SCODiA Report by MLBI Lab

Single-Cell Transcriptomic and Genomic Landscape of Lung Adenocarcinoma and Squamous Cell Carcinoma: Identifying Subtype-Specific Mechanisms and Therapeutic Targets

This single-cell analysis of lung cancer reveals distinct molecular and cellular landscapes for Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). UMAP embeddings show clear separation of these conditions, with aneuploid cells co-localizing with Lung Epithelial cells identified as tumor-origin. Copy Number Variation (CNV) analysis highlights subtype-specific genomic instability, including recurrent amplifications of oncogenes like SOX2 in Squamous samples. Significant differences are observed in T cell and macrophage subset populations, suggesting varied immune microenvironments. Condition-specific cell-cell interactions and surfaceome markers further delineate key signaling pathways, cell cycle regulation, and metabolic adaptations, providing a foundation for subtype-specific therapeutic targeting.

Contents

0. Dataset overview
1. Single-Cell UMAP Embedding Overview for Lung Cancer Conditions and Cell Types
2. UMAP Visualization of Major Cell Type Scores and Annotations
3. Marker Expression Dot Plot for `celltype_subset` Annotation Validation
4. Copy Number Variation (CNV) Analysis in Lung Epithelial Cells
5. UMAP Visualization of CNV Patterns across Cell Types, Ploidy, and Conditions in Lung Cancer
6. Minor Cell Type Population Analysis Across Lung Cancer Subtypes
7. T Cell Subset Population Analysis in Lung Adenocarcinoma and Squamous Cell Carcinoma
8. Differential T Cell Subset Proportions Between Lung Adenocarcinoma and Squamous Cell Carcinoma
9. Macrophage Subset Population Analysis in Lung Cancer Subtypes
10. Differential Macrophage Subpopulation Proportions in Lung Adenocarcinoma vs. Squamous Cell Carcinoma
11. Ploidy Analysis of Lung Epithelial Cells in Lung Adenocarcinoma and Squamous Cell Carcinoma Samples
12. Analysis of Cell-Cell Interaction Patterns in Lung Cancer Subtypes (Adenocarcinoma and Squamous Cell Carcinoma)
13. Condition-Specific Cell-Cell Interaction Patterns in Lung Adenocarcinoma and Squamous Cell Carcinoma
14. Condition-Specific Cell-Cell Interaction Patterns in Lung Cancer
15. Surfaceome Marker Expression in Lung Epithelial Cells Across Cancer Subtypes
16. Macrophage Condition-Specific Surface Marker Expression in Lung Adenocarcinoma and Squamous Cell Carcinoma
17. Fibroblast Condition-Specific Surfaceome Markers in Lung Cancer
18. T cell CD4+ Surfaceome Marker Expression in Squamous Lung Cancer
19. Differential Cell Cycle Gene Expression in Lung Epithelial Cells: Adenocarcinoma vs. Squamous Cell Carcinoma
20. Gene Ontology (GSA) Analysis of Up-regulated Genes in Lung Epithelial Cells Across Different Lung Cancer Conditions and Ploidy States
21. Gene Set Enrichment Analysis (GSEA) Across Lung Cancer Subtypes and Cell Types
22. Discussion
23. Query List

0. Dataset overview

Dataset Summary

• This dataset comprises 58757 cells and 37895 genes, derived from human Lung tissue.
• Key observational metadata (obs columns) include clinical details such as Tissue Diagnosis, Histology-reduced, Sex, Age, Stage, AJCC 8th ed, Smoking History, and Tumor Mutation Burden.
• Cellular annotations are provided at multiple levels: celltype_major (e.g., Lung Epithelial cell, Myeloid cell, T cell), celltype_minor, and celltype_subset.
• Conditions analyzed are 'Squamous' and 'Adeno', with 'Adeno' serving as the reference for differential analyses.
• Ploidy information is available, showing 96.1% Diploid, 3.9% Aneuploid, and 0.0% Unclear cells.
• Copy Number Variation (CNV) estimates are available, and 'Lung Epithelial cell' has been identified as the resolved tumor-origin cell type.

Inferred Biological Context

• The dataset is inferred to focus on Lung Cancer, specifically Adenocarcinoma and Squamous Cell Carcinoma, based on the conditions and clinical metadata provided.

Precomputed Results Summary

• Cell-Cell Interaction (CCI): Results from CellPhoneDB are available per condition (uns['CCI']) and per sample (uns['CCI_sample']).
• Differential Expression Genes (DEG): Results are available for each celltype_minor, comparing one condition against the rest (uns['DEG']). Specific cell types for DEG analysis include B cell, Endothelial cell, Fibroblast, Lung Epithelial cell, Macrophage, Mast cell, NK cell, Plasma cell, T cell CD4+, T cell CD8+.
• Gene Set Enrichment Analysis (GSEA): Results are available for each celltype_minor, comparing one condition against the rest (uns['GSEA']). Specific cell types for GSEA analysis include B cell, Endothelial cell, Fibroblast, Lung Epithelial cell, Macrophage, Mast cell, NK cell, Plasma cell, T cell CD4+, T cell CD8+.
• Gene Ontology (GO/GSA): Results are available for each celltype_minor, comparing one condition against the rest (uns['GSA_up']). Specific cell types for GO/GSA analysis include B cell, Endothelial cell, Fibroblast, Lung Epithelial cell, Macrophage, Mast cell, NK cell, Plasma cell, T cell CD4+, T cell CD8+.

1. Single-Cell UMAP Embedding Overview for Lung Cancer Conditions and Cell Types

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a series of UMAP (Uniform Manifold Approximation and Projection) plots, visualizing the global transcriptional landscape of 58,757 single cells from lung tissue. Cells are colored by different categorical metadata annotations: condition (Adenocarcinoma vs. Squamous Cell Carcinoma), sample, celltype_major, celltype_minor, ploidy_dec (aneuploidy status), and celltype_subset. This visualization aims to assess the overall structure of the dataset, the separation of biological groups, the quality of cell type annotations, and the distribution of ploidy status within the embedding.

Visual Summary

Embedding Structure and Data Quality

The UMAPs consistently show a well-resolved, complex cellular landscape, indicating successful dimensionality reduction and clustering. The overall embedding structure appears robust, with distinct clusters representing various cell populations. The high number of cells and genes in the AnnData suggests a comprehensive dataset for exploring lung cancer heterogeneity.

Condition-Specific Clustering

• condition UMAP: This plot clearly distinguishes between cells originating from Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). Adeno cells (maroon) comprise the majority of the dataset and form several large, interconnected clusters. Squamous cells (purple) form a relatively smaller but highly distinct cluster, predominantly localized to one specific region of the UMAP, with minimal intermixing with the main Adeno populations. This suggests substantial transcriptional differences between these two lung cancer subtypes.

Sample Distribution and Integration

• sample UMAP: While individual samples (e.g., NSC004.T1, NSC018.T2) contribute differentially to various parts of the UMAP, there is a general mixing of samples within broader cell type clusters rather than clear sample-specific separation of major cell populations. This pattern suggests effective integration of data across samples and minimal overt batch effects that would otherwise obscure biological signals.

Cell Type Hierarchy and Annotation Quality

• celltype_major UMAP: This plot demonstrates excellent separation of broad cell categories such as 'Lung Epithelial cell' (orange/peach), 'Myeloid cell' (yellow), and 'T cell' (cyan/light blue), forming prominent, well-defined clusters across the UMAP space. This indicates high-quality annotation at the major cell type level.
• celltype_minor UMAP: Providing a finer resolution, this UMAP shows that major cell type clusters further differentiate into distinct minor cell populations (e.g., 'T cell CD4+' vs. 'T cell CD8+', various 'Macrophage' subtypes, 'Airway Epithelial cell' vs. 'Alveolar Epithelial cell'). This hierarchical organization confirms the robustness and precision of cell type identification.
• celltype_subset UMAP: This most granular level of annotation further refines cell identities, revealing specific subtypes like 'Basal cell', 'Macrophage (M1)', 'T cell (Cytotoxic)', 'Alveolar type 2', etc. The visualization maintains clear separation for many of these subsets, indicating comprehensive cell type characterization.

Aneuploidy and Putative Tumor Cell Localization

• ploidy_dec UMAP: This plot is particularly informative. The vast majority of cells are 'Diploid' (yellow), consistent with the reported 96.1% diploid cells. Critically, the 'Aneuploid' cells (maroon), representing 3.9% of the total cells, do not appear randomly distributed but form distinct, concentrated clusters. These aneuploid clusters largely overlap with regions identified as 'Lung Epithelial cell' in the celltype_major UMAP and are predominantly associated with the Adeno condition. 'Unclear' cells are scarce, as noted (0.0%), and scatter sparsely.

Biological Interpretation

The UMAP visualizations provide crucial insights into the cellular composition and disease-associated alterations within lung cancer.

1. Distinct Cancer Subtype Biology: The clear separation of Adenocarcinoma and Squamous Cell Carcinoma cells on the UMAP strongly suggests that these conditions are driven by distinct transcriptional programs, which could influence their pathology, clinical behavior, and response to therapy.
2. Robust Cell Type Identification: The consistent and well-separated clustering observed across all levels of cell type annotation (major, minor, subset) indicates high confidence in the cell identity assignments. This provides a solid foundation for subsequent differential expression, pathway analysis, and cell-cell interaction studies, ensuring that analyses are conducted on biologically coherent cell populations.
3. Identification of Malignant Cells: The distinct clustering of Aneuploid cells within the Lung Epithelial cell compartment, especially co-localizing with the Adeno condition, strongly suggests that these are the malignant tumor cells. Aneuploidy is a hallmark of cancer, and its detection in specific epithelial cell clusters within a lung cancer context provides robust evidence for their neoplastic nature. This identification is critical for downstream analyses focusing on tumor-specific biology and interactions within the tumor microenvironment.
4. Tumor Microenvironment Complexity: The presence of a diverse array of immune cells (T cells, B cells, various myeloid cells including macrophages and dendritic cells) and stromal cells (fibroblasts, endothelial cells) alongside epithelial cells highlights the complex and heterogeneous nature of the lung tumor microenvironment, which is known to play a critical role in tumor progression and therapeutic resistance.
5. Data Quality Assurance: The absence of major batch-effect-driven clustering in the sample UMAP confirms that the biological signals, such as cell type and disease condition, are the primary drivers of the observed cellular organization, enhancing the reliability of downstream findings.

Annotation Notes

• The UMAP plots effectively visualize the complex cellular heterogeneity of the dataset and validate the quality of cell type annotations at multiple hierarchical levels.
• The distinct localization of aneuploid cells within the epithelial compartment serves as a strong indicator for tumor cell identification, aligning with the inferred disease context of lung cancer and the Resolved tumor-origin cell type being 'Lung Epithelial cell'.
• The overall embedding structure and distribution of conditions and cell types are consistent with expected biological patterns in single-cell lung cancer studies.

2. UMAP Visualization of Major Cell Type Scores and Annotations

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a Uniform Manifold Approximation and Projection (UMAP) visualization of single-cell RNA-seq data, displaying the scores for various major cell types across the embedding. The plots highlight the spatial distribution of cells based on their computed major cell type scores, the final major cell type assignments (celltype_major), and their ploidy status (ploidy_dec). The primary objective is to evaluate the quality of cell type annotation and understand the spatial organization of different cell populations, particularly in the context of lung cancer.

Visual Summary

The UMAP embedding shows distinct clusters representing different cell populations.

Cell Type Score Distribution (HiCAT_major_score plots):

• Each HiCAT_major_score plot (e.g., HiCAT_major_score: T cell, HiCAT_major_score: Myeloid cell, HiCAT_major_score: Lung Epithelial cell) effectively highlights regions where specific major cell types are enriched, indicated by higher scores (yellow/green). For instance, T cells form a large, distinct cluster on the right side of the UMAP, while Myeloid cells primarily occupy a large cluster on the left. Lung Epithelial cells are concentrated in the upper-central region. Plasma, B, Mast, Endothelial, and Stromal cells each form smaller, well-defined clusters in distinct parts of the embedding.

Final Major Cell Type Annotation (celltype_major plot):

• The celltype_major plot confirms the clear demarcation of these cell populations. The colors representing each major cell type (e.g., T cell in teal, Myeloid in yellow, Lung Epithelial in red) align very well with the high-score regions observed in the individual HiCAT_major_score plots. This indicates a high degree of confidence in the assigned cell identities.
• A small proportion of cells are labeled as 'unassigned' (dark purple), scattered across the UMAP, and forming some minor, less distinct clusters.

Ploidy Status (ploidy_dec plot):

• The ploidy_dec plot reveals that the majority of cells are Diploid (light yellow).
• Aneuploid cells (red) are localized to specific regions within the UMAP, primarily overlapping with the clusters identified as Lung Epithelial cells in the celltype_major plot, particularly in the upper-central region and some interconnected smaller branches. A very small number of 'Unclear' cells (dark blue) are also present but are almost imperceptible.

Biological Interpretation

The UMAP visualizations provide a comprehensive overview of the cellular landscape in the lung tissue samples.

1. Robust Cell Type Identification: The strong concordance between the HiCAT_major_score plots and the final celltype_major annotation demonstrates robust and well-separated clustering of major cell types. Each cell type occupies a largely distinct region in the embedding, suggesting that the underlying transcriptional profiles are sufficiently unique to differentiate these populations. This is crucial for downstream cell-type-specific analyses.
2. Tumor Cell Identification: A key finding is the clear co-localization of Aneuploid cells with the Lung Epithelial cell population. Given that 'Lung Epithelial cell' is the Resolved tumor-origin cell type and the context is inferred Lung Cancer, this strongly suggests that the aneuploid Lung Epithelial cells represent the malignant tumor cell population. This observation is consistent with the genomic instability (aneuploidy) often seen in cancer cells.
3. Tumor Microenvironment Composition: The presence of various immune and stromal cell types (T cells, Myeloid cells, B cells, Plasma cells, Mast cells, Endothelial cells, Stromal cells) alongside the presumed tumor cells indicates a complex tumor microenvironment. The distinct clustering of these immune and stromal components suggests their unique transcriptional states and functions within the tissue. For example, the large T cell and Myeloid cell populations point to active immune infiltration, which is common in lung cancer.
4. Annotation Quality: The well-defined clusters and minimal overlap between different major cell type scores, coupled with clear distinctions in the celltype_major plot, indicate high-quality cell type annotation at the major cell type level.
5. Unassigned Cells: The presence of 'unassigned' cells might represent rare cell populations, cells in transitional states, cells with low RNA complexity, or artifacts that could not be confidently assigned to an existing major cell type. Further investigation into these cells could reveal novel or ambiguous cell states.

Annotation Notes

• The HiCAT_major_score plots provide strong visual evidence supporting the accuracy and specificity of the major cell type assignments. The distinct and localized high-score regions for each cell type confirm the clear separation and identity of these populations.
• The spatial segregation of aneuploid cells specifically within the Lung Epithelial cell cluster is a critical finding for identifying the tumor compartment in this dataset, aligning perfectly with the biological expectation for lung cancer.
• While major cell types are well-resolved, the 'unassigned' population warrants further review. Exploring their transcriptional profiles might reveal if they represent under-represented cell types, specific transitional states, or cells of lower quality that could not be confidently classified. This might involve re-clustering or using different marker sets for finer-grained annotation.
• The UMAP embedding effectively captures the global structure and relationships between different cell populations, providing a reliable foundation for subsequent differential expression, pathway, or cell-cell interaction analyses.

3. Marker Expression Dot Plot for `celltype_subset` Annotation Validation

[Analysis Visualization Results]...

Analysis Overview

This analysis provides a marker expression dot plot, visualizing the expression patterns of key genes across various celltype_subset populations. The primary objective is to validate the assigned cell type annotations by examining whether the observed gene expression profiles align with known canonical markers for each cell subset. The plot displays the mean expression level (color intensity) and the fraction of cells expressing each gene within a given cell group (dot size). The 'common markers' section highlights genes that are frequently expressed across multiple distinct cell groups or are otherwise considered broadly significant markers.

Visual Summary

The dot plot effectively illustrates the specificity of marker gene expression across 35 distinct celltype_subset populations.

• Distinct Expression Patterns: The visualization shows clear diagonal blocks of high expression (dark, large dots) for numerous genes, indicating highly specific marker expression within particular celltype_subset groups. This "diagonal" pattern is a strong indicator of well-defined and uniquely identified cell populations. Red boxes further delineate these distinct marker sets for related cell types.
• Mean Expression and Fraction of Cells: The color intensity ranges from light red (low mean expression) to dark red (high mean expression), while dot size scales with the fraction of cells in a group expressing the gene (from small for ~20% to large for ~80% and above). This allows for a quick assessment of both marker prevalence and abundance within each cell type.
• 'Common Markers' Column: The far right column, labeled "common markers," displays genes that are expressed in a significant fraction of cells across multiple different celltype_subset groups (identified as markers in 3 or more groups based on analysis parameters). These include genes like PTPRC (CD45), CD4, CD8A, CD8B, CD3D, CD3E, MS4A1 (CD20), PECAM1 (CD31), and VWF, which are crucial for identifying broad immune or endothelial lineages.
• Cell Group Sizes: The bar plot on the right margin provides the absolute number of cells within each celltype_subset group, giving context to the robustness of the marker expression data for each population.

Biological Interpretation

The marker expression patterns strongly support the assigned celltype_subset annotations, demonstrating the specificity and fidelity of the cell type identification.

• B Cell Subsets: B cell (Breg), B cell (Follicular), B cell (MZ), and B cell (Memory) collectively exhibit high and specific expression of canonical B cell markers such as POU2F2 (OCT-2 transcription factor, important for B cell development) and MS4A1 (CD20, a widely used B cell surface marker), as well as CD19 and CD20.
• Endothelial Cells: Endothelial cell and Endothelial tip cell show robust expression of well-known endothelial markers including VWF, PECAM1 (CD31), and FLT1 (VEGFR1), consistent with their vascular identity.

Lung Epithelial Subsets:

• Basal cell populations are characterized by high expression of basal epithelial keratins like KRT5, KRT17, and KRT6A.
• Ciliated cells show specific expression of FOXJ1, a master regulator of ciliogenesis, and RSPA1.
• Goblet cells are identified by MUC5AC, a key mucin component.
• Secretory club cells express SCGB1A1 (Club cell secretory protein) and SNTN.
• Alveolar type 1 and Alveolar type 2 cells are clearly delineated by surfactant proteins such as SFTPB, SFTPC, SFTPD, SFTA2, and NAPSA.

Myeloid Cells:

• Mast cells show characteristic expression of KIT (CD117, mast cell growth factor receptor) and tryptases like TPSAB1 and TPSB2.
• Dendritic cell subsets (DC (Classical), DC (Inflammatory), DC (Plasmacytoid)) are distinguished by markers such as CD1A (for some DC subsets), CD83, GATA2, and IRF8.
• Macrophage subsets (M1, M2A, M2B, M2C, M2D) show expression of general macrophage markers like MSR1 and CD68, with CD163 being prominent in M2-like macrophages.
• Plasma Cells: These cells are strongly marked by JCHAIN, XBP1, MZB1, and PRDM1, indicating their role in immunoglobulin production.

Stromal Cells:

• Fibroblasts express key markers such as DCN (Decorin), COL1A1, COL1A2 (Collagen Type I), LUM (Lumican), COL5A1, FBLN1, and TAGLN, reflecting their extracellular matrix remodeling functions.
• Smooth muscle cells are identified by contractile proteins like MYH11, ACTA2 (alpha-smooth muscle actin), CALD1, and CNN1.
• Natural Killer (NK) Cells: NK cells display high expression of NKG7, KLRD1 (CD94), PRF1 (Perforin), GZMB (Granzyme B), and GNLY (Granulysin), consistent with their cytotoxic function.
• T Cell Subsets: All T cell subsets demonstrate pan-T cell markers like CD3D and CD3E. Specific subsets further express:
• CD4 in T cell CD4 Naive, T cell (Th1), T cell (Th17), T cell (Th2), T cell (Th22), T cell (Th9), T cell (Treg), and T cell (CD4 Memory).
• CD8A and CD8B in T cell (CD8 Naive), T cell (CD8 Memory), T cell (Cytotoxic), and T cell (CD8 Exhausted).
• Regulatory and exhausted T cells show markers like CTLA4 and TIGIT. Cytotoxic T cells express GZMA and GZMB.
• Innate Lymphoid Cells (ILCs): While less distinct markers are visible for ILC subsets (ILC1, ILC2, ILC3, ILCreg) compared to T/B cells in this particular marker set, NKG7 shows some expression in ILC1 and ILC3, suggesting shared cytotoxic potential or lineage markers.

The "common markers" section largely comprises a selection of well-established lineage-defining markers (e.g., PTPRC for leukocytes, CD3D/CD3E for T cells, MS4A1/CD19/CD20 for B cells, PECAM1/VWF for endothelial cells). Their presence in this section highlights their importance in defining these major cell populations across the dataset.

Annotation Notes

The marker expression dot plot provides strong evidence for the accuracy and robustness of the celltype_subset annotations. Each cell type largely exhibits a distinct and canonical gene expression signature, with minimal ambiguity or unexpected overlap, especially for the epithelial, immune, and stromal cell populations. This suggests that the current cell type assignments are reliable and well-supported by the underlying single-cell RNA-seq data. The clear segregation of markers for even finely resolved subsets underscores the high quality of the annotation. No significant mis-annotations or highly ambiguous populations were observed, confirming that the cell identities are largely validated by this marker analysis.

4. Copy Number Variation (CNV) Analysis in Lung Epithelial Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) within Lung Epithelial cells across various patient samples. Lung Epithelial cells were selected as the presumed tumor-origin cell type, consistent with the biological context of lung cancer (Adenocarcinoma and Squamous Cell Carcinoma). The primary goal is to visualize CNV patterns across the genome, grouped by sample, and to identify regions of significant copy number amplification. The results include a heatmap illustrating log2(CNR) values across genomic spots for each sample, and a summary identifying frequently amplified cytogenetic bands, along with their average copy number ratio or frequency.

Visual Summary

CNV Heatmap

The heatmap displays the log2(Copy Number Ratio, CNR) for Lung Epithelial cells, organized by individual samples on the y-axis and genomic spots (representing chromosomal locations) on the x-axis. Each row corresponds to a patient sample, and columns represent genomic regions ordered along chromosomes 1 through 22.

• Color Scale: Red indicates regions of amplification (log2(CNR) > 0), while blue indicates regions of deletion (log2(CNR) < 0). White/light yellow indicates regions with no significant change from the diploid state.
• Sample-Specific Patterns: A notable distinction is observed between samples explicitly labeled "Diploid NSC..." and other "NSC..." samples. The "Diploid NSC" samples generally show fewer and less pronounced CNV events (more white/light regions), consistent with their diploid classification. In contrast, many "NSC" samples, particularly NSC004.T1-T4, NSC018.T1, NSC019.T1, and NSC021.T1-T2, exhibit widespread and distinct patterns of both amplifications (red) and deletions (blue).
• Recurrent CNV Regions: Across several samples, particularly the non-diploid ones, recurrent amplifications are noticeable in regions like chromosome 1, chromosome 3, chromosome 8, and chromosome 17. Similarly, recurrent deletions can be observed in other regions, though amplifications appear more visually striking in this heatmap.

Summary of Significant Amplifications

The summary plot provides a detailed view of frequently amplified cytogenetic bands:

• Left Panel (Heatmap of Amplification Values): This heatmap shows the average copy number ratio (or a similar metric indicating amplification strength/frequency) for specific cytogenetic bands across different samples. Darker blue indicates higher amplification values.
• Samples like NSC004.T1-T4, NSC018.T1-T3, and NSC019.T1 show consistent amplification across multiple bands.
• Notable amplifications include 1p34.3:1p34.1, 1q21.3:1q21.3, 1q32.1:1q32.1 (NFASC), 3q21.3:3q23, 3q25.1:3q26.1, 3q26.33:3q29 (SOX2), 8q22.1:8q23.1 (EIF3E), and regions on chromosome 17 (e.g., 17q21.2:17q21.2) and chromosome 22 (e.g., 22q13.1:22q13.31).
• Right Panel (Frequency of Amplification): This bar plot quantifies the overall frequency (proportion) with which each listed cytogenetic band exhibits amplification across all analyzed samples.
• The highest frequency amplifications are observed at 1q21.3:1q21.3 (0.54), 1q42.13:1q42.2 (0.54), 3q26.33:3q29 (SOX2, 0.54), and 22q13.1:22q13.31 (0.54).
• Other highly frequent amplifications include 1p34.3:1p34.1 (0.46), 1q32.1:1q32.1 (NFASC, 0.38), 8q22.1:8q23.1 (EIF3E, 0.69 – notably high), and 17q21.2:17q21.2 (0.62).

Biological Interpretation

The observed CNV patterns in Lung Epithelial cells are highly indicative of genomic instability characteristic of cancer. The clear distinction between "Diploid" samples and those with extensive CNVs supports the identification of tumor cells (likely aneuploid cells) from the rest. The AnnData context indicates 3.9% aneuploid cells, which are the primary focus for cancer-related CNVs.

Several highly amplified regions contain genes frequently implicated in lung cancer:

• 3q26.33:3q29 (SOX2): This region harbors *SOX2*, a well-known oncogene and transcription factor crucial for self-renewal in various stem cells, including cancer stem cells. Amplification of *SOX2* is particularly common in Lung Squamous Cell Carcinoma (LSCC) and is associated with tumor initiation, progression, and resistance to therapy [1]. Its high amplification frequency (0.54) in this dataset is significant for lung cancer.
• 8q22.1:8q23.1 (EIF3E): This region includes *EIF3E* (Eukaryotic Translation Initiation Factor 3 Subunit E). Amplification of *EIF3E* can contribute to enhanced protein translation, a hallmark of cancer proliferation and survival. Its amplification frequency of 0.69 makes it one of the most frequently amplified regions in this analysis.
• 1q32.1:1q32.1 (NFASC): This region contains the *NFASC* gene, encoding Neurofascin, an axonal cell adhesion molecule. While less frequently discussed as a primary oncogene compared to SOX2, altered expression or copy number changes in adhesion molecules can play roles in cancer cell migration, invasion, and metastasis [2].
• 1q21.3:1q21.3: This region is known to host several genes involved in inflammation and immune response, such as the *S100A* gene cluster. Amplification of this region can be associated with tumor microenvironment modulation and cancer progression [3].
• 17q21.2:17q21.2: This region can contain genes involved in various cellular processes, and recurrent amplifications here are observed in different cancers.

The observation that some samples are explicitly labeled "Diploid" and indeed show minimal CNVs validates the ploidy assessment and the ability to distinguish likely non-tumor (or less aggressive tumor) cells from highly aneuploid tumor cells within the Lung Epithelial compartment. The copykat_pred and scevan_pred obs columns likely contributed to these ploidy assignments.

Clinical or Translational Implications

The identification of recurrent genomic amplifications in Lung Epithelial cells holds several clinical and translational implications for lung cancer:

• Biomarker Potential: Amplifications of genes like *SOX2* could serve as prognostic or predictive biomarkers, especially in specific lung cancer subtypes (e.g., Squamous Cell Carcinoma). Detecting these amplifications could help in patient stratification.
• Therapeutic Targets: Highly amplified oncogenes represent potential therapeutic vulnerabilities. For instance, *SOX2* amplification is actively being investigated as a target for novel anti-cancer therapies, given its role in cancer stemness and drug resistance [4].
• Disease Heterogeneity: The sample-to-sample variability in CNV patterns highlights the genomic heterogeneity of lung cancer, even within the same cell type (Lung Epithelial cells). This heterogeneity can influence treatment response and disease progression.
• Tumor Cell Identification: The ability to identify highly aneuploid samples with significant CNVs helps in confirming the tumor origin of Lung Epithelial cells and distinguishing them from normal epithelial cells in the tumor microenvironment.

References

1. SOX2 Amplification in Lung Cancer: Review on SOX2 in lung cancer. PubMed Search: SOX2 lung cancer amplification
2. NFASC and Cancer: Information on NFASC in cancer. GeneCards: NFASC
3. 1q21.3 Amplification: General information on 1q21.3 amplification in cancer. PubMed Search: 1q21.3 amplification cancer
4. Targeting SOX2: Research on therapeutic targeting of SOX2. PubMed Search: targeting SOX2 cancer therapy

5. UMAP Visualization of CNV Patterns across Cell Types, Ploidy, and Conditions in Lung Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes Uniform Manifold Approximation and Projection (UMAP) plots, generated from a single-cell RNA-seq dataset with an embedding informed by Copy Number Variation (CNV) patterns. The UMAPs visualize the global cellular landscape, colored by major cell type (celltype_major), minor cell type (celltype_minor), inferred ploidy status (ploidy_dec), disease condition (condition - Adenocarcinoma or Squamous Cell Carcinoma), and individual sample origin (sample). The primary objective is to evaluate the spatial distribution of cell populations, confirm tumor cell identity based on aneuploidy, understand how different lung cancer conditions (Adeno vs. Squamous) manifest in this CNV-driven space, and assess potential sample-specific effects or overall dataset integration.

Visual Summary

The UMAP plots effectively delineate distinct cellular clusters, reflecting the underlying biological and genomic heterogeneity of the lung cancer samples.

Cell Type Distribution (celltype_major, celltype_minor):

• The Lung Epithelial cell population, identified as the tumor-origin cell type in the data context, forms a prominent, extensive cluster, predominantly located in the central and upper-right regions of the UMAP.
• Immune cells (e.g., T cell, Myeloid cell, B cell, Plasma cell) and stromal cells (e.g., Stromal cell, Endothelial cell) form distinct, well-separated clusters, typically located at the periphery of the main epithelial mass. This clear segregation suggests unique transcriptional and CNV profiles for these host cells compared to epithelial cells.
• The celltype_minor plot further resolves these into more specific immune subsets (Macrophage, T cell CD4+, T cell CD8+, Dendritic cell) and epithelial subtypes (Airway Epithelial cell, Alveolar Epithelial cell), maintaining clear separation.

Ploidy Status (ploidy_dec):

• The ploidy_dec UMAP provides crucial insights into tumor cell identification. Aneuploid cells (maroon), which represent cells with abnormal chromosome numbers, are tightly clustered within a specific region that strongly overlaps with the main Lung Epithelial cell cluster.
• The vast majority of cells are Diploid (light yellow), forming the extensive background and encompassing all the immune and stromal cell clusters, as expected for normal, non-malignant cells.
• This clear spatial separation of aneuploid cells in the CNV-informed embedding validates the ability to distinguish tumor cells from normal host cells based on their genomic instability.

Condition (condition):

• A striking pattern is observed for the condition UMAP, where cells from Adeno (Adenocarcinoma, maroon) and Squamous (Squamous Cell Carcinoma, blue) conditions largely segregate.
• The Adeno cells constitute the majority of the primary tumor-associated cluster, overlapping with the large Aneuploid and Lung Epithelial cell regions.
• Squamous cells form a more distinct and somewhat separate cluster, often appearing as a specific projection, particularly towards the left arm of the UMAP. This indicates that these two major lung cancer subtypes possess differentiable CNV patterns that lead to their separation in this genomic-informed embedding.

Sample (sample):

• The sample UMAP highlights considerable inter-patient heterogeneity. While cells from different samples are somewhat intermingled within the Diploid (normal) cell regions, suggesting reasonable batch correction for non-malignant cells, distinct pockets or substructures within the Aneuploid (tumor) clusters are often dominated by cells from specific samples.
• For instance, the distinct Squamous cluster observed in the condition plot appears to be driven by a limited number of specific samples (e.g., NSC036.T1, NSC037.T1, NSC040.T1). This suggests patient-specific genomic profiles and tumor subclones contributing to the overall diversity.

Biological Interpretation

The CNV-informed UMAP embedding successfully organizes the single-cell data according to fundamental biological distinctions, particularly regarding cancer pathology:

• Genomic Instability as a Tumor Hallmark: The distinct clustering of Aneuploid cells, primarily within the Lung Epithelial cell population, robustly confirms that genomic instability, a hallmark of cancer, is a major driver of cellular separation in this embedding. This provides strong evidence for the identification of malignant epithelial cells. Aneuploidy is a common characteristic of solid tumors, including lung cancers, and can arise from chromosomal missegregation during cell division Ref: PubMed search for "aneuploidy lung cancer origin".
• Distinct Genomic Signatures of Lung Cancer Subtypes: The observed segregation of Adeno and Squamous cells within the aneuploid compartment highlights that these two histological subtypes of lung cancer possess unique and discernible CNV landscapes. These genomic differences are known to contribute to their distinct molecular pathways, clinical behaviors, and differential responses to targeted therapies Ref: PubMed search for "lung adenocarcinoma squamous cell carcinoma genomic differences".
• Tumor Microenvironment Integrity: The clear separation of immune and stromal cell types from the aneuploid epithelial cells indicates that these non-malignant components of the tumor microenvironment largely retain their diploid status and distinct genomic-transcriptional identities. This separation is crucial for understanding the composition and interactions within the tumor ecosystem, where distinct cell types play specific roles in tumor progression and immune response Ref: PubMed search for "tumor microenvironment cell types lung cancer".
• Patient-Specific Tumor Heterogeneity: The sample-specific patterns observed within the tumor clusters underscore the extensive inter-patient heterogeneity characteristic of lung cancer. Different patients often harbor unique sets of CNVs and mutations, which contribute to varied tumor evolution trajectories and therapeutic challenges.

Annotation Notes

• The strong concordance between the Lung Epithelial cell annotation and the Aneuploid status provides robust validation for both the cell type identification of tumor cells and the accuracy of the ploidy_dec calls.
• The clear separation of Adeno and Squamous conditions in the CNV-informed space confirms the biological distinctness of these annotations and their relevance to underlying genomic features.
• The distribution of cells from different samples indicates good integration of normal cell types while preserving patient-specific tumor CNV signatures, suggesting that the analysis effectively captures true biological heterogeneity rather than being dominated by batch effects.
• The minimal presence of 'unassigned' cells across the UMAPs implies a comprehensive and high-quality cell type annotation for the dataset.

6. Minor Cell Type Population Analysis Across Lung Cancer Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot visualizing the relative abundance of minor cell types across individual samples, categorized by two primary lung cancer conditions: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). Each stacked bar represents a single patient sample, and the colored segments within each bar indicate the percentage contribution of different minor cell types. This visualization is crucial for understanding the cellular heterogeneity within the tumor microenvironment of these lung cancer subtypes.

Visual Summary

The plot displays the cellular composition for 20 Adenocarcinoma samples and 6 Squamous Cell Carcinoma samples.

• Overall Composition: In both Adeno and Squamous samples, the tumor microenvironment is composed of a complex mix of epithelial, immune, and stromal cells. Macrophages, T cells (CD4+ and CD8+), and Fibroblasts appear to be consistently abundant components across many samples.

Epithelial Cells:

• Airway Epithelial cells (dark red) and Alveolar Epithelial cells (red), which include the Resolved tumor-origin cell type (Lung Epithelial cell), show noticeable differences between the two conditions. Many Adeno samples exhibit substantial proportions of these epithelial cells, often constituting 25-50% or more of the total cellularity (e.g., NSC016.T1, NSC019.T1, NSC036.T1, NSC010.T1, NSC037.T1).
• In contrast, Squamous samples generally display a lower proportion of these specific epithelial cell types, with some samples showing minimal representation.

Immune Cells:

• T cells (CD4+ and CD8+) (teal and blue) collectively represent a significant fraction in most samples from both conditions, indicating varying degrees of immune infiltration.
• Macrophages (light yellow) are consistently present and often constitute a large part of the immune infiltrate in both Adeno and Squamous tumors.
• Other immune cells like B cells (coral), Plasma cells (light green), NK cells (light yellow-green), Dendritic cells (orange), and ILCs (light orange) are present in lower and more variable proportions.
• Stromal Cells: Fibroblasts (orange) are also frequently observed as a notable component in both conditions, highlighting the presence of an active stromal compartment. Endothelial cells (light orange) and Smooth muscle cells (light green) are also present but in smaller proportions.
• Inter-sample Variability: Significant heterogeneity in cell type proportions is evident among individual samples within both the Adeno and Squamous groups, underscoring the patient-specific nature of the tumor microenvironment.
• "unassigned" cells (dark blue) generally account for a small percentage of cells in most samples, suggesting a high quality of cell type annotation.

Biological Interpretation

This cell type population analysis provides critical insights into the distinct cellular landscapes of Adenocarcinoma and Squamous Cell Carcinoma in the lung.

• Differential Tumor Cell Contributions: The generally higher proportion of Airway and Alveolar Epithelial cells in Adeno samples aligns with the typical origins of Adenocarcinoma from glandular epithelial cells (e.g., alveolar type II cells or club cells) in the lung. The lower relative abundance in Squamous samples might reflect different tumor cell origins, higher stromal or immune cell infiltration, or different epithelial-mesenchymal plasticity characteristics inherent to Squamous Cell Carcinoma.
• Tumor Microenvironment (TME) Composition: The prominent and variable presence of immune cells such as Macrophages, CD4+ T cells, and CD8+ T cells in both conditions highlights the active role of the immune system in lung cancer. Macrophages, particularly tumor-associated macrophages (TAMs), can have pro- or anti-tumor functions depending on their polarization state within the TME [PubMed Search: tumor associated macrophages lung cancer]. The varying levels of T cell infiltration suggest differences in immune surveillance and response across individual tumors and between subtypes.
• Stromal Remodeling: The consistent presence of Fibroblasts (which include cancer-associated fibroblasts, CAFs) in both subtypes underscores their critical role in extracellular matrix remodeling, angiogenesis, and supporting tumor growth and metastasis within the TME [GeneCards: FAP].
• Subtype-Specific Biology: The observed differences in the overall cellular architecture between Adeno and Squamous types suggest distinct underlying biological processes and cellular interactions that contribute to their unique pathologies. These differences could influence tumor progression, metastatic potential, and response to therapies.

Clinical or Translational Implications

• Biomarkers for Prognosis and Treatment Response: The distinct cellular profiles, particularly the differential proportions of epithelial and immune cells, could serve as valuable biomarkers. For instance, the specific composition of immune infiltrates (e.g., T cell density or macrophage polarization states, which can be further resolved at celltype_subset level) may predict patient response to immunotherapotherapies (e.g., immune checkpoint inhibitors) in either Adeno or Squamous lung cancer [PubMed Search: lung cancer immunotherapy biomarkers TME].
• Targeted Therapeutic Strategies: Understanding the unique cellular ecosystems of Adeno versus Squamous carcinoma can inform the development of subtype-specific therapeutic interventions. For example, if a specific immune cell type is consistently depleted in one subtype, strategies to augment its presence or function could be explored. Conversely, abundant pro-tumorigenic stromal components like fibroblasts might indicate targets for drugs that modulate CAF activity or collagen deposition.
• Addressing Tumor Heterogeneity: The significant inter-sample variability emphasizes the importance of single-cell resolution analyses for personalized medicine. Recognizing that even within the same cancer subtype, individual patients have unique cellular landscapes, allows for more tailored treatment approaches that account for this heterogeneity and potential mechanisms of treatment resistance.

7. T Cell Subset Population Analysis in Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of various T cell subsets, Innate Lymphoid Cells (ILCs), and NK cells within individual samples, grouped by lung cancer histology: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). Each bar represents a single patient sample, and the stacked segments within each bar show the percentage contribution of different immune cell subsets to the total T cell/ILC/NK cell population in that sample. This visualization helps to understand the composition of the adaptive and innate lymphoid infiltrate across different tumor types and patient samples, providing insight into the immune landscape of lung cancer.

Visual Summary

The stacked bar plots display the relative proportions of 20 distinct immune cell subsets, including various T helper cells (Th1, Th2, Th9, Th17, Th22, Tfh), T cytotoxic, T regulatory (Treg), T memory (CD4, CD8), T naive (CD4, CD8), exhausted T cells (CD8 Exhausted), NK cells, and ILC subsets (ILC1, ILC2, ILC3, ILCreg), for multiple samples within Adeno and Squamous conditions.

• Overall Dominance: Across both Adeno and Squamous conditions, the immune infiltrates are predominantly composed of various T cell subtypes, with ILCs and NK cells forming a smaller but consistent portion, generally at the base of the stacks.

Key T Cell Subsets

• T cell (Cytotoxic): This population (light yellow) appears to be a significant component in most samples, indicating an active cytotoxic immune response. There doesn't appear to be a striking difference in its proportion between Adeno and Squamous cell carcinoma at first glance, but some samples show higher or lower relative percentages.
• T cell (CD8 Exhausted): This subset (light orange) is also noticeable across samples in both conditions, suggesting the presence of exhausted anti-tumor T cells, a common feature in the tumor microenvironment.
• T cell (Treg): T regulatory cells (dark blue) are present in all samples, consistently occupying a small but noticeable proportion at the top of the stacks, which is expected for their role in immune suppression within tumors.
• T helper subsets: Various T helper subsets (Th1, Th17, Th2, Th9, Th22, Tfh) contribute to the overall T cell composition, with their proportions varying across samples. T cell (Th1) and T cell (Th17) (light green hues) appear relatively more prominent among the helper T cell populations compared to others like Th2, Th9, Th22, and Tfh.
• Innate Lymphoid Cells (ILCs) and NK cells: ILCs (red/burgundy hues) and NK cells (orange) are consistently present in smaller proportions at the bottom of the stacks across nearly all samples, indicating their contribution to the innate immune response. ILC2 (dark red) seems to be consistently present in a small proportion across most samples.
• Inter-sample Variability: While general trends are observed, there is notable heterogeneity in the exact proportions of different subsets among individual samples within both Adeno and Squamous groups. For example, some samples show a slightly higher proportion of cytotoxic T cells, while others might have a larger relative component of memory T cells.
• Comparison between Conditions: There is no immediately apparent dramatic difference in the overall T cell subset distribution patterns between the Adeno and Squamous groups. Both conditions exhibit a diverse T cell infiltrate with similar major contributing subsets. However, subtle shifts in the relative abundance of specific subsets might be present, warranting more detailed quantitative analysis.

Biological Interpretation

The detailed breakdown of T cell subsets and other lymphoid populations provides critical insights into the immune microenvironment of lung cancer.

• Adaptive Immune Response in Lung Cancer: The prevalence of various T cell subsets, including cytotoxic T cells, CD4+ and CD8+ memory T cells, and T helper populations (Th1, Th17), indicates an active adaptive immune response against both Adenocarcinoma and Squamous cell carcinoma.
• Cytotoxic T cells are crucial for directly killing tumor cells and are a primary target for immunotherapy [1]. Their presence suggests an ongoing anti-tumor effort.
• T helper cells, especially Th1 cells (associated with IFN-gamma production), are important for orchestrating cellular immunity and enhancing anti-tumor responses. Th17 cells have a more complex role, sometimes promoting anti-tumor immunity and sometimes tumor progression, depending on the context [2].

Immune Evasion and Suppression

• The consistent presence of T cell (CD8 Exhausted) cells is a hallmark of chronic antigen exposure in the tumor microenvironment, where T cells become dysfunctional and lose effector functions, contributing to immune evasion [3]. This suggests that even when a cytotoxic response is initiated, it often becomes compromised.
• T regulatory cells (Tregs) are known suppressors of anti-tumor immunity, hindering the activity of cytotoxic T cells and promoting tumor growth. Their consistent presence in both tumor types underscores an active immunosuppressive mechanism within the tumor microenvironment [4].
• Innate Lymphoid Cells (ILCs) and NK cells: These populations contribute to innate immunity within the tumor. NK cells are recognized for their direct cytotoxic activity against tumor cells, particularly those that have lost MHC-I expression. ILCs, while less studied in this context than T cells, play diverse roles in tissue homeostasis and inflammation, and their subsets (ILC1, ILC2, ILC3) can influence the immune landscape and tumor progression [5].
• Similarities Across Histologies: The generally similar profiles of T cell subsets between Adenocarcinoma and Squamous cell carcinoma suggest that common immunological mechanisms of anti-tumor immunity and immune evasion are at play, despite their distinct genetic and pathological features. However, subtle quantitative differences not immediately obvious from visual inspection could still hold biological significance.

Clinical or Translational Implications

Understanding the detailed composition of T cell and innate lymphoid cell populations within lung tumors has several clinical and translational implications:

• Immunotherapy Response Prediction: The balance between effector T cells (e.g., cytotoxic T cells) and suppressive cells (e.g., Tregs, exhausted T cells) is a key determinant of response to immune checkpoint blockade therapies. A higher ratio of functional effector T cells to suppressive cells might predict better responses [6]. The presence of exhausted T cells highlights a potential target for therapies aimed at reversing exhaustion.
• Biomarker Discovery: Distinct immune cell profiles or ratios of specific subsets could serve as prognostic biomarkers or predict response to specific treatments. For example, the relative abundance of Th1 vs. Th2 or Th17 cells could influence the effectiveness of certain immunotherapies.
• Targeting Immunosuppression: The consistent presence of Tregs and exhausted T cells suggests that strategies to deplete Tregs or reverse T cell exhaustion (e.g., via anti-PD-1/PD-L1 therapies, which target T cell exhaustion pathways) are highly relevant for both lung cancer subtypes.
• Heterogeneity in Patients: The observed inter-sample variability within each histological subtype indicates that patient-specific immune profiling is crucial for personalized treatment approaches. This heterogeneity might explain differential responses to standard therapies or immunotherapies among patients with the same lung cancer histology.

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References

[1] Cytotoxic T lymphocytes in cancer.

PubMed search: "cytotoxic T lymphocytes cancer"

[2] T helper 17 cells in cancer immunity.

PubMed search: "Th17 cells cancer immunity"

[3] T cell exhaustion in cancer.

PubMed search: "T cell exhaustion cancer"

[4] Regulatory T cells in cancer.

PubMed search: "regulatory T cells cancer"

[5] Innate lymphoid cells and NK cells in tumor immunity.

PubMed search: "innate lymphoid cells NK cells tumor immunity"

[6] Immune checkpoint blockade and T cell phenotypes.

PubMed search: "immune checkpoint blockade T cell phenotypes"

8. Differential T Cell Subset Proportions Between Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates differences in the proportions of various T cell subsets between lung adenocarcinoma (Adeno) and squamous cell carcinoma (Squamous) conditions. Box plots are generated for T cell populations exhibiting statistically significant differences (p < 0.1) based on celltype_subset annotation. These findings provide insights into the distinct immune microenvironments of these two major lung cancer subtypes.

Visual Summary

The box plots illustrate the distribution of celltype_subset proportions across Adeno and Squamous conditions, highlighting significant differences:

• Th2 cells show a significantly higher median proportion in Adeno (median ~10%) compared to Squamous (median ~6%), with a p-value of 0.00344.
• Th17 cells demonstrate a significantly higher median proportion in Squamous (median ~2.5%) compared to Adeno (median ~1.5%), with a p-value of 0.0469.
• T cell (CD4 Memory) populations tend to be higher in Adeno (median ~19%) compared to Squamous (median ~17%), with a p-value of 0.0554.
• T cell (CD8 Naive) cells exhibit a higher median proportion in Squamous (median ~2.5%) compared to Adeno (median ~0.8%), with a p-value of 0.0838.
• ILCreg (Innate Lymphoid Cell regulatory) populations appear higher in Adeno (median ~0.8%) compared to Squamous (median ~0.4%), with a p-value of 0.0878.
• An unassigned T cell subset also shows a higher proportion in Adeno (p=0.0724), though its biological interpretation is limited due to its uncharacterized nature.

Biological Interpretation

The observed differences in T cell subset proportions suggest distinct immune microenvironments in lung adenocarcinoma versus squamous cell carcinoma, which may influence tumor progression and response to therapy.

1. Th2-dominant response in Adenocarcinoma: The significantly higher proportion of Th2 cells in Adeno suggests a potential shift towards a type 2 immune response. Th2 cells are typically associated with allergic responses, humoral immunity, and tissue remodeling, but in cancer, they can sometimes contribute to immune suppression and tumor progression by suppressing anti-tumor Th1 responses, promoting M2 macrophage polarization, and fostering an immunosuppressive environment [1]. The higher proportion of T cell (CD4 Memory) in Adeno could also reflect a more established or distinct antigen-driven CD4 T cell response, potentially contributing to this Th2 bias.
2. Th17 and CD8 Naive enrichment in Squamous Cell Carcinoma: The elevated proportion of Th17 cells in Squamous might indicate a more inflammatory and potentially pro-tumorigenic microenvironment. Th17 cells are known to promote chronic inflammation, angiogenesis, and recruit other immune cells, although their role in cancer is context-dependent, sometimes showing anti-tumor effects [2]. The higher proportion of T cell (CD8 Naive) in Squamous could suggest a less active or less antigen-experienced cytotoxic T cell compartment, possibly indicating a "colder" immune microenvironment or different patterns of immune cell recruitment compared to Adeno, where T cells might have already undergone activation and differentiation into memory or effector phenotypes.
3. Potential Immune Regulation by ILCreg in Adenocarcinoma: The increased presence of ILCreg cells in Adeno is notable. While the precise roles of regulatory ILCs are still being fully elucidated, innate lymphoid cells (ILCs) are crucial in early immune responses and tissue homeostasis. Regulatory ILCs, similar to regulatory T cells, could contribute to immune suppression within the tumor microenvironment, potentially limiting effective anti-tumor immunity in adenocarcinoma [3].

These distinct immune profiles highlight fundamental differences in how the host immune system interacts with Adeno and Squamous tumors. A Th2-skewed response and increased regulatory cells in Adeno, contrasted with a Th17-driven inflammation and a higher proportion of naive CD8 T cells in Squamous, could underlie differences in tumor biology, progression, and therapeutic vulnerabilities.

Clinical or Translational Implications

Understanding the differential immune cell compositions between lung adenocarcinoma and squamous cell carcinoma can have several clinical implications:

• Prognostic Biomarkers: The distinct immune signatures (e.g., Th2 dominance in Adeno, Th17/CD8 Naive enrichment in Squamous) could serve as prognostic biomarkers, potentially predicting disease aggressiveness or patient outcomes for each subtype.
• Therapeutic Stratification: These findings suggest that immune-based therapies, such as immunocheckpoint inhibitors, might elicit different responses depending on the underlying T cell subset composition. For instance, tumors with higher levels of immune suppression (e.g., higher Th2 or ILCreg) might require combination therapies to overcome resistance.
• Targeted Immunotherapies: The identification of specific T cell subsets enriched in one tumor type over another could inform the development of subtype-specific immunotherapeutic strategies. For example, strategies aimed at modulating Th17 responses might be more relevant for Squamous, while targeting Th2-mediated suppression might be critical for Adeno.

These results underscore the importance of considering tumor histology when designing and evaluating immunotherapeutic approaches for lung cancer.

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References:

[1] Cardona, A. E. (2020). T-helper Type 2 Cells. *Reference Module in Biomedical Sciences*. https://www.sciencedirect.com/topics/neuroscience/t-helper-type-2-cells

[2] Gaffen, S. L., & Liu, X. (2020). IL-17 in Lung Disease. *Reference Module in Biomedical Sciences*. https://www.sciencedirect.com/topics/neuroscience/t-helper-type-17-cells

[3] Vivier, E., & Artis, D. (2018). Innate Lymphoid Cells in Cancer. *Nature Immunology*. PubMed Search: "ILC regulatory cancer"

9. Macrophage Subset Population Analysis in Lung Cancer Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional distribution of various macrophage subsets (M1, M2A, M2B, M2C, M2D) within the total macrophage population across individual lung cancer samples. Samples are categorized by their histological diagnosis: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). This stacked bar plot provides a visual representation of macrophage polarization states within the tumor microenvironment of each sample.

Visual Summary

The visualization displays the relative abundance of five distinct macrophage subsets for each patient sample, grouped by cancer subtype (Adeno or Squamous).

• Overall Macrophage Polarization: Both Adenocarcinoma and Squamous Cell Carcinoma samples exhibit a predominant presence of M2-like macrophage subsets (M2A, M2B, M2C, M2D) over the classically anti-tumorigenic Macrophage (M1) population. In most samples, the combined proportion of M2-like macrophages frequently exceeds 65-70% of the total macrophage population.
• Macrophage (M1) Distribution: The proportion of Macrophage (M1) (maroon) varies across samples but generally constitutes less than 35% of the total macrophages in both Adeno and Squamous cohorts.
• Dominant M2-like Subsets: Macrophage (M2A) (orange) and Macrophage (M2B) (light yellow) are consistently observed as the most abundant M2-like subsets in both conditions. Their individual contributions often range from 15% to 30% each across many samples.
• Minor M2-like Subsets: Macrophage (M2C) (light green) and Macrophage (M2D) (teal) generally represent smaller proportions, typically below 15% each within the macrophage compartment of most samples.
• Inter-sample Variability: While the general trend of M2-like dominance is consistent, there is noticeable sample-to-sample variability in the exact proportions of each macrophage subset within both the Adeno and Squamous groups.
• Comparison between Adeno and Squamous: Visually, the overall patterns of macrophage subset distribution appear broadly similar between Adenocarcinoma and Squamous Cell Carcinoma. Both conditions show a strong bias towards M2-like polarization. No stark, consistent differences in the relative proportions of specific macrophage subsets between the two conditions are immediately apparent from this visualization.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and undergo distinct polarization states with varying functions.

• M1 Macrophages: Classically activated M1 macrophages are typically characterized by their pro-inflammatory and anti-tumorigenic properties, contributing to tumor suppression through cytokine secretion and antigen presentation [PubMed search: "M1 macrophages tumor immunity"].
• M2 Macrophages (Tumor-Associated Macrophages - TAMs): M2-like macrophages, often termed Tumor-Associated Macrophages (TAMs) in cancer, represent a heterogeneous group generally associated with immune suppression, tissue repair, angiogenesis, and tumor progression. The M2A, M2B, M2C, and M2D classifications represent different M2-like polarization states induced by specific microenvironmental cues [GeneCards: CD163 (M2 macrophage marker)].
• M2A: Often induced by IL-4/IL-13, involved in allergic reactions and parasite infections, and can promote tissue repair.
• M2B: Induced by immune complexes and TLR/IL-1R agonists, involved in immune regulation.
• M2C: Induced by IL-10/TGF-β/glucocorticoids, primarily immunosuppressive and involved in tissue remodeling.
• M2D: Often induced by adenosine and hypoxia, promoting angiogenesis and tumor growth.

The observed dominance of M2-like macrophages in both Adenocarcinoma and Squamous Cell Carcinoma samples strongly suggests an immunosuppressive and pro-tumorigenic TME in these lung cancers. The high prevalence of M2A and M2B subtypes indicates active involvement in complex immune regulation, tissue remodeling, and potentially angiogenesis that favors tumor growth and progression. This polarization is a common mechanism by which tumors evade immune surveillance and promote their own survival.

Clinical or Translational Implications

The pervasive M2-like macrophage polarization in both Adenocarcinoma and Squamous Cell Carcinoma has several important clinical implications:

• Immunotherapy Resistance: A high infiltration of M2-like TAMs is frequently correlated with poor prognosis and resistance to immune checkpoint blockade therapies in various cancers, including lung cancer [PubMed search: "TAMs immunotherapy resistance lung cancer"]. The current findings suggest that overcoming M2-mediated immunosuppression may be crucial for improving treatment efficacy.
• Therapeutic Targets: Macrophage polarization pathways, specifically those leading to M2-like phenotypes, represent potential therapeutic targets. Strategies aimed at repolarizing M2-like TAMs towards an M1-like phenotype or depleting TAMs could enhance anti-tumor immunity and improve patient outcomes. Examples include CSF1R inhibitors, which target macrophage survival and differentiation [UniProt: CSF1R].
• Prognostic Marker: The relative abundance of M1 versus M2-like macrophages, or specific M2 subsets, could serve as a prognostic biomarker for disease progression or response to therapy. Further research would be needed to establish specific thresholds and associations.
• Subtype-Specific Targeting: While the general trend is similar, subtle differences in the precise M2 subset distribution between Adeno and Squamous samples, if statistically significant, might suggest the need for tailored therapeutic approaches that target specific polarization pathways dominant in each subtype.

10. Differential Macrophage Subpopulation Proportions in Lung Adenocarcinoma vs. Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis presents box plots visualizing the proportional distribution of specific Macrophage subsets within the tumor microenvironment, comparing two distinct lung cancer conditions: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). The goal is to identify and quantify statistically significant differences in these immune cell populations between the two cancer types, offering insights into their distinct immunological landscapes. The celltype_minor Macrophage was targeted, and differences in celltype_subset populations were assessed.

Visual Summary

The box plots illustrate the celltype proportion of four distinct Macrophage subsets—Mac (M2B), Mac (M2D), Mac (M2C), and Mac (M2A)—across the Adeno and Squamous conditions. Statistical significance was determined with a p-value cutoff of 0.1, and all displayed subsets show differences meeting this criterion.

1. Mac (M2B): This subset shows a significantly higher proportion in Squamous Cell Carcinoma compared to Adenocarcinoma (p = 0.00112). The median proportion for Mac (M2B) is notably higher in Squamous (~23%) than in Adeno (~14%).
2. Mac (M2D): Similar to M2B, Mac (M2D) proportions are significantly elevated in Squamous Cell Carcinoma relative to Adenocarcinoma (p = 0.0489). The median in Squamous is around 8.5%, while in Adeno, it is approximately 5.5%.
3. Mac (M2C): In contrast to M2B and M2D, Mac (M2C) exhibits a trend towards higher proportions in Adenocarcinoma compared to Squamous Cell Carcinoma (p = 0.0614). The median for Mac (M2C) is approximately 26% in Adeno and around 24% in Squamous.
4. Mac (M2A): This subset also shows a tendency for higher proportions in Adenocarcinoma compared to Squamous Cell Carcinoma (p = 0.076). The median proportion for Mac (M2A) in Adeno is around 18%, while in Squamous, it is roughly 16%.

Overall, the plots demonstrate distinct patterns of macrophage polarization between Adenocarcinoma and Squamous Cell Carcinoma, with M2B and M2D subsets being more prominent in Squamous, while M2C and M2A subsets show relatively higher, though marginally significant, proportions in Adeno. The spread of data points (stripplot) indicates variability within each condition, but the median differences are clear for the significant subsets.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and play diverse roles in cancer progression. Often broadly classified as M1 (pro-inflammatory, anti-tumor) or M2 (immunosuppressive, pro-tumor), the M2 phenotype itself is heterogeneous, further subdivided into M2A, M2B, M2C, and M2D based on their activation stimuli and functional characteristics.

The observed differential enrichment of specific M2 macrophage subsets between lung Adenocarcinoma (LUAD) and Squamous Cell Carcinoma (LUSC) highlights fundamental differences in their respective tumor microenvironments:

• Enrichment of Mac (M2B) and Mac (M2D) in Squamous Cell Carcinoma: M2B macrophages are often associated with immune regulation, Th2 responses, and can contribute to tumor immunosuppression [1]. M2D macrophages, also known as tumor-associated macrophages (TAMs), are linked to angiogenesis and tumor growth [2]. Their significantly higher proportions in LUSC suggest a more pronounced pro-tumorigenic and immunosuppressive environment driven by these specific M2 subtypes in squamous tumors compared to adenocarcinomas. This aligns with findings suggesting distinct immune evasion mechanisms in LUSC.
• Higher, though marginally significant, proportions of Mac (M2A) and Mac (M2C) in Adenocarcinoma: M2A macrophages are typically involved in wound healing and tissue repair, but in cancer, they can promote tumor growth and immunosuppression [3]. M2C macrophages are highly immunosuppressive, promote fibrosis, and are often associated with advanced tumor stages and poor prognosis [4]. The enrichment of these subsets in LUAD suggests that adenocarcinomas might employ different M2-driven immunosuppressive strategies compared to LUSC, possibly involving tissue remodeling and resolution of inflammation in a pro-tumorigenic context.

These findings underscore the immunological heterogeneity between LUAD and LUSC, indicating that each subtype fosters a distinct macrophage polarization landscape that may influence tumor progression, immune surveillance, and response to therapy.

Clinical or Translational Implications

The distinct macrophage subset profiles observed between lung Adenocarcinoma and Squamous Cell Carcinoma have several important clinical and translational implications:

1. Biomarkers for Subtype-Specific Prognosis: The differential prevalence of specific M2 macrophage subsets could serve as prognostic biomarkers, potentially indicating disease aggressiveness or recurrence risk in LUAD versus LUSC patients. For example, high levels of M2B/M2D in LUSC or M2A/M2C in LUAD might correlate with poorer outcomes.
2. Targeted Immunotherapy Strategies: Understanding the dominant M2 macrophage subsets in each lung cancer subtype could inform the development of more precise, subtype-specific immunotherapies. For instance, LUSC patients with elevated M2B/M2D populations might benefit from therapies that specifically repolarize these macrophages towards an M1-like anti-tumor phenotype or deplete them. Similarly, LUAD-specific strategies might target M2A/M2C pathways.
3. Monitoring Treatment Response: Changes in the proportions of these macrophage subsets could serve as dynamic biomarkers to monitor patient response to treatment, particularly immunotherapies. A shift from a pro-tumorigenic M2-dominant profile towards a more balanced or M1-dominant profile might indicate therapeutic efficacy.
4. Understanding Treatment Resistance: Differential macrophage populations could contribute to varying responses and resistance mechanisms to standard and emerging therapies, including checkpoint inhibitors, between LUAD and LUSC. Investigating these associations could lead to combination therapies that overcome macrophage-mediated immunosuppression.

These results emphasize the importance of considering lung cancer histological subtypes when designing clinical trials and developing personalized treatment approaches that account for the unique cellular composition of their respective tumor microenvironments.

References

1. M2B Macrophages: For general information on macrophage phenotypes, search PubMed: https://pubmed.ncbi.nlm.nih.gov/?term=M2B+macrophages+cancer
2. M2D Macrophages: For general information on M2D macrophages, search PubMed: https://pubmed.ncbi.nlm.nih.gov/?term=M2D+macrophages+tumor
3. M2A Macrophages: For general information on M2A macrophages, search PubMed: https://pubmed.ncbi.nlm.nih.gov/?term=M2A+macrophages+cancer
4. M2C Macrophages: For general information on M2C macrophages, search PubMed: https://pubmed.ncbi.nlm.nih.gov/?term=M2C+macrophages+tumor+prognosis

11. Ploidy Analysis of Lung Epithelial Cells in Lung Adenocarcinoma and Squamous Cell Carcinoma Samples

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (aneuploid, diploid, or unclear) of Lung Epithelial cells, identified as the tumor-origin cells, across various patient samples. The samples are categorized by their primary lung cancer histology: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). The goal is to compare the proportion of aneuploid and diploid cells within the tumor-origin population for each sample and identify potential differences between these two major lung cancer subtypes.

Visual Summary

The stacked bar plots display the relative proportions of aneuploid, diploid, and unclear Lung Epithelial cells for each individual sample, grouped by condition (Adeno or Squamous).

• Adenocarcinoma (Adeno) Samples: A notable heterogeneity in ploidy is observed. Some Adeno samples (e.g., NSC021.T1, NSC021.T2, NSC019.T2, NSC018.T1) exhibit a significant proportion of aneuploid cells, ranging from approximately 30% to over 60%. Conversely, many other Adeno samples (e.g., NSC016.T2, NSC010.T2, NSC010.T1, NSC020.T1) show a very low or negligible percentage of aneuploid cells, being almost entirely composed of diploid Lung Epithelial cells. The "Unclear" category is consistently very small, if present, across all Adeno samples.
• Squamous Cell Carcinoma (Squamous) Samples: In contrast to Adeno, Squamous samples display a more uniformly high proportion of aneuploid Lung Epithelial cells. Most samples (e.g., NSC004.T1, NSC004.T2, NSC004.T3, NSC004.T4) show over 70% aneuploidy, with others also demonstrating substantial aneuploid fractions (e.g., NSC009.T1 at ~58%, NSC009.T2 at ~38%). The proportion of diploid cells is correspondingly lower, and the "Unclear" category remains minimal.
• Comparison between conditions: A striking difference emerges where Squamous Cell Carcinoma samples generally present a higher and more consistent burden of aneuploid tumor-origin cells compared to Adenocarcinoma samples, which show greater variability in their aneuploidy levels.

Biological Interpretation

Aneuploidy, the state of having an abnormal number of chromosomes, is a pervasive feature of cancer and a major driver of genomic instability, tumor evolution, and drug resistance PMID: 33719293. The observed patterns in Lung Epithelial cells, confirmed as tumor-origin cells, provide important biological insights into the two lung cancer subtypes:

1. Confirmed Tumor Malignancy: The presence of aneuploid Lung Epithelial cells in a substantial number of samples, particularly in Squamous Cell Carcinoma, strongly supports the malignant nature of these cells, as aneuploidy is a hallmark of cancer PMID: 21327895.
2. Subtype-Specific Genomic Instability: The differential ploidy landscape between Adenocarcinoma and Squamous Cell Carcinoma suggests distinct underlying mechanisms of genomic instability or selective pressures during tumor development.

• Squamous Cell Carcinoma: The consistently high aneuploidy rates observed in Squamous samples are indicative of a generally more genomically unstable and complex karyotype, which is a known characteristic of Lung Squamous Cell Carcinomas PMID: 22960737. This extensive genomic disruption can contribute to aggressive tumor behavior.
• Adenocarcinoma: The considerable variability in aneuploidy within Adenocarcinoma samples reflects the molecular heterogeneity of this disease. Some Adenocarcinomas are highly aneuploid, similar to Squamous Cell Carcinoma, while others maintain a largely diploid state. Near-diploid tumors may represent earlier stages, less aggressive subtypes, or tumors driven by specific oncogenic alterations that do not necessarily induce widespread chromosomal instability.

3. Role of Diploid Tumor Cells: For Adenocarcinoma samples predominantly composed of diploid Lung Epithelial cells, it is plausible that these represent tumors that have either not yet accumulated significant chromosomal aberrations, or they are driven by specific genomic alterations (e.g., point mutations, fusions) that do not typically lead to gross aneuploidy. It is important to distinguish these from contamination by normal stromal or immune cells, but the data context specifies "Lung Epithelial cell" as the "Resolved tumor-origin cell type," reinforcing that these are indeed malignant cells.

Clinical or Translational Implications

The distinct ploidy patterns between lung adenocarcinoma and squamous cell carcinoma can have several clinical and translational implications:

1. Prognostic Value: High aneuploidy burden is often associated with poorer prognosis in various cancers, including lung cancer, reflecting increased genomic instability and potentially more aggressive tumor biology. The observed differences suggest that aneuploidy status in Lung Epithelial cells could serve as a prognostic marker, with Squamous Cell Carcinomas potentially having a less favorable prognosis generally linked to their higher aneuploidy rates, though further correlation with patient outcomes would be needed.
2. Treatment Stratification: Understanding the ploidy status can inform therapeutic strategies. Tumors with high aneuploidy might exhibit different sensitivities to chemotherapy or targeted therapies. For instance, highly aneuploid tumors might be more vulnerable to treatments that exacerbate mitotic stress or DNA damage. Conversely, near-diploid tumors might rely on different molecular dependencies. This highlights the importance of personalized medicine approaches tailored to the specific genomic landscape of each patient's tumor.
3. Biomarker Development: Ploidy status, as assessed by single-cell RNA-seq (e.g., via CNV inference), could potentially be developed into a biomarker to help distinguish between challenging cases of lung cancer subtypes or to identify patients who might benefit from specific therapeutic regimens.

12. Analysis of Cell-Cell Interaction Patterns in Lung Cancer Subtypes (Adenocarcinoma and Squamous Cell Carcinoma)

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using single-cell RNA-seq data from lung cancer patients, comparing Adenocarcinoma ('Adeno') and Squamous Cell Carcinoma ('Squamous') conditions. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between key cell types in the tumor microenvironment: Lung Epithelial cells (identified as tumor-origin cells), Macrophages, and T cells (CD4+ and CD8+ subsets). The plots highlight interactions with high mean expression (color intensity) and high statistical significance (dot size), displaying up to 80 prominent cell-cell interactions per condition.

Visual Summary

The provided dot plots illustrate distinct and shared cell-cell interaction landscapes for Adenocarcinoma and Squamous cell carcinoma. Each dot represents a unique ligand-receptor pair, with its size indicating the statistical significance (-log10(p-value)) and its color representing the interaction strength (log2(mean expression)).

CCI for Adeno

The Adeno plot reveals a rich and diverse set of interactions involving 'Diploid Lung Epi' (Lung Epithelial cells), 'Mac' (Macrophages), 'T CD4+', and 'T CD8+' cell types.

• Prominent Interactions with Lung Epithelial cells: 'Diploid Lung Epi' cells show significant self-interactions (e.g., ALCAM-CD6, CD99-PILRA, CD55-ADGRE5) and strong interactions with Macrophages (e.g., SPP1-CD44, CD99-PILRA, ICAM1_integrin complexes).
• Macrophage-Macrophage Interactions: 'Mac|Mac' interactions are abundant and strong, with highly significant pairs like SPP1-integrin_a3b1_complex, SPP1-CD44, and THBS1_integrin_a3b1_complex.
• T cell Interactions: 'T CD8+|T CD8+' and 'T CD4+|T CD4+' pairs show several significant interactions, including CD99-PILRA, CD55-ADGRE5, and PTPRC-PTPRC. 'Mac|T CD4+' interactions also appear, notably involving CD99-PILRA and ICAM1_integrin complexes.
• Overall, the Adeno tumor microenvironment appears to be characterized by extensive communication networks among all analyzed cell types, with significant involvement of the tumor-origin Lung Epithelial cells.

CCI for Squamous

The Squamous plot presents a comparatively sparser interaction profile, with fewer cell-cell pairs meeting the significance and mean expression cutoffs or being selected within the top 80 pairs.

• Absence of Lung Epithelial cell and T CD8+ Interactions: Notably, interactions involving 'Diploid Lung Epi' cells or 'T CD8+' cells are largely absent from the top 80 significant interactions shown for Squamous. This is a significant difference compared to Adeno.
• Macrophage Interactions Still Prominent: 'Mac|Mac' interactions remain a dominant feature, with SPP1-CD44 and THBS1_integrin_a3b1_complex showing high significance and strength, similar to Adeno.
• T CD4+ Interactions: 'T CD4+|T CD4+' and 'Mac|T CD4+' interactions are observed, with CD99-PILRA and CD55-ADGRE5 being notable.
• The Squamous microenvironment appears to have a less intricate direct interaction network among the selected cell types, particularly lacking the direct involvement of tumor-origin epithelial cells in the most prominent interactions depicted.

Comparison and Key Differences

The most striking difference is the extensive involvement of Lung Epithelial cells in cell-cell interactions in Adeno, which is largely absent in the displayed prominent interactions for Squamous. T CD8+ cell interactions are also more prominent in Adeno. Macrophage-Macrophage interactions (SPP1-CD44, THBS1_integrin_a3b1_complex) are consistently strong and significant in both conditions, suggesting their conserved importance in both lung cancer subtypes.

Biological Interpretation

The identified cell-cell interactions provide crucial insights into the biological processes within the tumor microenvironment (TME) of lung Adenocarcinoma and Squamous Cell Carcinoma.

• Tumor-Immune Cell Crosstalk in Adenocarcinoma: The significant interactions between 'Diploid Lung Epi' cells and 'Mac' cells in Adeno, particularly involving SPP1-CD44 and CD99-PILRA, highlight active communication between tumor cells and tumor-associated macrophages (TAMs).
• SPP1 (Osteopontin), secreted by tumor cells and TAMs, is known to promote tumor growth, metastasis, and immune evasion by binding to CD44 on various cell types, including macrophages. Its strong presence here suggests a pro-tumorigenic role for this axis in Adeno. GeneCards: SPP1 | GeneCards: CD44
• CD99-PILRA interactions between 'Diploid Lung Epi' and 'Mac' or T cells may modulate immune cell adhesion and activation, with PILRA acting as an inhibitory receptor. This could represent a mechanism by which tumor cells or TAMs regulate immune responses.
• ICAM1-integrin complexes (e.g., ICAM1_integrin_aL2_complex between 'Mac' and 'Diploid Lung Epi') facilitate cell adhesion and immune cell trafficking, suggesting active immune cell recruitment and interaction within the Adeno TME.
• Central Role of Macrophages: The high number of strong 'Mac|Mac' interactions, especially involving SPP1-CD44 and THBS1 (Thrombospondin-1)-integrin_a3b1_complex, underscores the central role of macrophages in shaping the TME of both Adeno and Squamous.
• THBS1 is a matricellular protein involved in angiogenesis, inflammation, and immune suppression, often secreted by TAMs. Its homotypic interactions or interactions with integrins on macrophages suggest complex self-regulation or spatial organization of TAMs. GeneCards: THBS1
• T-cell Interactions: Within T-cell populations, interactions like CD99-PILRA, CD55-ADGRE5, and PTPRC (CD45)-PTPRC are observed. CD45 homotypic interactions are critical for T-cell receptor signaling and T-cell activation. CD55 (complement decay-accelerating factor) interacting with ADGRE5 (CD97) is involved in cell adhesion and immune regulation. GeneCards: CD55 | GeneCards: ADGRE5 These interactions reflect the active state and communication within the T-cell compartment.
• Distinct TME Dynamics in Squamous: The relative absence of significant 'Diploid Lung Epi' and 'T CD8+' interactions in Squamous suggests a different mode of tumor-immune interaction compared to Adeno. This could imply that Squamous tumors might employ different strategies for immune evasion or have a TME where these specific ligand-receptor pairs play a less dominant role in the detected top interactions.

Clinical or Translational Implications

The distinct and shared cell-cell interaction patterns uncovered have significant implications for therapeutic development and biomarker discovery in lung cancer.

• Targeting SPP1-CD44 Axis: Given its strong and significant presence in Macrophage-Macrophage interactions in both Adeno and Squamous, and its involvement in Macrophage-Lung Epithelial cell interactions in Adeno, the SPP1-CD44 axis represents a promising pan-lung cancer therapeutic target. Modulating this pathway could inhibit tumor progression, reduce metastasis, and reprogram TAMs to a more anti-tumorigenic phenotype.
• Adeno-Specific Targets: The robust interactions involving Lung Epithelial cells in Adenocarcinoma, such as CD99-PILRA and ICAM1-integrin complexes, suggest potential Adeno-specific therapeutic avenues. Disrupting these interactions could interfere with tumor-macrophage crosstalk, immune cell infiltration, and potentially enhance anti-tumor immunity. These findings warrant further experimental validation to confirm their functional significance in Adeno pathogenesis.
• Biomarker Potential: The differential interaction profiles between Adeno and Squamous, particularly the prominence of 'Diploid Lung Epi' interactions in Adeno, could serve as biomarkers for distinguishing these subtypes or predicting response to subtype-specific therapies. Further studies could explore if the expression levels of these ligand-receptor pairs correlate with clinical outcomes or responsiveness to immunotherapy.
• Immunomodulation Strategies: The significant T-cell and Macrophage interactions, including those involving CD99-PILRA and CD55-ADGRE5, provide targets for immunomodulatory therapies. Manipulating these pathways could fine-tune immune cell activation, migration, and effector functions within the TME, potentially turning "cold" tumors "hot" or enhancing existing immunotherapies.
• Experimental Validation: The identified ligand-receptor pairs offer concrete hypotheses for *in vitro* and *in vivo* experimental validation. For instance, blocking antibodies against SPP1, CD44, PILRA, or specific integrin subunits could be tested in lung cancer models to evaluate their impact on tumor growth, immune cell composition, and therapeutic efficacy, thereby advancing target prioritization.

13. Condition-Specific Cell-Cell Interaction Patterns in Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interactions (CCIs) involving Lung Epithelial cells (both diploid and aneuploid, representing tumor and non-tumor epithelial cells) and major immune and stromal cell types in lung cancer patients, comparing Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) conditions. The goal is to identify significantly different interaction patterns between these two major lung cancer subtypes, uncover underlying signaling pathways, and assess their potential as therapeutic targets. The dot plot visualizes the strength and statistical significance of these interactions across individual samples for each condition.

Visual Summary

The dot plot effectively illustrates distinct cell-cell interaction landscapes between Adenocarcinoma and Squamous Cell Carcinoma samples.

• Condition-Specific Clustering: Samples are clearly separated by condition (Adeno at the top, Squamous at the bottom), and within each condition, groups of samples exhibit similar CCI profiles. This highlights the substantial differences in cell communication patterns between the two cancer subtypes.
• Interaction Strength and Significance: The color intensity (red scale) indicates the scaled log strength of the interaction, with darker red signifying stronger interactions. The size of the dot represents the -log10(p-value), with larger dots denoting more statistically significant interactions.

Differential Interaction Patterns:

• Adenocarcinoma (Adeno): A notable cluster of strong and significant interactions is observed in Adeno samples, particularly involving Aneuploid Lung Epithelial cells. Key interactions include those between Lung Epithelial cells (aneuploid) and various immune cells like Macrophages and T cells (CD4+, CD8+), as well as Plasma cells. Interactions such as APLP2-PIGR, THBS1-integrin, LGALS9-P4HB, CD55-ADGRE5, CEACAM6-CEACAM6, MPZL1-MPZL1, and GAS6-AXL are frequently observed as strong and significant in Adeno. Interactions involving Fibroblasts and Endothelial cells, often via collagen-integrin or VEGFA-NRP signaling, are also present.
• Squamous Cell Carcinoma (Squamous): In contrast, Squamous samples display a different set of prominent interactions. There is a high prevalence of interactions involving Fibroblasts, often forming homotypic (Fib-Fib) or heterotypic (Fib-Lung Epithelial) contacts via various collagen (COL) and fibronectin (FN1) integrin complexes. Other significant interactions include SPP1-CD44 (Mac-Lung.Epi), MIF-CD74 (Mac-Mac), JAG2-NOTCH3 (Fib-Fib), CD96-NECTIN1 (T CD4+-Lung.Epi), and CXCL14-CXCR4 (Lung.Epi-T CD8+).
• Cell Type Specificity: Both aneuploid and diploid Lung Epithelial cells participate in differential interactions with various immune (Macrophage, T cell, Plasma cell, Mast cell, NK cell, B cell) and stromal (Fibroblast, Endothelial cell) components, underscoring the complex interplay within the tumor microenvironment of each subtype.

Biological Interpretation

The observed condition-specific CCI patterns reveal distinct biological characteristics and microenvironmental compositions in lung Adenocarcinoma and Squamous Cell Carcinoma.

Interactions Predominant in Adenocarcinoma

1. GAS6-AXL Signaling (Lung.Epi (Aneuploid) - Plasma cell):

• Mechanism: Growth Arrest-Specific 6 (GAS6) is a ligand for the AXL receptor tyrosine kinase. AXL activation promotes tumor cell proliferation, survival, migration, and drug resistance, and is implicated in immune evasion. Plasma cells can contribute to the TME and potentially express AXL or GAS6, influencing tumor behavior.
• Biological Role: This pathway is a known oncogenic driver in various cancers, including lung adenocarcinoma, contributing to aggressive tumor phenotypes and resistance to therapy. The interaction suggests a pro-tumorigenic signal originating from or influencing aneuploid epithelial cells.
• Therapeutic Potential: High AXL expression correlates with poor prognosis. AXL inhibitors (e.g., bemcentinib, gilteritinib, cabozantinib) are actively being investigated in clinical trials for lung cancer and other malignancies. [PubMed Search]

2. LGALS9-P4HB (Lung.Epi (Aneuploid) - T cell CD4+):

• Mechanism: Galectin-9 (LGALS9) is an immune checkpoint molecule that binds to T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) on T cells, leading to T cell exhaustion and suppression. P4HB is a protein disulfide isomerase that can affect galectin-9 function or presentation.
• Biological Role: This interaction points towards an immune suppressive mechanism mediated by tumor-associated aneuploid epithelial cells in adenocarcinoma, contributing to T cell dysfunction and allowing immune escape.
• Therapeutic Potential: Targeting the LGALS9-TIM-3 axis with anti-TIM-3 antibodies (e.g., cobolimab) represents a strategy to reverse T cell exhaustion and enhance anti-tumor immunity. [PubMed Search]

3. THBS1-integrin_a3b1_complex (Macrophage - Lung.Epi (Aneuploid)):

• Mechanism: Thrombospondin-1 (THBS1) is a matricellular protein involved in cell-matrix interactions, angiogenesis, and immune regulation. Its interaction with integrin alpha3beta1 on aneuploid epithelial cells likely influences tumor cell adhesion, migration, and the recruitment/activation of tumor-associated macrophages.
• Biological Role: THBS1 can act as an anti-angiogenic factor but also promote tumor growth and metastasis in certain contexts. This interaction highlights the complex ECM remodeling and macrophage-tumor cell crosstalk in adenocarcinoma.
• Therapeutic Potential: Modulating THBS1 activity or blocking its integrin interactions could impact angiogenesis and immune responses. [PubMed Search]

4. VEGFA-NRP1/NRP2 (Fibroblast - Lung.Epi (Diploid)):

• Mechanism: Vascular Endothelial Growth Factor A (VEGFA) signaling, often co-opting Neuropilin-1 (NRP1) and Neuropilin-2 (NRP2) as co-receptors, is a primary driver of angiogenesis. This interaction suggests fibroblast-derived VEGFA influencing nearby epithelial cells.
• Biological Role: This pathway is fundamental for promoting tumor angiogenesis, providing nutrients and oxygen for tumor growth in adenocarcinoma, and is a well-established target in cancer therapy.
• Therapeutic Potential: Anti-VEGF agents like bevacizumab or ramucirumab are approved for lung cancer. Targeting neuropilins directly could also be a strategy. [PubMed Search]

Interactions Predominant in Squamous Cell Carcinoma

1. COL/FN1-Integrin Complexes (Fibroblast - Fibroblast, Fibroblast - Lung.Epi (Aneuploid)):

• Mechanism: Numerous interactions involving collagens (COL1A1, COL3A1, COL6A1, COL6A3, COL12A1) and fibronectin (FN1) with various integrin complexes (e.g., alpha1beta1, alpha2beta1, alpha5beta1, alpha6beta1) are prominent, particularly between fibroblasts and between fibroblasts and aneuploid lung epithelial cells. These interactions mediate cell adhesion, migration, and mechanotransduction.
• Biological Role: This robust signature points to significant extracellular matrix (ECM) remodeling and desmoplasia, a hallmark of squamous cell carcinoma, which creates a stiff, dense tumor microenvironment that promotes tumor growth, invasion, and resistance to therapy. Cancer-associated fibroblasts (CAFs) are key players in this process.
• Therapeutic Potential: Targeting specific integrins or ECM-modifying enzymes (e.g., LOXL2 inhibitors) could disrupt tumor progression and enhance drug delivery in desmoplastic tumors. [PubMed Search]

2. SPP1-CD44 (Macrophage - Lung.Epi (Aneuploid)):

• Mechanism: Secreted Phosphoprotein 1 (SPP1), also known as Osteopontin, binds to CD44 on aneuploid lung epithelial cells. This interaction activates downstream signaling pathways promoting cell survival, proliferation, migration, and metastasis. Macrophages are a major source of SPP1 in the TME.
• Biological Role: SPP1-CD44 signaling is strongly implicated in tumor aggressiveness, immune evasion, and drug resistance in SCC.
• Therapeutic Potential: Inhibitors targeting SPP1 or blocking CD44 (e.g., bivatuzumab mertansine) have been explored, aiming to reduce tumor growth and metastatic potential. [PubMed Search]

3. CD96-NECTIN1 (T cell CD4+ - Lung.Epi (Aneuploid)):

• Mechanism: CD96 is an immune checkpoint receptor found on T cells and NK cells, while NECTIN1 is its ligand, often expressed by tumor cells. This interaction can lead to inhibition of T cell activation and function.
• Biological Role: Similar to LGALS9-TIM-3, this represents another immune evasive mechanism employed by aneuploid tumor cells in SCC to suppress anti-tumor T cell responses.
• Therapeutic Potential: Monoclonal antibodies targeting CD96 are under investigation as novel immune checkpoint inhibitors to enhance anti-tumor immunity. [Link]

4. CXCL14-CXCR4 (Lung.Epi (Diploid) - T cell CD8+):

• Mechanism: Chemokine (C-X-C motif) Ligand 14 (CXCL14) is a chemokine that can interact with CXCR4, a G protein-coupled receptor. CXCR4 is widely expressed on immune cells and cancer cells, playing roles in cell trafficking and metastasis.
• Biological Role: This interaction might influence the recruitment, retention, or dysfunction of CD8+ T cells within the SCC microenvironment, potentially impacting anti-tumor immunity.
• Therapeutic Potential: CXCR4 antagonists (e.g., Plerixafor) are used in other clinical contexts and are being evaluated for their potential to inhibit tumor metastasis and improve responses to immunotherapy in cancer. [PubMed Search]

5. JAG2-NOTCH3 (Fibroblast - Fibroblast):

• Mechanism: Jagged2 (JAG2) is a ligand for Notch receptors, including NOTCH3. Notch signaling is crucial for cell development, differentiation, and tissue homeostasis, and its dysregulation contributes to cancer progression. This specific interaction suggests Notch activation within the stromal compartment.
• Biological Role: Dysregulated Notch signaling in CAFs can promote tumor growth, angiogenesis, and immunosuppression, contributing to the aggressive phenotype of SCC.
• Therapeutic Potential: Gamma-secretase inhibitors (GSIs) or anti-Notch antibodies are being investigated to target Notch signaling in cancer. [PubMed Search]

Clinical or Translational Implications

The distinct CCI profiles between lung adenocarcinoma and squamous cell carcinoma highlight their fundamentally different biological underpinnings, which have significant clinical and translational implications:

1. Personalized Therapy: The identified condition-specific interactions can guide the development of personalized therapeutic strategies. For instance, AXL inhibitors and anti-TIM-3 therapies may hold greater promise for adenocarcinoma, while targeting integrin-mediated ECM remodeling, SPP1-CD44, CD96, CXCR4, or Notch pathways might be more effective for squamous cell carcinoma.
2. Biomarker Discovery: The specific ligand-receptor pairs identified as significant in each condition could serve as diagnostic or prognostic biomarkers. For example, high expression of AXL or LGALS9 in adenocarcinoma tumor biopsies, or high SPP1/CD44 or specific collagen-integrin signatures in squamous cell carcinoma, could indicate disease subtype, aggressiveness, or likelihood of response to targeted therapies.
3. Combination Therapies: Understanding the specific immune suppressive (e.g., LGALS9-TIM-3, CD96-NECTIN1) or pro-tumorigenic (e.g., GAS6-AXL, SPP1-CD44, Notch) pathways allows for rational design of combination therapies. For example, combining immune checkpoint blockade with AXL inhibitors in adenocarcinoma or with integrin inhibitors in squamous cell carcinoma could yield synergistic effects.
4. Drug Repurposing: Existing drugs that modulate the identified pathways, such as tyrosine kinase inhibitors targeting AXL, anti-angiogenic agents targeting VEGF/NRP, or CXCR4 antagonists, could be repurposed or prioritized for clinical investigation in the respective lung cancer subtypes.
5. Understanding Treatment Resistance: Differential CCI patterns might explain varying responses to current standard treatments. For example, extensive ECM remodeling in SCC might hinder drug penetration, while specific immune evasion mechanisms in Adeno might affect immunotherapy responses.

In summary, this analysis provides valuable insights into the intricate cell-cell communication networks that characterize lung adenocarcinoma and squamous cell carcinoma, offering a foundation for developing more precise diagnostic tools and targeted therapeutic interventions.

14. Condition-Specific Cell-Cell Interaction Patterns in Lung Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interactions (CCIs) involving immune checkpoint and cell cycle pathway genes across different lung cancer samples (Adenocarcinoma and Squamous Cell Carcinoma). The primary objective was to identify significant interactions among Lung Epithelial cells (tumor-origin cells), major immune cells, and stromal cells, which differ between Adeno and Squamous conditions. The results are visualized as a dot plot, where dot color indicates interaction strength (scaled log strength) and dot size reflects statistical significance (-log10(p-value)).

Visual Summary

The dot plot effectively visualizes condition-specific CCI patterns across various patient samples.

• Sample Grouping: Samples are clearly grouped by condition on the y-axis, with Adenocarcinoma (Adeno) samples at the top and Squamous Cell Carcinoma (Squamous) samples at the bottom. The blue boxes highlight distinct clusters of interactions enriched in each condition.
• Interaction Grouping: Cell-cell interactions (CCI index) are displayed on the x-axis, also largely grouped by condition, allowing for easy comparison of active pathways.

Quantitative Representation

• Color Intensity: The gradient of red indicates the scaled log strength of the interaction, with darker red dots representing stronger interactions.
• Dot Size: The size of each dot corresponds to the -log10(p-value), meaning larger dots represent more statistically significant interactions.

Key Observations

• A distinct cluster of interactions (left blue box) shows high activity and significance predominantly within the Adeno samples (e.g., NSC010.T1, NSC016.T1-3, NSC018.T1-3, NSC020.T1-2). These include EGFR-ligand interactions involving diploid Lung Epithelial cells and endothelial cells, as well as LCK-CD8_receptor interactions between various immune cell types.
• Another prominent cluster of interactions (right blue box) is highly active and significant primarily within the Squamous samples (e.g., NSC004.T1-4, NSC009.T1-2). These largely involve EGFR-ligand interactions among macrophages, fibroblasts, plasma cells, and both diploid and aneuploid Lung Epithelial cells. LCK-CD8_receptor interactions among T cells are also observed.
• The plot clearly delineates interactions that are either enriched or more highly significant in one lung cancer subtype compared to the other, indicating differential biological processes.

Biological Interpretation

Condition-Specific Cell-Cell Interaction Patterns

Interactions Predominantly Active in Adenocarcinoma (Adeno)

In Adenocarcinoma samples, a cluster of interactions, often highly significant (large, dark red dots), suggests specific microenvironmental dynamics:

EGFR Ligand-Receptor Signaling (AREG_EGFR, HBEGF_EGFR)

• AREG_EGFR -- Lung.Epi (Dip) | Lung.Epi (Dip) and HBEGF_EGFR -- Lung.Epi (Dip) | Lung.Epi (Dip): These indicate autocrine or paracrine activation of the EGFR pathway among diploid Lung Epithelial cells. While "diploid" suggests non-aneuploid, these cells could be normal adjacent tissue, early transformed cells, or a subpopulation within the tumor with a stable karyotype. EGFR signaling is critical for epithelial cell growth and survival.
• AREG_EGFR -- Endo | Endo: Autocrine/paracrine EGFR signaling among endothelial cells. This points to active angiogenesis and vascular remodeling within the Adeno tumor microenvironment.

Immune Cell Interactions (CD93_IFNGR1, LCK_CD8_receptor)

• CD93_IFNGR1 -- Endo | Endo: This interaction between endothelial cells, involving the IFN-gamma receptor, suggests active modulation of angiogenesis and immune responses. CD93 is implicated in endothelial cell function and inflammation.
• LCK_CD8_receptor -- NK | T CD8+, LCK_CD8_receptor -- ILC | T CD8+, and LCK_CD8_receptor -- T CD8+ | T CD8+: These indicate significant interactions between various immune cell types (NK cells, ILCs, and CD8+ T cells) mediated by LCK and the CD8 receptor complex. LCK is a key kinase for T cell activation, suggesting ongoing immune cell communication and potentially attempts at anti-tumor immune responses within the Adeno microenvironment.

Interactions Predominantly Active in Squamous Cell Carcinoma (Squamous)

Squamous samples exhibit a distinct set of highly active and significant CCIs, emphasizing a different tumor microenvironment composition and signaling:

EGFR Ligand-Receptor Signaling (EREG_EGFR, HBEGF_EGFR, AREG_EGFR, EGF_EGFR)

• EREG_EGFR -- Mac | Fib, HBEGF_EGFR -- Mac | Fib: Strong interactions between macrophages (Mac) and fibroblasts (Fib) via Epiregulin (EREG) and Heparin-binding EGF-like growth factor (HBEGF) activating EGFR. This highlights the crucial role of the stromal compartment in Squamous tumors, with macrophages and fibroblasts likely promoting tumor growth and remodeling the extracellular matrix.
• EREG_EGFR -- Mac | Lung.Epi (Dip), AREG_EGFR -- Mast cell | Lung.Epi (Dip): Macrophages and Mast cells interacting with diploid Lung Epithelial cells via EGFR ligands. This indicates paracrine growth promotion signals from immune/stromal cells to epithelial cells.
• EREG_EGFR -- Mac | Lung.Epi (Aneup) and AREG_EGFR -- Lung.Epi (Aneup): Crucially, Epiregulin from macrophages and Amphiregulin (potentially autocrine or paracrine) are strongly activating EGFR on *aneuploid* Lung Epithelial cells, which represent the malignant tumor cells. This is a direct mechanism for promoting tumor cell proliferation and survival.
• EREG_EGFR -- Mac | Plasma, HBEGF_EGFR -- Plasma | Plasma, EGF_EGFR -- Plasma | Plasma, AREG_EGFR -- Mac | Plasma: Various EGFR ligand interactions involving plasma cells and macrophages, suggesting complex immune and stromal communication within the tumor microenvironment, potentially influencing humoral immunity or inflammation.

Immune Cell Interactions (LCK_CD8_receptor)

• LCK_CD8_receptor -- T CD8+ | T CD8+ and LCK_CD8_receptor -- T CD4+ | T CD8+: Similar to Adeno, interactions involving LCK and CD8 receptor are present, indicating active communication among T cells (CD8+ T cells with themselves and with CD4+ T helper cells). This is essential for coordinating adaptive immune responses.

Shared or Distinct Mechanisms

Both Adeno and Squamous subtypes show significant EGFR pathway activity, but the cellular sources of ligands and the target cells vary. Adeno shows more autocrine/paracrine EGFR signaling within epithelial and endothelial cells, while Squamous exhibits strong TME involvement with macrophages and fibroblasts driving EGFR activation on tumor cells. LCK-mediated T cell interactions are present in both, underscoring the universal importance of T cell immunity in the lung cancer microenvironment, albeit with slightly different interacting partners.

Clinical or Translational Implications

The identified cell-cell interactions, particularly those involving EGFR signaling and T cell function, offer critical insights into potential therapeutic strategies for lung cancer.

1. Targeting EGFR Signaling in Lung Cancer

The widespread and often strong activation of EGFR signaling, especially on aneuploid Lung Epithelial cells in Squamous carcinoma and across epithelial/endothelial cells in Adeno, underscores its role as a key driver of tumor growth and progression.

• Signaling Pathways and Mechanisms: Activation of EGFR by ligands (AREG, HBEGF, EREG, EGF) triggers downstream cascades such as the RAS/RAF/MEK/ERK pathway and the PI3K/AKT/mTOR pathway. These pathways collectively promote cell proliferation, survival, anti-apoptosis, angiogenesis, and metastasis.
• Reference: EGFR Signaling Pathway in Cancer - PubMed
• Potential as a Therapeutic Target: EGFR is a well-established oncogene and a highly validated therapeutic target in lung cancer. Targeting either the receptor itself or its activating ligands can disrupt tumor growth.

Relevant Therapeutic Agents

• EGFR Tyrosine Kinase Inhibitors (TKIs): Drugs like Gefitinib, Erlotinib, Afatinib, and Osimertinib are commonly used for *EGFR*-mutated NSCLC (more frequent in Adenocarcinoma). They directly inhibit the kinase activity of EGFR.
• Anti-EGFR Antibodies: Monoclonal antibodies such as Cetuximab and Necitumumab block ligand binding to the extracellular domain of EGFR, preventing its activation. Necitumumab is specifically approved for metastatic Squamous NSCLC in combination with chemotherapy.
• Ligand-Blocking Strategies: While specific drugs directly targeting EGFR ligands (AREG, HBEGF, EREG, EGF) are less common clinically, the strong presence of these interactions (e.g., EREG from macrophages to aneuploid Lung Epithelial cells in Squamous) suggests that inhibiting ligand production or activity could be a viable strategy, potentially in combination with EGFR TKIs to overcome resistance or enhance efficacy.
• Reference: Therapeutic targeting of EGFR in NSCLC - PubMed

2. Modulating T-Cell Immunity via LCK Signaling

LCK-CD8_receptor interactions were observed in both Adeno and Squamous conditions, indicating active T cell communication and activation pathways.

• Signaling Pathways and Mechanisms: LCK is a crucial non-receptor tyrosine kinase essential for initiating T cell receptor (TCR) signaling. Upon antigen recognition, LCK phosphorylates CD3 chains, leading to a cascade of events that result in T cell activation, proliferation, and effector functions (e.g., cytotoxicity). CD8 receptor acts as a co-receptor, enhancing TCR signaling in CD8+ T cells. Interactions between T cells, NK cells, and ILCs highlight the complex interplay within the immune microenvironment.
• Reference: LCK in T cell signaling - GeneCards
• Potential as a Therapeutic Target: While LCK itself is not typically a direct therapeutic target for enhancing anti-tumor immunity due to potential systemic immunosuppression, its central role in TCR signaling makes it a crucial component in strategies aimed at boosting T cell activity. Modulating its activity or downstream pathways could enhance the efficacy of immunotherapies.

Relevant Therapeutic Agents

• Immune Checkpoint Inhibitors (ICIs): Agents like Pembrolizumab, Nivolumab (anti-PD-1), and Atezolizumab (anti-PD-L1) work by removing inhibitory signals on T cells, thereby allowing LCK-mediated TCR signaling to proceed unhindered and restore anti-tumor immunity. These are standard treatments for various lung cancers.
• Adoptive Cell Therapies (ACT): Therapies such as CAR T-cells or engineered TCR T-cells aim to introduce T cells with enhanced tumor-recognition and activation capabilities, implicitly relying on robust LCK-mediated signaling for their effector functions.
• Combination Therapies: Understanding these LCK-mediated interactions can inform combination strategies, for instance, combining ICIs with agents that enhance T cell priming or activation.

3. Endothelial Cell Interactions and Angiogenesis

The CD93_IFNGR1 -- Endo | Endo interaction observed in Adenocarcinoma points towards specific endothelial cell biology.

• Signaling Pathways and Mechanisms: CD93 is a C-type lectin receptor expressed on endothelial cells and myeloid cells, involved in angiogenesis, inflammation, and phagocytosis. IFNGR1 is a subunit of the interferon-gamma receptor, mediating the effects of IFN-gamma, which can be pro- or anti-angiogenic and immunomodulatory. This interaction between endothelial cells may represent a mechanism of regulating vascular growth and immune cell infiltration in Adenocarcinoma.
• Reference: CD93 function in angiogenesis - PubMed
• Potential as a Therapeutic Target: Targeting angiogenesis is a well-established strategy in cancer treatment. Modulating CD93 or IFNGR1 signaling within endothelial cells could influence tumor blood supply and immune cell infiltration.

Relevant Therapeutic Agents

• Anti-angiogenic Agents: Bevacizumab (anti-VEGF antibody) is used in NSCLC to inhibit new blood vessel formation. While not directly targeting CD93 or IFNGR1, understanding specific endothelial interactions can guide combination therapies or identify novel targets for anti-angiogenic strategies.
• Immunomodulators: Given the role of IFNGR1, strategies that modulate the interferon pathway might also impact these interactions, influencing both angiogenesis and anti-tumor immunity.

15. Surfaceome Marker Expression in Lung Epithelial Cells Across Cancer Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes condition-specific surfaceome markers in Lung Epithelial cells from single-cell RNA-seq data, comparing Adenocarcinoma (Adeno), Diploid (likely non-malignant or less aggressive epithelial), and Squamous Cell Carcinoma (Squamous) conditions. The plot_markers_and_expression_dot tool was used to generate a dot plot displaying the mean expression level and the fraction of cells expressing each marker for up to 60 top surfaceome genes per condition. The markers were selected based on differential expression criteria, including a normalized percentage score (nz_pct_score > 0.183 or 0.25, nz_pct_FC > 1.25 or 1.75) and a p-value cutoff (0.05). This type of analysis is crucial for identifying potential diagnostic biomarkers, prognostic indicators, and therapeutic targets in different lung cancer subtypes.

Visual Summary

The dot plot effectively illustrates distinct surfaceome marker profiles for Lung Epithelial cells across the three conditions: Adeno, Diploid, and Squamous.

• Adeno-specific markers (Left Panel): This group shows a high prevalence (large dot size) and high expression (dark red color) of numerous markers in the Adeno samples (e.g., NSC016.T3, NSC018.T1, etc.) compared to Diploid and Squamous. Key examples include CEACAM5, MUC1, TACSTD2, EPCAM, SLC34A2, and ERBB2. These markers are largely absent or show very low expression in the Diploid and Squamous groups.
• Diploid-specific markers (Middle Panel): While generally showing lower and sparser expression of unique markers, some genes like CD74, HLA-DRA, and HLA-DRB1 show prominent expression in the Diploid group. These markers are also present in the Adeno group but at lower overall expression levels for many samples. General epithelial markers like EPCAM and CDH1 are also strongly expressed in this group, as expected.
• Squamous-specific markers (Right Panel): This panel highlights markers with high expression and prevalence specifically within the Squamous samples (e.g., NSC004.T3, NSC004.T1, etc.). Highly prominent markers include EGFR, CD44, SLC2A1, FGFR2, DSC3, and DSG2. These markers show minimal expression in the Adeno and Diploid conditions, indicating their specificity to the squamous phenotype.
• Overall Trend: The plot clearly demonstrates that Adeno and Squamous cell carcinomas exhibit highly divergent surfaceome marker profiles, reflecting their distinct pathological characteristics and cellular origins (e.g., adenocarcinoma typically arising from glandular cells, squamous cell carcinoma from squamous cells). The Diploid group likely represents non-malignant or basal epithelial cells, serving as a reference.

Biological Interpretation

The differential expression of surfaceome markers in Lung Epithelial cells provides critical insights into the molecular landscapes of lung adenocarcinoma and squamous cell carcinoma. Surface proteins are particularly important as they mediate cell-environment interactions, cell-cell communication, and are often amenable to therapeutic targeting.

Markers Enriched in Adenocarcinoma (Adeno)

The strong signal for markers like CEACAM5, MUC1, TACSTD2, EPCAM, SLC34A2, and ERBB2 in Adenocarcinoma suggests their involvement in adenocarcinoma-specific oncogenic processes, potentially related to altered cell adhesion, immune evasion, and growth signaling pathways.

• CEACAM5 (Carcinoembryonic Antigen-related Cell Adhesion Molecule 5): A cell adhesion molecule frequently overexpressed in various cancers, including lung adenocarcinoma. Its overexpression contributes to tumor growth, invasion, and metastasis, and it can suppress anti-tumor immune responses.
• MUC1 (Mucin 1, Cell Surface Associated): An oncogenic transmembrane glycoprotein that is aberrantly expressed and glycosylated in many adenocarcinomas. It plays roles in cell signaling, anti-apoptosis, and immune evasion, often promoting a more aggressive phenotype.
• TACSTD2 (TROP2, Trophoblast Cell Surface Antigen 2): A transmembrane glycoprotein involved in cell proliferation, migration, and invasion. Its overexpression is frequently observed in solid tumors, including lung adenocarcinoma, and is associated with poor prognosis.
• EPCAM (Epithelial Cell Adhesion Molecule): A pan-epithelial marker often overexpressed in epithelial cancers, including adenocarcinoma. It contributes to cell adhesion, proliferation, and differentiation, and is implicated in cancer stem cell biology and Wnt signaling.
• SLC34A2 (Solute Carrier Family 34 Member 2): A sodium-phosphate cotransporter. Its role in cancer is complex, but it can be dysregulated in lung adenocarcinoma, potentially influencing metabolic processes or cell survival.
• ERBB2 (HER2, Erb-B2 Receptor Tyrosine Kinase 2): A receptor tyrosine kinase and member of the EGFR family. Amplification or mutations in ERBB2 are known oncogenic drivers in a subset of lung adenocarcinomas, leading to constitutive activation of growth and survival pathways (e.g., MAPK, PI3K/Akt).

Markers Enriched in Squamous Cell Carcinoma (Squamous)

The high expression of EGFR, CD44, SLC2A1, FGFR2, DSC3, and DSG2 in Squamous Cell Carcinoma highlights distinct molecular vulnerabilities and phenotypic characteristics of this subtype, often linked to hyper-proliferation, altered metabolism, and maintenance of squamous differentiation.

• EGFR (Epidermal Growth Factor Receptor): A crucial receptor tyrosine kinase that drives cell proliferation, survival, and differentiation. While EGFR mutations are more common in adenocarcinoma, overexpression and amplification of EGFR are significant in squamous cell carcinoma, contributing to tumor growth and progression. It activates key oncogenic pathways like MAPK and PI3K/Akt.
• CD44 (CD44 Molecule): A cell surface glycoprotein involved in cell-cell interactions, adhesion, and migration. It is often considered a marker for cancer stem cells and is associated with tumor initiation, progression, and metastasis in squamous cell carcinoma.
• SLC2A1 (GLUT1, Solute Carrier Family 2 Member A1): A glucose transporter that facilitates cellular glucose uptake. Its high expression in squamous cell carcinoma reflects the Warburg effect, where cancer cells rely on aerobic glycolysis for energy, supporting rapid proliferation.
• FGFR2 (Fibroblast Growth Factor Receptor 2): A receptor tyrosine kinase that mediates signals from FGF ligands, regulating cell growth, proliferation, and differentiation. FGFR2 amplifications or fusions can be oncogenic drivers in a subset of squamous lung cancers.
• DSC3 (Desmocollin 3) and DSG2 (Desmoglein 2): These are components of desmosomes, specialized cell-cell adhesion structures. Their strong expression in squamous cell carcinoma is consistent with the highly cohesive and differentiated nature of squamous epithelia, though their dysregulation in cancer can affect cell-cell adhesion and contribute to altered tissue architecture.

Markers Enriched in Diploid Cells

The prominent expression of CD74, HLA-DRA, and HLA-DRB1 in the Diploid epithelial cell population is noteworthy.

• CD74 (Invariant Chain) and HLA-DRA/HLA-DRB1 (MHC Class II molecules): These molecules are critical for antigen presentation and immune activation. Their expression on non-malignant or less aggressive epithelial cells could suggest an active role in immune surveillance or an inflammatory response within the tumor microenvironment. This might also indicate a specific cellular state or subtype of non-malignant lung epithelial cells.

Clinical or Translational Implications

The identified surfaceome markers offer significant potential for clinical application in lung cancer, ranging from diagnostics and prognostics to targeted therapies and immunotherapies.

Established Therapeutic Targets and Clinical Relevance

• EGFR (Squamous): This is a well-established therapeutic target. EGFR inhibitors (TKIs like gefitinib, erlotinib, osimertinib) and monoclonal antibodies (e.g., necitumumab) are cornerstones of therapy for NSCLC, including squamous cell carcinoma with specific alterations or overexpression.
• Mechanisms: Drives oncogenic pathways.
• Agents: Tyrosine Kinase Inhibitors, Monoclonal Antibodies.
• Evidence: GeneCards: EGFR; PubMed: EGFR lung cancer therapy
• ERBB2 (HER2, Adeno): HER2 amplification or activating mutations occur in a subset of lung adenocarcinomas and are actionable targets.
• Mechanisms: Receptor tyrosine kinase signaling.
• Agents: Trastuzumab deruxtecan (ADC), Afatinib (TKI), Neratinib (TKI).
• Evidence: GeneCards: ERBB2; PubMed: HER2 lung adenocarcinoma therapy
• CEACAM5 (Adeno): High CEACAM5 expression is a prognostic marker and target for antibody-drug conjugates (ADCs) in various cancers.
• Mechanisms: Cell adhesion, proliferation, immune evasion.
• Agents: Tusamitamab ravtansine (anti-CEACAM5 ADC).
• Evidence: GeneCards: CEACAM5; PubMed: CEACAM5 lung cancer therapy
• TACSTD2 (TROP2, Adeno): TROP2 is a rapidly emerging therapeutic target.
• Mechanisms: Promotes proliferation, migration, invasion.
• Agents: Sacituzumab govitecan (ADC, approved in other cancers, in trials for NSCLC).
• Evidence: GeneCards: TACSTD2; PubMed: TROP2 lung cancer therapy
• EPCAM (Adeno): A common epithelial marker and target for therapeutic antibodies, especially in cancers with high EpCAM expression.
• Mechanisms: Cell adhesion, proliferation, cancer stem cell properties.
• Agents: Anti-EpCAM antibodies and ADCs (e.g., catumaxomab in previous development).
• Evidence: GeneCards: EPCAM; PubMed: EPCAM lung cancer therapy
• FGFR2 (Squamous): FGFR alterations (amplifications, fusions) are actionable in various cancers, and specific FGFR inhibitors are approved.
• Mechanisms: Receptor tyrosine kinase signaling.
• Agents: Pemigatinib, Erdafitinib (FGFR inhibitors).
• Evidence: GeneCards: FGFR2; PubMed: FGFR2 lung cancer therapy

Less Well-Characterized Markers with Potential as Therapeutic Targets

• MUC1 (Adeno): While extensively studied, MUC1 is a challenging target due to its mucinous nature. However, it remains a highly promising target for novel immunotherapies and ADCs.
• Mechanisms: Cell signaling, anti-apoptosis, immune evasion.
• Agents: MUC1-targeting antibodies, vaccines, CAR T-cells.
• Evidence: GeneCards: MUC1; PubMed: MUC1 lung adenocarcinoma therapy
• CD44 (Squamous): Its role in cancer stemness makes it an attractive target for eradicating therapy-resistant cancer cell populations.
• Mechanisms: Cell adhesion, migration, cancer stem cell properties, drug resistance.
• Agents: Anti-CD44 antibodies, aptamers (under investigation).
• Evidence: GeneCards: CD44; PubMed: CD44 squamous lung cancer therapy
• SLC2A1 (GLUT1, Squamous): Targeting cancer metabolism is a promising strategy. Inhibiting GLUT1 could selectively starve cancer cells.
• Mechanisms: Glucose uptake, Warburg effect.
• Agents: GLUT1 inhibitors (e.g., WZB117, BAY-876) often in combination therapies.
• Evidence: GeneCards: SLC2A1; PubMed: GLUT1 squamous lung cancer therapy
• CD74 (Diploid, and potentially Adeno/Squamous interaction): While prominent in Diploid cells, its role as a receptor for MIF and its involvement in immune processes make it a potential immunomodulatory target in the tumor microenvironment.
• Mechanisms: MHC class II antigen presentation, MIF receptor signaling.
• Agents: Targeting MIF-CD74 axis.
• Evidence: GeneCards: CD74; PubMed: CD74 lung epithelial

The strong and specific expression of desmosomal components (DSC3, DSG2) in squamous cell carcinoma provides insights into the cellular biology of this subtype and could potentially be explored as targets for therapies that disrupt cell-cell adhesion, or as very specific diagnostic/prognostic markers for squamous differentiation.

The marker expression profiles observed in this analysis serve as a valuable resource for guiding the development of personalized therapeutic strategies for lung cancer patients, allowing for more precise targeting based on specific histological subtypes.

• (The dot plot result has been saved as requested by the analysis parameters.)*

16. Macrophage Condition-Specific Surface Marker Expression in Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes condition-specific surface marker expression profiles in macrophages within lung cancer samples, comparing Adenocarcinoma (Adeno) against Squamous Cell Carcinoma (Squamous) types. The dot plot displays the mean expression level (color intensity) and the fraction of cells expressing the marker (dot size) for each identified surface marker across individual patient samples. The markers were selected based on a one-vs-rest comparison strategy with specific thresholds for non-zero percent score and fold change, prioritizing surfaceome genes. This provides insight into the phenotypic heterogeneity of tumor-associated macrophages (TAMs) between these two major lung cancer histologies.

Visual Summary

The dot plot reveals distinct patterns of macrophage surface marker expression between Adenocarcinoma and Squamous Cell Carcinoma samples.

• Adenocarcinoma-associated Markers (Left Panel): A cluster of markers, including *HLA-DQB1, HLA-DQA1, HLA-DMB, HLA-DMA, SLC02B1, EVI2B*, and notably *TREM2*, shows higher expression levels and a greater fraction of positive cells predominantly in Adeno samples (top rows within the Adeno group). These markers are largely absent or expressed at very low levels in Squamous samples. Expression of these markers is generally robust across most Adeno samples where they are present.
• Squamous Cell Carcinoma-associated Markers (Right Panel): A broader and more diverse set of markers is highly expressed in Squamous samples (bottom rows within the Squamous group). Key markers include *SLC2A3, CD44, CXCR4, TNFRSF1B, C5AR1, ITGAX, SLC2A14, CD15, EREG, FCGR3B, SLC11A1, THBD, ANPEP, SLC43A2, TREM1, ADGRE2, ADAM10, CSF3R, SLAMF6*, and *IL7R*. These markers are largely absent or expressed at very low levels in Adeno samples. Similar to the Adeno group, these markers show consistent expression across most Squamous samples identified with these markers.
• Sample Heterogeneity: Within both Adeno and Squamous groups, there is some sample-to-sample variability in the intensity and fraction of cells expressing specific markers, highlighting the heterogeneous nature of the tumor microenvironment across patients.
• Number of Markers: The Adeno group displays a more concentrated set of highly specific markers, primarily related to MHC Class II and TREM2, whereas the Squamous group exhibits a much wider range of distinctly upregulated surface markers.

Biological Interpretation

The observed condition-specific macrophage surface markers provide crucial insights into the functional state and potential roles of TAMs in Adenocarcinoma versus Squamous Cell Carcinoma of the lung.

Macrophage Phenotype in Adenocarcinoma

Macrophages in Adenocarcinoma samples are characterized by:

• MHC Class II Molecules: High expression of *HLA-DQB1, HLA-DQA1, HLA-DMB, HLA-DMA* suggests an active role in antigen presentation. While MHC Class II is typically associated with professional antigen-presenting cells and can be indicative of anti-tumor M1-like polarization, its function in TAMs can be complex. In some contexts, TAMs expressing MHC-II can present antigens in a tolerogenic manner, contributing to immune suppression [1].
• TREM2 (Triggering Receptor Expressed on Myeloid cells 2): Elevated *TREM2* expression is a significant finding. TREM2 is a transmembrane receptor primarily expressed on myeloid cells, including macrophages, and plays a critical role in immune regulation, particularly in tissue repair, chronic inflammation, and cancer. In the tumor microenvironment, TREM2-expressing macrophages are often associated with an M2-like, pro-tumorigenic phenotype, promoting tumor growth, angiogenesis, and immune suppression [2].
• Signaling Pathways: TREM2 signals through DAP12 (TYROBP) to activate downstream pathways involving PI3K/Akt, contributing to cell survival, proliferation, and inhibition of inflammatory responses.
• Role in TME: TREM2-expressing TAMs are known to clear apoptotic cells, promote tissue remodeling, and suppress anti-tumor immunity, often correlating with poor prognosis in various cancers, including lung cancer [3].
• Therapeutic Potential: TREM2 represents a promising therapeutic target. Inhibiting TREM2 signaling or depleting TREM2-positive TAMs could shift the immune balance towards anti-tumor responses.

Macrophage Phenotype in Squamous Cell Carcinoma

Macrophages in Squamous Cell Carcinoma samples show a more diverse and often inflammatory/pro-tumorigenic signature:

Cell Adhesion and Migration:

• CD44: A widely expressed cell surface glycoprotein involved in cell-cell adhesion, cell migration, and interaction with the extracellular matrix (ECM), particularly hyaluronic acid. It contributes to cancer progression, metastasis, and stemness [4].
• CXCR4: A chemokine receptor that binds CXCL12, crucial for cell migration, homing, and metastatic dissemination. The CXCL12-CXCR4 axis is a well-known pathway promoting tumor growth, angiogenesis, and immunosuppression in the TME [5].
• ITGAX (CD11c): Integrin alpha X, typically associated with dendritic cells, but also expressed on specific macrophage subsets. It mediates cell adhesion and can be involved in antigen capture and presentation.

Immune Regulation and Inflammation:

• TNFRSF1B (TNF Receptor Superfamily Member 1B / CD120b): A receptor for TNF-alpha, involved in apoptosis and inflammation. Its expression on macrophages suggests responsiveness to TNF-alpha, which can have complex pro- or anti-tumor effects depending on context.
• C5AR1 (Complement C5a Receptor 1): A receptor for the potent inflammatory mediator C5a. C5AR1 activation on TAMs can promote an immunosuppressive and pro-tumorigenic phenotype, contributing to tumor growth and metastasis [6].
• TREM1 (Triggering Receptor Expressed on Myeloid cells 1): Similar to TREM2, TREM1 is a myeloid cell-specific receptor. However, unlike TREM2, TREM1 is often associated with pro-inflammatory responses and exacerbates inflammation. Its role in cancer is complex, but it can contribute to tumor progression by enhancing inflammation and angiogenesis [7].
• CSF3R (Granulocyte Colony-Stimulating Factor Receptor): Receptor for G-CSF, which promotes the differentiation, proliferation, and survival of granulocytes and myeloid-derived suppressor cells (MDSCs). Its expression on macrophages suggests a potential role in myeloid cell recruitment and expansion, often linked to immunosuppression in cancer.
• FCGR3B (Fc Gamma Receptor IIIB / CD16b): A low-affinity Fc receptor for IgG, primarily found on neutrophils, but also on some macrophage subsets. It's involved in immune complex clearance and can mediate antibody-dependent cellular cytotoxicity (ADCC) or regulatory functions.

Growth Factors and Proteases:

• EREG (Epiregulin): A ligand for the Epidermal Growth Factor Receptor (EGFR). EREG can promote tumor cell proliferation, survival, and migration, as well as angiogenesis, by paracrine signaling between TAMs and tumor cells [8].
• ADAM10 (A Disintegrin And Metalloproteinase 10): A protease involved in shedding various cell surface proteins, including growth factor receptors and cytokines, thereby regulating cell signaling and cell-cell interactions in the TME.

Differences Between Adeno and Squamous Macrophages

The distinct marker profiles suggest different functional specialization of TAMs between Adeno and Squamous lung cancers:

• Adeno macrophages appear to have a more pronounced "repair" or "suppressive" phenotype, exemplified by TREM2 and potentially tolerogenic MHC II presentation. This might contribute to a highly immunosuppressive microenvironment conducive to Adeno growth.
• Squamous macrophages present a more complex picture, with markers suggesting active immune cell recruitment (CXCR4, C5AR1), strong involvement in ECM remodeling and cell adhesion (CD44, ITGAX), and a potential interplay with inflammatory and growth factor signaling (TNFRSF1B, TREM1, EREG, ADAM10, CSF3R). This could indicate a more inflammatory and dynamically interactive TME in Squamous, potentially driving different aspects of tumor progression.

Clinical or Translational Implications

The identification of these condition-specific macrophage surface markers provides valuable avenues for diagnostic, prognostic, and therapeutic development in lung cancer.

Therapeutic Targets

• TREM2 (Adenocarcinoma): Given its strong association with pro-tumorigenic macrophages, TREM2 is a high-priority therapeutic target in lung Adenocarcinoma.

Therapeutic Agents:

• Blocking antibodies: Monoclonal antibodies targeting TREM2 could inhibit its pro-tumorigenic signaling or deplete TREM2-positive TAMs. Preclinical studies are exploring such strategies [9].
• Small molecule inhibitors: Developing small molecules to modulate TREM2 activity is another potential approach.
• CD44 (Squamous Cell Carcinoma): CD44 is involved in tumor progression and metastasis.
• Therapeutic Agents: Anti-CD44 antibodies or small molecule inhibitors could target cancer stem cells and block cell adhesion/migration, potentially in combination with chemotherapy [10].
• CXCR4 (Squamous Cell Carcinoma): The CXCL12-CXCR4 axis is a well-established target in cancer.
• Therapeutic Agents: CXCR4 antagonists (e.g., Plerixafor, AMD3100) are already in clinical use for other indications and are being investigated in cancer to inhibit metastasis and enhance anti-tumor immunity [11].
• C5AR1 (Squamous Cell Carcinoma): Inhibition of C5AR1 could reduce complement-mediated inflammation and immunosuppression in the TME.
• Therapeutic Agents: C5AR1 antagonists are in various stages of development for inflammatory diseases and are being explored in oncology [12].
• EREG (Squamous Cell Carcinoma): Targeting EREG or its receptor EGFR (often aberrantly activated in cancer) could inhibit pro-tumorigenic signaling.
• Therapeutic Agents: EGFR inhibitors (e.g., Erlotinib, Osimertinib) are already widely used in lung cancer, and understanding EREG's role in TAMs could refine their application or lead to new combination strategies.
• CSF3R (Squamous Cell Carcinoma): Inhibiting CSF3R could reduce the recruitment and survival of immunosuppressive myeloid cells.
• Therapeutic Agents: Antibodies blocking CSF3R or inhibiting G-CSF signaling could be explored to remodel the myeloid compartment in Squamous lung cancer.

Biomarkers and Patient Stratification

These markers could serve as diagnostic or prognostic biomarkers. For instance, high *TREM2* expression on macrophages might identify Adenocarcinoma patients more likely to benefit from TREM2-targeting therapies, or predict prognosis. Similarly, the diverse set of Squamous markers could allow for sub-stratification of patients for targeted therapies.

Experimental Validation

Further experimental validation would involve:

• Flow cytometry or immunohistochemistry: Confirming protein expression of these markers on macrophages in patient samples.
• Functional assays: Investigating the impact of modulating these markers (e.g., using gene editing, antibodies, small molecules) on macrophage polarization, phagocytosis, cytokine production, and interaction with tumor cells *in vitro* and *in vivo* tumor models.
• Clinical trials: Ultimately, evaluating the efficacy of targeting these markers in stratified patient cohorts.

---

References:

[1] MHC Class II in TAMs: PubMed search on "tumor associated macrophages MHC class II immune suppression" (PubMed Search)

[2] TREM2 in Cancer: GeneCards entry for TREM2 (GeneCards).

[3] TREM2 in Lung Cancer: PubMed search on "TREM2 lung cancer prognosis" (PubMed Search)

[4] CD44 in Cancer: GeneCards entry for CD44 (GeneCards).

[5] CXCR4 in Cancer: PubMed search on "CXCL12 CXCR4 tumor microenvironment" (PubMed Search)

[6] C5AR1 in Cancer: PubMed search on "C5AR1 tumor associated macrophages" (PubMed Search)

[7] TREM1 in Cancer: PubMed search on "TREM1 tumor microenvironment" (PubMed Search)

[8] EREG in Cancer: GeneCards entry for EREG (GeneCards).

[9] TREM2 Therapeutics: PubMed search on "TREM2 antibody cancer therapy" (PubMed Search)

[10] CD44 Therapeutics: PubMed search on "CD44 targeting cancer therapy" (PubMed Search)

[11] CXCR4 Antagonists: PubMed search on "CXCR4 antagonist cancer" (PubMed Search)

[12] C5AR1 Therapeutics: PubMed search on "C5AR1 inhibitor cancer clinical trial" (PubMed Search)

17. Fibroblast Condition-Specific Surfaceome Markers in Lung Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Fibroblasts across Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) lung cancer samples using a one-vs-rest comparison. The dot plot visualizes the mean expression (color intensity) and fraction of expressing cells (dot size) for each marker gene in Fibroblasts from individual patient samples. The markers displayed are specifically enriched in either Adeno or Squamous conditions, filtered to include only surfaceome proteins with specific expression score and fold-change cutoffs (nz_pct_score ≥ 0.183; nz_pct FC > 1.25 for Adeno, > 1.36 for Squamous). This aims to characterize fibroblast subpopulations and their potential roles in different lung cancer histologies.

Visual Summary

The dot plot effectively distinguishes two main groups of markers: those predominantly expressed in Adeno samples and those enriched in Squamous samples.

• Adeno-enriched Markers (Top Block): A cluster of markers, including IL6ST, APLP2, CD74, ENG, and TFPI, show high mean expression and fraction of positive cells primarily in a subset of Adeno samples (e.g., NSC037.T1, NSC020.T2, NSC021.T2). Other Adeno samples show lower expression of these markers, suggesting heterogeneity within the Adeno-associated fibroblast population.
• Squamous-enriched Markers (Bottom Block): A much larger and more consistently expressed group of markers is observed across most Squamous samples (NSC004.T3, NSC004.T4, NSC004.T2, NSC004.T1). Prominent markers in this group include MMP14, MXRA8, CYSLTR1, FAP, SDC1, GPNMB, and LDLR, among many others. These markers generally exhibit both high mean expression and a large fraction of expressing cells across the Squamous samples, indicating a more uniform and robust fibroblast signature associated with this histology.
• Expression Pattern: The color intensity (red scale) indicates mean expression, with darker red signifying higher expression. The size of the dot indicates the fraction of cells expressing the marker, with larger dots representing a higher percentage. For instance, in Squamous samples, markers like FAP and MMP14 show large, dark red dots, indicating high expression in a large proportion of fibroblasts.
• Sample-level Heterogeneity: While clear condition-specific patterns emerge, individual samples within each condition still show some variability. For example, within the Adeno group, NSC037.T1 and NSC020.T2 show particularly strong expression of the Adeno-enriched markers compared to other Adeno samples.

Biological Interpretation

Fibroblasts, particularly Cancer-Associated Fibroblasts (CAFs), play a crucial role in shaping the tumor microenvironment (TME) and influencing tumor progression, metastasis, and response to therapy. The distinct surfaceome marker profiles observed in Fibroblasts from Adeno versus Squamous lung cancers suggest different functional states or subpopulations of CAFs driving histology-specific TME characteristics.

Key Markers and Their Biological Roles:

Adenocarcinoma-Enriched Fibroblast Markers:

1. IL6ST (Interleukin 6 Signal Transducer):

• Associated Signaling Pathways: IL6ST, also known as gp130, is the common signal-transducing receptor subunit for the IL-6 family of cytokines (e.g., IL-6, IL-11, LIF, OSM, CNTF). Upon ligand binding, it dimerizes with ligand-specific receptor alpha subunits and initiates JAK/STAT3, MAPK/ERK, and PI3K/AKT signaling pathways.
• Role in TME: In the lung cancer TME, IL-6/STAT3 signaling in CAFs can promote tumor growth, angiogenesis, immune evasion, and drug resistance by modulating ECM remodeling and secreting pro-tumorigenic factors. Activated fibroblasts can contribute to a pro-inflammatory and immunosuppressive TME. GeneCards: IL6ST, PubMed: IL-6/STAT3 in cancer
• Therapeutic Target Potential: Targeting IL-6/gp130 signaling has shown promise in various cancers. Blocking gp130 or its upstream ligands could inhibit pro-tumorigenic pathways in CAFs.
• Relevant Therapeutic Agents: Monoclonal antibodies targeting IL-6 (e.g., Siltuximab) or its receptor IL-6R (e.g., Tocilizumab, Sarilumab) are approved for inflammatory diseases and are being investigated in cancer. JAK/STAT3 pathway inhibitors are also under development.

2. ENG (Endoglin, CD105):

• Associated Signaling Pathways: ENG is a co-receptor for TGF-β, primarily expressed on endothelial cells and activated fibroblasts. It modulates TGF-β signaling, which is critical for angiogenesis, epithelial-mesenchymal transition (EMT), fibrosis, and immune suppression. ENG interaction with ALK1-Smad1/5/8 signaling promotes angiogenesis, while its interaction with ALK5-Smad2/3 inhibits it, depending on context.
• Role in TME: ENG-positive fibroblasts often represent a pro-angiogenic and pro-fibrotic CAF subpopulation. In lung cancer, these fibroblasts can contribute to tumor angiogenesis and dense ECM, hindering drug delivery and promoting tumor progression. GeneCards: ENG, PubMed: Endoglin cancer
• Therapeutic Target Potential: Due to its expression on activated endothelial cells and CAFs, ENG is an attractive target for anti-angiogenic and anti-fibrotic therapies.
• Relevant Therapeutic Agents: TRC105 (Carotuximab) is an anti-ENG monoclonal antibody that has been evaluated in clinical trials for various cancers, often in combination with anti-VEGF therapies.

3. CD74 (MHC Class II Invariant Chain):

• Associated Signaling Pathways: CD74 is primarily known as the MHC class II invariant chain, guiding MHC II molecule assembly and transport. However, it also functions as a cell surface receptor for Macrophage Migration Inhibitory Factor (MIF) and interacts with the CD44 receptor. MIF-CD74 signaling activates MAPK/ERK and NF-κB pathways, promoting cell proliferation, survival, and inflammation.
• Role in TME: CD74 expression on fibroblasts can mediate MIF-driven inflammatory and pro-tumorigenic responses. It can promote fibroblast activation, survival, and interaction with immune cells, contributing to an immunosuppressive or pro-inflammatory TME in lung cancer. GeneCards: CD74, PubMed: CD74 MIF cancer
• Therapeutic Target Potential: Targeting the MIF-CD74 axis is a promising strategy, particularly given MIF's role in inflammation, angiogenesis, and tumor growth.
• Relevant Therapeutic Agents: Small molecule inhibitors of MIF or anti-CD74 antibodies are being investigated as potential cancer therapeutics.

Squamous Cell Carcinoma-Enriched Fibroblast Markers:

1. FAP (Fibroblast Activation Protein alpha):

• Associated Signaling Pathways: FAP is a serine protease expressed by CAFs and plays roles in ECM remodeling, collagen degradation, and activation of growth factors. It contributes to a stiff, desmoplastic TME. FAP can activate various signaling pathways indirectly by modulating the availability of growth factors and cytokines.
• Role in TME: FAP is a canonical marker for CAFs across many cancers, including lung squamous cell carcinoma. FAP-positive CAFs promote tumor growth, angiogenesis, immunosuppression, and metastasis. They generate a highly desmoplastic stroma, which can impede drug delivery and promote epithelial-to-mesenchymal transition (EMT). GeneCards: FAP, PubMed: FAP cancer CAF
• Therapeutic Target Potential: FAP is considered an excellent therapeutic target due to its restricted expression in adult healthy tissues and high expression in the tumor stroma. Targeting FAP can reduce desmoplasia, enhance drug penetration, and inhibit tumor growth.
• Relevant Therapeutic Agents: FAP-targeted therapies include small molecule inhibitors, antibodies (e.g., FAP-5), antibody-drug conjugates (ADCs), and FAP-directed CAR-T cell therapies. Several are in preclinical and clinical development for various solid tumors.

2. MMP14 (Matrix Metalloproteinase 14, MT1-MMP):

• Associated Signaling Pathways: MMP14 is a membrane-bound metalloproteinase that degrades various ECM components (e.g., collagen, fibronectin) and activates other MMPs (e.g., MMP2). This proteolytic activity is crucial for cell invasion, migration, and angiogenesis. It can also activate latent growth factors.
• Role in TME: In lung squamous cell carcinoma, MMP14-expressing fibroblasts contribute significantly to ECM degradation, facilitating tumor cell invasion, intravasation, and metastasis. They promote an invasive phenotype in cancer cells and contribute to angiogenesis. GeneCards: MMP14, PubMed: MMP14 cancer invasion
• Therapeutic Target Potential: MMP14 is a critical mediator of tumor invasiveness and metastasis. Inhibiting its activity could curb tumor progression.
• Relevant Therapeutic Agents: Pan-MMP inhibitors have faced challenges in clinical trials due to toxicity. However, more selective MMP14 inhibitors are being developed, as are strategies to target MMP14 specifically in the TME.

3. SDC1 (Syndecan-1, CD138):

• Associated Signaling Pathways: SDC1 is a heparan sulfate proteoglycan that interacts with various growth factors (e.g., FGF, HGF, VEGF), chemokines, and ECM components (e.g., collagen, fibronectin), acting as a co-receptor. It modulates receptor tyrosine kinase (RTK) signaling, integrin signaling, and Wnt pathway activity, influencing cell adhesion, migration, proliferation, and survival.
• Role in TME: While best known as a plasma cell marker, SDC1 is also expressed by epithelial cells and fibroblasts. In the TME of squamous cell carcinoma, SDC1 on fibroblasts can promote cell adhesion, migration, and retention of growth factors, contributing to a supportive environment for tumor cells. Its shedding can also release pro-tumorigenic factors. GeneCards: SDC1, PubMed: Syndecan-1 cancer TME
• Therapeutic Target Potential: Targeting SDC1, particularly its shedding mechanisms or its interactions with growth factors, could disrupt key signaling pathways supporting tumor growth and progression.
• Relevant Therapeutic Agents: Monoclonal antibodies targeting SDC1, or strategies to inhibit SDC1 shedding, are under investigation.

4. GPNMB (Glycoprotein Non-Melanoma B):

• Associated Signaling Pathways: GPNMB is a transmembrane glycoprotein involved in cell adhesion, migration, and immune modulation. It can activate the PI3K/AKT and MAPK/ERK pathways, promoting cell survival and proliferation. It is also involved in lysosomal trafficking.
• Role in TME: GPNMB is expressed on various cell types in the TME, including CAFs, and its expression is often associated with tumor progression, invasion, and metastasis in several cancers, including lung cancer. In fibroblasts, GPNMB can contribute to an immunosuppressive TME and promote angiogenesis. GeneCards: GPNMB, PubMed: GPNMB cancer TME
• Therapeutic Target Potential: High expression of GPNMB in tumors makes it a promising target for antibody-drug conjugates (ADCs).
• Relevant Therapeutic Agents: Glembatumumab vedotin (CDX-011) is an ADC targeting GPNMB that has been evaluated in clinical trials for melanoma and breast cancer. Its potential for lung squamous cell carcinoma could be explored.

Clinical or Translational Implications

The identification of distinct fibroblast surfaceome markers for Adenocarcinoma and Squamous Cell Carcinoma highlights the histological-specific heterogeneity of the tumor microenvironment in lung cancer.

• Diagnostic/Prognostic Biomarkers: These condition-specific fibroblast markers could serve as novel diagnostic or prognostic biomarkers, distinguishing between lung cancer subtypes or predicting patient outcomes based on their TME characteristics.
• Targeted Therapy Development: The differential expression of these surfaceome markers offers opportunities for histology-specific targeted therapies. For instance, therapies targeting FAP or MMP14 might be particularly effective in Squamous Cell Carcinoma, while targeting IL6ST or ENG could be more relevant for Adenocarcinoma. This precision medicine approach could enhance therapeutic efficacy and reduce off-target effects.
• Combination Therapies: Understanding the specific signaling pathways active in CAFs of different histologies can guide rational combination therapies. For example, combining FAP inhibitors with chemotherapy or immunotherapy could improve responses in squamous lung cancer by remodeling the stroma and enhancing drug delivery or immune infiltration.
• Monitoring Treatment Response: Changes in the expression of these markers on circulating fibroblasts or in biopsy samples could potentially be used to monitor treatment response or disease progression.
• Experimental Validation: Further studies are warranted to validate the functional roles of these fibroblast subpopulations in each lung cancer histology. This could involve *in vitro* co-culture experiments, *in vivo* murine models, and analysis of patient cohorts to confirm the clinical relevance of these findings. Single-cell spatial transcriptomics could further elucidate the exact location and interaction partners of these specific fibroblast subsets within the TME.

18. T cell CD4+ Surfaceome Marker Expression in Squamous Lung Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome markers specific to CD4+ T cells in the context of Squamous lung carcinoma, comparing them against other conditions (likely Adenocarcinoma, as per the dataset context). The markers were selected based on differential expression criteria (nz_pct_score and nz_pct fold change) using a one-versus-rest comparison. The resulting dot plot illustrates the expression levels and prevalence of these identified markers across individual Squamous tumor samples. The goal is to highlight potential biomarkers and therapeutic targets on CD4+ T cells within the tumor microenvironment (TME).

Visual Summary

The dot plot displays the expression patterns of 11 surfaceome markers across 24 individual Squamous lung tumor samples (e.g., NSC037.T1, NSC016.T2). Each row represents a sample, and each column represents a gene marker.

• Dot Size: Corresponds to the fraction of CD4+ T cells within a given sample that express the marker (Fraction of cells in group (%)). Larger dots indicate higher prevalence.
• Dot Color Intensity: Represents the mean expression level of the marker within the cells expressing it (Mean expression in group). Darker red indicates higher mean expression.
• Right Bar Chart: Shows the total number of CD4+ T cells analyzed in each specific sample, providing context for the reliability of the observed marker expression.

Key Observations:

1. Condition Specificity: The plot focuses exclusively on markers identified as specific to the "Squamous" condition. No markers or samples from the "Adeno" condition are shown in this specific visualization, despite criteria being provided for both in the analysis parameters.
2. Marker Prominence: Several markers, including SLC2A3, CD44, CYSLTR1, ATP1A1, ITGB1, CD2, CD3D, and ITGB2, show relatively high mean expression (darker red dots) and a high fraction of expressing cells (larger dots) across many Squamous samples. This suggests these are consistently expressed on CD4+ T cells in this tumor type.
3. Variable Markers: OR4D9, GPR183, and SLC2A14 appear to be expressed at lower levels or in a smaller fraction of cells, indicating more variable or less universal presence on CD4+ T cells in these samples.
4. Inter-sample Heterogeneity: There is clear heterogeneity in marker expression across different patient samples. For example, samples within the red box (e.g., NSC004.T3, NSC004.T4, NSC004.T1) generally exhibit higher expression and prevalence for most markers compared to other samples like NSC037.T1 or NSC040.T1, which show sparser or lower expression.
5. Cell Count Context: The number of CD4+ T cells per sample varies significantly (e.g., NSC009.T1 has 51 cells, while NSC010.T1 has 1128 cells). While most samples have sufficient cell numbers for reliable analysis, lower cell counts (e.g., NSC009.T1 and NSC009.T2) might contribute to some of the observed variability or lower signal for certain markers.

Biological Interpretation

The identified surfaceome markers on CD4+ T cells in Squamous lung cancer samples provide insights into their functional state, interactions, and metabolic activity within the TME. CD4+ T cells are crucial orchestrators of immune responses, encompassing helper T cells (Th1, Th2, Th17, Th22, Tfh, Th9), regulatory T cells (Treg), and memory T cells, all of which play diverse roles in cancer immunity. Their precise function in the TME can be pro-tumorigenic, anti-tumorigenic, or exhausted.

Here's an interpretation of the prominent markers:

SLC2A3 (GLUT3):

• Signaling Pathways: Essential for glucose transport, central to cellular metabolism, particularly glycolysis. Activated T cells often undergo a metabolic shift towards aerobic glycolysis, relying on glucose for rapid proliferation and effector functions. GeneCards - SLC2A3
• Role in TME: High expression on CD4+ T cells suggests an active metabolic state, potentially indicating proliferating effector T cells or T cells adapted to the nutrient-depleted tumor environment.
• Therapeutic Potential: Modulating glucose metabolism can impact immune cell function. Inhibiting GLUT3 could selectively target highly metabolically active T cells.
• Relevant Agents: Metabolic inhibitors.

CD44:

• Signaling Pathways: Adhesion molecule and receptor for hyaluronic acid. Involved in cell adhesion, migration, and T cell activation pathways (e.g., PI3K/Akt, MAPK). GeneCards - CD44
• Role in TME: CD44 is crucial for T cell homing, trafficking to inflammatory sites, and interaction with extracellular matrix components and other cells within the TME. Its expression can mark memory or effector T cell populations.
• Therapeutic Potential: Targeting CD44 could potentially impede T cell migration or interaction, but its complex roles (in both pro- and anti-tumor immunity) necessitate careful consideration.

CYSLTR1:

• Signaling Pathways: Receptor for cysteinyl leukotrienes (e.g., LTC4, LTD4, LTE4), which are lipid mediators of inflammation and immune responses. GeneCards - CYSLTR1
• Role in TME: Its expression on CD4+ T cells suggests they are responsive to local inflammatory signals mediated by leukotrienes. This could influence T cell recruitment, differentiation, or suppressive functions within the tumor.
• Therapeutic Potential: CysLT1R antagonists (like montelukast, zafirlukast) are clinically approved for asthma and could be explored for their immunomodulatory effects in cancer, potentially impacting T cell function in the TME.

ATP1A1 (Na+/K+-ATPase α1 subunit):

• Signaling Pathways: A critical ion pump maintaining cellular ion gradients, essential for cell survival, volume regulation, and various signaling processes. It can also transduce signals via interaction with Src and MAPK pathways. GeneCards - ATP1A1
• Role in TME: Fundamental for T cell homeostasis and function. Altered expression or function could reflect changes in cell metabolism, proliferation, or stress responses in the TME.
• Therapeutic Potential: Cardiac glycosides (e.g., digoxin) target Na+/K+-ATPase and have been investigated for anti-cancer properties, but systemic toxicity is a significant concern.

GPR183 (EBI2):

• Signaling Pathways: A G protein-coupled receptor that acts as a chemokine-like receptor, binding to oxysterols (e.g., 7α,25-OHC). Involved in immune cell localization and migration. GeneCards - GPR183
• Role in TME: GPR183 can influence the positioning and homing of T cells within lymphoid tissues. In the TME, it might play a role in directing CD4+ T cells to specific niches, potentially impacting their interactions with other immune cells or tumor cells.
• Therapeutic Potential: Modulating GPR183 activity could alter immune cell trafficking patterns within and to the TME.

ITGB1 (CD29) and ITGB2 (CD18):

• Signaling Pathways: Both are beta subunits of integrin adhesion molecules. ITGB1 combines with various alpha chains (e.g., ITGA1-6) to form VLA (very late antigen) integrins, binding to extracellular matrix components like fibronectin and collagen. ITGB2 combines with ITGAL (CD11a) to form LFA-1, and ITGAM (CD11b) to form Mac-1, crucial for leukocyte adhesion and migration. GeneCards - ITGB1, GeneCards - ITGB2
• Role in TME: These integrins are vital for T cell adhesion, migration, and extravasation into tissues, including the tumor. They facilitate T cell-endothelial cell interactions and the formation of immunological synapses with antigen-presenting cells. High expression suggests an active migratory and interactive phenotype of CD4+ T cells.
• Therapeutic Potential: Integrins are targets for inhibiting cell migration in various diseases. Inhibitors could modulate T cell infiltration or retention within the TME, though the specific impact (pro- or anti-tumor) would depend on the targeted integrin and context.

CD2:

• Signaling Pathways: Adhesion molecule involved in T cell activation and costimulatory signaling. Binds to CD58 (LFA-3) on antigen-presenting cells. GeneCards - CD2
• Role in TME: Participates in forming the immunological synapse, promoting T cell adhesion, activation, and proliferation. Its presence indicates CD4+ T cells capable of engaging with APCs or tumor cells.
• Therapeutic Potential: Modulating CD2 interactions could affect T cell activation.

CD3D:

• Signaling Pathways: A crucial component of the T cell receptor (TCR) complex, essential for TCR assembly, surface expression, and signal transduction upon antigen recognition. GeneCards - CD3D
• Role in TME: Its robust expression is a hallmark of functional T cells, indicating their capacity for antigen-specific recognition. Maintenance of CD3D expression in tumor-infiltrating T cells is vital for effective anti-tumor immunity.
• Therapeutic Potential: While direct targeting of CD3D is usually broad and immunosuppressive, its presence is a prerequisite for T-cell engaging therapies, such as bispecific antibodies (e.g., those targeting CD3 and a tumor antigen).

OR4D9 and SLC2A14:

• Signaling Pathways: OR4D9 is an olfactory receptor, whose non-olfactory functions, particularly in immune cells, are still emerging and not fully understood. SLC2A14 is another glucose transporter (GLUT14), less characterized in immune cells compared to GLUT1 or GLUT3. GeneCards - OR4D9, GeneCards - SLC2A14
• Role in TME: Their roles in CD4+ T cells in the TME are less established and require further investigation. They could represent novel metabolic adaptations or sensing mechanisms.
• Therapeutic Potential: Currently, their therapeutic potential is largely unknown, but they could serve as novel biomarkers or targets if their specific functions in TME are elucidated.

Clinical or Translational Implications

The identification of these specific surfaceome markers on CD4+ T cells in Squamous lung cancer offers several clinical and translational avenues:

1. Biomarker Discovery: Markers like SLC2A3, CD44, CYSLTR1, ATP1A1, ITGB1, CD2, CD3D, and ITGB2 are highly expressed across multiple Squamous samples. Their consistent expression could make them valuable biomarkers for characterizing the functional state and tumor-infiltrating capacity of CD4+ T cells in Squamous lung cancer patients. This could aid in patient stratification or monitoring treatment response.
2. Therapeutic Targeting:

• Metabolic Reprogramming: The strong expression of glucose transporters like SLC2A3 (and SLC2A14 to a lesser extent) highlights the metabolic demands of CD4+ T cells in the Squamous TME. Strategies to modulate glucose metabolism, potentially through specific GLUT inhibitors, could be explored to fine-tune T cell functions or target specific pro-tumorigenic CD4+ T cell subsets (e.g., highly glycolytic Tregs).
• Immune Cell Trafficking and Adhesion: Markers like CD44, ITGB1, ITGB2, CD2, and GPR183 are critical for T cell migration, adhesion, and interaction. Modulating these adhesion molecules could alter the infiltration, retention, or interaction of CD4+ T cells within the TME, potentially enhancing anti-tumor responses or blocking pro-tumorigenic interactions. For instance, specific integrin inhibitors are already in clinical use for other diseases and could be investigated for their impact on immune cell dynamics in cancer.
• Inflammation Modulation: CYSLTR1 expression suggests CD4+ T cells are responsive to leukotriene-mediated inflammation. Repurposing existing CysLT1R antagonists could be a viable strategy to modify the inflammatory milieu in the TME and influence T cell behavior.

3. Understanding T cell Dysfunction/Exhaustion: The sustained expression of core T cell machinery like CD3D alongside adhesion and metabolic markers suggests these T cells possess the fundamental capacity for function, but their overall state (e.g., activated, exhausted, regulatory) would require further investigation with additional markers. Differential expression of these markers across patient samples suggests varying immune landscapes, which could impact immunotherapy effectiveness.
4. Further Validation: The markers identified here warrant further investigation in larger cohorts of Squamous lung cancer patients, as well as in *in vitro* and *in vivo* models, to validate their precise roles, establish their prognostic or predictive value, and assess their suitability as therapeutic targets.

The observed heterogeneity in marker expression across samples underlines the importance of personalized approaches to immunotherapy in lung cancer.

19. Differential Cell Cycle Gene Expression in Lung Epithelial Cells: Adenocarcinoma vs. Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a curated set of cell cycle pathway genes in Lung Epithelial cells, comparing two major lung cancer subtypes: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). The expression is quantified as the "expressing cell fraction (sample)," which represents the proportion of Lung Epithelial cells within each tumor sample that express a given gene. The objective was to identify genes with statistically significant differences in this fraction between the two conditions and visualize these differences using box plots. The analysis identified 24 cell cycle-related genes with significant differential expression, offering insights into subtype-specific cell cycle regulation.

Visual Summary

The visualization displays 24 box plots, each representing a distinct cell cycle gene and showing its "expressing cell fraction (sample)" in Lung Epithelial cells across Adenocarcinoma (Adeno, orange boxes) and Squamous Cell Carcinoma (Squamous, blue boxes) samples.

Genes with Higher Expressing Cell Fraction in Adenocarcinoma:

GADD45B (p=2.57e-06), CCND1 (p=2.13e-05), PTTG1 (p=0.000225), ANAPC7 (p=0.000573), HDAC2 (p=0.00146), ORC4 (p=0.00231), CDK4 (p=0.00247), and GADD45A (p=0.00297) demonstrate a significantly higher fraction of expressing cells in Adeno samples compared to Squamous samples. For these genes, the orange box plots (Adeno) are generally positioned higher than the blue box plots (Squamous), with minimal overlap in their interquartile ranges.

Genes with Higher Expressing Cell Fraction in Squamous Cell Carcinoma:

MCM7 (p=3.13e-05), HDAC1 (p=3.83e-05), YWHAG (p=9.44e-05), CDC27 (p=0.000158), TFDP1 (p=0.000202), CDKN2A (p=0.000235), GSK3B (p=0.000319), MCM4 (p=0.000327), MCM6 (p=0.000502), MYC (p=0.00053), MCM2 (p=0.000661), CDK6 (p=0.000652), TFDP2 (p=0.000744), SFN (p=0.00102), SMAD2 (p=0.0025), and SMAD3 (p=0.00488) exhibit a significantly higher fraction of expressing cells in Squamous samples. Conversely, their blue box plots (Squamous) are generally higher than their orange counterparts (Adeno), indicating a greater prevalence of expression in this subtype.

All observed differences are highly statistically significant (p-values < 0.05), suggesting robust and consistent distinctions in cell cycle gene expression between the two conditions at the single-cell sample level.

Biological Interpretation

The differential expression of cell cycle genes in Lung Epithelial cells highlights fundamental biological distinctions between Adenocarcinoma and Squamous Cell Carcinoma, reflecting varied strategies in proliferation, DNA damage response, and epigenetic regulation.

Differential Proliferative Programs:

• Adenocarcinoma (Adeno) Signature: A higher fraction of Adeno cells express key G1/S phase regulators like CCND1 (Cyclin D1) and CDK4, along with mitotic components such as PTTG1 (Securin) and ANAPC7 (a subunit of the Anaphase-Promoting Complex). ORC4, involved in DNA replication initiation, is also more prevalent. This profile suggests that Adeno cells might exhibit a more pronounced or dysregulated progression through the G1-S phase and mitosis GeneCards: CCND1, GeneCards: CDK4.
• Squamous Cell Carcinoma (Squamous) Signature: In contrast, a higher fraction of Squamous cells show increased expression of multiple Minichromosome Maintenance (MCM) proteins (MCM2, MCM4, MCM6, MCM7), which are crucial for DNA replication fork formation and progression. Proto-oncogenes MYC and CDK6, alongside transcription factors TFDP1 and TFDP2 (E2F partners), also display higher prevalence. This broad upregulation of DNA replication and cell cycle driving machinery suggests a generally highly proliferative state in Squamous cells, potentially driven by a broader activation of the replicative program PubMed search: MCM proteins cell cycle cancer.

Contrasting Regulatory and Stress Responses:

• Tumor Suppressors and Cell Cycle Checkpoints: Interestingly, CDKN2A (p16INK4a), a potent tumor suppressor that enforces G1 arrest, and SFN (14-3-3 sigma), involved in G2/M arrest and apoptosis, are expressed in a higher fraction of Squamous cells GeneCards: CDKN2A. This co-occurrence of high proliferative markers and tumor suppressors in Squamous cells could imply chronic cellular stress, an activated intrinsic feedback mechanism attempting to counteract rampant proliferation, or heterogeneity within the Squamous population where some cells are actively cycling and others are undergoing checkpoint activation or senescence.
• DNA Damage Response: GADD45A and GADD45B, stress-inducible genes involved in cell cycle arrest, DNA repair, and apoptosis, are found in a higher fraction of Adeno cells PubMed search: GADD45 DNA damage response cancer. This might indicate that Adeno cells experience a higher baseline of DNA damage or are more actively engaging their DNA damage response pathways, possibly due to oncogene-induced replication stress.
• Epigenetic Modulators: HDAC1 (Squamous) and HDAC2 (Adeno) show differential prevalence. As histone deacetylases, they play critical roles in chromatin remodeling and gene expression. Their subtype-specific differences suggest distinct epigenetic landscapes contributing to the unique cell cycle regulation and overall phenotype of each cancer type GeneCards: HDAC1, GeneCards: HDAC2.
• TGF-beta Signaling: SMAD2 and SMAD3, key mediators of the TGF-beta signaling pathway, are more prevalent in Squamous cells GeneCards: SMAD2, GeneCards: SMAD3. TGF-beta signaling has context-dependent roles in cancer, often suppressing tumor growth in early stages but promoting progression and metastasis in advanced disease. Its higher activity in Squamous cells could indicate a distinct role in their development or progression.

Clinical or Translational Implications

The identified subtype-specific differences in cell cycle gene expression in Lung Epithelial cells hold significant clinical and translational potential.

• Subtype-Specific Therapeutic Targets: The distinct activation patterns of cell cycle components suggest opportunities for tailored therapeutic strategies. For instance, the higher prevalence of CCND1 and CDK4 in Adenocarcinoma could indicate increased sensitivity to CDK4/6 inhibitors, a class of drugs already approved for other cancers. Conversely, the broad upregulation of MCM proteins, MYC, and CDK6 in Squamous cell carcinoma might point towards different vulnerabilities that could be exploited by targeting DNA replication initiation or MYC-driven pathways, though direct MYC targeting remains challenging.
• Biomarker Development: These differentially expressed genes could serve as valuable biomarkers for differentiating between Adenocarcinoma and Squamous Cell Carcinoma, aiding in diagnosis or guiding treatment selection. For example, a higher prevalence of CDKN2A expressing cells in a tumor biopsy might provide a molecular indicator supporting a Squamous diagnosis.
• Understanding Disease Progression: The complex interplay of proliferative drivers and tumor suppressors observed in Squamous cells (e.g., high MCMs/MYC alongside high CDKN2A/SFN) could reflect specific adaptive mechanisms or inherent stress responses characteristic of this subtype. Understanding these dynamics might reveal insights into their unique clinical behavior, such as their response to conventional therapies or their propensity for recurrence.
• Targeting DNA Damage Response in Adenocarcinoma: The elevated expression of GADD45A/B in Adenocarcinoma suggests that these cells might be under significant replication stress or prone to DNA damage. This could imply a therapeutic window for drugs that exploit DNA repair deficiencies or induce further DNA damage, potentially leading to synthetic lethality.

20. Gene Ontology (GSA) Analysis of Up-regulated Genes in Lung Epithelial Cells Across Different Lung Cancer Conditions and Ploidy States

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates Gene Ontology (GO) enrichment using Gene Set Analysis (GSA) for up-regulated differentially expressed genes (DEGs) specifically within Lung Epithelial cells. The comparisons are performed for three distinct cellular states/conditions: Adenocarcinoma (Adeno), Diploid cells, and Squamous Cell Carcinoma (Squamous), each compared against all other available conditions/ploidy states (_vs_others). This approach aims to identify the key biological processes and pathways that are significantly up-regulated in Lung Epithelial cells under these specific contexts, providing insights into their unique biological characteristics relevant to lung cancer.

The analysis focuses on 'up-regulated' genes, meaning the enriched pathways are driven by genes showing higher expression in the target group (Adeno, Diploid, or Squamous Lung Epithelial cells) compared to the 'others' reference group.

Visual Summary

The provided bar plots illustrate the top Gene Ontology terms enriched in up-regulated DEGs for Lung Epithelial cells under three different comparison contexts:

1. GSA for Lung Epithelial cell: Adeno_vs_others: This plot shows pathways enriched in Adenocarcinoma-associated Lung Epithelial cells compared to all other Lung Epithelial cells. Highly significant terms (with -log(p-val) and -log(q-val) often exceeding 40) primarily involve protein processing ("Protein processing in endoplasmic reticulum"), various neurodegenerative diseases ("Amyotrophic lateral sclerosis", "Pathways of neurodegeneration", "Huntington disease", "Alzheimer disease", "Parkinson disease", "Prion disease"), and terms related to infection and general cellular function ("Endocytosis", "Salmonella infection", "Spliceosome", "MAPK signaling pathway"). "Pathways in cancer" is also significantly enriched but not among the highest-ranking terms.
2. GSA for Lung Epithelial cell: Diploid_vs_others: This plot highlights pathways enriched in Diploid Lung Epithelial cells compared to all other Lung Epithelial cells (which would include Aneuploid, Adeno, and Squamous Lung Epithelial cells). The overall statistical significance (max -log(p-val) and -log(q-val) around 15) is considerably lower than in the Adeno and Squamous comparisons, indicating more subtle changes or fewer highly perturbed pathways. The most prominent terms are largely immune-related, such as "Phagosome", "Allograft rejection", "Graft-versus-host disease", "Antigen processing and presentation", and several infectious or autoimmune disease pathways.
3. GSA for Lung Epithelial cell: Squamous_vs_others: This plot presents pathways enriched in Squamous Cell Carcinoma-associated Lung Epithelial cells compared to all other Lung Epithelial cells. Similar to the Adeno comparison, this plot shows high statistical significance (max -log(p-val) and -log(q-val) exceeding 40) for many terms. The enriched pathways strongly overlap with those observed in Adeno, including neurodegenerative diseases ("Alzheimer disease", "Parkinson disease", "Amyotrophic lateral sclerosis", "Huntington disease", "Prion disease", "Pathways of neurodegeneration"), protein synthesis and processing ("Ribosome", "Spliceosome", "RNA transport"), metabolic dysregulation ("Non-alcoholic fatty liver disease", "Oxidative phosphorylation"), and "Pathways in cancer".

Biological Interpretation

The GSA results provide distinct biological insights into the molecular states of Lung Epithelial cells in different cancer contexts and ploidy statuses:

1. Common Pathological Signatures in Adenocarcinoma and Squamous Cell Carcinoma

• Widespread Cellular Stress and Dysregulation: Both Adeno and Squamous carcinoma Lung Epithelial cells show profound enrichment for pathways related to cellular stress, protein quality control, and metabolic reprogramming. Terms like "Protein processing in endoplasmic reticulum" (Adeno), "Ribosome", "Spliceosome", "RNA transport" (Squamous), and "Oxidative phosphorylation" (Squamous) indicate significant alterations in protein homeostasis and energy metabolism, which are hallmarks of cancer cells supporting rapid proliferation and survival under stress.
• "Neurodegenerative Disease" Overlap: A striking and shared feature between Adeno and Squamous conditions is the strong enrichment of numerous "neurodegenerative disease" pathways (e.g., Alzheimer disease, Parkinson disease, Amyotrophic lateral sclerosis, Huntington disease, Prion disease). While these diseases are distinct from cancer, their associated pathways often involve fundamental cellular processes like protein aggregation, mitochondrial dysfunction, oxidative stress, inflammation, and autophagy. The up-regulation of genes in these pathways in lung cancer epithelial cells suggests a shared molecular landscape of cellular stress responses, unfolded protein response, and altered proteostasis that contributes to oncogenesis and tumor progression.
• General Cancer Pathways: "Pathways in cancer" is enriched in both Adeno and Squamous conditions, confirming the malignant nature of these cells. However, its lower ranking compared to general stress and neurodegeneration-related terms suggests that the most significant up-regulation occurs in broader cellular machinery dysregulation rather than narrowly defined cancer-specific signaling modules. Signaling pathways like "MAPK signaling pathway" and "PI3K-Akt signaling pathway" are also noted, which are crucial for cell proliferation, survival, and differentiation in cancer.

2. Distinct Immunological Profile of Diploid Lung Epithelial Cells

• Immune Engagement and Surveillance: In stark contrast to the tumor-associated Adeno and Squamous cells, Diploid Lung Epithelial cells show significant enrichment for immune-related pathways. Terms such as "Phagosome", "Allograft rejection", "Antigen processing and presentation", and various infection/autoimmune disease terms (e.g., "Human T-cell leukemia virus 1 infection", "Epstein-Barr virus infection", "Herpes simplex virus 1 infection") are highly up-regulated. This suggests that Diploid Lung Epithelial cells, when compared to the broader heterogeneous 'others' (likely including aneuploid or more transformed tumor cells), maintain or activate robust immune surveillance mechanisms, host defense responses, or reflect a less transformed state that is actively interacting with the immune microenvironment.
• Lower Overall Dysregulation: The generally lower significance of enriched pathways in Diploid cells compared to Adeno and Squamous carcinoma cells implies that their transcriptomic changes are less extensive or less profoundly dysregulated than those in established tumor cells. This could represent healthy epithelial cells, cells in early-stage tumor development, or a distinct subpopulation of tumor cells with a less aggressive phenotype.

Clinical or Translational Implications

1. Biomarkers of Cellular Stress and Malignancy: The prominent enrichment of pathways related to protein processing, metabolic dysregulation, and general cellular stress in both Adeno and Squamous carcinoma cells highlights potential pan-cancer biomarkers reflecting the adaptive and survival mechanisms of tumor cells. Targeting these general stress response pathways, such as those involved in ER stress or oxidative phosphorylation, could be a broad therapeutic strategy for both lung adenocarcinoma and squamous cell carcinoma.
2. Shared Mechanisms with Neurodegenerative Diseases: The consistent appearance of neurodegenerative disease pathways suggests common molecular vulnerabilities. Investigating shared upstream regulators or key effector molecules between these seemingly disparate diseases could uncover novel therapeutic targets for lung cancer that modulate fundamental cellular stress responses or proteostasis.
3. Immunomodulation in Diploid Cells: The strong immune-related signature in Diploid Lung Epithelial cells suggests they play a more active role in immune responses, potentially as antigen-presenting cells or as targets of immune surveillance. Understanding why these pathways are up-regulated could reveal mechanisms of immune evasion in advanced tumors (where these pathways might be suppressed) or identify therapeutic avenues to enhance anti-tumor immunity by reactivating these responses in less transformed cells or the tumor microenvironment.
4. Stratification by Ploidy and Histology: The clear distinction between the GSA profiles of Diploid cells versus Adeno/Squamous cells underscores the importance of considering both tumor histology and cellular ploidy status in characterizing lung cancer biology. This could inform patient stratification for different treatment regimens, with therapies targeting immune activation potentially being more relevant for tumors with a higher proportion of immune-active diploid cells, and therapies addressing cellular stress or metabolic dysregulation for aneuploid or overtly malignant cells.

21. Gene Set Enrichment Analysis (GSEA) Across Lung Cancer Subtypes and Cell Types

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as a dot plot. The analysis was performed for multiple cell types identified in single-cell RNA-seq data from lung tissue, comparing gene expression profiles between 'Adeno' (Adenocarcinoma) and 'Squamous' (Squamous Cell Carcinoma) conditions against all other cells (the 'others' group). The plot displays the Normalized Enrichment Score (NES) using a divergent color map (RdBu_r), where red indicates enrichment (upregulation) and blue indicates depletion (downregulation) of a pathway in the test condition. The size of each dot reflects the statistical significance of the enrichment, corresponding to the -log(p-value), with larger dots indicating higher significance. The results capture the top 120 enriched or depleted pathways, providing a comprehensive overview of the altered biological processes within specific cell types across these two lung cancer subtypes.

Visual Summary

The dot plot displays a comprehensive overview of pathway activity across different cell types and lung cancer conditions. Each row represents a specific gene set or pathway, and each column corresponds to a unique cell type within either the "Adeno_vs_others" or "Squamous_vs_others" comparison.

Key Visual Observations:

• Differential Pathway Activity: A strong visual distinction is evident in pathway enrichment patterns between the Adeno and Squamous conditions, as well as across different cell types. Many dots are significantly colored red (enriched) or blue (depleted), indicating widespread changes in biological processes.

Condition-Specific Enrichment:

• Adenocarcinoma (Adeno_vs_others): Pathways like "Signaling pathways regulating pluripotency of stem cells," "MicroRNAs in cancer," "Central carbon metabolism in cancer," "PI3K-Akt signaling pathway," and "ECM-receptor interaction" show strong enrichment (red dots) in Lung Epithelial cells and Fibroblasts. Immune-related pathways are also differentially regulated in T cells and Macrophages.
• Squamous Cell Carcinoma (Squamous_vs_others): Lung Epithelial cells in this condition exhibit significant enrichment in pathways such as "HIF-1 signaling pathway," "AMPK signaling pathway," and also "MicroRNAs in cancer" and "Non-small cell lung cancer" pathways.

Cell Type-Specific Contributions:

• Lung Epithelial cells: As the resolved tumor-origin cell type, these cells show the most extensive and significant pathway enrichments related to cancer hallmarks, metabolism, and signaling in both Adeno and Squamous conditions.
• Fibroblasts: Consistently show enrichment in "ECM-receptor interaction," "Focal adhesion," and "Regulation of actin cytoskeleton" pathways, highlighting their role in extracellular matrix remodeling in the tumor microenvironment.
• Myeloid cells (Macrophages): Display significant changes in "Antigen processing and presentation," "Phagosome," and "Lysosome" pathways, reflecting their immune functions within the tumor.
• T cells (CD4+, CD8+): Show differential activity in immune signaling pathways such as "Th1 and Th2 cell differentiation," "JAK-STAT signaling pathway," and "T cell receptor signaling pathway."
• Endothelial cells: Are often associated with pathways like "Adherens junction," "Tight junction," and "ECM-receptor interaction," suggesting roles in vascular remodeling and integrity.
• Metabolic Reprogramming: Numerous metabolic pathways (e.g., "Glycolysis / Gluconeogenesis," "Citrate cycle (TCA cycle)," "Pentose phosphate pathway," "Oxidative phosphorylation") are differentially regulated across various cell types and conditions, underscoring the metabolic adaptations in the tumor microenvironment.
• Immune Response Pathways: Pathways related to immune function, such as "Antigen processing and presentation," "Fc gamma R-mediated phagocytosis," "Natural killer cell mediated cytotoxicity," and various T-cell differentiation pathways, show complex patterns of enrichment and depletion, reflecting the intricate immune landscape.

Biological Interpretation

The GSEA results provide profound insights into the distinct and shared biological processes driving lung adenocarcinoma and squamous cell carcinoma, as well as the intricate crosstalk within their respective tumor microenvironments.

1. Tumor Cell-Intrinsic Pathways (Lung Epithelial cells):

• Oncogenic Signaling and Stemness: In Adenocarcinoma epithelial cells, the strong enrichment of "PI3K-Akt signaling pathway" and "Signaling pathways regulating pluripotency of stem cells" highlights key drivers of tumor cell proliferation, survival, and potential stem-like features, which are often associated with aggressive disease and therapeutic resistance [UniProt: P27986 (AKT1)]. The activation of "MicroRNAs in cancer" pathways indicates a role for these small RNA molecules in regulating gene expression and promoting tumorigenesis.
• Metabolic Adaptations: Both Adeno and Squamous epithelial cells show extensive metabolic reprogramming. Notably, "HIF-1 signaling pathway" is strongly enriched in Squamous epithelial cells, suggesting adaptation to hypoxic conditions prevalent in these tumors and a shift towards glycolysis (Warburg effect) [PubMed search: HIF-1 alpha lung squamous carcinoma]. In contrast, Adeno epithelial cells show broader enrichment in other central carbon metabolism pathways.
• Tumor-Specific Signatures: The enrichment in "Non-small cell lung cancer" pathways specifically in epithelial cells underscores the direct involvement of these cells in the pathogenesis of both subtypes.

2. Tumor Microenvironment Contributions:

• Stromal Remodeling (Fibroblasts): The consistent enrichment of "ECM-receptor interaction" and "Focal adhesion" in fibroblasts points to their critical role in remodeling the extracellular matrix, facilitating tumor cell invasion, and contributing to desmoplasia. This creates a permissive environment for tumor growth and metastasis [PubMed search: cancer associated fibroblasts ECM remodeling lung cancer].

Immune Cell Dysregulation:

• Macrophages: Differential enrichment in "Antigen processing and presentation," "Phagosome," and "Lysosome" pathways in macrophages suggests their active, yet potentially dual, role in the tumor microenvironment—either attempting to clear tumor cells or becoming polarized towards an immunosuppressive phenotype (e.g., M2-like) that supports tumor growth [PubMed search: tumor associated macrophages lung cancer].
• T cells: The diverse enrichment patterns in "Th1 and Th2 cell differentiation" and other immune signaling pathways in CD4+ and CD8+ T cells indicate a complex immune response, potentially involving both anti-tumor immunity and mechanisms of T cell exhaustion or anergy within the tumor [PubMed search: T cell exhaustion lung cancer]. The distinct profiles between Adeno and Squamous might suggest different immune evasion strategies employed by the tumor cells or varying immune cell compositions.
• Angiogenesis and Vascular Support (Endothelial cells): Activation of "Adherens junction" and "Tight junction" pathways in endothelial cells indicates their involvement in forming new blood vessels (angiogenesis) to supply the growing tumor and maintain vascular integrity, crucial for tumor nutrient supply and metastasis.

3. Shared and Distinct Pathway Features:

• While both subtypes share some common cancer-related pathway enrichments (e.g., "MicroRNAs in cancer"), there are clear distinctions. For instance, the greater emphasis on "HIF-1 signaling pathway" in Squamous epithelial cells versus "PI3K-Akt signaling pathway" in Adeno epithelial cells suggests different primary survival and metabolic adaptation mechanisms.
• Immune pathways also show nuanced differences, indicating that the immune response landscape might vary significantly between Adeno and Squamous tumors.

Clinical or Translational Implications

The detailed GSEA results offer several avenues for clinical and translational applications in lung cancer management:

1. Targeted Therapy Development: The identification of subtype-specific oncogenic drivers, such as the prominent "PI3K-Akt signaling pathway" in Adenocarcinoma and "HIF-1 signaling pathway" in Squamous Cell Carcinoma, can guide the development of targeted therapies. Drugs specifically inhibiting these pathways could be more effective in patients whose tumors show strong enrichment of these pathways.
2. Biomarker Discovery for Diagnosis and Prognosis: Pathways consistently enriched or depleted in tumor cells or specific stromal cell types could serve as novel diagnostic or prognostic biomarkers. For instance, high activity in "Signaling pathways regulating pluripotency of stem cells" in tumor cells might correlate with more aggressive disease or resistance to conventional therapies.
3. Refining Immunotherapy Strategies: Understanding the distinct immune pathway activity in T cells and macrophages across Adeno and Squamous subtypes can inform more tailored immunotherapy approaches. For example, if Squamous tumors show a particular pattern of T cell exhaustion pathways, strategies to reverse this exhaustion might be particularly effective for this patient group.
4. Combination Therapies: The metabolic reprogramming identified, such as the activation of glycolysis-related pathways, suggests that combining standard treatments with metabolic inhibitors could be a promising strategy to enhance therapeutic efficacy in both lung cancer subtypes.
5. Understanding Treatment Resistance: Pathways involved in DNA repair (e.g., "Mismatch repair," "Nucleotide excision repair") or cellular stress responses, if enriched, could provide insights into mechanisms of resistance to chemotherapy or radiation, paving the way for resistance-modifying combination therapies.

22. Discussion

This comprehensive single-cell analysis elucidates the profound molecular and cellular heterogeneity distinguishing lung Adenocarcinoma and Squamous Cell Carcinoma, underscoring the necessity for subtype-specific therapeutic strategies. The initial UMAP embeddings clearly delineate Adeno and Squamous conditions, with aneuploid cells robustly localizing to Lung Epithelial cell clusters, confirming their tumor-origin and genomic instability as a primary driver of cellular separation.

Genomic and Proliferative Drivers: CNV analysis in Lung Epithelial cells highlights significant genomic instability. Squamous samples consistently exhibit higher aneuploidy and frequent amplifications of oncogenes such as SOX2 and EIF3E, indicative of broad genomic disruption. This is further corroborated by cell cycle gene expression, where Squamous cells show a higher prevalence of DNA replication machinery (MCMs, MYC, CDK6), contrasting with Adeno's emphasis on G1/S regulators (CCND1, CDK4) and DNA damage response genes (GADD45A/B). This suggests distinct proliferative strategies and inherent genomic stress responses between the two subtypes. Gene Set Analysis (GSA) also revealed common enrichment in neurodegenerative disease pathways for both Adeno and Squamous Lung Epithelial cells, pointing to shared, fundamental cellular stress and proteostasis dysregulation within the malignant compartment, a finding that warrants further exploration for broad therapeutic applicability.

Distinct Immune Microenvironments: The immune landscape is strikingly distinct. Adenocarcinoma exhibits a higher proportion of Th2 cells and regulatory Innate Lymphoid Cells (ILCreg), suggesting a bias towards type 2 immunity and potentially increased immune suppression. Conversely, Squamous Cell Carcinoma shows elevated Th17 and CD8 Naive T cell populations, indicative of a more inflammatory environment with potentially less antigen-experienced cytotoxic T cells. Macrophage polarization also differs: M2B and M2D subsets are enriched in Squamous, typically linked to immune regulation, angiogenesis, and tumor growth, while M2A and M2C subsets show a tendency for higher, albeit marginally significant, proportions in Adeno, associated with wound healing/tissue repair and immunosuppression, respectively. These distinct immune profiles signify different mechanisms of immune evasion and inflammation, critically impacting immunotherapy responsiveness.

Cell-Cell Communication and Surfaceome Signatures: Cell-cell interaction (CCI) analysis further distinguishes the subtypes. Adenocarcinoma features significant interactions involving aneuploid Lung Epithelial cells, notably GAS6-AXL signaling (promoting tumor growth) and LGALS9-P4HB (suggesting T cell exhaustion). Its epithelial cells also express unique surface markers like CEACAM5, MUC1, TACSTD2, and ERBB2. In contrast, Squamous Cell Carcinoma is characterized by extensive extracellular matrix remodeling, with prominent collagen/fibronectin-integrin interactions driven by fibroblasts, often marked by high FAP and MMP14 expression. Macrophages in Squamous are enriched in markers like CXCR4 and C5AR1, indicative of active recruitment and inflammatory signaling, alongside elevated EGFR ligand-receptor interactions from stromal cells to tumor cells, a known oncogenic driver. The shared SPP1-CD44 axis is prominent in macrophages in both subtypes, reinforcing their pro-tumorigenic role. GSEA further supports these findings, showing PI3K-Akt signaling and stemness pathways enriched in Adeno epithelial cells, while HIF-1 signaling and broader metabolic reprogramming are prominent in Squamous epithelial cells, alongside significant ECM-receptor interactions in fibroblasts of both conditions.

In conclusion, this analysis provides a high-resolution map of the distinct molecular and cellular features of lung Adenocarcinoma and Squamous Cell Carcinoma. The identified subtype-specific genomic alterations, immune dysregulations, and cell-cell communication networks offer a rich resource for developing precision diagnostic markers and targeted therapeutic interventions tailored to the unique biology of each lung cancer subtype.

Hypotheses:

1. Lung Adenocarcinoma and Squamous Cell Carcinoma exhibit distinct genomic instability profiles, with Squamous Cell Carcinoma displaying a more pervasive aneuploidy and specific oncogene amplifications (e.g., SOX2, EIF3E) driving its proliferative phenotype compared to Adenocarcinoma.
2. The immune microenvironments of lung Adenocarcinoma and Squamous Cell Carcinoma are differentially polarized, characterized by a Th2-skewed response and increased regulatory innate lymphoid cells in Adenocarcinoma, versus a Th17-driven inflammation and higher CD8 naive T cell proportions in Squamous Cell Carcinoma.
3. Cancer-associated fibroblasts (CAFs) in Squamous Cell Carcinoma drive a highly desmoplastic and invasive tumor microenvironment through prominent collagen/fibronectin-integrin interactions and expression of proteases like MMP14, contributing to its aggressive phenotype and drug resistance.
4. Tumor-associated macrophages (TAMs) in Adenocarcinoma are characterized by a pro-tumorigenic TREM2-positive phenotype, while in Squamous Cell Carcinoma, TAMs exhibit a broader inflammatory and migratory signature (e.g., CXCR4, C5AR1, EREG) reflecting distinct immune evasion mechanisms.
5. Subtype-specific cell-cell interactions, particularly EGFR ligand-receptor signaling from stromal cells to tumor cells in Squamous Cell Carcinoma and GAS6-AXL/LGALS9-TIM-3 interactions in Adenocarcinoma, represent key drivers of tumor growth and immune evasion that can be therapeutically targeted.

Potential therapeutic targets:

1. EGFR signaling pathway: Widespread and strong activation by various ligands (AREG, HBEGF, EREG, EGF) from tumor microenvironment cells (macrophages, fibroblasts, mast cells, plasma cells) on tumor cells, especially aneuploid Lung Epithelial cells in Squamous Cell Carcinoma. This pathway drives cell proliferation, survival, and differentiation. Evidence: Strong and significant EGFR ligand-receptor interactions observed in condition-specific CCI for both Adeno and Squamous. High EGFR expression on Squamous Lung Epithelial cells. Well-established oncogenic pathway in NSCLC. Validation: Clinical use of EGFR Tyrosine Kinase Inhibitors (Gefitinib, Erlotinib, Osimertinib, Afatinib) and anti-EGFR monoclonal antibodies (Necitumumab is approved for Squamous NSCLC).
2. AXL receptor tyrosine kinase: GAS6-AXL signaling (Lung Epithelial (Aneuploid) - Plasma cell) is predominant and significant in Adenocarcinoma, promoting tumor cell proliferation, survival, migration, drug resistance, and immune evasion. Evidence: Strong and significant GAS6-AXL interactions identified in Adenocarcinoma-specific CCI. AXL is a known oncogenic driver in various cancers. Validation: AXL inhibitors (e.g., bemcentinib, gilteritinib, cabozantinib) are currently undergoing investigation in clinical trials for lung cancer and other malignancies.
3. TIM-3 immune checkpoint receptor: LGALS9 (Galectin-9) from aneuploid Lung Epithelial cells interacting with TIM-3 on T cells (CD4+) in Adenocarcinoma suggests a mechanism for T cell exhaustion and immune suppression within the tumor microenvironment. Evidence: Strong and significant LGALS9-P4HB interaction in Adenocarcinoma-specific CCI, implicating active LGALS9. TIM-3 is a well-characterized inhibitory receptor on T cells. Validation: Anti-TIM-3 antibodies (e.g., cobolimab) are under investigation as novel immune checkpoint inhibitors to reverse T cell exhaustion and enhance anti-tumor immunity.
4. SPP1-CD44 axis: Secreted Phosphoprotein 1 (SPP1, Osteopontin) and its receptor CD44 show strong and significant interactions in Macrophage-Macrophage (both Adeno/Squamous) and Macrophage-Lung Epithelial (Adeno/Squamous) cells. This pathway promotes tumor growth, metastasis, and immune evasion, and CD44 is associated with cancer stem cell properties. Evidence: Prominent in general and condition-specific CCIs for both Adeno and Squamous. CD44 is highly expressed on Squamous Lung Epithelial cells and Macrophages. Validation: Inhibitors targeting SPP1 or blocking CD44 (e.g., bivatuzumab mertansine) have been explored in preclinical and early clinical studies.
5. FAP (Fibroblast Activation Protein alpha): FAP is a canonical marker for Cancer-Associated Fibroblasts (CAFs), highly and consistently expressed by fibroblasts in Squamous Cell Carcinoma. FAP+ CAFs drive extensive extracellular matrix remodeling, desmoplasia, and promote tumor growth, angiogenesis, and immunosuppression, contributing to an aggressive phenotype. Evidence: Highly enriched expression in Fibroblasts from Squamous Cell Carcinoma samples. Validation: FAP-targeted therapies, including small molecule inhibitors, antibodies, antibody-drug conjugates (ADCs), and FAP-directed CAR-T cell therapies, are in preclinical and clinical development for various solid tumors.
6. MMP14 (Matrix Metalloproteinase 14): MMP14 (MT1-MMP) is highly expressed by fibroblasts in Squamous Cell Carcinoma. This membrane-bound metalloproteinase degrades various ECM components and activates other MMPs, which is crucial for tumor cell invasion, intravasation, and metastasis. Evidence: Highly enriched expression in Fibroblasts from Squamous Cell Carcinoma samples. Validation: Selective MMP14 inhibitors are being developed to curb tumor progression and invasiveness, potentially enhancing the efficacy of other anti-cancer treatments.
7. TREM2 (Triggering Receptor Expressed on Myeloid cells 2): Elevated expression on macrophages in Adenocarcinoma. TREM2-expressing macrophages are often associated with an M2-like, pro-tumorigenic phenotype, promoting tumor growth, angiogenesis, and immune suppression by signaling through DAP12. Evidence: Highly enriched expression in Macrophages from Adenocarcinoma samples. Validation: Blocking antibodies or small molecule inhibitors of TREM2 are in preclinical development to shift the immune balance towards anti-tumor responses and deplete pro-tumorigenic TAMs.
8. CXCR4 chemokine receptor: Elevated expression on macrophages in Squamous Cell Carcinoma, and involved in CXCL14-CXCR4 interactions (Lung Epithelial - T CD8+). The CXCL12-CXCR4 axis is crucial for cell migration, homing, and metastatic dissemination, promoting tumor growth, angiogenesis, and immunosuppression. Evidence: Highly enriched expression in Macrophages from Squamous Cell Carcinoma samples and identified in condition-specific CCIs. Validation: CXCR4 antagonists (e.g., Plerixafor, AMD3100) are in clinical use for other indications and are being investigated in cancer to inhibit metastasis and enhance anti-tumor immunity.
9. CDK4/6 and cell cycle regulation: Differential expression of cell cycle genes indicates distinct proliferative programs. Specifically, CCND1 (Cyclin D1) and CDK4 are significantly higher in Adenocarcinoma Lung Epithelial cells, suggesting increased reliance on CDK4/6-driven cell cycle progression. Evidence: Statistically significant higher fraction of Lung Epithelial cells expressing CCND1 and CDK4 in Adenocarcinoma compared to Squamous Cell Carcinoma. Validation: CDK4/6 inhibitors (Palbociclib, Ribociclib, Abemaciclib) are approved for other cancers (e.g., breast cancer) and are being investigated for their potential efficacy in lung cancer, particularly in adenocarcinoma.

Follow-up validation ideas:

1. Validate differential T cell and macrophage subset proportions and marker expression (e.g., TREM2, CD44, FAP) using flow cytometry or immunohistochemistry in larger patient cohorts, correlating with clinical outcomes.
2. Investigate the spatial localization of specific fibroblast subtypes (e.g., FAP+ CAFs) and their interactions with tumor cells using spatial transcriptomics or multiplexed immunostaining. Confirm CNV regions (e.g., SOX2 amplification) by FISH in tissue sections.
3. Perform perturbation assays (in vitro using cell lines/organoids or in vivo using PDX models) with blocking antibodies or small molecule inhibitors against identified therapeutic targets (e.g., AXL, TIM-3, FAP, MMP14, SPP1-CD44 axis, CXCR4) to assess effects on tumor growth, metastasis, and immune modulation.
4. Utilize single-cell multi-omics technologies (e.g., ATAC-seq, proteomics) to complement transcriptomic data, providing a deeper understanding of regulatory mechanisms and protein-level validation of key surface markers and signaling pathways.
5. Test the prognostic or predictive value of identified biomarkers (e.g., specific cell cycle gene expression patterns, macrophage polarization states, surfaceome markers) in independent, well-annotated patient cohorts.

Limitations:

This analysis relies on single-cell RNA-seq data, and while robust, ploidy and CNV estimates are inferred rather than directly measured via genomic sequencing. The dataset provides a cross-sectional view, limiting the ability to observe dynamic changes in tumor evolution or treatment response over time. Despite comprehensive cell type annotation, a small fraction of 'unassigned' cells persist, potentially representing rare populations or technical artifacts. While strong correlations and biological plausibility are presented, direct causality of identified cell-cell interactions and marker functions requires experimental validation in in vitro and in vivo models. Furthermore, while the patient cohort is substantial, inter-patient heterogeneity within the Adenocarcinoma and Squamous subtypes suggests that some findings may not be universally generalizable, necessitating validation in larger, independent cohorts for broader clinical applicability.

23. Query List

1. Show UMAPs for condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset in two columns, and save the result.
2. Show major cell type scores on UMAP and save the result.
3. Show and save a marker expression dot plot for celltype_subset. SET target_cell = None and var_group_rotation = 45. Leave all other arguments at their default values.
4. Select Lung Epithelial cells as tumor-origin cells, show a CNV heatmap grouped by sample together with a summary of regions with significantly amplified copy numbers, and save the results.
5. Show and save UMAPs of CNV patterns colored by major cell type, minor cell type, ploidy results, condition, and sample in two columns.
6. Show and save a population bar plot of minor cell types.
7. Show and save a population bar plot of T cell subsets.
8. Show and save box plots of T cell subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
9. Show and save a population bar plot of Macrophage subsets.
10. Show and save box plots of Macrophage subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
11. Select Lung Epithelial cells as tumor-origin cells, show their ploidy populations as a bar plot, and save the result.
12. Show and save cell-cell interaction patterns involving Lung Epithelial cells (tumor-origin cells), Fibroblast cells, Macrophage cells, T cells, and other relevant cell types. Select at most 80 cell-cell interactions per group.
13. Find cell-cell interactions involving Lung Epithelial cells (tumor-origin cells), major immune and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms, its potential as a therapeutic target, and relevant therapeutic agents.
14. Find cell-cell interactions containing immune checkpoint and cell cycle pathway genes and involving Lung Epithelial cells (tumor-origin cells), major immune and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms, its potential as a therapeutic target, and relevant therapeutic agents.
15. Show marker expressions for Lung Epithelial cells (tumor-origin cells) in a dot plot, and save the result. Use only surfaceome markers, with at most 60 markers per condition. For the markers identified under each condition, distinguish between established therapeutic targets and less well-characterized markers with potential as therapeutic targets. For each marker, using the provided dataset context, describe the associated signaling pathways and mechanisms, relevant therapeutic agents, and supporting evidence.
16. Show marker expressions for Macrophage in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
17. Show marker expressions for Fibroblast in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
18. Show marker expressions for T cell CD4+ in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
19. Among cell cycle pathway genes, select those with statistically significant expression differences between conditions in Lung Epithelial cells (major disease-related cells), show box plots, and save the result. Set max_n_items_to_plot to 24 and choose ncols based on the total number of panels for an overall width-to-height ratio of approximately 2:3.
20. Show and save Gene Ontology (GSA) analysis results for Lung Epithelial cells (tumor-origin cells) as a bar plot.
21. Show Gene Set Enrichment Analysis results as a dot plot, and save it. Set the color map to RdBu_r, n_pws_to_show to 120, and target_cells to None.