SCODiA Report by MLBI Lab

Single-Cell Deconvolution of Tumor Microenvironment Heterogeneity in Lung Adenocarcinoma and Squamous Cell Carcinoma

This comprehensive single-cell RNA-sequencing analysis reveals distinct cellular and molecular landscapes differentiating lung adenocarcinoma (Adeno) from squamous cell carcinoma (Squamous). Aneuploid lung epithelial cells, identified as malignant, exhibit subtype-specific genomic alterations and transcriptional profiles. The tumor microenvironment (TME) shows marked differences in immune cell composition, macrophage polarization, and fibroblast activation states, leading to unique cell-cell interaction patterns. These findings highlight the critical need for histology-specific therapeutic strategies in lung cancer.

Contents

0. Dataset overview
1. UMAP Visualization of Lung Single-Cell RNA-seq Data Highlighting Condition, Sample, Cell Types, and Ploidy Status
2. UMAP Visualization of Major Cell Type Scores and Annotations in Lung Tissue
3. 세포아형 마커 유전자 발현 Dot Plot 분석
4. Tumor-Origin and Unassigned Cell CNV Heatmap Analysis
5. CNV-Informed UMAP Visualization of Cell Types, Ploidy, Conditions, and Samples
6. Minor Cell Type Population Analysis in Lung Adenocarcinoma and Squamous Cell Carcinoma
7. T Cell and Innate Lymphoid Cell Subset Composition Across Lung Adenocarcinoma and Squamous Cell Carcinoma Samples
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9. Assessment of Macrophage Cell Type Representation per Sample and Condition
10. Differential Macrophage Subset Proportions in Lung Adenocarcinoma vs. Squamous Cell Carcinoma
11. Ploidy Population Analysis of Tumor-Origin and Unassigned Cells in Lung Cancer Subtypes
12. Condition-Specific Cell-Cell Interaction Patterns in Lung Adenocarcinoma and Squamous Cell Carcinoma
13. Adenocarcinoma 및 편평상피세포암의 조건별 세포-세포 상호작용 분석
14. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Genes in Squamous Lung Carcinoma
15. Condition-Specific Cell-Cell Interaction Patterns in Lung Adenocarcinoma vs. Squamous Cell Carcinoma
16. Lung Epithelial Cell Condition-Specific Surfaceome Markers in Adenocarcinoma vs. Squamous Cell Carcinoma
17. Macrophage Condition-Specific Surfaceome Markers in Lung Cancer Subtypes
18. Condition-Specific Surface Markers in Lung Fibroblasts
19. T cell CD4+ Condition-Specific Surfaceome Markers in Lung Cancer Subtypes
20. Gene Ontology (GSA) Analysis for Lung Epithelial Cells: Condition and Ploidy-Specific Pathway Upregulation
21. Gene Set Enrichment Analysis Reveals Distinct Pathway Activities Across Lung Cancer Cell Types and Conditions
22. Discussion
23. Query List

0. Dataset overview

데이터셋 요약

• 데이터 형식: 58757개 세포와 37895개 유전자를 포함하는 AnnData 객체입니다.
• 종 및 조직: 인간 폐 조직에서 유래했습니다.
• 관측치 (obs) 정보: 'Tissue Diagnosis', 'Histology-reduced', 'Sex', 'Age at surgery', 'Stage', 'AJCC 8th ed', 'Type of Surgery', 'Smoking History', 'Years from quit date to surgery', 'Tumor Mutation Burden', 'Primary Tissue', 'Status', 'Survival from surgery', 'pid', 'sample', 'Tn', 'condition', 'copykat_pred', 'scevan_pred', 'sample_ext', 'celltype_major', 'celltype_minor', 'celltype_subset', 'cnv_ref_ind', 'ploidy_score', 'ploidy_dec', 'ploidy_init_group', 'cluster', 'sample_diversity_index', 'cnv_cluster', 'cnv_sample_diversity_index' 등의 정보를 포함합니다.
• 유전자 (var) 정보: 'gene_symbol', 'variable_genes', 'chr', 'spot_no', 'cytogenetic_band' 등의 정보를 포함합니다.
• 조건: Squamous, Adeno 두 가지 조건이 있습니다. DEG, GSEA, GSA_up 분석의 참조 조건은 Adeno입니다.
• 주요 세포 타입 (celltype_major): Lung Epithelial cell, Myeloid cell, Mast cell, Stromal cell, T cell, unassigned, Endothelial cell, B cell.
• 세부 세포 타입 (celltype_minor): Airway Epithelial cell, Macrophage, Mast cell, Fibroblast, ILC, T cell CD8+, unassigned, Endothelial cell, Plasma cell, T cell CD4+, B cell, Alveolar Epithelial cell, Dendritic cell, NK cell, Smooth muscle cell.
• 세포 서브셋 (celltype_subset): Basal cell, Macrophage (M2A), Mast cell, Macrophage (M1), Fibroblast, ILC2, T cell (Cytotoxic), ILC1, unassigned, Macrophage (M2D), Endothelial cell, Plasma cell, Macrophage (M2B), T cell (Tfh), T cell (Treg), T cell (Th2), Macrophage (M2C), LTI, T cell (Naive), B cell (Breg), T cell (Th22), B cell (Follicular), Alveolar type 2, ILCreg, DC (Classical), T cell (Th1), T cell (Th17), ILC3 (NCR-), Alveolar type 1, DC (Inflammatory), B cell (Memory), NK cell, T cell (Th9), Secretory club, Lymphatic Endothelial cell, B cell (MZ), Ionocyte, Ciliated cell, Endothelial tip cell, Smooth muscle cell, ILC3 (NCR+), DC (Plasmacytoid), Goblet cell.
• 종양 기원 세포 타입: Lung Epithelial cell.
• 배수성 (ploidy_dec): Aneuploid, Diploid.

사전 계산된 결과:

• uns['CCI']: 조건별 세포-세포 상호작용 (CellPhoneDB) 결과.
• uns['CCI_sample']: 샘플별 세포-세포 상호작용 (CellPhoneDB) 결과.
• uns['DEG']: 각 celltype_minor에 대해 한 조건과 나머지를 비교한 차등 발현 유전자 (DEG) 결과.
• uns['GSEA']: 각 celltype_minor에 대해 한 조건과 나머지를 비교한 유전자 세트 농축 분석 (GSEA) 결과.
• uns['GSA_up']: 각 celltype_minor에 대해 한 조건과 나머지를 비교한 GO(GSA) 결과.
• obs['ploidy_dec']: 배수성 추론 라벨 (Aneuploid/Diploid).
• obsm['X_cnv']: CNV 추정치.
• 분석 가능 세포 타입: DEG, GSEA, GSA/GO 분석을 위한 특정 세포 타입 (B cell, Fibroblast, ILC, Lung Epithelial cell, Macrophage, Mast cell, Plasma cell, T cell CD4+, T cell CD8+)이 존재합니다.

1. UMAP Visualization of Lung Single-Cell RNA-seq Data Highlighting Condition, Sample, Cell Types, and Ploidy Status

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a series of UMAP (Uniform Manifold Approximation and Projection) plots, providing a comprehensive overview of the single-cell RNA sequencing dataset. These visualizations allow us to explore the cellular heterogeneity, sample contributions, disease condition distributions, and ploidy status across the embedding. The plots highlight how cells cluster based on their transcriptional profiles and how these clusters relate to various biological and technical metadata annotations.

Visual Summary

• Condition: The UMAP shows a clear separation and enrichment of cells based on the 'condition' (Adeno or Squamous). Adenocarcinoma (Adeno, maroon) cells are broadly distributed across the central and larger clusters, while Squamous cell carcinoma (Squamous, purple) cells form distinct, somewhat smaller clusters, particularly at the periphery or in specific sub-regions. This suggests distinct cellular compositions or transcriptional states associated with these two lung cancer subtypes.
• Sample: Cells from different individual samples (NSCXXX.T1/T2/etc.) are generally well-interspersed across the major cell type clusters, indicating a largely successful integration of samples with minimal overt batch effects driving the primary separation of cell types. However, some smaller, more isolated clusters might show enrichment from specific samples, which could represent unique patient-specific biology or minor residual batch effects.
• Celltype_major: The major cell types show distinct and well-separated clusters on the UMAP, reflecting successful identification and annotation. Lung Epithelial cells (orange) and Myeloid cells (light green) form prominent and interconnected clusters, consistent with their central roles in the lung and tumor microenvironment. T cells (cyan), Stromal cells (light blue), B cells (maroon), and Endothelial cells (red) also form well-defined, albeit sometimes smaller, clusters. A small proportion of "unassigned" cells (dark blue) are present, scattered across the embedding.
• Celltype_minor: Further refinement into minor cell types reveals more granular populations. Airway Epithelial cells and Alveolar Epithelial cells (subsets of Lung Epithelial) show distinct localizations within the epithelial compartment. Macrophages (Mac) are a dominant Myeloid subset. Fibroblasts (Fib) are a major Stromal component. T cell CD4+ and T cell CD8+ populations are clearly delineated. These distinctions provide a higher resolution view of the cellular landscape.
• Ploidy_dec: Aneuploid cells (maroon) exhibit a striking pattern, primarily clustering in specific regions of the UMAP. These regions largely overlap with populations identified as Lung Epithelial cells and, to a lesser extent, some Myeloid cell populations. Diploid cells (yellow) are more broadly distributed across the UMAP. A very minor population of "Unclear" ploidy cells (dark blue) is also present. This distinct clustering of aneuploid cells is a key observation.
• Celltype_subset: This plot provides the highest resolution of cell identities. It reveals diverse subsets within major cell types, such as various macrophage polarizations (Mac_M1, Mac_M2A/B/C/D), specific T cell helper and regulatory subtypes (Th1, Th2, Treg, Tfh, T_Cyto, T_Naive), and fine-grained epithelial populations (Basal cell, Alveolar type 1/2, Secretory club cell, Ciliated cell). The segregation of these subsets further confirms the biological diversity captured in the dataset.

Biological Interpretation

The UMAP visualizations provide critical insights into the cellular composition and disease-associated features of the lung single-cell dataset.

1. Tumor-specific Cellular States and Microenvironment: The distinct clustering of cells by 'condition' (Adeno vs. Squamous) suggests that these two lung cancer types either harbor different cellular compositions within their tumor microenvironments or that the malignant epithelial cells themselves (and potentially other stromal or immune cells) exhibit distinct gene expression profiles. This aligns with the known biological differences and clinical presentations of lung adenocarcinoma and squamous cell carcinoma.
2. Aneuploidy as a Tumor Cell Marker: The strong co-localization of aneuploid cells (from ploidy_dec) with Lung Epithelial cell clusters (from celltype_major) is a highly significant finding. Given that "Lung Epithelial cell" is specified as the 'Tumor origin celltype' in the data context, this pattern strongly indicates that the aneuploid populations within these epithelial clusters represent the malignant tumor cells. Aneuploidy, the presence of an abnormal number of chromosomes, is a hallmark of most cancers, including lung cancer, and often drives tumor progression and heterogeneity [1]. The presence of aneuploidy in some myeloid cells could represent tumor-infiltrating myeloid cells that have acquired genomic instability, or less likely, technical artifacts, but the dominant signal is within the epithelial compartment.
3. Heterogeneity within Tumor Microenvironment (TME): The detailed celltype_minor and celltype_subset plots reveal a complex and diverse tumor microenvironment. The presence of multiple macrophage subtypes (M1, M2A-D), T cell subsets (cytotoxic, helper, regulatory), and various stromal components (Fibroblasts, Endothelial cells) underscores the intricate immune and stromal remodeling that occurs in lung cancer. The distinct distributions of these subsets on the UMAP suggest specialized roles or spatial organization within the tumor.
4. Data Quality and Annotation Robustness: The clear separation of major and minor cell types, consistent hierarchical clustering from broad to granular cell identities, and general mixing of samples (reducing concern for severe batch effects) all suggest a high quality of data processing, dimensionality reduction, and cell type annotation. The unassigned cell clusters are minimal, indicating comprehensive cell type identification.

Annotation Notes

The visualizations demonstrate robust cell type annotation and embedding structure. The hierarchical annotation from celltype_major to celltype_subset is consistent, with finer cell types appropriately nested within their broader categories. The ploidy_dec annotation effectively segregates cells by a key cancer-associated feature, and its strong co-localization with the tumor origin cell type provides high confidence in identifying the likely malignant populations. The overall UMAP structure reflects biological heterogeneity rather than technical artifacts.

References

1. Aneuploidy in Cancer: Bi, X., He, X. Chromosomal instability, aneuploidy, and tumor evolution. *Cell Mol Life Sci* 78, 3409–3425 (2021). https://pubmed.ncbi.nlm.nih.gov/33587002/

2. UMAP Visualization of Major Cell Type Scores and Annotations in Lung Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis provides a UMAP visualization of single-cell RNA-seq data from human lung tissue, displaying cell type scores for various major cell populations, ploidy status, and the final major cell type annotations. The primary goal is to assess the distribution of different cell types within the tissue and to evaluate the consistency of cell type scoring with the final cell type assignments, while also observing the spatial distribution of aneuploid cells.

Visual Summary

The UMAP projections reveal a complex landscape of cellular populations in the lung tissue.

Cell Type Scores (HiCAT_major_score plots):

• Each of the seven HiCAT_major_score plots (T cell, B cell, Myeloid cell, Mast cell, Endothelial cell, Stromal cell, Lung Epithelial cell) displays distinct high-score regions that generally correspond to specific clusters within the overall UMAP embedding. For example, the "HiCAT_major_score: T cell" plot shows a clear high-scoring region on the left-middle side, while "HiCAT_major_score: Lung Epithelial cell" shows high scores primarily in a large cluster towards the bottom-right. This indicates that the scoring method effectively discriminates between different major cell types.
• The intensity of the scores (yellow regions) for each cell type clearly delineates specific clusters or parts of clusters on the UMAP, suggesting good resolution in distinguishing these populations based on their gene expression profiles.

Ploidy Status (ploidy_dec plot):

• The ploidy_dec plot shows cells colored by their ploidy inference: Aneuploid (maroon), Diploid (light yellow), and Unclear (purple-blue).
• A significant population of Aneuploid cells is observed, forming distinct clusters, predominantly located in the lower-right region of the UMAP. The vast majority of cells are labeled as Diploid, scattered throughout the rest of the UMAP space. The "Unclear" category is minimally represented.

Major Cell Type Annotation (celltype_major plot):

• The celltype_major plot provides the definitive annotation for each cell, showing well-separated clusters for most cell types: B cell (maroon), Endothelial cell (orange), Lung Epithelial cell (light yellow), Mast cell (green-yellow), Myeloid cell (light green), Stromal cell (teal), T cell (blue), and unassigned (dark blue-grey).
• Comparing this plot with the individual HiCAT_major_score plots, there is strong concordance. For instance, the region with high "T cell" score perfectly aligns with the "T cell" cluster in the celltype_major plot. Similarly, the "Lung Epithelial cell" score aligns with the large yellow cluster.

Biological Interpretation

• Diverse Cellular Ecosystem: The UMAP clearly illustrates the heterogeneity of the lung tissue, identifying major immune populations (T cells, B cells, Myeloid cells, Mast cells), structural components (Stromal cells, Endothelial cells), and the tissue-specific epithelial cells (Lung Epithelial cells). The distinct clustering suggests well-defined gene expression signatures for these major cell types.
• Concordance of Cell Type Scores and Annotations: The high degree of overlap between the HiCAT_major_score plots and the celltype_major annotation plot indicates that the cell type scoring system is robust and accurately reflects the underlying cell identities. This strengthens the confidence in the cell type assignments provided by the celltype_major annotation.
• Identification of Potential Tumor Cells: Given that "Lung Epithelial cell" is specified as the Tumor origin celltype, the presence of a distinct cluster of Aneuploid cells, primarily overlapping with a subset of the "Lung Epithelial cell" population, is highly indicative of malignant lung epithelial cells. Aneuploidy is a hallmark of cancer, and its localized presence within the epithelial compartment strongly suggests the identification of tumor cells within the dataset.
• Immune Cell Infiltration and Stromal Components: The prominent clusters of T cells, Myeloid cells, and B cells suggest a significant immune infiltration, which is common in cancer microenvironments. The presence of Endothelial and Stromal cells indicates the vascular and structural components of the lung tissue, also integral to the tumor microenvironment.

Annotation Notes

The UMAP visualizations demonstrate high-quality cell type annotations. The distinct clustering of major cell types and the clear correspondence between cell type scores and final annotations indicate a well-resolved and accurately annotated dataset. The segregation of aneuploid cells, aligning with a subset of the tumor-originating cell type (Lung Epithelial cells), provides a strong biological validation for the identification of malignant cells. The minimal presence of "unassigned" cells further supports the comprehensiveness of the current cell type annotation.

3. 세포아형 마커 유전자 발현 Dot Plot 분석

[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱 데이터를 기반으로 AnnData 객체에 정의된 celltype_subset 그룹별 마커 유전자 발현 패턴을 시각화한 것입니다. plot_markers_and_expression_dot 도구를 사용하여 각 세포아형의 특징적인 유전자 발현을 점(dot) 크기와 색상 강도로 표현했습니다. 점의 크기는 해당 유전자를 발현하는 세포의 비율을 나타내고, 색상의 강도는 해당 유전자의 평균 발현량을 나타냅니다. 이 분석은 각 세포아형 주석의 품질과 고유한 분자적 특징을 확인하는 데 중점을 둡니다.

Visual Summary

제공된 Dot Plot은 X축에 선택된 마커 유전자들을, Y축에 AnnData의 celltype_subset에 정의된 38가지 세포아형들을 보여줍니다.

• 명확한 군집화 (Clear Clustering): 플롯의 대각선을 따라 짙은 색상의 큰 점들이 뚜렷하게 나타나, 각 celltype_subset이 고유한 마커 유전자 세트를 발현함을 시사합니다. 이는 세포아형 주석이 잘 이루어졌음을 강력히 지지합니다.
• 마커 유전자 특이성 (Marker Specificity): 대부분의 마커 유전자들은 특정 세포아형 그룹에서만 높게 발현되며 (크고 진한 점), 다른 세포아형에서는 발현이 없거나 매우 낮은 경향을 보입니다. 빨간색 사각형은 각 세포아형의 핵심 마커 유전자 그룹을 시각적으로 강조합니다.
• 발현 비율 및 강도 (Expression Fraction & Intensity): 점의 크기는 해당 유전자를 발현하는 세포의 비율을 나타내며, 큰 점은 해당 세포아형 내에서 유전자가 널리 발현됨을 의미합니다. 점의 색상은 유전자 발현의 평균 강도를 나타내며, 짙은 붉은색은 높은 발현량을 나타냅니다.
• 세포 그룹 크기 (Cell Group Size): 플롯의 오른쪽에 있는 막대 그래프는 각 celltype_subset에 속하는 세포의 수를 보여줍니다. 대부분의 세포아형이 분석에 충분한 수의 세포를 포함하고 있음을 확인할 수 있습니다.

Biological Interpretation

이 마커 발현 Dot Plot은 AnnData의 celltype_subset 주석이 생물학적으로 의미 있는 구별을 잘 포착하고 있음을 보여줍니다. 여러 주요 세포아형에서 잘 알려진 마커 유전자들이 특이적으로 발현되고 있습니다.

폐 상피 세포 (Lung Epithelial Cells)

• Alveolar type 1: AGER, CAV1과 같은 유전자들의 높은 발현은 제1형 폐포 상피 세포의 특징을 잘 나타냅니다 PubMed search: Alveolar type 1 cell markers.
• Alveolar type 2: SFTPB, SFTPC, SFTPA1, SFTPA2 등 폐 계면활성제 관련 유전자들의 특이적 발현은 제2형 폐포 상피 세포의 주석을 강력히 지지합니다 GeneCards: SFTPB.
• Basal cell: KRT5, KRT14, KRT15, KRT17, TP63과 같은 케라틴 및 전사인자의 발현은 폐 기도 기저 세포의 정체성을 잘 반영합니다 GeneCards: KRT5.
• Ciliated cell: DNAH12, FOXJ1 등 섬모 관련 유전자들의 발현은 섬모 세포의 특징입니다 GeneCards: FOXJ1.
• Goblet cell: MUC5AC의 높은 발현은 점액 분비 술잔 세포의 정체성을 확인합니다 GeneCards: MUC5AC.
• Secretory club: SCGB1A1, CEACAM5, CEACAM6 등의 발현은 기도 내 분비 클럽 세포 (이전 클라라 세포)의 특징입니다 GeneCards: SCGB1A1.

면역 세포 (Immune Cells)

• B cell (Breg, MZ, Memory): CD79A, CD79B, MS4A1 (CD20) 등 일반적인 B 세포 마커들의 발현이 확인되며, FCRL3는 B cell (MZ)에서 비교적 높게 발현됩니다.
• Plasma cell: SDC1 (CD138), PRDM1 (BLIMP1), XBP1, JCHAIN 등 형질 세포의 분화 및 기능에 중요한 유전자들이 특이적으로 높게 발현됩니다 GeneCards: SDC1.
• T cell (Cytotoxic): CD8A, CD8B, GZMA, GZMB, PRF1, NKG7과 같은 세포독성 T 세포 및 사이토카인/그랜자임 유전자 발현은 세포독성 T 세포의 특징을 보여줍니다 GeneCards: GZMB.
• T cell (Treg): FOXP3, CTLA4의 발현은 조절 T 세포 (Treg)의 핵심 마커로서, 해당 세포아형의 정체성을 명확히 합니다 GeneCards: FOXP3.
• Macrophage (M1, M2A, M2B, M2C): CD68, CD14, CD163, MSR1 등의 발현은 대식세포의 전반적인 특성을 보여주며, CD86은 M1-유사 대식세포에서, CD163은 M2-유사 대식세포에서 상대적으로 높은 발현을 보입니다. 이는 대식세포 아형 간의 미묘한 차이를 반영합니다.
• Dendritic cell (Classical, Inflammatory, Plasmacytoid): CD1A, CD1C는 고전적 DC (Classical DC)에서, CLEC4C (CD303)는 형질세포양 DC (Plasmacytoid DC)에서 특이적으로 발현되어 각 DC 아형의 주석이 정확함을 시사합니다 GeneCards: CLEC4C.
• Mast cell: KIT, TPSAB1, SRGN 등은 비만 세포의 대표적인 마커입니다 GeneCards: KIT.
• NK cell: KLRD1 (CD94), GZMB, NKG7 등이 자연살해 (NK) 세포에서 발현됩니다.

기질/내피 세포 (Stromal/Endothelial Cells)

• Fibroblast: COL1A1, DCN, COL3A1, ACTA2 등 콜라겐 및 세포외기질 관련 유전자들의 발현은 섬유아세포의 특징을 나타냅니다 GeneCards: COL1A1.
• Endothelial tip cell: ESM1, ANGPT2 등의 발현은 혈관 신생에 관여하는 내피 첨단 세포의 특이적인 마커입니다.

전반적으로, 플롯은 celltype_subset 주석이 생물학적 기반을 잘 갖추고 있으며, 각 세포 그룹이 특정 기능적 역할을 시사하는 마커 유전자 집합을 발현하고 있음을 보여줍니다. find_cfg에서 surfaceome_only: True 설정으로 인해 많은 표면 마커들이 포함되어 세포 표면 단백질 기반의 세포 식별 또는 분리에 유용할 수 있습니다. 일부 비표면 마커 (예: FOXP3, KRTs)도 강력한 특이성 때문에 포함된 것으로 보입니다.

Annotation Notes

이 Dot Plot은 AnnData 객체의 celltype_subset 주석의 품질을 검증하는 데 매우 유용합니다.

• 각 세포아형이 예상되는 마커 유전자를 특이적으로 발현하고 있어, 주석의 신뢰성이 높음을 시사합니다.
• 일부 아형 (예: Macrophage M1/M2 subtypes, DC subtypes) 간에는 마커 유전자 발현 패턴이 미묘하게 겹치는 부분이 관찰될 수 있으나, 이는 해당 세포 집단 내의 이질성과 연속적인 전이 상태를 반영하는 것일 수 있습니다.
• 세포 수가 적은 일부 아형 (예: ILC1, ILC2, ILC3, Endothelial tip cell, DC (Plasmacytoid))의 경우에도 특이적인 마커 발현이 명확하여, 희귀 세포 집단의 식별이 성공적이었음을 보여줍니다.
• 이러한 결과는 향후 더 심층적인 기능 분석 및 질병 관련 연구를 위한 견고한 세포 주석 기반을 제공합니다.

4. Tumor-Origin and Unassigned Cell CNV Heatmap Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) in cells identified as "Lung Epithelial cell" (the designated tumor-origin cell type) and "unassigned" cells from single-cell RNA-seq data, grouped by individual sample. The goal is to visualize CNV patterns across the genome for these specific cell populations and summarize significantly amplified regions, providing insights into tumor heterogeneity, ploidy status, and the nature of "unassigned" cells.

Visual Summary

The visualization consists of a CNV heatmap and associated summary plots detailing frequently amplified cytogenetic bands.

CNV Heatmap (log2(CNR))

• Overall Pattern: The heatmap displays log2(CNR) (Copy Number Ratio) values across genomic spots for selected cell groups. Red colors indicate amplifications (log2(CNR) > 0), while blue colors indicate deletions (log2(CNR) < 0). Most of the prominent signals observed are amplifications.
• Ploidy Distinction: Samples prefixed with "Diploid" (e.g., Diploid NSC004.T1, Diploid NSC009.T1) generally exhibit a relatively flat CNV profile, indicating a largely diploid genome, which is consistent with their inferred ploidy status. In contrast, cell groups without the "Diploid" prefix (e.g., NSC004.T1, NSC004.T2, NSC010.T1) show distinct and widespread CNV patterns, especially amplifications, consistent with aneuploidy.
• Intra-sample Heterogeneity: For sample NSC004, both "Diploid" and non-"Diploid" populations are present, demonstrating heterogeneity within the same patient sample where some cells maintain a diploid state while others exhibit significant aneuploidy.
• Recurrent Amplifications: Several regions show strong and recurrent amplifications across multiple aneuploid samples. Notably, there are visible amplification hot spots on chromosomes 1, 3, 7, 12, 17, and 22. Some samples also show deletions, for instance, on chromosome 6 in samples like NSC004.T1-T4.

Significant Amplification Summary

• The summary plots provide a detailed view of frequently amplified cytogenetic bands, specifically for the aneuploid populations of sample NSC004 (NSC004.T1, NSC004.T2, NSC004.T3, NSC004.T4).
• Specific Amplified Regions: The most consistently amplified regions in NSC004 are:
• 3q26.33:3q28: This region is amplified in 100% of the aneuploid NSC004 populations summarized, with copy number ratios (relative to a diploid baseline) ranging from 1.2 to 1.6. This region is particularly significant as it encompasses the SOX2 gene, a known oncogene frequently amplified in lung squamous cell carcinoma.
• Reference: GeneCards for SOX2
• Reference: PubMed search for SOX2 amplification lung cancer
• 1p34.2:1p33: Amplified in 80-90% of NSC004 aneuploid populations.
• 3q22.1:3q22.3 and 3q25.1:3q25.33: Show consistent amplification across NSC004 aneuploid populations. These regions on chromosome 3q are frequently co-amplified in various cancers.
• 12q13.13:12q13.13 and 17q21.2:17q21.2: Also show high frequency (100%) of amplification in the summarized NSC004 populations.
• Frequency: The bar plot on the right visually represents the frequency of these amplifications across the four summarized NSC004 aneuploid populations, with all listed regions showing 100% frequency except 6p12.2:6q12 (75%).

Biological Interpretation

• Tumor-Origin Cell Malignancy: The clear and extensive CNV patterns observed in the non-"Diploid" populations, particularly within the "Lung Epithelial cell" and potentially "unassigned" cells (as these are the target cell types), strongly indicate their malignant nature. Lung Epithelial cells are the tumor-origin cell type in this dataset, and their aneuploid status with significant CNVs is a hallmark of cancer.
• Validation of Ploidy Status: The distinct separation between "Diploid" and non-"Diploid" samples/populations within the heatmap provides strong validation for the ploidy_dec inference. Cells labeled "Diploid" show minimal CNVs, aligning with a non-malignant or normal genetic profile.
• Implications of "Unassigned" Cells: Since the analysis included both "Lung Epithelial cell" and "unassigned" cells, the observed CNV patterns in aneuploid samples represent a combined profile. If "unassigned" cells from these aneuploid samples contribute significantly to the observed CNVs, it suggests that a portion of these "unassigned" cells are likely malignant (tumor cells that could not be assigned a specific lung epithelial subtype) rather than non-malignant stromal or immune cells. Conversely, the "unassigned" cells in "Diploid" samples would likely contribute to the flat CNV profile, supporting a non-malignant identity.
• Key Oncogene Amplification: The consistent amplification of the 3q26.33:3q28 region, containing SOX2, is a critical finding. SOX2 is a transcription factor important for maintaining pluripotency and is frequently amplified and overexpressed in lung squamous cell carcinoma (LSCC), driving proliferation and tumor progression. The high frequency of this amplification across the aneuploid populations of sample NSC004 suggests it is a crucial oncogenic event in this tumor.
• Tumor Heterogeneity: The presence of both diploid and aneuploid populations within the same sample (e.g., NSC004) highlights significant intratumoral heterogeneity. This cellular diversity can have implications for tumor aggressiveness, treatment response, and disease progression.

Annotation Notes

• The clear distinction in CNV profiles between cells labeled "Diploid" and those without this prefix serves as an important internal validation of the ploidy_dec annotation.
• The finding that selected "Lung Epithelial cell" and "unassigned" populations in tumor samples exhibit extensive CNVs reinforces their classification as tumor cells or cells with malignant potential. For "unassigned" cells, the presence of CNVs suggests they are not simply normal cells that failed annotation, but potentially unclassified malignant cells. If specific "unassigned" populations consistently show tumor-associated CNVs across multiple samples, it might warrant further investigation into their specific cell identity and biological role.

5. CNV-Informed UMAP Visualization of Cell Types, Ploidy, Conditions, and Samples

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes a UMAP embedding constructed based on Copy Number Variation (CNV) estimates from single-cell RNA-seq data. The goal is to visualize the distribution of major and minor cell types, ploidy status (aneuploid vs. diploid), disease conditions (Adeno vs. Squamous), and individual samples across this CNV-informed dimensional reduction. This helps in understanding how genomic variations, particularly CNVs, shape the cellular landscape and correlate with biological annotations.

Visual Summary

The UMAP plots provide a comprehensive overview of cell distribution across the CNV landscape:

• celltype_major: The UMAP shows distinct clustering for major cell types. A large, central cluster predominantly comprises "Lung Epithelial cell" (orange), with other cell types such as "Myeloid cell" (light yellow), "T cell" (light blue), and "Stromal cell" (teal) forming separate, peripheral clusters. "B cell" (dark red) and "Endothelial cell" (maroon) also form distinct, smaller groups.
• celltype_minor: A more granular view reveals that within the "Lung Epithelial cell" major cluster, "Airway Epithelial cell" (dark red) and "Alveolar Epithelial cell" (maroon) show some distinct patterns. Similarly, "Macrophage" (Mac, light yellow) within the myeloid compartment and "Fibroblast" (Fib, orange) within the stromal compartment are clearly demarcated. T cell subtypes like "T cell CD4+" (light blue) and "T cell CD8+" (dark blue) also form separate but adjacent clusters.
• ploidy_dec: This plot reveals a striking separation based on ploidy status. A major, distinct cluster is composed primarily of "Aneuploid" (dark red) cells. "Diploid" (yellow) cells form separate clusters, largely corresponding to the non-epithelial cell populations observed in the celltype_major plot. A small number of "Unclear" cells (purple) are scattered.
• condition: The "Adeno" (Adenocarcinoma, dark red) condition cells heavily overlap with the "Aneuploid" and "Lung Epithelial cell" clusters. "Squamous" (Squamous cell carcinoma, purple) condition cells are also found within the aneuploid epithelial region, but they appear to form a somewhat distinct sub-cluster, suggesting a unique CNV profile or specific cellular composition for this condition.
• sample: A significant degree of sample-specific clustering is observed, particularly within the large aneuploid region. Cells from individual samples (e.g., NSC004.T1, NSC018.T2, NSC021.T1) often form visually distinguishable sub-groups, indicating heterogeneity in CNV patterns between patients. The diploid clusters, however, appear more mixed across different samples.

Biological Interpretation

The CNV-informed UMAP embedding effectively distinguishes cells based on their genomic integrity, offering crucial biological insights:

• Segregation of Malignant vs. Non-Malignant Cells: The most prominent feature is the clear separation of "Aneuploid" and "Diploid" cells. Given that lung cancers (Adeno and Squamous) originate from epithelial cells and are characterized by chromosomal instability leading to aneuploidy, the "Aneuploid" cluster likely represents the malignant tumor cells. Conversely, the "Diploid" clusters represent the non-malignant immune (Myeloid, T, B cells) and stromal populations (Fibroblasts, Endothelial cells) that typically maintain a diploid genome.
• Tumor Cell Identity and Origin: The "Lung Epithelial cell" population largely co-localizes with the "Aneuploid" and "Adeno" condition cells. This observation strongly supports the malignant identity of these epithelial cells in adenocarcinoma, consistent with their tissue of origin and disease pathology.
• Condition-Specific Genomic Signatures: While both "Adeno" and "Squamous" conditions show cells within the aneuploid compartment, their somewhat distinct clustering suggests that these two major histological types of lung cancer may harbor different underlying CNV profiles or patterns of genomic instability, contributing to their unique molecular and clinical characteristics.
• Tumor Heterogeneity: The sample-specific clustering within the aneuploid tumor cell compartment underscores the high inter-patient heterogeneity of lung cancer. Each patient's tumor exhibits a unique CNV landscape, resulting in distinct molecular profiles that are captured by this embedding. This highlights the importance of personalized approaches in cancer diagnostics and treatment.

Annotation Notes

• The clear and distinct separation between "Aneuploid" and "Diploid" cells on the CNV-informed UMAP strongly validates the quality and accuracy of the ploidy_dec inference.
• The strong co-localization of "Lung Epithelial cell" with the "Aneuploid" region and the "Adeno" condition reinforces the confidence in the cell type and disease condition annotations for the tumor cell compartment.
• The UMAP effectively captures relevant biological variation, providing a robust visualization of the dataset's structure based on CNV estimates.
• The presence of "unassigned" cells in minor cell type annotations suggests that a small fraction of cells could not be confidently classified and might warrant further investigation or refinement of annotation pipelines. Similarly, "Unclear" ploidy assignments represent a small, ambiguous population.

6. Minor Cell Type Population Analysis in Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot visualizing the proportional distribution of minor cell types within individual samples, grouped by lung cancer histology (Adenocarcinoma and Squamous Cell Carcinoma). Each bar represents a single sample, and the colored segments within each bar denote the relative abundance of different minor cell types, as identified by single-cell RNA sequencing. This provides an overview of the cellular heterogeneity and composition of the tumor microenvironment across different patients and cancer subtypes.

Visual Summary

The stacked bar plots display the relative proportions of 15 celltype_minor categories across multiple samples for both Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) conditions.

• Dominant Cell Types: In both Adeno and Squamous samples, Macrophages (light yellow) consistently represent a substantial proportion of the cellular landscape, often being the most abundant immune cell type. T cells (CD4+ and CD8+, teal and dark blue) are also prominently observed across most samples in both conditions.
• Epithelial Cell Distribution: Airway Epithelial cells (dark red) and Alveolar Epithelial cells (red) show a noticeable presence. In Adeno samples, there is considerable variability in the epithelial cell proportion, with some samples (e.g., NSC016.T1, NSC020.T1, NSC036.T1) showing a particularly high percentage of Alveolar Epithelial cells, while others have much lower proportions. Squamous samples generally exhibit a lower and less variable proportion of these epithelial cell types compared to the higher-epithelial Adeno samples.
• Stromal Components: Fibroblasts (orange-yellow) and Endothelial cells (orange) are present in varying but generally consistent proportions across samples in both conditions, indicating the presence of a supportive stromal microenvironment.
• Immune Subpopulations: Other immune cell types such as B cells, Plasma cells, Mast cells, NK cells, Dendritic cells, and ILCs (Innate Lymphoid Cells) are present in smaller, yet detectable, proportions across most samples, contributing to the overall immune infiltrate.
• Sample Heterogeneity: Both Adeno and Squamous groups exhibit notable heterogeneity in cell type proportions between individual samples. This is particularly pronounced in the Adeno group, where the epithelial component varies widely.

Biological Interpretation

The observed cell type distributions provide critical insights into the distinct tumor microenvironments (TME) of lung Adenocarcinoma and Squamous Cell Carcinoma.

• Tumor-Specific Cellularity: The variability in epithelial cell proportions, especially in Adeno samples, likely reflects differences in tumor cellularity or the specific characteristics of the tumor cells themselves. Given that the 'Tumor origin celltype' is 'Lung Epithelial cell', a high proportion of Alveolar Epithelial cells in some Adeno samples is consistent with the known origin of lung adenocarcinoma from alveolar type 2 cells or club cells. The generally lower and less variable epithelial component in Squamous samples might reflect distinct tumor architecture or differential host immune/stromal infiltration.
• Ubiquitous Macrophage Presence: The consistent and high abundance of Macrophages in both cancer types highlights their fundamental role in the lung TME. Macrophages are highly plastic cells that can exert pro-tumorigenic (e.g., M2-like) or anti-tumorigenic (e.g., M1-like) functions, influencing tumor progression, angiogenesis, and immune suppression. Their specific polarization (which would be captured at the celltype_subset level) would be crucial for understanding their functional impact.
• Adaptive Immune Response: The presence of both CD4+ and CD8+ T cells in the TME suggests an ongoing adaptive immune response in both cancer types. The relative proportions and functional states of these T cells are important determinants of anti-tumor immunity and response to immunotherapies.
• Stromal Contributions: The consistent presence of Fibroblasts and Endothelial cells underscores the importance of the stromal compartment in both lung cancer types. Fibroblasts contribute to extracellular matrix remodeling and can become cancer-associated fibroblasts (CAFs) that promote tumor growth and immunosuppression. Endothelial cells are vital for angiogenesis, supplying the tumor with nutrients and oxygen.

Clinical or Translational Implications

Understanding the cellular composition of the TME in lung cancer subtypes has several clinical implications:

• Histology-Specific TME Characteristics: The differences in epithelial and immune cell proportions between Adeno and Squamous indicate distinct TME characteristics that may influence disease progression and treatment response. For instance, a higher immune infiltrate might correlate with better responses to immune checkpoint inhibitors in certain patient subsets.
• Biomarker Identification: Distinct cellular profiles could serve as prognostic or predictive biomarkers. For example, a specific immune cell ratio or a high proportion of a particular stromal cell type might predict patient survival or response to targeted therapies.
• Therapeutic Targeting: Identifying predominant or differentially abundant cell types, such as macrophages or fibroblasts, could inform the development of novel therapies that target these non-malignant cells to reprogram the TME and enhance anti-tumor immunity. For example, strategies aimed at repolarizing tumor-associated macrophages or inhibiting CAF activity are actively being explored.
• Patient Stratification: The inter-sample heterogeneity within each cancer type suggests that individual patient-level TME characterization is crucial for precision medicine approaches, potentially enabling better stratification of patients for different treatment modalities.

7. T Cell and Innate Lymphoid Cell Subset Composition Across Lung Adenocarcinoma and Squamous Cell Carcinoma Samples

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the proportional distribution of T cell and related innate lymphoid cell (ILC) subsets within the "T cell" major cell type compartment for individual samples, stratified by lung cancer histological diagnoses: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). Each bar represents a distinct sample, and the stacked segments illustrate the relative abundance of various T cell (e.g., Cytotoxic, Naive, Th subtypes, Treg) and ILC (e.g., ILC1, ILC2, ILC3) populations.

Visual Summary

The stacked bar plots display the relative proportions of 21 distinct cell subsets, primarily encompassing T cell and innate lymphoid cell (ILC) populations, across individual patient samples. Samples are grouped by their diagnosis: Adeno (n=20 samples) and Squamous (n=7 samples).

• Differential ILC Distribution: A prominent observation is the notably higher and more variable proportion of ILCs (ILC1, ILC2, ILC3 (NCR+), ILC3 (NCR-), ILCreg) in Adeno samples compared to Squamous samples. In many Squamous samples, ILCs are almost absent or present in very low proportions. Conversely, several Adeno samples (e.g., NSC036.T1, NSC018.T1, NSC016.T1) show substantial contributions from various ILC subsets, particularly ILC1, ILC2, and ILC3 subtypes.
• Dominant T cell Subsets: T cell (Cytotoxic) (light yellow) and T cell (Naive) (pale yellow) populations consistently represent a large fraction of the total "T cell" major cell type across samples in both Adeno and Squamous conditions.
• Other T Helper Subsets: T cell (Th1), T cell (Th17), T cell (Th2), and T cell (Tfh) are present in varying proportions in both conditions, but no immediately striking or consistent differences in their overall contribution are apparent between Adeno and Squamous from this visualization alone, relative to the ILC differences.
• Regulatory T cells (Treg): T cell (Treg) (dark blue) populations appear to be present at low but consistent levels across most samples in both conditions.
• NK cells: Natural Killer (NK) cells (orange) are also present in both conditions, with some variability across samples.

Biological Interpretation

The observed differences in the composition of T cell and ILC subsets suggest distinct immune microenvironments in lung Adenocarcinoma versus Squamous Cell Carcinoma.

1. ILC-mediated Immunity in Adenocarcinoma: The more pronounced presence and variability of ILCs in Adeno samples points towards a potentially more active or diverse innate immune response involving these cells. ILCs are crucial early responders in immune surveillance and inflammation, often shaping adaptive immune responses [PubMed Search].

• ILC1s are known to produce IFN-$\gamma$ and are implicated in anti-tumor immunity by enhancing cytotoxic responses [PubMed Search]. Their increased presence in some Adeno samples might reflect an ongoing Type 1 immune response.
• ILC2s are associated with Type 2 inflammation and can promote tumor growth in some contexts, but also contribute to anti-tumor immunity depending on the tumor microenvironment [PubMed Search].
• ILC3s (NCR+ and NCR-) are involved in mucosal immunity and can have both pro- and anti-tumor roles, often by interacting with myeloid cells and shaping the inflammatory milieu [PubMed Search]. Their variable presence might reflect different stages of tumor-associated inflammation or distinct pathways of immune evasion.

2. T Cell Core Response: The consistent presence of Cytotoxic T cells suggests that both Adeno and Squamous tumors elicit a fundamental cytotoxic T cell response, which is crucial for tumor cell killing. Variations in their relative proportion across samples could reflect individual patient immune states or different levels of tumor immunogenicity. Naive T cells are also present, indicating a pool of unprimed T cells.
3. Distinct Immune Evasion Mechanisms: The relatively lower presence of ILCs in Squamous Cell Carcinoma samples might indicate either a different mode of immune evasion by these tumors, or a distinct developmental origin and microenvironment that is less conducive to ILC infiltration or survival. This contrasts with Adeno, where the innate lymphoid compartment appears more dynamic.

Clinical or Translational Implications

These findings highlight potential differences in the immune landscape between lung Adenocarcinoma and Squamous Cell Carcinoma at the level of T cell and ILC subsets.

• Biomarker Potential: The differential enrichment of ILC subsets, particularly ILC1s, in Adeno could serve as a potential biomarker to distinguish immune profiles between these lung cancer types. Further quantitative analysis would be needed to establish this.
• Immunotherapy Response: The varying immune cell composition could influence patient response to immunotherapies. For instance, tumors with higher ILC1 or cytotoxic T cell infiltration might respond differently to checkpoint blockade inhibitors compared to tumors with lower levels of these cells or different ILC subtype dominance. Understanding these distinctions could aid in patient stratification for personalized treatment strategies.
• Targeting ILCs: If ILCs play specific pro- or anti-tumor roles in Adenocarcinoma, they could represent novel therapeutic targets or modifiers of existing immunotherapies.

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9. Assessment of Macrophage Cell Type Representation per Sample and Condition

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to visualize the population of cells specifically identified as 'Macrophage' (from the celltype_minor annotation) across individual samples. The samples are grouped by their primary tumor conditions: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous), which are two major histological subtypes of Lung cancer.

Visual Summary

The visualization consists of two bar plots, each representing one of the lung cancer conditions: 'Adeno' and 'Squamous'. Within each plot, individual bars correspond to distinct samples (e.g., NSC010.T1, NSC004.T1). The y-axis indicates a percentage, ranging from 0 to 100. A single bar color (dark red) represents 'Macrophage'. Notably, all bars across all displayed samples, for both Adeno and Squamous conditions, indicate a 100% population for 'Macrophage'.

Biological Interpretation

The observed result, showing 100% 'Macrophage' population for every bar in both Adeno and Squamous conditions, primarily indicates an internal consistency check of the 'Macrophage' annotation. This means that within the specific subset of cells that were selected and presented for plotting based on the targets parameter {'obs_col': 'celltype_minor', 'value': 'Macrophage'}, all of them are indeed consistently identified as 'Macrophage'.

It is crucial to understand that this plot, in its current form, does not depict the *relative abundance* of macrophages within the entire cellular landscape of each tumor sample. In a typical heterogeneous tumor microenvironment, macrophages would constitute a certain proportion alongside other cell types such as T cells, B cells, stromal cells, and various epithelial cells. If the intention was to show the proportion of macrophages relative to *all cells* in each sample, the percentages would be expected to vary significantly across samples and would likely be much less than 100%.

Therefore, this visualization effectively confirms that the filtering and selection of cells annotated as 'Macrophage' were successful and uniform across the samples. It does not provide insights into differences in macrophage infiltration, density, or overall prevalence compared to other cell types between Adeno and Squamous conditions or across individual samples.

Annotation Notes

This plot serves more as an annotation validation step rather than an exploratory analysis of cell type proportions within the complex tumor microenvironment. To assess the actual relative abundance or compositional shifts of macrophages (or any other cell type) within the entire cellular population of each sample, the plotting configuration would need to compute proportions against the total cell count within each respective sample, not just within the pre-selected 'Macrophage' subset. Without such a comparison, no conclusions can be drawn regarding the differential presence or biological roles of macrophages in Adeno versus Squamous lung cancer based on this plot alone.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional differences of specific macrophage subset populations (Mac (M2A), Mac (M2B), Mac (M2C), and Mac (M2D)) within the tumor microenvironment between lung adenocarcinoma (Adeno) and lung squamous cell carcinoma (Squamous) conditions. The goal is to identify statistically significant shifts in these immune cell populations that may contribute to the distinct pathologies of these two major lung cancer types.

Visual Summary

The box plots illustrate the celltype proportion of four macrophage subsets (M2A, M2B, M2C, M2D) across Adeno and Squamous conditions. Each black dot represents the proportion from an individual sample, with box plots showing the median, interquartile range (IQR), and whiskers representing data spread.

• Macrophage (M2C) Proportion: The proportion of Mac (M2C) cells appears slightly higher in Adeno samples compared to Squamous samples, with median proportions around 27% and 24% respectively. This difference shows a borderline statistical significance (p = 0.07).
• Macrophage (M2A) Proportion: Similar to M2C, the Mac (M2A) proportion shows a trend of being higher in Adeno samples (median ~19%) compared to Squamous samples (median ~17%), also with a borderline statistical significance (p = 0.07).
• Macrophage (M2B) Proportion: A statistically significant difference is observed for Mac (M2B) cells (p ≤ 0.01). Squamous samples exhibit a noticeably higher proportion of Mac (M2B) cells (median ~22%) compared to Adeno samples (median ~14%).
• Macrophage (M2D) Proportion: The proportion of Mac (M2D) cells is also significantly different (p ≤ 0.05). Squamous samples show a higher proportion (median ~8.5-9%) of Mac (M2D) cells compared to Adeno samples (median ~6%).

In summary, Mac (M2B) and Mac (M2D) subsets are significantly more abundant in lung squamous cell carcinoma compared to lung adenocarcinoma, while Mac (M2C) and Mac (M2A) show a trend of being slightly more abundant in adenocarcinoma.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME), often differentiating into distinct phenotypes, broadly categorized as M1 (pro-inflammatory, anti-tumor) and M2 (anti-inflammatory, pro-tumor) subtypes. The subsets M2A, M2B, M2C, and M2D all fall under the M2 polarization spectrum, typically associated with functions that promote tumor growth, immune suppression, angiogenesis, and tissue remodeling.

The observed significant enrichment of Mac (M2B) and Mac (M2D) populations in Squamous cell carcinoma suggests distinct immune landscapes between these two lung cancer histologies.

• M2B macrophages are known to be induced by immune complexes and TLR agonists, producing a mix of pro- and anti-inflammatory cytokines. Their specific role in cancer can be complex, but their increased presence might indicate a specific type of inflammatory or immune-regulatory response prevalent in Squamous cell carcinoma.
• M2D macrophages are often considered highly immunosuppressive and pro-angiogenic. They are frequently associated with promoting tumor metastasis and poor prognosis. Their significant increase in Squamous samples could point towards a more immunosuppressive TME or enhanced pro-tumorigenic pathways specifically dominant in this subtype compared to adenocarcinoma. PubMed search: M2D macrophages cancer role

The trends of higher Mac (M2C) and Mac (M2A) proportions in Adenocarcinoma, though not reaching strong statistical significance (p=0.07), are also noteworthy.

• M2C macrophages are typically activated by IL-10 and TGF-β, contributing to tissue repair, immune tolerance, and matrix remodeling, often fostering an immunosuppressive environment that supports tumor progression.
• M2A macrophages are classically activated by IL-4 and IL-13, involved in allergic reactions and parasite infections, and can also contribute to tumor growth and progression through immune suppression and tissue repair mechanisms. PubMed search: M2A M2C macrophages tumor microenvironment

These differential enrichments of specific M2 macrophage subsets highlight that the distinct pathological features and responses to therapy observed in lung adenocarcinoma versus squamous cell carcinoma might be partly driven by differences in their myeloid cell composition and polarization states.

Clinical or Translational Implications

The distinct macrophage subset profiles in lung adenocarcinoma and squamous cell carcinoma carry potential clinical and translational implications:

1. Biomarker Potential: The differential abundance of M2B and M2D macrophages could serve as diagnostic or prognostic biomarkers to distinguish between these lung cancer subtypes or predict disease aggressiveness, particularly in Squamous cell carcinoma.
2. Therapeutic Targeting: If M2B and M2D macrophages play significant pro-tumorigenic roles in Squamous cell carcinoma, targeting these specific macrophage subsets or their activating pathways could represent a more effective therapeutic strategy for this cancer type. For example, therapies aimed at repolarizing M2 macrophages towards an M1 phenotype or inhibiting their recruitment might have differential efficacy depending on the predominant M2 subset.
3. Understanding Treatment Resistance: Differences in the immune microenvironment, including macrophage polarization, can influence response to immunotherapies. A higher prevalence of immunosuppressive M2B and M2D macrophages in Squamous cell carcinoma might contribute to varied responses to immune checkpoint inhibitors compared to Adenocarcinoma, warranting further investigation.
4. Histology-Specific Approaches: These findings underscore the importance of considering lung cancer histology when developing and applying immune-modulating therapies, rather than a "one-size-fits-all" approach.

11. Ploidy Population Analysis of Tumor-Origin and Unassigned Cells in Lung Cancer Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (Aneuploid, Diploid, Unclear) of a combined population of tumor-origin cells (Lung Epithelial cells) and unassigned cells across various lung cancer samples. The samples are categorized by two major lung cancer subtypes: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). This bar plot visually represents the proportion of each ploidy status within these selected cell populations for individual samples.

Visual Summary

The visualization displays stacked bar plots for each sample, grouped by condition (Adeno or Squamous). Each bar represents 100% of the selected cell population within that sample, with different colors indicating ploidy status: maroon for Aneuploid, orange for Diploid, and light green for Unclear.

Adenocarcinoma (Adeno) Samples:

• There is considerable heterogeneity in aneuploidy across Adeno samples. Some samples (e.g., NSC010.T1, NSC021.T1, NSC010.T2, NSC019.T2) exhibit a notable aneuploid fraction, ranging from approximately 30% to 50%.
• Conversely, several Adeno samples (e.g., NSC016.T2, NSC020.T2, NSC016.T1, NSC036.T1, NSC020.T1, NSC040.T1, NSC037.T1) show a relatively low proportion of aneuploid cells, often below 20%, with diploid cells being the dominant population.
• The "Unclear" ploidy category is generally small across Adeno samples, typically contributing less than 5% to the total cell population.

Squamous Cell Carcinoma (Squamous) Samples:

• Squamous samples appear to show a more consistently high proportion of aneuploid cells compared to Adeno. All depicted Squamous samples (NSC004.T3, NSC004.T4, NSC004.T2, NSC009.T1, NSC009.T2) demonstrate a substantial aneuploid fraction, generally ranging from approximately 30% to over 50%.
• The "Unclear" category is nearly negligible in Squamous samples, usually less than 1-2%.
• Diploid cells constitute the remaining proportion, making up about 50-70% in these samples.

Biological Interpretation

Aneuploidy, defined as an abnormal number of chromosomes, is a well-established hallmark of cancer and contributes to genomic instability, tumor heterogeneity, and resistance to therapy. The cells selected for this analysis include 'Lung Epithelial cells', which are designated as the tumor origin celltype, and 'unassigned' cells, some of which may also be malignant or critical components of the tumor microenvironment.

• Prevalence of Aneuploidy: The presence of aneuploid cells in both Adeno and Squamous samples aligns with the known biology of these lung cancers. Malignant cells commonly harbor chromosomal abnormalities, reflecting ongoing genomic instability during tumor development and progression [1].
• Subtype-Specific Differences: The observed trend suggests that aneuploidy might be more prevalent and consistently high in Squamous Cell Carcinoma samples than in Adenocarcinoma samples within this dataset. This could reflect distinct biological pathways driving genomic instability in these two major lung cancer subtypes. Squamous cell carcinoma is often associated with more severe genomic alterations and higher mutational burden in some contexts, which might contribute to a greater degree of aneuploidy [2].
• Heterogeneity within Subtypes: The significant variability in aneuploid fractions among Adeno samples highlights tumor heterogeneity, which is a common feature of cancer. Different tumors, even within the same histological subtype, can exhibit varying degrees of genomic instability and aneuploidy, potentially influenced by tumor stage, specific genetic drivers, or tumor microenvironment interactions.
• "Unassigned" Cells and Tumorigenesis: The inclusion of "unassigned" cells in this analysis means that the aneuploid fraction observed could represent not only clearly identified tumor-origin epithelial cells but also other potentially malignant cells that were difficult to classify, or possibly non-malignant cells affected by the tumor microenvironment (though non-malignant cells are typically diploid). Given the context that 'Lung Epithelial cells' are the tumor origin, it's reasonable to infer that the aneuploid population largely consists of these malignant epithelial cells.

Clinical or Translational Implications

• Diagnostic and Prognostic Biomarker: High levels of aneuploidy, particularly in tumor cells, are generally associated with aggressive tumor behavior and poor prognosis in many cancers, including lung cancer [1]. Monitoring aneuploidy could serve as a valuable diagnostic or prognostic marker, especially if distinct patterns emerge for Adeno versus Squamous subtypes.
• Therapeutic Targeting: The degree and specific patterns of aneuploidy can influence tumor response to certain therapies. Tumors with high genomic instability and aneuploidy might be more susceptible to treatments that exploit DNA damage response pathways (e.g., PARP inhibitors in certain contexts) or agents targeting mitotic machinery [3]. Conversely, high aneuploidy can also lead to drug resistance. Understanding these ploidy patterns can inform patient stratification for targeted therapies or chemotherapy regimens.
• Research Avenues: Further investigation into the specific genes or chromosomal regions affected in aneuploid cells within each subtype could reveal novel therapeutic targets or resistance mechanisms. Correlating these ploidy profiles with clinical outcomes (Survival from surgery, Stage) would provide significant translational insights.

---

References:

1. Aneuploidy in Cancer: Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011 Mar 4;144(5):646-74. PubMed Search: "Hallmarks of Cancer Aneuploidy"
2. Genomic Landscape of Lung Cancer Subtypes: Comprehensive genomic analysis of lung squamous cell carcinomas. Nature. 2012 Sep 27;489(7417):519-25. PubMed Search: "genomic landscape lung squamous adenocarcinoma"
3. Aneuploidy and Therapy: Santaguida MG, Amon A. The promises and perils of aneuploidy. Nat Rev Mol Cell Biol. 2015 Mar;16(3):141-52. PubMed Search: "aneuploidy cancer therapy"

12. 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 interaction (CCI) patterns in lung cancer, comparing Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) based on single-cell RNA-seq data. The focus is on interactions involving key cell types: Lung Epithelial cells (specifically distinguishing aneuploid tumor cells from diploid normal cells), Fibroblasts, Macrophages, and T cells. The dot plot visualizes the strength and significance of a maximum of 80 selected CCIs per condition across individual samples.

Visual Summary

The visualization clearly segregates samples by condition, Adeno and Squamous, revealing distinct CCI landscapes.

Adenocarcinoma (Adeno) Specific Patterns

• A prominent cluster of Adeno samples (top blue box) exhibits strong and significant interactions involving various collagen-integrin pairs (e.g., COL1A2, COL3A1, COL5A2, COL6A3, COL8A1, COL12A1) primarily between Aneuploid Lung Epithelial cells (likely tumor cells) and Fibroblasts. These interactions are characterized by high standardized mean expression (dark red dots) and significant p-values (larger dot sizes).
• Other notable interactions in Adeno involve APP_TREM2_receptor between Aneuploid Lung Epithelial cells and Macrophages.
• In contrast, a subset of Adeno samples (below the top blue box, above Squamous) shows markedly fewer and weaker interactions, indicating heterogeneity within the Adeno cohort.

Squamous Cell Carcinoma (Squamous) Specific Patterns

• The Squamous samples (bottom blue box) display a different set of highly active and significant CCIs, predominantly centered around immune cell interactions.
• Key interactions include ICAM1 with T cells and Macrophages (ICAM1_SPN--Mac|T cell, ICAM1_integrin_aLb2_complex--Mac|T cell, ICAM1_integrin_aMb2_complex--Mac|T cell, ICAM1_ITGAL--Mac|Lung.Epi(Aneuploid)), CCL20_CCR6 between Macrophages and T cells, and CXCL14_CXCR4 involving Aneuploid Lung Epithelial cells and T cells.
• Other significant interactions in Squamous include CD226_NECTIN2 (Mac-Lung Epi Aneuploid), HLA-F_LILRB1 (Mac-T cell), and Glutamate_byGLS_and_SLC1A3_GRM7 (Lung Epi Aneuploid-Macrophage).
• Similar to Adeno, there is heterogeneity within the Squamous cohort, with some samples showing stronger interaction profiles than others.
• Ploidy Distinction: The analysis effectively utilizes the ploidy_dec information, showing that many of the strongest tumor-associated interactions involve Lung Epi (Aneuploid) cells, confirming their origin as tumor-specific communications rather than normal epithelial cell interactions.

Biological Interpretation

The distinct CCI profiles observed between Adenocarcinoma and Squamous Cell Carcinoma reflect fundamental differences in their tumor microenvironments (TMEs) and underlying biology.

Adenocarcinoma: Stromal Remodeling and Fibrosis

• The overwhelming prevalence of collagen-integrin interactions between Aneuploid Lung Epithelial cells (tumor cells) and Fibroblasts suggests an active process of extracellular matrix (ECM) remodeling and potentially fibrosis in Adenocarcinoma. Integrins are crucial for cell-ECM adhesion and signaling, influencing cell survival, proliferation, migration, and invasion [1]. This robust tumor-fibroblast communication highlights the critical role of the tumor stroma in Adeno progression.
• The involvement of APP_TREM2_receptor between tumor cells and macrophages indicates potential roles in phagocytosis, inflammation, or immune modulation, as TREM2 is a key receptor on myeloid cells [2].
• These patterns align with the understanding that lung adenocarcinoma often presents with a desmoplastic stroma and extensive fibroblast activation.

Squamous Cell Carcinoma: Immune-Driven Microenvironment

• The strong presence of immune-related CCIs in Squamous Cell Carcinoma, particularly those involving ICAM1, CCL20-CCR6, and CXCL14-CXCR4, points to a TME characterized by active immune cell trafficking, recruitment, and modulation.
• ICAM1 (Intercellular Adhesion Molecule 1) plays a critical role in leukocyte adhesion and transmigration, affecting T cell activation and macrophage function [3]. Its interactions with T cells and macrophages suggest significant immune cell infiltration and potential immune checkpoint modulation.
• The CCL20-CCR6 axis is known to recruit CCR6-expressing cells, including Th17 cells, regulatory T cells (Tregs), and certain dendritic cells, to inflammatory sites and tumors, contributing to immune evasion or inflammation [4].
• CXCL14-CXCR4 interactions can influence immune cell recruitment and tumor progression, with CXCL14 having diverse roles depending on the context [5]. Its interaction with tumor cells and T cells suggests a role in shaping the immune response within the tumor.
• CD226_NECTIN2 and HLA-F_LILRB1 further underscore the complex interplay between tumor cells, macrophages, and T cells in modulating immune responses. The Glutamate_byGLS_and_SLC1A3_GRM7 interaction points to metabolic crosstalk that could influence immune cell function and tumor growth.
• These findings are consistent with the often more immune-infiltrated and inflammatory nature of squamous cell carcinoma compared to adenocarcinoma.

Clinical or Translational Implications

The distinct CCI patterns between lung Adenocarcinoma and Squamous Cell Carcinoma offer valuable insights for targeted therapeutic development and biomarker discovery.

Adenocarcinoma: Targeting Stromal Interactions

• The extensive collagen-integrin signaling in Adeno suggests that therapies targeting specific integrin receptors on tumor cells or fibroblasts could disrupt tumor-stroma communication, inhibit tumor invasion, and potentially reduce fibrosis. This might involve small molecule inhibitors or blocking antibodies against key integrin subunits or collagen receptors.
• Interfering with APP-TREM2 interactions could also modulate macrophage activity in the Adeno TME.

Squamous Cell Carcinoma: Immunomodulatory Strategies

• The strong immune-centric interactions in Squamous indicate that targeting these pathways could enhance anti-tumor immunity. For instance, modulating the ICAM1 pathway might influence T cell infiltration and activation.
• Inhibiting the CCL20-CCR6 axis could reduce the recruitment of immunosuppressive cells (like Tregs) or pro-tumorigenic inflammatory cells, thereby improving responses to immunotherapy.
• Strategies to disrupt CXCL14-CXCR4 signaling or other immune checkpoints highlighted by these interactions could be explored to re-sensitize tumors to existing immunotherapies or develop novel combinations.
• Understanding the metabolic crosstalk indicated by glutamate signaling could open avenues for metabolic reprogramming strategies.

Overall, these condition-specific CCI landscapes highlight the need for tailored therapeutic approaches in lung cancer, moving beyond a one-size-fits-all strategy.

References

1. Integrins in cancer: https://www.genecards.org/Search/Keyword?query=integrin%20cancer
2. TREM2 function: https://www.genecards.org/cgi-bin/carddisp.pl?gene=TREM2
3. ICAM1 in immunity: https://www.uniprot.org/uniprotkb/P05362/entry
4. CCL20-CCR6 axis in cancer: https://pubmed.ncbi.nlm.nih.gov/?term=CCL20+CCR6+cancer
5. CXCL14 in cancer: https://www.genecards.org/cgi-bin/carddisp.pl?gene=CXCL14

13. Adenocarcinoma 및 편평상피세포암의 조건별 세포-세포 상호작용 분석

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA-seq 데이터에서 파생된 두 가지 폐암 유형, 즉 폐선암(Adenocarcinoma, Adeno)과 편평상피세포암(Squamous Cell Carcinoma, Squamous) 간의 세포-세포 상호작용(Cell-Cell Interaction, CCI) 프로파일을 비교합니다. plot_cci_dots 도구를 사용하여 각 조건에서 가장 유의미하고 높은 발현을 보이는 세포-세포 쌍 및 리간드-수용체 쌍 상호작용을 시각화했습니다. 점의 크기는 상호작용의 통계적 유의성(-log10(p))을 나타내고, 색상은 리간드-수용체 쌍의 평균 발현 수준(log2(mean))을 나타냅니다. 최대 80개의 상호작용 쌍이 각 조건에 대해 표시되도록 설정되었습니다.

Visual Summary

Adenocarcinoma (Adeno) CCI 시각화

Adeno 조건에서는 훨씬 더 다양하고 복잡한 세포-세포 상호작용 양상이 관찰됩니다. 상호작용은 CD8+ T 세포, CD4+ T 세포, 대식세포(Macrophage, Mac), 자연살림프구(ILC), B 세포를 포함한 다양한 면역 세포와 이배체 폐 상피세포(Diploid Lung Epi) 사이에서 발생합니다.

• VEGFA-NRP1/NRP2/VEGFR1: T 세포(CD8+, CD4+), 대식세포, ILC, 이배체 폐 상피세포를 포함한 광범위한 세포 쌍에서 매우 강력하고 유의미한 상호작용으로 나타납니다. 이는 Adeno 미세환경에서 혈관신생 및 면역 조절의 중요한 역할을 시사합니다.
• SPP1-CD44/ITGAV/ITGB1/ITGB5: SPP1(Osteopontin) 관련 상호작용이 대식세포 간, T CD4+ 세포와 대식세포 간, 그리고 이배체 폐 상피세포와 대식세포 간에서 활발하게 나타납니다.
• ICAM1-integrin complexes: T 세포와 대식세포, 그리고 대식세포 간의 상호작용에서 ICAM1_integrin_aM_b2_complex, ICAM1_integrin_aL_b2_complex 등의 접착 분자 상호작용이 두드러집니다.
• ProstaglandinE2_e2_by_PTGES3_PTGER2: CD8+ T 세포와 대식세포 간의 상호작용에서 유의미하게 관찰되며, 면역억제 환경에 기여할 수 있음을 시사합니다.
• APOE-TREM2_receptor: 대식세포 간의 상호작용에서 중요하게 나타납니다.
• CCL20-CCR1/CCR2/CCR6: 대식세포와 이배체 폐 상피세포 간의 케모카인 신호전달이 관찰됩니다.

Squamous Cell Carcinoma (Squamous) CCI 시각화

Squamous 조건은 Adeno에 비해 훨씬 적고 집중적인 세포-세포 상호작용을 보입니다. 거의 모든 유의미한 상호작용은 형질세포(Plasma cell)와 대식세포 간, 또는 대식세포 자체 간에 국한되어 있습니다.

• APOE-TREM2_receptor: 형질세포와 대식세포 간, 그리고 대식세포 간 상호작용에서 매우 높은 유의성과 발현 수준을 보이며 가장 강력한 신호 중 하나입니다.
• HLA-E/F-LILRB2: 형질세포와 대식세포, 대식세포 간의 상호작용에서 MHC class I 관련 분자와 LILRB2의 상호작용이 관찰됩니다.
• ICAM1-integrin complexes: Adeno와 유사하게 ICAM1_integrin_aL_b2_complex, ICAM1_integrin_aM_b2_complex 등이 형질세포와 대식세포, 대식세포 간에 나타납니다.
• FN1_integrin_a5_b1_complex, COL2A1_integrin_a1_b1_complex: 기질 및 세포 접착과 관련된 인테그린 상호작용이 관찰됩니다.
• VEGFA-NRP1/NRP2: Adeno와 마찬가지로 VEGFA 신호전달이 형질세포와 대식세포 간, 대식세포 간에 나타나지만 Adeno만큼 광범위하지는 않습니다.

Biological Interpretation

두 가지 폐암 아형 간의 세포-세포 상호작용 프로파일은 뚜렷한 차이를 보이며, 이는 각 질병의 종양 미세환경(TME) 구성 및 면역 조절 메커니즘의 차이를 반영합니다.

TME 복잡성 및 세포 다양성:

• Adeno는 T 세포, 대식세포, ILC, B 세포 및 비악성 폐 상피세포를 포함하는 광범위한 세포 유형 간의 상호작용을 통해 더 복잡한 TME를 나타냅니다. 이는 Adeno에서 보다 활발하고 다양한 면역 및 기질 세포 반응이 존재함을 시사합니다.
• Squamous는 주로 대식세포와 형질세포에 집중된 상호작용을 보이며, TME의 면역 세포 구성이 Adeno와는 질적으로 다를 수 있음을 암시합니다.

주요 신호 전달 경로 비교:

• 혈관신생 및 면역 조절 (VEGFA-NRP1/NRP2/VEGFR1): Adeno에서 VEGFA 신호는 다양한 세포 유형에 걸쳐 광범위하게 활성화되어 있습니다. 이는 Adeno에서 혈관신생이 TME의 핵심 동인이며, 면역 세포 및 상피 세포의 기능에 영향을 미칠 수 있음을 강조합니다. [참고: GeneCards for VEGFA]
• 면역억제 및 염증 (ProstaglandinE2, SPP1): Adeno에서 T 세포와 대식세포 간의 PGE2 신호는 면역억제 환경을 조성할 수 있습니다. SPP1은 면역 조절, 전이, 섬유화에 관여하는 것으로 알려져 있으며, Adeno TME의 복잡성에 기여할 수 있습니다. [참고: GeneCards for SPP1]
• 골수 및 형질 세포 기능 (APOE-TREM2, HLA-E/F-LILRB2): Squamous에서는 APOE-TREM2 상호작용이 매우 두드러지게 나타나며, 이는 대식세포 기능 및 분극화에 중요한 역할을 할 수 있습니다. [참고: PubMed search for "TREM2 cancer macrophage"] 또한 HLA-E/F-LILRB2 상호작용은 골수성 세포에서 면역 억제 경로로 작용할 수 있음을 시사합니다. [참고: PubMed search for "LILRB2 cancer therapy"]
• 세포 접착 및 이동 (ICAM1-Integrins, FN1-Integrins): 두 조건 모두에서 면역 세포 이동 및 TME 내 세포 간 상호작용에 중요한 ICAM1 및 피브로넥틴(FN1) 관련 인테그린 상호작용이 관찰됩니다.

Clinical or Translational Implications

이러한 조건별 세포-세포 상호작용 프로파일의 차이는 폐선암과 편평상피세포암에 대한 맞춤형 치료 전략 및 진단 바이오마커 개발에 중요한 단서를 제공합니다.

치료 표적의 우선순위:

• Adeno: VEGFA 신호 전달은 광범위하게 활성화되어 있으므로, 항-VEGF 치료제(예: 베바시주맙)가 Adeno 환자에게 더 효과적일 수 있습니다. SPP1 및 PGE2 경로를 표적화하는 것은 Adeno의 면역억제 미세환경을 조절하고 항종양 면역 반응을 강화하는 전략이 될 수 있습니다.
• Squamous: APOE-TREM2 축은 Squamous의 대식세포 기능 조절을 위한 특정 표적이 될 수 있습니다. LILRB2 길항제와 같은 HLA-LILRB2 상호작용을 표적화하는 것은 Squamous에서 면역 회피 메커니즘을 극복할 잠재력을 가집니다.
• 바이오마커 개발: 각 조건에서 식별된 독특한 CCI 서명은 Adeno와 Squamous의 TME를 구별하고 특정 치료법에 대한 반응을 예측하는 바이오마커로 활용될 수 있습니다. 예를 들어, Adeno에서 높은 VEGFA 신호는 항혈관신생 요법에 대한 반응을 예측할 수 있는 반면, Squamous에서 강력한 APOE-TREM2 신호는 특정 대식세포 표적 치료법의 효과를 나타낼 수 있습니다.

실험적 검증:

• 이러한 예측된 세포-세포 상호작용은 공간 전사체학(spatial transcriptomics) 또는 다중 면역형광 염색(multiplex immunofluorescence)을 통해 조직 내에서 검증될 수 있습니다.
• 특정 세포 유형(예: Adeno의 대식세포-T 세포, Squamous의 대식세포-형질세포)을 사용한 시험관 내 공동 배양 실험은 이러한 리간드-수용체 쌍의 기능적 역할을 명확히 하는 데 도움이 될 수 있습니다.
• 관련 세포주 또는 오가노이드 모델에서 주요 리간드/수용체에 대한 유전자 교란(CRISPR/shRNA) 또는 약리학적 억제를 통해 종양 성장, 면역 회피 및 TME 구성에 미치는 영향을 평가할 수 있습니다.

14. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Genes in Squamous Lung Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) within single-cell RNA-seq data from lung tissue, specifically focusing on genes related to immune checkpoint and cell cycle pathways. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions, with a specific focus on the "Squamous" condition, likely representing Squamous Cell Carcinoma (SCC) of the lung. The analysis was constrained to a predefined list of genes associated with these critical biological processes.

Visual Summary

The provided dot plot, titled "CCI for Squamous", displays the cell-cell interactions. The plot is highly sparse, revealing only one statistically significant interaction that met the specified cutoffs (p-value < 0.05, mean expression > 0.01). This sole interaction is:

• CD93_IFNGR1 interaction between Macrophage and Macrophage (Mac|Mac).

The dot representing this interaction indicates its significance (-log10(p)) and mean expression (log2(m)), though the exact legend values are not visible in the snippet.

Biological Interpretation

The singular observation of a CD93-IFNGR1 interaction occurring within the macrophage population in squamous lung carcinoma warrants specific biological consideration:

• Autocrine/Paracrine Macrophage Signaling: The detection of a macrophage-macrophage interaction suggests an autocrine or paracrine signaling loop within the resident or infiltrating macrophage population. This indicates that macrophages are not only interacting with other cell types in the tumor microenvironment (TME) but also modulating their own activity or that of neighboring macrophages through specific ligand-receptor pairs.
• CD93 (C1qRp): CD93 is a C-type lectin-like receptor predominantly expressed on myeloid cells, including macrophages. It is known to play roles in cell adhesion, phagocytosis, and the regulation of inflammatory responses. CD93 has been implicated in the resolution of inflammation and immune cell migration. GeneCards: CD93
• IFNGR1 (Interferon Gamma Receptor 1): IFNGR1 is the alpha chain of the receptor for Interferon-gamma (IFN-$\gamma$), a crucial cytokine produced by T cells and NK cells that orchestrates anti-tumor immunity, antiviral responses, and inflammatory processes. Ligation of IFNGR1 initiates downstream signaling cascades, notably the JAK-STAT pathway, leading to the expression of IFN-$\gamma$-inducible genes that profoundly influence macrophage activation and polarization. GeneCards: IFNGR1
• Relevance in Squamous Lung Carcinoma: Macrophages are key components of the lung cancer TME and exhibit significant plasticity, adopting diverse phenotypes (e.g., pro-inflammatory M1, immunosuppressive M2) that can either inhibit or promote tumor growth. The interaction involving CD93 and IFNGR1 suggests a potential mechanism by which macrophages within squamous tumors regulate their own activation state or influence the functional reprogramming of adjacent macrophages. Given that IFN-$\gamma$ signaling is critical for effective anti-tumor immunity, an autocrine loop involving IFNGR1 could fine-tune macrophage responses to the broader immune landscape. While the precise ligand for IFNGR1 in this context is IFN-$\gamma$, and CD93 is typically considered a receptor, the detected interaction "CD93_IFNGR1" from CellPhoneDB output implies a specific ligand-receptor complex where CD93 might act as a ligand or a co-receptor, or it could represent a broader interaction complex. Further investigation into the specific mechanisms predicted by CellPhoneDB for this pair would clarify the precise roles.

Clinical or Translational Implications

• Macrophage Reprogramming: The identified CD93-IFNGR1 macrophage-macrophage interaction highlights an intrinsic regulatory circuit within the tumor-associated macrophage (TAM) population. Understanding how this specific interaction influences macrophage polarization (e.g., M1-like vs. M2-like phenotype) could offer insights into immune evasion or anti-tumor responses in squamous lung cancer.
• Potential Therapeutic Target: If this interaction is found to drive or maintain an immunosuppressive macrophage phenotype in squamous lung tumors, targeting either CD93 or IFNGR1 on macrophages could represent a novel immunotherapeutic strategy. For example, interfering with this autocrine loop might shift macrophage function towards an anti-tumorigenic state, potentially enhancing the efficacy of existing immune checkpoint inhibitors.
• Biomarker Development: The presence or specific activity of this CD93-IFNGR1 interaction could serve as a novel biomarker for assessing macrophage activity, predicting response to immunotherapy, or monitoring disease progression in patients with squamous lung carcinoma.
• Further Validation Needed: As this analysis detected only one interaction, it suggests that either the conditions for other interactions were not met (e.g., low expression, non-significant p-value) or that this interaction is particularly robust under the analysis parameters. Experimental validation (e.g., using *in vitro* co-culture models, *in vivo* tumor models) would be essential to elucidate the functional consequences of this CD93-IFNGR1 interaction on macrophage biology and tumor progression.

15. Condition-Specific Cell-Cell Interaction Patterns in Lung Adenocarcinoma vs. Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between lung Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) using single-cell RNA-seq data. The focus is on interactions involving major immune and stromal cell types: Myeloid cells (including Macrophages), Mast cells, T cells, Endothelial cells, B cells (including Plasma cells), and Stromal cells (including Fibroblasts). The results are visualized as a dot plot, where dot size reflects the statistical significance (-log10(p-value)) and dot color intensity represents the standardized mean expression of the ligand-receptor pair in each sample. Only the top 25 most significantly enriched CCIs per condition are displayed.

Visual Summary

The dot plot clearly delineates two major groups of cell-cell interactions, specifically enriched in either Adeno or Squamous conditions.

• Adeno-Enriched Interactions (Left Panel): This section shows a diverse array of strong and significant interactions across many Adeno samples. Key patterns include:
• Numerous interactions involving Macrophages (Mac) and Lung Epithelial cells (Aneuploid), such as various ICAM1-integrin complexes (e.g., ICAM1-integrin_aM_b2_complex--Mac|Lung.Epi(Aneuploid)) and APP-CD74.
• Prominent Macrophage-Macrophage interactions, particularly involving chemokine signaling (e.g., CCL4-CCR5, CCL3-CCR1, CCL3-CCR5).
• Interactions between Lung Epithelial cells (Aneuploid) and T cells (e.g., CEACAM5-CD8A), ILCs, and Plasma cells.
• Some Fibroblast-Lung Epithelial cell interactions (e.g., C3-C3AR1).
• Squamous-Enriched Interactions (Right Panel): This section highlights a distinct set of interactions, predominantly characterized by extensive communication between Fibroblasts (Fib) and Lung Epithelial cells (Aneuploid), particularly strong in samples like NSC004.T1-T4 and NSC009.T1-T2.
• A striking feature is the dominance of Collagen (COL) family interactions with Integrin complexes (e.g., COL1A2, COL3A1, COL5A2, COL6A3 interacting with integrin_a1b1_complex or integrin_a2b1_complex).
• Other notable interactions include PPIA-BSG between Lung Epithelial cells (Aneuploid) and Fibroblasts, and a few Macrophage-related interactions such as VEGFA-NRP1 (Mac|Mac) and AREG-EGFR (Mac|Lung.Epi(Aneuploid)).

Overall, the plot reveals a clear distinction in the tumor microenvironment composition and intercellular communication networks between Adeno and Squamous subtypes of lung cancer. The dot sizes and color intensities consistently indicate high significance and strong interaction strength for the displayed condition-specific CCIs.

Biological Interpretation

The differential CCI patterns between Adeno and Squamous lung cancer subtypes suggest distinct biological processes driving tumor progression and shaping the tumor microenvironment.

Adenocarcinoma Microenvironment: Immune-rich and Inflammatory:

• The abundance of Macrophage-Macrophage chemokine interactions (e.g., CCL4/CCL3 with CCR5/CCR1) suggests robust myeloid cell recruitment, activation, and communication within the tumor microenvironment of Adeno. This may contribute to a pro-inflammatory or immune-suppressive milieu depending on macrophage polarization. GeneCards: CCR5, GeneCards: CCR1
• Interactions between Aneuploid Lung Epithelial cells (likely tumor cells) and various immune cells (Macrophages, T cells, ILCs, Plasma cells) via adhesion molecules like ICAM1-integrins and immune checkpoints or signaling molecules such as CEACAM5-CD8A indicate complex crosstalk influencing immune evasion or response. CEACAM5 is a known tumor antigen. GeneCards: ICAM1, PubMed search: CEACAM5 CD8 T cell cancer
• The presence of APP-CD74 interactions also points towards potential roles in immune regulation and antigen presentation in Adeno. GeneCards: APP, GeneCards: CD74

Squamous Cell Carcinoma Microenvironment: Desmoplastic and ECM-driven:

• The striking enrichment of Fibroblast-Aneuploid Lung Epithelial cell interactions involving multiple Collagens (COL1A2, COL3A1, COL5A2, COL6A3) and Integrin complexes signifies a highly desmoplastic tumor microenvironment. This extensive extracellular matrix (ECM) remodeling, mediated by fibroblasts, is characteristic of Squamous cell carcinoma and plays a critical role in tumor stiffness, invasion, and metastasis. GeneCards: COL1A2, PubMed search: collagen integrin lung cancer squamous
• The presence of VEGFA-NRP1 between Macrophages suggests active angiogenesis, a process crucial for tumor growth and survival, within the Squamous tumor microenvironment. GeneCards: VEGFA
• AREG-EGFR signaling between Macrophages and Aneuploid Lung Epithelial cells further highlights a pro-tumorigenic axis, promoting tumor cell proliferation and survival in Squamous. GeneCards: AREG

The involvement of "Lung.Epi (Aneuploid)" in most of these significant interactions across both conditions is consistent with their role as tumor cells, distinguishing them from potentially non-malignant "Lung.Epi (Diploid)".

Clinical or Translational Implications

These distinct CCI signatures have important clinical and translational implications:

• Diagnostic and Prognostic Biomarkers: The identified condition-specific CCI pairs could serve as novel diagnostic or prognostic biomarkers. For instance, high levels of specific collagen-integrin interactions might indicate Squamous cell carcinoma and predict a more aggressive desmoplastic phenotype, while specific chemokine-receptor interactions might be indicative of Adeno.

Therapeutic Targets:

• In Adenocarcinoma, targeting specific chemokine receptors (e.g., CCR1, CCR5) could modulate macrophage infiltration and activation, potentially dampening pro-tumor inflammation or enhancing anti-tumor immunity. Disrupting ICAM1-integrin interactions could also impact tumor-immune cell adhesion and migration.
• In Squamous Cell Carcinoma, therapeutic strategies focusing on disrupting the extensive collagen-integrin interactions could inhibit ECM remodeling, reduce tumor stiffness, and impede tumor invasion and metastasis. This could involve integrin inhibitors or agents that modify the ECM. Furthermore, targeting the AREG-EGFR pathway or the VEGFA-NRP1 axis could offer avenues for inhibiting tumor growth and angiogenesis.
• Understanding Treatment Response: The differential microenvironmental features suggested by these CCI patterns could help explain variations in response to existing therapies (e.g., immunotherapy, anti-angiogenic agents) between Adeno and Squamous patients, and guide the development of subtype-specific treatment strategies.

16. Lung Epithelial Cell Condition-Specific Surfaceome Markers in Adenocarcinoma vs. Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Lung Epithelial cells, the designated tumor origin cell type, by comparing Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) conditions. The dot plot visualizes the expression of these markers across individual samples, showcasing differences in gene expression levels (color intensity) and the fraction of cells expressing the gene (dot size) within each sample. The focus on surfaceome markers highlights potential candidates for diagnostic, prognostic, or therapeutic applications due to their accessibility.

Visual Summary

The dot plot clearly delineates two distinct sets of surfaceome markers, one preferentially expressed in Adeno Lung Epithelial cells and another in Squamous Lung Epithelial cells.

• Adeno-specific markers: A cluster of genes, including *HLA-CD74*, *HLA-DRB1*, *HLA-DPA1*, *HLA-DPB1*, *HLA-DRB5*, *HLA-DQB1*, *MUC1*, *PIGR*, and *CEACAM6*, shows high expression (dark red, large dots) primarily in samples grouped under the "Adeno" condition. Notably, most of these Adeno-specific markers are highly expressed across a majority of the Adeno samples (e.g., NSC010.T1, NSC020.T2, NSC018.T1), with some variation in intensity and cell fraction. The samples showing strong Adeno marker expression generally do not have the "Diploid" prefix, aligning with an aneuploid tumor cell phenotype.
• Squamous-specific markers: A much larger group of genes, including *CD9*, *TM4SF1*, *ATP1B3*, *CD44*, *NTRK2*, *SDC1*, *PTPRF*, *DDR1*, *CD24*, *F11R*, *LEPR*, *ABCC5*, *GPC3*, *ALCAM*, *GPNMB*, *DSG2*, *CA12*, *CLDND1*, *EMP1*, *ITGA6*, *DSC3*, *NECTIN1*, and *SYPL1*, demonstrates strong and widespread expression in samples grouped under the "Squamous" condition (e.g., NSC004.T1, NSC004.T3, NSC004.T4, NSC004.T2). Similar to Adeno, these Squamous samples also lack the "Diploid" prefix, suggesting an aneuploid tumor origin. These markers are almost entirely absent in Adeno samples.
• Expression Patterns: Within each condition, the expression patterns are largely consistent across samples for their respective marker sets, with high mean expression (darker red) and a high fraction of expressing cells (larger dot size) for the most specific markers. The separation between the two conditions is very sharp, with minimal overlap in marker expression.

Biological Interpretation

The distinct sets of surfaceome markers for Adeno and Squamous Lung Epithelial cells highlight fundamental biological differences between these two major lung cancer subtypes.

Adenocarcinoma Markers

• The strong expression of HLA-DR family genes (HLA-DPA1, HLA-DPB1, HLA-DRB1, HLA-DRB5, HLA-DQB1) and CD74 in Adeno cells suggests an altered immune microenvironment or an active role in antigen presentation by these tumor cells. While HLA class II molecules are typically found on professional antigen-presenting cells, their aberrant expression on tumor cells has been observed in various cancers, including lung adenocarcinoma, and can influence anti-tumor immunity [NCBI].
• MUC1 is a well-known mucin protein often overexpressed in adenocarcinomas, playing roles in cell adhesion, signal transduction, and immune evasion [GeneCards]. Its presence reinforces the adenocarcinomatous phenotype.
• CEACAM6 (Carcinoembryonic Antigen Related Cell Adhesion Molecule 6) is an oncofetal protein associated with increased proliferation, survival, and metastasis in various cancers, including lung adenocarcinoma [PubMed Search].

Squamous Cell Carcinoma Markers

• The prominent expression of CD44 is particularly relevant for Squamous Cell Carcinoma (SCC). CD44 is a cell surface glycoprotein involved in cell adhesion and migration, and is often considered a stem cell marker. It is frequently overexpressed in SCCs and linked to tumor initiation, progression, and therapy resistance [NCBI].
• Desmogleins (DSG2, DSC3) and Nectin-1 (PVRL1/NECTIN1) are components of desmosomes and adherens junctions, respectively, which are crucial for cell-cell adhesion and maintaining epithelial tissue integrity. Their altered expression or specific isoforms can be characteristic of squamous differentiation and contribute to tumor biology in SCC.
• SDC1 (Syndecan-1) is a heparan sulfate proteoglycan involved in cell adhesion, growth factor binding, and cell signaling, frequently upregulated in SCCs and associated with invasion and metastasis [GeneCards].
• CA12 (Carbonic Anhydrase XII) is a transmembrane enzyme often overexpressed in various cancers, including SCC, and is involved in pH regulation and tumor hypoxia adaptation [NCBI].
• Other markers like ALCAM (CD166), GPNMB, and ITGA6 (Integrin alpha-6) are also cell adhesion molecules or involved in extracellular matrix interactions, consistent with the distinct adhesive and migratory properties of squamous epithelial cells and their cancerous counterparts.

The observation that both Adeno and Squamous marker-expressing samples are largely not labeled as "Diploid" is consistent with their identity as tumor-origin Lung Epithelial cells. Aneuploidy is a hallmark of cancer, and these distinct molecular profiles likely represent the malignant cells driving the pathology of each lung cancer subtype.

Clinical or Translational Implications

The identification of such clearly defined, condition-specific surfaceome markers has significant clinical and translational implications:

• Diagnostic Biomarkers: These markers could serve as highly specific diagnostic tools to differentiate between lung adenocarcinoma and squamous cell carcinoma, which is crucial for treatment selection. For instance, strong MUC1 and HLA-DR expression could indicate Adeno, while CD44 and DSG2 could indicate Squamous differentiation. This could be particularly valuable in cases where histological classification is ambiguous or for liquid biopsy approaches.
• Therapeutic Targets: As these are surfaceome proteins, they are directly accessible to antibody-based therapies, antibody-drug conjugates (ADCs), or CAR T-cell therapies.
• For Adenocarcinoma, targets like MUC1 or CEACAM6 could be explored, as they are known to be overexpressed in this subtype. The aberrant expression of HLA class II molecules also presents an interesting, albeit complex, target given its role in immune modulation.
• For Squamous Cell Carcinoma, highly expressed markers such as CD44, SDC1, DSG2, or CA12 represent promising therapeutic targets. CD44, in particular, has garnered attention as a target for cancer stem cells in SCC [NCBI].
• Prognostic Indicators: Further research could investigate if the expression levels of these markers correlate with patient prognosis, treatment response, or resistance, thereby informing personalized medicine strategies.
• Experimental Validation: These results provide a strong basis for experimental validation using techniques such as immunohistochemistry, flow cytometry, or functional assays in cell lines and patient-derived xenografts to confirm their diagnostic and therapeutic utility.

17. Macrophage Condition-Specific Surfaceome Markers in Lung Cancer Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers within the Macrophage cell population, comparing Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) samples from lung single-cell RNA-seq data. The dot plot visualizes the expression patterns of the top 50 surfaceome markers for each condition across individual patient samples, providing insights into the distinct macrophage phenotypes present in these two major lung cancer types.

Visual Summary

The dot plot effectively displays the differential expression of macrophage surface markers across Adeno and Squamous conditions.

• Sample Grouping: Samples are clearly segregated by tumor histology, with Adeno samples grouped at the top and Squamous samples at the bottom. Within each condition, individual patient samples (e.g., NSC018.T2, NSC009.T1) are shown.
• Marker Grouping: Markers predominantly expressed in Adeno samples are clustered on the left side of the plot, while markers highly expressed in Squamous samples are clustered on the right.

Expression Intensity and Prevalence:

• Dot Color (Mean Expression): Redder colors indicate higher mean expression of a gene within the Macrophage cells of that specific sample.
• Dot Size (Fraction of Cells): Larger dot sizes signify a higher percentage of Macrophage cells in that sample expressing the gene.
• Adeno-Specific Markers: A prominent cluster of markers, including HLA-DOA1, HLA-DMA, LY6E, HLA-DQB2, AXL, S100A12, ANPEP, TREM1, and ADGRE2, shows high expression (dark red, large dots) almost exclusively in the Adeno samples.
• Squamous-Specific Markers: Another distinct cluster of markers, such as IL7R, AQP9, ADAM8, MFSD12, SDC2, ITGA5, LTBR, P2RX7, CD300E, STAB1, ANKH, PCNX1, SLC8B1, FURIN, KCNMB1, IL1RAP, FGFR2, and SEMA4G, demonstrates high expression primarily in the Squamous samples.
• Sample Heterogeneity: While clear condition-specific patterns emerge, some heterogeneity is observed within each condition, with varying expression levels and fractions of expressing cells across individual patient samples.
• Cell Counts: The bar plot on the right indicates the total number of Macrophage cells contributing to the analysis for each sample, ranging from 47 to 2202 cells per sample.

Biological Interpretation

The identified condition-specific surfaceome markers suggest distinct functional states and roles for macrophages within the tumor microenvironment (TME) of lung Adenocarcinoma versus Squamous Cell Carcinoma.

Macrophage Phenotype in Adenocarcinoma

Macrophages in Adenocarcinoma samples appear to exhibit characteristics associated with antigen presentation and potentially diverse immune functions:

• Antigen Presentation: High expression of HLA-DOA1, HLA-DMA, and HLA-DQB2 (Major Histocompatibility Complex (MHC) Class II molecules) indicates a robust capacity for antigen presentation. This suggests that Adeno-associated macrophages might be more actively involved in interacting with T cells, though their specific role (pro- or anti-tumor) would require further functional characterization. GeneCards HLA-DMA
• Immune Modulation/Inflammation: TREM1 (Triggering Receptor Expressed on Myeloid cells 1) is a key amplifier of inflammatory responses, often associated with infection and cancer, promoting cytokine production.
• Growth and Survival Signaling: AXL (AXL Receptor Tyrosine Kinase) is frequently upregulated in various cancers, including lung cancer, playing roles in cell survival, proliferation, and resistance to therapy. Its presence on macrophages could indicate participation in pro-tumorigenic pathways. GeneCards AXL
• Other notable markers: LY6E is implicated in viral entry and cancer progression, while ANPEP (CD13) plays roles in cell growth, differentiation, and angiogenesis. ADGRE2 (CD97) is an adhesion GPCR involved in cell-cell interactions and immune regulation. S100A12 is a pro-inflammatory mediator.

Macrophage Phenotype in Squamous Cell Carcinoma

Macrophages in Squamous Cell Carcinoma samples show a different set of surface markers, potentially indicative of altered immune regulation, adhesion, and metabolic functions:

• Immune Cell Interaction & Survival: IL7R (CD127), while primarily known for T and B cell development, can be expressed on myeloid cells and might influence macrophage survival or interaction with other immune cells. LTBR (Lymphotoxin Beta Receptor) is involved in lymphoid organogenesis and immune responses, suggesting a role in shaping the local immune landscape.
• Adhesion and Migration: ITGA5 (Integrin alpha-5), forming part of the fibronectin receptor, is critical for cell adhesion and migration, suggesting that Squamous-associated macrophages might have distinct migratory or tissue-remodeling capabilities. GeneCards ITGA5 SDC2 (Syndecan-2) is a proteoglycan involved in cell adhesion, signaling, and tumor progression.
• Proteolytic and Metabolic Activity: ADAM8 (A Disintegrin And Metalloproteinase 8) contributes to cell adhesion, migration, and the proteolytic shedding of cell surface proteins, impacting the extracellular matrix. AQP9 (Aquaporin 9) is a water channel protein, its expression might reflect specific metabolic or migratory demands.
• Inflammation and Cell Death: P2RX7 (P2X Purinoceptor 7) is an ATP-gated ion channel involved in inflammation and regulated cell death pathways like pyroptosis.
• Scavenger Receptor Activity: STAB1 (Stabilin-1) is a scavenger receptor predominantly expressed on alternatively activated (M2-like) macrophages, suggesting roles in immune suppression or resolution of inflammation. GeneCards STAB1
• Other markers: FGFR2 (Fibroblast Growth Factor Receptor 2) can be involved in tumor growth and angiogenesis. CD300E and SEMA4G are involved in immune regulation.

The clear distinction in surface marker profiles strongly suggests that macrophages adopt subtype-specific functional programs in response to the unique microenvironments of Adenocarcinoma and Squamous Cell Carcinoma. This plasticity underscores the heterogeneity of tumor-associated macrophages (TAMs).

Clinical or Translational Implications

The identification of condition-specific surfaceome markers on macrophages holds significant clinical and translational potential:

• Diagnostic and Prognostic Biomarkers: These distinct surface marker panels could serve as novel biomarkers for differentiating between Adenocarcinoma and Squamous Cell Carcinoma, especially in biopsy samples where macrophage infiltration can be quantified and phenotyped via immunohistochemistry or flow cytometry. Differential expression of AXL in Adeno vs. STAB1 in Squamous could indicate differing disease progression or treatment responses.
• Therapeutic Targets: Surface molecules are highly accessible for targeted therapies.
• For Adenocarcinoma, high expression of AXL suggests that AXL inhibitors, some of which are already in clinical trials for other cancers, could be explored as a strategy to modulate macrophage functions or directly target AXL-expressing tumor cells and TAMs. PubMed AXL inhibitors cancer
• For Squamous Cell Carcinoma, markers like ITGA5, P2RX7, or FGFR2 could represent targets for interfering with macrophage adhesion, migration, inflammatory responses, or pro-tumorigenic signaling within the TME. Targeting STAB1 could modify the immune suppressive functions of M2-like macrophages.
• Immunotherapy Modulators: Understanding the distinct macrophage phenotypes based on their surface markers could inform more precise immunotherapy strategies. For example, identifying specific activation states (e.g., via HLA expression in Adeno) might help predict response to immune checkpoint inhibitors or guide macrophage-reprogramming therapies tailored to each lung cancer subtype.
• Experimental Validation: These identified markers provide excellent candidates for further experimental validation using techniques like flow cytometry or multiplex immunohistochemistry on tissue sections from lung cancer patients to confirm their protein expression, cellular localization, and association with clinical outcomes. Functional assays (e.g., macrophage polarization, phagocytosis, cytokine production) can then elucidate the precise roles of these markers in mediating macrophage-tumor interactions in a subtype-specific manner.

18. Condition-Specific Surface Markers in Lung Fibroblasts

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers in Fibroblast cells, comparing lung Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) conditions. The plot_markers_and_expression_dot tool was used to visualize the expression of up to 50 significantly differentially expressed surface markers per condition, as determined by a log2_FC cutoff of 1.5 and a pval_adj cutoff of 0.05. The resulting dot plot illustrates the mean expression level and the fraction of cells expressing each marker across various samples.

Visual Summary

The dot plot effectively delineates distinct surfaceome profiles for Fibroblasts derived from Adeno versus Squamous lung cancer samples.

• Sample Grouping: Samples are clearly clustered by condition, with Adeno samples (e.g., NSC016.T1, NSC021.T1) occupying the upper part of the plot and Squamous samples (NSC004.T2, NSC004.T1) at the bottom.
• Adenocarcinoma-Specific Markers: A cluster of genes on the left side of the plot, including IL6ST, MMP14, CYSLTR1, ATP1A1, PMEPA1, OR4D9, IL1R1, ITGB5, PTK7, MRC2, ADAM12, and PAM, shows high mean expression (darker red dots) and high prevalence (larger dot size) predominantly within the Adeno samples. IL6ST and MMP14 are particularly prominent in most Adeno samples.
• Squamous Cell Carcinoma-Specific Markers: A larger group of genes on the right side of the plot, such as TM9SF3, SLC2A3, SDC2, LRP10, UNC5B, CD82, MPZL1, SDC1, GPNMB, GJB2, FAP, APCDD1, GJA1, SLC39A14, NTM, LRRC15, and TMEM158, exhibits markedly higher mean expression and prevalence almost exclusively in the Squamous samples. FAP, GPNMB, SDC1, and LRRC15 stand out as highly expressed and widely prevalent markers within the Squamous fibroblast population.
• Expression Pattern: For condition-specific markers, the mean expression is high (dark red) and the fraction of expressing cells is large (large dots), indicating robust and widespread expression within the respective tumor types. Conversely, these markers show minimal to no expression in the fibroblasts from the opposing condition, highlighting their specificity.

Biological Interpretation

Fibroblasts within the tumor microenvironment (TME), often termed Cancer-Associated Fibroblasts (CAFs), play critical roles in tumor progression, immunosuppression, and therapeutic resistance. The observed condition-specific surfaceome markers suggest distinct functional states or sub-populations of CAFs in Adenocarcinoma versus Squamous Cell Carcinoma of the lung.

Adenocarcinoma-Associated Fibroblast Markers

Fibroblasts in Adenocarcinoma samples show elevated expression of markers indicative of inflammatory and pro-invasive capabilities:

• IL6ST (GP130): This is a shared receptor subunit for the IL-6 family of cytokines. Its high expression suggests that Adeno CAFs are actively involved in inflammatory signaling pathways, potentially promoting tumor growth and immune evasion through IL-6 family cytokines. PubMed search: IL6ST cancer associated fibroblasts
• MMP14 (MT1-MMP): A membrane-anchored matrix metalloproteinase, MMP14 is crucial for degrading the extracellular matrix (ECM) and facilitating tumor cell invasion and metastasis. Its upregulation points to an active role for Adeno CAFs in ECM remodeling and creating a pro-metastatic niche. GeneCards: MMP14
• CYSLTR1 (CysLT1 receptor): Part of the cysteinyl leukotriene pathway, CYSLTR1 is involved in inflammation and has been implicated in cancer progression, suggesting a link between inflammatory lipid mediators and Adeno CAF activity.
• ITGB5 (Integrin Beta-5): Integrins are cell surface receptors that mediate cell-ECM adhesion and signaling. ITGB5 has been associated with tumor cell invasion and resistance to therapy, implying Adeno CAFs contribute to these processes. GeneCards: ITGB5
• ADAM12: A disintegrin and metalloproteinase 12, often upregulated in various cancers, contributes to cell adhesion, migration, and proteolysis. Its presence suggests Adeno CAFs are involved in modulating the TME and supporting tumor cell motility.

Squamous Cell Carcinoma-Associated Fibroblast Markers

Fibroblasts in Squamous Cell Carcinoma samples exhibit a robust profile characterized by well-established CAF activation markers and metabolic regulators:

• FAP (Fibroblast Activation Protein): FAP is a highly specific and widely recognized marker of activated CAFs across many solid tumors, including lung cancer. Its strong expression in Squamous fibroblasts indicates a highly activated, pro-tumorigenic CAF phenotype involved in ECM remodeling, immune suppression, and promoting tumor growth. GeneCards: FAP
• LRRC15 (Leucine Rich Repeat Containing 15): Another recently identified and highly specific CAF marker, LRRC15 is involved in ECM organization and TME remodeling, often associated with immunotherapy resistance. Its distinct presence further supports a specific activated CAF state in Squamous tumors. PubMed search: LRRC15 cancer associated fibroblasts
• SDC1 (Syndecan-1): A heparan sulfate proteoglycan, SDC1 plays roles in cell adhesion, growth factor binding, and ECM interactions. It is frequently upregulated in CAFs and associated with aggressive tumor phenotypes and immune modulation. GeneCards: SDC1
• GPNMB (Osteoactivin): A transmembrane glycoprotein implicated in cell adhesion, migration, and immune regulation, GPNMB is often overexpressed in aggressive tumors and may contribute to their metastatic potential. GeneCards: GPNMB
• SLC2A3 (GLUT3): A glucose transporter, GLUT3 is involved in glucose uptake. Its high expression suggests increased metabolic activity in Squamous CAFs, potentially supporting the high energetic demands of the tumor microenvironment.
• GJB2 (Connexin 26): A gap junction protein, GJB2 facilitates direct cell-to-cell communication. Its presence suggests unique communication networks within the Squamous TME, potentially coordinating CAF functions with tumor cells or other stromal elements.

Distinct Phenotypes

The clear segregation of surface markers highlights fundamental differences in the biological activities and states of fibroblasts between lung Adenocarcinoma and Squamous Cell Carcinoma. This suggests that the TME architecture and cellular interactions orchestrated by CAFs are distinct in these two major subtypes of lung cancer.

Clinical or Translational Implications

The identification of these condition-specific surface markers in lung fibroblasts holds significant clinical and translational potential.

• Diagnostic and Prognostic Biomarkers: The distinct surfaceome profiles could serve as biomarkers to differentiate between Adenocarcinoma and Squamous Cell Carcinoma, particularly in challenging diagnostic cases, or to provide prognostic information. For instance, high FAP or LRRC15 expression in Squamous lesions might correlate with specific clinical outcomes.
• Therapeutic Targets: Surface proteins are highly accessible for therapeutic intervention.
• FAP: FAP is a well-established target for CAF depletion strategies, including FAP-targeted immunotherapies (e.g., FAP-CAR T cells, FAP-targeting antibodies or small molecules). Its strong specificity to Squamous fibroblasts suggests that FAP-targeted therapies might be particularly effective in Squamous Cell Carcinoma.
• LRRC15: As an emerging CAF target, LRRC15 could represent another promising avenue for modulating the Squamous TME.
• MMP14, IL6ST, ITGB5: For Adenocarcinoma, MMP14 could be targeted to inhibit ECM remodeling and invasion, while IL6ST could be a target to dampen pro-inflammatory signaling pathways driven by CAFs. ITGB5 could be targeted to disrupt CAF adhesion and signaling.
• Further investigation into the functional roles of these specific markers (e.g., CYSLTR1, ADAM12 in Adeno; SDC1, GPNMB, SLC2A3 in Squamous) could reveal additional novel therapeutic vulnerabilities in the respective tumor types.
• Personalized Medicine: Understanding these condition-specific CAF phenotypes could enable the development of more tailored treatment strategies, guiding the selection of CAF-targeting therapies based on the specific lung cancer subtype.
• Experimental Validation: These identified markers provide excellent candidates for further experimental validation in preclinical models and human cohorts, using techniques like immunohistochemistry, flow cytometry, or functional assays to confirm their roles and therapeutic potential.

19. T cell CD4+ Condition-Specific Surfaceome Markers in Lung Cancer Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression patterns of condition-specific surfaceome markers in CD4+ T cells, comparing Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) samples from lung tissue. The markers shown are a selection of up to 50 genes identified as differentially expressed between these conditions within the CD4+ T cell population. Dot size indicates the fraction of cells within each sample expressing a given gene, while color intensity represents the mean expression level.

Visual Summary

The dot plot effectively organizes samples by condition, with Adenocarcinoma samples forming the upper block (e.g., NSC037.T1 to NSC019.T1) and Squamous Cell Carcinoma samples forming the lower block (NSC004.T3 to NSC004.T2).

• Adenocarcinoma-Associated Markers: A distinct cluster of markers exhibits notably high mean expression (dark red dots) and high prevalence (large dot size) across the majority of Adenocarcinoma samples. These include genes such as BTN3A2, CYSLTR1, OR4D9, SLC2A14, ITGB2, SLC16A7, PIK3IP1, SERINC3, CLDND1, CD28, TIGIT, TNFRSF9, CTLA4, PRNP, LDLR, CD27, STT3B, IL6ST, CD47, CD82, LRP10, SLC2A11, FAS, NUP210, SLC12A6, PCNX1, OR52I1, GPR155, and SLC3A2. This indicates a unique surface protein signature for CD4+ T cells infiltrating Adenocarcinoma.
• Squamous Cell Carcinoma Patterns: In stark contrast, the same set of markers generally shows very low or absent expression and prevalence in the Squamous Cell Carcinoma samples. This suggests that these specific markers are largely not characteristic of CD4+ T cells in Squamous tumors, or that their expression levels are significantly lower than in Adenocarcinoma.
• The bar plot on the right indicates the number of CD4+ T cells contributing to each sample's expression profile, ranging from 71 to 823 cells. Despite this variability, the distinct expression patterns between conditions remain clear.

Biological Interpretation

The observed differential expression of surfaceome markers points to fundamental differences in the immunological state and functional characteristics of CD4+ T cells within the tumor microenvironment (TME) of Adenocarcinoma versus Squamous Cell Carcinoma of the lung.

• Immune Checkpoint and Co-stimulatory Landscape: Several highly expressed markers in Adenocarcinoma-associated CD4+ T cells are critical regulators of T cell function:
• Inhibitory Checkpoints: TIGIT and CTLA4 are well-known immune checkpoint receptors. Their high expression on CD4+ T cells suggests a potentially exhausted or suppressive phenotype, contributing to immune evasion within the Adenocarcinoma TME. GeneCards: TIGIT, GeneCards: CTLA4
• Co-stimulatory Molecules: CD28 and CD27 are crucial co-stimulatory molecules for T cell activation and survival. TNFRSF9 (also known as 4-1BB or CD137) is another co-stimulatory receptor. The co-expression of both inhibitory and stimulatory molecules suggests a complex regulatory state, potentially indicating chronically activated T cells that are also simultaneously receiving suppressive signals.
• FAS (CD95) is a death receptor involved in apoptosis, suggesting potential engagement in immune cell death pathways within the Adeno TME.

Adhesion and Metabolic Adaptations:

• ITGB2 (CD18), a subunit of beta-2 integrins, is vital for leukocyte adhesion and migration. Its elevated expression might indicate enhanced recruitment or tissue retention of CD4+ T cells in Adenocarcinoma.
• Several solute carrier family members (SLC2A14, SLC16A7, SLC2A11, SLC12A6, SLC3A2) suggest altered metabolic or transport processes, reflecting metabolic reprogramming of CD4+ T cells to adapt to the nutrient-deprived and immunosuppressive Adenocarcinoma TME.
• Distinct TME Profiles: The striking difference in these surfaceome markers between the two conditions underscores that CD4+ T cells adopt distinct functional and phenotypic states tailored to the specific context of Adenocarcinoma versus Squamous cell carcinoma, highlighting the unique immunological challenges posed by each cancer subtype.

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers in CD4+ T cells holds significant clinical and translational potential.

• Biomarker Development: Markers such highly expressed in Adenocarcinoma-associated CD4+ T cells, such as TIGIT, CTLA4, TNFRSF9, or specific SLC transporters, could serve as valuable biomarkers. If validated, these could aid in:
• Differential Diagnosis: Distinguishing between lung Adenocarcinoma and Squamous Cell Carcinoma based on the immune cell infiltrate, potentially through techniques like flow cytometry or immunohistochemistry on tumor biopsies.
• Prognosis and Prediction of Treatment Response: The specific expression profiles might correlate with disease progression or responsiveness to particular immunotherapies, especially immune checkpoint inhibitors. PubMed Search: CD4 TIGIT CTLA4 lung cancer prognosis
• Therapeutic Targeting: Surfaceome markers are prime candidates for therapeutic intervention. The elevated expression of immune checkpoint molecules like TIGIT and CTLA4 in Adenocarcinoma suggests that these pathways are actively engaged in immunosuppression in this subtype. This finding provides a strong rationale for:
• Targeted Immunotherapy: Exploring single-agent or combination immunotherapies that specifically target TIGIT, CTLA4, or other inhibitory pathways in lung Adenocarcinoma patients.
• Novel Therapeutic Strategies: Investigating the functional roles of other highly expressed surface markers (e.g., ITGB2, metabolic transporters) to identify new druggable targets that could modulate CD4+ T cell function and enhance anti-tumor immunity in Adenocarcinoma.
• Experimental Validation: Future studies should focus on validating these findings at the protein level using complementary techniques such as flow cytometry, mass cytometry, or spatial proteomics (e.g., multiplex immunofluorescence) to confirm their expression on the cell surface in patient samples and to investigate their functional impact on CD4+ T cell behavior in both cancer types.

20. Gene Ontology (GSA) Analysis for Lung Epithelial Cells: Condition and Ploidy-Specific Pathway Upregulation

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes Gene Set Analysis (GSA) to identify Gene Ontology (GO) terms and pathways that are significantly upregulated in Lung Epithelial cells under different conditions: Adenocarcinoma (Adeno) versus other conditions, Squamous cell carcinoma (Squamous) versus other conditions, and Diploid versus other ploidy states. The results are presented as bar plots, with terms ranked by their statistical significance (-log(p-val) and -log(q-val)).

Visual Summary

The visualization consists of three bar plots, each representing GSA results for Lung Epithelial cells under a specific comparison:

1. GSA_up for Lung Epithelial cell: Adeno_vs_others: This plot shows a substantial number of significantly upregulated GO terms and pathways in Adenocarcinoma lung epithelial cells. The p-values and q-values are generally very significant, with -log(p-val) extending beyond 15 and -log(q-val) beyond 10 for the top terms. Enriched categories predominantly include processes related to viral and bacterial infections, immune responses (e.g., antigen processing and presentation, phagosome), cellular metabolism (e.g., protein processing in endoplasmic reticulum, lipid and atherosclerosis), cell adhesion, and various cancer-related signaling pathways.
2. GSA_up for Lung Epithelial cell: Diploid_vs_others: This plot displays a much smaller number of enriched terms, and their significance is considerably lower compared to the Adeno and Squamous comparisons. While some terms show a p-value below the cutoff (e.g., "Metabolism of xenobiotics by cytochrome P450", "Antigen processing and presentation"), the corresponding -log(q-val) (adjusted p-value) for most terms is close to zero (indicated by the dashed line), suggesting that these enrichments are not statistically robust after multiple testing correction. This comparison indicates a less distinct or significant pathway upregulation in diploid lung epithelial cells relative to the general population (which includes aneuploid, likely tumor, cells).
3. GSA_up for Lung Epithelial cell: Squamous_vs_others: This plot reveals an extensive and highly significant upregulation of numerous pathways in Squamous cell carcinoma lung epithelial cells. The -log(p-val) and -log(q-val) values for the top terms are exceptionally high, reaching beyond 40, indicating very strong enrichment. The enriched terms span a broad range, including neurodegenerative disease pathways (e.g., Alzheimer, Parkinson, Huntington disease), fundamental cellular processes (e.g., ribosome, spliceosome, RNA transport, protein processing in endoplasmic reticulum, cell cycle, autophagy), infection responses, and a wide array of key oncogenic signaling pathways (e.g., PI3K-Akt, HIF-1, mTOR, MAPK, NF-kappa B).

Biological Interpretation

Distinct Biological Programs in Lung Adenocarcinoma (Adeno) Epithelial Cells

Lung epithelial cells in Adenocarcinoma demonstrate a significant upregulation of pathways involved in immune response and infection, particularly related to various viruses (Epstein-Barr virus, Kaposi sarcoma associated herpesvirus, Human T-cell leukemia virus 1, Human papillomavirus, Human cytomegalovirus, Influenza A) and bacteria (Pathogenic Escherichia coli, Shigellosis, Tuberculosis). This could suggest a heightened antiviral/antibacterial state, a response to chronic infection, or alterations in immune evasion mechanisms characteristic of the tumor microenvironment.

Furthermore, key cellular processes such as protein processing in the endoplasmic reticulum, phagosome/lysosome activity, and antigen processing and presentation are highly active. These indicate altered protein homeostasis, increased cellular catabolism, and a potentially active, albeit possibly dysregulated, role in interacting with the immune system.

The enrichment of "Pathways in cancer", MAPK, NF-kappa B, and PI3K-Akt signaling confirms the activation of established oncogenic drivers, while terms like "Cell adhesion molecules" and "Tight junction" suggest changes in cell-cell interactions crucial for tumor growth and metastasis.

Profound Cellular Dysregulation in Lung Squamous Cell Carcinoma (Squamous) Epithelial Cells

Squamous cell carcinoma epithelial cells exhibit an even more pronounced and widespread upregulation of pathways, indicative of severe cellular stress and uncontrolled proliferation. Remarkably, a large number of the top enriched terms are associated with neurodegenerative diseases (e.g., Alzheimer, Parkinson, Huntington disease pathways). While seemingly disparate from lung cancer, these pathways often converge on fundamental cellular mechanisms such as protein misfolding, aggregation, proteotoxicity, oxidative stress, and mitochondrial dysfunction. Their strong enrichment suggests that Squamous cells may be undergoing significant stress related to protein homeostasis and cellular integrity.

Core cellular machinery is highly dysregulated, including ribosome biogenesis, spliceosome activity, RNA transport, ubiquitin-mediated proteolysis, and protein processing in the endoplasmic reticulum. These findings point to extensive rewiring of protein synthesis, maturation, and degradation pathways, essential hallmarks of rapidly proliferating and highly stressed cancer cells.

Consistent with aggressive cancer, "Cell cycle" and "Autophagy" are upregulated, indicating altered cell proliferation and survival mechanisms. A comprehensive activation of major oncogenic signaling pathways (PI3K-Akt, mTOR, HIF-1, AMPK, FoxO, Hippo, NF-kappa B, MAPK) further underscores the aggressive, dysregulated nature of Squamous cell carcinoma.

Relative Stability of Diploid Lung Epithelial Cells

In stark contrast, diploid lung epithelial cells, when compared to other cell populations (predominantly aneuploid tumor cells), show minimal and less statistically robust pathway upregulation. The few terms identified, such as "Metabolism of xenobiotics by cytochrome P450" and "Antigen processing and presentation", suggest basic cellular maintenance and immune surveillance functions. However, the very low -log(q-val) for these terms indicates that these enrichments are not significant after correcting for multiple comparisons. This observation reinforces the concept that diploid cells are generally closer to a quiescent or normal state, lacking the extensive pathological pathway activation seen in the aneuploid and overtly malignant populations.

Clinical or Translational Implications

The distinct pathway enrichments in Adenocarcinoma versus Squamous cell carcinoma lung epithelial cells highlight fundamental biological differences between these major lung cancer subtypes.

• Targeted Therapies: The robust activation of oncogenic pathways like PI3K-Akt, MAPK, NF-kappa B, mTOR, and HIF-1 in both Adeno and Squamous cells, albeit with different magnitudes and specific pathway combinations, reinforces their validity as therapeutic targets. Subtype-specific pathway profiles could guide the selection of targeted therapies for patients with either Adenocarcinoma or Squamous cell carcinoma.
• Protein Homeostasis as a Vulnerability: The pronounced upregulation of pathways related to protein processing, folding, and degradation (e.g., ER stress, ubiquitin-proteasome system, ribosome, spliceosome) in both subtypes, particularly in Squamous, suggests potential vulnerabilities. Therapies targeting protein homeostasis, such as proteasome inhibitors or modulators of ER stress, could be explored.
• Immune Modulation: The consistent enrichment of infection and immune-related pathways may indicate mechanisms of immune evasion or chronic inflammatory states that contribute to tumor progression. This could inform the development of immunotherapeutic strategies or combination therapies that address the tumor's immune microenvironment.
• Prognostic Markers: The specific GSA signatures for Adeno and Squamous lung epithelial cells could potentially serve as prognostic biomarkers, indicating disease aggressiveness or response to therapy.
• The analysis of diploid cells provides a valuable context, showing that the identified oncogenic and stress-related pathways are characteristic of the diseased, likely aneuploid, state rather than a general feature of lung epithelial cells.

21. Gene Set Enrichment Analysis Reveals Distinct Pathway Activities Across Lung Cancer Cell Types and Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for various celltype_minor populations (B cell, Fibroblast, ILC, Lung Epithelial cell, Macrophage, Mast cell, Plasma cell, T cell CD4+, T cell CD8+) identified from single-cell RNA-seq data of human Lung tissue. The GSEA compares gene expression in each target cell type from one condition (Adenocarcinoma, Squamous cell carcinoma, or Diploid ploidy status for Lung Epithelial cells) against all other conditions/ploidy states. The dot plot visualizes the Normalized Enrichment Score (NES) and the statistical significance (-log(p-val)) of enriched pathways, providing insight into condition-specific biological processes.

Visual Summary

The dot plot effectively visualizes a complex landscape of pathway enrichment across different cell types and lung cancer conditions.

• Color Scale (NES): Red dots indicate pathways positively enriched (upregulated) in the comparison group (e.g., Adeno vs. others), while blue dots indicate negative enrichment (downregulated). A darker color signifies a stronger enrichment score.
• Dot Size (-log(p-val)): Larger dots represent higher statistical significance of the enrichment.
• Overall Pattern: There is a considerable diversity in pathway enrichment patterns across cell types and conditions. Some pathways show consistent enrichment or depletion across multiple cell types within a condition, while others are highly cell-type or condition-specific.

Prominent Enrichments:

• Many immune-related pathways (e.g., "Antigen processing and presentation", "Allograft rejection", "Th1 and Th2 cell differentiation", "Th17 cell differentiation", "Leukocyte transendothelial migration") show strong and diverse enrichment patterns, particularly in immune cell types like T cells, Macrophages, and B cells.
• Cancer-associated signaling pathways (e.g., "ErbB signaling pathway", "Hippo signaling pathway", "Hedgehog signaling pathway", "Focal adhesion", "ECM-receptor interaction", "Rap1 signaling pathway") appear frequently enriched or depleted, notably in Lung Epithelial cells and Fibroblasts.
• Metabolic pathways (e.g., "Cholesterol metabolism", "Pyruvate metabolism", "Metabolism of xenobiotics by cytochrome P450") are also variably enriched.

Biological Interpretation

Lung Epithelial Cells: Hallmarks of Cancer Subtypes and Ploidy

As the tumor origin cell type, Lung Epithelial cells show critical differences between Adenocarcinoma (Adeno) and Squamous cell carcinoma (Squamous), and also based on ploidy status (Diploid vs. others).

Adeno vs. Squamous:

• Adeno_vs_others (Lung Epithelial cell): Shows positive enrichment for "ErbB signaling pathway", "Hippo signaling pathway", "Focal adhesion", and "ECM-receptor interaction", suggesting a strong proliferative, migratory, and extracellular matrix remodeling phenotype often associated with adenocarcinomas. "Cholesterol metabolism" is also positively enriched, potentially indicating altered lipid metabolism to support rapid growth https://pubmed.ncbi.nlm.nih.gov/30678235/.
• Squamous_vs_others (Lung Epithelial cell): This comparison also shows enrichment for some of these cancer-associated pathways but with different intensities or specific pathways. For instance, "Metabolism of xenobiotics by cytochrome P450" is negatively enriched, which could reflect differences in drug metabolism or detoxification capacities between the subtypes.
• Diploid_vs_others (Lung Epithelial cell): This comparison highlights features of diploid (potentially less transformed or non-cancerous/early-stage tumor) lung epithelial cells relative to the general population. It shows negative enrichment for several cancer-related pathways, as expected, and positive enrichment for "Metabolism of xenobiotics by cytochrome P450", suggesting a more "normal" metabolic profile or drug response. "Protein processing in endoplasmic reticulum" shows negative enrichment, perhaps indicating less ER stress compared to aneuploid or malignant cells.

Immune Cell Compartment: Differential Immune Responses in the Tumor Microenvironment

Immune cells demonstrate distinct pathway enrichments, suggesting varied immune responses across conditions.

T cells (CD4+, CD8+):

• "Th1 and Th2 cell differentiation" and "Th17 cell differentiation" are variably enriched across conditions. For example, T cell CD4+ in Adeno shows negative enrichment for "Th1 and Th2 cell differentiation" and "Th17 cell differentiation", while T cell CD8+ in Adeno shows a positive enrichment for "Th17 cell differentiation". This could imply distinct T-cell polarization patterns within the Adeno TME, potentially influencing anti-tumor immunity or immunosuppression https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7323675/.
• "Antigen processing and presentation" is notably enriched in T cell CD8+ (Squamous_vs_others) and T cell CD4+ (Adeno_vs_others and Squamous_vs_others), indicating active antigen presentation mechanisms in these conditions.

Macrophages:

• "Phagosome" and "Antigen processing and presentation" pathways are positively enriched in Macrophages (Adeno_vs_others and Squamous_vs_others), suggesting their active role in debris clearance and immune initiation.
• "Metabolism of xenobiotics by cytochrome P450" and "Cholesterol metabolism" are also enriched, pointing to their metabolic reprogramming in the tumor context, which can influence their polarization (e.g., M1 vs. M2) https://pubmed.ncbi.nlm.nih.gov/31388056/.

B cells and Plasma cells:

• "Antigen processing and presentation" is enriched in B cells (Squamous_vs_others), suggesting their role in presenting antigens.
• Plasma cells (Adeno_vs_others) show enrichment for "Protein processing in endoplasmic reticulum" and "Ribosome", consistent with their primary function of antibody production and secretion.

Mast cells:

• "Fc epsilon RI signaling pathway" is a key mast cell activation pathway, and its enrichment in Mast cells (Adeno_vs_others and Squamous_vs_others) suggests their activation in both tumor types. Mast cells can play dual roles in cancer, both pro-tumorigenic and anti-tumorigenic https://pubmed.ncbi.nlm.nih.gov/31258671/.

ILCs (Innate Lymphoid Cells):

• ILCs show diverse immune pathway enrichments, such as "Allograft rejection" and "Inflammatory bowel disease" (which broadly represents inflammatory responses). Their involvement indicates their contribution to innate immune sensing and response within the lung TME.

Fibroblasts: Stromal Remodeling and Immune Crosstalk

Fibroblasts, crucial components of the tumor stroma, exhibit distinct pathway activities.

Adeno vs. Squamous:

• Both Adeno and Squamous conditions show enrichment for "ECM-receptor interaction" and "Focal adhesion", highlighting their role in extracellular matrix remodeling and cell-stroma interaction. These are critical for tumor growth, invasion, and metastasis https://pubmed.ncbi.nlm.nih.gov/30472477/.
• "Hedgehog signaling pathway" and "Hippo signaling pathway" are also enriched in Fibroblasts across both conditions, suggesting their involvement in regulating stromal plasticity and crosstalk with tumor cells.

Clinical or Translational Implications

The differential pathway enrichments observed across cell types and conditions provide potential avenues for understanding disease pathogenesis and identifying therapeutic targets.

• Subtype-Specific Targeting: The distinct signaling and metabolic pathway enrichments in Lung Epithelial cells between Adeno and Squamous (e.g., varying ErbB, Hippo, Focal adhesion activity) could inform subtype-specific therapeutic strategies. Targeting enriched pathways like ErbB in Adeno could be more efficacious.
• Immunomodulation: The diverse immune cell pathway activities suggest different immune landscapes in Adeno and Squamous. For example, understanding specific T cell polarization (Th1/Th2/Th17) in each subtype could guide immunotherapeutic approaches, potentially enhancing response to checkpoint inhibitors or directing adoptive cell therapies.
• Stromal Targeting: Fibroblasts' persistent involvement in ECM remodeling and key signaling pathways (e.g., Hippo, Hedgehog, Focal adhesion) underscores their importance in tumor progression. Targeting these pathways in fibroblasts could disrupt tumor-stroma interactions and improve drug delivery or efficacy.
• Metabolic Reprogramming: The enrichment of metabolic pathways (e.g., cholesterol, pyruvate, xenobiotics metabolism) in both tumor and immune cells suggests metabolic vulnerabilities that could be exploited therapeutically, potentially through metabolic inhibitors.
• Ploidy as a Biomarker: The distinct GSEA profile for Diploid Lung Epithelial cells compared to others indicates that ploidy status might reflect different biological stages or characteristics within the tumor, potentially serving as a prognostic marker or influencing treatment response.

22. Discussion

Our single-cell analysis reveals profound and distinct differences in the tumor microenvironments (TME) of lung adenocarcinoma (Adeno) and squamous cell carcinoma (Squamous), extending beyond their histological classification. At the genomic level, aneuploid lung epithelial cells consistently align with the tumor-origin cell type across both conditions, with Squamous samples exhibiting a more consistently high aneuploid fraction and recurrent amplification of the oncogene SOX2 (3q26.33:3q28). This suggests fundamental differences in genomic instability patterns driving these two subtypes.

The cellular composition of the TME also varies significantly. While macrophages are abundant in both, Squamous tumors show a significantly higher proportion of immunosuppressive M2B and M2D macrophage subsets, whereas Adeno tends towards higher M2A and M2C. This indicates distinct macrophage polarization states with potentially varied roles in immune evasion. Similarly, innate lymphoid cells (ILCs) are notably more prominent and variable in Adeno, suggesting a more dynamic innate immune response. The CD4+ T cell population in Adeno also shows increased expression of inhibitory immune checkpoints like TIGIT and CTLA4, alongside co-stimulatory molecules, hinting at a complex state of chronic activation and exhaustion.

Cell-cell interaction analysis further elucidates these distinctions. Adeno is characterized by extensive collagen-integrin interactions between aneuploid lung epithelial cells and fibroblasts, alongside macrophage-macrophage chemokine signaling (e.g., CCL4/CCL3-CCR5/CCR1) and tumor-immune adhesion molecules (ICAM1-integrins, CEACAM5-CD8A). This points to an immune-rich, inflammatory TME with significant stromal remodeling. In contrast, Squamous tumors display a highly desmoplastic TME, dominated by extensive collagen-integrin interactions between fibroblasts and aneuploid lung epithelial cells. Key immune-related CCIs (e.g., ICAM1, CCL20-CCR6, CXCL14-CXCR4) are also prominent, suggesting active immune cell trafficking and modulation. The unique CD93-IFNGR1 macrophage-macrophage interaction in Squamous highlights an intrinsic regulatory loop within the myeloid compartment.

The discovery of condition-specific surfaceome markers provides additional molecular granularity. Adeno tumor cells frequently express HLA class II molecules, MUC1, and CEACAM6, while Squamous tumor cells are rich in CD44, SDC1, DSG2, and CA12. Fibroblasts also exhibit distinct profiles, with Adeno-associated fibroblasts showing markers like IL6ST and MMP14, while Squamous fibroblasts are strongly positive for FAP and LRRC15. These distinct molecular signatures, combined with Gene Set Enrichment Analysis revealing differential activation of oncogenic pathways (ErbB, Hippo, PI3K-Akt, MAPK, NF-kappa B) and cellular stress responses (e.g., protein processing, neurodegenerative pathways predominantly in Squamous), underscore the unique pathogenic mechanisms at play in each subtype. Squamous epithelial cells, in particular, exhibit profound cellular dysregulation related to protein homeostasis, ribosome activity, and cell cycle, suggesting severe cellular stress and uncontrolled proliferation. Overall, these findings reveal that Adeno and Squamous tumors cultivate fundamentally different microenvironments and leverage distinct molecular pathways to drive their progression.

Hypotheses:

1. The distinct genomic alterations, particularly SOX2 amplification in squamous cell carcinoma, drive subtype-specific tumor cell phenotypes and influence the composition of the tumor microenvironment.
2. Differential macrophage polarization (e.g., M2B/M2D dominance in Squamous vs. M2A/M2C trends in Adeno) and immune checkpoint expression (e.g., TIGIT/CTLA4 in Adeno CD4+ T cells) lead to distinct immune evasion strategies in lung adenocarcinoma and squamous cell carcinoma.
3. The extensive collagen-integrin interactions between tumor cells and fibroblasts define a highly desmoplastic and pro-invasive microenvironment in squamous cell carcinoma, contrasting with an inflammatory yet stromal-remodeling microenvironment in adenocarcinoma.
4. Subtype-specific surfaceome markers on tumor cells, fibroblasts, and macrophages are functionally relevant in modulating cell-cell interactions and contribute to the differential clinical behavior and therapeutic responsiveness of lung adenocarcinoma and squamous cell carcinoma.

Potential therapeutic targets:

1. FAP (Fibroblast Activation Protein): FAP is highly and specifically expressed on activated Cancer-Associated Fibroblasts (CAFs) in Squamous Cell Carcinoma, playing a critical role in extracellular matrix remodeling, immune suppression, and tumor growth. Evidence: Section 18, Fibroblast condition-specific markers: FAP shows strong, widespread expression exclusively in Squamous fibroblasts. Validation: FAP-targeted therapies, such as FAP-CAR T cells or FAP-targeting antibodies/ADCs, could be tested in preclinical Squamous SCC models and clinical trials to disrupt CAF-mediated tumor support and immune evasion.
2. Collagen-Integrin Interactions (e.g., targeting integrin subunits ITGB1, ITGA1/2/5): Extensive collagen-integrin signaling between aneuploid Lung Epithelial cells and Fibroblasts is a hallmark of the highly desmoplastic microenvironment in Squamous Cell Carcinoma, critical for tumor stiffness, invasion, and metastasis. Evidence: Section 15, Condition-specific CCI patterns, Squamous-Enriched Interactions: Dominance of Collagen family interactions with Integrin complexes (e.g., COL1A2, COL3A1, COL5A2, COL6A3 interacting with integrin_a1b1_complex or integrin_a2b1_complex). Validation: Develop or repurpose integrin inhibitors or agents that modify the extracellular matrix to disrupt these interactions, testing efficacy in inhibiting tumor invasion and metastasis in Squamous cell carcinoma models.
3. AXL Receptor Tyrosine Kinase: AXL is highly expressed on macrophages in Adenocarcinoma, indicating its involvement in pro-tumorigenic pathways that support cell survival, proliferation, and resistance to therapy. Evidence: Section 17, Macrophage condition-specific markers: AXL shows high expression almost exclusively in Adeno macrophages. Validation: Evaluate existing AXL inhibitors, or develop novel ones, to modulate macrophage function towards an anti-tumorigenic state or directly target AXL-expressing macrophages and tumor cells in Adenocarcinoma.
4. TIGIT / CTLA4 (Immune Checkpoints): High expression of inhibitory checkpoints TIGIT and CTLA4 on CD4+ T cells in Adenocarcinoma suggests a state of T cell exhaustion or suppression, contributing to immune evasion. Evidence: Section 19, T cell CD4+ condition-specific markers: TIGIT and CTLA4 show high expression predominantly in Adeno CD4+ T cells. Validation: Clinical trials combining anti-TIGIT and/or anti-CTLA4 therapies, alone or with other immunotherapies, could be pursued for lung Adenocarcinoma patients to overcome T cell-mediated immunosuppression.
5. MUC1 / CEACAM6: MUC1 and CEACAM6 are highly expressed surface proteins on aneuploid Lung Epithelial cells in Adenocarcinoma, serving as established tumor antigens and promoting proliferation, survival, and metastasis. Evidence: Section 16, Lung Epithelial cell condition-specific markers: MUC1 and CEACAM6 show high expression specifically in Adeno tumor cells. Validation: Investigate MUC1/CEACAM6-targeting antibody-drug conjugates (ADCs) or CAR T-cell therapies in preclinical Adenocarcinoma models to specifically eliminate tumor cells.
6. SOX2 (indirect targeting): The SOX2 oncogene (3q26.33:3q28) is frequently and consistently amplified in aneuploid Squamous Cell Carcinoma cells, driving proliferation and tumor progression. Evidence: Section 4, Significant Amplification Summary: 3q26.33:3q28 (SOX2) is amplified in 100% of summarized aneuploid NSC004 populations. Validation: Develop therapies that indirectly target SOX2's downstream pathways or exploit vulnerabilities induced by its overexpression, testing efficacy in Squamous SCC models. This might involve inhibitors of transcription factors or epigenetic regulators that interact with SOX2.

Follow-up validation ideas:

1. Perform multiplex immunofluorescence or spatial transcriptomics on patient tumor sections to validate the co-localization of identified cell type-specific surface markers (e.g., MUC1/CD44 on tumor cells, FAP/LRRC15 on fibroblasts, AXL/STAB1 on macrophages) and to confirm predicted cell-cell interaction patterns in situ.
2. Conduct in vitro co-culture assays using primary tumor cells, fibroblasts, and immune cells from Adeno and Squamous patient models to functionally validate key ligand-receptor interactions (e.g., collagen-integrin pairs, CCL-CCR axes, CD93-IFNGR1, AREG-EGFR) and assess their impact on cell proliferation, migration, and immune modulation upon perturbation (e.g., gene knockdown, blocking antibodies).
3. Test the functional relevance of recurrent CNVs, such as SOX2 amplification in squamous cell carcinoma, through gene overexpression or knockdown experiments in relevant lung cancer cell lines or organoids, assessing impacts on cell proliferation, differentiation, and drug sensitivity.
4. Utilize patient-derived xenograft (PDX) or genetically engineered mouse models (GEMMs) to evaluate the therapeutic efficacy of targeting candidate molecules (e.g., AXL, FAP, TIGIT, specific integrins) in a subtype-specific manner and to assess their impact on tumor growth and immune microenvironment remodeling.
5. Analyze larger, independent cohorts of lung adenocarcinoma and squamous cell carcinoma patients, integrating single-cell data with bulk transcriptomics, proteomics, and clinical outcomes (survival, treatment response) to validate the prognostic and predictive value of identified cellular states, surface markers, and pathway activities.

Limitations:

This report is based on single-cell RNA sequencing data, providing a snapshot of cellular states and interactions but not fully capturing dynamic processes or spatial organization within the tissue. While copy number variation (CNV) inference and cell-cell interaction (CCI) predictions offer valuable insights into genomic instability and intercellular communication, they are computational inferences and require experimental validation to confirm functional causality. The generalizability of these findings may be influenced by the specific patient cohort and sample size. Furthermore, potential biases in cell type annotation or marker gene selection could exist. The analysis primarily focuses on major cell types and their subtypes, and the roles of rare cell populations or specific temporal changes during disease progression may not be fully resolved.

23. Query List

1. Show UMAP plots including condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset in 2 columns and save.
2. Show major celltype scores on UMAP and save.
3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
4. Select tumor-origin cells and unassigned cells, group them by sample, show a CNV heatmap, and include a summary of significantly amplified copy number regions, then save.
5. Show CNV patterns on UMAP, including major cell type, minor cell type, ploidy results, condition, and sample in 2 columns, then save.
6. Show a population bar plot for minor cell types and save.
7. Show a subset population bar plot for T cells and save.
8. For T cell subset populations, show box plots for statistically significant differences between conditions and save. Set ncols appropriately based on the total number of panels.
9. Show a subset population bar plot for macrophages and save.
10. For macrophage subset populations, show box plots for statistically significant differences between conditions and save. Set ncols appropriately based on the total number of panels.
11. Select tumor-origin cells and unassigned cells, then show a bar plot of their ploidy population and save.
12. Show cell-cell interaction patterns by condition, including tumor-origin cells (Lung Epithelial cell), fibroblasts, macrophages, and T cells, and save. Select a maximum of 80 cell-cell interactions per condition.
13. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
14. Select only genes related to immune checkpoint pathways and cell cycle pathways, then show cell-cell interactions for these genes and save.
15. Find statistically significant differences in cell-cell interactions between conditions for major immune and stromal cells, then show them as a dot plot and save. Set max_n_items_per_group = 25.
16. Show the condition-specific markers for tumor-origin cells (Lung Epithelial cell) as a dot plot and save the result. Use only surfaceome markers, up to 50 markers per condition.
17. Extract condition-specific markers for Macrophage, then show them as a dot plot and save. Include only surfaceome markers, up to 50 per condition.
18. Extract condition-specific markers for Fibroblast, then show them as a dot plot and save. Include only surfaceome markers, up to 50 per condition.
19. Extract condition-specific markers for T cell CD4+, then show them as a dot plot and save. Include only surfaceome markers, up to 50 per condition.
20. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
21. Show Gene Set Enrichment Analysis results for major cell types (B cell, Fibroblast, ILC, Lung Epithelial cell, Macrophage, Mast cell, Plasma cell, T cell CD4+, T cell CD8+) as a dot plot and save. Set color map to RdBu_r and n_pws_to_show = 80.