SCODiA Report by MLBI Lab

Single-Cell Atlas of Human Lung Cancer Reveals Genomic Instability, Dynamic Microenvironmental Remodeling, and Immune Evasion with Disease Progression

Single-cell RNA sequencing of human lung tissue uncovers profound cellular and molecular shifts from normal to early and advanced lung cancer. Aneuploid Lung Epithelial cells, identified as the tumor origin, exhibit significant genomic instability (e.g., frequent EGFR amplification) and drive extensive cell-cell interactions. The tumor microenvironment undergoes substantial remodeling, characterized by a progressive increase in pro-tumorigenic M2B macrophages, a decrease in anti-tumor NK cells, and dynamic changes in T cell subsets including elevated Th17 and naive T cells, and transiently increased Tregs in early stages. Cell-cell interaction analyses highlight prominent pro-tumorigenic (EGFR, TGF-beta via Integrin αVβ6, PGE2) and immune evasion (LILRB2-HLA-F, LGALS9-HAVCR2, TIGIT, CTLA4) pathways across diverse cell types. Condition-specific surfaceome markers further define these cellular states, identifying key oncogenes (EGFR, ERBB2, MET) on tumor epithelial cells, activated fibroblast markers (FAP, MMP14), and distinct macrophage phenotypes, all contributing to an immunosuppressive and pro-growth environment.

Contents

0. Dataset overview
1. UMAP Visualization of Lung Single-Cell RNA-seq Data by Condition, Sample, Cell Type, and Ploidy
2. UMAP Visualization of Major Cell Type Scores and Ploidy Status
3. Overall Celltype_subset Marker Expression Analysis
4. Copy Number Variation (CNV) Analysis in Lung Epithelial and Unassigned Cells Grouped by Sample
5. CNV-driven UMAP Visualization of Lung Single-Cell Atlas
6. 폐암 병기별 미세 세포 유형 구성 분석
7. T Cell Subset Population Analysis Across Normal, Early, and Advanced Lung Tumor Conditions
8. T Cell Subset Population Dynamics Across Lung Cancer Progression
9. Macrophage Cell Population Overview Across Lung Conditions
10. Changes in Macrophage Subpopulation Proportions Across Lung Cancer Progression
11. Tumor-Origin and Unassigned Cell Ploidy Landscape Across Lung Conditions
12. Advanced Lung Tumor Cell-Cell Interaction Landscape
13. Advanced Lung Cancer (Tumor(adv)) Cell-Cell Interaction Analysis
14. Immune Checkpoint and Cell Cycle Pathway-Related Cell-Cell Interactions in Lung Tissue
15. Condition-Specific Cell-Cell Interaction Patterns in Lung Tissue Microenvironment
16. Lung Epithelial Cell Condition-Specific Surfaceome Markers Across Disease Stages
17. Macrophage Condition-Specific Surfaceome Markers in Lung Tissue
18. Fibroblast Condition-Specific Surfaceome Markers in Lung Cancer
19. Condition-Specific Surfaceome Markers of CD4 T Cells in Lung Tissue
20. Differential Expression of Cell Cycle Genes in Lung Epithelial Cells During Lung Cancer Progression
21. Lung Epithelial Cell Gene Ontology (GSA) Analysis Across Ploidy and Disease Conditions
22. Major Cell Type Gene Set Enrichment Analysis in Lung Conditions
23. Discussion
24. Query List

0. Dataset overview

Dataset Summary

• 데이터 형식: 90,224개 세포와 23,489개 유전자를 포함하는 단일 세포 RNA 시퀀싱 AnnData입니다.
• 종 및 조직: 인간 폐 조직에서 유래한 데이터입니다.
• 관찰(obs) 정보: 세포 바코드, 환자 ID, 조직 기원, 병기, 흡연 여부 등 다양한 메타데이터와 함께 세포 유형(주요, 세부, 하위 유형) 및 조건(종양, 정상) 정보가 포함되어 있습니다.
• 유전자(var) 정보: 유전자 기호, 염색체, 세포유전학적 밴드 정보 등을 포함합니다.
• 조건: 주요 조건은 '종양(진행)', '종양(초기)', '정상'으로 구성되어 있으며, '정상'이 참조 조건으로 설정되어 있습니다.
• 주요 세포 유형: T 세포, 폐 상피세포, 골수세포, B 세포, 비만세포, 기질세포, 내피세포 등이 있습니다.
• 염색체 이수성 (Ploidy): 'Aneuploid' 및 'Diploid' 상태 정보가 포함되어 있습니다.
• 사전 계산된 결과: 세포-세포 상호작용 (CCI), 차등 발현 유전자 (DEG), 유전자 세트 농축 분석 (GSEA), 유전자 온톨로지 (GSA) 결과가 조건 및 참조 조건별로 저장되어 있습니다.

1. UMAP Visualization of Lung Single-Cell RNA-seq Data by Condition, Sample, Cell Type, and Ploidy

[Analysis Visualization Results]...

Analysis Overview

These UMAP visualizations present a comprehensive overview of the single-cell RNA-seq dataset from human lung tissue, colored by various annotations: disease condition, sample origin, major cell type, minor cell type, ploidy status, and granular cell subsets. The primary goal is to assess the overall structure of the dataset, the quality of cell type annotations, the presence of condition-specific populations, and the distribution of aneuploid cells.

Visual Summary

Condition UMAP

The UMAP colored by condition reveals a clear separation between 'Normal' and 'Tumor' cells. 'Normal' cells (dark red) predominantly occupy a distinct region, primarily on the left side of the embedding. Cells from 'Tumor(adv)' (light yellow) and 'Tumor(early)' (dark blue) conditions largely overlap and intermingle across multiple clusters in the central and right portions of the UMAP, though some areas show relative enrichment for one tumor stage over the other. This suggests that while normal tissue is transcriptionally distinct, early and advanced tumors share significant cellular components and states, possibly representing a continuum of disease or shared tumor microenvironment elements.

Sample UMAP

The sample UMAP displays a mosaic pattern, with cells from various samples intermingling broadly across the major clusters. This indicates that major sample-specific batch effects have been largely ameliorated during data processing and embedding, allowing biological variations to drive the clustering. While some minor clusters might show slight enrichment for specific samples, the overall mixing is good, which is critical for robust downstream analyses.

Cell Type UMAPs (Major, Minor, Subset)

The UMAPs colored by celltype_major, celltype_minor, and celltype_subset demonstrate excellent clustering and clear separation of distinct cell populations at increasing levels of granularity.

• Major Cell Types: Key populations like T cells (cyan), Myeloid cells (light green), and Lung Epithelial cells (orange) form large, well-defined clusters. Stromal cells (green) and Endothelial cells (red) also form distinct, though sometimes more diffuse, groupings.
• Minor Cell Types: Further resolution is evident, with distinct clusters for T cell CD8+ (dark blue) and T cell CD4+ (light blue), and various Macrophage subtypes (light yellow). Lung epithelial cells are resolved into Alveolar Epithelial cells (dark red) and Airway Epithelial cells (dark red).
• Cell Subsets: The most granular celltype_subset map reveals specific populations such as Alveolar type 1 (AT1) and Alveolar type 2 (AT2) cells within the Lung Epithelial cluster, various T cell (e.g., Cytotoxic, Naive, Treg) and Macrophage (e.g., M1, M2a-d) subtypes, and specific stromal and immune subsets. This level of detail confirms the high resolution of the single-cell data and the quality of the annotation. A population of 'unassigned' cells (purple) is present, particularly in the major cell type map, indicating cells that could not be confidently annotated with current markers.

Ploidy_dec UMAP

The ploidy_dec UMAP highlights the distribution of cells inferred to be 'Aneuploid' (dark red), 'Diploid' (light yellow), or 'Unclear' (dark blue). 'Diploid' cells are broadly distributed across the entire UMAP, as expected since they represent the majority of normal cells and potentially some non-cancerous cells within tumors. In contrast, 'Aneuploid' cells show a highly localized distribution, forming prominent, distinct clusters in specific regions, most notably in the upper-middle section of the UMAP.

Biological Interpretation

1. Tumor-Specific Cellular Landscape: The clear separation of 'Normal' cells from 'Tumor' cells (both early and advanced) in the condition UMAP underscores the significant transcriptional reprogramming that occurs during lung cancer development. The substantial overlap between 'Tumor(early)' and 'Tumor(adv)' populations suggests that certain cellular states or microenvironmental components are shared across different stages of tumor progression, while also indicating the presence of unique features that warrant further investigation through differential expression or pathway analyses.
2. Identification of Cancer Cells via Ploidy: The ploidy_dec UMAP provides a crucial insight into identifying the malignant cell population. The distinct clustering of Aneuploid cells strongly co-localizes with the main Lung Epithelial cell clusters, particularly those annotated as Alveolar type 2 (AT2) and Alveolar type 1 (AT1) in the celltype_subset map. Given that the Tumor origin celltype is specified as Lung Epithelial cell in the data context, this robustly confirms that these aneuploid epithelial cells represent the cancerous population. Aneuploidy, a hallmark of cancer, is thus spatially resolved to the malignant epithelial compartment within the tumor microenvironment [GeneCards: TP53 - Aneuploidy is often linked to TP53 mutations, UniProt: P04637].
3. Heterogeneous Tumor Microenvironment (TME): The broad distribution of immune cells (T cells, Myeloid cells, B cells, Mast cells, NK cells) and stromal cells (Fibroblasts, Endothelial cells) across the UMAP, often interspersed with the epithelial (and presumably malignant epithelial) clusters, reflects the complex and heterogeneous nature of the tumor microenvironment. These non-malignant cells are crucial players in tumor progression, immune evasion, and response to therapy. The detailed cell type annotations will enable in-depth analysis of their specific states and interactions within tumor versus normal contexts.
4. Annotation Quality and Dataset Resolution: The high degree of separation and fine granularity achieved in the cell type annotations (major, minor, and subset) validates the quality of the single-cell sequencing data and the annotation process. This robust clustering provides a reliable foundation for subsequent analyses, such as differential gene expression, pathway enrichment, and cell-cell interaction studies. The presence of 'unassigned' cells, though minor, suggests potential for further refinement or discovery of novel cell states.

Annotation Notes

The comprehensive UMAPs confirm the high resolution of the single-cell data and the quality of the cell type annotations down to specific subsets. The good mixing of samples suggests that the observed cellular structures are driven by biological variation rather than batch effects. The distinct clustering of aneuploid cells within the lung epithelial compartment provides a strong indicator of the tumor cell population, aligning well with the expected origin of lung cancer. Further investigations should focus on the biological characteristics of the 'unassigned' cell populations to determine if they represent novel cell types or transient states.

2. UMAP Visualization of Major Cell Type Scores and Ploidy Status

[Analysis Visualization Results]...

Analysis Overview

This analysis provides UMAP visualizations to assess the distribution of major cell types based on their specific gene expression scores (HiCAT_major_score), inferential ploidy status, and the final cell type annotations across the single-cell RNA-seq dataset. The goal is to visually confirm the distinctiveness of major cell populations and the consistency of their annotation within the reduced-dimension space.

Visual Summary

The UMAP plots display the landscape of 90,224 cells across 23,489 genes.

• HiCAT_major_score Plots (T cell, B cell, Myeloid cell, Mast cell, Endothelial cell, Stromal cell, Lung Epithelial cell): Each of these plots highlights the enrichment of marker genes for a specific major cell type, colored on a scale from low (purple) to high (yellow). Distinct clusters of cells with high scores are observed for each major cell type.
• T cells show a large, well-separated cluster on the right side of the UMAP.
• Myeloid cells form a prominent cluster in the upper-left/middle region.
• Lung Epithelial cells exhibit high scores in a distinct cluster in the upper-left.
• B cells, Mast cells, Endothelial cells, and Stromal cells each occupy smaller, yet clearly discernible, regions or clusters.
• ploidy_dec Plot: This plot categorizes cells as Aneuploid (maroon), Diploid (light yellow), or Unclear (purple). A significant proportion of cells in the upper-left cluster are labeled Aneuploid, while the majority of cells in other clusters are Diploid.
• celltype_major Plot: This plot displays the final major cell type annotations for all cells using distinct colors. The distribution of each annotated cell type strongly correlates with the regions showing high HiCAT_major_score for that specific cell type. For instance, the T cell-annotated cluster (teal) perfectly overlaps with the high-score T cell region. The Lung Epithelial cell-annotated cluster (yellow) aligns with the high-score Lung Epithelial cell region.

Biological Interpretation

The strong segregation of cells based on their major cell type scores into distinct clusters on the UMAP indicates that each major cell type possesses a unique and well-defined transcriptional profile. This robust separation provides confidence in the underlying cell type identification and annotation process.

Crucially, the co-localization of the Aneuploid cells (identified by ploidy_dec) predominantly within the Lung Epithelial cell cluster is a significant biological finding. Given that the Tumor origin celltype is specified as Lung Epithelial cell, this observation strongly suggests that these aneuploid Lung Epithelial cells represent the malignant (tumor) cell population within the dataset. Aneuploidy is a hallmark of cancer, reflecting chromosomal instability and abnormal chromosome numbers, which are characteristic features of tumor cells. The fact that immune and stromal cells, which are generally non-malignant, are largely classified as Diploid further supports this interpretation.

The consistent alignment between the HiCAT_major_score plots and the final celltype_major annotations demonstrates a high quality of cell type assignment. Each major cell type's specific gene expression signature accurately defines its corresponding cluster in the UMAP, validating the robustness of the clustering and annotation methodology. The presence of a small "unassigned" cluster indicates minor populations that did not clearly fit into the defined major cell types, which is common in complex single-cell datasets.

Annotation Notes

The visualizations provide strong evidence for well-defined and consistently annotated major cell types within this single-cell RNA-seq dataset. The UMAP embedding effectively separates distinct cellular populations based on their transcriptional profiles, and the cell type specific scoring method (HiCAT_major_score) directly supports the final celltype_major assignments. The clear identification of aneuploid cells within the Lung Epithelial cell population provides critical insight into the likely tumor cell compartment, which is consistent with the Tumor origin celltype provided in the data context. This robust cell type annotation forms a solid foundation for subsequent downstream analyses, such as differential gene expression, pathway analysis, and cell-cell interaction studies, especially when focusing on the tumor microenvironment.

3. Overall Celltype_subset Marker Expression Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression of marker genes across various Celltype_subset populations derived from single-cell RNA-seq data. The primary objective is to evaluate the distinctness and consistency of gene expression profiles for each cell subset, thereby serving as an important quality control step for the cell type annotations. Marker genes were identified and visualized, with dot size representing the fraction of cells expressing the gene within a group, and color intensity indicating the mean expression level.

Visual Summary

The dot plot effectively displays a matrix of Celltype_subset populations (y-axis) against their corresponding marker genes (x-axis). A striking feature is the clear diagonal pattern of enriched gene expression, highlighted by red boxes, indicating that each cell subset generally expresses a distinct set of genes at high levels and in a large fraction of cells within that group. This visual pattern strongly suggests robust and specific marker expression for the majority of annotated cell types.

• Distinct Cell Clusters: The plot shows well-separated clusters of highly expressed genes for most cell subsets, reinforcing the unique molecular identity of each Celltype_subset.
• Specificity of Markers: Genes generally exhibit high expression (dark red) and high prevalence (large dot size) within their assigned cell type, with minimal expression in other cell types. This indicates good specificity of the identified markers.
• Variable Marker Density: Some cell types, such as Alveolar type 2, Fibroblast, Smooth muscle cell, and Plasma cell, show a particularly dense and intense block of highly specific markers. Other subsets, like certain T cell subtypes, also show clear, albeit sometimes less dense, specific markers.
• "Unassigned" Group: As expected, the "unassigned" cell type shows no distinct block of highly expressed, specific markers, consistent with its indeterminate status.

Biological Interpretation

The observed marker gene expression patterns provide strong biological validation for the assigned Celltype_subset annotations, crucial for downstream analyses.

Lung Epithelial Cell Lineages

• Alveolar type 1 cells are characterized by high expression of *HOPX*, *EMP2*, *CEACAM6*, and *CAV1* GeneCards HOPX, consistent with their role in gas exchange.
• Alveolar type 2 cells show strong expression of surfactant protein genes (*SFTPB*, *SFTPA2*, *SFTPD*, *SFTPC*) and *MUC1*, confirming their identity as surfactant-producing cells critical for alveolar maintenance and repair GeneCards SFTPC.
• Basal cells express *KRT5* and *KRT15* GeneCards KRT5, characteristic of progenitor cells in the airway epithelium.
• Ciliated cells are defined by *FOXJ1*, *SNTN*, and *CCDC78* GeneCards FOXJ1, which are crucial for cilia structure and function.
• Goblet cells express *MUC5B* and *TFF3*, typical mucin-producing cells.
• Secretory club cells display *SCGB3A1* and *WFDC2*, markers of airway secretory cells involved in host defense.

Immune Cell Populations

• B cell subsets (Breg, MZ, Memory) show common B cell lineage markers like *POU2F2* and *CD24*.
• Plasma cells are distinctly marked by *JCHAIN*, *SDC1* (CD138), *XBP1*, and *MZB1* GeneCards JCHAIN, reflecting their immunoglobulin production and secretion function.
• Dendritic cell (DC) subsets: *DC (Classical)* express *CD1A*, *CD83*, *CD86* GeneCards CD1A; *DC (Inflammatory)* show *IRF4*, *IRF7*, *IRF8*; and *DC (Plasmacytoid)* express *LILRA4* and *SPIB*, validating the functional specialization of these antigen-presenting cells.
• Macrophage subsets exhibit shared and specific markers; for instance, *M1 Macrophages* express *IDO1*, and *M2D Macrophages* express *GATA2* and *KIT*, indicating their diverse polarization states.

T cell subsets

• T cell (Cytotoxic): Key markers include *CD8A* and *GZMB* GeneCards CD8A, consistent with their effector functions.
• T cell (Th1): Express *STAT1* and *IFNG*, indicative of type 1 helper T cell polarization.
• T cell (Th17): Show *RORA* and *BATF* expression, associated with IL-17 production.
• T cell (Th2): Characterized by *GATA3*, a master regulator of Th2 differentiation.
• NK cells are identified by *KLRD1* (CD94) and *KLRF1*, consistent with their cytotoxic roles.
• Mast cells: Highly expressed markers such as *KIT*, *TPSAB1*, and *TPSB2* GeneCards KIT confirm their identity.

Stromal and Endothelial Cells

• Fibroblasts are robustly marked by *COL1A1*, *COL1A2*, *DCN*, *LUM*, *PDGFRA*, and *S100A4* GeneCards COL1A1, reflecting their extracellular matrix production and structural roles.
• Smooth muscle cells express *ACTA2*, *TAGLN*, *CALD1*, *MYH11* GeneCards ACTA2, confirming their contractile phenotype.
• Endothelial tip cells show *DLL4* and *ESM1*, indicative of their specialized role in angiogenesis.
• Lymphatic Endothelial cells express *PDPN* and *PROX1* GeneCards PDPN, key markers for lymphatic vessel formation and function.

Annotation Notes

The comprehensive display of Celltype_subset marker genes confirms the high quality and distinctness of the single-cell annotations. The clear, specific expression patterns for known canonical markers across nearly all cell subsets provide strong evidence that the cell type assignments are biologically sound and reliable for further analysis. The minimal overlap in marker expression between different cell types further supports the resolution achieved in the cell type classification.

4. Copy Number Variation (CNV) Analysis in Lung Epithelial and Unassigned Cells Grouped by Sample

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) specifically within cells identified as "Lung Epithelial cell" (the designated tumor-origin cell type) and "unassigned" cells. The cells are grouped by individual sample, allowing for a comparison of genomic alterations across different patients or biopsy sites. The goal is to identify common or recurrent amplifications and deletions characteristic of these cell populations in the context of lung tissue, particularly relating to tumor progression. The ploidy inference for each sample (Diploid/Aneuploid) is incorporated into the heatmap visualization.

Visual Summary

CNV Heatmap (log2(CNR) per Genomic Spot)

The heatmap displays log2(Copy Number Ratio, CNR) values across genomic spots for selected cell types, grouped by sample. Red indicates genomic amplification (log2(CNR) > 0), while blue indicates genomic deletion (log2(CNR) < 0).

• Ploidy Distinction: Samples labeled "Diploid" (e.g., BRONCHO_58, BRONCHO_59, LUNG_109) generally exhibit a quieter CNV landscape, suggesting a more stable genome, although focal amplifications or deletions can still be observed. This suggests these samples might represent normal-like cells or early-stage tumors with limited genomic instability, or perhaps samples where the "Lung Epithelial cell" component is largely non-malignant.
• Widespread Aneuploidy: Many samples, particularly those from "LUNG" (e.g., LUNG_N01, LUNG_N06, LUNG_T18, LUNG_T19, LUNG_T30), show extensive and widespread patterns of both amplifications and deletions across multiple chromosomes. This widespread aneuploidy is highly characteristic of advanced solid tumors.
• Recurrent Amplifications: Notable recurrent amplifications (red regions) are observed across several samples. Prominent regions of amplification appear on chromosome arms such as 1q, 5p, 6q, 7p (including the region explicitly labeled for EGFR), 8q, 11q, 17q, 19q, and 20q.
• Recurrent Deletions: Conversely, recurrent deletions (blue regions) are also evident, particularly on chromosome arms such as 3p, 8p, 9p, 10q, 13q, and 16q.
• Inter-sample Heterogeneity: While some CNV patterns are recurrent, there is significant heterogeneity in the specific regions and extent of CNVs among different samples, indicating diverse clonal evolution paths.

Summary of Significantly Amplified Copy Number Regions

The lower heatmap quantifies the frequency of amplification for specific cytogenetic bands across the samples, complemented by a bar plot showing the overall frequency of these amplifications.

• Highly Frequent Amplifications: The bar plot on the right highlights several cytogenetic bands with high amplification frequencies. The most prominent include:
• 6q27:7p22.1: This broad region shows the highest frequency of amplification (0.73), suggesting a common genomic alteration.
• 7p12.1:7q11.23 (EGFR): This region, explicitly labeled for the *EGFR* gene, is amplified with a frequency of 0.64, indicating it is a very common event in these samples.
• 17q21.2:17q21.2 and 17q21.33:17q23.2: These adjacent regions on chromosome 17q also show high amplification frequencies (0.64 each).
• 1q21.3:1q23.2: This region on chromosome 1q has an amplification frequency of 0.55.
• Sample-Specific Contributions: The heatmap on the left shows which samples contribute most to these high-frequency amplifications. For example, the *EGFR* region (7p12.1:7q11.23) shows high amplification frequency (indicated by darker blue cells with values >0) in samples like EBUS_06, EBUS_28, LUNG_T18, LUNG_T19, LUNG_T20, LUNG_T30, LUNG_T31, and LUNG_T34. This indicates that these samples frequently harbor *EGFR* amplification.

Biological Interpretation

The observed CNV patterns in Lung Epithelial cells and unassigned cells provide strong evidence of genomic instability, a hallmark of cancer.

• Tumor-Origin Cells and Genomic Instability: The analysis focuses on "Lung Epithelial cell," which is designated as the tumor-origin cell type. The widespread CNVs, particularly in the "LUNG" samples, confirm that these cells are likely malignant or pre-malignant. The "unassigned" cells might represent diverse cell states, some of which could also be malignant or tumor-associated, or potentially cells with compromised quality or unusual transcriptional profiles.

Oncogene Amplifications:

• EGFR Amplification (7p12.1:7q11.23): The frequent amplification of the *EGFR* locus is highly significant. *EGFR* (Epidermal Growth Factor Receptor) is a well-known oncogene frequently altered in Non-Small Cell Lung Cancer (NSCLC). Amplification of *EGFR* can lead to increased protein expression and aberrant signaling, promoting cell proliferation, survival, and metastasis [1]. The data context also includes EGFR_mutation in the obs metadata, suggesting a critical role for EGFR signaling in these tumors.
• 1q Amplification: Gains on chromosome 1q are common in various cancers, including lung cancer, and can harbor genes such as *MDM4*, *PIK3C2B*, and *AKT3*, which are involved in cell cycle regulation and growth pathways.
• 6q/7p Amplification: The broad region 6q27:7p22.1 encompasses *EGFR* and other potential oncogenes.
• 8q Amplification: Gains in 8q often involve the *MYC* oncogene, a critical regulator of cell growth and division, whose amplification is associated with aggressive tumor phenotypes.
• 17q Amplification: This region can contain oncogenes such as *ERBB2* (HER2), another receptor tyrosine kinase, or *STAT3*, a transcription factor involved in various cellular processes.
• 20q Amplification: Gains on 20q are frequently observed in many cancers and can include oncogenes like *AURKA* (Aurora Kinase A) and *BCL2L1* (BCL-XL), which play roles in cell division and apoptosis, respectively.
• Tumor Suppressor Deletions: Recurrent deletions, such as those on 3p, 8p, 9p, and 13q, often indicate the loss of tumor suppressor genes.
• 3p Deletions: Common in lung cancer, this region harbors critical tumor suppressor genes like *FHIT* and *RASSF1*, involved in DNA repair and apoptosis.
• 9p Deletions: Often involve the *CDKN2A/B* locus, which encodes p16INK4a and p15INK4b, key cell cycle inhibitors, and *MTAP*. Loss of these genes leads to uncontrolled cell proliferation.
• Ploidy Status and Tumor Progression: The distinction between "Diploid" samples (some of which still show focal CNVs) and those with widespread aneuploidy is crucial. Diploid tumors with specific focal CNVs might represent earlier stages or less aggressive forms, while widespread aneuploidy is generally associated with more advanced and aggressive cancers.

Clinical or Translational Implications

The findings from this CNV analysis have significant clinical and translational relevance for lung cancer.

• Biomarker Identification and Stratification: The identification of recurrent CNVs, particularly *EGFR* amplification, serves as a crucial biomarker for lung cancer. This information can be used to stratify patients for prognosis and guide treatment decisions. The presence of *EGFR* amplification, potentially alongside EGFR_mutation (as indicated in obs), is a strong indicator for targeted therapies.
• Targeted Therapy Opportunities: For patients whose tumors exhibit *EGFR* amplification, targeted therapies such as EGFR tyrosine kinase inhibitors (TKIs) are a standard treatment approach [2]. The high frequency of *EGFR* amplification in this dataset suggests a significant proportion of these lung cancer patients could potentially benefit from such therapies.
• Prognostic Value: Certain CNV patterns or the extent of aneuploidy can be associated with tumor aggressiveness, risk of recurrence, and overall survival, providing prognostic insights for patient management.
• Monitoring Tumor Evolution and Resistance: Longitudinal monitoring of CNVs could help track tumor evolution, identify emerging resistance mechanisms (e.g., secondary *EGFR* mutations or other genomic alterations), and guide adaptive treatment strategies.
• Understanding Tumor Heterogeneity: The observed inter-sample heterogeneity in CNV patterns highlights the need for personalized medicine approaches, where treatment strategies are tailored to the specific genomic landscape of an individual patient's tumor.

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References

1. EGFR signaling in lung cancer:

PubMed Search: EGFR lung cancer signaling

2. EGFR TKIs in lung cancer:

PubMed Search: EGFR TKI lung cancer

5. CNV-driven UMAP Visualization of Lung Single-Cell Atlas

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the cellular landscape of the lung single-cell RNA-seq dataset using a UMAP embedding specifically computed based on Copy Number Variation (CNV) estimates (obsm['X_cnv']). The UMAP plots are colored by major cell type, minor cell type, inferred ploidy status, disease condition (Normal, Tumor(early), Tumor(adv)), and individual sample, allowing for a comprehensive assessment of CNV patterns across different cellular identities and disease states.

Visual Summary

The UMAP plots reveal distinct clustering patterns driven by CNV status, which largely correlate with cell type and disease condition:

Celltype_major and Celltype_minor:

• A large, central cluster predominantly comprises T cells, Myeloid cells, Stromal cells, and B cells, indicative of immune and stromal populations. These are generally well-mixed.
• Lung Epithelial cells (including Alveolar Epithelial cells and Airway Epithelial cells) form several more distinct, often peripheral clusters, particularly a prominent one in the top-right region and another smaller one at the bottom-middle. Endothelial cells also form separated clusters.
• Some "unassigned" cells form a small, isolated cluster.

Ploidy_dec:

• The large, central cluster aligns almost perfectly with "Diploid" cells (light yellow). This confirms that most immune and stromal cells, which dominate this region, maintain a normal diploid genomic state.
• The distinct peripheral clusters, especially the one at the top-right and the small cluster at the bottom, are overwhelmingly labeled as "Aneuploid" (maroon). A smaller cluster to the left-middle also shows a strong aneuploid signal.

Condition:

• "Normal" samples (maroon) almost exclusively populate the large central "Diploid" region, consistent with healthy tissue.
• "Tumor(adv)" (light yellow) and "Tumor(early)" (purple) conditions contribute cells across the entire UMAP, but critically, they are the sole contributors to the "Aneuploid" clusters. This indicates that aneuploidy is a hallmark of tumor cells in both early and advanced stages. Cells from tumor conditions are also present within the "Diploid" region, likely representing immune and stromal cells from the tumor microenvironment.

Sample:

• The sample plot shows a diverse contribution from multiple samples to different regions of the UMAP, suggesting relatively good integration and no dominant batch effects entirely separating samples. However, certain clusters, especially some of the aneuploid ones, show enrichment for specific samples, reflecting patient-specific CNV profiles and tumor heterogeneity.

Biological Interpretation

The CNV-driven UMAP effectively segregates cells based on their genomic stability, offering key biological insights:

• Malignant Cell Identification: The distinct "Aneuploid" clusters on the UMAP are largely composed of Lung Epithelial cells and are exclusively derived from "Tumor(early)" and "Tumor(adv)" conditions. This strongly suggests that these aneuploid epithelial cells represent the malignant cancer cell populations within the dataset. This finding is consistent with aneuploidy being a common characteristic of cancer cells, reflecting genomic instability during tumorigenesis PubMed Search: aneuploidy cancer biology.
• Tumor Microenvironment vs. Malignant Cells: The clear separation between "Diploid" and "Aneuploid" cells on this CNV-embedding UMAP highlights the distinction between the tumor microenvironment (TME) components and the malignant cells. The "Diploid" cluster, comprised mainly of immune and stromal cells from both normal and tumor samples, represents the non-malignant cells, including those supporting or interacting with the tumor.
• Early vs. Advanced Tumor Aneuploidy: Both "Tumor(early)" and "Tumor(adv)" conditions contribute significantly to the aneuploid clusters, suggesting that genomic instability leading to aneuploidy is present even in early stages of lung cancer progression. While the advanced tumors might show more extensive or complex CNVs, the presence of aneuploidy in early tumors is a critical indicator of malignancy.
• Cell Type-Specific Aneuploidy: As expected, the "Tumor origin celltype" (Lung Epithelial cell) is the primary cell type exhibiting aneuploidy. Other cell types (e.g., T cells, Myeloid cells, B cells) predominantly remain diploid, reinforcing their non-malignant role in the tumor context.
• Heterogeneity within Tumor Samples: The sample plot, when viewed in conjunction with the ploidy_dec and condition plots, indicates that different tumor samples contribute to various aneuploid regions, implying patient-specific CNV landscapes and tumor heterogeneity, even within the same condition stage. This highlights the unique genomic alterations that can arise in individual tumors.

Annotation Notes

The X_cnv UMAP embedding effectively distinguishes between cell populations based on their copy number profiles, providing strong support for the inferred ploidy_dec annotations. The spatial separation of aneuploid cells from diploid cells, and the clear association of aneuploidy with tumor conditions and epithelial cells, validates the quality of the CNV inference and its utility in identifying malignant cell populations. The clear segregation based on CNV status is a robust feature of this embedding.

6. 폐암 병기별 미세 세포 유형 구성 분석

[Analysis Visualization Results]...

Analysis Overview

제공된 단일 세포 RNA 시퀀싱 (scRNA-seq) 데이터를 바탕으로, 정상(Normal) 폐 조직 및 종양(Tumor) 조직(초기 및 진행성)에서 미세(minor) 세포 유형의 상대적 비율을 시각화한 막대 그래프입니다. 이 분석은 각 조건 및 개별 샘플 내에서 세포 구성의 변화를 이해하고, 폐암 발병 및 진행에 따른 미세 환경의 변화를 파악하는 데 중점을 둡니다.

Visual Summary

• 정상(Normal) 조직: 정상 폐 조직 샘플에서는 T cell CD4+, T cell CD8+ 및 Macrophage가 주요 면역 세포 집단으로 나타나며, Alveolar Epithelial cell 및 Airway Epithelial cell과 같은 상피 세포와 Fibroblast도 일정 비율로 존재합니다. 샘플별로 면역 세포 및 상피 세포의 비율에 약간의 차이가 관찰됩니다.
• 종양(early) 조직: 초기 종양 샘플에서는 Alveolar Epithelial cell의 비율이 일부 샘플(예: LUNG_T25, LUNG_T20, LUNG_T18)에서 현저히 증가하는 경향을 보입니다. 이는 종양 세포의 증식을 반영하는 것으로 해석될 수 있습니다. T cell CD4+ 및 T cell CD8+는 여전히 상당한 비율을 차지하며, Macrophage와 Fibroblast도 지속적으로 관찰됩니다. 일부 샘플에서는 Plasma cell의 비율이 정상 조직에 비해 다소 증가한 것으로 보입니다.
• 종양(adv) 조직: 진행성 종양 샘플(4개)에서는 'unassigned' 세포의 비율이 매우 높게 나타나는 것이 특징적입니다(특히 EBUS_49, EBUS_06). 이는 데이터의 기술적 특성, 기존 주석으로 설명되지 않는 특이한 세포 상태 또는 종양 특유의 고도로 이질적인 세포 집단을 나타낼 수 있습니다. Alveolar Epithelial cell도 여전히 상당한 비율을 차지하지만, 'unassigned' 세포 비율이 높아 다른 세포 유형의 상대적 비율 해석이 다소 제한적입니다. T cell과 Macrophage도 존재합니다.

Biological Interpretation

이러한 세포 구성의 변화는 폐암 미세 환경(TME)의 동적인 특성을 반영합니다.

• 종양 상피 세포의 증식: 'Tumor origin celltype'이 Lung Epithelial cell이라는 데이터 컨텍스트를 고려할 때, 초기 종양 샘플에서 Alveolar Epithelial cell의 현저한 증가는 악성 상피 세포의 클론성 확장 및 종양 형성의 핵심적인 특징을 시사합니다.

면역 세포 침윤 및 재편:

• T cell (CD4+ 및 CD8+)은 정상 조직에서도 풍부하며, 종양 초기 단계에서도 상당한 비율을 유지합니다. 이는 종양 항원에 대한 면역 반응이 활성화되어 있거나 종양 침윤 림프구(TILs)가 존재함을 나타냅니다. 그러나 종양 상피 세포의 증식으로 인해 상대적 비율이 감소한 것처럼 보일 수 있습니다.
• Macrophage는 모든 조건에서 일관되게 존재하며, 이는 폐의 항상성과 면역 감시 모두에 중요한 역할을 함을 보여줍니다. 종양 미세환경에서 Macrophage는 종양 관련 대식세포(TAMs)로 분화하여 종양 진행을 촉진할 수 있습니다.
• Plasma cell은 일부 종양 샘플에서 증가하는 경향을 보이며, 이는 종양 미세환경 내에서 체액성 면역 반응이 유도될 수 있음을 시사합니다.
• 기질 세포의 역할: Fibroblast는 정상 및 종양 조직 모두에서 존재하며, 종양 미세환경에서 암 관련 섬유아세포(CAFs)로 활성화되어 종양 성장, 침윤 및 전이에 기여할 수 있습니다.
• 'unassigned' 세포의 의미: 진행성 종양 샘플에서 'unassigned' 세포의 높은 비율은 중요한 관찰입니다. 이는 현재의 세포 유형 분류 체계로는 명확히 정의하기 어려운 고도로 이질적이거나 미분화된 종양 세포 집단을 나타낼 수 있습니다. 혹은 세포 상태의 특이성을 나타내거나, 심지어는 기술적인 이유(예: 불충분한 해상도 또는 희귀 세포 유형)로 인해 특정된 세포 유형으로 분류되지 않은 것일 수도 있습니다. 이러한 'unassigned' 세포에 대한 추가적인 특성 분석이 필요합니다.

Clinical or Translational Implications

• 질병 진행 바이오마커: Alveolar Epithelial cell의 증식은 폐암의 존재와 진행을 나타내는 주요 지표입니다. 종양 병기(초기 vs. 진행성)에 따른 특정 면역 세포(예: T cell, Plasma cell) 및 기질 세포(예: Fibroblast)의 상대적 구성 변화는 질병 진행 예측 또는 예후 인자로 활용될 가능성이 있습니다.
• 치료 반응 예측: T cell 및 Macrophage와 같은 면역 세포의 침윤 양상은 면역 관문 억제제(immune checkpoint inhibitors)와 같은 면역 치료에 대한 반응성을 예측하는 데 중요한 바이오마커가 될 수 있습니다. 예를 들어, T cell의 높은 침윤은 더 나은 치료 반응을 시사할 수 있습니다 https://pubmed.ncbi.nlm.nih.gov/33946358/.
• 'unassigned' 세포의 중요성: 진행성 종양에서 높은 비율을 차지하는 'unassigned' 세포는 잠재적으로 새로운 종양 관련 세포 아형이거나, 기존 치료에 대한 저항성을 유발하는 독특한 세포 상태일 수 있습니다. 이러한 세포의 특성 규명은 새로운 치료 표적을 발굴하는 데 기여할 수 있습니다.

7. T Cell Subset Population Analysis Across Normal, Early, and Advanced Lung Tumor Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot visualizing the relative proportions of T cell subsets and related innate lymphoid cells (ILCs), along with NK cells, within the T cell major cell type across individual samples from Normal, early-stage Tumor (Tumor(early)), and advanced-stage Tumor (Tumor(adv)) conditions in human lung tissue. The data is derived from single-cell RNA-seq, providing a granular view of the immune cell landscape within each sample. This type of visualization is crucial for understanding shifts in immune cell composition during disease progression, particularly in cancer.

Visual Summary

The bar plot shows the distribution of various immune cell subsets making up the T cell compartment (including T cells, NK cells, and ILCs) for each sample, normalized to 100%.

• Dominant Subsets Across Conditions: In all conditions (Normal, Tumor(early), Tumor(adv)), T cell (Cytotoxic) and T cell (Naive) constitute the largest proportions of the T cell compartment.

Normal Condition:

• T cell (Cytotoxic) is abundant, often making up 40-60% of the T cell compartment.
• T cell (Naive) is also highly represented, contributing significantly to the overall T cell population.
• T cell (Treg) (dark blue) is consistently present but in relatively low proportions across normal samples.
• NK cells and various ILC subsets (ILC1, ILC2, ILC3 (NCR+), ILC3 (NCR-), ILCreg, LTI) are present but generally constitute very minor populations.

Tumor (early) Condition:

• The overall composition appears broadly similar to Normal tissue, with T cell (Cytotoxic) and T cell (Naive) remaining major components.
• T cell (Treg) proportions seem comparable to, or slightly increased in some samples relative to, normal tissue.
• Other T helper (Th) subsets (e.g., Th1, Th17) and ILCs remain in smaller proportions.

Tumor (advanced) Condition:

• While T cell (Cytotoxic) remains a significant component, its *relative proportion* appears to be somewhat reduced in several advanced tumor samples (e.g., EBUS_28, EBUS_58) compared to many normal and early tumor samples, suggesting a potential shift in the immune microenvironment.
• A notable observation is a visually apparent increase in the relative proportion of T cell (Treg) (dark blue) in several advanced tumor samples (e.g., EBUS_06, EBUS_28, EBUS_58) compared to both normal and early tumor samples. This indicates a potential expansion of regulatory T cells with disease progression.
• The proportions of NK cells and ILCs remain low across advanced tumor samples.
• Sample Heterogeneity: There is visible inter-sample variability in cell subset proportions within each condition, highlighting the personalized nature of immune responses even within the same disease stage.

Biological Interpretation

The observed shifts in T cell subset populations provide insights into the immune landscape of lung cancer progression:

• Dominance of Cytotoxic T cells: The high prevalence of T cell (Cytotoxic) in both normal and tumor conditions suggests a strong presence of potentially anti-tumor immune cells. However, in the context of advanced tumors, these cells might be functionally exhausted or anergic due to the immunosuppressive tumor microenvironment (TME), rather than being fully effective antitumor effectors [PubMed: T cell exhaustion in cancer].
• Increase in Regulatory T cells (Tregs) in Advanced Tumor: The visually increased proportion of T cell (Treg) in advanced tumors is a critical finding. Tregs are potent immunosuppressive cells that inhibit effector T cell functions, promote immune tolerance, and facilitate tumor immune evasion. Their expansion is a well-documented mechanism by which tumors evade destruction by the immune system, contributing to disease progression [GeneCards: FOXP3]. This suggests a more immunosuppressive environment as the tumor advances.
• Naive T cells: The significant proportion of T cell (Naive) in normal tissue is expected for a healthy immune system. Their continued presence in tumors could reflect ongoing immune cell infiltration, but their functionality within the TME requires further investigation.
• Minority of NK and ILCs: The consistently low proportions of NK cells and ILCs across all conditions might indicate that, within the T cell major cell type, T cells themselves are the overwhelmingly dominant lymphoid population being profiled, or that these innate populations are generally less abundant in the lung TME compared to adaptive T cells. Specific changes within these minor populations might be obscured by the scale.

Clinical or Translational Implications

The observed immune cell shifts have significant clinical and translational implications for lung cancer:

• Prognostic and Predictive Biomarkers: The ratio of cytotoxic T cells to regulatory T cells (CTL/Treg ratio) is often considered a crucial indicator of anti-tumor immunity and has prognostic value in various cancers. A decline in this ratio due to increased Tregs in advanced tumors could serve as a biomarker for disease progression or poorer prognosis [PubMed: CTL Treg ratio cancer prognosis].
• Therapeutic Targeting of Tregs: The enrichment of T cell (Treg) in advanced lung tumors suggests that therapeutic strategies aimed at depleting or inhibiting Treg function could be beneficial. Such approaches, perhaps in combination with existing immunotherapies, could help reverse immune suppression in the TME and enhance anti-tumor responses [PubMed: Treg targeting cancer immunotherapy].
• Overcoming T cell Exhaustion: While cytotoxic T cells are abundant, their potential exhaustion in advanced tumors implies that therapies focusing on reinvigorating these cells (e.g., immune checkpoint inhibitors targeting PD-1/PD-L1) remain critical. Combining such therapies with Treg-targeting strategies could offer a synergistic approach to reactivate anti-tumor immunity.

8. T Cell Subset Population Dynamics Across Lung Cancer Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional differences of various T cell subsets (Th17, NK, Treg, Th1, Th2, Tfh, T_Naive, Th22) across three conditions: early-stage tumor (Tumor(early)), normal lung tissue (Normal), and advanced-stage tumor (Tumor(adv)). The goal is to identify significant shifts in these immune cell populations, which are crucial components of the anti-tumor immune response and can profoundly influence disease progression in lung cancer. The analysis uses a p-value cutoff of 0.1 for significance testing between groups.

Visual Summary

The box plots display the celltype proportion (as a percentage of total cells) for each T cell subset across the three conditions. Key observations include:

Elevated in Tumor Conditions:

• Th17 cells show significantly higher proportions in both early (p ≤ 0.001) and advanced (p = 0.06) tumors compared to normal tissue.
• Naive T cells (T_Naive) are significantly elevated in early tumors (p ≤ 0.01) and remain significantly higher in advanced tumors (p = 0.06) compared to normal.

Reduced in Tumor Conditions:

• Natural Killer (NK) cells are markedly and significantly reduced in both early (p ≤ 0.001) and advanced (p ≤ 0.05) tumors compared to normal tissue. Interestingly, their proportion is slightly higher in advanced tumors compared to early tumors (p = 0.09), though still significantly lower than normal.

Stage-Dependent Shifts:

• Regulatory T cells (Treg) are significantly elevated in early tumors (p ≤ 0.01) compared to normal, but this elevation is not sustained in advanced tumors (p = 0.37 vs Normal).
• Th1 cells are significantly increased in early tumors (p = 0.05) compared to normal, but their proportions return to levels similar to normal in advanced tumors (p = 0.71 vs Normal).
• Th2 cells are elevated in early tumors (p ≤ 0.01) but significantly decrease in advanced tumors (p ≤ 0.01 vs Normal; p ≤ 1e-4 vs Tumor(early)).
• T follicular helper (Tfh) cells are highly elevated in early tumors (p ≤ 1e-4) but their proportions are not significantly different from normal in advanced tumors (p = 0.24 vs Normal).
• Th22 cells are significantly elevated in early tumors (p ≤ 0.001) but show a significant decrease from early to advanced stages (p = 0.08) and are not significantly different from normal in advanced tumors (p = 0.72 vs Normal).

Biological Interpretation

The observed shifts in T cell subset populations reveal a dynamic and complex immune landscape in lung cancer that changes with disease progression.

1. Immunosuppressive and Pro-tumorigenic Environment:

• The consistent elevation of Th17 cells in both early and advanced tumors suggests a chronic inflammatory response that, in many cancer contexts, contributes to tumor growth, angiogenesis, and immune evasion [1].
• The initial increase in Treg cells in early tumors is a hallmark of immunosuppression, as Tregs are potent inhibitors of anti-tumor immunity. While their proportion appears to normalize in advanced stages, their early dominance could contribute to shaping the tumor microenvironment (TME) towards tolerance [2].
• The persistently high proportion of Naive T cells in both tumor stages compared to normal might indicate either a continuous influx of unprimed T cells into the TME or an impaired T cell activation and differentiation process within the tumor, preventing them from developing into effective effector cells.

2. Compromised Anti-tumor Immunity:

• The most striking finding is the significant reduction of NK cells in both early and advanced tumor conditions. NK cells are critical innate immune cells with direct cytotoxic activity against tumor cells and play a crucial role in immune surveillance [3]. Their depletion suggests a significant immune evasion mechanism employed by lung tumors from early stages. The slight increase in advanced tumors compared to early might be an attempted counter-response, but it's still insufficient to restore normal levels.
• While Th1 cells, known for their critical role in anti-tumor immunity through IFN-gamma production, are elevated in early tumors, their normalization in advanced stages might signify a weakening of the robust Th1-driven anti-tumor response as the disease progresses.

3. Dynamic Immune Reprogramming During Progression:

• The populations of Th2, Tfh, and Th22 cells show elevations in early tumors followed by a decrease or normalization in advanced tumors. This suggests a reprogramming of T helper cell responses. Th2 cells are often associated with humoral immunity and allergic responses, but their role in cancer is context-dependent. Tfh cells support B cell differentiation. Th22 cells are involved in tissue repair and inflammation. The decline of these populations from early to advanced stages suggests a shift away from certain types of inflammatory or helper responses, potentially leading to a more immunosuppressive or less dynamic TME in advanced disease.

Clinical or Translational Implications

• Biomarker Potential: The differential proportions of T cell subsets, particularly NK cells and Th17 cells, could serve as prognostic biomarkers for lung cancer progression. Monitoring these changes may offer insights into disease severity and immune status.
• Therapeutic Targets: The consistent suppression of NK cells highlights a potential therapeutic avenue. Strategies aimed at restoring or augmenting NK cell function (e.g., NK cell adoptive transfer, checkpoint inhibitors targeting NK cell inhibitory receptors, or cytokines like IL-15) could be beneficial in lung cancer patients [3].
• Stage-Specific Interventions: The dynamic shifts in other T cell subsets suggest that immune-modulating therapies might need to be tailored to the disease stage. For instance, interventions targeting Treg cells might be more effective in early lung cancer where their numbers are significantly elevated.
• Understanding Immune Evasion: The findings underscore key mechanisms of immune evasion in lung cancer, such as NK cell suppression and potential shifts in helper T cell subsets, which could inform the development of novel immunotherapeutic strategies.

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References:

[1] Th17 cells in cancer. PubMed Search: https://pubmed.ncbi.nlm.nih.gov/?term=Th17+cells+lung+cancer+role

[2] Regulatory T cells in cancer. PubMed Search: https://pubmed.ncbi.nlm.nih.gov/?term=Treg+cells+lung+cancer+immunosuppression

[3] Natural Killer cells in cancer. PubMed Search: https://pubmed.ncbi.nlm.nih.gov/?term=NK+cells+lung+cancer+anti-tumor

9. Macrophage Cell Population Overview Across Lung Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes the plot_celltype_population tool to visualize the distribution of a specific cell type, Macrophage (from celltype_minor), across different samples and conditions (Normal, Tumor(adv), Tumor(early)). The goal is to provide an overview of the representation of Macrophage cells within the dataset.

Visual Summary

The visualization consists of three bar plots, one for each condition: Normal, Tumor(adv) (advanced tumor), and Tumor(early) (early tumor). Each plot displays individual samples on the x-axis. For every sample across all three conditions, a single bar is present, consistently reaching the 100% mark on the y-axis, and is labeled as "Macrophage".

Biological Interpretation

The bar plots confirm the presence of Macrophages across all analyzed samples within the Normal, Tumor(adv), and Tumor(early) conditions. The observation that all bars are at 100% indicates that when the plot_celltype_population tool is queried for a specific celltype_minor (in this case, 'Macrophage'), it is showing that 100% of the cells within that designated celltype_minor are indeed the chosen cell type.

This plot primarily serves as a confirmation that the 'Macrophage' cell type is annotated and present in all the samples considered in the dataset. It does not illustrate the relative abundance of Macrophages compared to other major cell types, nor does it detail the distribution or proportions of different macrophage *subtypes* (e.g., M1, M2A, M2B, etc., which are available in celltype_subset) within the macrophage population itself across the various conditions. Therefore, this visualization verifies the consistency of macrophage identification across samples and conditions rather than highlighting shifts in macrophage frequencies or specific functional states.

Annotation Notes

This plot effectively validates that the 'Macrophage' annotation is consistently applied across all samples in the Normal, Tumor(adv), and Tumor(early) conditions. To gain deeper biological insights into macrophage involvement in lung cancer, further analyses would be required, such as:

1. Relative abundance of Macrophages: Comparing the proportion of Macrophages to other immune and stromal cells across conditions.
2. Macrophage subpopulation shifts: Analyzing the distribution of celltype_subset populations (e.g., Macrophage (M1), Macrophage (M2A)) across Normal, Tumor(early), and Tumor(adv) conditions to understand polarization states.
3. Differential gene expression: Investigating genes differentially expressed within Macrophages across conditions, or comparing specific macrophage subtypes.
4. Cell-cell interactions: Exploring how Macrophages interact with other cell types in the tumor microenvironment using tools like CellPhoneDB.

10. Changes in Macrophage Subpopulation Proportions Across Lung Cancer Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportions of different macrophage subsets (Macrophage (M2C), Macrophage (M2A), and Macrophage (M2B)) across three conditions: early-stage tumor (Tumor(early)), normal lung tissue (Normal), and advanced-stage tumor (Tumor(adv)). The goal is to identify significant shifts in these immune cell populations during lung cancer progression.

Visual Summary

The box plots illustrate the celltype proportion for three distinct macrophage subsets. Black dots represent individual sample measurements.

Macrophage (M2C) Proportions:

• The proportion of Mac (M2C) cells is comparable between Tumor(early) and Normal conditions (p = 0.74).
• However, in the Tumor(adv) condition, there is a notable decrease in Mac (M2C) cell proportion compared to both Normal (p = 0.08) and Tumor(early) (p = 0.09) conditions. The median proportion drops from approximately 25-28% in Normal/Tumor(early) to around 13% in Tumor(adv).

Macrophage (M2A) Proportions:

• Mac (M2A) cell proportion is significantly lower in Tumor(early) (median ~16%) compared to Normal (median ~23%) (p ≤ 0.05).
• This trend of reduction continues in Tumor(adv), where Mac (M2A) proportions are drastically lower (median ~6-7%) compared to both Normal (p ≤ 0.001) and Tumor(early) (p ≤ 0.01).

Macrophage (M2B) Proportions:

• Conversely, Mac (M2B) cell proportion shows a significant increase in Tumor(early) (median ~15%) compared to Normal (median ~9%) (p ≤ 0.05).
• This elevation is further pronounced in Tumor(adv) (median ~25%), where the proportion is significantly higher than in both Normal (p ≤ 0.01) and Tumor(early) (p ≤ 0.05).

In summary, as lung cancer progresses from early to advanced stages, there is a consistent decrease in Mac (M2A) and Mac (M2C) macrophage populations, while Mac (M2B) macrophages exhibit a significant and progressive increase.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and play diverse roles in cancer progression, often broadly categorized into M1 (anti-tumor) and M2 (pro-tumor) phenotypes. The M2 phenotype itself is heterogeneous, encompassing various subsets (M2A, M2B, M2C, M2D) with distinct functions, often involved in immune suppression, tissue remodeling, angiogenesis, and promoting tumor growth [1].

The observed shifts in specific macrophage subsets in lung cancer conditions suggest a dynamic re-orchestration of the immune landscape:

Decrease in Mac (M2A) and Mac (M2C) in advanced tumors:

• M2A macrophages are typically activated by IL-4/IL-13 and are involved in tissue repair and allergic responses. M2C macrophages are activated by IL-10/TGF-β and glucocorticoids, and are often associated with immunosuppression, immune complex clearance (efferocytosis), and tissue remodeling.
• The significant reduction of these specific M2 subsets in advanced lung tumors suggests that their specific pro-tumorigenic or immune-regulatory functions might be diminished or taken over by other immune cells or different macrophage populations in the late stages of cancer. Alternatively, it could indicate a shift away from tissue repair-associated or specific immune suppressive functions mediated by M2A/M2C, towards other mechanisms that are more critical for advanced tumor maintenance.

Increase in Mac (M2B) throughout tumor progression, peaking in advanced tumors:

• M2B macrophages are activated by immune complexes (e.g., IgG) and TLR agonists (e.g., LPS). They exhibit a mixed phenotype, capable of producing both pro-inflammatory (e.g., IL-6, TNF-α) and anti-inflammatory (e.g., IL-10) cytokines [2]. Their role in cancer is complex but often linked to promoting angiogenesis, metastasis, and modulating immune responses in a manner that supports tumor growth.
• The significant and progressive increase of M2B macrophages from normal tissue to early tumor and then to advanced tumor conditions strongly implies that this specific subset plays an increasingly crucial role in fostering the pro-tumorigenic microenvironment in lung cancer. This shift could contribute to immune evasion, inflammation-driven tumor growth, and metastatic potential. Given the Lung Epithelial cell origin of the tumor, these macrophages would be interacting closely with cancerous epithelial cells, influencing their proliferation and survival.

These findings highlight the plasticity of macrophage populations in the lung TME and suggest a highly specialized role for M2B macrophages in promoting lung cancer progression, particularly in advanced stages.

Clinical or Translational Implications

The differential changes in macrophage subset proportions, especially the prominent increase in Mac (M2B) in advanced lung cancer, have several clinical implications:

• Biomarker Potential: The proportions of Mac (M2A), Mac (M2B), and Mac (M2C) could serve as potential biomarkers for assessing disease stage or predicting progression in lung cancer patients. Monitoring these shifts could offer insights into the disease trajectory.
• Therapeutic Targets: Targeting specific macrophage subsets or their polarization pathways represents a promising therapeutic strategy. Given the increase in Mac (M2B) in advanced tumors, interventions aimed at inhibiting their recruitment, survival, or pro-tumorigenic functions could be explored. For example, therapies that re-educate these macrophages towards an M1-like anti-tumor phenotype, or deplete specific pro-tumorigenic macrophage subsets, could enhance anti-cancer immunity. This approach could be particularly relevant for advanced lung cancer, where these macrophages are most abundant.
• Immunotherapy Combinations: Understanding the specific macrophage landscape could help tailor combination immunotherapies. For instance, strategies combining checkpoint inhibitors with agents that modulate M2B macrophage activity might yield synergistic anti-tumor effects in lung cancer patients.

References

1. M2 Macrophage Subsets in Cancer: Review the roles of M2 macrophages in cancer, including M2A, M2B, M2C, and M2D subtypes. https://pubmed.ncbi.nlm.nih.gov/?term=M2+macrophage+subsets+cancer+review
2. M2B Macrophages in Tumor Microenvironment: Explore specific functions of M2B macrophages in cancer. https://pubmed.ncbi.nlm.nih.gov/?term=M2b+macrophages+tumor+microenvironment

11. Tumor-Origin and Unassigned Cell Ploidy Landscape Across Lung Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the ploidy status (Aneuploid, Diploid, or Unclear) of cells identified as "Lung Epithelial cell" (the designated tumor-origin cell type) and "unassigned" cells, sampled from Normal, Early Tumor (Tumor(early)), and Advanced Tumor (Tumor(adv)) lung tissues. The purpose is to assess the genomic stability of these specific cell populations across different disease stages at a single-cell resolution.

Visual Summary

The bar plot displays the percentage of Aneuploid, Diploid, and Unclear cells within the selected cell populations for individual samples, grouped by their disease condition:

• Normal Samples: All normal lung samples (e.g., LUNG_N20, LUNG_N18) show an overwhelmingly high proportion (nearly 100%) of Diploid cells. A negligible fraction appears as Aneuploid or Unclear. This indicates genomic stability in healthy lung tissue for the sampled cell types.
• Advanced Tumor Samples (Tumor(adv)): In stark contrast to normal samples, advanced tumor samples (e.g., EBUS_06, EBUS_28) exhibit a dominant proportion of Aneuploid cells, ranging from approximately 45% to over 95%. Diploid cells constitute a smaller fraction, and the "Unclear" category is generally low. This indicates significant genomic instability in advanced lung tumors.
• Early Tumor Samples (Tumor(early)): Early tumor samples present a more heterogeneous ploidy landscape. While many samples show a substantial proportion of Aneuploid cells (e.g., LUNG_T34, LUNG_T18, LUNG_T28 each above 60-80% aneuploid), others have a more balanced or even predominant Diploid population (e.g., LUNG_T30, LUNG_T19, LUNG_T09). The "Unclear" category is also more variable in early tumors, sometimes reaching notable proportions (e.g., LUNG_T30, LUNG_T19). This heterogeneity likely reflects the evolving nature of early-stage tumorigenesis.

Biological Interpretation

The observed ploidy patterns strongly correlate with the progression of lung cancer, particularly within the designated tumor-origin "Lung Epithelial cell" population:

• Genomic Stability in Normal Tissue: The consistent diploidy in normal lung epithelial and unassigned cells is expected for healthy somatic tissue, reflecting intact chromosomal integrity and cell cycle control.
• Aneuploidy as a Hallmark of Cancer: The marked increase in aneuploidy in tumor samples, especially in advanced stages, is a classic hallmark of cancer. Aneuploidy refers to an abnormal number of chromosomes and arises from genomic instability, which drives tumorigenesis by altering gene dosage, promoting tumor heterogeneity, and facilitating adaptation to selective pressures https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7966442/. Given that "Lung Epithelial cell" is the tumor-origin cell type, the aneuploid population in tumor samples is highly indicative of malignant cells.
• Tumor Progression and Genomic Evolution: The trend from predominantly diploid in normal tissue, to variable but significant aneuploidy in early tumors, and then to pervasive aneuploidy in advanced tumors, illustrates the genomic evolution during cancer progression. As tumors develop, they accumulate more chromosomal abnormalities, often leading to increased aneuploidy, which can confer growth advantages and contribute to more aggressive phenotypes.
• Heterogeneity in Early Tumors: The variability in ploidy profiles among early tumor samples suggests that early-stage lung cancers may have diverse genomic characteristics. Some may already harbor significant aneuploidy, while others might be in earlier stages of genomic disruption or contain a higher proportion of non-malignant cells mixed with nascent tumor cells. The "unassigned" cells in tumor samples might also contribute to this heterogeneity; if they are indeed malignant cells that couldn't be precisely classified, their ploidy would contribute to the overall aneuploid signal. If they represent stromal cells, they would likely be diploid.
• Diploid Cells in Tumor Microenvironment: The presence of diploid cells within tumor samples, particularly in early stages, could be attributed to several factors:
• Infiltration by non-malignant stromal cells (e.g., fibroblasts, endothelial cells) or immune cells, which would remain diploid.
• Residual healthy epithelial cells adjacent to the tumor.
• Subpopulations of early-stage tumor cells that have not yet acquired extensive aneuploidy but might harbor other oncogenic mutations.
• "Unclear" Ploidy Calls: The "Unclear" category might represent cells with ambiguous CNV signals, possibly due to low-quality single-cell data, complex and fragmented chromosomal rearrangements that defy clear classification, or cells in transitional states.

Clinical or Translational Implications

• Biomarker for Malignancy and Progression: The presence and extent of aneuploidy, particularly in lung epithelial cells, could serve as a valuable biomarker for distinguishing malignant from non-malignant lesions and for staging lung cancer. A higher proportion of aneuploid cells might indicate more aggressive disease.
• Prognostic Indicator: The degree of aneuploidy has been linked to patient prognosis in various cancers https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3656608/. Further analysis could explore whether the level of aneuploidy in early-stage lung epithelial cells correlates with recurrence risk or treatment response.
• Therapeutic Targeting: Aneuploidy often co-occurs with defects in DNA damage repair pathways or cell cycle checkpoints. Understanding the specific chromosomal aberrations underlying aneuploidy could potentially inform targeted therapeutic strategies that exploit these vulnerabilities in aneuploid tumor cells.
• Monitoring Disease Evolution: Tracking the ploidy landscape in liquid biopsies or repeat biopsies could offer insights into tumor evolution and potential resistance mechanisms during therapy.

12. Advanced Lung Tumor Cell-Cell Interaction Landscape

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the cell-cell interaction (CCI) patterns within the advanced tumor (Tumor(adv)) microenvironment of human lung tissue. Using CellPhoneDB, ligand-receptor interactions were identified among key cellular components: Aneuploid and Diploid Lung Epithelial cells (tumor origin), Macrophages, T cells (CD8+ and CD4+). The resulting dot plot illustrates the top 80 most significant interactions, where dot size reflects the significance of the interaction (-log10 p-value) and dot color represents the interaction strength (log2 mean expression). Fibroblast interactions, while included in the analysis scope, did not feature prominently in the top 80 interactions displayed for this specific condition.

Visual Summary

The dot plot for Tumor(adv) reveals a highly dynamic and intricate network of cellular communication, predominantly driven by tumor cells and immune infiltrates.

Dominant Interacting Cell Pairs

• Aneuploid Lung Epithelial cells are central to the interaction landscape, demonstrating numerous and strong interactions. These include extensive homotypic interactions (Aneuploid Lung Epi|Aneuploid Lung Epi) and robust heterotypic interactions with both Macrophages, T CD8+, and T CD4+ cells.
• Macrophage interactions are highly significant. Strong Macrophage|Macrophage communication is evident (e.g., via APP-TREM2 receptor), as are substantial interactions between Macrophages and Aneuploid Lung Epi cells.
• T cells (CD8+ and CD4+) engage in communication with Aneuploid Lung Epi cells and Macrophages, as well as among themselves.
• Diploid Lung Epithelial cells show fewer and generally weaker interactions compared to their Aneuploid counterparts, suggesting a different involvement in the advanced tumor context.
• Notably, no specific cell-cell pairs involving Fibroblasts are among the top 80 interactions shown, implying that for this advanced tumor stage and the selected cell types, other interactions are more prominent.

Key Ligand-Receptor Systems

• Prostaglandin E2 (PTGES3-PTGER4): This pathway shows highly significant and strong interactions, particularly between Aneuploid Lung Epi and Macrophages, and within Macrophages, highlighting a potentially crucial immunosuppressive and pro-tumorigenic axis.
• Integrin-mediated adhesion and signaling: Multiple complexes involving fibronectin (FN1) and TGFB1 (e.g., FN1_integrin_avb5_complex, TGFB1_integrin_avb6_complex) are prominent, predominantly involving Aneuploid Lung Epi cells.
• EGFR/ERBB family signaling: Ligand-receptor pairs such as AREG-EGFR, HBEGF-EGFR, HBEGF-ERBB2, BTC-ERBB3, and EREG-EGFR exhibit interactions, primarily involving Aneuploid Lung Epi cells.
• Immune checkpoint pathways: LILRB2-HLA-F_complex and LGALS9-HAVCR2 (Galectin-9-TIM-3) interactions are observed, often involving T cells and Aneuploid Lung Epi or Macrophages, indicative of immune modulation.
• Chemokine signaling: CCL3-CCR1, CCL5-CCR1, and CXCL10-CXCR3 interactions are present, involving T cells and Macrophages.
• WNT signaling: WNT7B-FZD6_LRP5 and WNT7B-FZD6_LRP6 show interactions, especially with Aneuploid Lung Epi.
• TWEAK-Fn14 (TNFSF12-TNFRSF12A): This pair shows interactions, including those involving T cells and Aneuploid Lung Epi.
• APP-CD74 and APOE-TREM2 receptor: These are particularly strong in Macrophage|Macrophage and Macrophage|Aneuploid Lung Epi interactions.
• Semaphorin-Neuropilin (Sema-NRP): Various SEMA ligands with NRP receptors are also observed.

Biological Interpretation

The observed CCI patterns provide critical insights into the biological mechanisms driving advanced lung tumor progression and shaping the tumor microenvironment (TME).

1. Aneuploid Tumor Cells Drive TME Remodeling and Immune Interaction: The extensive and strong interactions of Aneuploid Lung Epi cells both with themselves (homotypic) and with immune cells (heterotypic) suggest they actively orchestrate the TME. Homotypic interactions may support tumor cell cohesion, proliferation, and survival, while heterotypic interactions likely contribute to immune evasion and shaping an immunosuppressive environment.
2. Pro-tumorigenic Macrophage Polarization and Activity: The strong Macrophage|Macrophage and Macrophage|Aneuploid Lung Epi interactions, particularly via the Prostaglandin E2 (PTGES3-PTGER4) axis, are hallmarks of pro-tumorigenic (M2-like) macrophage polarization. Prostaglandin E2 is a potent immunosuppressive molecule that can inhibit T cell function, promote angiogenesis, and support tumor cell proliferation PubMed search: PGE2 tumor microenvironment immunosuppression. Interactions involving APP-CD74 and APOE-TREM2 receptor further support a role for macrophages in efferocytosis, inflammation resolution, and potentially immunosuppression within the TME GeneCards: TREM2.
3. T Cell Dysfunction and Immune Evasion: The presence of immune checkpoint interactions like LILRB2-HLA-F_complex and LGALS9-HAVCR2 (Galectin-9-TIM-3) involving T cells and other cells, suggests active mechanisms of immune suppression. LILRB2 (CD85d) binding to HLA-F on tumor cells or antigen-presenting cells delivers inhibitory signals to T cells, contributing to immune evasion PubMed search: LILRB2 HLA-F tumor immunity. Similarly, Galectin-9 interacting with TIM-3 is known to induce T cell exhaustion and apoptosis GeneCards: LGALS9, impairing anti-tumor immunity.
4. Oncogenic Signaling and Tumor Growth: The prominent EGFR/ERBB family signaling interactions (e.g., AREG-EGFR, HBEGF-EGFR/ERBB2) underscore the sustained activation of these critical pathways in Aneuploid Lung Epi cells, which are well-established drivers of proliferation and survival in lung cancer GeneCards: EGFR. WNT signaling (WNT7B-FZD6_LRP5/6) also plays crucial roles in cancer stemness, cell proliferation, and epithelial-mesenchymal transition (EMT), all contributing to advanced tumor progression.
5. ECM Remodeling and Invasive Potential: The significant involvement of FN1 and TGFB1 associated integrin complexes indicates extensive extracellular matrix (ECM) remodeling, which is vital for tumor cell migration, invasion, and metastasis. These interactions highlight the active processes supporting the invasive behavior of Aneuploid Lung Epi cells.
6. Fibroblast Role Not Dominant in Top Interactions: The absence of prominent fibroblast interactions in this visualization suggests that while fibroblasts are crucial in the TME, their most significant ligand-receptor activities within this specific set of cell-cell pairs and conditions might be less pronounced than the interactions between tumor cells and key immune populations in advanced disease.

Clinical or Translational Implications

The detailed CCI map offers valuable insights for developing therapeutic strategies and identifying biomarkers in advanced lung cancer.

1. Prioritizing Therapeutic Targets:

• PGE2 Pathway Inhibition: The strong involvement of the Prostaglandin E2 (PTGES3-PTGER4) axis indicates that targeting PGE2 synthesis or its receptor could be a promising strategy to suppress pro-tumorigenic and immunosuppressive signals in advanced lung tumors, potentially augmenting the efficacy of other therapies PubMed search: PTGER4 inhibitor cancer therapy.
• Novel Immune Checkpoint Blockade: The identified LILRB2-HLA-F and LGALS9-HAVCR2 (Galectin-9-TIM-3) interactions suggest potential targets for immunotherapy beyond conventional PD-1/PD-L1 blockade. Inhibiting these pathways could re-invigorate exhausted T cells and enhance anti-tumor immunity, especially in patients who are resistant to current immunotherapies PubMed search: LILRB2 inhibitor cancer immunotherapy.
• EGFR/ERBB Pathway Refinement: The continued importance of EGFR/ERBB family interactions in Aneuploid Lung Epi cells reinforces the utility of EGFR-targeted therapies. Investigating specific ligands or combining these therapies with inhibitors of other interacting pathways might improve outcomes.
• Myeloid-targeted strategies: Given the central role of macrophages, therapies aimed at repolarizing macrophages or blocking their pro-tumorigenic communication (e.g., via APP-TREM2 or PGE2) could re-sensitize tumors to immune attack or other treatments PubMed search: TREM2 inhibitor cancer.

2. Biomarker Development: The expression or activity of these critical ligand-receptor pairs could serve as prognostic biomarkers, indicating disease aggressiveness, or predictive biomarkers, identifying patients likely to respond to specific targeted or immunotherapies. For instance, high levels of PGE2-related interactions or specific integrin signatures might predict a more invasive disease course.
3. Rational Combination Therapies: The interconnected nature of these pathways suggests that targeting a single interaction might not be sufficient. Combination therapies, such as combining immune checkpoint inhibitors with agents that block PGE2 signaling or EGFR/ERBB pathways, could offer synergistic benefits by simultaneously disrupting multiple pro-tumorigenic and immunosuppressive axes.

Further experimental validation of these specific ligand-receptor interactions is crucial to confirm their functional roles in advanced lung cancer and translate these findings into effective clinical strategies.

13. Advanced Lung Cancer (Tumor(adv)) Cell-Cell Interaction Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates significant cell-cell interactions (CCIs) within the tumor microenvironment of advanced lung cancer (Tumor(adv) condition) using single-cell RNA-seq data. CellPhoneDB was used to infer ligand-receptor interactions, and the results are presented as a dot plot. The plot highlights up to 80 of the most significant interactions for the Tumor(adv) condition, filtered by a p-value cutoff of 0.05 and mean expression cutoff of 0.01. Dot size corresponds to the significance of the interaction (-log10(p-value)), and dot color represents the strength of the interaction (log2(mean expression)). The analysis includes various immune cells, stromal cells, and both aneuploid and diploid lung epithelial cells.

Visual Summary

The dot plot visualizes a complex network of cell-cell communication in advanced lung cancer.

• Highly interacting cell types: Prominent interactions are observed involving Aneuploid Lung Epithelial cells (likely tumor cells, given the "Lung Epithelial cell" tumor origin and "Aneuploid" status), Macrophages, T cells (CD8+ and CD4+), and NK cells.
• Key interaction pairs: Many interactions show high statistical significance (large dot size, indicating low p-values) across various cell-cell pairs.
• Strongest interactions by mean expression: Interactions exhibiting the highest mean expression (brighter yellow/green dots) are particularly notable. These include multiple Prostaglandin E2 (PGE2) receptor pathways (PTGER1, PTGER2, PTGER3, PTGER4), EGFR ligands (AREG, HBEGF) interacting with EGFR, and various integrin complexes.

Specific patterns

• Aneuploid Lung Epithelial cells engage in numerous high-mean interactions, both with themselves (autocrine signaling) and with various immune cells, especially Macrophages.
• Macrophages show strong self-interactions and robust communication with Aneuploid Lung Epithelial cells and T cells.
• Interactions between different T cell subsets (e.g., T CD8+|T CD4+) and between T cells and NK cells are also present.

Biological Interpretation

The observed cell-cell interactions provide crucial insights into the biology of advanced lung cancer, particularly concerning tumor progression and immune evasion:

• Aneuploid Lung Epithelial Cell-driven Communication: As the primary tumor cell population (derived from Lung Epithelial cells and characterized by aneuploidy), these cells exhibit significant interactions:
• Autocrine signaling: Interactions like AREG-EGFR and HBEGF-EGFR among Aneuploid Lung Epithelial cells suggest autocrine growth factor signaling, a common mechanism in cancer driving uncontrolled proliferation and survival. The EGFR pathway is a well-established oncogenic driver and therapeutic target in lung cancer. GeneCards: EGFR
• Interaction with Macrophages: Aneuploid Lung Epithelial cells show strong interactions with Macrophages, notably via the Prostaglandin E2 pathway (PGE2-PTGERs) and EGFR ligands. Macrophages, particularly M2-polarized tumor-associated macrophages (TAMs), are known to foster an immunosuppressive and pro-tumorigenic microenvironment. The PGE2 pathway is a critical mediator in this process, promoting TAM differentiation, angiogenesis, and tumor growth while suppressing anti-tumor immunity. PubMed Search: Prostaglandin E2 tumor microenvironment lung cancer

Macrophage Orchestration of the TME

• Macrophage-Macrophage interactions: Robust self-interactions, especially involving PGE2-PTGERs, indicate strong autocrine/paracrine signaling within the macrophage population. This can reinforce their pro-tumorigenic phenotype and amplify their effects on other cells in the tumor microenvironment (TME).
• Macrophage-T cell interactions: Interactions involving integrin complexes (e.g., ICAM1-integrin_aMb2_complex) are crucial for immune cell adhesion and migration. The balance of these interactions with the pro-tumorigenic signaling from Aneuploid Lung Epithelial cells and within macrophages likely contributes to T cell dysfunction and exhaustion in advanced tumors.
• Role of Prostaglandin E2 (PGE2) Pathway: This pathway consistently emerges with high mean expression in interactions involving Aneuploid Lung Epithelial cells and Macrophages. PGE2, often produced by tumor cells and immune cells (like macrophages) via COX-2, plays multifaceted roles in cancer, including promoting tumor cell proliferation, survival, angiogenesis, invasion, and suppressing anti-tumor immune responses (e.g., by inhibiting T cell activation and promoting Treg differentiation).
• Integrin Signaling: Various integrin complexes (e.g., FN1-integrin_aVb2_complex, integrin_aVb1_complex_ADGRES, ICAM1-integrin_aMb2_complex) are frequently observed across different cell-cell pairs. Integrins are essential for cell adhesion to the extracellular matrix and to other cells, mediating processes critical for tumor invasion, metastasis, and immune cell trafficking within the TME. PubMed Search: Integrin cancer progression

Clinical or Translational Implications

The identified cell-cell interactions and prominent pathways in advanced lung cancer highlight potential therapeutic targets and diagnostic biomarkers.

• Targeting the PGE2 Pathway: The strong and widespread involvement of the Prostaglandin E2 pathway, particularly between Aneuploid Lung Epithelial cells and Macrophages, suggests that targeting COX-2 (the enzyme producing PGE2) or its receptors (PTGERs) could be a highly effective therapeutic strategy. This could simultaneously inhibit tumor cell growth, reduce angiogenesis, and reverse macrophage-mediated immunosuppression, thereby enhancing anti-tumor immune responses. Non-steroidal anti-inflammatory drugs (NSAIDs) or more specific COX-2 inhibitors could be repurposed or novel inhibitors developed.
• EGFR Pathway Re-evaluation: The strong autocrine/paracrine EGFR signaling (AREG-EGFR, HBEGF-EGFR) underscores the continued relevance of EGFR as a therapeutic target, especially considering the EGFR_mutation status available in the obs metadata. For patients with activating EGFR mutations, EGFR tyrosine kinase inhibitors (TKIs) are standard of care, but these data suggest broader roles for EGFR signaling in the TME, even beyond direct tumor cell proliferation.
• Modulating Integrin-mediated Interactions: Given the diverse roles of integrins in tumor progression and immune cell function, inhibiting specific integrin complexes could disrupt tumor cell invasion and metastasis, as well as modulate immune cell recruitment and effector functions within the TME. Further investigation into specific integrin-ligand pairs could identify novel targets.
• Immunotherapy Enhancement: Understanding the pro-tumorigenic interactions, such as those involving Macrophages and PGE2, can inform strategies to overcome immune resistance. Combinatorial therapies that pair existing immunotherapies (e.g., checkpoint inhibitors) with agents targeting these immunosuppressive pathways (e.g., COX-2 inhibitors or macrophage-repolarizing agents) could significantly improve patient outcomes in advanced lung cancer. These results directly highlight specific ligand-receptor axes that could be experimentally validated for their contribution to immune evasion.

14. Immune Checkpoint and Cell Cycle Pathway-Related Cell-Cell Interactions in Lung Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) mediated by genes involved in immune checkpoint and cell cycle pathways across different lung tissue conditions: Normal, Early-stage Tumor (Tumor(early)), and Advanced-stage Tumor (Tumor(adv)). The plot_cci_dots tool was utilized to visualize these interactions, focusing on specific ligand-receptor pairs from a predefined list of genes relevant to the queried pathways. The results are aggregated by condition, allowing for a comparative assessment of CCI dynamics during lung tumor progression.

Visual Summary

The three dot plots display cell-cell interactions for Normal, Tumor(adv), and Tumor(early) conditions, respectively. Each plot's Y-axis represents interacting cell pairs (e.g., 'Mac|Mac' for Macrophage-Macrophage, 'T CD8+|Aneuploid Lung Epi' for CD8+ T cell-Aneuploid Lung Epithelial cell), while the X-axis lists specific ligand-receptor gene pairs identified from the immune checkpoint and cell cycle-related gene list.

• Dot Size: Corresponds to the statistical significance of the interaction, with larger dots indicating a smaller p-value (-log10(p)).
• Dot Color: Represents the interaction strength, quantified as the mean expression of the ligand-receptor pair (log2(m)), with brighter yellow indicating higher mean expression.

Key visual observations across conditions:

• Normal Condition: Shows active interactions primarily within immune cell populations (e.g., T CD8+|T CD8+, Mac|Mac) and within Diploid Lung Epithelial cells (Diploid Lung Epi|Diploid Lung Epi). Dominant interactions include AREG_EGFR within diploid epithelial cells and TGFB1_TGFbeta_receptor1/2 within macrophages. Co-stimulatory CD86_CD28 interactions are visible among T cells and between Macrophages and T CD4+ cells.
• Tumor Conditions (Early and Advanced): A significant shift is observed with the emergence of Aneuploid Lung Epi cells (representing tumor cells) as central players in cell-cell communication.
• EGFR Signaling: Interactions involving EGFR ligands (AREG_EGFR, EREG_EGFR, HBEGF_EGFR, TGFA_EGFR) are highly prominent and strong (bright yellow dots, large size) within Aneuploid Lung Epi cells (autocrine/paracrine) and between Aneuploid Lung Epi cells and other immune cells (Macrophages, NK cells, T CD8+). These interactions appear particularly strong in the Tumor(early) condition for Aneuploid Lung Epi autocrine signaling.
• TGF-beta Signaling: The TGFB1_integrin_avb6_complex interaction specifically emerges as highly significant and strong in both tumor conditions, largely involving Aneuploid Lung Epi cells interacting with themselves, macrophages, and NK cells. TGFB1_TGFbeta_receptor1/2 interactions also persist.
• Immune Cell Interactions: While interactions among immune cells (T-T, Mac-Mac) continue, their relative prominence compared to tumor-immune or tumor-tumor interactions appears altered. CD86_CD28 interactions, while present in Normal, are less emphasized in the top interactions shown for tumor conditions. IFNG_Type_II_IFNR signaling is noted between T CD8+ and Aneuploid Lung Epi cells in tumor conditions.

Biological Interpretation

1. Prominent Role of EGFR Signaling in Tumor Cells: The robust and highly significant interactions involving various EGFR ligands (AREG, EREG, HBEGF, TGFA) and the EGFR receptor within Aneuploid Lung Epi cells, as well as between Aneuploid Lung Epi and surrounding immune cells (Macrophages, NK cells), highlight the persistent and likely driving role of EGFR signaling in lung cancer. This signaling is active from early to advanced stages, suggesting it is critical for tumor cell proliferation, survival, and communication with the tumor microenvironment. The strong autocrine/paracrine EGFR activation within tumor epithelial cells can foster unchecked growth.
2. TGF-beta Pathway Activation via Integrin αVβ6: The striking appearance and strong signal of the TGFB1_integrin_avb6_complex in both early and advanced tumor conditions, particularly involving Aneuploid Lung Epi cells, is a critical finding. Integrin αVβ6, often upregulated on cancer cells, is known to activate latent TGF-β, leading to increased active TGF-β in the tumor microenvironment. Active TGF-β plays a multifaceted role in cancer, promoting tumor cell proliferation, epithelial-mesenchymal transition, angiogenesis, and significantly contributing to immunosuppression by inhibiting T cell functions and promoting regulatory T cells or pro-tumorigenic macrophage phenotypes. PubMed search: Integrin avb6 TGFB1 cancer
3. Tumor-Immune Cell Crosstalk in the TME:

• Macrophages as Key Interactors: Macrophages ('Mac') engage in extensive interactions with Aneuploid Lung Epi cells via both EGFR ligands and TGF-beta signaling in tumor conditions. This underscores the crucial role of tumor-associated macrophages (TAMs) in shaping the tumor microenvironment (TME) and potentially promoting tumor progression. TAMs can be polarized towards pro-tumorigenic M2 phenotypes, which support tumor growth and immune suppression.
• T Cell and NK Cell Engagement: While T CD8+ cells show IFNG_Type_II_IFNR interactions with Aneuploid Lung Epi (suggesting an immune response), they also interact via TGFB1_TGFbeta_receptor1, indicating exposure to immunosuppressive signals. NK cells also interact with Aneuploid Lung Epi cells via EGFR ligands and TGFB1_integrin_avb6_complex, suggesting complex immune modulation.
• Reduced Co-stimulation: The less prominent CD86_CD28 (a key co-stimulatory interaction for T cell activation) in tumor conditions compared to normal could imply a diminished T cell activating capacity or a shift towards an exhausted/anergic state in the tumor microenvironment.

4. Early Onset of Oncogenic Interactions: The observation that robust EGFR signaling and integrin αVβ6-mediated TGF-β activation are already highly prominent in Tumor(early) suggests that these pathways are activated early in lung cancer development and are sustained throughout disease progression, adapting to the evolving tumor microenvironment.

Clinical or Translational Implications

1. EGFR as a Therapeutic Target: The consistently strong EGFR signaling in tumor epithelial cells across tumor stages reinforces EGFR as a validated therapeutic target in lung cancer. Further investigation into the specific EGFR ligands driving these interactions (AREG, EREG, HBEGF, TGFA) could reveal nuances in pathway activation and potential resistance mechanisms to existing EGFR inhibitors, guiding more personalized treatment strategies. GeneCards: EGFR
2. Targeting TGF-beta Activation as an Immunotherapy Strategy: The significant involvement of the TGFB1_integrin_avb6_complex in tumor conditions presents a compelling therapeutic opportunity. Inhibiting integrin αVβ6 could prevent the activation of latent TGF-β, thereby alleviating immunosuppression in the TME and enhancing anti-tumor immune responses. This approach could be particularly beneficial in combination with other immunotherapies (e.g., checkpoint inhibitors) to overcome TGF-β-mediated resistance. UniProt: P01137 (TGFB1), GeneCards: ITGAV
3. Modulating Macrophage Crosstalk: The extensive interactions between macrophages and aneuploid lung epithelial cells suggest that targeting these specific communication pathways (e.g., via EGFR or TGF-beta signaling) could help re-educate pro-tumorigenic TAMs towards anti-tumor functions, offering another avenue for therapeutic intervention.
4. Biomarker Potential: The distinct patterns of CCI, particularly the strength of EGFR and TGFB1_integrin_avb6_complex interactions, could serve as prognostic or predictive biomarkers for patient stratification and treatment response in lung cancer. Analyzing these specific cell-cell communications might help identify patients who would benefit most from therapies targeting these pathways.

These findings strongly suggest that the interplay between EGFR and TGF-beta signaling, mediated by specific cell-cell interactions within the TME, is crucial for lung tumor progression and immune evasion. Targeting these pathways, especially the activation of TGF-beta by integrin αVβ6, holds promise for developing novel therapeutic strategies.

15. Condition-Specific Cell-Cell Interaction Patterns in Lung Tissue Microenvironment

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) among major immune cells (T cells, Myeloid cells, B cells, Mast cells, NK cells, Dendritic cells, Plasma cells) and stromal cells (Fibroblasts, Smooth muscle cells) when interacting with each other or with Lung Epithelial cells across different conditions: Normal, Tumor (advanced), and Tumor (early) in lung tissue. The dot plot visualizes the top 25 most significantly different CCIs for each condition, showing the standardized mean interaction strength (color intensity) and statistical significance (-log10(p) as dot size) for each sample. A critical distinction is made for Lung Epithelial cells based on their ploidy state: Diploid (Dip) in normal tissue and Aneuploid (Aneup) in tumor tissue, reflecting a key characteristic of cancer cells.

Visual Summary

The dot plot effectively illustrates condition-specific patterns of cell-cell interactions.

• Normal Condition Dominance: The leftmost section, corresponding to Normal lung samples (e.g., LUNG_N01 to LUNG_N34), exhibits a distinct cluster of highly active and significant CCIs (highlighted by the blue box). These interactions predominantly involve Diploid Lung Epithelial cells interacting with T cells (CD4+, CD8+) and Macrophages, and also Macrophage-Macrophage interactions. The dot sizes are generally large, indicating high statistical significance, and the dark red colors denote strong interaction means.
• Tumor (advanced) Specificity: The central Tumor(adv) section shows a different set of highly active and significant CCIs, particularly evident in the samples highlighted by the blue box. These interactions frequently involve Aneuploid Lung Epithelial cells engaging with Myeloid cells (Macrophages, NK cells, Dendritic cells), T cells, and B cells. Notable patterns include interactions related to Prostaglandin E2 signaling, Integrins, and ICAMs.
• Tumor (early) Characteristics: The rightmost Tumor(early) section also reveals a unique cluster of CCIs, distinct from both Normal and Tumor(adv) conditions, as indicated by its blue box. Interactions in this stage primarily involve Aneuploid Lung Epithelial cells with Macrophages and Fibroblasts. Interactions centered around Oncostatin M (OSM), VEGFA, and specific Integrins are prominent here.
• Ploidy-driven Shift: A striking feature is the clear demarcation of Lung Epithelial cell interactions: Lung.Epi(Dip) interactions are prevalent in Normal samples, while Lung.Epi(Aneup) interactions dominate in both early and advanced tumor samples, underscoring the genetic instability and altered cellular identity in cancer.

Biological Interpretation

The analysis highlights a profound remodeling of the lung tissue microenvironment (TME) during tumor progression, marked by a shift in cell-cell communication networks.

• Normal Homeostasis: In the normal lung, CCIs involving Lung.Epi(Dip) with T cells and Macrophages (e.g., ALCAM_CD6--Lung.Epi(Dip)|T CD4+, integrin_aLb2_complex--Mac|T CD8+) suggest healthy immune surveillance and epithelial-immune crosstalk crucial for maintaining tissue integrity and responding to routine challenges. Interactions like DHEAsulfate_bySULT2B_PPARg--Lung.Epi(Dip)|Mac might reflect metabolic and anti-inflammatory pathways active in maintaining lung health [1].
• Emergence of Aneuploid Epithelial Cells in Tumor: The switch from Lung.Epi(Dip) to Lung.Epi(Aneup) as a central interacting partner in tumor conditions is highly significant. Aneuploidy, a common feature of cancer cells, alters their gene expression profiles and surface protein landscape, leading to novel or dysregulated interactions with the surrounding immune and stromal cells [2].

Tumor (advanced) Microenvironment Remodeling

• The increased presence of ProstaglandinE2_byPTGES3_PTGER2--Fib|T CD8+ in advanced tumors points to an immunosuppressive environment where cancer-associated fibroblasts (CAFs) and T cells communicate via PGE2, a known mediator of immune evasion and tumor growth [3].
• Adhesion molecules like ICAMs and Integrins (e.g., ICAM1_ITGAL--Mac|Mac, F11R_integrin_aLb2_complex--Lung.Epi(Aneup)|T CD8+, ICAM1_integrin_aEb7_complex--Lung.Epi(Aneup)|NK) are crucial for cell migration, invasion, and immune cell trafficking within the TME. Their altered patterns suggest changes in immune cell recruitment, retention, and interaction with tumor cells.
• Interactions involving CDH1 (E-cadherin) with Lung.Epi(Aneup) and B cells (CDH1_integrin_aEb7_complex--Lung.Epi(Aneup)|B cell) or NK cells suggest complex roles beyond simple adhesion, possibly influencing immune cell function or tumor cell plasticity [4].

Tumor (early) Microenvironment Dynamics

• The strong OSM_OSMR--Mac|Lung.Epi(Aneup) interaction highlights Oncostatin M signaling, a cytokine pathway implicated in inflammation, fibrosis, and promoting cancer cell proliferation, invasion, and drug resistance, often through interactions with macrophages [5].
• VEGFA_NRP2--Mac|Mac suggests robust pro-angiogenic signaling within the macrophage population, critical for establishing the tumor vasculature even at early stages [6].
• Interactions involving MMP21_integrin_aLb2_complex--Mac|Mac and COL8A1_complex_ADGRE2--Fib|Mac point to extracellular matrix (ECM) remodeling and altered cell-matrix interactions, driven by macrophages and fibroblasts, which are fundamental processes for tumor invasion and metastasis [7].

Clinical or Translational Implications

These condition-specific CCI patterns offer valuable insights for biomarker discovery and therapeutic targeting in lung cancer.

• Diagnostic/Prognostic Biomarkers: Specific CCI signatures, particularly those distinguishing normal from early and advanced tumors, could serve as novel diagnostic or prognostic biomarkers. For instance, the presence of aneuploid epithelial cell interactions with components of the TME could indicate early tumor formation or progression.
• Therapeutic Targets: Key ligand-receptor pairs identified as differentially active represent potential therapeutic targets.
• Targeting the PGE2 pathway (e.g., ProstaglandinE2_byPTGES3_PTGER2) could help reverse immunosuppression in advanced lung cancer.
• Interfering with OSM-OSMR signaling (OSM_OSMR--Mac|Lung.Epi(Aneup)) might inhibit tumor growth and progression, especially in early stages where this interaction is prominent.
• Blocking VEGFA-NRP2 interactions (VEGFA_NRP2--Mac|Mac) could suppress angiogenesis, thereby limiting tumor nutrient supply and growth.
• Modulating Integrin-mediated interactions could impact tumor cell adhesion, migration, and immune cell function within the TME.
• Understanding Immune Evasion: The identification of CCIs that promote immunosuppression (e.g., PGE2, specific macrophage-epithelial interactions) provides avenues to develop immunomodulatory therapies that restore anti-tumor immunity.

---

References:

[1] DHEASulfate by SULT2B: GeneCards - SULT2B1. GeneCards

[2] Aneuploidy in Cancer: PubMed Search. PubMed Search

[3] Prostaglandin E2 in cancer: PubMed Search. PubMed Search

[4] E-cadherin in immune regulation: PubMed Search. PubMed Search

[5] Oncostatin M in cancer: PubMed Search. PubMed Search

[6] VEGFA-NRP2 in angiogenesis: PubMed Search. PubMed Search

[7] MMP21 in cancer: GeneCards - MMP21. GeneCards

16. Lung Epithelial Cell Condition-Specific Surfaceome Markers Across Disease Stages

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Lung Epithelial cells, comparing Normal, Tumor (early), and Tumor (advanced) conditions across various patient samples. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing the gene (dot size) for selected surfaceome genes, providing insights into potential diagnostic and therapeutic targets. The data is stratified by individual samples and their inferred ploidy status (Diploid/Aneuploid).

Visual Summary

The dot plot displays surfaceome marker expression across 90,224 Lung Epithelial cells from different samples, grouped by condition (Normal, Tumor(adv), Tumor(early)) and further by ploidy status (Diploid, non-Diploid).

• Normal Condition Markers: A distinct cluster of markers (e.g., CLDN18, LAMP3, ABCA3, AQP4) shows high expression and prevalence in Lung Epithelial cells from Normal samples. These markers are largely absent or expressed at very low levels in tumor samples.
• Specifically, Diploid LUNG_T28, Diploid LUNG_T09, Diploid LUNG_T08, Diploid LUNG_T19, Diploid LUNG_T06, Diploid LUNG_T25 samples within the "Normal" group show strong expression of CLDN18, LAMP3, ABCA3, and AQP4.
• Similarly, non-Diploid Normal samples (e.g., LUNG_N09, LUNG_N28) also exhibit high expression of these same markers, along with SUSD2, CD24, and LY6D.
• Tumor (Advanced) Condition Markers: A large set of genes demonstrates high expression and prevalence specifically in Tumor (advanced) samples. These include markers like EMP1, DSG2, IFNGR2, ADAM15, GPR160, TNFRSF1A, SLC39A4, ADAM9, SLC52A2, CEACAM6, BST2, ABCC3, ITGA3, MET, PLPP2, SERINC2, STEAP4, PLXNB2, ERBB2, DDR1, EGFR, EPHA2, PLAUR, SLC44A1, SLC44A4, and TNFRSF21.
• These markers show broad upregulation across multiple advanced tumor samples, irrespective of their ploidy status (although the majority of tumor samples appear to be non-Diploid).
• Tumor (Early) Condition Markers: The expression pattern in Tumor (early) samples shares many upregulated markers with Tumor (advanced) samples (e.g., EMP1, DSG2, IFNGR2, ADAM15, CEACAM6, BST2, ITGA3, MET, EGFR, ERBB2). However, some markers appear to be more prominent or specifically enriched in advanced stages, suggesting a progression-related signature.
• Ploidy Differences: While both Diploid and non-Diploid Normal samples share many common normal markers, within the tumor conditions, the analysis highlights that many of the highly expressed tumor markers are found across both Diploid and non-Diploid tumor cells, suggesting these represent general tumor-associated changes in Lung Epithelial cells. However, the non-Diploid tumor samples generally show a stronger and broader activation of tumor-associated markers.
• Sample Heterogeneity: There is clear heterogeneity in marker expression across individual samples within the same condition, particularly visible within the Tumor (advanced) and Tumor (early) groups. For instance, some tumor samples show higher expression of specific subsets of markers than others.

Biological Interpretation

This analysis provides a clear distinction of surfaceome profiles for Lung Epithelial cells in normal and cancerous states, as well as between early and advanced tumor stages.

Normal Epithelial Cell Markers:

• CLDN18 (Claudin-18): A tight junction protein critical for epithelial barrier function. CLDN18.2 isoform is a known gastric cancer therapeutic target, and its expression in normal lung epithelia is consistent with its role in tissue integrity.
• LAMP3 (Lysosomal Associated Membrane Protein 3): Involved in antigen presentation and immune response, also known as DC-LAMP, it can be expressed by certain epithelial cells.
• ABCA3 (ATP Binding Cassette Subfamily A Member 3): Crucial for pulmonary surfactant synthesis and secretion in alveolar type II cells. Its presence here is consistent with normal lung epithelial function GeneCards: ABCA3.
• AQP4 (Aquaporin-4): A water channel protein involved in water transport, expressed in various epithelial cells.
• CD24: A glycosylphosphatidylinositol-anchored protein involved in cell adhesion and signaling, often linked to stemness. Its expression in normal lung epithelial cells might reflect normal epithelial turnover or specific sub-lineage.

These markers collectively represent the homeostatic functions and cell identity of healthy Lung Epithelial cells.

Tumor-Associated Epithelial Cell Markers (Common to early and advanced tumors):

• EMP1 (Epithelial Membrane Protein 1): Associated with cell proliferation, differentiation, and migration, often overexpressed in various cancers and linked to poor prognosis PubMed: EMP1 cancer.
• DSG2 (Desmoglein 2): A desmosomal cadherin involved in cell-cell adhesion. Upregulation in cancer can be associated with altered adhesion properties and metastasis.
• IFNGR2 (Interferon Gamma Receptor 2): While IFN-gamma signaling is generally anti-tumorigenic, altered expression or signaling components can have complex roles in cancer immunity and progression.
• ADAM15 (ADAM Metallopeptidase Domain 15): A disintegrin and metalloproteinase, involved in cell adhesion, migration, and proteolysis, often upregulated in cancer and linked to invasion.
• CEACAM6 (Carcinoembryonic Antigen Related Cell Adhesion Molecule 6): A well-known oncofetal antigen, highly expressed in various adenocarcinomas including lung, promoting cell survival, proliferation, and metastasis GeneCards: CEACAM6. It is a strong candidate for therapeutic targeting.
• BST2 (Bone Marrow Stromal Antigen 2 / CD317): Also known as tetherin, an interferon-inducible protein, frequently overexpressed in various cancers and associated with tumor progression and immune evasion.
• ITGA3 (Integrin Subunit Alpha 3): Forms integrin α3β1, involved in cell adhesion to extracellular matrix and signaling, frequently implicated in cancer cell migration and invasion.
• MET (MET Proto-Oncogene, Receptor Tyrosine Kinase): A key receptor tyrosine kinase involved in cell growth, survival, and motility. MET overexpression or activation is a known oncogenic driver in lung cancer and a target for therapy PubMed: MET lung cancer.
• ERBB2 (Erb-B2 Receptor Tyrosine Kinase 2 / HER2): Another oncogenic receptor tyrosine kinase, a well-established therapeutic target in breast and gastric cancers, and also identified in a subset of lung cancers GeneCards: ERBB2. Its presence here suggests its potential as a target in lung epithelial tumors.
• EGFR (Epidermal Growth Factor Receptor): A critical receptor tyrosine kinase whose activating mutations and overexpression are major drivers in non-small cell lung cancer (NSCLC) and a primary target for tyrosine kinase inhibitors (TKIs). Its strong expression in tumor epithelial cells is highly expected PubMed: EGFR lung cancer.
• PLAUR (Plasminogen Activator, Urokinase Receptor): Involved in extracellular matrix degradation and cell migration, often upregulated in invasive cancers.
• Potential Progression Markers: Some markers like DDR1, ADAM15, GPR160, and TNFRSF1A seem to be more consistently or highly expressed in advanced tumors compared to early tumors, potentially indicating their role in disease progression or increased aggressiveness. Conversely, some markers like CLDN18, LAMP3, ABCA3, AQP4 are lost during tumorigenesis, representing dedifferentiation or altered cell state.

The presence of well-known oncogenic drivers like EGFR, ERBB2, and MET as highly expressed surfaceome markers in tumor Lung Epithelial cells is particularly significant.

Clinical or Translational Implications

The identified condition-specific surfaceome markers have substantial clinical and translational implications for lung cancer:

1. Diagnostic and Prognostic Biomarkers:

• Genes like CLDN18, LAMP3, ABCA3, and AQP4 could serve as biomarkers for distinguishing normal lung epithelial tissue from early-stage or advanced tumors.
• Conversely, robust tumor-specific markers such as CEACAM6, EMP1, BST2, ITGA3, MET, ERBB2, and EGFR could be used to identify cancer cells, monitor disease progression, or evaluate treatment response through liquid biopsies or imaging.

2. Therapeutic Targets:

• The strong and prevalent expression of surfaceome proteins like EGFR, ERBB2, and MET in tumor Lung Epithelial cells reaffirms their established roles as actionable therapeutic targets in lung cancer. These findings provide single-cell resolution evidence for their consistent expression at the cell surface in patient tumors.
• Other highly expressed tumor-specific surfaceome markers, such as CEACAM6, BST2, ITGA3, PLAUR, and DDR1, represent promising novel targets for antibody-drug conjugates (ADCs), chimeric antigen receptor (CAR) T-cell therapies, or bispecific antibodies. The "surfaceome only" filtering in this analysis is particularly relevant for these therapeutic modalities, as it focuses on proteins accessible for targeted interventions.
• Understanding the differential expression between early and advanced tumors (e.g., potential upregulation of DDR1 in advanced disease) could lead to stage-specific therapeutic strategies.

3. Understanding Tumor Heterogeneity: The observed sample-to-sample variability in marker expression underscores the importance of personalized medicine approaches. Not all tumors will express all markers to the same extent, necessitating biomarker testing for patient stratification in clinical trials. The integration of ploidy information (Diploid vs. non-Diploid samples) further refines our understanding of marker expression in different genetic contexts of lung cancer.

17. Macrophage Condition-Specific Surfaceome Markers in Lung Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that are specifically enriched in Macrophages across different conditions (Normal vs. Tumor (early)) within lung tissue. The plot_markers_and_expression_dot tool was utilized to visualize these condition-specific differentially expressed genes (DEGs), focusing on surface-expressed proteins due to their potential as therapeutic targets and diagnostic biomarkers. The results highlight distinct surface protein profiles for Macrophages in normal lung tissue compared to those in early-stage lung tumors.

Visual Summary

The dot plot effectively visualizes the expression patterns of identified macrophage surface markers across various samples, grouped by condition (Normal vs. Tumor (early)).

• Distinct Marker Sets: There is a clear segregation of markers, with one set predominantly expressed in Normal samples and another set showing preferential expression in Tumor (early) samples. This indicates a significant phenotypic shift in macrophages in the early tumor microenvironment.
• Normal Condition Markers: A large cluster of markers, including ADGRE5, SPN, ADAM17, ATP1B1, LPL, CLEC12A, TRPV2, ANPEP, S1PR4, SLC6A6, HCAR2, CLDN7, FFAR4, MME, and AMIGO2, shows high mean expression (dark red color) and broad prevalence (large dot size) across most Normal lung samples (e.g., LUNG_N06, LUNG_N09, LUNG_N08). These markers are largely absent or very lowly expressed in Tumor (early) samples.
• Tumor (early) Condition Markers: A distinct set of markers, notably GPR183, CD84, ABCA1, and FCGR2B, exhibits elevated expression specifically in Tumor (early) samples (e.g., LUNG_T09, LUNG_T08, LUNG_T20, LUNG_T18). Their expression intensity and prevalence vary among tumor samples but are markedly higher than in Normal samples.
• Sample Representation: The bar graphs on the right indicate the number of macrophage cells analyzed per sample (Fraction of cells in group (%)), providing context for the robustness of the expression data for each sample.

Biological Interpretation

The observed condition-specific surface markers for macrophages reflect their diverse functional roles and plasticity in response to the surrounding tissue microenvironment, particularly in the context of early lung cancer.

• Normal Lung Macrophages and Homeostasis: The markers prevalent in normal lung macrophages (e.g., ADGRE5, SPN, ADAM17, LPL, CLEC12A, HCAR2) are often associated with functions like tissue homeostasis, immune surveillance, phagocytosis of cellular debris, and lipid metabolism.
• ADGRE5 (CD97) is an adhesion G-protein coupled receptor involved in cell adhesion and immune regulation. GeneCards: ADGRE5
• LPL (Lipoprotein Lipase) plays a crucial role in lipid metabolism, which is essential for maintaining tissue health and energy balance. GeneCards: LPL
• CLEC12A (C-type lectin domain family 12 member A) is a myeloid-specific inhibitory receptor, likely involved in self-tolerance and regulating inflammatory responses. UniProt: Q5T098 (CLEC12A)
• HCAR2 (GPR109A) is a receptor for niacin and butyrate, known to mediate anti-inflammatory effects in various immune cells. PubMed Search: HCAR2 macrophage anti-inflammatory

These markers suggest a macrophage phenotype geared towards maintaining a quiescent and healthy tissue state.

• Early Lung Tumor Macrophages and Microenvironment Adaptation: The distinct set of surface markers in macrophages from early tumor samples indicates their adaptation to the tumor microenvironment, potentially contributing to tumor initiation or progression.
• GPR183 (EBI2) is a G protein-coupled receptor that, while known for B cell migration, has been implicated in macrophage function and infiltration into inflammatory or tumor sites. Its expression may suggest altered migratory patterns or interactions within the nascent tumor niche. PubMed: EBI2 macrophage tumor
• CD84 (SLAMF5) is a cell surface receptor of the Signaling Lymphocyte Activation Molecule (SLAM) family, involved in immune cell adhesion and signaling. Its upregulation could signify enhanced cell-cell interactions crucial for tumor-associated macrophage (TAM) functions, such as immune modulation or antigen presentation in a tumor context. GeneCards: CD84
• ABCA1 (ATP-binding cassette transporter A1) is critical for cholesterol efflux. Altered lipid metabolism is a common feature in TAMs, impacting their phenotype and function, often contributing to an immunosuppressive environment. PubMed: ABCA1 macrophage lipid tumor
• FCGR2B (CD32B) is an inhibitory Fc gamma receptor. Its expression on TAMs can dampen pro-inflammatory responses and immune effector functions, such as antibody-dependent cellular cytotoxicity (ADCC), thereby contributing to immune evasion in the tumor microenvironment. PubMed: FCGR2B tumor associated macrophage

These markers collectively point towards a macrophage phenotype that is adapting to and potentially promoting the early tumor environment, often characterized by altered metabolism and immunosuppressive functions.

Clinical or Translational Implications

The identification of condition-specific surface markers for macrophages in early lung cancer holds significant clinical and translational potential.

• Early Diagnostic and Prognostic Biomarkers: The distinct surfaceome signatures could serve as biomarkers for early detection of lung cancer or to stratify patients based on the macrophage immune landscape. For instance, increased expression of GPR183, CD84, ABCA1, and FCGR2B on macrophages could indicate the presence of an early tumor. These markers could be assessed via techniques like flow cytometry or imaging on biopsies.
• Therapeutic Targets for Immunomodulation: As surfaceome proteins, these markers represent accessible targets for immunomodulatory therapies.
• Targeting inhibitory receptors like FCGR2B on TAMs could potentially reprogram these cells towards an anti-tumor phenotype, enhancing immune surveillance and effector functions in the early stages of cancer.
• Modulating lipid metabolism via ABCA1 in TAMs could disrupt their pro-tumoral functions.
• Antibody-drug conjugates or CAR-macrophage approaches designed to specifically target these tumor-associated macrophage markers could offer novel therapeutic strategies for early lung cancer, aiming to either deplete pro-tumoral macrophages or re-educate them.
• Understanding Early Tumor Microenvironment: These markers provide insights into the earliest interactions between the nascent tumor and the immune system, which is crucial for developing prevention and early intervention strategies. Further functional studies are warranted to validate the causal roles of these markers in macrophage phenotype and tumor progression.

18. Fibroblast Condition-Specific Surfaceome Markers in Lung Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers that are specifically expressed in Fibroblasts under different conditions (Normal, Tumor (early)) within the lung tissue. By focusing on surface markers, we can pinpoint potential candidates for cell-type-specific targeting or diagnostic applications. The plot_markers_and_expression_dot tool was used to visualize the expression of the top 50 condition-specific surface markers for Fibroblasts, comparing Normal and Tumor (early) samples.

Visual Summary

The dot plot effectively visualizes the differential expression of surface markers across Fibroblast populations from Normal and early Tumor lung samples.

• Distinct Clustering by Condition: Samples clearly segregate into two main groups based on their gene expression profiles. Normal lung samples (LUNG_Nxx) cluster together at the top, characterized by high expression of a specific set of markers. Early tumor lung samples (LUNG_Txx) cluster below, showing high expression of a largely distinct set of markers. This indicates significant transcriptional reprogramming of fibroblasts in the tumor microenvironment even at early stages.
• Normal Fibroblast Markers: Genes such as SCARA5, GAS1, LEPR, GPRC5A, CD34, PI16, and CADM3 are highly expressed and detected in a large fraction of cells within the normal lung fibroblast population (as highlighted by the red box in the upper left). These markers appear to be largely absent or expressed at very low levels in tumor fibroblasts.
• Tumor (Early) Fibroblast Markers: A distinct set of genes, including prominent markers like PLXDC2, PTTG1IP, MMP14, TNFSF13B, FAP, TMEM204, PMEPA1, F2R, VCAM1, AOC3, IL1R1, SPINT2, CD82, and ITM2C, shows high expression and prevalence in fibroblasts from early tumor samples (as highlighted by the red box in the lower right). These markers are either absent or very lowly expressed in normal fibroblasts.
• Expression and Prevalence: For most identified markers, high mean expression (darker red color) correlates with a high fraction of cells expressing the gene (larger dot size), reinforcing their specificity to the respective conditions.
• Sample-Level Variability: While there's a clear condition-specific pattern, some intra-group variability in marker expression levels and prevalence can be observed between individual samples, which is common in biological data.

Biological Interpretation

The differential surfaceome profiles highlight a fundamental shift in fibroblast identity and function as they transition from normal tissue homeostasis to an early tumor-associated state.

• Normal Lung Fibroblast Identity: The identified markers for normal fibroblasts such as SCARA5 (scavenger receptor, involved in cell adhesion and immune response), GAS1 (growth arrest specific 1, a tumor suppressor gene often involved in inhibiting cell proliferation), LEPR (leptin receptor, involved in metabolism and inflammation), CD34 (a stem cell marker also found on some stromal and endothelial progenitors), and PI16 (protease inhibitor 16, involved in ECM organization) suggest roles in maintaining tissue structure, regulating cell growth, and mediating physiological responses within the healthy lung microenvironment. Their downregulation in tumor fibroblasts indicates a loss of these homeostatic functions.
• Early Tumor-Associated Fibroblast (CAF) Activation: The robust upregulation of specific surface markers in early tumor fibroblasts points towards their activation into a pro-tumorigenic phenotype, commonly referred to as Cancer-Associated Fibroblasts (CAFs).
• ECM Remodeling and Invasion: MMP14 (Matrix Metallopeptidase 14, also known as MT1-MMP) is a crucial enzyme involved in degrading the extracellular matrix (ECM) and facilitating tumor cell invasion and metastasis [GeneCards]. Its high expression in tumor fibroblasts underscores their role in restructuring the tumor microenvironment to support tumor growth. FAP (Fibroblast Activation Protein alpha) is a well-established canonical CAF marker also involved in ECM degradation, immunosuppression, and promoting tumor progression [GeneCards].
• Cell Adhesion and Migration: VCAM1 (Vascular Cell Adhesion Molecule 1) is an adhesion molecule involved in recruiting immune cells and promoting cancer cell adhesion and migration [GeneCards]. PLXDC2 (Plexin Domain Containing 2) is implicated in angiogenesis and tumor progression [GeneCards].
• Inflammation and Immune Modulation: IL1R1 (Interleukin 1 Receptor Type 1) mediates inflammatory responses that can contribute to tumor growth [GeneCards]. AOC3 (Amine Oxidase, Copper Containing 3, also known as VAP-1) is an adhesion molecule involved in immune cell trafficking and inflammation [GeneCards]. These suggest tumor fibroblasts are active participants in orchestrating the inflammatory and immunosuppressive tumor microenvironment.
• Proliferation and Signaling: PTTG1IP (PTTG1 Interacting Protein) is linked to cell proliferation and tumor growth [GeneCards]. PMEPA1 (Prostate Transmembrane Protein, Androgen Induced 1) is associated with TGF-beta signaling, a key pathway in CAF activation and tumor progression [GeneCards].

Clinical or Translational Implications

The identification of condition-specific surfaceome markers in Fibroblasts from early lung tumors has significant clinical and translational implications:

• Early Diagnostic and Prognostic Biomarkers: Markers like FAP, MMP14, VCAM1, and AOC3, which are distinctly upregulated on tumor fibroblasts, could serve as early diagnostic biomarkers for lung cancer or prognostic indicators of disease progression. Their detection, perhaps via liquid biopsy or specific imaging agents, could aid in early intervention.

Therapeutic Targets for Tumor Microenvironment Modulation:

• FAP stands out as a highly promising therapeutic target. As a well-established CAF marker, FAP-targeting strategies (e.g., FAP-targeting antibodies, small molecule inhibitors, or FAP-CAR T-cells) are actively being investigated in cancer therapy to reprogram the tumor microenvironment and enhance anti-tumor immunity [PubMed Search].
• MMP14 represents another valuable target due to its critical role in ECM remodeling and facilitating tumor cell invasion. Inhibitors of MMP14 could potentially impede tumor progression and metastasis.
• VCAM1 and AOC3, as adhesion molecules involved in immune cell recruitment and inflammation, could be targeted to disrupt the immunosuppressive tumor microenvironment and improve the efficacy of immunotherapies. Blocking these interactions might reduce immune evasion and enhance anti-tumor immune responses.
• The surface localization of these markers makes them highly accessible for antibody-drug conjugates (ADCs) or other cell-surface targeted therapies.
• Experimental Validation: The identified markers warrant further experimental validation using techniques like immunohistochemistry (IHC) or immunofluorescence on lung tissue sections to confirm their protein expression and localization. Flow cytometry could be used to isolate and further characterize these specific fibroblast populations. Functional studies (e.g., in vitro co-culture assays, in vivo murine models) would be essential to elucidate the precise roles of these markers in driving CAF activation and tumor progression, and to test the efficacy of targeting them.

19. Condition-Specific Surfaceome Markers of CD4 T Cells in Lung Tissue

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for CD4 T cells across normal, early-stage tumor, and advanced-stage tumor conditions in lung tissue. The results are presented as a dot plot, where dot size reflects the fraction of cells expressing a gene and color intensity represents the mean expression level within each sample. This approach helps pinpoint cell surface proteins that characterize CD4 T cell states in different disease contexts, offering potential insights into immune regulation, diagnostic biomarkers, and therapeutic targets.

Visual Summary

The dot plot effectively visualizes gene expression patterns for CD4 T cells across individual samples, grouped by condition (Normal, Tumor(adv), Tumor(early)).

• Normal Condition Cluster: Samples from normal lung tissue (LUNG_N01 to LUNG_N20) exhibit high expression and prevalence of genes such as ADGRE5, HLA-DRB5, LDLR, CD27, and SELL. These markers form a distinct cluster, suggesting a characteristic CD4 T cell phenotype in healthy lung.
• Tumor-Associated Cluster (Advanced): Samples from advanced tumors (EBU_49, BRONCHO_58, EBUS_06, EBUS_28) show a strong upregulation of several genes, including BTN3A2, LPAR6, SIRPG, SPINT2, SERINC5, TRABD2A, TMEM63A, TNFRSF4 (OX40), TIGIT, TNFRSF18 (GITR), and CTLA4. This cluster is particularly prominent in the advanced tumor samples, indicating a shift in CD4 T cell surface marker profile during disease progression.
• Tumor-Associated Cluster (Early): Samples from early-stage tumors (e.g., LUNG_T20, LUNG_T18, LUNG_T30) also show high expression of some markers identified in advanced tumors, notably TNFRSF4, TIGIT, TNFRSF18, and CTLA4. This suggests that some tumor-associated immune alterations begin in early stages, with some genes becoming even more pronounced in advanced disease.
• Differential Expression Intensity: The intensity of red color highlights strong mean expression, while larger dot sizes indicate a higher percentage of cells expressing the marker. For instance, in advanced tumor samples, genes like TIGIT and CTLA4 show both high expression and high prevalence across many cells, signifying their broad involvement.

Biological Interpretation

The observed condition-specific surfaceome markers in CD4 T cells reflect distinct functional states and interactions within the lung microenvironment, particularly in the context of tumor development.

Normal Lung CD4 T Cell Phenotype:

• The prominent expression of CD27 and SELL (CD62L) in normal lung CD4 T cells often indicates a naive or central memory T cell phenotype, involved in immune surveillance and homing to lymphoid organs for antigen presentation. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD27, https://www.genecards.org/cgi-bin/carddisp.pl?gene=SELL
• HLA-DRB5, an MHC Class II molecule, can be expressed by activated T cells or specific regulatory T cell subsets, suggesting a state of immune responsiveness or a unique population in the normal lung. https://www.genecards.org/cgi-bin/carddisp.pl?gene=HLA-DRB5
• ADGRE5 (CD97) is an adhesion GPCR involved in cell-cell interactions and inflammation, potentially important for CD4 T cell integration within the normal lung tissue. https://www.genecards.org/cgi-bin/carddisp.pl?gene=ADGRE5

Tumor-Associated CD4 T Cell Phenotype (Early and Advanced):

• The strong upregulation of TIGIT and CTLA4 in both early and advanced tumor conditions is highly significant. These are well-established inhibitory immune checkpoints associated with T cell exhaustion or regulatory T cell (Treg) function within the tumor microenvironment (TME). Their increased expression suggests an ongoing attempt by the tumor to suppress anti-tumor immunity. https://pubmed.ncbi.nlm.nih.gov/33979450/
• The concurrent high expression of TNFRSF4 (OX40) and TNFRSF18 (GITR), both co-stimulatory receptors, points to a complex immune landscape. While OX40 and GITR typically promote T cell activation and survival, their co-expression with inhibitory markers can reflect chronic activation leading to exhaustion or the presence of specific T cell subsets (e.g., activated Tregs) that are co-opting these pathways for immunosuppression. https://pubmed.ncbi.nlm.nih.gov/31332274/
• Markers like BTN3A2, LPAR6, and SIRPG show prominent expression in tumor conditions, particularly in advanced tumors. These molecules are involved in various immune regulatory processes, cell adhesion, and signaling, suggesting their potential roles in shaping the tumor-associated immune response. https://www.genecards.org/cgi-bin/carddisp.pl?gene=BTN3A2, https://www.genecards.org/cgi-bin/carddisp.pl?gene=LPAR6, https://www.genecards.org/cgi-bin/carddisp.pl?gene=SIRPG
• The enrichment of a broader set of markers (e.g., SPINT2, SERINC5, TRABD2A, TMEM63A) specifically in advanced tumor samples compared to early tumors highlights the progressive remodeling of CD4 T cell states as cancer progresses.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers on CD4 T cells holds significant clinical and translational potential for lung cancer.

Biomarker Development:

• The distinct profiles of surface markers could serve as diagnostic or prognostic biomarkers. For instance, the ratio or combined expression of markers like CD27/SELL versus TIGIT/CTLA4 could potentially differentiate normal lung from early or advanced tumor states.
• Monitoring the expression levels of these markers (e.g., TIGIT, CTLA4, OX40, GITR) on CD4 T cells in peripheral blood or tumor biopsies could provide insights into disease progression, response to therapy, or recurrence.

Therapeutic Targets for Immunotherapy:

• The robust upregulation of inhibitory immune checkpoints TIGIT and CTLA4 in both early and advanced tumors strongly supports their continued investigation as targets for immune checkpoint blockade (ICB) therapies in lung cancer. Combination therapies targeting multiple checkpoints may be particularly effective.
• The presence of co-stimulatory receptors OX40 and GITR in the tumor microenvironment suggests opportunities for agonist antibodies to enhance anti-tumor CD4 T cell responses. However, their co-expression with inhibitory markers necessitates careful consideration of the specific functional subsets (e.g., effector vs. regulatory T cells) where these are expressed.
• Further research into the roles of other tumor-associated markers like BTN3A2, LPAR6, and SIRPG could reveal novel pathways for immune modulation or cell-specific targeting to improve anti-tumor immunity.

Understanding Disease Progression and Resistance:

• The differences in marker profiles between early and advanced tumors provide a molecular foundation for understanding how CD4 T cell responses evolve with disease progression. This knowledge can inform the optimal timing and selection of immunotherapeutic interventions.
• These markers could also be investigated as potential indicators of resistance mechanisms to existing therapies, guiding the development of salvage regimens or combination strategies.

20. Differential Expression of Cell Cycle Genes in Lung Epithelial Cells During Lung Cancer Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis examines the expression profiles of a selected panel of cell cycle pathway-related genes within Lung Epithelial cells. The goal was to identify statistically significant differences in gene expression across three distinct conditions: Normal lung tissue, Early-stage Lung Tumor (Tumor(early)), and Advanced-stage Lung Tumor (Tumor(adv)). As Lung Epithelial cells are identified as the tumor origin cell type, these findings directly reflect intrinsic changes within the neoplastic cells. The results are presented as box plots, illustrating the distribution of gene expression (sample mean) for each gene across the conditions, with p-values indicating the statistical significance of pairwise comparisons.

Visual Summary

The visualization displays the expression levels of 27 cell cycle-related genes in Lung Epithelial cells across Normal, Tumor(early), and Tumor(adv) conditions.

• General Upregulation in Tumor Conditions: A pervasive pattern is the statistically significant increase in expression for many genes in both Tumor(early) and Tumor(adv) conditions compared to Normal lung epithelial cells. This includes core cell cycle regulators and components such as ANAPC10, ANAPC11, BUB3, CDC26, CDK4, CCND1, MCM3, MCM7, MYC, PCNA, SMAD3, TFDP1, TGFB1, and YWHAZ. Many of these comparisons show highly significant p-values (p $\leq$ 0.01 or $\leq$ 0.001), indicating a broad activation or dysregulation of cell cycle machinery in tumor cells.
• Higher Expression in Early Tumor: For several genes, including ANAPC10, ANAPC11, BUB3, CDKN1B, CDKN2A, CDKN2B, MCM3, MCM7, RB1, SMAD3, STAG2, TFDP1, and TFDP2, expression tends to be highest in Tumor(early) compared to Normal. While still significantly higher than normal, expression in Tumor(adv) for these genes is often similar to or slightly attenuated compared to Tumor(early). This pattern could suggest a peak of proliferative activity or a robust initial compensatory response in the early stages of tumorigenesis.
• Stage-Specific Downregulation: A particularly notable finding is the significant *decrease* in expression of GADD45G in Tumor(adv) compared to both Normal (p $\leq$ 1e-4) and Tumor(early) (p $\leq$ 0.001). This suggests a potential loss of growth arrest or DNA damage response mechanisms in advanced disease.
• Differential Upregulation in Advanced Tumor: ABL1 shows significantly higher expression in Tumor(adv) compared to both Normal and Tumor(early), indicating a potentially distinct signaling pathway activation or selective advantage in advanced disease.
• Consistent Differences Across Tumor Stages: Other genes such as ATM, CREBBP, EP300, HDAC1, HDAC2, MDM2, ORC4, PRKDC, TP53, WEE1, and several YWH family members also exhibit significant upregulation in tumor conditions relative to normal, reinforcing the widespread perturbation of cell cycle control.

Biological Interpretation

The observed upregulation of numerous cell cycle-related genes in Lung Epithelial cells across both early and advanced tumor conditions fundamentally highlights the role of deregulated cell proliferation in lung cancer pathogenesis. As these are the identified tumor origin cells, these findings directly reflect the core cellular processes driving tumor growth and progression.

• Accelerated Proliferative Activity: The elevated expression of key cell cycle drivers like CCND1 (Cyclin D1) and CDK4 promotes G1/S phase progression. Similarly, the consistent upregulation of MCM3 and MCM7, essential components of the DNA replication licensing complex, and PCNA, a marker for DNA synthesis, strongly indicates increased DNA replication and rapid cell division GeneCards: MCM7. The proto-oncogene MYC, a critical regulator of cell growth and proliferation, also shows significant elevation, further supporting heightened cellular proliferation.
• Altered Cell Cycle Checkpoint Control: Upregulation of ANAPC10, ANAPC11, ANAPC7 (components of the Anaphase-Promoting Complex/Cyclosome, APC/C, involved in mitotic exit) and BUB3 (a key component of the spindle assembly checkpoint) suggests that while cells are aggressively proliferating, there might also be an attempt to engage or a response to stress within cell cycle checkpoints. In cancer, these checkpoints are often compromised or overridden, leading to genomic instability, a hallmark of malignancy.
• Complex Regulation of Tumor Suppressors: The increased expression of cell cycle inhibitors/tumor suppressors such as CDKN1A (p21), CDKN1B (p27), CDKN2A (p16INK4a), CDKN2B (p15INK4b), and RB1 in tumor cells, particularly in early stages, is a complex observation. While these genes typically halt cell cycle progression, their elevated mRNA expression in cancer could reflect:

1. Compensatory attempts: Cells trying to counteract excessive proliferative signals.
2. Cellular stress response: Various stresses during tumorigenesis can activate these pathways.
3. Functional inactivation: The proteins might be rendered non-functional through mutations or post-translational modifications despite increased mRNA levels, a common mechanism in cancer. This highlights the importance of integrating protein-level data for a complete understanding.

• DNA Damage and Genomic Instability: Upregulation of ATM (Ataxia Telangiectasia Mutated) and PRKDC (DNA-dependent protein kinase catalytic subunit), central players in DNA damage response, implies that lung epithelial tumor cells are experiencing increased genomic stress. This stress likely arises from rapid, unchecked proliferation and oncogenic insults, necessitating active repair mechanisms to maintain cell viability PubMed Search: ATM DNA damage cancer.

Progression-Specific Mechanisms:

• The significant *downregulation* of GADD45G in advanced tumors is particularly crucial. GADD45G is known to induce cell cycle arrest, DNA repair, and apoptosis, acting as a tumor suppressor. Its reduced expression in Tumor(adv) suggests a potential loss of these critical growth-restricting and genome-protective functions, contributing to aggressive tumor behavior GeneCards: GADD45G.
• Elevated ABL1 expression in advanced tumors may indicate activation of alternative signaling pathways promoting proliferation and survival, as ABL1 is a proto-oncogene tyrosine kinase involved in various cellular processes and a therapeutic target in other malignancies GeneCards: ABL1.
• The upregulation of TGFB1 and SMAD3 suggests active TGF-beta signaling. While TGF-beta can inhibit growth in early stages, it often promotes invasion, metastasis, and immunosuppression in advanced cancers, underscoring its context-dependent roles in tumor progression PubMed Search: TGFB cancer dual role.

Clinical or Translational Implications

These findings have several important clinical and translational implications for lung cancer:

• Biomarker Discovery: Genes consistently upregulated in lung epithelial tumor cells, such as CCND1, CDK4, MCM3, MCM7, and MYC, could serve as valuable diagnostic or prognostic biomarkers. Their expression levels could help distinguish cancerous from normal tissue and potentially monitor disease progression.
• Targeted Therapies: The widespread dysregulation of cell cycle genes highlights their potential as therapeutic targets. For example, the elevated CDK4 expression suggests that CDK4/6 inhibitors, already approved for other cancers, could be investigated for efficacy in specific lung cancer subtypes PubMed Search: CDK4/6 inhibitors lung cancer. The increased ABL1 expression in advanced tumors suggests that ABL kinase inhibitors might be explored in personalized treatment strategies for advanced lung cancer.
• Stage-Specific Treatment Strategies: The differential expression patterns between early and advanced tumors, particularly the downregulation of GADD45G in Tumor(adv), indicate stage-specific alterations in tumor biology. This suggests that therapeutic strategies might need to be tailored to the stage of cancer, leveraging specific vulnerabilities present at different points of disease progression. For instance, therapies designed to restore GADD45G function or circumvent its loss could be particularly relevant for advanced lung cancer.
• Prognostic Indicators: The expression levels of these cell cycle genes, especially those showing distinct patterns across normal, early, and advanced stages, could be developed as prognostic indicators to predict patient outcomes, aggressiveness, or likelihood of response to specific therapies.

In summary, this analysis provides strong evidence for profoundly altered cell cycle regulation in lung epithelial cells during lung cancer development and progression, identifying key genes whose differential expression could inform biomarker development, therapeutic targeting, and stage-specific treatment approaches.

21. Lung Epithelial Cell Gene Ontology (GSA) Analysis Across Ploidy and Disease Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results (GSA) for Lung Epithelial cells, comparing different cellular states: Diploid vs. Aneuploid, Normal vs. Tumor (early/advanced), and Tumor(advanced) vs. Normal/Tumor(early), and Tumor(early) vs. Normal/Tumor(advanced). The goal is to identify biological pathways and processes significantly upregulated in Lung Epithelial cells under each specific condition relative to the "others" group within the same cell type, providing insights into condition-associated biology and cell-state shifts in the context of lung cancer.

Visual Summary

The provided bar plots illustrate the top significantly upregulated GO terms (Term) for Lung Epithelial cells under four distinct comparison conditions, sorted by their statistical significance (-log(p-val) and -log(q-val)).

1. Diploid_vs_others: Diploid Lung Epithelial cells show strong enrichment for immune response pathways, various infectious diseases (bacterial, viral, parasitic), and autoimmune conditions, along with phagosome activity.
2. Normal_vs_others: Normal Lung Epithelial cells exhibit prominent enrichment in metabolic pathways (e.g., PPAR signaling, fatty acid metabolism, cholesterol metabolism), cellular protective mechanisms (FoxO signaling, mitophagy), and growth control pathways (Hippo signaling), alongside immune and infection-related processes.
3. Tumor(adv)_vs_others: Lung Epithelial cells from advanced tumors are highly enriched in fundamental cellular processes associated with rapid proliferation and altered proteostasis (Ribosome, Spliceosome, Protein processing in endoplasmic reticulum, Cell cycle, DNA replication), cellular stress/dysfunction (pathways of neurodegeneration, autophagy), and multiple cancer-specific signaling pathways (mTOR, p53, ErbB - inferred from common cancer pathways).
4. Tumor(early)_vs_others: Lung Epithelial cells from early tumors share many enriched pathways with advanced tumors, including those for cellular machinery (Spliceosome, Ribosome, Protein processing in endoplasmic reticulum), cellular stress, and oncogenic signaling (ErbB, mTOR, Insulin signaling).

Biological Interpretation

Diploid vs. Aneuploid Lung Epithelial Cells

Diploid Lung Epithelial cells, representing a genetically stable state, demonstrate a robust upregulation of pathways related to immune response, inflammation, and host defense against various pathogens. Terms like "Staphylococcus aureus infection," "Asthma," "Rheumatoid arthritis," "Phagosome," and "Antigen processing and presentation" are highly enriched. This suggests that genetically stable epithelial cells maintain a vigilant role in immune surveillance and direct response to environmental challenges, a function that might be compromised in aneuploid cells, which often characterize precancerous or cancerous states. The "Intestinal immune network for IgA production" pathway, while seemingly distant from lung, can reflect a general mucosal immune competency or cross-reactivity in pathway annotations.

Normal vs. Tumor Lung Epithelial Cells

Healthy, Normal Lung Epithelial cells show a distinct metabolic signature, with significant enrichment in PPAR signaling pathway, biosynthesis and degradation of unsaturated fatty acids, and cholesterol metabolism. These pathways are crucial for maintaining lipid homeostasis and energy balance, reflecting the active, yet controlled, metabolic state of healthy cells. Furthermore, normal cells exhibit upregulation of Hippo signaling pathway, a key regulator of organ size and tumor suppression, and pathways like FoxO signaling and mitophagy, indicative of cellular stress response, longevity, and mitochondrial quality control. This profile suggests that normal lung epithelial cells are geared towards maintaining cellular integrity, metabolic fitness, and preventing uncontrolled proliferation, in stark contrast to tumor cells. They also retain a capacity for immune response and pathogen recognition, evidenced by "Phagosome" and various infection terms.

Tumor (Early and Advanced) Lung Epithelial Cells

Both early and advanced tumor Lung Epithelial cells display common characteristics of malignancy, reflecting substantial cell-state shifts from normal epithelium:

• Hyper-proliferation and altered protein homeostasis: A striking feature is the strong enrichment of pathways related to fundamental cellular machinery for growth and division, including "Ribosome," "Spliceosome," "Protein processing in endoplasmic reticulum," "Proteasome," "RNA transport," "Cell cycle," and "DNA replication." This indicates an elevated rate of protein synthesis, RNA processing, and cell division, which are hallmarks of rapid tumor growth.
• Cellular stress and dysfunction: A significant number of terms associated with "Pathways of neurodegeneration" (e.g., Alzheimer disease, Huntington disease, Parkinson disease) are highly enriched. While these diseases are distinct, their associated pathways often converge on common mechanisms of cellular stress, altered proteostasis (protein folding and degradation), and mitochondrial dysfunction, which are also prevalent in aggressive cancer cells. "Autophagy," "Ubiquitin mediated proteolysis," and "Thermogenesis" further underline these stress responses and metabolic reprogramming.
• Oncogenic signaling and evasion: Pathways such as "mTOR signaling pathway," "p53 signaling pathway" (more prominent in advanced tumors), "ErbB signaling pathway" (more prominent in early tumors), "Insulin signaling pathway," and "Viral carcinogenesis" are actively engaged, reflecting common drivers of cancer progression. The presence of "Proteoglycans in cancer" and "Regulation of actin cytoskeleton" suggests altered extracellular matrix interactions and increased cell motility/invasion.
• Infection and immune modulation: Various infection-related pathways (e.g., Salmonella, Coronavirus, Hepatitis, Human papillomavirus) are enriched in both early and advanced tumor cells. This could imply that tumor cells either exhibit altered susceptibility to certain pathogens or hijack these pathways for immune evasion and creating a pro-tumorigenic microenvironment.

Differences Between Early and Advanced Tumor Epithelial Cells

While significant overlap exists, subtle differences highlight progression:

• Advanced tumors show a more pronounced enrichment for direct proliferation markers like "Cell cycle" and "DNA replication," alongside "p53 signaling pathway," which is critical for cell cycle control and apoptosis and is often mutated or deregulated in advanced cancers.
• Early tumors more prominently feature "ErbB signaling pathway" and "Insulin signaling pathway," suggesting an early reliance on growth factor receptor signaling and metabolic shifts to drive initial transformation and uncontrolled growth.

Clinical or Translational Implications

1. Diagnostic and Prognostic Biomarkers: The distinct enrichment profiles observed for normal/diploid vs. tumor (early/advanced) lung epithelial cells can serve as a rich source for identifying novel diagnostic or prognostic biomarkers. For instance, dysregulation of metabolic pathways (e.g., PPAR signaling) could indicate early deviations from a healthy state, while specific activation of proliferative pathways (e.g., mTOR, ErbB) could indicate tumor presence and aggressiveness.
2. Therapeutic Targets: Pathways consistently upregulated in tumor cells, such as mTOR, ErbB, and p53 signaling, are well-established targets for cancer therapeutics. The differential enrichment observed here provides context for the specific activation of these pathways in lung epithelial cells during different stages of tumorigenesis, potentially guiding stage-specific therapeutic interventions. Targeting altered protein homeostasis pathways (e.g., ribosome biogenesis, proteasome activity) also represents a promising avenue for therapeutic development.
3. Understanding Tumor Microenvironment and Immune Evasion: The strong immune and infection-related signatures in normal/diploid cells, contrasted with their presence (and likely subversion) in tumor cells, emphasizes the critical role of epithelial cells in shaping the tumor microenvironment. Understanding how tumor epithelial cells alter host-pathogen interactions and immune signaling could lead to strategies for immunotherapy or therapies that restore anti-tumor immunity.
4. Metabolic Reprogramming: The clear shift from lipid-centric metabolism in normal cells to a proliferative and stress-response metabolic state in tumor cells highlights metabolic reprogramming as a therapeutic vulnerability. Targeting specific metabolic pathways in tumor cells, which are distinct from those in healthy cells, could offer selective anti-cancer approaches.

• PPAR signaling: https://www.genecards.org/cgi-bin/carddisp.pl?gene=PPARA&keywords=PPAR
• Hippo signaling pathway: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725515/ (Review on Hippo signaling in cancer)
• mTOR signaling pathway: https://www.genecards.org/cgi-bin/carddisp.pl?gene=MTOR&keywords=mTOR
• ErbB signaling pathway: https://www.genecards.org/cgi-bin/carddisp.pl?gene=ERBB2&keywords=ERBB

22. Major Cell Type Gene Set Enrichment Analysis in Lung Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for various major cell types across different lung conditions (Normal, Tumor(early), Tumor(adv)). The dot plot visualizes the Normalized Enrichment Score (NES) and statistical significance for a selection of 80 gene sets. Each column represents a specific cell type in a given condition compared against all other conditions for that same cell type (e.g., "T cell CD4+: Tumor(adv)_vs_others" compares CD4+ T cells in advanced tumor samples to CD4+ T cells from normal and early tumor samples). The color of each dot indicates the NES (red for positive/upregulated enrichment, blue for negative/downregulated enrichment), and the size of the dot reflects the statistical significance (-log(P-value)), with larger dots signifying higher significance.

Visual Summary

The dot plot reveals widespread and distinct pathway enrichments and depletions across different cell types and lung conditions.

• Condition-specific patterns: Generally, tumor conditions (Tumor(early) and Tumor(adv)) show a greater number of enriched (red) pathways compared to the Normal condition within the same cell type, indicating significant cellular reprogramming during tumorigenesis.
• "Pathways in cancer": This general cancer pathway is markedly enriched (large red dots) across multiple cell types in tumor conditions, including Lung Epithelial cells, Macrophages, Fibroblasts, and Endothelial cells. This highlights the broad impact of cancer on both tumor cells and the surrounding microenvironment.
• Metabolic Reprogramming: Pathways such as "Oxidative phosphorylation" and "Ribosome" are frequently enriched (red) in Lung Epithelial cells, Macrophages, Dendritic cells, Fibroblasts, and Endothelial cells in tumor conditions, suggesting increased metabolic activity and protein synthesis associated with proliferation and activation. "Glycolysis / Gluconeogenesis" is also notably enriched in Macrophages in tumor conditions.

Immune/Inflammatory Pathways

• "NF-kappa B signaling pathway" and "Cytokine-cytokine receptor interaction" are enriched (red) in Macrophages, Dendritic cells, Fibroblasts, and Endothelial cells in tumor conditions, pointing to an active inflammatory and communicative tumor microenvironment (TME).
• "PD-L1 expression and PD-1 checkpoint pathway" shows enrichment (red) in Macrophages (both Tumor(early) and Tumor(adv)) and T cell CD4+ (Tumor(adv)), suggesting immune evasion mechanisms are active.
• "Th1 and Th2 cell differentiation" is depleted (blue) in T cell CD4+ in both tumor conditions, contrasting with its enrichment in T cell CD8+ in Normal condition.
• Stromal Activation: Fibroblasts and Endothelial cells in tumor conditions consistently exhibit enrichment for pathways related to cancer, metabolism, and immune signaling, underscoring their active role in supporting tumor growth. "Axon guidance" pathway is also enriched in Endothelial cells in tumor conditions.
• Cellular Senescence: This pathway is enriched in Lung Epithelial cells in the Tumor(early) condition, potentially indicating early stress responses or a pre-malignant state, but depleted in T cell CD4+ in Tumor(adv).

Biological Interpretation

The GSEA results provide critical insights into the biological processes altered in different cell populations within the lung tumor microenvironment.

• Tumor Cell Intrinsic Changes (Lung Epithelial cells): The strong enrichment of "Pathways in cancer," "Oxidative phosphorylation," "Ribosome," and "Protein processing in endoplasmic reticulum" in tumor epithelial cells confirms their highly proliferative and metabolically active state, consistent with malignant transformation and rapid growth. The enrichment of "Cellular senescence" in early tumor epithelial cells might reflect a protective mechanism activated in response to oncogenic stress or a transient state prior to full malignant progression 1.

Immune Cell Dysregulation:

• The upregulation of "NF-kappa B signaling pathway" and "Cytokine-cytokine receptor interaction" in tumor-associated Macrophages and Dendritic cells suggests their active engagement in chronic inflammation and extensive cross-talk within the TME. These cells likely contribute to an immunosuppressive environment that favors tumor growth 2.
• The enrichment of "PD-L1 expression and PD-1 checkpoint pathway" in macrophages and advanced tumor CD4+ T cells is a direct indicator of active immune evasion, where tumor cells and associated immune cells suppress anti-tumor T cell responses 3.
• The depletion of "Th1 and Th2 cell differentiation" in tumor CD4+ T cells suggests a potential shift away from effective anti-tumor immune responses, which often rely on Th1 polarization, towards a more tolerogenic or anergic state.
• Metabolic Reprogramming in the TME: The widespread enrichment of "Oxidative phosphorylation" and "Glycolysis" in tumor-associated stromal and immune cells (e.g., Macrophages, Fibroblasts) highlights their metabolic adaptation to support tumor growth, often characterized by distinct metabolic preferences, such as the Warburg effect in rapidly proliferating cells and some immune subsets 4.
• Stromal Contribution to Tumor Progression: The consistent enrichment of cancer-related and pro-inflammatory pathways in Fibroblasts and Endothelial cells within tumor conditions underscores their transformation into Cancer-Associated Fibroblasts (CAFs) and tumor-associated endothelial cells. These cells actively remodel the extracellular matrix, promote angiogenesis, and secrete cytokines, collectively facilitating tumor progression 5. The enrichment of "Axon guidance" in endothelial cells could indicate their involvement in vascular remodeling, a process that shares molecular parallels with neural development 6.

Clinical or Translational Implications

The findings from this GSEA analysis offer several potential clinical and translational implications for lung cancer:

• Immunotherapy Targets: The strong enrichment of "PD-L1 expression and PD-1 checkpoint pathway" in tumor-associated macrophages and advanced tumor CD4+ T cells reinforces the rationale for PD-1/PD-L1 blockade in lung cancer. Targeting other immune checkpoint molecules or strategies to reverse T cell dysfunction (e.g., restoring Th1 differentiation) could be beneficial.
• Metabolic Intervention: The observed metabolic reprogramming, including enhanced oxidative phosphorylation and glycolysis in tumor cells and TME components, presents opportunities for targeted metabolic therapies. Inhibitors of specific metabolic enzymes or pathways could selectively impair tumor growth and support cells without severely impacting normal tissues.
• Targeting the Tumor Microenvironment: The widespread activation of inflammatory and cancer-promoting pathways in macrophages, fibroblasts, and endothelial cells suggests that therapies aimed at modulating the TME could be effective. This might include targeting NF-kappa B signaling, specific cytokine-cytokine interactions, or pathways involved in CAF activation and angiogenesis 7.
• Stage-Specific Therapies: The distinct enrichment pattern of "Cellular senescence" in early-stage tumor epithelial cells compared to later stages or other cell types might indicate unique vulnerabilities or therapeutic windows in early lung cancer, potentially suggesting strategies to induce or clear senescent cells 8.

References:

1. Cellular Senescence in Cancer:

PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=cellular+senescence+cancer+lung

2. NF-κB Signaling in TME:

PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=NF-kappaB+signaling+tumor+microenvironment

3. PD-L1/PD-1 Checkpoint in Cancer:

PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=PD-L1+PD-1+checkpoint+lung+cancer

4. Metabolic Reprogramming in Cancer:

PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=metabolic+reprogramming+lung+cancer

5. CAFs and Tumor Progression:

PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=cancer-associated+fibroblasts+lung+cancer

6. Axon Guidance in Angiogenesis:

PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=axon+guidance+angiogenesis+cancer

7. Targeting TME in Lung Cancer:

PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=targeting+tumor+microenvironment+lung+cancer

8. Senolytics in Cancer:

PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=senolytics+cancer+therapy

23. Discussion

The comprehensive single-cell analysis reveals a profoundly altered cellular landscape in lung cancer, driven primarily by aneuploid lung epithelial cells, the inferred tumor-origin population. These malignant cells exhibit widespread genomic instability, evident from their high aneuploidy across both early and advanced tumor stages, sharply contrasting with the genomic stability of normal cells. This genomic chaos is foundational to the subsequent transcriptional reprogramming and aberrant cell-cell communication observed.

The tumor microenvironment (TME) undergoes dramatic remodeling with disease progression. Key immune cell population shifts include a significant and progressive increase in pro-tumorigenic Macrophage (M2B) cells from normal to advanced tumors, accompanied by a decrease in other M2 macrophage subsets (M2A, M2C). This suggests a strong M2B-driven immunosuppressive and pro-angiogenic environment. Simultaneously, anti-tumor Natural Killer (NK) cells are markedly reduced in both early and advanced tumors, indicative of compromised immune surveillance. Within T cells, Th17 cells, associated with chronic inflammation, are elevated in tumors, while regulatory T cells (Tregs) show a significant but transient increase in early tumors, potentially contributing to early immunosuppression. Naive T cells also persist at higher proportions in tumors, suggesting impaired differentiation or continuous, ineffective immune cell infiltration.

Cell-cell interaction analyses highlight critical crosstalk networks. Aneuploid lung epithelial cells are central players, engaging in extensive homotypic and heterotypic interactions with immune and stromal cells. Prominent pro-tumorigenic and immunosuppressive pathways include robust EGFR signaling (autocrine/paracrine activation via AREG, HBEGF, EREG, TGFA), widespread TGF-beta activation via the integrin αVβ6 complex (TGFB1_integrin_avb6_complex), and significant Prostaglandin E2 (PGE2) signaling, particularly between tumor cells and macrophages. Immune checkpoint interactions like LILRB2-HLA-F and LGALS9-HAVCR2 (Galectin-9-TIM-3) are also prominent, indicating active immune evasion mechanisms.

Differential marker expression further dissects these cell-state changes. Tumor Lung Epithelial cells consistently upregulate oncogenic surface markers like EGFR, ERBB2, MET, and CEACAM6. Tumor-associated fibroblasts activate into CAFs, expressing FAP and MMP14, which are involved in ECM remodeling. Macrophages in early tumors adopt a distinct phenotype, characterized by GPR183, CD84, ABCA1, and the inhibitory FCGR2B, suggesting early adaptation towards a pro-tumorigenic, immunosuppressive state. CD4 T cells show increased expression of inhibitory checkpoints (TIGIT, CTLA4) and co-stimulatory receptors (OX40, GITR) in tumor conditions, reflecting chronic activation and exhaustion or specific regulatory functions.

Pathway analyses confirm these observations. Tumor epithelial cells exhibit hyper-proliferative pathways (Ribosome, Spliceosome, Cell cycle, DNA replication) and oncogenic signaling (mTOR, ErbB, p53), contrasting with the metabolic homeostasis and immune surveillance functions in normal cells. Collectively, this atlas delineates a multi-faceted landscape of lung cancer progression, where malignant epithelial cells drive genomic instability and establish an intricate, immunosuppressive, and pro-tumorigenic microenvironment through dynamic cell-cell interactions and transcriptional reprogramming of surrounding stromal and immune cells. These findings provide a rich resource for identifying novel biomarkers and therapeutic targets.

Hypotheses:

1. Aneuploidy in lung epithelial cells is an early and sustained event in lung cancer, directly contributing to altered gene expression, cell-cell interactions, and subsequent tumor progression by promoting uncontrolled proliferation and evasion of cell cycle checkpoints.
2. The progressive increase of M2B macrophages in advanced lung tumors, coupled with their strong interactions via Prostaglandin E2 (PGE2) signaling, actively promotes an immunosuppressive tumor microenvironment (TME), leading to T cell dysfunction and NK cell depletion.
3. Upregulation of the integrin αVβ6 complex on aneuploid lung epithelial cells facilitates the activation of latent TGF-β, which then suppresses anti-tumor immunity and promotes tumor invasiveness and extracellular matrix remodeling from early stages.
4. Increased expression of inhibitory immune checkpoints TIGIT and CTLA4 on CD4 T cells, alongside co-stimulatory receptors like OX40 and GITR, indicates a complex state of chronic activation leading to exhaustion or regulatory function, contributing to immune evasion in the TME.

Potential therapeutic targets:

1. EGFR: Consistently high expression and strong autocrine/paracrine signaling via multiple ligands (AREG, HBEGF, EREG, TGFA) in aneuploid Lung Epithelial cells from early to advanced stages. It is a well-established oncogenic driver in lung cancer. Evidence: High mean expression and statistical significance in cell-cell interaction (CCI) plots involving Aneuploid Lung Epithelial cells (Sections 12, 14). High expression as a surfaceome marker in Lung Epithelial cells (Section 16). Validation: Test efficacy of existing EGFR tyrosine kinase inhibitors (TKIs) or novel EGFR-targeting antibody-drug conjugates (ADCs) in patient-derived organoids/xenografts. Combine with inhibitors of interacting pathways to assess synergistic effects.
2. Integrin αVβ6 (ITGAV and ITGB6 subunits): Strong interaction of the TGFB1_integrin_avb6_complex in aneuploid Lung Epithelial cells in both early and advanced tumors, indicative of active TGF-beta activation, which is a major immunosuppressive and pro-tumorigenic pathway involved in invasion and fibrosis. Evidence: High statistical significance and mean expression in CCI plots, prominently involving Aneuploid Lung Epithelial cells and macrophages (Sections 12, 14, 15). Validation: Develop or test existing integrin αVβ6 inhibitors (e.g., neutralizing antibodies) in preclinical models to assess reduction of active TGF-β, reversal of immunosuppression, and inhibition of tumor invasion and growth.
3. Prostaglandin E2 (PGE2) Pathway (e.g., PTGES3 or PTGER4 receptor): Highly significant and strong interactions involving PGE2 receptors, particularly between aneuploid Lung Epithelial cells and Macrophages, and within Macrophages, in advanced tumors. PGE2 is a potent immunosuppressive and pro-tumorigenic mediator, promoting angiogenesis and tumor growth while inhibiting T cell function. Evidence: High statistical significance and mean expression for PTGES3-PTGER4 and other PTGER interactions in CCI plots for advanced tumors (Sections 12, 13, 15). Validation: Investigate COX-2 inhibitors or specific PTGER antagonists in preclinical models to assess their effects on macrophage polarization, T cell function, tumor angiogenesis, and overall tumor growth in vitro and in vivo.
4. FAP (Fibroblast Activation Protein alpha): Distinctly upregulated surfaceome marker on fibroblasts in early tumor samples, indicating activation into Cancer-Associated Fibroblasts (CAFs). FAP is involved in extracellular matrix (ECM) degradation, immunosuppression, and promoting tumor progression. Evidence: High mean expression and prevalence as a surfaceome marker in tumor-associated fibroblasts, contrasting with normal fibroblasts (Section 18). Validation: Utilize FAP-targeting antibody-drug conjugates (ADCs) or FAP-CAR T-cells in preclinical models to assess CAF depletion/reprogramming and its impact on tumor growth, metastasis, and the immune microenvironment. Investigate synergy with immunotherapies.
5. TIGIT and CTLA4: Robust upregulation of these inhibitory immune checkpoints on CD4 T cells in both early and advanced tumor conditions, suggesting active T cell exhaustion and immune evasion mechanisms are at play within the tumor microenvironment. Evidence: High mean expression and prevalence as surfaceome markers on CD4 T cells in tumor conditions, differentiating them from normal CD4 T cells (Section 19). Validation: Test combination immunotherapies targeting TIGIT and CTLA4 (potentially with existing PD-1/PD-L1 blockade) in preclinical models, assessing T cell reinvigoration, restoration of anti-tumor cytokine production, and anti-tumor efficacy.

Follow-up validation ideas:

1. Perform in vitro functional assays on isolated diploid and aneuploid lung epithelial cells from early tumor samples to compare their proliferation rates, self-renewal capacity, and specific gene expression profiles following perturbation of cell cycle regulators.
2. Inhibit PGE2 signaling in M2B macrophages (using COX-2 inhibitors or specific PTGER antagonists) in co-culture with T cells and tumor cells to assess restoration of T cell function (proliferation, cytokine production) and reduction of tumor cell proliferation/invasion.
3. Utilize gene knockdown or neutralizing antibodies against integrin αVβ6 in patient-derived lung cancer organoids or xenograft models to assess its impact on active TGF-β levels, immune cell infiltration, extracellular matrix remodeling, and tumor growth/metastasis.
4. Conduct multi-parameter flow cytometry and spatial transcriptomics on tumor-infiltrating lymphocytes to quantify TIGIT, CTLA4, OX40, and GITR co-expression on CD4 T cell subsets and map their precise localization relative to tumor cells and M2B macrophages, correlating with functional markers (e.g., IFN-γ, Granzyme B) and exhaustion markers (e.g., PD-1, LAG-3).

Limitations:

The single-cell RNA-seq data provides mRNA expression, which may not always directly correlate with protein levels or functional activity. Inferences made from CNV and cell-cell interaction analyses require experimental validation. The analysis provides a static snapshot of the cellular landscape; longitudinal studies would be needed for dynamic insights into progression. The presence of 'unassigned' cells, especially in advanced tumors, suggests unresolved heterogeneity or cell states that do not fit predefined categories, limiting a complete understanding of the TME. The observed inter-sample heterogeneity and varying sample sizes per condition (e.g., fewer advanced tumor samples) might limit the generalizability of some findings. All findings are currently based on bioinformatics analysis; direct functional validation in experimental models (in vitro, in vivo) is crucial to establish causality.

24. Query List

1. Show UMAPs with condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset, in 2 columns and save.
2. Show major cell type 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 by sample, show CNV heatmap, and include a summary of significantly amplified copy number regions. Save.
5. Show CNV patterns on UMAP, including major cell type, minor cell type, ploidy results, condition, and sample, in 2 columns and save.
6. Show population bar plot for minor cell types and save.
7. Show subset population bar plot for T cells and save.
8. If there are significant differences between conditions in T cell subset populations, show box plot and save. Set ncols appropriately based on the total number of panels.
9. Show subset population bar plot for Macrophages and save.
10. If there are significant differences between conditions in Macrophage subset populations, show box plot and save. Set ncols appropriately based on the total number of panels.
11. Select tumor-origin cells and unassigned cells, show their ploidy population as a bar plot and save.
12. Show cell-cell interaction patterns by condition, including tumor-origin cells (Lung Epithelial cell), fibroblasts, macrophages, and T cells, and save. Limit cell-cell interactions to a maximum of 80 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, show cell-cell interactions for these genes and save.
15. Find statistically significant differences in cell-cell interactions between conditions for major immune cells and stromal cells, show 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 Macrophages and show as a dot plot and save. Include only surfaceome markers, up to 50 per condition.
18. Extract condition-specific markers for Fibroblasts and show as a dot plot and save. Include only surfaceome markers, up to 50 per condition.
19. Extract condition-specific markers for CD4 T cells and show as a dot plot and save. Include only surfaceome markers, up to 50 per condition.
20. For major disease-related cells, select cell cycle pathway-related genes with statistically significant expression differences between conditions, show as a box plot and save. Set max_n_items_to_plot = 24, and set ncols appropriately for a 2x3 aspect ratio based on the total number of panels.
21. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
22. Show dot plot of Gene Set Enrichment Analysis results for major cell types and save. Use RdBu_r for the color map and set n_pws_to_show = 80.