SCODiA Report by MLBI Lab

Single-Cell Transcriptomic and Genomic Deconvolution of the Tumor Microenvironment in Lung Adenocarcinoma and Squamous Cell Carcinoma

This single-cell RNA-seq analysis extensively characterizes the tumor microenvironment (TME) across lung adenocarcinoma (Adeno) and squamous cell carcinoma (Squamous) samples. We reveal profound differences in cellular composition, genomic instability, cell-cell interaction networks, and cell-type-specific gene expression and pathway activation. Key distinctions highlight divergent immune evasion strategies and stromal support mechanisms, offering critical insights into the unique biology and potential therapeutic vulnerabilities of these two major non-small cell lung cancer (NSCLC) subtypes.

Contents

0. Dataset overview
1. UMAP Visualization of Single-Cell RNA-seq Data by Condition, Sample, Cell Type, and Ploidy
2. Major Cell Type Score and Annotation Mapping on UMAP
3. Celltype_subset Marker Expression Pattern Analysis for Annotation Validation
4. Genomic Copy Number Variation Patterns in Tumor-Origin and Unassigned Lung Cells
5. CNV- 기반 UMAP을 통한 세포 유형, 이수성 및 조건별 패턴 분석
6. Minor Cell Type Population Analysis in Lung Adenocarcinoma vs. Squamous Cell Carcinoma
7. Lymphoid Cell Subset Composition in Lung Adenocarcinoma and Squamous Cell Carcinoma
8. Differences in T Cell and ILC Subset Proportions Between Lung Adenocarcinoma and Squamous Cell Carcinoma
9. Macrophage Subset Population Analysis in Lung Adenocarcinoma vs. Squamous Cell Carcinoma
10. Macrophage Subset Proportion Differences Between Lung Adenocarcinoma and Squamous Cell Carcinoma
11. Ploidy Population Analysis of Tumor-Origin and Unassigned Cells in Lung Adenocarcinoma and Squamous Cell Carcinoma
12. Cell-Cell Interaction Analysis in Squamous Lung Cancer: Macrophage-Macrophage Signaling
13. Cell-Cell Interaction Analysis in Lung Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) Microenvironments
14. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Pathways in Lung Adenocarcinoma vs. Squamous Cell Carcinoma
15. Condition-Specific Cell-Cell Interaction Patterns in Lung Adenocarcinoma vs. Squamous Cell Carcinoma
16. Lung Epithelial Cell Condition-Specific Surfaceome Markers in Adenocarcinoma vs. Squamous Cell Carcinoma
17. Macrophage Condition-Specific Surfaceome Markers in Lung Cancer Subtypes
18. Fibroblast Condition-Specific Surfaceome Markers in Lung Cancer
19. T cell CD4+ condition-specific surfaceome markers in Lung Adenocarcinoma vs. Squamous Cell Carcinoma
20. Differential Expression of Cell Cycle Genes in Lung Epithelial Cells Across NSCLC Subtypes
21. Lung Epithelial Cell Pathway Enrichment Analysis in Lung Adenocarcinoma, Squamous Cell Carcinoma, and Diploid Cells
22. Lung Cancer Microenvironment: Cell-Type Specific Pathway Enrichment in Adenocarcinoma vs. Squamous Cell Carcinoma
23. Discussion
24. Query List

0. Dataset overview

Dataset Summary

• This dataset contains single-cell RNA-seq data from human Lung tissue.
• It includes 58,757 cells and 37,895 genes.
• Conditions: Squamous, Adeno
• Major Cell Types: Lung Epithelial cell, Myeloid cell, Mast cell, Stromal cell, T cell, Endothelial cell, B cell, unassigned.
• Minor Cell Types: Airway Epithelial cell, Macrophage, Mast cell, Fibroblast, ILC, T cell CD8+, T cell CD4+, Endothelial cell, Plasma cell, unassigned, NK cell, B cell, Dendritic cell, Alveolar Epithelial cell, Smooth muscle cell.
• Subset Cell Types: Includes various sub-populations like Basal cell, Macrophage (M2A), T cell (Cytotoxic), Plasma cell, and many more detailed cell types.
• Tumor Origin Cell Type: Lung Epithelial cell.
• Ploidy Status: Cells are classified as Aneuploid or Diploid.
• Precomputed Results: The dataset includes precomputed results for Cell-Cell Interaction (CCI), Differential Expression Genes (DEG), Gene Set Enrichment Analysis (GSEA), and Gene Ontology (GSA/GO).
• Reference Condition: For DEG, GSEA, and GSA_up analyses, 'Adeno' is used as the reference condition.
• Available Analyses: DEG, GSEA, and GSA/GO analyses are available for specific minor cell types including B cell, Dendritic cell, Fibroblast, ILC, Lung Epithelial cell, Macrophage, Mast cell, Plasma cell, T cell CD4+, T cell CD8+.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis presents six Uniform Manifold Approximation and Projection (UMAP) plots, visualizing a single-cell RNA-seq dataset comprising 58,757 cells from 37,895 genes. Each UMAP projection illustrates the relationships between cells based on their gene expression profiles, colored by different metadata attributes: cancer condition (Adenocarcinoma vs. Squamous Cell Carcinoma), individual patient sample, major cell type, minor cell type, ploidy status (Aneuploid vs. Diploid), and cell type subset. The goal is to provide an overview of the dataset's structure, identify distinct cell populations, and understand how conditions, samples, and cell intrinsic features distribute within the transcriptomic landscape.

Visual Summary

1. Condition UMAP:

• The UMAP colored by condition shows a clear separation between the "Adeno" (Adenocarcinoma) and "Squamous" (Squamous Cell Carcinoma) conditions.
• The "Adeno" cells (maroon) represent the vast majority of the dataset and broadly occupy most of the UMAP space, forming several large, interconnected clusters.
• "Squamous" cells (blue/purple) are much fewer in number and primarily localize to distinct, smaller clusters, suggesting a unique gene expression signature relative to the Adeno cells. There is minimal direct overlap in the core regions, indicating distinct overall transcriptomic profiles.

2. Sample UMAP:

• The sample UMAP reveals significant inter-sample heterogeneity. While some clusters appear to be dominated by cells from a single or a few samples, many samples contribute cells across multiple distinct regions of the UMAP.
• This indicates that individual samples contain diverse cell populations and that there are both sample-specific transcriptional patterns and shared cellular components across samples. The widespread distribution of various colors across the UMAP also suggests that potential batch effects are not overwhelmingly dominant, allowing for biologically meaningful clustering.

3. Major Cell Type UMAP:

• The celltype_major UMAP demonstrates distinct clustering of major cell types, indicating successful cell type annotation and separation based on gene expression.
• "Lung Epithelial cell" (orange) forms a large, central, and upper-left cluster, consistent with its role as the tumor origin cell type.
• Immune cells like "T cell" (teal) and "Myeloid cell" (light yellow) form prominent, well-separated clusters, primarily located in the lower-right and left-center regions, respectively.
• "Stromal cell" (green), "Endothelial cell" (red), and "B cell" (maroon) also form discrete, smaller clusters.
• A small, distinct "unassigned" cluster (purple) is present, suggesting a minor population that could not be confidently classified at this level.

4. Minor Cell Type UMAP:

• At the celltype_minor level, even finer resolution is observed, with related cell types clustering adjacently.
• Within the epithelial compartment, "Airway Epithelial cell" (maroon) and "Alveolar Epithelial cell" (red) show distinct but connected regions.
• "Macrophage" (light yellow) and "Dendritic cell" (orange) subdivide the "Myeloid cell" major cluster.
• "T cell CD4+" (light blue) and "T cell CD8+" (dark blue) clearly separate within the "T cell" major cluster.
• This detailed clustering confirms the robust identification of specific cell subpopulations within the dataset.

5. Ploidy Decision UMAP (ploidy_dec):

• The ploidy_dec UMAP shows a striking distribution: "Aneuploid" cells (maroon) predominantly co-localize with the large "Lung Epithelial cell" clusters, especially those associated with the "Adeno" condition.
• "Diploid" cells (light yellow) are widely distributed across the UMAP, encompassing most immune, stromal, and some epithelial cell populations.
• A very small "Unclear" category (blue/purple) is present, similar to the "unassigned" cell types.
• This pattern strongly suggests that the Aneuploid cells represent the malignant tumor cells, consistent with the biological understanding of cancer and the data context specifying "Lung Epithelial cell" as the tumor origin.

6. Cell Type Subset UMAP:

• The celltype_subset UMAP provides the highest resolution of cell types, showing numerous fine-grained clusters.
• Specific subtypes like "Basal cell", "Alveolar type 2", various "Macrophage" subtypes (e.g., Mac_M1, Mac_M2A), and diverse "T cell" subtypes (e.g., T_Cyto, T_Naive, Treg) are clearly delineated.
• This granular annotation confirms the rich cellular diversity captured in the dataset and the effectiveness of the clustering and annotation pipeline.
• Similar to the major cell type, a small population of "unassigned" cells (dark blue) is observed, likely representing rare or ambiguous cell states.

Biological Interpretation

The UMAP visualizations collectively provide a comprehensive landscape of the lung tumor microenvironment across two distinct lung cancer histologies: Adenocarcinoma and Squamous Cell Carcinoma.

• Distinct Disease States: The clear separation of Adeno and Squamous conditions on the UMAP suggests that these two lung cancer types possess distinct transcriptomic profiles, which is expected given their different cellular origins and pathological characteristics. Adenocarcinoma, originating from glandular epithelial cells, and Squamous Cell Carcinoma, originating from squamous epithelial cells, are known to have different molecular drivers and clinical behaviors. The high prevalence of Adeno cells aligns with its generally higher incidence compared to Squamous Cell Carcinoma in certain patient cohorts.
• Cellular Heterogeneity and Annotation Quality: The consistent and clear separation of cell types at major, minor, and subset levels validates the robustness of the single-cell RNA-seq data and the quality of the cell type annotation. The identification of specific subtypes like various macrophage polarization states (M1, M2A-D), diverse T cell subsets (Cytotoxic, Treg, Tfh), and distinct epithelial subtypes (Basal, AT1, AT2, Secretory club) provides a high-resolution view of cellular diversity within the lung tissue. This detailed annotation is crucial for understanding cell-type-specific contributions to disease.
• Identification of Malignant Cells via Ploidy: The strong co-localization of "Aneuploid" cells primarily with "Lung Epithelial cell" clusters in the Adeno condition provides compelling evidence for the successful identification of malignant tumor cells. Aneuploidy, or an abnormal number of chromosomes, is a hallmark of cancer cells and often distinguishes them from diploid non-malignant cells. Given that "Lung Epithelial cell" is specified as the tumor origin, this observation is highly consistent with malignant transformation within the epithelial compartment. This distinction is critical for downstream analyses, such as identifying tumor-specific gene expression or cell-cell interactions.
• Tumor Microenvironment Complexity: The presence of diverse immune cells (T cells, B cells, Myeloid cells including Macrophages and Dendritic cells), stromal cells (Fibroblasts, Endothelial cells), and different epithelial cell states highlights the complex cellular ecosystem within the lung tumor microenvironment. Understanding the precise composition and spatial organization of these cell types is essential for deciphering tumor progression, immune evasion, and response to therapy.

Annotation Notes

• Robust Cell Type Identification: The sequential clustering and annotation from major to subset cell types demonstrate high confidence in cell identity assignments, with generally distinct and well-separated clusters for most cell types.
• Minor "Unassigned" Populations: The presence of small, discrete "unassigned" clusters at both major and subset levels is common in single-cell datasets. These may represent rare cell types, transitional states, or cells with ambiguous transcriptional profiles that did not meet classification criteria, and their small proportion suggests high overall annotation quality.
• Ploidy as a Malignancy Marker: The clear distinction between Aneuploid and Diploid cells, especially its strong association with Lung Epithelial cells in the disease context, serves as a powerful validation for identifying tumor cells within the dataset.

This overview provides a strong foundation for further in-depth analyses, leveraging the high-resolution cell type and condition-specific information.

2. Major Cell Type Score and Annotation Mapping on UMAP

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of major cell type scores, inferred ploidy status, and assigned major cell type annotations on a Uniform Manifold Approximation and Projection (UMAP) embedding of single-cell RNA-seq data from human lung tissue. The plot_umap tool was used to generate these visualizations, providing an overview of the cellular landscape and validating cell type assignments.

Visual Summary

The UMAP plots display 58,757 cells, each colored according to different attributes:

• Cell Type Scores (HiCAT_major_score): Seven plots show the expression scores for major cell types (T cell, B cell, Myeloid cell, Mast cell, Endothelial cell, Stromal cell, Lung Epithelial cell). High scores (yellow/green) indicate strong enrichment of markers for that specific cell type in particular regions of the UMAP.
• Ploidy Status (ploidy_dec): One plot displays cells colored by their inferred ploidy: Aneuploid (red), Diploid (light yellow), or Unclear (purple).
• Major Cell Type Annotation (celltype_major): One plot shows cells colored by their final assigned major cell type (e.g., B cell, Endothelial cell, Lung Epithelial cell, Mast cell, Myeloid cell, Stromal cell, T cell, unassigned).

Key observations from the plots include:

• Distinct Cell Type Clustering: Each major cell type generally occupies a well-defined and distinct region or cluster within the UMAP embedding. For instance, T cells primarily cluster in the lower-right and upper-left-central regions, while Lung Epithelial cells form a large cluster in the central-left area.
• Concordance of Scores and Annotations: The distribution of high HiCAT_major_scores for each cell type strongly correlates with the regions assigned to that specific celltype_major annotation. For example, the high score for "Lung Epithelial cell" overlays precisely with the "Lung.Epi" annotated cluster. This indicates a high level of confidence in the cell type assignments.
• Aneuploidy Distribution: A significant population of Aneuploid cells (red in ploidy_dec plot) is predominantly localized within the large central-left cluster. This cluster, when compared to the celltype_major plot, is almost entirely comprised of Lung Epithelial cells. Diploid cells are widely distributed across other clusters, including immune and stromal populations.

Biological Interpretation

• Cellular Heterogeneity and Transcriptional Programs: The clear segregation of different major cell types on the UMAP demonstrates the substantial cellular heterogeneity within the human lung tissue analyzed. Each distinct cluster represents cells with unique transcriptional profiles, underlying their specialized functions and identities.
• Robust Cell Type Annotation: The strong concordance between the computed HICAT major cell type scores and the assigned celltype_major labels provides robust validation for the cell type annotation process. This increases confidence in downstream analyses that rely on these cell type definitions.
• Identification of Malignant Cells: Given that "Lung Epithelial cell" is specified as the "Tumor origin celltype" and a significant population of Aneuploid cells is found specifically within the Lung Epithelial cell cluster, this strongly suggests that these Aneuploid Lung Epithelial cells represent the malignant tumor cells. Aneuploidy is a hallmark of cancer, indicating chromosomal instability and abnormal chromosome numbers, which is characteristic of tumor cells PubMed Search: aneuploidy cancer biomarker.
• Tumor Microenvironment Composition: The presence and distinct clustering of various immune cells (T cells, B cells, Myeloid cells, Mast cells) and stromal cells (Fibroblast, Endothelial cells, Smooth muscle cells, etc., inferred from the "Stromal cell" and "Endothelial cell" major categories) highlight the complex composition of the tumor microenvironment (TME) in lung cancer. These cell types play crucial roles in tumor progression, immune surveillance, and therapeutic responses GeneCards: Lung Epithelial cell.

Annotation Notes

The visualization effectively confirms the quality and reliability of the major cell type annotations. The distinct clustering and the high correlation between HICAT scores and assigned labels suggest that the clustering and annotation pipeline has successfully identified major cell populations. The clear separation of the putatively malignant (Aneuploid Lung Epithelial) cells from the surrounding stromal and immune cells is a critical finding for further focused investigations into tumor biology.

3. Celltype_subset Marker Expression Pattern Analysis for Annotation Validation

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression patterns of key marker genes across various celltype_subset populations identified in the single-cell RNA-seq data from human lung tissue. The primary goal is to assess the quality and specificity of the cell type annotations by examining whether the identified markers align with known biological characteristics of each cell type. Each row represents a celltype_subset, and each column represents a marker gene. The size of the dot corresponds to the fraction of cells within that group expressing the gene, while the color intensity reflects the mean expression level. Red boxes highlight sets of markers predominantly expressed within specific cell type clusters.

Visual Summary

The dot plot reveals distinct and largely specific expression patterns for most celltype_subset populations. A prominent diagonal pattern is observed, where groups of genes are highly expressed and enriched within their corresponding cell type. This pattern is reinforced by the red boxes, which encapsulate clusters of highly specific markers for each annotated cell type. The intensity and size of the dots within these boxes indicate robust expression and high prevalence of these markers within their respective cell populations.

On the right, a bar plot displays the number of cells belonging to each celltype_subset, indicating varying cell population sizes but generally sufficient representation for marker analysis. Overall, the visualization strongly suggests well-defined cell type clusters based on differential gene expression.

Biological Interpretation (Annotation Validation)

The marker expression patterns observed in the dot plot provide strong biological validation for the assigned celltype_subset annotations:

Lung Epithelial Cells:

• Alveolar Type 1: Characterized by expression of *HOPX* [GeneCards] and *CEACAM6*, which are known markers for AT1 cells, supporting their identity.
• Alveolar Type 2: Clearly defined by high expression of surfactant proteins such as *SFTPB*, *SFTPC* [GeneCards], *SFTPA1*, and *SFTPA2*, along with *NAPSA* and *MUC1*, confirming their role in surfactant production.
• Basal Cells: Exhibit canonical basal cell markers including various keratins (*KRT5*, *KRT14*, *KRT15*, *KRT17*) and the transcription factor *TP63* [GeneCards], indicating robust annotation.
• Ciliated Cells: Show specific expression of genes involved in ciliary function and development, such as *FOXJ1* [GeneCards] and *DNAH12*, consistent with their motile cilia phenotype.
• Goblet Cells: Identified by the presence of *MUC5AC* [GeneCards], a key mucin component.
• Secretory Club Cells: Strongly marked by *SCGB1A1* [GeneCards] (Club cell secretory protein), a definitive marker of these airway epithelial cells.

Immune Cells:

• B Cell Subsets (Breg, MZ, Memory, Follicular): While shared B cell markers are expected (e.g., *EBF1* [GeneCards], *POU2AF1* [GeneCards]), the plot shows distinct patterns for each subset. For instance, Plasma cells are clearly demarcated by *XBP1* [GeneCards], *PRDM1* (BLIMP1), and *SDC1* (CD138) [GeneCards], reflecting their terminal differentiation.
• Dendritic Cells (Classical, Inflammatory, Plasmacytoid): Plasmacytoid DCs (pDC) are identified by specific expression of *IRF7* [GeneCards] and *LILRA4* [GeneCards], consistent with their unique role in antiviral immunity. Other DC subsets show distinct, albeit sometimes overlapping, marker profiles.
• Macrophage Subsets (M1, M2A, M2B, M2C, M2D): While M1/M2 polarization is complex, markers like *CD86* for M1-like populations and *SPP1* [GeneCards] for certain M2-like subsets (M2A/B/D) are visible, indicative of their functional diversity in the lung microenvironment.
• Mast Cells: Exhibit robust expression of canonical markers such as *TPSAB1* [GeneCards], *TPSB2*, *SRGN*, and *KIT* (CD117) [GeneCards], firmly establishing their identity.
• NK Cells: Marked by genes like *KLRF1* [GeneCards] and *NKG7* [GeneCards], consistent with their cytotoxic effector functions.
• T Cell Subsets (Cytotoxic, Naive, Tfh, Th1, Th17, Th2, Th22, Treg): These subsets are well-resolved by specific transcription factors and surface markers. For example, Cytotoxic T cells express *GZMK* [GeneCards] and *CD8A* [GeneCards]. Naive T cells show *SELL* (CD62L) [GeneCards]. Regulatory T cells (Treg) are clearly identified by *FOXP3* [GeneCards], *CTLA4* [GeneCards], and *TNFRSF18* (GITR) [GeneCards], which are critical for immune suppression. Th2 cells express *GATA3* [GeneCards].

Stromal and Endothelial Cells:

• Endothelial Cells (General and Tip): Identified by markers such as *ESM1* [GeneCards] and *DLL4* [GeneCards] (important for tip cells), indicating distinct endothelial populations.
• Fibroblasts: Display classic fibroblast markers including collagen genes (*COL1A1* [GeneCards], *COL3A1*, *COL6A2*), *DCN* (Decorin) [GeneCards], and *LUM* (Lumican) [GeneCards], which are integral to extracellular matrix production.
• Smooth Muscle Cells: Robustly identified by smooth muscle actins (*ACTA2* [GeneCards]) and myosins (*MYH11* [GeneCards]), consistent with their contractile phenotype.

Annotation Notes

The comprehensive display of celltype_subset marker expression generally provides strong evidence for high-quality and reliable cell type annotations within this dataset. The clear specificity and biological relevance of the identified markers for each population suggest that the clustering and annotation process has successfully delineated distinct cellular identities. While the find_cfg parameter indicated surfaceome_only: True, the plot correctly includes several key transcription factors (e.g., FOXP3, GATA3, IRF7) that are essential for defining specific immune cell subsets and epithelial lineages. Their inclusion enhances the biological precision of the annotations, even if they are not surface-expressed proteins. This thorough validation of celltype_subset identities is crucial for downstream analyses, ensuring that subsequent functional and comparative studies are built upon a solid foundation of accurate cell assignments.

4. Genomic Copy Number Variation Patterns in Tumor-Origin and Unassigned Lung Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis provides a visual and quantitative assessment of copy number variations (CNVs) in 'Lung Epithelial cell' (identified as the tumor-origin cell type) and 'unassigned' cell populations from single-cell RNA-seq data. Cells were grouped by individual patient samples, and the log2(Copy Number Ratio) was used to infer genomic amplifications and deletions. The objective is to identify recurrent genomic alterations, evaluate the consistency of CNV patterns with ploidy classifications, and characterize the genomic instability within these key cell populations in the context of lung tissue.

Visual Summary

The heatmap displays the log2(Copy Number Ratio) across the human genome for each cell group, illustrating genomic amplifications (red) and deletions (blue). The accompanying summary plots detail the mean log2(CNR) for specific cytogenetic bands and their frequency of amplification.

• Overall Genomic Instability: The heatmap reveals widespread genomic instability across most samples, characterized by numerous large-scale amplifications and deletions. This pattern is highly indicative of malignant or pre-malignant states within the analyzed cell populations.
• Recurrent CNV Patterns: Several genomic regions show recurrent alterations across multiple samples. Prominent amplifications are observed on chromosomes 3q, 5p, 8q, 12q, 14q, and 17q, while recurrent deletions are noted on 3p, 8p, 9p, and 13q. These patterns suggest common genomic drivers in the studied lung tumors.
• Ploidy Correlation: Samples labeled as 'Diploid' (e.g., 'Diploid NSC004.T1') generally exhibit a visibly lower burden of extensive CNVs compared to their non-Diploid counterparts or other samples. This observation supports the consistency between the inferred ploidy_dec annotation and the actual genomic copy number landscape.
• Summary of Significant Amplifications: The summary plots highlight specific cytogenetic bands that are significantly amplified. The right panel, showing the frequency of amplification, is particularly striking:
• Several bands, including 1p34.2:1p34.1, 3q22.1:3q23, 3q25.1:3q25.32, 3q26.33:3q28 (SOX2), 9q34.3:10p15.1, 12q13.13:12q13.13, 14q23.3:14q24.1, 17q21.2:17q21.2, 22q11.22:22q11.23, and 22q13.1:22q13.31, show a 100% frequency (1.00) of amplification across the summarized samples. This indicates highly recurrent genomic gains in these regions.
• The left panel, showing the mean log2(CNR) for patient NSC004's samples (T1-T4), illustrates the magnitude of these amplifications, with some regions like 3q22.1:3q23 reaching a mean log2(CNR) of 3.3 in NSC004.T2, signifying substantial copy number gains.

Biological Interpretation

The observed CNV profiles in 'Lung Epithelial cell' and 'unassigned' populations offer critical biological insights into lung cancer.

• Tumor-Specific Alterations: Given that 'Lung Epithelial cell' is defined as the tumor-origin cell type, the extensive and recurrent CNVs strongly indicate that these are indeed malignant cells. The presence of similar CNV patterns in 'unassigned' cells suggests that this population may also include tumor cells or tumor-associated cells exhibiting characteristic genomic aberrations.
• Role of Recurrent Amplifications: The highly recurrent amplifications identified (100% frequency) pinpoint genomic regions that likely harbor oncogenes or genes crucial for tumor initiation and progression in lung cancer. The amplification of 3q26.33:3q28, which includes the SOX2 gene, is a well-established oncogenic event, particularly prominent in lung squamous cell carcinoma. SOX2 is a transcription factor involved in maintaining pluripotency and self-renewal, and its overexpression can drive tumor cell proliferation and survival. GeneCards: SOX2
• Ploidy as a Marker of Genomic Instability: The distinction in CNV burden between 'Diploid' and other samples confirms the biological relevance of the ploidy_dec annotation. Diploid-classified cells, despite being from tumor samples, exhibit less severe genomic disorganization, potentially representing normal cells, early-stage tumor cells, or a less aggressive subclone, while aneuploid cells show the complex genomic rearrangements characteristic of advanced malignancies.

Clinical or Translational Implications

• Confirmation of Tumor Cell Identity: The clear and consistent CNV signatures in 'Lung Epithelial cell' populations serve as robust validation for their classification as tumor cells. The detection of similar CNV patterns in 'unassigned' cells warrants further investigation, as these cells may represent additional tumor components or tumor-infiltrating cells with shared genomic abnormalities.
• Potential Biomarker Discovery: The identified recurrent CNVs, such as the SOX2 amplification, represent candidate genomic biomarkers. These alterations could potentially be used for diagnostic purposes, predicting patient prognosis, or monitoring disease progression in lung cancer patients.
• Foundation for Further Research: While this analysis focuses on genomic patterns, the identification of key amplified regions and genes provides a foundation for subsequent functional studies to understand their precise roles in lung tumorigenesis and to explore their potential as therapeutic targets.

5. CNV- 기반 UMAP을 통한 세포 유형, 이수성 및 조건별 패턴 분석

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 기반으로 한 체세포 복제수 변이(CNV) 추정치를 사용하여 UMAP(Uniform Manifold Approximation and Projection) 임베딩을 시각화합니다. UMAP은 CNV 유사성에 따라 세포들을 2차원 공간에 배치하며, 각 패널은 이 CNV 기반 UMAP 위에 주요 세포 유형, 미성숙 세포 유형, 배수성(ploidy) 상태, 조건(Adeno 또는 Squamous) 및 개별 샘플 정보를 색상으로 오버레이하여 보여줍니다. 이 시각화는 세포 집단 간의 CNV 패턴 차이를 탐색하고, 종양 세포 식별, 아형별 CNV 특성, 그리고 데이터셋 내의 잠재적 이질성을 평가하는 데 중점을 둡니다.

Visual Summary

제공된 UMAP 시각화는 CNV 추정치를 기반으로 한 세포들의 분포를 여러 메타데이터 속성별로 색상화하여 보여줍니다.

주요 세포 유형 (celltype_major):

• Lung Epithelial cell (주황색)이 UMAP 공간의 중앙에서 가장 큰 클러스터를 형성하며, 이는 해당 데이터셋에서 이 세포 유형의 높은 비율을 시사합니다.
• Myeloid cell (연두색), Stromal cell (청록색), T cell (남색), Endothelial cell (짙은 빨강색)과 같은 다른 면역 및 기질 세포들은 Lung Epithelial cell 클러스터 주변 또는 독립적인 영역에 흩어져 별도의 클러스터를 형성하고 있습니다. 이는 각 주요 세포 유형이 CNV 수준에서 구별되는 프로파일을 가지고 있음을 나타냅니다.

미성숙 세포 유형 (celltype_minor):

• 주요 세포 유형 내의 세분화된 클러스터링을 보여줍니다. 예를 들어, Lung Epithelial cell 클러스터 내에서 Alveolar Epithelial cell (붉은 오렌지색)과 Airway Epithelial cell (짙은 빨강색)이 구별되어 나타납니다.
• Macrophage (밝은 노란색)와 Fibroblast (오렌지색) 또한 각각 Myeloid 및 Stromal cell 영역 내에서 명확하게 관찰됩니다. 이는 미성숙 세포 유형 수준에서도 CNV 기반의 세포 구분이 잘 이루어지고 있음을 확인시켜 줍니다.

배수성 결정 (ploidy_dec):

• Diploid (밝은 노란색) 세포들이 UMAP 공간의 대부분을 차지하며, 이는 비종양성(정상) 세포 집단을 나타낼 가능성이 높습니다.
• Aneuploid (짙은 빨강색) 세포들은 UMAP의 좌측 하단에서 뚜렷하고 밀집된 클러스터를 형성하고 있습니다. 이 클러스터는 Diploid 세포 집단과 명확하게 분리되어 있으며, 이는 종양 세포의 존재를 강력히 시사합니다.
• Unclear (보라색)로 분류된 소수의 세포들은 Diploid 및 Aneuploid 클러스터 사이에 산재되어 나타납니다.

조건 (condition):

• Adeno (짙은 빨강색) 세포는 UMAP 공간 전반에 걸쳐 넓게 분포하며, 특히 Aneuploid 클러스터와 상당 부분 겹칩니다.
• Squamous (남색) 세포는 UMAP의 좌측 상단에 상대적으로 작고 응집된 클러스터를 형성하여 Adeno 세포와 부분적으로 분리되는 양상을 보입니다. 이는 두 가지 다른 폐암 아형이 CNV 수준에서 차별적인 특성을 가지고 있음을 시사합니다.

샘플 (sample):

• 여러 샘플들이 다양한 색상으로 표시되어 UMAP 공간에 분포하고 있습니다.
• 대부분의 샘플에서 Diploid 및 Aneuploid 영역에 세포가 기여하고 있음을 보여주나, 특히 Squamous 클러스터는 소수의 특정 샘플(예: NSC036.T1, NSC037.T1, NSC040.T1)에 의해 주로 구성되는 경향을 보입니다. 이는 환자 간 CNV 이질성이 존재함을 나타내며, 특정 CNV 패턴이 샘플별로 편향될 수 있음을 시사합니다.

Biological Interpretation

이 CNV 기반 UMAP 분석은 폐 조직의 단일 세포 수준에서 복제수 변이의 중요한 생물학적 패턴을 드러냅니다.

• 종양 세포 식별 및 이질성: ploidy_dec 플롯에서 관찰된 Aneuploid 세포의 뚜렷한 클러스터는 악성 종양 세포 집단을 명확하게 나타냅니다. 이러한 Aneuploid 세포는 주로 Lung Epithelial cell에 해당하는 영역에 위치하며, 이는 Tumor origin celltype: Lung Epithelial cell이라는 데이터 컨텍스트와 일치합니다. 이는 CNV 분석이 종양 세포와 비종양 미세환경 세포를 효과적으로 구분할 수 있음을 보여줍니다.
• 세포 유형별 CNV 프로파일: celltype_major 및 celltype_minor 플롯은 다양한 면역 세포(T cell, B cell, Myeloid cell) 및 기질 세포(Stromal cell, Endothelial cell)가 주로 Diploid 영역에 분포하며, Lung Epithelial cell과 CNV 프로파일에서 명확하게 구별됨을 보여줍니다. 이는 TME(Tumor Microenvironment) 내의 비종양 세포들이 일반적으로 안정적인 유전체(diploid)를 유지한다는 생물학적 지식과 일치합니다.
• 폐암 아형별 CNV 특징: condition 플롯은 폐 선암종(Adeno)과 편평상피세포암(Squamous)이 CNV 수준에서 서로 다른 패턴을 보임을 시사합니다. Adeno 세포는 UMAP 공간에 더 넓게 퍼져 있어 Adeno 종양 내 CNV 이질성이 더 클 수 있음을 나타내는 반면, Squamous 세포는 비교적 응집된 별도의 클러스터를 형성하여 Squamous 특이적인 CNV 패턴이 존재할 가능성을 제시합니다. 이러한 CNV 패턴의 차이는 각 암 아형의 병리학적 및 분자적 특성을 반영할 수 있습니다.
• 환자 간 이질성: sample 플롯은 CNV 패턴이 샘플(환자) 간에 상당한 이질성을 가질 수 있음을 보여줍니다. 특히 특정 Squamous 클러스터가 소수의 샘플에서 주로 유래하는 것은 특정 환자들이 공유하는 독특한 CNV 프로파일이 존재할 수 있음을 나타내며, 이는 폐암의 정밀 의학적 접근에서 중요하게 고려될 수 있습니다.

Annotation Notes

• 세포 유형 분리: CNV 기반 UMAP은 celltype_major 및 celltype_minor 수준에서 세포 유형을 성공적으로 분리하고 있습니다. 이는 CNV 정보가 세포 유형 정체성을 구분하는 데 유용하게 활용될 수 있음을 나타냅니다.
• 배수성 결정의 명확성: ploidy_dec 라벨링은 Aneuploid 및 Diploid 세포 집단을 명확하게 구분하여 종양 세포와 비종양 세포의 구분에 높은 신뢰도를 제공합니다. 'Unclear'로 분류된 소수의 세포에 대한 추가적인 조사가 필요할 수 있으나, 전반적인 분리 품질은 우수합니다.
• 조건별 구분: 폐 선암종과 편평상피세포암은 CNV 프로파일 측면에서 뚜렷한 차이를 보이며, 이는 해당 조건 라벨링의 생물학적 타당성을 강화합니다.
• 샘플 통합: 특정 샘플들이 UMAP의 특정 영역에 집중되는 경향이 있지만, 이는 세포 유형 및 조건에 따른 생물학적 변이와도 일치합니다. 전반적으로 강력한 배치 효과(batch effect)로 인한 클러스터링보다는 생물학적 이질성을 반영하는 것으로 보입니다.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot displaying the proportional distribution of minor cell types across individual samples from patients with lung adenocarcinoma (Adeno) and squamous cell carcinoma (Squamous). The purpose is to visualize and compare the cellular composition of the tumor microenvironment (TME) between these two distinct lung cancer subtypes. Each bar represents a single sample, with stacked segments indicating the percentage of different minor cell types within that sample.

Visual Summary

The visualization consists of two main panels, one for Adenocarcinoma (Adeno) and one for Squamous Cell Carcinoma (Squamous), each showing stacked bar plots for individual samples (sample).

• Overall Cell Type Distribution: Both Adeno and Squamous samples exhibit a diverse cellular composition, including various immune cells (Macrophage, T cell CD4+, T cell CD8+, B cell, Dendritic cell, NK cell, Plasma cell, ILC, Mast cell) and stromal cells (Fibroblast, Endothelial cell, Smooth muscle cell), alongside epithelial cells.
• Dominant Cell Types in Adeno: In Adeno samples, Macrophages (light yellow) and Fibroblasts (light orange) are consistently prominent, often contributing significantly to the overall cell population. Alveolar Epithelial cells (dark red) are also a noticeable component in many Adeno samples, consistent with their origin. T cells (CD4+ and CD8+) are present but generally constitute a smaller fraction compared to macrophages and fibroblasts.
• Dominant Cell Types in Squamous: Squamous samples also show high proportions of Macrophages and Fibroblasts. However, Airway Epithelial cells (maroon) appear more prominent in Squamous samples compared to Adeno samples, while Alveolar Epithelial cells are less abundant.

Differences between Conditions:

• Epithelial Cells: There is a clear distinction in the predominant epithelial cell type. Adeno samples show a relatively higher proportion of Alveolar Epithelial cells, whereas Squamous samples tend to have a higher proportion of Airway Epithelial cells. This aligns with the known cellular origins of these cancer subtypes.
• Immune/Stromal Cells: Macrophages and Fibroblasts are abundant in both Adeno and Squamous TMEs, suggesting a significant role for these cell types in both cancer types. While their absolute proportions may vary between samples, their consistent presence highlights their importance.
• Variability within Conditions: There is noticeable heterogeneity in cell type proportions among individual samples within both the Adeno and Squamous groups, reflecting inter-patient variability in tumor microenvironment composition.
• Unassigned Cells: A segment labeled "unassigned" (dark blue) is present in some samples, particularly more pronounced in certain Squamous samples, suggesting that some cell populations might be novel or difficult to annotate with current markers, or represent cells undergoing significant stress/transformation.

Biological Interpretation

The observed differences in cell type populations between Adenocarcinoma and Squamous Cell Carcinoma provide important biological insights into their distinct tumor microenvironments:

• Epithelial Cell Origin and Pathogenesis: The relative abundance of Alveolar Epithelial cells in Adeno and Airway Epithelial cells in Squamous directly supports the widely accepted understanding of their tissue of origin. Adenocarcinomas are believed to originate from alveolar type II cells or club cells in the distal airways, while squamous cell carcinomas typically arise from squamous metaplasia of the bronchial epithelium in the proximal airways [1, 2].
• Inflammatory and Immunosuppressive Microenvironment: The consistent and often high proportion of Macrophages in both conditions points to a significant inflammatory component in both NSCLC subtypes. Tumor-associated macrophages (TAMs) are crucial players in the TME, often promoting tumor growth, angiogenesis, metastasis, and immunosuppression [3]. Their prevalence suggests both Adeno and Squamous cancers likely engage in substantial macrophage recruitment and polarization.
• Stromal Remodeling: The substantial presence of Fibroblasts (likely cancer-associated fibroblasts, CAFs) in both Adeno and Squamous indicates active stromal remodeling. CAFs contribute to extracellular matrix deposition, create a stiff microenvironment, and secrete growth factors and cytokines that support tumor progression and therapeutic resistance [4].
• T-cell Infiltration: While not the dominant population, the presence of CD4+ and CD8+ T cells in both conditions indicates an ongoing immune response, although its efficacy would depend on their activation state and functional phenotype (e.g., exhausted T cells vs. effector T cells). Further analysis of T cell subsets (celltype_subset) would provide deeper insights.
• Unassigned Cells: The presence of a notable "unassigned" population, particularly in some Squamous samples, warrants further investigation. These could represent novel cell states, highly dedifferentiated or transformed cells, or technical artifacts. Re-annotation or deeper characterization of these cells could reveal unique features of specific tumors.

Clinical or Translational Implications

Understanding the distinct cellular compositions of Adeno and Squamous TMEs has several clinical implications:

• Targeted Therapies: The differential abundance of specific epithelial cell types reinforces the need for subtype-specific diagnostic criteria and treatment strategies.
• Immunotherapy Response: The high infiltration of Macrophages in both subtypes suggests that therapies targeting TAMs (e.g., CSF1R inhibitors) could be beneficial, potentially alone or in combination with other immunotherapies [3]. The presence of T cells also indicates potential for checkpoint blockade therapies, but their functional state is critical for predicting response.
• Biomarker Discovery: Differences in the prevalence of specific immune or stromal cell populations could serve as prognostic biomarkers or predictors of response to specific treatments for Adeno versus Squamous lung cancer.
• Heterogeneity and Resistance: The sample-to-sample variability within each cancer type highlights the challenges of treating NSCLC, as a "one-size-fits-all" approach may not be effective. Personalized approaches considering individual TME composition might be more successful.

References

1. Adenocarcinoma Origin: Alveolar type 2 cells and Club cells as origin of lung adenocarcinoma. (PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=lung+adenocarcinoma+origin+alveolar+type+II+club+cell)
2. Squamous Cell Carcinoma Origin: Squamous cell carcinoma of the lung: from pathogenesis to new targeted therapies. (PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=lung+squamous+cell+carcinoma+origin+bronchial+epithelium)
3. Tumor-Associated Macrophages: Tumor-Associated Macrophages in Cancer Progression and Therapy. (PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=tumor+associated+macrophages+cancer+progression)
4. Cancer-Associated Fibroblasts: Cancer-associated fibroblasts in tumor microenvironment. (PubMed search: https://pubmed.ncbi.nlm.nih.gov/?term=cancer+associated+fibroblasts+tumor+microenvironment)

7. Lymphoid Cell Subset Composition in Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes single-cell RNA sequencing data to visualize the relative proportions of lymphoid cell subsets within the broader "T cell" major cell compartment across individual patient samples, stratified by lung cancer histological subtypes: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). The celltype_major category "T cell" is further resolved into celltype_minor subsets, which include Innate Lymphoid Cells (ILC), Natural Killer (NK) cells, CD4+ T cells, and CD8+ T cells. This visualization provides insight into the inherent heterogeneity of the lymphoid infiltrate within the tumor microenvironment of these lung cancer types.

Visual Summary

The stacked bar plots display the relative proportions of various lymphoid cell subsets within the defined "T cell" compartment for each analyzed sample in both Adeno and Squamous conditions.

• Dominant Subsets: In both Adeno and Squamous lung cancer samples, Innate Lymphoid Cells (ILC, dark red) and CD4+ T cells (light yellow/beige) are consistently the most abundant populations. These two cell types collectively often account for 70-90% of the profiled lymphoid cells in this compartment.
• CD8+ T cells: CD8+ T cells (pale yellow) are present in all samples but generally represent a smaller proportion, typically ranging from 10% to 30%.
• NK cells: Natural Killer (NK) cells (orange) are a minor component, consistently contributing less than 5% to the total lymphoid population in most samples across both conditions.
• Minimal Unassigned: The 'unassigned' category (teal) is negligible, indicating good annotation coverage for the T cell major group.
• Inter-sample Variability: Significant heterogeneity in the relative proportions of ILCs and CD4+ T cells is evident among individual patient samples within both Adeno and Squamous groups. For example, some Adeno samples (e.g., NSC037.T1) show a higher proportion of ILCs, while others (e.g., NSC018.T1, T2, T3) exhibit a relatively higher presence of CD4+ T cells.
• Condition Comparison: While sample numbers for Squamous are fewer, no stark and consistent differences in the overall proportional distribution of these lymphoid subsets are immediately apparent when comparing the Adeno and Squamous conditions. Both tumor types exhibit a lymphoid composition broadly dominated by ILCs and CD4+ T cells, with smaller but consistent contributions from CD8+ T cells.

Biological Interpretation

The visualization highlights the complex composition of the lymphoid compartment within lung cancer, emphasizing the interplay between innate and adaptive immune components.

• Broad Lymphoid Compartment: It's important to note that the celltype_major "T cell" here encompasses a broader definition of lymphoid cells including ILCs and NK cells, which are innate immune cells, in addition to conventional adaptive CD4+ and CD8+ T cells. This framing suggests a holistic view of lymphoid contributions to the tumor microenvironment (TME).
• Significance of ILCs: The substantial presence of ILCs across both Adeno and Squamous samples is a key finding. ILCs are diverse and critical for tissue homeostasis and immune responses. Their specific roles in cancer are context-dependent; some ILC subsets (e.g., ILC1) can mediate anti-tumor immunity, while others (e.g., ILC2, ILC3) might promote tumor growth or metastasis depending on the cytokine milieu PubMed Search: "ILC tumor microenvironment lung cancer". Their high abundance suggests they are active modulators of the lung cancer TME.
• CD4+ T cell Dominance: The relative abundance of CD4+ T cells over CD8+ T cells within this compartment is commonly observed in many solid tumors. CD4+ T cells include various helper T cell subsets (e.g., Th1, Th2, Th17, Tfh) and immunosuppressive regulatory T cells (Tregs). Without further sub-classification (e.g., from celltype_subset), it is challenging to infer the precise functional orientation of these CD4+ T cells, but their dominance implies a significant role in shaping the adaptive immune response.
• CD8+ T cells and Anti-tumor Immunity: CD8+ T cells are crucial for direct cytotoxic activity against tumor cells and are often associated with favorable prognosis. Their consistent presence, albeit at lower proportions, indicates an ongoing attempt by the adaptive immune system to control tumor growth. The ratio of CD8+ T cells to other immunosuppressive populations (e.g., Tregs within the CD4+ population, or certain ILC subsets) is often a critical determinant of clinical outcome.
• Minor NK Cell Presence: NK cells are potent innate anti-tumor effector cells. Their generally low proportions in this analysis might suggest either a limited infiltration in these specific samples or a relative enrichment of other lymphoid subsets.

Clinical or Translational Implications

Understanding the detailed composition of lymphoid cells within the lung cancer TME has significant clinical implications:

• Personalized Immunotherapy: The observed sample-to-sample variability underscores the need for personalized approaches to immunotherapy in lung cancer. A "one-size-fits-all" strategy might be suboptimal given the diverse immune landscapes.
• Biomarker Discovery and Therapeutic Targets: The high prevalence of ILCs and CD4+ T cells indicates that these populations are key players in lung cancer immunology.
• Further detailed characterization of ILC subsets (ILC1, ILC2, ILC3) and CD4+ T cell subsets (e.g., Th1, Th2, Tregs) is crucial for identifying specific pro- or anti-tumor immune modules. For instance, high infiltration of Tregs or specific pro-tumorigenic ILC subsets could indicate an immunosuppressive TME that might benefit from targeted therapies aimed at depleting these cells or modulating their function UniProt: FOXP3 (Treg marker).
• Conversely, strategies to enhance anti-tumor ILCs or CD4+ Th1 responses could be explored.
• Augmenting CD8+ T cell Responses: Given their critical role in direct tumor killing, strategies to increase the infiltration, activation, and persistence of CD8+ T cells could improve therapeutic efficacy, especially in samples where their proportions are relatively low. This could involve checkpoint blockade, adoptive cell therapy, or novel vaccination approaches.
• Consideration for NK Cell-based Therapies: The low proportion of NK cells suggests that NK cell-based therapies might require specific strategies to enhance NK cell recruitment and activation within the tumor, or might be more effective in a subset of patients with higher baseline NK cell infiltration.

8. Differences in T Cell and ILC Subset Proportions Between Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional differences of various T cell and Innate Lymphoid Cell (ILC) subsets between Lung Adenocarcinoma (Adeno) and Lung Squamous Cell Carcinoma (Squamous) conditions. Box plots, with individual data points overlaid, illustrate the distribution of celltype proportions for each subset, and statistical significance (p-values) highlights noteworthy differences between the two conditions. The reference condition for comparison is Adeno.

Visual Summary

The box plots reveal statistically significant differences in the proportions of several T cell and ILC subsets when comparing Squamous Cell Carcinoma to Adenocarcinoma:

Increased in Squamous Cell Carcinoma:

• Th17 cells show a significantly higher proportion in Squamous Cell Carcinoma compared to Adenocarcinoma (p ≤ 0.05).
• Regulatory T cells (Treg) also exhibit a significantly elevated proportion in Squamous Cell Carcinoma relative to Adenocarcinoma (p ≤ 0.01).

Decreased in Squamous Cell Carcinoma:

• Th1 cells are found in significantly lower proportions in Squamous Cell Carcinoma compared to Adenocarcinoma (p ≤ 0.05).
• Th2 cells demonstrate a significantly reduced proportion in Squamous Cell Carcinoma relative to Adenocarcinoma (p ≤ 0.01).
• ILC3(-) cells show a significantly lower proportion in Squamous Cell Carcinoma compared to Adenocarcinoma (p ≤ 0.05).
• Th9 cells are present in significantly lower proportions in Squamous Cell Carcinoma than in Adenocarcinoma (p ≤ 0.05).
• ILC1 cells also exhibit a significantly lower proportion in Squamous Cell Carcinoma when compared to Adenocarcinoma (p ≤ 0.05).

In summary, Squamous Cell Carcinoma samples tend to have higher proportions of Th17 and Treg cells, while having lower proportions of Th1, Th2, Th9, ILC1, and ILC3(-) cells, relative to Adenocarcinoma.

Biological Interpretation

The observed shifts in immune cell proportions suggest distinct immune microenvironments in Lung Adenocarcinoma versus Squamous Cell Carcinoma, which are two major histological subtypes of Non-Small Cell Lung Cancer (NSCLC).

1. Elevated Treg and Th17 in Squamous Cell Carcinoma:

• The increased proportion of Treg cells in Squamous Cell Carcinoma is a critical finding. Tregs are potent immunosuppressive cells that maintain immune tolerance and prevent autoimmunity. In the context of cancer, their enrichment often correlates with immune evasion, where they suppress anti-tumor immune responses, thereby promoting tumor growth and progression [1].
• Th17 cells are a subset of helper T cells known for producing IL-17 and playing complex roles in cancer. While they can mediate anti-tumor responses in some settings, they are often associated with pro-inflammatory and pro-tumorigenic activities, including promoting angiogenesis and recruiting other immunosuppressive cells [2]. Their higher prevalence in Squamous Cell Carcinoma, alongside Tregs, could indicate a distinct pro-inflammatory yet immunosuppressive milieu.

2. Reduced Anti-Tumorigenic and Regulatory Subsets in Squamous Cell Carcinoma:

• The decrease in Th1 cells in Squamous Cell Carcinoma is significant. Th1 cells are crucial for effective anti-tumor immunity, primarily through the production of IFN-gamma, which activates cytotoxic T lymphocytes and macrophages to eliminate cancer cells [3]. A lower proportion of Th1 cells suggests a weakened cellular immune response against the tumor.
• Th2 cells and Th9 cells also contribute to anti-tumor immunity, albeit through different mechanisms. Th2 cells are involved in humoral immunity and can sometimes activate anti-tumor responses, while Th9 cells produce IL-9, which can enhance mast cell activation and T cell-mediated anti-tumor effects [4]. Their reduction might further contribute to an overall less robust anti-tumor immune context.
• ILC1 and ILC3(-) are innate lymphoid cell subsets. ILC1s are IFN-gamma producing cells, functionally analogous to Th1 cells, and are generally associated with anti-tumor immunity [5]. The reduction in both ILC1s and ILC3(-) in Squamous Cell Carcinoma could signify a broader impairment of innate immune surveillance and response, potentially hindering the initiation and execution of effective anti-tumor immunity.

Collectively, these findings point towards a more immunosuppressive and less effectively anti-tumorigenic immune microenvironment in Squamous Cell Carcinoma compared to Adenocarcinoma. The balance shifts from pro-inflammatory and anti-tumorigenic Th1, Th2, Th9, ILC1, and ILC3(-) subsets towards immunosuppressive Treg and potentially pro-tumorigenic Th17 cells in Squamous Cell Carcinoma.

Clinical or Translational Implications

These distinct immune cell profiles between lung adenocarcinoma and squamous cell carcinoma have significant clinical implications:

• Prognosis and Disease Progression: The higher proportion of Tregs and lower proportions of Th1 and ILC1 cells in Squamous Cell Carcinoma suggest a more immune-evasive phenotype. This could translate to a poorer prognosis for Squamous Cell Carcinoma patients compared to Adenocarcinoma, as an attenuated anti-tumor immune response may allow for more aggressive tumor growth and metastasis.
• Response to Immunotherapy: These findings could help explain differential responses to immunotherapies, particularly checkpoint inhibitors (e.g., PD-1/PD-L1 inhibitors), between the two NSCLC subtypes. A highly immunosuppressive microenvironment with abundant Tregs might limit the efficacy of therapies designed to unleash T cell-mediated immunity. Understanding these differences could guide patient selection for specific immunotherapeutic approaches.
• Biomarker Development: The differential proportions of these T cell and ILC subsets could serve as potential biomarkers for distinguishing between Adenocarcinoma and Squamous Cell Carcinoma, or for predicting patient response to specific treatments. Monitoring these cell populations in tumor biopsies or peripheral blood could provide valuable prognostic or predictive information.
• Targeted Therapeutic Strategies: The enrichment of specific cell types, such as Tregs and Th17 cells, in Squamous Cell Carcinoma identifies them as potential therapeutic targets. Strategies aimed at depleting Tregs, modulating Th17 functions, or enhancing Th1/ILC1 activity could be explored as subtype-specific immunotherapeutic interventions to improve outcomes in Squamous Cell Carcinoma.

References

1. Treg in Cancer: GeneCards: FOXP3
2. Th17 in Lung Cancer: PubMed search: Th17 lung cancer
3. Th1 in Cancer Immunity: PubMed search: Th1 anti-tumor immunity
4. Th9 in Lung Cancer: PubMed search: Th9 lung cancer
5. ILC1 in Cancer: PubMed search: ILC1 cancer immunity

9. Macrophage Subset Population Analysis in Lung Adenocarcinoma vs. Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the relative proportions of different macrophage subsets (M1, M2A, M2B, M2C, M2D) within the total macrophage population across individual lung tumor samples. The samples are stratified by their histological diagnosis: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous), both major subtypes of Non-Small Cell Lung Cancer (NSCLC). This provides an overview of the macrophage polarization landscape within the tumor microenvironment of each sample and allows for comparison between the two cancer types.

Visual Summary

The stacked bar plots display the fractional composition of five macrophage subsets (M1, M2A, M2B, M2C, M2D) for each sample. Each bar represents 100% of the macrophages in a given sample, with colors indicating the different subsets:

Macrophage (M1) (dark red)

Macrophage (M2A) (orange)

Macrophage (M2B) (light yellow)

Macrophage (M2C) (pale green)

Macrophage (M2D) (teal green)

Key observations:

• Dominant Subsets: Macrophage (M1) and Macrophage (M2A) consistently represent the largest proportions of the macrophage population across nearly all samples in both Adeno and Squamous conditions. They often collectively account for 50-70% or more of the total macrophages.
• M2B Contribution: Macrophage (M2B) is also a notable component, typically contributing between 10-25% to the total macrophage pool.
• Minor Subsets: Macrophage (M2C) and Macrophage (M2D) are consistently present in much smaller proportions, often contributing less than 10% each to the total macrophage population. M2D, in particular, appears to be the least abundant subset.
• Inter-sample Variability: There is some heterogeneity in macrophage subset composition among individual samples within both the Adeno and Squamous groups. For example, some Adeno samples show a higher M1 proportion (e.g., NSC019.T1, NSC037.T1), while others have a more balanced M1/M2A distribution (e.g., NSC010.T2, NSC016.T2).

Adeno vs. Squamous Comparison:

• While both conditions show similar overall trends in dominant subsets (M1, M2A), there might be subtle differences. For instance, M1 macrophages appear to contribute slightly more variably in Adeno samples, occasionally reaching higher proportions than in Squamous samples.
• M2A and M2B appear consistently present in both conditions without stark visual differences in their average proportions.
• The minor M2C and M2D populations also show similar low abundances across both conditions, although in some Squamous samples, M2D might be relatively more pronounced than in Adeno, albeit still a small fraction of the total.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and exhibit remarkable plasticity, polarizing into various functional states often broadly categorized as M1-like (pro-inflammatory, anti-tumor) and M2-like (anti-inflammatory, pro-tumor) phenotypes. However, this M1/M2 paradigm is a simplification, and the observed distinct subsets (M2A, M2B, M2C, M2D) reflect more granular functional states.

• M1 Macrophages: These are typically considered "classically activated" and are associated with cytotoxic activity, pathogen clearance, and the production of pro-inflammatory cytokines (e.g., TNFα, IL-12). Their substantial presence in both Adeno and Squamous lung tumors suggests an ongoing inflammatory response within the TME, which can be either beneficial (anti-tumor) or detrimental depending on the overall context and balance with other immune cells. [Source: PubMed search for "M1 macrophages cancer"]
• M2 Macrophages (Subsets): The M2 classification encompasses diverse phenotypes involved in tissue repair, angiogenesis, immune regulation, and promoting tumor progression.
• M2A Macrophages: Often induced by IL-4 and IL-13, these are involved in allergic inflammation and parasitic infections. In cancer, they can contribute to tissue remodeling and immune suppression. Their prominent presence highlights their potential role in shaping the TME of lung cancer.
• M2B Macrophages: Induced by immune complexes and Toll-like receptor (TLR) agonists, M2B cells are pro-inflammatory but also produce anti-inflammatory cytokines. Their role in cancer is complex and context-dependent, often linked to immune modulation.
• M2C and M2D Macrophages: M2C (induced by IL-10 or TGF-β) are typically associated with immune suppression and tissue remodeling. M2D (also known as "regulatory macrophages" or some forms of Tumor-Associated Macrophages, TAMs) are often linked to angiogenesis and promoting tumor growth and metastasis, particularly through adenosine signaling. [Source: GeneCards for CD206 (M2A marker), CD163 (M2C marker)] The relatively low proportions of M2C and M2D in these samples suggest that while these pro-tumorigenic subsets are present, other M2-like populations (M2A, M2B) might play more numerically dominant roles in the macrophage compartment.

The differences in macrophage subset distribution between Adeno and Squamous lung cancer, though subtle in this plot, could signify distinct underlying immunological landscapes and potentially different vulnerabilities for therapeutic targeting. For instance, if Adeno generally harbors a slightly higher M1 presence, it might imply a different baseline immune activation state compared to Squamous.

Clinical or Translational Implications

The heterogeneous composition of macrophage subsets within lung tumors has significant clinical implications:

• Prognostic Value: The balance between M1 and various M2 subsets is often correlated with patient prognosis. A higher M1/M2 ratio is generally associated with better outcomes, while an abundance of pro-tumorigenic M2 subsets often indicates poorer prognosis and resistance to therapy in various cancers.
• Therapeutic Targeting: Macrophages, particularly M2-like TAMs, are increasingly recognized as therapeutic targets. Strategies include:
• Depleting TAMs: Using CSF1R inhibitors to block macrophage survival and recruitment.
• Reprogramming TAMs: Shifting M2-like macrophages towards an M1-like phenotype to enhance anti-tumor immunity. [Source: PubMed search for "macrophage reprogramming cancer therapy"]
• Histology-Specific Approaches: The observed differences, even subtle, between Adeno and Squamous subtypes suggest that macrophage-targeting therapies might need to be tailored to the specific histological diagnosis. Understanding these nuances could lead to more effective, subtype-specific immunotherapeutic strategies.
• Biomarker Potential: Further investigation into the specific gene expression profiles of these macrophage subsets within Adeno and Squamous tumors could identify novel biomarkers for patient stratification or response prediction to immunotherapy.

10. Macrophage Subset Proportion Differences Between Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential proportions of specific macrophage subsets, Macrophage (M2B) and Macrophage (M2D), across two distinct lung cancer conditions: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). The proportions are derived from single-cell RNA sequencing data of lung tissue. Box plots are used to visualize the distribution of celltype proportions for each subset and condition, with statistical significance indicated for observed differences.

Visual Summary

The visualization presents two box plots, one for Macrophage (M2B) and one for Macrophage (M2D), comparing their celltype proportions between Adeno and Squamous conditions.

• Macrophage (M2B): The celltype proportion of Macrophage (M2B) cells is significantly higher in the Squamous condition compared to the Adeno condition (p ≤ 0.05). The median proportion for M2B in Squamous is approximately 8%, while in Adeno, it is around 3.5-4%.
• Macrophage (M2D): Similarly, the celltype proportion of Macrophage (M2D) cells is also significantly elevated in the Squamous condition relative to the Adeno condition (p ≤ 0.01). The median proportion for M2D in Squamous is roughly 3.5%, whereas in Adeno, it is approximately 1-1.5%.

For both macrophage subsets, a clear upward shift in their representation is observed in Squamous Cell Carcinoma compared to Adenocarcinoma, with statistically robust differences.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME), and their polarization state significantly influences tumor progression. M2-polarized macrophages, including M2B and M2D, are generally associated with pro-tumoral functions, such as immune suppression, promotion of angiogenesis, tissue remodeling, and metastasis [1].

• M2B Macrophages: These macrophages are often induced by immune complexes and Toll-like receptor (TLR) agonists. They typically exhibit mixed M1 and M2 characteristics but generally contribute to chronic inflammation and tissue remodeling, which can support tumor growth and progression.
• M2D Macrophages (Tumor-Associated Macrophages - TAMs): M2D macrophages are a well-characterized subset of TAMs, often polarized by IL-6 and IL-10, and are potent promoters of angiogenesis, immune evasion, and tumor cell survival. Their presence is frequently correlated with poor prognosis in various cancers [2].

The findings indicate a significantly higher infiltration of both M2B and M2D macrophage subsets in Lung Squamous Cell Carcinoma (LUSC) compared to Lung Adenocarcinoma (LUAD). This suggests that the TME of LUSC might be characterized by a more pronounced pro-tumorigenic and immunosuppressive macrophage landscape compared to LUAD. This difference in macrophage composition could contribute to distinct immune evasion strategies and disease biology between these two major types of non-small cell lung cancer (NSCLC).

Clinical or Translational Implications

The differential enrichment of M2B and M2D macrophages between LUSC and LUAD has several potential clinical and translational implications:

1. Prognostic Biomarker: Higher proportions of M2B and M2D macrophages in LUSC could serve as prognostic indicators, potentially correlating with disease aggressiveness or patient outcomes.
2. Therapeutic Target: Given their established roles in promoting tumor growth and immune suppression, M2B and M2D macrophages represent attractive therapeutic targets. Strategies aimed at depleting these cells, reprogramming their polarization, or blocking their pro-tumoral functions could be more effective in LUSC than in LUAD, considering their higher abundance in LUSC [3].
3. Immunotherapy Response: The distinct macrophage profiles may influence response to existing immunotherapies, such as checkpoint inhibitors. A higher immunosuppressive M2 macrophage presence in LUSC might necessitate combination therapies that address both T-cell checkpoints and macrophage-mediated suppression.
4. Disease Heterogeneity: These findings underscore the immune heterogeneity between different histological subtypes of lung cancer, highlighting the importance of considering tumor-specific immune cell compositions when designing diagnostic and therapeutic strategies.

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References

1. M2 Macrophage Roles: PubMed search for "M2 macrophages tumor microenvironment" https://pubmed.ncbi.nlm.nih.gov/?term=M2+macrophages+tumor+microenvironment
2. M2D Macrophages (TAMs): PubMed search for "M2D macrophages tumor associated macrophages" https://pubmed.ncbi.nlm.nih.gov/?term=M2D+macrophages+tumor+associated+macrophages
3. Macrophage-targeted therapy in cancer: PubMed search for "macrophage targeted therapy cancer" https://pubmed.ncbi.nlm.nih.gov/?term=macrophage+targeted+therapy+cancer

11. Ploidy Population Analysis of Tumor-Origin and Unassigned Cells in Lung Adenocarcinoma and Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy distribution (Aneuploid, Diploid, Unclear) within lung epithelial cells (identified as tumor-origin cells) and unassigned cells across individual patient samples. The samples are categorized by their histological diagnosis: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). This visualization helps in understanding genomic instability within the presumed malignant compartment and uncharacterized cells, and how it varies between these two major lung cancer subtypes.

Visual Summary

The bar plots display the proportional representation of Aneuploid (maroon), Diploid (orange), and Unclear (light green) cell populations for each individual sample, grouped by condition (Adeno and Squamous).

Adenocarcinoma (Adeno) Samples:

• There is considerable sample-to-sample heterogeneity in the ploidy profiles among Adeno samples.
• A significant proportion of samples (e.g., NSC010.T1, NSC021.T1, NSC010.T2, NSC019.T2, NSC035.T1, NSC019.T1) show a substantial presence of Aneuploid cells, ranging from approximately 25% to over 60%.
• Conversely, several Adeno samples (e.g., NSC016.T1, NSC020.T2, NSC037.T1, NSC018.T1, NSC018.T3, NSC040.T1) are predominantly composed of Diploid cells, with aneuploid fractions often below 20%.
• The "Unclear" ploidy fraction is generally minimal across most Adeno samples.

Squamous Cell Carcinoma (Squamous) Samples:

• Squamous samples exhibit a more consistent and generally higher proportion of Aneuploid cells compared to Adeno samples.
• Across the displayed Squamous samples, the Aneuploid population consistently ranges from approximately 25% to 55%.
• All Squamous samples shown demonstrate a substantial aneuploid cell population, with fewer samples being largely diploid compared to the Adeno group.
• Similar to Adeno, the "Unclear" ploidy fraction is negligible in Squamous samples.

Biological Interpretation

The analysis of ploidy in tumor-originating Lung Epithelial cells and unassigned cells provides insights into the genomic landscape of lung cancer. Aneuploidy, defined as an abnormal number of chromosomes, is a hallmark of cancer and is associated with genomic instability, which drives tumor evolution and malignancy [1].

1. Tumor Genomic Instability: The presence of a significant aneuploid population in both Adeno and Squamous samples, particularly within the 'Lung Epithelial cell' compartment (which is identified as the tumor origin celltype in the data context), strongly indicates genomic instability characteristic of malignant transformation. If 'unassigned' cells also show high aneuploidy, it suggests they might represent tumor cells that could not be precisely classified by other means.
2. Histological Subtype Differences: The observation that Squamous Cell Carcinomas tend to have a higher and more consistent proportion of aneuploid cells compared to Adenocarcinomas suggests potential differences in their underlying mechanisms of genomic instability or tumor evolution. Squamous cell carcinomas are often associated with more severe genomic alterations, which aligns with a consistently higher aneuploid fraction. Adenocarcinomas, while also aneuploid, show greater inter-patient variability, possibly reflecting a broader range of molecular drivers and genomic instability levels among different patients.
3. Intra-Tumor Heterogeneity: The variability in ploidy profiles across individual samples within the Adeno group highlights significant inter-patient heterogeneity, even within the same cancer subtype. This suggests that while both are lung adenocarcinomas, the extent of genomic instability can differ substantially between patients.

Clinical or Translational Implications

• Prognostic and Predictive Value: Aneuploidy is a known prognostic factor in various cancers, often correlating with aggressive disease and poorer outcomes [2]. The observed differences in aneuploidy rates between Adeno and Squamous subtypes, and the heterogeneity within Adeno, could have implications for patient stratification and prognostication.
• Therapeutic Targeting: Tumors with high genomic instability and aneuploidy might exhibit different sensitivities to therapies, such as those targeting DNA repair pathways or inducing replication stress. Understanding the ploidy landscape could guide the development or selection of targeted therapies for specific patient subsets.
• Biomarker Development: The proportion of aneuploid cells could potentially serve as a biomarker for aggressiveness or response to certain treatments, particularly if further validated against clinical outcomes.

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References:

1. Aneuploidy in Cancer:

PubMed search: "aneuploidy cancer genomic instability"

2. Aneuploidy as Prognostic Marker:

PubMed search: "aneuploidy cancer prognostic marker"

12. Cell-Cell Interaction Analysis in Squamous Lung Cancer: Macrophage-Macrophage Signaling

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction patterns in single-cell RNA-seq data from lung tissue, specifically focusing on the Squamous condition. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between specified cell types. The user query requested interactions involving Lung Epithelial cells (tumor origin), Fibroblasts, Macrophages, and T cells (CD8+ and CD4+), for both Squamous and Adeno conditions, with a limit of 80 interactions per condition. The provided visualization specifically displays macrophage-macrophage (Mac|Mac) interactions within the Squamous condition. The size of each dot represents the -log10(p-value) of the interaction, indicating statistical significance, while the color intensity indicates the mean expression level (log2(mean)) of the interacting ligand-receptor pair.

Visual Summary

The provided dot plot illustrates 40 distinct ligand-receptor interactions occurring between macrophages within the Squamous tumor microenvironment.

• Dominant Interaction Partners: The plot exclusively shows interactions where both the sending and receiving cell types are Macrophages (Mac|Mac).
• High Significance and Expression: Many interactions are highly significant (indicated by larger dot sizes, reflecting lower p-values) and show strong mean expression (indicated by brighter yellow/green colors). This suggests robust autocrine signaling among macrophages in Squamous lung cancer.
• Key Interaction Categories: Several prominent categories of macrophage-macrophage communication are observed, including:
• TREM2 signaling: APOE_TREM2_receptor and APP_TREM2_receptor interactions are highly significant and show high expression.
• Adhesion and ECM interactions: FN1_integrin_a5b1_complex, ICAM1_integrin_aMb2_complex, ICAM1_integrin_aXb2_complex, and ICAM1_ITGAL are notable.
• Immunomodulatory signaling: HLA-F_LILRB1, HLA-F_LILRB2, IL10_IL10_receptor, IL1B_IL1_receptor, IL1RN_IL1_receptor, CD52_SIGLEC10, and SIRPA_CD47 are present.
• Angiogenesis-related: VEGFA_NRP1 and VEGFA_NRP2 also stand out.

Biological Interpretation

The strong autocrine macrophage-macrophage signaling observed in Squamous lung cancer suggests a highly active and self-sustaining macrophage population within the tumor microenvironment (TME).

• TREM2 Signaling (APOE/APP-TREM2): The prominent APOE_TREM2_receptor and APP_TREM2_receptor interactions are highly significant and show high expression. TREM2 (Triggering Receptor Expressed on Myeloid cells 2) is a key receptor on tumor-associated macrophages (TAMs) that often promotes an immunosuppressive and pro-tumoral phenotype. Activation of TREM2 by ligands like APOE and APP can drive macrophage survival, proliferation, and differentiation towards an M2-like, pro-tumorigenic state, contributing to tumor growth, angiogenesis, and immune evasion. PubMed search: TREM2 tumor associated macrophages lung cancer
• Autocrine Immunosuppression (IL10-IL10R, HLA-F-LILRB1/2): The presence of IL10_IL10_receptor interaction indicates autocrine IL-10 signaling. IL-10 is a potent immunosuppressive cytokine, and its autocrine loop reinforces an M2-like phenotype, dampening anti-tumor immune responses and promoting tumor tolerance. Similarly, HLA-F_LILRB1 and HLA-F_LILRB2 interactions, which are also significant, represent communication between an MHC class I molecule (HLA-F) and inhibitory receptors on myeloid cells (LILRB1/2), potentially leading to immune suppression. GeneCards: LILRB1, GeneCards: LILRB2
• Adhesion and Matrix Remodeling (Integrins, ICAM1): Interactions involving integrins (e.g., FN1_integrin_a5b1_complex) and ICAM1 (ICAM1_integrin_aMb2_complex, ICAM1_ITGAL) highlight the critical role of macrophages in modulating the extracellular matrix (ECM) and mediating cell-cell adhesion. These interactions facilitate macrophage migration, infiltration, and communication within the complex tumor microenvironment, potentially contributing to cancer progression and metastasis.
• Angiogenesis (VEGFA-NRP1/2): The strong VEGFA_NRP1 and VEGFA_NRP2 interactions suggest that macrophages in Squamous lung cancer are actively involved in promoting angiogenesis. VEGFA, often secreted by TAMs, drives new blood vessel formation, which is essential for tumor growth and spread. Neuropilins (NRP1/2) act as co-receptors for VEGFA and enhance its pro-angiogenic effects. UniProt: VEGFA, GeneCards: NRP1
• Other Immunomodulatory Signals: CD52_SIGLEC10 and SIRPA_CD47 interactions are also observed. SIGLEC10 is an inhibitory receptor, and CD47 is a "don't eat me" signal. These interactions can contribute to immune evasion by preventing phagocytosis and dampening immune responses.
• Inflammatory Signaling: The presence of IL1B_IL1_receptor suggests active inflammatory signaling, while IL1RN_IL1_receptor indicates regulation of IL-1beta activity, implying a complex balance of pro-inflammatory and anti-inflammatory mechanisms within the macrophage population.

Overall, the macrophage population in Squamous lung cancer appears to be extensively engaged in autocrine signaling that supports pro-tumorigenic functions, including immune suppression, angiogenesis, and interaction with the ECM.

Clinical or Translational Implications

The identified macrophage-macrophage interactions present several potential targets for therapeutic intervention in Squamous lung cancer:

• Targeting TREM2: Given the strong APOE/APP-TREM2_receptor signaling, blocking TREM2 or its ligands could reprogram TAMs from a pro-tumoral to an anti-tumoral phenotype, enhancing anti-cancer immunity. This represents a promising strategy for immune modulation in the tumor microenvironment.
• Interrupting IL-10 and HLA-F/LILRB pathways: Inhibiting autocrine IL-10 signaling or the HLA-F-LILRB pathways could counteract the immunosuppressive effects of macrophages, potentially sensitizing tumors to existing immunotherapies.
• Anti-angiogenic strategies: The robust VEGFA_NRP1/NRP2 interactions reinforce the role of TAMs in promoting angiogenesis. Combined therapies targeting both VEGFA and specific macrophage functions could be more effective than single-agent approaches in inhibiting tumor vascularization.
• Modulating Adhesion Molecules: Disrupting interactions involving ICAM1 and integrins could impair macrophage migration and infiltration, thereby reducing their pro-tumoral functions within the tumor microenvironment.

This analysis highlights specific intercellular communication hubs within the macrophage population that contribute to the unique biology of Squamous lung cancer. Further investigation into these pathways could lead to novel therapeutic strategies. It's important to note that this specific plot only shows Mac|Mac interactions for the Squamous condition; a full understanding would require examining all requested cell-cell interactions across both Squamous and Adeno conditions.

13. Cell-Cell Interaction Analysis in Lung Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) Microenvironments

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the most prominent cell-cell interactions (CCIs) within the tumor microenvironment of lung adenocarcinoma (Adeno) and lung squamous cell carcinoma (Squamous) using single-cell RNA sequencing data. CellPhoneDB was used to infer ligand-receptor interactions, and the results are presented as dot plots, highlighting the top 80 interactions for each condition based on their significance (p-value, indicated by dot size) and interaction strength (mean expression, indicated by dot color). The analysis aims to identify key communication pathways that may drive disease progression or serve as therapeutic targets.

Visual Summary

Overall Trends:

The analysis reveals distinct, yet also overlapping, cell-cell communication landscapes between lung adenocarcinoma and squamous cell carcinoma. The Adeno condition exhibits a broader and generally stronger network of interactions involving various immune cell types and diploid lung epithelial cells, whereas the Squamous condition, while showing significant interactions, appears to have a more focused set of top interactions primarily centered around macrophages and plasma cells within the top 80 pairs displayed.

Adenocarcinoma (Adeno) Specific Observations:

• Macrophage Centrality: Macrophages (Mac) are highly central, engaging in numerous interactions, including self-interactions (Mac|Mac), and cross-talk with T cells (Mac|T cell CD4+, Mac|T cell CD8+), ILCs (Mac|ILC), and B cells (Mac|B cell).
• Diploid Lung Epithelial Cell Involvement: "Diploid Lung Epi" cells are prominent interactors, particularly with macrophages and T cells. Notably, strong interactions include Diploid Lung Epi|Mac through HLA-F_LILRB1 and SPP1_integrin_aVb1_complex. Given that the tumor origin celltype is 'Lung Epithelial cell', these diploid epithelial cells likely represent non-malignant or less transformed epithelial cells within the tumor microenvironment that are actively communicating with immune cells.
• Immune Checkpoint and Modulatory Interactions: Key immune checkpoint interactions like PDCD1_CD274 (PD-1/PD-L1) are notably strong between Mac|T cell CD4+. Other significant immune modulatory pairs include LAGLS9_CLEC2D and VSIR_HLA-E in interactions involving T cells and macrophages.
• Adhesion and ECM-related interactions: SPP1_integrin_aVb1_complex shows high mean expression and significance across various cell pairs including Diploid Lung Epi|Mac, Mac|Mac, ILC|Mac, and B cell|Mac. ICAM1_integrin complexes are also frequently observed.
• Angiogenesis: VEGFA_NRP1 and VEGFA_NRP2 interactions are present, indicating potential roles in vascularization and tumor growth.

Squamous Cell Carcinoma (Squamous) Specific Observations:

• Fewer Top Interactions: The Squamous plot displays a considerably sparser interaction map within the top 80 selected pairs compared to Adeno, suggesting potentially fewer highly significant or abundant interactions met the display criteria.
• Macrophage and Plasma Cell Focus: Interactions are predominantly seen between macrophages (Mac|Mac, Mac|Plasma) and plasma cells (Plasma|Plasma).
• Adhesion and ECM Dominance: Integrin-mediated interactions, such as ICAM1_integrin complexes, FN1_integrin_a5b1_complex, and SPP1_integrin_aVb1_complex, are highly prominent, especially in Mac|Mac interactions.
• Shared Pathways: Some interactions, like HLA-F_LILRB1/2 and APOE_TREM2_receptor, are also observed, suggesting common immune regulatory mechanisms with Adeno. VEGFA_NRP1/2 are also present.
• Less Prominent Immune Checkpoints (among top 80): Canonical immune checkpoint interactions such as PD-1/PD-L1 are not among the top 80 significant interactions displayed for Squamous, unlike in Adeno. This does not mean they are absent, but rather that other interactions are more prominent by the selection criteria.

Biological Interpretation

The distinct cell-cell interaction profiles between Adeno and Squamous reflect their underlying biological differences in tumor microenvironment composition and immune evasion strategies.

1. Macrophage-Mediated Immunomodulation: In both lung cancer subtypes, macrophages emerge as central orchestrators of cell-cell communication. Their extensive interactions suggest a significant role in shaping the tumor microenvironment, including inflammation, immune suppression, and tissue remodeling. The high prevalence of integrin-related interactions (e.g., SPP1_integrin_aVb1_complex, ICAM1_integrin_aM2_complex, FN1_integrin_a5b1_complex) involving macrophages highlights their adhesive and migratory capabilities, which are crucial for their recruitment and function within the tumor. Osteopontin (SPP1) is known to promote tumor growth, metastasis, and immune suppression by modulating macrophage polarization and T cell function [4].
2. Immune Evasion Mechanisms in Adenocarcinoma: The strong presence of the PDCD1_CD274 (PD-1/PD-L1) axis in Adeno between macrophages and CD4+ T cells underscores a critical immune evasion pathway. This is a well-established mechanism in lung cancer, targeted by current immunotherapies [1]. The interactions involving LAGLS9_CLEC2D (Galectin-9/CLEC-2D) and VSIR_HLA-E (VISTA/HLA-E) further point to a complex interplay of inhibitory signals that can suppress anti-tumor immunity within the Adeno microenvironment [2, 3]. The prominent interactions of Diploid Lung Epi cells with immune cells via HLA-F_LILRB1/2 also suggest a role for non-malignant epithelial cells in modulating immune responses, potentially contributing to immune tolerance or evasion within the tumor microenvironment [5].
3. Distinct Immune Landscape in Squamous Carcinoma: The observed difference in the top CCI profiles for Squamous, with fewer immune checkpoint interactions compared to Adeno, suggests that squamous cell carcinoma might utilize different or additional immune evasion mechanisms, or that the specific macrophage-T cell PD-1/PD-L1 interactions are not as dominant or highly ranked in this subtype. The strong emphasis on adhesion and ECM-related interactions involving macrophages indicates a potential focus on tissue remodeling and stromal interactions in Squamous, which could also contribute to tumor progression and therapeutic resistance [9].
4. Shared Angiogenic Pathways: The presence of VEGFA_NRP1 and VEGFA_NRP2 interactions in both conditions highlights the common importance of vascular endothelial growth factor (VEGFA) signaling in promoting angiogenesis and potentially influencing immune cell function in both subtypes [6].

Clinical or Translational Implications

The identified cell-cell interactions provide valuable insights for therapeutic development and patient stratification in lung cancer:

1. Personalized Immunotherapy Strategies: The strong PDCD1_CD274 interaction in Adeno reinforces the clinical utility of PD-1/PD-L1 blockade for this subtype. For Squamous, where this interaction is less prominent in the top 80, it may suggest that a subset of patients might benefit less from PD-1/PD-L1 monotherapy, or that combination therapies targeting other pathways, such as the identified integrin-mediated adhesion or myeloid-specific interactions, could be more effective.
2. Targeting Myeloid Cell Function: Given the centrality of macrophages in both conditions, targeting specific macrophage-expressed receptors or their ligands (e.g., SPP1, LILRB1/2, TREM2) could be a promising therapeutic avenue for both Adeno and Squamous [7, 8]. Modulating macrophage polarization or recruitment could enhance anti-tumor immunity.
3. Disrupting Tumor Microenvironment Adhesion: The pervasive integrin-mediated interactions suggest that therapies targeting specific integrins or ECM components could disrupt tumor cell migration, invasion, and immune suppression. For instance, blocking the SPP1-integrin axis could interfere with tumor progression and immune escape in both subtypes.
4. Biomarker Identification and Patient Stratification: The differential presence and strength of specific CCI pairs between Adeno and Squamous could serve as predictive biomarkers for response to different immunotherapies. For example, high PDCD1_CD274 interaction strength might correlate with better response to anti-PD-1/PD-L1 in Adeno, while unique integrin signatures might guide therapy in Squamous.
5. Experimental Validation:

• _In vitro_ functional assays: Co-culture experiments using tumor cells, macrophages, and T cells from Adeno or Squamous models can validate the functional consequences of identified ligand-receptor pairs (e.g., how HLA-F_LILRB1/2 interactions between epithelial cells and macrophages affect T cell activation or macrophage polarization).
• _In vivo_ therapeutic targeting: Preclinical models could test the efficacy of blocking antibodies or small molecule inhibitors against key interaction components such as PD-L1, LILRB1/2, SPP1, or specific integrins, alone or in combination with existing therapies.
• Clinical correlative studies: Investigating the correlation between the strength of these CCI pathways in patient samples (e.g., using spatial transcriptomics or multiplex IHC) and clinical outcomes or response to therapy would be valuable.

14. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Pathways in Lung Adenocarcinoma vs. Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) within lung adenocarcinoma (Adeno) and squamous cell carcinoma (Squamous) microenvironments, specifically focusing on a curated list of genes related to immune checkpoint and cell cycle pathways. The goal is to identify significant communication patterns between different immune and stromal cell types that might differentiate these two lung cancer subtypes. The results are presented as dot plots, visualizing significant gene-cell pair interactions per condition.

Visual Summary

CCI for Adeno

The dot plot for Adeno reveals several highly significant cell-cell interactions involving the "LCK_CD8_receptor" functional module:

• T CD8+ | T CD8+: Strong self-interaction of CD8+ T cells.
• T CD4+ | T CD8+: Significant interaction between CD4+ and CD8+ T cells.
• Mac | T CD4+: Interaction between Macrophages and CD4+ T cells.
• Mac | ILC: Interaction between Macrophages and Innate Lymphoid Cells (ILCs).
• ILC | T CD8+: Interaction between ILCs and CD8+ T cells.

All these interactions show a high level of significance (p-value < 1e-10, as indicated by the large dot size corresponding to -log10(p) > 10) and moderate mean expression levels (log2(m) values ranging from 0.3 to 0.4). The recurring involvement of CD8+ and CD4+ T cells, Macrophages, and ILCs highlights a complex immune cellular network in Adenocarcinoma.

CCI for Squamous

The dot plot for Squamous shows a more restricted pattern:

• Mac | Mac: A single, highly significant self-interaction of Macrophages through the "CD93_IFNGR1" gene pair.

Similar to Adeno, this interaction demonstrates high significance (p-value < 1e-10) and a moderate mean expression level (log2(m) = 0.3). The striking difference is the absence of the diverse T cell-centric interactions observed in Adeno.

Biological Interpretation

The distinct CCI patterns observed in Adeno and Squamous conditions suggest fundamental differences in their tumor immune microenvironments, even when examining the same set of immune- and cell cycle-related genes.

Adenocarcinoma: Active T Cell-Centric Immune Signaling

The prominence of LCK_CD8_receptor interactions in Adeno points towards an active T cell-mediated immune response or dysregulation.

• LCK (Lymphocyte-specific protein tyrosine kinase) is a critical intracellular kinase indispensable for T cell receptor (TCR) signaling, especially in CD8+ and CD4+ T cells, where it phosphorylates the CD3 and CD8/CD4 co-receptor cytoplasmic tails upon antigen recognition. https://www.genecards.org/cgi-bin/carddisp.pl?gene=LCK
• CD8 (CD8 receptor) is a co-receptor on cytotoxic T lymphocytes, critical for recognizing MHC class I-peptide complexes and initiating T cell activation. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD8A
• The identified interactions (T CD8+|T CD8+, T CD4+|T CD8+, Mac|T CD4+, ILC|T CD8+, Mac|ILC) suggest widespread communication within the T cell compartment and between T cells, macrophages, and ILCs, driven by LCK/CD8-related signaling. This could indicate robust T cell activation, proliferation, or effector functions. However, the presence of these interactions does not solely imply effective anti-tumor immunity; it could also reflect an exhausted or dysfunctional T cell state that is still highly engaged in the microenvironment. The interactions between macrophages and T cells (Mac|T CD4+) are crucial for antigen presentation and shaping T cell responses.

Squamous Cell Carcinoma: Macrophage-Dominant Innate Immunity

The Squamous microenvironment, in contrast, shows a specific macrophage-macrophage interaction mediated by CD93_IFNGR1.

• CD93 (C-type lectin domain family 1, member A) is a cell surface receptor involved in various immune functions, including phagocytosis, inflammation, and cell adhesion. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD93
• IFNGR1 (Interferon Gamma Receptor 1) is a subunit of the receptor for Interferon-gamma (IFN-$\gamma$), a crucial cytokine for activating macrophages and modulating anti-tumor immunity. https://www.genecards.org/cgi-bin/carddisp.pl?gene=IFNGR1
• The Mac|Mac interaction via CD93_IFNGR1 suggests an autocrine or paracrine signaling loop among macrophages, potentially related to their activation or functional polarization within the Squamous tumor. IFN-$\gamma$ signaling through IFNGR1 is a hallmark of classically activated (M1-like) macrophages, which are often pro-inflammatory and anti-tumorigenic. However, macrophages are highly plastic and can also adopt pro-tumorigenic (M2-like) phenotypes. The involvement of CD93 might further modulate these macrophage functions, possibly in the context of tissue remodeling or debris clearance. The limited number of interactions shown, particularly the absence of explicit T cell interactions seen in Adeno, could suggest a more suppressed or distinct adaptive immune response in Squamous cell carcinoma, or that the relevant interactions are not captured by the selected gene set for this condition.

Clinical or Translational Implications

The distinct cell-cell interaction landscapes highlight potential differences in immune evasion mechanisms and therapeutic vulnerabilities between lung adenocarcinoma and squamous cell carcinoma.

1. Adenocarcinoma and T Cell-Targeted Therapies: The pervasive LCK/CD8-related interactions in Adeno suggest a highly engaged, albeit potentially dysregulated, T cell compartment. This might make Adeno patients more responsive to immunotherapies that aim to reactivate or augment T cell function, such as immune checkpoint inhibitors (e.g., anti-PD-1/PD-L1, anti-CTLA-4), by overcoming T cell exhaustion. Understanding the specific nature of these LCK/CD8 interactions (e.g., active vs. exhausted) could inform patient stratification.

• Potential for Biomarkers: High levels of LCK/CD8-related CCI could serve as a biomarker for "hot" tumors with an inflamed microenvironment, guiding immunotherapy decisions.
• Experimental Validation: Further single-cell functional assays (e.g., T cell activation, cytokine production) could be used to validate the functional implications of these LCK/CD8 interactions.

2. Squamous Cell Carcinoma and Macrophage-Targeted Interventions: The dominant macrophage self-interaction via CD93/IFNGR1 in Squamous indicates that macrophage biology plays a critical role in its immune microenvironment.

• Therapeutic Opportunities: Therapies targeting macrophage polarization or function, such as CSF1R inhibitors, or strategies to enhance IFN-$\gamma$ signaling, could be explored for Squamous.
• Understanding Immune Evasion: The lack of widespread T cell interactions observed might imply that Squamous tumors employ different strategies for immune evasion, potentially relying more on macrophage-mediated immunosuppression or a "cold" tumor environment.
• Experimental Validation: Investigating the polarization state of macrophages (M1 vs. M2) in Squamous tumors and the functional consequence of CD93-IFNGR1 axis activation could uncover novel therapeutic targets.

In summary, this analysis, focusing on immune checkpoint and cell cycle genes, reveals distinct immune communication patterns between lung adenocarcinoma and squamous cell carcinoma, highlighting the need for tailored therapeutic strategies that consider the unique cellular crosstalk within each tumor type.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCI) between Adenocarcinoma (Adeno) and Squamous cell carcinoma (Squamous) conditions in lung tissue, focusing on major immune cells (Myeloid, T cell, B cell) and stromal cells (Fibroblast). The results are presented as a dot plot, where each dot represents a specific ligand-receptor interaction pair between two cell types (CCI index) for individual samples. The color intensity reflects the standardized mean interaction strength, and the dot size indicates the statistical significance (-log10(p-value)) of the interaction. Importantly, many of the Lung Epithelial cell interactions involve aneuploid cells, likely representing the tumor cells themselves.

Visual Summary

The dot plot clearly segregates cell-cell interaction patterns based on the tumor condition (Adeno vs. Squamous).

• Adenocarcinoma (Adeno) Specific Interactions: The left portion of the plot, highlighted by the blue box, shows a prominent cluster of highly significant and strong interactions predominantly in Adeno samples. These interactions frequently involve Macrophage (Mac) and aneuploid Lung Epithelial (Lung.Epi (Aneup)) cells. Notable examples include C3_C3AR1 (Mac-Mac, Lung.Epi-Mac), ICAM1_ITGAL_complex (Mac-Mac, Mac-T cell CD4+, Lung.Epi-Mac), SPN_SIGLEC1 (Mac-Mac), and APP_CD74 (Lung.Epi-Mac). Some interactions also involve Fibroblasts and ILCs.
• Squamous Cell Carcinoma (Squamous) Specific Interactions: The right portion of the plot, also delineated by a blue box, reveals a distinct set of strong and significant interactions characteristic of Squamous samples. A striking pattern here is the extensive involvement of Fibroblasts (Fib) interacting with aneuploid Lung Epithelial (Lung.Epi (Aneup)) cells, as well as Fibroblast-T cell CD8+ interactions and Fibroblast-Fibroblast interactions. Key interactions include numerous collagen-integrin pairs such as COL6A3_integrin_a2b1_complex (Fib-Lung.Epi), COL3A1_integrin_a1b1_complex (Fib-Lung.Epi), COL1A2_integrin_a1b1_complex (Fib-Lung.Epi), and COL5A2_integrin_a2b1_complex (Fib-Lung.Epi). Other notable interactions involve MPZL1_MPZL1 (Fib-Lung.Epi) and THBS2_CD36 (Fib-Fib).
• Overall Pattern: The visualization effectively illustrates that Adenocarcinoma and Squamous cell carcinoma exhibit fundamentally different cell-cell interaction landscapes within their tumor microenvironments, with Adeno showing more pronounced immune (macrophage)-epithelial interactions and Squamous displaying extensive stromal (fibroblast)-epithelial and stromal-stromal interactions involving extracellular matrix components.

Biological Interpretation

The observed condition-specific CCI patterns highlight distinct immunological and stromal characteristics of the Adeno and Squamous tumor microenvironments, respectively.

• Adenocarcinoma TME (Immune-rich): The prominence of Macrophage-Macrophage and Macrophage-Aneuploid Lung Epithelial cell interactions in Adeno suggests a significant role for myeloid cells in shaping the TME.
• C3/C3AR1 signaling (complement component 3 and its receptor) is known to regulate inflammatory responses, cell adhesion, and migration. Its involvement in macrophage-macrophage and macrophage-tumor cell interactions could indicate a pro-tumorigenic inflammatory environment or immune evasion mechanisms in Adeno PMID: 29029961.
• ICAM1 (CD54) interactions with ITGAL (CD11a/CD18, LFA-1) are crucial for leukocyte adhesion and migration, and for immune cell-target cell recognition. Their strong presence implies active immune cell trafficking and potential immune cell recruitment or interaction with tumor epithelial cells GeneCards: ICAM1.
• APP (Amyloid Precursor Protein) and CD74 interaction could be involved in antigen presentation or immune modulation, given CD74's role as an invariant chain for MHC class II.

These interactions collectively suggest that Adenocarcinoma might rely heavily on macrophage-mediated processes, potentially related to immune suppression, inflammation, or angiogenesis within the TME.

• Squamous Cell Carcinoma TME (Stromal-rich): The strong signature of Fibroblast-Aneuploid Lung Epithelial cell interactions and Fibroblast-Fibroblast interactions in Squamous cell carcinoma points to extensive desmoplasia and extracellular matrix (ECM) remodeling, which are hallmarks of a highly reactive stroma.
• Collagen-integrin interactions (e.g., COL6A3, COL3A1, COL1A2 interacting with various integrin complexes) are critical for cell adhesion, migration, proliferation, and survival, and mediate communication between tumor cells and the surrounding stroma GeneCards: COL3A1. These interactions drive tumor invasion, metastasis, and therapy resistance by modulating ECM stiffness and signaling.
• MPZL1 (Myelin Protein Zero-like 1), also known as CD316, is a cell surface glycoprotein involved in cell adhesion and signal transduction. Its interaction between fibroblasts and epithelial cells suggests a role in maintaining tissue architecture or promoting tumor growth in a stromal-dependent manner.
• THBS2 (Thrombospondin 2), a matricellular protein, is involved in cell-matrix interactions, angiogenesis, and can promote tumor progression and metastasis. Its interaction among fibroblasts indicates active stromal remodeling GeneCards: THBS2.

These findings suggest that Squamous cell carcinoma development and progression are heavily influenced by a desmoplastic reaction, with fibroblasts and the ECM providing critical structural and signaling support to the tumor cells.

Clinical or Translational Implications

The distinct CCI patterns observed between Adeno and Squamous lung cancers carry significant clinical and translational implications:

• Subtype-Specific Therapeutic Targets: The identified ligand-receptor pairs represent potential subtype-specific therapeutic targets.
• In Adenocarcinoma, targeting macrophage-related interactions (e.g., C3/C3AR1, ICAM1/ITGAL, APP/CD74) could be explored to modulate the immune suppressive or inflammatory TME, potentially enhancing existing immunotherapies or developing novel macrophage-targeting strategies.
• In Squamous cell carcinoma, interventions aimed at disrupting collagen-integrin axes or inhibiting key stromal factors like THBS2 or MPZL1 could impede desmoplasia, reduce tumor stiffness, and inhibit invasion and metastasis. This might involve targeting specific integrin receptors or enzymes involved in collagen synthesis/cross-linking.
• Biomarkers for Diagnosis and Prognosis: The unique CCI signatures could serve as biomarkers for distinguishing between Adeno and Squamous subtypes, potentially aiding in more precise diagnosis or prognostication, especially in contexts where histological classification is challenging.
• Personalized Medicine Approaches: Understanding these differential interaction networks could pave the way for more personalized treatment strategies for lung cancer patients, tailoring therapies based on the dominant TME characteristics of their specific tumor subtype. This moves beyond genetic mutations to include the cellular ecosystem of the tumor.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis provides a dot plot visualization of condition-specific surfaceome markers in Lung Epithelial cells, comparing Lung Adenocarcinoma (Adeno) and Lung Squamous Cell Carcinoma (Squamous) conditions. The analysis specifically focused on surfaceome markers, identifying up to 50 top markers per condition, which are particularly relevant for potential diagnostic and therapeutic applications.

Visual Summary

The dot plot effectively illustrates the differential expression patterns of surfaceome markers across various patient samples, which are neatly grouped by their tumor condition (Adeno vs. Squamous).

• Distinct Condition-Specific Clusters: The plot clearly separates samples into two primary groups based on their unique marker expression profiles. The upper cluster of samples (e.g., NSC010.T1, NSC020.T2, NSC037.T1), designated as "Adeno", exhibits robust and widespread expression of a distinct set of markers. In contrast, the lower cluster of samples (NSC004.T3, NSC004.T1, NSC004.T4, NSC004.T2), representing the "Squamous" condition, displays high expression and prevalence for a different, extensive panel of surfaceome markers.
• Adeno-Specific Markers: Lung Epithelial cells from Adeno samples are predominantly characterized by high expression (indicated by dark red circles) and a high fraction of expressing cells (large circle size) for markers such as HLA-B, CD74, HLA-DRB1, HLA-DRA, MUC1, and MUC9. These markers show minimal to no expression in the Squamous samples.
• Squamous-Specific Markers: Lung Epithelial cells from Squamous samples show strong and widespread expression (large, dark red circles) across a broad spectrum of surfaceome markers. Key examples include CD44, SPINT2, NTRK2, SDC1, PTPRF, TMEM123, EFNA1, DDR1, CDH1, FGFR2, EGFR, and DSC2. These markers are largely absent or expressed at very low levels in the Adeno samples.
• Intermediate/Diploid Samples: An intermediate group of samples, explicitly labeled "Diploid NSC...", is clustered under the Adeno condition. These samples generally show very low expression for most markers, suggesting they might represent non-malignant epithelial cells or specific diploid tumor subclones that do not exhibit the strong marker signatures observed in the aneuploid tumor populations.
• Expression and Prevalence Indicators: The color intensity (red scale) of each dot signifies the mean expression level of the gene within that sample group, while the size of the dot represents the fraction of cells within that group expressing the gene. This combined visualization allows for clear identification of both highly expressed and broadly prevalent surface markers.

Biological Interpretation

The observed differential expression of these surfaceome markers reflects fundamental biological distinctions between lung adenocarcinoma and squamous cell carcinoma phenotypes.

Adenocarcinoma-Associated Markers:

• MHC Class I and II (HLA-B, HLA-DRB1, HLA-DRA): While frequently downregulated in advanced cancers to escape immune detection, their prominent expression in these Adeno samples suggests potential active immune surveillance mechanisms or specific immune-responsive states within these tumors. CD74, the invariant chain of MHC class II, is often co-expressed with MHC class II and has roles in antigen presentation and immune cell function, but can also be expressed by tumor cells UniProt CD74: P04233.
• Mucin Family Proteins (MUC1, MUC9): MUC1 is a well-established oncofetal glycoprotein frequently overexpressed in adenocarcinomas, where it contributes to tumor growth, metastasis, and immune evasion GeneCards MUC1. Its presence, along with MUC9, aligns with the glandular differentiation characteristics typical of adenocarcinomas.

Squamous Cell Carcinoma-Associated Markers:

• Cell Adhesion and Differentiation (CD44, SDC1, CDH1, DSC2, NECTIN1): CD44 is a prominent adhesion molecule often associated with cancer stem cells, epithelial-mesenchymal transition (EMT), and increased invasiveness, and is a known marker for squamous differentiation GeneCards CD44. SDC1 (Syndecan-1) and DSC2 (Desmocollin-2) are critical components of cell adhesion (proteoglycan and desmosomal cadherin, respectively) which are often upregulated in squamous epithelial tissues and tumors, reflecting their role in maintaining structural integrity UniProt SDC1: P18827, UniProt DSC2: Q08554. CDH1 (E-cadherin), a key adherens junction protein, and NECTIN1 (a nectin family cell adhesion molecule) further emphasize strong cell-cell adhesion characteristics of squamous epithelium.
• Growth Factor Receptors (EGFR, FGFR2, DDR1): The consistent and high expression of EGFR and FGFR2 is highly significant. EGFR is a major therapeutic target in lung cancer (especially adenocarcinoma, but also present in squamous) due to its role in cell proliferation and survival PubMed search: EGFR lung squamous. FGFR2 is another receptor tyrosine kinase whose alterations (fusions, amplifications) are actionable targets in various cancers PubMed search: FGFR2 lung cancer. DDR1 (Discoidin Domain Receptor 1), a collagen-activated receptor, is involved in cell adhesion, migration, and often overexpressed in cancer.
• NTRK2 (TrkB): This neurotrophic tyrosine receptor kinase plays roles in cell growth and differentiation; NTRK fusions are known oncogenic drivers and therapeutic targets in several cancer types GeneCards NTRK2.

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers in Lung Epithelial cells holds substantial clinical and translational potential for improved lung cancer diagnostics and therapeutics.

Enhanced Diagnostic and Prognostic Biomarkers:

• The clearly defined surfaceome profiles for Adenocarcinoma and Squamous Cell Carcinoma could serve as highly specific diagnostic tools, aiding in the accurate differentiation of these subtypes, especially in challenging biopsy samples or where tissue quantity is limited. For example, strong MUC1 expression might support an adenocarcinoma diagnosis, while robust CD44 and EGFR/FGFR2 expression could strongly indicate a squamous phenotype.
• These markers also warrant further investigation into their prognostic value, potentially correlating expression levels with disease progression, recurrence, or survival rates within each specific lung cancer subtype.

Novel Therapeutic Targets for Precision Medicine:

• Given that all identified markers are surface proteins, they represent excellent candidates for targeted therapies. EGFR and FGFR2 are already validated targets, and their high expression in squamous cells further emphasizes their therapeutic relevance.
• Other highly expressed, condition-specific surface proteins, such as CD44, SDC1, and NTRK2 in squamous cells, or CD74 and MUC1 in adenocarcinoma cells, could be explored as novel targets for advanced therapeutic modalities like antibody-drug conjugates (ADCs), bispecific antibodies, or chimeric antigen receptor (CAR) T-cell therapies. Such approaches would enable highly specific delivery of cytotoxic agents or immune cells directly to tumor cells, minimizing systemic toxicity.

Guidance for Experimental Validation and Drug Development:

• These findings provide a strong foundation for subsequent experimental validation using established techniques such as immunohistochemistry (IHC) on tumor tissue, flow cytometry on dissociated tumor cells, or mass spectrometry-based proteomics.
• Successful validation could lead to the development of new diagnostic assays or companion diagnostics to precisely select patients who are most likely to respond to specific targeted therapies.
• Ultimately, these marker discoveries can guide rational drug development strategies, focusing on subtype-specific surface proteins to create more effective and personalized treatment approaches for lung cancer patients.

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

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify surfaceome markers specific to Macrophages within two distinct lung cancer conditions: Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). Using single-cell RNA-seq data from the provided AnnData object, the plot_markers_and_expression_dot tool was employed to visualize differentially expressed genes. The analysis focused on surfaceome markers, with a maximum of 50 markers selected per condition based on expression specificity and fold change, providing insights into the distinct phenotypic profiles of macrophages in these tumor microenvironments.

Visual Summary

The dot plot effectively illustrates the condition-specific expression patterns of macrophage surfaceome markers across various patient samples.

• Distinct Marker Clusters: The plot clearly segregates markers into two main clusters based on their expression profiles. Genes on the left side (e.g., *HLA-DQA1*, *HLA-DMA*, *JAML*, *LY6E*, *AXL*, *HLA-DOB2*) exhibit high expression (indicated by darker red color) and high prevalence (larger dot size) predominantly in the top group of samples, which are associated with the Adenocarcinoma (Adeno) condition. Conversely, markers on the right side (e.g., *HLA-DQA2*, *SLC2A14*, *ANPEP*, *TREM1*, *ADGRE2*, *IL7R*, *AQP9*, *LTBR*, *SDC2*, *ITGA5*, *SLC2A11*, *P2RX7*, *CD300E*, *PCNX1*) show strong expression in the bottom group of samples, corresponding to the Squamous Cell Carcinoma (Squamous) condition.
• Condition Specificity: The red boxes highlight this clear separation. Adeno-associated markers are largely absent or expressed at very low levels in Squamous samples, and vice versa. This indicates a robust and distinct surfaceome signature for macrophages residing in Adenocarcinoma versus Squamous microenvironments.
• Heterogeneity within Conditions: While strong condition specificity is observed, there is some variability in marker expression levels and fraction of expressing cells even within samples belonging to the same condition. This suggests potential intra-condition heterogeneity in macrophage phenotypes.
• Cell Counts: The bar plot on the right displays the number of macrophage cells identified in each sample. This context helps confirm that the observed marker patterns are not merely due to low cell numbers but represent genuine expression differences in sizable cell populations.

Biological Interpretation

The identification of distinct macrophage surfaceome markers in Adenocarcinoma and Squamous Cell Carcinoma highlights the significant phenotypic plasticity and context-dependent functional states of tumor-associated macrophages (TAMs) in different lung cancer subtypes.

Adenocarcinoma-Associated Macrophages:

• Antigen Presentation and Immune Modulation: High expression of MHC Class II molecules (*HLA-DQA1*, *HLA-DMA*, *HLA-DOB2*) suggests a robust capacity for antigen presentation in Adeno-associated macrophages. While often associated with M1-like (pro-inflammatory) macrophages, certain MHC II isoforms can also be expressed by M2-like (immunosuppressive) macrophages, underscoring their complex role in shaping anti-tumor immunity. [PubMed: MHC class II molecules in cancer]
• Pro-Tumorigenic Signaling: The presence of *AXL* on Adeno macrophages is notable. AXL receptor tyrosine kinase is frequently implicated in tumor progression, promoting cancer cell survival, proliferation, and metastasis. In TAMs, AXL can contribute to an immunosuppressive microenvironment, suggesting that Adeno macrophages might play a role in immune evasion. [GeneCards: AXL]
• Cell Adhesion: *JAML* (Junctional Adhesion Molecule Like) indicates involvement in cell-cell interactions and leukocyte trafficking, important for macrophage recruitment and positioning within the tumor.

Squamous Cell Carcinoma-Associated Macrophages:

• Inflammatory and ECM Interaction: Markers such as *TREM1* (Triggering Receptor Expressed on Myeloid Cells 1) and *P2RX7* (P2X Purinoceptor 7) are typically associated with pro-inflammatory responses and inflammasome activation in myeloid cells. This suggests that macrophages in Squamous tumors might adopt a more inflammatory or M1-like phenotype, or contribute to chronic inflammation that can paradoxically fuel tumor growth. [GeneCards: TREM1] [GeneCards: P2RX7]
• ECM Remodeling and Adhesion: High expression of *ANPEP* (CD13), *SDC2* (Syndecan-2), and *ITGA5* (Integrin alpha-5) points towards active involvement in extracellular matrix (ECM) interactions, cell adhesion, and potentially matrix remodeling within the Squamous TME. *ANPEP* is linked to tumor angiogenesis and invasion, while *ITGA5* (a fibronectin receptor) mediates cell-matrix adhesion and migration, suggesting a more migratory or invasive phenotype for these macrophages. [GeneCards: ANPEP] [GeneCards: ITGA5]
• Metabolic Reprogramming: The expression of glucose transporters like *SLC2A14* and *SLC2A11* could imply distinct metabolic adaptations in Squamous-associated macrophages, potentially reflecting altered glucose metabolism to support their functions in this environment.

Overall, the data suggests that macrophages in Adenocarcinoma may have a more pronounced role in antigen presentation and AXL-mediated immune suppression, while those in Squamous Cell Carcinoma appear to be more involved in pro-inflammatory processes and extensive interactions with the extracellular matrix, reflecting distinct functional adaptations to their respective tumor microenvironments.

Clinical or Translational Implications

The identification of these condition-specific macrophage surfaceome markers carries significant clinical and translational potential:

• Diagnostic and Prognostic Biomarkers: These distinct marker profiles could serve as novel diagnostic tools to differentiate between Adenocarcinoma and Squamous Cell Carcinoma, or as prognostic indicators to predict disease progression or response to therapy based on the prevailing macrophage phenotype.

Targeted Immunotherapy Development:

• For Adenocarcinoma, targeting AXL on macrophages could be a promising therapeutic strategy to disarm their pro-tumorigenic and immunosuppressive functions. AXL inhibitors are already being explored in various cancers. [PubMed: AXL inhibitors]
• For Squamous Cell Carcinoma, modulating the activity of pro-inflammatory receptors like TREM1 or P2RX7, or inhibiting the functions of ANPEP or ITGA5 on macrophages, could potentially reprogram the tumor microenvironment to be less pro-tumorigenic or more susceptible to existing therapies.
• Patient Stratification: Understanding the unique macrophage signatures in each lung cancer subtype could enable more precise patient stratification for clinical trials, leading to personalized therapeutic approaches that effectively target the specific immune cell populations driving disease progression.
• Experimental Validation: These identified surface markers are strong candidates for further experimental validation using techniques like flow cytometry or immunohistochemistry on patient tumor samples. Functional studies in *in vitro* macrophage differentiation models or *in vivo* preclinical models would be crucial to confirm their roles in tumor biology and their potential as therapeutic targets.

18. Fibroblast Condition-Specific Surfaceome Markers in Lung Cancer

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes condition-specific surfaceome markers in Fibroblast cells from single-cell RNA-seq data, comparing lung Adenocarcinoma (Adeno) and Squamous cell carcinoma (Squamous) conditions. The dot plot displays the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) across individual samples clustered by diagnosis. Focusing on surfaceome proteins helps identify potential therapeutic targets or diagnostic biomarkers accessible on the cell surface.

Visual Summary

The dot plot clearly differentiates Fibroblast marker expression patterns between Adeno and Squamous conditions.

• Condition-Specific Patterns: A strong and extensive panel of surfaceome markers is predominantly highly expressed and prevalent in Fibroblasts from Adenocarcinoma samples. In contrast, Fibroblasts from Squamous cell carcinoma samples show generally lower expression and prevalence for most of these markers.
• Heterogeneity within Adenocarcinoma: Within the Adenocarcinoma group, two samples (NSC004.T1 and NSC004.T2) exhibit exceptionally high expression and near-universal prevalence (91-95% of cells) across a broad spectrum of markers, including IL6ST, CD74, MMP14, CYSLTR1, PMEPA1, ITGB5, PTK7, MRC2, ADAM12, SDC1, FAP, and LRRC15. This pattern is highlighted by the red box, indicating a distinct sub-phenotype or activation state of Fibroblasts in these specific Adeno samples.
• Squamous Condition: Fibroblasts from Squamous samples show less pronounced and less consistent expression of these specific surfaceome markers. While some markers like *MMP14*, *PMEPA1*, *MRC2*, and *SDC2* show moderate expression in a few Squamous samples (e.g., NSC021.T2), the overall signature is much weaker compared to Adenocarcinoma.
• Key Markers: Markers such as FAP and LRRC15, known to be associated with cancer-associated fibroblasts, show very high expression and prevalence in the Adeno samples, particularly in the NSC004 cluster.

Biological Interpretation

The observed differential expression of surfaceome markers in Fibroblasts points towards distinct functional states and roles of these stromal cells in Adenocarcinoma versus Squamous cell carcinoma of the lung.

• Cancer-Associated Fibroblast (CAF) Phenotypes: The robust expression of numerous pro-tumorigenic surfaceome markers in Adenocarcinoma Fibroblasts suggests a highly activated Cancer-Associated Fibroblast (CAF) phenotype.
• ECM Remodeling and Invasion: High expression of MMP14 (a matrix metalloproteinase) and ADAM12 (a disintegrin and metalloproteinase) indicates increased extracellular matrix (ECM) degradation and remodeling, facilitating tumor invasion and metastasis. MRC2 (Endo180) and SDC1 (Syndecan-1) further support roles in ECM interaction and cellular trafficking. GeneCards: MMP14 GeneCards: ADAM12
• Growth and Proliferation Signaling: IL6ST (gp130), a signal transducer for IL-6 family cytokines, suggests active inflammatory and growth signaling pathways within these CAFs, contributing to tumor growth and immune evasion. PTK7 (Protein Tyrosine Kinase 7) is also implicated in tumor cell proliferation and migration. GeneCards: IL6ST
• Cell Adhesion and Migration: ITGB5 (Integrin Beta 5) highlights enhanced cell-ECM adhesion and migratory capabilities. GeneCards: ITGB5
• Immunomodulation: CD74, while known for antigen presentation, also plays non-immune roles in cancer progression. GeneCards: CD74
• Distinct Adeno-specific CAF Subtype: The particularly strong signature in samples NSC004.T1 and NSC004.T2 suggests the presence of a highly activated, potentially aggressive CAF subtype or a specific microenvironmental context contributing to Adenocarcinoma progression in these patients. This cluster is enriched in well-established CAF markers like FAP (Fibroblast Activation Protein alpha) and emerging markers like LRRC15, which are critical players in tumor stroma interaction and immunosuppression. GeneCards: FAP PubMed Search: LRRC15 cancer-associated fibroblasts lung cancer
• Differences between Adeno and Squamous: The less prominent expression of these markers in Squamous carcinoma Fibroblasts implies either a different stromal composition, a less activated CAF state, or a reliance on distinct molecular pathways for stromal support in Squamous cell carcinoma. This differential expression highlights fundamental biological differences in the tumor microenvironment between these two major lung cancer subtypes.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers in Fibroblasts holds significant clinical and translational potential:

• Diagnostic and Prognostic Biomarkers: The distinct expression profiles, particularly for the Adenocarcinoma-associated markers (e.g., FAP, LRRC15, MMP14, SDC1), could serve as diagnostic markers to differentiate between lung Adenocarcinoma and Squamous cell carcinoma, or even to identify specific aggressive CAF subtypes within Adenocarcinoma. These markers could also have prognostic value, indicating patient stratification for different treatment responses.
• Therapeutic Targets: As these are surfaceome proteins, they represent highly accessible targets for therapeutic intervention.
• FAP is a particularly well-studied target for CAF depletion or reprogramming strategies, with ongoing clinical trials utilizing FAP-targeting antibodies or small molecules. PubMed Search: FAP fibroblast activation protein therapy cancer
• Other highly expressed surfaceome markers like LRRC15, PTK7, SDC1, and MMP14 could be investigated as novel targets for antibody-drug conjugates (ADCs), CAR-T cell therapy, or small molecule inhibitors to modulate CAF function and reduce tumor progression in Adenocarcinoma.
• Understanding Tumor Microenvironment: These markers provide insights into the distinct stromal-tumor interactions in different lung cancer subtypes, which can guide the development of tailored therapies that target the tumor microenvironment.
• Experimental Validation: The identified markers warrant further experimental validation (e.g., immunohistochemistry on patient samples, functional studies in 3D co-culture models or in vivo mouse models) to confirm their roles in tumor progression and assess their potential as therapeutic targets or biomarkers.

19. T cell CD4+ condition-specific surfaceome markers in Lung Adenocarcinoma vs. Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for CD4+ T cells, comparing Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) lung cancer contexts. The dot plot visualizes the expression of up to 50 top surfaceome markers across individual patient samples, grouped by disease condition. Dot size represents the fraction of cells expressing the gene within a sample group, while color intensity indicates the mean expression level. This analysis helps to pinpoint cell surface molecules that distinguish CD4+ T cell states between these two major lung cancer subtypes, which could serve as potential biomarkers or therapeutic targets.

Visual Summary

The dot plot displays the expression patterns of 24 selected surfaceome markers in CD4+ T cells across various patient samples.

• Condition-Specific Patterns: A clear distinction is observed between the Adeno and Squamous conditions. The majority of the identified markers show substantially higher mean expression (darker red dots) and a higher fraction of expressing cells (larger dot sizes) in samples derived from Adenocarcinoma patients compared to Squamous Cell Carcinoma patients. For instance, genes like *BTN3A2, CYSLTR1, OR4D9, ITGB2, CLEC2D, TIGIT, CTLA4, CD28, TNFRSF9*, and *CD27* are prominently expressed in Adeno samples, while their expression is largely absent or very low in Squamous samples.
• Heterogeneity within Adeno: While most Adeno samples show elevated expression of these markers, a distinct subgroup of Adeno samples (e.g., NSC004.T3, NSC004.T4, NSC004.T2, NSC004.T1 – highlighted by the bottom red box in the cropped image) exhibits particularly strong and widespread expression across almost all listed markers. This suggests potential intra-condition heterogeneity in CD4+ T cell activation or differentiation states.
• Gene Clustering: The dendrogram at the top clusters genes based on their co-expression patterns. This clustering suggests that many of these markers are co-expressed, potentially indicating coordinated transcriptional regulation or shared biological functions.
• Squamous Profile: In contrast to Adeno, CD4+ T cells from Squamous samples generally show minimal to no expression of these specific surfaceome markers, indicating that these markers are not characteristic of CD4+ T cells in the Squamous tumor microenvironment as defined by this analysis.

Biological Interpretation

The observed upregulation of numerous surfaceome markers on CD4+ T cells primarily in Adenocarcinoma samples provides significant biological insights into the distinct immune landscapes of these lung cancer types.

• Immune Checkpoints and Co-stimulatory Molecules: Several key immune checkpoint receptors and co-stimulatory molecules are highly expressed in Adeno-associated CD4+ T cells:
• TIGIT and CTLA4: These are well-known inhibitory receptors. Their increased expression suggests a state of T cell exhaustion or anergy within the Adenocarcinoma tumor microenvironment (TME), indicative of chronic antigen stimulation and attempts by the tumor to evade immune surveillance. GeneCards TIGIT, GeneCards CTLA4
• CD28, TNFRSF9 (CD137), CD27: These are co-stimulatory receptors essential for T cell activation, survival, and memory development. Their concurrent upregulation alongside inhibitory markers like TIGIT and CTLA4 suggests a complex and highly activated but potentially dysregulated state of CD4+ T cells in Adeno, where both activating and inhibitory signals are prominent. GeneCards CD28, GeneCards TNFRSF9, GeneCards CD27
• Cell Adhesion and Migration: *ITGB2* (integrin beta-2, a component of LFA-1) is involved in cell adhesion and T cell migration. Its expression could relate to T cell infiltration and interaction within the TME. GeneCards ITGB2

Other Receptors and Signaling Molecules:

• *CYSLTR1* (Cysteinyl Leukotriene Receptor 1) is involved in inflammatory responses.
• *CLEC2D* (C-type lectin domain family 2 member D) is known to regulate immune cell activation.
• *BTN3A2* (Butyrophilin Subfamily 3 Member A2) can be involved in antigen presentation and T cell activation, particularly gamma-delta T cells, but also found on alpha-beta T cells.
• Distinct T cell States in Adeno: The broad upregulation of these markers, especially in certain patient subgroups, points towards specific adaptive immune responses and T cell states that are more characteristic of Adenocarcinoma. This could include a higher proportion of activated, memory, or exhausted CD4+ T cells actively interacting with tumor cells or other immune cells in the TME. The paucity of these markers in Squamous T cells suggests different immune regulatory mechanisms or less pronounced T cell activation/exhaustion for these specific pathways.

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers carries important clinical and translational potential.

• Biomarkers for Patient Stratification: The differential expression of these surfaceome markers could serve as diagnostic or prognostic biomarkers to distinguish immune states in CD4+ T cells between Adenocarcinoma and Squamous Cell Carcinoma. Furthermore, the distinct expression profiles observed within Adeno patients (e.g., NSC004 samples) could enable the stratification of patients who might respond differently to various immunotherapies.
• Potential Therapeutic Targets: The significant upregulation of immune checkpoint molecules such as TIGIT and CTLA4 on CD4+ T cells in Adenocarcinoma patients highlights these as promising therapeutic targets. Blockade of these inhibitory pathways could potentially reinvigorate anti-tumor immune responses, suggesting a rationale for exploring anti-TIGIT or anti-CTLA4 therapies, perhaps in combination with existing treatments, specifically for Adenocarcinoma. PubMed Search: TIGIT cancer immunotherapy lung adenocarcinoma, PubMed Search: CTLA4 cancer immunotherapy lung adenocarcinoma
• Immunomodulation Strategies: The co-expression of both co-inhibitory and co-stimulatory markers suggests that multi-pronged immunomodulation strategies might be beneficial. For example, combining immune checkpoint blockade with agents that enhance co-stimulation (e.g., via CD28, TNFRSF9, or CD27 agonists) could be explored to achieve more robust and sustained anti-tumor immunity in Adenocarcinoma.
• Experimental Validation: These findings warrant further experimental validation using techniques such as flow cytometry or immunohistochemistry on tissue samples to confirm protein expression at the cellular level and to functionally characterize these CD4+ T cell subsets in the context of lung Adenocarcinoma.

20. Differential Expression of Cell Cycle Genes in Lung Epithelial Cells Across NSCLC Subtypes

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a curated set of cell cycle-related genes within Lung Epithelial cells (identified as tumor-origin cells) when comparing Squamous Cell Carcinoma (Squamous) and Adenocarcinoma (Adeno) conditions. The goal is to uncover subtype-specific alterations in cell cycle regulation that contribute to the distinct pathologies of these two major non-small cell lung cancer (NSCLC) types. Box plots visualize the gene expression levels, and statistical significance tests highlight differences between the conditions.

Visual Summary

The box plots display the sample mean gene expression for 24 cell cycle-related genes, comparing Squamous (blue) and Adeno (orange) conditions in Lung Epithelial cells. Each dot represents a sample.

• Widespread Upregulation in Squamous: A significant number of genes show higher expression in Squamous cells compared to Adeno cells. These include several 14-3-3 family members (YWHAB, YWHAQ, YWHAG, YWHAZ, YWHAE), histone deacetylases (HDAC1, HDAC2), cell cycle regulators (CDK4, TFDP1, TFDP2, MYC, ORC4, MCM7, BUB3, ANAPC11, RBX1), DNA damage/repair genes (GADD45A, PRKDC, RAD21), TGF-β pathway components (SMAD2, SMAD3), and tumor suppressors (CDKN2A, SFN). Many of these differences are highly statistically significant (p ≤ 0.001 to p ≤ 0.05).
• Specific Upregulation in Adeno: Fewer genes are significantly upregulated in Adeno cells. Notably, Cyclin D1 (CCND1), a key cell cycle promoter, and two cell cycle/DNA damage regulators (CDKN1A, GADD45B) exhibit higher expression in Adeno cells compared to Squamous cells, with high statistical significance (p ≤ 1e-4 to p = 0.05).
• Magnitude and Variability: The magnitude of expression differences varies, with genes like HDAC1, MYC, TFDP1, MCM7, CCND1, CDKN2A, and GADD45B showing particularly clear separation between the two conditions. Variability within each group is also evident, with some genes showing tighter clustering of expression values (e.g., TFDP1 in Squamous) and others displaying a broader range.

Biological Interpretation

The observed differential expression patterns highlight distinct mechanisms of cell cycle regulation and potential proliferative strategies between Lung Squamous Cell Carcinoma and Lung Adenocarcinoma in tumor cells.

1. Enhanced Proliferative Signature in Squamous Carcinoma:

• Core Cell Cycle Progression: Genes crucial for G1/S transition and DNA replication, such as CDK4 (a cyclin-dependent kinase), MYC (a proto-oncogene), TFDP1 and TFDP2 (E2F-associated transcription factors), and replication licensing factors like ORC4 and MCM7, are significantly upregulated in Squamous cells. This collectively suggests a more active and potentially accelerated cell cycle progression in Squamous tumors [1, 2].
• Epigenetic Regulation: HDAC1 and HDAC2, histone deacetylases that promote gene silencing and cell proliferation, are significantly elevated in Squamous cells. This indicates altered epigenetic landscapes that likely favor oncogenic gene expression and cell division in this subtype [3].
• 14-3-3 Protein Family Upregulation: Multiple members of the 14-3-3 protein family (YWHAB, YWHAQ, YWHAG, YWHAZ, YWHAE) are consistently upregulated in Squamous cells. These proteins are involved in a wide array of cellular processes, including cell cycle control, signal transduction, and apoptosis, often promoting cell survival and proliferation in cancer contexts. The concurrent upregulation of SFN (14-3-3 sigma), which can act as a tumor suppressor by promoting cell cycle arrest, alongside other proliferative markers, might suggest complex compensatory mechanisms or distinct context-dependent roles for specific 14-3-3 isoforms in Squamous tumors [4, 5].
• DNA Damage Response & Checkpoints: The upregulation of GADD45A (Growth Arrest and DNA Damage-inducible Protein Alpha) in Squamous cells could indicate increased DNA damage and an active but potentially overwhelmed DNA repair response, consistent with the genomic instability often observed in Squamous Cell Carcinoma [6]. Components of the anaphase-promoting complex (ANAPC11, RBX1) and spindle checkpoint protein BUB3 also show trends of upregulation, possibly reflecting higher mitotic activity.

2. Distinct Cell Cycle Drivers and Regulators in Adenocarcinoma:

• Cyclin D1 as a Key Driver: CCND1 (Cyclin D1), a major G1 cyclin, is markedly upregulated in Adeno cells. Overexpression of Cyclin D1 is a well-known oncogenic event that drives cell cycle progression and is frequently implicated in various cancers, including lung adenocarcinoma [7].
• Cell Cycle Inhibitor and DNA Damage Response: CDKN1A (p21), a potent cell cycle inhibitor often activated by TP53, shows higher expression in Adeno. Its upregulation could suggest a more active p53-mediated response or a distinct mechanism of cell cycle control in Adeno cells compared to Squamous cells [8]. Similarly, GADD45B, another DNA damage-inducible gene, is upregulated in Adeno, suggesting specific aspects of stress response or cell cycle regulation differ from Squamous (which shows GADD45A upregulation).

3. Contrasting Tumor Suppressor Expression:

• The upregulation of CDKN2A (p16INK4a) in Squamous cells, despite other pro-proliferative markers also being high, is a notable finding. CDKN2A is a critical tumor suppressor that inhibits CDK4/6 and is frequently inactivated in many cancers. Its higher expression in Squamous might represent an attempt by cells to induce cell cycle arrest in response to oncogenic stress (oncogene-induced senescence), or it could suggest that functional inactivation of CDKN2A occurs through mechanisms other than transcriptional downregulation (e.g., epigenetic silencing, mutation, or protein degradation) in some squamous tumors [9].

Clinical or Translational Implications

The distinct cell cycle gene expression profiles between Squamous and Adeno lung epithelial cells have several clinical implications:

• Subtype-Specific Therapeutic Vulnerabilities: The differential expression of key cell cycle regulators suggests that Squamous and Adeno tumors may respond differently to cell cycle-targeted therapies. For instance, the general upregulation of CDK4 in Squamous tumors might suggest a greater dependency on the CDK4/6 pathway, potentially making them more susceptible to CDK4/6 inhibitors compared to Adeno tumors, although Adeno has higher CCND1 which is also a target. The distinct patterns of HDAC1/2 upregulation in Squamous also highlight the potential utility of HDAC inhibitors in this subtype [3].
• Biomarker Identification: These differentially expressed genes could serve as subtype-specific biomarkers. For example, high expression of CDK4, MYC, or HDAC1/2 could be indicators for Squamous, while CCND1 could be a stronger marker for Adeno, aiding in diagnosis, prognosis, or therapeutic stratification.
• Understanding Resistance Mechanisms: The co-expression of pro-proliferative genes with tumor suppressors (e.g., high CDK4 and high CDKN2A in Squamous) could reflect underlying mechanisms of therapeutic resistance or tumor evolution, where cells overcome checkpoint controls despite activating components of growth arrest pathways. Further investigation at the protein level and functional assays would be critical to elucidate these complex interactions.
• Precision Medicine: Understanding these intrinsic differences in tumor cell biology is crucial for developing personalized treatment strategies for NSCLC patients based on their specific histological subtype.

---

References:

1. CDK4, MYC, TFDP1/2 in Cell Cycle: General knowledge about cell cycle regulation. For specific gene functions, refer to databases like GeneCards. GeneCards: CDK4, GeneCards: MYC, GeneCards: TFDP1.
2. ORC4, MCM7 in DNA Replication: Essential for initiation of DNA replication. GeneCards: ORC4, GeneCards: MCM7.
3. HDAC1, HDAC2 in Cancer: Involved in epigenetic regulation and frequently overexpressed in cancer. GeneCards: HDAC1, GeneCards: HDAC2.
4. 14-3-3 Proteins in Cancer: The 14-3-3 proteins are adapter proteins involved in various cell processes including cell cycle control. PubMed Search: 14-3-3 proteins cancer cell cycle.
5. SFN (14-3-3 Sigma) as Tumor Suppressor: GeneCards: SFN.
6. GADD45A in DNA Damage Response: GeneCards: GADD45A.
7. CCND1 in Cancer: Cyclin D1 is a major oncogene. GeneCards: CCND1.
8. CDKN1A (p21) in Cell Cycle Control: A key cell cycle inhibitor, often p53-dependent. GeneCards: CDKN1A.
9. CDKN2A (p16INK4a) as Tumor Suppressor: A crucial tumor suppressor frequently inactivated in cancer. GeneCards: CDKN2A.

21. Lung Epithelial Cell Pathway Enrichment Analysis in Lung Adenocarcinoma, Squamous Cell Carcinoma, and Diploid Cells

[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes Gene Ontology (GSA) to identify biological pathways and processes significantly enriched in Lung Epithelial cells under different conditions or characteristics: specifically, comparing Adenocarcinoma (Adeno) to other conditions, Diploid cells to other ploidy states, and Squamous Cell Carcinoma (Squamous) to other conditions. The results are presented as bar plots, showing the statistical significance (-log(p-val) and -log(q-val)) of enriched terms. This helps us understand the distinct biological activities and cellular states associated with these specific contexts within lung epithelial cells.

Visual Summary

The visualizations display three bar plots, each detailing GSA results for Lung Epithelial cells under a specific comparison:

1. "GSA_up for Lung Epithelial cell: Adeno_vs_others": This plot shows a rich array of significantly enriched pathways in lung epithelial cells from Adenocarcinoma. The top terms primarily relate to immune responses, viral infections, and canonical cancer signaling pathways. Many terms exhibit very high statistical significance (-log(p-val) and -log(q-val) extending beyond 15).
2. "GSA_up for Lung Epithelial cell: Diploid_vs_others": This plot shows fewer significantly enriched pathways in diploid lung epithelial cells compared to the other two conditions. The terms are mainly associated with fundamental metabolic processes and basic cellular functions, with lower overall statistical significance.
3. "GSA_up for Lung Epithelial cell: Squamous_vs_others": This plot also presents a large number of highly significant pathways enriched in lung epithelial cells from Squamous Cell Carcinoma. Similar to Adeno, cancer signaling and infection-related pathways are prominent, but there's a notable enrichment for pathways associated with protein homeostasis, cellular stress, and even several neurodegenerative diseases.

Biological Interpretation

Lung Epithelial Cell Pathways in Adenocarcinoma

Lung epithelial cells in adenocarcinoma show strong enrichment for:

• Immune and Inflammatory Responses: Pathways such as "Phagosome," "Antigen processing and presentation," "Leukocyte transendothelial migration," "Th1 and Th2 cell differentiation," "Th17 cell differentiation," "Chemokine signaling pathway," "IL-17 signaling pathway," and "TNF signaling pathway" are highly enriched. This suggests a highly active and complex immune microenvironment within lung adenocarcinoma, with epithelial cells playing a significant role in antigen presentation and modulating immune cell infiltration and activation.
• Viral Carcinogenesis and Infection: A remarkable number of terms related to various viral and bacterial infections ("Human cytomegalovirus infection," "Epstein-Barr virus infection," "Viral carcinogenesis," "Human T-cell leukemia virus 1 infection," "Kaposi sarcoma-associated herpesvirus infection," "Influenza A," "Herpes simplex virus 1 infection," "Tuberculosis," "Shigellosis," "Salmonella infection," "Pathogenic Escherichia coli infection," "Hepatitis C," "Staphylococcus aureus infection") are enriched. This could imply a role for oncoviruses in driving transformation, a generalized antiviral/antibacterial state in the tumor, or an altered susceptibility/response to pathogens within the tumor microenvironment.
• Canonical Cancer Signaling Pathways: Pathways like "Pathways in cancer," "Transcriptional misregulation in cancer," "PI3K-Akt signaling pathway", "MAPK signaling pathway", and "NF-kappa B signaling pathway" are highly enriched, confirming the activation of established oncogenic mechanisms in adenocarcinoma epithelial cells https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4925703/.
• Cellular Processes: Altered "Protein processing in endoplasmic reticulum," "Lysosome," "Endocytosis," "Cell adhesion molecules," "Focal adhesion," "Adherens junction," and "Tight junction" indicate significant changes in protein quality control, cell-cell communication, and tissue architecture, consistent with malignant transformation.

Lung Epithelial Cell Pathways in Diploid Cells

In contrast to the cancer-specific enrichments, diploid lung epithelial cells primarily show enrichment for:

• Fundamental Metabolic Processes: "Metabolism of xenobiotics by cytochrome P450," "Folate biosynthesis," "Steroid hormone biosynthesis," and "Arachidonic acid metabolism" are prominent. The enrichment of xenobiotic metabolism is particularly relevant for lung epithelial cells, which are constantly exposed to environmental toxins and play a critical role in detoxification processes https://pubmed.ncbi.nlm.nih.gov/20301490/.
• Basic Cellular Machinery: "Ribosome" enrichment underscores active protein synthesis.
• Stress Responses: Terms like "Chemical carcinogenesis," "Coronavirus disease," and "Salmonella infection" suggest a baseline capacity for responding to environmental stressors, carcinogens, and pathogens, which is expected for cells in a crucial barrier tissue like the lung.

This profile likely represents a more homeostatic or less pathologically perturbed state compared to the cancerous aneuploid counterparts, with a focus on core metabolic and defensive functions.

Lung Epithelial Cell Pathways in Squamous Cell Carcinoma

Lung epithelial cells in squamous cell carcinoma exhibit a distinct and extensive set of enriched pathways:

• Cancer-Related Signaling and Cell Cycle Regulation: Similar to adenocarcinoma, "Pathways in cancer," "Viral carcinogenesis," "Chemical carcinogenesis," "Cell cycle," "Apoptosis," "mTOR signaling pathway," "Insulin signaling pathway," "FoxO signaling pathway," "Hippo signaling pathway," "MAPK signaling pathway," and "PI3K-Akt signaling pathway" are strongly represented, highlighting their critical roles in squamous cell carcinoma development.
• Protein Homeostasis and Cellular Stress: A notable feature is the strong enrichment of pathways related to protein processing, degradation, and quality control: "Ribosome," "Protein processing in endoplasmic reticulum," "Ubiquitin mediated proteolysis," "Autophagy," "Mitophagy," "Lysosome," and "Proteasome." This suggests significant proteostasis dysfunction and heightened cellular stress responses in squamous epithelial cells, which are often hallmarks of highly proliferative and metabolically active cancer cells.
• Neurodegenerative and Systemic Disease Associations: A striking observation is the enrichment of pathways associated with neurodegenerative diseases (e.g., "Huntington disease," "Alzheimer disease," "Parkinson disease," "Amyotrophic lateral sclerosis") and other systemic conditions ("Non-alcoholic fatty liver disease," "Fluid shear stress and atherosclerosis"). This likely points to shared underlying mechanisms of cellular stress, protein misfolding, and mitochondrial dysfunction that are common across these disparate diseases and are critically altered in squamous carcinoma cells https://pubmed.ncbi.nlm.nih.gov/30282945/.
• Infection and Immune Response: As with adenocarcinoma, various infection-related pathways ("Human papillomavirus infection," "Shigellosis," "Coronavirus disease," "Human immunodeficiency virus 1 infection," "Hepatitis B," "Kaposi sarcoma-associated herpesvirus infection," "Human cytomegalovirus infection") are enriched, suggesting a complex interplay with pathogens and the immune system.

Comparative Insights

While both Adenocarcinoma and Squamous cell carcinoma epithelial cells show activation of general cancer pathways and robust immune/infection-related responses, key distinctions emerge:

• Adenocarcinoma epithelial cells appear to have a more pronounced enrichment of pathways directly involved in immune cell interaction and inflammatory processes.
• Squamous cell carcinoma epithelial cells demonstrate a stronger signature of proteostasis dysfunction and cellular stress, reflected in the enrichment of protein processing and degradation pathways, and intriguing links to neurodegenerative diseases. This might reflect specific metabolic demands or stress responses characteristic of squamous differentiation and transformation.

Clinical or Translational Implications

These findings offer valuable insights into the distinct biological landscapes of lung adenocarcinoma and squamous cell carcinoma:

• Disease Mechanisms: The detailed pathway analysis helps to elucidate the unique underlying molecular mechanisms driving each lung cancer subtype, particularly within the tumor-originating epithelial cells.
• Biomarker Discovery and Therapeutic Targeting: The identification of specific activated signaling pathways (e.g., PI3K-Akt, MAPK, mTOR, NF-kB) in both subtypes reinforces their importance as therapeutic targets. The distinct emphasis on protein homeostasis pathways in squamous cell carcinoma suggests potential vulnerabilities related to protein misfolding or degradation machinery that could be exploited therapeutically.
• Immune Modulation: The prominent enrichment of immune and infection-related pathways in both adenocarcinoma and squamous epithelial cells highlights the critical role of the tumor microenvironment and immune evasion/response mechanisms in lung cancer progression. This supports the rationale for immunotherapeutic approaches and underscores the need to understand pathogen interactions in these cancers.
• Metabolic Reprogramming: The metabolic pathways enriched in diploid cells could provide a baseline for understanding how these pathways are hijacked or altered during oncogenesis in aneuploid cells or specific cancer subtypes. Understanding the "Metabolism of xenobiotics" in normal vs. cancerous lung epithelial cells is also crucial for understanding responses to chemotherapy and environmental carcinogens.

22. Lung Cancer Microenvironment: Cell-Type Specific Pathway Enrichment in Adenocarcinoma vs. Squamous Cell Carcinoma

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a Gene Set Enrichment Analysis (GSEA) dot plot, revealing differentially regulated biological pathways across various major cell types (B cell, Dendritic cell, Fibroblast, ILC, Lung Epithelial cell, Macrophage, Mast cell, Plasma cell, T cell CD4+, T cell CD8+) in the context of Lung Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous). Each cell type is compared against all other conditions present in the dataset (denoted as "_vs_others"). The dot size indicates the statistical significance (-log10 P-value), and the color represents the Normalized Enrichment Score (NES), with red indicating enrichment (upregulation) and blue indicating depletion (downregulation) of the pathway in the specified cancer type compared to others.

Visual Summary

The dot plot effectively visualizes patterns of pathway enrichment and depletion across ten distinct cell types and two primary lung cancer conditions. Key visual patterns include:

• Widespread Pathway Alterations: Numerous pathways show significant enrichment or depletion (large, colored dots) across multiple cell types and conditions, indicating substantial biological reprogramming within the lung tumor microenvironment (TME).
• Lung Epithelial Cell Dominance: The "Lung Epithelial cell" category, representing the tumor cells, exhibits a high density of significantly enriched (red) and depleted (blue) pathways, particularly those directly related to cancer and metabolism.
• Immune Cell Heterogeneity: Immune cell types (e.g., Macrophage, T cells, Dendritic cells) display distinct sets of enriched and depleted immune-related pathways, highlighting their diverse roles and states within the TME.
• Stromal Activation: Fibroblasts consistently show enrichment in extracellular matrix (ECM) remodeling and adhesion pathways.
• Shared vs. Unique Patterns: While many pathways show similar enrichment patterns between Adeno_vs_others and Squamous_vs_others within the same cell type, some notable differences exist, suggesting distinct underlying biology between these two lung cancer subtypes.

Biological Interpretation

1. Tumor Cell Intrinsic Biology (Lung Epithelial Cells):

• Oncogenic Pathway Activation: Both Adeno and Squamous epithelial cells show strong enrichment for "Pathways in cancer", "Non-small cell lung cancer", "mTOR signaling pathway", and "p53 signaling pathway" (deep red, large dots). This reflects the core oncogenic processes driving tumor growth and proliferation in both subtypes [PubMed search: lung cancer mTOR p53].
• Metabolic Reprogramming: Significant enrichment of "Glutathione metabolism" and "Ribosome" pathways (red) suggests increased protein synthesis and enhanced antioxidant defense, crucial for rapidly proliferating cancer cells to cope with oxidative stress [UniProt: Glutathione S-transferase]. Conversely, depletion of "Mitophagy" and "Metabolism of xenobiotics by cytochrome P450" (blue) indicates altered mitochondrial quality control and drug metabolism, potentially contributing to cancer cell survival and resistance.
• Subtype-Specific Differences: "Hedgehog signaling pathway" appears more distinctly enriched in Lung Epithelial cell: Adeno_vs_others compared to Squamous. Conversely, "Phospholipase D signaling pathway" is enriched in Adeno but depleted in Squamous, suggesting differential activation of growth and survival pathways between the two histologies.

2. Stromal Cell Activation (Fibroblasts):

• ECM Remodeling: Fibroblasts from both Adeno and Squamous TMEs show robust enrichment in "ECM-receptor interaction", "Focal adhesion", and "Adherens junction" (red). This is characteristic of activated Cancer-Associated Fibroblasts (CAFs), which play a critical role in remodeling the extracellular matrix, promoting tumor cell migration, and contributing to the desmoplastic reaction often seen in solid tumors [PubMed search: CAF ECM remodeling cancer].
• Inflammatory Support: Enrichment of "NF-kappa B signaling pathway" and "Complement and coagulation cascades" in fibroblasts suggests their active participation in the inflammatory and pro-tumorigenic milieu.

3. Immune Cell Landscape Remodeling:

• Antigen Presentation: Dendritic cells and Macrophages in both Adeno and Squamous TMEs consistently show enrichment in "Antigen processing and presentation", underscoring their critical role in initiating and modulating immune responses against the tumor [UniProt: Major Histocompatibility Complex].
• Inflammatory T-cell Response: CD4+ T cells, in both Adeno and Squamous contexts, show enrichment in "Th17 cell differentiation" (red). Th17 cells are known to be pro-inflammatory and can have dual roles in cancer immunity, sometimes promoting tumor growth.
• Altered Cytotoxicity in CD8+ T cells: A notable finding is the depletion of "Natural killer cell mediated cytotoxicity" in CD8+ T cells for both Adeno and Squamous (blue). While CD8+ T cells are critical cytotoxic effectors, this specific pathway depletion might indicate a shift in their cytotoxic program away from NK-like mechanisms or a general suppression of this particular cytotoxic axis within the tumor microenvironment, potentially contributing to immune evasion.
• B cell and Plasma Cell Activity: B cells show an unexpected enrichment in "Intestinal immune network for IgA production" in both conditions. While IgA is typically associated with mucosal immunity, its presence in the lung TME could reflect specific adaptive immune responses or B cell lineage reprogramming. Plasma cells also show enrichment in "ABC transporters", indicating active efflux mechanisms that could be relevant for drug resistance or metabolite transport.
• Macrophage and Mast Cell Inflammation: Macrophages also exhibit enrichment in "NF-kappa B signaling pathway" and "Complement and coagulation cascades", highlighting their pro-inflammatory and immune-modulatory roles. Mast cells also show activated metabolic pathways like "Glutathione metabolism" (Adeno) and transport mechanisms ("ABC transporters" in Squamous), indicative of their dynamic roles in the TME.

Clinical or Translational Implications

The differential pathway enrichments observed across cell types in Lung Adenocarcinoma and Squamous Cell Carcinoma offer several clinical insights:

• Targeting Cancer-Specific Metabolism: The enrichment of "Glutathione metabolism" and "Ribosome" in lung epithelial cells suggests potential vulnerabilities in cancer cell metabolism that could be exploited therapeutically, for example, by targeting antioxidant pathways or protein synthesis [PubMed search: glutathione metabolism cancer therapy].
• Modulating the Stromal Compartment: The strong activation of ECM remodeling and inflammatory pathways in fibroblasts highlights CAFs as critical components of the TME. Therapeutic strategies aimed at inhibiting CAF activation or their pro-tumorigenic functions could be beneficial [PubMed search: CAF targeting cancer].
• Immunotherapy Refinements: The distinct immune pathway alterations, such as Th17 bias and altered CD8+ T cell cytotoxicity, underscore the need for personalized immunotherapeutic approaches tailored to the specific immune context of each lung cancer subtype. Interventions to restore robust cytotoxic T cell function or modulate the Th17 axis could improve treatment outcomes.
• Biomarker Discovery: Specific pathway enrichments unique to Adeno or Squamous epithelial cells, such as "Hedgehog signaling pathway" in Adeno or the differential "Phospholipase D signaling pathway", could serve as diagnostic or prognostic biomarkers and guide the selection of targeted therapies.

23. Discussion

The comprehensive single-cell analysis of human lung tissue from Adenocarcinoma (Adeno) and Squamous Cell Carcinoma (Squamous) patients provides a high-resolution view of their distinct tumor microenvironments (TMEs). UMAP visualizations confirmed robust cell type identification, with Lung Epithelial cells identified as the tumor origin. Ploidy analysis and CNV heatmaps revealed widespread aneuploidy, particularly in Lung Epithelial cells, strongly validating their malignant identity. Notably, recurrent amplification of 3q26.33:3q28, encompassing the SOX2 oncogene, was a prominent feature in Squamous samples, suggesting a specific genomic driver in this subtype.

Significant differences were observed in both immune and stromal compartments. Squamous cell carcinoma samples exhibited a more immunosuppressive T cell landscape, characterized by significantly higher proportions of regulatory T cells (Treg) and Th17 cells, coupled with decreased proportions of anti-tumorigenic Th1, Th2, Th9, ILC1, and ILC3(-) cells compared to Adenocarcinoma. Macrophages in Squamous tumors also skewed towards a more pro-tumorigenic phenotype, with significantly elevated proportions of M2B and M2D subsets. This suggests that Squamous tumors foster a highly immunosuppressive and pro-inflammatory TME, potentially hindering effective anti-tumor immunity. In contrast, Adeno CD4+ T cells displayed high expression of immune checkpoints like TIGIT and CTLA4, indicating a state of T cell activation and potential exhaustion, where inhibitory signals are actively engaged.

Cell-cell interaction (CCI) analyses further elucidated these TME distinctions. Adenocarcinoma featured a more T cell-centric immune signaling network, involving LCK/CD8-related interactions among T cells, macrophages, and ILCs, alongside prominent PD-1/PD-L1 interactions between macrophages and CD4+ T cells. This suggests an active but potentially suppressed adaptive immune response. Conversely, Squamous cell carcinoma exhibited a dominant macrophage-macrophage interaction via CD93/IFNGR1, hinting at a distinct macrophage activation state. Moreover, Squamous tumors showed extensive stromal remodeling, driven by widespread Fibroblast-Aneuploid Lung Epithelial cell and Fibroblast-Fibroblast interactions, notably involving numerous collagen-integrin pairs. This desmoplastic reaction appears to be a hallmark of the Squamous TME, providing structural and signaling support to tumor progression. Condition-specific surfaceome markers further reinforced these observations, with Adeno macrophages showing high MHC II and AXL, and Squamous macrophages expressing TREM1, P2RX7, ANPEP, and ITGA5, consistent with distinct functional adaptations.

Pathway enrichment analyses (GSA, GSEA) highlighted divergent intrinsic tumor cell biology. While both subtypes showed activation of canonical cancer pathways (e.g., PI3K-Akt, MAPK, NF-kB, mTOR, p53 signaling), Adenocarcinoma epithelial cells were more enriched in immune/inflammatory pathways. Squamous epithelial cells, however, displayed a unique signature of proteostasis dysfunction and heightened cellular stress, with strong enrichment in protein processing, ubiquitin-mediated proteolysis, autophagy, and intriguing links to neurodegenerative diseases, possibly reflecting specific metabolic demands or stress responses of squamous differentiation. Differential expression of cell cycle genes further delineated these differences; Squamous cells exhibited higher expression of core cell cycle regulators (CDK4, MYC, MCM7), HDACs, and multiple 14-3-3 proteins, suggesting an accelerated, epigenetically modified cell cycle, while Adeno showed higher CCND1 and p21 (CDKN1A), indicative of different cell cycle control mechanisms. The observed upregulation of CDKN2A in Squamous, despite other proliferative markers, may point to complex regulatory evasion or compensatory responses to oncogenic stress.

Collectively, these findings reveal that Lung Adenocarcinoma is characterized by a dynamic, T cell-centric, and macrophage-modulated immune TME, with an emphasis on immune checkpoints and antigen presentation. Lung Squamous Cell Carcinoma, in contrast, presents a more immunosuppressive macrophage-dominant landscape, with extensive stromal remodeling and distinct proteostasis vulnerabilities in tumor cells, possibly driven by specific genomic alterations like SOX2 amplification. These comprehensive insights are critical for developing histology-specific diagnostic, prognostic, and therapeutic strategies.

Hypotheses:

1. The distinct immune cell compositions, particularly the elevated Tregs and M2 macrophages in Squamous Cell Carcinoma, create a more profoundly immunosuppressive microenvironment that contributes to different immunotherapy responses compared to Adenocarcinoma.
2. The extensive collagen-integrin interactions and high expression of pro-tumorigenic CAFs (FAP, LRRC15) in Adenocarcinoma, versus the desmoplastic dominance in Squamous, signify distinct mechanisms of stromal support and ECM remodeling driving tumor progression in each subtype.
3. The SOX2 amplification in Squamous Cell Carcinoma drives unique cell cycle dysregulation and proteostasis dysfunction, leading to a specific metabolic and stress-response vulnerability compared to Adenocarcinoma.
4. The T cell-centric LCK/CD8-related interactions and prominent immune checkpoint expression (TIGIT, CTLA4) in Adenocarcinoma CD4+ T cells reflect a state of active T cell engagement that is subsequently suppressed, leading to exhaustion.
5. Differential expression of 14-3-3 proteins and HDACs in Squamous Cell Carcinoma contributes to an epigenetically altered and accelerated cell cycle, distinct from the CCND1-driven proliferation in Adenocarcinoma.

Potential therapeutic targets:

1. TIGIT / CTLA4 (on CD4+ T cells): Upregulated on CD4+ T cells in Adenocarcinoma, indicating T cell exhaustion. Blocking these inhibitory checkpoints can reinvigorate anti-tumor immune responses. Evidence: Analysis shows significant upregulation of TIGIT and CTLA4 on CD4+ T cells in Adenocarcinoma samples (Section 19). These are known immune checkpoint receptors targeted by existing immunotherapies (e.g., anti-TIGIT, anti-CTLA4 antibodies). Validation: Conduct clinical trials combining anti-TIGIT or anti-CTLA4 with PD-1/PD-L1 blockade in Adenocarcinoma patients. Validate TIGIT/CTLA4 protein expression via flow cytometry/IHC in patient biopsies.
2. AXL (on Macrophages): Highly expressed on macrophages in Adenocarcinoma. AXL contributes to pro-tumorigenic and immunosuppressive microenvironment. Evidence: AXL receptor tyrosine kinase shows high expression on Adeno-associated macrophages (Section 17). AXL signaling promotes cancer cell survival, proliferation, and immunosuppression by TAMs. Validation: Test AXL inhibitors in preclinical models of lung adenocarcinoma. Evaluate AXL protein expression on TAMs in patient samples via IHC/flow cytometry and correlate with response to AXL-targeted therapy.
3. TREM2 (on Macrophages): Prominent autocrine APOE/APP-TREM2 signaling among macrophages in Squamous Cell Carcinoma promotes pro-tumoral macrophage phenotypes. Evidence: Strong APOE_TREM2_receptor and APP_TREM2_receptor interactions observed in Squamous macrophages (Section 12). TREM2 activation drives M2-like macrophage survival, proliferation, and pro-tumorigenic functions. Validation: Investigate TREM2 inhibitors to reprogram TAMs in Squamous cell carcinoma models. Assess TREM2 expression on TAMs in Squamous patient samples and explore its prognostic/predictive value.
4. FAP / LRRC15 (on Cancer-Associated Fibroblasts): Highly activated CAFs expressing FAP and LRRC15 are prominent in Adenocarcinoma, contributing to ECM remodeling, tumor growth, and immunosuppression. Evidence: FAP and LRRC15 are highly expressed and prevalent in Adenocarcinoma fibroblasts, particularly in some aggressive samples (Section 18). Both are well-established CAF markers involved in tumor progression. Validation: Develop and test FAP-targeting ADCs or CAR-T cells in Adenocarcinoma preclinical models. Use IHC to confirm FAP/LRRC15 expression in human Adenocarcinoma biopsies and correlate with patient outcomes.
5. CDK4 / HDAC1 (in Squamous Epithelial Cells): Significantly upregulated in Squamous epithelial cells, driving accelerated cell cycle progression and epigenetic modifications critical for tumor growth. Evidence: CDK4 and HDAC1 are significantly upregulated in Lung Epithelial cells from Squamous Cell Carcinoma (Section 20), suggesting their role in enhanced proliferation and epigenetic reprogramming. Validation: Test CDK4/6 inhibitors and HDAC inhibitors (alone or in combination) in Squamous lung cancer cell lines and patient-derived organoids. Evaluate efficacy in preclinical models and assess CDK4/HDAC1 protein expression in Squamous patient tumors.
6. SOX2 (genomic amplification in Squamous): Recurrent amplification of the SOX2 oncogene is a key genomic driver in Squamous Cell Carcinoma, promoting tumor cell proliferation and survival. Evidence: The 3q26.33:3q28 region, encompassing SOX2, shows 100% frequency of amplification across summarized Squamous samples (Section 4). SOX2 is a known oncogene, particularly in lung squamous cell carcinoma. Validation: Develop and test direct or indirect inhibitors of SOX2 activity in SOX2-amplified Squamous cell lines and xenografts. Use FISH or droplet digital PCR (ddPCR) to identify SOX2-amplified patients for targeted therapies.

Follow-up validation ideas:

1. Perform flow cytometry and multiplex immunohistochemistry (IHC) or immunofluorescence (IF) on independent lung tumor cohorts to validate the differential proportions of T cell subsets (Treg, Th1, Th17) and macrophage subsets (M1, M2B, M2D) between Adenocarcinoma and Squamous Cell Carcinoma. This will confirm the cellular composition differences at the protein level and assess their spatial organization within the tumor microenvironment.
2. Utilize spatial transcriptomics or high-plex imaging (e.g., CODEX, IMC) to confirm the identified cell-cell interaction patterns (e.g., PD-1/PD-L1 in Adeno macrophages/T cells, collagen-integrin interactions in Squamous fibroblasts/tumor cells) and their spatial proximity. This would provide direct evidence of ligand-receptor co-localization in situ.
3. Conduct in vitro co-culture experiments using patient-derived organoids or cell lines of Adenocarcinoma and Squamous epithelial cells with isolated T cells, macrophages, or fibroblasts, to functionally validate the impact of identified CCI pathways (e.g., LCK/CD8, TREM2, FAP, integrin axes) on tumor cell proliferation, immune cell function, or stromal remodeling upon specific perturbations (e.g., blocking antibodies or genetic knockdowns).
4. Employ FISH or targeted sequencing to validate the frequency and functional impact of SOX2 gene amplification and other recurrent CNVs in a larger cohort of Squamous Cell Carcinoma samples. Functional studies using CRISPR/Cas9 to modulate SOX2 expression in Squamous cell lines could assess its role in cell cycle, proteostasis, and proliferation.
5. Investigate the functional consequences of differentially expressed cell cycle genes (e.g., CDK4, MYC, HDAC1/2 in Squamous; CCND1, CDKN1A in Adeno) using shRNA knockdown or overexpression in relevant lung cancer cell lines, followed by cell cycle assays, proliferation assays, and assessment of drug sensitivity to cell cycle inhibitors (e.g., CDK4/6 inhibitors, HDAC inhibitors).
6. Analyze the expression of identified surfaceome markers (e.g., TIGIT, CTLA4, AXL, TREM1, FAP, LRRC15, CD44, EGFR) via flow cytometry, IHC, or mass spectrometry-based proteomics in fresh tumor dissociates or tissue sections from a validation cohort to confirm protein expression and guide therapeutic target selection.

Limitations:

This report is based on observational single-cell RNA-seq data and precomputed analyses. While robust cellular and molecular distinctions are identified between Adenocarcinoma and Squamous Cell Carcinoma, causal relationships cannot be definitively established without functional experimental validation. Inter-sample and intra-tumor heterogeneity are evident, and the selected cell populations for specific analyses (e.g., 'unassigned' cells in CNV or specific cell types for DGE/GSEA) introduce context-specific interpretations. The biological relevance of some inferred cell-cell interactions and pathway enrichments requires further experimental confirmation in appropriate in vitro and in vivo models. Furthermore, the selection of top markers or pathways for visualization, while informative, does not capture the entirety of molecular differences. The current data does not directly address patient clinical outcomes, and therapeutic target nominations require extensive preclinical and clinical validation.

24. Query List

1. Show UMAPs including condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset, arranged in 2 columns, and save it.
2. Show major cell type scores on UMAP and save it.
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 a CNV heatmap, include a summary of significantly amplified copy number regions, and save it.
5. Show CNV patterns on a UMAP. Include major cell type, minor cell type, ploidy results, condition, and sample, arranged in 2 columns, and save it.
6. Show a population bar plot for minor cell types and save it.
7. Show a subset population bar plot for T cells and save it.
8. For T cell subsets, show box plots for statistically significant differences between conditions, if any, and save it. Set ncols appropriately based on the total number of panels.
9. Show a subset population bar plot for macrophages and save it.
10. For macrophage subsets, show box plots for statistically significant differences between conditions, if any, and save it. Set ncols appropriately based on the total number of panels.
11. Select tumor-origin cells and unassigned cells, show a bar plot of their ploidy population, and save it.
12. Show cell-cell interaction patterns including Lung Epithelial cells (tumor origin), fibroblasts, macrophages, and T cells, for each condition, and save it. Select up to 80 cell-cell interactions per condition.
13. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
14. Select only genes related to immune checkpoint pathways and cell cycle pathways, show cell-cell interactions for these genes, and save it.
15. Find statistically significant differences in cell-cell interactions between conditions for major immune cells (e.g., Myeloid, T cell, B cell) and stromal cells (e.g., Fibroblast), show them as a dot plot, and save it. Set max_n_items_per_group to 25.
16. Show the condition-specific markers for tumor-origin cells (Lung Epithelial cell) as a dot plot and save the result. Use only surfaceome markers, up to 50 markers per condition.
17. Extract condition-specific markers for Macrophage, show them as a dot plot, and save it. Filter for surfaceome markers only, up to 50 per condition.
18. Extract condition-specific markers for Fibroblast, show them as a dot plot, and save it. Filter for surfaceome markers only, up to 50 per condition.
19. Extract condition-specific markers for T cell CD4+, show them as a dot plot, and save it. Filter for surfaceome markers only, up to 50 per condition.
20. Select genes related to Cell cycle pathways that show statistically significant expression differences between conditions in tumor-origin cells (Lung Epithelial cells), show box plots, and save it. Set max_n_items_to_plot to 24, and ncols appropriately for a 2x3 aspect ratio.
21. Show Gene Ontology (GSA) analysis results for epithelial cells as bar plots and save the result.
22. Show a dot plot of Gene Set Enrichment Analysis results for major cell types (B cell, Dendritic cell, Fibroblast, ILC, Lung Epithelial cell, Macrophage, Mast cell, Plasma cell, T cell CD4+, T cell CD8+) and save it. Use 'RdBu_r' for the color map and set n_pws_to_show to 80.