SCODiA Report by MLBI Lab

Single-Cell Landscape of Pancreatic Ductal Adenocarcinoma: Genomic Instability, Microenvironment Remodeling, and Therapeutic Targets

This comprehensive single-cell analysis characterizes the cellular and molecular landscape of Pancreatic Ductal Adenocarcinoma (PDAC). It reveals a significant expansion of aneuploid Ductal cells as the malignant population, accompanied by a profoundly reprogrammed tumor microenvironment (TME) featuring immunosuppressive macrophages, diverse T cell subsets, and a desmoplastic stroma. The study highlights widespread genomic instability, metabolic shifts, and a rewired cell-cell interaction network, identifying critical pathways and surface markers with therapeutic potential.

Contents

  1. Dataset overview
  2. UMAP Visualization of Pancreatic Single-Cell Data by Condition, Sample, Cell Type, and Ploidy
  3. UMAP Visualization of Major Cell Type Scores, Ploidy, and Cell Type Annotations in Pancreatic scRNA-seq Data
  4. Marker Gene Expression for celltype_subset Validation
  5. Ductal Cell Copy Number Variation Analysis in Pancreatic Samples
  6. CNV-based UMAP Visualization of Pancreatic Cells by Major Cell Type, Ploidy, Condition, and Sample
  7. Population Bar Plot of Minor Cell Types in Pancreas
  8. T Cell and Innate Lymphoid Cell Subpopulation Dynamics in Pancreatic Ductal Adenocarcinoma
  9. Analysis of T Cell Subset Population Changes in Pancreatic Ductal Adenocarcinoma
  10. Macrophage Subset Population Analysis in Pancreatic Ductal Adenocarcinoma
  11. Ploidy Analysis of Ductal Cells in Pancreatic Ductal Adenocarcinoma (PDAC)
  12. Cell-Cell Interaction Analysis in Pancreatic Ductal Adenocarcinoma (PDAC)
  13. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Pathways in Pancreatic Ductal Adenocarcinoma
  14. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma
  15. Ductal Cell Condition-Specific Surfaceome Markers in Pancreatic Ductal Adenocarcinoma
  16. Macrophage Condition-Specific Surfaceome Marker Expression in Pancreatic Ductal Adenocarcinoma
  17. T cell CD4+ Condition-Specific Surfaceome Marker Expression in Pancreatic Ductal Adenocarcinoma
  18. Cell Cycle Pathway Gene Expression in Pancreatic Ductal Adenocarcinoma Ductal Cells
  19. Gene Ontology (GSA) Analysis of Ductal Cells in Pancreatic Tissue
  20. Gene Set Enrichment Analysis of Pancreatic Cell Types in PDAC
  21. Discussion
  22. Query List

0. Dataset overview

데이터셋 요약

사전 계산된 결과

추론된 생물학적/질병 문맥

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a series of Uniform Manifold Approximation and Projection (UMAP) plots, visualizing the global transcriptional landscape of single-cell RNA-sequencing data from pancreatic tissue. Each UMAP is colored by a different metadata attribute: experimental condition, individual sample, major cell type, minor cell type, ploidy status, and cell type subset. These visualizations are crucial for assessing the overall data structure, the quality of cell type annotations, potential batch effects, and the distribution of key biological features such as ploidy in the context of Pancreatic Ductal Adenocarcinoma (PDAC).

Visual Summary

Condition UMAP

The UMAP colored by 'condition' (Adj_normal vs. PDAC) reveals a substantial separation between cells from Adj_normal (adjacent normal) and PDAC (Pancreatic Ductal Adenocarcinoma) conditions. Cells from PDAC samples predominantly form distinct clusters, particularly in the upper and central regions of the embedding, while Adj_normal cells occupy other, somewhat separated regions. However, there are also areas of mixing, indicating shared cell populations or transcriptomic states between the two conditions, likely representing non-transformed stromal or immune cells present in both microenvironments. The overall impression is that a significant portion of the cellular heterogeneity is driven by the disease state.

Sample UMAP

The 'sample' UMAP shows a complex pattern, with various samples contributing to different regions of the UMAP space. While there are some sample-specific clusters, indicating unique patient- or sample-specific cellular compositions or transcriptional profiles, a substantial degree of mixing across samples is also observed within the major cell type clusters. This mixing suggests that batch effects are not overwhelmingly dominant and that cells of similar types from different samples are generally co-embedded, which is desirable for downstream comparisons. Adj_N samples cluster together, as do several PDAC samples (e.g., PDAC_11A, PDAC_11B, PDAC_12, PDAC_13, PDAC_15, PDAC_16) forming a distinct large cluster, reinforcing the condition-specific separation.

Celltype_major UMAP

This plot illustrates the organization of major cell types within the UMAP space. Distinct clusters are visible for most major cell types, such as Acinar cells, Ductal cells, Stromal cells, T cells, and Myeloid cells, indicating robust segregation based on their transcriptional profiles. Notably, 'Ductal cells' form a large, central cluster, aligning with the inferred tumor-origin cell type in PDAC. Other cell types like T cells and Stromal cells occupy broad, distinct regions, suggesting their diverse roles and states within the pancreas and tumor microenvironment. A small proportion of 'unassigned' cells are scattered, but do not form a major distinct cluster, suggesting good overall annotation coverage.

Celltype_minor UMAP

The 'celltype_minor' UMAP provides a more granular view of cell type identities. It refines the major clusters, showing sub-populations within larger groups. For example, 'T cell CD4+' and 'T cell CD8+' emerge as distinct sub-clusters within the broader T cell population. Similarly, 'Macrophage' cells (part of Myeloid) and 'Stellate cell' and 'Fibroblast' (part of Stromal) are clearly demarcated. The 'Ductal cell' population remains a prominent and relatively cohesive cluster, as expected for the inferred tumor population.

Ploidy_dec UMAP

The 'ploidy_dec' UMAP highlights the distribution of cells classified as Aneuploid, Diploid, or Unclear. A prominent cluster of 'Aneuploid' cells (colored burgundy) is observed, largely overlapping with a significant portion of the 'Ductal cell' cluster identified in the cell type UMAPs. This strong co-localization supports the inference that these aneuploid cells represent the malignant Ductal cell population. The vast majority of cells are 'Diploid' (light yellow), as expected for normal cells, and they are broadly distributed across the UMAP space, encompassing most of the non-Ductal cell clusters. The 'Unclear' cells are a very minor population and appear scattered.

Celltype_subset UMAP

This UMAP offers the highest resolution of cell type annotation. It further deconstructs minor cell types into specific subsets, such as various macrophage polarizations (Mac_M1, Mac_M2A, M2B, M2C, M2D), T helper subsets (Th1, Th2, Th9, Th17, Th22, Tfh), and B cell subsets (B cell (Memory), B cell (MZ), B cell (Follicular), B cell (Breg), Plasma cell). This level of detail confirms the rich cellular heterogeneity captured by the single-cell data and the refined annotation process. The 'Ductal cell' cluster remains distinct, and its internal heterogeneity, if any, is not heavily subdivided into distinct subsets beyond the main "Ductal" classification at this level.

Biological Interpretation

The UMAP visualizations provide strong evidence for the presence of PDAC-associated cellular changes within the pancreatic tissue.

  1. Disease-Specific Cellular Landscape: The clear separation of PDAC and Adj_normal conditions in the UMAP indicates significant transcriptomic differences driven by the disease. This suggests a global shift in cellular composition or gene expression patterns in the tumor microenvironment compared to adjacent normal tissue.
  2. Tumor Cell Identification: The 'Ductal cell' population, identified as the resolved tumor-origin cell type, forms a large and distinct cluster. Crucially, this cluster shows a strong overlap with cells classified as 'Aneuploid' in the 'ploidy_dec' UMAP. This convergence of cell type annotation and ploidy status robustly supports the identification of these 'Ductal cells' as the malignant tumor cells characteristic of PDAC. The presence of 15% aneuploid cells within the dataset, predominantly localized to this Ductal cluster, further reinforces this interpretation.
  3. Heterogeneity of the Tumor Microenvironment: While the tumor cells themselves form a relatively cohesive cluster, the remaining UMAP space is occupied by diverse stromal and immune cell types (Fibroblasts, Stellate cells, various T cell, B cell, Myeloid, ILC populations). The co-existence and spatial arrangement of these cells, particularly in the PDAC-enriched regions of the UMAP, highlight the complex interplay within the tumor microenvironment (TME) in PDAC. The mixing of samples within these stromal and immune cell clusters suggests common cellular responses or conserved cell types across different patients.
  4. Refined Cell Type Resolution: The progressive increase in detail from major to minor to subset cell types demonstrates the successful deep phenotyping of the pancreatic single-cell landscape. The identification of various immune and stromal subsets (e.g., M1/M2 macrophages, diverse T cell helper subsets, different fibroblast types) is critical for understanding their specific roles in tumor progression, immune surveillance, and therapeutic resistance in PDAC.

Annotation Notes

The UMAP embeddings exhibit well-defined clusters that largely correspond to the annotated cell types, indicating a high quality of cell type identification and annotation. The separation based on 'condition' and the strong co-localization of 'Ductal cells' with 'Aneuploid' cells reinforce the biological validity of the annotations and the inference of PDAC. The presence of a small proportion of 'unassigned' cells is typical in single-cell datasets and does not detract from the overall annotation quality, as they do not form a major distinct cluster. The embedding structure appears robust, with distinct populations showing clear boundaries, suggesting that the underlying transcriptional differences are well-captured.

2. UMAP Visualization of Major Cell Type Scores, Ploidy, and Cell Type Annotations in Pancreatic scRNA-seq Data

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Analysis Overview

This analysis presents a series of UMAP (Uniform Manifold Approximation and Projection) plots derived from single-cell RNA-sequencing data of the human pancreas, with a focus on Pancreatic Ductal Adenocarcinoma (PDAC). The plots visualize the distribution of cells based on their expression profiles. Specifically, the visualizations show:

The purpose of these visualizations is to assess the quality of cell type annotations, understand the underlying cellular heterogeneity, and identify populations with specific characteristics, such as aneuploidy, which is relevant in the context of PDAC.

Visual Summary

  1. Major Cell Type Scores (HICAT_major_score plots):
  1. Ploidy Status (ploidy_dec plot):
  1. Major Cell Type Annotation (celltype_major plot):

Biological Interpretation

  1. Consistent Cell Type Annotation:

The HICAT_major_score plots demonstrate strong concordance with the celltype_major annotation. For each major cell type, high scores are predominantly localized to the same regions where those cell types are ultimately annotated. This indicates that the chosen marker genes and scoring method effectively delineate distinct cell populations, thereby supporting the robustness and accuracy of the cell type annotations. The clear separation of immune cells, stromal cells, endothelial cells, and pancreatic parenchymal cells reflects the expected cellular composition and heterogeneity of the pancreas, especially in a disease context.

  1. Ductal Cell Heterogeneity and Tumor Origin:

Ductal cells, identified as the resolved tumor-origin cell type in the data context, form a large and somewhat heterogeneous cluster on the UMAP. The HICAT_major_score: Ductal cell plot shows a broad distribution of high scores, potentially reflecting different states or sub-populations within the ductal compartment, which is expected in PDAC due to tumor evolution and interaction with the microenvironment.

  1. Aneuploidy as a Tumor Cell Signature:

The ploidy_dec plot critically highlights a significant population of aneuploid cells. This large aneuploid cluster spatially overlaps extensively with a major portion of the cells annotated as "Ductal cell" in the celltype_major plot. This strong association provides compelling evidence that these aneuploid cells represent the malignant epithelial compartment of PDAC, consistent with the known genomic instability and aneuploidy characteristic of most solid tumors, including pancreatic adenocarcinoma. The distinct clustering of aneuploid cells suggests a clear separation of tumor cells from the surrounding diploid stromal and immune compartments. The reported 15% aneuploid cells aligns with the visual representation of a substantial tumor cell population.

  1. Pancreatic Islet Cell Representation:

The relatively lower scores and more diffuse clustering of Alpha, Beta, Delta, Epsilon, and Gamma (PP) cells indicate that these pancreatic endocrine cell types, while present, constitute smaller populations in this dataset or exhibit more subtle transcriptional differences compared to the more abundant exocrine, immune, and stromal compartments. Their proximity on the UMAP suggests shared developmental origins or functional relationships within the islets.

Annotation Notes

3. Marker Gene Expression for celltype_subset Validation

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Analysis Overview

This analysis presents a marker expression dot plot for various celltype_subset populations identified in the single-cell RNA-seq data from the Pancreas, specifically in the context of Pancreatic Ductal Adenocarcinoma (PDAC). The plot visualizes the mean expression level (color intensity) and the fraction of cells expressing a gene (dot size) for a selected set of marker genes across each cell type subset. The primary goal is to validate the assigned cell type annotations by examining the specificity and strength of expression of known cell-type-specific marker genes.

Visual Summary

The dot plot effectively displays distinct blocks of gene expression, where specific sets of genes are highly and widely expressed within particular celltype_subset populations. This "diagonal" pattern, with intense red dots (high expression) and large dot sizes (high fraction of cells) concentrated along the main diagonal, indicates good separation and unique marker profiles for most cell types.

Biological Interpretation

The observed marker gene expression patterns strongly support the assigned celltype_subset identities within the pancreatic microenvironment, particularly relevant to PDAC.

The distinct expression patterns of established markers for each celltype_subset provide strong evidence that the cell identities are well-defined and accurately annotated. This level of resolution in celltype_subset is crucial for understanding the cellular heterogeneity and complex interactions within the pancreatic tumor microenvironment.

Annotation Notes

The marker expression dot plot serves as an excellent validation of the celltype_subset annotations. The high specificity and strong expression of known lineage markers for each subset suggest robust and reliable cell type assignments. The clear separation of marker profiles for most cell types indicates that the clustering and annotation process has successfully resolved distinct cellular populations. This forms a solid foundation for subsequent analyses, such as differential gene expression or cell-cell interaction studies, ensuring that biological insights are drawn from correctly identified cell populations. There are no major anomalies or ambiguities in marker expression that would suggest misannotation or poor resolution of any celltype_subset.

4. Ductal Cell Copy Number Variation Analysis in Pancreatic Samples

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Analysis Overview

This analysis visualizes Copy Number Variations (CNVs) in Ductal cells, the resolved tumor-origin cell type, across various pancreatic samples. The results include a heatmap showing log2(Copy Number Ratio, CNR) for individual samples grouped by condition, along with a summary of significantly amplified genomic regions, highlighting their frequency across samples and within specific cytogenetic bands. The primary goal is to identify common CNV patterns in Ductal cells from PDAC samples compared to adjacent normal samples.

Visual Summary

CNV Heatmap (log2(CNR) by Genomic Spot)

The heatmap displays log2(CNR) values across chromosomes (genomic spots) for Ductal cells, grouped by sample and condition. A red color indicates amplification (log2(CNR) > 0), while blue indicates deletion (log2(CNR) < 0).

Summary of Significantly Amplified Regions

The provided summary heatmap and bar plot highlight specific cytogenetic bands that show significant amplification in Ductal cells across PDAC samples.

1q21.3:1q23.2 (frequency 0.8)

1q42.12:2p25.2 (frequency 0.64)

3q21.3:3q26.1 (frequency 0.64)

7q21.11:7q21.11 (frequency 0.50)

8p12:8q12.2 (DDHD2, LSM1) (frequency 0.36)

8q22.1:9p24.1 (GSDMD, INTS8, EIF3E) (frequency 0.43)

Biological Interpretation

The observed CNV patterns in Ductal cells from PDAC samples are highly consistent with the known genomic instability characteristic of pancreatic ductal adenocarcinoma.

Implications of Specific Amplified Regions:

Genes in Amplified Regions:

Clinical or Translational Implications

The identification of recurrent CNV patterns in Ductal cells from PDAC patients has several clinical and translational implications:

Annotation Notes

The analysis confirms that the Ductal cells from PDAC patients exhibit significant genomic alterations, aligning with their designation as the resolved tumor-origin cell type and the presence of PDAC. The distinction between "Diploid PDAC" and "PDAC" samples in the heatmap suggests that a global ploidy assessment might not fully capture the complexity of regional CNVs. This highlights the importance of detailed CNV analysis for accurate tumor characterization.

References

  1. Genomic Instability in Cancer: PubMed Search: "genomic instability cancer aneuploidy"
  2. Chromosome 1q Amplifications in Cancer: PubMed Search: "chromosome 1q amplification cancer"
  3. GSDMD in Cancer: GeneCards: GSDMD
  4. EIF3E in Cancer: GeneCards: EIF3E

5. CNV-based UMAP Visualization of Pancreatic Cells by Major Cell Type, Ploidy, Condition, and Sample

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Analysis Overview

This analysis presents five UMAP (Uniform Manifold Approximation and Projection) plots, where the embedding itself is based on Copy Number Variation (CNV) patterns. Each plot colors the single cells by a different metadata annotation: major cell type (celltype_major), minor cell type (celltype_minor), ploidy determination (ploidy_dec), disease condition (condition), and individual sample (sample). The primary goal is to visualize how cells with similar genomic CNV profiles cluster and to observe the distribution of different cell annotations within this CNV-informed space, particularly focusing on the malignant cell populations.

Visual Summary

Cell Type Distribution on CNV UMAP

Ploidy Status and Condition Association

Sample-Level Variation

Biological Interpretation

Annotation Notes

6. Population Bar Plot of Minor Cell Types in Pancreas

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Analysis Overview

This analysis presents a stacked bar plot illustrating the proportional distribution of minor cell types across individual samples from both 'Adj_normal' (adjacent normal pancreas) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. Each bar represents a single sample, with different colored segments indicating the relative abundance of distinct minor cell populations. This visualization is crucial for understanding the cellular composition shifts associated with PDAC and identifying potential changes in the tumor microenvironment.

Visual Summary

The bar plot clearly delineates the cellular landscape between adjacent normal pancreas and PDAC samples, revealing significant shifts in cell type proportions.

  1. Adjacent Normal Pancreas (Adj_normal) Samples:
    • The 'Adj_normal' samples (AdjN_3, AdjN_1, AdjN_2) exhibit a relatively consistent cellular composition.
    • Acinar cells (dark red) are the predominant cell type, constituting a large majority (approximately 60-70%) of the total cell population in these samples.
    • Other cell types, such as Macrophages (light yellow), T cells (CD4+ teal, CD8+ dark blue), and Ductal cells (orange), are present in much smaller proportions.
  2. Pancreatic Ductal Adenocarcinoma (PDAC) Samples:
    • In stark contrast to 'Adj_normal', the PDAC samples demonstrate a highly altered and heterogeneous cellular composition.
    • Ductal cells (orange) show a marked increase in proportion across most PDAC samples, frequently becoming the dominant cell type or a substantial component (e.g., PDAC_6, PDAC_3, PDAC_8, PDAC_13, PDAC_4). This is consistent with the Ductal cell being identified as the resolved tumor-origin cell type.
    • Conversely, Acinar cells (dark red) are drastically reduced or nearly absent in most PDAC samples, indicating a loss of normal pancreatic tissue.
    • Stellate cells (light green) and Fibroblasts (light orange), both components of the stromal compartment, are noticeably increased in several PDAC samples (e.g., Stellate cells in PDAC_8, PDAC_11A, PDAC_15, PDAC_1; Fibroblasts in PDAC_10), suggesting an expanded desmoplastic stroma.
    • Macrophages (light yellow) are consistently present and appear to contribute a larger proportion in many PDAC samples compared to adjacent normal tissue.
    • T cells (CD4+ and CD8+) are also observed in varying proportions across PDAC samples, with some samples showing notable immune infiltration (e.g., PDAC_16, PDAC_6, PDAC_3).
    • There is considerable sample-to-sample heterogeneity within the PDAC group, highlighting the diversity of tumor microenvironments among patients. For example, some samples like PDAC_16 are rich in immune cells, while others like PDAC_10 are dominated by Ductal cells and fibroblasts.

Biological Interpretation

The observed shifts in cell type proportions provide critical insights into the pathology of Pancreatic Ductal Adenocarcinoma.

  1. Tumor Cell Expansion and Parenchymal Loss: The significant increase in Ductal cell proportion in PDAC samples directly reflects the expansion of cancerous epithelial cells, consistent with PDAC originating from ductal epithelial cells. The concomitant decrease or near disappearance of Acinar cells indicates the destruction and replacement of normal pancreatic parenchyma by the growing tumor mass.
  2. Desmoplastic Stromal Remodeling: The prominent increase in Stellate cells and Fibroblasts in PDAC samples is a hallmark of the severe desmoplastic reaction characteristic of this cancer. Pancreatic stellate cells are key drivers of fibrosis, secreting extracellular matrix components that contribute to the dense, immune-suppressive, and drug-resistant tumor microenvironment (TME) [PubMed search: Pancreatic stellate cells fibrosis PDAC]. This dense stroma poses a significant barrier to therapeutic delivery and immune cell infiltration, impacting treatment efficacy.
  3. Immune Landscape Alterations:
    • The increased presence of Macrophages in PDAC samples is consistent with the recruitment and accumulation of tumor-associated macrophages (TAMs). TAMs are known to play a crucial role in promoting tumor growth, angiogenesis, metastasis, and immunosuppression within the PDAC TME [PubMed search: Tumor-associated macrophages PDAC].
    • The varying proportions of T cells (CD4+ and CD8+) across PDAC samples suggest diverse immune responses. While T cells are present, their functional state (e.g., exhausted, anergic) would require further investigation. The desmoplastic stroma and immunosuppressive factors from other cells (like TAMs) often limit effective anti-tumor immunity in PDAC.
  4. Tumor Heterogeneity: The substantial variation in cell type proportions among individual PDAC samples underscores the high degree of inter-patient heterogeneity in PDAC. This cellular diversity can have profound implications for disease progression, therapeutic response, and patient outcomes.

Clinical or Translational Implications

Understanding the cellular composition of PDAC tumors has significant clinical and translational implications:

  1. Biomarker Potential: Specific cell type proportions, particularly the stromal and immune components, could serve as prognostic biomarkers for disease aggressiveness and patient survival. For example, a higher stromal content or specific immune cell infiltrates might correlate with poorer prognosis or differential responses to therapy.
  2. Therapeutic Targeting Strategies: The prominence of stromal cells (Stellate cells, Fibroblasts) and immunosuppressive cells (Macrophages) highlights them as attractive therapeutic targets. Strategies aimed at depleting or reprogramming pancreatic stellate cells, or inhibiting TAM functions, are actively being investigated to overcome drug resistance and enhance anti-tumor immunity in PDAC [PubMed search: Stromal targeting PDAC therapy].
  3. Personalized Medicine: The observed inter-patient heterogeneity emphasizes the need for personalized treatment approaches in PDAC. Profiling the cellular composition of individual tumors could guide treatment decisions, potentially enabling the selection of therapies most likely to be effective for a given patient based on their specific TME characteristics. This could involve combining conventional chemotherapy with stromal-targeting agents or immunomodulators.

7. T Cell and Innate Lymphoid Cell Subpopulation Dynamics in Pancreatic Ductal Adenocarcinoma

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Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of T cell and Innate Lymphoid Cell (ILC) subsets within the overall T cell major population. The data is stratified by individual samples and grouped by 'Adj_normal' (adjacent normal pancreas tissue) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. The goal is to identify shifts in immune cell composition within the T cell lineage in the context of PDAC.

Visual Summary

The bar plot reveals distinct patterns in T cell and ILC subset distribution between adjacent normal and PDAC samples:

Biological Interpretation

The observed shifts in T cell and ILC subset populations in PDAC underscore a significant reprogramming of the immune microenvironment in the context of pancreatic cancer.

The varied presence of these ILC subsets suggests a complex interplay of innate immune mechanisms within the PDAC TME.

The balance between these effector and regulatory T cell populations is crucial for determining the overall anti-tumor immune response.

Clinical or Translational Implications

The distinct immune cell profiles observed in PDAC provide valuable insights for potential clinical applications:

8. Analysis of T Cell Subset Population Changes in Pancreatic Ductal Adenocarcinoma

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Analysis Overview

This analysis presents box plots illustrating the proportions of various T cell subsets in Pancreatic Ductal Adenocarcinoma (PDAC) tissue compared to adjacent normal pancreatic tissue (Adj_normal). The objective was to identify T cell populations that show statistically significant differences between these two conditions, providing insights into the immune landscape alterations in PDAC. The populations are derived from the celltype_subset annotation level, filtered for T cell major cell types.

Visual Summary

The box plots display the proportion of eight distinct T cell subsets, with each panel representing a single subset and comparing its distribution across PDAC and Adj_normal conditions. Statistical significance (p-value) for the observed differences is indicated above each comparison.

  1. T cell (Cytotoxic) (T_Cyto): This subset shows a highly significant decrease in proportion in PDAC samples compared to Adj_normal (p=7.65e-11). The median proportion of cytotoxic T cells is notably lower in PDAC.
  2. T cell (Naive) (T_Naive): A significant increase in the proportion of naive T cells is observed in PDAC samples relative to Adj_normal (p=5.63e-07).
  3. T cell (Treg): Regulatory T cells (Tregs) are significantly elevated in PDAC samples compared to Adj_normal (p=0.00065), with a higher median and broader distribution in the tumor samples.
  4. T cell (Th9): The proportion of Th9 cells is significantly increased in PDAC compared to Adj_normal (p=0.00142).
  5. T cell (Tfh): T follicular helper cells (Tfh) show a significant increase in PDAC samples relative to adjacent normal tissue (p=0.00249).
  6. T cell (Th17): Th17 cells are significantly more abundant in PDAC samples compared to Adj_normal (p=0.00254).
  7. T cell (Th2): A significant increase in the proportion of Th2 cells is observed in PDAC samples compared to Adj_normal (p=0.00604).
  8. T cell (Th22): Th22 cell proportions are also significantly higher in PDAC samples relative to Adj_normal (p=0.00655).

In summary, across almost all statistically significant T cell subsets, there is a marked increase in various helper T cell populations and Tregs in PDAC, contrasting sharply with a pronounced decrease in cytotoxic T cells.

Biological Interpretation

The observed shifts in T cell subset proportions in PDAC tissue suggest a profoundly altered tumor immune microenvironment (TME) that likely favors tumor progression and immune evasion.

Collectively, these findings paint a picture of a PDAC TME characterized by a severe deficiency in direct tumor-killing lymphocytes (cytotoxic T cells) and an overabundance of cells (Tregs, various Th subsets) that can either directly suppress anti-tumor immunity, promote pro-tumorigenic inflammation, or represent an ineffective immune response.

Clinical or Translational Implications

These findings have significant clinical and translational implications for PDAC:

  1. Prognostic Biomarkers: The altered proportions of T cell subsets, particularly the reduction of cytotoxic T cells and expansion of Tregs, could serve as prognostic biomarkers for PDAC. Patients with a more "cold" (low cytotoxic T cell infiltration) and immunosuppressive (high Treg infiltration) TME are often associated with poorer outcomes.
  2. Therapeutic Targets: The dominance of immunosuppressive cells (Tregs) and the scarcity of effector cells (T_Cyto) highlight potential targets for immunotherapy. Strategies aimed at depleting Tregs, enhancing cytotoxic T cell function, or modulating the differentiation and function of other helper T cell subsets could be explored.
  3. Resistance to Immunotherapy: The observed immune landscape, especially the profound decrease in cytotoxic T cells, suggests that PDAC may be inherently resistant to immunotherapies that rely on pre-existing anti-tumor T cell responses, such as checkpoint inhibitors, as these cells are scarce. Combination therapies might be necessary to "re-ignite" the anti-tumor immune response.
  4. Understanding Disease Progression: These population shifts provide a deeper understanding of how the immune system is dysregulated in PDAC, contributing to disease progression and metastasis. Further research into the specific cytokine environments driving these T cell polarizations is warranted.

References

  1. Cytotoxic T cells in cancer: PubMed Search: "cytotoxic T cells cancer"
  2. Regulatory T cells in cancer: PubMed Search: "regulatory T cells cancer immunosuppression"
  3. Th17 cells in PDAC: PubMed Search: "Th17 cells pancreatic cancer"

9. Macrophage Subset Population Analysis in Pancreatic Ductal Adenocarcinoma

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Analysis Overview

This analysis presents a population bar plot visualizing the relative proportions of different macrophage subsets (Macrophage M1, M2A, M2B, M2C, M2D) across individual samples from both "Adj_normal" (adjacent normal pancreas) and "PDAC" (Pancreatic Ductal Adenocarcinoma) conditions. The aim is to identify potential shifts in macrophage polarization within the tumor microenvironment compared to normal tissue.

Visual Summary

The visualization reveals distinct differences in macrophage subset composition between adjacent normal and PDAC samples:

Biological Interpretation

Macrophages are critical components of the tumor microenvironment, and their polarization state plays a pivotal role in cancer progression.

The observed shift from an M1-dominant state in adjacent normal tissue to an increased proportion of M2-like macrophages, particularly M2A, in PDAC samples is consistent with the well-established role of TAMs in pancreatic cancer. This macrophage repolarization creates an immunosuppressive and pro-tumorigenic microenvironment, facilitating tumor growth, invasion, and resistance to therapy. The heterogeneity in PDAC samples could reflect different stages of disease progression, genetic backgrounds, or responses to the evolving tumor microenvironment.

Clinical or Translational Implications

The pronounced shift towards an M2-like macrophage phenotype in PDAC has several clinical and translational implications:

10. Ploidy Analysis of Ductal Cells in Pancreatic Ductal Adenocarcinoma (PDAC)

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Analysis Overview

This analysis investigates the ploidy status (Diploid, Aneuploid, or Unclear) specifically within the 'Ductal cell' population, which has been identified as the resolved tumor-origin cell type. The distribution of ploidy populations is visualized as a stacked bar plot, stratified by individual samples and comparing 'Adj_normal' (adjacent normal tissue) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. This helps to understand the genomic stability of Ductal cells in healthy versus diseased states.

Visual Summary

The bar plot displays the percentage of Ductal cells categorized by ploidy status across various individual samples from both 'Adj_normal' and 'PDAC' conditions.

Biological Interpretation

The observed ploidy patterns within Ductal cells strongly align with the biological characteristics of pancreatic cancer.

Clinical or Translational Implications

These findings have several potential clinical and translational implications for PDAC:

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References:

  1. Diploidy in normal cells: A fundamental characteristic of somatic cells. PubMed search for "human normal somatic cell ploidy" https://pubmed.ncbi.nlm.nih.gov/?term=human+normal+somatic+cell+ploidy
  2. Aneuploidy in cancer: Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. *Cell*, 144(5), 646-674. PubMed search for "aneuploidy hallmark cancer" https://pubmed.ncbi.nlm.nih.gov/?term=aneuploidy+hallmark+cancer
  3. Aneuploidy and tumor progression: Sansregret, L. L., & van Steensel, B. (2017). The physiological and pathological consequences of aneuploidy. *Nature Reviews Molecular Cell Biology*, 18(11), 683-695. PubMed search for "aneuploidy tumor progression heterogeneity" https://pubmed.ncbi.nlm.nih.gov/?term=aneuploidy+tumor+progression+heterogeneity
  4. Aneuploidy and therapeutic response: Ben-David, U., & Amon, A. (2020). Context-dependent effects of aneuploidy on cancer cell fitness. *Nature Reviews Cancer*, 20(2), 101-116. PubMed search for "aneuploidy cancer therapy response" https://pubmed.ncbi.nlm.nih.gov/?term=aneuploidy+cancer+therapy+response

11. Cell-Cell Interaction Analysis in Pancreatic Ductal Adenocarcinoma (PDAC)

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns in pancreatic tissue under both adjacent normal (Adj_normal) and Pancreatic Ductal Adenocarcinoma (PDAC) conditions. The primary goal is to identify key communication axes involving Ductal cells (particularly tumor-originating aneuploid Ductal cells), Fibroblast cells, Macrophage cells, and T cells (CD8+ and CD4+). CellPhoneDB was used to infer ligand-receptor interactions, and the results are visualized as dot plots, where dot size indicates the significance of the interaction (-log10(p-value)) and dot color represents the interaction strength (log2(mean expression)). Up to 80 most significant interactions per group are displayed.

Visual Summary

The provided dot plots reveal distinct cell-cell interaction landscapes between the Adj_normal and PDAC conditions.

Adjacent Normal (Adj_normal) Condition:

Pancreatic Ductal Adenocarcinoma (PDAC) Condition:

Biological Interpretation

The analysis highlights a profound remodeling of the cellular communication network in PDAC compared to the adjacent normal pancreas. This shift is characterized by the emergence of "Aneuploid Ductal" cells as central orchestrators of the TME, engaging in extensive cross-talk with resident immune cells like Macrophages and T cells.

  1. Tumor-Immune Landscape Remodeling: In PDAC, the immune-centric communication observed in normal tissue is supplanted by a complex network heavily influenced by tumor cells. This extensive interaction network is critical for tumor progression, immune suppression, and metastasis.
  2. Key Pro-tumorigenic Axes in PDAC:
    • SPP1-CD44 Axis: The strong interaction between SPP1 (Osteopontin) and CD44, particularly between Macrophages and Aneuploid Ductal cells, is highly significant. SPP1 is a matricellular protein often overexpressed in PDAC, promoting cell adhesion, migration, invasion, and M2 macrophage polarization, contributing to an immunosuppressive TME. CD44 is a receptor for SPP1 and a known cancer stem cell marker PubMed Search: SPP1 CD44 cancer pancreas.
    • CEACAM1-CEACAM6 Interactions: Carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) are frequently dysregulated in cancer. Their robust interactions in PDAC, especially involving tumor cells and macrophages, can mediate cell adhesion, immune evasion, and promote tumor growth and metastasis PubMed Search: CEACAM1 CEACAM6 cancer pancreas.
    • Ephrin/Eph Receptor Signaling: The strong presence of Ephrin-Eph interactions (e.g., EPHA4-EPHB2, EPHA6-EPHB3) involving Aneuploid Ductal cells underscores their role in regulating cell migration, invasion, and vascular remodeling within the TME, all critical processes in cancer progression.
    • Integrin-Mediated Adhesion: Interactions involving various integrin complexes (e.g., with COL17A1, LAMC1, CDH1) highlight altered cell-extracellular matrix and cell-cell adhesion dynamics in PDAC, facilitating tumor cell invasion and metastasis.
    • ANXA1-FPR1 Signaling: Annexin A1 (ANXA1) and Formyl Peptide Receptor 1 (FPR1) regulate inflammatory processes. In the PDAC TME, this axis between tumor cells and macrophages can influence immune responses, potentially contributing to immune evasion or shaping the inflammatory milieu.
    • PLAU-PLAUR System: The urokinase-type plasminogen activator (PLAU) and its receptor (PLAUR) are known to be involved in pericellular proteolysis, facilitating tissue degradation, cell migration, and invasion, crucial for metastatic spread in PDAC.
    • Altered Lipid Metabolism (Desmosterol-DHCR24/NR1H2): The presence of interactions involving desmosterol and its related enzymes/receptors suggests that altered lipid metabolism, a hallmark of cancer, plays a role in intercellular communication within the PDAC TME, potentially impacting tumor growth and immune cell function.
  3. Potential Immune Evasion: The decreased visibility of HLA-E-mediated interactions in PDAC compared to normal tissue might suggest that tumor cells are downregulating or altering these interactions to evade immune surveillance by NK cells and T cells, which recognize HLA-E.

Clinical or Translational Implications

The identified shifts in cell-cell interaction patterns in PDAC offer several potential clinical and translational avenues:

12. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Pathways in Pancreatic Ductal Adenocarcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) within the pancreas, specifically focusing on a user-defined set of genes associated with immune checkpoint and cell cycle pathways. The interactions are compared between 'Adj_normal' (adjacent normal tissue) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. The plot_cci_dots tool, leveraging CellPhoneDB results, visualizes the statistical significance (dot size, -log10(p)) and interaction strength (dot color, log2(mean expression)) of ligand-receptor pairs between different cell type combinations. While the user query included a broad list of genes related to immune checkpoints and cell cycle, the plot_cci_dots tool specifically identifies interactions involving ligand-receptor pairs from this list, which are then displayed if they meet the significance cutoffs.

Visual Summary

The provided dot plots illustrate significant cell-cell interactions for selected gene pairs under 'Adj_normal' and 'PDAC' conditions.

Adj_normal Condition:

Key Gene Pairs:

PDAC Condition:

Key Gene Pairs:

Biological Interpretation

The comparison between Adj_normal and PDAC conditions reveals critical shifts in cell-cell communication related to tumor progression and immune modulation in pancreatic cancer.

  1. Emergence of Oncogenic Signaling in Tumor Cells:
    • In PDAC, the prominent interactions involving TGFA-EGFR and AREG-EGFR within Aneuploid Ductal cells, and between Aneuploid Ductal cells and Macrophages, are highly significant. Epidermal Growth Factor Receptor (EGFR) is a well-established oncogene in PDAC, driving cell proliferation, survival, and migration. The increased autocrine/paracrine activation of EGFR by its ligands (TGFA, AREG) within the tumor cell compartment suggests a key mechanism for tumor growth and potentially resistance to therapy [1].
    • The strong presence of TGFB1_integrin_avb6_complex interactions, particularly involving Aneuploid Ductal cells (autocrine/juxtacrine) and Macrophages, is highly concerning. While TGF-beta has context-dependent roles, in advanced PDAC, it is primarily pro-tumorigenic, promoting epithelial-mesenchymal transition (EMT), immunosuppression, and fibrosis in the tumor microenvironment (TME). Integrin αvβ6 is known to activate latent TGF-beta1, and its expression is often elevated in PDAC, correlating with invasion and metastasis [2].
  2. Reprogrammed Immune Cell Interactions in PDAC:
    • Macrophages: Macrophages show robust interactions with Aneuploid Ductal cells in PDAC, particularly via TGFB1_integrin_avb6_complex, TGFA-EGFR, and CD86-CD28. Macrophages in the PDAC TME, often polarized towards an M2-like phenotype, are known to promote tumor growth, angiogenesis, and immune evasion [3]. Their interactions with tumor cells via TGF-beta and EGFR signaling likely contribute to this pro-tumorigenic function.
    • T Cell Co-stimulation: The strong CD86-CD28 interaction between Macrophages and T CD4+ cells in PDAC is notable. CD28 is a crucial co-stimulatory receptor on T cells that, upon binding to CD86 (on antigen-presenting cells like macrophages), enhances T cell activation and proliferation. However, in the highly immunosuppressive PDAC TME, this co-stimulation might not translate to effective anti-tumor immunity. Instead, it could contribute to chronic activation leading to exhaustion or supporting regulatory T cell (Treg) function, especially if macrophages are M2-polarized.
    • IFN-gamma Signaling: IFNG-Type_II_IFNR interactions persist in T CD8+ and Macrophage interactions in PDAC. Interferon-gamma (IFN-γ) is a key cytokine produced by activated T cells and NK cells, playing a critical role in anti-tumor immunity. While its presence indicates some level of immune activity, the effectiveness of this signaling in PDAC, often characterized by immune exclusion and resistance to checkpoint blockade, requires further investigation.
  3. Context of Cell Cycle Genes:
    • The initial query included a wide array of cell cycle-related genes (e.g., CDKs, cyclins, E2Fs). While these are not directly observed as ligand-receptor pairs in the CCI plots, the identified EGFR and TGF-beta signaling pathways are crucial upstream regulators of cell cycle progression. Activation of EGFR often leads to uncontrolled cell division, and TGF-beta signaling can either inhibit or promote cell cycle, depending on the cellular context and disease stage. Therefore, the observed surface interactions are highly relevant to the dysregulation of cell cycle in PDAC, which is driven by these extracellular cues.

Clinical or Translational Implications

The identified cell-cell interactions involving immune checkpoint and cell cycle-related pathways offer several avenues for therapeutic intervention and experimental validation in PDAC.

  1. Targeting EGFR and TGF-beta Signaling:
    • The strong EGFR signaling (TGFA-EGFR, AREG-EGFR) within and around Aneuploid Ductal cells reinforces EGFR as a potential therapeutic target in PDAC. While EGFR inhibitors (e.g., erlotinib, cetuximab) have shown limited efficacy as monotherapy in PDAC, these findings suggest that combination therapies or patient stratification based on specific ligand-receptor activation patterns could be beneficial 1.
    • The prominent role of TGFB1_integrin_avb6_complex in PDAC interactions suggests that targeting integrin αvβ6 or the TGF-beta pathway more broadly could inhibit tumor progression, EMT, and potentially reverse immunosuppression in the TME. Several TGF-beta pathway inhibitors are under investigation in clinical trials for various cancers, and their specific application in PDAC, perhaps combined with EGFR inhibitors, warrants further exploration 2.
  2. Modulating Macrophage-T Cell Interactions:
    • The strong CD86-CD28 interaction between Macrophages and T CD4+ cells highlights the importance of macrophage-T cell communication. Strategies to reprogram tumor-associated macrophages (TAMs) from an M2-like, pro-tumorigenic phenotype to an M1-like, anti-tumorigenic phenotype could enhance anti-tumor immunity. This might involve inhibiting macrophage recruitment, promoting M1 polarization, or combining with immune checkpoint blockade.
    • Given the general immune-suppressive nature of PDAC, understanding the precise functional consequences of IFNG signaling in this context is crucial. Strategies to enhance functional IFN-γ responses from T cells could be beneficial.
  3. Biomarker Development and Patient Stratification:
    • The specific ligand-receptor pairs identified, such as TGFA-EGFR, AREG-EGFR, and TGFB1_integrin_avb6_complex, could serve as predictive biomarkers to identify PDAC patients who are most likely to respond to targeted therapies against EGFR or TGF-beta pathways. High expression or interaction strength of these pairs in patient biopsies could guide treatment decisions.
  4. Experimental Validation:
    • Further experimental validation is crucial. This could involve using *in vitro* co-culture systems of primary PDAC cells with immune cells and fibroblasts to confirm the functional roles of these interactions. *In vivo* studies using patient-derived xenografts or genetically engineered mouse models of PDAC could evaluate the efficacy of targeting these pathways alone or in combination with existing therapies. Spatial transcriptomics or multiplex immunohistochemistry on human PDAC samples could spatially resolve these interactions and correlate them with clinical outcomes.
  5. Unaddressed Checkpoints: The absence of PDCD1 (PD-1) or CD274 (PD-L1) among the most significant interactions in this analysis, despite their inclusion in the query, suggests that other immune checkpoint mechanisms or co-stimulatory/co-inhibitory signals might be more dominant or significant in the context of these specific cell-cell interactions, or simply not meeting the significance cutoffs set for visualization. This does not preclude their importance but highlights the need for broader analysis or condition-specific evaluations.

References:

[1] GeneCards - EGFR. Available at: https://www.genecards.org/cgi-bin/carddisp.pl?gene=EGFR

[2] PubMed search: integrin avb6 TGFB1 PDAC. Available at: https://pubmed.ncbi.nlm.nih.gov/?term=integrin+avb6+TGFB1+PDAC

[3] PubMed search: macrophages pancreatic cancer immune suppression. Available at: https://pubmed.ncbi.nlm.nih.gov/?term=macrophages+pancreatic+cancer+immune+suppression

13. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interactions (CCIs) involving major immune and stromal cell types, comparing Pancreatic Ductal Adenocarcinoma (PDAC) samples with adjacent normal (Adj_normal) pancreatic tissue. The CellPhoneDB tool was used to identify ligand-receptor interactions, and the results are visualized as a dot plot showing the scaled interaction strength and statistical significance (-log10(p-value)) for each interaction across individual samples. The primary focus is on interactions involving Stromal cells, T cells, Myeloid cells (Macrophages), Mast cells, and B cells, as specified by the user query.

Visual Summary

The dot plot effectively illustrates significant differences in cell-cell communication patterns between Adj_normal and PDAC samples.

Biological Interpretation

The observed increase in significant cell-cell interactions in PDAC samples, particularly involving T cells and macrophages, underscores the complex and highly interactive nature of the tumor microenvironment (TME) in pancreatic cancer. These interactions are crucial for tumor progression, immune evasion, and stromal remodeling.

Below, we detail several prominent interactions observed to be highly active in PDAC, their associated signaling pathways, and biological mechanisms:

CCL4-CCR5 (T CD8+ <-> Mac):

ICAM1-integrin_aLb2_complex (ICAM1 on Mac <-> integrin on T CD8+):

SEMA4D-PLXNB2 (Mac <-> Mac):

CD47-SIRPA (Mac <-> Mac, or Mac <-> T CD8+ / NK):

HLA-E-NKG2A (HLA-E on Mac <-> NKG2A on T CD8+ / NK):

Clinical or Translational Implications

The pervasive increase in specific immune and stromal cell-cell interactions in PDAC samples compared to normal tissue highlights several promising avenues for therapeutic intervention.

14. Ductal Cell Condition-Specific Surfaceome Markers in Pancreatic Ductal Adenocarcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers within Ductal cells from pancreatic single-cell RNA-sequencing data. The dot plot visualizes the expression of up to 60 differentially expressed surfaceome genes in Ductal cells across individual samples, distinguishing between "Adj_normal" (adjacent normal pancreas) and "PDAC" (Pancreatic Ductal Adenocarcinoma) conditions, including samples annotated as "Diploid PDAC". The size of each circle represents the fraction of cells in a given sample group expressing the marker, while the color intensity indicates the mean expression level within those cells. The primary goal is to identify potential diagnostic biomarkers and therapeutic targets accessible on the cell surface.

Visual Summary

The dot plot clearly delineates two major expression patterns corresponding to the "Adj_normal" and "PDAC" conditions for Ductal cells.

Biological Interpretation

The analysis reveals robust sets of surfaceome markers specific to normal and malignant Ductal cells in the context of PDAC. These markers provide insights into the altered cell biology of tumor cells and highlight potential avenues for therapeutic intervention.

Markers Enriched in Adjacent Normal Ductal Cells

These markers often represent normal physiological functions of pancreatic Ductal cells and are significantly downregulated or lost in cancer.

Markers Enriched in PDAC Ductal Cells (Potential Therapeutic Targets)

The identified markers in PDAC Ductal cells are crucial for understanding tumor progression and developing targeted therapies. We can categorize them based on their therapeutic potential.

Established Therapeutic Targets / Strongly Implicated Oncogenes

  1. MSLN (Mesothelin):
    • Signaling/Mechanism: A glycosylphosphatidylinositol-anchored protein involved in cell adhesion and signaling, particularly via MUC16/CA125 interaction. Overexpression promotes cell proliferation, migration, and invasion in various cancers, including PDAC PubMed Search: Mesothelin PDAC cancer mechanism.
    • Therapeutic Agents: An established target for antibody-drug conjugates (ADCs) like anetumab ravtansine and mirvetuximab soravtansine, and CAR T-cell therapies PubMed Search: Mesothelin targeted therapy PDAC.
    • Evidence: Highly expressed in many PDAC cases, correlating with poor prognosis.
  2. MET (MET proto-oncogene, Receptor tyrosine kinase):
    • Signaling/Mechanism: Receptor for Hepatocyte Growth Factor (HGF). Upon ligand binding, MET dimerization and autophosphorylation activate downstream pathways (e.g., RAS-MAPK, PI3K-AKT, STAT3) promoting cell proliferation, survival, migration, and invasion (epithelial-mesenchymal transition) GeneCards: MET.
    • Therapeutic Agents: Tyrosine kinase inhibitors (TKIs) targeting MET (e.g., crizotinib, capmatinib, tepotinib) are in clinical development or approved for MET-driven cancers PubMed Search: MET inhibitor PDAC clinical trial.
    • Evidence: MET amplification, mutation, or overexpression contributes to PDAC pathogenesis and resistance to chemotherapy.
  3. SLC2A1 (GLUT1):
    • Signaling/Mechanism: Glucose transporter 1, facilitating glucose uptake into cells. Upregulated in numerous cancers to support enhanced glycolysis (Warburg effect), crucial for rapid proliferation UniProt: P11166.
    • Therapeutic Agents: Glucose uptake inhibitors, though clinical application can be challenging due to off-target effects. Targeting metabolic pathways is an active area of research.
    • Evidence: High GLUT1 expression is a hallmark of many aggressive tumors, including PDAC, and is associated with poor prognosis.
  4. ERBB2 (HER2):
    • Signaling/Mechanism: A member of the epidermal growth factor receptor (EGFR) family of receptor tyrosine kinases. Forms heterodimers with other EGFR family members, leading to activation of downstream growth and survival pathways (e.g., RAS-MAPK, PI3K-AKT) GeneCards: ERBB2.
    • Therapeutic Agents: Monoclonal antibodies (e.g., trastuzumab, pertuzumab) and TKIs (e.g., lapatinib) are established therapies for HER2-positive breast and gastric cancers. These are being explored in PDAC for HER2-amplified cases PubMed Search: HER2 PDAC therapy.
    • Evidence: HER2 amplification/overexpression occurs in a subset of PDAC patients, suggesting it as a viable target for patient stratification.

Less Well-Characterized Markers with Potential as Therapeutic Targets

  1. EPHA2 (Ephrin Receptor A2):
    • Signaling/Mechanism: A receptor tyrosine kinase that interacts with ephrin ligands. Plays roles in cell adhesion, migration, invasion, and angiogenesis. Dysregulation contributes to tumor progression and metastasis in various cancers UniProt: P29317.
    • Therapeutic Agents: Antibodies and small molecule inhibitors targeting EPHA2 are under investigation in preclinical and early clinical studies.
    • Evidence: Overexpression of EPHA2 is frequently observed in PDAC and is associated with aggressive disease.
  2. CDCP1 (CUB domain containing protein 1):
    • Signaling/Mechanism: A transmembrane glycoprotein that, upon cleavage, promotes SRC kinase activation, leading to enhanced cell proliferation, migration, invasion, and survival. It interacts with growth factor receptors and integrins GeneCards: CDCP1.
    • Therapeutic Agents: Monoclonal antibodies and inhibitors targeting CDCP1 are in preclinical development for several solid tumors.
    • Evidence: Elevated CDCP1 expression is linked to tumor progression and poor prognosis in PDAC and other cancers.
  3. ITGA6 (Integrin Alpha 6):
    • Signaling/Mechanism: Forms heterodimers with ITGB1 (integrin beta 1) to create the laminin receptor, or with ITGB4 (integrin beta 4) to form a hemidesmosome component. Involved in cell-extracellular matrix adhesion, cell survival, migration, and is implicated in cancer stem cell biology UniProt: P00750.
    • Therapeutic Agents: Anti-integrin antibodies have been developed, with some showing promise in oncology by disrupting tumor-stromal interactions and metastasis.
    • Evidence: ITGA6 overexpression is observed in various cancers, including PDAC, where it can promote metastatic potential and resistance to therapy.
  4. ADAM9 (ADAM metallopeptidase domain 9):
    • Signaling/Mechanism: A member of the ADAM (A Disintegrin And Metalloprotease) family. Possesses both protease and adhesion domains, involved in shedding of cell surface proteins (e.g., growth factors, receptors) and cell-cell/cell-matrix interactions, thereby influencing tumor growth and invasion GeneCards: ADAM9.
    • Therapeutic Agents: Small molecule inhibitors targeting ADAM proteases are under investigation, though achieving specificity can be challenging.
    • Evidence: Upregulated in PDAC and associated with tumor progression and metastasis.
  5. PLAU (Urokinase-type plasminogen activator):
    • Signaling/Mechanism: A serine protease that converts plasminogen to plasmin, initiating a proteolytic cascade that degrades the extracellular matrix. This process facilitates tumor cell invasion, migration, and metastasis UniProt: P04930.
    • Therapeutic Agents: Inhibitors targeting PLAU or its receptor (uPAR) have been explored in preclinical settings to block tumor invasion and metastasis.
    • Evidence: Elevated PLAU expression is a common feature in many aggressive cancers, including PDAC, correlating with invasiveness.

Clinical or Translational Implications

The identification of condition-specific surfaceome markers in Ductal cells has significant clinical and translational implications for PDAC:

15. Macrophage Condition-Specific Surfaceome Marker Expression in Pancreatic Ductal Adenocarcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes a dot plot to visualize the expression patterns of surfaceome markers in Macrophage cells across different conditions (Adj_normal and PDAC) and individual samples. The primary goal is to identify markers that are differentially expressed in macrophages from PDAC samples compared to adjacent normal pancreatic tissue, understand their biological roles within the tumor microenvironment, and assess their potential as therapeutic targets in Pancreatic Ductal Adenocarcinoma (PDAC). The plot shows both the mean expression level (color intensity) and the fraction of cells expressing the marker (dot size) for each gene within each sample group.

Visual Summary

The dot plot displays the expression of 40 surfaceome markers in macrophage populations from three 'Adj_normal' samples and multiple 'PDAC' samples.

Biological Interpretation

The observed upregulation of numerous surfaceome markers in macrophages within PDAC samples strongly suggests a distinct phenotype of tumor-associated macrophages (TAMs) that play a crucial role in the pancreatic tumor microenvironment (TME). This altered expression profile reflects their engagement in various pro-tumorigenic processes, including immune suppression, angiogenesis, metastasis, and extracellular matrix remodeling.

Here, we highlight several prominent markers significantly upregulated in PDAC macrophages and discuss their biological roles and potential implications:

SIRPA (Signal Regulatory Protein Alpha) and CD47

TGFBR2 (Transforming Growth Factor Beta Receptor Type 2)

ITGAM (Integrin Alpha M, CD11b)

ADAM17 (A Disintegrin And Metalloprotease 17, TACE)

TLR4 (Toll-like Receptor 4)

ICAM1 (Intercellular Adhesion Molecule 1, CD54)

Clinical or Translational Implications

The identification of highly expressed surfaceome markers on macrophages in PDAC samples offers several potential clinical and translational avenues:

Further research focusing on the precise functional roles of these markers on PDAC macrophages in *in vivo* models and clinical trials is warranted to validate their utility as therapeutic targets and biomarkers.

16. T cell CD4+ Condition-Specific Surfaceome Marker Expression in Pancreatic Ductal Adenocarcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression of surfaceome markers in CD4+ T cells across various samples from both adjacent normal (Adj_normal) and Pancreatic Ductal Adenocarcinoma (PDAC) conditions. The aim is to identify markers that are differentially expressed between these conditions, providing insights into the state and function of CD4+ T cells within the PDAC tumor microenvironment. Each dot represents a gene-sample pair, with its size indicating the fraction of cells expressing the marker in that sample group (prevalence) and its color intensity (from white to dark red) reflecting the mean expression level among expressing cells. The analysis specifically focused on surfaceome markers to highlight potential targets for therapeutic intervention or diagnostic applications.

Visual Summary

The dot plot clearly segregates markers into two main groups based on their preferential expression: those enriched in Adj_normal samples and those predominantly expressed in PDAC samples.

Biological Interpretation

The differential expression of surfaceome markers in CD4+ T cells between adjacent normal tissue and PDAC samples highlights significant alterations in T cell activation, differentiation, trafficking, and immune modulation within the tumor microenvironment (TME).

Markers Enriched in Adj_normal CD4+ T cells:

  1. CD69 (Early Activation Marker/Tissue Residency)
    • Associated Signaling Pathways: Involved in early T cell activation, T cell receptor (TCR) signaling, and downstream signaling leading to tissue retention. It can interact with S1P1 (S1PR1) to prevent egress from tissues. GeneCards: CD69
    • Role in TME / T cell CD4+: High expression in adjacent normal tissue suggests a baseline activated state or a population of tissue-resident memory (Trm) CD4+ T cells. Trm cells play a crucial role in local immune surveillance. Its reduced expression in many PDAC samples might indicate a shift away from this tissue-resident, surveilling phenotype, or the influx of other T cell subsets.
    • Potential as Therapeutic Target: While CD69 is an activation marker, directly targeting it therapeutically is complex due to its broad role. However, it can serve as a biomarker for tissue-resident T cell populations.
    • Relevant Therapeutic Agents: No direct therapeutic agents targeting CD69 are commonly used, but it's a marker used in immunological studies to identify activated or tissue-resident lymphocytes.
  2. KLRB1 (CD161) (NK1.1 Antigen)
    • Associated Signaling Pathways: KLRB1 is an inhibitory receptor that, upon ligand binding (e.g., LLT1), can downregulate NK cell and T cell activation. UniProt: KLRB1
    • Role in TME / T cell CD4+: CD161 is expressed on subsets of CD4+ T cells, including Th17 cells and some regulatory T cells (Tregs), and can influence their cytokine production and effector functions. Its presence in normal tissue may reflect a balanced immune regulatory environment. Its differential expression in PDAC could imply altered Th17/Treg balance or shifts in effector functions.
    • Potential as Therapeutic Target: Modulating KLRB1 signaling could theoretically influence T cell activation and differentiation. However, its complex role as an inhibitory receptor might require nuanced approaches.
    • Relevant Therapeutic Agents: Research is ongoing into targeting inhibitory receptors to modulate anti-tumor immunity.

Markers Enriched in PDAC CD4+ T cells:

  1. GPR183 (EBI2) (Epstein-Barr Virus Induced Gene 2)
    • Associated Signaling Pathways: A G protein-coupled receptor activated by oxysterols (e.g., 7α,25-dihydroxycholesterol). Crucial for lymphocyte migration and positioning within lymphoid organs and tertiary lymphoid structures (TLS). GeneCards: GPR183
    • Role in TME / T cell CD4+: Upregulation in PDAC CD4+ T cells suggests altered migratory patterns or an attempt to form or maintain TLS within the tumor. TLS formation in PDAC can be prognostic, sometimes associated with better outcomes due to local immune activation, but its overall impact is complex and context-dependent.
    • Potential as Therapeutic Target: Modulating GPR183 activity could influence lymphocyte trafficking to and within the TME, potentially enhancing anti-tumor immunity by recruiting specific immune subsets.
    • Relevant Therapeutic Agents: GPR183 antagonists or agonists are under investigation for modulating immune responses in various diseases, including cancer.
  2. CD27 (TNF Receptor Superfamily Member 7)
    • Associated Signaling Pathways: A costimulatory receptor on T cells that binds to CD70. Signaling through CD27 promotes T cell activation, proliferation, survival, and differentiation into memory and effector cells. UniProt: CD27
    • Role in TME / T cell CD4+: High expression in PDAC CD4+ T cells may indicate ongoing T cell activation and the presence of memory or effector T cell populations. However, chronic stimulation in the TME can also lead to exhaustion, where CD27 expression can be maintained or altered. It's a key marker for T cell maturation and function.
    • Potential as Therapeutic Target: CD27 agonists have been explored in cancer immunotherapy to enhance T cell responses.
    • Relevant Therapeutic Agents: Varlilumab (anti-CD27 agonist antibody) has been evaluated in clinical trials for various cancers.
  3. ITGB1 (CD29) (Integrin Beta-1)
    • Associated Signaling Pathways: Forms heterodimers with various alpha integrins (e.g., ITGA4, ITGA5) to create adhesion receptors (e.g., VLA-4, VLA-5). Involved in cell-extracellular matrix (ECM) interactions, cell-cell adhesion, and signaling pathways regulating cell survival, proliferation, and migration. GeneCards: ITGB1
    • Role in TME / T cell CD4+: Upregulation of ITGB1 suggests enhanced adhesive capabilities of CD4+ T cells, which is critical for their infiltration into the dense, desmoplastic PDAC TME and their retention within it. It mediates interactions with fibronectin and collagen, abundant in the PDAC ECM. This could imply a subset of CD4+ T cells attempting to engage with the tumor microenvironment.
    • Potential as Therapeutic Target: Integrin inhibitors have been developed to target tumor cell adhesion and invasion, but targeting integrins on immune cells for specific modulation of their TME infiltration or function is an area of research.
    • Relevant Therapeutic Agents: Several integrin-targeting drugs exist (e.g., natalizumab for autoimmune disease), but their application in cancer, specifically targeting immune cell integrins, is still under investigation.
  4. IL10RA (CD210) (Interleukin 10 Receptor Subunit Alpha)
    • Associated Signaling Pathways: Binds to IL-10, leading to activation of JAK1/STAT3 signaling, which mediates the potent immunosuppressive effects of IL-10. GeneCards: IL10RA
    • Role in TME / T cell CD4+: Increased expression of IL10RA on CD4+ T cells in PDAC indicates that these cells are highly responsive to IL-10, a key immunosuppressive cytokine abundant in the PDAC TME, often produced by tumor cells or myeloid-derived suppressor cells (MDSCs). This responsiveness contributes to an overall immunosuppressive environment that hinders anti-tumor immunity.
    • Potential as Therapeutic Target: Blocking IL-10/IL10RA signaling could reduce immunosuppression and enhance anti-tumor responses. This could be a promising strategy in PDAC.
    • Relevant Therapeutic Agents: Antibodies targeting IL-10 or its receptor are being explored in various contexts, including cancer, to overcome immunosuppression.
  5. TNFRSF14 (HVEM) (Herpesvirus Entry Mediator)
    • Associated Signaling Pathways: A member of the TNF receptor superfamily with complex interactions. It binds both the costimulatory ligand LIGHT (TNFSF14) and the inhibitory ligand BTLA (B and T Lymphocyte Attenuator). Its signaling can either promote or inhibit T cell activation depending on the context and available ligands. GeneCards: TNFRSF14
    • Role in TME / T cell CD4+: Upregulation in PDAC CD4+ T cells suggests a dynamic and potentially dysregulated immune checkpoint landscape. If BTLA is also expressed on these T cells or other immune cells in the TME, it could contribute to T cell exhaustion or anergy. If LIGHT is present, it might provide costimulation, but the balance is critical. In the immunosuppressive PDAC TME, increased HVEM might favor inhibitory signals.
    • Potential as Therapeutic Target: Modulating the HVEM-BTLA-LIGHT axis represents a potential immunotherapeutic strategy. Blocking BTLA or agonistically targeting LIGHT could enhance anti-tumor immunity.
    • Relevant Therapeutic Agents: Antibodies targeting BTLA (e.g., BMS-986218) are in clinical development as immune checkpoint inhibitors.

Clinical or Translational Implications

The distinct surfaceome marker profiles of CD4+ T cells in PDAC, compared to adjacent normal tissue, offer several translational avenues:

These findings highlight the complexity of the immune landscape in PDAC and provide a foundation for further research into the functional implications of these surface markers and their potential exploitation for therapeutic benefit.

17. Cell Cycle Pathway Gene Expression in Pancreatic Ductal Adenocarcinoma Ductal Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigated the expression fraction of a curated list of cell cycle pathway genes within Ductal cells, comparing Pancreatic Ductal Adenocarcinoma (PDAC) samples against adjacent normal pancreatic tissue (Adj_normal). The 'expressing cell fraction (sample)' metric quantifies the proportion of Ductal cells within each sample that express a given gene. The objective was to identify genes with statistically significant differences in this fraction between the two conditions, highlighting potential dysregulation of cell cycle control in PDAC.

Visual Summary

The visualization presents 24 box plots, each representing a distinct cell cycle pathway gene. In every plot, the 'expressing cell fraction (sample)' is compared between "Adj_normal" and "PDAC" conditions for Ductal cells.

Key observations from the plots are:

Biological Interpretation

The observed widespread and statistically significant increase in the expressing cell fraction of cell cycle pathway genes in PDAC Ductal cells compared to adjacent normal Ductal cells is a strong indicator of uncontrolled cell proliferation, a hallmark of cancer. Ductal cells are the resolved tumor-origin cell type for PDAC, making these findings directly relevant to tumor biology.

Several categories of cell cycle genes are prominently upregulated:

Oncogenes and Tumor Suppressors with Complex Roles:

Other Pro-proliferative/Growth Signaling Genes:

The collective upregulation of these cell cycle components confirms a state of heightened proliferative activity in PDAC Ductal cells, consistent with the aggressive nature of this malignancy.

Clinical or Translational Implications

The consistent upregulation of cell cycle genes in PDAC Ductal cells highlights the aggressive proliferative capacity of these tumor cells and offers several clinical and translational insights:

Overall, these findings underscore the fundamental role of cell cycle dysregulation in PDAC pathogenesis and provide a strong basis for further investigation into these specific genes as diagnostic markers or therapeutic avenues.

18. Gene Ontology (GSA) Analysis of Ductal Cells in Pancreatic Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results (Gene Set Analysis, GSA) for Ductal cells within pancreatic tissue. Given that Ductal cells are identified as the resolved tumor-origin cell type in the context of Pancreatic Ductal Adenocarcinoma (PDAC), understanding their pathway alterations is crucial. The analysis compares gene expression profiles across different conditions or ploidy states to identify significantly enriched biological processes. Three comparisons are shown:

  1. Adj_normal_vs_others: Pathways upregulated in Ductal cells from adjacent normal tissue compared to all other conditions (predominantly PDAC).
  2. Diploid_vs_others: Pathways upregulated in Diploid Ductal cells compared to all other ploidy states (predominantly Aneuploid cells, often associated with malignancy).
  3. PDAC_vs_others: Pathways upregulated in Ductal cells from PDAC samples compared to all other conditions (predominantly adjacent normal tissue, as 'Adj_normal' is the reference condition).

The bar plots display the top enriched GO terms, ordered by their statistical significance, with bar length representing -log(p-val) and -log(q-val).

Visual Summary

GSA_up for Ductal cell: Adj_normal_vs_others

The top enriched pathways in Ductal cells from adjacent normal tissue are predominantly associated with core metabolic and digestive functions. Highly significant terms include "Pancreatic secretion", "Protein digestion and absorption", "Maturity onset diabetes of the young", and various amino acid and fatty acid metabolic pathways (e.g., "Valine, leucine and isoleucine degradation", "Fatty acid degradation"). This profile reflects the normal physiological role of pancreatic ductal cells in coordinating exocrine functions.

GSA_up for Ductal cell: Diploid_vs_others

Ductal cells identified as Diploid show enrichment for a smaller set of pathways. Notably, "Bile secretion" and "Pancreatic secretion" are highly significant, mirroring the normal functional aspects seen in Adj_normal cells. Also present are "Proximal tubule bicarbonate reclamation" (a metabolic/transport process), "Complement and coagulation cascades" (immune/inflammatory responses), "cAMP signaling pathway", and "PI3K-Akt signaling pathway" (fundamental cellular signaling networks). This suggests that diploid ductal cells, which would include the healthy fraction and potentially early-stage non-aneuploid tumor cells, largely maintain normal functions while also engaging basic signaling and immune-related processes.

GSA_up for Ductal cell: PDAC_vs_others

In stark contrast, Ductal cells from PDAC samples exhibit a widespread upregulation of pathways indicative of malignancy, cellular stress, and dysregulation. The top enriched terms are highly diverse and include:

Biological Interpretation

The GO enrichment analysis highlights a dramatic shift in the biological processes active in Ductal cells, transitioning from a healthy, functional state to a malignant, highly dysregulated state in PDAC.

  1. Loss of Normal Pancreatic Function: In adjacent normal tissue and diploid cells, Ductal cells are characterized by pathways essential for pancreatic exocrine function, metabolism, and maintaining cellular homeostasis. The prominence of "Pancreatic secretion" and various metabolic pathways underscores their role in digestion and nutrient processing.
  2. Activation of Oncogenic Programs in PDAC: In PDAC-associated Ductal cells, these normal functions are largely suppressed or reprogrammed, replaced by pathways that support aggressive tumor growth and survival.
    • Metabolic Reprogramming and Protein Synthesis/Degradation: The upregulation of terms related to ubiquitin-mediated proteolysis, ER protein processing, ribosome function, and RNA transport reflects the high metabolic demand and protein synthesis rates required for rapid cell proliferation in cancer. This often leads to proteotoxic stress, which is managed by mechanisms like autophagy and the unfolded protein response (UPR) [PubMed search: protein quality control cancer].
    • Hyperactivated Signaling Pathways: Key growth and survival pathways such as mTOR and insulin signaling are consistently dysregulated in many cancers, promoting cell growth, proliferation, and angiogenesis [GeneCards: MTOR, GeneCards: INS]. Their enrichment here confirms their central role in PDAC pathogenesis.
    • Evasion of Cell Death and Senescence: Alterations in apoptosis and cellular senescence pathways allow cancer cells to escape natural tumor suppressive mechanisms, contributing to uncontrolled growth and survival.
    • Shared Mechanisms with Other Diseases: The enrichment of terms related to various infectious diseases and neurodegenerative disorders often indicates a generalized cellular stress response, immune dysregulation, or shared protein quality control issues that are leveraged by cancer cells. For example, altered mitochondrial function, inflammation, and proteostasis are common threads across these seemingly disparate diseases [PubMed search: inflammation cancer neurodegeneration]. The "Complement and coagulation cascades" also appearing in diploid cells suggests that aspects of immune or hemostatic systems may be involved early or constitutively.

Clinical or Translational Implications

The profound shift in Ductal cell biology during PDAC progression revealed by this GSA has several clinical implications:

19. Gene Set Enrichment Analysis of Pancreatic Cell Types in PDAC

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results across various pancreatic cell types, comparing different conditions or cellular states within each cell type. The primary goal is to identify pathways that are significantly enriched or depleted, providing insights into the biological processes altered in Pancreatic Ductal Adenocarcinoma (PDAC) and its associated microenvironment.

The dot plot visualizes the Normalized Enrichment Score (NES) and statistical significance (-log(p-value)) for 120 selected pathways. Each column represents a specific cell type (celltype_minor) under a particular comparison group (Group_vs_others), while each row corresponds to a specific gene set or pathway.

Key parameters for the visualization:

Plot Type: Dot plot

Visual Summary

The GSEA dot plot reveals distinct and highly significant pathway alterations across multiple cell types within the pancreatic microenvironment, particularly pronounced in tumor-associated conditions.

  1. Ductal Cells - Tumor Core Signatures:
    • PDAC vs. Others (PDAC Ductal vs. Adj_normal Ductal): This comparison shows a strong enrichment (large red dots) for pathways central to cancer biology, including "Central carbon metabolism in cancer", "Cell cycle", "PI3K-Akt signaling pathway", "Ras signaling pathway", "HIF-1 signaling pathway", "p53 signaling pathway", "ECM-receptor interaction", and "Cell adhesion molecules". Conversely, pathways such as "Oxidative phosphorylation", "Insulin secretion", "Insulin signaling pathway", and "PPAR signaling pathway" are significantly depleted (large blue dots).
    • Diploid vs. Others (Diploid Ductal vs. Aneuploid Ductal): This comparison exhibits a strikingly inverse pattern compared to "PDAC vs. others" for Ductal cells. Pathways enriched in PDAC Ductal cells are depleted in Diploid Ductal cells, and vice versa, strongly suggesting that the "Diploid" state for Ductal cells is characteristic of a less tumor-like or non-tumorigenic phenotype compared to the "others" (likely aneuploid, tumor-associated Ductal cells).
    • Adj_normal vs. Others (Adj_normal Ductal vs. PDAC Ductal): This comparison largely mirrors the Diploid_vs_others column for Ductal cells, showing depletion of oncogenic pathways and enrichment of metabolic pathways, as expected for healthy tissue.
  2. Tumor Microenvironment (TME) - Immune and Stromal Remodeling:
    • Macrophages (PDAC vs. Others): Macrophages in PDAC show enrichment for "Antigen processing and presentation", "Fc gamma R-mediated phagocytosis", "HIF-1 signaling pathway", "IL-17 signaling pathway", "TNF signaling pathway", "ECM-receptor interaction", and "Cell adhesion molecules". This indicates significant activation and remodeling within the TME.
    • T cells (CD4+ and CD8+; PDAC vs. Others): Both CD4+ and CD8+ T cells exhibit similar patterns to macrophages, with enrichment in "HIF-1 signaling pathway", "IL-17 signaling pathway", "TNF signaling pathway", "ECM-receptor interaction", and "Cell adhesion molecules". Depletion of "Oxidative phosphorylation" and "Insulin signaling pathway" is also observed.
    • NK Cells (PDAC vs. Others): NK cells show enrichment for "Natural killer cell mediated cytotoxicity" in both Adj_normal and PDAC conditions, which is expected for their primary function. They also show enrichment in "HIF-1 signaling pathway" and "TNF signaling pathway" in the PDAC context.
    • Endothelial and Smooth Muscle Cells (PDAC vs. Others): These stromal components show strong enrichment for "ECM-receptor interaction", "Cell adhesion molecules", "HIF-1 signaling pathway", and "PI3K-Akt signaling pathway", indicative of significant extracellular matrix remodeling and altered vascularization/stromal activation in the tumor.
  3. General Trends Across Cell Types:
    • Many cell types in the PDAC condition consistently show significant enrichment of pathways related to inflammation ("TNF signaling pathway", "IL-17 signaling pathway") and cellular stress/hypoxia ("HIF-1 signaling pathway").
    • A common pattern of depletion for "Oxidative phosphorylation" and insulin-related pathways (e.g., "Insulin secretion", "Insulin signaling pathway") is observed across multiple cell types in the PDAC context, suggesting widespread metabolic reprogramming.
    • "ECM-receptor interaction" and "Cell adhesion molecules" are broadly enriched in PDAC-associated cells, highlighting the pervasive nature of TME remodeling.

Biological Interpretation

The GSEA results provide a comprehensive view of the biological processes dysregulated in PDAC, extending beyond the tumor cells themselves to the surrounding microenvironment.

  1. Hallmarks of Cancer in Ductal Cells: The robust enrichment of "Cell cycle", "PI3K-Akt signaling pathway", "Ras signaling pathway", and "p53 signaling pathway" in PDAC Ductal cells aligns with established hallmarks of cancer, including uncontrolled proliferation and altered survival signaling [1]. The pronounced enrichment of "Central carbon metabolism in cancer" and depletion of "Oxidative phosphorylation" suggest a significant metabolic shift, characteristic of the Warburg effect, where cancer cells favor aerobic glycolysis even in the presence of oxygen [2]. The observed inverse patterns in Diploid_vs_others further solidify these pathways as indicators of tumor progression and malignancy within the Ductal cell lineage.
  2. Tumor Microenvironment Remodeling: The consistent enrichment of "ECM-receptor interaction" and "Cell adhesion molecules" across stromal (Endothelial, Smooth muscle) and immune cells in PDAC indicates extensive remodeling of the extracellular matrix and altered cell-cell interactions, which are critical for tumor growth, invasion, and metastasis [3]. "HIF-1 signaling pathway" enrichment across multiple PDAC-associated cell types suggests a prevalent hypoxic environment within the tumor, driving adaptive responses in both tumor and stromal cells [4].
  3. Immune Landscape in PDAC:
    • The upregulation of "Antigen processing and presentation" and "Fc gamma R-mediated phagocytosis" in PDAC macrophages suggests an active role in immune responses, although this can be pro- or anti-tumor depending on the specific macrophage polarization (e.g., M1 vs M2).
    • Enrichment of "TNF signaling pathway" and "IL-17 signaling pathway" in macrophages and T cells points to a highly inflammatory TME, which can promote tumor growth and suppress anti-tumor immunity in PDAC [5].
    • While "Natural killer cell mediated cytotoxicity" is expected in NK cells, its sustained enrichment in the PDAC context suggests potential ongoing immune surveillance or a compensatory response to tumor presence.
  4. Metabolic Dysfunction Beyond Tumor Cells: The widespread depletion of "Oxidative phosphorylation" and "Insulin signaling pathway" across various PDAC-associated cell types, including immune and stromal cells, suggests systemic metabolic dysregulation influencing the entire tumor ecosystem. This could impact nutrient availability and energy metabolism for both tumor and host cells.

Clinical or Translational Implications

These GSEA findings highlight several potential avenues for clinical translation in PDAC:

  1. Therapeutic Targeting:
    • Oncogenic Pathways: The prominent role of PI3K-Akt, Ras, and HIF-1 signaling pathways in PDAC Ductal cells suggests these as prime targets for developing specific inhibitors. Targeting central carbon metabolism could also disrupt energy supply to tumor cells.
    • TME Modulation: Pathways like ECM-receptor interaction and cell adhesion molecules, highly active in stromal and immune cells, represent potential targets to disrupt tumor-stroma interactions and metastasis.
    • Inflammation: Targeting inflammatory pathways (TNF, IL-17) could mitigate the pro-tumorigenic inflammatory microenvironment.
  2. Biomarker Discovery: The identified enriched/depleted pathways could serve as diagnostic or prognostic biomarkers. For instance, high activity in "Central carbon metabolism in cancer" or low activity in "Oxidative phosphorylation" in circulating tumor cells or biopsies could indicate PDAC presence or progression.
  3. Immunotherapy Strategies: The altered immune cell pathways, including those related to antigen presentation and inflammatory signaling, could inform the development of more effective immunotherapeutic approaches tailored to the unique immune landscape of PDAC. Understanding the specific polarization of macrophages and T cells based on these pathways would be crucial.
  4. Ploidy as a Prognostic Indicator: The distinct GSEA profiles between Diploid and PDAC Ductal cells underscore the biological significance of ploidy status. Further investigation into ploidy-associated gene expression could identify markers for distinguishing less aggressive Ductal cells from transformed, aneuploid tumor cells.

References:

[1] Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell, 144(5), 646-674. PubMed search: Hanahan Weinberg Hallmarks of Cancer 2011

[2] Warburg, O. (1956). On the origin of cancer cells. Science, 123(3191), 309-314. PubMed search: Warburg cancer metabolism

[3] Eble, J. A., & Kalluri, R. (2017). The role of the extracellular matrix in cancer. In Cancer Biology (pp. 53-73). Springer, Cham. PubMed search: extracellular matrix cancer

[4] Semenza, G. L. (2010). HIF-1: upstream and downstream of cancer metabolism. Current opinion in genetics & development, 20(1), 51-56. PubMed search: Semenza HIF-1 cancer metabolism

[5] Giese, N. A., Wente, M. N., Esposito, I., Berberat, P. O., Büchler, M. W., & Friess, H. (2006). Role of inflammatory response in pancreatic cancer. World Journal of Gastroenterology, 12(28), 4619. PubMed search: inflammation pancreatic cancer

20. Discussion

The single-cell analysis of pancreatic tissue provides a detailed and multi-faceted characterization of Pancreatic Ductal Adenocarcinoma (PDAC), clearly distinguishing the disease state from adjacent normal tissue. UMAP visualizations underscore a global transcriptomic shift driven by the disease, with a prominent 'Ductal cell' cluster co-localizing strongly with 'Aneuploid' cells, robustly confirming their identity as the malignant population. This is further supported by the Ductal cell ploidy analysis, demonstrating a progressive increase in aneuploidy across PDAC samples, in stark contrast to the stable diploid state in adjacent normal tissue.

Genomic analysis of Ductal cells revealed extensive Copy Number Variations (CNVs) in PDAC samples, including recurrent amplifications on chromosomes 1q, 3q, 7q, and 8q, harboring genes like GSDMD and EIF3E. The CNV-based UMAP embedding further validated the clear segregation of aneuploid, tumor-origin Ductal cells from diploid stromal and immune components. This pervasive genomic instability, even in samples initially classified as 'Diploid PDAC,' highlights the complexity of tumor genetics beyond simple ploidy classification.

Significant remodeling of the tumor microenvironment (TME) is a hallmark of PDAC, as evidenced by drastic shifts in cell type proportions. Acinar cells are largely depleted, while Ductal cells, Stellate cells, Fibroblasts, and Macrophages show substantial expansion. The immune landscape is profoundly altered, characterized by a significant decrease in cytotoxic T cells alongside an increase in immunosuppressive regulatory T cells (Tregs) and various helper T cell subsets (Th9, Th17, Th2, Th22, Tfh). Macrophages in PDAC display a striking shift from an M1-dominant to an M2-like pro-tumorigenic phenotype, consistent with their role in promoting immune evasion and tumor progression. The emergence of diverse Innate Lymphoid Cell (ILC) subsets in PDAC also points to a complex innate immune response.

Cell-cell interaction (CCI) analyses reveal a dramatic rewiring of communication networks in PDAC. Aneuploid Ductal cells emerge as central hubs, engaging in extensive cross-talk with Macrophages and T cells through crucial ligand-receptor axes like SPP1-CD44, CEACAM1-CEACAM6, and various Ephrin/Eph receptors. Oncogenic EGFR signaling (TGFA-EGFR, AREG-EGFR) and pro-tumorigenic TGF-beta signaling (TGFB1-integrin_avb6_complex) are highly active within and between Ductal cells and macrophages. Immune-specific CCIs highlight upregulated CCL4-CCR5, SEMA4D-PLXNB2, and crucially, CD47-SIRPA and HLA-E-NKG2A, pathways implicated in immune cell recruitment, function modulation, and immune evasion.

Surfaceome marker expression further differentiates cell states. PDAC Ductal cells upregulate key oncogenes and adhesion molecules such as MSLN, MET, SLC2A1 (GLUT1), ERBB2 (HER2), EPHA2, CDCP1, ITGA6, ADAM9, and PLAU, while losing normal ductal markers like GP2 and SCTR. Macrophages in PDAC show a distinct TAM phenotype with high expression of SIRPA, TGFBR2, ITGAM, ADAM17, TLR4, and ICAM1. CD4+ T cells in PDAC exhibit increased expression of GPR183, CD27, ITGB1, IL10RA, and TNFRSF14 (HVEM), indicative of altered trafficking, activation, and responsiveness to immunosuppressive signals.

Finally, Gene Ontology (GSA) and Gene Set Enrichment Analysis (GSEA) confirm widespread metabolic reprogramming (e.g., enrichment of 'Central carbon metabolism in cancer,' depletion of 'Oxidative phosphorylation'), hyperactive cell cycle pathways, and dysregulation of PI3K-Akt, Ras, and HIF-1 signaling in PDAC Ductal cells. Immune and stromal cells in the PDAC TME also show enrichment in inflammatory (TNF, IL-17) and stress-related (HIF-1) pathways, reflecting the hostile tumor environment. Notably, the upregulation of tumor suppressors like RB1 and TP53 in PDAC Ductal cells, despite active proliferation, suggests potential functional inactivation or compensatory mechanisms common in cancer. These collective findings underscore the profound molecular and cellular changes driving PDAC progression, offering a rich resource for identifying novel therapeutic strategies.

Hypotheses:

  1. Ductal cells in PDAC exhibit comprehensive genomic instability (aneuploidy, recurrent CNVs) and metabolic reprogramming, driving uncontrolled proliferation, survival, and escape from normal cellular regulation.
  2. The PDAC tumor microenvironment is fundamentally immunosuppressive, characterized by a decrease in functional cytotoxic T cells, an increase in regulatory T cells, and a shift of macrophages towards an M2-like phenotype that actively promotes tumor growth and immune evasion.
  3. Specific ligand-receptor interactions, such as SPP1-CD44, CD47-SIRPA, and the activation of EGFR/TGF-beta pathways, act as key mediators of pro-tumorigenic cross-talk between malignant Ductal cells and components of the tumor microenvironment, facilitating invasion, metastasis, and immune suppression.
  4. The unique surfaceome signatures identified on PDAC Ductal cells, tumor-associated macrophages, and CD4+ T cells can serve as accessible biomarkers for disease progression and as direct targets for novel precision immunotherapies or targeted agents.

Potential therapeutic targets:

  1. MSLN (Mesothelin): MSLN is highly and specifically upregulated on PDAC Ductal cells, which are the malignant population. It is involved in cell adhesion, proliferation, and invasion, and its expression correlates with poor prognosis. Evidence: Section 14 (Ductal Cell Condition-Specific Surfaceome Markers) clearly shows strong, widespread upregulation of MSLN in PDAC Ductal cells, with minimal expression in normal counterparts. The explanation notes its established role in cancer progression and as a therapeutic target. Validation: Confirm MSLN expression in patient biopsies via IHC. Test efficacy of anti-MSLN antibody-drug conjugates (ADCs) or CAR T-cell therapies in PDAC patient-derived xenograft (PDX) models or organoids. Monitor response in MSLN-positive patients in clinical trials.
  2. MET (MET proto-oncogene): MET is a receptor tyrosine kinase that is significantly upregulated on PDAC Ductal cells. Activation of MET by its ligand HGF drives cell proliferation, survival, migration (EMT), and invasion, contributing to tumor progression and potential chemotherapy resistance. Evidence: Section 14 (Ductal Cell Condition-Specific Surfaceome Markers) shows high expression of MET in PDAC Ductal cells. The explanation details its oncogenic signaling via RAS-MAPK, PI3K-AKT, STAT3 pathways. Validation: Assess MET activation/expression levels in PDAC patient tumors. Test known MET tyrosine kinase inhibitors (e.g., crizotinib, capmatinib, tepotinib) in PDAC cell lines and PDX models, especially in cases with high MET expression or amplification. Investigate combination therapies with other agents.
  3. CD47-SIRPA Axis: CD47 is a 'don't eat me' signal frequently overexpressed on PDAC tumor cells, interacting with SIRPA on macrophages to inhibit phagocytosis. SIRPA is highly expressed on PDAC-associated macrophages, making this axis a critical immune evasion mechanism for tumor cells. Evidence: Section 15 (Macrophage Condition-Specific Surfaceome Markers) shows prominent SIRPA upregulation on PDAC macrophages. Section 13 (Condition-Specific Cell-Cell Interaction Patterns) highlights CD47-SIRPA as a significantly differential interaction in PDAC, suggesting its active role in immune evasion. Validation: Test anti-CD47 antibodies (e.g., Magrolimab) or SIRPA-Fc fusion proteins in PDAC *in vitro* co-culture models with macrophages to enhance phagocytosis of tumor cells. Evaluate the anti-tumor efficacy of these agents, alone or in combination with chemotherapy/immunotherapy, in PDX models.
  4. TGF-beta Pathway (via TGFBR2 / Integrin αvβ6): The TGF-beta pathway is a major immunosuppressive and pro-tumorigenic pathway in the PDAC TME. TGFBR2, highly expressed on PDAC macrophages, mediates TGF-beta signaling which promotes M2-like polarization and immune suppression. The TGFB1-integrin_avb6_complex is a strong interaction in PDAC involving Aneuploid Ductal cells and Macrophages, activating latent TGF-beta. Evidence: Section 15 (Macrophage Surfaceome Markers) indicates high TGFBR2 expression. Section 12 (CCI of Immune Checkpoint & Cell Cycle Pathways) shows strong TGFB1-integrin_avb6_complex interactions in PDAC, linking tumor cells and macrophages. GSA/GSEA (Sections 18, 19) also highlight dysregulated signaling pathways (mTOR, PI3K-Akt) often modulated by TGF-beta. Validation: Evaluate TGF-beta receptor kinase inhibitors (e.g., galunisertib) or anti-TGF-beta ligand/integrin αvβ6 antibodies in PDAC models. Assess their ability to reprogram TAMs from an M2-like to an M1-like phenotype, reduce fibrosis, and enhance anti-tumor immunity. Combine with T cell-targeted immunotherapies.
  5. SLC2A1 (GLUT1): GLUT1 is a glucose transporter highly upregulated in PDAC Ductal cells. Its increased expression reflects the metabolic reprogramming (Warburg effect) characteristic of rapidly proliferating cancer cells, making glucose uptake a critical vulnerability. Evidence: Section 14 (Ductal Cell Condition-Specific Surfaceome Markers) shows significant upregulation of SLC2A1 in PDAC Ductal cells. Section 19 (GSEA) indicates strong enrichment of 'Central carbon metabolism in cancer' and depletion of 'Oxidative phosphorylation' in PDAC Ductal cells. Validation: Test glucose metabolism inhibitors or GLUT1-specific inhibitors in PDAC cell lines and organoids to assess their impact on proliferation and viability. Combine with other therapies to target metabolic vulnerabilities in PDAC models. Evaluate GLUT1 expression as a potential predictive biomarker.

Follow-up validation ideas:

  1. Functional Validation of Genomic Drivers (CNVs): Use CRISPR-Cas9 to knock down or overexpress genes within frequently amplified regions (e.g., GSDMD, EIF3E on 8q22.1:9p24.1) in PDAC cell lines. Evaluate effects on cell proliferation, survival, invasion, and metastatic potential in both 2D and 3D organoid cultures, followed by *in vivo* xenograft models.
  2. Immune Cell Phenotyping and Function: Employ multicolor flow cytometry or mass cytometry on freshly dissociated PDAC tumor samples (and adjacent normal tissue) to precisely characterize the activation state, exhaustion markers, and cytokine profiles of T cell subsets (e.g., CD8+ T_Cyto, Tregs) and macrophage polarization (M1/M2 markers, e.g., CD68, CD163, CD206). Perform *in vitro* co-culture assays to assess the immunosuppressive capacity of patient-derived Tregs/TAMs on effector T cell proliferation and function.
  3. Perturbation of Key Cell-Cell Interactions: Utilize blocking antibodies or genetic interference (siRNA/shRNA knockdown, CRISPR knockout) against identified ligand-receptor pairs (e.g., SPP1, CD44, CD47, SIRPA, EGFR, TGFBR2) in *in vitro* co-culture models of PDAC cells with patient-derived fibroblasts, stellate cells, and immune cells. Assess effects on cell migration, invasion, proliferation, and immune cell function (e.g., phagocytosis, T cell activation).
  4. Spatial Omics for Microenvironment Context: Apply spatial transcriptomics (e.g., Visium, MERFISH) or multiplex immunofluorescence/imaging mass cytometry (IMC) to human PDAC tissue sections. Spatially resolve the distribution of identified cell types (Ductal, Macrophage, T cells), surfaceome markers (e.g., MSLN, MET, SIRPA, IL10RA), and specific ligand-receptor interactions, correlating spatial proximity with functional states and clinical outcomes like patient survival or treatment response.
  5. Metabolic Reprogramming Functional Assays: Measure glucose uptake, lactate production, oxygen consumption rate (OCR), and extracellular acidification rate (ECAR) using a Seahorse Analyzer in isolated PDAC Ductal cells and tumor-associated stromal/immune cells (e.g., macrophages). Use stable isotope tracer-based metabolomics (e.g., 13C-glucose, 13C-glutamine) to confirm altered metabolic fluxes characteristic of the Warburg effect and other metabolic adaptations.
  6. Validation in Preclinical Models and Patient Cohorts: Test the efficacy of therapeutic agents targeting identified pathways (e.g., anti-CD47 antibodies, MET inhibitors, TGF-β pathway blockers) in patient-derived xenograft (PDX) models or genetically engineered mouse models (GEMMs) of PDAC. Validate marker expression (e.g., MSLN, HER2, GLUT1) and pathway activation (e.g., phospho-AKT, phospho-STAT3) in a larger cohort of human PDAC patient samples using immunohistochemistry (IHC) or RNA-seq, correlating with clinical data.

Limitations:

This report provides a detailed single-cell analysis; however, it represents a static snapshot of the pancreatic tumor microenvironment. The dynamic evolution of PDAC over time, during treatment, or in response to therapy cannot be fully inferred from this dataset. Cell type and state inferences are based on transcriptomic data, and while robust, direct protein expression or functional validation of every finding would strengthen the conclusions. While CNVs are estimated, comprehensive genomic profiling methods might offer higher resolution. Furthermore, cell-cell interaction predictions are computational and require experimental verification of direct physical interactions and their functional consequences. Therapeutic targets suggested herein are based on strong *in silico* and biological evidence but necessitate extensive preclinical and clinical validation before translation. Finally, the observed genomic and phenotypic heterogeneity within 'PDAC' samples, including 'Diploid PDAC' cells exhibiting malignant characteristics, underscores the complex and variable nature of this disease, suggesting that simple classifications may not fully capture the underlying biology.

21. Query List

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