SCODiA Report by MLBI Lab

Comprehensive Single-Cell Profiling of Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Remodeling and Therapeutic Vulnerabilities

This study employed single-cell RNA sequencing to characterize Pancreatic Ductal Adenocarcinoma (PDAC) and adjacent normal pancreas, uncovering profound cellular and molecular alterations within the tumor microenvironment. Aneuploid ductal cells, confirmed as the malignant population, exhibit extensive genomic instability and reprogrammed oncogenic pathways. The PDAC microenvironment is marked by significant desmoplastic reaction, altered macrophage polarization, and a highly immunosuppressive T cell landscape with prominent exhaustion, collectively highlighting critical disease mechanisms and potential therapeutic avenues.

Contents

  1. Dataset overview
  2. UMAP Visualization of Single-Cell Pancreatic Data by Condition, Sample, Cell Type, and Ploidy
  3. UMAP Visualization of Major Cell Type Scores, Ploidy, and Cell Type Annotations in Pancreatic Tissue
  4. Marker Expression Profile Across Pancreatic Cell Subsets
  5. Copy Number Variation Analysis in Pancreatic Ductal Cells
  6. CNV-Based UMAP Analysis of Pancreatic Single-Cell Data
  7. Cell Type Population Analysis in Pancreatic Ductal Adenocarcinoma (PDAC)
  8. Population Analysis of T Cell Subsets in Pancreatic Ductal Adenocarcinoma
  9. Differential Proportions of T Cell Subsets in Pancreatic Ductal Adenocarcinoma (PDAC)
  10. Population Analysis of Macrophage Subsets in Pancreatic Ductal Adenocarcinoma
  11. Ploidy Population Analysis of Ductal Cells in Pancreatic Ductal Adenocarcinoma (PDAC)
  12. Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma (PDAC) Microenvironment
  13. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma
  14. Differential Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma (PDAC) Microenvironment
  15. Ductal Cell Condition-Specific Surfaceome Markers in Pancreatic Cancer
  16. Macrophage Condition-Specific Surfaceome Marker Expression in Pancreatic Ductal Adenocarcinoma (PDAC)
  17. Surfaceome Marker Expression in T cell CD4+ Subsets in Pancreatic Ductal Adenocarcinoma
  18. Differential Expression of Cell Cycle Genes in PDAC Ductal Cells
  19. Gene Ontology (GSA) Analysis for Ductal Cells in Pancreatic Ductal Adenocarcinoma (PDAC)
  20. Gene Set Enrichment Analysis (GSEA) of Pancreatic Ductal Adenocarcinoma Cell Types
  21. Discussion
  22. Query List

0. Dataset overview

Dataset Summary

Precomputed Results Available

Inferred Disease Context

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) plots, providing a visual overview of the single-cell RNA-seq dataset from the Pancreas. The UMAPs are colored by key metadata features: condition (Adj_normal vs. PDAC), sample, celltype_major, celltype_minor, ploidy_dec (diploid, aneuploid, unclear), and celltype_subset. The primary goal of this visualization is to assess the overall data structure, the distribution of different cell populations, and their relationships with disease status and ploidy, which is critical for identifying tumor cells and characterizing the tumor microenvironment.

Visual Summary

Condition and Sample Distribution

Cell Type Annotation and Hierarchical Structure

Ploidy Status

Biological Interpretation

The UMAP visualizations collectively provide a robust overview of the pancreatic single-cell landscape in the context of PDAC.

  1. Identification of Malignant Cells: The most striking observation is the clear co-localization of aneuploid cells with ductal cells within the PDAC condition. Given that Pancreatic Ductal Adenocarcinoma originates from ductal epithelial cells and aneuploidy is a hallmark of cancer, this strong association indicates that these aneuploid ductal cells represent the malignant tumor cell population. This finding is consistent with the inferred disease context and the resolved tumor-origin cell type being Ductal cell.
  2. Tumor Microenvironment (TME) Composition: The PDAC-enriched region of the UMAP is not solely composed of malignant ductal cells but also contains distinct clusters of stromal cells (e.g., Fibroblasts, Stellate cells) and various immune cell types (e.g., Macrophages, T cells, B cells). This highlights the complex cellular heterogeneity of the tumor microenvironment, where non-malignant cells play critical roles in tumor progression and immune responses.
  3. Disease-Associated Cell Type Shifts: The clear separation of conditions on the UMAP suggests significant differences in cell type composition and/or transcriptional states between healthy adjacent tissue and PDAC. The enrichment of specific cell types, beyond just malignant cells, within the PDAC-dominant regions warrants further investigation to understand their contribution to disease pathology.
  4. Annotation Quality: The well-separated and hierarchically consistent clusters for cell types across major, minor, and subset levels confirm the high quality of cell type annotation and the ability of the embedding to capture biological distinctions.

Clinical or Translational Implications

The clear identification of malignant aneuploid ductal cells within the PDAC context provides a critical foundation for downstream analyses aimed at understanding tumor biology. This allows for focused investigation into the molecular characteristics of the tumor cells themselves, as well as the non-malignant cell types comprising the tumor microenvironment. This detailed cellular map can inform:

These UMAPs serve as a strong starting point for deeper investigation into the cellular and molecular mechanisms driving PDAC pathogenesis and progression.

2. UMAP Visualization of Major Cell Type Scores, Ploidy, and Cell Type Annotations in Pancreatic Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the single-cell RNA-sequencing data from pancreatic tissue (including Pancreatic Ductal Adenocarcinoma, PDAC, and adjacent normal samples) on a Uniform Manifold Approximation and Projection (UMAP) embedding. The plots display:

  1. HiCAT_major_score: The score reflecting the confidence or enrichment of major cell type markers for each cell, across various major cell types. This helps to understand the distinctness and localization of cell populations based on their gene expression profiles.
  2. ploidy_dec: The ploidy status (Diploid, Aneuploid, or Unclear) for each cell, indicating genomic stability or instability.
  3. celltype_major: The final assigned major cell type annotation for each cell, serving as a reference for assessing the HiCAT scores.

The primary goal is to assess cell identity, embedding structure, and the distribution of ploidy in relation to cell types within the dataset, particularly in the context of PDAC.

Visual Summary

The UMAP visualizations reveal a complex cellular landscape, characteristic of pancreatic tissue and its associated tumor microenvironment.

HiCAT Major Cell Type Scores Validation:

Biological Interpretation

The observed patterns provide key insights into the cellular composition and disease biology within the pancreas, particularly in the context of PDAC.

Annotation Notes

3. Marker Expression Profile Across Pancreatic Cell Subsets

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a marker expression dot plot, visualizing the expression patterns of various genes across different celltype_subset populations derived from single-cell RNA sequencing data of the Pancreas in the context of PDAC and adjacent normal tissue. The primary goal is to validate and understand the distinct molecular signatures that define each annotated cell subset, ensuring the accuracy of cell type assignments based on known marker genes. The dot plot displays the mean expression level of each gene (color intensity) and the fraction of cells expressing the gene (dot size) within each cell type.

Visual Summary

The dot plot effectively illustrates distinct and often specific expression patterns of marker genes for the diverse cell subsets.

Biological Interpretation

The marker expression patterns observed largely confirm the biological identity of the annotated celltype_subset populations, aligning with established knowledge of pancreatic cellular architecture and immune cell biology.

Annotation Notes

The comprehensive marker expression dot plot provides strong evidence supporting the robust and accurate annotation of celltype_subset populations. The distinct and expected expression profiles for key marker genes across the various pancreatic and immune cell types indicate that the cell clustering and labeling are highly reliable. The clear separation of even closely related cell subsets, such as different B cell or T cell types, demonstrates the granularity and quality of the annotation. The identification of specific stromal components like Stellate cells, which are pathologically relevant in PDAC, further enhances the biological utility of this dataset. The presence of common markers is also expected, representing genes that are broadly expressed within certain lineages or fundamental cellular processes, and does not detract from the specificity of unique markers for each subset.

4. Copy Number Variation Analysis in Pancreatic Ductal Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates copy number variations (CNVs) within Ductal cells, identified as the tumor-origin cell type in Pancreatic Ductal Adenocarcinoma (PDAC). The analysis compares CNV profiles across different samples, including adjacent normal pancreatic tissue (Adj_normal) and various PDAC patient samples, some of which are initially classified as 'Diploid PDAC'. The results are presented as a heatmap showing log2(CNR) values across genomic spots and a summary heatmap highlighting significantly amplified cytogenetic bands, along with their frequencies across PDAC samples. This provides insight into the chromosomal instability and recurrent genomic alterations characteristic of PDAC in its presumed cells of origin.

Visual Summary

CNV Heatmap of Ductal Cells

The heatmap displays log2(CNR) values for Ductal cells, grouped by sample. Red indicates genomic amplification (log2(CNR) > 0), and blue indicates genomic deletion (log2(CNR) < 0).

Summary of Significant Amplified Regions

The summary plots highlight significantly amplified cytogenetic bands across the PDAC samples.

Biological Interpretation

The observed CNV patterns in Ductal cells provide critical insights into the genomic instability driving PDAC. The clear distinction between the largely stable adjacent normal samples and the highly aberrant PDAC samples underscores the role of CNVs in pancreatic cancer pathogenesis.

Clinical or Translational Implications

The identification of recurrent and widespread CNVs, particularly amplifications of known oncogenes like *SOX2*, *EGFR*, and *ERBB2*, in the tumor-origin Ductal cells of PDAC has several important clinical implications:

This analysis validates the expected genomic instability in PDAC Ductal cells and identifies specific recurrent CNV events with potential clinical relevance, reinforcing the value of single-cell genomic profiling in unraveling cancer biology.

5. CNV-Based UMAP Analysis of Pancreatic Single-Cell Data

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a Uniform Manifold Approximation and Projection (UMAP) visualization of single-cell RNA-sequencing data from the pancreas, with the embedding specifically computed based on inferred Copy Number Variation (CNV) patterns. The cells are colored by celltype_major, celltype_minor, ploidy_dec (derived ploidy decision), condition (Adj_normal vs. PDAC), and sample. The primary goal is to visualize the cellular landscape based on genomic integrity, identify tumor cells, and assess the distribution of different cell types and samples within this CNV-informed space.

Visual Summary

The UMAP plots reveal a distinct separation of cells driven by their CNV profiles.

Cell Types (celltype_major, celltype_minor):

Sample:

Biological Interpretation

The CNV-driven UMAP provides compelling biological insights into the cellular composition of pancreatic tissues in the context of PDAC.

  1. Identification of Tumor Cells: The clear segregation of aneuploid cells, almost exclusively originating from the PDAC condition and largely identified as Ductal cells, strongly supports their classification as neoplastic pancreatic cancer cells. This effectively delineates the malignant tumor cell compartment from the non-malignant cells within the tissue.
  2. Tumor Heterogeneity: The observation of multiple, somewhat separated aneuploid clusters within the PDAC condition suggests significant genomic heterogeneity among tumor cells. These distinct clusters might represent different subclones or populations of tumor cells with varying CNV profiles, reflecting the complex evolutionary landscape of PDAC.
  3. Normal and Tumor Microenvironment Cells: The large diploid compartment contains a diverse array of non-malignant cell types, including immune cells (T cells, Myeloid cells), stromal cells (Fibroblasts, Smooth muscle cells), and normal pancreatic epithelial cells (Acinar, Alpha, and normal Ductal cells). Their presence in both Adj_normal and PDAC conditions, coupled with their diploid status, indicates they are likely responsive, but not transformed, components of the tumor microenvironment or healthy tissue.
  4. Robust CNV-Based Cell Identity: The UMAP successfully leverages CNV patterns to reinforce cell identity and disease status. The explicit use of cnv=True in the embedding configuration demonstrates that genomic alterations are a primary driver of the observed cellular organization, accurately distinguishing tumor cells from normal cells and effectively separating tumor-associated cells from those derived from healthy adjacent tissue.

Annotation Notes

This UMAP analysis provides strong evidence for the quality and biological interpretability of the cell type and ploidy annotations within the dataset. The clear separation of aneuploid cells (predominantly Ductal cells from PDAC samples) from diploid cells (a mix of non-tumor cell types from both conditions) confirms that:

6. Cell Type Population Analysis in Pancreatic Ductal Adenocarcinoma (PDAC)

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents the proportion of minor cell types across individual samples, categorized by their condition: "Adj_normal" (adjacent normal pancreas tissue) and "PDAC" (Pancreatic Ductal Adenocarcinoma). The stacked bar plot visualizes the cellular composition of each sample, highlighting differences in the tumor microenvironment (TME) compared to normal tissue and revealing heterogeneity within PDAC samples. This provides a foundational understanding of the cellular landscape underlying PDAC.

Visual Summary

The visualization displays stacked bar plots representing the percentage contribution of various minor cell types within each sample. Samples are grouped by condition: 'Adj_normal' (3 samples) and 'PDAC' (16 samples).

Biological Interpretation

The observed cellular population shifts align strongly with the known pathological features of Pancreatic Ductal Adenocarcinoma.

  1. Tumor Cell Dominance: The dramatic increase in Ductal cell populations in PDAC samples directly reflects the malignant expansion of ductal epithelial cells, which are the cell of origin for this cancer. This confirms the presence and varying cellularity of the tumor across samples.
  2. Desmoplastic Reaction: The significant presence of Fibroblasts in PDAC samples is a hallmark of the severe desmoplastic reaction characteristic of pancreatic cancer. These fibroblasts are often cancer-associated fibroblasts (CAFs) that contribute substantially to the dense, immunosuppressive tumor microenvironment, promoting tumor growth, invasion, and resistance to therapy PubMed Search: pancreatic cancer associated fibroblasts desmoplasia.
  3. Loss of Normal Tissue: The reduction or absence of Acinar cells in PDAC samples signifies the replacement of normal exocrine pancreatic tissue by the advancing tumor and its associated stroma.
  4. Immune Landscape: The presence of various immune cells, particularly Macrophages and T cells, indicates immune infiltration within the PDAC TME. The relative abundance of Macrophages is particularly relevant, as tumor-associated macrophages (TAMs) are known to foster an immunosuppressive environment and contribute to disease progression in PDAC GeneCards: CD68 (Macrophage marker). The proportions of T cells can vary and may represent functionally exhausted or suppressed populations in this highly immunosuppressive context.
  5. Inter-tumor Heterogeneity: The notable variability in cell type proportions among different PDAC samples underscores the inherent biological heterogeneity of this disease across patients. This heterogeneity can influence disease progression, treatment response, and patient outcomes.

Clinical or Translational Implications

The detailed characterization of cell type populations in PDAC has several clinical and translational implications:

  1. Diagnostic and Prognostic Markers: Changes in the proportions of specific cell types (e.g., high Ductal cell fraction, significant CAF infiltration, specific immune cell profiles) could serve as potential diagnostic biomarkers or indicators of disease aggressiveness.
  2. Therapeutic Targeting: The observed cellular composition highlights key components of the PDAC tumor microenvironment that could be targeted therapeutically. For instance, strategies aimed at depleting or reprogramming CAFs, modulating TAM activity, or enhancing T cell infiltration and function are actively being explored in PDAC PubMed Search: PDAC tumor microenvironment targeted therapy.
  3. Patient Stratification: The inter-patient heterogeneity in cell type proportions suggests that patients could be stratified into distinct subgroups based on their TME composition. This stratification could inform personalized treatment approaches, predicting response to specific therapies, such as immunotherapies or stromal-targeting agents.

7. Population Analysis of T Cell Subsets in Pancreatic Ductal Adenocarcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative abundance of various T cell subsets and Innate Lymphoid Cells (ILCs) across individual samples from both 'Adj_normal' (adjacent normal pancreas) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. The celltype_major 'T cell' was selected, and its breakdown into celltype_subset populations was visualized. The plot normalizes the cell counts for each sample to 100%, allowing for direct comparison of proportional changes in immune cell composition within the T cell compartment.

Visual Summary

The stacked bar plot provides a clear overview of the T cell and ILC subset distribution:

Biological Interpretation

The observed shifts in T cell and ILC populations in PDAC samples provide critical insights into the immune landscape of this aggressive cancer:

Clinical or Translational Implications

The findings underscore key challenges and potential opportunities for immunotherapy in PDAC:

8. Differential Proportions of T Cell Subsets in Pancreatic Ductal Adenocarcinoma (PDAC)

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differences in the proportions of various T cell subsets between Pancreatic Ductal Adenocarcinoma (PDAC) tissue and adjacent normal (Adj_normal) pancreatic tissue. Box plots are used to visualize the distribution of celltype proportions for each T cell subset across the two conditions, with statistical significance determined by p-values (cutoff at 0.1). This helps to identify T cell populations that are significantly expanded or contracted in the PDAC tumor microenvironment.

Visual Summary

The box plots illustrate the proportions of eight distinct T cell subsets, comparing PDAC samples (blue boxes) to adjacent normal samples (orange boxes). Statistically significant differences (p < 0.1) were observed for all eight subsets:

Increased Proportions in PDAC (compared to Adj_normal):

Decreased Proportions in PDAC (compared to Adj_normal):

Biological Interpretation

The observed shifts in T cell subset proportions paint a complex picture of the immune microenvironment in PDAC, which is known for its highly immunosuppressive nature.

  1. Impaired Anti-tumor Immunity: The most striking finding is the significant reduction in Cytotoxic T cells (T_Cyto) and T cell (CD8 Memory) populations in PDAC. Cytotoxic T cells, primarily CD8+, are critical effectors of adaptive anti-tumor immunity, responsible for directly killing cancer cells. Their diminished presence suggests a compromised ability of the immune system to eradicate tumor cells in PDAC. The reduction in CD8 Memory T cells further indicates a potential deficit in sustained, long-term immune surveillance and response against the tumor PubMed search: CD8 T cells in cancer immunotherapy.
  2. Complex Role of Helper T Cells: While effector cytotoxic T cells are decreased, several CD4+ helper T cell subsets are increased in PDAC:

In summary, PDAC appears to be characterized by a shift from a potentially protective cytotoxic T cell response towards a more heterogeneous CD4+ T cell landscape, where even traditionally "anti-tumor" Th1 cells might be functionally impaired, and other subsets (like Th22) could contribute to a pro-tumorigenic inflammatory state. This reinforces the concept of PDAC as an immune-excluded or immunosuppressed tumor.

Clinical or Translational Implications

The distinct T cell subset profiles observed in PDAC versus adjacent normal tissue carry significant clinical and translational implications:

Therapeutic Targeting:

9. Population Analysis of Macrophage Subsets in Pancreatic Ductal Adenocarcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents the proportional distribution of distinct macrophage subsets (Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), Macrophage (M2D)) across individual samples derived from adjacent normal pancreatic tissue (Adj_normal) and Pancreatic Ductal Adenocarcinoma (PDAC) conditions. The primary objective is to identify potential shifts in macrophage polarization within the PDAC tumor microenvironment compared to the non-tumorigenic state.

Visual Summary

The stacked bar plot effectively illustrates the relative abundance of various macrophage subsets within each sample:

Biological Interpretation

Macrophages are highly plastic immune cells that play a crucial role in the tumor microenvironment (TME) of various cancers, including Pancreatic Ductal Adenocarcinoma (PDAC). They can polarize into different functional phenotypes, broadly categorized as M1 (pro-inflammatory, anti-tumorigenic) and M2 (anti-inflammatory, pro-tumorigenic).

The observed shift towards a higher proportional representation of Macrophage (M1) within the macrophage population of PDAC samples is a significant finding. This pattern suggests that in this dataset's PDAC samples, a robust pro-inflammatory macrophage response is present. This could imply:

  1. Active Anti-tumor Immunity: The increased M1 population may reflect an active, albeit potentially insufficient, immune response mounted by the host against the tumor. M1 macrophages are known to produce pro-inflammatory cytokines and contribute to direct tumor cell killing or antigen presentation.
  2. Heterogeneity of PDAC TME: The general consensus often describes the PDAC TME as highly immunosuppressive and rich in M2-like Tumor-Associated Macrophages (TAMs) that promote tumor growth. The finding of increased M1 proportions here highlights the substantial heterogeneity within the PDAC TME and suggests that specific patient samples or disease stages may exhibit distinct immune landscapes. It's important to note that this plot shows *relative proportions* within the macrophage compartment, and not necessarily the *absolute numbers* of different macrophage types.
  3. Dynamic Polarization: Macrophage polarization is not static but dynamically influenced by local TME cues. The M1 enrichment could point to specific cytokine environments or cellular interactions within these PDAC tumors that drive macrophages towards a pro-inflammatory state.
  4. Specific Macrophage Subtype Definitions: The specific markers used to define the celltype_subset annotations (M1, M2A, M2B, M2C, M2D) in this dataset may capture distinct functional states, providing a nuanced view of macrophage biology in PDAC.

Clinical or Translational Implications

The findings regarding macrophage subset distribution in PDAC carry several potential clinical and translational implications:

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (Diploid, Aneuploid, or Unclear) of Ductal cells across different samples from 'Adj_normal' (adjacent normal pancreas) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. The objective is to understand how the genomic integrity of the presumed tumor-originating cell type changes in the disease state compared to normal tissue.

Visual Summary

The stacked bar plot clearly illustrates the distribution of ploidy populations within Ductal cells for each sample, separated by condition.

Biological Interpretation

The observed ploidy patterns strongly support the biological hallmarks of cancer, specifically in the context of PDAC originating from Ductal cells.

  1. Aneuploidy as a Hallmark of Cancer: The dramatic increase in aneuploid Ductal cells in PDAC samples compared to adjacent normal tissue is a well-established characteristic of malignancy. Aneuploidy, defined as an abnormal number of chromosomes, is a pervasive feature of cancer cells and is often associated with genomic instability, tumor progression, and resistance to therapy PubMed Search: aneuploidy cancer hallmarks.
  2. Ductal Cell Malignancy: Given that Ductal cell is the Resolved tumor-origin cell type for PDAC, the finding of extensive aneuploidy specifically within this cell population provides strong evidence of their malignant transformation. This reinforces the understanding that Ductal cells are the primary drivers of PDAC pathogenesis.
  3. Tumor Heterogeneity: The considerable variability in the proportion of aneuploid Ductal cells across different PDAC samples reflects the inherent genetic and genomic heterogeneity within human tumors. This heterogeneity can arise from different stages of tumor evolution, varying degrees of clonal selection, or distinct patient-specific genomic landscapes. The samples with lower aneuploidy might represent earlier stages or less aggressive forms of the disease, while those with very high aneuploidy could indicate more advanced or aggressive tumors.
  4. Implications for Tumor Microenvironment: While this analysis focuses on Ductal cells, their ploidy status likely influences their interactions with the tumor microenvironment. Aneuploid cancer cells can exhibit altered gene expression, metabolic profiles, and signaling pathways, which in turn can modify the surrounding stromal and immune cell populations.

Clinical or Translational Implications

The findings regarding Ductal cell ploidy have several potential clinical and translational implications for PDAC:

  1. Diagnostic and Prognostic Biomarker: The presence and proportion of aneuploid Ductal cells could serve as a valuable diagnostic marker for PDAC, distinguishing malignant lesions from benign conditions. Furthermore, the degree of aneuploidy might be a prognostic indicator, with higher aneuploidy potentially correlating with more aggressive disease behavior, higher recurrence rates, or poorer patient outcomes PubMed Search: aneuploidy PDAC prognosis.
  2. Therapeutic Stratification: Understanding the ploidy status of tumor cells could inform personalized treatment strategies. Tumors with high levels of aneuploidy may exhibit different sensitivities or resistances to conventional chemotherapy or targeted therapies compared to those that are predominantly diploid. For instance, highly aneuploid tumors might be more susceptible to therapies that exploit vulnerabilities arising from chromosomal instability (e.g., inhibitors of spindle assembly checkpoint components or DNA damage response pathways).
  3. Monitoring Disease Progression: Tracking changes in Ductal cell ploidy over time, especially in liquid biopsy samples or serial tumor biopsies, could offer insights into tumor evolution, response to treatment, and potential for recurrence.

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB, focusing on Ductal cells (specifically Aneuploid Ductal cells in the PDAC context), Fibroblasts, Macrophages, and T cells (CD4+ and CD8+) in both adjacent normal (Adj_normal) and Pancreatic Ductal Adenocarcinoma (PDAC) conditions. The goal is to identify significant ligand-receptor interactions that distinguish the tumor microenvironment from normal pancreatic tissue, with an emphasis on identifying potential therapeutic targets and understanding disease mechanisms. The analysis captured the top 80 significant interactions based on p-value and mean expression for each condition. It is important to note that while Fibroblasts were included in the target cell types, no significant interactions involving them were among the top 80 pairs displayed in these specific plots. Similarly, Ductal cell interactions were not observed in the Adj_normal condition within the top 80 interactions.

Visual Summary

The dot plots illustrate significant cell-cell interactions, where dot size corresponds to the negative log10 of the p-value (significance) and dot color represents the log2 of the mean expression of the ligand-receptor pair (strength).

Adj_normal Condition

PDAC Condition

Biological Interpretation

The observed CCI patterns reveal a profound remodeling of the pancreatic microenvironment in PDAC, driven by the emergence of aneuploid tumor cells.

  1. Tumor Cell-Driven Remodeling: The absence of significant Ductal cell interactions in Adj_normal compared to the robust interaction network formed by Aneuploid Ductal cells in PDAC underscores the central role of tumor cells in shaping the tumor microenvironment (TME). This indicates that malignant transformation induces unique intercellular communication pathways essential for tumor progression.
  2. Adhesion and Invasion: The prominent homotypic (Aneuploid Ductal|Aneuploid Ductal) and heterotypic (Aneuploid Ductal|Macrophage) interactions involving Integrins (e.g., ICAM1-integrin complexes, CDH1-integrin, COL17A1-integrin) and EPH receptors (EPHA/B family) suggest enhanced cell-cell adhesion, motility, and potentially invasion within the tumor. Integrins are crucial for cell-extracellular matrix and cell-cell interactions, often hijacked by cancer cells for migration and metastasis. GeneCards: ITGAL, GeneCards: EPHA2
  3. Immune Evasion and Macrophage Reprogramming:
  1. Altered T Cell Surveillance: Interactions between Aneuploid Ductal cells and T cells (CD4+, CD8+) reveal altered immune recognition. The presence of HLA-E_CD94:NKG2C interactions suggests a potential mechanism for tumor cells to suppress Natural Killer (NK) cells and certain T cell subsets, as NKG2C can be an inhibitory receptor. Other HLA class I interactions indicate ongoing antigen presentation or modulation. The TNFRSF10B and TNFRSF10C interactions (TRAIL receptors) may point to pathways regulating tumor cell apoptosis sensitivity or resistance.

Clinical or Translational Implications

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

  1. Therapeutic Targeting of Immune Evasion Pathways:
  1. Targeting Tumor Cell Adhesion and Invasion:
  1. Biomarkers for Disease Progression and Treatment Response:
  1. Combination Therapies: Given the complex interplay of these interactions, combination therapies might be particularly effective. For example, combining immune checkpoint inhibitors (e.g., anti-CD47) with agents that modulate the immunosuppressive TME (e.g., PGE2 pathway inhibitors or SPP1 inhibitors) could lead to synergistic anti-tumor effects in PDAC, a notoriously difficult-to-treat cancer.

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates significant cell-cell interactions (CCIs) between Ductal cells, major immune cells (T cell CD8+, Macrophage, T cell CD4+, Mast cell, B cell, NK cell, Plasma cell, Dendritic cell, ILC), and stromal cells (Smooth muscle cell, Endothelial cell, Fibroblast) in human pancreatic tissue. The CCIs were compared between Adj_normal (adjacent normal pancreas) and PDAC (Pancreatic Ductal Adenocarcinoma) conditions to identify condition-specific communication patterns. The dot plot visualizes the strength (scaled log strength, color intensity) and significance (-log10(p), dot size) of these interactions across individual samples for each condition.

Visual Summary

The dot plot reveals distinct patterns of cell-cell communication between Adj_normal and PDAC conditions:

Biological Interpretation

The observed shifts in cell-cell interaction patterns between Adj_normal and PDAC reveal critical biological processes characteristic of the Pancreatic Ductal Adenocarcinoma tumor microenvironment (TME).

  1. Remodeling of the Immune Microenvironment: The most striking feature is the dramatic increase in strong and significant interactions involving macrophages, CD8+ T cells, CD4+ T cells, and NK cells within PDAC samples. This indicates a highly active and dysregulated immune cell crosstalk in the tumor.
  1. Shift from Baseline Interactions: Interactions involving Endothelial cells and NK cells, which are present in Adj_normal, appear to be either altered or superseded by other dominant interactions in PDAC. The baseline homeostatic interactions are replaced by more pro-tumorigenic ones.
  2. Role of Ductal Cells (Tumor Origin): While the analysis was designed to include Ductal cells, the most prominent differential interactions are between immune and stromal cell types. This suggests that the tumor-originating Ductal cells might primarily drive the recruitment and education of these immune and stromal cells, which then extensively interact with each other to establish the immunosuppressive and pro-tumorigenic TME. The initial query was to identify interactions *involving* Ductal cells, but the data highlight that many key changes are in the immune and stromal compartments that constitute the TME.

Clinical or Translational Implications

The identified cell-cell interactions represent potential therapeutic targets for overcoming immune evasion and stromal barriers in PDAC.

  1. Targeting the SIRPA-CD47 Axis:
  1. Modulating ICAM1-Integrin Interactions:
  1. Targeting SEMA4D Signaling:
  1. Rebalancing HLA-E-NKG2A (KLRK1) Immune Checkpoint:

These findings highlight a complex interplay of immune and stromal cells within the PDAC microenvironment that could be exploited for developing novel therapeutic strategies, potentially in combination with existing treatments like chemotherapy or checkpoint inhibitors.

13. Differential Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma (PDAC) Microenvironment

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interaction (CCI) patterns involving Ductal cells (the inferred tumor-origin cell type), major immune cell types (T cell CD8+, Macrophage, T cell CD4+, Mast cell, B cell, ILC, NK cell, Dendritic cell, Plasma cell), and stromal cells (Smooth muscle cell, Fibroblast, Endothelial cell). The focus is on interactions mediated by genes involved in immune checkpoint and cell cycle pathways, comparing Pancreatic Ductal Adenocarcinoma (PDAC) samples with adjacent normal (Adj_normal) pancreatic tissue. The plot_dot_for_cci_with_signif_difference tool was used to visualize these interactions, identifying statistically significant differences between conditions based on a t-test and filtering for specified target genes.

Visual Summary

The dot plot visualizes the strength and significance of cell-cell interactions across individual samples, grouped by condition (Adj_normal and PDAC).

Dominant Interaction Themes in PDAC:

Biological Interpretation

The observed cell-cell interaction landscape reveals profound changes in the PDAC tumor microenvironment compared to adjacent normal tissue, driven by a complex interplay between malignant Ductal cells, immune cells, and stromal components. These interactions facilitate critical hallmarks of cancer, including sustained proliferative signaling, immune evasion, tumor-promoting inflammation, and angiogenesis.

Here, we detail key signaling pathways and mechanisms highlighted by the analysis:

  1. Transforming Growth Factor-beta (TGF-beta) Signaling

References:

  1. PubMed Search: TGF-beta fibrosis pancreatic cancer
  2. PubMed Search: TGF-beta immune suppression pancreatic cancer
  3. Epidermal Growth Factor Receptor (EGFR) Signaling

References:

  1. GeneCards: EGFR
  2. PubMed Search: EGFR signaling pancreatic cancer
  3. CD47-SIRPA Immune Checkpoint Signaling

References:

  1. GeneCards: CD47
  2. PubMed Search: CD47 SIRPA immune evasion cancer
  3. Integrin-Mediated Cell Adhesion and Remodeling

References:

  1. GeneCards: ITGAV
  2. PubMed Search: Integrin alphaVbeta6 pancreatic cancer
  3. VEGF-VEGFR Angiogenesis Signaling

References:

  1. GeneCards: VEGFA
  2. PubMed Search: VEGF angiogenesis pancreatic cancer

Clinical or Translational Implications

The profound dysregulation of cell-cell communication in PDAC highlights several pathways and specific interactions as promising therapeutic targets, aiming to disrupt the pro-tumorigenic microenvironment and enhance anti-tumor responses.

  1. Targeting TGF-beta Signaling:

Relevant Agents:

References:

  1. PubMed Search: Galunisertib pancreatic cancer
  2. Targeting EGFR Signaling:

Relevant Agents:

References:

  1. PubMed Search: Erlotinib pancreatic cancer
  2. Targeting CD47-SIRPA Immune Evasion:

Relevant Agents:

References:

  1. PubMed Search: Magrolimab pancreatic cancer
  2. Targeting Integrin αVβ6:

Relevant Agents:

References:

  1. PubMed Search: Anti-integrin alphaVbeta6 antibody pancreatic cancer
  2. Targeting VEGF-VEGFR Angiogenesis:

Relevant Agents:

References:

  1. PubMed Search: Bevacizumab pancreatic cancer

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

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies surfaceome marker genes differentially expressed in Ductal cells across 'Adj_normal', 'Diploid PDAC', and 'PDAC' conditions, leveraging single-cell RNA-seq data from human pancreas tissue. The primary goal is to discover cell-type-specific markers, particularly those enriched in PDAC Ductal cells, which may serve as established or potential therapeutic targets for Pancreatic Ductal Adenocarcinoma (PDAC). The dot plot visualizes the mean expression level and the fraction of cells expressing each marker gene within different samples.

Visual Summary

The dot plot displays the expression profiles of approximately 70 surfaceome markers in Ductal cells across various individual samples, categorized by their condition: 'Adj_normal', 'Diploid PDAC', and 'PDAC'.

Biological Interpretation

The observed surfaceome marker expression patterns reveal critical biological changes in Ductal cells during PDAC progression.

Markers Enriched in Adjacent Normal Ductal Cells:

Markers Highly Expressed in PDAC Ductal Cells:

These markers represent significant alterations in cancer cell biology, influencing proliferation, survival, invasion, and immune evasion.

Established Therapeutic Targets:

Potential Therapeutic Targets:

Clinical or Translational Implications

The identification of these Ductal cell surfaceome markers has significant clinical and translational implications for PDAC:

  1. Diagnostic and Prognostic Biomarkers: Markers highly specific to PDAC Ductal cells, such as MSLN, CEACAM6, ERBB3, and MET, could be developed into novel diagnostic tools (e.g., liquid biopsies, imaging agents) or prognostic indicators, helping to identify PDAC earlier or predict disease course.
  2. Therapeutic Target Validation: The strong upregulation of established therapeutic targets like MSLN, ERBB3, MET, CD47, and ITGB1 in PDAC Ductal cells provides direct evidence for their relevance in this specific cancer type. This supports the rationale for existing clinical trials and the development of new targeted therapies against these molecules in PDAC.
  3. Novel Drug Development: Less characterized but highly expressed markers such as CD151, PLAUR, SDC1, LY6E, and ADAM9 represent promising avenues for developing novel therapeutic agents. These could include monoclonal antibodies, antibody-drug conjugates, or small molecule inhibitors specifically designed to target PDAC cells while sparing normal tissues.
  4. Stratification for Personalized Medicine: The cell-type-specific and condition-specific expression patterns observed at single-cell resolution highlight the potential for patient stratification. Patients whose tumors highly express certain markers could be candidates for specific targeted therapies, moving towards personalized treatment approaches.
  5. Understanding Tumor Biology: The roles of these surfaceome proteins in cell adhesion, growth factor signaling, immune evasion, and ECM remodeling provide deeper insights into the fundamental biological processes driving PDAC progression, metastasis, and therapeutic resistance. This understanding can guide the development of combination therapies or strategies to overcome resistance.

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

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for Macrophages within pancreatic tissue, comparing Pancreatic Ductal Adenocarcinoma (PDAC) samples against adjacent normal (Adj_normal) samples. The dot plot visualizes the mean expression level and the fraction of cells expressing each marker across various individual samples grouped by condition. The analysis focused on surfaceome markers to identify potential targets accessible for therapeutic intervention or as biomarkers. Markers were selected based on a one-vs-rest comparison, applying thresholds for non-zero percentage score (≥ 0.25) and fold change (FC > 1.75 for Adj_normal_vs_others; FC > 1.69 for PDAC_vs_others) to ensure robust differential expression.

Visual Summary

The dot plot, titled "Macrophage condition-specific markers," effectively illustrates distinct expression profiles between macrophages in Adj_normal and PDAC conditions.

Biological Interpretation

The observed upregulation of numerous surfaceome markers in PDAC macrophages points to their active and distinct role within the tumor microenvironment (TME) compared to macrophages in adjacent normal tissue. This strongly suggests a population of tumor-associated macrophages (TAMs) with pro-tumorigenic functions.

Here is a detailed interpretation of several highly upregulated and biologically significant markers in PDAC macrophages:

ITGAX (CD11c):

CXCR4:

BSG (CD147):

ITGAM (CD11b):

TGFBR2:

TLR4:

Clinical or Translational Implications

The identification of these highly expressed surfaceome markers on PDAC macrophages has significant clinical and translational implications:

  1. Biomarker Discovery: The distinctive expression profiles of these markers (e.g., CXCR4, BSG, TGFBR2, TLR4) could serve as diagnostic or prognostic biomarkers for PDAC, reflecting the extent of TAM infiltration and their pro-tumorigenic activation state. They might also predict response to specific therapies.
  2. Therapeutic Targeting: Many of these markers represent druggable targets. Therapies aimed at inhibiting these receptors (e.g., CXCR4 antagonists, TGFBR2 inhibitors, anti-CD147 antibodies) could directly modulate the function or recruitment of pro-tumorigenic macrophages, thereby reducing immune suppression, fibrosis, and metastasis in PDAC.
  3. Combination Therapies: Given the complex and immunosuppressive nature of the PDAC TME, combining TAM-targeting therapies with conventional chemotherapy, radiation, or other immunotherapies (e.g., checkpoint inhibitors) could yield synergistic anti-tumor effects. Modulating TAMs might overcome resistance mechanisms by altering the TME's immunosuppressive landscape.
  4. Reprogramming TAMs: Beyond simple depletion, understanding the signaling pathways activated by these surface markers can inform strategies to "reprogram" TAMs from an M2-like, pro-tumorigenic phenotype to an M1-like, anti-tumorigenic phenotype, effectively turning tumor allies into tumor adversaries.
  5. Patient Stratification: Future studies could investigate if specific patterns of these macrophage markers correlate with patient subgroups, allowing for more precise patient stratification and personalized treatment approaches.

16. Surfaceome Marker Expression in T cell CD4+ Subsets in Pancreatic Ductal Adenocarcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the surfaceome marker expression profiles of CD4+ T cells derived from Pancreatic Ductal Adenocarcinoma (PDAC) and adjacent normal pancreatic tissue (Adj_normal) samples. Using a one-vs-rest comparison, surface markers were identified based on their non-zero percentage score (≥ 0.25) and fold change (FC > 1.75). The dot plot visualizes the mean expression levels and the fraction of cells expressing these markers across individual samples. The primary goal is to identify condition-specific markers, understand their biological implications for CD4+ T cells in the PDAC tumor microenvironment, and assess their potential as therapeutic targets.

Visual Summary

The dot plot displays the expression of 40 selected surfaceome markers in CD4+ T cells across 2 adjacent normal samples and 15 PDAC samples.

Biological Interpretation

The observed upregulation of numerous surfaceome markers on CD4+ T cells in PDAC compared to adjacent normal tissue indicates a significant functional alteration and activation state of these cells within the tumor microenvironment (TME). This altered phenotype likely reflects their engagement with tumor cells, stromal cells, and other immune cells, influencing disease progression and immune evasion.

Below are interpretations for key upregulated markers observed in PDAC CD4+ T cells:

CXCR4 (C-X-C Motif Chemokine Receptor 4)

CD44 (CD44 Molecule)

CD47 (CD47 Molecule)

IL10RA (Interleukin 10 Receptor Subunit Alpha)

CD96 (CD96 Molecule)

CCR7 (C-C Motif Chemokine Receptor 7)

Clinical or Translational Implications

The distinct surfaceome profiles of CD4+ T cells in PDAC versus adjacent normal tissue offer valuable insights for therapeutic development and biomarker discovery.

Therapeutic Targets:

17. Differential Expression of Cell Cycle Genes in PDAC Ductal Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the "expressing cell fraction" of a predefined set of cell cycle pathway genes in Ductal cells, comparing Pancreatic Ductal Adenocarcinoma (PDAC) samples to adjacent normal (Adj_normal) pancreatic tissue. The goal was to identify genes with statistically significant differences in the proportion of expressing cells between these two conditions, focusing on genes relevant to cell cycle regulation. Box plots illustrate these differences for 24 selected genes, with p-values indicating statistical significance.

Visual Summary

The visualization presents 24 box plots, each representing a distinct cell cycle-related gene. For nearly all plotted genes, Ductal cells from PDAC samples exhibit a significantly higher "expressing cell fraction" compared to Ductal cells from Adj_normal samples. The p-values associated with each comparison consistently fall below the 0.05 significance threshold, indicating robust statistical differences. The box plots for PDAC samples generally show a higher median and wider interquartile range, reflecting both a greater proportion of expressing cells and often more variability across PDAC samples. For instance, genes like GSK3B, TGFB1, ANAPC1, SFN, MCM7, CDC16, MYC, ORC2, and WEE1 demonstrate particularly pronounced increases in expressing cell fraction in PDAC. Even genes typically associated with cell cycle inhibition or tumor suppression, such as RB1, TP53, CDKN2D, and CDKN1B, show an increased expressing cell fraction in PDAC, which warrants further biological interpretation.

Biological Interpretation

The observed widespread upregulation in the *expressing cell fraction* of cell cycle pathway genes in Ductal cells from PDAC samples strongly indicates a significant shift towards an increased proliferative state and dysregulated cell cycle control in the tumor microenvironment. This is a hallmark of cancer, where cells escape normal growth restraints and continuously divide.

  1. Increased Proliferative Capacity: Many genes upregulated in PDAC are key drivers of cell cycle progression. For example:
  1. Altered Tumor Suppressor and Oncogene Signaling:
  1. DNA Damage Response and Genomic Instability: Genes like ATR (Ataxia-Telangiectasia and Rad3-related protein) and RAD21 (a cohesin complex component) are involved in DNA damage response and maintaining genomic integrity. Their upregulation may suggest increased genomic instability and activation of DNA repair pathways, which are common features in cancer cells as they undergo rapid division and accumulate mutations.

Overall, the data robustly shows that a significantly higher fraction of Ductal cells in PDAC are actively engaged in processes related to cell cycle progression and DNA replication, consistent with the highly proliferative nature of pancreatic cancer. The pattern of expression for both oncogenes and tumor suppressors highlights the complex regulatory landscape within the tumor, where mechanisms designed to control growth are often overridden or co-opted.

Clinical or Translational Implications

The widespread dysregulation of cell cycle gene expression in PDAC Ductal cells points to several potential clinical and translational implications:

References:

  1. TGFB1 in Pancreatic Cancer: [PubMed Search: TGFB1 pancreatic cancer]
  2. GSK3B in Pancreatic Cancer: [PubMed Search: GSK3B pancreatic cancer proliferation]
  3. WEE1 Inhibitors: [GeneCards: WEE1]
  4. ATR Inhibitors: [GeneCards: ATR]

18. Gene Ontology (GSA) Analysis for Ductal Cells in Pancreatic Ductal Adenocarcinoma (PDAC)

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the functional pathways enriched by up-regulated genes (Gene Set Analysis - GSA, using Gene Ontology terms) in Ductal cells, comparing different conditions and ploidy states. The comparisons performed are:

  1. Adj_normal_vs_others: Identifying pathways up-regulated in Ductal cells from adjacent normal pancreatic tissue compared to other (likely PDAC) ductal cells.
  2. Diploid_vs_others: Identifying pathways up-regulated in Diploid Ductal cells compared to other (likely aneuploid) ductal cells.
  3. PDAC_vs_others: Identifying pathways up-regulated in Ductal cells from PDAC tissue compared to other (likely adjacent normal) ductal cells.

The results are presented as bar plots, where the length of the bar corresponds to the statistical significance (-log(p-val) and -log(q-val)) of pathway enrichment. Only up-regulated differentially expressed genes (DEGs) were considered for pathway enrichment.

Visual Summary

The provided bar plots illustrate the top enriched Gene Ontology (GO) terms for up-regulated genes in Ductal cells across three distinct comparisons:

Biological Interpretation

The GSA results provide crucial insights into the functional alterations occurring in Ductal cells under normal, diploid, and PDAC conditions.

  1. Ductal Cells in Adjacent Normal Pancreas (Adj_normal_vs_others):
  1. Diploid Ductal Cells (Diploid_vs_others):
  1. Ductal Cells in Pancreatic Ductal Adenocarcinoma (PDAC_vs_others):

In summary, normal ductal cells show metabolic functions, while PDAC ductal cells exhibit a profound rewiring of cellular machinery, driven by established oncogenic pathways and characterized by massive protein turnover and dysregulation, consistent with an aggressive malignant phenotype. Diploid ductal cells show a distinct, albeit limited, functional profile potentially related to maintaining specific ductal functions or differing from aneuploid cells in their PI3K-Akt signaling status.

Clinical or Translational Implications

The distinct pathway enrichments observed in PDAC ductal cells compared to adjacent normal tissue highlight potential therapeutic targets and diagnostic markers.

These GSA results provide a functional landscape of ductal cell state in PDAC, guiding further investigation into the precise mechanisms driving the disease and identifying potential pathways for therapeutic intervention.

19. Gene Set Enrichment Analysis (GSEA) of Pancreatic Ductal Adenocarcinoma Cell Types

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for various cell types identified in single-cell RNA-seq data from human pancreas, comparing both "Adj_normal" and "PDAC" conditions against all other cell types within their respective conditions (one-vs-rest comparison). The analysis also includes a detailed comparison of "Diploid" and "Aneuploid" Ductal cells within the PDAC context. The aim is to identify biological pathways that are significantly enriched or suppressed in specific cell types and conditions, providing insights into the molecular changes associated with Pancreatic Ductal Adenocarcinoma (PDAC). The dot plot visualizes the Normalized Enrichment Score (NES) and statistical significance (-log(p-val)) for 120 selected pathways.

Visual Summary

The dot plot effectively visualizes GSEA results across different cell types and conditions.

Overall, the plot reveals a distinct pattern of pathway deregulation in PDAC conditions, particularly in Ductal cells (both diploid and aneuploid), and various immune and stromal cell types within the tumor microenvironment.

Biological Interpretation

The GSEA results highlight significant alterations in biological pathways across different cell types in PDAC, underscoring the complex molecular landscape of the tumor and its microenvironment.

  1. Ductal Cells (Tumor Origin) Show Profound Changes, Exacerbated by Aneuploidy:
  1. Immune Cell Activation and Evasion in the Tumor Microenvironment:
  1. Stromal and Endothelial Cell Contributions:
  1. Metabolic Reprogramming is Widespread:

Clinical or Translational Implications

The comprehensive GSEA results provide several clinically relevant insights for PDAC:

20. Discussion

This comprehensive single-cell analysis of Pancreatic Ductal Adenocarcinoma (PDAC) and adjacent normal pancreas tissues has unveiled critical insights into the cellular and molecular underpinnings of this aggressive malignancy. The UMAP visualizations consistently identified aneuploid ductal cells as the malignant population, co-localizing distinctly with the PDAC condition. This finding was robustly supported by CNV analysis of ductal cells, which demonstrated widespread genomic amplifications, notably of oncogenes like SOX2, EGFR, and ERBB2, even in samples initially classified as 'Diploid PDAC,' indicating pervasive genomic instability as a hallmark of the disease. Ploidy population analysis further confirmed a significant shift towards aneuploidy in PDAC ductal cells compared to normal counterparts, validating their transformed state.

The tumor microenvironment (TME) in PDAC exhibited dramatic remodeling. Population analyses revealed a significant increase in ductal cells and fibroblasts (reflecting desmoplasia) in PDAC samples, coupled with a reduction in normal acinar cells. The immune landscape was profoundly altered: a striking increase in CD8+ Exhausted T cells and T regulatory cells (Tregs) was observed, alongside a reduction in Natural Killer (NK) cells. Moreover, box plots of T cell subsets highlighted a significant decrease in cytotoxic T cells (T_Cyto) and CD8+ Memory T cells in PDAC, while several CD4+ helper T cell subsets (Th22, CD4 Memory, Th9, CD4 Naive, Tfh, Th1) were significantly increased. This pattern paints a clear picture of an immunosuppressive TME, where effector anti-tumor immunity is compromised despite the presence of various helper T cell subsets that may be dysfunctional or suppressed. Interestingly, within the macrophage compartment, an increased proportional representation of M1 macrophages was observed in most PDAC samples, which, while typically associated with anti-tumor immunity, might signify an active but ultimately ineffective inflammatory response or a unique, context-dependent polarization state that warrants further investigation.

Cell-cell interaction (CCI) analyses provided mechanistic detail on the TME remodeling. Profoundly altered CCI patterns emerged in PDAC, with extensive interactions involving aneuploid ductal cells, macrophages, and T cells. Key interactions mediating immune evasion included the CD47-SIRPA axis between aneuploid ductal cells and macrophages, and the presence of PDCD1-CD274 (PD-1/PD-L1) interactions in immune cells. Pro-tumorigenic interactions were mediated by the SPP1-Integrin axis (driving TAM recruitment/polarization), widespread Integrin and EPH receptor signaling (facilitating adhesion/invasion), and the Prostaglandin E2 pathway (promoting immunosuppression). Moreover, the analysis revealed strong TGF-beta signaling (driving fibrosis and immune suppression), EGFR signaling (promoting proliferation), and VEGF-VEGFR interactions (driving angiogenesis), all critically active in the PDAC TME. Surfaceome marker analysis further delineated these changes, identifying significant upregulation of established therapeutic targets (e.g., MSLN, ERBB3, MET, CD47, ITGB1) and promising novel markers (e.g., CEACAM6, CD151, PLAUR, SDC1) on PDAC ductal cells. Similarly, PDAC macrophages showed upregulation of markers like CXCR4, BSG, ITGAM, and TGFBR2, indicating their active, pro-tumorigenic roles. CD4+ T cells in PDAC exhibited increased expression of markers related to trafficking, activation, and immune checkpoints, including CXCR4, CD47, IL10RA, and CD96, suggesting a complex interplay of responsiveness and dysfunction. Furthermore, the elevated expressing cell fraction of numerous cell cycle genes (e.g., MCM7, MYC) in PDAC ductal cells underscored their hyperproliferative state, even with increased expression fractions for some tumor suppressors (e.g., RB1, TP53), possibly reflecting compensatory or dysfunctional regulatory mechanisms.

Gene Ontology (GSA) and Gene Set Enrichment Analysis (GSEA) results synthesized these findings at a pathway level. PDAC ductal cells demonstrated profound enrichment for protein homeostasis pathways (ubiquitin-mediated proteolysis, ER processing), core cancer pathways (mTOR, PI3K-Akt, ErbB, Cell cycle, Wnt), and metabolic reprogramming (HIF-1 signaling, glycolysis). Notably, aneuploid ductal cells exhibited even stronger enrichment of these cancer-associated pathways, highlighting the intensified malignant phenotype linked to genomic instability. Immune cells showed pathways consistent with activation (phagosome in macrophages, NK cell mediated cytotoxicity) but also clear signs of immune evasion (PD-L1/PD-1 checkpoint pathway in T cells). These integrated findings provide a detailed cellular and molecular atlas of PDAC, validating established oncogenic mechanisms, illuminating the intricate immunosuppressive TME, and identifying novel vulnerabilities.

Hypotheses:

  1. Aneuploid ductal cells in PDAC actively drive an extensive desmoplastic reaction and immunosuppressive microenvironment by secreting pro-fibrotic factors (e.g., TGF-beta) and immune-modulatory ligands (e.g., SPP1, CD47).
  2. The observed increased proportion of M1 macrophages in some PDAC samples represents a state of chronic, ineffective inflammation rather than robust anti-tumor immunity, potentially driven by DAMPs and TLR4 signaling from tumor or stromal cells.
  3. The concurrent reduction of cytotoxic T cells and expansion of exhausted/regulatory T cells, alongside upregulation of multiple immune checkpoints (CD47, CD96, HLA-E/NKG2A, PD-1), is orchestrated by tumor cells and stromal components to foster a highly privileged immune evasion environment in PDAC.
  4. Recurrent amplifications of oncogenes (e.g., SOX2, EGFR, ERBB2) and widespread dysregulation of cell cycle genes (e.g., MYC, MCM7) in aneuploid ductal cells are central to PDAC's aggressive proliferative capacity and genomic instability, driving resistance to conventional therapies.
  5. The intricate network of cell-cell interactions, particularly involving integrins, EPH receptors, and various growth factor pathways (EGFR, TGF-beta, VEGF), collectively orchestrates tumor invasion, metastasis, and angiogenesis in PDAC.

Potential therapeutic targets:

  1. CD47: Overexpressed on PDAC Ductal cells and CD4+ T cells, CD47 forms a critical 'don't eat me' signal with SIRPA on macrophages, enabling immune evasion by preventing phagocytosis of tumor cells and potentially protecting dysfunctional T cells from clearance. Evidence: High expression on Ductal cells (Sec 14) and CD4+ T cells (Sec 16) in PDAC. Strong CD47-SIRPA interactions observed between Aneuploid Ductal cells and Macrophages (Sec 11, 13). Validation: Magrolimab and other anti-CD47 antibodies are in clinical trials for various cancers, including PDAC. Preclinical validation can include demonstrating enhanced phagocytosis of PDAC cells and improved anti-tumor immune responses in vivo upon CD47 blockade.
  2. TGF-beta Pathway (TGFBR1/TGFB1/2/3): The TGF-beta pathway is a master regulator of PDAC desmoplasia and immunosuppression. It activates cancer-associated fibroblasts (CAFs), promotes ECM remodeling, and suppresses anti-tumor immunity, creating a formidable barrier to therapy. Evidence: Robust TGFB1/2/3 interactions observed between Ductal cells and Fibroblasts, Macrophages, and T cells (Sec 13). Macrophages in PDAC show upregulation of TGFBR2 (Sec 15). GSA for PDAC ductal cells strongly enriched for TGF-beta signaling (Sec 18). Validation: TGFBR1 kinase inhibitors (e.g., Galunisertib) and anti-TGF-beta antibodies (e.g., Fresolimumab) are in clinical development for PDAC. Preclinical validation involves assessing reduction of fibrosis, increased immune cell infiltration, and enhanced efficacy of chemotherapy or immunotherapy.
  3. EGFR/ERBB3/MET: EGFR, ERBB2, and ERBB3 are key receptor tyrosine kinases frequently dysregulated in PDAC. EGFR and ERBB2 exhibit recurrent genomic amplifications, while ERBB3 and MET are highly expressed surface markers on PDAC Ductal cells, driving proliferation, survival, and invasion. Evidence: CNV analysis shows frequent amplifications of EGFR and ERBB2 in Ductal cells (Sec 4). Ductal cell marker analysis shows high expression of ERBB3 and MET in PDAC (Sec 14). GSEA/GSA highlight ErbB signaling pathway enrichment in PDAC ductal cells (Sec 18, 19). Validation: EGFR inhibitors (e.g., Erlotinib) and HER2-targeted therapies are approved for other cancers and are being investigated for PDAC. Further validation for ERBB3 and MET can involve preclinical testing of specific inhibitors and patient stratification based on expression levels or genomic alterations.
  4. CXCR4: CXCR4 is highly upregulated on PDAC macrophages and CD4+ T cells. The CXCL12-CXCR4 axis is crucial for recruiting immunosuppressive cells like TAMs into the TME and influencing T cell trafficking and retention, contributing to a pro-tumorigenic and immunosuppressive environment. Evidence: High expression of CXCR4 on PDAC macrophages (Sec 15) and CD4+ T cells (Sec 16). Critical for macrophage recruitment and pro-tumorigenic functions. GSEA highlights chemotaxis and immune cell migration pathways. Validation: CXCR4 antagonists (e.g., Plerixafor) are in clinical development for various cancers, including PDAC, to inhibit TAM infiltration, alter T cell localization, and enhance anti-tumor immunity, often in combination with other therapies.

Follow-up validation ideas:

  1. Spatial Transcriptomics/Proteomics: Validate the spatial distribution and co-localization of aneuploid ductal cells, specific immune subsets (e.g., M1 macrophages, exhausted T cells), and key ligand-receptor interactions (e.g., CD47-SIRPA, SPP1-integrin) within the intact PDAC tumor microenvironment.
  2. Flow Cytometry/Immunohistochemistry: Quantify and confirm the altered proportions of T cell subsets (e.g., reduced Cytotoxic T cells, increased exhausted CD8+ T cells, Tregs) and macrophage polarization (e.g., M1 vs. M2 markers) in fresh PDAC tumor samples compared to normal pancreas.
  3. FISH/CNV-seq on Isolated Ductal Cells: Independently validate specific recurrent CNV amplifications (e.g., SOX2, EGFR, ERBB2) in sorted aneuploid ductal cells from PDAC patients, correlating these with disease progression or treatment response.
  4. In Vitro/In Vivo Perturbation Assays: Functionally validate the roles of key therapeutic targets (e.g., MSLN, ERBB3, MET, CD47, CXCR4, TGFBR2) and their associated pathways (e.g., TGF-beta, EGFR) in PDAC tumor cell proliferation, survival, and immune evasion using CRISPR/shRNA knockdowns or overexpression in PDAC cell lines and patient-derived xenograft models.
  5. Cell-Cell Co-culture Experiments: Functionally test the impact of identified cell-cell interactions (e.g., Ductal-Macrophage CD47-SIRPA, Ductal-Fibroblast TGFB1-TGFBR1) on tumor cell phagocytosis, fibroblast activation, or T cell suppression in controlled in vitro environments.
  6. Validation Cohorts (Bulk/Single-cell): Confirm the identified gene expression signatures (e.g., surfaceome markers for Ductal, Macrophage, CD4+ T cells) and pathway enrichments (GSEA/GSA) in independent cohorts of PDAC patients, potentially correlating findings with clinical outcomes.

Limitations:

This single-cell RNA sequencing study provides a snapshot of the PDAC microenvironment; dynamic changes over time and tumor evolution cannot be fully captured. While robust, CNV inference from scRNA-seq may not detect all subtle genomic alterations. Cell type annotations, relying on marker expression, may not encompass all functional states or extremely rare cell populations. Computational predictions of cell-cell interactions require experimental validation to confirm their biological relevance and precise directionality. The generalizability of findings is limited by the specific cohort analyzed and requires validation in larger, independent PDAC patient cohorts. Functional implications derived from gene expression changes and pathway enrichments are inferential; direct functional studies are necessary to establish causality. The heterogeneous 'one-vs-rest' comparison groups in certain analyses may mask more nuanced condition-specific differences, and the distinction between 'Diploid PDAC' and 'Aneuploid PDAC' highlights heterogeneity but warrants further clinical and biological characterization.

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, Fibroblasts, Macrophages, T cells, and other relevant cell types. Select at most 80 cell-cell interactions per group.
  12. Find cell-cell interactions involving Ductal cells, major immune and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms, its potential as a therapeutic target, and relevant therapeutic agents.
  13. Find cell-cell interactions containing immune checkpoint and cell cycle pathway genes and involving Ductal cells, major immune and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms, its potential as a therapeutic target, and relevant therapeutic agents.
  14. 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 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+ 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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