SCODiA Report by MLBI Lab

Single-Cell Landscape of Pancreatic Ductal Adenocarcinoma: Genomic Instability, Immune Reprogramming, and Oncogenic Signaling

This single-cell RNA-seq analysis reveals the intricate cellular landscape of Pancreatic Ductal Adenocarcinoma (PDAC) compared to adjacent normal tissue. UMAP analysis distinctly clusters malignant, aneuploid ductal cells that dominate PDAC samples, while marker gene expression validates cell type annotations. The PDAC tumor microenvironment (TME) exhibits significant immune and stromal cell shifts, pronounced genomic instability with recurrent CNVs in tumor cells, and a dense network of condition-specific cell-cell interactions. Gene Ontology and GSEA highlight widespread activation of oncogenic, inflammatory, and metabolic reprogramming pathways across various cell types, underscoring the aggressive nature and complex biology of PDAC.

Contents

  1. Dataset overview
  2. UMAP Visualization of Pancreatic Single-Cell RNA-Seq Data by Key Metadata
  3. Pancreatic Single-Cell UMAP Gene Expression Analysis and Cell Type Annotation Validation
  4. Celltype_subset Marker Expression Dot Plot Analysis
  5. CNV Analysis of Ductal and Unassigned Cells in Pancreatic Samples
  6. CNV-Informed UMAP Analysis of Pancreatic Single-Cell RNA-seq Data
  7. Minor Cell Type Population Analysis in Pancreatic Tissues
  8. T-cell Subset Population Analysis in Pancreatic Ductal Adenocarcinoma (PDAC) vs. Adjacent Normal Tissue
  9. Macrophages Subset Population Analysis in Pancreatic Ductal Adenocarcinoma (PDAC)
  10. PDAC 조건에서 T 세포 아형 및 관련 림프구 집단의 변화 분석
  11. Ductal Cell (Tumor-Origin) and Unassigned Cell Ploidy Population Analysis in Pancreatic Tissue
  12. PDAC 조건에서의 세포-세포 상호작용 패턴 분석
  13. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma (PDAC)
  14. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions in Pancreatic Tissue
  15. Condition-Specific Cell-Cell Interaction Patterns in Pancreatic Ductal Adenocarcinoma
  16. Ductal Cell Condition-Specific Surfaceome Markers in Pancreatic Cancer
  17. Macrophage Condition-Specific Surfaceome Markers in Pancreatic Cancer
  18. Condition-Specific Surfaceome Markers in Pancreatic Cancer CD4+ T cells
  19. Ductal Cell Cycle Genes Show Significant Upregulation in Pancreatic Ductal Adenocarcinoma (PDAC)
  20. Ductal and Acinar Cell Gene Ontology (GSA) Analysis in Pancreatic Conditions
  21. Gene Set Enrichment Analysis (GSEA) of Pancreatic Cell Types in PDAC
  22. Discussion
  23. Query List

0. Dataset overview

Dataset Summary

Precomputed Results

Important Cell Types for Analysis

1. UMAP Visualization of Pancreatic Single-Cell RNA-Seq Data by Key Metadata

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents UMAP (Uniform Manifold Approximation and Projection) visualizations of single-cell RNA-seq data from the human pancreas, colored by various metadata features: condition (Adj_normal vs. PDAC), individual sample, major cell type, minor cell type, inferred ploidy status, and cell type subsets. These plots provide an overview of the dataset structure, cell type heterogeneity, disease-associated population shifts, and the distribution of ploidy status, essential for understanding the overall quality of annotations and the underlying biological landscape.

Visual Summary

Condition and Sample Distribution

Cell Type Annotations (Major, Minor, Subset)

Ploidy Status

Biological Interpretation

The UMAP visualizations provide a comprehensive overview of the cellular landscape in pancreatic tissue, highlighting key differences between adjacent normal and PDAC conditions.

  1. Tumor Microenvironment Composition: The presence of diverse immune cells (T cells, B cells, Macrophages, Dendritic cells, Mast cells, NK cells, ILCs), endothelial cells, and stromal cells (Fibroblasts, Stellate cells, Smooth muscle cells) alongside epithelial cells (Ductal, Acinar) indicates a robust representation of the pancreatic tumor microenvironment (TME) in the PDAC samples. This rich dataset allows for detailed investigations into cell-cell interactions and cell-type specific responses.
  2. Malignant Cell Identification: The strong co-localization of "Ductal cells" (specifically those from PDAC samples) with "Aneuploid" status is a crucial finding. This confirms the identity of the malignant cell population as transformed ductal cells, which is consistent with the biology of PDAC (Pancreatic Ductal Adenocarcinoma) GeneCards: KRAS, TP53, SMAD4. This also validates the accuracy of the ploidy_dec annotation for distinguishing tumor cells from normal epithelial and stromal cells.
  3. Condition-Specific Cell Population Shifts: The distribution of "PDAC" and "Adj_normal" cells suggests that while some core pancreatic cell types are present in both conditions, there are likely alterations in cell type proportions or transcriptional states within specific populations in PDAC. For example, the expansion of certain immune cell subsets or the altered state of stromal cells would contribute to the unique UMAP topology seen in PDAC.
  4. Annotation Quality: The consistent clustering and distinct separation of cells across all three levels of cell type annotation (celltype_major, celltype_minor, celltype_subset) demonstrate high-quality and robust cell type identification. This provides a strong foundation for downstream differential expression, pathway analysis, and cell-cell interaction studies.

Clinical or Translational Implications

Annotation Notes

2. Pancreatic Single-Cell UMAP Gene Expression Analysis and Cell Type Annotation Validation

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the expression of a panel of canonical marker genes across the UMAP embedding of pancreatic single-cell RNA-seq data. The primary goal is to validate the celltype_minor annotations by observing whether specific gene markers are highly expressed within their expected cell populations. The UMAP plots display the expression levels of 12 key genes, alongside a UMAP colored by celltype_minor annotations for direct comparison.

Visual Summary

The UMAP plots clearly delineate distinct clusters of cells corresponding to various pancreatic cell types. The expression patterns of the investigated genes are highly localized to specific regions of the UMAP, which largely align with the celltype_minor annotations:

Biological Interpretation

The strong concordance between the spatial distribution of canonical marker gene expression and the celltype_minor annotations provides robust validation for the cell type assignments in this single-cell RNA-seq dataset.

  1. Immune Cell Identity: The clear separation of T cells (CD3D+, CD4+, CD8A+), B cells (CD79A+, MS4A1+), plasma cells (MZB1+), and myeloid cells (CD14+, LYZ+) demonstrates successful resolution of distinct immune cell populations within the pancreatic microenvironment. The presence of T cells, B cells, macrophages, and dendritic cells is expected in both healthy and diseased pancreatic tissue.
  1. Pancreatic Parenchymal and Stromal Cell Identification: The expression patterns confirm the presence and correct annotation of major pancreatic resident cell types:
  1. Embedding Structure and Annotation Quality: The distinct and non-overlapping expression of cell-type-specific markers across different UMAP regions indicates a high quality of cell clustering and subsequent cell type annotation. The embedding effectively separates distinct biological cell states.

Annotation Notes

The comprehensive validation of celltype_minor annotations using canonical markers confirms the reliability of the cell type assignments, which is fundamental for subsequent downstream analyses such as differential gene expression (DEG), gene set enrichment analysis (GSEA), or cell-cell interaction (CCI) studies. This robust annotation foundation ensures that findings derived from these cell types are biologically sound.

3. Celltype_subset Marker Expression Dot Plot Analysis

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression patterns of marker genes across various celltype_subset populations identified in the single-cell RNA-seq data from human pancreas. The primary goal is to assess the quality of cell type annotations by examining whether the assigned cell subsets exhibit characteristic expression of known marker genes. Each dot represents a marker gene within a specific cell subset, with its size indicating the fraction of cells expressing the gene and its color intensity (red scale) representing the mean expression level. Markers were selected based on non-zero percentage score, focusing on surfaceome genes.

Visual Summary

The dot plot effectively visualizes distinct sets of marker genes highly expressed within specific celltype_subset populations.

Biological Interpretation

The marker expression patterns observed in the dot plot provide strong evidence supporting the robustness and accuracy of the celltype_subset annotations.

Annotation Notes

The strong and specific expression patterns of known marker genes across the various celltype_subset populations confirm the high quality and reliability of the cell type annotations. The distinct gene signatures for each subset allow for clear differentiation between cell types and support the biological relevance of the identified populations within the pancreatic tissue. This robust annotation serves as a solid foundation for subsequent analyses, such as differential gene expression, cell-cell interaction, or pathway enrichment studies.

4. CNV Analysis of Ductal and Unassigned Cells in Pancreatic Samples

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[Analysis Visualization Results]...

Analysis Overview

This analysis provides an interpretation of copy number variation (CNV) patterns in "Ductal cell" (identified as the tumor-origin cell type) and "unassigned" cells from single-cell RNA-seq data, across various pancreatic samples. The results include a heatmap illustrating log2(Copy Number Ratio) values for individual genomic spots across different samples, grouped by sample identity. Additionally, a summary plot highlights significantly amplified genomic regions, displaying both the extent of CNV activity per sample and the overall frequency of these amplifications across all samples. The goal is to identify common and sample-specific genomic alterations relevant to pancreatic disease, particularly in the context of pancreatic ductal adenocarcinoma (PDAC).

Visual Summary

CNV Heatmap (log2(CNR))

The main heatmap visualizes log2(CNR) values across approximately 1500 genomic spots, ordered by chromosome, for "Ductal cell" and "unassigned" cells from different samples.

Significant Amplified Copy Number Regions Summary

The summary plots provide a detailed view of significant CNAs:

Biological Interpretation

The CNV analysis provides critical insights into the genomic landscape of Ductal cells, the tumor-origin cell type in PDAC, and "unassigned" cells which likely include malignant cells.

Clinical or Translational Implications

The recurrent CNVs identified in Ductal and unassigned cells offer potential avenues for clinical and translational research in PDAC:

5. CNV-Informed UMAP Analysis of Pancreatic Single-Cell RNA-seq Data

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the single-cell RNA-sequencing data on a UMAP embedding, specifically incorporating Copy Number Variation (CNV) estimates into the dimensionality reduction. This allows for the clustering and separation of cells based not only on their gene expression profiles but also on their genomic integrity. The UMAP plots are colored by celltype_major, celltype_minor, ploidy_dec (inferred ploidy status), condition (Adj_normal vs. PDAC), and sample to provide a comprehensive view of cell identity, genomic alterations, and their distribution across different biological contexts.

Visual Summary

The UMAP visualizations reveal clear patterns regarding cell identity, genomic status, and disease condition:

Celltype Distribution (celltype_major and celltype_minor):

Ploidy Status (ploidy_dec):

Condition Distribution (condition):

Sample Distribution (sample):

Biological Interpretation

The CNV-informed UMAP embedding effectively delineates distinct cell populations based on their genomic state, offering key biological insights into pancreatic ductal adenocarcinoma (PDAC):

  1. Identification of Malignant Cells: The most prominent finding is the clear separation and clustering of aneuploid cells. Given that Ductal cells are specified as the "Tumor origin celltype" and aneuploidy is a hallmark of cancer, the strong co-localization of aneuploid cells with Ductal cell clusters (particularly the large cluster on the right) and their exclusive presence in the "PDAC" condition strongly indicate that these aneuploid Ductal cells represent the malignant tumor cells. The CNV information has successfully separated these tumor cells from the surrounding diploid stromal and immune cells.
  2. Genomic Instability in PDAC: The significant proportion of aneuploid cells specifically within the PDAC samples underscores the widespread genomic instability characteristic of pancreatic cancer. These cells form distinct clusters, suggesting different subclones or states of aneuploidy within the tumor mass.
  3. Tumor Microenvironment Composition: The large, central clusters, predominantly composed of diploid cells from both "Adj_normal" and "PDAC" conditions, represent the diverse tumor microenvironment. This includes various immune cells (T cells, Myeloid cells) and stromal cells (Fibroblasts, Stellate cells, Smooth muscle cells), which are recruited to or reside within both normal and cancerous pancreatic tissue. Their largely diploid status confirms they are non-malignant components.
  4. Preservation of Normal Pancreatic Architecture: The distinct cluster of Acinar cells at the bottom, which are predominantly diploid and can originate from both normal and tumor-adjacent tissue, suggests that this normal pancreatic epithelial component is well-preserved and distinct from the malignant Ductal cells in this dataset.
  5. Sample-Specific Tumor Heterogeneity: The observation that different PDAC samples contribute to distinct parts of the aneuploid Ductal cell clusters highlights the inter-patient heterogeneity of PDAC. While all identified tumor cells share aneuploidy and ductal origin, their specific CNV profiles or expression states, as reflected in the UMAP, can vary between patients.

Clinical or Translational Implications

This analysis provides a robust framework for identifying and characterizing malignant cell populations in PDAC based on their genomic alterations.

6. Minor Cell Type Population Analysis in Pancreatic Tissues

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[Analysis Visualization Results]...

Analysis Overview

이 분석은 Adj_normal (정상 인접 조직)과 PDAC (췌장 췌관선암) 조건에서 단일 세포 RNA 시퀀싱 데이터를 기반으로 한 minor cell type의 상대적인 세포 개체군 분포를 시각화한 것입니다. 각 막대는 개별 샘플을 나타내며, 서로 다른 색상은 다양한 minor cell type을 의미합니다. 이 플롯은 각 샘플 내에서 특정 세포 유형이 차지하는 비율을 보여주어 조건 간의 세포 구성 변화를 비교하는 데 도움을 줍니다.

Visual Summary

Biological Interpretation

이 세포 개체군 분석 결과는 췌장암 발병 및 진행과 관련된 조직학적 및 면역학적 변화를 명확하게 보여줍니다.

  1. 정상 췌장 구조의 파괴 및 종양 세포 증식: Adj_normal 샘플에서 Acinar cell이 우세한 것은 정상 췌장 외분비 조직의 주요 구성원을 반영합니다. 반면, PDAC 샘플에서 Acinar cell의 현저한 감소와 Ductal cell (종양 기원 세포)의 증가는 종양으로 인한 정상 조직의 파괴 및 암세포의 무분별한 증식을 나타냅니다. 췌장 췌관선암은 췌관 상피세포에서 기원하며, 이는 Ductal cell의 증가로 확인됩니다 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6908920/.
  2. 종양 미세환경 (TME) 재구성:

Clinical or Translational Implications

이러한 세포 개체군 변화는 PDAC의 진단, 예후 및 치료 전략 개발에 중요한 시사점을 제공합니다.

  1. 바이오마커 개발: Ductal cell의 비정상적인 증식, Acinar cell의 감소, 그리고 Macrophage, Fibroblast, Stellate cell 등 기질 세포의 증가는 PDAC 진단을 위한 조직학적 및 분자 바이오마커로서 활용될 수 있습니다.
  2. 치료 표적:
  1. 환자 계층화 및 예후 예측: T 세포 침윤의 이질성은 PDAC 환자를 면역 반응에 따라 계층화하고 면역 관문 억제제와 같은 면역 치료에 대한 반응을 예측하는 데 활용될 수 있습니다. T 세포 침윤이 높은 환자는 면역 치료에 더 잘 반응할 수 있습니다.
  2. 세포 기반 치료 개발: 특정 세포 유형 (예: 종양을 직접 공격하는 T cell CD8+)의 비율 변화를 이해하는 것은 세포 기반 면역 치료법을 개발하고 최적화하는 데 도움이 될 수 있습니다.

7. T-cell Subset Population Analysis in Pancreatic Ductal Adenocarcinoma (PDAC) vs. Adjacent Normal Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the proportional distribution of various T cell and related innate lymphoid cell (ILC) subsets within the overall 'T cell' major population across individual samples from both adjacent normal pancreas and Pancreatic Ductal Adenocarcinoma (PDAC) conditions. The aim is to identify shifts in immune cell composition associated with PDAC.

Visual Summary

The stacked bar plots display the relative proportions of T cell and ILC subsets for each sample. Samples are grouped by condition: 'Adj_normal' (3 samples) and 'PDAC' (13 samples).

  1. Adjacent Normal Pancreas (Adj_normal): The T cell compartment in adjacent normal tissue samples is predominantly composed of T cell (Naive) and T cell (Cytotoxic) populations. T cell (Treg) is also present but generally constitutes a smaller proportion. Other ILCs and helper T cell subsets are minimal.
  2. Pancreatic Ductal Adenocarcinoma (PDAC): A significant shift in the T cell subset composition is observed in PDAC samples compared to adjacent normal tissues.
  1. Inter-sample Heterogeneity: There is considerable heterogeneity in T cell and ILC subset compositions among individual PDAC samples, reflecting the diverse immune landscapes characteristic of this cancer.

Biological Interpretation

The observed shifts in T cell and ILC subset populations in PDAC highlight a complex immune reprogramming within the tumor microenvironment compared to normal pancreas.

  1. Immune Evasion and Immunosuppression: The consistent increase in T cell (Treg) populations in PDAC samples is a strong indicator of an immunosuppressive microenvironment. Tregs are crucial in suppressing anti-tumor immune responses, contributing to tumor immune evasion in pancreatic cancer [1].
  2. Innate Lymphoid Cell Involvement in PDAC: The expanded presence and diversity of ILCs (ILC1, ILC2, ILC3) in PDAC suggest their active participation in the disease pathology.
  1. T Cell Exhaustion/Differentiation: The relative shift away from predominantly naive T cells towards more diverse effector and regulatory populations in PDAC suggests active immune cell recruitment, differentiation, and potentially exhaustion within the tumor. The variability in T cell (Cytotoxic) proportions could reflect differences in anti-tumor efficacy or the degree of immune suppression across tumors.

Clinical or Translational Implications

The distinct immune cell signatures observed have several potential clinical implications for PDAC:

  1. Prognostic Biomarkers: The specific composition and ratios of T cell and ILC subsets, particularly the elevated T cell (Treg) and ILC populations, could serve as prognostic biomarkers to predict disease aggressiveness and patient outcomes in PDAC [1].
  2. Therapeutic Targets: Targeting immunosuppressive cells like T cell (Treg) (e.g., via depletion or inhibition) could be a viable strategy to enhance anti-tumor immunity and improve the efficacy of existing therapies or immunotherapies in PDAC [3]. Understanding the precise roles of different ILC subsets in PDAC progression could also uncover novel therapeutic targets for modulating the tumor microenvironment.
  3. Response to Immunotherapy: The immune cell landscape, especially the balance between cytotoxic effector cells and immunosuppressive cells, can significantly influence a patient's response to immunotherapies like checkpoint inhibitors. Patients with a highly immunosuppressive TME (e.g., high Tregs, specific ILCs) might require combination therapies to overcome resistance.

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References

  1. Regulatory T cells in pancreatic cancer:
  1. ILC2s and fibrosis in cancer:
  1. Targeting Tregs in cancer immunotherapy:

8. Macrophages Subset Population Analysis in Pancreatic Ductal Adenocarcinoma (PDAC)

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the proportional distribution of different macrophage subsets (M1, M2A, M2B, M2C, M2D) across individual samples from both "Adj_normal" (adjacent normal pancreas tissue) and "PDAC" (Pancreatic Ductal Adenocarcinoma) conditions. The aim is to understand the shifts in macrophage polarization within the tumor microenvironment compared to non-malignant tissue.

Visual Summary

The stacked bar plots display the relative proportions of macrophage subsets within each sample.

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and are broadly classified into M1 (pro-inflammatory, anti-tumorigenic) and M2 (pro-tumoral, immune suppressive, tissue repair) phenotypes.

The observed predominance of Macrophage (M1) in PDAC samples is a notable finding, as tumor-associated macrophages (TAMs) in many solid tumors, including PDAC, are often reported to be skewed towards an M2-like phenotype, which promotes tumor growth, angiogenesis, and immune evasion [1].

Several biological interpretations can be considered for this observation:

Clinical or Translational Implications

The distinct macrophage landscape observed in PDAC samples has several potential clinical and translational implications:

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References:

  1. Tumor-Associated Macrophages in Pancreatic Cancer: [PubMed Search: "pancreatic cancer tumor-associated macrophages M1 M2"

](PubMed Search)

  1. Chronic Inflammation and Cancer: [PubMed Search: "chronic inflammation cancer progression"

](PubMed Search)

  1. Macrophage Reprogramming in Cancer Therapy: [PubMed Search: "macrophage reprogramming cancer therapy"

](PubMed Search)

9. PDAC 조건에서 T 세포 아형 및 관련 림프구 집단의 변화 분석

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[Analysis Visualization Results]...

Analysis Overview

본 분석은 단일 세포 RNA 시퀀싱 데이터를 활용하여 췌장암(PDAC) 조직과 인접 정상(Adj_normal) 조직 간의 T 세포 아형 및 일부 관련 림프구 집단의 상대적 비율 차이를 비교한 결과입니다. celltype_major가 'T cell'인 세포들을 대상으로 celltype_subset 수준에서 각 아형의 비율 변화를 박스 플롯으로 시각화하고 통계적 유의성을 평가했습니다.

Visual Summary

제공된 박스 플롯은 6가지 림프구 아형(ILCreg, Tfh, T_Naive, ILC1, Th1, T_Cyto)의 PDAC 및 Adj_normal 조직 내에서의 비율 분포를 보여줍니다. 각 플롯에는 개별 샘플의 데이터 포인트(점), 중앙값(박스 안의 선), 사분위수 범위(박스), 및 이상치(동그라미)가 표시되어 있습니다. 주요 관찰 결과는 다음과 같습니다:

참고: ILCreg 및 ILC1은 T 세포와는 다른 계통의 선천성 림프구(Innate Lymphoid Cells)이지만, 본 분석 결과에 포함되어 함께 해석되었습니다.

Biological Interpretation

이 결과는 췌장암(PDAC)의 종양 미세환경(TME)에서 T 세포 및 관련 림프구 집단 구성에 중요한 변화가 있음을 시사합니다.

  1. 세포독성 T 세포(T_Cyto)의 감소: 가장 두드러진 발견은 PDAC 조직에서 종양 세포 살상에 핵심적인 역할을 하는 세포독성 T 세포의 비율이 현저히 감소한다는 것입니다. 이는 PDAC TME가 강력한 면역 억제 환경임을 명확하게 보여주며, 항종양 면역 반응이 효과적으로 작동하지 못하고 있음을 나타냅니다 PubMed search: Pancreatic cancer immune evasion cytotoxic T cells.
  2. 미분화 T 세포(T_Naive)의 증가: PDAC에서 미분화 T 세포의 비율이 증가하는 것은, 종양으로 유입되는 T 세포들이 효과적인 종양 반응성 T 세포로 분화 및 활성화되지 못하고 있음을 시사할 수 있습니다. 이는 TME 내의 면역 억제 요인(예: 조절 T 세포, 골수 유래 억제 세포, 면역 체크포인트 분자)으로 인해 T 세포 활성화가 저해되기 때문일 수 있습니다.
  3. Tfh 세포의 증가: Tfh 세포는 주로 B 세포 반응을 조절하며 항체 생산을 돕는 역할을 합니다 GeneCards: TFC. PDAC TME 내 Tfh 세포의 증가는 종양 관련 3차 림프 구조 형성 또는 특정 B 세포 반응의 변화와 관련될 수 있지만, 고형암에서의 Tfh 역할은 복잡하고 문맥 의존적입니다. 일부 연구에서는 Tfh가 항종양 면역에 기여하거나, B 세포를 통해 종양 진행을 촉진할 수도 있음을 제시합니다.
  4. 조절형 선천성 림프구(ILCreg)의 증가: ILCreg는 면역 억제 기능을 수행하는 것으로 알려져 있으며, 이들의 증가는 PDAC의 면역 억제 TME 형성에 기여할 수 있습니다 PubMed search: Regulatory ILCs cancer.
  5. ILC1 및 Th1 세포의 경미한 증가 경향: ILC1 및 Th1 세포는 주로 IFN-γ를 생산하여 세포 매개 면역 반응 및 항종양 반응을 유도하는 것으로 알려져 있습니다. 이들의 증가 경향은 면역 체계가 종양에 대항하려는 시도를 반영할 수 있으나, 동시에 세포독성 T 세포의 감소가 관찰되므로, 이러한 방어 메커니즘이 PDAC의 강력한 면역 억제 환경에 의해 극복되고 있을 가능성을 시사합니다.

전반적으로, PDAC TME는 세포독성 T 세포가 고갈되고 미분화 T 세포, Tfh 세포, 조절형 선천성 림프구가 증가하는 특징적인 면역 프로파일을 가지는 것으로 해석됩니다. 이는 췌장암의 진행에 중요한 면역 회피 메커니즘을 형성합니다.

Clinical or Translational Implications

10. Ductal Cell (Tumor-Origin) and Unassigned Cell Ploidy Population Analysis in Pancreatic Tissue

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Analysis Overview

이 분석은 인접 정상(Adj_normal) 및 췌장 선암(PDAC) 조건에서 췌장 조직의 Ductal cell (종양 기원 세포)과 unassigned cells의 핵형(ploidy) 분포를 평가한 것입니다. Single-cell RNA-seq 데이터를 기반으로 각 샘플 내 세포 집단의 이수성(Aneuploid), 이배성(Diploid) 및 불명확(Unclear) 핵형 비율을 비교하여, 종양 발생 및 진행에 따른 게놈 불안정성을 탐색합니다.

Visual Summary

제공된 바 플롯은 인접 정상 및 PDAC 샘플에서 Ductal cell과 unassigned cells의 ploidy 분포를 보여줍니다.

PDAC 샘플

Biological Interpretation

Clinical or Translational Implications

11. PDAC 조건에서의 세포-세포 상호작용 패턴 분석

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Analysis Overview

이 분석은 췌장암(PDAC) 조건에서 특정 세포 유형 간의 세포-세포 상호작용(CCI) 패턴을 CellPhoneDB를 사용하여 시각화한 결과입니다. 주요 관심 세포 유형은 Ductal cell, Fibroblast, Macrophage, T cell CD4+, T cell CD8+였습니다. 특히, 암세포의 근원인 Ductal cell은 ploidy(염색체 수성)에 따라 Diploid Ductal과 Aneuploid Ductal로 세분화되어 분석되었습니다. 그림은 각 세포쌍 간의 상호작용에 대한 통계적 유의성(-log10(p))과 평균 발현 강도(log2(m))를 나타내며, PDAC 미세환경의 복잡한 통신 네트워크를 조명합니다.

Visual Summary

주어진 CellPhoneDB 닷 플롯은 PDAC 조건에서 상위 80개 세포-세포 상호작용 쌍을 보여줍니다.

Biological Interpretation

PDAC의 종양 미세환경(TME)은 고도로 복잡하며, 다양한 세포 유형 간의 상호작용은 질병 진행에 필수적입니다.

Clinical or Translational Implications

  1. Aneuploid Ductal cell 표적 치료: EGFR (AREG-EGFR, TGFA-EGFR), TGF-beta (TGFB1-TGFBR1), Ephrin (EPHA/EFNA/EFNB) 신호전달 경로는 Aneuploid Ductal cell에서 강력하게 활성화되어 있으므로, 이들 경로를 표적으로 하는 치료제(예: EGFR 저해제)는 PDAC 환자에게 직접적인 항암 효과를 제공할 잠재력이 있습니다. 특히, 기존 EGFR 저해제의 효능을 개선하거나, 다른 표적과 병용하는 전략이 고려될 수 있습니다.
  2. TAM 재프로그래밍을 통한 면역 강화: Macrophage 간의 APOE-TREM2_receptor 상호작용은 TAM의 면역억제 역할을 강화하는 중요한 경로일 수 있습니다. TREM2를 표적으로 하는 전략은 TAM을 항종양 표현형으로 재프로그래밍하여 면역항암치료의 효과를 높일 수 있는 잠재적인 치료 접근법이 될 수 있습니다.
  3. 미세환경 조절을 통한 전이 억제: Ductal cell 및 Macrophage에서 나타나는 Integrin 복합체 (CDH1-integrin, LAMC1-integrin, PLAUR-integrin) 상호작용은 세포의 접착, 이동, 침습 및 ECM 리모델링에 중요한 역할을 합니다. 이들 상호작용을 차단함으로써 종양의 전이를 억제하고 섬유화된 TME를 완화하여 약물 전달을 개선할 수 있습니다.
  4. 면역 체크포인트 조절 및 병용 요법: Macrophage와 T 세포 간의 CD86-CD28/CTLA4 상호작용은 면역 체크포인트 억제제 반응에 대한 중요한 정보를 제공합니다. PDAC에서 면역 체크포인트 억제제의 제한적인 효능을 고려할 때, 이러한 특정 상호작용을 조절하는 치료법(예: CTLA4 억제제)을 다른 TME 표적 치료와 병용하여 T 세포의 항종양 활성을 회복시키는 전략이 필요할 수 있습니다.

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

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Analysis Overview

This analysis investigates the cell-cell interaction (CCI) landscape in pancreatic tissue, comparing healthy adjacent normal samples (Adj_normal) with Pancreatic Ductal Adenocarcinoma (PDAC) samples. Using single-cell RNA sequencing data, the plot_dot_for_cci_with_signif_difference tool was employed to identify and visualize the most significant CCIs (up to 80 per condition based on minimum p-value) across individual samples. The visualization displays the strength of interactions (standardized sample mean, color intensity) and their statistical significance (-log10(p-value), dot size) for various ligand-receptor pairs between specific cell types.

Visual Summary

The dot plot effectively illustrates distinct CCI patterns between the Adj_normal and PDAC conditions.

Key Interaction Categories

Biological Interpretation

The observed condition-specific CCI patterns provide critical insights into the biological processes driving PDAC progression.

Clinical or Translational Implications

The detailed characterization of condition-specific CCIs in PDAC offers significant clinical and translational opportunities.

13. Immune Checkpoint and Cell Cycle Related Cell-Cell Interactions in Pancreatic Tissue

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Analysis Overview

This analysis utilizes single-cell RNA sequencing data from pancreatic tissue (human) to identify cell-cell interactions (CCI) involving a predefined set of immune checkpoint and cell cycle-related genes. The interactions are visualized using dot plots, comparing two conditions: "Adj_normal" (adjacent normal tissue) and "PDAC" (Pancreatic Ductal Adenocarcinoma). The objective is to understand how these critical pathways mediate cell communication in both healthy and diseased pancreatic microenvironments.

Visual Summary

The analysis produced two dot plots, one for the "Adj_normal" condition and one for the "PDAC" condition, displaying significant cell-cell interaction pairs and the associated ligand-receptor interactions. The size of each dot represents the -log10(p-value) of the interaction, indicating statistical significance, while the color intensity reflects the log2(mean) expression of the interacting gene pair, indicating interaction strength.

Adj_normal Condition

PDAC Condition

Biological Interpretation

The analysis highlights distinct patterns of immune checkpoint-related cell-cell interactions between healthy adjacent pancreatic tissue and PDAC.

  1. Baseline Immune Regulation in Adj_normal Tissue:
  1. Altered Immune Checkpoint Landscape in PDAC:
  1. Absence of Cell Cycle Gene Interactions:

Clinical or Translational Implications

The differential patterns of immune checkpoint interactions between "Adj_normal" and "PDAC" conditions have significant clinical and translational implications for pancreatic cancer immunotherapy.

  1. Therapeutic Target Prioritization:
  1. Biomarker Development:
  1. Understanding Tumor Microenvironment:
  1. Experimental Validation:

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

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Analysis Overview

This analysis identifies statistically significant differences in cell-cell interactions (CCIs) between 'Adj_normal' (adjacent normal pancreas tissue) and 'PDAC' (Pancreatic Ductal Adenocarcinoma) conditions. The investigation focused on major immune and stromal cell types: Myeloid cells, T cells, Endothelial cells, Mast cells, B cells, and Stromal cells. CellPhoneDB was used to compute CCIs per condition and per sample, followed by statistical testing to identify interactions significantly different between conditions. The results are visualized as a dot plot, showing the strength and statistical significance of specific ligand-receptor interactions between defined cell types across individual samples.

Visual Summary

The dot plot effectively illustrates distinct patterns of cell-cell communication characterizing the adjacent normal pancreatic tissue versus PDAC samples.

Biological Interpretation

The observed differences in CCI patterns reflect a profound reshaping of the cellular communication network in PDAC compared to normal pancreatic tissue, primarily driven by altered immune and stromal cell interactions.

Clinical or Translational Implications

The distinct CCI patterns observed between normal and PDAC tissues offer several potential clinical and translational avenues.

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

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Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers in Ductal cells, the tumor-origin cell type in Pancreatic Ductal Adenocarcinoma (PDAC). The plot_markers_and_expression_dot tool was used to visualize the expression of up to 50 surfaceome markers in Ductal cells across individual samples, categorized by their condition (Adj_normal vs. PDAC). The results provide insights into potential cell surface proteins that differentiate cancerous ductal cells from their normal counterparts.

Visual Summary

The dot plot clearly illustrates a striking difference in surfaceome marker expression between Ductal cells from "Adj_normal" and "PDAC" samples.

Biological Interpretation

The significant upregulation of a broad panel of surfaceome markers in PDAC ductal cells compared to adjacent normal ductal cells points towards profound changes in the cell surface proteome during malignant transformation. These surface proteins play critical roles in cell adhesion, signaling, immune evasion, and nutrient transport, all of which are fundamental processes in cancer biology.

Oncogenic Signaling and Growth:

Cell Adhesion, Migration, and Invasion:

Other Noteworthy Markers:

The robust and widespread upregulation of these surfaceome markers in PDAC ductal cells suggests a coordinated phenotypic shift in the cell surface landscape that supports cancer-specific functions.

Clinical or Translational Implications

The identification of a comprehensive panel of highly upregulated surfaceome markers in PDAC ductal cells has significant clinical and translational implications:

Further validation through immunohistochemistry on tissue microarrays or flow cytometry on patient samples would be crucial to confirm these findings and assess their clinical utility.

16. Macrophage Condition-Specific Surfaceome Markers in Pancreatic Cancer

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Analysis Overview

This analysis aimed to identify surfaceome markers that distinguish macrophages from pancreatic ductal adenocarcinoma (PDAC) tissue compared to adjacent normal pancreatic tissue (Adj_normal) samples. The dot plot visualizes the expression of up to 50 top differentially expressed surfaceome genes in macrophages across individual patient samples, grouped by condition. The size of each dot represents the fraction of cells within that sample expressing the gene, while the color intensity indicates the mean expression level.

Visual Summary

The dot plot clearly segregates macrophage populations based on their gene expression profiles according to the tissue origin.

Biological Interpretation

The observed differential expression of surfaceome markers points to a significant phenotypic shift in macrophages within the PDAC tumor microenvironment compared to those in adjacent normal pancreatic tissue. This suggests the presence of distinct macrophage populations with potentially different functional roles.

Macrophages in the PDAC microenvironment appear to adopt a tumor-associated macrophage (TAM) phenotype, characterized by the upregulation of several pro-tumorigenic and immunosuppressive markers:

Immune Evasion and Suppression:

Pro-tumorigenic and Inflammatory Roles:

Macrophage Activation and Differentiation Markers:

The relatively low expression of most of these markers in adjacent normal macrophages suggests a quiescent or homeostatic macrophage state in healthy tissue. The presence of GP2 in adjacent normal macrophages is interesting. GP2 is known as a marker for M cells in Peyer's patches and is a major component of pancreatic zymogen granules. Its surface expression on adjacent normal macrophages could indicate a specific interaction with healthy pancreatic acinar cells or involvement in maintaining pancreatic homeostasis, potentially through phagocytosis of zymogen granule components or cellular debris [GeneCards: GeneCards].

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers on macrophages in PDAC has several potential clinical implications:

17. Condition-Specific Surfaceome Markers in Pancreatic Cancer CD4+ T cells

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Analysis Overview

This analysis aimed to identify surfaceome markers that are differentially expressed in CD4+ T cells between pancreatic ductal adenocarcinoma (PDAC) and adjacent normal (Adj_normal) pancreatic tissues. The single-cell RNA sequencing (scRNA-seq) data, spanning 26871 cells and 23239 genes from human pancreas, was interrogated for CD4+ T cells. The plot_markers_and_expression_dot tool was used to visualize up to 50 significant surfaceome markers per condition, prioritizing those that are distinctively expressed. The dot plot displays both the mean expression level (color intensity) and the fraction of cells expressing the gene (dot size) across individual samples within each condition.

Visual Summary

The dot plot clearly differentiates the surfaceome marker profiles of CD4+ T cells from adjacent normal tissue (AdjN_2) versus PDAC samples.

Biological Interpretation

The observed surfaceome marker landscape points towards significant alterations in CD4+ T cell phenotypes and functions within the PDAC tumor microenvironment compared to adjacent normal tissue.

Cell Adhesion and Migration:

T cell Activation and Signaling:

Clinical or Translational Implications

The identified surfaceome markers hold significant promise for both diagnostic and therapeutic applications in PDAC.

Potential Therapeutic Targets:

18. Ductal Cell Cycle Genes Show Significant Upregulation in Pancreatic Ductal Adenocarcinoma (PDAC)

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Analysis Overview

This analysis investigates the differential expression of genes related to the Cell Cycle pathway in Ductal cells, comparing the Pancreatic Ductal Adenocarcinoma (PDAC) condition to the Adjacent Normal (Adj_normal) condition. The findings are presented as box plots showing gene expression distributions across samples, along with statistical significance for the observed differences.

Visual Summary

The visualization displays box plots for 24 distinct cell cycle-related genes, comparing their expression in Ductal cells from Adj_normal versus PDAC samples.

Biological Interpretation

The observed widespread upregulation of cell cycle-related genes in Ductal cells within PDAC is a strong indicator of enhanced proliferative activity and represents a fundamental hallmark of cancer. Considering that Ductal cells are identified as the tumor origin cell type in this dataset, these findings directly reflect the transformed state of the tumor cells.

Clinical or Translational Implications

The findings underscore the highly proliferative nature of Ductal cells in PDAC, a key driver of tumor growth and aggressiveness.

19. Ductal and Acinar Cell Gene Ontology (GSA) Analysis in Pancreatic Conditions

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Analysis Overview

This analysis investigates Gene Ontology (GO) pathway enrichment (specifically for upregulated genes, GSA_up) in Ductal cells and Acinar cells. The comparisons are performed across different conditions (Adj_normal vs others, PDAC vs others) and for Ductal cells, also by ploidy status (Diploid vs others). Ductal cells are explicitly identified as the tumor origin cell type for Pancreatic Ductal Adenocarcinoma (PDAC). The results are presented as a dot plot, where dot size and color intensity reflect the statistical significance (-log10(p-value)) of pathway enrichment.

Visual Summary

The visualization provided is a dot plot, not a bar plot as requested by the user query, illustrating Gene Ontology pathway enrichment. Each row represents a specific GO pathway, and each column represents a comparison case (e.g., "Ductal cell: PDAC_vs_others"). The size and color intensity of each dot correspond to the negative logarithm of the adjusted p-value (-log10(P)), with larger, darker red dots indicating more significant enrichment.

Key visual observations include:

Biological Interpretation

The GSA_up analysis highlights distinct biological processes activated in Ductal and Acinar cells, particularly in the context of PDAC.

Ductal Cells in PDAC Exhibit Extensive Oncogenic Activation:

Normal/Diploid Ductal Cells Show Minimal Oncogenic Signatures:

Acinar Cells Exhibit Stress Responses Rather Than Transformation:

Clinical or Translational Implications

The distinct pathway enrichments observed have several important clinical and translational implications for PDAC:

20. Gene Set Enrichment Analysis (GSEA) of Pancreatic Cell Types in PDAC

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Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results for key cell types within the pancreas, comparing their transcriptional profiles in different conditions. The dot plot visualizes the enrichment or depletion of various biological pathways across Acinar cells, Ductal cells, Endothelial cells, Macrophages, Mast cells, CD4+ T cells, and CD8+ T cells. The comparisons include cells from adjacent normal tissue ("Adj_normal_vs_others"), Pancreatic Ductal Adenocarcinoma (PDAC) tissue ("PDAC_vs_others"), and specifically, diploid Ductal cells ("Diploid_vs_others") against all other cells in the dataset. Normalized Enrichment Score (NES) indicates the direction and magnitude of pathway enrichment (red for upregulation, blue for downregulation), while dot size reflects statistical significance (-log(p-value)).

Visual Summary

The GSEA dot plot effectively summarizes the pathway activity across various cell types and conditions.

Biological Interpretation

The GSEA results provide a comprehensive view of the biological processes dysregulated in various pancreatic cell types in the context of PDAC. The interpretation is based on comparing specific cell types in a given condition (e.g., PDAC) against all other cells in the dataset, effectively highlighting their unique molecular signatures.

Common Dysregulations Across PDAC Cell Types

Ductal Cell Insights (Tumor Origin)

Immune Cell Dynamics

Stromal and Endothelial Cell Contributions

Acinar Cell Changes

Clinical or Translational Implications

The comprehensive GSEA results reveal several therapeutically targetable pathways and potential biomarkers in PDAC:

21. Discussion

The comprehensive single-cell analysis of pancreatic tissue provides critical insights into the pathology of Pancreatic Ductal Adenocarcinoma (PDAC), highlighting fundamental differences from adjacent normal tissue.

Aneuploid ductal cells consistently emerge as the malignant population, exhibiting widespread genomic instability with recurrent amplifications, notably affecting regions harboring oncogenes like EGFR (7q) and EIF3E/GSDMD (8q) (Sections 1, 4, 5, 10). This genomic rewiring is a hallmark of PDAC, driving its aggressive proliferation and providing a molecular fingerprint of the disease.

Profound remodeling of the tumor microenvironment (TME) is a central finding. Cell population shifts indicate a significant expansion of ductal cells (tumor cells) and an infiltration of diverse immune (macrophages, T cells, ILCs) and stromal components (fibroblasts, stellate cells) within PDAC samples (Section 6). The T cell compartment in PDAC shows a notable decrease in cytotoxic T cells and an increase in naive T cells, T follicular helper (Tfh) cells, and ILCreg, collectively suggesting a shift toward an immunosuppressive state (Sections 7, 9).

An interesting and potentially notable finding is the observed predominance of M1-like macrophages in PDAC samples compared to adjacent normal tissue (Section 8). While tumor-associated macrophages (TAMs) in many solid tumors, including PDAC, are often characterized by an M2-like, pro-tumoral and immunosuppressive phenotype, this observation could indicate a sustained pro-inflammatory state, a context-dependent functional impairment of these M1-like cells, or specific molecular subtypes of PDAC. Further investigation into their functional state and polarization in the PDAC TME is warranted.

Cell-cell interaction (CCI) analysis further illuminates the intricate communication network within the PDAC TME (Sections 11, 12, 13, 14). Aneuploid ductal cells engage in extensive autocrine/paracrine signaling, including oncogenic EGFR (AREG-EGFR, TGFA-EGFR) and EMT-promoting TGFB1-TGFBR1 interactions, driving their self-sufficiency and invasiveness. Macrophages play a central role, engaging in immune-modulatory interactions with T cells (e.g., CD86-CD28/CTLA4) and establishing immunosuppressive axes (e.g., SIRPA-CD47, HLA-E-NKG2A) that contribute to immune evasion. Integrin-mediated interactions are also prominent, facilitating cell adhesion, migration, and stromal remodeling crucial for tumor invasion and metastasis.

Gene set enrichment analyses (GSA and GSEA) reveal widespread activation of oncogenic, inflammatory, and metabolic reprogramming pathways across various PDAC cell types (Sections 19, 20). Malignant ductal cells display robust enrichment of DNA damage response, cell cycle, PI3K-AKT-mTOR, MAPK, and VEGFA-VEGFR2 signaling pathways, indicative of uncontrolled proliferation and angiogenesis. Macrophages adopt a pro-tumorigenic phenotype with activated IL-4, IFN-I, TLR signaling, and oxidative stress pathways. Metabolic shifts towards glycolysis (HIF1A/PPARG regulation) are also observed across multiple cell types.

The upregulation of numerous cell cycle-related genes (e.g., ORC2, MYC, CCND1) in PDAC ductal cells directly correlates with their enhanced proliferative activity (Section 18). Furthermore, condition-specific surfaceome marker analyses identify distinct panels of highly upregulated surface proteins on PDAC ductal cells (e.g., MET, ERBB2, ITGB6, PLAUR) and macrophages (e.g., SIRPA, BSG, IL6R, TGFBR2) (Sections 15, 16, 17). These cell-surface molecules not only delineate altered cellular phenotypes but also represent highly accessible targets for therapeutic intervention.

In summary, this single-cell analysis provides a multi-faceted view of PDAC, characterized by malignant ductal cell genomic instability and hyperproliferation, a profoundly reprogrammed immunosuppressive and pro-tumorigenic TME, and a complex network of oncogenic and inflammatory cell-cell interactions. These findings collectively offer a rich resource for understanding PDAC pathogenesis and identifying novel diagnostic, prognostic, and therapeutic strategies.

Hypotheses:

  1. The observed M1 macrophage dominance in PDAC samples indicates a functionally impaired pro-inflammatory response, rather than an effective anti-tumor immune response, due to the highly immunosuppressive PDAC microenvironment.
  2. The recurrent CNVs in EGFR, EIF3E, and GSDMD in aneuploid ductal cells are critical drivers of tumor proliferation and survival, and targeting these pathways will significantly impact tumor growth.
  3. The elevated cell-cell interactions involving integrin pathways and immune checkpoints (e.g., CD47-SIRPA, HLA-E-NKG2A) between aneuploid ductal cells and macrophages actively promote immune evasion and metastatic potential in PDAC.

Potential therapeutic targets:

  1. EGFR: Frequently amplified in PDAC, promoting proliferation, survival, and migration, and exhibiting strong autocrine/paracrine signaling in aneuploid ductal cells. Evidence: CNV analysis shows frequent amplification of 7q14.1:7q21.12 (EGFR) in PDAC Ductal cells (up to ~67% frequency, Section 4). CCI analysis shows strong AREG-EGFR and TGFA-EGFR interactions within Aneuploid Ductal cells (Section 11). GSA shows EGF/EGFR Signaling Pathway enrichment in PDAC Ductal cells (Section 19). Upregulation of ERBB2, another EGFR family member, also seen in Ductal cell surfaceome (Section 15). Validation: Evaluate efficacy of EGFR-targeted therapies (e.g., gefitinib, erlotinib) in PDAC patient subsets with EGFR amplifications, potentially in combination with other treatments.
  2. CD47/SIRPA axis: CD47 acts as a 'don't eat me' signal, inhibiting phagocytosis by macrophages, contributing to immune evasion. Upregulated in PDAC tumor cells and CD4+ T cells. SIRPA is the macrophage receptor. Evidence: CCI analysis (Sections 12, 13, 14) shows strong SIRPA-CD47 interactions (e.g., Mac|Mac, Mac|T CD8+). Section 17 highlights CD47 upregulation on PDAC CD4+ T cells. Section 16 shows SIRPA upregulation on PDAC macrophages. Validation: Test anti-CD47 antibodies or SIRPA inhibitors to promote macrophage phagocytosis of tumor cells and enhance anti-tumor immunity in PDAC models.
  3. TGF-beta signaling (TGFB1/TGFBR1): TGF-beta signaling promotes immunosuppression, fibrosis, epithelial-mesenchymal transition (EMT), and tumor progression in PDAC. Evidence: CCI analysis (Section 11) shows strong TGFB1-TGFBR1 interactions within Aneuploid Ductal cells and also with macrophages. GSA also shows TGF-beta Receptor Signaling pathway enrichment in PDAC Ductal cells (Section 19). Section 16 shows TGFBR2 upregulation on PDAC macrophages. Validation: Evaluate TGF-beta pathway inhibitors (e.g., galunisertib) in PDAC models, especially in combination with immunotherapies or agents targeting fibrosis.
  4. HLA-E/NKG2A axis: HLA-E engaging NKG2A on T and NK cells suppresses anti-tumor immunity, allowing tumor cells to evade immune surveillance. Evidence: CCI analysis (Sections 12, 14) shows HLA-E-CD94:NKG2A (Mac|T CD8+) and HLA-E_KLRC1 (Mac|T CD8+) interactions in PDAC. Section 17 highlights HLA-F (another MHC class I molecule) upregulation on PDAC CD4+ T cells, suggesting broader MHC class I immune evasion mechanisms. Validation: Develop or repurpose therapies targeting HLA-E or NKG2A to reactivate T/NK cell responses in PDAC.
  5. MET: MET is a receptor tyrosine kinase frequently overexpressed or activated in PDAC, promoting cell proliferation, survival, invasion, and metastasis. Highly accessible surfaceome marker. Evidence: Dot plot (Section 15) shows strong upregulation of MET as a surfaceome marker in PDAC Ductal cells compared to normal. Validation: Develop antibodies or antibody-drug conjugates (ADCs) targeting MET, or evaluate MET inhibitors in PDAC models and patient cohorts.
  6. ITGB6 (Integrin Beta 6): ITGB6 is an integrin subunit crucial for cell-extracellular matrix interactions, often associated with epithelial-mesenchymal transition (EMT), fibrosis, tumor invasion, and metastasis in PDAC. Highly accessible surfaceome marker. Evidence: Dot plot (Section 15) shows strong upregulation of ITGB6 as a surfaceome marker in PDAC Ductal cells compared to normal. CCI analysis (Sections 12, 14) shows various integrin-mediated interactions in PDAC. Validation: Develop antibodies or inhibitors targeting ITGB6 to disrupt tumor-stroma interactions and inhibit tumor invasion/metastasis in PDAC models.
  7. PI3K-AKT-mTOR pathway: This pathway is consistently activated in PDAC Ductal cells and other TME cells, driving proliferation, survival, and metabolic reprogramming, central to cancer cell growth and resistance to therapy. Evidence: GSA (Section 19) shows strong enrichment of 'PI3K-AKT-mTOR signaling pathway' in PDAC Ductal cells. GSEA (Section 20) confirms 'PI3K-Akt Signaling Pathway' upregulation across multiple PDAC cell types (Ductal, Endothelial, Macrophage, T cells). Cell cycle genes show high expression in PDAC Ductal cells (Section 18). Validation: Evaluate PI3K/AKT/mTOR inhibitors, alone or in combination, in PDAC models and patient cohorts to inhibit tumor proliferation and survival.
  8. MAPK signaling pathway: A key pro-survival and proliferative pathway consistently activated in PDAC, driving cell growth and resistance to therapy. Evidence: GSEA (Section 20) confirms 'MAPK Signaling Pathway' upregulation across multiple PDAC cell types (Ductal, Endothelial, Macrophage, T cells). Validation: Evaluate MAPK pathway inhibitors (e.g., MEK inhibitors), alone or in combination with other therapies, in PDAC models and patient cohorts.

Follow-up validation ideas:

  1. Perform in vitro co-culture experiments with PDAC cells and patient-derived M1-like macrophages to assess their functional anti-tumor activity (e.g., cytokine production, phagocytosis, T cell activation capacity) under TME-mimicking conditions.
  2. Use CRISPR/Cas9 or small molecule inhibitors to target EGFR, EIF3E, or GSDMD in PDAC cell lines (preferably with corresponding amplifications) and assess effects on cell proliferation, survival, and invasion in vitro and in patient-derived xenograft (PDX) models.
  3. Employ neutralizing antibodies or genetic knockdowns to disrupt identified cell-cell interactions (e.g., CD47-SIRPA, integrins, TGFB1-TGFBR1) in relevant in vitro (e.g., 3D organoids, co-cultures) and in vivo (e.g., orthotopic PDAC models) models to evaluate their impact on tumor growth, metastasis, and immune modulation.
  4. Apply spatial transcriptomics or multiplexed imaging (e.g., IMC, CyTOF) to patient PDAC samples to spatially resolve the identified cell types, marker expression, and cell-cell interactions in situ, confirming their co-localization and validating functional hypotheses.
  5. Correlate the expression of identified surfaceome markers (e.g., MET, ERBB2, SIRPA, CD96) or the activity of enriched pathways (e.g., PI3K-AKT-mTOR, MAPK) in patient biopsies with clinical outcomes (e.g., survival, response to therapy) to establish their prognostic or predictive value.

Limitations:

Single-cell RNA-seq captures mRNA, not protein levels or post-translational modifications, which can be critical for protein function. CellPhoneDB analysis infers cell-cell interactions based on ligand-receptor expression; actual functional interactions require experimental validation. Ploidy inference (Aneuploid/Diploid) is based on gene expression patterns (CNV estimates) and might not capture all genomic alterations or mosaicism. The 'unassigned' cells remain, and while they show some malignant characteristics, their precise identity is unclear. The observed M1 macrophage dominance in PDAC (Section 8) contradicts common understanding (M2 dominance) in many solid tumors, requiring further functional validation.

22. Query List

  1. Show UMAPs colored by condition, sample, celltype_major, celltype_minor, ploidy_dec, and celltype_subset in 2 columns, and save it.
  2. Show expression of CD3D, CD4, CD8A, CD79A, MS4A1, MZB1, CD14, LYZ, FBLN1, NOTCH3, EPCAM, MUC1, CD34 on UMAP, along with minor celltype annotation. Set ncols=4 and save it.
  3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
  4. Show a CNV heatmap for Ductal cell (tumor-origin) and unassigned cells, grouped by sample, along with a summary of significantly amplified copy number regions, and save it.
  5. Show CNV patterns on UMAP, including major celltype, minor celltype, ploidy results, condition, and sample in 2 columns, and save it.
  6. Show population bar plot for minor cell types and save it.
  7. Show subset population bar plot for T cells and save it.
  8. Show subset population bar plot for macrophages and save it.
  9. If there are significant differences between conditions in T cell subset population, show them as box plots and save them. Determine ncols appropriately based on the total number of panels.
  10. Show a ploidy population bar plot for Ductal cell (tumor-origin) and unassigned cells, and save it.
  11. Show cell-cell interaction patterns per condition, focusing on Ductal cell, Fibroblast, Macrophage, T cell CD4+, and T cell CD8+, up to 80 interactions per condition, and save it.
  12. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
  13. Show cell-cell interactions for genes related to immune checkpoint and cell cycle pathways, and save it.
  14. Find statistically significant differences in cell-cell interactions between conditions for major immune and stromal cells, show them as a dot plot, set max_n_items_per_group = 25, and save it.
  15. Show the condition-specific markers for tumor-origin cells (Ductal cell) as a dot plot and save the result. Use only surfaceome markers, up to 50 markers per condition.
  16. Extract condition-specific markers for macrophages, show them as a dot plot, include only surfaceome markers, up to 50 per condition, and save it.
  17. Extract condition-specific markers for CD4 T cells, show them as a dot plot, include only surfaceome markers, up to 50 per condition, and save it.
  18. For genes related to the Cell cycle pathway, find statistically significant differences in expression between conditions for Ductal cell, Macrophage, and T cell CD8+, show them as box plots, set max_n_items_to_plot = 24, and determine ncols such that the width x height ratio is approximately 2x3, and save it.
  19. Show Gene Ontology (GSA) analysis results as a bar plot for Ductal cell and Acinar cell, and save it.
  20. Show Gene set enrichment analysis (GSEA) results as a dot plot for Acinar cell, Ductal cell, Endothelial cell, Macrophage, Mast cell, T cell CD4+, T cell CD8+. Use 'RdBu_r' for color map, set n_pws_to_show = 80, and save it.
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