Abstract B115: Integrated spatial and multi-omic analysis reveals functional niches in the pancreatic cancer microenvironment
Bibliographic record
Abstract
Abstract The tumour microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is comprised of heterogenous malignant epithelium and diverse immune infiltrates within a dense, fibrotic stroma. This multi-compartmental TME includes myeloid and lymphoid lineages, cancer-associated fibroblasts (CAFs), mural and endothelial cells, and tumour cells embedded in a rich extracellular matrix (ECM). Efforts to profile and perturb compartments or cellular phenotypes in isolation have often led to unintended adverse effects on neighbouring cells, compromising drug efficacy. This drives the need for an integrated, spatially-resolved understanding of the architecture and molecular features underlying the organization of the TME. To address this, we leveraged single-cell RNA sequencing (scRNAseq) data from eight distinct datasets to design three custom multiplexed imaging mass cytometry (IMC) panels against tumour, immune, and stromal targets. These were applied to three serial sections of a tissue microarray of 221 PDAC patients. Segmentation and clustering of 7.9 million cells revealed 83 distinct cell types and their functional states, including biophysical responses. Advanced image alignment across serial sections enabled the incorporation of TME-wide features and resolved eight spatially recurrent PDAC microenvironments neighbouring phenotypically distinct epithelial ducts. Two microenvironments, ECM-rich and immune-suppressed, were associated with worse overall survival and frequently co-occurred with a third, stiff matrix, characterized by stroma enriched in phospho-myosin light chain 2 expressing CAFs. In contrast, the immune infiltrated stroma microenvironment was linked to improved clinical outcomes and often co-occurred with CD105+ fibrovascular and immune infiltrated microenvironments. Matched whole-genome sequencing (n = 182/221) revealed that patients dominant in ECM-rich regions harboured 12p11.21 (KRAS) and 8q24.21 (MYC and POU5F1B) amplifications, and 17q22 (RNF43) deletions, and stiff matrix were associated with 13q33.3 (LATS2) deletion. To link spatial niches to proteomic signatures, we performed whole-slide IMC-guided laser capture microdissection and mass spectrometry, and further integrated differentially expressed proteins with transcriptomes from an external scRNAseq cohort (n = 163). ECM-rich and immune suppressed regions showed signatures of mechanically induced immune suppression, including NETosis and innate immune surveillance via pattern recognition receptors. Contrastingly, immune infiltrated microenvironments were enriched for cytotoxic activity and immune trafficking programs, while immune infiltrated stroma displayed signatures associated with oxidative stress responses. These findings define unique spatial ecosystems within the PDAC TME that are linked to specific genomic alterations, functional states and clinical outcomes, highlighting their therapeutic relevance and potential as targets to overcome the challenges of treating PDAC. Citation Format: Noor Shakfa, Ferris Nowlan, Tiak Ju Tan, Sibyl Drissler, Elizabeth Sunnucks, Jennifer L. Gorman, Chengxin Yu, Michael J. Geuenich, Sheng-Ben Liang, Barbara Gruenwald, Ayelet Borgida, Cassandra J. Wong, Brendon Seale, Zhen Yuan Lin, Edward L. Chen, Golnaz Abazari, Miralem Mrkonjic, Julie M. Wilson, Kieran R. Campbell, Robert C. Grant, Anne-Claude Gringas, Grainne M. O'Kane, Faiyaz Notta, Steven Gallinger, Hartland W. Jackson. Integrated spatial and multi-omic analysis reveals functional niches in the pancreatic cancer microenvironment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B115.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".