Abstract 155: Spatial analysis of tumor-immune-stromal cellular phenotypes and microenvironments in pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is notoriously resistant or refractory to both modern and conventional therapies. These tumors are characterized by heterogeneous malignant epithelial lesions and immune cells embedded within a highly dense and complex stroma. The unique, multi-compartmental tumor microenvironment (TME) —encompassing diverse lineages such as fibroblasts, mural cells, endothelial cells, lymphoid and myeloid populations, cancer cells, and a rich extracellular matrix—indicates the presence of intricate cellular networks that drive tumor progression. Efforts to profile and therapeutically target specific compartments or cellular phenotypes have often led to unintended adverse effects on neighboring cells and their microenvironments, compromising drug efficacy. To identify vulnerabilities in these interactions and precisely quantify intra- and inter-tumoral spatial heterogeneity, we utilized single-cell RNA sequencing data from eight distinct datasets to design separate custom multiplexed histopathology panels for imaging mass cytometry against tumor, immune, and stromal targets. Three serial sections of a tissue microarray containing 4 cores from 221 patients on each were stained with compartment-specific panels. Cell segmentation and clustering of 7.9 million cells in our dataset revealed 104 unique cell types, their functional states along with biophysical responses. We identified the presence of multiple epithelial subtypes within a single patient, along with single-compartment cellular organization and phenotypes associated with patient outcomes. Advanced image alignment using pixel classification and image transformation across serial sections enabled integration of TME-wide information, defining reproducible microenvironments neighboring malignant ducts. These microenvironments demonstrated immune and stromal patterns closely associated with specific epithelial phenotypes, carrying significant prognostic implications. Laser capture microdissection combined with mass spectrometry provided deeper insights into the biological processes within our microenvironments. We further identified associations between our microenvironments with bulk-RNAseq and specific genomic aberrations. These findings contribute to a nuanced understanding of the TME in PDAC and highlight the therapeutic potential in addressing this complexity to overcome the challenges in treating patients with PDAC. Citation Format: Noor Shakfa, Ferris Nowlan, Elizabeth Sunnucks, Sibyl Drissler, Tiak Ju Tan, Chengxin Yu, Jennifer L. Gorman, Michael J. Geuenich, Sheng-Ben Liang, Edward L. Chen, Golnaz Abazari, Barbara Gruenwald, Ayelet Borgida, Cassandra J. Wong, Brendon Seale, Zhen Yuan Lin, Miralem Mrkonjic, Julie M. Wilson, Kieran R. Campbell, Anne-Claude Gringas, Grianne M. O'Kane, Faiyaz Notta, Steven Gallinger, Hartland W. Jackson. Spatial analysis of tumor-immune-stromal cellular phenotypes and microenvironments in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 155.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".