Abstract B102: A model for the origins of transcriptional heterogeneity in human pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Transcriptional heterogeneity in pancreatic ductal adenocarcinoma can be separated into two major phenotypes, commonly referred to as Classical and Basal-like. Substantial work in the field has further refined the phenotypes and highlighted associations with outcome and treatment. However, there remains the fundamental question of how these transcriptional phenotypes originate in the disease. In mice, oncogenic reprogramming by Kras plays a central role in phenotype identity, but how these processes unfold in human tumours is largely unresolved. Based on analysis of the transcriptomes of 490 microdissected patient tumours, 48 single-cell RNA-seq and 10 single-cell multiome profiles, we distilled transcriptional heterogeneity into four high-fidelity phenotypes defined by distinct biological programs. Strikingly, on initial investigation, none of the phenotypes reflected the heterogeneity of the normal pancreas, as is common with cancers of other tissues. Upon investigating temporal emergence, the transcriptional phenotypes were found to arise in two phases from fundamentally different biological processes. Early phenotypes were found to emerge in non-aneuploid cells, and were unexpectedly present in the absence of KRAS mutations in humans. In contrast, late-emerging phenotypes developed after KRAS mutations with the onset of aneuploidy and were decoupled from tumour initiation. Biologically, early phenotypes derived from cell dedifferentiation processes whereas late phenotypes derived from lineage plasticity and epithelial remodelling. Transition from the early to late phenotypes fueled significant tumour heterogeneity and influenced therapeutic responses to KRAS inhibition. Together, these findings help redefine the ontogeny of human pancreatic cancer phenotypes. Citation Format: Sabrina Ge, Paul Tonon, Gun Ho Jang, Yu Zhang, Karen Ng, Eugenia Flores Figueroa, Julie M. Wilson, Anna Dodd, Amy Zhang, Amelia Xu, Michelle Chan-Seng-Yue, Ayah Elqaderi, Ilinca Lungu, Stephanie Ramotar, Shawn Hutchinson, Daniela Bevacqua, Ayelet Borgida, Spring Holter, Pathum Kossinna, Ruth Isserlin, Veronique Voisin, Gary D. Bader, Erica Tsang, Robert C. Grant, Grainne O'Kane, David Tuveson, Oren Parnas, Federico Gaiti, Jennifer J. Knox, Steven Gallinger, Faiyaz Notta. A model for the origins of transcriptional heterogeneity in human pancreatic ductal adenocarcinoma [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 B102.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".