Abstract A007: Pancreatic cancer comprises co-existing transcriptional states regulated by distinct master regulator programs
Bibliographic record
Abstract
Abstract Despite extensive efforts, reproducible assessment of pancreatic ductal adenocarcinoma (PDA) heterogeneity and plasticity at the single cell level remains elusive. Systematic, network-based analysis of single cell RNA-seq profiles showed that most PDA tumors comprise three coexisting lineages whose aberrant transcriptional state is mechanistically controlled by distinct regulatory programs. These lineages were characterized by the aberrant activation of either gastrointestinal lineage markers (GLS), transcriptional effectors of morphogen pathways (MOS) and acinar to ductal metaplasia markers (ALS). Each lineage was characterized by cells in two different cell states determined by the differential activation of MEK signaling (M+/M-) and high cellular plasticity. These states were confirmed in multiple cohorts, cell lines, PDX models and harmonized with bulk profile analyses. Master regulators (MRs) of GLS and MOS state were predictive of patient’s survival in bulk profiles. Cross-state plasticity was confirmed by lineage tracing assays, while pooled CRISPR/Cas9 assays confirmed the essentiality of identified MR proteins. Finally, mechanistic MR-mediated cell state control was confirmed by MR expression-mediated reprogramming of MOS cells to a GLS state. Our work provided a mechanistic model of pancreatic cancer heterogeneity and testable hypothesis to target cell state-specific pancreatic cancer dependencies. Citation Format: Pasquale Laise, Mikko Turunen, Hans Carlo Maurer, Alvaro Curiel Garcia, Ela Elyada, Bernhard Schmierer, Lorenzo Tomassoni, Jeremy Worley, Mariano J. Alvarez, Jordan Kesner, Xiangtian Tan, Somnath Tagore, Ester Calvo Fernandez, Kelly Wong, Alexander L. E. Wang, Sabrina Ge, Alina C. Iuga, Aaron T. Griffin, Winston Wong, Gulam A. Manji, Faiyaz Notta, David A. Tuveson, Kenneth P. P. Olive, Andrea Califano. Pancreatic cancer comprises co-existing transcriptional states regulated by distinct master regulator programs [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A007.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".