Abstract A025: Cell of origin does not underlie transcription heterogeneity in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) it though to develop through induction of different precursor lesions, however, the normal cell type that gives rise to these precancerous lesions and PDAC is unclear. Recent studies using mouse models and ex vivo cultured human cells suggest that the cellular origin of PDAC may be either acinar or ductal cells. Potential transcriptional markers associated with PDAC of different cellular origins have been identified. Here, we interrogate whether these markers are predictive of cellular of origin across models using mouse models where acinar and ductal cells (Ptf1aCreER or Sox9CreER) expressing oncogenic Kras in the absence of Trp53 or Pten give rise to PDAC tumors. We found that acinar- and ductal-cell-derived PDAC exhibit transcriptional differences both in vivo and in vitro when comparing within one genotype or sample source type (e.g. bulk tumor or primary cell lines). However, previously predicted markers of cellular origin had variable expression across models and sample types suggestive of differences specific to those sample sets. Using our larger sample sets and multiple genotypes and source types, we proposed a more comprehensive set of cell of origin markers for consideration. Application of these markers to our own data sets, however, failed to properly segregate acinar- and ductal-cell-derived PDAC into separate groups. This suggests that the transcriptional heterogeneity observed in our sample set was not driven by the tumors arising from different cellular origins. We also examined the relationship between cell of origin and patient PDAC transcriptional subtypes using our mouse model datasets. Our findings support previous research indicating that mouse models of acinar-cell-derived PDAC favor those of a classical subtype. However, our mouse PDAC samples all express similar or very low levels of the genes denoting the basal subtype, suggesting that the basal subtype was not strongly present in any of the datasets we examined. In summary, our data suggest that acinar and ductal cells converge into a very similar PDAC transcriptional state and that, if differences can be identified, they will likely be identified by non-transcript based readouts. Citation Format: Ken Chu, Alex Lee, Karnjit Sarai, Yan Dou, Wesley Hunt, Emma Croft, Atefeh Samani, Farnaz Taghizedeh, Claire Dubois, Stephane Flibotte, Janel Kopp. Cell of origin does not underlie transcription heterogeneity in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A025.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".