Abstract 4396: Multiomics assessment of the cancer and stromal compartments of patient-derived pancreatic xenografts reveals clinically-relevant subtypes and novel targeted therapies
Bibliographic record
Abstract
Abstract Patient-derived xenografts (PDX) are appearing as a prime approach for preclinical studies despite being insufficiently characterized as a model of the human disease and its diversity. In this work, 29 PDX were obtained from either surgery or endoscopic ultrasound-guided fine needle aspirate of pancreatic adenocarcinoma. The extensive genomic profiling of these pancreatic PDX, revealed two clinically-relevant subtypes having broad similarities with human primary tumors. These subtypes are defined by highly specific DNA methylation and transcriptomic profiles (mRNA, miRNA or lncRNA) but are not distinguishable by exonic mutations or copy number aberrations. Moreover, by specifically analyzing the stroma transcriptome, as defined by the expression of murine transcripts, we found that it is able to stratify the patients with the same efficiency than the analysis of grafted human tumor cells. This finding suggest that transformed pancreatic cells drive the composition of their own stroma. Finally, the multiomics analysis pinpoints novel therapeutic targets, one of which we demonstrate to be an efficient method for treating pancreatic cancer. Overall, we show that PDX are trustworthy pre-clinical models of pancreatic adenocarcinoma including of unresectable tumors. Their multiomics profiling allow the independent analysis of the uncontaminated cancer or stromal compartments and discloses several original therapeutics targets. Citation Format: Remy Nicolle, Yuna Blum, Laetitia Marisa, Celine Loncle, Odile Gayet, Vincent Moutardier, Olivier Turrini, Marc Giovannini, Benjamin Bian, Martin Bigonnet, Marion Rubis, Nabila Elarouci, Lucile Armenoult, Mira Ayadi, Pauline Duconseil, Mohamed Gasmi, Mehdi Ouaissi, Aurélie Maignan, Gwen Lomberk, Jean-Marie Boher, Jacques Ewald, Erwan Bories, Jonathan Garnier, Anthony Goncalves, Flora Poizat, Jean-Luc Raoul, Veronique Secq, Stephane Garcia, Philippe Grandval, Marine Barraud-Blanc, Emmanuelle Norguet, Marine Gilabert, Jean-Robert Delpero, Julie Roques, Ezequiel Calvo, Fabienne Guillaumond, Sophie Vasseur, Raul Urrutia, Aurélien de Reyniès, Nelson Dusetti, Juan Iovanna. Multiomics assessment of the cancer and stromal compartments of patient-derived pancreatic xenografts reveals clinically-relevant subtypes and novel targeted therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4396. doi:10.1158/1538-7445.AM2017-4396
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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 | 0.000 |
| 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) | 0.002 | 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".