Abstract A48: Multi-omics characterization of PDAC subtypes using PDX reveals that epigenetic but not genetic analysis permit a clinically relevant classification
Bibliographic record
Abstract
Abstract Genome-wide molecular profiles have been proven to be beneficial for the identification of clinically relevant tumor subtypes in many neoplastic diseases. While pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death, very few genomic and transcriptomic studies have been conducted. This is mainly due to the difficulty to obtain a suitable cohort of PDAC tumor samples. The major obstacle is the usually high proportion of non-transformed stromal cells, which can greatly hinder the analysis of carcinogenic-specific processes. Moreover, tumor cohorts requiring resection samples can be biased by the exclusion of inoperable patients, representing 85% of all patients presenting PDAC. In this study, we generated Patient Derived Xenografts (PDX) with samples collected from 29 patients using either Endoscopic Ultrasound-Guided Fine-Needle Aspirates or, for operable patients, resections. The transcriptomic profiles, of both mRNA and miRNA, and the epigenetic landscape of early PDX passages consistently identified two tumor-specific molecular subtypes: a well differentiated group often referred to as classical, and an undifferentiated group previously recognized as Basal, Quasi-Mesenchymal or Squamous. Of all the genetic alterations determined by Copy Number analysis and exome sequencing, none were specific to any of the two subtypes, which therefore could not be discriminated solely on genetic basis. These two PDAC subtypes are characterized by distinct epigenetic and transcriptomic profiles of which the analysis revealed the deregulation of several pathways previously imputed in PDAC development and carcinogenesis in general. In particular, we showed that the Wnt pathway as well as several metabolic pathways, including cytochrome P450 genes recently implicated in drug resistance, are both epigenetically and transcriptionally deregulated. Altogether, our results provide new insights on pancreatic carcinogenesis and suggest that PDAC phenotypical heterogeneity is mainly driven by epigenetic rather than genetic events. Citation Format: Remy Nicolle, Yuna Blum, Laetitia Marisa, Jonathan Garnier, Benjamin Bian, Celine Loncle, Martin Bigonnet, Odile Gayet, Vincent Moutardier, Pauline Duconseil, Mohamed Gasmi, Mehdi Ouaissi, Olivier Turrini, Marc Giovannini, Aurélie Maignan, Jean-Marie Boher, Jacques Ewald, Erwan Bories, Marc Barthet, Anthony Goncalves, Flora Poizat, Jean-Luc Raoul, Veronique Secq, Stephane Garcia, Philippe Grandval, Marine Barraud-Blanc, Emmanuelle Norguet, Marine Gilabert, Jean-Robert Delpero, Ezequiel Calvo, Aurélien de Reyniès, Nelson Dusetti, Juan Iovanna.{Authors}. Multi-omics characterization of PDAC subtypes using PDX reveals that epigenetic but not genetic analysis permit a clinically relevant classification. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr A48.
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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 | 0.000 |
| 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".