A subgroup of pancreatic adenocarcinoma is sensitive to the 5-aza-dC DNA methyltransferase inhibitor
Bibliographic record
Abstract
// Odile Gayet 1 , Celine Loncle 1 , Pauline Duconseil 1 , Marine Gilabert 1 , Maria Belen Lopez 1 , Vincent Moutardier 1, 2 , Olivier Turrini 1, 3 , Ezequiel Calvo 4 , Jacques Ewald 3 , Marc Giovannini 3 , Mohamed Gasmi 5 , Erwan Bories 3 , Marc Barthet 5 , Mehdi Ouaissi 6 , Anthony Goncalves 3 , Flora Poizat 3 , Jean Luc Raoul 3 , Veronique Secq 1, 2 , Stephane Garcia 1, 2 , Patrice Viens 3 , Nelson Dusetti 1 , Juan Iovanna 1 1 Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Parc Scientifique et Technologique de Luminy, Marseille, France 2 Hôpital Nord, Marseille, France 3 Institut Paoli-Calmettes, Marseille, France 4 Centre Génomique du Centre de recherche du CHUL Research Center, Quebec, Canada 5 Hôpital Nord, Département de Gastroentérologie, Marseille, France 6 Hôpital de la Timone, Marseille, France Correspondence to: Nelson Dusetti, e-mail: nelson.dusetti@inserm.fr Juan Iovanna, e-mail: juan.iovanna@inserm.fr Received: August 05, 2014 Accepted: November 02, 2014 Published: December 03, 2014 ABSTRACT Pancreatic Ductal Adenocarcinoma (PDAC) is a disease with a great heterogeneity in the response to treatments. To improve the responsiveness to treatments there are two different approaches, the first one consist to develop new and more efficient drugs that intent to cure all patients and the second one is to use already-approved drugs, alone or in combination, but selecting beforehand the most sensitive patients. In this work we explored the efficiency of the second possibility. We developed a collection of 17 PDAC samples collected by Endoscopic Ultrasound-Guided Fine-Needle Aspiration (EUS-FNA) or surgery and preserved as xenografts and as primary cultures. This collection was characterized at molecular level by a transcriptomic analysis using an Affymetrix approach. In this paper we present data demonstrating that a subgroup of PDAC responds to low doses of 5-aza-dC. These tumors show a specific RNA expression profile that could serve as a marker, but there is no correlation with Dnmt1 , Dnmt3A or Dnmt3B expression. Responder tumors corresponded to well-differentiated samples and longer survival patients. In conclusion, we present data obtained with the well-known drug 5-aza-dC as a proof of concept that a drug that seems to be inefficient in solid tumors in general could be applicable to a particular subgroup of patients with PDAC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".