Mots-Clés
Database, multiomics, transcriptomics, pancreatic cancer, translational research
Description
Project: Creation of a Multi-omics Database from Preclinical Models to Identify New Molecular Targets for Overcoming Pancreatic Cancer Resistance
Job opening for a 2-year fixed-term contract (CDD). Post-doc or engineer level.
Skills/Qualifications
- Advanced degree (MSc/Ph.D.) in bioinformatics or a related field.
- Strong experience in multi-omic data analysis, utilizing genomic, transcriptomic, and other -omic datasets.
- Proficiency in programming languages such as Python, R, or similar, for bioinformatic analysis.
- Knowledge of cancer biology, especially pancreatic cancer, is highly desirable.
- Experience in database management and bioinformatics tool development skills.
- Excellent collaboration and communication skills.
Research Center in Oncology of Marseille: 'Pancreatic Cancer' Team - Nelson Dusetti/Juan IovannaPaoli-Calmettes Institute: Emmanuel Mitry, Brice Chanez
Pancreatic adenocarcinoma ranks among cancers with the poorest prognosis, and despite innovations in oncology, effective therapeutic options remain limited. Cytotoxic chemotherapy remains the standard treatment, and its administration currently is not guided by tumor biology, despite its significant heterogeneity. In an era where personalized medicine appears to be the optimal choice for most cancers, it becomes urgent to tailor pancreatic cancer treatment to its various subtypes and, particularly, their sensitivity to available drugs, while awaiting the arrival of more effective treatments. In this clinical and biological context, we aim to address the following question: Is it possible to establish clinical prognosis and predict the chemosensitivity of pancreatic cancer based on its molecular characteristics?
1- Construction of the PaCaOmics Preclinical Models Collection
In recent years, we have developed complementary strategies to customize existing treatments for pancreatic cancer and identify more effective therapies. We established a tumor biobank based on in vivo and in vitro preclinical models, including patient-derived xenografts (PDX), two-dimensional primary cell cultures, and three-dimensional organoids. This "breathing" biobank (150 PDX, >100 2D primary cell cultures, >150 3D organoids) can be frozen and thawed, providing an unlimited source of microtumors useful in various clinical and/or preclinical studies. One of its major advantages is the study of tumor sensitivity to known drugs and the assessment of new anticancer molecules (clinical trial PaCaOmics 2012-2017 NCT01692873 (1-15)). Organoids, by better preserving the original tumor's characteristics compared to other in vitro models, exhibit sensitivity to treatments, heterogeneity, and molecular features defining pancreatic tumor subtypes. Moreover, these models recapitulate the significant heterogeneity observed in patients. Through these strategies, all pancreatic cancer patients can be included in our studies, obtaining microtumor fragments through endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) for locally advanced or metastatic tumors, as well as pancreatic resection specimens for surgically treated patients.
2- Multi-omics Characterization of the PaCaOmics Preclinical Models Collection
We developed high-throughput studies applied to these preclinical models. These studies led to the creation of sensitivity tests (chemograms) covering all chemotherapy molecules currently validated for pancreatic cancer treatment (5FU, oxaliplatin, irinotecan, gemcitabine, and paclitaxel). These functional tests, based on quantifying viable cells based on drug concentration (in vitro) or tumor size in xenografts (in vivo) post-treatment, were then associated with omics studies (genomic, transcriptomic, methylomic, proteomic, secretomic, epigenomic, etc.). Different models and chemogram systems developed complementarily allowed us to identify, despite differences between models, mechanisms of pancreatic cancer chemoresistance. Thus, we identified several transcriptomic signatures predicting tumor aggressiveness and sensitivity to different chemotherapy molecules. These results were then retrospectively validated in several patient cohorts. The results of these works have been published (1-15). These studies generated a substantial amount of data regarding the sensitivity of models and their molecular characteristics (multi-omics), biological material in the form of PDX, primary cultures, and organoids. They also enhanced our biological and bioinformatics expertise. Four patents were filed to protect our findings (16-19), one of which was licensed to a Marseille-based startup, PREDICTING MED. Overall, these results demonstrated, by establishing a robust proof of concept, that combining chemotherapy sensitivity data with the analysis of cancer cell phenotype through an omics approach can reveal pancreatic tumor chemosensitivity.
3- Project: Creation of a Multi-omics Database to Identify New Molecular Targets for Overcoming Pancreatic Cancer Resistance
The goal of this project is to centralize, normalize, and associate all omics data obtained to date into a single database. This data collection represents 10 years of work and over 60 publications from our team in collaboration with clinicians from the Paoli-Calmettes Institute. Molecular data will be linked to clinical data from the 220 patients in the PaCaOmics trial, along with chemosensitivity tests conducted on preclinical models. We will then develop bioinformatic algorithms to query and exploit this database through an intuitive interface accessible to the scientific community, especially teams working on pancreatic cancer at CRCM. This advanced tool will facilitate the exploitation of a large amount of data, allowing the cross-referencing of various omics studies and identifying models relevant to patients selected based on the study's interest. This information source will be one of the richest and most comprehensive for this cancer, enabling research and the identification of new molecular targets and mechanisms involved in pancreatic tumor resistance. It will provide tools to sensitize them to existing treatments and detect vulnerabilities that, when exploited, will lead to the development of innovative and specific treatments.
This project follows the funding initiative 'Big Data: Use of Massive Data in Cancer Treatment', granted by Ligue CD13 to the Paoli-Calmettes Institute (IPC) to establish the necessary computer infrastructure for collecting and storing research and clinical data at IPC. This infrastructure is part of the Health Data Warehouse (EDS), co-financed by IPC, European FEDER-Région funds, and the Public Investment Bank, as part of the EDS Méditerranée (including IPC, APHM, and Nice hospitals), recently labeled.
The project will be led by Drs. Nelson Dusetti/Juan Iovanna, directors of the 'Pancreatic Cancer' team at CRCM, and Drs. Emmanuel Mitry, Director of Clinical Research at IPC, and Brice Chanez, medical oncologist at IPC. It will benefit from the expertise of medical and scientific teams from CRCM, IPC, and APHM, representing the leading French force in pancreatic cancer research.
References for Key Publications of the PaCaOmics Project
1- Nicolle R et al. Pancreatic Adenocarcinoma Therapeutic Targets Revealed by Tumor-Stroma Cross-Talk Analyses in Patient-Derived Xenografts. Cell Rep. 2017 Nov 28;21(9):2458-2470.
2- Lomberk G et al. Distinct epigenetic landscapes underlie the pathobiology of pancreatic cancer subtypes. Nat Commun. 2018 May 17;9(1):1978.
3- Juiz N et al, Pancreatic Cancer Heterogeneity can be Explained Beyond the Genome. Front Oncol. 2019 Apr 5;9:246.
4- Lomberk, G., Dusetti, N., Iovanna, J. et al. Emerging epigenomic landscapes of pancreatic cancer in the era of precision medicine. Nat Commun 2019 10, 3875 (2019).
5- Duconseil P, et al. Transcriptomic analysis predicts survival and sensitivity to anticancer drugs of patients with a pancreatic adenocarcinoma. Am J Pathol. 2015 Apr;185(4):1022-32.
6- B. Bian et al. Gene expression profiling of patient-derived pancreatic cancer xenografts predicts sensitivity to the BET bromodomain inhibitor JQ1: implications for individualized medicine. EMBO molecular medicine, 9 (2017), pp. 482–497.
7- Lan W et al. E2F signature is predictive for the pancreatic adenocarcinoma clinical outcome and sensitivity to E2F inhibitors, but not for the response to cytotoxic-based treatments. J. Sci Rep. 2018 May 29;8(1).
8- Rémy Nicolle et al. Establishment of a pancreatic adenocarcinoma molecular gradient (PAMG) that predicts the clinical outcome of pancreatic cancer. EBioMedicine. 2020 Jul;57:102858.
9- Rémy Nicolle et al. A transcriptomic signature to predict adjuvant gemcitabine sensitivity in pancreatic. Ann Oncol. 2021 Feb;32(2):250-260.
10- Fraunhoffer N et al. A Transcriptomic-Based Tool to Predict Gemcitabine Sensitivity in Advanced Pancreatic Adenocarcinoma. Gastroenterology. 2023 Mar;164(3):476-480.e4.
11- Nicolas Fraunhoffer et al/ Development and clinical validation of news transcriptomic tools for predicting the response to individual drug of the mFOLFIRINOX regimen in patients with pancreatic ductal adenocarcinoma. iScience under press.
12- Natalia Juiz, Abdessamad Elkaoutari, Martin Bigonnet, Odile Gayet, Julie Roques, Rémy Nicolle, Juan Iovanna, Nelson Dusetti. Basal-like and Classical cells coexistence in pancreatic cancer revealed by single cell analysis. FASEB J. 2020 Sep;34(9):12214-12228.
13- Hoare O et al. Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models. Cancers (Basel). 2021 May 19;13(10):2473.
14- Fraunhoffer NA et al. Inhibition of glucuronidation in pancreatic cancer improves gemcitabine anticancer activity. Cancer Commun (Lond). 2022 Nov;42(11):1212-1216.
15- Rémy Nicolle, Jean-Baptiste Bachet, Alexandre Harlé, Juan Iovanna, P. Hammel, Vinciane Rebours, Anthony Turpin, Meher Ben Abdelghani, Alice Wei, Emmanuel Mitry, Anthony Lopez, James Biagi, Eric François, Pascal Artru, Aurélien Lambert, Daniel Renouf, Laure Monard, Marjorie Mauduit, Nelson Dusetti, Thierry Conroy, Jérome Cros. Prediction of adjuvant gemcitabine-sensitivity in resectable pancreatic adenocarcinoma using the GemPred RNA signature: an ancillary study of the PRODIGE-24/CCTG PA6 clinical trial. J Clin Oncol. 2023 Nov 14:JCO2202668.
16- Rapid methodology for obtaining molecular signatures from Biopsy derived pancreatic organoids (BDPO). Application N° EP16200075.6 Dusetti Nelson, Iovanna Juan, Bian Benjamin, Bigonnet Martin. SATT Sud-Est.
17- IN VITRO METHOD FOR IDENTIFYING EFFICIENT THERAPEUTIC MOLECULES FOR TREATING PANCREATIC DUCTAL ADENOCARCINOMA. Application N° EP20305052. January 2020. Inventors : TURRINI Olivier, GILABERT Marine, GIOVANNINI Marc, NICOLLE Rémy, BLUM Yuna, IOVANNA Juan, DUSETTI Nelson. Institut Paoli Calmettes.
18- METHOD FOR DETERMINING A REFERENCE TUMOR AGGRESSIVENESS MOLECULAR GRADIENT FOR A PANCREATIC DUCTAL ADENOCARCINOMA. Application N° 19305522.5 déposée le 23/04/2019. Inventors : TURRINI Olivier, GILABERT Marine, GIOVANNINI Marc, NICOLLE Rémy, BLUM Yuna, IOVANNA Juan, DUSETTI Nelson. Institut Paoli Calmettes. Institut Paoli Calmettes.
19- BIOMARKERS FOR PROGNOSING RESPONSE TO TREATMENT – PDAC. Inventors : IOVANNA Juan, DUSETTI Nelson. SATT Sud-est.