Drug treatments for metastatic renal cell carcinoma (RCC) have evolved considerably over the past twenty years with the availability of an increasing number of targeted therapies based on protein kinase inhibitors and immunotherapies. At the same time, molecular profiling technologies (next-generation sequencing, LC-MS/MS) have made it possible to obtain multi-omic characterizations of large cohorts of renal tumors. One of the main challenges in precision medicine is to optimize the choice of treatments for patients not only by using biological and clinical information about their tumors, but also according to their genomic characteristics. In this context, the KATY project (https://katy-project.eu/), funded from the European Union’s Horizon 2020 Framework Programme, has set out to build a precise personalised medicine system empowered by Artificial Intelligence (AI). The novel AI tool aims to predict the response of kidney cancer to targeted therapies and identify the molecular evidence to support these predictions. Most importantly, the KATY system will offer human interpretable knowledge that clinicians and clinical researchers can trust, adequately evaluate and effectively use in their everyday working routine.
(1) In collaboration with KATY partners, he/she will collect public multi-omics (genome, bulk and single-cell transcriptome, proteome, phospho-proteome, epigenome, metabolome) renal data collections generated from cell models and normal/tumor tissues. Biological, clinical, demographical and therapeutic response data from patients will also be collected. He/she will also work on the aggregation of existing knowledge regarding biological pathways, molecular interactions, clinical trials, and drugs targets/side effects from public databases.
(2) He/she will work on the homogeneous bioinformatic reprocessing on omics data when needed based on the best bioinformatics practices established with researchers from the consortium.
(3) He/she will perform advanced feature engineering approaches from quantitative measurements of genes, proteins and metabolites, to model perturbations at higher levels of abstraction. In particular, we will predict the cellular heterogeneity of the tumor microenvironment using cellular deconvolution methods, and perform network-based modeling of molecular deregulations.
(4) Finally, the bioinformatician will rely on the results generated previously to perform the deep molecular and cellular characterizations of renal cancer subtypes which will be used to develop AI-based predictive models of patients responses to targeted therapies.
CEA, the French Atomic and Alternative Energy Commission is a public research and technology organization founded in 1945 and active in four main areas: defense and security, low-carbon energies, information technologies and health technologies. The CEA currently counts 16,000 employees including 1,600 PhD students and 300 post-docs. It has been ranked the most innovative public research organization in Europe and ranked second in the world in the "Top 25 Global Innovators -Government" ranking (Reuters, 2017). The Interdisciplinary Research Institute of Grenoble (IRIG) aims at exploring biological processes on a molecular scale and to carry out research in technologies for life sciences and health. The research project will take place in the IMAC team, member of the Biology and Biotechnology for Health Laboratory at IRIG, CEA Grenoble. IMAC is a multi-disciplinary team composed of cancer biologists, clinicians and bioinformaticians.
The recruited bioinformatician will benefit from interactions with a PhD student and a post-doctoral researcher who will soon be recruited by the CEA as part of KATY. Furthermore, he/she will be integrated into the multi-disciplinary work environment of the KATY consortium, made up of clinicians, biologists, bioinformaticians, developers, and AI experts from different countries.
Expected experiences and skills:
Engineering or Master degree in bioinformatics with at least 2 years of experience.
Good experience in bioinformatics
programming language (Python, R); unix command line
processing, exploration and visualization of omics data
network / systems biology
machine learning; biostatistics
Good knowledge of biology, oncology and omics data
transcriptome, proteome, epigenome, metabolome
cancer biology, cell types, biological pathways
rigor, organization and ability to work in team and interact with people of different backgrounds
curiosity and willingness to improve one's scientific and technological skills and productivity
The researcher will be recruited on a 2-year work contract financed by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017453.
If you want to join this exciting and ambitious research project, please send your CV, a cover letter, a letter of recommendation and the name of at least one referent to firstname.lastname@example.org. The position starts as early as May 2021.