Mots-Clés

miRNAs and transcriptomic biomarkers multi-block data analysis thyroid cancer ionizing radiation

Description

Environnement, organisation

The research project relies on a closed collaboration between the IRSN (Radiobiology of Accidental Exposure Laboratory (LRAcc, SERAMED, IRSN) and the CEA (Institute of cellular and molecular radiobiology).  Experimental research will take place in the LRAcc which develops operational methods and tools used for the diagnosis and prognosis of radiation-induced organ damage.

The Institute for Radiation Protection and Nuclear Safety (IRSN) is the French public expert, with industrial and commercial activities, in nuclear and radiation risks, and its activities cover all the related scientific and technical issues. The Institute is supervised jointly by the French Minister of the Ecological transition, the French Minister of Defense, and the French Ministers of Energy transition, Research and Health.

The Atomic Energy and Alternative Energies Commission (CEA) is a research organization specializing in the fields of nuclear and renewable energies (low carbon), defense and security, technological research for industry and fundamental research (material sciences, natural sciences of life and health). The CEA is a public industrial and commercial establishment (EPIC) placed under the triple supervision of the Ministry of Ecology, the Ministry of Higher Education and the Ministry of the Armed Forces.

Scientific context

Thyroid exposure to ionizing radiation (IR) during childhood, following radioactive iodide contamination or external exposure is a recognized risk factor for the development of cancers by epidemiology studies, but for thyroid doses above 50mGy. As the risk decreases with the dose and the frequency of spontaneous thyroid cancers increases with age, statistical epidemiological methods are no longer applicable to estimate a significant risk associated with exposure to lower doses. In addition, no specific clinical, anatomo-pathological or genetic criterion distinguish radiation-induced from spontaneous thyroid tumors. Therefore, to date, we cannot objectively answer to scientific and societal questioning concerning the impact on public health of an exposure to the thyroid at low doses. This led to long lasting societal debates along the track of the Chernobyl cloud over Western Europe, after the Fukushima accident in Japan or concerning thyroid exposure of children during repeated diagnostics by medical imaging using IR. For the exposed population, suspicion of a low IR dose effect may lead to thyroid cancers over-screening which will mainly reveal small subclinical cancers thanks to improved diagnostic practices.

Work context and objectives

The global aim of this IRSN/CEA collaborative project, funded by Electricity of France (EDF) is to establish molecular signatures able to discriminate a sporadic thyroid cancer from a radio-induced thyroid cancer at low and high doses.

The project generated several datasets of plasma miRNome, RNAseq and exome measured on twenty five patients both in a tumoral samples as well as their surrounding healthy tissues.

With the purpose of identifying molecular signatures allowing a classification between several samples groups according to their etiology and radiation exposure levels, the proposed postdoctoral project aims to mobilize dimension reduction and machine learning approaches adapted to the large dimension of the data with the objective of visualizing, integrating and structuring data at different scales from the integration of omics data (microRNA, transcriptomic and exomes) to a classification score including demographic parameter including age, gender…etc. These approaches allow to maximize the association of multi-omic molecular expressions with higher scale variables according to certain relevant dimensions of the data space, with the ultimate aim of  thyroid sample classification.

For a more mechanistic purpose, interaction networks between molecular entities will also be modeled using graphical models (e.g. Gaussian lasso graphical model) specifically designed to detect the main pathways impacted by irradiation doses, sample tumoral statue and demographic determinants of health. This will make possible to infer mechanistic networks as well as a clustering of molecular entities from bioinformatics enrichment based on microRNAs gene target databases (inter-species conservation, tissue expression, genomic organization, validated or predicted target genes to identify the relevant biological/metabolic pathways that may be involved in the systemic response to thyroid radiation exposure.

Required profile

Applicants should have a PhD in Bioinformatics, Biostatistics or applied mathematics. Training in Multi-Omics integration would be an asset, as well as previous experience in biological network inference.

The selected candidate will work in close interdisciplinary collaboration with scientists having expertise in applied mathematics and radiobiology. The position includes leading bioinformatic analysis and manuscript writing in collaboration with the research team. The selected candidate will be encouraged to present the findings of the project at scientific conferences as well as to administrative authorities.

Télétravail : occasionnel

Informations complémentaires :

The start date is fixed from December 2024. The duration of the position is 18 months and it will be located at the Institute for Radiological Protection and Nuclear Safety (IRSN), Fontenay-aux-Roses (92), France. Salary will be based on qualifications and experience according to the IRSN salary grid. Applications will be considered until the position is filled.

Applicants are encouraged to submit their CV, a short research statement, at least one manuscript (published or unpublished) and the names of 2-3 reference persons. 

Please send applications and potential enquiries to Mohamed Amine Benadjaoud (mohamedamine.benadjaoud@irsn.fr)