Missions

Identification of the clonal composition of a tumor from panel-sequencing data

The postdoc is part of the ANR Identhic project, focused on reconstructing the evolutionary history of tumors from partial sequencing data. The candidate will interact with the 4 main members of the project as well as with masters and PhD students working on complementary aspects of the project.
The candidate will be responsible for developing methodological statistical approaches to identify subgroups of cells exhibiting homogeneous behavior based on sequencing data, first under complete observation, and then under partial observation.

More details : https://alice.cleynen.fr/wp-content/uploads/2024/09/Postdoc.pdf

Activities

- Familiarization with data, data curation, annotation of patient samples
- Development of deconvolution methods to infer clonal composition from full sequencing data
- Benchmarking of the method
- Extension of methods to partial sequencing today using biology-informed priors, development of imputation methods in this context
- Implementation of the methods in efficient codes
- Presentation and discussion of the results with members of the consortium
- Manuscript drafting for dissemination of the results in the community
- Participation to workshops and conferences

Skills

PhD in biostatistics or bioinformatics
Taste for programming, in particular fluent knowledge of R or python
Prior usage of at least one type of sequencing data

Work Context

The recruited researcher will carry out research tasks in IMAG, within the EPS team under the supervision of Alice Cleynen and Sophie Lèbre.
Located on the Campus Triolet of the University of Montpellier, IMAG is one of the gateways to mathematics in “Occitanie-est” Region. It comprises about 170 members and is structured into 4 research teams : Analysis, Numerical Analysis, and Scientific Computing(ACSIOM), Didactics and Epistemology of Mathematics (DEMA), Probability and Statistics (EPS), and Geometry, Topology, and Algebra (GTA).

Personalized medicine is an emerging approach in healthcare that gained momentum with the advent of genomic technologies in the early 2000s. It focuses on tailoring medical decisions to an individual patient's predicted response. A notable example is the classification of breast cancer into five subtypes based on gene expression profiles, as well as the development of monoclonal antibodies, which are particularly effective in HER2-positive patients.
For personalized and precision oncology to reach its full potential, we must deepen our understanding of tumor evolution at the patient level. This involves identifying the initial events that trigger cancer formation and the successive driver events that lead to therapeutic resistance, relapses, and uncontrolled tumor growth.
As sequencing technologies become more accessible, it is now common practice to use gene panels—targeted sequencing of selected genes known to be associated with cancer through mutations, expression changes, or copy-number alterations—at the time of diagnosis. This helps identify potential abnormalities in tumor cells that can guide treatment and prognosis. Translational research labs are actively developing new sequencing panels to uncover both known and novel events at the RNA and DNA levels, with the goal of improving patient outcomes.
This process generates large volumes of heterogeneous data, collected at multiple time points from different patients
The general aim of the postdoc project will be to develop a statistical approach to identify the clonal composition of the tumor from panel-sequencing data.