I am currently a PhD candidate at the Center for Computational Biology from the Ecole Mines Paristech, and the RT2 Lab (tumor residue and response to treatment) at Institut Curie under the supervision of Jean-Philippe Vert and Fabien Reyal.

I obtained a bachelor in Molecular and Cellular Biology in 2013 from ENS Ulm, and then specialized in applied Mathematics, with a Master Degree in Mathematics, Vision and Machine Learning in 2015 from ENS Paris Saclay.

I am interested in the analysis of high-throughput sequencing data from cancer genomes. In particular, I am working on statistical methods to identify the different subpopulations which constitute a sequenced tumor sample.

Tumor cells keep accumulating new genetic alterations, and phases of clonal expansion, leading to a mosaic structure.

At each cellular division, tumor cells acquire new genomic alterations. Most of them have no effect on cellular functions, but some of them may bring a selective advantage to their carrier. As a result of positive selection or simply neutral evolution, some cancer cells might undergo clonal expansion until they represent the totality of the tumor or a substantial part of it. This process results in a tumor composed of a mosaic of cell subpopulations with specific mutations.

Intra-tumor heterogeneity and tumor evolution understanding could have implications in important aspects of cancer clinical management, such as ability to metastase, treatment resistance, tumor screening, and personalized medicine.

Keywords: Intra-tumor Heterogeneity, Genomics, Machine Learning, Evolution


Email adress: judith [dot] abecassis (at) mines-paristech [dot] fr

Mailing address: Judith Abécassis
CBIO — MINES ParisTech
60 boulevard Saint-Michel
75272 Paris Cedex 06

twitter: @judithabk6

ORCID: 0000-0002-5818-8304

Github scholar