Keywords: Genomics, biostatistics, bioinformatics
Topics: multi-omics integration, network inference, signal deconvolution.
Honored to receive this prize! I presented our recent results on Cancer heterogeneity quantification using a deconvolution approach and its clinical impacts. Thank you #ESMO19 and thank you to all my collaborators!
We are organizing together with the BMC TIMC-IMAG team – CNRS, the 2nd edition of an international health data challenge on statistical methods to quantify the cell populations in a tumor using omics data, one of the main current challenges in cancer genomics! Join us for this very exciting event ! More info https://tinyurl.com/hadaca2019
During this International Conference, I presented our collaborative work with Didier Jean’s team (Functional genomics of solid tumor, Inserm Unit U1162, Hôpital Saint-Louis, Paris) about MPM heterogeneity characterization using molecular gradients.
I presented a collaborative work with Didier Jean’s team (Functional genomics of solid tumor, Inserm Unit U1162, Hôpital Saint-Louis, Paris) about MPM heterogeneity characterization using molecular gradients. In our study, we show using a deconvolution approach, that molecular gradients bring new light on intra-tumor heterogeneity of MPM, leading to a reconsideration of existant MPM molecular classifications. We further show that this new way of thinking the pathology provides a significant contribution to clinical applications with implications in prognosis and therapeutic strategies, including immunotherapies and targeted therapies.
Scientific Paper: http://www.cell.com/cell-reports/abstract/S2211-1247(17)31606-6
In brief (french):