Artificial Intelligenece to study tumor heterogeneity

High throughput multi-omic cancer studies have described the inter-tumor heterogeneity and led to well defined molecular classifications. Nevertheless, these classifications only reflect the most abundant tumor subtype in the examined sample, thus neglecting intra-tumor heterogeneity, a major source of therapeutic resistance. As advanced microdissection techniques to isolate a cell population of interest from heterogeneous clinical tissue are not feasible in daily practice, bioinformatic tools to estimate intra-tumor heterogeneity are therefore urgently needed.

Our aim is to develop integrative models of tumor heterogeneity and to infer biological behavior and associated clinical indicators using new computational methods based on Artificial Intelligence. We propose to take up this challenge by developing original methods applied to the study of heterogeneous tumor samples using benchmark datasets. First, we intend to develop an approach inspired by machine learning methods to address the problem of subtype classification accounting, for intra-tumor heterogeneity. We will then assess the impact of multi-omic data integration and feature selection in tumor heterogeneity quantification. In parallel, we will study the spatial tumor heterogeneity using microdissections of complex tumor tissues. Finally, we will apply the developed algorithms to a large pancreatic tumor cohort and decipher the clinical impact of intra-heterogeneity and its spatial properties.

Faculty Supervisor:

Mathieu Lavallée-Adam

Student:

Partner:

Université Grenoble Alpes

Discipline:

Life Sciences

Sector:

Education

University:

University of Ottawa

Program: