Phenotypic deconvolution of cancer cell populations

Abstract: Tumor heterogeneity is an important driver of treatment failure in cancer, as therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. In this seminar, I will demonstrate how to leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This approach allows us to reliably identify tumor subpopulations exhibiting differential drug responses and estimate their drug sensitivities and frequencies within the bulk population. I will discuss the advantages and disadvantages of using deterministic versus stochastic birth-death population models. These methods are applied to synthetically generated cell populations, mixed cell-line in vitro experiments, and multiple myeloma patient samples.

machine learningprobabilitystatistics theory

Audience: advanced learners

( paper )

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.