Cancer Systems Pharmacology

Wednesday April 28, 2021

10:00 am - 11:30 am Eastern Time (ET)

View session on the EB Virtual Platform (EB registration required)


Chair :

James Costello
University of Colorado Anschutz Medical Campus

Laura Heiser
Oregon Health & Science University

Systems pharmacology is the application of mathematical and computational modeling to understand the underlying molecular mechanisms that mediate therapeutic response and treament resistance, and to identify optimal treatment strategies. This session will cover diverse applications in cancer systems pharmacology. First, we will explore how measurements of drug response can be modeled as the integration of many cell-intrinsic and -extrinsic factors. Second, we will consider how mutations differentially affect protein function and how these mutations define and alter cellular states. Third, we will present the application of computational models to characterize the relationship between signaling networks and cellular decisions. Finally, we will investigate how heterotypic cell-to-cell interactions differentially affect response to drugs. Each topic combines experimental and mathematical approaches to demonstrate the power of systems pharmacology approaches.


Marc Hafner - Genentech

Integrative Modeling of Drug Response that Can Accelerate Drug Discovery

A major challenge in oncology drug development is to translate results from large in vitro screens performed in cancer cell lines into successful drugs for patients. The first step towards this goal is to handle in vitro drug response data with rigor of genomics data and quantify them to best capture the response phenotype. With that in place, one can model in vivo response using first-principle models and obtain meaningful in vivo predictions.

Elizabeth Brunk - University of California, San Diego

Systematic Characterization of Functionally-relevant Mutational Landscapes

In cancer, the majority of variants are unspecified, even in the most common oncogenes. I will present recent work on systematically identifying variants that drive specific molecular and clinical phenotypes. Using multi-omics datasets and functional screens in combination with structural genomics, we find that functionally-relevant clusters of mutations emerge in unique and non-obvious ways, which can pave the way to matching treatments to genomes and increasing confidence in the clinical implications of mutations.

Pamela Kreeger - University of Wisconsin, Madison

Systems approaches to Ovarian Cancer Metastasis

Ovarian cancer metastasis is a complex and deadly process that involves interactions between numerous cell types in diverse microenvironments. Using bioengineered models, we are analyzing this process in vitro, with the goal of identifying mechanisms that support tumor detachment, passage in the peritoneal fluid, reattachment to new sites, and remodeling of the microenvironment. A key tool supporting our efforts is the use of data-driven modeling techniques such as partial least squares regression.

Michael Lee - University of Massachusetts Medical School

Micro-environmental Regulation of Drug-Drug Interactions

A central limitation in the development of effective therapies is our inability to predict drug-drug interactions. Using large-scale screening of drug combinations, in concert with statistical modeling, we find ‘rules’ that aid in the prediction of non-additive drug-drug interactions

Last Updated: February 8, 2021
Key Dates
April 27 – 30, 2021

ASPET Annual Meeting at EB 2021

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