Abstract 1568: Predicting stochastic proliferation and death in response to drugs with mechanistic models tailored to genomic, transcriptomic, and proteomic data
Authors: Mehdi Bouhaddou, Anne Marie Barrette, Rick J. Koch, Marc R. Birtwistle
Journal: Cancer Research (2017)

Abstract Over the past decade we have seen a shift in cancer therapy from broadly cytotoxic drugs to molecular therapies targeting “driver” mutations. Although targeted therapy has seen great success for some cancers (e.g. imatinib for leukemia), it has struggled with poor efficacy in treating other cancers that can sometimes possess multiple “driver” mutations. This highlights the complex, and at times non-intuitive, interplay between multiple players in a signaling cascade, which can be highly dependent on the biological context – that is, gene expression levels and mutational architecture – of a tumor or cell line. A quantitative, mechanistic, biologically-tailored understanding of how these signaling dynamics drive proliferation and death could improve precision pharmacology approaches to treat cancer. Here, we constructed the first highly detailed, large-scale ordinary differential equation (ODE) mechanistic mathematical model depicting the most commonly mutated cancer signaling pathways across human cancers, as indicated by a pan-cancer analysis by The Cancer Genome Atlas (TCGA). The model includes the RTK/Ras/MAPK, PI3K/AKT/mTOR, CDK/RB cell cycle, p53/MDM2 DNA damage response, and BCL/Caspases apoptosis pathways. The adjustable parameters of the model can be informed by measurements from patients or cell lines, including copy number alterations, mutations, and gene expression levels. This single-cell model links stochastic gene expression processes to quantitative signaling dynamics, and once tailored to a biological context can be used to simulate the effect of various anti-cancer therapies on cell fate behavior such as proliferation and death for a population of cells. The first instance of the model integrated genomic, transcriptomic, and proteomic data from the MCF10A cell line, a non-transformed cell line with predictable phenotypic behaviors. We trained the model using western blot and flow cytometry experiments to refine various biochemical parameters and phenotypic outcomes. Many fundamental questions in signal transduction arose during this process, such as how EGF and insulin synergize to drive S-phase entry or how a specific biological context confers sensitivity or resistance to inhibitors of the ERK and AKT pathways. Simultaneously, we are tailoring the model to patient-derived genetic information from primary glioblastoma tumors and screening brain-penetrable compounds in a patient-specific manner. In conclusion, a quantitative, mechanistic, biologically-tailored mathematical model depicting the major cancer pathways allows us to probe the mechanisms that underlie how signaling dynamics drive proliferation and death in response to various perturbations, and gain insight into their dependence on the biological context of cell lines and patient tumors. Note: This abstract was not presented at the meeting. Citation Format: Mehdi Bouhaddou, Anne Marie Barrette, Rick J. Koch, Marc R. Birtwistle. Predicting stochastic proliferation and death in response to drugs with mechanistic models tailored to genomic, transcriptomic, and proteomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1568. doi:10.1158/1538-7445.AM2017-1568