Computational approaches to drug sensitivity prediction and personalized cancer therapy
Noah Berlow · ThinkTech (Texas Tech University) · 2015
This dissertation represents the accumulated research in the field of Personalized Cancer Therapy performed as a Graduate Student at Texas Tech University. This research has focused on the design, implementation, and validation of computational, data-driven models of drug sensitivity and their application to personalized cancer therapy. This has resulted in projects of varying depth in the following subjects. Probabilistic Computational Modeling of Tumor Sensitivity to Targeted Therapeutic Compounds: A key open problem in the field of Systems Medicine is the drug sensitivity prediction problem: given a new cancer patient and a list of drugs or drug combinations, what will the patient response (sensitivity) to the different compounds be. Robust solutions to this problem translate to viable approaches to Personlized Therapy, where therapy assignment to cancer patients is based on the underlying patient and cancer biology, instead of a one-size-fits-all approach. The transition to personalized therapy is a primary need for the clinical oncologist community, who are often faced with a dearth of viable treatment options for relapsed, unresponsive, or high risk patients. An integrative model of drug sensitivity, focusing on functional drug screen data and informed by available genetic data, was developed to address this issue; in silico modeling results are presented in this section. Regression modeling of drug sensitivity from the CCLE Database: As another form of in silico validation, the change in drug sensitivity prediction following integration of drug-target inhibition data to an existing dataset was tested. The Cancer Cell Line Encyclopedia (CCLE) database consists of 24 anticancer drugs profiled across 479 hum-origin cancer cell lines. These cell lines underwent thorough genetic characterization, with exome sequencing, copy number variation, and gene expression sequencing data available. A few of these anti-cancer agents also have known drug-target inhibitions profiles; these commonalities are utilized to show that integration of dataset improves sensitivity prediction; in silico modeling results are presented in this section. Model-driven combination therapy design: in vitro and in vivo validation: The in silico validation of the tumor sensitivity modeling constituted the first step in development of this computational approach. The next key step was translation from in silico validation to in vitro (in glass) and in vivo (in life) validation. Biological experimentation was required to move the computational approach closer to clinical viablity. As part of this research, a year was spent in the laboratory of key collaborator, Dr. Charles Keller. The biological validations performed showed that functional data-based modeling was capable of translating to successful biological outcomes. Design of Dynamic Network Models from Static Models and Expression experiments: The computational approach presented here is based on data that acts as a single timepoint snapshot of a biological system. However, tumor cells are never at rest; they are constantly undergoing a myriad of necessary biological processes. The cellular processes exist on numerous biological pathways and have a vast number of potential ways to interact. Because of this, there are upstream and downstream biological processes; by intervening in upstream processes, the downstream processes may respond without need for intervention. The static computational model was informed with a small set of gene expression experiments to construct dynamic, upstream-downstream and parallel process models of tumor sensitivity and performed in silico valdiations of the approach. Analysis of Drug Screen Information Gain and Drug Screen Design : The monetary and time cost of producing functional drug screens for high-throughput screening of new patient cancer samples, as well as the limited population of patient cancer cells available for testing, are key practical constraints in preclinical testing scenarios. As such, maximizing the usable information gained from a functional drug screen is extremely important when the data is used to inform clinical decisions for patients. This work establishes a metric for comparing expected information gain from a drug screen of an arbitrary size, and establishes a framework for drug selection for new drug screens.