UC Santa Cruz

Since response to therapy can differ greatly between cancer patients, a precision medicine approach to treating cancer based on the uniqueness of patient tumors could greatly improve response rate and quality of life. High-throughput assays provide the means to probe multiple types of genomic alterations across a patient’s cancer genome. By leveraging prior knowledge about genetic pathways, I have created tools to address the challenges of tackling large multi-dimensional datasets to make biological sense of the data. I developed PATHMARK to identify clusters of genes that are dysregulated together forming networks that offer insights into disease mechanisms and treatment strategies. PATHMARK utilizes conventional univariate differential analysis with a filter on pathway interactions to identify sub-networks that are significantly more connected than by chance. I developed PARADIGM-SHIFT to predict the functional impact of mutations detected from whole-exome sequencing data. PARADIGM-SHIFT analyzes the inferred activities of a network surrounding a mutated gene, comparing the levels between mutant and wild-type samples. The approach predicts if mutations are likely to be neutral, gain-of-function, or loss-of-function. I demonstrate how inferences about mutations in novel genes and in non-coding regions can be gleaned from models trained on known coding mutations. The predictions form “molecular machines” that link events together based on shared pathway alteration. With the growing number of available datasets and computational tools, it has become increasingly important to make analyses easily accessible and reproducible. To support these ideals, I have developed my tools to be compatible within the Galaxy system, which has enabled collaborators to apply my tools to analyze their data.