DOI: 10.25549/usctheses-c40-372269
title: Statistical methods for cancer genome analysis

description: Cancer is a genetic disorder, caused by somatic mutations, mutations not inherited from parents, in the genome. With the advent and advancement of second generation sequencing, large amount of genomic data is made available for us to study in depth the mechanism of this disease and to create novel diagnostic and therapeutic options for cancer patients. ❧ This thesis aims to establish novel statistical models using information from cancer genome to understand this complex disease, to nominate personal cancer driver events and to nominate desirable treatment strategies. As cancer is a highly complex disorder, we approached this challenge in three consecutive steps, described in order in three chapters accordingly. ❧ The first chapter tackles this challenge in a prove-of-concept manner, testing the hypothesis that we can use modeling strategy to nominate driver event for genetic disorders. To simply this hypothesis, we used a simple disease model, Mendelian disorder, a spectrum of genetic disorders caused by mutations occurred in one gene. Through studying the contribution of genetic variants to Mendelian disorder, we integrated ten independent molecular features, established kernelled support vector machines that outperformed eighteen different models and demonstrated the potential of modeling patterns of disease causing mechanisms of pathogenic variants across the genome. ❧ The second chapter extends initial findings in the first chapter to a more complex disease model, cancer. Cancer is a highly complex disease, caused by a repertoire of somatic mutations in the cancer genome. How to model this complex disorder, identify the underlying causal factors and propose corresponding personalized treatment strategies is what we are trying to tackle in this chapter. We proposed to address these questions using a three-layer statistical framework, iCAGES (short for integrated Cancer Genome Score), that integrates information from molecular features of somatic variants, to prior knowledge of cancer driver genes, to FDA drug databases and consecutively predicts personalized cancer driver mutations, genes and effective drugs. Our model showed superior performance than all other current methods evaluated on ~14,000 cancer patients (to our knowledge the largest cancer genome cohort available) and demonstrated the potential of using hierarchical statistical models to predict personalized cancer drivers to facilitate precision medicine in cancer. ❧ The third chapter further extends the idea of using hierarchical statistical models to facilitate precision medicine in cancer to deep learning with high dimensional molecular cancer profile. The goal is to integrate high dimensional molecular features from cancer patients to accurately predict their survival probably. With the advancement of deep learning technology and its corresponding platform Tensorflow developed by Google, we proposed a group lasso deep learning framework that group different molecular profiles from each group based on the underlying biology, integrated information from molecular and clinical signatures from TCGA cancer patients and showed superior performance than all current modeling strategies, demonstrating the potential of using complex statistical models in the context of deep learning to facilitate cancer patients’ prognosis prediction.