Title

Clinical and Translational Cancer Research by Genomics and Transcriptomics Data Analysis

Date of Award

Spring 2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Biology and Bioinformatics

First Advisor

Gerstein, Mark

Abstract

As the second most-frequent cause of death worldwide, cancer comprises a collection of diseases characterized by uncontrollable cell division and spread into surrounding tissues. Diagnosis and treatment of cancer are very challenging due to its huge heterogeneity and rapid progression. Leveraging remarkable achievements in biomedical experimental procedures and techniques, more comprehensive knowledge and deeper understanding of cancer biology have been discovered and applied to interpret the molecular intricacy and variations of cancer at multiple levels, spanning genome, epigenome, transcriptome, proteome and metabolome. With the advancement of high-throughput sequencing technologies, enormous amount of data from each level (i.e. omics data) has been generated in significantly decreasing time and cost manner, which have responded to major challenges of stratified medicine in oncology, including patients’ phenotyping, biomarker discovery, and therapy design. Omics data provides an opportunity to investigate molecular origins of cancer from diverse contributions, including DNA mutations, differential expression of RNA and proteins, as well as metabolic abnormalities. In this dissertation, with focuses on breast cancer and prostate cancer, we presented six genomics and transcriptomics studies to investigate the clinical and translational significance conveyed by comprehensive bioinformatics analysis. First, we introduced three whole-genome sequencing studies where a wide selection of genomic features were examined, including somatic and germline single-nucleotide variants, copy number variants and structural variants, as well as mutational signatures and clonal architectures. Our analysis illustrated innate drug resistance and suggested candidate therapeutic targets. Next, by systematic analysis of RNA sequencing data, we demonstrated the immune background and predictive biomarkers of pathological complete response for triple-negative breast cancer patients treated with neoadjuvant chemotherapy. Finally, utilizing data from targeted gene expression panels, we revealed how the immune microenvironment changes during neoadjuvant chemotherapy of primary breast cancer, and uncovered the complexity of chemotherapy and immunotherapy resistance mechanisms. Altogether, this dissertation presented six applications of genomics and transcriptomics data analysis to breast cancer and prostate cancer research, which expanded our understanding of early diagnosis and therapeutic strategy for cancer by employing in-depth bioinformatics analysis.

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