Integrated systems approach for mechanistic understanding of human cancers



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Research described in this dissertation has focused on cancer systems biology, aiming to tackle fundamental questions underlying genotype-phenotype relationships. I combine my unique computational and experimental expertise to build a quantitative understanding of genetic/epigenetic variation and signaling network perturbation in cancer. Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. To resolve this challenge, we developed e-MutPath, a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. In a specific context, alterations in immune-related pathways are common hallmarks of cancer. Herein we developed a Network-based Integrative model to Prioritize Potential immune respondER genes (NIPPER). Using an interactome network propagation framework integrated with drug associated gene signatures, we identified potential immunomodulatory drug candidates. By developing an integrated multi-omics model, I further discovered widespread epigenetic perturbations in colorectal cancer, with a clear dependency on tumor sidedness. This result provides a possible reason why right-sided colorectal cancer leads to overall worse prognosis. More importantly, to reveal molecular interaction networks, I invented a high- throughput wet-lab technology to investigate contextual dependencies in the human RNA- protein interactome with Exosome-mediated RNA-Protein Trafficking (ExRPT). Combined with single-molecule barcoding and detection, our platform aims to provide a flexible interface that integrates complex libraries of recombinant RNAs, coupled with fluorescence-based sorting and barcode sequencing. Additionally, lipid vesicles are thought to have a protective role in shielding cargo RNA from enzymatic degradation and provide a closed environment to maintain RNA stability. Through this we seek to associate conditions that alter epigenetic signaling networks that respond to the extracellular environment and drive diverse disease states. Last but not least, we developed a predictive framework ‘i-Modern’, a robust deep learning framework for integrating multi-omics data to efficiently and precisely stratify cancer patients and predict survival prognosis. Together, the landmark multi-omics signatures identified here may serve as potential therapeutic targets in cancer.


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