Multimodal and Multiomic Integration in Precision Oncology

Abstract

Precision oncology is an ever-evolving field that seeks to utilize genomic profiling to offer personalized therapeutics based on a patient’s genetic, molecular, and phenotypic profile. Recent advancements in sequencing technologies have revolutionized our ability to identify new biomarkers, improve cancer subtype characterizations, understand their mechanism of action, and match them to potential therapeutics. Despite its promises, there are still many challenges for precision oncology in clinical practice. These include biological factors such as tumor heterogeneity and mechanism of developing drug resistance, as well as computational factors such as interpretation of high-throughput sequencing and accurate patient profiling. These factors indicate the need for stronger comprehensive genomic profiling, as reliable characterization of patients is essential for predicting responses to specific treatments, determining prognosis, and discovering novel therapeutic targets. In this dissertation, I present a comprehensive overview of my work exploring the multimodal and multiomic aspects that underline current biomedical research. This body of work captures the varied analysis from identifying biomarkers from multiomic data, applications of precision oncology in a cohort study, and lastly analysis of experimental validation through cell lines and mice models. These studies not only bridge the gaps between high-throughput data and real-world clinical applications but also underscores the importance of high-quality genomic studies in precision medicine. In the first chapter, I highlight the applications of multi-omics in precision medicine through a cohort study combining genomics, proteomics, post-translational modifications, metabolomics, and lipidomics. Through novel computational algorithms, we provide comprehensive subtyping of acute myeloid leukemia (AML) that captures not only the genetic heterogeneity of AML, but also its immune hierarchy. We identified novel markers for specific subtypes with prognostic potential and revealed extensive metabolomic reprogramming driven by a divergent MYC and mTOR activity. These results demonstrate how usage of new sequencing technologies and novel multi-omic approaches can lead to discovery of new subtype-specific biomarkers and a deeper understanding of metabolomic changes in cancer. In my second chapter, I demonstrate how knowledge of biomarkers can be translated into actionable items to help inform real-life clinical decisions through a cohort of prostate cancer patients. With NGS sequencing becoming more accessible, more patients have the opportunity to receive multiple NGS sequencing, giving us a rare opportunity to track the progression of prostate cancer evolution. Our retrospective analysis revealed complex relationships between asynchronous sequencing testing practices and its ability to detect new actionable alterations. These complex asynchronous datasets are summarized in our study, providing a resource to guide therapy choices. In my third and fourth chapters, I demonstrate mechanistic studies utilizing a variety of advanced sequencing technologies including ChIP-seq, single-cell RNA (scRNA), as well as HiC sequencing, which we leveraged to understand the heterogeneity in T-cells upon stimulation, and uncover a regulatory mechanism in Ewing’s sarcoma which is dependent on long GGAA microsatellites. In summary, precision oncology remains as one of the most promising directions for cancer research and is largely shaped by the quality of data. As new sequencing technologies become more accessible, new methods to approach, analyze, and integrate data becomes more crucial for patient profiling. This work highlights and demonstrates a comprehensive workflow for modern precision oncology research from identification of novel biomarkers, applications of biomarkers in clinical settings, and experimental validations using novel sequencing technologies.