Leveraging Cancer Molecular Classification to Improve Prediction of Patient Prognosis and Elucidate Subtype-specific Regulatory Mechanisms


Student thesis: Doctoral Thesis

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Award date5 May 2020


Since different tumor molecular subtypes with distinct molecular characteristics and clinical outcomes, while exploring the key regulatory mechanisms of different subtypes, we can also combine the characteristics of subtypes to achieve accurate risk assessment and management for patients, as well as search for potential new therapeutic targets. For colorectal cancer (CRC), patients classified to CMS4 have the highest relapse rate and the worst relapse-free survival. Therefore, we hypothesize that taking into consideration of molecular subtyping will greatly promote the performance of colon cancer recurrence risk prediction. For ovarian cancer, the mesenchymal subtype has a relatively poor overall survival. Therefore, the quest for identification of the driving molecular events underlying the poor prognosis mesenchymal subtype is indispensable for more precise targeting of the patient with ovarian cancer.

The work in this thesis focuses on leveraging cancer molecular classification to improve prediction of patient prognosis and elucidate subtype-specific regulatory mechanisms, especially driving molecular events underlying the poor prognosis mesenchymal subtype. The studies in each chapter are described below:

Chapter 1: An introduction of transcriptome-based molecular subtyping, and the consensus molecular subtypes recently in colorectal cancer (CRC) and high-grade serous ovarian cancer (HGSOC). We discuss the biological understanding of CRC and HGSOC heterogeneity and clinical associations, as well as their limitations. Furthermore, we highlight and discuss major challenges preventing effective translation of cancer subtyping into routine clinical practice, and possible solutions, alternative strategies, and new opportunities.

Chapter 2: This chapter introduces CMS-Pro (Consensus Molecular Subtype-Prognosticator), a novel dual-layer prognostic assay that integrates colon cancer subtyping and subtype-specific relapse probability to predict the recurrence risk for stage II colon cancer patients.

Chapter 3: In this chapter, we describe a network-based systems biology approach enabling the identification of subtype-specific drivers to gain insights into the molecular determinants of distinct ovarian cancer subtypes. Our multidimensional network analysis identified miR-508-3p as a master regulator that defines the mesenchymal subtype and provides a novel prognostic biomarker to improve the management of this disease.

Chapter 4: This chapter illustrates a landscape view of the immune infiltrates in different subtypes of ovarian cancer by the deconvolution algorithm (CIBERSORT). In particular, using this integrated computational analysis together with further functional experiments, we identified an immune-associated cellular, molecular, and clinical network that highlights the defining role of plasma cells in the mesenchymal identity of ovarian cancer.