PROBING TUMOR HETEROGENITY WITH BIG DATA AND SINGLE-CELL APPROACHES

Abstract

Circulating tumor cells (CTCs) are precursors of cancer metastasis. Yet, little is known about the biology, and the clinical value, of these cells, largely owing to the technical challenges and their inherent complex nature of heterogeneity. Here, we aim to address such heterogeneity by analyzing CTCs at the single-cell resolution. This thesis is split into three parts, 1) to derive robust gene signatures for early-stage cancer diagnosis (prevention) and prognosis (prediction) using large-scale transcriptomic data, 2) to assess the impact of intra-tumor heterogeneity (ITH) on risk prediction using this newly developed multi-gene classifier, and 3) to provide novel tailored strategy to refine prognostication using single-CTC-based liquid biopsy. By leveraging informatics, microfluidics and genomics approaches, we demonstrate how molecular alterations found in patient-derived CTCs can advance our understanding of metastasis and optimize patient management in clinical practice.