Single cell quantification of genomic instability in cancer as a determinant of therapeutic response

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PROJECT ABSTRACT Tumor genetic heterogeneity is an extensive feature of cancer biology and underlies patient response to therapy. One aspect of tumor heterogeneity that has been difficult to study is heterogeneity of large genomic aberrations, including high level amplifications a few megabases in size, whole or partial chromosomal gains and losses and whole genome duplications. This is because identifying these aberrations in subclonal populations (present in <100% of cells) is extremely challenging when sequencing tumors in “bulk”. Single cell genomics however, can resolve these alterations at cellular resolution enabling precise quantification of heterogeneity at these genomic length scales. To comprehensively investigate the extent and consequences of intra-tumor heterogeneity generated by these types of genomic aberrations I will leverage recent advances in robust highly scalable single cell whole genome sequencing and my expertise in computational modeling. In the K99 phase of the award I will investigate how differences in the ability of cells to repair their genomes results in different patterns of genetic heterogeneity, and how such cellular diversity can cause differential response to treatment in high grade serous ovarian cancer, a cancer driven by genomic instability. In the independent phase of the award I will focus on heterogeneity and evolutionary dynamics of extra-chromosomal DNA, small circular pieces of DNA that cause high level amplification of oncogenes. The results of this proposal have the potential to give fundamental new insight into the biology of genomic instability and enable better predication of patient response to therapy and identification of therapeutic vulnerability that may be exploited. This proposal also describes a training plan to advance my career to an independent investigator, combining computational modeling inspired by evolutionary theory, machine learning and high-resolution genomics to quantify cancer evolution in order to better predict patient response to therapy and uncover the mechanisms driving cancer progression. During the K99 phase I will be supported by an interdisciplinary team of experts in single cell genomics, cancer evolution, ovarian cancer biology and genomic instability. I will broaden my knowledge of machine learning, genomic instability and scalable bioinformatics software engineering and improve my communication and leadership skills vital for my transition.