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

> **NIH NIH K99** · SLOAN-KETTERING INST CAN RESEARCH · 2022 · $99,798

## Abstract

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.

## Key facts

- **NIH application ID:** 10357908
- **Project number:** 5K99CA256508-02
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Marc Williams
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $99,798
- **Award type:** 5
- **Project period:** 2021-03-03 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10357908

## Citation

> US National Institutes of Health, RePORTER application 10357908, Single cell quantification of genomic instability in cancer as a determinant of therapeutic response (5K99CA256508-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10357908. Licensed CC0.

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