# Monitoring tumor subclonal heterogeneity over time and space

> **NIH NIH U24** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $755,943

## Abstract

PROJECT SUMMARY
 DNA sequencing and new computational approaches have yielded detailed maps of clonal variation in
human cancer. While changes in clonal structure over time and under the selective pressure of treatment have
been extensively studied in hematologic malignancies, solid cancers are less well characterized owing to the
relative lack of suitable tumor material. Analyses of breast and ovarian cancer have demonstrated substantial
clonal variation between metastatic sites and polyclonal heterogeneity within individual tumor deposits, yet our
understanding of the dynamics of clonal change in breast and ovarian cancer and its role in therapeutic
response and the emergence of resistance is in its infancy.
 By combining expertise in mutation detection and genomic analysis with access to unique patient
cohorts, this proposal will develop critically needed methods to identify all genomic changes in tumors in order
to resolve a tumor's clonal substructure as it evolves over time or space in response to treatment. We will
apply our tools in two key patient cohorts: 1) longitudinal samples from early stage, neoadjuvant breast cancer
patients biopsied before, during, and after the completion of initial chemotherapy; and 2) tumor cells from
metastatic breast and ovarian cancer patients at multiple time-points during their treatment with multiple
courses of chemotherapy (breast and ovarian) and at time of autopsy (ovarian).
 The Specific Aims are to: (1) Develop and apply comprehensive mutation detection to identify
the genetic lesions that develop in patient tumors over time during the course of chemotherapy, or at
multiple distinct metastatic lesions. Using these tools, we will measure the cellular prevalence of mutations
among multiple biopsies from both breast and ovarian patient cohorts. (2) Comprehensively prioritize
mutations based on the likelihood that they drive tumor evolution. We will use these methods to prioritize
consequential mutations and to gain insight into the potential mechanisms underlying clonal evolution. (3)
Delineate tumor subclone structure and its evolution across longitudinal tumor biopsies and multiple
metastatic lesions. By estimating the cellular prevalence of all forms of mutation in each biopsy, these
innovations will enable a better understanding of how tumor subclone populations evolve over time and space
and evade response to chemotherapy. (4) Create an interactive, web-based software platform for the
analysis exploration of tumor subclone structure. In summary, the proposed research will devise and apply
new algorithms that will improve our understanding of the dynamics of breast and ovarian cancer evolution
over time and space.

## Key facts

- **NIH application ID:** 9980298
- **Project number:** 5U24CA209999-05
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Gabor T Marth
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $755,943
- **Award type:** 5
- **Project period:** 2016-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9980298

## Citation

> US National Institutes of Health, RePORTER application 9980298, Monitoring tumor subclonal heterogeneity over time and space (5U24CA209999-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9980298. Licensed CC0.

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