# Project 1: Dynamic Genomic and Microenvironmental Models of Acquired Chemoresistance

> **NIH NIH U54** · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · 2021 · $565,218

## Abstract

ABSTRACT
Breast and ovarian cancers are heterogeneous diseases, as a typical tumor contains multiple “subclones”,
which are defined as evolutionarily related subpopulations of cells with a different complement of somatically
acquired DNA mutations and phenotypes. When chemotherapeutic agents are administered to the patient,
some of these subclones may gain a selective advantage and develop resistance to the treatment, resulting in
cancer relapse and progression. For this reason, it is imperative to identify these subclones and their evolution
across treatment; and to understand how the genomic aberrations within these subclones drive resistance to
chemotherapy. We will integrate experimental biology and computational models across temporal samples of
patient tumors as they develop a resistant state in order to better understand and combat refractory and
terminal cancer. To enable the study of tumor heterogeneity evolution in patients, we will utilize a highly unique
collection of metastatic tumor cells from breast and ovarian cancer patients before, during, and after
treatments, often across multiple courses of chemotherapy, as well as tumors from a clinical trial taken before
and after therapy. We use deep sequencing to find genomic aberrations at each of these time points, and
develop systems models to identify the subclones and follow phenotypic changes and their functional impacts
of subclone evolution in response to chemotherapy. We hypothesize that 1) Dynamical systems models based
on the evolution of subclone structure and acquisition of oncogenic phenotypes during treatment can identify
key factors in the development of a chemo-resistant state; and 2) We can delay development of a chemo-
resistant cancer state by inhibiting development of phenotypes that emerge over time commonly during
treatment. We will model resistant cancer cell populations and both extrinsic and immune microenvironmental
factors to identify critical features of acquired resistance and apply these models to a clinical trial aimed at
blocking transition to a resistant cancer state. While these components can exhibit co-dependencies, by their
nature they can also have vulnerabilities based on these interactive features, and if one can inhibit dependent
relationships within a population it may be possible to shift the equilibrium of a tumor from a chemoresistant
state to a sensitive state. The algorithms and procedures we are developing in this proposal will for a rational
basis for real-time patient monitoring and making treatment choices for refractory patients. The outcomes of
this research will deliver approaches to block or reverse the transition to a resistant state for advanced stage
breast and ovarian cancer patients.

## Key facts

- **NIH application ID:** 10207529
- **Project number:** 5U54CA209978-06
- **Recipient organization:** BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
- **Principal Investigator:** ANDREA Hope BILD
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $565,218
- **Award type:** 5
- **Project period:** 2017-05-15 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10207529

## Citation

> US National Institutes of Health, RePORTER application 10207529, Project 1: Dynamic Genomic and Microenvironmental Models of Acquired Chemoresistance (5U54CA209978-06). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10207529. Licensed CC0.

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