# Project 2: Targeting Cooperative Phenotypes Common in Spatial Heterogeneity

> **NIH NIH U54** · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · 2020 · $303,885

## Abstract

ABSTRACT
Recent studies in primary tumors have found a remarkable degree of intratumor heterogeneity, where a single
tumor is comprised of a range of subclones exhibiting a diversity of phenotypes, including molecular profiles,
proliferation capacity, and response to therapies. Although heterogeneity is now widely reported, few studies
have investigated the heterogeneity of metastatic tumors at the end stage, despite the fact that metastatic cancer
is estimated to be responsible for over 90% of cancer deaths. For breast and ovarian cancer, tumors that
progress to metastasis are refractory to treatment. Therefore, there is a great need to determine the mechanisms
by which subclonal diversity can affect the metastatic phenotype and underlie the difficulties in treatment.
Studying metastatic tumors is difficult due to the challenges in collecting patient tissues. While primary tissues
are typically obtained through biopsy, this is rarely performed for metastatic sites. To address this difficulty, we
have developed both a rapid autopsy strategy where we collect fresh samples of metastatic tumors within hours
of patient death, as well as collections of metastatic tumor biopsies in the clinical trial setting prior to and after
drug treatment. These collections enable us to profile multiple metastatic sites and investigate the association
between metastatic sites and subclonal evolution in an isogenic background. We propose to leverage this unique
data set to investigate the relationship between evolution of tumor subclones during metastatic progression and
the phenotypic profiles of these tumors.
We hypothesize that, despite the diversity in their genetic mutation profiles, metastatic tumors exhibit
clonal dynamics that ultimately leads to convergence on more common cooperative phenotypic
networks, and that targeting the key dependencies within this network will lead to increased collapse of
the metastatic tumor population. To investigate this, we will profile the tumors by whole genome sequencing,
whole exome sequencing, and single cell RNA sequencing. This data, coupled with our newly developed
algorithms for dissecting subclonal populations using tree reconstruction algorithms, for eliciting phenotypes from
gene expression profiles using Bayesian statistics, and for simulating phenotypic evolution using mathematical
models from ecology; will enable us to understand (Aim 1) the subclonal heterogeneity that underlies metastatic
initiation and progression; (Aim 2) how cooperative functions evolve to a chemo-refractory signaling network,
and therapeutic strategies to target it; and (Aim 3) how these dynamics are manifested human tumors in a clinical
trial.
Our investigations represent the first characterization of the clonal dynamics of a large multisite metastatic
cohort, and will provide a new framework for understanding and treating end-stage tumors based on the evolution
of cooperative phenotypes. We will develop these models on patient samples and ...

## Key facts

- **NIH application ID:** 9959359
- **Project number:** 5U54CA209978-05
- **Recipient organization:** BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
- **Principal Investigator:** JEFFREY T CHANG
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $303,885
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9959359

## Citation

> US National Institutes of Health, RePORTER application 9959359, Project 2: Targeting Cooperative Phenotypes Common in Spatial Heterogeneity (5U54CA209978-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9959359. Licensed CC0.

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