# Mechanistic maps of adaptive responses to therapeutic stress to optimize combination therapies.

> **NIH NIH U01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2021 · $540,450

## Abstract

Summary. In triple-negative breast cancer and high-grade serous ovarian cancer, the emergence of resistance
to therapy is virtually inevitable and contributes to dismal long-term patient outcomes. The team will test the
hypothesis that tumor ecosystems rapidly adapt to stress engendered by therapies, leading to the rapid
emergence of resistance. As a corollary, blocking adaptive responses in tumor cells and the immune
microenvironment will interdict the emergence of resistance. The objective is to monitor mechanisms underlying
adaptive responses across temporal and spatial scales with single-cell precision, predict responses to untested
combinatorial perturbations, and validate predicted drug combinations, fueling future clinical trials. An interactive
team with diverse and complementary expertise and long collaboration history has been assembled: cancer and
systems biology and therapeutics (Mills, contact PI, OHSU), computational biology/image analysis (Korkut, PI,
MDACC; Goecks, OHSU), bioinformatics and systems biology (Liang, PI, MDACC), single-cell transcriptomics
and proteomics (Mohammed, OHSU), molecular and anatomic pathology (Corless, OHSU; Sahin, MDACC), and
ovarian and breast cancer translational research (Westin, MDACC; Mitri, OHSU). We will pursue two specific
aims. Aim 1. Develop novel algorithms to create mechanistic maps of adaptive responses to therapeutic stress.
The team will innovate algorithms to build data-driven and predictive models encompassing tumor cell signaling,
microenvironment, and immune modulation. An extensive pre-existing longitudinal proteomics dataset of cell
lines, xenografts, novel murine transplantable syngeneic models, PDXs, and patient samples will serve as the
experimental data and constraints driving model construction. The modeling approaches will identify cellular
vulnerabilities arising from adaptive responses to therapeutic stress and predict responses to untested
combinatorial perturbations. The team will also determine whether therapeutic targeting “steers” proteomically
heterogeneous tumors to a more therapeutically tractable homogenous state. For this purpose, we will use state-
of-the-art multiplexed imaging-based proteomics technologies to formulate and implement data-driven models
at spatial and single-cell precision. The single-cell, data-driven modeling will demonstrate how targeted therapies
alter the tumor and immune microenvironment, leading to therapeutic vulnerabilities that new targeted therapy
or immunotherapy combinations could exploit. Aim 2. Validate rational drug combinations targeting adaptive
responses to therapy in relevant settings. The team will use cell lines, xenografts, PDXs, and novel murine
transplantable syngeneic models to validate the therapeutic tractability of the rational drug combinations
predicted by the data-driven models under Aim 1. Importantly, the experimental assessment will inform and
improve the computational models through iterative data acquisition and...

## Key facts

- **NIH application ID:** 10212771
- **Project number:** 1U01CA253472-01A1
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Anil Korkut
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $540,450
- **Award type:** 1
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10212771

## Citation

> US National Institutes of Health, RePORTER application 10212771, Mechanistic maps of adaptive responses to therapeutic stress to optimize combination therapies. (1U01CA253472-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10212771. Licensed CC0.

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