# Decoding the Heterogeneity in Chemo-Immunomodulation to Unlock the Potential of Chemoimmunotherapy in Metastatic Triple-Negative Breast Cancer

> **NIH NIH DP5** · UNIVERSITY OF FLORIDA · 2024 · $381,250

## Abstract

PROJECT SUMMARY/ABSTRACT
 Triple-negative breast cancer (TNBC) is a highly heterogeneous and aggressive subtype of breast cancer
characterized by the highest potential for metastasis and thus the worst clinical outcomes of all breast cancer
subtypes. The outcome in metastatic TNBC (mTNBC) is particularly poor with a median overall survival time of
< 16 months. Chemoimmunotherapeutic treatment strategies employing chemotherapeutics, including both
conventional compounds and targeted therapies, and PD-1 blockade have proven efficacious for a variety of
solid tumor malignancies, however, the efficacy of anti-PD-1 chemoimmunotherapy is limited in the context of
mTNBC with an estimated overall progression-free survival of < 10 months and overall survival < 2 years for the
best-case scenarios. While the outcomes observed thus far represent a minor improvement for mTNBC, anti-
PD-1 chemoimmunotherapy has achieved > 40% 5-year overall survival with greater improvements in response
rate and progression-free survival in other solid tumor malignancies. Thus, further studies are needed to optimize
anti-PD-1 chemoimmunotherapeutic strategies in mTNBC.
 Despite the essential synergistic role that chemotherapy plays in anti-PD-1 chemoimmunotherapeutic
treatment strategies, the identification and selection of the optimal chemotherapeutic agent(s) that best synergize
with PD-1 blockade for an individual patient remain a critically unmet need. Previous studies have identified that
the immunomodulatory effects of chemotherapy (chemo-immunomodulation) are imperative for anti-PD-1
chemoimmunotherapeutic efficacy, thus, understanding the factors that influence chemo-immunomodulation
may be critical for optimizing anti-PD-1 chemoimmunotherapeutic treatment strategies. Nevertheless, while
studies have sought to understand genomic and transcriptomic features that influence chemoresistance, few
studies have focused on factors that influence chemo-immunomodulation. The objectives of this proposal are to
identify, characterize, and establish the clinical relevance of genomic and transcriptomic features that influence
chemo-immunomodulation and utilize the data generated from the studies herein to develop a machine-learning-
based model predictive of chemo-immunomodulation by select agents. Aim 1, 2A, and 2B of this proposal will
identify and characterize genomic and transcriptomic factors that influence chemo-immunomodulation using bulk
and single-cell RNA-sequencing approaches in in vitro, in vivo, and ex vivo models of mTNBC. Aim 2C will
establish the clinical relevance of these findings for selected chemotherapeutics using preclinical mTNBC murine
models. Aim 3 seeks to utilize the abundance of data generated through single-cell RNA-sequencing to generate
preliminary machine learning models that are predictive of chemo-immunomodulation for selected
chemotherapeutic agents. The results of this study may provide information that guides novel approaches in
designing, optimi...

## Key facts

- **NIH application ID:** 10933411
- **Project number:** 5DP5OD036134-02
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Mohammed Olusoji Gbadamosi
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $381,250
- **Award type:** 5
- **Project period:** 2023-09-22 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10933411

## Citation

> US National Institutes of Health, RePORTER application 10933411, Decoding the Heterogeneity in Chemo-Immunomodulation to Unlock the Potential of Chemoimmunotherapy in Metastatic Triple-Negative Breast Cancer (5DP5OD036134-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10933411. Licensed CC0.

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