# Developing Digital Pathology Biomarkers for Response to Neoadjuvant and Adjuvant Chemotherapy in Breast Cancer

> **NIH NIH F32** · UNIVERSITY OF CHICAGO · 2021 · $75,815

## Abstract

Project Summary
 Although tremendous strides have been made in uncovering the biology of breast cancer, selection of
chemotherapy regimens for early breast cancer is based predominantly on receptor status and stage.
However, numerous other factors are associated with response, including gene expression patterns and tumor
genetics, but these are not uniformly available for patients. Hematoxylin and eosin stained pathology is
routinely obtained for all patients with breast cancer, and contains a wealth of information beyond grade. For
example, the pattern and amount of tumor infiltrating lymphocytes has long been recognized as a predictor of
response to chemotherapy, but quantification is challenging.
 Deep learning is an emerging discipline with particular promise in the domain of image recognition,
wherein models can learn from repeated exposure to sample images to recognize any candidate features of
interest. Using deep learning, our group and others have successfully used histology to predict a variety of
tumor specific factors linked with response to treatment, including receptor status, gene expression patterns,
driver mutations, and tumor infiltrating lymphocytes. These features can be accurately detected at point of
care, without the extended turn-around time and expense associated with specialized molecular testing. We
hypothesize that deep learning on histology can identify novel morphologic and spatial features of breast
cancer tumors that in turn can predict response to chemotherapy in early breast cancer. We will take
advantage of a rich institutional cohort of over 600 patients who received neoadjuvant chemotherapy and over
2000 patients with long term survival data to curate a well annotated database suited for deep learning on
digital histology. Our patient cohort also features diverse demographics with inclusion of minority patients often
underrepresented in public datasets, ensuring applicability of our findings to all patients with breast cancer. We
will use this dataset to develop a deep learning histologic biomarker of chemotherapy response in early stage
breast cancer. This deep learning biomarker will be compared to standard markers of response to determine if
deep learning on histology provides independent predictive value, allowing better identification of candidates
for intensification or de-intensification of standard anthracycline and taxane based chemotherapy.

## Key facts

- **NIH application ID:** 10315227
- **Project number:** 1F32CA265232-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Frederick Matthew Howard
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $75,815
- **Award type:** 1
- **Project period:** 2021-08-02 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10315227

## Citation

> US National Institutes of Health, RePORTER application 10315227, Developing Digital Pathology Biomarkers for Response to Neoadjuvant and Adjuvant Chemotherapy in Breast Cancer (1F32CA265232-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10315227. Licensed CC0.

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