# Integrating Clinical, Pathologic, and Immune Features to Predict Breast Cancer Recurrence and Chemotherapy Benefit

> **NIH NIH K08** · UNIVERSITY OF CHICAGO · 2023 · $201,055

## Abstract

Abstract
 Breast cancer is the leading cause of cancer death for women globally, with over 2.3 million cases
diagnosed each year. Most cases are hormone receptor positive and effectively treated with anti-estrogen
therapy, but some patients have aggressive disease and are at risk for recurrence and death without
chemotherapy. Gene expression based recurrence assays, such as OncotypeDX, were designed to predict
recurrence on hormonal therapy and are used to select patients for chemotherapy. However, these assays are
expensive (> $3,000 per test), take considerable time to perform leading to treatment delays, and testing is
underutilized or frankly unavailable in low resource settings in the US and globally. Conversely, every patient
with breast cancer has a biopsy to confirm the diagnosis, which is routinely analyzed by pathologist to determine
subtype of breast cancer and grade. Deep learning is an emerging technique for quantitative image analysis,
and can identify non-intuitive features from pathology, including gene expression patterns. In preliminary work, I
have demonstrated that deep learning on pathology samples can provide rapid and cost-effective prediction of
OncotypeDX score using readily available data, and can identify patients at low risk of recurrence on hormonal
therapy.
 However, OncotypeDX remains an imperfect predictor of chemotherapy benefit, as it was developed to
predict recurrence on hormonal therapy. By refining my deep learning biomarker to incorporate clinical and
immune features of breast cancer, I can improve accuracy in prediction of chemotherapy benefit and thus the
ability to personalize treatment. First, I will capitalize on the recent expansion of clinical data in the National
Cancer Data Base to develop a more accurate clinical models of prognosis and chemotherapy benefit. Next, I
will use multiplex immunofluorescence to better characterize spatial and cell density features associated with
chemotherapy benefit, and use deep learning models to infer these features from standard hematoxylin and
eosin stained digital pathology. Finally, I will integrate these clinical and immune models with my existing deep
learning pathologic model and validate the integrated model in a multi-institutional cohort. The result of this work
will result in a prognostic and predictive deep learning biomarker that makes accurate predictions from readily
available clinical, pathologic, and inferred immune features. This approach has the potential to reduce
chemotherapy delays due to rapid turnaround time, combat healthcare disparities through improved availability
of testing, and improve personalization of treatment by tailoring a biomarker for prediction of chemotherapy
benefit.

## Key facts

- **NIH application ID:** 10723924
- **Project number:** 1K08CA283261-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Frederick Matthew Howard
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $201,055
- **Award type:** 1
- **Project period:** 2023-07-07 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10723924

## Citation

> US National Institutes of Health, RePORTER application 10723924, Integrating Clinical, Pathologic, and Immune Features to Predict Breast Cancer Recurrence and Chemotherapy Benefit (1K08CA283261-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10723924. Licensed CC0.

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