# Identifying responders to chemotherapy in invasive lobular carcinoma of the breast: development of a multivariable clinical prediction tool

> **NIH NIH K08** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $255,444

## Abstract

Project Summary/Abstract
 Invasive lobular carcinoma (ILC) is the second most prevalent breast cancer, which is the most
common malignancy affecting women in the United States. Although ILC has unique molecular and clinical
features, it is not well-studied, and no specific therapeutic strategies exist for it. One of the major challenges in
the treatment of women with ILC is determining whether cytotoxic chemotherapy should be utilized or not.
Currently available gene expression assays (e.g. Oncotype and Mammaprint tests) classify the majority of ILC
tumors as molecularly “low risk,” which suggests that cytotoxic chemotherapy will be ineffective. However, ILC
is more likely than other types of breast cancer to present at advanced stages with lymph node involvement,
making these patients clinically “high risk.” This “clinical high risk” status drives chemotherapy use in patients
with ILC, despite discordant results from molecular assays. Indeed, the majority of node-positive ILC patients
receive chemotherapy, despite the absence of data suggesting benefit for any individual patient. There is a
huge need to improve patient selection, so that chemotherapy can be utilized only in patients who will benefit
from it, while others can be spared its toxic side effects. In parallel, for patients with predicted poor response to
standard chemotherapy, we need personalized approaches that target the unique molecular pathways involved
in ILC. There have been recent advances in our understanding of ILC, and several groups have now identified
ILC specific gene signatures that show significant heterogeneity within this group of tumors. Given this newly
available data, we can now start incorporating ILC specific tools into clinical practice and develop tailored
treatment strategies for women with ILC. In this proposal, I will address this via the following three
approaches. First, I will evaluate a novel early indicator of chemotherapy responsiveness in ILC, improving our
ability to determine whether a tumor has responded or not. Given the relatively small numbers of ILC patients
in clinical trials, I will conduct a pooled analysis using 12 combined datasets from breast cancer patients
treated with pre-operative (neoadjuvant) chemotherapy. Second, I will leverage the recent discovery of ILC-
specific gene expression signatures and the data available in the I-SPY2 Trial to develop a predictive tool to
identify chemotherapy responders (Chemotherapy in Lobular breast cancer Effectiveness and Response
[CLEAR] score). Finally, I will conduct a pilot study testing a novel, targeted agent in combination with
endocrine therapy in the I-SPY2 Trial, through a new arm termed the Endocrine Optimization Pathway. This
project addresses an important, relevant clinical issue, utilizes new datasets and molecular signatures not
previously available, and importantly, will allow me to develop skills and knowledge in a mentored setting that
will facilitate my ability to design and cond...

## Key facts

- **NIH application ID:** 10890145
- **Project number:** 5K08CA256047-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Rita Mukhtar
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $255,444
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10890145

## Citation

> US National Institutes of Health, RePORTER application 10890145, Identifying responders to chemotherapy in invasive lobular carcinoma of the breast: development of a multivariable clinical prediction tool (5K08CA256047-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10890145. Licensed CC0.

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