New Tool Predicts Chemotherapy Response in Triple-Negative Breast Cancer

Researchers at The University of Texas MD Anderson Cancer Center have developed a new computational approach designed to better account for changes in gene expression within tumors relative to their unique microenvironments to improve chemotherapy response predictions for patients with triple-negative breast cancer (TNBC).

The study, led by Wenyi Wang, professor of Bioinformatics and Computational Biology, and colleagues, was published in Cell Reports Medicine. This new tool aims to improve upon current methods by using a process known as deconvolution to break down, calculate and interpret cellular differences. By making these analytical approaches more accessible to researchers without extensive computational backgrounds, the team hopes to translate these findings into practical tools that the broader cancer research community can apply to advance precision medicine for patients with cancer.

Main data that support the findings

In a study involving a dataset of 575 patients with TNBC across ethnically diverse cohorts, the researchers found that their new approach outperformed current methods for predicting how patients respond to chemotherapy. The tool utilizes a biomarker known as tumor-specific total mRNA expression (TmS) to accurately sort patients into two categories: those with high-TmS, indicating a favorable prognosis, and those with low-TmS, indicating a poor prognosis.

The data show that this prognostic biomarker applies across different populations while also revealing novel insights into population-level characteristics of TNBC. Specifically, the research highlighted key differences in the tumor microenvironments of high-TmS Western and Asian ethnic groups. These findings suggest that the biomarker could eventually allow clinicians to match additional treatments that are likely to work more effectively for each specific population. The TmS biomarker serves as an effective starting point for patient stratification, which helps optimize the selection of treatments for patients with cancer.

Trial details

The development of this tool was a collaborative effort involving MD Anderson’s Institute for Data Science in Oncology (IDSO) and the Department of Breast Medical Oncology. The researchers focused on improving deconvolution strategies, which are used to measure cell composition. While there are currently 43 different deconvolution methods available, existing strategies often fail to consider changes in gene expression that occur within tumors in relation to their unique microenvironments.

To address this gap, the researchers developed an integrative bulk analysis that considers the ratio of tumor cells relative to non-tumor cells. This approach identifies cancer-specific mechanisms by factoring in tumor-specific total mRNA expression. While normal cells have mRNA expression directly proportional to chromosome numbers, cancer cells possess an abnormal number of chromosomes. The TmS biomarker factors in these changes in gene expression relative to chromosome numbers. Furthermore, the tool accounts for changes in RNA activities in tumor microenvironment cells as compared to the tumor cells themselves. Wang and her colleagues aimed to create a comprehensive guide for these methods to help researchers understand which approach might work best for study-specific goals.

The researchers noted that while the results are promising, further validation is needed to advance this computational tool into the clinic. The study focused on the accuracy of the TmS biomarker as a prognostic tool to optimize treatment selection across diverse populations. Because this research involves a computational approach and the analysis of gene expression data rather than the administration of a new drug or physical intervention, the report does not list clinical side effects or safety risks to the patients involved in the data cohorts. The focus remains on the potential of the TmS biomarker to improve the precision of chemotherapy response predictions and to ensure that treatment selection is tailored to the biological characteristics of the tumor and its microenvironment.

Reference

1. “New biomarker predicts chemotherapy response in triple-negative breast cancer,” news release; https://www.mdanderson.org/newsroom/research-newsroom/new-biomarker-predicts-chemotherapy-response-in-triple-negative-breast-cancer.h00-159852978.html

Editor's note: This article is for informational purposes only and is not a substitute for professional medical advice, as your own experience will be unique. Use this article to guide discussions with your oncologist. Content was generated with AI and reviewed by a human editor.

For more news on cancer updates, research and education, don’t forget to subscribe to CURE®’s newsletters here.