New Biomarker Predicts Chemotherapy Response in Triple-Negative Breast Cancer

Triple-negative breast cancer is an aggressive form of breast cancer in which patients often show widely varying responses to chemotherapy. Predicting who will benefit from treatment remains challenging, especially because tumors interact dynamically with their surrounding microenvironment. Existing computational tools typically focus on cell composition but overlook gene expression changes relative to the tumor’s unique biological context. Researchers have now developed a new computational biomarker that more accurately predicts chemotherapy response and identifies patients who may benefit from alternative therapies.

Researchers at The University of Texas MD Anderson Cancer Center (Houston, TX, USA) have developed a novel integrative analysis approach incorporating tumor-specific total mRNA expression, known as the TmS biomarker. The method builds on deconvolution strategies by accounting for the ratio of tumor to non-tumor cells and adjusting for chromosomal abnormalities in cancer cells. By integrating gene expression data with microenvironment context, the tool captures cancer-specific biological mechanisms often missed by traditional models.

In a dataset of 575 patients with triple-negative breast cancer across diverse ethnic cohorts, the TmS biomarker successfully stratified patients into high-TmS groups with a favorable prognosis and low-TmS groups with poorer outcomes. The study, published in Cell Reports Medicine, showed that TmS outperformed existing computational methods in predicting chemotherapy response. The analysis also revealed population-specific differences between Western and Asian tumors, offering insights into tailored treatment strategies.

The TmS biomarker may serve as an effective starting point for patient stratification, guiding chemotherapy decisions and identifying candidates for alternative therapies. Its ability to capture microenvironment-driven gene expression changes positions it as a promising tool for precision oncology. Although further clinical validation is required, researchers believe the approach could improve treatment optimization across diverse populations and advance data-driven decision-making in breast cancer care.

“Deconvolution strategies are not one size fits all,” said Professor Wenyi Wang, PhD, senior author of the study. “We’re focused on making these methods more accessible to researchers without extensive computational backgrounds, with the goal of translating these powerful analytical approaches into practical tools that the broader cancer research community can readily apply to advance precision medicine.”

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