Researchers at The University of Texas MD Anderson Cancer Center have developed a computational tool that improves predictions of chemotherapy response in patients with triple-negative breast cancer (TNBC). The new method, led by Wenyi Wang, Ph.D., professor of Bioinformatics and Computational Biology, was published in Cell Reports Medicine.
The approach builds on existing deconvolution methods, which analyze cellular differences within tumors. According to the research team, their technique is distinct because it accounts for changes in gene expression within tumors relative to their specific microenvironments. Current classification strategies typically measure cell composition but do not consider these dynamic changes.
Wang and colleagues previously published a guide outlining 43 available deconvolution methods to help researchers choose appropriate tools for their studies. Despite this variety, the team noted limitations in how current approaches address tumor complexity.
To overcome these challenges, the researchers worked with MD Anderson's Institute for Data Science in Oncology and Department of Breast Medical Oncology to create an integrative bulk analysis method. This includes consideration of tumor-specific total mRNA expression (TmS), which takes into account the ratio of tumor cells to non-tumor cells. This helps identify mechanisms specific to cancer cells.
"While normal cells have mRNA expression directly proportional to chromosome numbers, cancer cells have an abnormal number of chromosomes," the release states. "The TmS biomarker factors this in, accounting for gene expression changes relative to chromosome numbers in cancer cells. This biomarker further factors in changes in RNA activities in tumor microenvironment cells as compared to tumor cells."
Testing the TmS biomarker on data from 575 TNBC patients across ethnically diverse groups showed it could distinguish between those with favorable prognosis (high-TmS) and poor prognosis (low-TmS). The new tool outperformed existing prediction methods for chemotherapy response and may serve as a basis for better patient stratification when selecting treatments.
The study also found that while the prognostic biomarker applies broadly across populations, there are notable differences between high-TmS Western and Asian ethnic groups' tumor microenvironments. These findings suggest clinicians might be able to tailor additional therapies based on population-specific characteristics.
Although further clinical validation is needed before widespread adoption, the results indicate that the TmS biomarker could help optimize treatment choices for diverse TNBC patient populations.
