Researchers have developed a new computational approach designed to better account for changes in gene expression within tumors relative to their unique microenvironments. This approach outperformed current methods for predicting chemotherapy response in patients with triple-negative breast cancer.
Data from the new tool, developed by Wenyi Wang, PhD, Professor of Bioinformatics and Computational Biology at The University of Texas MD Anderson Cancer Center, and colleagues, was published in Cell Reports Medicine. It aims to improve upon similar methods to predict treatment responses using an approach known as deconvolution, which involves breaking down, calculating, and interpreting cellular differences. This approach also revealed novel insights into population-level characteristics of triple-negative breast cancer.
“Deconvolution strategies are not one-size-fits-all,” Dr. Wang said. “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.”
Knowing that there are many computational tools available, Dr. Wang and colleagues recently published a comprehensive guide that details 43 of these deconvolution methods in Nature Reviews Cancer. Their goal was to help researchers without extensive computational backgrounds to understand which method might work best for their study-specific goals.
However, while existing classification strategies measure cell composition, they do not take into consideration the changes that occur in gene expression within tumors in relation to their unique microenvironments.
To address this, the researchers collaborated with MD Anderson’s Institute for Data Science in Oncology (IDSO) and Department of Breast Medical Oncology to develop an integrative bulk analysis that also considers tumor-specific total mRNA expression (TmS). This approach accounts for the ratio of tumor cells relative to nontumor cells as a way of identifying cancer-specific mechanisms.
While normal cells have mRNA expression directly proportional to chromosome numbers, cancer cells have an abnormal number of chromosomes. 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.
Current Study Findings
In a data set of 575 patients with triple-negative breast cancer across ethnically diverse cohorts, the TmS biomarker was able to accurately sort patients as being high-TmS (favorable prognosis) or low-TmS (poor prognosis).
The biomarker outperformed current methods to predict chemotherapy response, highlighting its potential as an effective starting point for patient stratification to optimize treatment selection.
Importantly, this prognostic biomarker applies across populations, while also highlighting that there are key differences in the tumor microenvironments of high-TmS Western and Asian ethnic groups that could allow clinicians to also match additional treatments likely to work more effectively for each population.
While further validation is needed to advance this tool in the clinic, these results suggest that the TmS biomarker is a promising approach to optimize treatment selection across diverse populations.
DISCLOSURE: This study was supported by the National Cancer Institute, Department of Defense, the Cancer Prevention and Research Institute of Texas (CPRIT), the American Cancer Society, and Lyda Hill Philanthropies. For full disclosures of the study authors, visit cell.com.
