Researchers characterized cancer cell-specific features in the tumor microenvironment (TME) of early-stage triple-negative breast cancer (TNBC) tissues, identifying specific macrophage subtypes associated with chemotherapy response. The researchers developed a 13-gene panel and a machine learning model that can predict which patients are more likely to respond to treatment, laying the groundwork for developing novel diagnostic approaches and personalized therapeutic strategies. This represents one of the first large-scale single-cell genomic studies of TNBC, providing an unprecedented view of both cancer cell biology and the unique TNBC tumor microenvironment. The study was led by Nicholas Navin, Ph.D., chair of Systems Biology, and Clinton Yam, M.D., associate professor of Breast Medical Oncology.
“This study provides novel insights into the gene-expression programs and the different cell states of the tumor microenvironment in patients with triple-negative breast cancer,” Navin said. “Importantly, we’ve identified certain programs and macrophage subtypes that are associated with good responses to neoadjuvant chemotherapy, which has tremendous potential to improve patient outcomes.”