HOUSTON, MAY 13, 2026 ― Researchers at The University of Texas MD Anderson Cancer Center have 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.
The study, published today in Nature , was led by Nicholas Navin, Ph.D. , chair of Systems Biology , and Clinton Yam, M.D. , associate professor of Breast Medical Oncology . 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.
"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."
What is triple-negative breast cancer?
TNBC is an aggressive subtype of breast cancer that is commonly treated with chemotherapy. However, outcomes vary widely among patients, highlighting the need to understand what's causing these differences in response.
Previous studies have shown that TNBC tumors can have very different characteristics at the genetic and cellular level, but there haven't been many studies that take a comprehensive look at these differences. Further, it was not clear how the cells in the TME interact with each other and how they respond to chemotherapy treatment.
How did researchers identify the different cell types?
Using pre-chemotherapy tissue samples from patients with TNBC, the researchers performed single-cell analysis of over 427,000 cells from 101 patients and spatial transcriptomic analysis of tumors from 44 patients. They compared that data to normal breast tissue profiles from the Human Breast Cell Atlas , a comprehensive reference of cell types and cell states in adult human breast tissue.
The researchers were able to categorize TNBC tumors into four patient-level "archetypes" based on gene expression in the cancer cells. They found a coordinated set of 13 highly expressed cancer-specific genes – known as a transcriptional signature – that drives the different cell populations within tumors.
They also characterized 49 immune cell states that form eight consistent types of cell neighborhoods within the TME associated with each archetype and with chemotherapy response.
What are the main implications of the study regarding approaches to diagnose and treat TNBC?
While previous studies focused on T cells, this study notably highlighted the important role of specific subtypes of macrophages – a type of immune cell – and cancer-cell transcriptional signatures associated with either pro- or anti- tumor activity.
Together, these findings led researchers to develop a 13-gene transcriptional signature panel as well as a machine learning model that may help inform future efforts to better understand which tumors are more likely to respond to chemotherapy prior to treatment.
The ability to better predict responses to chemotherapy in TNBC represents an important step forward in understanding how tumor-specific features relate to treatment outcomes. Though further prospective studies are needed before clinical application, these findings also highlight certain features of TNBC tumors, specifically the macrophage subtypes, that could guide future efforts toward more tailored therapeutic strategies.
"These insights provide an important foundation for improving our understanding of why different TNBC tumors respond differently to chemotherapy, and the findings have strong potential to inform future strategies aimed at better predicting treatment response and guiding more individualized care for patients with triple-negative breast cancer," Yam said. "It's an exciting and meaningful step toward more precise approaches to breast cancer treatment, with the promise of ultimately improving outcomes and quality of life for our patients."
