Researchers at The University of Texas MD Anderson Cancer Center have identified immune cell and tumor-specific features in triple-negative breast cancer (TNBC) that may help predict which patients are most likely to respond to chemotherapy before treatment begins, according to a study published in Nature. Using single-cell and spatial transcriptomic analyses of pretreatment tumor samples, the team identified specific macrophage subtypes and cancer-cell gene expression programs associated with response to neoadjuvant chemotherapy (NAC). The team also developed a 13-gene panel and a machine learning model that could help classify tumors according to their likelihood of responding to chemotherapy.
“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,” said Nicholas Navin, PhD, chair of systems biology at MD Anderson. “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.”
TNBC accounts for between 10% and 20% of breast cancer cases. Because it lacks estrogen, progesterone, and HER2 receptors, treatment options are limited, resulting in a higher rate of recurrence compared with other form of breast cancer. Chemotherapy is the main treatment approach, particularly in early-stage disease, where neoadjuvant chemotherapy can achieve pathological complete response in 40% to 50% of patients. However, treatment outcomes vary widely from patient to patient, and researchers have been looking for ways that can better predict response before therapy begins.
For this study, the researchers analyzed pretreatment core biopsy samples from treatment-naive patients with early-stage TNBC. They performed single-cell RNA sequencing on 427,857 cells collected from 101 patients and spatial transcriptomic profiling on tumors from 44 patients. The findings also were compared with normal breast tissue data from the Human Breast Cell Atlas.
Based on their testing the researchers classified TNBC tumors into four patient-level “archetypes” based on cancer-cell gene expression patterns. They also identified 13 metaprograms that reflected heterogeneity within tumors at the single-cell level.
The tumor microenvironment consisted of 49 immune and stromal cell states organized into eight cellular communities, or ecotypes, defined by the co-occurrence of cancer cells and surrounding immune cell populations. Researchers found these cellular neighborhoods were associated both with tumor archetypes and chemotherapy response.
The study homed in on macrophages, a type of immune cell that has received less attention in TNBC research than T cells. The investigators said that seven of eight macrophage cell states were significantly associated with treatment response, while none of the 14 T-cell and natural killer-cell states showed significant associations with NAC response.
Macrophage subtypes linked to interferon signaling and complement activity, identified as Mac-IFN and Mac-lip-C1Q, were more abundant in patients who achieved pathological complete response. By comparison, two macrophages associated with angiogenesis and extracellular matrix remodeling, called Mac-angio and Mac-ECM, were enriched in patients with residual disease after chemotherapy.
The team also found that tumors linked to good response to NAC showed increased interferon signaling and elevated expression of human leukocyte antigen class II genes. Researchers said these findings indicate that cancer cells themselves may actively participate in modulating immune signaling related to chemotherapy response.
As part of their work, the researchers developed a 13-gene transcriptional signature panel developed from the single-cell analyses that can be used as a predictive model for chemotherapy response. Researchers said the model’s predictions correlated with chemotherapy response and overall survival across multiple public TNBC cohorts.
These new findings have the potential to influence how patients with TNBC are treated in the future by helping clinicians identify which patients are more likely to benefit from standard chemotherapy and which patients may need alternative therapeutic strategies earlier.
In addition, “these findings suggest that targeting specific macrophage subtypes could potentially provide new therapeutic opportunities in TNBC,” the researchers wrote.
The MD Anderson team noted that the study is one of the first large-scale single-cell genomic studies of TNBC integrating cancer cells, immune cells and treatment-response data. Earlier research exploring tumor heterogeneity has often lacked therapy response information, focused only on cancer cells or immune cells separately, or included relatively small patient cohorts.
Whether single-cell RNA seq could eventually become a basis for predictive diagnostics remains an open question. Today, the method is still expensive and technically challenging, two hindrances to it wider adoption. The researchers noted, however, that advances in sample multiplexing and other methods compatible with formalin-fixed paraffin-embedded tissue could make it feasible in the future.
Clinton Yam, MD, an associate professor of breast medical oncology at MD Anderson, said the findings could support more individualized approaches to TNBC care.
“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.”
Future research will focus on validating the predictive models in prospective patient cohorts and evaluating TNBC treated with chemo-immunotherapy, which has become the standard of care when TNBC is detected early. The researchers also plan to study longitudinal tumor samples collected before, during, and after treatment to better understand how cancer cells and the tumor microenvironment evolve over time and how those changes relate to chemotherapy response and survival.
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