A research team from Fudan University Shanghai Cancer Center and Shanghai Medical College has developed a new breast cancer classification system that predicts patient response to immune checkpoint inhibitors, according to a July 3 publication in Cancer Biology & Medicine. The system is based on the cancer-immunity cycle, which outlines the step-by-step process of the anti-tumor immune response.
The researchers created a "CIC score" measuring activity across six key steps in this cycle. By analyzing these scores, patients were classified into three distinct clusters. The first cluster (C1) represents an "immune-cold" tumor with low immune infiltration and poor prognosis, while the third cluster (C3) represents an "immune-hot" tumor with high immune cell infiltration and strong response to immunotherapy.
The study found that the second cluster (C2) was characterized by an intermediate profile with a defect in antigen presentation. Despite having a high tumor mutational burden—usually linked to better immunotherapy outcomes—these tumors showed frequent human leukocyte antigen loss of heterozygosity and an immunosuppressive environment rich in dysfunctional dendritic cells and regulatory T cells. Multi-omic analyses identified unique metabolic dependencies for each group: C1 tumors were enriched for sphingolipid metabolism, while C2 tumors depended on serine metabolism. The enzyme PSAT1 was highlighted as a key regulator in C2; its knockdown reduced expression of immunosuppressive molecules such as PD-L1 and TGFB1.
"The CIC provides a powerful framework for understanding how tumors evade the immune system," the authors said. "By building a comprehensive score that captures the efficiency of this entire cycle, we've moved beyond the simple 'hot' and 'cold' tumor paradigm to identify distinct, actionable defects. This allows us to not only predict which patients will benefit from current immunotherapies but also to see exactly where the cycle is breaking down, pointing us toward new, more targeted combination strategies to fix those breaks and improve outcomes for a wider range of patients."
According to the researchers, this classification offers immediate implications for clinical practice by providing biomarkers that could help stratify breast cancer patients based on their likelihood of responding to therapy. They say it may also guide development of novel combination treatments targeting specific mechanisms underlying resistance or susceptibility within each subtype.
