The advent of immune checkpoint inhibitors (ICIs) has marked a paradigm shift in cancer therapy, yet a significant proportion of breast cancer patients fail to respond to these therapies. The cancer-immune cycle (CIC) is a conceptual framework that maps the step-by-step process of the antitumor immune response, from the release of tumor cell antigens to the killing of tumor cells by T cells. A defect in any step can stop the entire cycle and render immunotherapy ineffective. However, most research has focused on single steps, which fail to capture the complexity of the immune response. Due to these limitations, a more holistic and systematic approach is urgently needed to accurately assess the patient’s immune status and guide therapeutic decisions.
Researchers from the Department of Breast Surgery at Shanghai Fudan University Cancer Center and the Department of Oncology at Shanghai Medical College, Fudan University developed a new classification system for breast cancer based on CIC. Published (DOI: 10.20892/j.issn.2095-3941.2025.0611) in Cancer Biology & Medicine in 2026, the study details how this new framework can predict patient response to ICIs and identifies new therapeutic targets to overcome treatment resistance.
The team developed a “CIC score” to measure the activity of six key steps in the anti-tumor immune response. Analyzing the outcome of each step, they classified patients into three distinct CIC groups. The first cluster (C1) was characterized as an “immune-cold” tumor with low immune infiltration, poor prognosis, and an abundance of immunosuppressive M2 macrophages. In stark contrast, the third cluster (C3) represented an “immune-hot” tumor, showing high immune cell infiltration, active T cells, and the best response to ICI treatment.
The most unexpected finding was the second cluster (C2), an intermediate subtype with a unique defect in antigen presentation. Despite a high tumor mutational load (TMB), which usually indicates a response to immunotherapy, C2 tumors showed frequent loss of human leukocyte antigen (HLA) heterozygosity and an immunosuppressive tumor microenvironment (TME) enriched with dysfunctional dendritic cells (DCs). Polyomic analyzes revealed specific metabolic dependencies for each group, with C1 showing enrichment of sphingolipid metabolism and C2 showing a strong dependence on serine metabolism. In particular, the enzyme PSAT1 was identified as a key metabolic regulator in C2, and its destruction in cancer cells reduced the expression of key immunosuppressive molecules such as *PD-L1* and TGFB1.
“CIC provides a powerful framework for understanding how tumors evade the immune system,” the authors said. “By building a comprehensive score that captures the effectiveness of this entire cycle, we’ve gone beyond the simple “hot” and “cold” volume paradigm to identify distinct, actionable defects. This allows us not only to predict which patients will benefit from current immunotherapies, but also to see exactly where the cycle is interrupted, leading us to new, more targeted combination strategies to correct these interruptions and improve outcomes for a wider range of patients.”
This new classification system has immediate and far-reaching implications for clinical practice. It provides a powerful biomarker, the CIC score, that could be used to stratify breast cancer patients, identifying those most likely to respond to ICI therapy and sparing others from unnecessary side effects. More importantly, the discovery of distinct immune evasion mechanisms in each subtype paves the way for new combination therapies. For patients with C1 tumors, therapies may need to focus on converting the “cold” microenvironment to a “warm” one, while for C2 patients, strategies to enhance antigen presentation, possibly by targeting PSAT1 or overcoming HLA loss, could be key.
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