A new study has developed a framework to classify breast cancer based on the cancer-immunity cycle (CIC), potentially improving how patient response to immunotherapy is predicted. By analyzing the activity of six key steps in this cycle, researchers identified three distinct subtypes of breast cancer, shedding light on why some patients respond to treatment while others do not and unveiling new biological targets, such as the metabolic enzyme PSAT1, for personalized therapies.
Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, but many breast cancer patients do not respond. The CIC is a conceptual framework mapping the anti-tumor immune response from antigen release to tumor cell killing. A defect in any step can halt the cycle and render immunotherapy ineffective. However, most research has focused on individual steps, failing to capture the complexity of the immune response. A more holistic approach is needed to assess immune status and guide treatment.
Researchers from Fudan University Shanghai Cancer Center and Shanghai Medical College developed a new classification system based on the CIC. Published in Cancer Biology & Medicine (DOI: 10.20892/j.issn.2095-3941.2025.0611), the study details how this framework can predict response to ICIs and identify new therapeutic targets.
The team developed a 'CIC score' measuring activity of six key steps. They classified patients into three clusters. The first cluster (C1) was 'immune-cold' with low immune infiltration, poor prognosis, and immunosuppressive M2 macrophages. The third cluster (C3) was 'immune-hot' with high immune cell infiltration and best response to ICI therapy. The second cluster (C2) was an intermediate subtype with a defect in antigen presentation. Despite high tumor mutational burden (TMB), C2 tumors exhibited human leukocyte antigen (HLA) loss of heterozygosity and an immunosuppressive tumor microenvironment enriched with dysfunctional dendritic cells and regulatory T cells. Multi-omic analyses revealed metabolic dependencies: C1 showed sphingolipid metabolism enrichment, and C2 showed strong dependency on serine metabolism. The enzyme PSAT1 was identified as a key metabolic regulator in C2, and its knockdown reduced expression of immunosuppressive molecules like 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.'
This classification has implications for clinical practice. It provides a robust biomarker, the CIC score, to stratify patients and identify those likely to respond to ICI therapy while sparing others from side effects. The discovery of distinct immune-evasion mechanisms paves the way for novel combination therapies. For C1 tumors, treatments might focus on converting the 'cold' microenvironment into a 'hot' one, while for C2 patients, strategies to enhance antigen presentation by targeting PSAT1 or overcoming HLA loss could be key. The study was supported by grants from the National Key Research and Development Project of China and the National Natural Science Foundation of China.
