A new study published in Cancer Biology & Medicine has introduced a framework to classify breast cancer based on the cancer-immunity cycle (CIC), potentially transforming how clinicians predict patient response to immunotherapy and identify new treatment targets. The research, conducted by scientists from Fudan University Shanghai Cancer Center and Shanghai Medical College, analyzed six key steps of the CIC to develop a “CIC score” that categorizes patients into three distinct clusters, each with unique immune profiles and vulnerabilities.
The cancer-immunity cycle is a conceptual model describing the step-by-step process of the anti-tumor immune response, from antigen release to T-cell killing of tumor cells. Defects in any step can halt the cycle and render immune checkpoint inhibitors (ICIs) ineffective. While ICIs have revolutionized cancer treatment, many breast cancer patients do not respond, highlighting the need for a more holistic assessment of immune status. This study addresses that gap by evaluating the entire cycle rather than individual steps.
Using the CIC score, the team identified three clusters: C1, an “immune-cold” tumor with low immune infiltration, poor prognosis, and abundance of immunosuppressive M2 macrophages; C3, an “immune-hot” tumor with high immune infiltration, active T cells, and the best response to ICI therapy; and C2, an intermediate subtype with a unique defect in antigen presentation. Despite high tumor mutational burden (TMB), which typically predicts immunotherapy responsiveness, C2 tumors exhibited frequent 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 specific metabolic dependencies for each cluster. C1 tumors showed enrichment in sphingolipid metabolism, while C2 tumors displayed a strong dependency on serine metabolism. Notably, the enzyme PSAT1 was identified as a key metabolic regulator in C2, and its knockdown reduced expression of immunosuppressive molecules such as PD-L1 and TGFB1. This finding suggests that targeting PSAT1 could restore antigen presentation and enhance immunotherapy efficacy in C2 patients.
“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.”
The classification system has immediate clinical implications. The CIC score could serve as a robust biomarker to stratify patients, identifying those most likely to benefit from ICI therapy while sparing others from unnecessary side effects. Additionally, the discovery of distinct immune-evasion mechanisms in each subtype 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—potentially by targeting PSAT1 or overcoming HLA loss—could be key.
The study was published in Cancer Biology & Medicine (DOI: 10.20892/j.issn.2095-3941.2025.0611), an open-access journal indexed in SCOPUS, MEDLINE, and SCI. The research was supported by grants from the National Key Research and Development Project of China and the National Natural Science Foundation of China.
