Researchers from Fudan University Shanghai Cancer Center and Shanghai Medical College have developed a novel classification system for breast cancer based on the cancer-immunity cycle (CIC), potentially transforming how patient response to immunotherapy is predicted. The study, published in Cancer Biology & Medicine (DOI: 10.20892/j.issn.2095-3941.2025.0611), analyzed six key steps of the anti-tumor immune response to create a "CIC score" that identifies three distinct subtypes of breast cancer.
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but many breast cancer patients do not respond. The CIC framework maps the step-by-step process from antigen release to tumor killing by T cells, and a defect in any step can halt the cycle. Previous research focused on individual steps, missing the holistic picture. This new study provides a systematic approach to assess immune status and guide treatment.
The team classified patients into three clusters. C1, termed "immune-cold," showed low immune infiltration, poor prognosis, and immunosuppressive M2 macrophages. C3, "immune-hot," had high immune cell infiltration, active T cells, and the best ICI response. The unexpected C2 subtype had a unique defect in antigen presentation despite high tumor mutational burden (TMB). C2 tumors exhibited frequent HLA loss of heterozygosity and an immunosuppressive microenvironment enriched with dysfunctional dendritic cells and regulatory T cells. Multi-omic analyses revealed metabolic dependencies: C1 enriched in sphingolipid metabolism, C2 dependent 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 prediction of which patients will benefit from current immunotherapies and points to new combination strategies to fix cycle breaks.
The classification has immediate clinical implications. The CIC score can stratify patients, identifying those likely to respond to ICIs and sparing others side effects. Discovery of distinct immune-evasion mechanisms paves the way for novel combination therapies. For C1 tumors, treatments might focus on converting the "cold" microenvironment to "hot"; for C2, enhancing antigen presentation by targeting PSAT1 or overcoming HLA loss could be key.
