A new study published in Cancer Biology & Medicine introduces a classification system for breast cancer based on the cancer-immunity cycle (CIC), offering a more comprehensive approach to predicting patient response to immunotherapy and identifying novel treatment targets. The research, conducted by scientists from Fudan University Shanghai Cancer Center and Shanghai Medical College, analyzed the activity of six key steps in the anti-tumor immune response to develop a 'CIC score' that categorizes breast cancer into three distinct subtypes.
Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has transformed cancer treatment, but many breast cancer patients do not respond. The cancer-immunity cycle maps the step-by-step process of the immune system's attack on tumors, from antigen release to T-cell killing. A defect in any step can halt the cycle, rendering immunotherapy ineffective. Previous research focused on individual steps, missing the holistic picture. This new study takes a systematic approach by evaluating the entire cycle.
Using the CIC score, the team classified patients into three clusters. The first cluster (C1) represents 'immune-cold' tumors with low immune infiltration, poor prognosis, and an abundance of immunosuppressive M2 macrophages. The third cluster (C3) is 'immune-hot,' characterized by high immune cell infiltration, active T cells, and the best response to ICI therapy. The second cluster (C2) was an unexpected intermediate subtype with a unique defect in antigen presentation. Despite a 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 (DCs) and regulatory T cells (Tregs).
Multi-omic analyses revealed specific metabolic dependencies for each cluster. C1 tumors showed enrichment in sphingolipid metabolism, while C2 tumors had a strong dependency on serine metabolism. Notably, the enzyme PSAT1 was identified as a key metabolic regulator in C2, and its knockdown in cancer cells reduced the expression of immunosuppressive molecules like PD-L1 and TGFB1. This finding suggests that targeting PSAT1 could restore antigen presentation and overcome treatment resistance in C2 patients.
'The CIC provides a powerful framework for understanding how tumors evade the immune system,' the authors stated. '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.'
The implications for clinical practice are significant. The CIC score could serve as a robust biomarker to stratify patients, identifying those most likely to respond to ICIs and sparing others from unnecessary side effects. 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. For C2 patients, strategies to enhance antigen presentation, such as targeting PSAT1 or overcoming HLA loss, could be key.
The study was funded by the National Key Research and Development Project of China and the National Natural Science Foundation of China. The full research is available via the DOI 10.20892/j.issn.2095-3941.2025.0611 in Cancer Biology & Medicine.
