The advent of immune checkpoint inhibitors (ICIs) represents a paradigm shift in cancer treatment, but 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 anti-tumor immune responses, from the release of cancer cell antigens to the killing of tumor cells by T cells. A defect in any one step can halt the entire cycle and render immunotherapy ineffective. However, most studies focus on individual steps and fail to capture the complexity of the immune response. Because of these limitations, a more comprehensive and systematic approach is urgently needed to accurately assess a patient’s immune status and guide treatment decisions.
Researchers from the Department of Breast Surgery, Fudan University Shanghai Cancer Center and the Department of Oncology, Fudan University Shanghai Medical College, have developed a new classification system for breast cancer based on the CIC. Public (DOI: 10.20892/j.issn.2095-3941.2025.0611) Cancer biology and medicine In 2026, a detailed study will explore how this new framework can predict patient response to ICIs and identify new therapeutic targets to overcome treatment resistance.
The research team developed a “CIC score” that measures the activity of six key steps in the anti-tumor immune response. By analyzing the scores for each step, they classified patients into three different CIC clusters. The first cluster (C1) was characterized as an “immune cold” tumor with low immune infiltrate, poor prognosis, and abundant immunosuppressive M2 macrophages. In stark contrast, the third cluster (C3) represented “immune hot” tumors, with high immune cell infiltration, activated T cells, and the best response to ICI treatment.
The most unexpected finding was the second cluster (C2), which is an intermediate subtype with specific defects in antigen presentation. Despite their high tumor mutational burden (TMB), which usually indicates responsiveness to immunotherapy, C2 tumors frequently exhibited loss of human leukocyte antigen (HLA) heterozygosity and an immunosuppressive tumor microenvironment (TME) rich in dysfunctional dendritic cells (DCs) and regulatory T cells (Tregs). Multiomic analysis revealed specific metabolic dependence of each cluster, with C1 showing enrichment in sphingolipid metabolism and C2 showing strong dependence on serine metabolism. In particular, the enzyme PSAT1 has been identified as an important metabolic regulator of C2, and its knockdown 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 constructing a comprehensive score that captures the efficiency of this entire cycle, we are now able to go beyond a simple “hot” and “cold” tumor paradigm to identify clear and addressable deficiencies. This allows us to not only predict which patients will benefit from current immunotherapies, but also to understand exactly where the cycle is breaking, leading to new, more targeted combination strategies to correct those breaks and improve outcomes for a wider range of patients.. ”
This new classification system has immediate and far-reaching implications for clinical practice. It provides the CIC score, a powerful biomarker that can be used to stratify breast cancer patients, identify those most likely to respond to ICI therapy, and protect other patients from unnecessary side effects. More importantly, the discovery of distinct immune evasion mechanisms in each subtype opens the way to novel combination therapies. For patients with C1 tumors, treatment may need to focus on converting a “cold” to a “hot” microenvironment, whereas for C2 patients, strategies to enhance antigen presentation, potentially by targeting PSAT1 or overcoming HLA loss, may be key.
sauce:
Chinese Academy of Sciences
Reference magazines:
Xiao, D. others. (2026). Molecular subtypes based on the cancer immune cycle in breast cancer predict response to immune checkpoint inhibitors. Cancer biology and medicine. DOI: 10.20892/j.issn.2095-3941.2025.0611. https://www.cancerbiomed.org/content/early/2026/03/05/j.issn.2095-3941.2025.0611
