ReviewCancers2026
Immunotherapies for Breast Cancer: From Checkpoint Inhibition to Emerging Cellular Therapies.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Harnessing immunity against breast cancer: from checkpoints to cell therapies.Journal of translational medicine · 2026Review
- Breast Cancer: Epidemiology, Molecular Classification, Diagnostics and Evolving Treatment Paradigms.Molecules (Basel, Switzerland) · 2026Review
- Molecular Profiling and Targeted Therapeutic Strategies in Breast Cancer: Clinical Integration of HER2, CDK4/6, and PI3K Inhibition with Trastuzumab, Abemaciclib and Alpelisib.Journal of clinical medicine · 2026Review
- NUPR1 in breast cancer: mechanisms and potential applications.Frontiers in physiology · 2026Review
- Control of breast cancer patient-derived xenografts by CD70-targeted HIT-CAR T cells.Frontiers in immunology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer remains a leading cause of cancer-related morbidity and mortality worldwide, with therapeutic response being shaped by the unique biology of each breast cancer subtype. Immunotherapy has emerged as a transformative approach in selected disease subtypes, with the most successful results being found in relation to triple negative breast cancer (TNBC). Immune checkpoint inhibitors (ICIs) have transformed the management of many solid tumours. In breast cancer, they have demonstrated clinical benefit in TNBC when combined with chemotherapy, establishing a new standard of care in both early-stage and metastatic settings. However, the majority of breast cancers exhibit intrinsic or acquired resistance to checkpoint blockade, driven by low tumour immunogenicity and an immunosuppressive tumour microenvironment. Recent advances in cellular immunotherapy could represent the next frontier in the therapeutic landscape of breast cancer. Chimeric antigen receptor (CAR) T cell targeting antigens such as HER2, ROR1, MUC1, mesothelin, and B7-H3 are entering early-phase clinical evaluation with results eagerly awaited. Parallel approaches, including tumour-infiltrating lymphocyte (TIL) therapy, T cell receptor (TCR)-engineered T cells, and CAR-natural killer (CAR-NK) platforms, offer alternative mechanisms to overcome antigen presentation barriers and immune evasion. This review summarises current clinical evidence for immunotherapies in breast cancer, highlights emerging cellular strategies, and discusses key challenges including antigen specificity, off-tumour toxicity, and tumour microenvironment-mediated resistance. Future progress will likely depend on rational combination approaches and next-generation engineered immune cell platforms to achieve durable and personalised clinical benefit.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.