Cancer
September 1, 2026 · via Europe PMC
IMMU-NO versus yes: Applicability of immune checkpoint inhibitors in gynecologic cancers.
Manning WB, Youssef AM, Yeku OO.
AI summary — for doctors, generated from the abstract only
Immune checkpoint inhibitors have clear, biomarker-guided benefit in endometrial and cervical cancers, but only limited benefit in ovarian cancer, with KEYNOTE-B96 a notable exception in platinum-resistant disease.
What was studied
A narrative review of the immunotherapy landscape across gynecologic cancers, detailing where ICI use is and is not supported by evidence.
Key findings
- In endometrial cancer, MMR status is the main predictive biomarker, with FDA approvals for ICI monotherapy and combinations
- ICIs plus chemotherapy show benefit regardless of MMR status in first-line advanced/metastatic endometrial cancer
- In cervical cancer, ICIs approved with chemoradiation and with chemotherapy for recurrent/metastatic disease
- In ovarian cancer, low TMB, immunosuppressive microenvironment and lack of biomarkers limit efficacy; most combinations failed
- KEYNOTE-B96 (pembrolizumab plus weekly paclitaxel ± bevacizumab) is an exception in platinum-resistant ovarian cancer
Clinical relevance
This review supports MMR-guided ICI use in endometrial cancer and approved ICI combinations in cervical cancer, while underscoring the limited and selective role of ICIs in ovarian cancer.
Who should care
Limitations
Narrative review; quantitative effect sizes of individual trials are not provided in the abstract.
Immunotherapy is now standard in many endometrial and cervical cancers, but ovarian cancer remains largely resistant.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Immune checkpoint inhibitors (ICIs) have transformed the treatment paradigm for patients with gynecologic malignancies. ICIs have led to improved survival outcomes in uterine and cervical cancer, although with only modest success in ovarian cancer. In endometrial cancer, The Cancer Genome Atlas provided the framework for the molecular classification of endometrial cancer, enabling identification of the predominant predictive biomarker: mismatch repair (MMR) status. US Food and Drug Administration approvals for ICI monotherapy and in combination with targeted therapy based on MMR status quickly followed. Further research demonstrated a role for ICIs in combination with chemotherapy regardless of MMR status in the initial treatment of advanced or metastatic disease. In cervical cancer, ICIs are approved for use in combination with chemotherapy-sensitized radiation and in combination with systemic chemotherapy in patients who have recurrent or metastatic disease. In ovarian cancer, the tumor microenvironment, low tumor mutational burden, and the absence of a reliable predictive biomarker have limited the efficacy of ICIs, with most combination approaches failing to demonstrate meaningful benefit. A notable exception is KEYNOTE-B96, a randomized trial of pembrolizumab versus placebo with weekly paclitaxel, with or without bevacizumab, in patients with platinum-resistant ovarian cancer. Continued investigation into predictive biomarkers, resistance mechanisms, and alternative therapeutic targets is critically needed to extend the benefits of immunotherapy to patients with ovarian cancer. In this review, the authors describe the current immunotherapy landscape across gynecologic cancers, highlighting when ICI use is, and is not, supported by the evidence.
DOI
10.1002/cncr.70611
PMID
42745396
PMCID
PMC13578834
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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