Evidence map›Paper›PMID 42041198›Full record

SynthesisCancer medicine2026

A Systematic Review and Meta-Analysis of the Impact of Tumour Mutation Burden on Survival Outcomes in Solid Tumours.

Aijia Meng, Alexander Yuile, Hao-Wen Sim, Subotheni Thavaneswaran, Humaira Noor, Jacky Yeung, Ashish Mehta, Joseph Powell, Ashraf Zaman

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Aijia MengGarvan Institute of Medical Research, Sydney, New South Wales, Australia.ORCID https://orcid.org/0009-0005-9419-2246
Alexander YuileDepartment of Medical Oncology, Royal North Shore Hospital, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-7242-0528
Hao-Wen SimGarvan Institute of Medical Research, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-2530-0523
Subotheni ThavaneswaranGarvan Institute of Medical Research, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-8946-6709
Humaira NoorDepartment of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, California, USA.ORCID https://orcid.org/0000-0001-8887-8122
Jacky YeungDepartment of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID https://orcid.org/0000-0001-7336-5738
Ashish MehtaGarvan Institute of Medical Research, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-0187-4477
Joseph PowellGarvan Institute of Medical Research, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-5070-4124
Ashraf ZamanGarvan Institute of Medical Research, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-2492-5281

Funding

Charlie Teo Foundation PR11197
6 · The paper itself

Abstract

backgroundTumour mutation burden (TMB) is an emerging pan-cancer biomarker with predictive value for immune checkpoint inhibitor (ICI) outcomes, yet evidence is inconsistent due to methodological variability and cut-off thresholds. This systematic review and meta-analysis evaluated the impact of TMB on overall survival (OS) and progression-free survival (PFS) across solid tumours in ICI-treated cohorts and its predictive relevance in non-ICI-treated cohorts.

methodsFollowing PRISMA 2020 guidelines, we searched PubMed, Scopus, ScienceDirect and Cochrane for studies published between 2010 and 2024 reporting hazard ratios (HRs) and 95% confidence intervals (CIs) for OS and PFS in high- versus low-TMB cohorts. High and low TMB were defined by study-specific cut-offs, and ultra-high TMB was defined as the top 20% of cohort-specific values. Study quality was assessed with the Newcastle-Ottawa Scale; heterogeneity with I

results5278 patients across 28 studies were analysed. High TMB, defined by cohort-specific cut-offs, was significantly associated with improved OS and PFS, particularly in non-small cell lung cancer (OS: HR = 0.56), selected gastrointestinal cancers (OS: HR = 0.36), and advanced/recurrent tumours (OS: HR = 0.52). Benefits were greatest in ICI-treated patients, especially with combined anti-PD-L1/PD-1 and anti-CTLA-4 therapy (OS: HR = 0.47; PFS: HR = 0.50). Chemotherapy-treated cohorts also showed better outcomes, but less consistently (OS: HR = 0.60; PFS: HR = 0.55). Ultra-high TMB had better OS than the universal 10 mut/Mb cut-off (HR = 0.44 vs. 0.58). Non-beneficial associations were observed in glioma and penile squamous cell carcinoma, highlighting disease-specific variability. Sequencing platforms and cut-off definitions remained sources of heterogeneity.

conclusionTMB demonstrates prognostic relevance and predictive utility in a histology- and treatment-context-dependent manner, with the most consistent associations in selected ICI-treated tumours. Associations in non-ICI-treated cohorts were weaker and inconsistent, indicating putative predictive value. Standardising TMB assessment and refining relevant thresholds are essential for optimising its role in precision oncology.

trial registrationPROSPERO Registration Number: CRD42024608809.

Indexed as

Biomarkers, TumorImmune Checkpoint InhibitorsMutationNeoplasmsHumansPrognosisProgression-Free SurvivalBiomarkers, TumorImmune Checkpoint Inhibitorsimmunotherapymeta‐analysisoverall survivalprogression‐free survivaltumour mutation burden

Identifiers

PMID42041198
PMCPMC13113419

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.