New guidance aims to standardize tumor mutational burden in cancer

Immune checkpoint inhibitors have transformed cancer care, yet only a minority of patients respond to monotherapy, and immune-related adverse events remain a major concern. That has intensified the search for biomarkers that can better predict benefit before treatment begins. Tumor mutational burden (TMB) emerged as one of the leading candidates because it reflects the number of tumor mutations that may generate neoantigens capable of triggering antitumor immune responses. But enthusiasm has been tempered by a practical problem: different sample types, sequencing panels, thresholds, bioinformatics pipelines, and reporting styles can all produce different answers. Because of these challenges, and because TMB values vary greatly across cancer types, there is a pressing need for deeper research and more standardized clinical guidance.

Developed by Yangtze River Delta Lung Cancer Cooperation Group (East China LUngcaNcer Group), Youth Committee, published (DOI: 10.20892/j.issn.2095-3941.2025.0351) in Cancer Biology & Medicine, this expert consensus outlines how TMB should be defined, tested, standardized, and clinically interpreted to improve its use in real-world cancer immunotherapy practice in China.

The document's central message is that TMB should not be treated as a loose biomarker, but as a carefully controlled clinical measurement. The experts recommend prioritizing recently prepared paraffin-embedded tumor tissue, ensuring adequate tumor-cell content, and using validated DNA extraction and quality-control procedures before sequencing begins. For detection, the consensus identifies whole-exome sequencing as the gold standard, while recognizing targeted panel sequencing as the more practical clinical option when it is properly validated against whole-exome results. To improve reliability, the group recommends panel coverage of at least 1.0 Mb, sequencing depth of at least 200×, and standardized bioinformatics pipelines that can accurately remove germline variants and reduce platform-related bias. The paper also emphasizes that panel design matters: overly small panels can distort TMB estimates, while broader panels can better recapitulate whole-exome results. Just as importantly, the authors argue that thresholds should not be universal. TMB values differ dramatically across tumor types, and clinically useful cutoffs should be linked to actual immunotherapy outcomes rather than arbitrary statistical divisions. The consensus, therefore, moves the field away from a simplistic "high versus low" model and toward a more nuanced framework in which assay design, cancer type, and treatment context all shape meaning.

"TMB is no longer just a research concept," the expert consensus suggests in effect. "Its value now depends on whether clinicians and laboratories can measure it consistently, interpret it correctly, and place it in the right treatment context." By stressing sample quality, assay harmonization, cancer-specific thresholds, and multidisciplinary interpretation, the paper presents TMB not as a stand-alone answer, but as a decision-support tool that becomes most powerful when used carefully and in combination with broader molecular and clinical evidence.

The implications are practical and immediate. For oncologists, the consensus offers a clearer path for deciding when TMB testing may help expand access to immunotherapy, especially in patients with advanced solid tumors who have limited treatment options. For laboratories, it provides a framework for standardizing workflows from specimen handling to reporting language. For the broader field, it highlights a maturing phase in precision oncology: success now depends not only on discovering biomarkers, but on making them reproducible, comparable, and clinically interpretable. If widely adopted, this roadmap could help TMB move from a promising but uneven biomarker to a more dependable part of personalized cancer care.