tumor mutation burden

Tumor mutation burden (TMB) is a genomic biomarker that quantifies the number of somatic mutations in a tumor and is used primarily to predict response to immune checkpoint inhibitors. It is reviewed as a conventional predictive biomarker, including in mismatch repair-deficient endometrial cancer where it helps assess response or resistance. TMB is also described as an immunotherapy-response-related biomarker with context-dependent associations, including links to prkd3. Recent work has improved TMB estimation in tumor-only sequencing through varnet t, which classifies TMB-high status. In NSCLC, TMB was used as a comparator and reported to be inferior to deep hypergraph for nsclc (DHGN) for identifying likely immunotherapy responders. Overall, TMB remains a clinically relevant but imperfect biomarker, with newer models and multi-omic approaches refining how it is measured and interpreted.

Immunotherapy response prediction

  • TMB was evaluated as a conventional biomarker for predicting response or resistance to immune checkpoint inhibitors in mismatch repair-deficient endometrial cancer. (PMID:41835337)
  • In a pan-cancer analysis, TMB showed immunotherapy-response-related associations that varied in a context-dependent manner with prkd3. (PMID:41931575)
  • A clinician-deployable NSCLC model reported that DHGN outperformed TMB for identifying patients likely to benefit from immunotherapy. (PMID:42008547)
  • The review framed TMB as a standard biomarker in the broader checkpoint-inhibitor response landscape. (PMID:41835337)

Mutation burden estimation and classification

  • varnet t was developed to improve tumor-only variant calling and estimate mutation burden more accurately. (PMID:41957035)
  • The method explicitly supports classification of TMB-high status from tumor-only sequencing data. (PMID:41957035)
  • This advances practical TMB assessment when matched normal samples are unavailable. (PMID:41957035)

Comparative biomarker performance

  • In NSCLC, TMB served as a comparator biomarker and was reported to be less effective than deep hypergraph for nsclc for predicting immunotherapy benefit. (PMID:42008547)
  • The finding highlights that TMB may be useful but not always the best discriminator of responders. (PMID:42008547)
  • The literature suggests biomarker performance can depend on disease context and model design. (PMID:41835337; PMID:42008547)