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TMB in NSCLC: A Broken Dream?

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Predicting immunotherapy response in cancer is crucial. Tumor mutation burden (TMB) shows promise but faces detection challenges, suggesting combined biomarker models may improve patient selection for immune checkpoint inhibitors.

Keywords:
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Area of Science:

  • Oncology
  • Immunotherapy
  • Genomics

Background:

  • Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, including non-small-cell lung cancer.
  • However, patient response to ICIs varies significantly, necessitating predictive biomarkers for effective patient selection.

Purpose of the Study:

  • To evaluate Tumor Mutation Burden (TMB) as a predictive biomarker for ICI response.
  • To identify challenges hindering TMB's clinical utility and explore strategies for enhanced predictive accuracy.

Main Methods:

  • Review of current literature and clinical practices regarding TMB assessment.
  • Analysis of factors influencing TMB detection and interpretation.
  • Exploration of multivariable models incorporating TMB and other biomarkers.

Main Results:

  • TMB is a potential biomarker for predicting response to immune checkpoint inhibitors.
  • Clinical application of TMB is limited by detection difficulties and biological, methodological, and economic issues.
  • Multivariable models combining TMB with PD-L1 expression or other biomarkers show potential for greater predictive power.

Conclusions:

  • While TMB is a promising biomarker, its widespread clinical use is impeded by practical challenges.
  • Integrating TMB with other biomarkers in predictive models may enhance patient stratification for immunotherapy.