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Updated: Aug 31, 2026
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
TMB in NSCLC: A Broken Dream?
Sara Bravaccini1, Giuseppe Bronte1, Paola Ulivi1
1IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", 47014 Meldola, Italy.
Abstract:
Although immune checkpoint inhibitors have changed the treatment paradigm of a variety of cancers, including non-small-cell lung cancer, not all patients respond to immunotherapy in the same way. Predictive biomarkers for patient selection are thus needed. Tumor mutation burden (TMB), defined as the total number of somatic/acquired mutations per coding area of a tumor genome (Mut/Mb), has emerged as a potential predictive biomarker of response to immune checkpoint inhibitors. We found that the limited use of TMB in clinical practice is due to the difficulty in its detection and compounded by several different biological, methodological and economic issues. The incorporation of both TMB and PD-L1 expression or other biomarkers into multivariable predictive models could result in greater predictive power.
Insights
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.
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.
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