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Updated: Jun 20, 2025
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
Tumour mutational burden is overestimated by target cancer gene panels
Hu Fang1,2, Johanna Bertl3, Xiaoqiang Zhu1
1School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Background:
Tumour mutational burden (TMB) has emerged as a predictive marker for responsiveness to immune checkpoint inhibitors (ICI) in multiple tumour types. It can be calculated from somatic mutations detected from whole exome or targeted panel sequencing data. As mutations are unevenly distributed across the cancer genome, the clinical implications from TMB calculated using different genomic regions are not clear.
Methods:
Pan-cancer data of 10,179 samples were collected from The Cancer Genome Atlas cohort and 6,831 cancer patients with either ICI or non-ICI treatment outcomes were derived from published papers. TMB was calculated as the count of non-synonymous mutations and normalised by the size of genomic regions. Dirichlet method, linear regression and Poisson calibration models are used to unify TMB from different gene panels.
Results:
We found that panels based on cancer genes usually overestimate TMB compared to whole exome, potentially leading to misclassification of patients to receive ICI. The overestimation is caused by positive selection for mutations in cancer genes and cannot be completely addressed by the removal of mutational hotspots. We compared different approaches to address this discrepancy and developed a generalised statistical model capable of interconverting TMB derived from whole exome and different panel sequencing data, enabling TMB correction for patient stratification for ICI treatment. We show that in a cohort of lung cancer patients treated with ICI, when using a TMB cutoff of 10 mut/Mb, our corrected TMB outperforms the original panel-based TMB.
Conclusion:
Cancer gene-based panels usually overestimate TMB, and these findings will be valuable for unifying TMB calculations across cancer gene panels in clinical practice.
Insights
Cancer gene panels often overestimate tumour mutational burden (TMB), potentially misclassifying patients for immune checkpoint inhibitors (ICI). A new statistical model corrects TMB discrepancies between whole exome and panel data, improving patient stratification for ICI therapy.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Tumour mutational burden (TMB) is a key biomarker for predicting response to immune checkpoint inhibitors (ICI).
- TMB calculation methods vary depending on whether whole exome or targeted panel sequencing data is used.
- The uneven distribution of mutations across the cancer genome creates uncertainty in TMB interpretation from different genomic regions.
Purpose of the Study:
- To investigate discrepancies in TMB calculations between whole exome sequencing and targeted gene panels.
- To develop a generalized statistical model for harmonizing TMB values across different sequencing approaches.
- To improve patient stratification for immune checkpoint inhibitor (ICI) therapy by correcting TMB values.
Main Methods:
- Collected pan-cancer data from The Cancer Genome Atlas (10,179 samples) and ICI/non-ICI treatment outcomes from published studies (6,831 patients).
- Calculated TMB as non-synonymous mutations normalized by genomic region size.
- Employed Dirichlet method, linear regression, and Poisson calibration models to unify TMB from various gene panels.
Main Results:
- Cancer gene panels tend to overestimate TMB compared to whole exome sequencing, potentially leading to misclassification for ICI treatment.
- This overestimation is attributed to positive selection of mutations in cancer genes and is not fully resolved by removing mutational hotspots.
- A generalized statistical model was developed to interconvert TMB values from whole exome and panel data, demonstrating improved performance in a lung cancer cohort treated with ICI.
Conclusions:
- Cancer gene-based panels frequently overestimate TMB, impacting clinical decision-making for ICI therapy.
- The developed generalized statistical model offers a method for unifying TMB calculations across different panels.
- These findings are crucial for standardizing TMB assessment in clinical practice to ensure accurate patient stratification for immunotherapy.
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