AJLM2402 - CME/CMLE - Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES)-Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES)

Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES)

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The study, published in the American Journal of Clinical Pathology, investigates the variability and challenges in whole-exome sequencing (WES)-based tumor mutational burden (TMB) analysis among clinical laboratories. The research emphasizes the need for standardizing TMB estimation in clinical settings to improve the reliability of predicting patients’ responses to immune checkpoint inhibitors (ICIs).<br /><br />The study outlines key points:<br />1. **Standardization Need**: There is significant variability in TMB estimation across labs due to factors such as variant calls, limit of detection (LOD), region of interest (ROI) sizes, and TMB cutoffs.<br />2. **Promising Potential**: Despite challenges, 50% of the laboratories showed consistent performance in WES-based TMB analysis, suggesting potential for standardization.<br /><br />The research involved 24 laboratories in China and utilized in silico reference data sets to assess the reliability and comparability of WES-based TMB analysis under routine conditions. The analysis included evaluating referred somatic variants’ precision and recall and calculating TMB. The results indicated widespread underestimation of TMB scores, with significant variability among laboratories. High interlaboratory variability was noted in interpreting TMB cutoffs.<br /><br />Several factors contributed to the observed variability:<br />1. **Variant Detection**: Different bioinformatics tools and parameters resulted in variable precision and recall among laboratories.<br />2. **Region Size**: Varying sizes of ROIs used for TMB calculation affected the estimations.<br />3. **False Positives/Negatives**: Splicing site variants and deletions were common causes of false negatives and positives.<br />4. **TMB Cutoffs**: Discrepancies in TMB cutoffs further added to the inconsistency in clinical interpretation.<br /><br />The findings underscore the need for harmonized analytical procedures and stringent validation of LOD for more accurate TMB estimation. The study also recommends that experienced clinical doctors should lead the interpretation of TMB scores to minimize variability caused by differing cutoff points among labs.<br /><br />The research concludes that improving the accuracy and comparability of WES-based TMB analysis is crucial for reliably evaluating patients' responses to immunotherapy, thereby aiding clinical decision-making. Further studies focusing on standardizing each step of the bioinformatics pipeline and determining optimal TMB cutoffs for different cancer types are needed to achieve consistent results.

Keywords

whole-exome sequencing

tumor mutational burden

clinical laboratories

standardization

immune checkpoint inhibitors

interlaboratory variability

bioinformatics tools

region of interest

false positives

clinical decision-making