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Reliability of panel-based mutational signatures for immune-checkpoint-inhibition efficacy prediction in non-small
H C Donker1, K Cuppens2, G Froyen3
1Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Global Computational Biology & Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany.
Objectives:
Mutational signatures (MS) are gaining traction for deriving therapeutic insights for immune checkpoint inhibition (ICI). We asked if MS attributions from comprehensive targeted sequencing assays are reliable enough for predicting ICI efficacy in non-small cell lung cancer (NSCLC).
Methods:
Somatic mutations of m = 126 patients were assayed using panel-based sequencing of 523 cancer-related genes. In silico simulations of MS attributions for various panels were performed on a separate dataset of m = 101 whole genome sequenced patients. Non-synonymous mutations were deconvoluted using COSMIC v3.3 signatures and used to test a previously published machine learning classifier.
Results:
The ICI efficacy predictor performed poorly with an accuracy of 0.51-0.09+0.09, average precision of 0.52-0.11+0.11, and an area under the receiver operating characteristic curve of 0.50-0.09+0.10. Theoretical arguments, experimental data, and in silico simulations pointed to false negative rates (FNR) related to panel size. A secondary effect was observed, where deconvolution of small ensembles of point mutations lead to reconstruction errors and misattributions.
Conclusion:
MS attributions from current targeted panel sequencing are not reliable enough to predict ICI efficacy. We suggest that, for downstream classification tasks in NSCLC, signature attributions be based on whole exome or genome sequencing instead.
Insights
Mutational signatures from targeted sequencing are unreliable for predicting immune checkpoint inhibition efficacy in non-small cell lung cancer. Whole exome or genome sequencing is recommended for accurate therapeutic insights.
Area of Science:
- Genomics
- Cancer Research
- Immunotherapy
Background:
- Mutational signatures (MS) offer therapeutic insights for immune checkpoint inhibition (ICI).
- Predicting ICI efficacy in non-small cell lung cancer (NSCLC) is crucial for treatment selection.
Purpose of the Study:
- To evaluate the reliability of MS attributions from targeted sequencing for predicting ICI efficacy in NSCLC.
- To determine if current targeted sequencing assays are sufficient for clinical decision-making regarding ICI.
Main Methods:
- Assayed somatic mutations in 126 NSCLC patients using panel-based sequencing of 523 cancer genes.
- Performed in silico simulations of MS attributions on whole-genome sequenced data from 101 patients.
- Deconvoluted mutations using COSMIC v3.3 signatures and tested a machine learning classifier.
Main Results:
- The ICI efficacy predictor showed poor performance (accuracy 0.51, AUC 0.50).
- False negative rates and misattributions were linked to targeted panel size and small mutation ensembles.
- In silico simulations confirmed issues with targeted sequencing for MS attribution.
Conclusions:
- MS attributions from current targeted panel sequencing are not reliable for predicting ICI efficacy.
- Whole exome or genome sequencing is recommended for accurate MS attribution in NSCLC downstream classification tasks.
