Reliability of panel-based mutational signatures for immune-checkpoint-inhibition efficacy prediction in non-small cell lung cancer

H C Donker; K Cuppens; G Froyen; H J M Groen; T J N Hiltermann; B Maes; E Schuuring; P-J Volders; G A Lunter; B van Es

doi:10.1016/j.lungcan.2023.107286

Reliability of panel-based mutational signatures for immune-checkpoint-inhibition efficacy prediction in non-small cell lung cancer

Lung Cancer. 2023 Aug:182:107286. doi: 10.1016/j.lungcan.2023.107286. Epub 2023 Jul 3.

Authors

H C Donker¹, K Cuppens², G Froyen³, H J M Groen⁴, T J N Hiltermann⁵, B Maes⁶, E Schuuring⁷, P-J Volders⁸, G A Lunter⁹, B van Es¹⁰

Affiliations

¹ Department 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. Electronic address: [email protected].
² Department of Pulmonology and Thoracic Oncology, Jessa Hospital, Hasselt, Belgium; Department of Thoracic Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Faculty of Medicine and Life Sciences - LCRC, Hasselt University, Diepenbeek, Belgium. Electronic address: [email protected].
³ Faculty of Medicine and Life Sciences - LCRC, Hasselt University, Diepenbeek, Belgium; Laboratory of Molecular Diagnostics, Dept Clinical Biology, Jessa Hospital, Hasselt, Belgium.
⁴ Department of Pulmonary Diseases, University of Groningen and University Medical Center Groningen, the Netherlands. Electronic address: [email protected].
⁵ Department of Pulmonary Diseases, University of Groningen and University Medical Center Groningen, the Netherlands. Electronic address: [email protected].
⁶ Faculty of Medicine and Life Sciences - LCRC, Hasselt University, Diepenbeek, Belgium; Laboratory of Molecular Diagnostics, Dept Clinical Biology, Jessa Hospital, Hasselt, Belgium. Electronic address: [email protected].
⁷ Department of Pathology, University of Groningen and University Medical Center Groningen, the Netherlands. Electronic address: [email protected].
⁸ Laboratory of Molecular Diagnostics, Dept Clinical Biology, Jessa Hospital, Hasselt, Belgium. Electronic address: [email protected].
⁹ Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, Oxford University, Oxford, UK. Electronic address: [email protected].
¹⁰ Central Diagnostic Laboratory, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, 3508 GA Utrecht, the Netherlands; MedxAI, Theophile de Bockstraat 77-1, 1058VA Amsterdam, the Netherlands. Electronic address: [email protected].

PMID: 37421934
DOI: 10.1016/j.lungcan.2023.107286

Abstract

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.

Keywords: Immunotherapy; Mutational signatures; Non-small cell lung cancer; Panel sequencing.

MeSH terms

Aged
Aged, 80 and over
Carcinoma, Non-Small-Cell Lung* / drug therapy
Carcinoma, Non-Small-Cell Lung* / genetics
Computer Simulation
DNA Mutational Analysis* / methods
Datasets as Topic
Female
Humans
Immune Checkpoint Inhibitors* / therapeutic use
Lung Neoplasms* / drug therapy
Lung Neoplasms* / genetics
Machine Learning
Male
Middle Aged
Point Mutation
Treatment Outcome

Substances

Immune Checkpoint Inhibitors