What question did this study set out to answer?

To evaluate the role of somatic copy number variants in the comparative genomic analysis of multifocal non-small cell lung cancer using a Bayesian approach.

April 5, 2026

Abstract 5423: A Bayesian framework for evaluating somatic copy number variants into comparative genomic analysis of multifocal non-small cell lung cancer.

Key Points

To evaluate the role of somatic copy number variants in the comparative genomic analysis of multifocal non-small cell lung cancer using a Bayesian approach.
Analyzed single nucleotide variants (SNVs), gene fusions, small and large-scale copy number variants in tumor nodules.
Used a tumor-only 515-gene next generation sequencing panel for genomic profiling.
Conducted retrospective analysis of 34 patients with multiple cancer nodules.
Applied a Bayesian framework to determine the probability of shared events among tumor nodules.
32.4% of patients had 2 or more shared SNVs and shared lCNVs.
23.5% of patients had no shared genomic alterations.
Clonal relatedness probability was over 99% for tumors with multiple shared alterations.
Whole chromosome or arm lCNVs sometimes shared between nodules with different SNVs.

Abstract

Abstract Introduction: Clinical staging of multifocal non-small cell lung cancer (NSCLC) is facilitated by comparative genomic analysis of separate tumor nodules, but the role of genome-wide copy number profiling for this purpose is uncertain. Methods: We analyzed single nucleotide variants (SNVs), gene fusions, small copy number variants (gene amplifications and homozygous deletions) (sCNVs), and large-scale copy number variants (lCNVs) in a retrospective cohort of 34 patients with 1 lung cancer nodule tested on a previously validated tumor-only 515-gene next generation sequencing (NGS) panel (MayoComplete Solid Tumor Panel). For patients with 2 shared SNVs, a Bayesian approach was formulated to calculate the probability of shared versus independent events occurring in different tumor nodules in the same patient. The prevalence of specific lCNVs in NSCLC was derived from an independent dataset of 100 NSCLC peers tested by the same panel, and the prevalence of SNVs, fusions, and sCNVs in NSCLC was derived from publicly available data. Alterations unique to different tumor nodules and suspected germline alterations were excluded from the Bayesian analysis. Results: There were 54 total comparisons for 34 patients. Eleven of the patients (32.4%) had =2 shared SNVs along with one or more shared lCNVs also identified in each patient. Eight patients (23.5%) had no shared SNVs, gene fusions, sCNVs, or lCNVs. Using the Bayesian approach for the remaining fifteen (44.1%) patients with at least one shared alteration (SNVs, sCNVs, and lCNVs), the probability of clonal relatedness ranged from 73% for a shared whole arm or whole chromosome lCNV to 99% for tumors with two or more shared alterations including multiple or complex lCNVs and SNVs. Whole chromosome or whole arm lCNVs were sometimes shared between tumor nodules with different driver SNVs. Conclusions: Incorporating lCNVs into comparative genomic analysis provides additional evidence of clonal relatedness in a subset of multifocal NSCLCs. However, differences in bioinformatics pipelines for calling lCNVs, concerns over the imprecision of genome-wide copy number profiling, and uncertainty regarding the temporal relationship between lCNVs and carcinogenesis (including somatic events possibly related to “field cancerization”) create challenges for integrating lCNVs into comparative genomic analysis with standard clinical workflows for tumor-only NGS on paraffin embedded tissue sections. Our simplified approach mitigates some of the uncertainty introduced by incorporating lCNVs into clonality determination. Citation Format: Claire Teigen, Ying-Chun Lo, Sounak Gupta, Stephanie Smoley, Beth Pitel, Hussam Al Kateb, Gopi Sivasankaran, Benjamin Kipp, Ande Rumilla, Gang Zheng, Kevin Halling, Katherine Geiersbach, . A Bayesian framework for evaluating somatic copy number variants into comparative genomic analysis of multifocal non-small cell lung cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5423.

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