Abstract
A CMS approved test for lung cancer is based on tumor-only analysis of a targeted 35 gene panel, specifically excluding the use of the patient’s normal germline tissue. However, this tumor-only approach increases the risk of mistakenly identifying germline single nucleotide polymorphisms (SNPs) as somatically-derived cancer driver mutations (false positives). 621 patients with 30 different cancer types, including lung cancer, were studied to compare the precision of tumor somatic variant calling in 35 genes using tumor-only DNA sequencing versus tumor-normal DNA plus RNA sequencing. When sequencing of lung cancer was performed using tumor genomes alone without normal germline controls, 94% of variants identified were SNPs and thus false positives. Filtering for common SNPs still resulted in as high as 48% false positive variant calling. With tumor-only sequencing, 29% of lung cancer patients had a false positive variant call in at least one of twelve genes with directly targetable drugs. RNA analysis showed 18% of true somatic variants were not expressed. Thus, sequencing and analysis of both normal germline and tumor genomes is necessary for accurate identification of molecular targets. Treatment decisions based on tumor-only analysis may result in the administration of ineffective therapies while also increasing the risk of negative drug-related side effects.
Highlights
In 2016, the Centers for Medicare and Medicaid Services (CMS) authorized coverage of a tumor-only DNA sequencing-based test of 35 genes intended to inform lung cancer treatment
This tumor-only approach increases the risk of mistakenly identifying germline single nucleotide polymorphisms (SNPs) as somatically-derived cancer driver mutations. 621 patients with 30 different cancer types, including lung cancer, were studied to compare the precision of tumor somatic variant calling in 35 genes using tumor-only DNA sequencing versus tumor-normal DNA plus RNA sequencing
Whole-genome DNA sequencing of 45 lung cancer patients’ tumor and normal genomes resulted in the identification of 802 missense or nonsense proteinaltering single nucleotide variants (SNVs) in the panel of 35 genes associated with lung cancer etiology
Summary
In 2016, the Centers for Medicare and Medicaid Services (CMS) authorized coverage of a tumor-only DNA sequencing-based test of 35 genes intended to inform lung cancer treatment. Bioinformatics and statistical methods have been developed for the purpose of identifying somatic mutations with tumor-only sequencing data, including an extension to a well-known variant calling algorithm that incorporates new statistical and machine learning components [3], as well as a method that leverages ancestry information and allele fraction to improve the identification of somatic and germline variants [4]. While these bioinformatics and statistical methods have reduced false positive rates, the rates remain unacceptably high for use with tumor-only sequencing in a clinical setting. The problem of identifying mutations of germline origin from tumor-only sequencing has recently been highlighted [6] and tumor-normal sequencing and analysis was shown to be significantly better at identifying inherited cancer susceptibility mutations than guideline-based germline testing [7]
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