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BioinformaticsJCO Clinical Cancer Informatics
Peer-reviewed journal article

Identification of community-consensus clinically relevant variants and development of single molecule molecular inversion probes using the CIViC database

Erica Kay BarnelliD, Adam WaalkesiD, Matthew MosioriD, Kelsi M. PenewitiD, Kelsy C. CottoiD, Arpad M. DanosiD, Lana M. ShetaiD, Katie Marie CampbelliD, Kilannin KrysiakiD, Damian Tobias RiekeiD, Nicholas C. SpiesiD, Zachary L. SkidmoreiD, Colin C. PritchardiD, Todd A. FehnigeriD, Ravindra UppaluriiD, Ramaswamy GovindaniD, Malachi GriffithiD, Stephen J. SalipanteiD, Obi L. GriffithiD

Abstract

Clinical targeted sequencing panels are important for identifying actionable variants for cancer patients, however, there are currently no strategies to create impartial and rationally-designed panels to accommodate rapidly growing knowledge within the field. Here we use the Clinical Interpretations of Variants in Cancer database (CIViC) in conjunction with single-molecule molecular inversion probe (smMIP) capture to identify and design probes targeting clinically relevant variants in cancer. In total, 2,027 smMIPs were designed to target 111 eligible CIViC variants. The total genomic region covered by the CIViC smMIPs reagent was 61.5 kb. When compared to existing genome or exome sequencing results (n = 27 cancer samples from 5 tumor types), CIViC smMIP sequencing demonstrated a 95% sensitivity for variant detection (n = 61/64 variants). Variant allele frequency for variants identified on both sequencing platforms were highly concordant (Pearson correlation = 0.885; n = 61 variants). Moreover, for individuals with paired tumor/normal samples (n = 12), 182 clinically relevant variants missed by original sequencing were discovered by CIViC smMIPs sequencing. This design paradigm demonstrates the utility of an open-sourced database built on attendant community contributions for each variant with peer-reviewed interpretations. Use of a public repository for variant identification, probe development, and variant annotation could provide a transparent approach to build a dynamic next-generation sequencing-based oncology panel.

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02 /Specification & Classification

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