A Validation Framework for Somatic Copy Number Detection in Targeted Sequencing Panels

Abstract

Somatic copy number alterations (SCNAs) in tumors are clinically significant diagnostic, prognostic, and predictive biomarkers. SCNA detection from targeted next-generation sequencing panels is increasingly common in clinical practice; however, detailed descriptions of optimization and validation of SCNA pipelines for small targeted panels are limited. This study describes the validation and implementation of a tumor-only SCNA pipeline using CNVkit, augmented with custom modules and optimized for clinical implementation by testing reference materials and clinical tumor samples with different classes of copy number variation (CNV; amplification, single copy loss, and biallelic loss). Using wet-bench and in silico methods, various parameters impacting CNV calling, including assay-intrinsic variables (establishment of normal reference and sequencing coverage), sample-intrinsic variables (tumor purity and sample quality), and CNV algorithm-intrinsic variables (bin size), were optimized. The pipeline was trained and tested on an optimization cohort and validated using an independent cohort with a sensitivity and specificity of 100% and 93%, respectively. Using custom modules, intragenic CNVs with breakpoints within tumor suppressor genes were uncovered. Using the validated pipeline, re-analysis of 28 pediatric solid tumors that had been previously profiled for mutations identified SCNAs in 86% (24/28) samples, with 46% (13/28) samples harboring findings of potential clinical relevance. Our report highlights the importance of rigorous establishment of performance characteristics of SCNA pipelines and presents a detailed validation framework for optimal SCNA detection in targeted sequencing panels.

Figure 1

Optimizing normal reference parameters for SCNA analysis. A: Mean sequencing coverage of normal peripheral blood samples. B: Verifying fitness of normal samples by performing a sex-matched normal versus normal analysis (females: top panel; males: bottom panel). Each normal sample (gray rectangles) was compared one on one against other normal comparators (x axis). Box plots summarize the gene-level log2(FC) values. C: Box and density plots summarizing log2(FC) ratio of 15 peripheral blood normal samples (7 males and 8 females), after removing the outlier normal, show a narrow distribution of values centered around zero. Dashed red line represents log2(FC) threshold for copy number loss. D: Box plots summarizing increase in the median absolute deviation (blue dots) for tumor samples (optimization cohort) when the normal reference pool is down sampled to decreasing coverages (Kruskal-Wallis P = 1.3 × 10⁻⁷). E: Change in sensitivity (blue) and specificity (red) of the SCNA pipeline when tumor samples are compared against normal reference pool at different down-sampled coverages. n = 16 (A); n = 11 (D). ∗∗P < 0.01, ∗∗∗∗P < 0.0001 (Wilcoxon rank-sum test).

Figure 2

Establishing congruence between SCNA derived from copy number array and targeted sequencing panel data. A: Comparison of HT-29 cell line gene-level log2(FC) between copy number array [array comparative genomic hybridization (aCGH)] and Texas Children's Hospital (TCH) solid sequencing data (R² = 0.90). B: Comparison of MOLT-4 cell line gene-level log2(FC) between copy number array (OncoScan) and TCH heme sequencing data (R² = 0.95). C and D: Overall SCNA profile of HT-29 cell line using aCGH data from NCI-60 CellMiner (C) and TCH solid targeted sequencing data (D). E and F: SCNA profile of HAP1 cell line before recentering (E) and after recentering (F).

Figure 3

Optimizing tumor sample parameters for SCNA analysis. A: Mean sequencing coverage of tumor samples in the optimization cohort. B: Box plots summarizing the increase in median absolute deviation (blue dots) for tumor samples (optimization cohort) when their coverages are down sampled to decreasing coverages (Kruskal-Wallis P = 1.4 × 10⁻¹⁵). C: Change in sensitivity (blue) and specificity (red) of the SCNA pipeline when tumor samples are down sampled to different coverages. D: Change in sensitivity (blue) and specificity (red) of the SCNA pipeline with varying bin size. E–G: Studying limit of detection by diluting samples T01 (purity, 70%; E), T06 (purity, 80%; F), and T07 (purity, 90%; G) that have confirmed CCND1 amplification, PTEN heterozygous loss, and SMARCB1 homozygous loss, respectively, by orthogonal gold standard method with normal reference in silico. H: Diluting T17 (previously confirmed SMARCB1 heterozygous loss) with normal reference in silico and wet bench. Comparing log2(FC) values between theoretical (green), in silico (dark red), and wet-bench (golden yellow) dilutions to verify minimum tumor fraction at which loss is detected using log2(FC) threshold of <−0.4 (red). Fraction of tumor sample at log2(FC) threshold for theoretical and wet-bench dilutions marked by blue and purple dashed lines, respectively. Line plots show good concordance between log2(FC) values of theoretical (green) and in silico dilutions (dark red). Copy number thresholds marked by red dashed line [amplifications, log2(FC) > 1.5; and loss, log2(FC) < −0.4], and fraction of tumor sample at the threshold has been marked by blue dashed line. n = 11 (A and B). ∗P < 0.05, ∗∗∗∗P < 0.0001 (Wilcoxon rank-sum test).

Figure 4

Evaluating precision of the SCNA pipeline. A: Reproducibility measured using HT-29 cell line showed concordant gene-level log2(FC) values (R² = 0.95) when compared against its replicate (HT-29R2). B: Repeatability measured using T25 shows concordant gene-level log2(FC) values (R² = 0.98) when compared against its replicate (T25R2). C: Interquartile range (IQR) of gene-level log2(FC) measured per gene (blue) using replicates of the positive control sample, HT-29. D: Genes crossed the set threshold [log2(FC) < −0.4 and log2(FC) > 1.5; blue] for making gene-level SCNA calls consistently in all replicates, except for IRF2 and STAG2 (outliers; black). n = 124 genes (C); n = 18 replicates (C). TCH, Texas Children's Hospital.

Figure 5

Identifying allelic imbalance and intragenic events. A: Neuroblastoma sample wild type for chromosome 11q SCNA supported by segments (orange) centered around log2(FC) = 0 and heterozygous single-nucleotide polymorphism (SNP) means centered close to 0.5 variant allele fraction (VAF; orange), as seen in the SNP allele fraction track. B: Neuroblastoma sample with allelic imbalance in chromosome 11q, supported by a segment (orange) in chromosome 11q with log2(FC) = −1.3 and heterozygous SNP means (orange) showing imbalance with SNPs clustering close to 0 and 1 VAF, as seen in the SNP allele fraction track.

Figure 6

Assessing diagnostic utility of SCNA analysis. A: Oncoprint summarizing the diagnostic utility of integrating copy number analysis to the Texas Children's Hospital (TCH) solid gene mutation panel. DNA extracted from 28 available pediatric cancer samples was evaluated for copy number changes and somatic point mutations. B: Sunburst plot highlighting diagnostic (orange), therapeutic (green), and prognostic (yellow) utility of including copy number analysis as part of TCH solid. AMP, amplification; ATRT, atypical teratoid rhabdoid tumor; Chr, chromosome; CNLOH, copy-neutral loss of heterozygosity; GNT, glioneuronal tumor; LOH, loss of heterozygosity; LS, liposarcoma; MB, medulloblastoma; MEGNT, malignant epithelioid glioneuronal tumor; MUT, mutation; NB, neuroblastoma; WT, Wilms tumor.