Development and validation of a targeted gene sequencing panel for application to disparate cancers

Abstract

Next generation sequencing has revolutionised genomic studies of cancer, having facilitated the development of precision oncology treatments based on a tumour's molecular profile. We aimed to develop a targeted gene sequencing panel for application to disparate cancer types with particular focus on tumours of the head and neck, plus test for utility in liquid biopsy. The final panel designed through Roche/Nimblegen combined 451 cancer-associated genes (2.01 Mb target region). 136 patient DNA samples were collected for performance and application testing. Panel sensitivity and precision were measured using well-characterised DNA controls (n = 47), and specificity by Sanger sequencing of the Aryl Hydrocarbon Receptor Interacting Protein (AIP) gene in 89 patients. Assessment of liquid biopsy application employed a pool of synthetic circulating tumour DNA (ctDNA). Library preparation and sequencing were conducted on Illumina-based platforms prior to analysis with our accredited (ISO15189) bioinformatics pipeline. We achieved a mean coverage of 395x, with sensitivity and specificity of >99% and precision of >97%. Liquid biopsy revealed detection to 1.25% variant allele frequency. Application to head and neck tumours/cancers resulted in detection of mutations aligned to published databases. In conclusion, we have developed an analytically-validated panel for application to cancers of disparate types with utility in liquid biopsy.

Figure 1

Performance metrics for PV1 and PV2. (A) Box plots presenting data comparisons between PV1 (n = 80 samples tested) and PV2 (n = 111 samples tested) for unique reads (in millions; M), percent duplication, target coverage and percent base coverage at >20x and >100x. (B) Mean coverage over coding regions of shared genes was compared between PV1 and PV2 after additional probes to target poorly-covered regions were added to the latter. (C) Changes to individual shared targets between PV1 and PV2 are also presented. (D) The final mean coverage for PV2 is presented following the addition of 139 new genes for a total of 451 targeted genes, as well as (E) the final percentage of targeted coding bases in PV2 with a percent mean coverage of >100x. Dashed lines in panels (B–E) represent mean coverage. ***p < 0.001, ns = not significant.

Figure 2

Intragenic copy number detection and comparison to commercial panels. (A) Control genomic DNA samples were acquired from kConFab for sensitivity testing. Three of these samples included known exon duplications in BRCA1, TP53 and MSH2, which were assessed by the DeCON tool. Exons are numbered along the x-axis, and those of normal copy number are presented as blue dots. Amplifications are shown in red. The TP53 and AURKB genes are on opposing DNA strands hence the presence of the latter and its exons in this Figure. A similar genetic-overlap is observed for MSH2 to the left of the panel. (B) A commercially available pool of synthetic oligos against a normal genomic background was also obtained. Mutations were provided at variant allele frequencies (VAF) of 5–15% and 15–35%, or at germline frequencies. Presented are the number of detected and missed variants in our PV1 and PV2 panels relative to what was expected in AcroMetrix. This was compared to three other panels [AmpliSeq Cancer Hotspot Panel v2 (CHPv2), Illumina TruSeq Amplicon – Cancer Panel (TSCAP) and TruSight Tumor Panel 26 (TSTP)], the data for which were provided by the AcroMetrix manufacturer. Percent values on the right indicate the proportion of AcroMetrix variants actually targeted by the panels.

Figure 3

Copy number evaluation in breast cancer cell lines. (A) Heat map representation of ~300 targeted genes in the SK-BR-3 and BT-474 breast cancer cells lines. Data was extracted from PV1-captured DNA and processed through CNVkit and compared to those expected according to the Broad Institute’s Cancer Cell Line Encyclopedia (CCLE). Copy number deletions are represented in blue and as copy numbers approach diploidy colours become white. Amplification in copy number is represented in red. (B) Regression analysis was carried out using the correlation coefficient to quantify the strength of relationship between PV1 called variants in SK-BR-3 and (C) BT-474, and those expected according to CCLE. An r-value of >0.7 is considered a strong, positive relationship. (D–G) Ten lowest (red) and ten largest CNV (blue) changes as detected by PV1 in SK-BR-3 (D) and BT-474 (F). These were compared to expected values according to CCLE (E,G). Note the log₂ y-axes and the gene names on the x-axes. Complete data is presented in Supplementary Fig. S4.

Figure 4

Rare variants are detectable in FPTS, OSCC and cSCC samples, at a similar ratio to online databases. Our validated panel was applied to cohorts of patients with FPTS (n = 18) (A), OSCC (n = 39) (B), and cSCC (n = 27) (C), for the detection of rare (≤1% control population) variations of various categories. Data for OSCC and cSCC was compared to TCGA and Pickering/Inman databases^,, with genes arranged in order of most commonly mutated within those databases. Variants were restricted to high and medium impact. No such database exists for pituitary tumours, and genes were restricted to the 8 described in FPTS. Variants of any impact were included in the data presented herein for FPTS.