Automated next-generation profiling of genomic alterations in human cancers

Abstract

The lack of validated, distributed comprehensive genomic profiling assays for patients with cancer inhibits access to precision oncology treatment. To address this, we describe elio tissue complete, which has been FDA-cleared for examination of 505 cancer-related genes. Independent analyses of clinically and biologically relevant sequence changes across 170 clinical tumor samples using MSK-IMPACT, FoundationOne, and PCR-based methods reveals a positive percent agreement of >97%. We observe high concordance with whole-exome sequencing for evaluation of tumor mutational burden for 307 solid tumors (Pearson r = 0.95) and comparison of the elio tissue complete microsatellite instability detection approach with an independent PCR assay for 223 samples displays a positive percent agreement of 99%. Finally, evaluation of amplifications and translocations against DNA- and RNA-based approaches exhibits >98% negative percent agreement and positive percent agreement of 86% and 82%, respectively. These methods provide an approach for pan-solid tumor comprehensive genomic profiling with high analytical performance.

Fig. 1. Overview of sample preparation and analysis for determination of sequence and structural alterations, TMB, and MSI by elio tissue complete.

Genomic libraries were prepared using DNA extracted from cell line or FFPE tissue, and following hybrid capture and PCR amplification, DNA libraries were sequenced using the Illumina NextSeq. Next-generation sequencing data were analyzed using the VariantDx bioinformatics pipeline through alignment to the human reference genome assembly for the identification of sequence mutations, including single base substitutions (SBSs) and insertions/deletions (indels). Candidate variants were filtered through the PGDx Cerebro algorithm, designed to distinguish genuine somatic mutation calls from technical artifacts and used to determine an elio-predicted exome tumor mutation burden (eTMB) score. Microsatellite status was determined using 68 mononucleotide tracts and specific sequence mutation contexts. Structural variants were identified with the Digital Karyotyping (DK) and Personalized Analysis of Rearranged Ends (PARE) algorithms. TMB tumor mutation burden, MSI microsatellite instability, MAF mutant allele fraction, WES whole-exome sequencing.

Fig. 2. Genomic landscape of sequence variants identified by elio tissue complete.

Distribution of variant consequences identified among genes included in elio tissue complete across 112 FFPE tumor specimens analyzed (a), with specific alterations in select driver genes highlighted in (b). c The landscape of mutations identified, by consequence, per sample demonstrated a wide dynamic range in the number and type of variants identified across the cohort, with the tumor types indicated below each case. Two cases, in which no alterations were identified, are not displayed. Source data are provided as a Source Data file.

Fig. 3. In silico and experimental comparison of the targeted panel and whole-exome TMB performance.

a The correlation of in silico predicted tumor mutation burden (TMB) for panels of different size (100 kilobases to 2.5 megabases, Mb) to observed whole-exome sequencing (WES) TMB for samples in The Cancer Genome Atlas (TCGA) suggested that panels of >1 Mb provide accurate TMB measurements. TMB analyses were performed in a metastatic population weighted according to the relative frequency of late-stage cases per year. b Comparison of the elio-predicted exome TMB (eTMB) using elio tissue complete and WES of tumor and matched-normal samples in a cohort of 106 non-small cell lung cancer (NSCLC) FFPE and cell-line samples resulted in high concordance in a cross-validated analysis (Pearson correlation = 0.909, P < 0.0001 and Spearman rho = 0.855, P < 0.0001). c Evaluation of eTMB in an independent cohort of 307 FFPE-derived pan tumor samples demonstrated high correlation to WES (Pearson correlation = 0.949, P < 0.0001 and Spearman rho = 0.870, P < 0.0001). d Distribution of eTMB scores in the independent cohort of 307 FFPE-derived tumors by tumor type, with the number of each tumor type captured in (c). The boxes represent the 25th and 75th percentile (interquartile range, IQR), while the median is reflected by the middle line of the box. The whiskers represent 1.5*IQR, with outliers plotted as points not connected to the whiskers. The Pearson and Spearman correlation coefficients were calculated using a two-sided test, and no adjustments were made for multiple comparisons. Source data are provided as a Source Data file. Muts/Mb mutations per megabase.

Fig. 4. Analytical precision and reproducibility of elio tissue complete eTMB.

Precision and reproducibility of elio-predicted exome tumor mutation burden (eTMB) measured in mutations per megabase (muts/Mb) across three independent clinical laboratories using 21 FFPE tumor and cell-line samples. At each site, each sample was analyzed by two operators on two sequencing instruments across three non-consecutive days as indicated. The coefficient of variation (CV) and standard deviation (SD) for each sample are indicated, demonstrating high performance of eTMB across standard clinical laboratory variables. Source data are provided as a Source Data file.

Fig. 5. Training and analytical validation of the elio tissue complete MSI detection algorithm.

a 725 FFPE cancer samples were evaluated using the combination tract-based and substitution score microsatellite instability (MSI) algorithm. A subset of these samples (n = 73) had confirmed MSI status through multiplex PCR analyses. After plotting both the fraction of positive homopolymer tracts and the signature weight matrix score, a decision boundary was determined to separate the cluster of known microsatellite stable (MSS) cases from known microsatellite instability-high (MSI-H) cases. Analysis for the presence or absence of deleterious mutations in genes involved in mismatch repair was used to segregate the population of samples close to the decision boundary line. b An independent cohort of 223 FFPE cancer samples with confirmed MSI status was analyzed with the elio tissue complete MSI algorithm. c Detailed analyses of each of the 68 mononucleotide tracts employed in the tract-based peak finding algorithm demonstrated >40% of tracts have perfect specificity in MSS cases and that a combination of high-sensitivity and high-specificity tracts was employed in the peak finding algorithm. Source data are provided as a Source Data file. CRC colorectal cancer, NSCLC non-small cell lung cancer.

Fig. 6. Analytical precision and reproducibility of the elio tissue complete MSI detection algorithm.

Precision and reproducibility of the microsatellite instability (MSI) status from elio tissue complete were evaluated across three independent clinical laboratories using 21 FFPE and cell-line samples. At each site, each sample was analyzed by two operators on two sequencing instruments across three non-consecutive days as indicated. The absolute value of the coefficient of variation (CV) and standard deviation (SD) for each sample are indicated. Samples with a combined score above the dotted line are considered microsatellite instability-high, demonstrating high performance of MSI across standard clinical laboratory variables. Source data are provided as a Source Data file.