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Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

While tissue biopsies and imaging techniques remain the current standard of care, minimally invasive liquid biopsy tests are emerging as compelling complements in the clinical management of cancer patients. The ability to accurately detect low frequency somatic mutations, tumor mutational burden (TMB) and microsatellite instability (MSI) biomarkers from a single plasma sample using a broad-based NGS panel is of high value for biomarker discovery, detection and monitoring. Herein we report the results of our analytical validation of Illumina's TruSight Oncology 500 (TSO500) ctDNA assay which employs a hybrid-capture approach for target enrichment of 523 clinically-relevant cancer genes with unique molecular indices (UMIs) to enable detection of low frequency variants, CNVs, DNA fusions, TMB and MSI analysis. To characterize performance of small variant detection (SNVs and indels) and TMB analysis using the TSO500 ctDNA assay, we utilized various contrived reference standards with >1,500 unique variants below 5% variant allele frequency (VAF) and over 50 uncharacterized diseased and 20 healthy plasma samples. Cell-free DNA (cfDNA) isolates from healthy donors were used to evaluate the false positive (FP) rate as well as to evaluate potential impacts of blood collection tube (BCT) type on variant calling. For the tube type comparison, we procured 5 matched sets of healthy donor blood in both EDTA and Streck cfDNA BCTs. Where possible, unexpected somatic variants detected in healthy donor samples were orthogonally confirmed using droplet digital PCR (ddPCR). Additionally, to evaluate robustness of input on variant detection and TMB precision, cfDNA isolates from late stage cancer patients were tested at varying input amounts (10, 20, 30ng) in the TSO500 ctDNA assay. Analytical sensitivity, specificity and positive predictive value (PPV) were evaluated using reference standards. We observed >99% sensitivity for detecting SNVs and >98% for indels at or above 0.5% VAF. Additionally, we observed very high specificity and PPV (>99%), with a false positive rate of < 2 FP per 1.3 Mb down to 0.2% VAF. Further support for high specificity was observed in healthy donor samples where 25/26 apparent false positive somatic variants (0.2-0.7% VAF) were confirmed using ddPCR. Robustness of input studies revealed comparable coverage depth and uniformity can be achieved with 20 - 30 ng of cfDNA. Precision of variant detection and TMB will be reported for a subset of the diseased plasma sample cohort. In conclusion, the TSO500 ctDNA assay enables robust detection of low frequency variants and other clinically relevant biomarkers in EDTA and Streck derived plasma samples with at least 20ng of cfDNA input. Citation Format: Kirsten C. Verhein, Gunjan Hariani, Stephanie B. Hastings, Patrick Hurban. Analytical validation of Illumina's TruSight Oncology 500 ctDNA assay [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3114.

Checkpoint inhibitor (CPI) therapy demonstrates a remarkable clinical benefit in many cancer types. However, the ability to successfully select patients who will benefit from CPIs is still limited. Tumor mutational burden (TMB), a measure of the number of somatic mutations per coding area of tumor genome, is a putative biomarker of response showing great promise in CPI and immunotherapy combination trials. The ability to measure TMB from tumor biopsies or plasma samples will be important for clinical adoption of this biomarker. Herein we report on initial performance evaluation of Illumina’s TruSight™ Oncology 500 gene (TSO500) NGS assay for the analysis of TMB in FFPE tissue and plasma cell-free (cf)DNA specimens. Illumina’s TSO500 assay employs hybrid-capture based approach for target enrichment coupled with unique molecular indices to enable low frequency variant detection of single nucleotide variants and indels. This comprehensive cancer panel interrogates relevant cancer biomarkers in >500 cancer genes (~2 Mb) from as little as 40 ng of FFPE DNA or 30 ng of cfDNA. In addition to variant calls, Illumina’s analysis pipeline reports a TMB score and microsatellite instability (MSI) status. Results obtained with the TSO500 TMB assay were compared to our validated whole exome sequencing (WES) TMB assessment for FFPE tissue specimens, with and without matched normal. As previously reported, the WES TMB tumor-only pipeline uses somatic variant classifications determined using a random forest model to generate TMB scores from analysis of tumor FFPE specimens. TSO500 TMB and MSI statues were evaluated in a set of 50 tumor FFPE specimens obtained from late-stage (III+) colorectal and bladder cancer patients. TSO500 TMB results correlated well with the WES-derived TMB results, although some differences were observed in the magnitude of high TMB scores. TSO500 TMB levels correlated strongly with the MSI status, with 100% of MSI-high tumors demonstrating high TMB scores (> 20), and greater than 85% of MSI-low tumors characterized as TMB-low ( Citation Format: Stephanie B. Hastings, Gunjan Hariani, Victor J. Weigman, Tingting Jiang, Chen Zhao, Rajiv Raja, Traci Pawlowski, Philip Brohawn, Patrick Hurban. Panel-based tumor mutational burden (TMB) analysis of matched tumor and plasma specimens using Illumina’s TruSight Oncology 500 next-generation sequencing assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1346.

Cancer genomic profiles created by analysis of targeted Next Generation Sequencing (NGS) panels is emerging as a powerful tool for making informed clinical decisions. Of the critical informatics challenges to address, accurate mutation calls and allele frequency estimations after accounting for PCR-mediated artifacts are debated. The process of sample preparation for NGS sequencing involves amplification by PCR. While PCR is relatively error-free, mistakes early in DNA synthesis can be compounded, driving detection of spurious mutations and having an adverse impact on clinical reporting. Previous reports have addressed the utility of detecting and removing PCR duplicate reads in Mendelian applications but have rarely examined its use with targeted NGS panels. We performed deduplication with 3 widely used tools (Samtools, Samblaster, and Picard) to understand sensitivity to call low frequency alleles and any impact on false positive/negative rates. Furthermore, we evaluated effects of duplicate removal on targeted panels of varying sizes and effects of sample matrices using replicates of 7 verified reference samples with several digitally confirmed alleles with frequencies ranging from 1-5%. It is not practical to perform deduplication on PCR-enriched panels, therefore, we assessed 3 different hybrid enrichment panels of varying size (387kb, 1.3Mb, and 54Mb). Deduplication by Picard resulted in a greater decrease in the mean depth for the smaller panels (32-59%) compared to Exome (15%), showing that higher molecular diversity lowers duplication rates. Uniformity (percent of ROI with depth within 20% of the mean depth) improved 6-18% after deduplication for the smaller panels, but only 1% for the Exome. Independent of panel size, about 32% of the total reads were marked as duplicates, reducing the power to call low frequency variants by 18%. Importantly, after added sequencing 95-96% of onco-specific variants were detected post-deduplication with a lower limit of detection of 3% compared to 2.5% pre-removal. For low-quality DNA samples we find no benefit in added sequencing for any panel. We also generally observed higher sensitivity (0% to 10% for SNV and -3% to 3% for indels) post deduplication. Molecular diversity also varies by sample type. Intact DNA show higher molecular diversity and lower duplication rates than degraded FFPE samples. We profiled mixed-quality FFPE samples (n = 85), good-quality fresh frozen samples (n = 3), and NA12878 (n = 1) on our smallest panel and noted duplication rates of 64±14%, 40±5% and 30% respectively. On average, deduplication cut the number of SNV calls by 17.4%, with the FFPE samples affected the most (2-88%). From our analysis we recommend performing deduplication during analysis of targeted panels. While we observed the most benefit for smaller panels with low uniformity, improved variant sensitivity was seen regardless of panel size. During experimental design, we advise a worksheet to guide deduplication decisions. Citation Format: Jeran Stratford, Gunjan Hariani, Jeff Jasper, Chad Brown, Wendell Jones, Victor J. Weigman. Impact of duplicate removal on low frequency NGS somatic variant calling. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5276.