Establishing reference samples for detection of somatic mutations and germline variants with NGS technologies

Abstract

71We characterized two reference samples for NGS technologies: a human triple-negative 72 breast cancer cell line and a matched normal cell line. Leveraging several whole-genome 73 sequencing (WGS) platforms, multiple sequencing replicates, and orthogonal mutation 74 detection bioinformatics pipelines, we minimized the potential biases from sequencing 75 technologies, assays, and informatics. Thus, our "truth sets" were defined using evidence from 76 21 repeats of WGS runs with coverages ranging from 50X to 100X (a total of 140 billion reads). 77These "truth sets" present many relevant variants/mutations including 193 COSMIC mutations 78 and 9,016 germline variants from the ClinVar database, nonsense mutations in BRCA1/2 and 79 missense mutations in TP53 and FGFR1. Independent validation in three orthogonal 80 experiments demonstrated a successful stress test of the truth set. We expect these reference 81 materials and "truth sets" to facilitate assay development, qualification, validation, and 82 proficiency testing. In addition, our methods can be extended to establish new fully 83 characterized reference samples for the community. 84 85 86

Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing Data

“…For full-spectrum analysis of the somatic mutation detection problem, we used the first comprehensive whole-genome characterized reference tumor-normal paired breast cancer cell lines (HCC1395 and HCC1395BL), developed by the Somatic Mutation Working Group of the SEQC-II consortium 12,13 . We leveraged high-confidence somatic mutations (39,536 SNVs and 2,020 INDELs) derived by the consortium as our ground truth set ( Suppl.…”

“…We used several different training models in our analysis. First, we used the already available model published recently 11 which was trained using in silico spike-ins from the DREAM Challenge Stage 3 dataset 13 . Despite the large discrepancy between the sample types, sequencing platforms, coverages, spike-in mutation frequencies, and heterogeneity of the samples used to train the DREAM3 model, this model outperformed other conventional techniques across the real cancer datasets of diverse characteristics by more than ∼4% by the mean F1-score averaged across different samples for both SNVs and INDELs ( Figure 1a ).…”

“…We used the SEQC-II reference matched samples, a human triple-negative breast cancer cell line (HCC1395) and a matched B lymphocyte derived normal cell line (HCC1395BL) in our analysis. Detailed sample information can be found in the SEQC-II reference samples manuscripts 12,13 . The SEQC-II somatic mutation working group has established a gold standard truth set of somatic mutations for these samples 13 ( Suppl.…”

“…Detailed sample information can be found in the SEQC-II reference samples manuscripts 12,13 . The SEQC-II somatic mutation working group has established a gold standard truth set of somatic mutations for these samples 13 ( Suppl. Figure 1 ).…”

“…Recently, the Somatic Mutation Working Group in the FDA-led Sequencing Quality Control Phase II (SEQC-II) consortium developed reference matched tumor-normal samples: a human triple-negative breast cancer cell line (HCC1395) and a matched B lymphocyte derived normal cell line (HCC1395BL) 12,13 . Using orthogonal sequencing technologies, multiple sequencing replicates, and multiple bioinformatics analysis pipelines, the SEQC-II consortium has developed a well-defined “Gold Set” of somatic Single Nucleotide Variants (SNVs) and insertion/deletions (INDELs) for HCC1395.…”