Zook, Justin M

Summary Cancer genome sequencing is essential for understanding tumor evolution and advancing precision medicine. 1 However, reference gaps and germline variants obscure detection of small and large somatic variants and methylation in repetitive regions. 1–3 It is common for tumor cells to gain or lose chromosome arms due to somatic structural changes that occur inside highly repetitive satellite DNA sequences in the centromeres. 4 To identify the full spectrum of somatic variants, including complex rearrangements, we construct and curate near-complete, haplotype-resolved assemblies of the most recent common ancestor of an early-passage broadly-consented hypodiploid pancreatic cancer cell line and matched normal tissues. The tumor assembly completely recapitulates all 35 tumor chromosomes observed with karyotyping, with multiple translocation-induced hybrid chromosomes. The hybrid chromosomes contain putative functional dicentric and fused centromeres, nested foldback inversions causing 14 breakpoints with a haplotype switch in a single event, and centromeric satellite tandem duplications up to 136 kbp. Direct comparison of tumor and normal assembly haplotypes uncovers >7,000 variants altering >1 Mbp of sequence in repetitive regions that have been hidden by reference gaps and germline variants. 44 % of somatic small variants change representation because they alter germline variants on GRCh38, impacting mutational signatures and kataegis/omikli clusters. Most somatic LINE insertions originate from two hypomethylated non-reference germline LINE insertions, highlighting their impact on insertion mutation burden. These assemblies demonstrate that centromeric, acrocentric, and telomeric regions conventionally excluded from analysis harbor extensive somatic and epigenetic changes. Resolving complete tumor genomes enables a deeper understanding of cancer structural plasticity and the endpoints of breakage-fusion-bridge cycles. These assembled, curated paired normal-tumor benchmarks will serve as a critical foundation for developing future algorithms to characterize the most intractable regions of cancer genomes.

Abstract Background: Established laboratory methods, including next-generation sequencing (NGS), can accurately and inexpensively detect certain classes of clinically important mutations in readily accessible protein coding regions of a patient's DNA. These events include single nucleotide variants (SNVs), small insertions/deletions (indels), and sometimes medium sized copy-number deletions or duplications (del/dups). However, other mutation types with clinical relevance are known, and indeed data on over 80,000 patients show that pathogenic, medically important variants of other, more technically challenging types are prevalent in genes related to hereditary breast and ovarian cancer (see associated abstract by Lincoln et al.). We sought to develop and evaluate a reference standard by which laboratories may be able to assess and improve their performance on representative examples of complex, technically “hard” mutations in medically important genes associated with breast, ovarian, and other cancers. Methods: We selected a diverse set of 23 challenging alterations, uncovered by the aforementioned clinical study, all of which are considered pathogenic and potentially actionable under ACMG and NCCN guidelines when uncovered in the germline DNA of a patient. These mutations are in the BRCA1, BRCA2, MLH1, MSH2, MSH6, and PMS2 genes. We generated a single synthetic DNA specimen with all 23 mutations introduced into a known genomic background. Following presentation of this general strategy at a 2016 meeting, 7 laboratories volunteered to collaboratively evaluate this approach. This DNA was created, validated, blinded and provided to these laboratories who sequenced it using a total of 9 different NGS based workflows, including 5 validated clinical tests with customized methods and two sequencing technology vendor (Illumina, Ion Torrent) default pipelines. Multiple target capture biochemical methods were used, as was whole genome sequencing. Results: Twelve of the 23 variants were detected by all 9 laboratory workflows, but just 2 workflows detected all 23. Many, but not all, of these test limitations were previously known. Importantly, evidence of each variant was present in the raw data, suggesting that this strategy is compatible with the diverse biochemical methods used in NGS laboratories today. Raw data for the synthetic variants mimicked that of the endogenous ones (including presenting similar technical artifacts which make these variants “hard”) demonstrating that controls such as these may be useful in the development of methods with improved sensitivity. The vendor-supplied bioinformatics pipelines fared the worst, reinforcing the importance of carefully selecting bioinformatics algorithms and parameters in any laboratory developed test. Discussion: Medically important but technically challenging mutations are prevalent in genes involved in hereditary breast, ovarian, and other cancers. Although patient specimens are also critical, synthetic controls may help efficiently assess the analytic range of any clinical test, highlighting certain strengths and limitations, and can help laboratories develop new methods to improve sensitivity for these challenging variants. Citation Format: Lincoln S, Zook J, Truty R, Chowdhury S, Fellowes A, Mahamdallie S, Ferber M, Cleveland M, Huang C, Tomson F, Klee E, DeSilva W, Seal S, Aradhya S, Nussbaum R, Garlick R, Kingsmore S, Rahman N, Salit M, Shirts B. An interlaboratory study of complex mutation detection in genes associated with hereditary breast and ovarian cancer highlights both successes and current challenges [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-06-08.