Optical Genome Mapping Identifies Novel Recurrent Structural Alterations in Childhood ETV6::RUNX1+ and High Hyperdiploid Acute Lymphoblastic Leukemia.

The mutational landscape of B-cell precursor acute lymphoblastic leukemia (BCP-ALL), the most common pediatric cancer, is not fully described partially because commonly applied short-read next generation sequencing has a limited ability to identify structural variations. By combining comprehensive analysis of structural variants (SVs), single-nucleotide variants (SNVs), and small insertions-deletions, new subtype-defining and therapeutic targets may be detected. We analyzed the landscape of somatic alterations in 60 pediatric patients diagnosed with the most common BCP-ALL subtypes, ETV6::RUNX1+ and classical hyperdiploid (HD), using conventional cytogenetics, single nucleotide polymorphism (SNP) array, whole exome sequencing (WES), and the novel optical genome mapping (OGM) technique. Ninety-five percent of SVs detected by cytogenetics and SNP-array were verified by OGM. OGM detected an additional 677 SVs not identified using the conventional methods, including (subclonal) IKZF1 deletions. Based on OGM, ETV6::RUNX1+ BCP-ALL harbored 2.7 times more SVs than HD BCP-ALL, mainly focal deletions. Besides SVs in known leukemia development genes (ETV6, PAX5, BTG1, CDKN2A), we identified 19 novel recurrently altered regions (in n ≥ 3) including 9p21.3 (FOCAD/HACD4), 8p11.21 (IKBKB), 1p34.3 (ZMYM1), 4q24 (MANBA), 8p23.1 (MSRA), and 10p14 (SFMBT2), as well as ETV6::RUNX1+ subtype-specific SVs (12p13.1 (GPRC5A), 12q24.21 (MED13L), 18q11.2 (MIB1), 20q11.22 (NCOA6)). We detected 3 novel fusion genes (SFMBT2::DGKD, PDS5B::STAG2, and TDRD5::LPCAT2), for which the sequence and expression were validated by long-read and whole transcriptome sequencing, respectively. OGM and WES identified double hits of SVs and SNVs (ETV6, BTG1, STAG2, MANBA, TBL1XR1, NSD2) in the same patient demonstrating the power of the combined approach to define the landscape of genomic alterations in BCP-ALL.

A multi-platform reference for somatic structural variation detection.

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

Haplotype threading: accurate polyploid phasing from long reads.

Resolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. Polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes.We present WHATSHAP POLYPHASE, a novel two-stage approach that addresses these challenges by (i) clustering reads and (ii) threading the haplotypes through the clusters. Our method outperforms the state-of-the-art in terms of phasing quality. Using a real tetraploid potato dataset, we demonstrate how to assemble local genomic regions of interest at the haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap.

Genotyping inversions and tandem duplications.

MOTIVATION: Next Generation Sequencing (NGS) has enabled studying structural genomic variants (SVs) such as duplications and inversions in large cohorts. SVs have been shown to play important roles in multiple diseases, including cancer. As costs for NGS continue to decline and variant databases become ever more complete, the relevance of genotyping also SVs from NGS data increases steadily, which is in stark contrast to the lack of tools to do so. RESULTS: We introduce a novel statistical approach, called DIGTYPER (Duplication and Inversion GenoTYPER), which computes genotype likelihoods for a given inversion or duplication and reports the maximum likelihood genotype. In contrast to purely coverage-based approaches, DIGTYPER uses breakpoint-spanning read pairs as well as split alignments for genotyping, enabling typing also of small events. We tested our approach on simulated and on real data and compared the genotype predictions to those made by DELLY, which discovers SVs and computes genotypes, and SVTyper, a genotyping program used to genotype variants detected by LUMPY. DIGTYPER compares favorable especially for duplications (of all lengths) and for shorter inversions (up to 300 bp). In contrast to DELLY, our approach can genotype SVs from data bases without having to rediscover them. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/jana_ebler/digtyper.git. CONTACT: t.marschall@mpi-inf.mpg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.