Integrated analysis of whole-genome paired-end and mate-pair sequencing data for identifying genomic structural variations in multiple myeloma

Myeloma immunoglobulin rearrangement and translocation detection through targeted capture sequencing

Multiple myeloma is a plasma cell neoplasm characterized by clonal immunoglobulin V(D)J signatures and oncogenic immunoglobulin gene translocations. Additional subclonal genomic changes are acquired with myeloma progression and therapeutic selection. PCR-based methods to detect V(D)J rearrangements can have biases introduced by highly multiplexed reactions and primers undermined by somatic hypermutation, and are not readily extended to include mutation detection. Here, we report a hybrid-capture approach (CapIG-seq) targeting the 3' and 5' ends of the V and J segments of all immunoglobulin loci that enable the efficient detection of V(D)J rearrangements. We also included baits for oncogenic translocations and mutation detection. We demonstrate complete concordance with matched whole-genome sequencing and/or PCR clonotyping of 24 cell lines and report the clonal sequences for 41 uncharacterized cell lines. We also demonstrate the application to patient specimens, including 29 bone marrow and 39 cell-free DNA samples. CapIG-seq shows concordance between bone marrow and cfDNA blood samples (both contemporaneous and follow-up) with regard to the somatic variant, V(D)J, and translocation detection. CapIG-seq is a novel, efficient approach to examining genomic alterations in myeloma.

Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software

In recent years, many software packages for identifying structural variants (SVs) using whole-genome sequencing data have been released. When published, a new method is commonly compared with those already available, but this tends to be selective and incomplete. The lack of comprehensive benchmarking of methods presents challenges for users in selecting methods and for developers in understanding algorithm behaviours and limitations. Here we report the comprehensive evaluation of 10 SV callers, selected following a rigorous process and spanning the breadth of detection approaches, using high-quality reference cell lines, as well as simulations. Due to the nature of available truth sets, our focus is on general-purpose rather than somatic callers. We characterise the impact on performance of event size and type, sequencing characteristics, and genomic context, and analyse the efficacy of ensemble calling and calibration of variant quality scores. Finally, we provide recommendations for both users and methods developers.

A number of computational methods have been developed for calling structural variants (SVs) using short read sequencing data. Here, the authors perform a comprehensive benchmarking analysis comparing 10 general-purpose callers and provide recommendations for both users and methods developers.

Molecular Cytogenetics Guides Massively Parallel Sequencing of a Radiation-Induced Chromosome Translocation in Human Cells

Chromosome rearrangements are large-scale structural variants that are recognized drivers of oncogenic events in cancers of all types. Cytogenetics allows for their rapid, genome-wide detection, but does not provide gene-level resolution. Massively parallel sequencing (MPS) promises DNA sequence-level characterization of the specific breakpoints involved, but is strongly influenced by bioinformatics filters that affect detection efficiency. We sought to characterize the breakpoint junctions of chromosomal translocations and inversions in the clonal derivatives of human cells exposed to ionizing radiation. Here, we describe the first successful use of DNA paired-end analysis to locate and sequence across the breakpoint junctions of a radiation-induced reciprocal translocation. The analyses employed, with varying degrees of success, several well-known bioinformatics algorithms, a task made difficult by the involvement of repetitive DNA sequences. As for underlying mechanisms, the results of Sanger sequencing suggested that the translocation in question was likely formed via microhomology-mediated non-homologous end joining (mmNHEJ). To our knowledge, this represents the first use of MPS to characterize the breakpoint junctions of a radiation-induced chromosomal translocation in human cells. Curiously, these same approaches were unsuccessful when applied to the analysis of inversions previously identified by directional genomic hybridization (dGH). We conclude that molecular cytogenetics continues to provide critical guidance for structural variant discovery, validation and in "tuning" analysis filters to enable robust breakpoint identification at the base pair level.

Hi-C as a tool for precise detection and characterisation of chromosomal rearrangements and copy number variation in human tumours

Chromosomal rearrangements occur constitutionally in the general population and somatically in the majority of cancers. Detection of balanced rearrangements, such as reciprocal translocations and inversions, is troublesome, which is particularly detrimental in oncology where rearrangements play diagnostic and prognostic roles. Here we describe the use of Hi-C as a tool for detection of both balanced and unbalanced chromosomal rearrangements in primary human tumour samples, with the potential to define chromosome breakpoints to bp resolution. In addition, we show copy number profiles can also be obtained from the same data, all at a significantly lower cost than standard sequencing approaches.

Technological considerations for genome-guided diagnosis and management of cancer

Technological, methodological, and analytical advances continue to improve the resolution of our view into the cancer genome, even as we discover ways to carry out analyses at greater distances from the primary tumor sites. These advances are finally making the integration of cancer genomic profiling into clinical practice feasible. Formalin fixation and paraffin embedding, which has long been the default pathological biopsy medium, is now being supplemented with liquid biopsy as a means to profile the cancer genomes of patients. At each stage of the genomic data generation process-sample collection, preservation, storage, extraction, library construction, sequencing, and variant calling-there are variables that impact the sensitivity and specificity of the analytical result and the clinical utility of the test. These variables include sample degradation, low yields of nucleic acid, and low variant allele fractions (proportions of assayed molecules carrying variant allele(s)). We review here the most common pre-analytical and analytical factors relating to routine cancer patient genome profiling, some solutions to common challenges, and the major sample preparation and sequencing technology choices available today.

Gene integrated set profile analysis: a context-based approach for inferring biological endpoints

The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an 'integrate by intersection' (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance.