Copy number variant analysis using genome-wide mate-pair sequencing.

Copy number variation (CNV) is a common form of structural variation detected in human genomes, occurring as both constitutional and somatic events. Cytogenetic techniques like chromosomal microarray (CMA) are widely used in analyzing CNVs. However, CMA techniques cannot resolve the full nature of these structural variations (i.e. the orientation and location of associated breakpoint junctions) and must be combined with other cytogenetic techniques, such as karyotyping or FISH, to do so. This makes the development of a next-generation sequencing (NGS) approach capable of resolving both CNVs and breakpoint junctions desirable. Mate-pair sequencing (MPseq) is a NGS technology designed to find large structural rearrangements across the entire genome. Here we present an algorithm capable of performing copy number analysis from mate-pair sequencing data. The algorithm uses a step-wise procedure involving normalization, segmentation, and classification of the sequencing data. The segmentation technique combines both read depth and discordant mate-pair reads to increase the sensitivity and resolution of CNV calls. The method is particularly suited to MPseq, which is designed to detect breakpoint junctions at high resolution. This allows for the classification step to accurately calculate copy number levels at the relatively low read depth of MPseq. Here we compare results for a series of hematological cancer samples that were tested with CMA and MPseq. We demonstrate comparable sensitivity to the state-of-the-art CMA technology, with the benefit of improved breakpoint resolution. The algorithm provides a powerful analytical tool for the analysis of MPseq results in cancer.

SVAtools for junction detection of genome-wide chromosomal rearrangements by mate-pair sequencing (MPseq).

Mate-pair sequencing (MPseq), using long-insert, paired-end genomic libraries, is a powerful next-generation sequencing-based approach for the detection of genomic structural variants. SVAtools is a set of algorithms to detect both chromosomal rearrangements and large (>10 kb) copy number variants (CNVs) in genome-wide MPseq data. SVAtools can also predict gene disruptions and gene fusions, and characterize the genomic structure of complex rearrangements. To illustrate the power of SVAtools' junction detection methods to provide comprehensive molecular karyotypes, MPseq data were compared against a set of samples previously characterized by traditional cytogenetic methods. Karyotype, FISH and chromosomal microarray (CMA), performed for 29 patients in a clinical laboratory setting, collectively revealed 285 breakpoints in 87 rearrangements. The junction detection methods of SVAtools detected 87% of these breakpoints compared to 48%, 42% and 57% for karyotype, FISH and CMA respectively. Breakpoint resolution was also reported to 1 kb or less and additional genomic rearrangement complexities not appreciable by standard cytogenetic techniques were revealed. For example, 63% of CNVs detected by CMA were shown by SVAtools' junction detection to occur secondary to a rearrangement other than a simple deletion or tandem duplication. SVAtools with MPseq provides comprehensive and accurate whole-genome junction detection with improved breakpoint resolution, compared to karyotype, FISH, and CMA combined. This approach to molecular karyotyping offers considerable diagnostic potential for the simultaneous detection of both novel and recurrent genomic rearrangements in hereditary and neoplastic disorders.

Integrated Genomic Analysis of Pancreatic Ductal Adenocarcinomas Reveals Genomic Rearrangement Events as Significant Drivers of Disease.

Many somatic mutations have been detected in pancreatic ductal adenocarcinoma (PDAC), leading to the identification of some key drivers of disease progression, but the involvement of large genomic rearrangements has often been overlooked. In this study, we performed mate pair sequencing (MPseq) on genomic DNA from 24 PDAC tumors, including 15 laser-captured microdissected PDAC and 9 patient-derived xenografts, to identify genome-wide rearrangements. Large genomic rearrangements with intragenic breakpoints altering key regulatory genes involved in PDAC progression were detected in all tumors. SMAD4, ZNF521, and FHIT were among the most frequently hit genes. Conversely, commonly reported genes with copy number gains, including MYC and GATA6, were frequently observed in the absence of direct intragenic breakpoints, suggesting a requirement for sustaining oncogenic function during PDAC progression. Integration of data from MPseq, exome sequencing, and transcriptome analysis of primary PDAC cases identified limited overlap in genes affected by both rearrangements and point mutations. However, significant overlap was observed in major PDAC-associated signaling pathways, with all PDAC exhibiting reduced SMAD4 expression, reduced SMAD-dependent TGFß signaling, and increased WNT and Hedgehog signaling. The frequent loss of SMAD4 and FHIT due to genomic rearrangements strongly implicates these genes as key drivers of PDAC, thus highlighting the strengths of an integrated genomic and transcriptomic approach for identifying mechanisms underlying disease initiation and progression.

Identification of independent primary tumors and intrapulmonary metastases using DNA rearrangements in non-small-cell lung cancer.

PURPOSE: Distinguishing independent primary tumors from intrapulmonary metastases in non-small-cell carcinoma remains a clinical dilemma with significant clinical implications. Using next-generation DNA sequencing, we developed a chromosomal rearrangement-based approach to differentiate multiple primary tumors from metastasis. METHODS: Tumor specimens from patients with known independent primary tumors and metastatic lesions were used for lineage test development, which was then applied to multifocal tumors. Laser capture microdissection was performed separately for each tumor. Genomic DNA was isolated using direct in situ whole-genome amplification methodology, and next-generation sequencing was performed using an Illumina mate-pair library protocol. Sequence reads were mapped to the human genome, and primers spanning the fusion junctions were used for validation polymerase chain reaction. RESULTS: A total of 41 tumor samples were sequenced (33 adenocarcinomas [ADs] and eight squamous cell carcinomas [SQCCs]), with a range of three to 276 breakpoints per tumor identified. Lung tumors predicted to be independent primary tumors based on different histologic subtype did not share any genomic rearrangements. In patients with lung primary tumors and paired distant metastases, shared rearrangements were identified in all tumor pairs, emphasizing the patient specificity of identified breakpoints. Multifocal AD and SQCC samples were reviewed independently by two pulmonary pathologists. Concordance between histology and genomic data occurred in the majority of samples. Discrepant tumor samples were resolved by genome sequencing. CONCLUSION: A diagnostic lineage test based on genomic rearrangements from mate-pair sequencing demonstrates promise for distinguishing independent primary from metastatic disease in lung cancer.