Next generation mapping reveals novel large genomic rearrangements in prostate cancer.

Weerachai Jaratlerdsiri,Claire Yang,Eva K.F Chan,Peter I Croucher,Desiree C Petersen,Palak Sheth,M.S Riana Bornman,Vanessa M Hayes

doi:10.18632/oncotarget.15802

Abstract

Complex genomic rearrangements are common molecular events driving prostate carcinogenesis. Clinical significance, however, has yet to be fully elucidated. Detecting the full range and subtypes of large structural variants (SVs), greater than one kilobase in length, is challenging using clinically feasible next generation sequencing (NGS) technologies. Next generation mapping (NGM) is a new technology that allows for the interrogation of megabase length DNA molecules outside the detection range of single-base resolution NGS. In this study, we sought to determine the feasibility of using the Irys (Bionano Genomics Inc.) nanochannel NGM technology to generate whole genome maps of a primary prostate tumor and matched blood from a Gleason score 7 (4 + 3), ETS-fusion negative prostate cancer patient. With an effective mapped coverage of 35X and sequence coverage of 60X, and an estimated 43% tumor purity, we identified 85 large somatic structural rearrangements and 6,172 smaller somatic variants, respectively. The vast majority of the large SVs (89%), of which 73% are insertions, were not detectable ab initio using high-coverage short-read NGS. However, guided manual inspection of single NGS reads and de novo assembled scaffolds of NGM-derived candidate regions allowed for confirmation of 94% of these large SVs, with over a third impacting genes with oncogenic potential. From this single-patient study, the first cancer study to integrate NGS and NGM data, we hypothesise that there exists a novel spectrum of large genomic rearrangements in prostate cancer, that these large genomic rearrangements are likely early events in tumorigenesis, and they have potential to enhance taxonomy.

Highlights

Structural genomic rearrangements appear to be highly abundant and complex in the prostate cancer genome, with potential to contribute directly to oncogenic events and provide a molecular signature for subtype classification [1]
While long-read sequencing methods, such as single-molecule sequencing from Pacific Biosystems (PacBio) and Oxford Nanopore, are improving structural variation (SV) detection [8, 9], they are still limited by relatively high costs, low throughput and relatively high error rates
Fluorescent labels act as molecular markers allowing for the reconstruction of whole genome maps. In this first study, using Next generation mapping (NGM) for the detection of somatic SVs > 1 Kb in a matched normal-tumor prostate cancer pair, we demonstrated the potential of targeted next generation sequencing (NGS) interrogation for large SV validation

Summary

Introduction

Structural genomic rearrangements appear to be highly abundant and complex in the prostate cancer genome, with potential to contribute directly to oncogenic events and provide a molecular signature for subtype classification [1]. Genomic rearrangements have been used to clinically subclassify primary prostate cancer [2, 3]. Accurate detection of structural variations (SVs) greater than one kilobase (Kb) in length using shortread (up to hundreds of bases) generation sequencing (NGS) data is, difficult. In clinically relevant www.impactjournals.com/oncotarget prostate cancer genome sequencing, this has been further challenged by tumor heterogeneity and frequent stromal contaminants. Short-read NGS detection of SVs, including large deletions, insertions or duplications, inversions and translocations, is based on differences in local depth of coverage and sequence read orientation relative to a reference genome [4]. As no single informatics tool can detect the full range of SVs regarding size and subtype [5], integrated methods have been proposed [6, 7], with de novo assembly of tumor genomes remaining a challenge. While long-read (up to thousands of bases) sequencing methods, such as single-molecule sequencing from Pacific Biosystems (PacBio) and Oxford Nanopore, are improving SV detection [8, 9], they are still limited by relatively high costs, low throughput and relatively high error rates

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Conclusion