AbstractCopy 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.
Novel Targeted Therapy Strategies for Biliary Tract Cancers and Hepatocellular Carcinoma
Abstract Motivation The ability to produce and analyze whole genome sequencing (WGS) data from samples with structural variations (SV) generated the need to visualize such abnormalities in simplified plots. Conventional two-dimensional representations of WGS data frequently use either circular or linear layouts. There are several diverse advantages regarding both these representations, but their major disadvantage is that they do not use the two-dimensional space very efficiently. We propose a layout, termed the Genome U-Plot, which spreads the chromosomes on a two-dimensional surface and essentially quadruples the spatial resolution. We present the Genome U-Plot for producing clear and intuitive graphs that allows researchers to generate novel insights and hypotheses by visualizing SVs such as deletions, amplifications, and chromoanagenesis events. The main features of the Genome U-Plot are its layered layout, its high spatial resolution and its improved aesthetic qualities. We compare conventional visualization schemas with the Genome U-Plot using visualization metrics such as number of line crossings and crossing angle resolution measures. Based on our metrics, we improve the readability of the resulting graph by at least 2-fold, making apparent important features and making it easy to identify important genomic changes. Results A whole genome visualization tool with high spatial resolution and improved aesthetic qualities. Availability and implementation An implementation and documentation of the Genome U-Plot is publicly available at https://github.com/gaitat/GenomeUPlot. Supplementary information Supplementary data are available at Bioinformatics online.
Retention of Interstitial Genes between
TMPRSS2
and
ERG
Is Associated with Low-Risk Prostate Cancer
Abstract Over the recent years significant knowledge has been accumulated on which drugs can be therapeutically beneficial for a given patient based on mutational profiling of their tumors using selected gene panels. Much less, however, is known under which conditions targeting of genes which are amplified could be beneficial. Apart from successfully targeting HER2-positive cancers, many of which frequently carry amplified ERBB2 gene, no other targeted therapy based on DNA amplifications or gains of the genes is offered to the patients due to lack of clinical response data. Using mate pair sequencing we have profiled 60 high grade serous ovarian tumors to compare genomic rearrangements and copy number changes. The analyses revealed that these tumors carry highly rearranged genome. Copy number changes amplifications, gains and losses were very common and included genes potentially therapeutically targetable. In contrast, only a few of targetable fusion genes were identified in a set of 60 cases. The tumors were propagated in immunocompromised mice (mouse avatars), sequenced and showed identical to original patient tumor landscape of rearrangements. Using these mouse avatar models we tested whether a therapy inhibiting selected based on genomic amplification targets provided a benefit compared to standard chemotherapy. Mice which harbored tumors showing amplifications of CDKs (CDK2, 4 and 9) and ERBB3 were treated with flavopiridol and pertuzumab respectively, and response was compared to that of standard for ovarian cancer chemotherapy (carboplatin and paclitaxel combination). We observed that application of either agent, significantly inhibited tumor growth in treated mice as compared to untreated. However, neither drug alone caused tumor regression. The treatment regimen then was modified to administer chemotherapy and targeted therapy together. A mouse avatars in which genomic analyses revealed amplification at loci involving AKT3 and RICTOR genes was treated with chemotherapy alone and in combination with MK2206 (AKT inhibitor) or MK8669 (mTOR inhibitor). The high level of both target proteins was confirmed by immunoblotting. Significant regression of tumor was observed after 6 weeks of treatment. Addition of either targeted drug provided an extra benefit in killing tumor cells compared to chemotherapy alone. The mice are followed to study tumor recurrence. These data indicated that genomic information obtained using next generation sequencing can aid therapy decisions by tailoring treatments to individual tumor makeup. Avatar models in conjunction with genomic analyses of the original tumors are invaluable for testing specific targeted therapies selected based on amplification/gains of putative genes-targets and for studying mechanisms of tumor recurrence and therapy resistance. Citation Format: Faye R. Harris, Lin Yang, Xiaonan Hou, Konstantinos Leventakos, Saravut J. Weroha, George Vasmatzis, Irina V. Kovtun. Targeting genes which are amplified in ovarian cancer. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr A34.