The Impact of Whole Genome Data on Therapeutic Decision-Making in Metastatic Prostate Cancer: A Retrospective Analysis

Crumbaker, Megan; Chan, Eva; Gong, Ting-Ting; Corcoran, Niall M.; Jaratlerdsiri, Weerachai; Lyons, Ruth J.; Haynes, Anne‐Maree; Kulidjian, Anna; Kalsbeek, Anton M.F.; Petersen, Desiree C.; Stricker, Phillip D.; Jamieson, Christina; Hovens, Christopher M.; Joshua, Anthony M.; Hayes, Vanessa M.

doi:10.3390/cancers12051178

Cited by 11 publications

(22 citation statements)

References 125 publications

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“…The numbers of SVs simulated were based on previous studies that have suggested the presence of 5,000 to 10,000 polymorphic SVs in the human genome [ 15 ] and between a handful to up to 1000 somatic SVs across a range of tumour types [ 16 ]. These findings support what we have also observed in our own work [ 17 , 18 ]. It is worth noting that, very similar results were observed when the number of germline SVs were reduced to match the number of somatic SVs except for two callers ( S1 File ).…”

Section: Methodssupporting

confidence: 94%

Shiny-SoSV: A web-based performance calculator for somatic structural variant detection

2020

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Somatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applications that could predict the performance of somatic structural variant detection. To address this gap, we developed Shiny-SoSV, a user-friendly webbased calculator for determining the impact of common variables on the sensitivity, precision and F1 score of somatic structural variant detection, including choice of variant detection tool, sequencing depth of coverage, variant allele fraction, and variant breakpoint resolution. Using simulation studies, we determined singular and combinatoric effects of these variables, modelled the results using a generalised additive model, allowing structural variant detection performance to be predicted for any combination of predictors. Shiny-SoSV provides an interactive and visual platform for users to easily compare individual and combined impact of different parameters. It predicts the performance of a proposed study design, on somatic structural variant detection, prior to the commencement of benchwork. Shiny-SoSV is freely available at https://hcpcg.shinyapps.io/Shiny-SoSV with accompanying user's guide and example use-cases.

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Section: Methodssupporting

confidence: 94%

Shiny-SoSV: A web-based performance calculator for somatic structural variant detection

2020

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“…Some prior studies have begun to demonstrate the utility of Bionano UHMW DNA isolation protocols in solid tissue tumor analysis. These include studies of lung squamous cell carcinoma and metastatic prostate carcinoma [7,34,35]. This current report demonstrates the utility of the a DNA isolation protocol and SV analysis in a wide variety of solid tissue types, and expands the feasibility of such analysis for previously unused human tissue types.…”

Section: Discussionmentioning

confidence: 72%

Identification of Somatic Structural Variants in Solid Tumors By Optical Genome Mapping

Goldrich

LaBarge

Chartrand

et al. 2021

Preprint

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Genomic structural variants comprise a significant fraction of somatic mutations driving cancer onset and progression. However, such variants are not readily revealed by standard next generation sequencing. Optical genomic mapping (OGM) surpasses short read sequencing in detecting large (>500bp) and complex structural variants (SVs) but requires isolation of ultra-high molecular weight DNA from the tissue of interest. We have successfully applied a protocol involving a paramagnetic nanobind disc to a wide range of solid tumors. Using as little as 6.5mg of input tumor tissue, we show successful extraction of high molecular weight genomic DNA that provides a high genomic map rate and effective coverage by optical mapping. We demonstrate the utility of the system at identifying somatic SVs affecting functional and cancer-related genes for each sample. Duplicate/triplicate analysis of select samples shows intra-sample reliability but also intra-sample heterogeneity. We also demonstrate that simply filtering SVs based on a GRCh38 human control database provides high positive and negative predictive values for true somatic variants. Our results indicate that the solid tissue DNA extraction protocol, OGM and SV analysis can be applied to a wide variety of solid tumors to capture SVs across the entire genome with functional importance in cancer prognosis and treatment.

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“…Despite its success in visualizing SVs in liquid tumors and cell lines, OGM has not yet seen widespread application in solid tissue tumors, due primarily to the difficulty of obtaining high-quality, high-molecular-weight DNA from solid tumor samples. Nonetheless, previous work has shown the feasibility of high-quality high-molecular-weight DNA isolation and analysis using earlier workflow iterations [ 33 ], and recent feasibility studies have shown the importance of OGM application to solid tumor analysis [ 7 , 34 , 35 ]. Peng et al demonstrated large SVs not detected by WGS implicated in metastatic lung squamous cell carcinoma [ 7 ], and Jaratlerdiri et al and Crumbaker et al similarly found SVs impacting oncogenic and tumor-suppressing genes not identified by NGS or WGS alone in prostate cancer [ 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Somatic Structural Variants in Solid Tumors by Optical Genome Mapping

Goldrich

LaBarge

Chartrand

et al. 2021

JPM

View full text Add to dashboard Cite

Genomic structural variants comprise a significant fraction of somatic mutations driving cancer onset and progression. However, such variants are not readily revealed by standard next-generation sequencing. Optical genome mapping (OGM) surpasses short-read sequencing in detecting large (>500 bp) and complex structural variants (SVs) but requires isolation of ultra-high-molecular-weight DNA from the tissue of interest. We have successfully applied a protocol involving a paramagnetic nanobind disc to a wide range of solid tumors. Using as little as 6.5 mg of input tumor tissue, we show successful extraction of high-molecular-weight genomic DNA that provides a high genomic map rate and effective coverage by optical mapping. We demonstrate the system’s utility in identifying somatic SVs affecting functional and cancer-related genes for each sample. Duplicate/triplicate analysis of select samples shows intra-sample reliability but also intra-sample heterogeneity. We also demonstrate that simply filtering SVs based on a GRCh38 human control database provides high positive and negative predictive values for true somatic variants. Our results indicate that the solid tissue DNA extraction protocol, OGM and SV analysis can be applied to a wide variety of solid tumors to capture SVs across the entire genome with functional importance in cancer prognosis and treatment.

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The Impact of Whole Genome Data on Therapeutic Decision-Making in Metastatic Prostate Cancer: A Retrospective Analysis

Cited by 11 publications

References 125 publications

Shiny-SoSV: A web-based performance calculator for somatic structural variant detection

Shiny-SoSV: A web-based performance calculator for somatic structural variant detection

Identification of Somatic Structural Variants in Solid Tumors By Optical Genome Mapping

Identification of Somatic Structural Variants in Solid Tumors by Optical Genome Mapping

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