Assessment of the Clinical Relevance of BRCA2 Missense Variants by Functional and Computational Approaches

Guidugli, Lucia; Shimelis, Hermela; Masica, David L.; Pankratz, V. Shane; Lipton, Gary; Singh, Namit; Hu, Chunling; Monteiro, Alvaro N.A.; Lindor, Noralane M.; Goldgar, David E.; Karchin, Rachel; Iversen, Edwin S.; Couch, Fergus J.

doi:10.1016/j.ajhg.2017.12.013

Cited by 73 publications

(121 citation statements)

References 42 publications

(61 reference statements)

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“…Presently, stand‐alone use of in silico methods for HBOC diagnosis is discouraged (Ernst et al, ). Nonetheless, it is considered that these methods can be fruitfully combined with the results of functional assays, to provide an alternative to multifactorial models in the absence of family information (Guidugli et al, ). The tools presented in this work are amenable to this type of approach because of their extreme simplicity and interpretability.…”

Section: Discussionmentioning

confidence: 99%

“…Obtaining good estimates of the functional impact and cancer risk of BRCA1 and BRCA2 sequence variants plays a vital role in the diagnosis and management of inherited breast and ovarian cancers (Eccles et al, 2015;Findlay et al, 2018;Guidugli et al, 2018;Moreno et al, 2016;Paluch-Shimon et al, 2016). A priori, in silico tools, can be used to obtain these estimates; however, their moderate success rate restricts their applicability (Ernst et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…To develop our method we used experimental HDR results available from the literature: 44 variants for BRCA1 (Starita et al, 2015) and 185 variants for BRCA2 (Guidugli et al, 2013(Guidugli et al, , 2018 proteins. However, to reinforce the strength of the signal, relative to experimental noise, we did not employ the full datasets.…”

Section: The Mlr Methodsmentioning

confidence: 99%

“…The final number of HDR values was 28 for BRCA1. For BRCA2, we worked with 92 HDR values that corresponded to 56 variants (some had been tested twice; Guidugli et al, 2013Guidugli et al, , 2018.…”

Section: The Mlr Methodsmentioning

confidence: 99%

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BRCA1 ‐ and BRCA2 ‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge

et al. 2019

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BRCA1 and BRCA2 (BRCA1/2) germline variants disrupting the DNA protective role of these genes increase the risk of hereditary breast and ovarian cancers. Correct identification of these variants then becomes clinically relevant, because it may increase the survival rates of the carriers. Unfortunately, we are still unable to systematically predict the impact of BRCA1/2 variants. In this article, we present a family of in silico predictors that address this problem, using a gene‐specific approach. For each protein, we have developed two tools, aimed at predicting the impact of a variant at two different levels: Functional and clinical. Testing their performance in different datasets shows that specific information compensates the small number of predictive features and the reduced training sets employed to develop our models. When applied to the variants of the BRCA1/2 (ENIGMA) challenge in the fifth Critical Assessment of Genome Interpretation (CAGI 5) we find that these methods, particularly those predicting the functional impact of variants, have a good performance, identifying the large compositional bias towards neutral variants in the CAGI sample. This performance is further improved when incorporating to our prediction protocol estimates of the impact on splicing of the target variant.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: The Mlr Methodsmentioning

confidence: 99%

Section: The Mlr Methodsmentioning

confidence: 99%

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BRCA1 ‐ and BRCA2 ‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge

et al. 2019

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show abstract

“…The method development has seen excellent opportunities created by the growing public databases (such as TCGA (Weinstein et al, 2013), dbSNP (Sherry et al, 2001), ClinVar (Landrum et al, 2016) and dbNSFP (Liu et al, 2016)), benchmark studies (Martelotto et al, 2014;Guidugli et al, 2018), and community experiments (in particular, CAGI (Hoskins et al, 2017)). Indeed, some algorithms are widely and successfully applied for functional prediction of genetic variants, such as SIFT (Ng and Henikoff, 2003), PolyPhen2 (Adzhubei et al, 2013), MutationTaster2 (Adzhubei et al, 2013), SNAP (Bromberg and Rost, 2007;Hecht et al, 2015), MutPred (Pejaver et al, 2017a,b), and Evolutionary Action (Katsonis andLichtarge, 2014, 2017).…”

mentioning

confidence: 99%

Predicting Pathogenicity of Missense Variants with Weakly Supervised Regression

Cao

Sun

Karimi

et al. 2019

Preprint

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Quickly growing genetic variation data of unknown clinical significance demand computational methods that can reliably predict clinical phenotypes and deeply unravel molecular mechanisms. On the platform enabled by the Critical Assessment of Genome Interpretation (CAGI), we develop a novel “weakly supervised” regression (WSR) model that not only predicts precise clinical significance (probability of pathogenicity) from inexact training annotations (class of pathogenicity) but also infers underlying molecular mechanisms in a variant‐specific manner. Compared to multiclass logistic regression, a representative multiclass classifier, our kernelized WSR improves the performance for the ENIGMA Challenge set from 0.72 to 0.97 in binary area under the receiver operating characteristic curve (AUC) and from 0.64 to 0.80 in ordinal multiclass AUC. WSR model interpretation and protein structural interpretation reach consensus in corroborating the most probable molecular mechanisms by which some pathogenic BRCA1 variants confer clinical significance, namely metal‐binding disruption for p.C44F and p.C47Y, protein‐binding disruption for p.M18T, and structure destabilization for p.S1715N.

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Germline testing for homologous recombination repair genes—opportunities and challenges

Hirsch

Gieldon

Sutter

et al. 2020

Genes Chromosomes & Cancer

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Pathogenic variants in the BRCA1 and BRCA2 genes are well known causes of hereditary breast and ovarian cancer. Other genes involved in the homologous recombination pathway can also be associated with increased probability of cancer development, for example, breast and ovarian cancer, prostate and pancreatic cancer, colorectal cancer, and even childhood tumors like medulloblastoma. Traditionally, patients and families likely to harbor a genetic predisposition have been identified using personal and family history. Several checklists and risk prediction tools have proven to be useful in the clinic. Through the widespread application of next generation sequencing of tumor tissue, a growing number of individuals with genetic cancer predisposition is now identified molecularly, even in the absence of a suggestive family history. Any constitutional variant identified during molecular genetic testing has to be assessed for its relevance, both functionally and in the context of patient phenotype. Variant curation is time consuming, but has been increasingly standardized by introduction of several guidelines to allow reliable and reproducible classification of constitutional variants. Variant classification by expert panels using data mining tools, evidence‐based decision trees and gene specific criteria represents the gold standard. Participation of geneticists in molecular tumor boards facilitates the curation of potential constitutional variants, germline validation and thus directing the patient to appropriate counselling and care pathways. Due to the high relevance of germline variants for treatment and surveillance of the index patient and predictive testing and surveillance of relatives, only pathogenic or likely pathogenic variants must be used for clinical decision‐making.

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Assessment of the Clinical Relevance of BRCA2 Missense Variants by Functional and Computational Approaches

Cited by 73 publications

References 42 publications

BRCA1 ‐ and BRCA2 ‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge

BRCA1 ‐ and BRCA2 ‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge

Predicting Pathogenicity of Missense Variants with Weakly Supervised Regression

Germline testing for homologous recombination repair genes—opportunities and challenges

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