scholarly journals Management of Gene Variants of Unknown Significance: Analysis Method and Risk Assessment of the VHL Mutation p.P81S (c.241C>T)

2016 ◽  
Vol 18 (1) ◽  
pp. 93-103
Author(s):  
Daniela Alosi ◽  
Marie Bisgaard ◽  
Sophie Hemmingsen ◽  
Lotte Krogh ◽  
Hanne Mikkelsen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Gene Variants ◽  
Analysis Method ◽  
Variants Of Unknown Significance ◽  
Significance Analysis ◽  
Unknown Significance

scholarly journals NGS Evaluation of a Bernese Cohort of Unexplained Erythrocytosis Patients

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1951
Author(s):  
Katarzyna Aleksandra Jalowiec ◽  
Kristina Vrotniakaite-Bajerciene ◽  
Annina Capraru ◽  
Tatiana Wojtovicova ◽  
Raphael Joncourt ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Molecular Data ◽  
Gene Variants ◽  
Reference Center ◽  
Variants Of Unknown Significance ◽  
Molecular Features ◽  
Unknown Significance ◽  
Unknown Gene ◽  
The Relationship ◽  
Generation Sequencing

(1) Background: Clinical and molecular data on patients with unexplained erythrocyto-sis is sparse. We aimed to analyze the clinical and molecular features of patients with congenital erythrocytosis in our tertiary reference center. (2) Methods: In 34 patients with unexplained erythrocytosis, a 13-gene Next-Generation Sequencing erythrocytosis panel developed at our center was conducted. (3) Results: In 6/34 (18%) patients, eight different heterozygous gene variants were found. These patients were, therefore, diagnosed with congenital erythrocytosis. Two patients had two different gene variants each. All variants were characterized as variants of unknown significance as they had not previously been described in the literature. The rest of the patients (28/34, 82%) had no detected gene variants. (4) Conclusions: Our experience shows that the NGS panel can be helpful in determining the reasons for persistent, unexplained erythrocytosis. In our cohort of patients with erythrocytosis, we identified some, thus far unknown, gene variants which may explain the clinical picture. However, further investigations are needed to determine the relationship between the molecular findings and the phenotype.

scholarly journals Predicting ovarian/breast cancer pathogenic risks of BRCA1 gene variants of unknown significance

2020 ◽  
Author(s):  
Hui-Heng Lin ◽  
Hongyan Xu ◽  
Hongbo Hu ◽  
Zhanzhong Ma ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Random Forest ◽  
Clinical Significance ◽  
Brca1 Gene ◽  
Gene Variants ◽  
Sequencing Data ◽  
Forest Model ◽  
Variants Of Unknown Significance ◽  
Sequencing Technologies ◽  
Unknown Significance

AbstractThe difficulty of early diagnosis for ovarian cancer is an important cause of the high mortal rates of ovarian cancer patients. Instead of symptom-based diagnostic methods, modern sequencing technologies enable the access of human’s genetic information via reading DNA/RNA molecules’ nucleotide base sequences. In such way, genes’ mutations and variants could be identified and hence a better clinical diagnosis in molecular level could be expected. However, as sequencing technologies gain more popularity, novel gene variants with unknown clinical significance are found, giving difficulties to interpretations of patients’ genetic data, precise disease diagnoses as well as the making of therapeutic strategies and decisions. In order to solve these issues, it is of critical importance to figure out ways to analyze and interpret such variants. In this work, BRCA1 gene variants with unknown clinical significance were identified from clinical sequencing data, and then we developed machine learning models so as to predict the pathogenicity for variants with unknown clinical significance. Amongst, in performance benchmarking, our optimized random forest model scored 0.85 in area under receiver-operating characteristic curve, which outperformed other models. Finally, we applied the optimized random forest model to predict the pathogenic risks of 7 BRCA1 variants of unknown clinical significances identified from our sequencing data, and 6315 variants of unknown clinical significance in ClinVar database. As a result, our model predicted 4724 benign and 1591 pathogenic variants, which helped the interpretation of these variants of unknown significance and diagnosis.

Genetic risk assessment for hereditary RCC: Report from the consensus panel meeting.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 615-615
Author(s):  
Michael Daneshvar ◽  
Neil Mendhiratta ◽  
Ramaprasad Srinivasan ◽  
Eric Jonasch ◽  
Mark Wayne Ball ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Genetic Testing ◽  
Kidney Cancer ◽  
Genetic Risk ◽  
Genetic Counselors ◽  
Genetic Risk Assessment ◽  
Variants Of Unknown Significance ◽  
Modified Delphi ◽  
Unknown Significance ◽  
Patient Advocates

615 Background: While many genes are now known to be associated with hereditary kidney cancer syndromes, there is a paucity of guidelines or uniform consensus on genetic testing for these patients. An expert panel was organized to assess who, what, when and how patients should be evaluated and what testing should be initiated. Methods: A national, multidisciplinary, panel of experts in urology, medical oncology, clinical geneticists, genetic counselors and patient advocates with background and knowledge in hereditary syndromic kidney cancer convened in person in September 2019. A renal cell carcinoma (RCC) genetic risk assessment questionnaire consisting of 52 questions was compiled prior to the meeting using modified Delphi methodology. The questions were then discussed and reviewed with uniform consensus defined as a minimum of 85% agreement in accordance with the National Comprehensive Cancer Network criteria. Results: The panel consisted of twenty-six attendees represented by urologists (43%), medical oncologist (23%), genetic counselors (13%), clinical geneticists (7%), and patient advocates (3%). The questionnaire consisted of fifty-five statements focusing on who, what, when and how genetic testing should be performed in a patient suspected of hereditary RCC syndrome. A >85% agreement was reached on 30/52 statements with 18/25 (72%) achieving consensus addressing “who”, 2/6 (33%) achieving consensus in “what’ category, 2/7 (29%) in ‘when’ and 4/6 (67%) on how. The questions with least consensus were found in the “what/when?” category with only 4/13 questions with minimum 85% agreement. Specific areas of debate included an age cutoff for prompting a genetic risk assessment as well as need for familial testing in patients with variants of unknown significance. Conclusions: Despite experience of the panel in management of hereditary RCC, the consensus was reached only on 66% of genetic testing. While many issues will need to be discussed further, those statements with consensus may be used to guide physicians and patients on who, what, when and how genetic RCC risk assessment should be performed.

scholarly journals Predicting Ovarian/Breast Cancer Pathogenic Risks of Human BRCA1 Gene Variants of Unknown Significance

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hui-Heng Lin ◽  
Hongyan Xu ◽  
Hongbo Hu ◽  
Zhanzhong Ma ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Random Forest ◽  
Clinical Significance ◽  
High Throughput Sequencing ◽  
Brca1 Gene ◽  
Random Forest Model ◽  
Gene Variants ◽  
Sequencing Data ◽  
Forest Model ◽  
Variants Of Unknown Significance ◽  
Unknown Significance

High-throughput sequencing is gaining popularity in clinical diagnoses, but more and more novel gene variants with unknown clinical significance are being found, giving difficulties to interpretations of people’s genetic data, precise disease diagnoses, and the making of therapeutic strategies and decisions. In order to solve these issues, it is of critical importance to figure out ways to analyze and interpret such variants. In this work, BRCA1 gene variants with unknown clinical significance were identified from clinical sequencing data, and then, we developed machine learning models so as to predict the pathogenicity for variants with unknown clinical significance. Through performance benchmarking, we found that the optimized random forest model scored 0.85 in area under receiver operating characteristic curve, which outperformed other models. Finally, we applied the best random forest model to predict the pathogenicity of 6321 BRCA1 variants from both sequencing data and ClinVar database. As a result, we obtained the predictive pathogenic risks of BRCA1 variants of unknown significance.

A method of high-throughput functional evaluation of EGFR gene variants of unknown significance in cancer

2017 ◽  
Vol 9 (416) ◽  
pp. eaan6566 ◽  
Author(s):  
Shinji Kohsaka ◽  
Masaaki Nagano ◽  
Toshihide Ueno ◽  
Yoshiyuki Suehara ◽  
Takuo Hayashi ◽  
...  
Keyword(s):  
High Throughput ◽  
Functional Evaluation ◽  
Gene Variants ◽  
Egfr Gene ◽  
Variants Of Unknown Significance ◽  
Unknown Significance

scholarly journals Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance

2017 ◽  
Vol 10 (5) ◽  
Author(s):  
Bian Li ◽  
Jeffrey L. Mendenhall ◽  
Brett M. Kroncke ◽  
Keenan C. Taylor ◽  
Hui Huang ◽  
...  
Keyword(s):  
Functional Impact ◽  
Variants Of Unknown Significance ◽  
Unknown Significance

scholarly journals Very Elevated IgE, Atopy, and Severe Infection: A Genomics-Based Diagnostic Approach to a Spectrum of Diseases

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
A. Chin ◽  
S. Balasubramanyam ◽  
C. M. Davis
Keyword(s):  
Autosomal Dominant ◽  
Severe Infection ◽  
Severe Atopic Dermatitis ◽  
Genomic Testing ◽  
Atopic Diseases ◽  
Variants Of Unknown Significance ◽  
Hyper Ige Syndrome ◽  
Heterozygous Variant ◽  
Unknown Significance ◽  
Multiple Variants

Elevated IgE has been long recognized as an important clinical marker of atopy but can be seen in a myriad of conditions. The discovery of autosomal dominant STAT3 deficiency marked the first recognition of hyper-IgE syndrome (HIES) and the first primary immunodeficiency linked to elevated IgE. Since then, genomic testing has increased the number of defects with associated mutations causing hyper-IgE syndrome and atopic diseases with FLG, DOCK8, SPINK5, and CARD11, among others. A spectrum of recurrent infections and atopy are hallmarks of elevated IgE with significant phenotypic overlap between each underlying condition. As treatment is predicated on early diagnosis, genomic testing is becoming a more commonly used diagnostic tool. We present a 6-year-old male patient with markedly elevated IgE and severe atopic dermatitis presenting with staphylococcal bacteremia found to have a heterozygous variant in FLG (p.S3247X) and multiple variants of unknown significance in BCL11B, ZAP70, LYST, and PTPRC. We review the genetic defects underpinning elevated IgE and highlight the spectrum of atopy and immunodeficiency seen in patients with underlying mutations. Although no one mutation is completely causative of the constellation of symptoms in this patient, we suggest the synergism of these variants is an impetus of disease.

scholarly journals A computational and structural analysis of germline and somatic variants affecting the DDR mechanism, and their impact on human diseases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorena Magraner-Pardo ◽  
Roman A. Laskowski ◽  
Tirso Pons ◽  
Janet M. Thornton
Keyword(s):  
Prostate Cancer ◽  
Dna Damage ◽  
Dna Damage Response ◽  
Protein Interaction ◽  
Three Dimensional ◽  
Protein Protein Interaction ◽  
Variants Of Unknown Significance ◽  
Damage Response ◽  
Dna Variants ◽  
Unknown Significance

AbstractDNA-Damage Response (DDR) proteins are crucial for maintaining the integrity of the genome by identifying and repairing errors in DNA. Variants affecting their function can have severe consequences since failure to repair damaged DNA can result in cells turning cancerous. Here, we compare germline and somatic variants in DDR genes, specifically looking at their locations in the corresponding three-dimensional (3D) structures, Pfam domains, and protein–protein interaction interfaces. We show that somatic variants in metastatic cases are more likely to be found in Pfam domains and protein interaction interfaces than are pathogenic germline variants or variants of unknown significance (VUS). We also show that there are hotspots in the structures of ATM and BRCA2 proteins where pathogenic germline, and recurrent somatic variants from primary and metastatic tumours, cluster together in 3D. Moreover, in the ATM, BRCA1 and BRCA2 genes from prostate cancer patients, the distributions of germline benign, pathogenic, VUS, and recurrent somatic variants differ across Pfam domains. Together, these results provide a better characterisation of the most recurrent affected regions in DDRs and could help in the understanding of individual susceptibility to tumour development.

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