Associated Papers
Splicing Predictions, Splicing Assays, and Variant Classification Using ACMG/AMP Guidelines: Challenges Observed with BRCA1 and BRCA2 Variants.
Santamariña M et al.
Clinical Chemistry, 2026
Analyzing the effects of BRCA1/2 variants on mRNA splicing by minigene assay.
Dong Z et al.
Journal of Human Genetics, 2023
OncoPan®: An NGS-Based Screening Methodology to Identify Molecular Markers for Therapy and Risk Assessment in Pancreatic Ductal Adenocarcinoma.
Tibiletti MG et al.
Biomedicines, 2022
35625944 (PubMed )
Supplement: biomedicines-10-01208-s001.zip › biomedicines-1723870-supplementary.pdf
Human BRCA pathogenic variants were originated during recent human history.
Li J et al.
Life Science Alliance, 2022
35165121 (PubMed )
Supplement: LSA-2021-01263_TableS2.xlsx › BRCA1
Analysis of Italian BRCA1/2 Pathogenic Variants Identifies a Private Spectrum in the Population from the Bergamo Province in Northern Italy.
Figlioli G et al.
Cancers, 2021
33573335 (PubMed )
Supplement: cancers-13-00532-s001.zip › cancers-1044931-supplementary.xlsx › Suppl_Table
Clinical outcome of breast cancer in carriers of BRCA1 and BRCA2 mutations according to molecular subtypes.
De Talhouet S et al.
Scientific Reports, 2020
32341426 (PubMed )
Supplement: 41598_2020_63759_MOESM1_ESM.pdf
Criteria of the German Consortium for Hereditary Breast and Ovarian Cancer for the Classification of Germline Sequence Variants in Risk Genes for Hereditary Breast and Ovarian Cancer.
Wappenschmidt B et al.
Geburtshilfe Und Frauenheilkunde, 2020
Investigating the effect of 28 BRCA1 and BRCA2 mutations on their related transcribed mRNA.
Quiles F et al.
Breast Cancer Research and Treatment, 2016
Characterization of three alternative transcripts of the BRCA1 gene in patients with breast cancer and a family history of breast and/or ovarian cancer who tested negative for pathogenic mutations.
Gambino G et al.
International Journal of Molecular Medicine, 2015
Analysis of 30 putative BRCA1 splicing mutations in hereditary breast and ovarian cancer families identifies exonic splice site mutations that escape in silico prediction.
Wappenschmidt B et al.
Plos One, 2012
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