Genetic Modifiers of Risk of BRCA1- and BRCA2-Related Breast and Ovarian Cancers

2009 ◽  
pp. 107-129
Author(s):  
Georgia Chenevix-Trench ◽  
Antonis C. Antoniou
Keyword(s):  
Genetic Modifiers ◽  
Brca1 And Brca2 ◽  
Ovarian Cancers

scholarly journals A new hybrid record linkage process to make epidemiological databases interoperable: application to the GEMO and GENEPSO studies involving BRCA1 and BRCA2 mutation carriers

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yue Jiao ◽  
Fabienne Lesueur ◽  
Chloé-Agathe Azencott ◽  
Maïté Laurent ◽  
Noura Mebirouk ◽  
...  
Keyword(s):  
Record Linkage ◽  
Gold Standard ◽  
Brca2 Mutation ◽  
Epidemiological Studies ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Genetic Modifiers ◽  
Brca1 And Brca2 ◽  
Mutation Carriers

Abstract Background Linking independent sources of data describing the same individuals enable innovative epidemiological and health studies but require a robust record linkage approach. We describe a hybrid record linkage process to link databases from two independent ongoing French national studies, GEMO (Genetic Modifiers of BRCA1 and BRCA2), which focuses on the identification of genetic factors modifying cancer risk of BRCA1 and BRCA2 mutation carriers, and GENEPSO (prospective cohort of BRCAx mutation carriers), which focuses on environmental and lifestyle risk factors. Methods To identify as many as possible of the individuals participating in the two studies but not registered by a shared identifier, we combined probabilistic record linkage (PRL) and supervised machine learning (ML). This approach (named “PRL + ML”) combined together the candidate matches identified by both approaches. We built the ML model using the gold standard on a first version of the two databases as a training dataset. This gold standard was obtained from PRL-derived matches verified by an exhaustive manual review. Results The Random Forest (RF) algorithm showed a highest recall (0.985) among six widely used ML algorithms: RF, Bagged trees, AdaBoost, Support Vector Machine, Neural Network. Therefore, RF was selected to build the ML model since our goal was to identify the maximum number of true matches. Our combined linkage PRL + ML showed a higher recall (range 0.988–0.992) than either PRL (range 0.916–0.991) or ML (0.981) alone. It identified 1995 individuals participating in both GEMO (6375 participants) and GENEPSO (4925 participants). Conclusions Our hybrid linkage process represents an efficient tool for linking GEMO and GENEPSO. It may be generalizable to other epidemiological studies involving other databases and registries.

scholarly journals Management of hereditary ovarian cancer

2011 ◽  
Vol 152 (40) ◽  
pp. 1596-1608 ◽  
Author(s):  
József Gábor Joó ◽  
Szabolcs Ládi ◽  
B. Zsolt Nagy ◽  
Zoltán Langmár
Keyword(s):  
Ovarian Cancer ◽  
Hereditary Ovarian Cancer ◽  
Brca1 And Brca2 ◽  
Ovarian Cancers ◽  
Brca2 Mutations ◽  
Histological Phenotype ◽  
Brca1 Brca2 ◽  
Brca1 And Brca2 Mutations ◽  
High Level ◽  
Associated Tumors

Mutations in BRCA1 and BRCA2 genes account for the majority of hereditary breast and ovarian cancers. Approximately 10% of cases of ovarian cancer are due to germline mutations in BRCA1 and BRCA2. Ovarian cancer associated with BRCA1 and BRCA2 mutations has a distinct histological phenotype. This type of cancer is predominantly of serous or endometrioid histology and is high grade. Patients with BRCA1 or BRCA2 mutations should be offered risk-reducing salpingo-oophorectomy by age 40 years, or when childbearing is complete. Nowadays there are no differences between the treatments provided for sporadic and hereditary ovarian cancer, although there are indications that targeted therapy is effective in women with BRCA1/BRCA2-associated tumors. Retrospective studies reveal a high level of sensitivity to platinum agents in BRCA-associated tumors and initial trials show good efficacy and tolerability for polyADP-ribose polymerase inhibitors in mutation carriers with advanced ovarian cancers. These agents might also potentially be used in chemoprevention. Authors review the current management of hereditary ovarian cancer. Orv. Hetil., 2011, 152, 1596–1608.

scholarly journals Genetic Predisposition to Breast and Ovarian Cancers: How Many and Which Genes to Test?

2020 ◽  
Vol 21 (3) ◽  
pp. 1128 ◽  
Author(s):  
Davide Angeli ◽  
Samanta Salvi ◽  
Gianluca Tedaldi
Keyword(s):  
Ovarian Cancer ◽  
Genetic Predisposition ◽  
Genetic Disorders ◽  
Molecular Techniques ◽  
Ovarian Cancer Risk ◽  
Brca1 And Brca2 ◽  
New Genes ◽  
Ovarian Cancers ◽  
Genes Encoding ◽  
Predisposition Genes

Breast and ovarian cancers are some of the most common tumors in females, and the genetic predisposition is emerging as one of the key risk factors in the development of these two malignancies. BRCA1 and BRCA2 are the best-known genes associated with hereditary breast and ovarian cancer. However, recent advances in molecular techniques, Next-Generation Sequencing in particular, have led to the identification of many new genes involved in the predisposition to breast and/or ovarian cancer, with different penetrance estimates. TP53, PTEN, STK11, and CDH1 have been identified as high penetrance genes for the risk of breast/ovarian cancers. Besides them, PALB2, BRIP1, ATM, CHEK2, BARD1, NBN, NF1, RAD51C, RAD51D and mismatch repair genes have been recognized as moderate and low penetrance genes, along with other genes encoding proteins involved in the same pathways, possibly associated with breast/ovarian cancer risk. In this review, we summarize the past and more recent findings in the field of cancer predisposition genes, with insights into the role of the encoded proteins and the associated genetic disorders. Furthermore, we discuss the possible clinical utility of genetic testing in terms of prevention protocols and therapeutic approaches.

scholarly journals Genetic modifiers of cancer risk for BRCA1 and BRCA2 mutation carriers

2011 ◽  
Vol 22 ◽  
pp. i11-i17 ◽  
Author(s):  
R.L. Milne ◽  
A.C. Antoniou
Keyword(s):  
Cancer Risk ◽  
Brca2 Mutation ◽  
Genetic Modifiers ◽  
Brca1 And Brca2 ◽  
Mutation Carriers

Identification of BRCA1 and BRCA2 genetic modifiers.

2009 ◽  
Author(s):  
JD Weyandt ◽  
C Snyder ◽  
HT Lynch ◽  
E Gillanders ◽  
TN Holmes ◽  
...  
Keyword(s):  
Genetic Modifiers ◽  
Brca1 And Brca2
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