Literature DB >> 29622727

Current detection rates and time-to-detection of all identifiable BRCA carriers in the Greater London population.

Ranjit Manchanda1,2,3, Oleg Blyuss4,5, Faiza Gaba1,2, Vladimir Sergeevich Gordeev6, Chris Jacobs7,8, Matthew Burnell4, Carmen Gan2, Rohan Taylor9, Clare Turnbull1, Rosa Legood10, Alexey Zaikin4, Antonis C Antoniou11, Usha Menon3, Ian Jacobs12.   

Abstract

BACKGROUND: BRCA carrier identification offers opportunities for early diagnoses, targeted treatment and cancer prevention. We evaluate BRCA- carrier detection rates in general and Ashkenazi Jewish (AJ) populations across Greater London and estimate time-to-detection of all identifiable BRCA carriers.
METHODS: BRCA carrier data from 1993 to 2014 were obtained from National Health Service genetic laboratories and compared with modelled predictions of BRCA prevalence from published literature and geographical data from UK Office for National Statistics. Proportion of BRCA carriers identified was estimated. Prediction models were developed to fit BRCA detection rate data. BRCA carrier identification rates were evaluated for an 'Angelina Jolie effect'. Maps for four Greater London regions were constructed, and their relative BRCA detection rates were compared. Models developed were used to predict future time-to-identify all detectable BRCA carriers in AJ and general populations.
RESULTS: Until 2014, only 2.6% (3072/111 742 estimated) general population and 10.9% (548/4985 estimated) AJ population BRCA carriers have been identified in 16 696 608 (AJ=190 997) Greater London population. 57% general population and 54% AJ mutations were identified through cascade testing. Current detection rates mirror linear fit rather than parabolic model and will not identify all BRCA carriers. Addition of unselected ovarian/triple-negative breast cancer testing would take >250 years to identify all BRCA carriers. Doubling current detection rates can identify all 'detectable' BRCA carriers in the general population by year 2181, while parabolic and triple linear rates can identify 'detectable' BRCA carriers by 2084 and 2093, respectively. The linear fit model can identify 'detectable' AJ carriers by 2044. We did not find an Angelina Jolie effect on BRCA carrier detection rates. There was a significant difference in BRCA detection rates between geographical regions over time (P<0.001).
CONCLUSIONS: The majority of BRCA carriers have not been identified, missing key opportunities for prevention/earlier diagnosis. Enhanced and new strategies/approaches are needed. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  Brca; detection rate; genetic testing; prediction; time to detection

Mesh:

Year:  2018        PMID: 29622727     DOI: 10.1136/jmedgenet-2017-105195

Source DB:  PubMed          Journal:  J Med Genet        ISSN: 0022-2593            Impact factor:   6.318


  18 in total

1.  Can Automated Alerts in the Electronic Health Record Encourage Referrals for Genetic Counseling and Testing Among Patients at High Risk for Hereditary Cancer Syndromes?

Authors:  Kristin K Zorn; Melinda E Simonson; Jennifer L Faulkner; Cyndee L Carr; Joshua Acuna; Tiffany L Hall; John F Jenkins; Karen L Drummond; Geoffrey M Curran
Journal:  JCO Oncol Pract       Date:  2022-03-22

2.  Strategies to enhance identification of hereditary breast cancer gene carriers.

Authors:  Sonya Reid; Lucy B Spalluto; Tuya Pal
Journal:  Expert Rev Mol Diagn       Date:  2020-09-11       Impact factor: 5.225

Review 3.  Cancer genetics, precision prevention and a call to action.

Authors:  Clare Turnbull; Amit Sud; Richard S Houlston
Journal:  Nat Genet       Date:  2018-08-29       Impact factor: 38.330

4.  Comparison of a Cancer Family History Collection and Risk Assessment Tool - ItRunsInMyFamily - with Risk Assessment by Health-Care Professionals.

Authors:  Jordon B Ritchie; Brandon M Welch; Caitlin G Allen; Lewis J Frey; Heath Morrison; Joshua D Schiffman; Alexander V Alekseyenko; Brian Dean; Chanita Hughes Halbert; Cecelia Bellcross
Journal:  Public Health Genomics       Date:  2021-12-06       Impact factor: 2.132

5.  Economic Evaluation of Population-Based BRCA1/BRCA2 Mutation Testing across Multiple Countries and Health Systems.

Authors:  Ranjit Manchanda; Li Sun; Shreeya Patel; Olivia Evans; Janneke Wilschut; Ana Carolina De Freitas Lopes; Faiza Gaba; Adam Brentnall; Stephen Duffy; Bin Cui; Patricia Coelho De Soarez; Zakir Husain; John Hopper; Zia Sadique; Asima Mukhopadhyay; Li Yang; Johannes Berkhof; Rosa Legood
Journal:  Cancers (Basel)       Date:  2020-07-17       Impact factor: 6.639

6.  Population based germline testing for primary cancer prevention.

Authors:  Ranjit Manchanda; Rosa Legood
Journal:  Oncotarget       Date:  2018-09-04

Review 7.  Population Based Testing for Primary Prevention: A Systematic Review.

Authors:  Ranjit Manchanda; Faiza Gaba
Journal:  Cancers (Basel)       Date:  2018-11-05       Impact factor: 6.639

8.  Population genomic screening of all young adults in a health-care system: a cost-effectiveness analysis.

Authors:  Lei Zhang; Yining Bao; Moeen Riaz; Jane Tiller; Danny Liew; Xun Zhuang; David J Amor; Aamira Huq; Lara Petelin; Mark Nelson; Paul A James; Ingrid Winship; John J McNeil; Paul Lacaze
Journal:  Genet Med       Date:  2019-02-18       Impact factor: 8.822

9.  Expanding the search for germline pathogenic variants for breast cancer. How far should we go and how high should we jump? The missed opportunity!

Authors:  Hikmat Abdel-Razeq
Journal:  Oncol Rev       Date:  2021-06-24

10.  A Cost-effectiveness Analysis of Multigene Testing for All Patients With Breast Cancer.

Authors:  Li Sun; Adam Brentnall; Shreeya Patel; Diana S M Buist; Erin J A Bowles; D Gareth R Evans; Diana Eccles; John Hopper; Shuai Li; Melissa Southey; Stephen Duffy; Jack Cuzick; Isabel Dos Santos Silva; Alec Miners; Zia Sadique; Li Yang; Rosa Legood; Ranjit Manchanda
Journal:  JAMA Oncol       Date:  2019-10-03       Impact factor: 31.777

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