Comment on “The Predictive Capacity of Personal Genome Sequencing”

Begg, Colin B.; Pike, Malcolm C.

doi:10.1126/scitranslmed.3004162

Cited by 4 publications

(3 citation statements)

References 7 publications

(10 reference statements)

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“…Therefore the population percentile of an individual’s risk equals that of her risk score, and the efficacy of percentile-based stratification depends on the variances of the risk score distributions in the population and among future breast cancer cases. For the theoretically derived distribution of fully known risk scores, these variances can be estimated using the arguments of Pharoah (3,4) and Begg and Pike (5), as described in the Supplementary Materials and Methods.…”

Section: Methodsmentioning

confidence: 99%

The Role of Genome Sequencing in Personalized Breast Cancer Prevention

Sieh

Rothstein

McGuire

et al. 2014

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background There is uncertainty about the benefits of using genome-wide sequencing to implement personalized preventive strategies at the population level, with some projections suggesting little benefit. We used data for all currently known breast cancer susceptibility variants to assess the benefits and harms of targeting preventive efforts to a population subgroup at highest genomic risk of breast cancer. Methods We used the allele frequencies and effect sizes of 86 known breast cancer variants to estimate the population distribution of breast cancer risks and evaluate the strategy of targeting preventive efforts to those at highest risk. We compared the efficacy of this strategy to that of a “best-case” strategy based on a risk distribution estimated from breast cancer concordance in monozygous twins, and to strategies based on previously estimated risk distributions. Results Targeting those in the top 25% of the risk distribution would include approximately half of all future breast cancer cases, compared to 70% captured by the best-case strategy and 35% based on previously known variants. In addition, current evidence suggests that reducing exposure to modifiable nongenetic risk factors will have greatest benefit for those at highest genetic risk. Conclusions These estimates suggest that personalized breast cancer preventive strategies based on genome sequencing will bring greater gains in disease prevention than previously projected. Moreover these gains will increase with increased understanding of the genetic etiology of breast cancer. Impact These results support the feasibility of using genome-wide sequencing to target the women who would benefit from mammography screening.

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Section: Methodsmentioning

confidence: 99%

The Role of Genome Sequencing in Personalized Breast Cancer Prevention

Sieh

Rothstein

McGuire

et al. 2014

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…They concluded that the predictive value of this approach was small. This study raised much debate [ 79 - 81 ]. Begg and Golan criticized the analytic caveats in this study and proved that WGS could theoretically offer more optimistic risk prediction compared with what presented by Roberts et al [ 79 , 80 ].…”

Section: Current Personalized Medicine Applicationsmentioning

confidence: 99%

“…This study raised much debate [ 79 - 81 ]. Begg and Golan criticized the analytic caveats in this study and proved that WGS could theoretically offer more optimistic risk prediction compared with what presented by Roberts et al [ 79 , 80 ]. As pointed out by Topol, the predictive capacity of WGS is unlikely to be sufficiently powerful until the sequences of many individuals with the same well-defined trait and advanced analytic approaches are available [ 81 ].…”

Section: Current Personalized Medicine Applicationsmentioning

confidence: 99%

Jumping on the Train of Personalized Medicine: A Primer for Non- Geneticist Clinicians: Part 3. Clinical Applications in the Personalized Medicine Area

Li¹,

Meyre

2014

CPSR

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The rapid decline of sequencing costs brings hope that personal genome sequencing will become a common feature of medical practice. This series of three reviews aim to help non-geneticist clinicians to jump into the fast-moving field of personalized genetic medicine. In the first two articles, we covered the fundamental concepts of molecular genetics and the methodologies used in genetic epidemiology. In this third article, we discuss the evolution of personalized medicine and illustrate the most recent success in the fields of Mendelian and complex human diseases. We also address the challenges that currently limit the use of personalized medicine to its full potential.

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