A Pragmatic Testing-Eligibility Framework for Population Mutation Screening: The Example of <i>BRCA1/2</i>

Best, Ana F.; Tucker, Margaret A.; Frone, Megan; Greene, Mark H.; Peters, June A.; Katki, Hormuzd A.

doi:10.1158/1055-9965.epi-18-0584

Cited by 6 publications

(8 citation statements)

References 40 publications

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“…Whether the high prevalence of clinically important germline mutations in OC patients justifies population-wide screening is a vivid matter of debate [44][45][46][47][48]. We emphasize that we found BRCA1/2 mutations in 14.5% of OC patients with no family cancer history who would currently not be revealed presymptomatically without population screening.…”

Section: Discussionmentioning

confidence: 68%

Multigene Panel Germline Testing of 1333 Czech Patients with Ovarian Cancer

Lhotová

Zemánková

Vočka

et al. 2020

Cancers

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Ovarian cancer (OC) is the deadliest gynecologic malignancy with a substantial proportion of hereditary cases and a frequent association with breast cancer (BC). Genetic testing facilitates treatment and preventive strategies reducing OC mortality in mutation carriers. However, the prevalence of germline mutations varies among populations and many rarely mutated OC predisposition genes remain to be identified. We aimed to analyze 219 genes in 1333 Czech OC patients and 2278 population-matched controls using next-generation sequencing. We revealed germline mutations in 18 OC/BC predisposition genes in 32.0% of patients and in 2.5% of controls. Mutations in BRCA1/BRCA2, RAD51C/RAD51D, BARD1, and mismatch repair genes conferred high OC risk (OR > 5). Mutations in BRIP1 and NBN were associated with moderate risk (both OR = 3.5). BRCA1/2 mutations dominated in almost all clinicopathological subgroups including sporadic borderline tumors of ovary (BTO). Analysis of remaining 201 genes revealed somatic mosaics in PPM1D and germline mutations in SHPRH and NAT1 associating with a high/moderate OC risk significantly; however, further studies are warranted to delineate their contribution to OC development in other populations. Our findings demonstrate the high proportion of patients with hereditary OC in Slavic population justifying genetic testing in all patients with OC, including BTO.

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Section: Discussionmentioning

confidence: 68%

Multigene Panel Germline Testing of 1333 Czech Patients with Ovarian Cancer

Lhotová

Zemánková

Vočka

et al. 2020

Cancers

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“…First, Ashkenazi Jewish ancestry without a personal cancer history is now included as a scenario for which genetic testing may be considered. The rate of the 3 founder pathogenic variants in this population is 2.2% to 2.5%, [1][2][3] which is equivalent to a level of risk for some other testing criteria, such as breast cancer diagnosed at age #45 years. In addition, studies have shown that genetic testing based on clinical guidelines emphasizing a family history of breast, ovarian, prostate, or other cancers missed approximately 38% to 56% of mutation carriers with Ashkenazi Jewish ancestry, 1,2,4,5 providing some evidence to support population-based genetic testing among the Ashkenazi Jewish population.…”

Section: Testing Criteria For High-penetrance Breast And/or Ovarian Cmentioning

confidence: 95%

NCCN Guidelines Insights: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 1.2020

Daly

Pilarski

Yurgelun

et al. 2020

Journal of the National Comprehensive Cancer Network

387

435

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The NCCN Guidelines for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic provide recommendations for genetic testing and counseling for hereditary cancer syndromes, and risk management recommendations for patients who are diagnosed with syndromes associated with an increased risk of these cancers. The NCCN panel meets at least annually to review comments, examine relevant new data, and reevaluate and update recommendations. These NCCN Guidelines Insights summarize the panel’s discussion and most recent recommendations regarding criteria for high-penetrance genes associated with breast and ovarian cancer beyond BRCA1/2, pancreas screening and genes associated with pancreatic cancer, genetic testing for the purpose of systemic therapy decision-making, and testing for people with Ashkenazi Jewish ancestry.

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“…Instead, a lower risk threshold, below 10%, might identify most all mutation-carriers, yet avoid unnecessary testing for most women. We recently showed that a low 0.78% risk-threshold would identify 80% of Ashkenazi-Jewish mutation-carriers yet test only 44% of Ashkenazi-Jewish women(24). We use AUC, MRS, NBI, and Net Benefit to throw light on properties of BRCAPRO to select women for BRCA1/2 testing at risk thresholds between 0%−10%.…”

Section: Informativeness Of Risk Models To Select People For Brca1/2 mentioning

confidence: 99%

“…We calculated each volunteer’s risk of carrying a mutation, based on their self-reported family-history of breast/ovarian cancers, using BRCAPRO. Here M is the BRCAPRO risk score, and because BRCAPRO is a well-calibrated risk model(24), m 0 = R , i.e. the cutpoint m 0 equals the risk threshold R .…”

Section: Informativeness Of Risk Models To Select People For Brca1/2 mentioning

confidence: 99%

“…In light of plummeting costs, thresholds below the current US/UK 10% guideline should be considered. We recently showed that a threshold of 0.78% identified 80% of Ashkenazi-Jewish mutation-carriers by testing only 44% of Ashkenazi-Jewish women(24). In this paper, we consider AUC, MRS, NBI, and Net Benefit to better understand the properties of different risk thresholds.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying risk stratification provided by diagnostic tests and risk predictions: Comparison to AUC and decision curve analysis

Katki

2019

Statistics in Medicine

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A property of diagnostic tests and risk models deserving more attention is risk stratification, defined as the ability of a test or model to separate those at high absolute risk of disease from those at low absolute risk. Risk stratification fills a gap between measures of classification (i.e. AUC) that do not require absolute risks and decision-analysis that requires not only absolute risks but also subjective specification of costs and utilities. We introduce Mean Risk Stratification (MRS) as the average change in risk of disease (posttest-pretest) revealed by a diagnostic test or risk model dichotomized at a risk threshold. MRS is particularly valuable for rare conditions, where AUC can be high but MRS can be low, identifying situations that temper overenthusiasm for screening with the new test/model. We apply MRS to the controversy over who should get testing for mutations in BRCA1/2 that cause high risks of breast and ovarian cancers. To reveal different properties of risk-thresholds to refer women for BRCA1/2 testing, we propose an eclectic approach considering MRS and other metrics. The value of MRS is to interpret AUC in the context of BRCA1/2 mutation prevalence, provide a range of risk thresholds at which a risk model is "optimally informative", and to provide insight into why Net Benefit arrives to its conclusion.

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A Pragmatic Testing-Eligibility Framework for Population Mutation Screening: The Example of BRCA1/2

Cited by 6 publications

References 40 publications

Multigene Panel Germline Testing of 1333 Czech Patients with Ovarian Cancer

Multigene Panel Germline Testing of 1333 Czech Patients with Ovarian Cancer

NCCN Guidelines Insights: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 1.2020

Quantifying risk stratification provided by diagnostic tests and risk predictions: Comparison to AUC and decision curve analysis

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