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Preoperative genetic testing impacts surgical decision making in BRCA mutation carriers with breast cancer: a retrospective cohort analysis.

ABSTRACT: The impact of timing of genetic testing on surgical decision making in women with breast cancer and BRCA mutation is not well known.Women who were found to carry a deleterious BRCA mutation and had been diagnosed with breast cancer were identified from a database at Beaumont Health. Women who had received BRCA positive results at least a day prior to their index surgery were considered to be aware of their mutation status prior to surgery. Baseline characteristics and surgical choices were compared between women who were aware of their mutation status prior to surgery and those who were not. Fischer's exact test was used for categorical variables and Mann-Whitney U-Test was used for continuous variables.A total of 220 patients were included in the final analysis, 208 (94.5%) with unilateral breast cancer and 12 (5.5%) with bilateral breast cancer. Out of the 208 patients with unilateral breast cancer, 106 (51.0%) patients were aware of their mutation status prior to index surgery while 102 (49%) were not. A significantly (p < 0.05) higher proportion of women underwent contralateral prophylactic mastectomy in the group that was aware of their mutation status prior to index surgery compared to the group that was not (76.4% vs 14.7%).Our study demonstrates that knowledge of BRCA mutation status impacts surgical decision making in favor of bilateral mastectomy in patients who are aware of their results prior to index surgery. This finding supports the practice of preoperative genetic testing in patients with newly diagnosed breast cancer.

SUBMITTER: Yadav S

PROVIDER: S-EPMC5530488 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:Identification of BRCA mutations in breast cancer (BC) patients influences treatment and survival and may be of importance for their relatives. Testing is often restricted to women fulfilling high-risk criteria. However, there is limited knowledge of the sensitivity of such a strategy, and of the clinical aspects of BC caused by BRCA mutations in less selected BC cohorts. The aim of this report was to address these issues by evaluating the results of BRCA testing of BC patients in South-Eastern Norway.1371 newly diagnosed BC patients were tested with sequencing and Multi Ligation Probe Amplification (MLPA). Prevalence of mutations was calculated, and BC characteristics among carriers and non-carriers compared. Sensitivity and specificity of common guidelines for BRCA testing to identify carriers was analyzed. Number of identified female mutation positive relatives was evaluated.A pathogenic BRCA mutation was identified in 3.1%. Carriers differed from non-carriers in terms of age at diagnosis, family history, grade, ER/PR-status, triple negativity (TNBC) and Ki67, but not in HER2 and TNM status. One mutation positive female relative was identified per mutation positive BC patient. Using age of onset below 40 or TNBC as criteria for testing identified 32-34% of carriers. Common guidelines for testing identified 45-90%, and testing all below 60 years identified 90%. Thirty-seven percent of carriers had a family history of cancer that would have qualified for predictive BRCA testing. A Variant of Uncertain Significance (VUS) was identified in 4.9%.Mutation positive BC patients differed as a group from mutation negative. However, the commonly used guidelines for testing were insufficient to detect all mutation carriers in the BC cohort. Thirty-seven percent had a family history of cancer that would have qualified for predictive testing before they were diagnosed with BC. Based on our combined observations, we suggest it is time to discuss whether all BC patients should be offered BRCA testing, both to optimize treatment and improve survival for these women, but also to enable identification of healthy mutation carriers within their families. Health services need to be aware of referral possibility for healthy women with cancer in their family.

Project description:BackgroundSurgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.MethodsCochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.ResultsRequirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.ConclusionTraditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

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Preoperative genetic testing impacts surgical decision making in BRCA mutation carriers with breast cancer: a retrospective cohort analysis.

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