Effects of Social Determinants and Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions.

Context: The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. Objective: Evaluate aggregate contribution of social determinants, comorbidity, and gene-x-drug interactions to moderate 90-day hospital readmission. Study Design and Analysis: Non-concurrent cohort study; Multivariable logistic regression Setting: Hospital/integrated healthcare delivery system in northern Illinois Population Studied: 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel Outcome Measure: 90-day hospital readmission (primary outcome) Results: Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45-10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61-4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77-3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88-2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47-3.07) (p < 0.0001), median household income (OR = 1.63, 1.03-2.58) (p = 0.035), male gender (OR = 1.47, 1.21-1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18-1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08-1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). Conclusions: These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.

Personalized medicine in a community health system: the NorthShore experience.

Genomic and personalized medicine implementation efforts have largely centered on specialty care in tertiary health systems. There are few examples of fully integrated care systems that span the healthcare continuum. In 2014, NorthShore University HealthSystem launched the Center for Personalized Medicine to catalyze the delivery of personalized medicine. Successful implementation required the development of a scalable family history collection tool, the Genetic and Wellness Assessment (GWA) and Breast Health Assessment (BHA) tools; integrated pharmacogenomics programming; educational programming; electronic medical record integration; and robust clinical decision support tools. To date, more than 225,000 patients have been screened for increased hereditary conditions, such as cancer risk, through these tools in primary care. More than 35,000 patients completed clinical genetic testing following GWA or BHA completion. An innovative program trained more than 100 primary care providers in genomic medicine, activated with clinical decision support and access to patient genetic counseling services and digital healthcare tools. The development of a novel bioinformatics platform (FLYPE) enabled the incorporation of genomics data into electronic medical records. To date, over 4,000 patients have been identified to have a pathogenic or likely pathogenic variant in a gene with medical management implications. Over 33,000 patients have clinical pharmacogenomics data incorporated into the electronic health record supported by clinical decision support tools. This manuscript describes the evolution, strategy, and successful multispecialty partnerships aligned with health system leadership that enabled the implementation of a comprehensive personalized medicine program with measurable patient outcomes through a genomics-enabled learning health system model that utilizes implementation science frameworks.

Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes.

CONTEXT: Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations. OBJECTIVE: This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D. METHODS: Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients. RESULTS: The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D. CONCLUSION: PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.

Cancer-associated thrombosis by cancer sites and inherited factors in a prospective population-based cohort.

Cancer-associated thrombosis (CAT) is common and associated with mortality. We estimated CAT rate by cancer sites and inherited factors among cancer patients from the UK Biobank (N =70,406). The 12-month CAT rate after cancer diagnosis was 2.37% overall but varied considerably among cancer sites. Among the 10 cancer sites classified as 'high-risk' of CAT by the National Comprehensive Cancer Network guidelines, 6 had CAT rate <5%. In contrast, 5 cancer sites classified as 'average-risk' by the guidelines had CAT rate >5%. For inherited risk factors, both known mutation carriers in two genes (F5/F2) and polygenic score for venous thromboembolism (VTE) (PGSVTE) were independently associated with increased CAT risk. While F5/F2 identified 6% patients with high genetic-risk for CAT, adding PGSVTE identified 13 % patients at equivalent/higher genetic-risk to CAT than that of F5/F2 mutations. Findings from this large prospective study, if confirmed, provide critical data to update guidelines for CAT risk assessment.

Refining Risk for Alzheimer's Disease Among Heterozygous APOEɛ4 Carriers.

In a large population-based cohort, we show not all heterozygous APOEɛ4 carriers are at increased risk for Alzheimer's disease (AD); a significantly higher AD proportion was only found for ɛ3/ɛ4, not ɛ2/ɛ4. Among ɛ3/ɛ4 carriers (24% in the cohort), the AD proportion differed considerably by polygenic risk score (PRS). In particular, the AD proportion was lower than the entire cohort for subjects in the bottom 20-percentile PRS and was higher than that of homozygous ɛ4 carriers for subjects at the top 5th-percentile PRS. Family history was no longer a significant predictor of AD risk after adjusting APOE and PRS.

Ovarian cancer pathology characteristics as predictors of variant pathogenicity in BRCA1 and BRCA2.

BACKGROUND: The distribution of ovarian tumour characteristics differs between germline BRCA1 and BRCA2 pathogenic variant carriers and non-carriers. In this study, we assessed the utility of ovarian tumour characteristics as predictors of BRCA1 and BRCA2 variant pathogenicity, for application using the American College of Medical Genetics and the Association for Molecular Pathology (ACMG/AMP) variant classification system. METHODS: Data for 10,373 ovarian cancer cases, including carriers and non-carriers of BRCA1 or BRCA2 pathogenic variants, were collected from unpublished international cohorts and consortia and published studies. Likelihood ratios (LR) were calculated for the association of ovarian cancer histology and other characteristics, with BRCA1 and BRCA2 variant pathogenicity. Estimates were aligned to ACMG/AMP code strengths (supporting, moderate, strong). RESULTS: No histological subtype provided informative ACMG/AMP evidence in favour of BRCA1 and BRCA2 variant pathogenicity. Evidence against variant pathogenicity was estimated for the mucinous and clear cell histologies (supporting) and borderline cases (moderate). Refined associations are provided according to tumour grade, invasion and age at diagnosis. CONCLUSIONS: We provide detailed estimates for predicting BRCA1 and BRCA2 variant pathogenicity based on ovarian tumour characteristics. This evidence can be combined with other variant information under the ACMG/AMP classification system, to improve classification and carrier clinical management.

Association of Reported Candidate Monogenic Genes With Lung Cancer Risk.

INTRODUCTION/BACKGROUND: Published studies on association of germline monogenic genes and lung cancer risk were inconsistent. Our objective is to assess the validity of reported candidate monogenic genes for their association with lung cancer. MATERIALS AND METHODS: A systematic review of published papers prior to August 2022 was performed first to identify all genes where germline mutations were associated with lung cancer risk. We then performed a confirmation study in 2,050 lung cancer cases and 198,553 controls in the UK Biobank (UKB). Germline mutations of these genes were identified from sequencing data and annotated using The American College of Medical Genetics criteria. The robust SKAT-O, a gene-based analysis that properly controls for false positives due to unbalanced case-control ratio, was used for association tests adjusting for age at recruitment, gender, and genetic background. RESULTS: The systematic review identified 12 genes that were statistically significantly associated with lung cancer risk in at least one study (P < .05), including ATM, BLM, BRCA2, BRIP1, CHEK2, FANCA, FANCD2, MSH6, PMS1, RAD51C, RAD51D, and TP53. When pathogenic/likely pathogenic mutations were aggregated within each gene, the association was confirmed for ATM (P = 4.47E-4) at the study-wise significance level (P < .0042, Bonferroni correction for 12 tests). Suggestive evidence of association was found for 2 other genes, BRCA2 (P = .007) and TP53 (P = .03). Among these 3 genes, the lung cancer risks range from 1.95 (BRCA2) to 5.28 (TP53). CONCLUSION: This study provides statistical evidence for association of previously reported genes and lung cancer risk and has clinical utility for risk assessment and genetic counseling.

Reliability of Ancestry-specific Prostate Cancer Genetic Risk Score in Four Racial and Ethnic Populations.

Background: Reliability of prostate cancer (PCa) genetic risk score (GRS), that is, the concordance between its estimated risk and observed risk, is required for genetic testing at the individual level. Reliability data are lacking for non-European racial/ethnic populations, which hinders its clinical use and exacerbates racial disparity. Objective: To calibrate PCa ancestry-specific GRS in four racial/ethnic populations. Design setting and participants: PCa ancestry-specific GRSs, calculated from published risk-associated single-nucleotide polymorphisms in corresponding racial/ethnic populations, were evaluated in men who participated in 23andMe, Inc. genetic testing and consented for research, including 888 086 of European (EUR), 81 109 of Hispanic (HIS), 30 472 of African (AFR), and 13 985 of East Asian (EAS) ancestry, as classified by 23andMe's ancestry composition algorithm. Outcome measurements and statistical analysis: The concordance between the observed and estimated PCa risks at ten ancestry-specific GRS deciles was measured primarily by using the calibration slope (ß), where 1 represents a perfect calibration. Platt scaling was used to correct the systematic bias of GRS. Results and limitations: A linear trend of an increased observed PCa prevalence in men with higher ancestry-specific GRS deciles was found in each racial population (all p -trend < 0.001). A calibration analysis revealed a systematic bias of GRS; ß was considerably lower than 1 (0.73, 0.64, 0.66, and 0.75 in EUR, HIS, AFR, and EAS ancestries, respectively). This bias was reduced after the Platt scaling correction: ß for scaled GRS in the testing dataset (40% of individuals) approximated 1 for all groups (0.95, 1.05, 1.02, and 1.01 in EUR, HIS, AFR, and EAS populations, respectively). The generalizability of the Platt correction needs to be validated in independent cohorts. Conclusions: A systematic bias of ancestry-specific GRS in the direction of an overestimated risk for men in the highest decile was found in EUR and non-EUR populations. GRS is well calibrated after correction and is appropriate for genetic testing at the individual level for personalized PCa screening. Patient summary: A corrected genetic risk score is more reliable (supported by the observed prostate cancer [PCa] risk) and appropriate for genetic testing for personalized PCa screening.

Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System.

The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.

Inherited risk assessment and its clinical utility for predicting prostate cancer from diagnostic prostate biopsies.

BACKGROUND: Many studies on prostate cancer (PCa) germline variants have been published in the last 15 years. This review critically assesses their clinical validity and explores their utility in prediction of PCa detection rates from prostate biopsy. METHODS: An integrative review was performed to (1) critically synthesize findings on PCa germline studies from published papers since 2016, including risk-associated single nucleotide polymorphisms (SNPs), polygenic risk score methods such as genetic risk score (GRS), and rare pathogenic mutations (RPMs); (2) exemplify the findings in a large population-based cohort from the UK Biobank (UKB); (3) identify gaps for implementing inherited risk assessment in clinic based on experience from a healthcare system; (4) evaluate available GRS data on their clinical utility in predicting PCa detection rates from prostate biopsies; and (5) describe a prospective germline-based biopsy trial to address existing gaps. RESULTS: SNP-based GRS and RPMs in four genes (HOXB13, BRCA2, ATM, and CHEK2) were significantly and consistently associated with PCa risk in large well-designed studies. In the UKB, positive family history, RPMs in the four implicated genes, and a high GRS (>1.5) identified 8.12%, 1.61%, and 17.38% of men to be at elevated PCa risk, respectively, with hazard ratios of 1.84, 2.74, and 2.39, respectively. Additionally, the performance of GRS for predicting PCa detection rate on prostate biopsy was consistently supported in several retrospective analyses of transrectal ultrasound (TRUS)-biopsy cohorts. Prospective studies evaluating the performance of all three inherited measures in predicting PCa detection rate from contemporary multiparametric MRI (mpMRI)-based biopsy are lacking. A multicenter germline-based biopsy trial to address these gaps is warranted. CONCLUSIONS: The complementary performance of three inherited risk measures in PCa risk stratification is consistently supported. Their clinical utility in predicting PCa detection rate, if confirmed in prospective clinical trials, may improve current decision-making for prostate biopsy.

Calibration of polygenic risk scores is required prior to clinical implementation: results of three common cancers in UKB.

BACKGROUND: SNP-based polygenic risk scores have recently been adopted in the clinic for risk assessment of some common diseases. Their validity is supported by a consistent trend between their percentile rank and disease risk in populations. However, for clinical use at the individual level, the reliability of score values is necessary considering they are directly used to calculate remaining lifetime risk. OBJECTIVES: We assessed the reliability of polygenic score values to estimate prostate cancer (PCa), breast cancer (BCa) and colorectal cancer (CRC) risk in three incident cohorts from the UK Biobank (n>500 000). METHODS: Cancer-specific Genetic Risk Score (GRS), a well-established population-standardised polygenic risk score, was calculated. RESULTS: A systematic bias was found between estimated risks (GRS values) and observed risks; ß (95% CI) was 0.67 (0.58-0.76), 0.74 (0.65-0.84) and 0.82 (0.75-0.89), respectively, for PCa, BCa and CRC, all significantly lower than 1.00 (perfect calibration), p<0.001. After applying a correction factor derived from a training data set, the ß for corrected GRS values in an independent testing data set were 1.09 (1.05-1.13), 1.00 (0.88-1.12) and 1.08 (0.96-1.21), respectively, for PCa, BCa and CRC. CONCLUSION: Assessing the calibration of polygenic risk scores is necessary and feasible to ensure their reliability prior to clinical implementation.

The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System.

Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines exist for many medications commonly prescribed prior to hospital discharge, yet there are limited data regarding the contribution of gene-x-drug interactions to hospital readmissions. The present study evaluated the relationship between prescription of CPIC medications prescribed within 30 days of hospital admission and 90-day hospital readmission from 2010 to 2020 in a study population (N = 10,104) who underwent sequencing with a 14-gene pharmacogenetic panel. The presence of at least one pharmacogenetic indicator for a medication prescribed within 30 days of hospital admission was considered a gene-x-drug interaction. Multivariable logistic regression analyzed the association between one or more gene-x-drug interactions with 90-day readmission. There were 2211/2354 (93.9%) admitted patients who were prescribed at least one CPIC medication. Univariate analyses indicated that the presence of at least one identified gene-x-drug interaction increased the risk of 90-day readmission by more than 40% (OR = 1.42, 95% confidence interval (CI) 1.09-1.84) (p = 0.01). A multivariable model adjusting for age, race, sex, employment status, body mass index, and medical conditions slightly attenuated the effect (OR = 1.32, 95% CI 1.02-1.73) (p = 0.04). Our results suggest that the presence of one or more CPIC gene-x-drug interactions increases the risk of 90-day hospital readmission, even after adjustment for demographic and clinical risk factors.

The integration of personalized medicine into health systems: progress and a path forward.

Tweetable abstract Fueled by technological advancements and the integration efforts of many pioneer health systems, personalized medicine is now being clinically implemented at measurable but incomplete levels system-wide.

Physician-directed genetic screening to evaluate personal risk for medically actionable disorders: a large multi-center cohort study.

BACKGROUND: The use of proactive genetic screening for disease prevention and early detection is not yet widespread. Professional practice guidelines from the American College of Medical Genetics and Genomics (ACMG) have encouraged reporting pathogenic variants that confer personal risk for actionable monogenic hereditary disorders, but only as secondary findings from exome or genome sequencing. The Centers for Disease Control and Prevention (CDC) recognizes the potential public health impact of three Tier 1 actionable disorders. Here, we report results of a large multi-center cohort study to determine the yield and potential value of screening healthy individuals for variants associated with a broad range of actionable monogenic disorders, outside the context of secondary findings. METHODS: Eligible adults were offered a proactive genetic screening test by health care providers in a variety of clinical settings. The screening panel based on next-generation sequencing contained up to 147 genes associated with monogenic disorders within cancer, cardiovascular, and other important clinical areas. Sequence and intragenic copy number variants classified as pathogenic, likely pathogenic, pathogenic (low penetrance), or increased risk allele were considered clinically significant and reported. Results were analyzed by clinical area and severity/burden of disease using chi-square tests without Yates' correction. RESULTS: Among 10,478 unrelated adults screened, 1619 (15.5%) had results indicating personal risk for an actionable monogenic disorder. In contrast, only 3.1 to 5.2% had clinically reportable variants in genes suggested by the ACMG version 2 secondary findings list to be examined during exome or genome sequencing, and 2% had reportable variants related to CDC Tier 1 conditions. Among patients, 649 (6.2%) were positive for a genotype associated with a disease of high severity/burden, including hereditary cancer syndromes, cardiovascular disorders, or malignant hyperthermia susceptibility. CONCLUSIONS: This is one of the first real-world examples of specialists and primary care providers using genetic screening with a multi-gene panel to identify health risks in their patients. Nearly one in six individuals screened for variants associated with actionable monogenic disorders had clinically significant results. These findings provide a foundation for further studies to assess the role of genetic screening as part of regular medical care.

Observed evidence for guideline-recommended genes in predicting prostate cancer risk from a large population-based cohort.

BACKGROUND: Germline testing for prostate cancer (PCa) is now recommended by the National Comprehensive Cancer Network. While multi-gene testing has been proposed, evidence for their association with PCa risk is not well established. METHODS: We tested associations of pathogenic/likely pathogenic mutations in 10 guideline-recommended genes (ATM, BRCA1, BRCA2, CHEK2, PALB2, MLH1, MSH2, MSH6, PMS2, and HOXB13) with PCa risk in the UK Biobank, a population-based cohort. Mutations were annotated based on prostate-specific transcripts using the American College of Medical Genetics and Genomics standards. Associations were tested in 4399 PCa cases and 85,403 unaffected male controls using logistic regression adjusting for age and genetic background. p < .005 was considered significant based on Bonferroni correction. RESULTS: Among the 10 tested genes, significantly higher mutation carrier rates in PCa cases versus controls were found for four genes at p < .005; HOXB13, BRCA2, ATM, and CHEK2, with odds ratios (95% confidence interval) estimated at 4.96 (3.62-6.69), 3.23 (2.23-4.56), 2.95 (2.01-4.22), 1.94 (1.43-2.58), respectively. No significant association was found between mutation carrier status and age at PCa diagnosis or family history of PCa. Despite the large sample size of this study, statistical power remains limited, especially for genes where pathogenic mutation carrier rates are extremely rare (<0.03%). CONCLUSION: Observed evidence for PCa risk was found for four of the 10 guideline-recommended genes in this large population-based study. Mutations in these four genes can be interpreted with confidence in genetic counseling for PCa risk assessment. Evidence for the remaining six genes needs to be further evaluated in larger studies.

Implementing Primary Care Mediated Population Genetic Screening Within an Integrated Health System.

INTRODUCTION: Genetic screenings can have a large impact on enabling personalized preventive care. However, this can be limited by the primary use of medical history-based screenings in determining care. The purpose of this study was to understand the impact of DNA10K, a population-based genetic screening program mediated by primary care physicians within an integrated health system to emphasize its contribution to preventive healthcare. METHODS: Construction of the patient experience as part of DNA10K shaped the context for PCP engagement within the program. A cross-sectional analysis of patient consents, orders, tests, and results of nearly 10,000 patients within the primary care specialties of family medicine, internal medicine or obstetrics/gynecology between April 1, 2019 and January 22, 2020 was conducted. RESULTS: Across all specialties, a median number of 7.5 cancer and cardiovascular disease variants per PCP was found. The average age of the study population was 49.6 years. Over 8% of these patients had at least one actionable genetic risk variant and almost 2% of patients had at least one CDC Tier 1 variant. The median numbers of patients per PCP with either hereditary breast and ovarian cancer, Lynch Syndrome, or Familial Hypercholesterolemia was 1 (Interquartile Range 0-2). DISCUSSION: The analysis of test results and the engagement of an integrated healthcare system in the implementation of a genetic screening program suggests that it can have a large impact on population health outcomes and minimal referral burden to PCPs if identified risks can lead to preventive care.

The predictive ability of the 313 variant-based polygenic risk score for contralateral breast cancer risk prediction in women of European ancestry with a heterozygous BRCA1 or BRCA2 pathogenic variant.

PURPOSE: To evaluate the association between a previously published 313 variant-based breast cancer (BC) polygenic risk score (PRS313) and contralateral breast cancer (CBC) risk, in BRCA1 and BRCA2 pathogenic variant heterozygotes. METHODS: We included women of European ancestry with a prevalent first primary invasive BC (BRCA1 = 6,591 with 1,402 prevalent CBC cases; BRCA2 = 4,208 with 647 prevalent CBC cases) from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), a large international retrospective series. Cox regression analysis was performed to assess the association between overall and ER-specific PRS313 and CBC risk. RESULTS: For BRCA1 heterozygotes the estrogen receptor (ER)-negative PRS313 showed the largest association with CBC risk, hazard ratio (HR) per SD = 1.12, 95% confidence interval (CI) (1.06-1.18), C-index = 0.53; for BRCA2 heterozygotes, this was the ER-positive PRS313, HR = 1.15, 95% CI (1.07-1.25), C-index = 0.57. Adjusting for family history, age at diagnosis, treatment, or pathological characteristics for the first BC did not change association effect sizes. For women developing first BC < age 40 years, the cumulative PRS313 5th and 95th percentile 10-year CBC risks were 22% and 32% for BRCA1 and 13% and 23% for BRCA2 heterozygotes, respectively. CONCLUSION: The PRS313 can be used to refine individual CBC risks for BRCA1/2 heterozygotes of European ancestry, however the PRS313 needs to be considered in the context of a multifactorial risk model to evaluate whether it might influence clinical decision-making.

Patient-Reported Outcomes and Experiences with Population Genetic Testing Offered Through a Primary Care Network.

Aims: To explore patient experiences in a large-scale primary care-based, preemptive genetic testing program. Methods: Patients who received genetic results from the initiative were invited to participate in an online survey 3 weeks postresult disclosure. A 6-month follow-up survey was sent to assess changes over time. Results: The initial survey was completed by 1646 patients, with 544 completing the 6-month follow-up survey. The following outcomes were high overall: patient-reported understanding of results (cancer: 87%; cardiac: 86%); perceived utility (75%); positive emotions (relieved: 66.8%; happy: 62.0%); family result sharing (67.6%); and satisfaction (87%), although analysis by demographic factors identified groups who may benefit from additional education and emotional support. Results-related health behaviors and discussions with providers increased over time (screening procedures 6.1% to 14.2% p < 0.001; provider discussion 10.3% to 25.3%, p < 0.001), and were more likely to take place for patients with positive cancer and/or cardiac results (39.8% vs. 7.6%, p < 0.001). Forty-seven percent of patients reported insurance discrimination concerns, and most (79.4%) were not familiar with privacy and nondiscrimination laws. Concerns regarding discrimination and negative emotions decreased between the two survey time points (privacy issues 44.6% to 35.1% p < 0.001; life insurance discrimination concerns 35.5% to 29.6%, p = 0.001; anxiety 8.1% to 3.3%, p < 0.001; and uncertainty 19.8% to 12.8%, p < 0.001). These findings led to the development and integration of additional patient resources to improve program implementation. Conclusion: Our findings highlight patient experiences with and areas of need in a community-based genomic screening pilot initiative using a mixed primary care/genetics provider model to deliver precision medicine.