Precise, Genotype-First Breast Cancer Prevention: Experience With Transferring Monogenic Findings From a Population Biobank to the Clinical Setting

Systematic review of the uptake and outcomes from returning secondary findings to adult participants in research genomic testing

The increasing use of genomic sequencing in research means secondary findings (SF) is more frequently detected and becoming a more pressing issue for researchers. This is reflected by the recent publication of multiple guidelines on this issue, calling for researchers to have a plan for managing SF prior to commencing their research. A deeper understanding of participants' experiences and outcomes from receiving SF is needed to ensure that the return of SF is conducted ethically and with adequate support. This review focuses on the uptake and outcomes of receiving actionable SF for research participants. This review included studies from January 2010 to January 2023. Databases searched included Medline, Embase, PsycINFO, and Scopus. Of the 3903 studies identified, 29 were included in the analysis. The uptake of SF ranged between 20% and 97%, and outcomes were categorized into psychological, clinical, lifestyle and behavioral, and family outcomes. The results indicate there is minimal psychological impact from receiving SF. Almost all participants greatly valued receiving SF. These findings highlight considerations for researchers when returning results, including the importance of involving genetic health professionals in consenting, results return process, and ensuring continuity of care by engaging healthcare providers.

The Prevalence and Molecular Landscape of Lynch Syndrome in the Affected and General Population

BACKGROUND

Lynch syndrome (LS) is the most frequent genetically pre-disposed colorectal cancer (CRC) syndrome, accounting for 2-3% of all CRC cases. In Estonia, ~1000 new cases are diagnosed each year. This retroactive and prospective study aimed to estimate the prevalence of LS and describe disease-causing variants in mismatch repair (MMR) genes in a diagnostic setting and in the Estonian general population.

METHODS

LS data for the diagnostic cohort were gathered from 2012 to 2022 and data for the general population were acquired from the Estonian Biobank (EstBB). Furthermore, we conducted a pilot study to estimate the improvement of LS diagnostic yield by raising the age limit to >50 years for immunohistochemistry analysis of MMR genes.

RESULTS

We estimated LS live birth prevalence between 1930 and 2003 in Estonia at 1:8638 (95% CI: 1: 9859-7588). During the study period, we gathered 181 LS individuals. We saw almost a six-fold increase in case prevalence, probably deriving from better health awareness, improved diagnostic possibilities and the implementation of MMR IHC testing in a broader age group.

CONCLUSION

The most common genes affected in the diagnostic and EstBB cohorts were MLH1 and PMS2 genes, respectively. The LS diagnosis mean age was 44.8 years for index cases and 36.8 years (p = 0.003) for family members. In the MMR IHC pilot study, 29% had LS.

Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients

The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case-control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual's BC risk (OR = 1.37; 95% CI = 1.03-1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.