Dataset Information

Clinical implications of incorporating genetic and non-genetic risk factors in CanRisk-based breast cancer risk prediction.

ABSTRACT:

Background

Breast cancer (BC) risk prediction models consider cancer family history (FH) and germline pathogenic variants (PVs) in risk genes. It remains elusive to what extent complementation with polygenic risk score (PRS) and non-genetic risk factor (NGRFs) data affects individual intensified breast surveillance (IBS) recommendations according to European guidelines.

Methods

For 425 cancer-free women with cancer FH (mean age 40·6 years, range 21-74), recruited in France, Germany and the Netherlands, germline PV status, NGRFs, and a 306 variant-based PRS (PRS₃₀₆) were assessed to calculate estimated lifetime risks (eLTR) and estimated 10-year risks (e10YR) using CanRisk. The proportions of women changing country-specific European risk categories for IBS recommendations, i.e. ≥20 % and ≥30 % eLTR, or ≥5 % e10YR were determined.

Findings

Of the women with non-informative PV status, including PRS₃₀₆ and NGRFs changed clinical recommendations for 31·0 %, (57/184, 20 % eLTR), 15·8 % (29/184, 30 % eLTR) and 22·4 % (41/183, 5 % e10YR), respectively whereas of the women tested negative for a PV observed in their family, clinical recommendations changed for 16·7 % (25/150), 1·3 % (2/150) and 9·5 % (14/147). No change was observed for 82 women with PVs in high-risk genes (BRCA1/2, PALB2). Combined consideration of eLTRs and e10YRs identified BRCA1/2 PV carriers benefitting from IBS <30 years, and women tested non-informative/negative for whom IBS may be postponed.

Interpretation

For women who tested non-informative/negative, PRS and NGRFs have a considerable impact on IBS recommendations. Combined consideration of eLTRs and e10YRs allows personalizing IBS starting age.

Funding

Horizon 2020, German Cancer Aid, Federal Ministry of Education and Research, Köln Fortune.

SUBMITTER: Tuchler A

PROVIDER: S-EPMC10749276 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Clinical implications of incorporating genetic and non-genetic risk factors in CanRisk-based breast cancer risk prediction.

Tüchler Anja A De Pauw Antoine A Ernst Corinna C Anota Amélie A Lakeman Inge M M IMM Dick Julia J van der Stoep Nienke N van Asperen Christi J CJ Maringa Monika M Herold Natalie N Blümcke Britta B Remy Robert R Westerhoff Anke A Stommel-Jenner Denise J DJ Frouin Eléonore E Richters Lisa L Golmard Lisa L Kütting Nadine N Colas Chrystelle C Wappenschmidt Barbara B Rhiem Kerstin K Devilee Peter P Stoppa-Lyonnet Dominique D Schmutzler Rita K RK Hahnen Eric E

Breast (Edinburgh, Scotland) 20231129

<h4>Background</h4>Breast cancer (BC) risk prediction models consider cancer family history (FH) and germline pathogenic variants (PVs) in risk genes. It remains elusive to what extent complementation with polygenic risk score (PRS) and non-genetic risk factor (NGRFs) data affects individual intensified breast surveillance (IBS) recommendations according to European guidelines.<h4>Methods</h4>For 425 cancer-free women with cancer FH (mean age 40·6 years, range 21-74), recruited in France, German ...[more]

PMID: 38061307

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Project description:ImportancePolygenic risk scores (PRSs) have shown promise in breast cancer risk prediction; however, limited studies have been conducted among Asian women.ObjectiveTo develop breast cancer risk prediction models for Asian women incorporating PRSs and nongenetic risk factors.Design, setting, and participantsThis diagnostic study included women of Asian ancestry from the Asia Breast Cancer Consortium. PRSs were developed using data from genomewide association studies (GWASs) of breast cancer conducted among 123 041 women with Asian ancestry (including 18 650 women with breast cancer) using 3 approaches: (1) reported PRS for women with European ancestry; (2) breast cancer-associated single-nucleotide variations (SNVs) identified by fine-mapping of GWAS-identified risk loci; and (3) genomewide risk prediction algorithms. A nongenetic risk score (NGRS) was built, including 7 well-established nongenetic risk factors, using data of 416 case participants and 1558 control participants from a prospective cohort study. PRSs were initially validated in an independent data set including 1426 case participants and 1323 control participants and further evaluated, along with the NGRS, in the second data set including 368 case participants and 736 control participants nested within a prospective cohort study.Main outcomes and measuresLogistic regression was used to examine associations of risk scores with breast cancer risk to estimate odds ratios (ORs) with 95% CIs and area under the receiver operating characteristic curve (AUC).ResultsA total of 126 894 women of Asian ancestry were included; 20 444 (16.1%) had breast cancer. The mean (SD) age ranged from 49.1 (10.8) to 54.4 (10.4) years for case participants and 50.6 (9.5) to 54.0 (7.4) years for control participants among studies that provided demographic characteristics. In the prospective cohort, a PRS with 111 SNVs developed using the fine-mapping approach (PRS111) showed a prediction performance comparable with a genomewide PRS that included more than 855 000 SNVs. The OR per SD increase of PRS111 score was 1.67 (95% CI, 1.46-1.92), with an AUC of 0.639 (95% CI, 0.604-0.674). The NGRS had a limited predictive ability (AUC, 0.565; 95% CI, 0.529-0.601). Compared with the average risk group (40th-60th percentile), women in the top 5% of PRS111 and NGRS were at a 3.84-fold (95% CI, 2.30-6.46) and 2.10-fold (95% CI, 1.22-3.62) higher risk of breast cancer, respectively. The prediction model including both PRS111 and NGRS achieved the highest prediction accuracy (AUC, 0.648; 95% CI, 0.613-0.682).Conclusions and relevanceIn this study, PRSs derived using breast cancer risk-associated SNVs had similar predictive performance in Asian and European women. Including nongenetic risk factors in models further improved prediction accuracy. These findings support the utility of these models in developing personalized screening and prevention strategies.

Project description:IntroductionMutations in BRCA1 and BRCA2 confer high risks of breast cancer and ovarian cancer. The risk prediction algorithm BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) may be used to compute the probabilities of carrying mutations in BRCA1 and BRCA2 and help to target mutation screening. Tumours from BRCA1 and BRCA2 mutation carriers display distinctive pathological features that could be used to better discriminate between BRCA1 mutation carriers, BRCA2 mutation carriers and noncarriers. In particular, oestrogen receptor (ER)-negative status, triple-negative (TN) status, and expression of basal markers are predictive of BRCA1 mutation carrier status.MethodsWe extended BOADICEA by treating breast cancer subtypes as distinct disease end points. Age-specific expression of phenotypic markers in a series of tumours from 182 BRCA1 mutation carriers, 62 BRCA2 mutation carriers and 109 controls from the Breast Cancer Linkage Consortium, and over 300,000 tumours from the general population obtained from the Surveillance Epidemiology, and End Results database, were used to calculate age-specific and genotype-specific incidences of each disease end point. The probability that an individual carries a BRCA1 or BRCA2 mutation given their family history and tumour marker status of family members was computed in sample pedigrees.ResultsThe cumulative risk of ER-negative breast cancer by age 70 for BRCA1 mutation carriers was estimated to be 55% and the risk of ER-positive disease was 18%. The corresponding risks for BRCA2 mutation carriers were 21% and 44% for ER-negative and ER-positive disease, respectively. The predicted BRCA1 carrier probabilities among ER-positive breast cancer cases were less than 1% at all ages. For women diagnosed with breast cancer below age 50 years, these probabilities rose to more than 5% in ER-negative breast cancer, 7% in TN disease and 24% in TN breast cancer expressing both CK5/6 and CK14 cytokeratins. Large differences in mutation probabilities were observed by combining ER status and other informative markers with family history.ConclusionsThis approach combines both full pedigree and tumour subtype data to predict BRCA1/2 carrier probabilities. Prediction of BRCA1/2 carrier status, and hence selection of women for mutation screening, may be substantially improved by combining tumour pathology with family history of cancer.

Dataset Information

Clinical implications of incorporating genetic and non-genetic risk factors in CanRisk-based breast cancer risk prediction.

Background

Methods

Findings

Interpretation

Funding

Publications

Clinical implications of incorporating genetic and non-genetic risk factors in CanRisk-based breast cancer risk prediction.

Similar Datasets

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