Two Methods to Properly Evaluate the Calibration Of A Disease Risk Prediction Tool; Evaluation of one of the Nurses' Health Study (Nhs) Based Breast Cancer Risk Score on a French Cohort

2006 ◽  
Vol 163 (suppl_11) ◽  
pp. S228-S228
Author(s):  
V Viallon ◽  
F Clavel-Chapelon ◽  
S Ragusa
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Disease Risk ◽  
Health Study ◽  
Prediction Tool ◽  
Tool Evaluation ◽  
French Cohort

scholarly journals The Discovery of Breast Cancer Risk Gene and Establishment of Prediction Model Based on Estrogen Metabolism Regulation

2020 ◽  
Author(s):  
Feng Zhao ◽  
Zhixiang Hao ◽  
Yanan Zhong ◽  
Yinxue Xu ◽  
Meng Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Healthy Subjects ◽  
Estrogen Metabolism ◽  
Polygenic Risk Score ◽  
Polygenic Risk

Abstract Background In this study, we aim to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes with breast cancer (BC) and establish a risk prediction model based on polygenic risk score. Methods Unrelated BC patients and healthy subjects were recruited for analysis of the estrogen levels and the single nucleotide polymorphisms (SNPs) of estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes which was calculated using a Bayesian approach. The independent sample t test was used to evaluate the difference between PRS scores of BC and healthy subjects. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (ROC). Results The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulated in the serum of BC patients. Then,we established PRS model to evaluate the role of multiple genes SNPs. The PRS model 1 (M1) was established from 6 GWAS-identified high risk genes SNPs. On the basis of M1, we added 7 estrogen metabolism enzyme genes SNPs to establish PRS model 2 (M2). The independent sample t test results show that there is no difference between BC and healthy subjects in M1 (P = 0.17), however, there is significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results also show that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than M1 (AUC = 54.56%). Conclusion Estrogens and the related metabolic enzymes gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme genes SNPs has a good ability in breast cancer risk prediction, and the accuracy is greatly improved comparing PRS model only includes GWAS-identified genes SNPs.

Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction

Radiology ◽  
2020 ◽  
Vol 294 (2) ◽  
pp. 265-272 ◽  
Author(s):  
Karin Dembrower ◽  
Yue Liu ◽  
Hossein Azizpour ◽  
Martin Eklund ◽  
Kevin Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Mammographic Density ◽  
Risk Score ◽  
Density Score

scholarly journals Breast cancer risk prediction using a clinical risk model and polygenic risk score

2016 ◽  
Vol 159 (3) ◽  
pp. 513-525 ◽  
Author(s):  
Yiwey Shieh ◽  
Donglei Hu ◽  
Lin Ma ◽  
Scott Huntsman ◽  
Charlotte C. Gard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Risk Model ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

scholarly journals Is Mammographic Breast Density an Endophenotype for Breast Cancer?

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3916
Author(s):  
Ellie Darcey ◽  
Nina McCarthy ◽  
Eric K. Moses ◽  
Christobel Saunders ◽  
Gemma Cadby ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Density ◽  
Disease Risk ◽  
Health Study ◽  
Mammographic Breast Density ◽  
Dense Area ◽  
Cross Sectional ◽  
Meta Analyses

Mammographic breast density (MBD) is a strong and highly heritable predictor of breast cancer risk and a biomarker for the disease. This study systematically assesses MBD as an endophenotype for breast cancer—a quantitative trait that is heritable and genetically correlated with disease risk. Using data from the family-based kConFab Study and the 1994/1995 cross-sectional Busselton Health Study, participants were divided into three status groups—cases, relatives of cases and controls. Participant’s mammograms were used to measure absolute dense area (DA) and percentage dense area (PDA). To address each endophenotype criterion, linear mixed models and heritability analysis were conducted. Both measures of MBD were significantly associated with breast cancer risk in two independent samples. These measures were also highly heritable. Meta-analyses of both studies showed that MBD measures were higher in cases compared to relatives (β = 0.48, 95% CI = 0.10, 0.86 and β = 0.41, 95% CI = 0.06, 0.78 for DA and PDA, respectively) and in relatives compared to controls (β = 0.16, 95% CI = −0.24, 0.56 and β = 0.16, 95% CI = −0.21, 0.53 for DA and PDA, respectively). This study formally demonstrates, for the first time, that MBD is an endophenotype for breast cancer.

scholarly journals A New Breast Cancer Risk Prediction Tool for French Women

2006 ◽  
Vol 163 (suppl_11) ◽  
pp. S72-S72
Author(s):  
V Viallon ◽  
F Clavel-Chapelon ◽  
S Ragusa
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Tool

scholarly journals Analysis of Italian BRCA1/2 Pathogenic Variants Identifies a Private Spectrum in the Population from the Bergamo Province in Northern Italy

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 532
Author(s):  
Gisella Figlioli ◽  
Arcangela De Nicolo ◽  
Irene Catucci ◽  
Siranoush Manoukian ◽  
Bernard Peissel ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Disease Risk ◽  
Northern Italy ◽  
Genetic Isolate ◽  
National Population ◽  
Pathogenic Variants ◽  
High Breast Cancer Risk

Germline pathogenic variants (PVs) in the BRCA1 or BRCA2 genes cause high breast cancer risk. Recurrent or founder PVs have been described worldwide including some in the Bergamo province in Northern Italy. The aim of this study was to compare the BRCA1/2 PV spectra of the Bergamo and of the general Italian populations. We retrospectively identified at five Italian centers 1019 BRCA1/2 PVs carrier individuals affected with breast cancer and representative of the heterogeneous national population. Each individual was assigned to the Bergamo or non-Bergamo cohort based on self-reported birthplace. Our data indicate that the Bergamo BRCA1/2 PV spectrum shows less heterogeneity with fewer different variants and an average higher frequency compared to that of the rest of Italy. Consistently, four PVs explained about 60% of all carriers. The majority of the Bergamo PVs originated locally with only two PVs clearly imported. The Bergamo BRCA1/2 PV spectrum appears to be private. Hence, the Bergamo population would be ideal to study the disease risk associated with local PVs in breast cancer and other disease-causing genes. Finally, our data suggest that the Bergamo population is a genetic isolate and further analyses are warranted to prove this notion.

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