Dataset Information

Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones.

ABSTRACT: BACKGROUND:Models that predict the risk of estrogen receptor (ER)-positive breast cancers may improve our ability to target chemoprevention. We investigated the contributions of sex hormones to the discrimination of the Breast Cancer Surveillance Consortium (BCSC) risk model and a polygenic risk score comprised of 83 single nucleotide polymorphisms. METHODS:We conducted a nested case-control study of 110 women with ER-positive breast cancers and 214 matched controls within a mammography screening cohort. Participants were postmenopausal and not on hormonal therapy. The associations of estradiol, estrone, testosterone, and sex hormone binding globulin with ER-positive breast cancer were evaluated using conditional logistic regression. We assessed the individual and combined discrimination of estradiol, the BCSC risk score, and polygenic risk score using the area under the receiver operating characteristic curve (AUROC). RESULTS:Of the sex hormones assessed, estradiol (OR 3.64, 95% CI 1.64-8.06 for top vs bottom quartile), and to a lesser degree estrone, was most strongly associated with ER-positive breast cancer in unadjusted analysis. The BCSC risk score (OR 1.32, 95% CI 1.00-1.75 per 1% increase) and polygenic risk score (OR 1.58, 95% CI 1.06-2.36 per standard deviation) were also associated with ER-positive cancers. A model containing the BCSC risk score, polygenic risk score, and estradiol levels showed good discrimination for ER-positive cancers (AUROC 0.72, 95% CI 0.65-0.79), representing a significant improvement over the BCSC risk score (AUROC 0.58, 95% CI 0.50-0.65). CONCLUSION:Adding estradiol and a polygenic risk score to a clinical risk model improves discrimination for postmenopausal ER-positive breast cancers.

SUBMITTER: Shieh Y

PROVIDER: S-EPMC5669824 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones.

Shieh Yiwey Y Hu Donglei D Ma Lin L Huntsman Scott S Gard Charlotte C CC Leung Jessica W T JWT Tice Jeffrey A JA Ziv Elad E Kerlikowske Karla K Cummings Steven R SR

Breast cancer research and treatment 20170808 2

<h4>Background</h4>Models that predict the risk of estrogen receptor (ER)-positive breast cancers may improve our ability to target chemoprevention. We investigated the contributions of sex hormones to the discrimination of the Breast Cancer Surveillance Consortium (BCSC) risk model and a polygenic risk score comprised of 83 single nucleotide polymorphisms.<h4>Methods</h4>We conducted a nested case-control study of 110 women with ER-positive breast cancers and 214 matched controls within a mammo ...[more]

PMID: 28791495

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Project description:Background:Polygenic risk scores (PRS) for breast cancer can be used to stratify the population into groups at substantially different levels of risk. Combining PRS and environmental risk factors will improve risk prediction; however, integrating PRS into risk prediction models requires evaluation of their joint association with known environmental risk factors. Methods:Analyses were based on data from 20 studies; datasets analysed ranged from 3453 to 23 104 invasive breast cancer cases and similar numbers of controls, depending on the analysed environmental risk factor. We evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS with reproductive history, alcohol consumption, menopausal hormone therapy (MHT), height and body mass index (BMI). We tested the null hypothesis of multiplicative joint associations for PRS and each of the environmental factors, and performed global and tail-based goodness-of-fit tests in logistic regression models. The outcomes were breast cancer overall and by estrogen receptor (ER) status. Results:The strongest evidence for a non-multiplicative joint associations with the 77-SNP PRS was for alcohol consumption (P-interaction?=?0.009), adult height (P-interaction?=?0.025) and current use of combined MHT (P-interaction?=?0.038) in ER-positive disease. Risk associations for these factors by percentiles of PRS did not follow a clear dose-response. In addition, global and tail-based goodness of fit tests showed little evidence for departures from a multiplicative risk model, with alcohol consumption showing the strongest evidence for ER-positive disease (P?=?0.013 for global and 0.18 for tail-based tests). Conclusions:The combined effects of the 77-SNP PRS and environmental risk factors for breast cancer are generally well described by a multiplicative model. Larger studies are required to confirm possible departures from the multiplicative model for individual risk factors, and assess models specific for ER-negative disease.

Project description:BackgroundThis study aimed to investigate whether an immunohistochemical prognostic model (IHC4 score) can predict the prognosis and the chemotherapy benefit in patients with estrogen receptor-positive (ER+)/human epidermal growth receptor 2-negative (HER2-) metastatic breast cancer (MBC).Materials and methodsWe developed a method to calculate the modified IHC4 (mIHC4) scores based on routine pathological reports and compared them with the original IHC4 scores that were much more difficult to calculate. Univariate and multivariate analyses were used to study the prognostic factors of progression-free survival (PFS) and overall survival (OS). The predictive value of mIHC4 score was also investigated.ResultsThe Sun Yat-sen Memorial Hospital data set included 315 patients with newly diagnosed ER+ MBC with a median follow-up of 25.6 months. Univariate and multivariate analysis showed that higher mIHC4 scores in metastatic lesions, but not the ones in primary tumors, were significantly associated with worse PFS and OS. The prognostic value of mIHC4 scores for PFS was validated using an independent Chinese Society of Clinical Oncology- Breast Cancer (CSCO-BC) data set. More importantly, subpopulation treatment effect pattern plot analysis showed that first-line endocrine therapy achieved better PFS and OS than chemotherapy in low-risk patients with ER+/HER2- MBC, whereas first-line chemotherapy was associated with improved PFS and OS compared with endocrine therapy in high-risk ones. The predictive value of mIHC4 score for PFS in selecting first-line endocrine therapy versus chemotherapy was also confirmed in the CSCO-BC data set.ConclusionmIHC4 scores in metastatic lesions are prognostic for the PFS and OS in patients with ER+ MBC. Low or high mIHC4 score may indicate the survival benefit in choosing first-line endocrine therapy or chemotherapy in patients with ER+/HER2- MBC, respectively.Implications for practiceThe modified IHC4 (mIHC4) score is easy to implement and able to predict patients with advanced and/or metastatic breast cancer. In addition, with the help of the mIHC4 score, physicians might be able to recommend chemotherapy or endocrine therapy as the first-line treatment for patients with high and low risk as predicted by the mIHC4 score.

Dataset Information

Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones.

Publications

Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones.

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