Dataset Information

Germline Variation and Breast Cancer Incidence: A Gene-Based Association Study and Whole-Genome Prediction of Early-Onset Breast Cancer.

ABSTRACT: Background: Although germline genetics influences breast cancer incidence, published research only explains approximately half of the expected association. Moreover, the accuracy of prediction models remains low. For women who develop breast cancer early, the genetic architecture is less established.Methods: To identify loci associated with early-onset breast cancer, gene-based tests were carried out using exome array data from 3,479 women with breast cancer diagnosed before age 50 and 973 age-matched controls. Replication was undertaken in a population that developed breast cancer at all ages of onset.Results: Three gene regions were associated with breast cancer incidence: FGFR2 (P = 1.23 × 10-5; replication P < 1.00 × 10-6), NEK10 (P = 3.57 × 10-4; replication P < 1.00 × 10-6), and SIVA1 (P = 5.49 × 10-4; replication P < 1.00 × 10-6). Of the 151 gene regions reported in previous literature, 19 (12.5%) showed evidence of association (P < 0.05) with the risk of early-onset breast cancer in the early-onset population. To predict incidence, whole-genome prediction was implemented on a subset of 3,076 participants who were additionally genotyped on a genome wide array. The whole-genome prediction outperformed a polygenic risk score [AUC, 0.636; 95% confidence interval (CI), 0.614-0.659 compared with 0.601; 95% CI, 0.578-0.623], and when combined with known epidemiologic risk factors, the AUC rose to 0.662 (95% CI, 0.640-0.684).Conclusions: This research supports a role for variation within FGFR2 and NEK10 in breast cancer incidence, and suggests SIVA1 as a novel risk locus.Impact: This analysis supports a shared genetic etiology between women with early- and late-onset breast cancer, and suggests whole-genome data can improve risk assessment. Cancer Epidemiol Biomarkers Prev; 27(9); 1057-64. ©2018 AACR.

SUBMITTER: Scannell Bryan M

PROVIDER: S-EPMC6125194 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Germline Variation and Breast Cancer Incidence: A Gene-Based Association Study and Whole-Genome Prediction of Early-Onset Breast Cancer.

Scannell Bryan Molly M Argos Maria M Andrulis Irene L IL Hopper John L JL Chang-Claude Jenny J Malone Kathleen E KE John Esther M EM Gammon Marilie D MD Daly Mary B MB Terry Mary Beth MB Buys Saundra S SS Huo Dezheng D Olopade Olofunmilayo I OI Genkinger Jeanine M JM Whittemore Alice S AS Jasmine Farzana F Kibriya Muhammad G MG Chen Lin S LS Ahsan Habibul H

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20180613 9

<b>Background:</b> Although germline genetics influences breast cancer incidence, published research only explains approximately half of the expected association. Moreover, the accuracy of prediction models remains low. For women who develop breast cancer early, the genetic architecture is less established.<b>Methods:</b> To identify loci associated with early-onset breast cancer, gene-based tests were carried out using exome array data from 3,479 women with breast cancer diagnosed before age 50 ...[more]

PMID: 29898891

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Project description:BACKGROUND The early death of patients is a global cancer issue. We aimed to identify the risk factors for early death in stage IV breast cancer. Predictive nomograms for early death evaluation were generated based on the risk factors. MATERIAL AND METHODS Based on the Surveillance, Epidemiology, and End Results (SEER) database, patients diagnosed with IV breast cancer were selected. The risk factors for early death (survival time ?1 year) were identified using logistic regression model analysis. Predictive nomograms were constructed and internal validation was performed. RESULTS A total of 5998 (32.6%) breast cancer patients were diagnosed as early death in the construction cohort. Age older than 50 years, unmarried status, black race, uninsured status, triple-negative type, grade (II and III), tumor size >5 cm, and metastasis to lung, liver, and brain were risk factors for total early death, while Luminal B subtype, N1 stage, and surgical interventions were associated with lower risk of early death. As for cancer-specific and non-cancer-specific early death, several factors were not consistent between the 2 groups. Nomograms for all-cause, cancer-specific, and non-cancer-specific early death were constructed. The calibration curve showed satisfactory agreement. The areas under the ROC curve (AUC) were 78.3% (95% CI: 77.7-78.9%), 75.8% (75.1-76.4%), and 72.3% (71.6-72.9%), respectively. In the validation cohort, a total of 689 (19.3%) patients were diagnosed as early death and the calibration curve showed satisfactory agreement. The AUCs of the all-cause, cancer-specific, and non-cancer-specific early death prediction were 74.0% (95% CI: 72.5-75.4%), 73.5% (72.0-74.9%), and 68.6% (67.0-70.1%), respectively. CONCLUSIONS Nomograms were generated to predict early death, with good calibration and discrimination. The predictive model can provide a reference for identifying cases with high risk of early death among stage IV breast cancer patients and play an auxiliary role in guiding individual treatment.

Dataset Information

Germline Variation and Breast Cancer Incidence: A Gene-Based Association Study and Whole-Genome Prediction of Early-Onset Breast Cancer.

Publications

Germline Variation and Breast Cancer Incidence: A Gene-Based Association Study and Whole-Genome Prediction of Early-Onset Breast Cancer.

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