title: Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers.
abstract: Relationships between some risk factors and breast cancer incidence are known to vary by tumor subtype. However, breast tumors can be classified according to a number of markers, which may be correlated, making it difficult to identify heterogeneity of risk factors with specific tumor markers when using standard competing-risk survival analysis. In this paper, we propose a constrained competing-risk survival model that allows for assessment of heterogeneity of risk factor associations according to specific tumor markers while controlling for other markers. These methods are applied to Nurses' Health Study data from 1980-2006, during which 3,398 incident invasive breast cancers occurred over 1.4 million person-years of follow-up. Results suggested that when estrogen receptor (ER) and progesterone receptor (PR) status are mutually considered, some risk factors thought to be characteristic of "estrogen-positive tumors," such as high body mass index during postmenopause and increased height, are actually significantly associated with PR-positive tumors but not ER-positive tumors, while other risk factors thought to be characteristic of "estrogen-negative tumors," such as late age at first birth, are actually significantly associated with PR-negative rather than ER-negative breast cancer. This approach provides a strategy for evaluating heterogeneity of risk factor associations by tumor marker levels while controlling for additional tumor markers.
authors: Channing Division of Network Medicine, Brigham andWomen’s Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA. stbar@channing.harvard.edu
authors: Bernard
authors: B
authors: Rosner
authors: Robert J
authors: RJ
authors: Glynn
authors: Rulla M
authors: RM
authors: Tamimi
authors: Wendy Y
authors: WY
authors: Chen
authors: Graham A
authors: GA
authors: Colditz
authors: Walter C
authors: WC
authors: Willett
authors: Susan E
authors: SE
authors: Hankinson
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000209:etiology* / Q000378:metabolism; D003661:Decision Support Techniques; D005260:Female; D005500:Follow-Up Studies; D006306:Health Surveys; D006801:Humans; D008875:Middle Aged; D016016:Proportional Hazards Models; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism*; D011980:Receptors, Progesterone / Q000378:metabolism*; D018570:Risk Assessment; D012307:Risk Factors; D011795:Surveys and Questionnaires
chemical_list: D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; C508053:ERBB2 protein, human
keywords: breast cancer; competing risks; proportional hazards model
references: Cancer Lett. 2004 Dec 8;216(1):115-21
references: 15500955
references: Breast Cancer Res Treat. 2008 May;109(1):123-39
references: 17578664
references: J Biol Chem. 1978 Apr 10;253(7):2223-8
references: 632265
references: Breast Cancer Res Treat. 2012 Jan;131(1):159-67
references: 21830014
references: Am J Epidemiol. 2000 Nov 15;152(10):950-64
references: 11092437
references: Nat Rev Cancer. 2005 May;5(5):388-96
references: 15864280
references: Breast Cancer Res. 2006;8(4):R43
references: 16859501
references: Am J Epidemiol. 2005 Nov 15;162(10):975-82
references: 16207808
references: Biometrics. 1995 Jun;51(2):524-32
references: 7662841
references: J Natl Cancer Inst. 2004 Feb 4;96(3):218-28
references: 14759989
references: J Natl Cancer Inst. 2011 Feb 2;103(3):250-63
references: 21191117
references: Biometrika. 2010 Sep;97(3):683-698
references: 22822252