title: Mathematical Modeling Links Pregnancy-Associated Changes and Breast Cancer Risk.
abstract: Recent debate has concentrated on the contribution of bad luck to cancer development. The tight correlation between the number of tissue-specific stem cell divisions and cancer risk of the same tissue suggests that bad luck has an important role to play in tumor development, but the full extent of this contribution remains an open question. Improved understanding of the interplay between extrinsic and intrinsic factors at the molecular level is one promising route to identifying the limits on extrinsic control of tumor initiation, which is highly relevant to cancer prevention. Here, we use a simple mathematical model to show that recent data on the variation in numbers of breast epithelial cells with progenitor features due to pregnancy are sufficient to explain the known protective effect of full-term pregnancy in early adulthood for estrogen receptor-positive (ER+) breast cancer later in life. Our work provides a mechanism for this previously ill-understood effect and illuminates the complex influence of extrinsic factors at the molecular level in breast cancer. These findings represent an important contribution to the ongoing research into the role of bad luck in human tumorigenesis.Cancer Res; 77(11); 2800-9. ©2017 AACR.
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.|Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, United Kingdom.|Department of Computer Science, University College London, London, United Kingdom.|Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.
authors.forename: Daniel
authors.initials: D
authors.lastname: Temko
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
authors.forename: Yu-Kang
authors.initials: YK
authors.lastname: Cheng
authors.affiliation: Department of Medical Oncology, Dana-Farber Cancer Institute, and Department of Medicine, Harvard Medical School, Boston, Massachusetts. michor@jimmy.harvard.edu kornelia_polyak@dfci.harvard.edu.
authors.forename: Kornelia
authors.initials: K
authors.lastname: Polyak
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts. michor@jimmy.harvard.edu kornelia_polyak@dfci.harvard.edu.
authors.forename: Franziska
authors.initials: F
authors.lastname: Michor
mesh_terms: D001943:Breast Neoplasms / Q000209:etiology* / Q000473:pathology; D005260:Female; D006801:Humans; D008962:Models, Theoretical; D011247:Pregnancy; D011252:Pregnancy Complications, Neoplastic / Q000209:etiology*; D012306:Risk