Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies.

Journal: Genetic epidemiology

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Affiliated Institutions:  Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia. Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia. ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia. Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia. Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, South Korea. Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia. Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.

Abstract summary 

Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.

Authors & Co-authors:  Hopper Li MacInnis Dowty Nguyen Bui Dite Esser Ye Makalic Schmidt Goudey Alpen Kapuscinski Win Dugué Milne Jayasekara Brooks Malta Calais-Ferreira Campbell Young Nguyen-Dumont Sung Giles Buchanan Winship Terry Southey Jenkins

Study Outcome 

Source Link: Visit source

Statistics
Citations :  Acheampong, T., Kehm, R. D., Terry, M. B., Argov, E. L., & Tehranifar, P. (2020). Incidence trends of breast cancer molecular subtypes by age and race/ethnicity in the US from 2010 to 2016. JAMA Network Open, 3(8), e2013226. https://doi.org/10.1001/jamanetworkopen.2020.13226
Authors :  31
Identifiers
Doi : 10.1002/gepi.22555
SSN : 1098-2272
Study Population
Male,Female
Mesh Terms
Other Terms
DEPTH;ICE CRISTAL;ICE FALCON;VALID;breast cancer;causation;colorectal cancer;familial confounding;family data;statistical methods;twin studies
Study Design
Study Approach
Country of Study
Publication Country
United States