Open AccessTracing Lung Cancer Risk Factors Through Mutational Signatures in Never-Smokers
Author(s) -
Maria Teresa Landi,
Naoise C Synnott,
Jennifer Rosenbaum,
Tongwu Zhang,
Bin Zhu,
Jianxin Shi,
Wei Zhao,
Michael Kebede,
Jian Sang,
Jiyeon Choi,
Laura Mendoza,
Marwil Pacheco,
Belynda Hicks,
Neil E. Caporaso,
Mustapha Abubakar,
Dmitry A. Gordenin,
David C. Wedge,
Ludmil B. Alexandrov,
Nathaniel Rothman,
Qing Lan,
Montserrat García-Closas,
Stephen J. Chanock
Publication year - 2020
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwaa234
Subject(s) - lung cancer , genome , computational biology , dna methylation , medicine , dna sequencing , bioinformatics , biology , genetics , oncology , gene , gene expression
Epidemiologic studies often rely on questionnaire data, exposure measurement tools, and/or biomarkers to identify risk factors and the underlying carcinogenic processes. An emerging and promising complementary approach to investigate cancer etiology is the study of somatic "mutational signatures" that endogenous and exogenous processes imprint on the cellular genome. These signatures can be identified from a complex web of somatic mutations thanks to advances in DNA sequencing technology and analytical algorithms. This approach is at the core of the Sherlock-Lung study (2018-ongoing), a retrospective case-only study of over 2,000 lung cancers in never-smokers (LCINS), using different patterns of mutations observed within LCINS tumors to trace back possible exposures or endogenous processes. Whole genome and transcriptome sequencing, genome-wide methylation, microbiome, and other analyses are integrated with data from histological and radiological imaging, lifestyle, demographic characteristics, environmental and occupational exposures, and medical records to classify LCINS into subtypes that could reveal distinct risk factors. To date, we have received samples and data from 1,370 LCINS cases from 17 study sites worldwide and whole-genome sequencing has been completed on 1,257 samples. Here, we present the Sherlock-Lung study design and analytical strategy, also illustrating some empirical challenges and the potential for this approach in future epidemiologic studies.