Faculty, Staff and Student Publications
Publication Date
8-1-2023
Journal
Journal of Thoracic Oncology
Abstract
INTRODUCTION: Mosaic chromosomal alterations (mCAs) detected in white blood cells represent a type of clonal hematopoiesis (CH) that is understudied compared with CH-related somatic mutations. A few recent studies indicated their potential link with nonhematological cancers, especially lung cancer.
METHODS: In this study, we investigated the association between mCAs and lung cancer using the high-density genotyping data from the OncoArray study of INTEGRAL-ILCCO, the largest single genetic study of lung cancer with 18,221 lung cancer cases and 14,825 cancer-free controls.
RESULTS: We identified a comprehensive list of autosomal mCAs, ChrX mCAs, and mosaic ChrY (mChrY) losses from these samples. Autosomal mCAs were detected in 4.3% of subjects, in addition to ChrX mCAs in 3.6% of females and mChrY losses in 9.6% of males. Multivariable logistic regression analysis indicated that the presence of autosomal mCAs in white blood cells was associated with an increased lung cancer risk after adjusting for key confounding factors, including age, sex, smoking status, and race. This association was mainly driven by a specific type of mCAs: copy-neutral loss of heterozygosity on autosomal chromosomes. The association between autosome copy-neutral loss of heterozygosity and increased risk of lung cancer was further confirmed in two major histologic subtypes, lung adenocarcinoma and squamous cell carcinoma. In addition, we observed a significant increase of ChrX mCAs and mChrY losses in smokers compared with nonsmokers and racial differences in certain types of mCA events.
CONCLUSIONS: Our study established a link between mCAs in white blood cells and increased risk of lung cancer.
Keywords
Male, Female, Humans, Lung Neoplasms, Chromosome Aberrations, Carcinoma, Squamous Cell, Cohort Studies, Smoking, Mosaic chromosomal alterations, Clonal hematopoiesis, Lung cancer risk
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons
Comments
Associated Data
PMID: 37150255