Publications by authors named "E Fabianova"
Background: Increased lung-cancer risks for low socioeconomic status (SES) groups are only partially attributable to smoking habits. Little effort has been made to investigate the persistent risks related to low SES by quantification of potential biases.
Methods: Based on 12 case-control studies, including 18 centers of the international SYNERGY project (16,550 cases, 20,147 controls), we estimated controlled direct effects (CDE) of SES on lung cancer via multiple logistic regression, adjusted for age, study center, and smoking habits, and stratified by sex.
Tobacco smoke, alone or combined with alcohol, is the predominant cause of head and neck cancer (HNC). Here, we further explore how tobacco exposure contributes to cancer development by mutational signature analysis of 265 whole-genome sequenced HNC from eight countries. Six tobacco-associated mutational signatures were detected, including some not previously reported.
View Article and Find Full Text PDFLarge-scale biorepositories and databases are essential to generate equitable, effective, and sustainable advances in cancer prevention, early detection, cancer therapy, cancer care, and surveillance. The Mutographs project has created a large genomic dataset and biorepository of over 7,800 cancer cases from 30 countries across five continents with extensive demographic, lifestyle, environmental, and clinical information. Whole-genome sequencing is being finalized for over 4,000 cases, with the primary goal of understanding the causes of cancer at eight anatomic sites.
View Article and Find Full Text PDFArticle Synopsis
- The study assessed how different dimensions of the quantitative job-exposure matrix (SYN-JEM) impact the correlation between silica exposure and lung cancer risk, using data from 16,901 lung cancer cases and 20,965 controls from global studies.
- The analysis revealed that including all dimensions of SYN-JEM resulted in the best fit for predicting lung cancer odds, while omitting job-specific estimates led to a poor model fit.
- The findings suggest that to accurately model the exposure-response relationship between silica and lung cancer, it’s crucial to use all relevant factors, including job specifics, time, and region in the analysis.
Background: While much research has been done to identify individual workplace lung carcinogens, little is known about joint effects on risk when workers are exposed to multiple agents.
Objectives: We investigated the pairwise joint effects of occupational exposures to asbestos, respirable crystalline silica, metals (i.e.