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Large-scale systematic analysis of exposure to multiple cancer risk factors and the associations between exposure patterns and cancer incidence.

ABSTRACT: Exposures to cancer risk factors such as smoking and alcohol are not mutually independent. We aimed to identify risk factor exposure patterns and their associations with sociodemographic characteristics and cancer incidence. We considered 120,771 female and, separately, 100,891 male participants of the Australian prospective cohort 45 and Up Study. Factor analysis grouped 36 self-reported variables into 8 combined factors each for females (largely representing 'smoking', 'alcohol', 'vigorous exercise', 'age at childbirth', 'Menopausal Hormone Therapy', 'parity and breastfeeding', 'standing/sitting', 'fruit and vegetables') and males (largely representing 'smoking', 'alcohol', 'vigorous exercise', 'urology and health', 'moderate exercise', 'standing/sitting', 'fruit and vegetables', 'meat and BMI'). Associations with cancer incidence were investigated using multivariable logistic regression (4-8 years follow-up: 6193 females, 8749 males diagnosed with cancer). After multiple-testing correction, we identified 10 associations between combined factors and cancer incidence for females and 6 for males, of which 14 represent well-known relationships (e.g. bowel cancer: females 'smoking' factor Odds Ratio (OR) 1.16 (95% Confidence Interval (CI) 1.08-1.25), males 'smoking' factor OR 1.15 (95% CI 1.07-1.23)), providing evidence for the validity of this approach. The catalogue of associations between exposure patterns, sociodemographic characteristics, and cancer incidence can help inform design of future studies and targeted prevention programmes.

SUBMITTER: Steinberg J

PROVIDER: S-EPMC7841154 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Large-scale systematic analysis of exposure to multiple cancer risk factors and the associations between exposure patterns and cancer incidence.

Steinberg Julia J Yap Sarsha S Goldsbury David D Nair-Shalliker Visalini V Banks Emily E Canfell Karen K O'Connell Dianne L DL

Scientific reports 20210127 1

Exposures to cancer risk factors such as smoking and alcohol are not mutually independent. We aimed to identify risk factor exposure patterns and their associations with sociodemographic characteristics and cancer incidence. We considered 120,771 female and, separately, 100,891 male participants of the Australian prospective cohort 45 and Up Study. Factor analysis grouped 36 self-reported variables into 8 combined factors each for females (largely representing 'smoking', 'alcohol', 'vigorous exe ...[more]

PMID: 33504831

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Project description:BACKGROUND: In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family.Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics. RESULTS: Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics.Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (BKD), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data. CONCLUSION: Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from http://cdb.ics.uci.edu/.

Dataset Information

Large-scale systematic analysis of exposure to multiple cancer risk factors and the associations between exposure patterns and cancer incidence.

Publications

Large-scale systematic analysis of exposure to multiple cancer risk factors and the associations between exposure patterns and cancer incidence.

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