A Generalized Integration Approach to Association Analysis with Multi-category Outcome: An Application to a Tumor Sequencing Study of Colorectal Cancer and Smoking

Abstract

Cancer is a heterogeneous disease, and rapid progress in sequencing and -omics technologies has enabled researchers to characterize tumors comprehensively. This has stimulated an intensive interest in studying how risk factors are associated with various tumor heterogeneous features. The Cancer Prevention Study-II (CPS-II) cohort is one of the largest prospective studies, particularly valuable for elucidating associations between cancer and risk factors. In this article, we investigate the association of smoking with novel colorectal tumor markers obtained from targeted sequencing. However, due to cost and logistic difficulties, only a limited number of tumors can be assayed, which limits our capability for studying these associations. Meanwhile, there are extensive studies for assessing the association of smoking with overall cancer risk and established colorectal tumor markers. Importantly, such summary information is readily available from the literature. By linking this summary information to parameters of interest with proper constraints, we develop a generalized integration approach for polytomous logistic regression model with outcome characterized by tumor features. The proposed approach gains the efficiency through maximizing the joint likelihood of individual-level tumor data and external summary information under the constraints that narrow the parameter searching space. We apply the proposed method to the CPS-II data and identify the association of smoking with colorectal cancer risk differing by the mutational status of APC and RNF43 genes, neither of which is identified by the conventional analysis of CPS-II individual data only. These results help better understand the role of smoking in the etiology of colorectal cancer. Supplementary materials for this article are available online.

Keywords:

• Case–control study
• Empirical likelihood
• Etiologic heterogeneity
• Polytomous logistic regression
• Statistical efficiency
• Summary information

Supplementary Materials

The web appendices contain various technical details and additional results for simulation studies and data application. The data and R codes to implement the simulation studies and data analysis are provided, with detailed descriptions.

National Institutes of Health;

Disclosure Statement

The authors report there are no competing interests to declare.

Acknowledgments

The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. This study was conducted with Institutional Review Board approval. The authors thank the CPS-II participants and study management group for their invaluable contributions to this research. The authors would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program. The authors are grateful to many insightful discussions with Drs. Ulrike Peters and Peter Campbell. The authors gratefully acknowledge three referees, an associate editor, and the editor for their many valuable comments and suggestions, which have significantly improved the article.

Additional information

Funding

This work is funded by R01 CA 189532 and supported in part by Scientific Computing Infrastructure at Fred Hutch funded by ORIP grant S10OD028685.