Mining Cancer-Specific Disease Comorbidities from a Large Observational Health Database

ABSTRACT: Cancer comorbidities often reflect the complex pathogenesis of cancers and provide valuable clues to discover the underlying genetic mechanisms of cancers. In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System.

We stratified 3,354,043 patient based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group.

We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose comorbidity relationships with cancers are relatively stable among all patients. Literature evidences demonstrate that these stable cancer comorbidities reflect the pathogenesis of cancers.

We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis. Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

KEYWORDS: data mining, cancer informatics, systems biology, network analysis, cancer comorbidity, colorectal cancer

SUPPLEMENT: Computational advances in Cancer Informatics (A)

CITATION: Chen and Xu. Mining Cancer-Specific Disease Comorbidities from a Large Observational Health Database. Cancer Informatics 2014:13(s1) 37–44 doi: 10.4137/CIn.s13893.

RECEIVED: March 19, 2014. RESUMBITTED: April 29, 2014. ACCEPTED FOR PUBLICATION: April 30, 2014.

ACADEMIC EDITOR: JT Efird, Editor in Chief

TYPE: Original Research

FUNDING: YC and RX are funded by Case Western Reserve University/Cleveland Clinic CTSA Grant (UL1 RR024989), the training grant in Computational Genomic Epidemiology of Cancer (CoGEC), and the American Cancer Society Pilot Awards.

COMPETING INTERESTS: Authors disclose no potential conflicts of interest.

COPYRIGHT: © the authors, publisher and licensee Libertas Academica Limited. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.

CORRESPONDENCE: [email protected]

This paper was subject to independent, expert peer review by a minimum of two blind peer reviewers. All editorial decisions were made by the independent academic editor. All authors have provided signed confirmation of their compliance with ethical and legal obligations including (but not limited to) use of any copyrighted material, compliance with ICMJE authorship and competing interests disclosure guidelines and, where applicable, compliance with legal and ethical guidelines on human and animal research participants. Provenance: the authors were invited to submit this paper.

Introduction

Disease phenotype relationship often reflects overlapping pathogenesis,^1–3 thus has been used to predict genetic origins of diseases^4–7 and discover drug treatments.^8,9

Disease comorbidity is an important aspect of disease phenotype. The comorbidity patterns often lead to unexpected disease links¹⁰ and offer novel insights to explain genetic mechanisms for diseases.^11,12

Specifically, the comorbidity patterns of cancers have impacts on cancer prognosis,^13,14 treatment decisions,¹⁵ and cancer mechanism understanding. A few recent researches probed the underlying genetic factors to explain the cooccurrence between cancer and autoimmune diseases,^16,17 metabolic diseases,¹⁸ and inflammatory diseases.¹⁹

The common genetic factors between cancer and comorbidity have also been applied to develop cancer treatments.²⁰

In this study, our goal is systematical mining and analyzing cancer-specific comorbidities. Systematic comorbidity studies have been conducted previously, but not focusing on cancer comorbidities. Rzhetsky et al developed a statistical model to analyze a database of hospital medical records. They identified co-occurrence relationships among 160 diseases and emphasized on psychiatry disorders.²¹

Park et al and Hidalgo et al detected comorbidity patterns from the Medicare claims with statistical measures. Their study focused on elderly patients aged 65 years or older.^22,23 Roque et al mined disease correla­tions from the free text in electrical medical records of a psychiatric hospital.²⁴

Different from existing works, we extracted comorbidity patterns specifically for cancers without restricting ages and genders of the patients. We also investigated the effects of age and gender on cancer comorbidity patterns.

We extracted cancer-specific comorbidities from the FDA Adverse Event Reporting System (FAERS) with a data mining approach. The FAERS database contains records of 3,354,043 patients (male and female at all age levels), 1,138 cancers of different types and stages, and 8,974 non-cancer health problems.

These data offer rich resources for the network-based analysis of cancer comorbidity patterns among diverse patient populations. FAERS has been extensively mined for detecting post-market drug safety signals, but its use in mining disease comorbidity patterns has not been explored.

We first investigated the demographics of patients and demonstrated that the data are valuable for comorbidity mining. Then, we stratified the patients based on age and gender, and developed an automatic approach to extract comorbidity patterns from each patient group.

Different from previous studies, which used statistical approaches to calculate pairwise disease commodity measures, we applied association rule learning to mine comorbidity patterns among multiple diseases. Comparing the comorbidity patterns among different patient groups, we were able to extract population-specific cancer comorbidities and investigate the effect of age and gender on comorbid relationships.