Discussion

In this study, we mined cancer-specific comorbidity from large-scale data in the adverse event reporting system. Our approach flexibly detects comorbidity patterns for one or multiple types of cancers based on network analysis.

Comparisons of cancer comorbidities among stratified patient groups show that many comorbidity patterns for cancers depend on patient age and gender.

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The resulting comorbidity relationships of our approach can be applied to detect cancer pathogenesis in the future work. Previous phenotype-based systematic gene prioritization approaches4–6 and genome-wide analyses12,52 usually assume that all patients are equal or only stratify patients by races.

Our results demonstrate the importance of age and gender for cancer comorbidity, and suggest stratifying patients based on these two factors when incorporating cancer comorbidities in phenotype-driven approaches to identify cancer genetic mechanisms.

We currently detect cancer comorbidities based on disease co-occurrence patterns. These co-occurrence patterns may indicate that cancers and their comorbidities increase the risk of each other in a mutual way.

In addition, the comorbidity patterns can be caused not only by common genetic basis between cancers and other diseases, but also by various factors, such as environmental factors, treatment-induced factors, and similar patient lifestyles. Incorporating more comprehensive patient-level data may help refine the disease relationships. 

For example, we may infer whether a drug treating cancers induce their comorbidities with the time series data that describe if the patients develop the diseases before or after taking the drugs.

Our result may be biased toward the diseases whose drugs have high toxicity. FAERS collects data based on the adverse event reports of drug from medical product manufacturers, health professionals, and the public.

Hence, the diseases without drug treatments are not included in the data. In addition, if a drug has high toxicity and causes many adverse events, the disease treated by the drug tends to appear frequently in the database and has a higher chance to co-occur with other diseases. One advantage of the association rule mining approach is that the confidence scores of comorbidity patterns involving frequent diseases were automatically downweighted.

In addition, our study can be enhanced with a method to identify inverse cancer comorbidities, which also provide interesting clues of disease pathogenesis and mechanisms.

Recent studies have used the inverse cancer comorbidity to gain insight into central nervous disorders.28,29 They are based on serendipitous epidemiological evidences of inverse comorbidities. A systematic analysis of all inverse cancer comorbidities may offer invaluable opportunities to understand cancers and other complex diseases.

Conclusions

We mined and analyzed cancer comorbidities through large-scale data mining among millions of patients. Our results show that cancers have comorbidity relationships with various kinds of diseases. Literature evidences demonstrated that comorbidity patterns reflect complex cancer pathophysiology and mechanisms.

Also, cancer comorbidity patterns change with patient ages and genders. The stratified comorbidity patterns based on age and gender may lead to more reliable discoveries in understanding cancer pathogenesis.

Author Contributions

Conceived and designed the experiments: RX. Analyzed the data: YC. Wrote the first draft of the manuscript: YC. Contributed to the writing of the manuscript: RX. Agree with manuscript results and conclusions: YC, RX. Jointly developed the structure and arguments for the paper: YC, RX. Made critical revisions and approved final version: YC, RX. All authors reviewed and approved of the final manuscript.

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Source: Cancer Informatics

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