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Rigshospitalet - a part of Copenhagen University Hospital
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A large-cohort, longitudinal study determines pre-cancer disease routes across different cancer types

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  1. MTH1 inhibitor TH1579 induces oxidative DNA damage and mitotic arrest in acute myeloid leukemia

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  2. Association of Genomic Domains in BRCA1 and BRCA2 with Prostate Cancer Risk and Aggressiveness

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  3. Transcriptome-Wide Association Study Identifies New Candidate Susceptibility Genes for Glioma

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  4. Genetic data from nearly 63,000 women of European descent predicts DNA methylation biomarkers and epithelial ovarian cancer risk

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  1. Vaccination Against Herpes Zoster

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  2. Contemporary antiretrovirals and body-mass index: a prospective study of the RESPOND cohort consortium

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  3. Low-dose hydrocortisone in patients with COVID-19 and severe hypoxia: the COVID STEROID randomised, placebo-controlled trial

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Although many diseases are associated with cancer, the full spectrum of temporal disease correlations across cancer types has not yet been characterized. A population-wide study of longitudinal disease trajectories is needed to interrogate the general medical histories of cancer patients. Here we performed a retrospective study covering a 20-year period, using 6.9 million patients from the Danish National Patient Registry linked to 0.7 million cancer patients from the Danish Cancer Registry. Statistical analysis identified all significant disease associations occurring prior to cancer diagnoses. These associations were used to build frequently occurring, longitudinal disease trajectories. Across 17 cancer types, a total of 648 significant diagnoses correlated directly with a cancer, while 168 diagnosis trajectories of time-ordered steps were identified for seven cancer types. The most common diseases across cancer types involved cardiovascular, obesity, and genitourinary diseases. A comprehensive, publicly available web tool of interactive illustrations for all cancer disease associations is provided. By exploring the pre-cancer landscape using this large dataset, we identify disease associations that can be used to derive mechanistic hypotheses for future cancer research.

Original languageEnglish
JournalCancer Research
Volume79
Issue number4
Pages (from-to)864-872
Number of pages9
ISSN0008-5472
DOIs
Publication statusPublished - 15 Feb 2019

Bibliographical note

©2018 American Association for Cancer Research.

ID: 56392918