A basis for translational cancer research on aetiology, pathogenesis and prognosis: Guideline for standardised and population-based linkages of biobanks to cancer registries

In the field of oncology, a close integration of cancer research and patient care is indispensable. Although an exchange of data between health care providers and other institutions such as cancer registries has already been established in Germany, it does not take advantage of internationally coordinated health data standards. Translational cancer research would also benefit from such standards in the context of secondary data use. This paper employs use cases from the German Cancer Consortium (DKTK) to show how this gap can be closed using a harmonised FHIR-based data model, and how to apply it to an existing federated data platform.

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A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

International Journal of Molecular Sciences ◽

10.3390/ijms22062822 ◽

2021 ◽

Vol 22 (6) ◽

pp. 2822

Author(s):

Efstathios Iason Vlachavas ◽

Jonas Bohn ◽

Frank Ückert ◽

Sylvia Nürnberg

Keyword(s):

Cancer Research ◽

Clinical Information ◽

Disease Diagnosis ◽

Molecular Data ◽

Molecular Networks ◽

Biological Knowledge ◽

Omics Data ◽

Translational Cancer Research ◽

Using Data ◽

Biological Entities

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

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Incidence and Trends of the Leading Cancers with Elevated Incidence Among American Indian and Alaska Native Populations, 2012–2016

American Journal of Epidemiology ◽

10.1093/aje/kwaa222 ◽

2020 ◽

Author(s):

Stephanie C Melkonian ◽

Hannah K Weir ◽

Melissa A Jim ◽

Bailey Preikschat ◽

Donald Haverkamp ◽

...

Keyword(s):

American Indian ◽

Cancer Incidence ◽

Alaska Native ◽

Cancer Registries ◽

Population Based ◽

Incidence Rates ◽

Breast Cancers ◽

Age Adjusted Rates ◽

Incident Cases ◽

Hispanic White

Abstract Cancer incidence varies among American Indian and Alaska Native (AI/AN) populations, as well as between AI/AN and White populations. This study examined trends for cancers with elevated incidence among AI/AN compared with non-Hispanic White populations and estimated potentially avoidable incident cases among AI/AN populations. Incident cases diagnosed during 2012–2016 were identified from population-based cancer registries and linked with the Indian Health Service patient registration databases to improve racial classification of AI/AN populations. Age-adjusted rates (per 100,000) and trends were calculated for cancers with elevated incidence among AI/AN compared with non-Hispanic White populations (rate ratio >1.0), by region. Trends were estimated using joinpoint regression analyses. Expected cancers were estimated by applying age-specific cancer incidence rates among non-Hispanic White populations to population estimates for AI/AN populations. Excess cancer cases among AI/AN populations were defined as observed minus expected cases. Liver, stomach, kidney, lung, colorectal and female breast cancers had higher incidence rate among AI/AN populations across most regions. Between 2012 and 2016, nearly 5,200 excess cancers were diagnosed among AI/AN populations, with the largest number of excess cancers (1,925) occurring in the Southern Plains region. Culturally informed efforts may reduce cancer disparities associated with these and other cancers among AI/AN populations.

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