scholarly journals Working with Human Tissues for Translational Cancer Research

10.3791/53189 ◽  
2015 ◽  
Author(s):  
Alexandre Reuben ◽  
Vancheswaran Gopalakrishnan ◽  
Heidi E. Wagner ◽  
Christine N. Spencer ◽  
Jacob Austin-Breneman ◽  
...  
Keyword(s):  
Cancer Research ◽  
Human Tissues ◽  
Translational Cancer Research

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

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.

scholarly journals Stem Cells in Translational Cancer Research

2015 ◽  
Vol 2015 ◽  
pp. 1-2
Author(s):  
Oswaldo Keith Okamoto ◽  
Ander Matheu ◽  
Luca Magnani
Keyword(s):  
Stem Cells ◽  
Cancer Research ◽  
Translational Cancer Research

scholarly journals A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens

2015 ◽  
Vol 75 (24) ◽  
pp. 5194-5201 ◽  
Author(s):  
Rebecca S. Jacobson ◽  
Michael J. Becich ◽  
Roni J. Bollag ◽  
Girish Chavan ◽  
Julia Corrigan ◽  
...  
Keyword(s):  
Cancer Research ◽  
Clinical Data ◽  
Translational Cancer Research

scholarly journals Mission‐oriented translational cancer research – health economics

2019 ◽  
Vol 13 (3) ◽  
pp. 636-647 ◽  
Author(s):  
Bengt Jönsson ◽  
Richard Sullivan
Keyword(s):  
Cancer Research ◽  
Health Economics ◽  
Translational Cancer Research

scholarly journals Proteomics and Mass Spectrometry for Cancer Biomarker Discovery

2007 ◽  
Vol 2 ◽  
pp. 117727190700200 ◽  
Author(s):  
Ming Lu ◽  
Kym F. Faull ◽  
Julian P. Whitelegge ◽  
Jianbo He ◽  
Dejun Shen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Cancer Research ◽  
Disease Management ◽  
Early Detection Of Cancer ◽  
Biomarker Discovery ◽  
Protein Biomarkers ◽  
Bioinformatic Tools ◽  
Translational Cancer Research ◽  
Basic Concepts ◽  
Detection And Diagnosis

Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management.

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