crcTRP: A Translational Research Platform for Colorectal Cancer

Colorectal cancer is a leading cause of cancer mortality in both developed and developing countries. Transforming basic research results into clinical practice is one of the key tasks of translational research, which will greatly improve the diagnosis and treatments of colorectal cancer. In this paper, a translational research platform for colorectal cancer, named crcTRP, is introduced. crcTRP serves the colorectal cancer translational research by providing various types of biomedical information related with colorectal cancer to the community. The information, including clinical data, epidemiology data, individual omics data, and public omics data, was collected through a multisource biomedical information collection solution and then integrated in a clinic-omics database, which was constructed with EAV-ER model for flexibility and efficiency. A preliminary exploration of conducting translational research on crcTRP was implemented and worked out a set of clinic-genomic relations, linking clinical data with genomic data. These relations have also been applied to crcTRP to make it more conductive for cancer translational research.

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A clinical omics database integrating epidemiology, clinical, and omics data for colorectal cancer translational research

2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) ◽

10.1109/bmei.2011.6098677 ◽

2011 ◽

Author(s):

Ling Zheng ◽

Li Wang ◽

Da Wang ◽

Ning Deng ◽

Xudong Lu ◽

...

Keyword(s):

Colorectal Cancer ◽

Translational Research ◽

Omics Data

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The Clinical, Prognostic and Predictive Value of CD44 and Its Isoforms in Colorectal Cancer: A Systematic Analysis of Clinical Data

SSRN Electronic Journal ◽

10.2139/ssrn.3228856 ◽

2018 ◽

Author(s):

Tao Huang ◽

Susu Han ◽

Xiyu Wang ◽

Xing Wu ◽

Shanshan Liu ◽

...

Keyword(s):

Colorectal Cancer ◽

Clinical Data ◽

Predictive Value ◽

Systematic Analysis

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Genomic Data Management in Big Data Environments: The Colorectal Cancer Case

Lecture Notes in Computer Science - Advances in Conceptual Modeling ◽

10.1007/978-3-030-01391-2_36 ◽

2018 ◽

pp. 319-329 ◽

Cited By ~ 1

Author(s):

Ana León Palacio ◽

Alicia García Giménez ◽

Juan Carlos Casamayor Ródenas ◽

José Fabián Reyes Román

Keyword(s):

Colorectal Cancer ◽

Big Data ◽

Data Management ◽

Genomic Data ◽

Cancer Case ◽

Colorectal Cancer Case

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PINSPlus: a tool for tumor subtype discovery in integrated genomic data

Bioinformatics ◽

10.1093/bioinformatics/bty1049 ◽

2018 ◽

Vol 35 (16) ◽

pp. 2843-2846 ◽

Cited By ~ 15

Author(s):

Hung Nguyen ◽

Sangam Shrestha ◽

Sorin Draghici ◽

Tin Nguyen

Keyword(s):

Personal Computer ◽

Genomic Data ◽

Supplementary Information ◽

Omics Data ◽

Tumor Subtype ◽

Supplementary Data ◽

Significant Survival ◽

Survival Differences

Abstract Summary Since cancer is a heterogeneous disease, tumor subtyping is crucial for improved treatment and prognosis. We have developed a subtype discovery tool, called PINSPlus, that is: (i) robust against noise and unstable quantitative assays, (ii) able to integrate multiple types of omics data in a single analysis and (iii) dramatically superior to established approaches in identifying known subtypes and novel subgroups with significant survival differences. Our validation on 12,158 samples from 44 datasets shows that PINSPlus vastly outperforms other approaches. The software is easy-to-use and can partition hundreds of patients in a few minutes on a personal computer. Availability and implementation The package is available at https://cran.r-project.org/package=PINSPlus. Data and R script used in this manuscript are available at https://bioinformatics.cse.unr.edu/software/PINSPlus/. Supplementary information Supplementary data are available at Bioinformatics online.

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Molecular genetics of hereditary nonpolyposis colorectal cancer; basic research with clinical applications

Cancer Genetics and Cytogenetics ◽

10.1016/s0165-4608(97)82503-4 ◽

1996 ◽

Vol 91 (2) ◽

pp. 107

Author(s):

L.A. Aaltonen

Keyword(s):

Colorectal Cancer ◽

Molecular Genetics ◽

Hereditary Nonpolyposis Colorectal Cancer ◽

Basic Research ◽

Clinical Applications ◽

Hereditary Nonpolyposis

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Connecting the Bench with the Bedside

Systemic Approaches in Bioinformatics and Computational Systems Biology - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-61350-435-2.ch011 ◽

2011 ◽

pp. 249-267

Author(s):

Yue Wang Webster ◽

Ernst R Dow ◽

Mathew J Palakal

Keyword(s):

Complex System ◽

Colorectal Cancer ◽

Conceptual Framework ◽

Translational Research ◽

Dynamic Process ◽

Hybrid Approach ◽

Disease Area ◽

Cancer Disease ◽

Clinical Observations ◽

Hypotheses Generation

Even though numerous tools and technologies have been developed to meet this need with various degrees of success, a conceptual framework is needed to fully realize the value of those tools and technologies. The authors propose Complex System (CS) to be the logical foundation of such a framework. Since translational research is a spiral and dynamic process. With the CS mindset, they designed a multi-layer architecture called HyGen (Hypotheses Generation Framework) to address the challenges faced by translational researchers. In order to evaluate the framework, the authors carried out heuristic and quantitative tests in Colorectal Cancer disease area. The results demonstrate the potential of this hybrid approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases, which may eventually lead to the discovery of novel disease targets, biomarkers and therapies.

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Integration of Clinical and Genomic Data for Decision Support in Cancer

Encyclopedia of Healthcare Information Systems ◽

10.4018/978-1-59904-889-5.ch097 ◽

2008 ◽

pp. 768-776

Author(s):

Yorgos Goletsis ◽

Themis P. Exarchos ◽

Nikolaos Giannakeas ◽

Markos G. Tsipouras ◽

Dimitrios I. Fotiadis

Keyword(s):

Decision Support ◽

Molecular Biology ◽

Early Diagnosis ◽

Clinical Data ◽

Support System ◽

Genomic Data ◽

Biological Information ◽

Tumour Suppressor Genes ◽

Suppressor Genes ◽

Cancer Diseases

In this article, we address decision support for cancer by exploiting clinical data and identifying mutations on tumour suppressor genes. The goal is to perform data integration between medicine and molecular biology by developing a framework where clinical and genomic features are appropriately combined in order to handle cancer diseases. The constitution of such a decision support system is based on (a) cancer clinical data and (b) biological information that is derived from genomic sources. Through this integration, real time conclusions can be drawn for early diagnosis, staging and more effective cancer treatment.

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Translational research in agriculture. Can we do it better?

Crop and Pasture Science ◽

10.1071/cp20066 ◽

2020 ◽

Vol 71 (6) ◽

pp. 517 ◽

Cited By ~ 3

Author(s):

John B. Passioura

Keyword(s):

Abiotic Stress ◽

Translational Research ◽

Carbon Balance ◽

Basic Research ◽

Peer Review System ◽

Review System ◽

Important Goal ◽

Common Term ◽

Use Efficiency ◽

Effective Translation

‘Translational research’ became an increasingly common term when it was realised that much agriculturally inspired basic research failed to contribute to the improvement of crops. Most of the failure has come from laboratory-based attempts to ameliorate abiotic stresses. Dealing with biotic stress has been much more successful; the control of pests and weeds is often enabled by transforming crops with single genes, for such genes have little or no influence on a crop’s metabolism. By contrast, abiotic stress varies with the weather; i.e. crops respond systemically, over a range of levels of organisation (e.g. cells, tissues, organs), with many feedbacks and feedforwards. Drought is the most pervasive form of abiotic stress. There are 4600 papers that have searched, ineffectively, for ‘drought resistance’, a term that usually defies useful definition. By contrast, dealing with a measured, limited water supply (e.g. seasonal rainfall), rather than with ‘drought’, has effectively increased water-limited yield through agronomic innovation based on improving water-use efficiency. ‘Salt tolerance’ has similar difficulties; nevertheless, physiological knowledge has revealed effective single genes, in contrast to the failures of empirical gene prospecting. Another important goal has been to increase potential crop yield by exploring mechanistic opportunities to improve photosynthetic efficiency. These attempts have not, so far, succeeded, perhaps because they have rarely broached physiological responses beyond carbon balance, such as metabolic responses to environmental challenges that may affect meristematic development. A major reason for the predominant failure of translational research from laboratory to field is that the peer-review system is too narrow; i.e. reviewers have the same backgrounds as the authors. Effective translation will require the addition of reviewers who can assess the pathway from laboratory to field.

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