The Nexus of Forces in Action – Use-Case 14: Translational Research – Bench to Bedside



Provide ability to quickly apply translational research at the bench-side to the patients on the bed as personalized care.

Primary Industry Sectors


Business Value

Reducing the cost of translational research data and knowledge management, enabling non-statisticians to perform exploratory analyses, facilitating cross-study analyses, disease treatment, life-saving.

Key Business Functions

Research, treatment, data analysis (genomic, proteomic, and transcriptomic)

Primary Actors

Clinical researcher

Secondary Actors

Bio-informatician, bio-statistician

Machine Actors

Medical history, patient statistical analysis, medical research analytics, mobile devices to input and display information at bedside, variety of medical devices connected to the Internet

Key Technologies

Big data analysis, multi-channel data collection and processing, high-performance computing potentially on cloud, statistical analysis, IoT

Main Scenarios

Clinical researchers conduct disease (cancer) research, which is referred to as bench-side, while treating the patients on the bedside. Their study of molecular diagnostics involves studying the genomic and proteomic expression patterns to distinguish between the normal, pre-disease, and post-disease tissue or blood samples at the molecular level.

Molecular diagnostics involves processing of large amounts of data. Such data is collected, analyzed, and stored for each patient. The clinical researchers combine the data belonging to multiple cancer patients and derive the expression patterns of interest. This analysis process involves selecting cohorts of patients based on their clinical and/or measured molecular characteristics and running different kinds of statistical analysis algorithms on this data.

Bio-informaticians and bio-statisticians help these clinical researchers in data analysis. Initially they may take the datasets and conduct data analysis. Over a period of time this research will evolve into algorithms that can be re-used on the fly when the clinical researchers want to process data.

In the future, an oncologist can take blood and a biopsy specimen, run the tests to identify the genetic expression patterns, and identify the type of disease. Also the patient’s expression pattern, combined with the history of analyses available from several previous cancer patients, can point to the type of chemotherapy that the patient would respond to. During the treatment the protein expression patterns may be used to make sure that the treatment is effectively disrupting the targeted cellular/gene pathway in the tumor. All this can happen very quickly due to the processing power and the intelligence of the IT systems that process and store the data.

Ultimately, the clinicians will be interested in the easiest ways to apply the results of their analysis and research (at bench) to the patient (at the bedside). The suggested treatments from such a platform will be patient-specific, enabling personalized treatment with minimum side effects.

See the article by Anna Laura Van der Laan and Marianne Boenink: Beyond Bench and Bedside – Disentangling the Concept of Translational Research in Springer.

Key Data

Master Data

Patients' clinical and disease data (genomic, molecular, proteomic, transcriptomic analysis, etc.)

Current Observations Data

Current clinical data, pathology data, and other genomic and molecular analysis of a single patient

Historical Data

Disease data collected over a period of time for thousands of patients in the past, treatments given in the past, responders, and non-responders

Query Data

Cohort building, identifying the target set of patient information

Action Taken Data

Responders and non-responders

Real Business Examples


U-BIOPRED (Unbiased BIOmarkers in PREDiction of respiratory disease outcomes) is a research project using information and samples from adults and children to learn more about different types of asthma to ensure better diagnosis and treatment for each person. The project is collecting a rich set of study participant characterization data: clinical, patient reported outcomes, imaging, and “omics” (proteomics, lipidomics, trancriptomics, breathomics, physiomics, genomics) with a plan to integrate the data into a sub-phenotyping “handprint of severe asthma”. (See the U-BIOPRED website.)

The project set up a European Translation Research and Knowledge Management Services (eTRIKS) platform and established a process that enabled:

  • Data integration
  • Curation support
  • Organization of the analysis process
  • Data repository
  • Relation of collected data to other studies

Cancer Centers

There is relevant work at:

  • The University of Delaware Translation Cancer Research Center
  • The Leonard and Madlyn Abrams Family Cancer Research Institute (AFCRI)
  • The US National Cancer Institute (NCI). The Translational Research Program (TRP) is the home of the Specialized Programs of Research Excellence (SPOREs) – a cornerstone of NCI’s efforts to promote collaborative, inter-disciplinary translational cancer research. SPORE grants involve both basic and clinical/applied scientists and support projects that will result in new and diverse approaches to the prevention, early detection, diagnosis, and treatment of human cancers. (See the NCI TRP website).

Additional Considerations

Existing Interoperability Standards

The Clinical Data Interchange Standards Consortium (CDISC) is an open, multi-disciplinary, neutral, non-profit Standards Development Prganization (SDO) that has been working through productive, consensus-based collaborative teams, since its formation in 1997, to develop global standards and innovations to streamline medical research and ensure a link with healthcare.

The UK Medical Research Council lists some data standards for sharing research data. (See the UK Medical Research Council website.)

The US Food and Drug Administration (FDA) lists data standards and terminology standards for information submitted to the Center for Devices and Radiological Health. (See the FDA website.)

Comments on Context

Ethical policies of data collection of patient records and conditions.


Availability of patient data for analysis and research.