• Trust is the core underlying issue for concerns regarding consent, data privacy and security, accountability, and data ownership. Consumers and patients want their data to be protected, and they want medicine and health care to be advanced.

In this session, four cancer use cases were presented as examples of successful informatics-supported approaches to managing large, complex datasets. Panelists discussed data collection, storage, and retrieval; data analysis and reporting; and data sharing.


Spyro Mousses, vice president in the Office of Innovation and director of the Center for BioIntelligence at the Translational Genomics Research Institute (TGen), described a clinical trial using molecularly guided individualized therapy in pediatric cancer as a case example of successful alignment of biomedical science and informatics. To begin, Mousses described what he called “the evolution from evidence-based medicine to information-enabled medicine to intelligence-based medicine” and TGen’s “N = 1” approach to drug development (Figure 4-1).

An N = 1 Approach to Clinical Research

When seeking to develop a drug for a deadly disease such as cancer, investigators generally start with a broad target population from which they select a representative study cohort for a clinical trial comparing one therapeutic option against another. Evidence-based decisions on treatment are informed by statistical outcomes of the trial. For example, if the data indicate a 30 percent response rate to therapeutic option 1 and a 20 percent response rate to option 2, then therapeutic option 1 would be the drug of choice to be developed for all patients in the original target population (Figure 4-1A). Such a statistical approach is not ideal when dealing with a disease or condition that is clinically heterogeneous and molecularly complex, Mousses said.

Moving beyond basic evidence-based medicine, information-enabled

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