Impact of integrated translational research on clinical exome sequencing.

PURPOSE: Exome sequencing often identifies pathogenic genetic variants in patients with undiagnosed diseases. Nevertheless, frequent findings of variants of uncertain significance necessitate additional efforts to establish causality before reaching a conclusive diagnosis. To provide comprehensive genomic testing to patients with undiagnosed disease, we established an Individualized Medicine Clinic, which offered clinical exome testing and included a Translational Omics Program (TOP) that provided variant curation, research activities, or research exome sequencing. METHODS: From 2012 to 2018, 1101 unselected patients with undiagnosed diseases received exome testing. Outcomes were reviewed to assess impact of the TOP and patient characteristics on diagnostic rates through descriptive and multivariate analyses. RESULTS: The overall diagnostic yield was 24.9% (274 of 1101 patients), with 174 (15.8% of 1101) diagnosed on the basis of clinical exome sequencing alone. Four hundred twenty-three patients with nondiagnostic or without access to clinical exome sequencing were evaluated by the TOP, with 100 (9% of 1101) patients receiving a diagnosis, accounting for 36.5% of the diagnostic yield. The identification of a genetic diagnosis was influenced by the age at time of testing and the disease phenotype of the patient. CONCLUSION: Integration of translational research activities into clinical practice of a tertiary medical center can significantly increase the diagnostic yield of patients with undiagnosed disease.

Experience with precision genomics and tumor board, indicates frequent target identification, but barriers to delivery.

BACKGROUND: The ability to analyze the genomics of malignancies has opened up new possibilities for off-label targeted therapy in cancers that are refractory to standard therapy. At Mayo Clinic these efforts are organized through the Center for Individualized Medicine (CIM). RESULTS: Prior to GTB, datasets were analyzed and integrated by a team of bioinformaticians and cancer biologists. Therapeutically actionable mutations were identified in 65% (92/141) of the patients tested with 32% (29/92) receiving genomically targeted therapy with FDA approved drugs or in an independent clinical trial with 45% (13/29) responding. Standard of care (SOC) options were continued by 15% (14/92) of patients tested before exhausting SOC options, with 71% (10/14) responding to treatment. Over 35% (34/92) of patients with actionable targets were not treated with 65% (22/34) choosing comfort measures or passing away. MATERIALS AND METHODS: Patients (N = 165) were referred to the CIM Clinic between October 2012 and December 2015. All patients received clinical genomic panel testing with selected subsets receiving array comparative genomic hybridization and clinical whole exome sequencing to complement and validate panel findings. A genomic tumor board (GTB) reviewed results and, when possible, developed treatment recommendations. CONCLUSIONS: Treatment decisions driven by tumor genomic analysis can lead to significant clinical benefit in a minority of patients. The success of genomically driven therapy depends both on access to drugs and robustness of bioinformatics analysis. While novel clinical trial designs are increasing the utility of genomic testing, robust data sharing of outcomes is needed to optimize clinical benefit for all patients.

Implementing individualized medicine into the medical practice.

There is increasing recognition that genomic medicine as part of individualized medicine has a defined role in patient care. Rapid advances in technology and decreasing cost combine to bring genomic medicine closer to the clinical practice. There is also growing evidence that genomic-based medicine can advance patient outcomes, tailor therapy and decrease side effects. However the challenges to integrate genomics into the workflow involved in patient care remain vast, stalling assimilation of genomic medicine into mainstream medical practice. In this review we describe the approach taken by one institution to further individualize medicine by offering, executing and interpreting whole exome sequencing on a clinical basis through an enterprise-wide, standalone individualized medicine clinic. We present our experience designing and executing such an individualized medicine clinic, sharing lessons learned and describing early implementation outcomes.

The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data.

Mayo Clinic's Enterprise Data Trust is a collection of data from patient care, education, research, and administrative transactional systems, organized to support information retrieval, business intelligence, and high-level decision making. Structurally it is a top-down, subject-oriented, integrated, time-variant, and non-volatile collection of data in support of Mayo Clinic's analytic and decision-making processes. It is an interconnected piece of Mayo Clinic's Enterprise Information Management initiative, which also includes Data Governance, Enterprise Data Modeling, the Enterprise Vocabulary System, and Metadata Management. These resources enable unprecedented organization of enterprise information about patient, genomic, and research data. While facile access for cohort definition or aggregate retrieval is supported, a high level of security, retrieval audit, and user authentication ensures privacy, confidentiality, and respect for the trust imparted by our patients for the respectful use of information about their conditions.