individual biomarkers, the complexity of data sharing with other scientists, and the high degree of hope placed in the promise of omics-enabled technologies and medical care.

Omics-based tests, and indeed all clinical laboratory tests, are subject to a different regulatory framework than drugs. Specifically, there are more pathways for regulation of in vitro diagnostic test devices—the category under which omics-based tests fall—than there are for drugs. Tests can be developed, validated, and placed into clinical use either through review by the Food and Drug Administration (FDA) or through validation and performance in a specific laboratory, also called laboratory-developed tests (LDTs). Any clinical laboratory that reports tests for clinical management of patients falls under the purview of the Clinical Laboratory Improvement Act (a CLIA-certified clinical laboratory) that provides a baseline level of oversight with respect to test development and the quality of laboratory operations. While FDA has the authority for regulatory oversight of all tests used in patient care, it has not defined a regulatory framework that includes oversight of LDTs and has only reviewed LDT tests that it has determined to be of high complexity and therefore high risk to patients. This alternate LDT pathway is not possible for drug development, and all drugs must be approved by FDA. It is precisely this LDT pathway that allows academic medical centers to move omics-based tests from discovery to clinical use without external regulatory review of the new test, and places a new and mostly unrecognized demand on academic institutions to provide proper oversight for omics-based test development, validation, and clinical implementation. While pharmaceutical and medical device companies follow well-established medical product development pathways and have many process controls in place for strong oversight of development, clinical validation, and manufacturing, academic institutions are not as accustomed to overseeing the development of medical products.

The frequent lack of a clear biological rationale further distinguishes omics-based tests from most other clinical laboratory tests based on a single analyte. The biological rationale behind a single-analyte test is frequently quite evident: The test is useful because the gene, RNA, protein, or metabolite plays an understood role in the disease pathology or other biological process under investigation. Examples of single-analyte tests include human epidermal growth factor receptor 2 (HER2) testing of breast cancers or measuring low-density lipoprotein (LDL) cholesterol level in the blood for cardiac risk assessment. In contrast, the biological rationale for the set of biomarkers in an omics-based test frequently is not well-defined scientifically. This difference puts an additional burden on the statisticians and bioinformatics experts involved in test validation to ensure that the biological data and computational model are scientifically sound. Because of the increased risk of overfitting large datasets in the development of the

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