Biomarker Discovery and Translation in Metabolomics

Abstract

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Keywords: Biomarkers; NMR spectroscopy; cancer; cardiovascular disease; commercialization; diabetes; diagnostics; inborn errors of metabolism; mass spectrometry; metabolomics; neurological disorder; statistical analysis; translation; validation.

Fig. (1)

The number of metabolomics research studies published over the past 10 years has increased exponentially. The number of papers listed in PubMed using the following keyword searches: (a) “metabolomics”, (b) “metabolomics” and “biomarker” and (c) “metabolomics” and “translational.”

Fig. (2)

The major steps involved in the translation of a biomarker candidate from the lab to the clinic. In actual practice, some steps may be combined, such as Discovery and Pre-Validation, or there may be multiple Validation steps. Typically, the Development step precedes the final validation step, which is then followed by the commercialization process that may take a number of forms and offered on one or more platforms.

Fig. (3)

(a) ROC curve generated from the PLS-DA model based on eleven serum metabolite markers for detection of breast cancer recurrence; the model was cross validated using a leave-one-patient-out procedure. The red circle compares the sensitivity and specificity for the conventional breast cancer marker used for recurrence test. The area under the ROC curve is 0.88. The sensitivity and specificity at two selected cutoff values are shown in Table 1 [Reproduced with permission from ref. 105].

Fig. (4)

Results of the MCCV (200 iterations) shown in ROC space for PLS-DA models based on 3 metabolites used to discriminate hepatocellular carcinoma and hepatitis C patients. Each blue diamond represents an iteration of the true model; each red square represents an iteration of the permutation model [Reproduced with permission from ref. 63].