Metabolomics: Granularizing Diagnostics
Article Sep 11, 2017 | Angelo DePalma, Ph.D.
Precision or personalized medicine seeks to provide the right drug to the right patient at the right time. Hence the significance of the principal omics: disciplines of genomics, proteomics, and last but not least metabolomics, as diagnostic enablers.
Primacy among the ‘omics is debatable, but the notion that metabolomics reflects the most accurate picture of disease states has reached significant momentum. “Almost every factor affecting health exerts its influence by altering metabolite levels,” says Mike Milburn, Ph.D., Chief Scientific Officer at Metabolon (Morrisville, North Carolina, USA).
Where clinical chemistry blood tests typically quantify individual species for example, glucose or cholesterol, metabolomics measures hundreds or even thousands of metabolites to provide a nuanced view of disease states.
Metabolon employs standard liquid chromatography-mass spectrometry (LC-MS) for metabolomic studies. Its proprietary informatics and processing platform, Precision MetabolomicsTM, overcomes the “big data” challenge, a natural consequence of measuring hundreds or thousands of small-molecule entities with widely differing concentrations in a single sample. Precision Metabolomics enables “n of 1” studies -- meaningful clinical trials on a single patient, Milburn adds:
Diagnostic metabolomics resembles other medical testing, where results are compared against readings from healthy individuals or a reference population. Many metabolites serve that purpose but none on its own is sufficiently specific or diagnostic for a diagnosis -- otherwise it would comprise a standalone test. Hence the reliance on metabolite panels or networks, which together may provide a clearer view of disease states than any single diagnostic molecule.
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Complementing genetic profiling
Calculating statistical relevance for an assembly of metabolites takes serious computing power, particularly given the huge concentration dynamic range of metabolomic elements. Mapping metabolites to known metabolic pathways is even harder, but the rewards, such as pinpointing metabolic disruptions that genes or protein levels fail to uncover, are what diagnostic metabolomics is all about.
Metabolomics complements genetic profiling by covering the combined effects of diet and lifestyle, drug treatment, genes, and the microbiome. While genomics reveals potential risk, metabolomics chemically profiles the current state of health irrespective of genes.
Understanding the effects and penetrance of genetic mutations on phenotypes is a major goal of genomics. Penetrance, is the proportion of individuals carrying a specific gene variant who also express that gene’s associated trait, or phenotype -- in this context clinical symptoms. “Since metabolomics measures a molecular phenotype, it addresses the issue of the phenotypic impact of gene variance in genes that also affect metabolism,” Milburn explains.
In collaboration with Baylor College of Medicine and the Houston Health Sciences Center, researchers at Metabolon published an 80-subject study that combined whole exome sequencing with global metabolomics data to assess the clinical significance of genetic mutations. Investigators showed that metabolomics was an effective complement to next-generation sequencing for disease risk analysis, disease monitoring, and drug management. Apropos to the current point, data demonstrated that some individuals who possessed a gene variant associated with disease, but normal metabolites could, in fact, be healthy. Conversely, some individuals with mutations considered benign but who were sick had metabolomics profiles indicative of disease.
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Amanda L Souza Manager, Metabolomics Program Manager, CMD Life Science
No single analytic
One of the wonders of metabolomics is the relative lack of constraint regarding analytic modality, sample origin, or data analytics. For example, numares (Regensburg, Germany), a medical diagnostics company, recently announced results from a retrospective study that supports the application of metabolomic biomarker networks to a non-invasive diagnostic for bladder cancer. numares plans to analyze data from a prospective study to validate this hypothesis in late 2018.
numares has acquired 300 retrospective urine samples and is applying artificial intelligence towards identification of metabolomic signals that differentiate bladder cancer patients from healthy individuals.
numares’ analysis platform, proton nuclear magnetic resonance (1H-NMR), is sensitive and handles wide variations in concentration, but has a reputation for inaccessibility due to its high instrument cost and the high level of expertise required to conduct NMR experiments. To further complicate matters, numares conducts NMR on urine, which contains hundreds of compounds and metabolites. NMR’s traditional strength is the analysis of pure compounds.
Yet numares hopes that through engineering, discovery, and advanced informatics it can transform NMR into a suitable diagnostic platform. “We are making NMR accessible to non-experts,” says Dr. Philipp Pagel, Chief Medical Officer. “But to achieve that we need to define characteristics of a complex NMR spectrum that we can plug into an equation that generates a probability that cancer is present.” That number, he says, and not complex spectra, are what physicians will see.
Available urine-based diagnostics for bladder cancer are unreliable in the presence of trace blood, termed microhematuria. Fewer than five percent of patients with microhematuria are diagnosed with bladder cancer, yet many undergo costly, unpleasant cystoscopy (urethral examination) to rule out malignancy. A metabolomic test will complement, not replace current diagnostic protocols, and could obviate the need for many cystocopies.
NMR is by no means the only metabolomic approach to bladder cancer. Early in 2017, scientists from Agilent Technologies (Santa Clara, California, USA) reported on a study based on the microplate-based AbsoluteIDQ p180 kit from Biosocrates Life Sciences (Innsbruck, Austria). The kit is designed as a preparative front end to liquid chromatography or flow injection analysis coupled with mass spectrometry. A paper published last year in Analytical Chemistry demonstrated the inter-lab reliability of AbsoluteIDQ.
GENOMICS HAS MET ITS MATCH – and it’s now more insightful than ever
Genomic research has helped unlock important discoveries and medical advances, but identifying the “key signals” from the massive volume of genetic data produced genotyping, NGS and RNA expression remains challenging.
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Metabolomic analysis: It’s all about data
Since metabolomics is all about correlations and test execution, metabolomics tool companies will always seek out organizations possessing historical patient data and test development know-how.
In July, 2017, SRI International (Menlo Park, California, USA) and Thermo Fisher Scientific (San Jose, California) announced results from a collaboration that combined the capabilities of high-resolution orbitrap LC/MS (from Thermo Fisher) with curated, organism-specific metabolic pathway and genome data (maintained by SRI) to enable metabolomic analysis.
Researchers now have a direct link between the Thermo Fisher’s small-molecule Compound Discoverer 2.1 software and SRI’s BioCyc, a collection of 11,000 databases that electronically references the metabolic pathways and genomes of many organisms. Overlaying these pathways interactively facilitates the biological interpretation of metabolomics experiments.
“A pathway is a linked set of biochemical reactions,” explains Peter Karp, Ph.D., director of the Bioinformatics Research Group at SRI. “When more than one metabolite within a pathway changes there is more confidence that the change is real.” When scientists are unfamiliar with the significance of a given metabolite, studying pathways in which it is involved often clarifies that role.
BioCyc is not tied to Thermo Fisher hardware or instrumentation. Karp and coworkers at SRI have published an introductory paper on its use, and a 2017 update on using BioCyc to discover metabolites that change between normal and diseased states, and to infer biological processes in which those metabolites participate.
Metabolomic Technologies (MT; Edmonton, Alberta) recently inked agreements with Atlantic Diagnostic Laboratories and Evolution Laboratories, both clinical testing companies, to use Metabolomic Technologies’ PolypDx™, a urine-based diagnostic test for adenomatous polyps, a precursor to colorectal cancer.
When tested in a Canadian clinical trial involving 1,000 patients, PolypDx™ was significantly more sensitive than fecal-based screening tests in detecting precancerous growths. Patients who screen positive for polyps are then referred for colonoscopy.
PolypDx™, an LC-MS based test conducted by certified diagnostic laboratories, measures levels of three metabolites (ascorbate, carnitine, and succinate), which when adjusted for age and sex and entered into a predictive algorithm generate the test results.
Its development involved clinical characterization of hundreds of different metabolites in one thousand asymptomatic patients undergoing colonoscopy. Metabolite data was correlated to biopsy results, specifically to clinically confirmed adenomatous polyps.
Metabolomic Technologies is researching alternatives to the analytical platform needed to quantitatively measure these metabolites. This includes a number of immunoassay platforms, as well as some Point of Care (POC) options.
PolypDx is much more sensitive but not as specific as hemoccult tests. David Chang, PhD CEO explains that like any other test PolypDx can be tuned to be greater specificity or sensitivity, which represents a tradeoff between false positives and false negatives. “Our test is still much more sensitive to adenomatous polyps than the hemoccult tests. Hemoccult tests are tuned to be very specific, leading to very few false positives, but they miss many patients with polyps. We seek a good balance, as a population-based screen, to maximize patient outcomes.”
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Molecularly specific subsets of the metabolome, for example the lipidome (fats expressed in cells) are already in clinical use. In June 2017, OWL Metabolomics (Derio, Spain) entered a multi-year collaboration with ProSciento (Chula Vista, California, USA) to apply OWL’s virtual biopsy to diagnose non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). The companies will establish a database of prospective clinical study participants using OWL’s non-invasive, lipidomics-based assays.
OWL’s OWLiver and OWLiver Care products are the first metabolomics-based tests for NASH and NAFLD, respectively. The assays use less than 0.3 ml of blood, whereas conventional confirmatory testing involves invasive liver biopsy.
ProSciento maintains a database of more than 30,000 active study participants enriched for diabetes, obesity, and related metabolic conditions. Its partnership with OWL will help to identify individuals eligible for enrollment in NAFLD/NASH clinical trials through non-invasive blood tests.
OWL’s metabolomic analyses focus on lipids, including fatty acids, acylcarnitines, glycerolipids, glycerophospholipids, sphingophospholipids, cholesterol esters, and bile acids plus amino acids and other polar metabolites. “As no single method analyzes the entire metabolome, we use four different sample preparation methods in which metabolites are fractionated into pools of molecules with similar physiochemical properties,” says Cristina Alonso, Ph.D., Services Manager at OWL.
For OWLiver and OWLiver Care tests, metabolite selection is based on the relationship between NAFLD and circulating triglycerides. Serum concentrations of specific triglycerides also correlates to their levels in the liver, as demonstrated early in 2017 by researchers at OWL scientists and academic collaborators.
Although their principal research focus is liver diseases, OWL is branching its lipidomics approach into dermatology, autophagy and nerve regeneration, and cancer.
The future of metabolomics in diagnostics
The success of diagnostic metabolomics depends on reducing or overcoming its complexity, strengthening relationships between metabolites and target diseases, and convincing insurers to pay for what amounts to an additional layer of testing: in other words, the interplay of science and economics.
That metabolomics will continue to improve and evolve towards its stated objective is a near certainty. Whether the granularity it adds to well-established diagnostic paradigms actually benefits patients and healthcare systems remains an open question.
Clearly this is the case where a simple blood or urine test reduces the need for invasive procedures. But even then the technology must be generally accessible and physicians must trust it. Given the inertia of modern health systems, that is by no means guaranteed.
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