Magnetic Resonance Spectroscopy to Monitor Tumor Metabolism: Not a Biomarker Yet But a Promising Concept

In the October 12^th issue of PLos One, Alessia Lodi and Sabrina Ronen from UCSF published the results of their work on the use of Magnetic Resonance Spectroscopy (MRS) to monitor the metabolic activity of tumor cells (link).  Although this work was entirely conducted in vitro on cell lines, the concept presented in this paper offer a glimpse at a possible new approach to monitor drug effect early during treatment.  Indeed, there is ample evidence that anti-cancer drugs alter the metabolic profile of cancer cells before producing detectable effects on tumor size (detectable by CT scan or MRI) or even overall metabolic activity (detectable by FDG-PET).

The utility of MRS to monitor early metabolic changes induced by drugs in cancer cells has been demonstrated before.  However, these earlier studies focused on single metabolites which limited their observations to the specific drug – cell combination studied.  In this work, the authors expanded on earlier work MRS use for the monitoring of cancer cell metabolism by used an unbiased 1H MRS-based metabolomics approach to investigate the overall metabolic consequences of treatment with the phosphoinositide 3-kinase inhibitor LY294002 and the heat shock protein 90 inhibitor 17AAG in prostate and breast cancer cell lines.

Obviously, translating this concept to human patients, in which complexity will be several orders of magnitude greater, will not be easy but one can speculate that as MRS technology further progresses, tracking multiple metabolites in vivo will become trivial. 


Thierry Sornasse for Integrated Biomarker Strategy

Catching Metabolic Pathways in the Act: Navigating the Heavy Water World

A press release on September 16^th on Market Watch about KineMed caught my attention (link).  KineMed, based in Emeryville CA, has developed new proteomics and metabolomics tools that enable the monitoring of metabolic flux through complex biological pathways by exploiting the power of deuterated water (or heavy water: ²H₂O) labeling.  By monitoring the kinetic of predictable mass shift of molecules of interest by mass spectrometry, the scientists at KineMed have been able to ascertain complex dynamic processes such as blood clotting, complement cascade activation, epidermal turnover in psoriasis patients, anterograde neuronal transport in ALS and PD patients, and DNA turnover rate in leukemia and breast cancer (see a video presentation by Marc K. Hellerstein, M.D., Ph.D.; co-founder of KineMed)

Because of its non-radioactive nature and ease of deployment (deuterated water is simply administered as a glass of water), this technique offers the prospect of identifying new biomarkers related to disease processes, drug mechanism of action, and drug toxicities.  It is important to remember though that this technique does not allow for in situ metabolism monitoring and thus still requires sample collection.  Therefore, the usual limitations associated with the collection of biosamples do apply to this new technique.

Thierry Sornasse for Integrated Biomarker Strategy

Pairing GWAS with in-depth metabolomics: assigning functions to genetic variants

In the September 1^st issue of Nature (reference), scientists from the Helmholtz Zentrum Munchen Institute in Munich, Germany, the Wellcome Trust/Sanger Centre, King’s College, and Metabolon, Inc. present the most comprehensive Genome Wide Association Study (GWAS) aimed at identifying relationship between individual genetic variations and specific metabolic pathways.  Using ultra-high performance LC-MS and GC-MS, the levels of over 250 metabolites, representing over 60 metabolic pathways, were analyzed in serum samples from volunteers enrolled in the German KORA F4 study (n= 1768) and in the British TwinsUK study (n= 1052).  From these measures, over 37,000 metabolic traits (concentrations or ratios of metabolite pairs) were derived and their association with about 600,000 SNPs was assessed.  The team identified 37 independent genetic loci with genome-wide significant associations with metabolic traits, 23 of which represented novel associations.  Moreover, among these 37 genetic loci, 15 overlapped with known disease-associated genetic loci, shedding new light on possible new pathobiological mechanisms of diseases such as diabetes, kidney failure, venous thromboembolism, and coronary artery disease. 

This remarkable work represents a major evolution in the field of GWAS by providing a means to place genetic information within a functional biological context.  Indeed, despite identifying thousands of disease risk loci, most GWAS are cataloging exercises offering little or no information about the biological processes potentially associated with the identified genetic variants.  

Thierry Sornasse for Integrated Biomarker Strategy