System Biology-Based Prognostic Biomarkers of Clinical Complications in Acutely Injured Patients

In the September 13^th issue of PLoS One (link), John D. Storey and colleagues report on inflammation-related gene expression signatures associated with differential clinical outcome in acute trauma patients.  Specifically, the authors analyzed the expression of inflammation-related genes in 168 blunt-force trauma patients over a 28-day period.  The genes and gene pathways that clustered differently between patients’ clinical outcome subgroups (based on Marshall multiple organ failure clinical score) were assembled into predictive modules of clinical outcomes.  Of particularly interest, the down-regulation of MHC II expression within 48 hours of trauma and up-regulation of p38-MAPK within 100 hours of trauma were particularly robust independent predictors of negative clinical outcome in this patient sample.

Considering that up to 60% of late trauma mortality is caused by infections, sepsis, and multiple organ failure multiple organ, the management of these inflammation-related complications remains a major unmet medical need.  In particular, the ability to predict the individual patient clinical trajectory early during trauma treatment remains a significant challenge for the medical community.  Therefore, the prospect of using gene expression as a prognostic biomarker to manage the care of trauma patients is of particular significance.

Thierry Sornasse for Integrated Biomarker Strategy

Brain Imaging Biomarker of Pain: I see how you feel

Our perception of biomarkers tends to be limited to the realm of measures that provide information about disease and drug activity.  In fact, biomarkers can provide a means to assess additional biological processes relevant to patient well being such as anxiety and pain.  In paper published in the September 13^th issue of PLoS One (link), a team of the Department of Anesthesia, Stanford University describes a new functional MRI-based (fMRI) biomarker for the identification of pain.  Because the sensation of pain can be subjective and can occur in the absence of detectable injury, the standard for assessing pain is based on patient self report.  While this traditional measure is readily assessable, it does not differentiate between the sensory and the psychological components of pain perception.  In addition, patient self reported pain assessment is impossible in individuals who are not able to communicate.  Therefore, development of an objective biomarker of pain is of great interest to the medical community. 

The team at Stanford performed a pilot study involving 24 individuals who were monitored by fMRI while being subjected to painful and non-painful thermal stimuli.  Using the results from the first 8 volunteers, the team used Support Vector Machine learning to develop a predictive model that then validated on the remaining 16 individual volunteers.  In this setting, the model accurately identified the type of stimulus with 81 % accuracy.

While the size of this study is not sufficient to draw definitive conclusions, it is tempting to speculate that the future of pain management in patients who are unable to communicate may improve dramatically

Thierry Sornasse for Integrated Biomarker Strategy

Prognosis of conversion from MCI to AD: of verbal memory, brain volume, and CSF biomarkers

In the September 2011 issue of the Archives of General Psychiatry (reference), Dr. Goldberg and colleagues report the results of the first study that examined the respective predictive values of cognitive measures, brain imaging, and cerebrospinal fluid (CSF) biomarkers in determining the risk of conversion from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD).

In contrast with the multiple recent publications derived from the Alzheimer’s Disease Neuroimaging Initiative about biomarkers in AD (ADNI; see earlier post), this work identified measures of delayed verbal memory (Logical Memory delayed recall and Auditory Verbal Learning Test delayed recall) as the most reliable predictors of progression from MCI to AD.  While brain volume assessed by MRI (Left middle temporal lobe thickness) was identified as an additional predictive factor, the levels of Ab42 and Tau in the CSF did not add significant predictive value to their model (systematic stepwise logistic regression).

In commentary provided to Medscape (link), the lead author urged caution in interpreting this finding by stating that “Biomarkers unarguably work. However, cognitive markers, which are less expensive and less invasive, also work and provide strong complementary information”.

In my mind, the question is not so much whether cognitive assessment tools work better than CSF biomarkers but more about the applicability of these findings to the general practice of medicine.  Indeed, while CSF biomarkers are objective measures, the results of even the best cognitive tests are partially subjective: the skills of the person administering the test can have an influence on the results.  Therefore, one can wonder if, in the hands of the average neurologist or neuropsychiatrist, the verbal memory testing would perform as well and would outperform the objective measure provided by CSF biomarkers.

Thierry Sornasse for Integrated Biomarker Strategy

FDA Pharmacogenomic Biomarkers in Drug Labels

The list of pharmacogenomic biomarkers included the labels of FDA approved drugs has grown substantially over the last 10 years.  The most recent update from the FDA (Table of Pharmacogenomic Biomarkers in Drug Labels; 08/25/2011) lists 109 pharmacogenomic biomarkers included in the labels of 97 drugs (the labels of some drugs such as Imatinib and Warfarin include more than one pharmacogenomic biomarkers).

From a regulatory perspective, these biomarkers can be included in different sections of the drug labels (e.g. box warning, contraindication, clinical pharmacology), informing the prescribing physicians and the patients about identification of responders / non-responders, avoiding adverse events, and optimizing drug dosage.  The label information about pharmacogenomic biomarker can describe:

• Drug exposure and clinical response variability
• Risk for adverse events
• Genotype-specific dosing
• Mechanisms of drug action
• Polymorphic drug target and disposition genes

Functionally, the majority of the pharmacogenomic biomarkers currently included in the label of approved drug fall into the category of safety and efficacy markers related to drug exposure due to altered drug metabolism.  Indeed, 60 of the 109 pharmacogenomic biomarkers belong to the liver cytochrome P450 enzymes (CYP) which play a critical role in drug metabolism.  Other functional variants of enzymes involved in drug metabolism such as dihydropyrimidine dehydrogenase (DPD) and thiopurine S-methyltransferase (TPMT) also fall into this category. 

Although still representing a minority of cases, the number of drug efficacy pharmacogenomic biomarkers included in cancer drug labels has been growing (i.e. response biomarkers, predictive biomarkers).  In general, these biomarkers are designed to assist in the prescription decision by testing for the presence of the drug target. 

Examples:

• Imatinib: C-kit, BCR-Abl, PDGFR
• Trastuzumab: Her2/neu
• Vemurafenib: BRAF
• Tositumomab: CD20

As the field of biomarker development in support of drug development evolves, it is expected that this list of pharmacogenomic biomarkers included in drug labels will grow substantially, making the promise of personalized medicine a reality.

Thierry Sornasse for Integrated Biomarker Strategy

Diagnostic On-the-Go: Cell phone, microchip, ELISA, and ovarian cancer

In the September 1^st issue of Lab on a Chip, Wang and colleagues report a proof-of-concept for an easily deployable, point-of-care diagnostic system for the detection of the ovarian cancer HE4 biomarker (reference).  The team combined a simple microchip-based ELISA platform with the imaging capability of modern portable phones.  Interestingly, the performances (sensitivity and specificity) of the portable phone camera appeared to be superior to a stand-alone CCD camera.

Although this work may seem anecdotal at first glance, it constitutes a valuable step towards increased diagnostic accessibility through the translation of a standard “high-tech” laboratory method to a “low-tech” broadly deployable platform.

Thierry Sornasse for Integrated Biomarker Strategy

Low Cost Blood Protein Detection System: Of Aptamers, Gold, and Resonance

In the September 1^st issue of Biomedical Optics Express (reference), Zheng and colleagues present a proof of concept study for a novel type of biosensor for the detection of proteins in blood.  Briefly, the team immobilized amine-terminated aptamers – artificial oligonucleotides engineered to bind specific ligands – onto a gold modified surface and used Surface Plasmon Resonance (SPR) to detect the binding of the ligand; in this case thrombin.  This prototype sensor showed good performances (sensitivity, linearity, and reversibility) for the intended ligand (thrombin), in the presence or absence of high levels (400 nM) of BSA, suggesting that this technology could be applied to direct detection of reasonably abundant factors in blood.

Considering the relative inexpensive nature of the manufacturing process of this new biosensor and the relative simplicity of SPR detection, it is tempting to speculate that this technology could solve the issue of cost for current and new blood diagnostics.  Time will tell if the reported performance of this prototype biosensor will be reproduced for other blood proteins.

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