Transcriptional response to GAA deficiency (Pompe disease) in infantile-onset patients

Pompe disease is a genetic disorder resulting from a deficiency of lysosomal acid alpha-glucosidase (GAA) that manifests as a clinical spectrum with regard to symptom severity and rate of progression. In this study, we used microarrays to examine gene expression from the muscle of two cohorts of infantile-onset Pompe patients to identify transcriptional differences that may contribute to the disease phenotype. We found strong similarities among the gene expression profiles generated from biceps and quadriceps, and identified a number of signaling pathways altered in both cohorts. We also found that infantile-onset Pompe patient muscle had a gene expression pattern characteristic of immature or regenerating muscle, and exhibited many transcriptional markers of inflammation, despite having few overt signs of inflammatory infiltrate. Further, we identified genes exhibiting correlation between expression at baseline and response to therapy. This combined dataset can serve as a foundation for biological discovery and biomarker development to improve the treatment of Pompe disease. (C) 2012 Elsevier Inc. All rights reserved.

Salivary Cortisol, Alpha-Amylase and Heart Rate Variation in Response to Dental Treatment in Children

Objective. Anxiety and stress are usually related to the dental treatment situation. The objective was to investigate salivary cortisol and alpha-amylase levels (salivary biomarkers) and heart rate in children undergoing a minor dental procedure (dental prophylaxis). Study design. In total, 31 children (range 84-95 months) of both genders without caries or history of dental treatment/pain/trauma were selected. Three saliva samples were gathered: one prior to dental prophylaxis, one immediately after, and one ten minutes later Weight and height were assessed, and heart rate was evaluated prior to and during the procedure. Data were analyzed by correlation tests and t-test/Wikoxon (alpha=0.05). Results. Higher cortisol and amylase levels were observed before prophylaxis compared to afterward. Cortisol and amylase levels did not show a significant correlation, nor did salivary biomarkers and body mass index. However, heart rate and amylase levels showed a significant positive correlation. Conclusions. In the studied sample, certain anticipation of the dental treatment was observed because higher cortisol and amylase levels were observed before, rather than after, the event; moreover, a significant correlation between amylase levels and heart rate was observed. Thus, salivary biomarkers may be a valuable tool for evaluating anxiety-producing events, such as dental treatment, in children.

Predicting and monitoring cancer treatment response with diffusion-weighted MRI

An imaging biomarker that would provide for an early quantitative metric of clinical treatment response in cancer patients would provide for a paradigm shift in cancer care. Currently, nonimage based clinical outcome metrics include morphology, clinical, and laboratory parameters, however, these are obtained relatively late following treatment. Diffusion-weighted MRI (DW-MRI) holds promise for use as a cancer treatment response biomarker as it is sensitive to macromolecular and microstructural changes which can occur at the cellular level earlier than anatomical changes during therapy. Studies have shown that successful treatment of many tumor types can be detected using DW-MRI as an early increase in the apparent diffusion coefficient (ADC) values. Additionally, low pretreatment ADC values of various tumors are often predictive of better outcome. These capabilities, once validated, could provide for an important opportunity to individualize therapy thereby minimizing unnecessary systemic toxicity associated with ineffective therapies with the additional advantage of improving overall patient health care and associated costs. In this report, we provide a brief technical overview of DW-MRI acquisition protocols, quantitative image analysis approaches and review studies which have implemented DW-MRI for the purpose of early prediction of cancer treatment response.

Reverse-translational biomarker validation of Abnormal Repetitive Behaviors in mice: an illustration of the 4P's modeling approach

The NIMH's new strategic plan, with its emphasis on the "4P's" (Prediction, Pre-emption, Personalization, and Populations) and biomarker-based medicine requires a radical shift in animal modeling methodology. In particular 4P's models will be non-determinant (i.e. disease severity will depend on secondary environmental and genetic factors); and validated by reverse-translation of animal homologues to human biomarkers. A powerful consequence of the biomarker approach is that different closely related disorders have a unique fingerprint of biomarkers. Animals can be validated as a highly specific model of a single disorder by matching this 'fingerprint'; or as a model of a symptom seen in multiple disorders by matching common biomarkers. Here we illustrate this approach with two Abnormal Repetitive Behaviors (ARBs) in mice: stereotypies and barbering (hair pulling). We developed animal versions of the neuropsychological biomarkers that distinguish human ARBs, and tested the fingerprint of the different mouse ARBs. As predicted, the two mouse ARBs were associated with different biomarkers. Both barbering and stereotypy could be discounted as models of OCD (even though they are widely used as such), due to the absence of limbic biomarkers which are characteristic of OCD and hence are necessary for a valid model. Conversely barbering matched the fingerprint of trichotillomania (i.e. selective deficits in set-shifting), suggesting it may be a highly specific model of this disorder. In contrast stereotypies were correlated only with a biomarker (deficits in response shifting) correlated with stereotypies in multiple disorders, suggesting that animal stereotypies model stereotypies in multiple disorders.

Design, conduct, and analysis of a multicenter, pharmacogenomic, biomarker study in matched patients with severe sepsis treated with or without drotrecogin Alfa (activated)

A genomic biomarker identifying patients likely to benefit from drotrecogin alfa (activated) (DAA) may be clinically useful as a companion diagnostic. This trial was designed to validate biomarkers (improved response polymorphisms (IRPs)). Each IRP (A and B) contains two single nucleotide polymorphisms that were associated with a differential DAA treatment effect.

Identification of pathogen and host-response markers correlated with periodontal disease

BACKGROUND: Periodontitis is the major cause of tooth loss in adults and is linked to systemic illnesses, such as cardiovascular disease and stroke. The development of rapid point-of-care (POC) chairside diagnostics has the potential for the early detection of periodontal infection and progression to identify incipient disease and reduce health care costs. However, validation of effective diagnostics requires the identification and verification of biomarkers correlated with disease progression. This clinical study sought to determine the ability of putative host- and microbially derived biomarkers to identify periodontal disease status from whole saliva and plaque biofilm. METHODS: One hundred human subjects were equally recruited into a healthy/gingivitis group or a periodontitis population. Whole saliva was collected from all subjects and analyzed using antibody arrays to measure the levels of multiple proinflammatory cytokines and bone resorptive/turnover markers. RESULTS: Salivary biomarker data were correlated to comprehensive clinical, radiographic, and microbial plaque biofilm levels measured by quantitative polymerase chain reaction (qPCR) for the generation of models for periodontal disease identification. Significantly elevated levels of matrix metalloproteinase (MMP)-8 and -9 were found in subjects with advanced periodontitis with Random Forest importance scores of 7.1 and 5.1, respectively. The generation of receiver operating characteristic curves demonstrated that permutations of salivary biomarkers and pathogen biofilm values augmented the prediction of disease category. Multiple combinations of salivary biomarkers (especially MMP-8 and -9 and osteoprotegerin) combined with red-complex anaerobic periodontal pathogens (such as Porphyromonas gingivalis or Treponema denticola) provided highly accurate predictions of periodontal disease category. Elevated salivary MMP-8 and T. denticola biofilm levels displayed robust combinatorial characteristics in predicting periodontal disease severity (area under the curve = 0.88; odds ratio = 24.6; 95% confidence interval: 5.2 to 116.5). CONCLUSIONS: Using qPCR and sensitive immunoassays, we identified host- and bacterially derived biomarkers correlated with periodontal disease. This approach offers significant potential for the discovery of biomarker signatures useful in the development of rapid POC chairside diagnostics for oral and systemic diseases. Studies are ongoing to apply this approach to the longitudinal predictions of disease activity.

EGFR exon-level biomarkers of the response to bevacizumab/erlotinib in non-small cell lung cancer

Activating epidermal growth factor receptor (EGFR) mutations are recognized biomarkers for patients with metastatic non-small cell lung cancer (NSCLC) treated with EGFR tyrosine kinase inhibitors (TKIs). EGFR TKIs can also have activity against NSCLC without EGFR mutations, requiring the identification of additional relevant biomarkers. Previous studies on tumor EGFR protein levels and EGFR gene copy number revealed inconsistent results. The aim of the study was to identify novel biomarkers of the response to TKIs in NSCLC by investigating whole genome expression at the exon-level. We used exon arrays and clinical samples from a previous trial (SAKK19/05) to investigate the expression variations at the exon-level of 3 genes potentially playing a key role in modulating treatment response: EGFR, V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) and vascular endothelial growth factor (VEGFA). We identified the expression of EGFR exon 18 as a new predictive marker for patients with untreated metastatic NSCLC treated with bevacizumab and erlotinib in the first line setting. The overexpression of EGFR exon 18 in tumor was significantly associated with tumor shrinkage, independently of EGFR mutation status. A similar significant association could be found in blood samples. In conclusion, exonic EGFR expression particularly in exon 18 was found to be a relevant predictive biomarker for response to bevacizumab and erlotinib. Based on these results, we propose a new model of EGFR testing in tumor and blood.

IMPROVING QUANTITATIVE TREATMENT RESPONSE MONITORING WITH DEFORMABLE IMAGE REGISTRATION

Quantitative imaging with 18F-FDG PET/CT has the potential to provide an in vivo assessment of response to radiotherapy (RT). However, comparing tissue tracer uptake in longitudinal studies is often confounded by variations in patient setup and potential treatment induced gross anatomic changes. These variations make true response monitoring for the same anatomic volume a challenge, not only for tumors, but also for normal organs-at-risk (OAR). The central hypothesis of this study is that more accurate image registration will lead to improved quantitation of tissue response to RT with 18F-FDG PET/CT. Employing an in-house developed “demons” based deformable image registration algorithm, pre-RT tumor and parotid gland volumes can be more accurately mapped to serial functional images. To test the hypothesis, specific aim 1 was designed to analyze whether deformably mapping tumor volumes rather than aligning to bony structures leads to superior tumor response assessment. We found that deformable mapping of the most metabolically avid regions improved response prediction (P<0.05). The positive predictive power for residual disease was 63% compared to 50% for contrast enhanced post-RT CT. Specific aim 2 was designed to use parotid gland standardized uptake value (SUV) as an objective imaging biomarker for salivary toxicity. We found that relative change in parotid gland SUV correlated strongly with salivary toxicity as defined by the RTOG/EORTC late effects analytic scale (Spearman’s ρ = -0.96, P<0.01). Finally, the goal of specific aim 3 was to create a phenomenological dose-SUV response model for the human parotid glands. Utilizing only baseline metabolic function and the planned dose distribution, predicting parotid SUV change or salivary toxicity, based upon specific aim 2, became possible. We found that the predicted and observed parotid SUV relative changes were significantly correlated (Spearman’s ρ = 0.94, P<0.01). The application of deformable image registration to quantitative treatment response monitoring with 18F-FDG PET/CT could have a profound impact on patient management. Accurate and early identification of residual disease may allow for more timely intervention, while the ability to quantify and predict toxicity of normal OAR might permit individualized refinement of radiation treatment plan designs.

Taking personalized medicine seriously: Biomarker approaches in phase IIb/III studies in major depression and schizophrenia

The success rate in the development of psychopharmacological compounds is insufficient. Two main reasons for failure have been frequently identified: 1) treating the wrong patients and 2) using the wrong dose. This is potentially based on the known heterogeneity among patients, both on a syndromal and a biological level. A focus on personalized medicine through better characterization with biomarkers has been successful in other therapeutic areas. Nevertheless, obstacles toward this goal that exist are 1) the perception of a lack of validation, 2) the perception of an expensive and complicated enterprise, and 3) the perception of regulatory hurdles. The authors tackle these concerns and focus on the utilization of biomarkers as predictive markers for treatment outcome. The authors primarily cover examples from the areas of major depression and schizophrenia. Methodologies covered include salivary and plasma collection of neuroendocrine, metabolic, and inflammatory markers, which identified subgroups of patients in the Netherlands Study of Depression and Anxiety. A battery of vegetative markers, including sleep-electroencephalography parameters, heart rate variability, and bedside functional tests, can be utilized to characterize the activity of a functional system that is related to treatment refractoriness in depression (e.g., the renin-angiotensin-aldosterone system). Actigraphy and skin conductance can be utilized to classify patients with schizophrenia and provide objective readouts for vegetative activation as a functional marker of target engagement. Genetic markers, related to folate metabolism, or folate itself, has prognostic value for the treatment response in patients with schizophrenia. Already, several biomarkers are routinely collected in standard clinical trials (e.g., blood pressure and plasma electrolytes), and appear to be differentiating factors for treatment outcome. Given the availability of a wide variety of markers, the further development and integration of such markers into clinical research is both required and feasible in order to meet the benefit of personalized medicine. This article is based on proceedings from the "Taking Personalized Medicine Seriously-Biomarker Approaches in Phase IIb/III Studies in Major Depression and Schizophrenia" session, which was held during the 10th Annual Scientific Meeting of the International Society for Clinical Trials Meeting (ISCTM) in Washington, DC, February 18 to 20, 2014.

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.

Specific microbial biomarker signatures from Congo fan sediments of ODP site 175-1075, Congo soils, wetland and estuary sediments

Methane (CH4) is a strong greenhouse gas known to have perturbed global climate in the past, especially when released in large quantities over short time periods from continental or marine sources. It is therefore crucial to understand and, if possible, quantify the individual and combined response of these variable methane sources to natural climate variability. However, past changes in the stability of greenhouse gas reservoirs remain uncertain and poorly constrained by geological evidence. Here, we present a record from the Congo fan of a highly specific bacteriohopanepolyol (BHP) biomarker for aerobic methane oxidation (AMO), 35-aminobacteriohopane-30,31,32,33,34-pentol (aminopentol), that identifies discrete periods of increased AMO as far back as 1.2 Ma. Fluctuations in the concentration of aminopentol, and other 35-aminoBHPs, follow a pattern that correlates with late Quaternary glacial-interglacial climate cycles, with highest concentrations during warm periods. We discuss possible sources of aminopentol, and the methane consumed by the precursor methanotrophs, within the context of the Congo River setting, including supply of methane oxidation markers from terrestrial watersheds and/or marine sources (gas hydrate and/or deep subsurface gas reservoir). Compound-specific carbon isotope values of -30 per mil to -40 per mil for BHPs in ODP 1075 and strong similarities between the BHP signature of the core and surface sediments from the Congo estuary and floodplain wetlands from the interior of the Congo River Basin, support a methanotrophic and likely terrigenous origin of the 35-aminoBHPs found in the fan sediments. This new evidence supports a causal connection between marine sediment BHP records of tropical deep sea fans and wetland settings in the feeding river catchments, and thus tropical continental hydrology. Further research is needed to better constrain the different sources and pathways of methane emission. However, this study identifies the large potential of aminoBHPs, in particular aminopentol, to trace and, once better calibrated and understood, quantify past methane sources and fluxes from terrestrial and potentially also marine sources.

Stable carbon isotopic composition of biomarker lipids of DSDP Hole 31-302

The middle Paleocene through early Eocene long-term gradual warming was superimposed by several transient warming events, such as the Paleocene-Eocene Thermal Maximum (PETM) and Eocene Thermal Maximum 2 (ETM2). Both events show evidence for extreme global warming associated with a major injection of carbon into the ocean-atmosphere system, but the mechanisms of carbon injection and many aspects of the environmental response are still poorly understood. In this study, we analyzed the concentration and stable carbon isotopic (d13C) composition of several sulfur-bound biomarkers derived from marine photoautotrophs, deposited in the Arctic Ocean at ~85°N, during ETM2. The presence of sulfur-bound biomarkers across this event points toward high primary productivity and anoxic bottom water conditions. The previously reported presence of isorenieratene derivatives indicates euxinic conditions in the photic zone, likely caused by a combination of enhanced primary productivity and salinity stratification. The negative carbon isotope excursion measured at the onset of ETM2 for several biomarkers, ranges between 3 per mil and 4.5 per mil, much larger than the ~1.4 per mil recorded in marine carbonates elsewhere, suggesting substantial enhanced isotopic fractionation by the primary producers likely due to a significant rise in pCO2. In the absence of biogenic carbonates in the ETM2 section of our core we use coeval planktonic d13C from elsewhere to estimate surface water d13C in the Arctic Ocean and then apply the relation between isotopic fractionation and pCO2, originally calibrated for haptophyte alkenones, to three selected organic biomarkers (i.e., S-bound phytane, C35 hopane, and a C25 highly branched isoprenoid). This yields pCO2 values potentially in the range of four times preindustrial levels. However, these estimates are uncertain because of a lack of knowledge on the importance of pCO2 on photosynthetic isotopic fractionation.

Plasma Epstein-Barr virus (EBV) DNA is a biomarker for EBV-positive Hodgkin's lymphoma

Purpose: Latent Epstein-Barr virus (EBV) genomes are found in the malignant cells of approximately one-third of Hodgkin's lymphoma (HL) cases. Detection and quantitation of EBV viral DNA could potentially be used as a biomarker of disease activity. Experimental Design: Initially, EBV-DNA viral load was prospectively monitored from peripheral blood mononuclear cells (PBMC) in patients with HL. Subsequently, we analyzed viral load in plasma from a second cohort of patients. A total of 58 patients with HL (31 newly diagnosed, 6 relapsed, and 21 in long-term remission) were tested. Using real-time PCR, 43 PBMC and 52 plasma samples were analyzed. Results: EBV-DNA was detectable in the plasma of all EBV-positive patients with HL prior to therapy. However, viral DNA was undetectable following therapy in responding patients (P = 0.0156), EBV-positive HL patients in long-term remission (P = 0.0011), and in all patients with EBV-negative HL (P = 0.0238). Conversely, there was no association seen for the EBV-DNA load measured from PBMC in patients with active EBV-positive HL patients as compared with EBV-negative HL, or patients in long-term remission. EBV-DNA load in matched plasma/PBMC samples were not correlated. Conclusions: We show that free plasma EBV-DNA has excellent sensitivity and specificity, and can be used as a noninvasive biomarker for EBV-positive HL and that serial monitoring could predict response to therapy. Additional prospective studies are required to further evaluate the use of free plasma EBV-DNA as a biomarker for monitoring response to treatment in patients with EBV-positive HL.

Microbialites and lipid biomarker from coral reefs off the volcanic islands Tahiti and Vanuatu and from the nonvolcanic sites Belize and the Maldives

The occurrence of microbialites in post-glacial coral reefs has been interpreted to reflect an ecosystem response to environmental change. The greater thickness of microbialites in reefs with a volcanic hinterland compared to thinner microbial crusts in reefs with a non-volcanic hinterland led to the suggestion that fertilization of the reefal environment by chemical weathering of volcanic rocks stimulated primary productivity and microbialite formation. Using a molecular and isotopic approach on reef-microbialites from Tahiti (Pacific Ocean), it was recently shown that sulfate-reducing bacteria favored the formation of microbial carbonates. To test if similar mechanisms induced microbialite formation in other reefs as well, the Tahitian microbialites are compared with similar microbialites from coral reefs off Vanuatu (Pacific Ocean), Belize (Caribbean Sea, Atlantic Ocean), and the Maldives (Indian Ocean) in this study. The selected study sites cover a wide range of geological settings, reflecting variable input and composition of detritus. The new lipid biomarker data and stable sulfur isotope results confirm that sulfate-reducing bacteria played an intrinsic role in the precipitation of microbial carbonate at all study sites, irrespective of the geological setting. Abundant biomarkers indicative of sulfate reducers include a variety of terminally-branched and mid chain-branched fatty acids as well as mono-O-alkyl glycerol ethers. Isotope evidence for bacterial sulfate reduction is represented by low d34S values of pyrite (-43 to -42 per mill) enclosed in the microbialites and, compared to seawater sulfate, slightly elevated d34S and d18O values of carbonate-associated sulfate (21.9 to 22.2 per mill and 11.3 to 12.4 per mill, respectively). Microbialite formation took place in anoxic micro-environments, which presumably developed through the fertilization of the reef environment and the resultant accumulation of organic matter including bacterial extracellular polymeric substances (EPS), coral mucus, and marine snow in cavities within the coral framework. ToF-SIMS analysis reveals that the dark layers of laminated microbialites are enriched in carbohydrates, which are common constituents of EPS and coral mucus. These results support the hypothesis that bacterial degradation of EPS and coral mucus within microbial mats favored carbonate precipitation. Because reefal microbialites formed by similar processes in very different geological settings, this comparative study suggests that a volcanic hinterland is not required for microbialite growth. Yet, detrital input derived from the weathering of volcanic rocks appears to be a natural fertilizer, being conductive for the growth of microbial mats, which fosters the development of particularly abundant and thick microbial crusts.