Assessment of estrogenic exposure in brown trout (Salmo trutta) in a Swiss midland river: integrated analysis of passive samplers, wild and caged fish, and vitellogenin mRNA and protein

This field study examined the vitellogenin (VTG) biomarker response under conditions of low and fluctuating activities of environmental estrogenicity. The present study was performed on immature brown trout (Salmo trutta) exposed to the small river Luetzelmurg, which is located in the prealpine Swiss midland region and receives effluents from a single sewage treatment plant (STP). To understand better factors influencing the relationship between estrogenic exposure and VTG induction, we compared VTG levels in caged (stationary) and feral (free-ranging) fish, VTG levels in fish from up- and downstream of the STP, and two different methods for quantifying VTG (enzyme-linked immunosorbent assay vs real-time reverse transcription-polymerase chain reaction), and we used passive samplers (polar organic chemical integrative sampler [POCIS]) to integrate the variable, bioaccumulative estrogenic load in the river water over time. The POCIS from the downstream site contained approximately 20-fold higher levels of bioassay-derived estrogen equivalents than the POCIS from the upstream site. In feral fish, this site difference in estrogenic exposure was reflected in VTG protein levels but not in VTG mRNA. In contrast, in caged fish, the site difference was evident only for VTG mRNA but not for VTG protein. Thus, the outcome of VTG biomarker measurements varied with the analytical detection method (protein vs mRNA) and with the exposure modus (caged vs feral). Our findings suggest that for environmental situations with low and variable estrogenic contamination, a multiple-assessment approach may be necessary for the assessment of estrogenic exposure in fish.

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.

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.