Multiplex assays for biomarker research and clinical application: translational science coming of age

Over the last decade, translational science has come into the focus of academic medicine, and significant intellectual and financial efforts have been made to initiate a multitude of bench-to-bedside projects. The quest for suitable biomarkers that will significantly change clinical practice has become one of the biggest challenges in translational medicine. Quantitative measurement of proteins is a critical step in biomarker discovery. Assessing a large number of potential protein biomarkers in a statistically significant number of samples and controls still constitutes a major technical hurdle. Multiplexed analysis offers significant advantages regarding time, reagent cost, sample requirements and the amount of data that can be generated. The two contemporary approaches in multiplexed and quantitative biomarker validation, antibody-based immunoassays and MS-based multiple (or selected) reaction monitoring, are based on different assay principles and instrument requirements. Both approaches have their own advantages and disadvantages and therefore have complementary roles in the multi-staged biomarker verification and validation process. In this review, we discuss quantitative immunoassay and multiple reaction monitoring/selected reaction monitoring assay principles and development. We also discuss choosing an appropriate platform, judging the performance of assays, obtaining reliable, quantitative results for translational research and clinical applications in the biomarker field.

Proteomics in cardiovascular surgery

Proteomics describes, analogous to the term genomics, the study of the complete set of proteins present in a cell, organ, or organism at a given time. The genome tells us what could theoretically happen, whereas the proteome tells us what does happen. Therefore, a genomic-centered view of biologic processes is incomplete and does not describe what happens at the protein level. Proteomics is a relatively new methodology and is rapidly changing because of extensive advances in the underlying techniques. The core technologies of proteomics are 2-dimensional gel electrophoresis, liquid chromatography, and mass spectrometry. Proteomic approaches might help to close the gap between traditional pathophysiologic and more recent genomic studies, assisting our basic understanding of cardiovascular disease. The application of proteomics in cardiovascular medicine holds great promise. The analysis of tissue and plasma/serum specimens has the potential to provide unique information on the patient. Proteomics might therefore influence daily clinical practice, providing tools for diagnosis, defining the disease state, assessing of individual risk profiles, examining and/or screening of healthy relatives of patients, monitoring the course of the disease, determining the outcome, and setting up individual therapeutic strategies. Currently available clinical applications of proteomics are limited and focus mainly on cardiovascular biomarkers of chronic heart failure and myocardial ischemia. Larger clinical studies are required to test whether proteomics may have promising applications for clinical medicine. Cardiovascular surgeons should be aware of this increasingly pertinent and challenging field of science.

Potentials and pitfalls of clinical peptidomics and metabolomics

Clinical peptidomics and metabolomics are two emerging "-omics" technologies with the potential not only to detect disease-specific markers, but also to give insight into the disease dependency of degradation processes and metabolic pathway alterations. However, despite their rapid evolution and major investments, a clinical breakthrough, such as the approval of a major cancer biomarker, is still out of sight. What are the reasons for this failure? In this review we focus on three important factors: sensitivity, specificity and the avoidance of bias. The way to clinical implementation of peptidomics and metabolomics is still hampered by many of the problems that had to be solved for genomics and proteomics in the past, as well as new ones that require the creation of new analytic, computational and interpretative techniques. The greatest challenge, however, will be the integration of information from different "-omics" subdisciplines into straightforward answers to clinical questions, for example, in the form of new, superior "meta-markers".

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

Identification of protein targets of nevirapine reactive metabolites using click chemistry and mass spectrometry-based differential proteomics

Abstract : Adverse drug reactions (ADRs) are undesirable effects caused after administration of a single dose or prolonged administration of drug or result from the combination of two or more drugs. Idiosyncratic drug reaction (IDR) is an adverse reaction that does not occur in most patients treated with a drug and does not involve the therapeutic effect of the drug. IDRs are unpredictable and often life-threatening. Idiosyncratic reaction is dependent on drug chemical characteristics or individual immunological response. IDRs are a major problem for drug development because they are usually not detected during clinical trials. In this study we focused on IDRs of Nevirapine (NVP), which is a non-nucleoside reverse transcriptase inhibitor used for the treatment of Human Immunodeficiency Virus (HIV) infections. The use of NVP is limited by a relatively high incidence of skin rash. NVP also causes a rash in female Brown Norway (BN) rats, which we use as animal model for this study. Our hypothesis is that idiosyncratic skin reactions associated with NVP treatment are due to post-translational modifications of proteins (e.g., glutathionylation) detectable by MS. The main objective of this study was to identify the proteins that are targeted by a reactive metabolite of Nevirapine in the skin. The specific objectives derived from the general objective were as follow: 1) To implement the click chemistry approach to detect proteins modified by a reactive NVP-Alkyne (NVP-ALK) metabolite. The purpose of using NVP-ALK was to couple it with Biotin using cycloaddition Click Chemistry reaction. 2) To detect protein modification using Western blotting and Mass Spectrometry techniques, which is important to understand the mechanism of NVP induced toxicity. 3) To identify the proteins using MASCOT search engine for protein identification, by comparing obtained spectrum from Mass Spectrometry with theoretical spectrum to find a matching peptide sequence. 4) To test if the drug or drug metabolites can cause harmful effects, as the induction of oxidative stress in cells (via protein glutathionylation). Oxidative stress causes cell damage that mediates signals, which likely induces the immune response. The results showed that Nevirapine is metabolized to a reactive metabolite, which causes protein modification. The extracted protein from the treated BN rats matched 10% of keratin, which implies that keratin was the protein targeted by the NVP-ALK.

Microfluidics-based single-cell functional proteomics microchip for portraying protein signal transduction networks within the framework of physicochemical principles, with applications in fundamental and translational cancer research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research.

The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow.

The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM).

The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model.

The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out.

In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.

Clinical Accuracy of the MedGem Indirect Calorimeter for Measuring Resting Energy Expenditure in Cancer Patients

OBJECTIVE: To compare, in patients with cancer and in healthy subjects, measured resting energy expenditure (REE) from traditional indirect calorimetry to a new portable device (MedGem) and predicted REE. DESIGN: Cross-sectional clinical validation study. SETTING: Private radiation oncology centre, Brisbane, Australia. SUBJECTS: Cancer patients (n = 18) and healthy subjects (n = 17) aged 37-86 y, with body mass indices ranging from 18 to 42 kg/m(2). INTERVENTIONS: Oxygen consumption (VO(2)) and REE were measured by VMax229 (VM) and MedGem (MG) indirect calorimeters in random order after a 12-h fast and 30-min rest. REE was also calculated from the MG without adjustment for nitrogen excretion (MGN) and estimated from Harris-Benedict prediction equations. Data were analysed using the Bland and Altman approach, based on a clinically acceptable difference between methods of 5%. RESULTS: The mean bias (MGN-VM) was 10% and limits of agreement were -42 to 21% for cancer patients; mean bias -5% with limits of -45 to 35% for healthy subjects. Less than half of the cancer patients (n = 7, 46.7%) and only a third (n = 5, 33.3%) of healthy subjects had measured REE by MGN within clinically acceptable limits of VM. Predicted REE showed a mean bias (HB-VM) of -5% for cancer patients and 4% for healthy subjects, with limits of agreement of -30 to 20% and -27 to 34%, respectively. CONCLUSIONS: Limits of agreement for the MG and Harris Benedict equations compared to traditional indirect calorimetry were similar but wide, indicating poor clinical accuracy for determining the REE of individual cancer patients and healthy subjects.

The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting

Objective: To compare the effectiveness of the STRATIFY falls tool with nurses’ clinical judgments in predicting patient falls. Study Design and Setting: A prospective cohort study was conducted among the inpatients of an acute tertiary hospital. Participants were patients over 65 years of age admitted to any hospital unit. Sensitivity, specificity, and positive predictive value (PPV) and negative predictive values (NPV) of the instrument and nurses’ clinical judgments in predicting falls were calculated. Results: Seven hundred and eighty-eight patients were screened and followed up during the study period. The fall prevalence was 9.2%. Of the 335 patients classified as being ‘‘at risk’’ for falling using the STRATIFY tool, 59 (17.6%) did sustain a fall (sensitivity50.82, specificity50.61, PPV50.18, NPV50.97). Nurses judged that 501 patients were at risk of falling and, of these, 60 (12.0%) fell (sensitivity50.84, specificity50.38, PPV50.12, NPV50.96). The STRATIFY tool correctly identified significantly more patients as either fallers or nonfallers than the nurses (P50.027). Conclusion: Considering the poor specificity and high rates of false-positive results for both the STRATIFY tool and nurses’ clinical judgments, we conclude that neither of these approaches are useful for screening of falls in acute hospital settings.

Attention and memory bias for body image and health related information using an Emotional Stroop task in a non-clinical sample

It has been proposed that body image disturbance is a form of cognitive bias wherein schemas for self-relevant information guide the selective processing of appearancerelated information in the environment. This threatening information receives disproportionately more attention and memory, as measured by an Emotional Stroop and incidental recall task. The aim of this thesis was to expand the literature on cognitive processing biases in non-clinical males and females by incorporating a number of significant methodological refinements. To achieve this aim, three phases of research were conducted. The initial two phases of research provided preliminary data to inform the development of the main study. Phase One was a qualitative exploration of body image concerns amongst males and females recruited through the general community and from a university. Seventeen participants (eight male; nine female) provided information on their body image and what factors they saw as positively and negatively impacting on their self evaluations. The importance of self esteem, mood, health and fitness, and recognition of the social ideal were identified as key themes. These themes were incorporated as psycho-social measures and Stroop word stimuli in subsequent phases of the research. Phase Two involved the selection and testing of stimuli to be used in the Emotional Stroop task. Six experimental categories of words were developed that reflected a broad range of health and body image concerns for males and females. These categories were high and low calorie food words, positive and negative appearance words, negative emotion words, and physical activity words. Phase Three addressed the central aim of the project by examining cognitive biases for body image information in empirically defined sub-groups. A National sample of males (N = 55) and females (N = 144), recruited from the general community and universities, completed an Emotional Stroop task, incidental memory test, and a collection of psycho-social questionnaires. Sub-groups of body image disturbance were sought using a cluster analysis, which identified three sub-groups in males (Normal, Dissatisfied, and Athletic) and four sub-groups in females (Normal, Health Conscious, Dissatisfied, and Symptomatic). No differences were noted between the groups in selective attention, although time taken to colour name the words was associated with some of the psycho-social variables. Memory biases found across the whole sample for negative emotion, low calorie food, and negative appearance words were interpreted as reflecting the current focus on health and stigma against being unattractive. Collectively these results have expanded our understanding of processing biases in the general community by demonstrating that the processing biases are found within non-clinical samples and that not all processing biases are associated with negative functionality

The relationship between deformity correction and clinical outcomes after thoracoscopic scoliosis surgery

Surgical treatment of scoliosis is quantitatively assessed in the clinic using radiographic measures of deformity correction, as well as the rib hump, but it is important to understand the extent to which these quantitative measures correlate with self-reported improvements in patients’ quality of life following surgery. The purpose of this prospective study was to evaluate the relationship between clinical outcomes of thoracoscopic anterior scoliosis surgery and deformity correction using the Scoliosis Research Society questionnaire (SRS-24). Patients undergoing thoracoscopic anterior scoliosis correction report good SRS scores which are comparable to those reported in previous studies for both open and thoracoscopic scoliosis correction procedures. Major Cobb correction is a significant predictor of patient satisfaction when comparing subgroups of patients with the highest and lowest major curve corrections.

The relationship between personality characteristics and postconcussion symptoms in a non-clinical sample

Postconcussion symptoms are relatively common in the acute recovery period following mild traumatic brain injury (MTBI). However, for a small subset of patients, self reported postconcussion symptoms continue long after injury. Many factors have been proposed to account for the presence of persistent postconcussion symptoms. The influence of personality traits has been proposed as one explanation. The purpose of this study was to examine the relation between postconcussion-like symptom reporting and personality traits in a sample of 96 healthy participants. Participants completed the British Columbia Postconcussion Symptom Inventory (BC-PSI) and the Millon Clinical Multiaxial Inventory III (MCMI-III). There was a strong positive relation between the majority of MCMI-III scales and postconcussion-like symptom reporting. Approximately half of the sample met the International Classification of Diseases-10 Criterion C symptoms for Postconcussional Syndrome (PCS). Compared with those participants who did not meet this criterion, the PCS group had significant elevations on the negativistic, depression, major depression, dysthymia, anxiety, dependent, sadistic, somatic, and borderline scales of the MCMI-III. These findings support the hypothesis that personality traits can play a contributing role in self reported postconcussion-like symptoms.