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Results for “biomarker discovery”

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At least 19 recordsLinked to original sources

PubMed2005

Synergistic approaches to clinical oncology biomarker discovery.

Biomarkers in the clinical oncology field can have tremendous therapeutic impact especially if the marker is detected before clinical symptoms. This impact can be extended to the evaluation of clinical oncology treatments allowing evaluation of potential compounds to determine their efficacy in the disease treatment. The discovery of clinical biomarkers can consume time, resources and costs. Therefore, it is important that the most effective strategies are employed to discover these biomarkers. These strategies may include the integration of available genomic, proteomic and histopathological technologies, which could reduce the costs and aid in the validation of the biomarker. Certainly the type of biomarker needed to address a particularly defined problem will drive the type of technology. However, a single biomarker to diagnose a specific cancer can be as elusive as relying on a single technology. This review examines some of the technologies used to discover biomarkers and presents the use of combinatorial technical synergies to discover and validate potential clinical oncology biomarkers.

Biomarkers, Tumor↗

PubMed2006

Two-dimensional protein electrophoresis: from molecular pathway discovery to biomarker discovery in neurological disorders.

Two-dimensional protein electrophoresis (2-DE) has undergone many technical improvements in the past 30 years, resulting in an analytical method that is unparalleled in the resolution of complex protein mixtures and capable of quantifying changes in protein expression from a wide variety of tissues and samples. The technique has been applied in many studies of neurologic disease to identify changes in spot patterns that correlate with disease. The true power of the technique emerges when it is coupled to state-of-the-art methods in mass spectrometry, which enable identification of the protein or proteins contained within a spot of interest on a 2-DE map. Investigators have successfully applied the technique to gain improved understanding of neurologic disease mechanisms in humans and in animal models and to discover biomarkers that are useful in the clinical setting. An important extension to these efforts that has not been realized thus far is the desire to profile changes in protein expression that result from therapy to help relate disease-modifying effects at the molecular level with clinical outcomes. Here we review the major advances in 2-DE methods and discuss specific examples of its application in the study of neurologic diseases.

Animals↗

PubMedDate not supplied

Utilizing human blood plasma for proteomic biomarker discovery.

Candidate proteomic biomarker discovery from human plasma holds both incredible clinical potential as well as significant challenges. The dynamic range of proteins within plasma is known to exceed 10(10), and many potential biomarkers are likely present at lower protein abundances. At present, proteomic based MS analyses provide a dynamic range typically not exceeding approximately 10(3) in a single spectrum, and approximately 10(4)-10(6) when combined with on-line separations (e.g., reversed-phase gradient liquid chromatography), and thus are generally insufficient for low level biomarker detection directly from human plasma. This limitation is providing an impetus for the development of experimental methodologies and strategies to increase the possible number of detections within this biofluid. Discussed is the diversity of available approaches currently used by our laboratory and others to utilize human plasma as a viable medium for biomarker discovery. Various separation, depletion, enrichment, and quantitative efforts as well as recent improvements in MS capabilities have resulted in measurable improvements in the detection and identification of lower abundance proteins (by approximately 10-10(2)). Despite these improvements, further advances are needed to provide a basis for discovery of candidate biomarkers at very low levels. Continued development of depletion and enrichment techniques, coupled with improved pre-MS separations (both at the protein and peptide level) holds promise in extending the dynamic range of proteomic analysis.

Biomarkers↗

PubMedDate not supplied

Capillary electrophoresis-mass spectrometry as a powerful tool in clinical diagnosis and biomarker discovery.

Proteome analysis is now emerging as key technology for deciphering biological processes and the discovery of biomarkers for diseases from tissues and body fluids. The complexity and wide dynamic range of protein expression poses a formidable challenge to both peptide separation technologies and mass spectrometry (MS). Here we review the efforts that have been undertaken to date, focussing on capillary electrophoresis coupled to mass spectrometry (CE-MS). We discuss CE-MS from an application point of view evaluating its merits and vices in regard to biomarker discovery and clinical applications. As examples, we present the use of CE-MS for the determination of protein patterns in urine, serum, and other body fluids. Finally, the benefits and limitations of CE-MS for the analysis of proteins in clinical samples are discussed against the background of alternative technologies.

Animals↗

PubMedDate not supplied

Place of pattern in proteomic biomarker discovery.

The role of pattern in biomarker discovery and clinical diagnosis is examined in its historical context. The use of MS-derived pattern is treated as a logical extension of prior applications of non-MS-derived pattern. Criticisms pertaining to specific technology platforms and analytic methodologies are considered separately from the larger issues of pattern utility and deployment in biomarker discovery. We present a hybrid strategy that marries the desirable attributes of high-information content MS pattern with the capability to obtain identity, and explore the key steps in establishing a data analysis pipeline for pattern-based biomarker discovery.

Biomarkers↗

PubMed2006

Proteomic-based biomarker discovery with emphasis on cerebrospinal fluid and multiple sclerosis.

Discovery of disease specific biomarkers in human body fluids has become an important challenge in clinical proteomics. Facing the increasing threat of degenerative and disabling diseases like cancer, cardiovascular, neurological and inflammatory diseases in large parts of the world's population, there is an urgent need to improve early diagnostics. In this review we discuss possibilities and limitations connected to using mass spectrometry based proteomics in the search for novel biomarkers, with focus on multiple sclerosis as a typical representative for the large group of non-curable degenerative and disabling disease with the lack of specific tests for early diagnosis. Careful control of the pre-analytical phase including sampling, storage and fractionation of samples, in addition to a thoroughly considered patient selection, is important in order to avoid false biomarkers to appear in the resulting mass spectra. Furthermore, advanced computational tools are needed in order to discover potential biomarkers from the enormous data amounts generated by the mass spectrometers. The development of such computer tools is a research field currently in the start phase and could prove to be a bottle neck in the biomarker discovery the next years. Therefore, a rather detailed review of the most used computational and pre-analytical methods is given in this review. Mass spectrometry based biomarker discovery is undoubtedly still in its early infancy. However, in light of the potential of this technology to provide deep coverage of the body fluid proteomes, it will certainly consolidate its role in developing molecular medicine into clinical practice.

Biomarkers↗

PubMed2005

Biomarker discovery in biological fluids.

Discovery of novel protein biomarkers is essential for successful drug discovery and development. These novel protein biomarkers may aid accelerated drug efficacy, response, or toxicity decision making based on their enhanced sensitivity and/or specificity. These biomarkers, if necessary, could eventually be converted into novel diagnostic marker assays. Proteomic platforms developed over the past few years have given us the ability to rapidly identify novel protein biomarkers in various biological matrices from cell cultures (lysates, supernatants) to human clinical samples (serum, plasma, and urine). In this article, we delineate an approach to biomarker discovery. This approach is divided into three steps, (i) identification of markers, (ii) prioritization of identified markers, and (iii) preliminary validation (qualification) of prioritized markers. Using drug-induced idiosyncratic hepatotoxicity as a case study, the article elaborates methods and techniques utilized during the three steps of biomarker discovery process. The first step involves identification of markers using multi-dimensional protein identification technology. The second step involves prioritization of a subset of marker candidates based on several criteria such as availability of reagent set for assay development and literature association to disease biology. The last step of biomarker discovery involves development of preliminary assays to confirm the bio-analytical measurements from the first step, as well as qualify the marker(s) in pre-clinical models, to initiate future marker validation and development.

14-3-3 Proteins↗

PubMed2006

Biomarker discovery from the plasma proteome using multidimensional fractionation proteomics.

Because biomarkers are typically low in abundance, the crucial step of biomarker discovery is to efficiently separate clinically relevant sets of proteins that might define disease stages and/or predict disease development. It is anticipated that a multi-dimensional fractionation system (MDFS) will provide an efficient means of separating low abundance proteins from plasma proteins, resulting in the extension of the detection limit. However, when using an MDFS to analyze the plasma proteome it is important to consider how sample processing, yield, resolution and throughput potential may influence the detection limit. This review evaluates the recent advances in MDFS research with respect to '4RS criterion' (4R: resolution, reproducibility, recovery, and robustness; 4S: simplicity, speed, selectivity and sensitivity) and discusses perspectives for future plasma-derived biomarker discovery.

Biomarkers↗

PubMed2004

Biomarker discovery by comprehensive phenotyping for autoimmune diseases.

There is a well-recognized but unmet need for biological markers to characterize disease type, status, progression, and response to therapy in autoimmune diseases. We are developing and applying an integrated bioanalytical platform and clinical research program to facilitate comprehensive differential phenotyping of patient samples and enable the discovery of biomarkers. Our measurement platform includes microvolume laser scanning cytometry for the quantification of hundreds of cellular parameters in whole blood and other samples, liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry for the quantification of proteins and low molecular weight biomolecules in serum and other fluids or tissues, and specific immunoassays for the quantification of trace proteins in serum. We describe the technologies and discuss initial applications to the analysis of subjects with rheumatoid arthritis (RA) and healthy controls.

Arthritis, Rheumatoid↗

PubMedDate not supplied

Evaluation of biomarker discovery approaches to detect protein biomarkers of acute renal allograft rejection.

Management of host responses to allografts by immunosuppressive therapy is the cornerstone of transplantation medicine, but it is still deficient in one important element: biomarkers that are readily accessible and predict the fate of the transplant early, specifically, and reliably. Using a Brown Norway (BN)-to-Lewis rat renal allograft model of kidney transplantation, this study aims at evaluating two proteomic approaches to discover biomarkers for acute rejection: SELDI-MS technology and 2D gel electrophoresis combined with mass spectrometry. Several novel potential serum biomarkers have been identified for follow up. Overall, the conclusion is that apparently at the serum protein level, dramatic changes only occur at a stage where kidney function is already severely affected. Multivariate analysis of serum profiles suggests that there is an ensemble of subtle changes, comprising a proteomic signature of acute rejection at an early stage, a more detailed evaluation of which might provide novel opportunities for the diagnosis of acute rejection. Profiling of the excreted proteins indicates that urine might even present the earliest signs of the rejection process.

Animals↗

PubMed2002

Clinical proteomics for cancer biomarker discovery and therapeutic targeting.

As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the "software" of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the "wiring diagram" of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.

Algorithms↗

PubMed2025

Proteomic-based biomarker discovery reveals panels of diagnostic biomarkers for early identification of heart failure subtypes.

BACKGROUND: Limited access to echocardiography can delay the diagnosis of suspected heart failure (HF), which in turn postpones the initiation of optimal guideline-directed medical therapy. Although natriuretic peptides like B-type natriuretic peptide (BNP) are valuable biomarkers for diagnosing and managing HF, the utility of combining BNP with other blood-based biomarkers to predict subtypes of new-onset HF remains underexplored. OBJECTIVES: This study sought to investigate and evaluate the diagnostic significance of adding blood-based biomarkers to BNP for identifying heart failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF), with the goal of enhancing diagnostic assays beyond BNP measurements. METHODS: We identified candidate blood protein biomarkers using untargeted proteomics workflows from a cohort of individuals recruited to the STOP-HF trial who were at risk of HF and subsequently developed either HFpEF or HFrEF over time ("HF progressors"; n = 40). Candidate biomarkers were verified in an independent cohort (n = 52) from a community-based rapid access HF diagnostic clinic. The biological processes associated with these proteins were assessed, and the diagnostic values of biomarker panels were evaluated using a machine learning approach. RESULTS: Within HF progressors, we identified 3 proteins associated with HFpEF development: vascular cell adhesion protein 1 (VCAM1), insulin-like growth factor 2 (IGF2), and inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3). Additionally, 4 proteins were linked to HFrEF development: C-reactive protein (CRP), interleukin-6 receptor subunit beta (IL6RB), phosphatidylinositol-glycan-specific phospholipase D (PHLD), and noelin (NOE1). These findings were verified in an independent cohort to distinguish HF subtypes from controls. Moreover, a random forest algorithm demonstrated that combining these candidate biomarkers with BNP measurement significantly improved the prediction of HF subtypes. CONCLUSIONS: We identified candidate proteins linked to HFpEF and HFrEF in a longitudinal HF progressor cohort and validated them in a community-based cohort. Adding these proteins to BNP led to a significant improvement in HF subtype prediction. Study results have clinical implications for blood-based screening of HF subtypes using panels of biomarkers, particularly in resource-limited settings.

Humans↗

PubMed2001

Data mining of spectroscopic data for biomarker discovery.

The goals of precise diagnosis, prevention and treatment of disease can be realized through the discovery of biological markers. Spectroscopic tools can simultaneously detect and quantify multiple small molecule and macromolecular components of biological samples, and are therefore ideal methods for the discovery of previously uncharacterized markers. However, the identification of meaningful spectral features is complicated by the lack of foreknowledge of the molecular nature of a disease, spectral noise and biological variability that is uncorrelated with the disease state. Pattern recognition techniques, both statistical and machine-learning, have been increasingly used in recent years with spectroscopic data to identify markers and classify patients into disease subsets. This review summarizes recent developments, limitations and future prospects in the use of data mining techniques with magnetic resonance spectroscopy, mass spectrometry and optical spectroscopy for the discovery of biomarkers.

Biomarkers↗

PubMed2005

The use of proteomics in biomarker discovery in neurodegenerative diseases.

Biomarkers for neurodegenerative diseases should reflect the central pathogenic processes of the diseases. The field of clinical proteomics is especially well suited for discovery of biomarkers in cerebrospinal fluid (CSF), which reflects the proteins in the brain under healthy conditions as well as in several neurodegenerative diseases. Known proteins involved in the pathology of neurodegenerative diseases are, respectively, normal tau protein, beta-amyloid (1-42), synaptic proteins, amyloid precursor protein (APP), apolipoprotein E (apoE), which previously have been studied by protein immunoassays. The objective of this paper was to summarize results from proteomic studies of differential protein patterns in neurodegenerative diseases with focus on Alzheimer's disease (AD). Today, discrimination of AD from controls and from other neurological diseases has been improved by simultaneous analysis of both beta-amyloid (1-42), total-tau, and phosphorylated tau, where a combination of low levels of CSF-beta-amyloid 1-42 and high levels of CSF-tau and CSF-phospho-tau is associated with an AD diagnosis. Detection of new biomarkers will further strengthen diagnosis and provide useful information in drug trials. The combination of immunoassays and proteomic methods show that the CSF proteins express differential protein patterns in AD, FTD, and PD patients, which reflect divergent underlying pathophysiological mechanisms and neuropathological changes in these diseases.

Alzheimer Disease↗

PubMed2005

High-throughput biomarker discovery and identification by mass spectrometry.

Native peptides and proteins are of increasing interest in biomedical research because they hold promise to represent a large number of useful diagnostic and therapeutic biomarkers. Discovery attempts from patient samples have to deal with the complexity of biology from a disease perspective as well as with a high individual variability. High throughput screening of samples is therefore the strategy of choice to detect relevant peptidic biomarkers, and requires a high order of automation particularly in the detection process. In this contribution, a novel technical approach employing a fully automated MALDI-TOF/TOF mass spectrometer is described. This approach combines high throughput biomarker discovery with the identification of corresponding endogenous peptides in one instrument and from the same set of samples. The degree of automation allows the analysis of thousands of chromatographic fractions corresponding to up to one hundred patient samples per day. The applied relative quantification via Differential Peptide Display((R)) is performed in a label-free way and shows a dynamic range of up to four orders of magnitude in the accessible peptide concentrations. The typical limit of detection is in the mid- to low-picomolar range for body fluids such as blood plasma, urine and cerebrospinal fluid. Sequence assignment via MALDI-TOF/TOF mass spectrometry is carried out either in an overview approach, characterizing rapidly the peptide composition e.g. of a novel sample, or in a directed approach, analyzing a list of biomarker candidates deduced from statistically significant abundance differences from the biomarker discovery process.

Biomarkers↗

PubMedDate not supplied

Perspective: a program to improve protein biomarker discovery for cancer.

Biomarkers for cancer risk, early detection, prognosis, and therapeutic response promise to revolutionize cancer management. Protein biomarkers offer tremendous potential in this regard due to their great diversity and intimate involvement in physiology. An effective program to discover protein biomarkers using existing technology will require team science, an integrated informatics platform, identification and quantitation of candidate biomarkers in disease tissue, mouse models of disease, standardized reagents for analyzing candidate biomarkers in bodily fluids, and implementation of automation. Technology improvements for better fractionation of the proteome, selection of specific biomarkers from complex mixtures, and multiplexed assay of biomarkers would greatly enhance progress.

Animals↗

PubMed2005

Proteomics as a tool for clinically relevant biomarker discovery and validation.

The excitement associated with clinical applications of proteomics was initially focused on its potential to serve as a vehicle for both biomarker discovery and drug discovery and routine clinical sample analysis. Some approaches were thought to be able to "identify" mass spectral characteristics that distinguished between control and disease samples, and thereafter it was believed that the same tool could be employed to screen samples in a high-throughput clinical setting. However, this has been difficult to achieve, and the early promise is yet to be fully realized. While we see an important place for mass spectrometry in drug and biomarker discovery, we believe that alternative strategies will prove more fruitful for routine analysis. Here we discuss the power and versatility of 2D gels and mass spectrometry in the discovery phase of biomarker work but argue that it is better to rely on immunochemical methods for high-throughput validation and routine assay applications.

Animals↗

PubMed2006

Clinical proteomics: the promises and challenges of mass spectrometry-based biomarker discovery.

The lack of effective surrogates or biomarkers for biologic and disease state is a major hindrance to effective cancer care. Many laboratories have been using mass spectrometry as the means to discover new biomarkers. The success of these efforts relies on effective collaboration between clinicians and mass spectroscopists. This review describes many of the common proteomic experimental methodologies and evaluates the current state of proteomics-based biomarker discovery--starting from sample collection through sample processing and ultimately sample measurement. Guidelines are given for the evaluation of experimental design and for the gauging of result significance. Particular emphasis is placed on those aspects strongly associated with reproducibility from both clinical and laboratory perspectives.

Biomarkers↗