Biomarker Discovery Pipeline Research Articles

A lectin affinity workflow targeting glycosite-specific, cancer-related carbohydrate structures in trypsin-digested human plasma

Glycans are cell-type-specific, posttranslational protein modifications that are modulated during developmental and disease processes. As such, glycoproteins are attractive biomarker candidates. Here, we describe a mass spectrometry-based workflow that incorporates lectin affinity chromatography to enrich for proteins that carry specific glycan structures. As increases in sialylation and fucosylation are prominent among cancer-associated modifications, we focused on Sambucus nigra agglutinin (SNA) and Aleuria aurantia lectin (AAL), lectins which bind sialic acid- and fucose-containing structures, respectively. Fucosylated and sialylated glycopeptides from human lactoferrin served as positive controls, and high-mannose structures from yeast invertase served as negative controls. The standards were spiked into Multiple Affinity Removal System (MARS) 14-depleted, trypsin-digested human plasma from healthy donors. Samples were loaded onto lectin columns, separated by HPLC into flow-through and bound fractions, and treated with peptide: N-glycosidase F to remove N-linked glycans. The deglycosylated peptide fractions were interrogated by ESI HPLC-MS/MS. We identified a total of 122 human plasma glycoproteins containing 247 unique glycosites. Importantly, several of the observed glycoproteins (e.g., cadherin 5 and neutrophil gelatinase-associated lipocalin) typically circulate in plasma at low nanogram per milliliter levels. Together, these results provide mass spectrometry-based evidence of the utility of incorporating lectin-separation platforms into cancer biomarker discovery pipelines.

Microarray based gene expression: a novel approach for identification and development of potential drug and effective vaccine against visceral Leishmaniasis.

Visceral Leishmaniasis (VL) is the well-recognized infectious disease among the complex of Leishmaniasis (cutaneous, mucocutaneous, visceral) in tropical and subtropical countries. Treatments for VL are unsatisfactory till date and alarming rise of drug resistance is a serious problem. Vaccines to control VL have shown some promise, but none are in current clinical use. Therefore, urgent needs for new and effective anti-leishmanials are pre-requisite. To identify the potential factors, DNA microarray an advance, high throughput technology, has open the possibility of discovering new genes that can contribute to vaccine initiatives or serve as potential drug targets. It has been successfully applied to many of the protozoan parasites and identified some new genes as targets. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose. After the completion of genome sequencing of Leishmania major and L. infantum, advancement in microarray technologies provide new approaches to study the pattern of gene expression profile during differentiation and development of parasite. It has the potential to improve our understanding of pathogenicity, mechanism of drug resistance and virulence factors by identifying up/down regulated gene and characterizing the respective gene expression. Keeping these backgrounds in mind, we reviewed the data obtained from genome-wide wide expression profiling in Leishmania that focuses on applications of microarray in novel vaccine/drug targets discovery for VL and discuss the potential avenues for their future investigation. Ultimately this will be able to translate the findings into the development of novel therapeutic approaches and targets for VL. Keywords: Visceral Leishmaniasis, Microarray, Stage-specific, Gene expression profiling, Gene discovery, Novel vaccine/drug targets

The Bottleneck in the Cancer Biomarker Pipeline and Protein Quantification through Mass Spectrometry–Based Approaches: Current Strategies for Candidate Verification

Although robust discovery-phase platforms have resulted in the generation of large numbers of candidate cancer biomarkers, a comparable system for subsequent quantitative assessment and verification of all candidates is lacking. Established immunoassays and available antibodies permit analysis of small subsets of candidates; however, the lack of commercially available reagents, coupled with high costs and lengthy production and purification times, have rendered the large majority of candidates untestable. Mass spectrometry (MS), and in particular multiple reaction monitoring (MRM)-MS, has emerged as an alternative technology to immunoassays for quantification of target proteins. Novel biomarkers are expected to be present in serum in the low (microg/L-ng/L) range, but analysis of complex serum or plasma digests by MS has yielded milligram per liter limits of detection at best. The coupling of prior sample purification strategies such as enrichment of target analytes, depletion of high-abundance proteins, and prefractionation, has enabled reliable penetration into the low microgram per liter range. This review highlights prospects for candidate verification through MS-based methods. We first outline the biomarker discovery pipeline and its existing bottleneck; we then discuss various MRM-based strategies for targeted protein quantification, the applicability of such methods for candidate verification, and points of concern. Although it is unlikely that MS-based protein quantification will replace immunoassays in the near future, with the expected improvements in limits of detection and specificity in instrumentation, MRM-based approaches show great promise for alleviating the existing bottleneck to discovery.

Cross-platform method for identifying candidate network biomarkers for prostate cancer

Discovering biomarkers using mass spectrometry (MS) and microarray expression profiles is a promising strategy in molecular diagnosis. Here, the authors proposed a new pipeline for biomarker discovery that integrates disease information for proteins and genes, expression profiles in both genomic and proteomic levels, and protein-protein interactions (PPIs) to discover high confidence network biomarkers. Using this pipeline, a total of 474 molecules (genes and proteins) related to prostate cancer were identified and a prostate-cancer-related network (PCRN) was derived from the integrative information. Thus, a set of candidate network biomarkers were identified from multiple expression profiles composed by eight microarray datasets and one proteomics dataset. The network biomarkers with PPIs can accurately distinguish the prostate patients from the normal ones, which potentially provide more reliable hits of biomarker candidates than conventional biomarker discovery methods.

Target discovery from data mining approaches

Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.

MS-BID: a Java package for label-free LC-MS-based comparative proteomic analysis

MS-BID (MS Biomarker Discovery Platform) is an integrative computational pipeline for biomarker discovery using LC-MS-based comparative proteomic analysis. This platform consists of several computational tools for: (i) detecting peptides in the collected patterns; (ii) matching detected peptides across a number of LC-MS datasets and (iii) selecting discriminatory peptides between classes of samples. MS-BID source codes, binaries and documentations are freely available under LGPL from http://tools.proteomecenter.org/msBID.php.

Advances in ovarian cancer proteomics: the quest for biomarkers and improved therapeutic interventions

Epithelial ovarian cancer is the leading cause of cancer-related death among gynecological cancers due to the asymptomatic nature of the disease, a lack of early detection markers and the development of resistance to current chemotherapeutic agents. Currently available tests (CA-125, transvaginal ultrasound or combination of both) lack the sensitivity and specificity to be useful as an efficient screening tool for surveillance of the general population. Thus, there is an urgent need for the development and validation of new molecular markers that would be both specific and sensitive indicators of disease onset, as well as progression. Proteomic profiling has emerged as a powerful tool to study ovarian cancer in an unbiased way at the molecular level, to monitor the effects of given treatment options and for the discovery of biomarkers. In this review we discuss the challenges associated with proteomics-based biomarker discovery and some recent concepts to potentially overcome these hurdles. Recent proteomics work on ovarian cancer cells and tissues will be discussed in light of obtaining new insights into fundamental biological processes, as well as their potential integration with ongoing biomarker discovery pipelines.

Research Profile: A new pipeline for biomarker discovery and validation

Research Profile: A new pipeline for biomarker discovery and validation

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.