Prognostic and Predictive Biomarkers in Resected Colon Cancer: Current Status and Future Perspectives for Integrating Genomics into Biomarker Discovery

The number of agents that are potentially effective in the adjuvant treatment of locally advanced resectable colon cancer is increasing. Consequently, it is important to ascertain which subgroups of patients will benefit from a specific treatment. Despite more than two decades of research into the molecular genetics of colon cancer, there is a lack of prognostic and predictive molecular biomarkers with proven utility in this setting. A secondary objective of the Pan European Trials in Adjuvant Colon Cancer-3 trial, which compared irinotecan in combination with 5-fluorouracil and leucovorin in the postoperative treatment of stage III and stage II colon cancer patients, was to undertake a translational research study to assess a panel of putative prognostic and predictive markers in a large colon cancer patient cohort. The Cancer and Leukemia Group B 89803 trial, in a similar design, also investigated the use of prognostic and predictive biomarkers in this setting. In this article, the authors, who are coinvestigators from these trials and performed similar investigations of biomarker discovery in the adjuvant treatment of colon cancer, review the current status of biomarker research in this field, drawing on their experiences and considering future strategies for biomarker discovery in the postgenomic era. The Oncologist 2010; 15: 390-404

Diagnostic and prognostic biomarker discovery strategies for autoimmune disorders.

Current clinical, laboratory or radiological parameters cannot accurately diagnose or predict disease outcomes in a range of autoimmune disorders. Biomarkers which can diagnose at an earlier time point, predict outcome or help guide therapeutic strategies in autoimmune diseases could improve clinical management of this broad group of debilitating disorders. Additionally, there is a growing need for a deeper understanding of multi-factorial autoimmune disorders. Proteomic platforms offering a multiplex approach are more likely to reflect the complexity of autoimmune disease processes. Findings from proteomic based studies of three distinct autoimmune diseases are presented and strategies compared. It is the authors' view that such approaches are likely to be fruitful in the movement of autoimmune disease treatment away from reactive decisions and towards a preventative stand point.

A TMA De-Arraying Method for High Throughput Biomarker Discovery in Tissue Research

Background: Tissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide.

Methodology: This study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap.

Conclusion: This study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly.

Implications for Powering Biomarker Discovery Studies

This study examined variations in gene expression between FFPE blocks within tumors of individual patients. Microarray data were used to measure tumor heterogeneity within and between patients and disease states. Data were used to determine the number of samples needed to power biomarker discovery studies. Bias and variation in gene expression were assessed at the intrapatient and interpatient levels and between adenocarcinoma and squamous samples. A mixed-model analysis of variance was fitted to gene expression data and model signatures to assess the statistical significance of observed variations within and between samples and disease states. Sample size analysis, adjusted for sample heterogeneity, was used to determine the number of samples required to support biomarker discovery studies. Variation in gene expression was observed between blocks taken from a single patient. However, this variation was considerably less than differences between histological characteristics. This degree of block-to-block variation still permits biomarker discovery using either macrodissected tumors or whole FFPE sections, provided that intratumor heterogeneity is taken into account. Failure to consider intratumor heterogeneity may result in underpowered biomarker studies that may result in either the generation of longer gene signatures or the inability to identify a viable biomarker. Moreover, the results of this study indicate that a single biopsy sample is suitable for applying a biomarker in nonsmall-cell lung cancer. © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology.

PICan: An integromics framework for dynamic cancer biomarker discovery

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

MS-based clinical proteomics: biomarker discovery in men’s cancer

Importance of biomarker discovery in men’s cancer diagnosis and prognosis Each year around 10,000 men in the UK die as a result of prostate cancer (PCa) making it the 3rd most common cancer behind lung and breast cancer; worldwide more than 670,000 men are diagnosed every year with the disease [1]. Current methods of diagnosis of PCa mainly rely on the detection of elevated prostate-specific antigen (PSA) levels in serum and/or physical examination by a doctor for the detection of an abnormal prostate. PSA is a glycoprotein produced almost exclusively by the epithelial cells of the prostate gland [2]. Its role is not fully understood, although it is known that it forms part of the ejaculate and its function is to solubilise the sperm to give them the mobility to swim. Raised PSA levels in serum are thought to be due to both an increased production of PSA from the proliferated prostate cells, and a diminished architecture of affected cells, allowing an easier distribution of PSA into the wider circulatory system.

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.

MALDI mass spectrometry in prostate cancer biomarker discovery

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) is a highly versatile and sensitive analytical technique, which is known for its soft ionisation of biomolecules such as peptides and proteins. Generally, MALDI MS analysis requires little sample preparation, and in some cases like MS profiling it can be automated through the use of robotic liquid-handling systems. For more than a decade now, MALDI MS has been extensively utilised in the search for biomarkers that could aid clinicians in diagnosis, prognosis, and treatment decision making. This review examines the various MALDI-based MS techniques like MS imaging, MS profiling and proteomics in-depth analysis where MALDI MS follows fractionation and separation methods such as gel electrophoresis, and how these have contributed to prostate cancer biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Genomics based biomarker discovery in oral squamous cell carcinoma

 The present thesis showed signaling mechanisms and pathways essential for oral cancer progression through genomics approach. It has identified markers that are of diagnostic, prognostic and therapeutic importance. It has also shown that aspirin is a potential drug in oral cancer treatment.

Mass spectrometry-based protein profiling strategies for biomarker discovery in liver and inflammatory bowel diseases

The study of protein expression profiles for biomarker discovery in serum and in mammalian cell populations needs the continuous improvement and combination of proteins/peptides separation techniques, mass spectrometry, statistical and bioinformatic approaches. In this thesis work two different mass spectrometry-based protein profiling strategies have been developed and applied to liver and inflammatory bowel diseases (IBDs) for the discovery of new biomarkers. The first of them, based on bulk solid-phase extraction combined with matrix-assisted laser desorption/ionization - Time of Flight mass spectrometry (MALDI-TOF MS) and chemometric analysis of serum samples, was applied to the study of serum protein expression profiles both in IBDs (Crohn’s disease and ulcerative colitis) and in liver diseases (cirrhosis, hepatocellular carcinoma, viral hepatitis). The approach allowed the enrichment of serum proteins/peptides due to the high interaction surface between analytes and solid phase and the high recovery due to the elution step performed directly on the MALDI-target plate. Furthermore the use of chemometric algorithm for the selection of the variables with higher discriminant power permitted to evaluate patterns of 20-30 proteins involved in the differentiation and classification of serum samples from healthy donors and diseased patients. These proteins profiles permit to discriminate among the pathologies with an optimum classification and prediction abilities. In particular in the study of inflammatory bowel diseases, after the analysis using C18 of 129 serum samples from healthy donors and Crohn’s disease, ulcerative colitis and inflammatory controls patients, a 90.7% of classification ability and a 72.9% prediction ability were obtained. In the study of liver diseases (hepatocellular carcinoma, viral hepatitis and cirrhosis) a 80.6% of prediction ability was achieved using IDA-Cu(II) as extraction procedure. The identification of the selected proteins by MALDITOF/ TOF MS analysis or by their selective enrichment followed by enzymatic digestion and MS/MS analysis may give useful information in order to identify new biomarkers involved in the diseases. The second mass spectrometry-based protein profiling strategy developed was based on a label-free liquid chromatography electrospray ionization quadrupole - time of flight differential analysis approach (LC ESI-QTOF MS), combined with targeted MS/MS analysis of only identified differences. The strategy was used for biomarker discovery in IBDs, and in particular of Crohn’s disease. The enriched serum peptidome and the subcellular fractions of intestinal epithelial cells (IECs) from healthy donors and Crohn’s disease patients were analysed. The combining of the low molecular weight serum proteins enrichment step and the LCMS approach allowed to evaluate a pattern of peptides derived from specific exoprotease activity in the coagulation and complement activation pathways. Among these peptides, particularly interesting was the discovery of clusters of peptides from fibrinopeptide A, Apolipoprotein E and A4, and complement C3 and C4. Further studies need to be performed to evaluate the specificity of these clusters and validate the results, in order to develop a rapid serum diagnostic test. The analysis by label-free LC ESI-QTOF MS differential analysis of the subcellular fractions of IECs from Crohn’s disease patients and healthy donors permitted to find many proteins that could be involved in the inflammation process. Among them heat shock protein 70, tryptase alpha-1 precursor and proteins whose upregulation can be explained by the increased activity of IECs in Crohn’s disease were identified. Follow-up studies for the validation of the results and the in-depth investigation of the inflammation pathways involved in the disease will be performed. Both the developed mass spectrometry-based protein profiling strategies have been proved to be useful tools for the discovery of disease biomarkers that need to be validated in further studies.

Radiation metabolomics. 4. UPLC-ESI-QTOFMS-Based metabolomics for urinary biomarker discovery in gamma-irradiated rats

Radiation metabolomics has aided in the identification of a number of biomarkers in cells and mice by ultra-performance liquid chromatography-coupled time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) and in rats by gas chromatography-coupled mass spectrometry (GCMS). These markers have been shown to be both dose- and time-dependent. Here UPLC-ESI-QTOFMS was used to analyze rat urine samples taken from 12 rats over 7 days; they were either sham-irradiated or γ-irradiated with 3 Gy after 4 days of metabolic cage acclimatization. Using multivariate data analysis, nine urinary biomarkers of γ radiation in rats were identified, including a novel mammalian metabolite, N-acetyltaurine. These upregulated urinary biomarkers were confirmed through tandem mass spectrometry and comparisons with authentic standards. They include thymidine, 2'-deoxyuridine, 2'deoxyxanthosine, N(1)-acetylspermidine, N-acetylglucosamine/galactosamine-6-sulfate, N-acetyltaurine, N-hexanoylglycine, taurine and, tentatively, isethionic acid. Of these metabolites, 2'-deoxyuridine and thymidine were previously identified in the rat by GCMS (observed as uridine and thymine) and in the mouse by UPLC-ESI-QTOFMS. 2'Deoxyxanthosine, taurine and N-hexanoylglycine were also seen in the mouse by UPLC-ESI-QTOFMS. These are now unequivocal cross-species biomarkers for ionizing radiation exposure. Downregulated biomarkers were shown to be related to food deprivation and starvation mechanisms. The UPLC-ESI-QTOFMS approach has aided in the advance for finding common biomarkers of ionizing radiation exposure.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Radiation metabolomics. 3. Biomarker discovery in the urine of gamma-irradiated rats using a simplified metabolomics protocol of gas chromatography-mass spectrometry combined with random forests machine learning algorithm

Abstract Radiation metabolomics employing mass spectral technologies represents a plausible means of high-throughput minimally invasive radiation biodosimetry. A simplified metabolomics protocol is described that employs ubiquitous gas chromatography-mass spectrometry and open source software including random forests machine learning algorithm to uncover latent biomarkers of 3 Gy gamma radiation in rats. Urine was collected from six male Wistar rats and six sham-irradiated controls for 7 days, 4 prior to irradiation and 3 after irradiation. Water and food consumption, urine volume, body weight, and sodium, potassium, calcium, chloride, phosphate and urea excretion showed major effects from exposure to gamma radiation. The metabolomics protocol uncovered several urinary metabolites that were significantly up-regulated (glyoxylate, threonate, thymine, uracil, p-cresol) and down-regulated (citrate, 2-oxoglutarate, adipate, pimelate, suberate, azelaate) as a result of radiation exposure. Thymine and uracil were shown to derive largely from thymidine and 2'-deoxyuridine, which are known radiation biomarkers in the mouse. The radiation metabolomic phenotype in rats appeared to derive from oxidative stress and effects on kidney function. Gas chromatography-mass spectrometry is a promising platform on which to develop the field of radiation metabolomics further and to assist in the design of instrumentation for use in detecting biological consequences of environmental radiation release.

Novel ageing-biomarker discovery using data-intensive technologies

Ageing is accompanied by many visible characteristics. Other biological and physiological markers are also well-described e.g. loss of circulating sex hormones and increased inflammatory cytokines. Biomarkers for healthy ageing studies are presently predicated on existing knowledge of ageing traits. The increasing availability of data-intensive methods enables deep-analysis of biological samples for novel biomarkers. We have adopted two discrete approaches in MARK-AGE Work Package 7 for biomarker discovery; (1) microarray analyses and/or proteomics in cell systems e.g. endothelial progenitor cells or T cell ageing including a stress model; and (2) investigation of cellular material and plasma directly from tightly-defined proband subsets of different ages using proteomic, transcriptomic and miR array. The first approach provided longitudinal insight into endothelial progenitor and T cell ageing.This review describes the strategy and use of hypothesis-free, data-intensive approaches to explore cellular proteins, miR, mRNA and plasma proteins as healthy ageing biomarkers, using ageing models and directly within samples from adults of different ages. It considers the challenges associated with integrating multiple models and pilot studies as rational biomarkers for a large cohort study. From this approach, a number of high-throughput methods were developed to evaluate novel, putative biomarkers of ageing in the MARK-AGE cohort.