The discovery and validation of colorectal cancer biomarkers

A Meta-Analysis of Proteomic Blood Markers of Colorectal Cancer

BACKGROUND

Early diagnosis will significantly improve the survival rate of colorectal cancer (CRC); however, the existing methods for CRC screening were either invasive or inefficient. There is an emergency need for novel markers in CRC's early diagnosis. Serum proteomics has gained great potential in discovering novel markers, providing markers that reflect the early stage of cancer and prognosis prediction of CRC. In this paper, the results of proteomics of CRC studies were summarized through a meta-analysis in order to obtain the diagnostic efficiency of novel markers.

METHODS

A systematic search on bibliographic databases was performed to collect the studies that explore blood-based markers for CRC applying proteomics. The detection and validation methods, as well as the specificity and sensitivity of the biomarkers in these studies, were evaluated. Newcastle- Ottawa Scale (NOS) case-control studies version was used for quality assessment of included studies.

RESULTS

Thirty-four studies were selected from 751 studies, in which markers detected by proteomics were summarized. In total, fifty-nine proteins were classified according to their biological function. The sensitivity, specificity, or AUC varied among these markers. Among them, Mammalian STE20-like protein kinase 1/ Serine threonine kinase 4 (MST1/STK4), S100 calcium-binding protein A9 (S100A9), and Tissue inhibitor of metalloproteinases 1 (TIMP1) were suitable for effect sizes merging, and their diagnostic efficiencies were recalculated after merging. MST1/STK4 obtained a sensitivity of 68% and a specificity of 78%. S100A9 achieved a sensitivity of 72%, a specificity of 83%, and an AUC of 0.88. TIMP1 obtained a sensitivity of 42%, a specificity of 88%, and an AUC of 0.71.

CONCLUSION

MST1/STK4, S100A9, and TIMP1 showed excellent performance for CRC detection. Several other markers also presented optimized diagnostic efficacy for CRC early detection, but further verification is still needed before they are suitable for clinical use. The discovering of more efficient markers will benefit CRC treatment.

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Targeted Proteomics for Multiplexed Verification of Markers of Colorectal Tumorigenesis

Targeted proteomic methods can accelerate the verification of multiple tumor marker candidates in large series of patient samples. We utilized the targeted approach known as selected/multiple reaction monitoring (S/MRM) to verify potential protein markers of colorectal adenoma identified by our group in previous transcriptomic and quantitative shotgun proteomic studies of a large cohort of precancerous colorectal lesions. We developed SRM assays to reproducibly detect and quantify 25 (62.5%) of the 40 selected proteins in an independent series of precancerous and cancerous tissue samples (19 adenoma/normal mucosa pairs; 17 adenocarcinoma/normal mucosa pairs). Twenty-three proteins were significantly up-regulated (n = 17) or downregulated (n = 6) in adenomas and/or adenocarcinomas, as compared with normal mucosa (linear fold changes ≥ ±1.3, adjusted p value <0.05). Most changes were observed in both tumor types (up-regulation of ANP32A, ANXA3, SORD, LDHA, LCN2, NCL, S100A11, SERPINB5, CDV3, OLFM4, and REG4; downregulation of ARF6 and PGM5), and a five-protein biomarker signature distinguished neoplastic tissue from normal mucosa with a maximum area under the receiver operating curve greater than 0.83. Other changes were specific for adenomas (PPA1 and PPA2 up-regulation; KCTD12 downregulation) or adenocarcinoma (ANP32B, G6PD, RCN1, and SET up-regulation; downregulated AKR1B1, APEX1, and PPA1). Some changes significantly correlated with a few patient- or tumor-related phenotypes. Twenty-two (96%) of the 23 proteins have a potential to be released from the tumors into the bloodstream, and their detectability in plasma has been previously reported. The proteins identified in this study expand the pool of biomarker candidates that can be used to develop a standardized precolonoscopy blood test for the early detection of colorectal tumors.

Mass spectrometry based translational proteomics for biomarker discovery and application in colorectal cancer

Colorectal cancer (CRC) is a leading cause of cancer-related death in the world. Clinically, early detection of the disease is the most effective approach to tackle this tough challenge. Discovery and development of reliable and effective diagnostic tools for the assessment of prognosis and prediction of response to drug therapy are urgently needed for personalized therapies and better treatment outcomes. Among many ongoing efforts in search for potential CRC biomarkers, MS-based translational proteomics provides a unique opportunity for the discovery and application of protein biomarkers toward better CRC early detection and treatment. This review updates most recent studies that use preclinical models and clinical materials for the identification of CRC-related protein markers. Some new advances in the development of CRC protein markers such as CRC stem cell related protein markers, SRM/MRM-MS and MS cytometry approaches are also discussed in order to address future directions and challenges from bench translational research to bedside clinical application of CRC biomarkers.

Label-free quantitative mass spectrometry reveals a panel of differentially expressed proteins in colorectal cancer

To identify potential biomarkers involved in CRC, a shotgun proteomic method was applied to identify soluble proteins in three CRCs and matched normal mucosal tissues using high-performance liquid chromatography and mass spectrometry. Label-free protein profiling of three CRCs and matched normal mucosal tissues were then conducted to quantify and compare proteins. Results showed that 67 of the 784 identified proteins were linked to CRC (28 upregulated and 39 downregulated). Gene Ontology and DAVID databases were searched to identify the location and function of differential proteins that were related to the biological processes of binding, cell structure, signal transduction, cell adhesion, and so on. Among the differentially expressed proteins, tropomyosin-3 (TPM3), endoplasmic reticulum resident protein 29 (ERp29), 18 kDa cationic antimicrobial protein (CAMP), and heat shock 70 kDa protein 8 (HSPA8) were verified to be upregulated in CRC tissue and seven cell lines through western blot analysis. Furthermore, the upregulation of TPM3, ERp29, CAMP, and HSPA8 was validated in 69 CRCs byimmunohistochemistry (IHC) analysis. Combination of TPM3, ERp29, CAMP, and HSPA8 can identify CRC from matched normal mucosal achieving an accuracy of 73.2% using IHC score. These results suggest that TPM3, ERp29, CAMP, and HSPA8 are great potential IHC diagnostic biomarkers for CRC.

Proteomics for discovery of candidate colorectal cancer biomarkers

Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in Europe and other Western countries, mainly due to the lack of well-validated clinically useful biomarkers with enough sensitivity and specificity to detect this disease at early stages. Although it is well known that the pathogenesis of CRC is a progressive accumulation of mutations in multiple genes, much less is known at the proteome level. Therefore, in the last years many proteomic studies have been conducted to find new candidate protein biomarkers for diagnosis, prognosis and as therapeutic targets for this malignancy, as well as to elucidate the molecular mechanisms of colorectal carcinogenesis. An important advantage of the proteomic approaches is the capacity to look for multiple differentially expressed proteins in a single study. This review provides an overview of the recent reports describing the different proteomic tools used for the discovery of new protein markers for CRC such as two-dimensional electrophoresis methods, quantitative mass spectrometry-based techniques or protein microarrays. Additionally, we will also focus on the diverse biological samples used for CRC biomarker discovery such as tissue, serum and faeces, besides cell lines and murine models, discussing their advantages and disadvantages, and summarize the most frequently identified candidate CRC markers.

Identification alpha-2-HS-glycoprotein precursor and tubulin beta chain as serology diagnosis biomarker of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) remains a major worldwide cause of cancer-related morbidity and mortality largely due to the insidious onset of the disease. The current clinical procedures utilized for disease diagnosis are invasive, unpleasant, and inconvenient. Hence, the need for simple blood tests that could be used for the early detection is crucial for its ultimate control and prevention.

METHODS

The present work is a case-control study focused on proteomic analysis of serum of healthy volunteers and CRC patients by the ClinProt profiling technology based on mass spectrometry. This approach allowed to identifying a pattern of proteins/peptides able to differentiate the studied populations. Moreover, some of peptides differentially expressed in the serum of patients as compared to healthy volunteers were identified by LTQ Orbitrap XL.

RESULTS

A Quick Classifier Algorithm was used to construct the peptidome patterns (m/z 1208, 1467, 1505, 1618, 1656 and 4215) for the identification of CRC from healthy volunteers with accuracy close to 100% (>CEA, P < 0.05). Peaks at m/z 1505 and 1618 were identified as alpha-2-HS-glycoprotein precursor and tubulin beta chain, respectively.

CONCLUSIONS

Alpha-2-HS-glycoprotein precursor and tubulin beta chain could be involved in the pathogenesis of CRC and perform as potential serology diagnosis biomarker.

VIRTUAL SLIDES

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/4796578761089186.

Evaluation of specific fecal protein biochips for the diagnosis of colorectal cancer

AIM: To develop and initially test a potential fecal protein biochip for the screening of colorectal cancer (CRC).

METHODS: Fecal protein from 20 colorectal cancer patients and 20 healthy controls were extracted from all of the fecal samples and screened for proteomic differences using a Biotin label-based protein array. Candidate proteins were then verified by ELISA. Finally, we will select out the significant protein and a seven-target multiplex fecal protein biochip was generated and tested for 20 fecal samples to determine the effectiveness of the biochip on identifying CRC. And the value of the protein biochip would be discussed.

RESULTS: After tested by protein biochip of the fecal protein from 20 colorectal cancer patients and 20 healthy controls and levels of calprotectin, M2-pyruvatekinase, angiopoietin-2, fibroblast growth factor-23 (FGF-23), proteins of the matrix metalloproteinase, thrombopoietin (TPO) and interleukin-13 (IL-13) were significantly different between CRC and healthy controls. The sensitivity of all the seven proteins combined was 0.7, specificity was 0.4, and area under the receiver operating characteristics was 0.729. The most promising combinations of test proteins were FGF-23, TPO, and IL-13, reaching a sensitivity of 0.7 and a specificity of 0.7. The combination of FGF-23 and TPO scored highest with sensitivity of 0.7 and specificity of 0.8. Its mean that the combination of FGF-23 and TPO has the highest value for the diagnosis of CRC in our study.

CONCLUSION: A protein biochip composed of proteins found to be elevated in the feces of colorectal cancer patients has great potential as a noninvasive diagnostic for colorectal cancer. The addition of new protein biomarkers and technologies, as they are discovered, is an excellent avenue of future research.

Metabolomic profiling in colorectal cancer: opportunities for personalized medicine

Colorectal cancer (CRC) is one of the most common types of cancer in the world, with high prevalence and mortality. Understanding the alterations of cancer metabolism and identifying reliable biomarkers would facilitate the development of novel technologies of CRC screening and early diagnosis, as well as new approaches to providing personalized medicine. Metabolomics, as an emerging molecular phenotyping approach, provides a clinical platform technology with an unprecedented amount of metabolic readout information, which is ideal for theranostic biomarker discovery. Metabolic signatures can link the unique pathophysiological states of patients to personalized health monitoring and intervention strategies. This article presents an overview of the metabolomic studies of CRC with a focus on recent advances in the biomarker discovery in serum, urine, fecal water and tissue samples for cancer diagnosis. The development and application of metabolomics towards personalized medicine, including early diagnosis, cancer staging, treatment and drug discovery are also discussed.

The cancer secretome, current status and opportunities in the lung, breast and colorectal cancer context

Despite major improvements on the knowledge and clinical management, cancer is still a deadly disease. Novel biomarkers for better cancer detection, diagnosis and treatment prediction are urgently needed. Proteins secreted, shed or leaking from the cancer cell, collectively termed the cancer secretome, are promising biomarkers since they might be detectable in blood or other biofluids. Furthermore, the cancer secretome in part represents the tumor microenvironment that plays a key role in tumor promoting processes such as angiogenesis and invasion. The cancer secretome, sampled as conditioned medium from cell lines, tumor/tissue interstitial fluid or tumor proximal body fluids, can be studied comprehensively by nanoLC-MS/MS-based approaches. Here, we outline the importance of current cancer secretome research and describe the mass spectrometry-based analysis of the secretome. Further, we provide an overview of cancer secretome research with a focus on the three most common cancer types: lung, breast and colorectal cancer. We conclude that the cancer secretome research field is a young, but rapidly evolving research field. Up to now, the focus has mainly been on the discovery of novel promising secreted cancer biomarker proteins. An interesting finding that merits attention is that in cancer unconventional secretion, e.g. via vesicles, seems increased. Refinement of current approaches and methods and progress in clinical validation of the current findings are vital in order to move towards applications in cancer management. This article is part of a Special Issue entitled: An Updated Secretome.

Predicting outcomes in radiation oncology--multifactorial decision support systems

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

Comprehensive fluorogenic derivatization-liquid chromatography/tandem mass spectrometry proteomic analysis of colorectal cancer cell to identify biomarker candidate

Existing colorectal cancer biomarkers are insufficient for providing a quick and accurate diagnosis, which is critical for a good prognosis. More appropriate biomarkers are thus needed. To identify new colorectal cancer biomarker candidates, we conducted a comprehensive differential proteomic analysis of six cancer cell lines and a normal cell line, utilizing a fluorogenic derivatization-liquid chromatography-tandem mass spectrometry (FD-LC-MS/MS) approach. Two sets of intracellular biomarker candidates were identified: one for colorectal cancer, and the other for metastatic colorectal cancer. Our results suggest that cooperative expression of FABP5 and cyclophilin A might be linked to Her2 signaling. Upregulation of LDHB and downregulation of GAPDH suggest the existence of a specific nonglycolytic energy production pathway in metastatic colorectal cancer cells. Downregulation of 14-3-3ζ/δ, cystatin-B, Ran and thioredoxin could be a result of their secretion, which then stimulates metastasis via activity in the sera and ascitic fluids. We propose a possible flow scheme to describe the dynamics of protein expression in colorectal cancer cells leading to tumor progression and metastasis via cell proliferation, angiogenesis, disorganization of actin filaments and epithelial-mesenchymal transition. Our results suggest that colorectal tumor progression may be regulated by signaling mediated by Her2, hypoxia, and TGFβ.

Proteomics to study the diversity and dynamics of proteasome complexes: from fundamentals to the clinic

This article covers the latest contributions of proteomics to the structural and functional characterization of proteasomes and their associated proteins, but also to the detection of proteasomes as clinical biomarkers in diseases. Proteasomes are highly heterogenous supramolecular complexes and constitute important cellular proteases controlling the pool of proteins involved in key cellular functions. The comprehension of the structure/function relationship of proteasomes is therefore of major interest in biology. Numerous biochemical methods have been employed to purify proteasomes, and have led to the identification of complexes of various compositions - depending on the experimental conditions and the type of strategy used. In association with protein separation and enrichment techniques, modern mass spectrometry instruments and mass spectrometry-based quantitative methods, they have led to unprecedented breakthroughs in the in-depth analysis of the diversity and dynamics of proteasome composition and localization under various stimuli or pathological contexts. Proteasome inhibitors are now used in clinics for the treatment of cancer, and recent studies propose that the proteasome should be considered as a predictive biomarker for various pathologies.

Non-invasive detection of candidate pregnancy protein biomarkers in the feces of captive polar bears (Ursus maritimus)

Currently, there is no method of accurately and non-invasively diagnosing pregnancy in polar bears. Specific proteins may exhibit altered profiles in the feces of pregnant bears, but predicting appropriate candidate proteins to investigate is speculative at best. The objective of this study was to identify potential pregnancy biomarker proteins based on their increased abundance in the feces of pregnant polar bears compared to pseudopregnant females (controls) using two-dimensional in-gel electrophoresis (2D-DIGE) and mass spectrometry (MS). Three 2D-DIGE gels were performed to evaluate fecal protein profiles from controls (n=3) and pregnant polar bears (n=3). There were 2224.67±52.39 (mean±SEM) spots resolved per gel. Of these, only five proteins were elevated in the pregnant group (P<0.05), and seven additional spots tended to be higher (0.05<P<0.10). All 12 were submitted for MS analysis and the identities of 11 were ascertained with a >99.9% confidence interval. The 11 spots represented seven distinct proteins, five of which were significantly more abundant in the pregnant group: IgGFc-binding protein, filamin-C, carboxypeptidase B, transthyretin, and immunoglobulin heavy chain variable region. To our knowledge, this was the first study that employed 2D-DIGE to identify differentially expressed proteins in fecal samples to characterize a physiological condition other than those related to gastrointestinal disorders. These promising results provided a strong foundation for ensuing efforts to develop a non-invasive pregnancy assay for use in both captive and wild polar bears.

Activated leukocyte cell adhesion molecule (CD166)--its prognostic power for colorectal cancer patients

BACKGROUND

The activated leukocyte cell adhesion molecule (ALCAM, CD166) has been reported to be involved in tumorigenesis of colorectal cancer (CRC) and to function as a cancer stem cell marker. Controversial data exist regarding the prognostic power of ALCAM expression in CRC. Here, we evaluate the expression of ALCAM in a cohort of CRC patients and its usage as a prognostic marker for survival.

MATERIALS AND METHODS

Tissue specimens from 299 patients with CRC treated between 1993 and 2006 were analyzed via ALCAM immunohistochemistry (clone MOG/07) using a tissue microarray. Results were correlated with clinical, histopathological, and patient survival data (Chi-square test, Kaplan-Meier analysis, and log-rank test, respectively). Multivariate analysis also was performed (Cox regression).

RESULTS

ALCAM is expressed in most primary (76%) and secondary (62%) CRC lesions (P = 0.014). Immunohistochemistry revealed an inverse association with tumor grading (P = 0.002) but not with any other clinical or histopathological data. Kaplan-Meier survival analysis revealed a significant overall survival benefit in the group of ALCAM-positive patients (P = 0.019). Multivariate analysis showed that ALCAM is an independent positive prognostic marker for overall survival (P = 0.023).

CONCLUSIONS

ALCAM expression is a positive prognostic marker for overall survival of CRC patients, and its detection might help to optimize the existing prognostic staging system. Elevated expression in higher differentiated tumors might indicate a potential role in the early steps of tumorigenesis, and its loss might be associated with reduced cellular adhesion, resulting in a higher metastatic potential of the tumor. Further studies must be conducted investigating these hypotheses.

Characterization of the gastrointestinal microbiota in health and inflammatory bowel disease

The enteric bacterial flora play a key role in maintaining health. Inflammatory bowel disease is associated with quantitative and qualitative alterations in the microbiota. Early characterization of the microbiota involved culture-dependent techniques. The advent of metagenomic techniques, however, allows for structural and functional characterization using culture-independent methods. Changes in diversity, together with quantitative alterations in specific bacterial species, have been identified. The functional significance of these changes, and their pathogenic role, remain to be elucidated.

Quantitative targeted absolute proteomics-based ADME research as a new path to drug discovery and development: methodology, advantages, strategy, and prospects

An understanding of the functional roles of proteins, for example, in drug absorption, distribution, metabolism, elimination, toxicity, and efficacy (ADMET/efficacy), is important for drug discovery and development. Equally, detailed information about protein expression is required. Recently, a new protein quantification method, called quantitative targeted absolute proteomics (QTAP), has been developed on the basis of separation and identification of protein digests by liquid chromatography-linked tandem mass spectrometry with multiple reaction monitoring. Target peptides for quantification are selected only from sequence information, so time-consuming procedures such as antibody preparation and protein purification are unnecessary. In this review, we introduce the technical features of QTAP and summarize its advantages with reference to recently reported results. These include the evaluation of species differences of blood-brain barrier protein levels among human, monkey, and mouse. The high selectivity of QTAP and its ability to quantify multiple proteins simultaneously make it possible to determine the absolute expression levels of many proteins in tissues and cells in both physiological and disease states. Knowledge of absolute expression amounts, together with data on intrinsic protein activity, allows us to reconstruct in vivo protein function, and this should be an efficient strategy to predict ADMET/efficacy of drug candidates in humans in various disease states.