An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics

An Overview of Advances in Rare Cancer Diagnosis and Treatment

Cancer stands as the leading global cause of mortality, with rare cancer comprising 230 distinct subtypes characterized by infrequent incidence. Despite the inherent challenges in addressing the diagnosis and treatment of rare cancers due to their low occurrence rates, several biomedical breakthroughs have led to significant advancement in both areas. This review provides a comprehensive overview of state-of-the-art diagnostic techniques that encompass new-generation sequencing and multi-omics, coupled with the integration of artificial intelligence and machine learning, that have revolutionized rare cancer diagnosis. In addition, this review highlights the latest innovations in rare cancer therapeutic options, comprising immunotherapy, targeted therapy, transplantation, and drug combination therapy, that have undergone clinical trials and significantly contribute to the tumor remission and overall survival of rare cancer patients. In this review, we summarize recent breakthroughs and insights in the understanding of rare cancer pathophysiology, diagnosis, and therapeutic modalities, as well as the challenges faced in the development of rare cancer diagnosis data interpretation and drug development.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives

Colorectal cancer (CRC) ranks third among the most commonly occurring cancers worldwide, and it causes half a million deaths annually. Alongside mechanistic study for CRC detection and treatment by conventional techniques, new technologies have been developed to study CRC. These technologies include genomics, transcriptomics, proteomics, and metabolomics which elucidate DNA markers, RNA transcripts, protein and, metabolites produced inside the colon and rectum part of the gut. All these approaches form the omics arena, which presents a remarkable opportunity for the discovery of novel prognostic, diagnostic and therapeutic biomarkers and also delineate the underlying mechanism of CRC causation, which may further help in devising treatment strategies. This review also mentions the latest developments in metagenomics and culturomics as emerging evidence suggests that metagenomics of gut microbiota has profound implications in the causation, prognosis, and treatment of CRC. A majority of bacteria cannot be studied as they remain unculturable, so culturomics has also been strengthened to develop culture conditions suitable for the growth of unculturable bacteria and identify unknown bacteria. The overall purpose of this review is to succinctly evaluate the application of omics technologies in colorectal cancer research for improving the diagnosis and treatment strategies.

Implementation of MALDI Mass Spectrometry Imaging in Cancer Proteomics Research: Applications and Challenges

Studying the proteome–the entire set of proteins in cells, tissues, organs and body fluids—is of great relevance in cancer research, as differential forms of proteins are expressed in response to specific intrinsic and extrinsic signals. Discovering protein signatures/pathways responsible for cancer transformation may lead to a better understanding of tumor biology and to a more effective diagnosis, prognosis, recurrence and response to therapy. Moreover, proteins can act as a biomarker or potential drug targets. Hence, it is of major importance to implement proteomic, particularly mass spectrometric, approaches in cancer research, to provide new crucial insights into tumor biology. Recently, mass spectrometry imaging (MSI) approaches were implemented in cancer research, to provide individual molecular characteristics of each individual tumor while retaining molecular spatial distribution, essential in the context of personalized disease management and medicine.

Colorectal carcinoma screening: Established methods and emerging technology

Colorectal carcinoma screening programs have shown success in lowering both the incidence and mortality rate of colorectal carcinoma at a population level, in part because this carcinoma is relatively slow growing and has an identifiable premalignant lesion. Still, many patients do not undergo the recommended screening for colorectal carcinoma, and of those who do, a subset may be over- or under-diagnosed by the currently available testing methods. The primary purpose of this article is to review the data regarding currently available colorectal cancer screening modalities, which include fecal occult blood testing, direct colonic visualization, and noninvasive imaging techniques. In addition, readers will be introduced to a variety of biomarkers that may serve as stand-alone or adjunct tests in the future. Finally, there is a brief discussion of the current epidemiologic considerations that public health officials must address as they create population screening guidelines. The data we provide as laboratory physicians and scientists are critical to the construction of appropriate recommendations that ultimately decrease the burden of disease from colorectal carcinoma.

Serum peptidome profiling for the diagnosis of colorectal cancer: discovery and validation in two independent cohorts

Colorectal cancer (CRC) is one of the most common malignant neoplasms worldwide. Except for the existing fecal occult blood test, colonoscopy and sigmoidoscopy, no widely accepted in vitro diagnostic methods have been available. To identify potential peptide biomarkers for CRC, serum samples from a discovery cohort (100 CRC patients and 100 healthy controls) and an independent validation cohort (91 CRC patients and 91 healthy controls) were collected. Peptides were fractionated by weak cation exchange magnetic beads (MB-WCX) and analysed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Five peptides (peaks at m/z 1895.3, 2020.9, 2080.7, 2656.8 and 3238.5) were identified as candidate biomarkers for CRC. A diagnostic panel based on the five peptides can discriminate CRC patients from healthy controls, with an accuracy of 91.8%, sensitivity of 95.6%, and specificity of 87.9% in the validation cohort. Peptide peaks at m/z 1895.3, 2020.9 and 3238.5 were identified as the partial sequences of complement component 4 (C4), complement component 3 (C3) and fibrinogen α chain (FGA), respectively. This study potentiated peptidomic analysis as a promising in vitro diagnostic tool for diagnosis of CRC. The identified peptides suggest the involvement of the C3, C4 and FGA in CRC pathogenesis.

The use of Gene Ontology terms and KEGG pathways for analysis and prediction of oncogenes

BACKGROUND

Oncogenes are a type of genes that have the potential to cause cancer. Most normal cells undergo programmed cell death, namely apoptosis, but activated oncogenes can help cells avoid apoptosis and survive. Thus, studying oncogenes is helpful for obtaining a good understanding of the formation and development of various types of cancers.

METHODS

In this study, we proposed a computational method, called OPM, for investigating oncogenes from the view of Gene Ontology (GO) and biological pathways. All investigated genes, including validated oncogenes retrieved from some public databases and other genes that have not been reported to be oncogenes thus far, were encoded into numeric vectors according to the enrichment theory of GO terms and KEGG pathways. Some popular feature selection methods, minimum redundancy maximum relevance and incremental feature selection, and an advanced machine learning algorithm, random forest, were adopted to analyze the numeric vectors to extract key GO terms and KEGG pathways.

RESULTS

Along with the oncogenes, GO terms and KEGG pathways were discussed in terms of their relevance in this study. Some important GO terms and KEGG pathways were extracted using feature selection methods and were confirmed to be highly related to oncogenes. Additionally, the importance of these terms and pathways in predicting oncogenes was further demonstrated by finding new putative oncogenes based on them.

CONCLUSIONS

This study investigated oncogenes based on GO terms and KEGG pathways. Some important GO terms and KEGG pathways were confirmed to be highly related to oncogenes. We hope that these GO terms and KEGG pathways can provide new insight for the study of oncogenes, particularly for building more effective prediction models to identify novel oncogenes. The program is available upon request.

GENERAL SIGNIFICANCE

We hope that the new findings listed in this study may provide a new insight for the investigation of oncogenes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Development of a test to identify bladder cancer in the urine of patients using mass spectroscopy and subcellular localization of the detected proteins

The survival rate for bladder cancer is much better when the disease is detected early, so improvements in methodology for early detection would be beneficial. When urine contains neoplastic urothelial cells, it carries biomarkers of the disease. This study aims to develop a test for the detection of urothelial carcinoma in the urine. The sediments from urines of ten patients with carcinoma and ten randomly selected normal controls were tested for cancer biomarkers using high-resolution mass spectroscopy. 212 unique individual proteins were identified. Most of them occurred only once or twice in the entire cohort of cases. When sorting the detected proteins by their subcellular compartments, we were able to develop a test that differentiates between the two sets. When the combination of nuclear and red blood cell proteins was used as the discriminating function, the level of statistical significance was p=0.003, the sensitivity was 90%, the specificity 67% and the area under the Receiver-Operating Characteristic curve (ROC) was 94%. When the lack of any detectible proteins, which includes nuclear proteins, was included as a criterion indicating benign urine, the specificity increased to 80%. This use of cellular compartment localization of the detected proteins in the discriminating function is less restrictive than requiring the presence of specific proteins, and we were able to develop a screening test with this less stringent criterion. This approach can be applied to other tumors, such as breast, lung and colon cancers, where the need for a simple screening test is even greater.

Introducing biomarker panel in esophageal, gastric, and colon cancers; a proteomic approach

Cancer research is an attractive field in molecular biology and medicine. By applying large-scale tools such as advanced genomics and proteomics, cancer diagnosis and treatment have been improved greatly. Cancers of esophagus, gastric, and colon accounted for major health problem globally. Biomarker panel could bring out the accuracy for cancer evaluation tests as it can suggest a group of candidate molecules specified to particular malignancy in a way that distinguishing malignant tumors from benign, differentiating from other diseases, and identifying each stages with high specificity and sensitivity. In this review, a systematic search of unique protein markers reported by several proteomic literatures are classified in their specific cancer type group as novel panels for feasible accurate malignancy diagnosis and treatment. About thousands of introduced proteins were studied; however, a small number of them belonged to a specific kind of malignancy. In conclusion, despite the fact that combinatorial biomarkers appear to be hopeful, more evaluation of them is crucial to achieve the suitable biomarker panel for clinical application. This effort needs more investigations and researches for finding a specific and sensitive panel.

Biomarker Signature Discovery from Mass Spectrometry Data

Mass spectrometry based high throughput proteomics are used for protein analysis and clinical diagnosis. Many machine learning methods have been used to construct classifiers based on mass spectrometry data, for discrimination between cancer stages. However, the classifiers generated by machine learning such as SVM techniques typically lack biological interpretability. We present an innovative technique for automated discovery of signatures optimized to characterize various cancer stages. We validate our signature discovery algorithm on one new colorectal cancer MALDI-TOF data set, and two well-known ovarian cancer SELDI-TOF data sets. In all of these cases, our signature based classifiers performed either better or at least as well as four benchmark machine learning algorithms including SVM and KNN. Moreover, our optimized signatures automatically select smaller sets of key biomarkers than the black-boxes generated by machine learning, and are much easier to interpret.

Proteomic analysis of colorectal cancer: discovering novel biomarkers

Colorectal cancer is one of the most common cancers in the Western world. When detected at an early stage, the majority of cancers can be cured with current treatment modalities. However, most cancers present at an intermediate stage. The discovery of sensitive and specific biomarkers has the potential to improve preclinical diagnosis of primary and recurrent colorectal cancer, and holds the promise of prognostic and therapeutic application. Current biomarkers such as carcinoembryonic antigen lack sensitivity and specificity for general population screening. This review aims to highlight the role of current proteomic technologies in the discovery and validation of potential biomarkers with a view to translation to the clinic.

Combining proteomics, serum biomarkers and bioinformatics to discriminate between esophageal squamous cell carcinoma and pre-cancerous lesion

OBJECTIVE

Biomarker assay is a noninvasive method for the early detection of esophageal squamous cell carcinoma (ESCC). Searching for new biomarkers with high specificity and sensitivity is very important for the early detection of ESCC. Serum surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS) is a high throughput technology for identifying cancer biomarkers using drops of sera.

METHODS

In this study, 185 serum samples were taken from ESCC patients in a high incidence area and screened by SELDI. A support vector machine (SVM) algorithm was adopted to analyze the samples.

RESULTS

The SVM patterns successfully distinguished ESCC from pre-cancerous lesions (PCLs). Also, types of PCL, including dysplasia (DYS) and basal cell hyperplasia (BCH), and healthy controls (HC) were distinguished with an accuracy of 95.2% (DYS), 96.6% (BCH), and 93.8% (HC), respectively. A marker of 25.1 kDa was identified in the ESCC patterns whose peak intensity was observed to increase significantly during the development of esophageal carcinogenesis, and to decrease obviously after surgery.

CONCLUSIONS

We selected five ESCC biomarkers to form a diagnostic pattern which can discriminate among the different stages of esophageal carcinogenesis. This pattern can significantly improve the detection of ESCC.

Differential expression of colon cancer associated transcript1 (CCAT1) along the colonic adenoma-carcinoma sequence

BACKGROUND

The transition from normal epithelium to adenoma and, to invasive carcinoma in the human colon is associated with acquired molecular events taking 5-10 years for malignant transformation. We discovered CCAT1, a non-coding RNA over-expressed in colon cancer (CC), but not in normal tissues, thereby making it a potential disease-specific biomarker. We aimed to define and validate CCAT1 as a CC-specific biomarker, and to study CCAT1 expression across the adenoma-carcinoma sequence of CC tumorigenesis.

METHODS

Tissue samples were obtained from patients undergoing resection for colonic adenoma(s) or carcinoma. Normal colonic tissue (n = 10), adenomatous polyps (n = 18), primary tumor tissue (n = 22), normal mucosa adjacent to primary tumor (n = 16), and lymph node(s) (n = 20), liver (n = 8), and peritoneal metastases (n = 19) were studied. RNA was extracted from all tissue samples, and CCAT1 expression was analyzed using quantitative real time-PCR (qRT-PCR) with confirmatory in-situ hybridization (ISH).

RESULTS

Borderline expression of CCAT1 was identified in normal tissue obtained from patients with benign conditions [mean Relative Quantity (RQ) = 5.9]. Significant relative CCAT1 up-regulation was observed in adenomatous polyps (RQ = 178.6 ± 157.0; p = 0.0012); primary tumor tissue (RQ = 64.9 ± 56.9; p = 0.0048); normal mucosa adjacent to primary tumor (RQ = 17.7 ± 21.5; p = 0.09); lymph node, liver and peritoneal metastases (RQ = 11,414.5 ± 12,672.9; 119.2 ± 138.9; 816.3 ± 2,736.1; p = 0.0001, respectively). qRT-PCR results were confirmed by ISH, demonstrating significant correlation between CCAT1 up-regulation measured using these two methods.

CONCLUSION

CCAT1 is up-regulated across the colon adenoma-carcinoma sequence. This up-regulation is evident in pre-malignant conditions and through all disease stages, including advanced metastatic disease suggesting a role in both tumorigenesis and the metastatic process.

Candidate biomarker verification: Critical examination of a serum protein pattern for human colorectal cancer

PURPOSE

We critically examine a candidate serum protein pattern for human colorectal cancer (CRC) with respect to reproducibility, sample handling, and disease specificity.

EXPERIMENTAL DESIGN

Serum samples from CRC patients, patients with benign colon tumors and healthy individuals, were obtained at two collection sites and analyzed by SELDI-TOF MS on 8 days, over a period of 5 weeks. The spectra were subjected to multivariate analysis. Tissues from normal colon and CRC were analyzed by SELDI-TOF MS. Selected mass peaks were identified.

RESULTS

Using an elaborate experimental design we developed a multivariate classifier that correctly classified CRC and control serum measured on an independent day. The classifier did not discriminate between samples from CRC patients and patients with benign colon tumors, and, secondly, did not correctly classify serum from an independent collection site. All discriminatory mass peaks were identified as high abundant plasma proteins. Tissue profiling provided support of increased proteolytic activity in CRC tissue.

CONCLUSION AND CLINICAL RELEVANCE

Critical verification did not justify advancing the identified CRC serum protein pattern into clinical validation without improvement. We believe that proteomics biomarker research could benefit if the presented, or a similar, verification scheme was more commonly employed in explorative biomarker studies.

Identification of a new protein biomarker for colorectal cancer diagnosis

As one of the most common cancers, colorectal cancer (CRC) is a major public health issue worldwide. Thus, the identification of novel biomarkers to aid in the early diagnosis of CRC is crucial. The aim of the present study was to identify a novel protein biomarker for CRC, and to identify its structure. In this study, a total of 99 serum samples from 73 CRC patients and 26 healthy controls were collected and analyzed by SELDI-TOF-MS. The biomarkers were separated using HPLC and detected with MALDI-TOF-MS. The qualified peaks were ranked by p-value of non-parametric tests and the top 10 peaks displaying significant differences were selected. Among the 10 protein biomarkers, the concentration of a 3.9‑kDa protein in the serum of the CRC patients was much lower than that in the healthy controls. Therefore, the 3.9‑kDa protein was selected as a biomarker for CRC and its separation and purification were performed. The structure of the 3.9-kDa protein biomarker was determined by LC-MS/MS, and was confirmed to be a fragment of serine/theonine kinase 4 (MST1/STK4). The 3.9‑kDa protein biomarker had high sensitivity and specificity for CRC, and its potential clinical application warrants further investigation.

Screening for colorectal cancer: established and emerging modalities

It has been estimated that >95% of cases of colorectal cancer (CRC) would benefit from curative surgery if diagnosis was made at an early or premalignant polyp stage of disease. Over the past 10 years, most developed nation states have implemented mass population screening programs, which are typically targeted at the older (at-risk) age group (>50-60 years old). Conventional screening largely relies on periodic patient-centric investigation, particularly involving colonoscopy and flexible sigmoidoscopy, or else on the fecal occult blood test. These methods are compromised by either low cost-effectiveness or limited diagnostic accuracy. Advances in the development of diagnostic molecular markers for CRC have yielded an expanding list of potential new screening modalities based on investigations of patient stool (for colonocyte DNA mutations, epigenetic changes or microRNA expression) or blood specimens (for plasma DNA mutations, epigenetic changes, heteroplasmic mitochondrial DNA mutations, leukocyte transcriptome profile, plasma microRNA expression or protein and autoantibody expression). In this Review, we present a critical evaluation of the performance data and relative merits of these various new potential methods. None of these molecular diagnostic methods have yet been evaluated beyond the proof-of-principle and pilot-scale study stage and it could be some years before they replace existing methods for population screening in CRC.

Identification of biomarkers for colorectal cancer through proteomics-based approaches

The early detection of colorectal cancer is one of the great challenges in the battle against this disease. However, owing to its heterogeneous character, single markers are not likely to provide sufficient diagnostic power to be used in colorectal cancer population screens. This review provides an overview of recent studies aimed at the discovery of new diagnostic protein markers through proteomics-based approaches. It indicates that studies that start with the proteomic analysis of tumor tissue or tumor cell lines (near the source) have a high potential to yield novel and colorectal cancer-specific biomarkers. In the next step, the diagnostic accuracy of these candidate markers can be assessed by a targeted ELISA assay using serum from colorectal cancer patients and healthy controls. Instead, direct proteomic analysis of serum yields predominantly secondary markers composed of fragments of abundant serum proteins that may be associated with tumor-associated protease activity, and alternatively, immunoproteomic analysis of the serum antibody repertoire provides a valuable tool to identify the molecular imprint of colorectal cancer-associated antigens directly from patient serum samples. The latter approach also allows a relatively easy translation into targeted assays. Eventually, multimarker assays should be developed to reach a diagnostic accuracy that meets the stringent criteria for colorectal cancer screening at the population level.

Proteomic serum biomarkers and their potential application in cancer screening programs

Early diagnosis of cancer is of pivotal importance to reduce disease-related mortality. There is great need for non-invasive screening methods, yet current screening protocols have limited sensitivity and specificity. The use of serum biomarkers to discriminate cancer patients from healthy persons might be a tool to improve screening programs. Mass spectrometry based proteomics is widely applied as a technology for mapping and identifying peptides and proteins in body fluids. One commonly used approach in proteomics is peptide and protein profiling. Here, we present an overview of profiling methods that have the potential for implementation in a clinical setting and in national screening programs.

[Research progress in SELDI-TOF MS and its clinical applications]

Proteinchip profiling is a powerful and innovative proteomic technology for the discovery of biomarkers and the development of diagnostic/prognostic assays. On the basis of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), Ciphergen’s proteinchip system offers a single, unified, and high throughput platform for a multitude of proteomic research applications. Proteins are the major functional components of the cell. The study of proteomics helps to better understand the mechanism of a disease. Remarkable findings in disease biomarkers have shed light on the early diagnosis, monitoring, and prognosis of various diseases, especially for cancer. In this paper, the development and technology of SELDI-TOF MS are introduced. The research progress and encouraging research results in malignancies, infectious diseases, neurological diseases, and diabetes mellitus using SELDI-TOF MS are reviewed. This paper concludes by evaluating the pros and cons, and the future perspectives are also expounded.

Influence of sample storage duration on serum protein profiles assessed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS)

BACKGROUND

Issues have been raised concerning the robustness and validity of alleged serum markers discovered by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). Pre-analytical variables have been shown to exert a profound effect on protein profiles, irrespective of true biological variation. However, little is known about the possible effects of sample storage duration on protein profiles. We, therefore, aimed to investigate the effects of extended storage duration on the serum protein profile.

METHODS

Archival sera from 140 breast cancer patients, stored at -30 degrees C for 1-11 years, were profiled by SELDI-TOF MS using immobilised metal affinity capture (IMAC) arrays, a condition applied in the majority of breast cancer biomarker discovery studies.

RESULTS

Fourteen peak clusters, structurally identified as C3a des-arginine anaphylatoxin and multiple fragments of albumin and fibrinogen, were found to be significantly associated with sample storage duration by five distinct patterns. These proteins have been described previously as potential cancer markers, rendering them specific to both disease and sample handling issues.

CONCLUSIONS

To prevent experimental variation being interpreted erroneously as disease associated variation, assessment of potential confounding pre-analytical parameters is a pre-requisite in biomarker discovery and validation studies. In addition, with respect to the different (non-)linear patterns observed in the current study, simply performing linear corrections to account for sample storage duration will not necessarily suffice.

Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective

Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.

Mining novel biomarkers for prognosis of gastric cancer with serum proteomics

Background

Although gastric caner (GC) remains the second cause of cancer-related death, useful biomarkers for prognosis are still unavailable. We present here the attempt of mining novel biomarkers for GC prognosis by using serum proteomics.

Methods

Sera from 43 GC patients and 41 controls with gastritis as Group 1 and 11 GC patients as Group 2 was successively detected by Surface Enhanced Laser Desorption/ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) with Q10 chip. Peaks were acquired by Ciphergen ProteinChip Software 3.2.0 and analyzed by Zhejiang University-ProteinChip Data Analysis System (ZJU-PDAS). CEA level were evaluated by chemiluminescence immunoassay.

Results

After median follow-up periods of 33 months, Group 1 with 4 GC patients lost was divided into 20 good-prognosis GC patients (overall survival more than 24 months) and 19 poor-prognosis GC patients (no more than 24 months). The established prognosis pattern consisted of 5 novel prognosis biomarkers with 84.2% sensitivity and 85.0% specificity, which were significantly higher than those of carcinoembryonic antigen (CEA) and TNM stage. We also tested prognosis pattern blindly in Group 2 with 66.7% sensitivity and 80.0% specificity. Moreover, we found that 4474-Da peak elevated significantly in GC and was associated with advanced stage (III+IV) and short survival (p < 0.03).

Conclusion

We have identified a number of novel biomarkers for prognosis prediction of GC by using SELDI-TOF-MS combined with sophisticated bioinformatics. Particularly, elevated expression of 4474-Da peak showed very promising to be developed into a novel biomarker associated with biologically aggressive features of GC.

An integrated LC-ESI-MS platform for quantitation of serum peptide ladders. Application for colon carcinoma study

Mounting evidence indicates that MS analysis of the human blood peptidome allows to distinguish between cancer and non-cancer samples, giving promise for a new MS-based diagnostic tool. However, several aspects of already published work have been criticized and demand for more methodical approach has been formulated. Motivated by this we undertook a systematic study of the plasma and serum peptidome using an integrated ESI-LC-MS-based platform, equipped with new data analysis tools for relative and absolute peptide quantitation. We used a high resolution LC-ESI-MS to analyze well-separated MS signals corresponding to peptides, and measured the variability of >1000 peptide signal amplitudes across a set of plasma and serum samples from healthy individuals. By spiking serum samples with known amounts of isotopically labeled versions of a selected set of peptides we measured the variability of their absolute concentration in this sample set and demonstrated a strong influence of clotting time on the concentration of these peptides in serum. Finally, we used this new LC-ESI-MS analytical platform for the differential analysis of healthy versus colon cancer serum samples and found that it was possible to distinguish the two groups with 89.8% sensitivity and 94.6% specificity.

Clinical significance of tumor markers and an emerging perspective on colorectal cancer

Serum carcinoembryonic antigen (CEA) and CA19-9, a carbohydrate antigen recognized by the monoclonal antibody NS19-9, are commonly used as classical tumor markers in colorectal cancer (CRC) clinics. The roles of tumor markers include: (1) diagnostic screening (diagnostic markers); (2) prediction of prognosis after treatment (prognostic markers); and (3) judgment tools for treatment effect (surveillance markers). Tumor markers can be evaluated in serum, stools, or even in tissues depending on the clinical purpose. The American Society for Clinical Oncology recommends that CEA is the only marker of choice for monitoring the response of metastatic disease to systemic therapy at present. In the present paper, we are the first to review the clinical significance of the classical tumor markers CEA and CA19-9 in serum, allowing for our original data, and present our view on the newly emerging biomarkers in CRC. Novel promising biomarkers for diagnostic, prognostic, and surveillance purposes are reviewed and considered, some of which are anticipated for further validation. For diagnostic markers, urine or serum might replace fecal samples in the near future. On the other hand, prognostic or predictive markers for treatment sensitivity may be identified from the molecular profiles of primary cancer tissues. Selection of patients who are sensitive to chemotherapy will reduce the number of patients who undergo harmful chemotherapy with no effectiveness. The optimal tumor markers would be generalized, easy to assess, and accurate, and such markers are eagerly anticipated to enable personalized tailored therapy for CRC patients.

Assessment of serum proteomics to detect large colon adenomas

A noninvasive blood test that could reliably detect early colorectal cancer or large adenomas would provide an important advance in colon cancer screening. The purpose of this study was to determine whether a serum proteomics assay could discriminate between persons with and without a large (> or =1 cm) colon adenoma. To avoid problems of "bias" that have affected many studies about molecular markers for diagnosis, specimens were obtained from a previously conducted study of colorectal cancer etiology in which bloods had been collected before the presence or absence of neoplasm had been determined by colonoscopy, helping to assure that biases related to differences in sample collection and handling would be avoided. Mass spectra of 65 unblinded serum samples were acquired using a nanoelectrospray ionization source on a QSTAR-XL mass spectrometer. Classification patterns were developed using the ProteomeQuest algorithm, performing measurements twice on each specimen, and then applied to a blinded validation set of 70 specimens. After removing 33 specimens that had discordant results, the "test group" comprised 37 specimens that had never been used in training. Although in the primary analysis, no discrimination was found, a single post hoc analysis, done after hemolyzed specimens had been removed, showed a sensitivity of 78%, a specificity of 53%, and an accuracy of 63% (95% confidence interval, 53-72%). The results of this study, although preliminary, suggest that further study of serum proteomics, in a larger number of appropriate specimens, could be useful. They also highlight the importance of understanding sources of "noise" and "bias" in studies of proteomics assays.

Detection of Colorectal Cancer by Serum and Tissue Protein Profiling: A Prospective Study in a Population at Risk

Colorectal cancer (CRC) is the second most common cause of cancer-related death in Europe and its prognosis is largely dependent on stage at diagnosis. Currently, there are no suitable tumour markers for early detection of CRC. In a retrospective study we previously found discriminative CRC serum protein profiles with surface enhanced laser desorption ionisation—time of flight mass spectrometry (SELDI-TOF MS). We now aimed at prospective validation of these profiles. Additionally, we assessed their applicability for follow-up after surgery and investigated tissue protein profiles of patients with CRC and adenomatous polyps (AP). Serum and tissue samples were collected from patients without known malignancy with an indication for colonoscopy and patients with AP and CRC during colonoscopy. Serum samples of controls (CON; n = 359), patients with AP (n = 177) and CRC (n = 73), as well as tissue samples from AP (n = 52) and CRC (n = 47) were analysed as described previously. Peak intensities were compared by non-parametric testing. Discriminative power of differentially expressed proteins was assessed with support vector machines (SVM). We confirmed the decreased serum levels of apolipoprotein C-1 in CRC in the current population. No differences were observed between CON and AP. Apolipoprotein C-I levels did not change significantly within 1 month post-surgery, although a gradual return to normal levels was observed. Several proteins differed between AP and CRC tissue, among which a peak with similar mass as apolipoprotein C-1. This peak was increased in CRC compared to AP. Although we prospectively validated the serum decrease of apolipoprotein C-1 in CRC, serum protein profiles did not yield SVM classifiers with suitable sensitivity and specificity for classification of our patient groups.

Proteomic profiling of urine for the detection of colon cancer

Background

Colorectal cancer is the second most common cause of cancer related death in the developed world. To date, no blood or stool biomarkers with both high sensitivity and specificity for potentially curable early stage disease have been validated for clinical use. SELDI and MALDI profiling are being used increasingly to search for biomarkers in both blood and urine. Both techniques provide information predominantly on the low molecular weight proteome (<15 kDa). There have been several reports that colorectal cancer is associated with changes in the serum proteome that are detectable by SELDI and we hypothesised that proteomic changes would also be detectable in urine.

Results

We collected urine from 67 patients with colorectal cancer and 72 non-cancer control subjects, diluted to a constant protein concentration and generated MALDI and SELDI spectra. The intensities of 19 peaks differed significantly between cancer and non-cancer patients by both t-tests and after adjusting for confounders using multiple linear regressions. Logistic regression classifiers based on peak intensities identified colorectal cancer with up to 78% sensitivity at 87% specificity. We identified and independently quantified 3 of the discriminatory peaks using synthetic stable isotope peptides (an 1885 Da fragment of fibrinogen and hepcidin-20) or ELISA (β2-microglobulin).

Conclusion

Changes in the urine proteome may aid in the early detection of colorectal cancer.

Blood markers for early detection of colorectal cancer: a systematic review

BACKGROUND

Despite different available methods for colorectal cancer (CRC) screening and their proven benefits, morbidity, and mortality of this malignancy are still high, partly due to low compliance with screening. Minimally invasive tests based on the analysis of blood specimens may overcome this problem. The purpose of this review was to give an overview of published studies on blood markers aimed at the early detection of CRC and to summarize their performance characteristics.

METHOD

The PUBMED database was searched for relevant studies published until June 2006. Only studies with more than 20 cases and more than 20 controls were included. Information on the markers under study, on the underlying study populations, and on performance characteristics was extracted. Special attention was given to performance characteristics by tumor stage.

RESULTS

Overall, 93 studies evaluating 70 different markers were included. Most studies were done on protein markers, but DNA markers and RNA markers were also investigated. Performance characteristics varied widely between different markers, but also between different studies using the same marker. Promising results were reported for some novel assays, e.g., assays based on SELDI-TOF MS or MALDI-TOF MS, for some proteins (e.g., soluble CD26 and bone sialoprotein) and also for some genetic assays (e.g., L6 mRNA), but evidence thus far is restricted to single studies with limited sample size and without further external validation.

CONCLUSIONS

Larger prospective studies using study populations representing a screening population are needed to verify promising results. In addition, future studies should pay increased attention to the potential of detecting precursor lesions.

Cancer biomarker discovery via low molecular weight serum proteome profiling - Where is the tumor?

Time-course analyses of rapidly processed serum performed in parallel by SELDI and nanoscale LC-MS/MS have revealed the temporal correlation of several literature-based disease markers with ex vivo driven events such that their in vivo existence in healthy subjects is questionable. Identification by MS/MS reveals these putative biomarkers to be byproducts of the coagulation cascade and platelet activation and suggests plasmatic analysis may be preferred. In a pilot plasmatic study, a cohort of naïve prostate cancer (PCa) samples were uniformly distinguished from their age-matched controls (n = 20) on the basis of multiple peptidic components; most notably by a derivative of complement C(4) at 1863 m/z (GLEEELQFSLGSKINVK, C4(1353-1369) ). The fully tryptic nature of this and other putative PCa discriminants is consistent with the cleavage specificity of common blood proteases and questions the need for tumor-derived proteolytic activities as has been proposed. In light of the known correlation of disregulated hemostasis with malignant disease, we suggest the underlying differentiating phenomena in these types of analyses may lie in the temporal disparity of sample activation such that the case (patient) samples are preactivated while the control samples are not.

From the genome to the proteome--biomarkers in colorectal cancer

BACKGROUND AND AIMS

Colorectal cancer is the second leading cause of cancer-related death. Current clinical practice in colorectal cancer screening (fecal occult blood test, FOBT; colonoscopy) has contributed to a reduction of mortality. However, despite these screening programs, about 70% of carcinomas are detected at advanced tumor stages (UICC III/IV) presenting poor patient prognosis. Thus, innovative tools and methodologies for early cancer detection can directly result in improving patient survival rates.

PATIENTS/METHODS

Biomedical research has advanced rapidly in recent years with the availability of technologies such as global gene and protein expression profiling. Comprehensive tumor profiling has become a field of intensive research aiming at identifying biomarkers relevant for improved diagnostics and therapeutics.

RESULTS

In this paper, we report a comprehensive review of genomic, transcriptomic, and proteomic approaches for biomarker identification in tissue and blood with a main emphasis on two-dimensional gel-electrophoresis (2-DE) and mass spectrometry analyses.

CONCLUSION

Proteomics-based technologies enable to distinguish the healthy patient from the tumor patient with high sensitivity and specificity and could greatly improve common classification systems and diagnostics. However, this progress has not yet been transferred from bench to bedside but could open the door to a more accurate and target specific personalized medicine with improved patient survival.

Technology insight: the application of proteomics in gastrointestinal disease

Analysis of the human genome has increased our knowledge of the genes that are associated with disease. At the same time, however, it has become clear that having complete DNA sequences alone is not sufficient to elucidate the biological functions of the proteins that they encode. For this reason, proteomics-the analysis of proteins-has become increasingly attractive, because the proteome reflects both the intrinsic genetic programming of a cell and the impact of its immediate environment. The principal goals of clinical proteomics are to identify biomarkers for the early diagnosis of disease and potential targets for therapeutic intervention. Other goals include the identification of biomarkers for the early detection of disease recurrence (relapse) and how they might be combined with diagnostic imaging techniques to improve the sensitivity for detecting disease. This Review describes conventional proteomic technologies, their strengths and limitations, and demonstrates their application to clinical practice, with specific reference to their use in the gastroenterology field.

Sample preparation for serum/plasma profiling and biomarker identification by mass spectrometry

In this article, we present an overview of the different strategies for sample preparation for identification by mass spectrometry (MS) of biomarkers from serum and/or plasma. We consider the effects of the variables involved in sample collection, handling and storage, and describe different approaches for removal of high abundance proteins and serum/plasma fractionation. We review the advantages and disadvantages of such techniques as centrifugal ultrafiltration, different formats for solid phase extraction, organic solvent extraction, gel and capillary electrophoresis, and liquid chromatography. We also discuss a variety of current proteomic methods and their main applications for biomarker-related studies.

SELDI-TOF MS profiling of plasma proteins in ovarian cancer

OBJECTIVE

Proteomic profiling of plasma or serum is a technique to identify new biomarkers in disease. The objective of this study was to identify new plasma biomarkers in ovarian cancer patients using mass spectrometry protein profiling and artificial intelligence.

METHODS

A total of 65 plasma samples obtained from women with ovarian cancer (n = 35) and age-matched disease-free controls (n = 30) were applied to anion exchange protein chips for protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS).

RESULTS

SELDI-TOF MS was highly reproducible in detecting ovarian tumor-specific protein profiles. One protein peak (relative molecular mass, Mr, 11,537 Da) was identified in plasma from women with ovarian cancer but not in controls. Two peaks, Mr 5,147 and 8,780 Da, were present in the plasma of controls but not of women with ovarian cancer. After a training analysis, classification analysis generated by univariant or linear combination split was performed to reach a discriminant protein signature pattern. After cross validation, a sensitivity of 84% and specificity of 89% for all studied cases and controls was reached.

CONCLUSION

This study clearly demonstrates that the combined technology of SELDI-TOF MS and artificial intelligence is effective in distinguishing protein expression between normal and ovarian cancer plasma. The identified protein peaks may be candidate proteins for early detection of ovarian cancer or evaluation of therapeutic response.

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.

Combination of SELDI-TOF-MS and data mining provides early-stage response prediction for rectal tumors undergoing multimodal neoadjuvant therapy

OBJECTIVE

We investigated whether proteomic analysis of the low molecular weight region of the serum proteome could predict histologic response of locally advanced rectal cancer to neoadjuvant radiochemotherapy (RCT).

SUMMARY BACKGROUND DATA

Proteomic analysis of serum is emerging as a powerful new modality in cancer, in terms of both screening and monitoring response to treatment. No study has yet assessed its ability to predict and monitor the response of rectal cancer to RCT.

METHODS

Sequential serum samples from 20 patients undergoing RCT were prospectively collected. Time points sampled were as follows: pretreatment, 24/48 hours, 1 week, 2 weeks, 3 weeks, 5 weeks (last day of RCT), and presurgery. Response to treatment was measured using a 5-point tumor regression grade (TRG) based on the degree of residual tumor to fibrosis. All serum samples were analyzed in duplicate using surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS). Support vector machine (SVM) analysis of spectra was used to generate a predictive algorithm for each time point based on proteins that were maximally differentially expressed between good and poor responders. This algorithm was then tested using leave-one-out cross validation.

RESULTS

In total, 230 spectra were generated representing all available time points from 9 good responders (TRG 1+2) and 11 poor responders (TRG 3-5). SVM analysis indicated that changes within the serum proteome at the 24/48 hours time point into treatment provided optimal classification accuracy. In more detail, a cohort of 14 protein peaks were identified that collectively differentiated between good and poor responders, with 87.5% sensitivity and 80% specificity.

CONCLUSIONS

Serum proteomic analysis may represent an early response predictor in multimodal treatment regimens of rectal cancer. These data suggest that this novel, minimally invasive modality may be a useful adjunct in the multimodal management of rectal cancer, and in the design of future clinical trials.

Plasma proteomic profiling for detecting and differentiating in situ and invasive carcinomas of the uterine cervix

The objective of this study was to identify multiple plasma protein markers that might be characteristic of in situ and invasive cervical cancers. Plasma samples obtained from patients with in situ cervical cancer (carcinoma in situ [CIS], n= 32), from patients with early invasive cervical cancer without lymph node metastasis (squamous cell carcinoma [SCC], n= 60), and from age-matched disease-free controls (n= 37) were analyzed by cation-exchange protein chips and surface-enhanced laser desorption and ionization time-of-flight mass spectrometry. A classification tree defined by six protein peaks could discriminate 84 of the 92 cancers (CIS and SCC) and 36 of the 37 controls, with 91% sensitivity and 97% specificity. In comparing the CIS and SCC samples, two protein peaks with Mr values of 6586.41 and 3805.68 were able to classify 55 of the 60 SCC and 31 of the 32 CIS samples, with 92% sensitivity and 97% specificity. This study demonstrates for the first time the feasibility of differentiating in situ and invasive cervical cancers through plasma protein profiling. Identification of the proteins different in invasive and in situ cancer may be of great value in the understanding of cervical cancer invasion and in the development of novel therapeutic intervention.

Validation of SELDI-TOF MS serum protein profiles for renal cell carcinoma in new populations

Currently, no suitable biomarker for the early detection or follow-up of renal cell carcinoma (RCC) is available. We aimed to validate previously reported potential serum biomarkers for RCC obtained with Surface Enhanced Laser Desorption Ionisation-Time of Flight Mass Spectrometry (SELDI-TOF MS) in our laboratory using distinct patient populations. Two sets of sera from RCC patients and healthy controls (HC) were gathered from different institutes and analysed according to published procedures. The first set (40 RCC, 32 HC) consisted of mainly presurgery samples from patients with disease stages I-IV. The second set (26 RCC, 27 HC) were mostly sera from patients with stage-IV disease, drawn after nephrectomy. Only the increased expression of the previously found serum amyloid-alpha (SAA) peak cluster could be validated in a similar RCC patient subset in both our populations in two independent analyses. It was seen both in early- and late-stage disease and in pre- and postsurgery samples. These results were also confirmed by ELISA. Other previously identified biomarker candidates (mass-to-charge ratio's (m/z) 3900, 4107, 4153, 5352 and 5987) proved difficult to reproduce upon duplicate analysis. Modification of the analytical protocol for these markers resulted in their detection, but we did not achieve satisfactory classification of patients and controls with these alleged biomarkers in any of our two sample sets. Instead, two new peaks (m/z 4289 and 8151) were identified with better performance (sensitivity and specificity approximately 65-90%) for separating patients from controls in the first sample set. Concluding, only the SAA peak cluster was validated as a robust RCC biomarker candidate, which is present in a specific subset of these patients, regardless of disease stage or nephrectomy status. In addition, two new peaks were seen which might prove useful as biomarkers, provided these are validated in new populations.

New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples

AIM: To identify a multi serum protein pattern as well as single protein markers using surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) for detection and differentiation of liver fibrosis (F1-F2), liver cirrhosis (F4) and hepatocellular carcinoma (HCC) in patients with chronic hepatitis C virus (HCV).

METHODS: Serum samples of 39 patients with F1/F2 fibrosis, 44 patients with F4 fibrosis, 34 patients with HCC were applied to CM10 arrays and analyzed using the SELDI-TOF ProteinChip System (PBS-IIc; Ciphergen Biosystems) after anion-exchange fractionation. All patients had chronic hepatitis C and histologically confirmed fibrosis stage/HCC. Data were analyzed for protein patterns by multivariate statistical techniques and artificial neural networks.

RESULTS: A 4 peptide/protein multimarker panel (7486, 12 843, 44 293 and 53 598 Da) correctly identified HCCs with a sensitivity of 100% and specificity of 85% in a two way-comparison of HCV-cirrhosis versus HCV-HCC training samples (AUROC 0.943). Sensitivity and specificity for identification of HCC were 68% and 80% for random test samples. Cirrhotic patients could be discriminated against patients with F1 or F2 fibrosis using a 5 peptide/protein multimarker pattern (2873, 6646, 7775, 10 525 and 67 867 Da) with a specificity of 100% and a sensitivity of 85% in training samples (AUROC 0.976) and a sensitivity and specificity of 80% and 67% for random test samples. Combination of the biomarker classifiers with APRI score and alfa-fetopotein (AFP) improved the diagnostic performance. The 6646 Da marker protein for liver fibrosis was identified as apolipoprotein C-I.

CONCLUSION: SELDI-TOF-MS technology combined with protein pattern analysis seems a valuable approach for the identification of liver cirrhosis and hepatocellular carcinoma in patients with chronic hepatitis C. Most probably a combination of different serum markers will help to identify liver cirrhosis and early-stage hepatocellular carcinomas in the future.

Urinary biomarker profiling in transitional cell carcinoma

Urinary biomarkers or profiles that allow noninvasive detection of recurrent transitional cell carcinoma (TCC) of the bladder are urgently needed. We obtained duplicate proteomic (SELDI) profiles from 227 subjects (118 TCC, 77 healthy controls and 32 controls with benign urological conditions) and used linear mixed effects models to identify peaks that are differentially expressed between TCC and controls and within TCC subgroups. A Random Forest classifier was trained on 130 profiles to develop an algorithm to predict the presence of TCC in a randomly selected initial test set (n = 54) and an independent validation set (n = 43) several months later. Twenty two peaks were differentially expressed between all TCC and controls (p < 10(-7)). However potential confounding effects of age, sex and analytical run were identified. In an age-matched sub-set, 23 peaks were differentially expressed between TCC and combined benign and healthy controls at the 0.005 significance level. Using the Random Forest classifier, TCC was predicted with 71.7% sensitivity and 62.5% specificity in the initial set and with 78.3% sensitivity and 65.0% specificity in the validation set after 6 months, compared with controls. Several peaks of importance were also identified in the linear mixed effects model. We conclude that SELDI profiling of urine samples can identify patients with TCC with comparable sensitivities and specificities to current tumor marker tests. This is the first time that reproducibility has been demonstrated on an independent test set analyzed several months later. Identification of the relevant peaks may facilitate multiplex marker assay development for detection of recurrent disease.

Application of serum protein fingerprint in diagnosis of papillary thyroid carcinoma

To find new biomarkers and establish serum protein fingerprint models for early diagnosis and preoperative staging of papillary thyroid carcinoma, we employed SELDI-TOF-MS and bioinformatics tools. A total of 116 samples were analyzed in this study. The first 80 samples were analyzed by SELDI-TOF-MS and two biomarker patterns were identified. Pattern 1 distinguishes patients with papillary thyroid carcinoma from healthy individuals. Pattern 2 distinguishes papillary thyroid carcinoma from benign thyroid nodes. The remaining 29 samples were analyzed on the second day and served as an independent test set. The analysis of this independent test set yielded a specificity of 80.0% and a sensitivity of 88.9% for pattern 1 and a specificity of 80.0% and a sensitivity of 80.0% for pattern 2. Two additional biomarker patterns were identified to distinguish different stages of the papillary thyroid carcinoma (pattern 3) with an accuracy of 77.1% and different pathological types of thyroid carcinoma (pattern 4) with an accuracy of 88.1%. Taken together, the SELDI-TOF-MS technique combined with bioinformatics approaches can not only facilitate the discovery of better biomarkers for papillary thyroid carcinoma but also provide a useful tool for molecular diagnosis in the future.

Human body fluid proteome analysis

The focus of this article is to review the recent advances in proteome analysis of human body fluids, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, and amniotic fluid, as well as its applications to human disease biomarker discovery. We aim to summarize the proteomics technologies currently used for global identification and quantification of body fluid proteins, and elaborate the putative biomarkers discovered for a variety of human diseases through human body fluid proteome (HBFP) analysis. Some critical concerns and perspectives in this emerging field are also discussed. With the advances made in proteomics technologies, the impact of HBFP analysis in the search for clinically relevant disease biomarkers would be realized in the future.

Multivariate analysis approach to the plasma protein profile of patients with advanced colorectal cancer

The aim of the present study was to identify the pattern of plasma protein species of interest as markers of colorectal cancer (CRC). Using matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS), the plasma protein profile was determined in nine stage IV CRC patients (study group) and nine clean-colon healthy subjects (control group). Multivariate analysis methods were employed to identify distinctive disease patterns at protein spectrum. In the study and control groups, cluster analysis (CA) on the complete MALDI-MS spectra plasma protein profile showed a distinction between CRC patients and healthy subjects, thus allowing the identification of the most discriminating ionic species. Principal component analysis (PCA) and linear discriminant analysis (LDA) yielded similar grouping results. LDA with leave-one-out cross validation achieved a correct classification rate of 89% in both the patients and the healthy subjects.

Current status and prospects of clinical proteomics studies on detection of colorectal cancer: Hopes and fears

Colorectal adenocarcinoma (CRC) is the third most common type of cancer and the fourth most frequent cause of death due to cancer worldwide. Given the natural history of CRC, early diagnosis appears to be the most appropriate tool to reduce disease-related mortality. A field of recent interest is clinical proteomics, which was reported to lead to high sensitivity and specificities for early detection of several solid tumors. This emerging field uses mass spectrometry-based protein profiles/patterns of easy accessible body fluids to distinguish cancer from non-cancer patients. These discrepancies may be a result of: (1) proteins being abnormally produced or shed and added to the serum proteome, (2) proteins clipped or modified as a consequence of the disease process, or (3) proteins subtracted from the proteome owing to disease-related proteolytic degradation pathways. Therefore, protein pattern diagnostics would provide easy and reliable tools for detection of cancer. This paper focuses on the current status of clinical proteomics research in oncology and in colorectal cancer especially, and will reflect on pitfalls and fears in this relatively new area of clinical medicine, which are reproducibility issues and pre-analytical factors, statistical issues, and identification and nature of discriminating proteins/peptides.

Technology Insight: renal proteomics--at the crossroads between promise and problems

Knowledge of the human genome has fertilized research in the embryonic field of proteomics. The aim of this Review is to examine the recent application of emerging proteomic technologies to diagnosis of renal disease. We discuss the roles, efficacy and diagnostic potential of different proteomic approaches, focusing on current difficulties and potential solutions. Our rudimentary knowledge of the healthy human urine proteome is described, as are studies that have sought to use the urinary proteome as a tool for diagnosis of renal disease. Vignettes of renal proteome are also presented. The integral role of bioinformatics, and the need for standardized sample preservation and reporting of results, are discussed.

Plasma proteomic pattern as biomarkers for ovarian cancer

Early detection of ovarian cancer remains a challenge. Pathologic changes within an organ might be reflected in proteomic patterns in serum or plasma. The objective of this study was to identify new plasma biomarkers in ovarian cancer patients using mass spectrometry (MS) protein profiling and artificial intelligence. The study included 35 women with ovarian cancer and 30 age-matched disease-free controls. For plasma protein signature analysis, the protein chip array surface-enhanced laser desorption/ionization (SELDI) analysis was performed. The strong anion exchange (SAX) and weak cation exchange (WCX) chips were used for analysis. After a training analysis by SAX and WCX protein chips, learning algorithm and clustering analysis was performed to reach a discriminate pattern of protein signature. SELDI mass spectroscopy was highly reproducible in detecting ovarian tumor-specific protein profiles. Four specific protein peaks were identified in plasma of women with ovarian cancer, but not in controls, with relative molecular masses of 6190.48, 5147.06, 11522.6, and 11537.7 d. Two peaks, with Mr 5295.5 and 8780.48 d, were present in plasma of control but not in women with ovarian cancer. A sensitivity of 90-96.3% and specificity of 100% for this studied cases and controls were reached. This study clearly demonstrates that the combined technology of SELDI-MS and artificial intelligence is effective in distinguishing protein expression between normal and ovary cancer plasma. The identified gained and lost protein peaks in plasma may provide as candidate proteins to be used for the detection or monitoring ovarian cancer.

Identification of serum biomarkers for colon cancer by proteomic analysis

Colorectal cancer (CRC) is often diagnosed at a late stage with concomitant poor prognosis. Early detection greatly improves prognosis; however, the invasive, unpleasant and inconvenient nature of current diagnostic procedures limits their applicability. No serum-based test is currently of sufficient sensitivity or specificity for widespread use. In the best currently available blood test, carcinoembryonic antigen exhibits low sensitivity and specificity particularly in the setting of early disease. Hence, there is great need for new biomarkers for early detection of CRC. We have used surface-enhanced laser desorbtion/ionisation (SELDI) to investigate the serum proteome of 62 CRC patients and 31 noncancer subjects. We have identified proteins (complement C3a des-arg, α1-antitrypsin and transferrin) with diagnostic potential. Artificial neural networks trained using only the intensities of the SELDI peaks corresponding to identified proteins were able to classify the patients used in this study with 95% sensitivity and 91% specificity.

Detection of colorectal cancer using MALDI-TOF serum protein profiling

Serum protein profiling is a promising approach for classification of cancer versus non-cancer samples. The objective of our study was to assess the feasibility of mass spectrometry based protein profiling for the discrimination of colorectal cancer (CRC) patients from healthy individuals. In a randomized block design, pre-operative serum samples obtained from 66 colorectal cancer patients and 50 controls were used to generate MALDI-TOF protein profiles. After pre-processing of the spectra, linear discriminant analysis with double cross-validation was used to classify protein profiles. A total recognition rate (92.6%), sensitivity (95.2%) and specificity (90.0%) for the detection of CRC were shown. The area under the curve of the classifier was 97.3%, and demonstrated the high, significant separation power of the classifier. Double cross-validation shows that classification can be attributed to information in the protein profile. Although preliminary, the high sensitivity and specificity indicate the potential usefulness of serum protein profiles for the detection of colorectal cancer.