Proteins are potent biomarkers to detect colon cancer progression

Colon cancer is the most common type of cancer and major cause of death worldwide. The detection of colon cancer is difficult in early stages. However, the secretory proteins have been used as ideal biomarker for the detection of colon cancer progress in cancer patients. Serum/tissue protein expression could help general practitioners to identify colon cancer at earlier stages. By this way, we use the biomarkers to evaluate the anticancer drugs and their response to therapy in cancer models. Recently, the biomarker discovery is important in cancer biology and disease management. Also, many measurable specific molecular components have been studied in colon cancer therapeutics. The biomolecules are mainly DNA, RNA, metabolites, enzymes, mRNA, aptamers and proteins. Thus, in this review we demonstrate the important protein biomarker in colon cancer development and molecular identification of protein biomarker discovery.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Biomarkers for the lung cancer diagnosis and their advances in proteomics

Over a last decade, intense interest has been focused on biomarker discovery and their clinical uses. This interest is accelerated by the completion of human genome project and the progress of techniques in proteomics. Especially, cancer biomarker discovery is eminent in this field due to its anticipated critical role in early diagnosis, therapy guidance, and prognosis monitoring of cancers. Among cancers, lung cancer, one of the top three major cancers, is the one showing the highest mortality because of failure in early diagnosis. Numerous potential DNA biomarkers such as hypermethylations of the promoters and mutations in K-ras, p53, and protein biomarkers; carcinoembryonic antigen (CEA), CYFRA21-1, plasma kallikrein B1 (KLKB1), Neuron-specific enolase, etc. have been discovered as lung cancer biomarkers. Despite extensive studies thus far, few are turned out to be useful in clinic. Even those used in clinic do not show enough sensitivity, specificity and reproducibility for general use. This review describes what the cancer biomarkers are for, various types of lung cancer biomarkers discovered at present and predicted future advance in lung cancer biomarker discovery with proteomics technology.     

Lectin precipitation using phytohemagglutinin-L(4) coupled to avidin-agarose for serological biomarker discovery in colorectal cancer

N-acetylglucosaminyltransferase V (GnT-V) has been reported to be upregulated in malignant cancer cells, and its targets have been sought after with regard to biomarker identification. The low capacity and high false positive rates of 2-DE gel-based lectin blots using phytohemagglutinin-L(4) (L-PHA) prompted us to develop a novel protocol for identifying GnT-V targets, in which serum proteins were subjected to immunodepletion, alkylation, and lectin precipitation using L-PHA coupled to avidin-agarose bead complexes, and tryptic digestion. Proteins captured by L-PHA conjugates were analyzed by a nano-LC-FT-ICR/LTQ MS. Here, we report 26 candidate biomarkers for colorectal cancer (CRC) that show 100% specificity and sensitivities of greater than 50%. Not only can these candidate proteins be used as analytes for validation, but the novel protocol described herein can be applied to biomarker discovery in nonCRCs.     

Advances in proteomic prostate cancer biomarker discovery

Prostate cancer is the most common non-cutaneous cancer in men in the United States. For reasons largely unknown, the incidence of prostate cancer has increased in the last two decades, in spite or perhaps because of a concomitant increase in serum prostate-specific antigen (PSA) screening. While PSA is acknowledged not to be an ideal biomarker for prostate cancer detection, it is however widely used by physicians due to lack of an alternative. Thus, the identification of a biomarker(s) that can complement or replace PSA represents a major goal for prostate cancer research. Screening complex biological specimens such as blood, urine, and tissue to identify protein biomarkers has become increasingly popular over the last decade thanks to advances in proteomic discovery methods. The completion of human genome sequence together with new development in mass spectrometry instrumentation and bioinformatics has been a major driving force in biomarker discovery research. Here we review the current state of proteomic applications as applied to various sample sources including blood, urine, tissue, and “secretome” for the purpose of prostate cancer biomarker discovery. Additionally, we review recent developments in validation of putative markers, efforts at systems biology approach, and current challenges of proteomics in biomarker discovery.     

Biomarker discovery in biological fluids

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

The offer of chemistry to targeted therapy in cancer

Cancer therapy is facing the big challenge of destroying selectively tumour cells without harming the normal tissues. Chemotherapy was trying from the beginning to kill malignant cells because of their proliferative activity since normal cells are in general quiescent. Meanwhile side effects were produced due to the destruction of some normal cells that need regular proliferation. The discovery of biomarkers led to the identification of molecular targets within tumour cells in order to kill them selectively. Chemistry followed the progress of biomarkers biotechnology by the production of target specific antagonists which were the subject of many patents. Meanwhile novel problems of tumour resistance appeared and made the battle against cancer a non stop development of new strategies and new weapons. As a consequence, paralleled activities of patenting biomarkers and chemical antagonists are continuously generated. The offer of chemistry does not actually limit the efficiency of Targeted therapy but the identification of biomarkers is still missing the exclusive specificity to tumour cells.     

Circulating tumour cells and cancer stem cells: A role for proteomics in defining the interrelationships between function,phenotype and differentiation with potential clinical applications

Research on the discovery and implementation of valid cancer biomarkers is one of the most challenging fields in oncology and oncoproteomics in particular. Moreover, it is generally accepted that an evaluation of cancer biomarkers from the blood could significantly enable biomarker assessments by providing a relatively non-invasive source of representative tumour material. In this regard, circulating tumour cells (CTCs) isolated from the blood of metastatic cancer patients have significant promise. It has been demonstrated that localised and metastatic cancers may give rise to CTCs, which are detectable in the bloodstream. Despite technical difficulties, recent studies have highlighted the prognostic significance of the presence and number of CTCs in the blood. Future studies are necessary not only to detect CTCs but also to characterise them. Furthermore, another pathogenically significant type of cancer cells, known as cancer stem cells (CSCs) or more recently termed circulating tumour stem cells (CTSCs), appears to have a significant role as a subpopulation of CTCs.     

Biomarkers of human cutaneous squamous cell carcinoma from tissues and cell lines identified by DNA microarrays and qRT-PCR

Squamous cell carcinoma (SCC) is the second most common form of skin cancer in Caucasians. Here we report on the identification of biomarkers of human cutaneous SCC cell lines in vitro and tissue samples in vivo using DermArray and PharmArray DNA microarrays, consisting of ca. 7400 unique human cDNAs. Differentially expressed genes were identified in two facial skin SCC cell lines (SCC 12 and SCC 13) compared to normal keratinocytes, and three cutaneous SCC tissue samples compared to normal skin. Quantitative validations of up- and down-regulated biomarkers were performed by qRT-PCR on 23 biomarker genes for the cell lines and 20 biomarker genes for the tumor tissues. In addition, three oral SCC cell lines were also included in the qRT-PCR validations for comparison, and the biomarker profiles were highly similar between the cutaneous and the oral SCC cell lines for all 23 biomarkers examined. The expression profiles for a variety of non-cutaneous SCC types, such as head-and-neck, oral, and lung, have been previously published. This report is the first to describe biomarkers for cutaneous SCC in two contexts, in vitro and in vivo. Although there was minimal overlap between the two different contexts using DNA microarrays, five genes were found common to both the cell lines and tissues, namely fibronectin 1, annexin A5, glyceraldehyde 3-phosphate dehydrogenase, zinc-finger protein 254, and huntingtin-associated protein interacting protein. Some of our previously published biomarkers of normal keratinocytes were down-regulated in SCC, suggestive of the dedifferentiated status of the transformed cells. While recent reports have identified some of the same genes as SCC biomarkers, for instance in head-and-neck cancer, thereby validating our approach, we have identified some novel biomarkers for cutaneous disease. These biomarker lists may be useful in molecular diagnostics of non-melanoma skin cancer, and a subset of the biomarkers might serve as suitable targets for drug discovery efforts of therapies for SCC.     

Cell-specific aptamer probes for membrane protein elucidation in cancer cells

Disease biomarkers play critical roles in the management of various pathological conditions of diseases. This involves diagnosing diseases, predicting disease progression and monitoring the efficacy of treatment modalities. While efforts to identify specific disease biomarkers using a variety of technologies has increased the number of biomarkers or augmented information about them, the effective use of disease-specific biomarkers is still scarce. Here, we report that a high expression of protein tyrosine kinase 7 (PTK7), a transmembrane receptor protein tyrosine kinase-like molecule, was discovered in a series of leukemia cell lines using whole cell aptamer selection. With the implementation of a two-step strategy (aptamer selection and biomarker discovery), combined with mass spectrometry, PTK7 was ultimately identified as a potential biomarker for T-cell acute lymphoblastic leukemia (T-ALL). Specifically, the aptamers for T-ALL cells were selected using the cell-SELEX process, without any prior knowledge of the cell biomarker population, conjugated with magnetic beads and then used to capture and purify their binding targets on the leukemia cell surface. This demonstrates that a panel of molecular aptamers can be easily generated for a specific type of diseased cells. It further demonstrates that this two-step strategy, that is, first selecting cancer cell-specific aptamers and then identifying their binding target proteins, has major clinical implications in that the technique promises to substantially improve the overall effectiveness of biomarker discovery. Specifically, our strategy will enable efficient discovery of new malignancy-related biomarkers, facilitate the development of diagnostic tools and therapeutic approaches to cancer, and markedly improve our understanding of cancer biology.     

Integrative analysis of N-linked human glycoproteomic data sets reveals PTPRF ectodomain as a novel plasma biomarker candidate for prostate cancer

In an attempt to identify prostate cancer biomarkers with greater diagnostic and prognostic capabilities, we have developed an integrative proteomic discovery workflow focused on N-linked glycoproteins that refines the target selection process. In this work, hydrazide-based chemistry was used to identify N-linked glycopeptides from 22Rv1 prostate cancer cells cultured in vitro, which were compared with glycopeptides identified from explanted 22Rv1 murine tumor xenografts. One hundred and four human glycoproteins were identified in the former analysis and 75 in the latter, with 40 proteins overlapping between data sets. Of the 40 overlapping proteins, 80% have multiple literature references to the neoplastic process and ～40% to prostatic neoplasms. These include a number of well-known prostate cancer-associated biomarkers, such as prostate-specific membrane antigen (PSMA). By integrating gene expression data and available literature, we identified members of the overlap data set that deserve consideration as potential prostate cancer biomarkers. Specifically, the identification of the extracellular domain of protein tyrosine phosphatase receptor type F (PTPRF) was of particular interest due to the direct involvement of PTPRF in the control of β-catenin signaling, as well as dramatically elevated gene expression levels in the prostate compared to other tissues. In this investigation, we demonstrate that the PTPRF E-subunit is more abundant in human prostate tumor tissue compared to normal control and also detectable in murine plasma by immunoblot and ELISA. Specifically, PTPRF distinguishes between animals xenografted with the 22Rv1 cells and control animals as early as 14 days after implantation. This result suggests that the ectodomain of PTPRF has the potential to function as a novel plasma or tissue-based biomarker for prostate cancer. The workflow described adds to the literature of potential biomarker candidates for prostate cancer and demonstrates a pathway to developing new diagnostic assays.     

Towards proteome standards: The use of absolute quantitation in high-throughput biomarker discovery

The use of proteomics to profile biological fluids and identify therein biomarkers for cancer and other diseases was initially received with considerable excitement. However, results have fallen short of the expectations. Traditionally, protein biomarkers have been identified by measurement of relative expression changes between case and control samples from which differentially expressed proteins are then considered to represent biomarker candidates.We argue that current individual proteomics-based biomarker discovery studies lack the statistical strength for the identification of high-confidence biomarkers. Instead, multi-group efforts are necessary to facilitate the generation of sufficient sample sizes. This is contingent on the ability to collate and cross-compare data from different studies, which will require the use of a common metric or standards.Though profound, the technical challenges for absolute protein quantification can be overcome. The use of matrix specific, shared standards for absolute quantitation presents an opportunity to facilitate the much needed, but currently impossible, comparisons of different studies. In addition to community-wide approaches to standardize pre-analytical biomarker research studies, it is also important to establish means to integrate experimental data from different studies in order to assess the usefulness of proposed biomarkers with sufficient statistical certainty.     

Identification of miRNA-103 in the cellular fraction of human peripheral blood as a potential biomarker for malignant mesothelioma--a pilot study

Background

To date, no biomarkers with reasonable sensitivity and specificity for the early detection of malignant mesothelioma have been described. The use of microRNAs (miRNAs) as minimally-invasive biomarkers has opened new opportunities for the diagnosis of cancer, primarily because they exhibit tumor-specific expression profiles and have been commonly observed in blood of both cancer patients and healthy controls. The aim of this pilot study was to identify miRNAs in the cellular fraction of human peripheral blood as potential novel biomarkers for the detection of malignant mesothelioma.

Methodology/Principal Findings

Using oligonucleotide microarrays for biomarker identification the miRNA levels in the cellular fraction of human peripheral blood of mesothelioma patients and asbestos-exposed controls were analyzed. Using a threefold expression change in combination with a significance level of p<0.05, miR-103 was identified as a potential biomarker for malignant mesothelioma. Quantitative real-time PCR (qRT-PCR) was used for validation of miR-103 in 23 malignant mesothelioma patients, 17 asbestos-exposed controls, and 25 controls from the general population. For discrimination of mesothelioma patients from asbestos-exposed controls a sensitivity of 83% and a specificity of 71% were calculated, and for discrimination of mesothelioma patients from the general population a sensitivity of 78% and a specificity of 76%.

Conclusions/Significance

The results of this pilot study show that miR-103 is characterized by a promising sensitivity and specificity and might be a potential minimally-invasive biomarker for the diagnosis of mesothelioma. In addition, our results support the concept of using the cellular fraction of human blood for biomarker discovery. However, for early detection of malignant mesothelioma the feasibility of miR-103 alone or in combination with other biomarkers needs to be analyzed in a prospective study.     

Identification of CD44 as a surface biomarker for drug resistance by surface proteome signature technology

We developed surface proteome signatures (SPS) for identification of new biomarkers playing a role in cancer drug resistance. SPS compares surface antigen expression of different cell lines by immunocytochemistry of a phage display antibody library directed to surface antigens of HT1080 fibrosarcoma cells. We applied SPS to compare the surface proteomes of two epithelial derived cancer cell lines, MCF7 and NCI/ADR-RES, which is drug resistant because of overexpression of the P-glycoprotein (P-gp) drug efflux pump. Surface proteomic profiling identified CD44 as an additional biomarker that distinguishes between these two cell lines. CD44 immunohistochemistry can distinguish between tumors derived from these lines and predict tumor response to doxorubicin in vivo. We further show that CD44 plays a role in drug resistance, independently of P-gp, in NCI/ADR-RES cells and increases expression of the antiapoptotic protein Bcl-xL. Our findings illustrate the utility of SPS to distinguish between cancer cell lines and their derived tumors and identify novel biomarkers involved in drug resistance.     

Rapid discovery and identification of a tissue-specific tumor biomarker from 39 human cancer cell lines using the SELDI ProteinChip platform

Useful biomarkers are needed for early detection of cancers. To demonstrate the potential diagnostic usefulness of a new proteomic technology, we performed Expression Difference Mapping analysis on 39 cancer cell lines from 9 different tissues using ProteinChip technology. A protein biomarker candidate of 12kDa was found in colon cancer cells. We then optimized the purification conditions for this biomarker by utilizing Retentate Chromatography mass spectrometry (RC-MS). The optimized purification conditions developed "on-chip" were directly transferred to conventional chromatography to purify the biomarker, which was identified as prothymosin-alpha by ProteinChip time-of-flight mass spectrometry (TOF MS) and ProteinChip-Tandem MS systems. The relative expression level of prothymosin-alpha between colon cancer cells and normal colon mucosal cells was evaluated on the same ProteinChip platform. Prothymosin-alpha expression in colon cancer cells was clearly higher than in normal colon cells. These results indicate that prothymosin-alpha could be a potential biomarker for colon cancer, and that the ProteinChip platform could perform the whole process of biomarker discovery from screening to evaluation of the identified marker.     

Tumour Control Probability in Cancer Stem Cells Hypothesis

The tumour control probability (TCP) is a formalism derived to compare various treatment regimens of radiation therapy, defined as the probability that given a prescribed dose of radiation, a tumour has been eradicated or controlled. In the traditional view of cancer, all cells share the ability to divide without limit and thus have the potential to generate a malignant tumour. However, an emerging notion is that only a sub-population of cells, the so-called cancer stem cells (CSCs), are responsible for the initiation and maintenance of the tumour. A key implication of the CSC hypothesis is that these cells must be eradicated to achieve cures, thus we define TCP_S as the probability of eradicating CSCs for a given dose of radiation. A cell surface protein expression profile, such as CD44high/CD24low for breast cancer or CD133 for glioma, is often used as a biomarker to monitor CSCs enrichment. However, it is increasingly recognized that not all cells bearing this expression profile are necessarily CSCs, and in particular early generations of progenitor cells may share the same phenotype. Thus, due to the lack of a perfect biomarker for CSCs, we also define a novel measurable TCP_CD+, that is the probability of eliminating or controlling biomarker positive cells. Based on these definitions, we use stochastic methods and numerical simulations parameterized for the case of gliomas, to compare the theoretical TCP_S and the measurable TCP_CD+. We also use the measurable TCP to compare the effect of various radiation protocols.     

The nucleotide-independent Fe(III)-binding site is located on beta subunit of the mitochondrial F(1)-ATPase

The need for methods to identify disease biomarkers is underscored by the survival-rate of patients diagnosed at early stages of cancer progression. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a novel approach to biomarker discovery that combines two powerful techniques: chromatography and mass spectrometry. One of the key features of SELDI-TOF MS is its ability to provide a rapid protein expression profile from a variety of biological and clinical samples. It has been used for biomarker identification as well as the study of protein-protein, and protein-DNA interaction. The versatility of SELDI-TOF MS has allowed its use in projects ranging from the identification of potential diagnostic markers for prostate, bladder, breast, and ovarian cancers and Alzheimer's disease, to the study of biomolecular interactions and the characterization of posttranslational modifications. In this minireview we discuss the application of SELDI-TOF MS to protein biomarker discovery and profiling.     

Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery

A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes>2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation.