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PROTEOMICS - Clinical Applications, 2012
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.
Revelation of Proteomic Indicators for Colorectal Cancer in Initial Stages of Development
Molecules, 2020
Background: Colorectal cancer (CRC) at a current clinical level is still hardly diagnosed, especially with regard to nascent tumors, which are typically asymptotic. Searching for reliable biomarkers of early diagnosis is an extremely essential task. Identification of specific post-translational modifications (PTM) may also significantly improve net benefits and tailor the process of CRC recognition. We examined depleted plasma samples obtained from 41 healthy volunteers and 28 patients with CRC at different stages to conduct comparative proteome-scaled analysis. The main goal of the study was to establish a constellation of protein markers in combination with their PTMs and semi-quantitative ratios that may support and realize the distinction of CRC until the disease has a poor clinical manifestation. Results: Proteomic analysis revealed 119 and 166 proteins for patients in stages I–II and III–IV, correspondingly. Plenty of proteins (44 proteins) reflected conditions of the immune response, lipid metabolism, and response to stress, but only a small portion of them were significant (p < 0.01) for distinguishing stages I–II of CRC. Among them, some cytokines (Clusterin (CLU), C4b-binding protein (C4BP), and CD59 glycoprotein (CD59), etc.) were the most prominent and the lectin pathway was specifically enhanced in patients with CRC. Significant alterations in Inter-alpha-trypsin inhibitor heavy chains (ITIH1, ITIH2, ITIH3, and ITIH4) levels were also observed due to their implication in tumor growth and the malignancy process. Other markers (Alpha-1-acid glycoprotein 2 (ORM2), Alpha-1B-glycoprotein (A1BG), Haptoglobin (HP), and Leucine-rich alpha-2-glycoprotein (LRG1), etc.) were found to create an ambiguous core involved in cancer development but also to exactly promote tumor progression in the early stages. Additionally, we identified post-translational modifications, which according to the literature are associated with the development of colorectal cancer, including kininogen 1 protein (T327-p), alpha-2-HS-glycoprotein (S138-p) and newly identified PTMs, i.e., vitamin D-binding protein (K75-ac and K370-ac) and plasma protease C1 inhibitor (Y294-p), which may also contribute and negatively impact on CRC progression. Conclusions: The contribution of cytokines and proteins of the extracellular matrix is the most significant factor in CRC development in the early stages. This can be concluded since tumor growth is tightly associated with chronic aseptic inflammation and concatenated malignancy related to loss of extracellular matrix stability. Due attention should be paid to Apolipoprotein E (APOE), Apolipoprotein C1 (APOC1), and Apolipoprotein B-100 (APOB) because of their impact on the malfunction of DNA repair and their capability to regulate mTOR and PI3K pathways. The contribution of the observed PTMs is still equivocal, but a significant decrease in the likelihood between modified and native proteins was not detected confidently.
Journal of Mass Spectrometry, 2006
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.
Oncology Reports, 2010
Colorectal cancer (CRC) is the second most common cause of cancer related death. Prognosis is highly dependent on stage at diagnosis making early detection mandatory. This study aimed to identify novel disease specific biomarkers of CRC, validate our previously identified biomarkers of CRC and identify serum biomarkers predicting treatment response and for monitoring. Serum of patients with metastatic CRC was collected, according to a predefined schedule, prior to start of standard first-line chemotherapy with oxaliplatin and capecitabine and serially before each 3 weekly treatment cycle and analyzed for proteomic profile by standardized SELDI-TOF MS. Serum proteomic mass spectrometry data of all subjects were processed using the tbimass R-package and proteomic profiles of CRC patients were compared with those of matched normal control subjects. Furthermore, changes in proteomic profiles during the course of chemotherapy were recorded according to treatment response. In total, 42 patients with advanced CRC were treated and mean follow-up was 13.5 months. The response rate was 50% and the median overall survival 19.5 months (95% CI: 16-23). By comparing CRC patients and healthy controls we identified 13 potential biomarkers of CRC (m/z 2.0-31.9 kDa) whereas two proteins, m/z 14060 and 28100 Da (apolipoprotein A-I), were highly significant (p<0.0001). Comparison of responding and non-responding patients identified 6 proteins potentially predicting response, where of m/z 3330 Da was significant (p=0.007). Serial analysis identified 2 proteins, m/z 2022 and 28100 Da, that changed during chemotherapy in accordance with response. We identified 13 m/z values discriminating between CRC patients and healthy controls, including the previously identified apolipoprotein A-I as a candidate biomarker for CRC and treatment monitoring.
Clinical Cancer Research, 2011
Purpose: Identification of novel biomarkers of cancer is important for improved diagnosis, prognosis, and therapeutic intervention. This study aimed to identify marker genes of colorectal cancer (CRC) by combining bioinformatics analysis of gene expression data and validation experiments using patient samples and to examine the potential connection between validated markers and the established oncogenes such as c-Myc and K-ras. Experimental Design: Publicly available data from GenBank and Oncomine were meta-analyzed leading to 34 candidate marker genes of CRC. Multiple case-matched normal and tumor tissues were examined by RT-PCR for differential expression, and 9 genes were validated as CRC biomarkers. Statistical analyses for correlation with major clinical parameters were carried out, and RNA interference was used to examine connection with major oncogenes. Results: We show with high confidence that 9 (ECT2, ETV4, DDX21, RAN, S100A11, RPS4X, HSPD1, CKS2, and C9orf140) of the 34 candidate genes are expressed at significantly elevated levels in CRC tissues compared to normal tissues. Furthermore, high-level expression of RPS4X was associated with nonmucinous cancer cell type and that of ECT2 with lack of lymphatic invasion while upregulation of CKS2 was correlated with early tumor stage and lack of family history of CRC. We also demonstrate that RPS4X and DDX21 are regulatory targets of c-Myc and ETV4 is downstream to K-ras signaling. Conclusions: We have identified multiple novel biomarkers of CRC. Further analyses of their function and connection to signaling pathways may reveal potential value of these biomarkers in diagnosis, prognosis, and treatment of CRC. Clin Cancer Res; 17(4); 700-9. Ó2011 AACR.
BMC Cancer, 2010
Background: Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Methods: Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the 'GenePattern' suite of computational tools (Broad Institute, MIT, USA). Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, 'leave-one-out' cross-validation method. Results: 73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P ≤ 0.01, false discovery rate ≤ 0.05). Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/ normal mucosa status of specimens in an independent test spectra dataset with 100% sensitivity and specificity (95% confidence interval: 67.9-99.2%). Supervised prediction using 'leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = < 0.001; receiver-operator characteristics -ROC -error, 0.171); disease recurrence was correctly predicted in 5/6 cases and disease-free survival (median follow-up time, 25 months) was correctly predicted in 22/23 cases (P = < 0.001; ROC error, 0.105). A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212).
British Journal of Cancer
Background The TNM system is used to assess prognosis after colorectal cancer (CRC) diagnosis. Other prognostic factors reported include histopathological assessments of the tumour, tumour mutations and proteins in the blood. As some of these factors are strongly correlated, it is important to evaluate the independent effects they may have on survival. Methods Tumour samples from 2162 CRC patients were visually assessed for amount of tumour stroma, severity of lymphocytic infiltrate at the tumour margins and the presence of lymphoid follicles. Somatic mutations in the tumour were assessed for 2134 individuals. Pre-surgical levels of 4963 plasma proteins were measured in 128 individuals. The associations between these features and prognosis were inspected by a Cox Proportional Hazards Model (CPH). Results Levels of stroma, lymphocytic infiltration and presence of lymphoid follicles all associate with prognosis, along with high tumour mutation burden, high microsatellite instability a...
Prognostic value of tumor progression-related gene expression in colorectal cancer patients
Journal of Cancer Research and Clinical Oncology, 2012
Purpose: Colorectal cancer (CRC) is driven by genetic alterations causing its progression. Besides accepted tumor suppressor-and onco-genes, a series of genes have been identified, which contribute to transformation into a more malignant stage. We investigated whether the expression level of such genes, alone or in combination, could add to predict the prognosis of CRC patients. Methods: Tumor samples from 118 CRC patients were screened in a retrospective analysis by qRT-PCR for expression of the four tumor progression associated genes osteopontin (Opn), transforming growth factor β (Tgf-β), matrix metalloproteinase-2 (Mmp-2), and cyclooxigenase-2 (Cox-2). The resulting qRT-PCR values were related to those of housekeeping genes. All patients were clustered for similar expression levels between the four genes with R statistical software using the package pvclust, which provides bootstrap agglomerative hierarchical clustering. Clusters with similar expression of the four genes were analyzed for correlation with UICC stages and survival time. Results: Expression of the four genes varied considerably within the cohort of patients. Cluster analysis of patients revealed a subgroup (n= 33) who in comparison with the other patients showed 10fold higher expression levels of all four genes (p<0.001, respectively). However, there was no correlation between patients expressing high or low levels of these four genes and known parameters of clinical prognosis (UICC stages, survival time). Conclusions: In conclusion, tenfold increased expression levels of Opn, Tgf-β, Mmp-2 and Cox-2 in a subset of CRC patients did not predict for a clinical outcome that is different from that of the remaining patients.
Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel
Clinical Proteomics, 2019
Background: One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current populationbased screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). Methods: A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. Results: Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. Conclusion: MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.