Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

Monoclonal Antibodies Against Tumour-Associated Carbohydrate Antigens

Glycomic profiling of tumour tissues consistently shows alterations in N- and O-glycosylation profiles of glycoproteins and glycolipids compared to healthy tissues, with important functional implications for cancer cell biology. The overexpression of tumour-associated carbohydrate antigens (TACAs), as a result of aberrant glycosylation in tumours, is usually correlated with poor prognosis and survival of cancer patients. In tumours, TACAs are associated with worse tumour progression than the deletion and inactivation of tumour suppressor genes. The findings of TACAs acting are not merely tumour markers but also constitute part of the machinery in inducing cancer metastasis and invasiveness further strengthen the scientific rationales for immunotherapy targeting TACAs. Despite the attractiveness of the TACAs, there are very few anti-glycan monoclonal antibodies (mAbs), as glycans usually induce low-affinity IgM responses. This chapter provides an overview of TACAs, direct killing anti-glycan mAbs, and introduces two murine mAbs (FG88 mAbs) that recognise Lewis carbohydrate antigens overexpressed on tumour glycoconjugates with high functional affinity. Although the production of anti-glycan mAbs against cancers is not new, the production of high-affinity IgG anti-glycan mAbs is novel. FG88 mAbs definitely have great potential in cancer therapy and serve as valuable tools in glycobiology research

Ensemble Learning of Colorectal Cancer Survival Rates

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved.Comment: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013, pp. 82 - 86, 201

Ensemble learning of colorectal cancer survival rates

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved

An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates

This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient’s biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not

An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates

This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient’s biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not

Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to cluster this dataset and important subsets of it in an effort to characterize the data and validate existing standards for tumour classification. It is apparent from optimal clustering that existing tumour classification is largely unrelated to immunological factors within a patient and that there may be scope for re-evaluating treatment options and survival estimates based on a combination of tumour physiology and patient histochemistry.Comment: Federated Conference on Computer Science and Information Systems (FedCSIS), pp 187-191, 201

Total loss of MHC class I is an independent indicator of good prognosis in breast cancer

Tumours can be recognised by CTL and NK cells. CTL recognition depends on expression of MHC Class I loaded with peptides from tumour antigens. In contrast, loss of MHC Class I results in NK activation. In our study a large set of samples from patients with primary operable invasive breast cancer was evaluated for the expression of MHC Class I heavy and light by immunohistochemical staining of 439 breast carcinomas in a tissue microarray. Forty-seven percent (206 of 439) of breast carcinomas were considered negative for HLA Class I heavy chain (HC10), whereas lack of anti-β2m-antibody staining was observed in 39% (167 of 424) of tumours, with only 3% of the β2m-negative tumours expressing detectable HLA Class I heavy chain. Correlation with patient outcome showed direct relationship between patient survival and HLA-negative phenotype (log rank = 0.004). A positive relationship was found between the intensity of expression of MHC Class I light and heavy chains expression and histological grade of invasive tumour (p < 0.001) and Nottingham Prognostic Index (p < 0.001). To investigate whether HLA Class I heavy and light chains expression had independent prognostic significance, Cox multivariate regression analysis, including the parameters of tumour size, lymph node stage, grade and intensity of HC10 and anti-β2m staining, was carried out. In our analysis, lymph node stage (p < 0.001), tumour grade (p = 0.005) and intensity of MHC Class I light and heavy chains expression were shown to be independent prognostic factors predictive of overall survival (p-values HC10 = 0.047 and β2m = 0.018)

The role of MUC1 and MUC3 in the biology and prognosis of colorectal cancer

BACKGROUND: MUC1 and MUC3 are from a large family of glycoproteins with an aberrant expression profile in various malignancies. Much interest has been focused on the role of these proteins in the development and progression of colorectal cancer; however, no previous studies have included the highly confounding variable of vascular invasion in their survival analysis. Using high throughput tissue microarray technology we assessed the prognostic value of MUC1 and MUC3 expression in the largest cohort of colorectal cancer patients to date. We propose that tumours lacking expression of MUC1 and MUC3 will be more likely to metastasise, due to previously observed loss of cell-cell adhesion, and this will therefore lead to more aggressive cancers with poorer prognosis. METHODS: A tissue micro-array was prepared from tumour samples of 462 consecutive patients undergoing resection of a primary colorectal cancer. A comprehensive prospectively recorded data base with mean follow up of 75 months was collected and included common clinicopathological variables and disease specific survival. Immunohistochemical analysis of MUC1 and MUC3 expression was performed using antibodies NCL-MUC1 and 1143/B7 respectively, results were correlated with the variables within the database. RESULTS: Positive expression of MUC1 and MUC3 was seen in 32% and 74% of tumours respectively. On univariate analysis no correlation was seen with either MUC1 or MUC3 and any of the clinicopathological variables including tumour grade and stage, vascular invasion and tumour type. Kaplan-Meier analysis demonstrated a significant reduction in disease specific survival with MUC1 positive tumours (p = 0.038), this was not seen with MUC3 (p = 0.552). On multivariate analysis, using Cox proportional hazards model, MUC1 expression was shown to be an independent marker of prognosis (HR 1.339, 95%CI 1.002–1.790, p = 0.048). CONCLUSION: MUC1 expression in colorectal cancer is an independent marker of poor prognosis, even when vascular invasion is included in the analysis. These results support previous studies suggesting a role for MUC1 in colorectal cancer development possibly through its effects on cell adhesion

Evidence that the p53 negative / Bcl-2 positive phenotype is an independent indicator of good prognosis in colorectal cancer: A tissue microarray study of 460 patients

BACKGROUND: Advances in our understanding of the molecular biology of colorectal cancer have fuelled the search for novel molecular prognostic markers to complement existing staging systems. Markers assessed in combination may perform better than those considered individually. Using high-throughput tissue microarray technology, we describe the prognostic value of combined p53 / Bcl-2 status in colorectal cancer. PATIENTS AND METHODS: Tumour samples from 462 patients who underwent elective surgery to resect a primary colorectal cancer between 1994 and 2000 (mean follow-up of 75 months) were assembled in tissue microarray format. Clinico-pathological data including tumour grade, stage, vascular invasion status along with disease specific survival data has been collected prospectively. Immunohistochemical analysis of p53 and Bcl-2 expression was performed using antibodies DO-7 (p53) and 124 (Bcl-2), and results correlated with known clinico-pathological variables and outcomes. RESULTS: Abnormal nuclear p53 accumulation and Bcl-2 overexpression were detected in 221/445 (49.6%) and199/437 (45.5%) tumours respectively, with a significant inverse correlation between the two markers (p = 0.023). On univariate analysis no correlations were found between either marker and standard clinico-pathological variables, however nuclear p53 expression was associated with a significantly reduced survival (p = 0.024). Combined analysis of the two markers indicated that 112/432 (24.2%) cases displayed a p53(-)/Bcl-2(+) phenotype, this occurring more frequently in earlier stage tumours. Kaplan-Meier analysis revealed a significant survival advantage in these p53(-)/Bcl-2(+) tumours compared with the remaining cases (p = 0.0032). On multivariate analysis using the Cox proportional hazards model, neither p53 expression nor Bcl-2 expression alone were of independent prognostic significance, however the combined p53(-)/Bcl-2(+) phenotype was significantly associated with a good prognosis in this series (HR 0.659, 95%CI 0.452–0.959, p = 0.029). CONCLUSION: Patient stratification by combined p53 / Bcl-2 phenotype provides stage-independent prognostic information in colorectal cancer. Specifically, that up to a quarter of patients display a good prognosis p53(-)/Bcl-2(+) phenotype. This may indicate a more clinically indolent phenotype and a subset of patients for whom less aggressive adjuvant treatment appropriate