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

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

Integrated transcriptional profiling and genomic analyses reveal RPN2 and HMGB1 as promising biomarkers in colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease that is associated with a gradual accumulation of genetic and epigenetic alterations. Among all CRC stages, stage II tumors are highly heterogeneous with a high relapse rate in about 20-25 % of stage II CRC patients following surgery. Thus, a comprehensive analysis of gene signatures to identify aggressive and metastatic phenotypes in stage II CRC is desired for a more accurate disease classification and outcome prediction. By utilizing a Cancer Array, containing 440 oncogenes and tumor suppressors to profile mRNA expression, we identified a larger number of differentially expressed genes in poorly differentiated stage II colorectal adenocarcinoma tissues, compared to their matched normal tissues. Ontology and Ingenuity Pathway Analysis (IPA) indicated that these genes are involved in functional mechanisms associated with several transcription factors. Genomic alterations of these genes were also investigated through The Cancer Genome Atlas (TCGA) database, utilizing 195 published CRC specimens. The percentage of genomic alterations in these genes was ranked based on their mRNA expression, copy number variations and mutations. This data was further combined with published microarray studies from a large set of CRC tumors classified based on prognostic features. This led to the identification of eight candidate genes including RPN2, HMGB1, AARS, IGFBP3, STAT1, HYOU1, NQO1 and PEA15 that were associated with the progressive phenotype. In particular, RPN2 and HMGB1 displayed a higher genomic alteration frequency in CRC, compared to eight other major solid cancers. Immunohistochemistry was performed on additional 78 stage I-IV CRC samples, where RPN2 protein immunostaining exhibited a significant association with stage III/IV tumors, distant metastasis, and poor differentiation, indicating that RPN2 expression is associated with poor prognosis. Further, our study revealed significant transcriptional regulatory mechanisms, networks and gene signatures, underlying CRC malignant progression and phenotype warranting future clinical investigations.published_or_final_versio

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

Integration of Genome Scale Data for Identifying New Biomarkers in Colon Cancer: Integrated Analysis of Transcriptomics and Epigenomics Data from High Throughput Technologies in Order to Identifying New Biomarkers Genes for Personalised Targeted Therapies for Patients Suffering from Colon Cancer

Colorectal cancer is the third most common cancer and the leading cause of cancer deaths in Western industrialised countries. Despite recent advances in the screening, diagnosis, and treatment of colorectal cancer, an estimated 608,000 people die every year due to colon cancer. Our current knowledge of colorectal carcinogenesis indicates a multifactorial and multi-step process that involves various genetic alterations and several biological pathways. The identification of molecular markers with early diagnostic and precise clinical outcome in colon cancer is a challenging task because of tumour heterogeneity. This Ph.D.-thesis presents the molecular and cellular mechanisms leading to colorectal cancer. A systematical review of the literature is conducted on Microarray Gene expression profiling, gene ontology enrichment analysis, microRNA and system Biology and various bioinformatics tools. We aimed this study to stratify a colon tumour into molecular distinct subtypes, identification of novel diagnostic targets and prediction of reliable prognostic signatures for clinical practice using microarray expression datasets. We performed an integrated analysis of gene expression data based on genetic, epigenetic and extensive clinical information using unsupervised learning, correlation and functional network analysis. As results, we identified 267-gene and 124-gene signatures that can distinguish normal, primary and metastatic tissues, and also involved in important regulatory functions such as immune-response, lipid metabolism and peroxisome proliferator-activated receptors (PPARs) signalling pathways. For the first time, we also identify miRNAs that can differentiate between primary colon from metastatic and a prognostic signature of grade and stage levels, which can be a major contributor to complex transcriptional phenotypes in a colon tumour

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

Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry.

Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection

Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer.

The recognition that colorectal cancer (CRC) is a heterogeneous disease in terms of clinical behaviour and response to therapy translates into an urgent need for robust molecular disease subclassifiers that can explain this heterogeneity beyond current parameters (MSI, KRAS, BRAF). Attempts to fill this gap are emerging. The Cancer Genome Atlas (TGCA) reported two main CRC groups, based on the incidence and spectrum of mutated genes, and another paper reported an EMT expression signature defined subgroup. We performed a prior free analysis of CRC heterogeneity on 1113 CRC gene expression profiles and confronted our findings to established molecular determinants and clinical, histopathological and survival data. Unsupervised clustering based on gene modules allowed us to distinguish at least five different gene expression CRC subtypes, which we call surface crypt-like, lower crypt-like, CIMP-H-like, mesenchymal and mixed. A gene set enrichment analysis combined with literature search of gene module members identified distinct biological motifs in different subtypes. The subtypes, which were not derived based on outcome, nonetheless showed differences in prognosis. Known gene copy number variations and mutations in key cancer-associated genes differed between subtypes, but the subtypes provided molecular information beyond that contained in these variables. Morphological features significantly differed between subtypes. The objective existence of the subtypes and their clinical and molecular characteristics were validated in an independent set of 720 CRC expression profiles. Our subtypes provide a novel perspective on the heterogeneity of CRC. The proposed subtypes should be further explored retrospectively on existing clinical trial datasets and, when sufficiently robust, be prospectively assessed for clinical relevance in terms of prognosis and treatment response predictive capacity. Original microarray data were uploaded to the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress/) under Accession Nos E-MTAB-990 and E-MTAB-1026. © 2013 Swiss Institute of Bioinformatics. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland

Pancancer analysis of DNA methylation-driven genes using MethylMix.

Aberrant DNA methylation is an important mechanism that contributes to oncogenesis. Yet, few algorithms exist that exploit this vast dataset to identify hypo- and hypermethylated genes in cancer. We developed a novel computational algorithm called MethylMix to identify differentially methylated genes that are also predictive of transcription. We apply MethylMix to 12 individual cancer sites, and additionally combine all cancer sites in a pancancer analysis. We discover pancancer hypo- and hypermethylated genes and identify novel methylation-driven subgroups with clinical implications. MethylMix analysis on combined cancer sites reveals 10 pancancer clusters reflecting new similarities across malignantly transformed tissues