Intelligent techniques using molecular data analysis in leukaemia: an opportunity for personalized medicine support system

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient’s genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.Haneen Banjar, David Adelson, Fred Brown, and Naeem Chaudhr

Examining lipid metabolism of colorectal adenomas and carcinomas using Rapid Evaporative Ionisation Mass Spectrometry (REIMS)

Background There is an unmet need for real-time intraoperative colorectal tissue recognition, which would promote personalised oncologic decision making. Rapid Evaporative Ionization Mass Spectrometry (REIMS) analyses the composition of cellular lipids through the aerosol generated from electrosurgical instruments, providing a novel diagnostic platform and surgeon feedback. Thesis Hypothesis Colorectal lipid metabolism and cellular lipid composition are associated with the phenotype of colorectal adenomas and carcinomas, which can be leveraged for tissue recognition in vivo. Methods This thesis contains three work packages. First, a method for REIMS spectral quality control was developed based on a human dataset and analysis of a porcine model assessed the spectral impact of technical and environmental factors. Second, an ex vivo spectral reference database was constructed from analysis of human colorectal tissues, assessing the ability of REIMS for tissue recognition. Finally, REIMS was translated into the operating theatre, for proof-of-principle application of during transanal minimally invasive surgery (TAMIS). Results Sensitivity analyses revealed seven minimum quality criteria for REIMS spectra to be included in all future statistical analyses, with quality also impacted by low diathermy power, coagulation mode and tissue contamination. Based on tissue of 161 patients, REIMS could differentiate colorectal normal, adenoma and cancer tissue with 91.1% accuracy, and disease from normal with 93.5% accuracy. REIMS could risk-stratify adenomas by predicting grade of dysplasia, however not histological features of poor prognosis in cancers. 61 pertinent lipid metabolites were structurally identified. REIMS was coupled to TAMIS in seven patients. Optimisation of the workflow successfully increased signal intensity, with tissue recognition showing high accuracy in vivo and identification of a cancer-involved margin. Discussion This thesis demonstrates that REIMS can be optimised and applied for accurate real-time colorectal tissue recognition based on cellular lipid composition. This can be translated in vivo, with promising results during first-in-man mass spectrometry-coupled TAMIS.Open Acces

Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology

Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes

Discovery and application of colorectal cancer protein markers for disease stratification

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise methods of CRC stratification are needed. The intracellular protein expression from 28 CRC primary tumours and corresponding normal intestinal mucosa was analysed using saturation-DIGE/MS and Explorer antibody microarrays. Changes in protein abundance were identified at each stage of CRC. Proteins associated with proliferation, glycolysis, reduced adhesion, endoplasmic reticulum stress, angiogenesis, and response to hypoxia represent changes to CRC and its microenvironment during development. Molecular changes in CRC cells and their microenvironment can be incorporated into clinic-pathological data to help sub-classify tumours and personalise treatment. DotScan antibody microarray analysis was used to profile the surface proteome of cells derived from 50 CRC samples and corresponding normal intestinal mucosa. Fluorescence multiplexing enabled the analysis of two different sub-populations of cells from each sample: EpCAM+ cells (CRC cells or normal epithelial cells in normal mucosa) and CD3+ T-cells (tumour-infiltrating lymphocytes). Unsupervised hierarchical clustering of the CRC and T-cell surface profiles defined four clinically relevant clusters, which showed some correlation with histopathological and clinical characteristics such as cancer cell differentiation, peri-tumoural inflammation and stimulation of infiltrating T-cells. The observed relationship between the surface antigen expression profiles of patients’ CRC cells and their corresponding tumour infiltrating T-cells suggests that CRC surface proteins may play a direct role in influencing the activity (and hence surface protein expression) of neighbouring T-cells and/or vice versa. We conclude that the application of surface profiling may provide improved patient stratification, allowing more reliable prediction of disease progression and patient outcome

Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics

Introduction: The last 20 years have seen significant improvements in the analytical capabilities of biological mass spectrometry. Studies using advanced mass spectrometry (MS) have resulted in new insights into cell biology and the aetiology of diseases as well as its use in clinical applications. Areas Covered: This review will discuss recent developments in MS-based technologies and their cancer-related applications with a focus on proteomics. It will also discuss the issues around translating the research findings to the clinic and provide an outline of where the field is moving. Expert Opinion: Proteomics has been problematic to adapt for the clinical setting. However, MS-based techniques continue to demonstrate potential in novel clinical uses beyond classical cancer proteomics

Discovery and application of colorectal cancer protein markers for disease stratification

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise methods of CRC stratification are needed. The intracellular protein expression from 28 CRC primary tumours and corresponding normal intestinal mucosa was analysed using saturation-DIGE/MS and Explorer antibody microarrays. Changes in protein abundance were identified at each stage of CRC. Proteins associated with proliferation, glycolysis, reduced adhesion, endoplasmic reticulum stress, angiogenesis, and response to hypoxia represent changes to CRC and its microenvironment during development. Molecular changes in CRC cells and their microenvironment can be incorporated into clinic-pathological data to help sub-classify tumours and personalise treatment. DotScan antibody microarray analysis was used to profile the surface proteome of cells derived from 50 CRC samples and corresponding normal intestinal mucosa. Fluorescence multiplexing enabled the analysis of two different sub-populations of cells from each sample: EpCAM+ cells (CRC cells or normal epithelial cells in normal mucosa) and CD3+ T-cells (tumour-infiltrating lymphocytes). Unsupervised hierarchical clustering of the CRC and T-cell surface profiles defined four clinically relevant clusters, which showed some correlation with histopathological and clinical characteristics such as cancer cell differentiation, peri-tumoural inflammation and stimulation of infiltrating T-cells. The observed relationship between the surface antigen expression profiles of patients’ CRC cells and their corresponding tumour infiltrating T-cells suggests that CRC surface proteins may play a direct role in influencing the activity (and hence surface protein expression) of neighbouring T-cells and/or vice versa. We conclude that the application of surface profiling may provide improved patient stratification, allowing more reliable prediction of disease progression and patient outcome

From Correlation to Causality: Does Network Information improve Cancer Outcome Prediction?

Motivation: Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. A widely used approach is high-throughput experiments that aim to explore predictive signature genes which would provide identification of clinical outcome of diseases. Microarray data analysis helps to reveal underlying biological mechanisms of tumor progression, metastasis, and drug-resistance in cancer studies. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. The experimental or computational noise in data and limited tissue samples collected from patients might furthermore reduce the predictive power and biological interpretability of such signature genes. Nevertheless, signature genes predicted by different studies generally represent poor similarity; even for the same type of cancer. Integration of network information with gene expression data could provide more efficient signatures for outcome prediction in cancer studies. One approach to deal with these problems employs gene-gene relationships and ranks genes using the random surfer model of Google's PageRank algorithm. Unfortunately, the majority of published network-based approaches solely tested their methods on a small amount of datasets, questioning the general applicability of network-based methods for outcome prediction. Methods: In this thesis, I provide a comprehensive and systematically evaluation of a network-based outcome prediction approach -- NetRank - a PageRank derivative -- applied on several types of gene expression cancer data and four different types of networks. The algorithm identifies a signature gene set for a specific cancer type by incorporating gene network information with given expression data. To assess the performance of NetRank, I created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and one in-house dataset. Results: NetRank performs significantly better than classical methods such as foldchange or t-test as it improves the prediction performance in average for 7%. Besides, we are approaching the accuracy level of the authors' signatures by applying a relatively unbiased but fully automated process for biomarker discovery. Despite an order of magnitude difference in network size, a regulatory, a protein-protein interaction and two predicted networks perform equally well. Signatures as published by the authors and the signatures generated with classical methods do not overlap -- not even for the same cancer type -- whereas the network-based signatures strongly overlap. I analyze and discuss these overlapping genes in terms of the Hallmarks of cancer and in particular single out six transcription factors and seven proteins and discuss their specific role in cancer progression. Furthermore several tests are conducted for the identification of a Universal Cancer Signature. No Universal Cancer Signature could be identified so far, but a cancer-specific combination of general master regulators with specific cancer genes could be discovered that achieves the best results for all cancer types. As NetRank offers a great value for cancer outcome prediction, first steps for a secure usage of NetRank in a public cloud are described. Conclusion: Experimental evaluation of network-based methods on a gene expression benchmark dataset suggests that these methods are especially suited for outcome prediction as they overcome the problems of random gene signatures and noisy expression data. Through the combination of network information with gene expression data, network-based methods identify highly similar signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. In general allows the integration of additional information in gene expression analysis the identification of more reliable, accurate and reproducible biomarkers and provides a deeper understanding of processes occurring in cancer development and progression.:1 Definition of Open Problems 2 Introduction 2.1 Problems in cancer outcome prediction 2.2 Network-based cancer outcome prediction 2.3 Universal Cancer Signature 3 Methods 3.1 NetRank algorithm 3.2 Preprocessing and filtering of the microarray data 3.3 Accuracy 3.4 Signature similarity 3.5 Classical approaches 3.6 Random signatures 3.7 Networks 3.8 Direct neighbor method 3.9 Dataset extraction 4 Performance of NetRank 4.1 Benchmark dataset for evaluation 4.2 The influence of NetRank parameters 4.3 Evaluation of NetRank 4.4 General findings 4.5 Computational complexity of NetRank 4.6 Discussion 5 Universal Cancer Signature 5.1 Signature overlap – a sign for Universal Cancer Signature 5.2 NetRank genes are highly connected and confirmed in literature 5.3 Hallmarks of Cancer 5.4 Testing possible Universal Cancer Signatures 5.5 Conclusion 6 Cloud-based Biomarker Discovery 6.1 Introduction to secure Cloud computing 6.2 Cancer outcome prediction 6.3 Security analysis 6.4 Conclusion 7 Contributions and Conclusion

Extracellular vesicle microRNA as liquid biopsy biomarkers in early stage colorectal cancer patients

New Zealand has one of the highest rates of colorectal cancer (CRC) in the world. Current strategies to diagnose CRC, including population level stool-based screening, are flawed due to poor sensitivity in early disease and limited participation. Patients have expressed a preference for a blood-based test, or ‘liquid biopsy’, if it were available. A liquid biopsy that has parity with, or outperforms current stool based screening would likely lead to earlier diagnosis of CRC and improved patient outcomes. Extracellular Vesicles (EVs) are produced in abundance by CRC cells, their contents also reflect their parent cell of origin. microRNA (miRNA) are small non-coding RNA molecules, which are dysregulated in CRC cells compared to normal cells. Identifying EV miRNA in the bloodstream of early stage CRC patients that are sufficiently different from healthy patients may enable identification of CRC from a liquid biopsy. Our study examined four miRNAs, miR-19a, miR-23a, miR-183 and miR-1246, in both tumour tissue and plasma EVs that have previously been reported as dysregulated in CRC. miRNA expression of the candidate miRNAs was in examined in CRC tumour tissue vs normal colonic mucosa, and in plasma EVs of stage I and II CRC patients vs healthy controls, by RT-qPCR. Changes in expression in tissue were assessed to establish if they would translate to circulating EVs. Furthermore, any differences between the miRNA expression in plasma EVs were examined to assess for a capacity to differentiate early stage CRC patients from healthy controls and be useful as a liquid biopsy biomarker in CRC. From our results, miR-1246 and miR-183 were significantly overexpressed in tumour tissue compared to normal colonic mucosa. However, neither miRNA was consistently expressed in the circulating EVs. In the plasma analysis, miR-19a was significantly downregulated in EVs of CRC patients. It demonstrated significant differences even when stage I patients alone were compared against controls and it also had superior sensitivity to CEA in our cohort. This appears to be the first study to document a significant decrease in miR-19 in the EVs of early stage CRC patients. From our results, significant variance from the current literature was seen. Some possible factors for this variance may include differences in methodology and control selection. Although, plasma EV miRNA display great promise as liquid biopsy biomarkers, there are a number of challenges in identifying a marker specific for CRC. Standardisation of methodology may help identify candidate miRNAs that offer the greatest potential. Finding a liquid biopsy biomarker for CRC that is equally effective, or outperforms, current faecal-based methods would likely increase uptake in screening. The subsequent effects would include decreased healthcare costs due to earlier diagnoses of CRC with improved patient outcomes for the growing millions of people who will suffer from this disease

Patient model for colon and colorectal cancer care trajectory simulation

Almost half Canadians (41% women and 46% men) will develop cancer during their lifetime and 88% of them are older than fifty [1]. Lung, breast, colon, colorectal and prostate cancers represent more than half of all new cancer cases (52%). Breast cancer is the leading type of cancer among women, while colon and colorectal cancer are the third most common cancer among men and women. Cancer is the leading cause of death in Canada and in the world with 29.8% of the population affected, compared to 26.6% for cardiovascular diseases [2]. Furthermore, in 2000, cancer was the fourth most expensive disease in Canada with $17.4 billion spent. Colon and Colorectal cancer are considered the second leading cause of cancer death among men and the third among women. Cancer treatment is characterized by the convergence of many services including ambulatory, hospitals, clinical, nutritional, psychological, and sports medicine, which coordination and integration condition treatment success and patient quality of life. In order to reduce the impact of this disease and increase the cure rate and the patient quality of life, it is necessary to develop and evaluate new therapeutic and organizational approaches. This study deals with this goal and is the first methodological step toward creating a simulation platform of care trajectories of colorectal cancer patients. This simulation platform aims at simulating many elements of the hospital environment, from care resources to patient physiology and psychology profiles, in order to evaluate the many impacts of organizational changes of care trajectories. First, this paper describes the general scope of this simulation project and presents a state of the art of agentbased simulation. Next, the general conceptual model of the simulation is described. Then, we present our cancer evolution, which is then tested and validated using two separate experiments