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

Characterization of Metabolic, Diffusion, and Perfusion Properties in GBM: Contrast-Enhancing versus Non-Enhancing Tumor.

BackgroundAlthough the contrast-enhancing (CE) lesion on T1-weighted MR images is widely used as a surrogate for glioblastoma (GBM), there are also non-enhancing regions of infiltrative tumor within the T2-weighted lesion, which elude radiologic detection. Because non-enhancing GBM (Enh-) challenges clinical patient management as latent disease, this study sought to characterize ex vivo metabolic profiles from Enh- and CE GBM (Enh+) samples, alongside histological and in vivo MR parameters, to assist in defining criteria for estimating total tumor burden.MethodsFifty-six patients with newly diagnosed GBM received a multi-parametric pre-surgical MR examination. Targets for obtaining image-guided tissue samples were defined based on in vivo parameters that were suspicious for tumor. The actual location from where tissue samples were obtained was recorded, and half of each sample was analyzed for histopathology while the other half was scanned using HR-MAS spectroscopy.ResultsThe Enh+ and Enh- tumor samples demonstrated comparable mitotic activity, but also significant heterogeneity in microvascular morphology. Ex vivo spectroscopic parameters indicated similar levels of total choline and N-acetylaspartate between these contrast-based radiographic subtypes of GBM, and characteristic differences in the levels of myo-inositol, creatine/phosphocreatine, and phosphoethanolamine. Analysis of in vivo parameters at the sample locations were consistent with histological and ex vivo metabolic data.ConclusionsThe similarity between ex vivo levels of choline and NAA, and between in vivo levels of choline, NAA and nADC in Enh+ and Enh- tumor, indicate that these parameters can be used in defining non-invasive metrics of total tumor burden for patients with GBM

Silenced ZNF154 Is Associated with Longer Survival in Resectable Pancreatic Cancer

Pancreatic cancer has become the third leading cause of cancer-related death in the Western world despite advances in therapy of other cancerous lesions. Late diagnosis due to a lack of symptoms during early disease allows metastatic spread of the tumor. Most patients are considered incurable because of metastasized disease. On a cellular level, pancreatic cancer proves to be rather resistant to chemotherapy. Hence, early detection and new therapeutic targets might improve outcomes. The detection of DNA promoter hypermethylation has been described as a method to identify putative genes of interest in cancer entities. These genes might serve as either biomarkers or might lead to a better understanding of the molecular mechanisms involved. We checked tumor specimens from 80 patients who had undergone pancreatic resection for promoter hypermethylation of the zinc finger protein ZNF154. Then, we further characterized the effects of ZNF154 on cell viability and gene expression by in vitro experiments. We found a significant association between ZNF154 hypermethylation and better survival in patients with resectable pancreatic cancer. Moreover, we suspect that the cell growth suppressor SLFN5 might be linked to a silenced ZNF154 in pancreatic cancer

The influence of student engagement with online pre laboratory work modules on academic performance in first year chemistry

Information and communications technologies are increasingly being incorporated into teaching activities in higher education. They offer the opportunity to improve student learning experiences provided they are used in an educationally sound way. This may be through triggering student interest, improving engagement and, in turn, helping students to develop a deeper understanding of particular concepts. A previous study examined students’ engagement with two specific online prelaboratory work modules to determine how students engage with them, and correlate this engagement with learning style (Tasker, Miller, Kemmett and Bedgood Jnr 2003). The present study extends that work with a broader range of students and on a much larger scale, to investigate the claim that student engagement with these modules improves academic performance in first year chemistry

Interventions to improve latent and active tuberculosis treatment completion rates in underserved groups in low incidence countries:a scoping review

BACKGROUND: People in underserved groups have higher rates of tuberculosis (TB) and poorer treatment outcomes compared with people with no social risk factors.OBJECTIVES: This scoping review aimed to identify interventions that improve TB treatment adherence or completion rates.ELIGIBILITY CRITERIA: Studies of any design focusing on interventions to improve adherence or completion of TB treatment in underserved populations in low incidence countries.SOURCES OF EVIDENCE: MEDLINE, Embase and Cochrane CENTRAL were searched (January 2015 to December 2023).CHARTING METHODS: Piloted data extraction forms were used. Findings were tabulated and reported narratively. Formal risk of bias assessment or synthesis was not undertaken.RESULTS: 47 studies were identified. There was substantial heterogeneity in study design, population, intervention components, usual care and definition of completion rates. Most studies were in migrants or refugees, with fewer in populations with other risk factors (eg, homelessness, imprisonment or substance abuse). Based on controlled studies, there was limited evidence to suggest that shorter treatment regimens, video-observed therapy (compared with directly observed therapy), directly observed therapy (compared with self-administered treatment) and approaches that include tailored health or social support beyond TB treatment may lead to improved outcomes. This evidence is mostly observational and subject to confounding. There were no studies in Gypsy, Roma and Traveller populations, or individuals with mental health disorders and only one in sex workers. Barriers to treatment adherence included a lack of knowledge around TB, lack of general health or social support and side effects. Facilitators included health education, trusted relationships between patients and healthcare staff, social support and reduced treatment duration.CONCLUSIONS: The evidence base is limited, and few controlled studies exist. Further high-quality research in well-defined underserved populations is needed to confirm the limited findings and inform policy and practice in TB management. Further qualitative research should include more people from underserved groups.</p

Assessing Landscape Functions with Broad-Scale Environmental Data: Insights Gained from a Prototype Development for Europe

We examine the advantages and disadvantages of a methodological framework designed to analyze the poorly understood relationships between the ecosystem properties of large portions of land, and their capacities (stocks) to provide goods and services (flows). These capacities (stocks) are referred to as landscape functions. The core of our assessment is a set of expert- and literature-driven binary links, expressing whether specific land uses or other environmental properties have a supportive or neutral role for given landscape functions. The binary links were applied to the environmental properties of 581 administrative units of Europe with widely differing environmental conditions and this resulted in a spatially explicit landscape function assessment. To check under what circumstances the binary links are able to replace complex interrelations, we compared the landscape function maps with independently generated continent-wide assessments (maps of ecosystem services or environmental parameters/indicators). This rigorous testing revealed that for 9 out of 15 functions the straightforward binary links work satisfactorily and generate plausible geographical patterns. This conclusion holds primarily for production functions. The sensitivity of the nine landscape functions to changes in land use was assessed with four land use scenarios (IPCC SRES). It was found that most European regions maintain their capacity to provide the selected services under any of the four scenarios, although in some cases at other locations within the region. At the proposed continental scale, the selected input parameters are thus valid proxies which can be used to assess the mid-term potential of landscapes to provide goods and service

Identification of dfrA14 in two distinct plasmids conferring trimethoprim resistance in Actinobacillus pleuropneumoniae

OBJECTIVES: The objective of this study was to determine the distribution and genetic basis of trimethoprim resistance in Actinobacillus pleuropneumoniae isolates from pigs in England. METHODS: Clinical isolates collected between 1998 and 2011 were tested for resistance to trimethoprim and sulphonamide. The genetic basis of trimethoprim resistance was determined by shotgun WGS analysis and the subsequent isolation and sequencing of plasmids. RESULTS: A total of 16 (out of 106) A. pleuropneumoniae isolates were resistant to both trimethoprim (MIC >32 mg/L) and sulfisoxazole (MIC ≥256 mg/L), and a further 32 were resistant only to sulfisoxazole (MIC ≥256 mg/L). Genome sequence data for the trimethoprim-resistant isolates revealed the presence of the dfrA14 dihydrofolate reductase gene. The distribution of plasmid sequences in multiple contigs suggested the presence of two distinct dfrA14-containing plasmids in different isolates, which was confirmed by plasmid isolation and sequencing. Both plasmids encoded mobilization genes, the sulphonamide resistance gene sul2, as well as dfrA14 inserted into strA, a streptomycin-resistance-associated gene, although the gene order differed between the two plasmids. One of the plasmids further encoded the strB streptomycin-resistance-associated gene. CONCLUSIONS: This is the first description of mobilizable plasmids conferring trimethoprim resistance in A. pleuropneumoniae and, to our knowledge, the first report of dfrA14 in any member of the Pasteurellaceae. The identification of dfrA14 conferring trimethoprim resistance in A. pleuropneumoniae isolates will facilitate PCR screens for resistance to this important antimicrobial

CT stress perfusion imaging for detection of haemodynamically relevant coronary stenosis as defined by FFR

Objectives: To evaluate the diagnostic accuracy (DA) of CT-myocardial perfusion imaging (CT-MPI) and a combined approach with CT angiography (CTA) for the detection of haemodynamically relevant coronary stenoses in patients with both suspected and known coronary artery disease. Design: Prospective, non-randomised, diagnostic study. Setting: Academic hospital-based study. Patients: 65 patients (42 men age 70.4 +/- 9) with typical or atypical chest pain. Interventions: CTA and CT-MPI with adenosine stress using a fast dual-source CT system. At subsequent invasive angiography, FFR measurement was performed in coronary arteries to define haemodynamic relevance of stenosis. Main outcome measures: We tried to correlate haemodynamically relevant stenosis (FFR <0.80) to a reduced myocardial blood flow (MBF) as assessed by CT-MPI and determined the DA of CT-MPI for the detection of haemodynamically relevant stenosis. Results: Sensitivity and negative predictive value (NPV) of CTA alone were very high (100% respectively) for ruling out haemodynamically significant stenoses, specificity, Positive predictive value (PPV) and DA were low (43.8, 67.3 and 72%, respectively). CT-MPI showed a significant increase in specificity, PPV and DA for the detection of haemodynamically relevant stenoses (65.6, 74.4 and 81.5%, respectively) with persisting high sensitivity and NPV for ruling out haemodynamically relevant stenoses (97% and 95.5% respectively). The combination of CTA and CT-MPI showed no further increase in detection of haemodynamically significant stenosis compared with CT-MPI alone. Conclusions: Our data suggest that CT-MPI permits the detection of haemodynamically relevant coronary artery stenoses with a moderate DA. CT may, therefore, allow the simultaneous assessment of both coronary morphology and function

Molecular Characterization of the Onset and Progression of Colitis in Inoculated Interleukin-10 Gene-Deficient Mice: A Role for PPARα

The interleukin-10 gene-deficient (Il10−/−) mouse is a model of human inflammatory bowel disease and Ppara has been identified as one of the key genes involved in regulation of colitis in the bacterially inoculated Il10−/− model. The aims were to (1) characterize colitis onset and progression using a histopathological, transcriptomic, and proteomic approach and (2) investigate links between PPARα and IL10 using gene network analysis. Bacterial inoculation resulted in severe colitis in Il10−/− mice from 10 to 12 weeks of age. Innate and adaptive immune responses showed differences in gene expression relating to colitis severity. Actin cytoskeleton dynamics, innate immunity, and apoptosis-linked gene and protein expression data suggested a delayed remodeling process in 12-week-old Il10−/− mice. Gene expression changes in 12-week-old Il10−/− mice were related to PPARα signaling likely to control colitis, but how PPARα activation might regulate intestinal IL10 production remains to be determined