Immersive analytics for oncology patient cohorts

This thesis proposes a novel interactive immersive analytics tool and methods to interrogate the cancer patient cohort in an immersive virtual environment, namely Virtual Reality to Observe Oncology data Models (VROOM). The overall objective is to develop an immersive analytics platform, which includes a data analytics pipeline from raw gene expression data to immersive visualisation on virtual and augmented reality platforms utilising a game engine. Unity3D has been used to implement the visualisation. Work in this thesis could provide oncologists and clinicians with an interactive visualisation and visual analytics platform that helps them to drive their analysis in treatment efficacy and achieve the goal of evidence-based personalised medicine. The thesis integrates the latest discovery and development in cancer patients’ prognoses, immersive technologies, machine learning, decision support system and interactive visualisation to form an immersive analytics platform of complex genomic data. For this thesis, the experimental paradigm that will be followed is in understanding transcriptomics in cancer samples. This thesis specifically investigates gene expression data to determine the biological similarity revealed by the patient's tumour samples' transcriptomic profiles revealing the active genes in different patients. In summary, the thesis contributes to i) a novel immersive analytics platform for patient cohort data interrogation in similarity space where the similarity space is based on the patient's biological and genomic similarity; ii) an effective immersive environment optimisation design based on the usability study of exocentric and egocentric visualisation, audio and sound design optimisation; iii) an integration of trusted and familiar 2D biomedical visual analytics methods into the immersive environment; iv) novel use of the game theory as the decision-making system engine to help the analytics process, and application of the optimal transport theory in missing data imputation to ensure the preservation of data distribution; and v) case studies to showcase the real-world application of the visualisation and its effectiveness

P5 eHealth: An Agenda for the Health Technologies of the Future

This open access volume focuses on the development of a P5 eHealth, or better, a methodological resource for developing the health technologies of the future, based on patients’ personal characteristics and needs as the fundamental guidelines for design. It provides practical guidelines and evidence based examples on how to design, implement, use and elevate new technologies for healthcare to support the management of incurable, chronic conditions. The volume further discusses the criticalities of eHealth, why it is difficult to employ eHealth from an organizational point of view or why patients do not always accept the technology, and how eHealth interventions can be improved in the future. By dealing with the state-of-the-art in eHealth technologies, this volume is of great interest to researchers in the field of physical and mental healthcare, psychologists, stakeholders and policymakers as well as technology developers working in the healthcare sector

Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects

Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient

Continuing the sequence?:Towards an economic evaluation of whole genome sequencing for the diagnosis of rare diseases in Scotland

Funding This research was made possible through access to the data and findings generated by Scotland’s four regional genetics centres at NHS Grampian, Lothian, Tayside and Greater Glasgow and Clyde. These four centres participated in Scotland’s involvement in the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health) and funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructure. Acknowledgements The authors would like to thank the Scottish Genomes Partnership for their support with this work. The Scottish Genomes Partnership is funded by the Chief Scientist Office of the Scottish Government Health Directorates [SGP/1] and The Medical Research Council Whole Genome Sequencing for Health and Wealth Initiative (MC/PC/15080). We are grateful for the contributions of the funding bodies; Scottish Regional Genetics centres at NHS Lothian, Tayside, Grampian and Greater Glasgow and Clyde, clinicians and healthcare teams who contributed to the provision of data as well as the analyses and interpretation of results. We also thank Morad Ansari, Christine Bell, Martin McClatchey, Nicola Williams, Austin Diamond, Jonathan Berg, Jon Warner, Alexis Duncan, Amy Rowlatt, and Tessa Coupar for their help and advice during the SGP Project, and Michael Doherty, Florence Richards and Quinn Heppe for help with costing the standard testing pathway. We thank Professor Tim Aitman for commenting on earlier drafts of the paper. We thank all participants who took part in the valuation study. The University of Aberdeen and the Chief Scientist Office of the Scottish Government Health and Social Care Directorates fund the Health Economics Research Unit (HERU). This study would not be possible without the families, patients, clinicians, nurses, research scientists, laboratory staff, and the wider Scottish Genomes Partnership team to whom we give grateful thanks.Peer reviewedPublisher PD

Predictors of physician comfort in using pharmacogenomics data in clinical practice: A cross-sectional study

Objective: Utilization of pharmacogenomics data in clinical practice is a critical step towards individual and precision medicine. This is a cross-sectional study conducted by incorporating several variables as outlined in the survey report to assess and analyze the reasons or behaviors that could influence clinicians to use or not use pharmacogenomics. Methods: In this study, we conducted a cross-sectional quantitative survey among primary physicians practicing in Kettering Health Network facilities. 1,201 invitations were sent out and 135 Physicians participated in the survey. Physicians were requested by email to participate in a survey containing 14 multiple choice questions regarding their understanding and beliefs regarding pharmacogenomics, as well as questions about specific professional details which were intended to explore how physician characteristics affected familiarity, and comfort and confidence in using pharmacogenomics data in patient care. Statistical Package for the Social Sciences (standard version 25) was used for statistical analysis and consent was obtained from all study participants through the survey link. Results: The ratings of the familiarly, comfort, and confidence with pharmacogenetics were highly intercorrelated (r = 0.81-0.87).  Accordingly, we summed the three ratings to form a composite score of the three items; hereafter referred to as “scale scores”.  Possible scores ranged from 5 to 15, whereas actual scores ranged from 3 to 15 (Mean = 6.32, SD = 3.12). Scale scores were not statistically significantly correlated with age (r = 0.12, p < 0.17) or number of years in practice (r = 0.11, p < 0.22), and were only weakly (inversely) correlated with number of hours spent in patient care each week (r = -0.17, p < 0.05). Conclusion: In our study, physicians who had some education in the field of pharmacogenomics were more likely to use pharmacogenomics data in clinical practice. We have further characterized that continuing medical education (CME), more than medical education or residency training significantly predicts familiarity, confidence, or comfort in using pharmacogenomics data. Therefore, pharmacogenomics should be integrated in the CME for practicing clinicians as well as graduate medical education

Towards a Long-Read Sequencing Approach for the Molecular Diagnosis of RPGRORF15 Genetic Variants

Sequencing of the low-complexity ORF15 exon of RPGR, a gene correlated with retinitis pigmentosa and cone dystrophy, is difficult to achieve with NGS and Sanger sequencing. False results could lead to the inaccurate annotation of genetic variants in dbSNP and ClinVar databases, tools on which HGMD and Ensembl rely, finally resulting in incorrect genetic variants interpretation. This paper aims to propose PacBio sequencing as a feasible method to correctly detect genetic variants in low-complexity regions, such as the ORF15 exon of RPGR, and interpret their pathogenicity by structural studies. Biological samples from 75 patients affected by retinitis pigmentosa or cone dystrophy were analyzed with NGS and repeated with PacBio. The results showed that NGS has a low coverage of the ORF15 region, while PacBio was able to sequence the region of interest and detect eight genetic variants, of which four are likely pathogenic. Furthermore, molecular modeling and dynamics of the RPGR Glu-Gly repeats binding to TTLL5 allowed for the structural evaluation of the variants, providing a way to predict their pathogenicity. Therefore, we propose PacBio sequencing as a standard procedure in diagnostic research for sequencing low-complexity regions such as RPGRORF15, aiding in the correct annotation of genetic variants in online databases

Point-of-care diagnostics of childhood central nervous system infections, with a focus on usability in low-resource settings

Background: The inaccessibility of laboratory services sustains the high burden of paediatric infectious diseases, such as central nervous system (CNS) infections, in low-resource settings. New contextually fit and well-implemented point-of-care tests (POCTs) could relieve such a burden and narrow the diagnostic divide between rich and poor. Yet, current disengagement between product developers, end-users, and implementors of POCTs impedes their clinical use and utility in low-resource settings. Also, the lack of evidence gathered through field evaluations of many diagnostic instruments in low-resource settings raises questions of their clinical utility there. Objectives: The main aim of this thesis was to provide clinical and contextual guidance for developers of new POCTs for CNS infection diagnosis with high utility, especially in low-resource settings; and to implementors of POCT services towards their optimized clinical benefit. This was addressed through a multidisciplinary combination of qualitative, laboratory, and clinical studies. Methods: Qualitative focus group discussions were conducted with health care workers (HCW) in Mbarara, Uganda (Paper I), and in Stockholm, Sweden (Paper III). Discussions were audio recorded and transcribed verbatim. Qualitative content analysis with an inductive approach was pursued in for data analysis. Comparisons between the two settings were discussed. In Paper II, a vertical flow DNA microarray printed on paper was developed for the detection of Neisseria meningitidis – a major aetiology of paediatric bacterial CNS infection worldwide. The analytical performance of the microarray was laboratory evaluated on DNA extracted from the bacteria, through the detection of the ctrA gene sequence specific to N. meningitidis. In Paper IV, a commercially available polymerase chain reaction (PCR) instrument with the capability of multiplex single-sample cerebrospinal fluid (CSF) microbiology was prospectively field-evaluated for the diagnosis of paediatric CNS infection in Mbarara, Uganda. Clinical turnaround time (cTAT) was defined as time spent from lumbar puncture until reporting of microbiology analyses to clinicians. The PCR instrument’s influence on clinical and patient-centered outcomes (yield, cTAT, duration of hospitalization and antibiotic exposure, patient outcome) was compared to that of bacterial culture. Results: Fifty and 24 HCWs of different professions participated in the qualitative studies in Mbarara and Stockholm, respectively, expressing greater similarities than differences in perspectives of POCT use. POCTs were routinely used at both sites and credited for facilitating differential diagnostics and clinical decision-making. While the Ugandan setting with low laboratory accessibility was highly dependent on POCTs for sample analyses, the Swedish setting credited their use for having clinical and social value. Contrary to the described beneficial aspects, current POCTs were deemed contextually unfit in Mbarara, and their use to cause clinical distraction in Stockholm. Deficient implementation of POCT services was exposed in both places. Requests for ideal POCTs were aligned with those stipulated by the ‘ASSURED’ criteria of the World Health Organization. Specific POCTs for infectious diseases, including CNS infections, were requested. The laboratory study demonstrated an analytical sensitivity of 38 copies of ctrA per assay, with high specificity. The clinical study enrolled 212 children aged 0-12 years who were suspected of having CNS infection, with 193 of them being evaluated using the commercially available PCR instrument. A vast majority of children had been pre-administered antibiotics prior to lumbar puncture. Bacterial yield for the instrument was 12 % vs. 1.5 % for culture, with the addition of the instrument’s detection of viruses in 23 samples. Median cTAT for the instrument was 4.2 hours vs. 2 days for culture. Use of the instrument was associated with a statistically significant shorter antibiotic exposure of bacteria-negative vs. positive patients of five days, measured as from the time of reporting of laboratory results to the responsible clinicians. Similarly, its use was associated with a significantly shorter hospitalization for all-negative patients (five days) compared to those with any microorganism detected by it. No statistically significant differences in patient outcome were found due to its use, nor by its detection of any microorganisms. Conclusion: Point-of-care tests provide laboratory means to settings without laboratory capacity and to situations in need of timely results, and we could show how rapid molecular methods for CSF analysis could benefit paediatric children with suspected CNS infection. Yet, without any observed benefits in patient outcome, and at a cost not financially bearable in most lowresource settings. Contextually fit POCTs for paediatric CNS infections are needed in lowresource settings. Yet, there are design flaws in current POCTs and in implementations for their use, limiting their clinical benefits. Collaborative engagement of product developers, clinicians, laboratory professionals, and health policymakers would better serve low-resource settings with contextually fit POCTs and allow for their optimized implementation. The ‘POCTEST' framework for such an engagement is proposed in this thesis. Finally, as we provided proof of concept for a newly developed paper-printed molecular method, we will pursue its development towards contextual clinical utility in low-resource settings. Should we succeed, we hope to contribute to a decrease in preventable childhood mortality in such settings

MiMiR - an integrated platform for microarray data sharing, mining and analysis

Background: Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data.Results: A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package.Conclusion: The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies

Bioinformatics for personal genomics: development and application of bioinformatic procedures for the analysis of genomic data

In the last decade, the huge decreasing of sequencing cost due to the development of high-throughput technologies completely changed the way for approaching the genetic problems. In particular, whole exome and whole genome sequencing are contributing to the extraordinary progress in the study of human variants opening up new perspectives in personalized medicine. Being a relatively new and fast developing field, appropriate tools and specialized knowledge are required for an efficient data production and analysis. In line with the times, in 2014, the University of Padua funded the BioInfoGen Strategic Project with the goal of developing technology and expertise in bioinformatics and molecular biology applied to personal genomics. The aim of my PhD was to contribute to this challenge by implementing a series of innovative tools and by applying them for investigating and possibly solving the case studies included into the project. I firstly developed an automated pipeline for dealing with Illumina data, able to sequentially perform each step necessary for passing from raw reads to somatic or germline variant detection. The system performance has been tested by means of internal controls and by its application on a cohort of patients affected by gastric cancer, obtaining interesting results. Once variants are called, they have to be annotated in order to define their properties such as the position at transcript and protein level, the impact on protein sequence, the pathogenicity and more. As most of the publicly available annotators were affected by systematic errors causing a low consistency in the final annotation, I implemented VarPred, a new tool for variant annotation, which guarantees the best accuracy (>99%) compared to the state-of-the-art programs, showing also good processing times. To make easy the use of VarPred, I equipped it with an intuitive web interface, that allows not only a graphical result evaluation, but also a simple filtration strategy. Furthermore, for a valuable user-driven prioritization of human genetic variations, I developed QueryOR, a web platform suitable for searching among known candidate genes as well as for finding novel gene-disease associations. QueryOR combines several innovative features that make it comprehensive, flexible and easy to use. The prioritization is achieved by a global positive selection process that promotes the emergence of the most reliable variants, rather than filtering out those not satisfying the applied criteria. QueryOR has been used to analyze the two case studies framed within the BioInfoGen project. In particular, it allowed to detect causative variants in patients affected by lysosomal storage diseases, highlighting also the efficacy of the designed sequencing panel. On the other hand, QueryOR simplified the recognition of LRP2 gene as possible candidate to explain such subjects with a Dent disease-like phenotype, but with no mutation in the previously identified disease-associated genes, CLCN5 and OCRL. As final corollary, an extensive analysis over recurrent exome variants was performed, showing that their origin can be mainly explained by inaccuracies in the reference genome, including misassembled regions and uncorrected bases, rather than by platform specific errors