Doctor of Philosophy

dissertationDespite the advancements in therapies, next-generation sequencing, and our knowledge, breast cancer is claiming hundreds of thousands of lives around the world every year. We have therapy options that work for only a fraction of the population due to the heterogeneity of the disease. It is still overwhelmingly challenging to match a patient with the appropriate available therapy for the optimal outcome. This dissertation work focuses on using biomedical informatics approaches to development of pathwaybased biomarkers to predict personalized drug response in breast cancer and assessment of feasibility integrating such biomarkers in current electronic health records to better implement genomics-based personalized medicine. The uncontrolled proliferation in breast cancer is frequently driven by HER2/PI3K/AKT/mTOR pathway. In this pathway, the AKT node plays an important role in controlling the signal transduction. In normal breast cells, the proliferation of cells is tightly maintained at a stable rate via AKT. However, in cancer, the balance is disrupted by amplification of the upstream growth factor receptors (GFR) such as HER2, IGF1R and/or deleterious mutations in PTEN, PI3KCA. Overexpression of AKT leads to increased proliferation and decreased apoptosis and autophagy, leading to cancer. Often these known amplifications and the mutation status associated with the disease progression are used as biomarkers for determining targeting therapies. However, downstream known or unknown mutations and activations in the pathways, crosstalk iv between the pathways, can make the targeted therapies ineffective. For example, one third of HER2 amplified breast cancer patients do not respond to HER2-targeting therapies such as trastuzumab, possibly due to downstream PTEN loss of mutation or PIK3CA mutations. To identify pathway aberration with better sensitivity and specificity, I first developed gene-expression-based pathway biomarkers that can identify the deregulation status of the pathway activation status in the sample of interest. Second, I developed drug response prediction models primarily based on the pathway activity, breast cancer subtype, proteomics and mutation data. Third, I assessed the feasibility of including gene expression data or transcriptomics data in current electronic health record so that we can implement such biomarkers in routine clinical care

Health information systems

Healthcare is an information intensive industry in which quality and timely information is a critical resource. There are a wide range of information systems in health that perform different functions but all are involved in the management of data and information. This chapter provides an overview of Health Information Systems and their use in supporting healthcare

Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple "yes" or "no" responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real-world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a "lingua franca" to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects

The emergence of openness in open source projects : the case of openEHR

The meaning of openness in open source is both intrinsically unstable and dynamic, and tends to fluctuate with time and context. We draw on a very particular open-source project primarily concerned with building rigorous clinical concepts to be used in electronic health records called openEHR. openEHR explains how openness is a concept that is purposely engaged with, and how, in this process of engagement, the very meaning of open matures and evolves within the project. Drawing on rich longitudinal data related to openEHR we theorise the evolving nature of openness and how this idea emerges through two intertwined processes of maturation and metamorphosis. While metamorphosis allows us to trace and interrogate the mutational evolution in openness, maturation analyses the small, careful changes crafted to build a very particular understanding of openness. Metamorphosis is less managed and controlled, whereas maturation is representative of highly precise work carried out in controlled form. Both processes work together in open-source projects and reinforce each other. Our study reveals that openness emerges and evolves in open-source projects where it can be understood to mean rigour; ability to participate; open implementation; and an open process. Our work contributes to a deepening in the theorisation of what it means to be an open-source project. The multiple and co-existing meanings of ‘open’ imply that open-source projects evolve in nonlinear ways where each critical meaning of openness causes a reflective questioning by the community of its continued status and existence

Engineering requirements of a Herpes simplex virus patient registry: discovery phase of a real-world evidence platform to advance pharmacogenomics and personalized medicine

Comprehensive pharmacogenomic understanding requires both robust genomic and demographic data. Patient registries present an opportunity to collect large amounts of robust, patient-level data. Pharmacogenomic advancement in the treatment of infectious diseases is yet to be fully realised. Herpes simplex virus (HSV) is one disease for which pharmacogenomic understanding is wanting. This paper aims to understand the key factors that impact data collection quality for medical registries and suggest potential design features of an HSV medical registry to overcome current constraints and allow for this data to be used as a complement to genomic and clinical data to further the treatment of HSV. This paper outlines the discovery phase for the development of an HSV registry with the aim of learning about the users and their contexts, the technological constraints and the potential improvements that can be made. The design requirements and user stories for the HSV registry have been identified for further alpha phase development. The current landscape of HSV research and patient registry development were discussed. Through the analysis of the current state of the art and thematic user analysis, potential design features were elucidated to facilitate the collection of high-quality, robust patient-level data which could contribute to advances in pharmacogenomic understanding and personalised medicine in HSV. The user requirements specification for the development of an HSV registry has been summarised and implementation strategies for the alpha phase discussed

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

<p>Abstract</p> <p>Background</p> <p>In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.</p> <p>Discussion</p> <p>Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the <it>Roadmap for National Action on Clinical Decision Support </it>commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.</p> <p>Summary</p> <p>A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.</p

Workshop on the EHCR

This deliverable provides a summary report of a workshop on Electronic Health Records that was organised and delivered as the main focus of Workpackage 16 of the Semantic Mining project. The workshop was held as day three of a three-day series of events held in Brussels in late November 2004, under the umbrella and with kind support of the EUROREC organisation. This report provides a brief summary of that event, and includes in Annex 1 the complete delegate pack as printed and issued to all persons attending the event, This delegate pack included printed copies of all slides and screenshots used throughout the day. The workshop was well attended, and in particular the organisers are pleased to report that some very productive discussions took place that will act as the stimulus for new threads of research collaboration between various Semantic Mining partners, under the work plan of Workpackage 26. The organisers are grateful for the support of the EUROREC organisation in facilitating the organisation of this workshop and for lending their support to it through their web site and a personal endorsement of the event