21st century schools face significant challenges as they move towards providing opportunities for learners which recognize and build on their strengths and abilities. The process of supporting young people to develop the desire and the confidence to recognise personal potential and to manage their ongoing learning is a priority. Communication and collaboration are key to learners becoming informed active participants in their own learning and experiencing successful outcomes in today's society. Our old models of learning where pre packaged parcels of knowledge were delivered to students by teachers will no longer suffice. As we respond to the new meaning of knowledge in the 21st century and begin to view knowledge as an active process, it is clear that many of the top down structures and organisational practices present in New Zealand secondary schools need change. The idea of personalisation in order to support independent learners to reach their potential is a familiar one for many teachers and is one of the ideals which may have brought them into the teaching profession. However, the institutional contexts in which they operate can act not as a driving force for personalised learning but as a barrier to it. In seeking to find one possible way in which secondary school systems can be re shaped around the needs of the learner, this study examines the role of online mentoring with experts outside the school. This small scale qualitative study uses ethnographic methods to gather data from twelve secondary school year thirteen physical education students and their teacher as they engage in an eight week online project with expert sports coaches at Auckland University of Technology. Eleven of the students were boys. In examining the impact which online mentoring might have on this group of learners and their teacher, rich data was collected via web transcripts, observation, image data and interviews. The research findings reveal that students found a high degree of satisfaction with the process and placed value on having the opportunity to pursue personalised goals as they worked with mentors in a collaborative online environment. Teacher behaviour and practice underwent change in the project with the teacher becoming repositioned within the group in the role of learner. In a process where authoritarian approaches were replaced by collaborative group action and inquiry, students reported an enhanced ability to think deeply, to manage their own learning and to relate in highly skilled ways with others. Students' perceptions about the ways in which they were working were analysed using the New Zealand Curriculum Key Competencies. As students focused their inquiry past the level of curriculum goals and onto real world personal goals, several experienced a shift in perception concerning their own learning potential and expressed surprised at their own level of competence. The fact that eleven out of the twelve students were boys makes this shift in personal learning expectation worthy of further investigation in the quest for improving academic outcomes for boys. Finally, this study may have relevance for the ways in which the Key Competencies have meaning in secondary schools. The study demonstrated that the emergence of competencies such as self management and relating to others was assisted by changes in teacher behaviour and action. As authoritarian approaches were replaced by a collaborative model where independent learning with others was supported, learners began to exhibit the personal competencies described by the New Zealand Curriculum (2008). These competencies which include Thinking, Using Language, symbols and texts, Managing self, Relating to others and Participating and contributing occurred as a natural consequence of a learning model which was shaped to fit the learner; a personalised approach to learning with support from online mentors.

Mobile phone technologies are transforming how young people think, work, play and relate to each other. However, a central concern for the thesis is that education policy and practice far too often resembles an industrial model that is standardised, mechanistic and linear and that rarely reflects the informational, dynamic and creative lives of young people. In particular, the educational project fails to connect with the way young people use their mobile phone technologies to multi-task, connect, and create content at a precipitous rate. This thesis focuses on the ways in which mobile phone technology is now a significant influence in the way young people develop a sense of self, and a sense of identity and agency that permeates the way they engage with education. The specific research questions that follow from this are: how are young peoples’ identities shaping the meaning and use of mobile phones within (im)material culture? How is the relationship between identity and the creation and use of social space being defined through mobile phone technology? And, taken together how might these processes of identity development influence the way the educational project develops in the future? This thesis addressed these aims by conducting a visual ethnographic study over three years, using participation observation in a sixth-form college in the UK that included video interviews with seven college students. The research has produced a conceptual framework that documents a number of key findings that include: (a) the mobile phone has an immediate symbolic value to young people providing signals about the user’s identity, or presentation of the self; (b) the mobile phone also helps facilitate the performance of lived experiences and is actively part of assisting in various forms of agency. (c) The mobile phone enables a constant flow of (re)presentations of young people that reflects a fluidity of identity that characterises key aspects of contemporary social life. Finally, (d) the mobile phone also supports and enhances the maintenance of social space through the maintenance of social groups and also crucially, the feeling of being oneself. The main conclusion drawn from this research is that too often education systems overlook that fact that learning for young people is typically, and inevitably, personal and yet at the same time located in connected, information-driven environments that are predisposed to digital technologies. Therefore, this research argues for educational policy makers and practitioners to think creatively about how to develop education in ways that fundamentally support young people in their (re)construction of a personalised landscape for learning through their mobile phone technologies.

La lutte contre les maladies chroniques nécessite la mise en œuvre de nouvelles stratégies de prédiction du risque et de prévention. La médecine personnalisée représente une approche sophistiquée pour réussir la prise en charge des morbidités de populations vieillissantes. Dans le cadre de ces travaux de thèse inspirés par les principes de la médecine personnalisée, nous décrivons une approche qui associe plusieurs méthodologies «-omiques». Nous avons utilisé un modèle de «dénominateur commun» pour les maladies chroniques afin d'identifier des biomarqueurs associés aux facteurs de risque et aux voies biologiques de maladies courantes. Par l’étude de variants génétiques localisés dans la région comportant le gène TREM2, nous avons identifié une association entre le rs6918289 et à la fois de taux élevés de TNF-α et une augmentation de l’épaisseur intima-média de l’artère fémorale. Grâce à des études d’association panépigénomique (EWAS), nous avons identifié de nouveaux marqueurs épigénétiques liés à des facteurs de risque de maladies courantes. Un site CpG de méthylation était associé à une augmentation du tour de taille, contribuant à expliquer la régulation épigénétique de l’obésité abdominale. De plus, une étude EWAS des taux de triglycérides a permis d’identifier deux sites CpG significatifs. L’un de ces deux sites a pu être confirmé dans le tissu adipeux. Une étude EWAS a également été réalisée pour décrire la régulation épigénétique des concentrations de VEGF-A. 20 sites CpG ont pu être identifiés ainsi et leurs relations avec la régulation du VEGF-A ont été examinées par analyse bioinformatique poussée. Les liens entre le VEGF-A et 11 cytokines ont également été étudiés. Le taux de protéine VEGF-A était associé à IL-4, MCP1 et EGF. Les associations entre les cytokines et des isoformes spécifiques de l’ARNm du VEGF-A ont également été évaluées : le VEGF165 était associé à MCP1 et IL-1α, et le VEGF189 à IL-4 et IL-6. Nous avons étudié le rôle du VEGF-A et LT dans l’athérosclérose. Cela a permis d’identifier un variant génétique lié au VEGF-A associé à l’attrition des télomères qui pourrait constituer un dénominateur commun pour les maladies chroniques. L’utilisation de diverses méthodologies pour étudier les facteurs de risque et les voies impliquées dans des maladies chroniques courantes a permis d’identifier de nouveaux marqueurs diagnostiques qui pourraient améliorer la prédiction du risque de maladie basée sur le profil génétique de chaque individu. / The fight against common chronic diseases requires the implementation of new risk prediction and prevention strategies. Personalised medicine offers sophisticated approaches for management of the morbidities of the ageing population. In this thesis, inspired by the principles of personalised medicine, we describe an integrative approach combining “-omics” methodologies. We use a model of a “common denominator” for cardiovascular disease (CVD) and other chronic diseases to identify biomarkers linked with common diseases risk factors and molecular pathways. With the investigation of genetic variants, located in the region of the TREM2 gene, we identified the association of SNP rs6918289 with increased levels of TNF-α and intima-media thickness of the femoral artery. With the use of epigenome-wide association studies (EWAS), we identified novel epigenetic biomarkers related to common diseases risk factors: central obesity and lipid levels. One methylation site (CpG) was associated with increased waist circumference (cg16170243), which could explain the epigenetic regulation of central obesity. Moreover, an EWAS of the triglyceride levels identified two significant CpG sites, one of which was replicated in the adipose tissue (cg04580029), giving insights into epigenetic regulation of lipid levels. An EWAS was also used to study the epigenetics of VEGF-A levels; 20 CpG sites were identified and their relations with VEGF-A regulation were analysed through detailed bioinformatics analysis. VEGF-A was further investigated for its relation with 11 cytokines. VEGF-A protein levels were associated with IL-4, MCP1 and EGF. Specific VEGF-A mRNA isoforms were also investigated for their association with cytokines; VEGF165 showed associations with MCP1 and IL-1α and VEGF189 with IL-4 and IL-6. Together with another important biomarker, TL, we studied the role of VEGF-A in atherosclerosis and identified one VEGF-A related genetic variant associated with telomere attrition, which could present a common denominator of chronic diseases. The employment of diverse methodologies for the investigation of common chronic diseases risk factors and pathways provided new diagnostic markers and generated results, which could help to improve the diseases risk prediction based on the individual genetic “make-up”. New insights into associations between different biomarkers might help in understanding the pathophysiological pathways common between CVDs and other chronic diseases.

Bakgrund: Reumatoid artrit (RA) är en kronisk autoimmun sjukdom som drabbar ungefär 0,3 – 1 % av världens population. Sjukdomen orsakar stort lidande och minskad livskvalitet hos den enskilde individen. Att synliggöra individens livserfarenheter av RA ger vårdpersonal bättre förutsättningar till att ge en god personcentrerad vård. Syfte: Att belysa personers erfarenheter av att leva med reumatoid artrit.  Metod: Kvalitativ litteraturstudie. För att identifiera relevanta artiklar har databaserna Cinahl och PubMed använts. Studien baseras på 11 artiklar som analyserades med hjälp av Fribergs femstegsmodell.   Resultat: Fyra kategorier med 11 subkategorier identifierades (1) Att uppleva livsbegräsningar, (2) Att hitta sig själv, (3) Att finna mening och balans i livet och (4) Att möta andra – privat och offentligt.   Konklusion: Resultatet belyser vilka djupgående effekter diagnosen RA har på individens alla aspekter i livet. RA tvingar individen till självreflektion, försöka återfå mening och balans i tillvaron samt till att omdefiniera sociala roller både privat och offentligt. Studiens resultat kan bli ett viktigt redskap i den personcentrerade vården. Framtida studier inom ämnesområdet kan utgå från sjuksköterskans perspektiv i mötet med individen med RA. / Background: Rheumatoid arthritis is a chronic autoimmune disease affecting 0,3 - 1 % of the world's population and causes great suffering and reduced quality of life. By recognizing the experiences of individuals affected by RA, healthcare professionals can provide an effective personalised approach.  Aim: To enlighten people's experiences of living with rheumatoid arthritis.  Methods: A qualitative literature study. We used a systematic search of two electronic databases Cinahl and PubMed. The study is based on 11 articles that were analysed using Friberg's five-step model.  Results: We identified four categories, with 11 subcategories, (1) To experience life limitations (2) To redefine yourself (3) Finding meaning and balance in life and (4) To meet others – in private and public.  Conclusion: The result highlights the effect that rheumatoid arthritis has on all aspects of the individual's life. RA forces the individual to self-reflect, to regain a sense of purpose and to redefine social roles. The result of the study can be an important tool in the personalised approach. Future studies could focus on the nurse's perspective in their encounters with individuals affected by RA.

The metaverse has seen increased usages in its capabilities as an educational tool by immersing users in virtual scenarios. This technology is inaccessible for user groups that require higher degrees of accessibility and personalization, as most metaverse implementations do not adjust to the user’s needs. One such group are individuals with Autism Spectrum Disorder (ASD), rendering them unable to use immersive learning to foster skills that enable independent living. To solve this issue, this thesis aims to employ design science to produce an artefact that answers the research question: ”How can machine learning-based adaptations to 3D-environments be integrated into existing virtual reality platforms in order to increase accessibility for users with ASD?”. By the use of literature studies along with personas supplied from a research project, four different user profiles were established to represent users with ASD. Requirements for the artefact to be produced were then established by exploring possible stakeholders affected by the artefact. Employing data generative techniques, a neural network was trained to predict how the virtual environment should be augmented given the user’s specific characteristics. This neural network was then integrated into a virtual environment set up via Unity, by use of participatory modelling. The resulting artefact was then evaluated against the established requirements, via an experiment that mirrors a typical morning routine meant to increase the person’s skills in independent living. The results of this evaluation found that the artefact could handle known user profiles with a high accuracy (87.7%). The artefact also proved effective in approximating what kind of aide should be presented for the unknown user profiles (8/10 cases). The use of modern development tooling also proved satisfactory in aiding developers to use the artefact to create accessible environments with ease. The artefact’s use of neural networks proved an effective way to model complex user groups, though further ethnographic studies and non-synthetic data is needed to validate this capability.

Background: Head and neck squamous cell carcinomas (HNSCCs) are complex and highly aggressive tumours that develop in the mouth, throat, salivary glands and nose. HNSCCs account for more than half a million cases annually and are the sixth most common cancer worldwide. Alcohol, tobacco and human papilloma virus (HPV) infection are the well-known causes for HNSCC. The current options for treatment are surgery, radiotherapy, chemotherapy or a combination of therapies. Locally advanced HNSCC patients show heterogenous response to standard treatments and the survival after 5 years is about 50%. Therefore, there is a need to identify biomarkers to predict outcome and improve personalised therapies. The recent advancement in next generation sequencing technologies has allowed for understanding the molecular characteristics of the tumour and identify patients at high risk that are unresponsive to the standard treatment. HPV-associated oropharyngeal carcinoma have shown a very high rate of loco-regional control (LRC) and overall survival (OS) after postoperative radio- chemotherapy (PORT-C) and are being assessed for treatment de-escalation strategies to reduce toxicity in clinical trials. The treatment response of HPV-negative HNSCC, however, is still heterogeneous and novel biomarkers are required to identify subgroups of patients for treatment adaptation. Objectives: The overall aim of the thesis is to develop biomarkers to identify patients at high risk for future treatment adaptations and improve personalised radiotherapy based on the biological differences in HNSCC patients. For this purpose, novel gene signatures were developed and validated using machine learning approaches and biological information in order to predict LRC in patients with locally advanced HNSCC. The novel gene signatures will help to identify patients at high risk that do not respond to standard treatments and to further understand the molecular mechanisms involved in heterogenous treatment response. Materials and methods: The data from a total of 504 locally advanced HNSCC patients of the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG) treated with postoperative radiotherapy (PORT) or postoperative radiochemotherapy (PORT-C) were evaluated. Data from 60 mice bearing xenografts of ten established human HNSCC cell lines were also evaluated. Gene expression analyses was performed using the GeneChip Human Transcriptome Array 2.0 and nanoString analyses. Differential gene expression analysis, Cox regression analysis and machine learning algorithms were used to develop gene signatures. Models were built on the training cohort and then applied on an independent validation cohort. Results: The patients with HPV-negative HNSCC that were treated with PORT-C were classified into the four molecular subtypes basal, mesenchymal, atypical and classical that were previously reported for HNSCC patients treated with primary radio(chemo)therapy or surgery and were related to LRC. The mesenchymal subtype had the worst prognosis as compared to the other subtypes. These tumours were associated with overexpression of epithelial-mesenchymal transition genes and DNA repair genes. A novel 6-gene signature was developed and validated based on full-transcriptome data using machine-learning approaches that was prognostic for LRC in patients with HPV-negative HNSCC treated with PORT-C. The 6-gene signature consisted of four individual genes CAV1, GPX8, IGLV3-25, TGFBI and one metagene combining the highly correlated genes, INHBA and SERPINE1. The identified gene signature was combined with the clinical parameters, T stage and tumour localisation as well as the stem-cell marker CD44 and the 15-gene hypoxia- associated classifier and this improved the performance of the model. Previously identified prognostic gene signatures and molecular-subtype classification were back-translated from HNSCC patients to pre-clinical tumour models. The tumour models were classified into the four subtypes basal, mesenchymal, atypical and classical, similar to the patients. The mesenchymal tumours were significantly associated with a higher TCD50 as compared to other subtypes. A novel 2-gene signature consisting of FN1 and SERPINE1 was developed based on tumour models and patient data using differential gene expression analysis. The 2-gene signature was prognostic for the TCD50 in tumour models and was successfully validated on an independent PORT-C patient cohort for LRC. A matched-pair analysis was performed between patients that were treated with postoperative radiochemotherapy and patients that were treated with postoperative radiotherapy. A 2- metagene signature, consisting of KRT6A, KRT6B, KRT6C forming one metagene and SPRR1A, SPRR1B, SPRR2A, SPRR2C forming the second metagene, was identified. The novel predictive signature stratified patients into high and low risk groups. The high-risk group patients that received PORT-C showed higher LRC as compared to the high-risk patients that received PORT. Thus, the predictive gene signature identified patients that were considered to be at intermediate risk according to clinical factors but were at biologically high risk for the development of loco-regional recurrences after PORT. These patients might benefit from PORT-C treatment. Conclusions: In this thesis, novel gene signatures were identified by combining machine learning and biological information to stratify locally advanced HNSCC patients into high and low risk groups for loco-regional control. This information could be used in the future, e.g. to adjust radiotherapy doses based on the risk group. The developed gene signatures could be combined with other gene signatures or the molecular subtype stratification to develop potential combined treatment approaches. Within the DKTK-ROG framework, the gene signatures will be incorporated with biomarkers developed on the same cohort at the other DKTK-ROG partner sites using the data from different omics platforms in the future. This would help to better understand the molecular basis of heterogenous treatment response in HNSCC patients and uncover novel targets for therapies. The thesis also provides a valuable insight into the applicability of preclinical tumour models to study the efficacy of personalised radiotherapy treatments. Overall, the gene signatures identified in this thesis were from retrospective studies and have to be validated in prospective studies before their application in interventional clinical trials to improve personalised radiotherapy treatments. Additionally, the methods used in the thesis to identify the gene signatures could be used and applied across different cancer datasets for identification of biomarkers. Therefore, this thesis has provided a basis for future studies on personalized treatment of HNSCC based on their genetic profile.:Content Abbreviations VII Tables XII 1 Introduction 1 2 Biological and statistical background 6 2.1 Head and neck squamous cell carcinoma 6 2.1.1 Tumourigenesis 6 2.1.2 Biomarkers: clinical and genomics 9 2.2 Statistics 12 General statistical analyses 17 2.3 Gene expression analyses 18 3 Molecular subtypes and mechanisms of radioresistance 20 3.1 Introduction and motivation 20 3.2 Patient cohort and experimental design 21 3.2.1 Patient cohort 21 3.2.2 Clinical endpoints and statistical analysis 23 3.2.3 Experimental design 23 3.3 Results 26 3.3.1 Prognostic factors for LRC and OS 26 3.3.2 Death as competing risk 26 3.3.3 Multivariable Cox regression for improved prognosis 29 3.3.4 Molecular subtypes in HPV-negative HNSCC patients 31 3.3.5 Molecular subtypes are prognostic for LRC after PORT-C 33 3.4 Discussion 36 4 A novel 6-gene signature for LRC prognosis 39 4.1 Introduction and motivation 39 4.2 Patient cohort and experimental design 40 4.2.1 Patient cohorts 40 4.2.2 Clinical endpoints and statistical analysis 41 4.2.3 Experimental design 41 4.3 Results 44 4.3.1 Characteristics of the patient cohorts 44 4.3.2 Development of the 6-gene signature prognostic for LRC 45 4.3.3 Combination of the 6-gene signature and clinical parameters 47 4.3.4 Extension with CD44 and the 15-gene hypoxia signature 48 4.3.5 Prognostic for secondary endpoints 49 4.3.6 Technical validation using nanoString technology 52 4.3.7 Death as competing risk 56 4.4 Discussion 58 5 Biomarker development in preclinical tumour models and HNSCC patients 62 5.1 Introduction and motivation 62 5.2 Patient cohort and experimental design 64 5.2.1 Patient derived xenograft tumour models 64 5.2.2 Patient cohorts 64 5.2.3 Clinical endpoints and statistical analysis 65 5.2.4 Experimental design 65 5.3 Results 68 5.3.1 Molecular subtypes 68 5.3.2 Development of the 2-gene signature 70 5.3.3 Technical validation using the nanoString technology 71 5.3.4 Back-translation of gene signatures in xenograft models 75 5.4 Discussion 79 6 PORT-C improves LRC in intermediate-risk patients 82 6.1 Introduction and motivation 82 6.2 Patient cohort and experimental design 83 6.2.1 Patient cohorts 84 6.2.2 Clinical endpoints and statistical analysis 84 6.2.3 Experimental design 84 6.3 Results 87 6.3.1 Characteristics of the patient cohorts 87 6.3.2 Propensity score matching analysis 88 6.3.3 Development of the predictive 2-metagene signature 90 6.4 Discussion 93 7 Conclusion and future perspectives 96 8 Summary 99 9 Zusammenfassung 102 Appendix 105 A. Supplementary Figures 105 B. Supplementary Tables 110 Bibliography 116 Erklärungen 149

Yes / The early diagnosis and personalised treatment of diseases are facilitated by machine learning. The quality of data has an impact on diagnosis because medical data are usually sparse, imbalanced, and contain irrelevant attributes, resulting in suboptimal diagnosis. To address the impacts of data challenges, improve resource allocation, and achieve better health outcomes, a novel visual learning approach is proposed. This study contributes to the visual learning approach by determining whether less or more synthetic data are required to improve the quality of a dataset, such as the number of observations and features, according to the intended personalised treatment and early diagnosis. In addition, numerous visualisation experiments are conducted, including using statistical characteristics, cumulative sums, histograms, correlation matrix, root mean square error, and principal component analysis in order to visualise both original and synthetic data to address the data challenges. Real medical datasets for cancer, heart disease, diabetes, cryotherapy and immunotherapy are selected as case studies. As a benchmark and point of classification comparison in terms of such as accuracy, sensitivity, and specificity, several models are implemented such as k-Nearest Neighbours and Random Forest. To simulate algorithm implementation and data, Generative Adversarial Network is used to create and manipulate synthetic data, whilst, Random Forest is implemented to classify the data. An amendable and adaptable system is constructed by combining Generative Adversarial Network and Random Forest models. The system model presents working steps, overview and flowchart. Experiments reveal that the majority of data-enhancement scenarios allow for the application of visual learning in the first stage of data analysis as a novel approach. To achieve meaningful adaptable synergy between appropriate quality data and optimal classification performance while maintaining statistical characteristics, visual learning provides researchers and practitioners with practical human-in-the-loop machine learning visualisation tools. Prior to implementing algorithms, the visual learning approach can be used to actualise early, and personalised diagnosis. For the immunotherapy data, the Random Forest performed best with precision, recall, f-measure, accuracy, sensitivity, and specificity of 81%, 82%, 81%, 88%, 95%, and 60%, as opposed to 91%, 96%, 93%, 93%, 96%, and 73% for synthetic data, respectively. Future studies might examine the optimal strategies to balance the quantity and quality of medical data.

The shift of media from traditional forms to new digital ones has raised the possibility of new kinds of media services, including mobile television. In today¿s communications market, mobile phones are of increasing importance to users and, since mobile devices are connected most of the time, they have a high degree of location independence. The availability of 3G technology and the mobile devices needed to implement mobile television are now established and available. Mobile television is expected to be an important new service that could penetrate the market place and provide new applications, as well as create a market for new players and new investments, if the appropriate price, content and philosophy for content design are found. This research explores the many potential application areas for mobile TV, with a particular focus on advertising. Various organisations that seek success in this market can utilise the potential for advertising on mobile TV. Ultimately, mobile device users are able to use mobile TV for entertainment and information sourcing. However, a number of challenging issues remain to be addressed. The features that appealed to the consumers were studied in this research. Surveys were conducted to obtain an understanding of consumers¿ opinions and needs regarding the mobile TV experience. Many users clearly do like to interact with video content on mobile devices. Interactive mobile TV advertising can benefit users who will be able to use an essentially ¿free¿ mobile TV service, funded by an advertising model. This research proposes an environment for interactive advertising on mobile TV and discussion of an implementation of the proposed designs.

The tumour microenvironment comprises the non-cancerous cells present in the tumour mass (fibroblasts, endothelial, and immune cells), as well as signalling molecules and extracellular matrix. Tumour growth, invasion, metastasis, and response to therapy are influenced by the tumour microenvironment. Therefore, characterising the cellular and molecular components of the tumour microenvironment, and understanding how they influence tumour progression, represent a crucial aim for the success of cancer therapies. High-grade serous ovarian cancer provides an excellent opportunity to systematically study the tumour microenvironment due to its clinical presentation of advanced disseminated disease and debulking surgery being standard of care. This thesis first presents a case report of a long-term survivor (>10 years) of metastatic high-grade serous ovarian cancer who exhibited concomitant regression/progression of the metastatic lesions (5 samples). We found that progressing metastases were characterized by immune cell exclusion, whereas regressing metastases were infiltrated by CD8+ and CD4+ T cells. Through a T cell - neoepitope challenge assay we demonstrated that pre- dicted neoepitopes were recognised by the CD8+ T cells obtained from blood drawn from the patient, suggesting that regressing tumours were subjected to immune attack. Immune excluded tumours presented a higher expression of immunosuppressive Wnt signalling, while infiltrated tumours showed a higher expression of the T cell chemoattractant CXCL9 and evidence of immunoediting. These findings suggest that multiple distinct tumour immune microenvironments can co-exist within a single individual and may explain in part the hetero- geneous fates of metastatic lesions often observed in the clinic post-therapy. Second, this thesis explores the prevalence of intra-patient tumour microenvironment het- erogeneity in high-grade serous ovarian cancer at diagnosis (38 samples from 8 patients), as well as the effect of chemotherapy on the tumour microenvironment (80 paired samples from 40 patients). Whole transcriptome analysis and image-based quantification of T cells from treatment-naive tumours revealed highly prevalent variability in immune signalling and distinct immune microenvironments co-existing within the same individuals at diagnosis. ConsensusTME, a method that generates consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods that predict immune cell populations using bulk RNA data was developed. ConsensusTME improved accuracy and sensitivity of T cell and leukocyte deconvolutions in ovarian cancer samples. As previously observed in the case report, Wnt signalling expression positively correlated with immune cell exclusion. To evaluate the effect of chemotherapy on the tumour microenvironment, we compared site-matched and site-unmatched tumours before and after neoadjuvant chemotherapy. Site- matched samples showed increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, unlike site-unmatched samples where heterogeneity could not be accounted for. In addition, low levels of immune activation pre-chemotherapy were found to be correlated with immune activation upon chemotherapy treatment. These results cor- roborate that the tumour-immune interface in advanced high-grade serous ovarian cancer is intrinsically heterogeneous, and that chemotherapy induces an immunogenic effect mediated by cytotoxic cells. Finally, the different deconvolution methods were benchmarked along with ConsensusTME in a pan-cancer setting by comparing deconvolution scores to DNA-based purity scores, leukocyte methylation data, and tumour infiltrating lymphocyte counts from image analysis. In so far as it has been benchmarked, unlike the other methods, ConsensusTME performs consistently among the top three methods across cancer-related benchmarks. Additionally, ConsensusTME provides a dynamic and evolvable framework that can integrate newer de- convolution tools and benchmark their performance against itself, thus generating an ever updated version. Overall, this thesis presents a systematic characterisation of the tumour microenvironment of high grade serous ovarian cancer in treatment-naive and chemotherapy treated samples, and puts forward the development of an integrative computational method for the systematic analysis of the tumour microenvironment of different tumour types using bulk RNA data.

Recent decades have seen increasing recognition of the importance of patient involvement during patient-professional interactions and promotion of preventative and long term approaches to healthcare for those with long-term conditions. The concepts of 'shared decision making' and 'personalised care planning' have both been advocated by patient groups, policy-makers, professional bodies and academia as best practice. During shared decision making, patients and healthcare professionals work in equal partnership to decide the best course of action. Shared decision making is a central tenet of personalised care planning, as it aims to foster partnerships between patients and healthcare professionals when making decisions, but personalised care planning also describes an overall approach to healthcare that is forward-planning and preventative, rather than episodic and reactive. Despite the breadth of support for such approaches, in reality they are not routinely adopted. Multiple Sclerosis (MS) is a heterogeneous neurodegenerative long term condition, which is unpredictable with limited treatments available. Such uncertainty and complexity position MS as an interesting long term condition to explore decisional and information needs. This doctoral research comprises of three methods stages. Firstly, two systematic reviews assessing the effectiveness of personalised care planning for people with long-term conditions and people with MS were carried out. Secondly, 22 in-depth semi-structured qualitative interviews were carried out with people with MS across the UK to explore experiences of decision making and interactions with healthcare professionals. Purposive sampling was carried out and data saturation determined sample size. A modified grounded theory approach was used and thematic analysis of interview data was carried out. Lastly, a series of structured qualitative interviews were carried out with 6 consultant neurologists. This stage was iterative in that problematic areas identified during analysis of interview data from stage 2 were presented to neurologists in infographic form to further examine issues raised. Framework analysis was carried out on neurologist interview data to examine their interpretations and potential solutions. Although there appears to be some evidence demonstrating that personalised care planning is effective for people with long term conditions, such favourable effects were not demonstrated in the context of MS. Based on the findings from the systematic reviews it is unclear whether personalised care planning is effective for people with MS and there is a clear gap in the literature examining this. Findings from the interview stages suggest there are key areas which are lacking in terms of information and decisional support. Such areas included the type and amount of information around the time of diagnosis, support when choosing disease modifying drugs and discussions about approaches outside mainstream medicine. Findings from neurologist interview data corroborated those from MS interview data, but through examination of issues raised it also highlighted some of the complexities and challenges of involving patients and enacting shared decision making in reality. This research identified key areas that require improvement for people with MS in terms of provision of the information and decisional support. Although in theory personalised care planning and shared decision making are positioned as best practice, in reality it is unclear whether they are effective or appropriate for people with MS. The way in which such approaches are enacted are complex and require careful consideration. Potential barriers and pitfalls identified within this study suggest a lack of clarity in how to respond to challenges and further investigation into how patient involvement is enacted is needed.