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Glioblastoma (GBM) is a highly aggressive adult brain cancer, characterised by poor prognosis and a dismal five-year survival rate. Despite significant knowledge gains in tumour biology, meaningful advances in patient survival remain elusive. The field of neuro-oncology faces many disease obstacles, one being the paucity of faithful models to advance preclinical research and guide personalised medicine approaches. Recent technological developments have permitted the maintenance, expansion and cryopreservation of GBM explant organoid (GBO) tissue. GBOs represent a translational leap forward and are currently the state-of-the-art in 3D in vitro culture system, retaining brain cancer heterogeneity, and transiently maintaining the immune infiltrate and tumour microenvironment (TME). Here, we provide a review of existing brain cancer organoid technologies, in vivo xenograft approaches, evaluate in-detail the key advantages and limitations of this rapidly emerging technology, and consider solutions to overcome these difficulties. GBOs currently hold significant promise, with the potential to emerge as the key translational tool to synergise and enhance next-generation omics efforts and guide personalised medicine approaches for brain cancer patients into the future. [ABSTRACT FROM AUTHOR]

Representative models of intestinal diseases are transforming our knowledge of the molecular mechanisms of disease, facilitating effective drug screening and avenues for personalised medicine. Despite the emergence of 3D in vitro intestinal organoid culture systems that replicate the genetic and functional characteristics of the epithelial tissue of origin, there are still challenges in reproducing the human physiological tissue environment in a format that enables functional readouts. Here, we describe the latest platforms engineered to investigate environmental tissue impacts, host-microbe interactions and enable drug discovery. This highlights the potential to revolutionise knowledge on the impact of intestinal infection and inflammation and enable personalised disease modelling and clinical translation. [ABSTRACT FROM AUTHOR]

The extensive heterogeneity and the limited availability of effective targeted therapies contribute to the challenging prognosis and restricted survival observed in triple-negative breast cancer (TNBC). Recent research indicates the aberrant expression of diverse tyrosine kinases (TKs) within this cancer, contributing significantly to tumor cell proliferation, survival, invasion, and migration. The contemporary paradigm shift towards precision medicine has highlighted TKs and their receptors as promising targets for pharmacotherapy against a range of malignancies, given their pivotal roles in tumor initiation, progression, and advancement. Intensive investigations have focused on various monoclonal antibodies (mAbs) and small molecule inhibitors that specifically target proteins such as epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptor (VEGFR), cellular mesenchymal-epithelial transition factor (c-MET), human epidermal growth factor receptor 2 (HER2), among others, for combating TNBC. These agents have been studied both in monotherapy and in combination with other chemotherapeutic agents. Despite these advances, a substantial terrain of unexplored potential lies within the realm of TK targeted therapeutics, which hold promise in reshaping the therapeutic landscape. This review summarizes the various TK targeted therapeutics that have undergone scrutiny as potential therapeutic interventions for TNBC, dissecting the outcomes and revelations stemming from diverse clinical investigations. A key conclusion from the umbrella clinical trials evidences the necessity for in-depth molecular characterization of TNBCs for the maximum efficiency of TK targeted therapeutics, either as standalone treatments or a combination. Moreover, our observation highlights that the outcomes of TK targeted therapeutics in TNBC are substantially influenced by the diversity of the patient cohort, emphasizing the prioritization of individual patient genetic/molecular profiles for precise TNBC patient stratification for clinical studies. [ABSTRACT FROM AUTHOR]

The KidGen Collaborative's Policy Implementation Workshop 2023 celebrated the 10th anniversary of Australia's first kidney genetics clinic in Brisbane. This event marked the establishment of a national network now comprising 19 kidney genetics clinics across Australia, all dedicated to providing equitable access to genomic testing for families affected by genetic kidney diseases. The workshop reflected on past progress and outlined future objectives for kidney genetics in Australia, recognising the collaborative efforts of clinical teams, researchers, and patients. Key insights from the workshop are documented in the proceedings. [ABSTRACT FROM AUTHOR]

Genetic heterogeneity in ovarian cancer indicates the need for personalised treatment approaches. Currently, very few G-protein coupled receptors (GPCRs) have been investigated for active targeting with nanomedicines such as antibody-conjugated drugs and drug-loaded nanoparticles, highlighting a neglected potential to develop personalised treatment. To address the genetic heterogeneity of ovarian cancer, a future personalised approach could include the identification of unique GPCRs expressed in cancer biopsies, matched with personalised GPCR-targeted nanomedicines, for the delivery of lethal drugs to tumour tissue before, during and after surgery. Here we report on the systematic analysis of public ribonucleic acid-sequencing (RNA-seq) gene expression data, which led to prioritisation of 13 GPCRs as candidates with frequent overexpression in ovarian cancer tissues. Subsequently, primary ovarian cancer cells derived from ascites and ovarian cancer cell lines were used to confirm frequent gene expression for the selected GPCRs. However, the expression levels showed high variability within our selection of samples, therefore, supporting and emphasising the need for the future development of case-to-case personalised targeting approaches. [ABSTRACT FROM AUTHOR]

Introduction: n-of-1 trials are undertaken to optimise the evaluation of health technologies in individual patients. They involve a single patient receiving treatments, both interventional and control, consecutively over set periods of time, the order of which is decided at random. Although n-of-1 trials are undertaken in medical research it could be argued they have the utility to be undertaken more frequently. We undertook the National Institute for Health Research (NIHR) commissioned DIAMOND (Development of generalisable methodology for n-of-1 trials delivery for very low volume treatments) project to develop key points to assist clinicians and researchers in designing and conducting n-of-1 trials. Methods: The key points were developed by undertaking a stakeholder workshop, followed by a discussion within the study team and then a stakeholder dissemination and feedback event. The stakeholder workshop sought to gain the perspectives of a variety of stakeholders (including clinicians, researchers and patient representatives) on the design and use of n-of-1 trials. A discussion between the study team was held to reflect on the workshop and draft the key points. Lastly, the stakeholders from the workshop were invited to a dissemination and feedback session where the proposed key points were presented and their feedback gained. Results: A set of 22 key points were developed based on the insights from the workshop and subsequent discussions. They provide guidance on when an n-of-1 trial might be a viable or appropriate study design and discuss key decisions involved in the design of n-of-1 trials, including determining an appropriate number of treatment periods and cycles, the choice of comparator, recommended approaches to randomisation and blinding, the use of washout periods and approaches to analysis. Conclusions: The key points developed in the project will support clinical researchers to understand key considerations when designing n-of-1 trials. It is hoped they will support the wider implementation of the study design. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. Results: We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. Conclusions: The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security. [ABSTRACT FROM AUTHOR]

Background: There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. Methods: Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. Results: In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17–1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47–1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39–1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). Conclusion: The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death. [ABSTRACT FROM AUTHOR]

Background: Monogenetic inborn errors of metabolism cause a wide phenotypic heterogeneity that may even differ between family members carrying the same genetic variant. Computational modelling of metabolic networks may identify putative sources of this inter-patient heterogeneity. Here, we mainly focus on medium-chain acyl-CoA dehydrogenase deficiency (MCADD), the most common inborn error of the mitochondrial fatty acid oxidation (mFAO). It is an enigma why some MCADD patients—if untreated—are at risk to develop severe metabolic decompensations, whereas others remain asymptomatic throughout life. We hypothesised that an ability to maintain an increased free mitochondrial CoA (CoASH) and pathway flux might distinguish asymptomatic from symptomatic patients. Results: We built and experimentally validated, for the first time, a kinetic model of the human liver mFAO. Metabolites were partitioned according to their water solubility between the bulk aqueous matrix and the inner membrane. Enzymes are also either membrane-bound or in the matrix. This metabolite partitioning is a novel model attribute and improved predictions. MCADD substantially reduced pathway flux and CoASH, the latter due to the sequestration of CoA as medium-chain acyl-CoA esters. Analysis of urine from MCADD patients obtained during a metabolic decompensation showed an accumulation of medium- and short-chain acylcarnitines, just like the acyl-CoA pool in the MCADD model. The model suggested some rescues that increased flux and CoASH, notably increasing short-chain acyl-CoA dehydrogenase (SCAD) levels. Proteome analysis of MCADD patient-derived fibroblasts indeed revealed elevated levels of SCAD in a patient with a clinically asymptomatic state. This is a rescue for MCADD that has not been explored before. Personalised models based on these proteomics data confirmed an increased pathway flux and CoASH in the model of an asymptomatic patient compared to those of symptomatic MCADD patients. Conclusions: We present a detailed, validated kinetic model of mFAO in human liver, with solubility-dependent metabolite partitioning. Personalised modelling of individual patients provides a novel explanation for phenotypic heterogeneity among MCADD patients. Further development of personalised metabolic models is a promising direction to improve individualised risk assessment, management and monitoring for inborn errors of metabolism. [ABSTRACT FROM AUTHOR]

Background: Personalised medicine (PM) has been fostered by technological and medical advances, but all stakeholders, including healthcare professionals, citizens and policy makers, should achieve adequate health literacy to promote PM implementation. The "Integrating China in the International Consortium for Personalised Medicine" (IC2PerMed) project, funded by the International Consortium for Personalised Medicine, focuses on this issue by highlighting the need to educate healthcare professionals and empower citizens. Within the aforementioned project, building on a mapping of European and Chinese policies in PM, experts in the field of PM participated in an online workshop and a following two-round Delphi survey, in order to identify the priority areas of intervention for healthcare professionals' education and curricula, engagement and empowerment of citizens and patients. Results: Nine experts completed the survey and reached a consensus on seventeen priorities: seven were related to health professionals' education and curricula, whereas ten on citizen and patients' awareness and empowerment. Conclusion: These priorities emphasized the importance of education and health literacy, multidisciplinary and international collaboration, public trust, and consideration of ethical, legal, and social issues. The present experience highlights the relevance of the involvement of stakeholders in informing decision-makers, developing appropriate national plans, strategies, and policies, and ensuring the adequate implementation of PM in health systems. [ABSTRACT FROM AUTHOR]

Background: ICU risk assessment tools, routinely used for predicting population outcomes, are not recommended for evaluating individual risk. The state of health of single patients is mostly subjectively assessed to inform relatives and presumably to decide on treatment decisions. However, little is known how subjective and objective survival estimates compare. Methods: We performed a prospective cohort study in mechanically ventilated critically ill patients across five European centres, assessed 62 objective markers and asked the clinical staff to subjectively estimate the probability of surviving 28 days. Results: Within the 961 included patients, we identified 27 single objective predictors for 28-day survival (73.8%) and pooled them into predictive groups. While patient characteristics and treatment models performed poorly, the disease and biomarker models had a moderate discriminative performance for predicting 28-day survival, which improved for predicting 1-year survival. Subjective estimates of nurses (c-statistic [95% CI] 0.74 [0.70–0.78]), junior physicians (0.78 [0.74–0.81]) and attending physicians (0.75 [0.72–0.79]) discriminated survivors from non-survivors at least as good as the combination of all objective predictors (c-statistic: 0.67–0.72). Unexpectedly, subjective estimates were insufficiently calibrated, overestimating death in high-risk patients by about 20% in absolute terms. Combining subjective and objective measures refined discrimination and reduced the overestimation of death. Conclusions: Subjective survival estimates are simple, cheap and similarly discriminative as objective models; however, they overestimate death risking that live-saving therapies are withheld. Therefore, subjective survival estimates of individual patients should be compared with objective tools and interpreted with caution if not agreeing. Trial registration ISRCTN ISRCTN59376582, retrospectively registered October 31st 2013. [ABSTRACT FROM AUTHOR]

Background: Pharmacists' contribution to pharmacogenomics (PGx) implementation in clinical practice is vital, but a great proportion of them are not aware of PGx and its applications. This highlights the university education's crucial role to prepare pharmacists to face future challenges in such a constantly evolving and demanding environment. Objectives: Our study aims to examine pharmacy students' training satisfaction, knowledge, self-confidence and attitudes towards PGx on their intentions for postgraduate training in PGx and personalised medicine (PM). Methods: An initial model on students' intention to pursue postgraduate training in PGx and PM and its predicting factors, based on the Theory of Planned Behaviour (TPB), was proposed. Based on it, a questionnaire was developed and distributed to 346 pharmacy students of all study years, capturing the selected factors influencing students' intentions to postgraduate training in PGx and PM, as well as their demographics. Structural equation modelling (SEM) analysis was employed to determine the effects of both the examined factors and demographics on students' intentions. Results: Students did not consider themselves adequately prepared for using PGx in clinical practice. Their attitudes towards PGx implementation were the most important factor influencing their intentions to pursue postgraduate training in PGx and PM. Other factors such as self-confidence and training satisfaction also affected students' intentions, but to a lower extent. Students of the last two study years (40% of the whole sample) and male (36%) students stated to be less willing to pursue PGx-related studies in the future. Only 10% of the participants claimed to have undergone a recent PGx or genetic test, but this did not affect their intentions. Conclusion: There is an important gap in pharmacy school curriculum regarding PGx and PM training which coupled with the slow rate of PGx and PM implementation into clinical practice seems to restrain students' aspiration to further expand their knowledge and horizons in terms of PGx and PM. [ABSTRACT FROM AUTHOR]

Background: Fibrinolysisis is essential for vascular blood flow maintenance and is triggered by endothelial and platelet release of tissue plasminogen activator (t-PA). In certain critical conditions, e.g. sepsis, acute respiratory failure (ARF) and trauma, the fibrinolytic response is reduced and may lead to widespread thrombosis and multi-organ failure. The mechanisms underpinning fibrinolysis resistance include reduced t-PA expression and/or release, reduced t-PA and/or plasmin effect due to elevated inhibitor levels, increased consumption and/or clearance. This study in critically ill patients with fibrinolysis resistance aimed to evaluate the ability of t-PA and plasminogen supplementation to restore fibrinolysis with assessment using point-of-care ClotPro viscoelastic testing (VET). Methods: In prospective, observational studies, whole-blood ClotPro VET evaluation was carried out in 105 critically ill patients. In 32 of 58 patients identified as fibrinolysis-resistant (clot lysis time > 300 s on the TPA-test: tissue factor activated coagulation with t-PA accelerated fibrinolysis), consecutive experimental whole-blood VET was carried out with repeat TPA-tests spiked with additional t-PA and/or plasminogen and the effect on lysis time determined. In an interventional study in a patient with ARF and fibrinolysis resistance, the impact of a 24 h intravenous low-dose alteplase infusion on coagulation and fibrinolysis was prospectively monitored using standard ClotPro VET. Results: Distinct response groups emerged in the ex vivo experimental VET, with increased fibrinolysis observed following supplementation with (i) t-PA only or (ii) plasminogen and t-PA. A baseline TPA-test lysis time of > 1000 s was associated with the latter group. In the interventional study, a gradual reduction (25%) in serial TPA-test lysis times was observed during the 24 h low-dose alteplase infusion. Conclusions: ClotPro viscoelastic testing, the associated TPA-test and the novel experimental assays may be utilised to (i) investigate the potential mechanisms of fibrinolysis resistance, (ii) guide corrective treatment and (iii) monitor in real-time the treatment effect. Such a precision medicine and personalised treatment approach to the management of fibrinolysis resistance has the potential to increase treatment benefit, while minimising adverse events in critically ill patients. Trial registration: VETtiPAT-ARF, a clinical trial evaluating ClotPro-guided t-PA (alteplase) administration in fibrinolysis-resistant patients with ARF, is ongoing (ClinicalTrials.gov NCT05540834; retrospectively registered September 15th 2022). [ABSTRACT FROM AUTHOR]

Background: Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Methods: These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations. Results: Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Conclusions: Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future. [ABSTRACT FROM AUTHOR]

Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman's TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. Results: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets. [ABSTRACT FROM AUTHOR]

Background: For patients with therapy-refractory persistent spinal pain syndrome type II (PSPS-T2), spinal cord stimulation (SCS) may serve as an effective minimally invasive treatment. Despite the evidence that SCS can improve return to work (RTW), only 9.5 to 14% of patients implanted with SCS are effectively capable of returning to work. Thus, it seems that current post-operative interventions are not effective for achieving RTW after SCS implantation in clinical practice. The current objective is to examine whether a personalised biopsychosocial rehabilitation programme specifically targeting RTW alters the work ability in PSPS-T2 patients after SCS implantation compared to usual care.Methods: A two-arm, parallel-group multicentre randomised controlled trial will be conducted including 112 patients who will be randomised (1:1) to either (a) a personalised biopsychosocial RTW rehabilitation programme of 14 weeks or (b) a usual care arm, both with a follow-up period until 12 months after the intervention. The primary outcome is work ability. The secondary outcomes are work status and participation, pain intensity, health-related quality of life, physical activity and functional disability, functional capacities, sleep quality, kinesiophobia, self-management, anxiety, depression and healthcare expenditure.Discussion: Within the OPERA project, we propose a multidisciplinary personalised biopsychosocial rehabilitation programme specifically targeting RTW for patients implanted with SCS, to tackle the high socio-economic burden of patients that are not re-entering the labour market. The awareness is growing that the burden of PSPS-T2 on our society is expected to increase over time due to the annual increase of spinal surgeries. However, innovative and methodologically rigorous trials exploring the potential to decrease the socio-economic burden when patients initiate a trajectory with SCS are essentially lacking.Trial Registration: ClinicalTrials.gov NCT05269212. Registered on 7 March 2022. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is a very prevalent disease in men. Patients are monitored regularly during and after treatment with repeated assessment of prostate-specific antigen (PSA) levels. Prognosis of localised prostate cancer is generally good after treatment, and the risk of having a recurrence is usually estimated based on factors measured at diagnosis. Incorporating PSA measurements over time in a dynamic prediction joint model enables updates of patients' risk as new information becomes available. We review joint model strategies that have been applied to model time-dependent PSA trajectories to predict time-to-event outcomes in localised prostate cancer.Methods: We identify articles that developed joint models for prediction of localised prostate cancer recurrence over the last two decades. We report, compare, and summarise the methodological approaches and applications that use joint modelling accounting for two processes: the longitudinal model (PSA), and the time-to-event process (clinical failure). The methods explored differ in how they specify the association between these two processes.Results: Twelve relevant articles were identified. A range of methodological frameworks were found, and we describe in detail shared-parameter joint models (9 of 12, 75%) and joint latent class models (3 of 12, 25%). Within each framework, these articles presented model development, estimation of dynamic predictions and model validations.Conclusions: Each framework has its unique principles with corresponding advantages and differing interpretations. Regardless of the framework used, dynamic prediction models enable real-time prediction of individual patient prognosis. They utilise all available longitudinal information, in addition to baseline prognostic risk factors, and are superior to traditional baseline-only prediction models. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC. [ABSTRACT FROM AUTHOR]

Background: Interleukin (IL)-18 is a marker of inflammasome activation, and high baseline plasma IL-18 is associated with increased mortality in patients with sepsis-induced ARDS. The aim of this analysis was to determine if simvastatin was associated with benefit in patients with ARDS and high plasma IL-18.Methods: In this secondary analysis of the HARP-2 study, we compared 28-day mortality and response to simvastatin according to baseline plasma IL-18 using cox proportional hazards analysis. Separately, monocyte-derived macrophages from healthy volunteers were pre-incubated with simvastatin or rosuvastatin before stimulation with ATP and LPS, and the effect on secreted IL-18 and IL-1β compared.Results: 511 patients from HARP-2 had available data. High baseline plasma IL-18 (≥ 800 pg/ml) was associated with increased 28-day mortality (high IL-18 30.6% vs. low IL-18 17.5%; HR 1.89 [95% CI 1.30-2.73]; p = 0.001). Allocation to simvastatin in patients with high baseline plasma IL-18 was associated with a lower probability of 28-day mortality compared with placebo (24.0% vs 36.8%; p = 0.01). Finally, simvastatin, but not rosuvastatin, reduced stimulated macrophage secretion of IL-18 and IL-1β.Conclusion: In patients with high baseline plasma IL-18, simvastatin is associated with a higher probability of survival, and this effect may be due to reduced inflammasome activation. These data suggest that baseline plasma IL-18 may allow a personalised treatment approach by identifying patients with ARDS who could benefit from simvastatin therapy. [ABSTRACT FROM AUTHOR]

Background: Using participatory methods to engage end-users in the development and design of eHealth is important to understand and incorporate their needs and context. Within participatory research, recent social distancing practice has forced a transition to digital communication platforms, a setting that warrants deeper understanding. The aim of this study was to describe the experiences of, and evaluate a digital co-creation process for developing an eHealth tool for people with chronic obstructive pulmonary disease (COPD).Methods: The co-creation was guided by Participatory appreciative action and reflection, where a convenience sample (n = 17), including persons with COPD, health care professionals, relatives and a patient organization representative participated in six digital workshops. User instructions, technical equipment, and skilled support were provided if necessary. Workshops centred around different topics, with pre-recorded films, digital lectures and home assignments to up-skill participants. Process validity, experiences and ownership in the co-creation process were evaluated by repeated respondent validation, member checking, questionnaires and by assessing attendance. Data was analysed quantitatively or qualitatively as appropriate.Results: The co-creators were in general satisfied with the digital format of the workshops. Mean attendance and perceived engagement in workshops was high and the experience described as enjoyable. Engagement was facilitated by up-skilling activities and discussions in small groups. Few had used digital communication previously, and feelings ranging from excitement to concern were expressed initially. Technical issues, mainly audio related, were resolved with support. At completion, skills using equipment and digital platform surpassed expectations. Few disadvantages with the digital format were identified, and advantages included reduced travel, time efficiency and reduced infection risk.Conclusions: Experiences of digital co-creation were overwhelmingly positive, despite initial barriers related to computer naivety and use of digital equipment and platforms. The high level of satisfaction, engagement, attendance rates, and agreement between individual and group views suggests that a digital co-creation process is a feasible method. Several important success factors were identified, such as the provision of information and education on discussion topics in advance of workshops, as well as the smaller group discussions during workshops. The knowledge gained herein will be useful for future digital co-creation processes. [ABSTRACT FROM AUTHOR]

Background: The promise of personalised medicine (PM) to transform healthcare has sparked great enthusiasm in the last years. Yet, its lack of consensus around the nature and scope of the concept has ended in terminological confusion amongst the users in primary care. We aimed to investigate the perceptions of doctors and their patients in response to this evolving concept. This present article focuses on the general understanding of personalised medicine, underlining the confusion over the concept. Methods: Semi-structured comprehensive interviews were conducted with 10 general practitioners (GPs) and 10 of their patients. The purposive sampling took into account the doctor's age, sex, and place of practice (rural/urban); each doctor recruited one patient of the same age and sex. Each interview began with the same open-ended question about the participant's knowledge of the topic, after which a working definition was provided to continue the discussion. Using the grounded theory method, the analysis consisted of open coding, axial coding and selective coding. Results: From our present analysis focusing on the general understanding of PM, three main themes representing the concept emerged. The first two representations being "centred on the person as a whole" and "focused on alternative and complementary methods", in which the therapeutic relationship was stated as key. The third theme "medicine open to innovation" involved the few participants who had a good understanding of the concept and could associate personalised medicine with genomics. For those who value therapeutic relationship, the risks of accepting innovation could result in "fast-food" medicine and interpersonal barriers. Discussion: PM is predominantly unfamiliar in family medicine. It is misinterpreted as a holistic or integrative type of medicine. This semantic confusion probably lies in the choice of the label "personalised" or from the lack of a uniform definition for the term. [ABSTRACT FROM AUTHOR]

Background: Currently, numerous antihypertensive drugs from different pharmacological classes are available; however, blood pressure control is achieved in only less than a third of patients treated for hypertension. Moreover, providing optimal and personalised treatment for hypertension is challenging. Therefore, in this study, we propose a 'drug-related attributes' sensitive spectrum. This novel concept can assist clinicians in selecting an optimal antihypertensive drug and improve blood pressure control after examining the attributes of a patient.Methods: We collected clinical data on attributes related to hypertension and its therapy of inpatients from West China Hospital who received metoprolol therapy and constructed the sensitive spectrum using data-visualisation tools.Results: Our analysis revealed that haematocrit, haemoglobin, serum creatinine, serum cystatin C, serum urea, age, sex, systolic pressure, diastolic pressure, pulse pressure, and heart rate are metoprolol-related attributes.Conclusion: Our study showed that all metoprolol-related attributes identified are reasonable and helpful in improving the personalisation of metoprolol therapy. The proposed drug-related attributes spectrum can help personalise antihypertensive medication. Moreover, data-visualisation tools can be effectively used to mine the drug-related attributes sensitive spectrum. [ABSTRACT FROM AUTHOR]

Since the discovery of the first MDM2 inhibitors, we have gained deeper insights into the cellular roles of MDM2 and p53. In this review, we focus on MDM2 inhibitors that bind to the p53-binding domain of MDM2 and aim to disrupt the binding of MDM2 to p53. We describe the basic mechanism of action of these MDM2 inhibitors, such as nutlin-3a, summarise the determinants of sensitivity to MDM2 inhibition from p53-dependent and p53-independent points of view and discuss the problems with innate and acquired resistance to MDM2 inhibition. Despite progress in MDM2 inhibitor design and ongoing clinical trials, their broad use in cancer treatment is not fulfilling expectations in heterogenous human cancers. We assess the MDM2 inhibitor types in clinical trials and provide an overview of possible sources of resistance to MDM2 inhibition, underlining the need for patient stratification based on these aspects to gain better clinical responses, including the use of combination therapies for personalised medicine. [ABSTRACT FROM AUTHOR]

Background: Despite the progress of research and treatment for breast cancer, still up to 30% of the patients afflicted will develop distant disease. Elongation of survival and maintaining the quality of life (QoL) become pivotal issues guiding the treatment decisions. One possible approach to optimise survival and QoL is the use of patient-reported outcomes (PROs) to timely identify acute disease-related burden. We present the protocol of a trial that investigates the effect of real-time PRO data captured with electronic mobile devices on QoL in female breast cancer patients with metastatic disease.Methods: This study is a randomised, controlled trial with 1:1 randomisation between two arms. A total of 1000 patients will be recruited in 40 selected breast cancer centres. Patients in the intervention arm receive a weekly request via an app to complete the PRO survey. Symptoms will be assessed by study-specific optimised short forms based on the EORTC QLQ-C30 domains using items from the EORTC CAT item banks. In case of deteriorating PRO scores, an alarm is sent to the treating study centre as well as to the PRO B study office. Following the alarm, the treating breast cancer centre is required to contact the patient to inquire about the reported symptoms and to intervene, if necessary. The intervention is not specified and depends on the clinical need determined by the treating physician. Patients in the control arm are prompted by the app every 3 months to participate in the PRO survey, but their response will not trigger an alarm. The primary outcome is the fatigue level 6 months after enrolment. Secondary endpoints include among others hospitalisations, use of rescue services and overall QoL.Discussion: Within the PRO B intervention group, we expect lower fatigue levels 6 months after intervention start, higher levels of QoL, less unplanned hospitalisations and less emergency room visits compared to controls. In case of positive results, our approach would allow a fast and easy transfer into clinical practice due to the use of the already nationwide existing IT infrastructure of the German Cancer Society and the independent certification institute OnkoZert.Trial Registration: DRKS (German Clinical Trials Register) DRKS00024015 . Registered on 15 February 2021. [ABSTRACT FROM AUTHOR]

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided. [ABSTRACT FROM AUTHOR]

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ .Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application., (© 2024. The Author(s).)

Background: In the presence of heterogeneous treatment effects, it is desirable to divide patients into subgroups based on their expected response to treatment. This is formalised via a personalised treatment recommendation: an algorithm that uses biomarker measurements to select treatments. It could be that multiple, rather than single, biomarkers better predict these subgroups. However, finding the optimal combination of multiple biomarkers can be a difficult prediction problem.Methods: We described three parametric methods for finding the optimal combination of biomarkers in a personalised treatment recommendation, using randomised trial data: a regression approach that models outcome using treatment by biomarker interactions; an approach proposed by Kraemer that forms a combined measure from individual biomarker weights, calculated on all treated and control pairs; and a novel modification of Kraemer's approach that utilises a prognostic score to sample matched treated and control subjects. Using Monte Carlo simulations under multiple data-generating models, we compare these approaches and draw conclusions based on a measure of improvement under a personalised treatment recommendation compared to a standard treatment. The three methods are applied to data from a randomised trial of home-delivered pragmatic rehabilitation versus treatment as usual for patients with chronic fatigue syndrome (the FINE trial). Prior analysis of this data indicated some treatment effect heterogeneity from multiple, correlated biomarkers.Results: The regression approach outperformed Kraemer's approach across all data-generating scenarios. The modification of Kraemer's approach leads to improved treatment recommendations, except in the case where there was a strong unobserved prognostic biomarker. In the FINE example, the regression method indicated a weak improvement under its personalised treatment recommendation algorithm.Conclusions: The method proposed by Kraemer does not perform better than a regression approach for combining multiple biomarkers. All methods are sensitive to misspecification of the parametric models. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the potential of induction chemotherapy as an indicator of the management of advanced hypopharyngeal carcinoma with cervical oesophageal invasion. Methods: Sixty-eight patients admitted to our hospital between February 2003 and November 2016 with stage IVB hypopharyngeal carcinoma with cervical oesophageal invasion were retrospectively analysed. Patients were divided into two groups according to the treatment they selected following an explanation of the different treatments available. Patients in group A received induction chemotherapy and had (1) complete/partial remission following chemotherapy and radiotherapy/concurrent chemoradiotherapy or (2) stable disease following chemotherapy and surgery. Patients in group B underwent surgery followed by adjuvant radiotherapy/concurrent chemoradiotherapy. Survival analyses were performed using the Kaplan–Meier method, and differences between the groups were evaluated using the log-rank test. Laryngeal and oesophageal retention rates were compared using the cross-tabulation test. Results: The 3- and 5-year overall survival rates were 22.86% and 11.43% in group A and 24.25% and 6.06% in group B, respectively (all P > 0.05). The laryngeal and oesophageal retention rates were 40.0% and 74.3% in group A and 0.0% and 27.3% in group B, respectively (all P < 0.01). There was no statistically significant difference in the incidence of post-operative complications between the two groups (group A 8.6%, group B 12.1%; P > 0.05). Conclusions: Induction chemotherapy may be an appropriate first choice to ensure laryngeal and oesophageal preservation in the individualised treatment of advanced hypopharyngeal carcinoma with cervical oesophageal invasion. [ABSTRACT FROM AUTHOR]

The editorial comments on a recently published study in which 242 patients, with "stable" chronic kidney disease, recruited during a hospital stay, were randomised either to receiving support from nephrologists (co-management by primary care physicians and nephrologists), or to be managed by primary care physicians with written instructions and nephrology consultations on demand. After a mean follow-up of 4 years, the results in terms of dialysis start, hospitalisation and death were similar for both groups.This study gave the possibility to discuss about the options of follow-up of CKD patients, including on one side the advantage of a greater involvement of primary care physicians, who could oversee care by applying a common set of simplified guidelines, and on the other one the importance of a direct and deep involvement of the specialists that seems necessary in particular if personalised approaches have to be pursuit. The data of the present study are somehow in disagreement with the literature, usually suggesting better outcomes in intensive treatment, in which specialists are directly involved. The literature is heterogeneous, the goals vary and the populations are differently selected. The compliance issue is probably one of the missing pieces of the puzzle, and specific interventions should also be tailored to "reluctant" patients. Guidelines should probably be staring points for improvement, and not the standard of care; the study herein discussed may suggest that primary care physicians may be of great help in granting a good standard of care, hopefully as a baseline for further improvement, and personalised care. [ABSTRACT FROM AUTHOR]

This commentary presents the vision of the International Consortium for Personalised Medicine (ICPerMed) on how personalised medicine (PM) will lead to the next generation of healthcare by 2030. This vision focuses on five perspectives: individual and public engagement, involvement of health professionals, implementation within healthcare systems, health-related data, and the development of sustainable economic models that allow improved therapy, diagnostic and preventive approaches as new healthcare concepts for the benefit of the public. We further identify four pillars representing transversal issues that are crucial for the successful implementation of PM in all perspectives. The implementation of PM will result in more efficient and equitable healthcare, access to modern healthcare methods, and improved control by individuals of their own health data, as well as economic development in the health sector. [ABSTRACT FROM AUTHOR]

Background: Infliximab (INX) and other tumour necrosis factor inhibitors (TNFi) have revolutionised the treatment of several immune mediated inflammatory diseases. Still, many patients do not respond sufficiently to therapy or lose efficacy over time. The large interindividual variation in serum drug concentrations on standard doses and the development of anti-drug antibodies are thought to be major reasons for treatment failures. Therapeutic drug monitoring (TDM), an individualised treatment strategy based on systematic assessments of serum drug concentrations, has been proposed as a clinical tool to optimise efficacy of INX treatment. TDM seems reasonable both from a clinical and an economical point of view, but the effectiveness of this treatment strategy has not yet been demonstrated in randomised clinical trials. The NORwegian DRUg Monitoring study (NOR-DRUM) aims to assess the effectiveness of TDM, both with regard to the achievement of remission in patients starting INX treatment (part A) as well as to maintain disease control in patients on INX treatment (part B).Methods: The NOR-DRUM study is a randomised, open, controlled, parallel-group, comparative, multi-centre, national, superiority, phase IV study with two separate parts, NOR-DRUM A and NOR-DRUM B. Patients with rheumatoid arthritis, psoriatic arthritis, spondyloarthritis, ulcerative colitis, Crohn's disease and psoriasis are included. In both study parts participants are randomised 1:1 to either TDM of infliximab (intervention group) or to standard treatment with infliximab without knowledge of drug levels or ADAb status (control group). NOR-DRUM A will include 400 patients starting INX therapy. The primary outcome is remission at 30 weeks. In NOR-DRUM B, 450 patients on maintenance treatment with INX will be included. The primary endpoint is occurrence of disease worsening during the 52-week study period.Discussion: As the first trial to assess the effectiveness, safety and cost-effectiveness of TDM in patients receiving TNFi for a range of immune mediated inflammatory diseases, we hope that the NOR-DRUM study will contribute to the advancement of evidence based personalised treatment with biological medicines.Trial Registration: Clinicaltrials.gov, NCT03074656. Registered on 090317. [ABSTRACT FROM AUTHOR]

Background: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore (https://bioconductor.org/packages/singscore). Results: We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS < 25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. Conclusions: The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification. [ABSTRACT FROM AUTHOR]

Background: Establishing the cancer type and site of origin is important in determining the most appropriate course of treatment for cancer patients. Patients with cancer of unknown primary, where the site of origin cannot be established from an examination of the metastatic cancer cells, typically have poor survival. Here, we evaluate the potential and limitations of utilising gene alteration data from tumour DNA to identify cancer types. Methods: Using sequenced tumour DNA downloaded via the cBioPortal for Cancer Genomics, we collected the presence or absence of calls for gene alterations for 6640 tumour samples spanning 28 cancer types, as predictive features. We employed three machine-learning techniques, namely linear support vector machines with recursive feature selection, L1-regularised logistic regression and random forest, to select a small subset of gene alterations that are most informative for cancer-type prediction. We then evaluated the predictive performance of the models in a comparative manner. Results: We found the linear support vector machine to be the most predictive model of cancer type from gene alterations. Using only 100 somatic point-mutated genes for prediction, we achieved an overall accuracy of 49.4 ± 0.4% (95% confidence interval). We observed a marked increase in the accuracy when copy number alterations are included as predictors. With a combination of somatic point mutations and copy number alterations, a mere 50 genes are enough to yield an overall accuracy of 77.7 ± 0.3%. Conclusions: A general cancer diagnostic tool that utilises either only somatic point mutations or only copy number alterations is not sufficient for distinguishing a broad range of cancer types. The combination of both gene alteration types can dramatically improve the performance. [ABSTRACT FROM AUTHOR]

There is increasing international policy and clinical interest in developing learning health systems and delivering precision medicine, which it is hoped will help reduce variation in the quality and safety of care, improve efficiency, and lead to increasing the personalisation of healthcare. Although reliant on similar policies, informatics tools, and data science and implementation research capabilities, these two major initiatives have thus far largely progressed in parallel. In this opinion piece, we argue that they should be considered as complementary, synergistic initiatives whereby the creation of learning health systems infrastructure can support and catalyse the delivery of precision medicine that maximises the benefits and minimises the risks associated with treatments for individual patients. We illustrate this synergy by considering the example of treatments for asthma, which is now recognised as an umbrella term for a heterogeneous group of related conditions. [ABSTRACT FROM AUTHOR]

Background: Personalised treatment that is uniquely tailored to an individual's phenotype has become a key goal of clinical and pharmaceutical development across many, particularly chronic, diseases. For type 2 diabetes, the importance of the underlying clinical heterogeneity of the condition is emphasised and a range of treatments are now available, with personalised approaches being developed. While a close connection between risk factors for type 2 diabetes and gestational diabetes has long been acknowledged, stratification of screening, treatment and obstetric intervention remains in its infancy.Conclusions: Although there have been major advances in our understanding of glucose tolerance in pregnancy and of the benefits of treatment of gestational diabetes, we argue that far more vigorous approaches are needed to enable development of companion diagnostics, and to ensure the efficacious and safe use of novel therapeutic agents and strategies to improve outcomes in this common condition. [ABSTRACT FROM AUTHOR]

Background: Cystinuria is an inherited disease that results in the formation of cystine stones in the kidney, which can have serious health complications. Two genes (SLC7A9 and SLC3A1) that form an amino acid transporter are known to be responsible for the disease. Variants that cause the disease disrupt amino acid transport across the cell membrane, leading to the build-up of relatively insoluble cystine, resulting in formation of stones. Assessing the effects of each mutation is critical in order to provide tailored treatment options for patients. We used various computational methods to assess the effects of cystinuria associated mutations, utilising information on protein function, evolutionary conservation and natural population variation of the two genes. We also analysed the ability of some methods to predict the phenotypes of individuals with cystinuria, based on their genotypes, and compared this to clinical data. Results: Using a literature search, we collated a set of 94 SLC3A1 and 58 SLC7A9 point mutations known to be associated with cystinuria. There are differences in sequence location, evolutionary conservation, allele frequency, and predicted effect on protein function between these mutations and other genetic variants of the same genes that occur in a large population. Structural analysis considered how these mutations might lead to cystinuria. For SLC7A9, many mutations swap hydrophobic amino acids for charged amino acids or vice versa, while others affect known functional sites. For SLC3A1, functional information is currently insufficient to make confident predictions but mutations often result in the loss of hydrogen bonds and largely appear to affect protein stability. Finally, we showed that computational predictions of mutation severity were significantly correlated with the disease phenotypes of patients from a clinical study, despite different methods disagreeing for some of their predictions. Conclusions: The results of this study are promising and highlight the areas of research which must now be pursued to better understand how mutations in SLC3A1 and SLC7A9 cause cystinuria. The application of our approach to a larger data set is essential, but we have shown that computational methods could play an important role in designing more effective personalised treatment options for patients with cystinuria. [ABSTRACT FROM AUTHOR]

Over the last decade, there have been major advances in the understanding of the genetic basis of hyperuricaemia and gout as well as of the pharmacogenetics of urate-lowering therapy. Key findings include the reporting of 28 urate-associated loci, the discovery that ABCG2 plays a central role on extra-renal uric acid excretion, the identification of genes associated with development of gout in the context of hyperuricaemia, recognition that ABCG2 variants influence allopurinol response, and the impact of HLA-B*5801 testing in reducing the prevalence of allopurinol hypersensitivity in high-risk populations. These advances, together with the reducing cost of whole genome sequencing, mean that integrated personalised medicine approaches may soon be possible in clinical practice. Genetic data may inform assessment of disease prognosis in individuals with hyperuricaemia or established gout, personalised lifestyle advice, selection and dosing of urate-lowering therapy, and prevention of serious medication adverse effects. In this article, we summarise the discoveries from genome-wide association studies and discuss the potential for translation of these findings into clinical practice. [ABSTRACT FROM AUTHOR]

Background: Precision medicine is a new and important topic in psychiatry. Psychiatry has not yet benefited from the advanced diagnostic and therapeutic technologies that form an integral part of other clinical specialties. Thus, the vision of precision medicine as applied to psychiatry - 'precision psychiatry' - promises to be even more transformative than in other fields of medicine, which have already lessened the translational gap.Discussion: Herein, we describe 'precision psychiatry' and how its several implications promise to transform the psychiatric landscape. We pay particular attention to biomarkers and to how the development of new technologies now makes their discovery possible and timely. The adoption of the term 'precision psychiatry' will help propel the field, since the current term 'precision medicine', as applied to psychiatry, is impractical and does not appropriately distinguish the field. Naming the field 'precision psychiatry' will help establish a stronger, unique identity to what promises to be the most important area in psychiatry in years to come.Conclusion: In summary, we provide a wide-angle lens overview of what this new field is, suggest how to propel the field forward, and provide a vision of the near future, with 'precision psychiatry' representing a paradigm shift that promises to change the landscape of how psychiatry is currently conceived. [ABSTRACT FROM AUTHOR]

Background: Docetaxel based therapy is one of the first line chemotherapeutic agents for the treatment of metastatic castrate-resistant prostate cancer. However, one of the major obstacles in the treatment of these patients is docetaxel-resistance. Defining the mechanisms of resistance so as to inform subsequent treatment options and combinations represents a challenge for clinicians and scientists. Previous work by our group has shown complex changes in pro and anti-apoptotic proteins in the development of resistance to docetaxel. Targeting these changes individually does not significantly impact on the resistant phenotype but understanding the central signalling pathways and transcription factors (TFs) which control these could represent a more appropriate therapeutic targeting approach.Methods: Using a number of docetaxel-resistant sublines of PC-3 cells, we have undertaken a transcriptomic analysis by expression microarray using the Affymetrix Human Gene 1.0 ST Array and in conjunction with bioinformatic analyses undertook to predict dysregulated TFs in docetaxel resistant prostate cancer. The clinical significance of this prediction was ascertained by performing immunohistochemical (IHC) analysis of an identified TF (SRF) in the metastatic sites from men who died of advanced CRPC. Investigation of the functional role of SRF was examined by manipulating SRF using SiRNA in a docetaxel-resistant PC-3 cell line model.Results: The transcription factors identified include serum response factor (SRF), nuclear factor kappa-B (NFκB), heat shock factor protein 1 (HSF1), testicular receptor 2 & 4 (TR2 &4), vitamin-D and retinoid x receptor (VDR-RXR) and oestrogen-receptor 1 (ESR1), which are predicted to be responsible for the differential gene expression observed in docetaxel-resistance. IHC analysis to quantify nuclear expression of the identified TF SRF correlates with both survival from date of bone metastasis (p = 0.003), survival from androgen independence (p = 0.00002), and overall survival from prostate cancer (p = 0.0044). Functional knockdown of SRF by siRNA demonstrated a reversal of apoptotic resistance to docetaxel treatment in the docetaxel-resistant PC-3 cell line model.Conclusions: Our results suggest that SRF could aid in treatment stratification of prostate cancer, and may also represent a therapeutic target in the treatment of men afflicted with advanced prostate cancer. [ABSTRACT FROM AUTHOR]

Neuroblastoma is an embryonic malignancy of early childhood originating from neural crest cells and showing heterogeneous biological, morphological, genetic and clinical characteristics. The correct stratification of neuroblastoma patients within risk groups (low, intermediate, high and ultra-high) is critical for the adequate treatment of the patients. High-throughput technologies in the Omics disciplines are leading to significant insights into the molecular pathogenesis of neuroblastoma. Nonetheless, further study of Omics data is necessary to better characterise neuroblastoma tumour biology. In the present review, we report an update of compounds that are used in preclinical tests and/or in Phase I-II trials for neuroblastoma. Furthermore, we recapitulate a number of compounds targeting proteins associated to neuroblastoma: MYCN (direct and indirect inhibitors) and downstream targets, Trk, ALK and its downstream signalling pathways. In particular, for the latter, given the frequency of ALK gene deregulation in neuroblastoma patients, we discuss on second-generation ALK inhibitors in preclinical or clinical phases developed for the treatment of neuroblastoma patients resistant to crizotinib. We summarise how Omics drive clinical trials for neuroblastoma treatment and how much the research of biological targets is useful for personalised medicine. Finally, we give an overview of the most recent druggable targets selected by Omics investigation and discuss how the Omics results can provide us additional advantages for overcoming tumour drug resistance. [ABSTRACT FROM AUTHOR]

Background: The idea of personalised medicine (PM) has gathered momentum recently, attracting funding and generating hopes as well as scepticism. As PM gives rise to differing interpretations, there have been several attempts to clarify the concept. In an influential paper published in this journal, Schleidgen and colleagues have proposed a precise and narrow definition of PM on the basis of a systematic literature review. Given that their conclusion is at odds with those of other recent attempts to understand PM, we consider whether their systematic review gives them an edge over competing interpretations.Discussion: We have found some methodological weaknesses and questionable assumptions in Schleidgen and colleagues' attempt to provide a more specific definition of PM. Our perplexities concern the lack of criteria for assessing the epistemic strength of the definitions that they consider, as well as the logical principles used to extract a more precise definition, the narrowness of the pool from which they have drawn their empirical data, and finally their overlooking the fact that definitions depend on the context of use. We are also worried that their ethical assumption that only patients' interests are legitimate is too simplistic and drives all other stakeholders' interests-including those that are justifiable-underground, thus compromising any hope of a transparent and fair negotiation among a plurality of actors and interests.Conclusion: As an alternative to the shortcomings of attempting a semantic disciplining of the concept we propose a pragmatic approach. Rather than considering PM to be a scientific concept in need of precise demarcation, we look at it as an open and negotiable concept used in a variety of contexts including at the level of orienting research goals and policy objectives. We believe that since PM is still more an ideal than an achieved reality, a plurality of visions is to be expected and we need to pay attention to the people, reasons and interests behind these alternative conceptions. In other words, the logic and politics of PM cannot be disentangled and disagreements need to be tackled addressing the normative and strategic conflicts behind them. [ABSTRACT FROM AUTHOR]

Biobanks have been heralded as essential tools for translating biomedical research into practice, driving precision medicine to improve pathways for global healthcare treatment and services. Many nations have established specific governance systems to facilitate research and to address the complex ethical, legal and social challenges that they present, but this has not lead to uniformity across the world. Despite significant progress in responding to the ethical, legal and social implications of biobanking, operational, sustainability and funding challenges continue to emerge. No coherent strategy has yet been identified for addressing them. This has brought into question the overall viability and usefulness of biobanks in light of the significant resources required to keep them running. This review sets out the challenges that the biobanking community has had to overcome since their inception in the early 2000s. The first section provides a brief outline of the diversity in biobank and regulatory architecture in seven countries: Australia, Germany, Japan, Singapore, Taiwan, the UK, and the USA. The article then discusses four waves of responses to biobanking challenges. This article had its genesis in a discussion on biobanks during the Centre for Health, Law and Emerging Technologies (HeLEX) conference in Oxford UK, co-sponsored by the Centre for Law and Genetics (University of Tasmania). This article aims to provide a review of the issues associated with biobank practices and governance, with a view to informing the future course of both large-scale and smaller scale biobanks. [ABSTRACT FROM AUTHOR]

Background: The volume and complexity of patient data - especially in personalised medicine - is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare.Methods: Semantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don't provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository.Results: A web-based tool called ODMedit ( https://odmeditor.uni-muenster.de/ ) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal.Conclusion: Uniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository. [ABSTRACT FROM AUTHOR]