Shaping the future of kidney genetics in Australia: proceedings from the KidGen policy implementation workshop 2023.

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.

The clinical heterogeneity of adult onset Still's disease may underlie different pathogenic mechanisms. Implications for a personalised therapeutic management of these patients.

Adult-onset Still's disease (AOSD) is a rare inflammatory disease of unknown aetiology usually affecting young adults and manifesting with a clinical triad of spiking fever, arthritis, and evanescent cutaneous rash. AOSD may be considered a highly heterogeneous disease, despite a similar clinical presentation, the disease course may be completely different. Some patients may have a single episode of the disease whereas others may evolve toward a chronic course and experience life-threatening complications. On these bases, to dissect the clinical heterogeneity of this disease, four different subsets were identified combining the manifestations at the beginning with possible diverse outcomes over time. Each one of these derived subsets would be characterised by a prominent different clinical feature from others, thus proposing dissimilar underlying pathogenic mechanisms, at least partially. Consequently, a distinct management of AOSD may be suggested to appropriately tailor the therapeutic strategy to these patients, according to principles of the precision medicine. These findings would also provide the rationale to recognise a different genetic and molecular profile of patients with AOSD. Taking together these findings, the basis for a precision medicine approach may be suggested in AOSD, which would drive a tailored therapeutic approach in these patients. A better patient stratification may also help in arranging specific designed studies to improve the management of patients with AOSD. Behind these different clinical phenotypes, distinct endotypes of AOSD may be suggested, probably differing in pathogenesis, outcomes, and response to therapies.

Cellular and molecular diversity in Rheumatoid Arthritis.

Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease. RA mainly affects synovial joints, with inflammation of the synovial membrane (synovitis), characterised by neo-angiogenesis, hyperplasia of lining layer, and immune cell infiltration that drive local inflammation and, if untreated, can lead to joint destruction and disability. In parallel to the well-known clinical heterogeneity, the underlying synovitis can also be significantly heterogeneous, both at cellular and molecular level, which can at least in part explain why despite the availability of highly effective treatment options, a large proportion of patients are resistant to some individual treatments. The assimilation of recent high-throughput data from analysis at the single-cell level with rigorous and high-quality clinical outcomes obtained from large randomised clinical trials support the definition of disease and treatment response endotypes. Looking ahead, the integration of histological and molecular signatures from the diseased tissue into clinical algorithms may help decision making in the management of patients with Rheumatoid Arthritis in clinical practice.

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

Potential New Inflammatory Markers in Bronchiectasis: A Literature Review.

Specific molecular and inflammatory endotypes have been identified for chronic respiratory disorders, including asthma and COPD (chronic obstructive pulmonary disease). These endotypes correspond with clinical aspects of disease, enabling targeted medicines to address certain pathophysiologic pathways, often referred to as "precision medicine". With respect to bronchiectasis, many comorbidities and underlying causes have been identified. Inflammatory endotypes have also been widely studied and reported. Additionally, several genes have been shown to affect disease progression. However, the lack of a clear classification has also hampered our understanding of the disease's natural course. The aim of this review is, thus, to summarize the current knowledge on biomarkers and actionable targets of this complex pathologic condition and to point out unmet needs, which are required in the design of effective diagnostic and therapeutic trials.

Revolutionising immune research with organoid-based co-culture and chip systems.

The intertwined interactions various immune cells have with epithelial cells in our body require sophisticated experimental approaches to be studied. Due to the limitations of immortalised cell lines and animal models, there is an increasing demand for human in vitro model systems to investigate the microenvironment of immune cells in normal and in pathological conditions. Organoids, which are self-renewing, 3D cellular structures that are derived from stem cells, have started to provide gap-filling tissue modelling solutions. In this review, we first demonstrate with some of the available examples how organoid-based immune cell co-culture experiments can advance disease modelling of cancer, inflammatory bowel disease and tissue regeneration. Then, we argue that to achieve both complexity and scale, organ-on-chip models combined with cutting-edge microfluidics-based technologies can provide more precise manipulation and readouts. Finally, we discuss how genome editing techniques and the use of patient-derived organoids and immune cells can improve disease modelling and facilitate precision medicine. To achieve maximum impact and efficiency, these efforts should be supported by novel infrastructures such as organoid biobanks, organoid facilities, as well as drug screening and host-microbe interaction testing platforms. All these together or in combination can allow researchers to shed more detailed, and often patient-specific, light on the crosstalk between immune cells and epithelial cells in health and disease.

Bacteriophage therapy against ESKAPE bacterial pathogens: Current status, strategies, challenges, and future scope.

The ESKAPE pathogens are the primary threat due to their constant spread of drug resistance worldwide. These pathogens are also regarded as opportunistic pathogens and could potentially cause nosocomial infections. Most of the ESKAPE pathogens have developed resistance to almost all the antibiotics that are used against them. Therefore, to deal with antimicrobial resistance, there is an urgent requirement for alternative non-antibiotic strategies to combat this rising issue of drug-resistant organisms. One of the promising alternatives to this scenario is implementing bacteriophage therapy. This under-explored mode of treatment in modern medicine has posed several concerns, such as preferable phages for the treatment, impact on the microbiome (or gut microflora), dose optimisation, safety, etc. The review will cover a rationale for phage therapy, clinical challenges, and propose phage therapy as an effective therapeutic against bacterial coinfections during pandemics. This review also addresses the expected uncertainties for administering the phage as a treatment against the ESKAPE pathogens and the advantages of using lytic phage over temperate, the immune response to phages, and phages in combinational therapies. The interaction between bacteria and bacteriophages in humans and countless animal models can also be used to design novel and futuristic therapeutics like personalised medicine or bacteriophages as anti-biofilm agents. Hence, this review explores different aspects of phage therapy and its potential to emerge as a frontline therapy against the ESKAPE bacterial pathogen.

Tumour budding as a prognostic biomarker in biopsies and resections of neoadjuvant-treated rectal adenocarcinoma.

BACKGROUND: Tumour budding (TB) is a marker of tumour aggressiveness which, when measured in rectal cancer resection specimens, predicts worse outcomes and response to neoadjuvant therapy. We investigated the utility of TB assessment in the setting of neoadjuvant treatment. METHODS AND RESULTS: A single-centre, retrospective cohort study was conducted. TB was assessed using the hot-spot International Tumour Budding Consortium (ITBCC) method and classified by the revised ITBCC criteria. Haematoxylin and eosin (H&E) and AE1/AE3 cytokeratin (CK) stains for ITB (intratumoural budding) in biopsies with PTB (peritumoural budding) and ITB (intratumoural budding) in resection specimens were compared. Logistic regression assessed budding as predictors of lymph node metastasis (LNM). Cox regression and Kaplan-Meier analyses investigated their utility as a predictor of disease-free (DFS) and overall (OS) survival. A total of 146 patients were included; 91 were male (62.3%). Thirty-seven cases (25.3%) had ITB on H&E and 79 (54.1%) had ITB on CK assessment of biopsy tissue. In univariable analysis, H&E ITB [odds (OR) = 2.709, 95% confidence interval (CI) = 1.261-5.822, P = 0.011] and CK ITB (OR = 2.165, 95% CI = 1.076-4.357, P = 0.030) predicted LNM. Biopsy-assessed H&E ITB (OR = 2.749, 95% CI = 1.258-6.528, P = 0.022) was an independent predictor of LNM. In Kaplan-Meier analysis, ITB identified on biopsy was associated with worse OS (H&E, P = 0.003, CK: P = 0.009) and DFS (H&E, P = 0.012; CK, P = 0.045). In resection specimens, CK PTB was associated with worse OS (P = 0.047), and both CK PTB and ITB with worse DFS (PTB, P = 0.014; ITB: P = 0.019). In multivariable analysis H&E ITB predicted OS (HR = 2.930, 95% CI = 1.261-6.809) and DFS (HR = 2.072, 95% CI = 1.031-4.164). CK PTB grading on resection also independently predicted OS (HR = 3.417, 95% CI = 1.45-8.053, P = 0.005). CONCLUSION: Assessment of TB using H&E and CK may be feasible in rectal cancer biopsy and post-neoadjuvant therapy-treated resection specimens and is associated with LNM and worse survival outcomes. Future management strategies for rectal cancer might be tailored to incorporate these findings.

Ex vivo therapeutic screening of metastatic cSCC: A review of methodological considerations for clinical implementation.

Cutaneous squamous cell carcinoma (cSCC) is the second most common malignancy worldwide, with most deaths caused by locally advanced and metastatic disease. Treatment of resectable metastases is typically limited to invasive surgery with adjuvant radiotherapy; however, many patients fail to respond and there is minimal data to predict response or propose effective alternatives. Precision medicine could improve this, though genomic biomarkers remain elusive in the high mutational background and genomic complexity of cSCC. A phenotypic approach to precision medicine using patient-derived ex vivo tumour models is gaining favour for its capacity to directly assess biological responses to therapeutics as a functional, predictive biomarker. However, the use of ex vivo models for guiding therapeutic selection has yet to be employed for metastatic cSCC. This review will therefore evaluate the existing experimental models of metastatic cSCC and discuss how ex vivo methods could overcome the shortcomings of these existing models. Disease-specific considerations for a prospective methodological pipeline will also be discussed in the context of precision medicine.

Pharmacy students' attitudes and intentions of pursuing postgraduate studies and training in pharmacogenomics and personalised medicine.

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.

Mind over matter: the microbial mindscapes of psychedelics and the gut-brain axis.

Psychedelics have emerged as promising therapeutics for several psychiatric disorders. Hypotheses around their mechanisms have revolved around their partial agonism at the serotonin 2 A receptor, leading to enhanced neuroplasticity and brain connectivity changes that underlie positive mindset shifts. However, these accounts fail to recognise that the gut microbiota, acting via the gut-brain axis, may also have a role in mediating the positive effects of psychedelics on behaviour. In this review, we present existing evidence that the composition of the gut microbiota may be responsive to psychedelic drugs, and in turn, that the effect of psychedelics could be modulated by microbial metabolism. We discuss various alternative mechanistic models and emphasize the importance of incorporating hypotheses that address the contributions of the microbiome in future research. Awareness of the microbial contribution to psychedelic action has the potential to significantly shape clinical practice, for example, by allowing personalised psychedelic therapies based on the heterogeneity of the gut microbiota.

The Haemato-Oncology Frailty (HOF) score to assess frailty in lymphoma.

Morbidity and mortality during chemotherapy in older adults with haematological malignancy can be unpredictable. The Haemato-Oncology Frailty (HOF) score was previously found to predict outcomes in a cohort of patients with plasma cell myeloma. In this study, we assess its utility in assessing frailty in patients with lymphoma, and compare its performance to that of two other frailty scores. The HOF score was able to predict progression-free survival in this population, and was also shown to have potential in assessing the dynamism of frailty during chemotherapy. It performed well when compared to the Charlson Comorbidity Index (CCI) score and the Haematopoietic Cell Transplantation-Specific Comorbidity Index (HCTCI), although the study was not powered to assess for non-inferiority. The HOF score is a new score with the potential for application in different haematological malignancies.

Molecular Heterogeneity in Leiomyosarcoma and Implications for Personalised Medicine.

OPINION STATEMENT: Leiomyosarcoma (LMS) is one of the more common subtypes of soft tissue sarcomas (STS), accounting for about 20% of cases. Differences in anatomical location, risk of recurrence and histomorphological variants contribute to the substantial clinical heterogeneity in survival outcomes and therapy responses observed in patients. There is therefore a need to move away from the current one-size-fits-all treatment approach towards a personalised strategy tailored for individual patients. Over the past decade, tissue profiling studies have revealed key genomic features and an additional layer of molecular heterogeneity among patients, with potential utility for optimal risk stratification and biomarker-matched therapies. Furthermore, recent studies investigating intratumour heterogeneity and tumour evolution patterns in LMS suggest some key features that may need to be taken into consideration when designing treatment strategies and clinical trials. Moving forward, national and international collaborative efforts to aggregate expertise, data, resources and tools are needed to achieve a step change in improving patient survival outcomes in this disease of unmet need.

Opportunities and Challenges in Soft Tissue Sarcoma Risk Stratification in the Era of Personalised Medicine.

OPINION STATEMENT: Soft tissue sarcomas (STS) are a rare and heterogeneous group of cancers. Treatment options have changed little in the past thirty years, and the role of neoadjuvant chemotherapy is controversial. Accurate risk stratification is crucial in STS in order to facilitate clinical discussions around peri-operative treatment. Current risk stratification tools used in clinic, such as Sarculator, use clinicopathological characteristics and may be specific to anatomical site or to histology. More recently, risk stratification tools have been developed using molecular or immunological data. Combining Sarculator with other risk stratification tools may identify novel patient groups with differential clinical outcomes. There are several considerations when translating risk stratification tools into widespread clinical use, including establishing clinical utility, health economic value, being applicable to existing clinical pathways, having strong real-world performance, and being supported by investment into infrastructure. Future work may include incorporation of novel modalities and data integration techniques.

Integrating China in the International Consortium for Personalised Medicine: a position paper on innovation and digitalization in Personalized Medicine.

BACKGROUND: The advent of Personalized Medicine (PM) holds significant promise in revolutionizing healthcare by tailoring treatments to individual patients based on their data. However, its successful implementation requires the seamless integration of innovative technologies and presents formidable challenges in terms of sustainability. To tackle these challenges head-on, the International Consortium for Personalized Medicine (ICPerMed) was established, and the IC2PerMed project, as part of this consortium, seeks to foster collaboration between the European Union (EU) and China in the field of Personalized Medicine. Based on the results collected by the project, the objective of this study is to discern the key priorities for the implementation of Personalised Medicine concerning Information and Communication Technologies (ICT) and Big Data and digital solutions, with a particular emphasis on data management and protection. METHODS: A Delphi survey was conducted to gather expert's consensus on the main priorities for actions on Information and Communication Technologies (ICT) and Big Data and digital solutions in the field of Personalized Medicine. RESULTS: The survey identified seven priorities in the area of Big Data and digital solutions, including data interoperability, standards, security measures, and international partnerships. Additionally, twelve priorities were identified for the innovation-to-market process, emphasizing cost-effectiveness, need assessment, and value definition in resource allocation. CONCLUSIONS: The effective implementation of new technologies in Personalized Medicine research and practice is essential for the advancement of healthcare systems in both the European and Chinese contexts. The identified priorities play a pivotal role in promoting the sustainability of health systems and driving innovation in the implementation of Personalized Medicine. Addressing challenges related to data interoperability, standards, security, international collaboration, cost-effectiveness, and value assessment is of utmost importance in order to propel the progress of Personalized Medicine in healthcare systems.

Some uncommon cystic lesions in the anterior head and neck region: Pitfalls to be avoided on cytology.

Cystic lesions of the anterior head and neck region are a challenging and frequent finding on cytological smears. The scant amount of cellular material in cystic slides poses the greatest difficulty to interpretation, so that frequently they are diagnosed as inadequate or with minimal cellular component. Despite the majority of cystic lesions being benign, a minor portion consist of malignant cystic entities. In these latter cases, the evidence of very scant malignant cells can be misdiagnosed and/or underestimated, leading to a false negative diagnosis. Many papers have already described and detailed the range of possible benign and malignant cystic lesions in head and neck. In the current review we have focused on the less common entities that often lead to serious misinterpretation.

Regulating diagnosis-Molecular and regulatory sub-stratifications of lung cancer treatment.

The sociology of diagnosis has shown that diagnosis not only serves to label the underlying cause of disease but also to provide access to services and resources. Elaborating on this double-affordance of diagnosis, this article examines how precision medicine reconfigures diagnosis as a label and as a process in regulatory and clinical settings. Reporting from an ethnographic case study of the introduction of immunotherapy for lung cancer, the paper unfolds the uncertainties involved in dissecting diagnosis into layers and examines the efforts and negotiations it takes to enable these layers to work both as clinical entities and regulative entities with the purpose of delineating access to treatment. I suggest that the work of subdividing diseases into molecularly defined categories for the purpose of delineating treatment-eligible populations can be labelled 'diagnostic sub-stratification' and argue that it is pertinent to understand the political capacity of this strategy. Diagnostic sub-stratification involves a push of diagnosis from the clinic 'up' into the regulatory system and 'out' into the laboratories, obscuring who is accountable for the diagnostic categories employed to define patients' treatment access.

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics.

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.

Personalised modelling of clinical heterogeneity between medium-chain acyl-CoA dehydrogenase patients.

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.

Evidence for Therapeutic Drug Monitoring of Atypical Antipsychotics.

Second-generation antipsychotics (SGAs), also known as atypical antipsychotics, are a newer class of antipsychotic drugs used to treat schizophrenia, bipolar disorder, and related psychiatric conditions. The plasma concentration of antipsychotic drugs is a valid measure of the drug at its primary target structure in the brain, and therefore determines the efficacy and safety of these drugs. However, despite the well-known high variability in pharmacokinetics of these substances, psychiatric medication is usually administered in uniform dosage schedules. Therapeutic drug monitoring (TDM), as the specific method that can help personalised medicine in dose adjustment according to the characteristics of the individual patient, minimizing the risk of toxicity, monitoring adherence, and increasing cost-effectiveness in the treatment, thus seems to be an elegant tool to solve this problem. Non-response to therapeutic doses, uncertain adherence to medication, suboptimal tolerability, or pharmacokinetic drug-drug interactions are typical indications for TDM of SGAs. This review aims to summarize an overview of the current knowledge and evidence of the possibilities to tailor the dosage of selected SGAs using TDM, including the necessary pharmacokinetic parameters for personalised pharmacotherapy.