Precision Medicine Framework Research Articles

Molecular profiling reveals novel therapeutic targets and clonal evolution in ovarian clear cell carcinoma

BackgroundOvarian clear cell carcinoma (OCCC) has a disproportionately high incidence among women in East Asia. Patients diagnosed with OCCC tend to experience worse clinical outcomes than those with high-grade serous carcinoma (HGSC) at advanced stages. The unfavorable prognosis of OCCC can be partly attributed to its frequent resistance to conventional chemotherapy. Within a precision medicine framework, we sought to provide a comprehensive molecular characterization of OCCC using whole-exome sequencing to uncover potential molecular targets that may inform novel therapeutic strategies.MethodsWe performed whole-exome sequencing analysis on tumor-normal paired samples from 102 OCCC patients. This comprehensive genomic characterization of a substantial cohort of OCCC specimens was coupled with an analysis of clonal progression.ResultsOn analyzing 102 OCCC samples, ARID1A (67%) and PIK3CA (49%) emerged as the most frequently mutated driver genes. We identified tier 1 or 2 clinically actionable molecular targets in 40% of cases. This included DNA mismatch repair deficiency (n = 1), as well as BRCA2 (n = 1), PIK3CA (n = 36), KRASG12C (n = 1), and ATM (n = 4) mutations. Furthermore, 45% of OCCC samples displayed ARID1A biallelic loss. Interestingly, we identified previously unreported mutations in the 5’ untranslated region of the TERT gene that harbored an adverse prognostic significance. Clock-like mutational processes and activated APOBECs were major drivers of somatic point mutations. Mutations arising from DNA mismatch repair deficiency were uncommon. Reconstruction of clonal evolution revealed that early genetic events likely driving tumorigenesis included mutations in the ARID1A, PIK3CA, TERT, KRAS, and TP53 genes.ConclusionsOur study provides a comprehensive characterization of the genomic landscape and clonal evolution in OCCC within a substantial cohort. These findings unveil potentially actionable molecular alterations that could be leveraged to develop targeted therapies.

Deciphering pain: molecular mechanisms and neurochemical pathways-challenges and future opportunities.

This review delves into the intricate biological underpinnings of pain perception. It encompasses nociceptive signaling pathways, the molecular mechanisms involved, and the subjective experience of discomfort in humans. The initial focus is on nociceptor transduction, where specialized neurons transform noxious stimuli into electrical impulses. Subsequently, the review explores the central nervous system, elucidating how these signals are processed and modulated by critical elements such as ion channels, receptors, and neurotransmitters (e.g., substance P, glutamate, GABA). Shifting gears toward chronic pain, the review examines the concept of neuroplasticity, highlighting its potential to induce maladaptive responses through alterations in neural networks. The burgeoning field of pain genomics, alongside established genetic research, offers valuable insights that could pave the way for a framework of personalized pain management strategies. Finally, the review emphasizes the significance of these molecular insights in facilitating accurate therapeutic interventions. The overarching objective is to establish an integrative framework for precision medicine in pain management by incorporating this information alongside biopsychosocial models. This framework serves to translate the heterogeneous landscape of pain mechanisms into a coherent roadmap for the development of effective therapies.

Immunotherapeutic and Targeted Strategies for Managing Brain Metastases from Common Cancer Origins: A State-of-the-Art Review.

This review examines contemporary strategies for managing brain metastases (BM) from common cancers such as lung, breast, and melanoma. We evaluate the efficacy and applicability of targeted therapies and immunotherapies, exploring their potential to cross the blood-brain barrier and improve patient outcomes. Recent studies have shown that tyrosine kinase inhibitors, immune checkpoint inhibitors, and ADCs effectively treat BM. These treatments can overcome the challenges posed by the blood-brain barrier and improve therapeutic outcomes. ADCs are promising because they can deliver cytotoxic agents directly to tumor cells, which reduces systemic toxicity and increases drug delivery efficiency to the brain. Personalized medicine is becoming increasingly significant in treatment decisions, with biomarkers playing an essential role. Advances in molecular genetics and drug development have led to more refined treatments, emphasizing the precision medicine framework. The management of BM is evolving, driven by drug efficacy, resistance mechanisms, and the need for personalized medicine. Integrating ADCs into treatment regimens represents a significant advancement in targeting metastatic brain tumors. Despite these advances, BM management still presents considerable challenges, requiring ongoing research and multi-institutional trials to optimize therapeutic strategies. This review outlines the current state and future directions in treating BM, highlighting the critical need for continued innovation and comprehensive clinical evaluations to improve survival rates and quality of life for affected patients.

DNA methylation and non-coding RNAs in metabolic disorders: Epigenetic Roles of Nutrients, Dietary Patterns and Weight Loss Interventions for Precision Nutrition.

Dysregulation of epigenetic processes and abnormal epigenetic profiles are associated with various metabolic disorders. Nutrition, as an environmental factor, can induce epigenetic changes through both direct exposure and transgenerational inheritance, continuously altering gene expression and shaping the phenotype. Nutrients consumed through food or supplementation, such as vitamin B12, folate, vitamin B6, and choline, play a pivotal role in DNA methylation, a critical process for gene regulation. Additionally, there is mounting evidence that the expression of non-coding RNAs (ncRNAs) can be modulated by the intake of specific nutrients and natural compounds, thereby influencing processes involved in the onset and progression of metabolic diseases. Evidence suggests that dietary patterns, weight-loss interventions, nutrients and nutritional bioactive compounds can modulate the expression of various miRNAs and DNA methylation levels, contributing to development of metabolic disorders such as obesity and type 2 diabetes. Furthermore, several studies have proposed that DNA methylation and microRNA (miRNA) expression could serve as biomarkers for the effects of weight-loss programs. Despite ongoing debate regarding the effects of nutrient supplementation on DNA methylation levels and the expression of ncRNAs, certain DNA methylation marks and ncRNA expressions might predict the risk of metabolic disorders and act as biomarkers for forecasting the success of therapies within the framework of precision medicine and nutrition. The role of DNA methylation and miRNA expression as potential mediators of the effects of weight loss underscores their potential as biomarkers for the outcomes of weight loss programs. This highlights the influence of dietary patterns and weight-loss interventions on the regulation of miRNA expression and DNA methylation levels, suggesting an interaction between these epigenetic factors and the body's response to weight loss.

Redox Homeostasis and Molecular Biomarkers in Precision Therapy for Cardiovascular Diseases.

Precision medicine is envisioned as the future of cardiovascular healthcare, offering a more tailored and effective method for managing cardiovascular diseases compared to the traditional one-size-fits-all approaches. The complex role of oxidative stress in chronic diseases within the framework of precision medicine was carefully explored, delving into the cellular redox status and its critical involvement in the pathophysiological complexity of cardiovascular diseases (CVDs). The review outlines the mechanisms of reactive oxygen species generation and the function of antioxidants in maintaining redox balance. It emphasizes the elevated reactive oxygen species concentrations observed in heart failure and their detrimental impact on cardiovascular health. Various sources of ROS within the cardiovascular system are examined, including mitochondrial dysfunction, which contributes to oxidative stress and mitochondrial DNA degradation. The article also addresses oxidative stress's role in myocardial remodeling, a process pivotal to the progression of heart diseases. By integrating these aspects, the review underscores the importance of redox homeostasis and identifies molecular biomarkers that can enhance precision therapy for CVDs. The insights provided aim to pave the way for targeted therapeutic strategies that mitigate oxidative stress, thereby improving patient outcomes in cardiovascular medicine.

Precision medicine for obesity: current evidence and insights for personalization of obesity pharmacotherapy.

Obesity is a chronic and complex disease associated with increased morbidity, mortality, and financial burden. It is expected that by 2030 one of two people in the United States will have obesity. The backbone for obesity management continues to be lifestyle interventions, consisting of calorie deficit diets and increased physical activity levels, however, these interventions are often insufficient to achieve sufficient and maintained weight loss. As a result, multiple patients require additional interventions such as antiobesity medications or bariatric interventions in order to achieve clinically significant weight loss and improvement or resolution of obesity-associated comorbidities. Despite the recent advances in the field of obesity pharmacotherapy that have resulted in never-before-seen weight loss outcomes, comorbidity improvement, and even reduction in cardiovascular mortality, there is still a significant interindividual variability in terms of response to antiobesity medications, with a subset of patients not achieving a clinically significant weight loss. Currently, the trial-and-error paradigm for the selection of antiobesity medications results in increased costs and risks for developing side effects, while also reduces engagement in weight management programs for patients with obesity. The implementation of a precision medicine framework to the selection of antiobesity medications might help reduce heterogeneity and optimize weight loss outcomes by identifying unique subsets of patients, or phenotypes, that have a better response to a specific intervention. The detailed study of energy balance regulation holds promise, as actionable behavioral and physiologic traits could help guide antiobesity medication selection based on previous mechanistic studies. Moreover, the rapid advances in genotyping, multi-omics, and big data analysis might hold the key to discover additional signatures or phenotypes that might respond better to a certain intervention and might permit the widespread adoption of a precision medicine approach for obesity management.

Precision medicine: Externally validated explainable AI support tool for predicting sustainability of infliximab and vedolizumab in ulcerative colitis

Drug sustainability (DS), a surrogate marker for drug efficacy, is important, especially when aiming for precision medicine. However, it lacks reliable prediction methods. To develop and externally validate a web-based artificial intelligence(AI)-derived tool for predicting DS of infliximab and vedolizumab in patients with moderate-to-severe Ulcerative Colitis (UC). Data from three Israeli centers included infliximab or vedolizumab patients treated for >54 weeks. Sustainability meant no corticosteroids, hospitalizations or surgeries. Machine learning techniques predicted >54-week and overall DS using baseline clinical data. The model was developed using data from 246 patients from Rabin Medical Center and externally validated on 67 patients from Rambam Health Care Campus and Sheba Medical Center. No significant difference in DS was observed across the datasets. Most patients were biologic-naïve and primarily treated with vedolizumab. The model performed well, with an area under the ROC curve of 0.86, and showed good accuracy (65.5 %-76.9 %) across the test sets. The study introduces a novel, AI-based tool for predicting >54-week DS of infliximab and vedolizumab in moderate-to-severe UC, using baseline parameters. This can aid clinical decision-making in the framework of precision medicine, promising to optimize disease management while maintaining physician autonomy.

First report: PLATFORM study for precision treatment of rare tumors in China

The purpose of this study was to present the preliminary results of the PLATFORM Study, which aimed to evaluate the effectiveness of precision treatment for rare tumors in China. This study involved a phase II, open-label, non-randomized, multi-arm, single-center clinical trial. Patients with advanced rare solid tumors, who had not responded to standard treatment, were enrolled. The primary objective was to assess the safety and efficacy of targeted therapies in patients with actionable genetic alterations and immune checkpoint inhibitors in patients lacking actionable genetic alterations. Out of the 922 cases screened, 107 patients underwent mutation detection, with a final enrollment of 64 cases for the study. Among these, 26 cases received targeted therapy, and 38 cases underwent immunotherapy. The study encompassed over 40 types of rare tumors. The overall objective response rate (ORR) was 7.0%, with a disease control rate (DCR) of 70%. Targeted therapy showed a higher ORR of 17.8% and a DCR of 100%. The median progression-free survival (PFS) was 4 months overall, with targeted therapy showing a median PFS of 5 months and immunotherapy showing a median PFS of 3 months. In conclusion, from this preliminary analysis, targeted therapy within the precision medicine framework demonstrated promising therapeutic potential for rare tumors. However, monotherapy immunotherapy exhibited limited efficacy, highlighting the challenges in overcoming tumor-specific variations. These findings underscore the importance of further research and the exploration of combination therapies to improve outcomes for patients with rare tumors.

A Communication and Decision-Making Framework for Pediatric Precision Medicine.

Advances in genomic testing have been pivotal in moving childhood cancer care forward, with genomic testing now a standard diagnostic tool for many children, adolescents, and young adults with cancer. Beyond oncology, the role of genomic testing in pediatric research and clinical care is growing, including for children with developmental differences, cardiac abnormalities, and epilepsy. Despite more standard use in their patients, pediatricians have limited guidance on how to communicate this complex information or how to engage parents in decisions related to precision medicine. Drawing from empirical work in pediatric informed consent and existing models of shared decision-making, we use pediatric precision cancer medicine as a case study to propose a conceptual framework to approach communication and decision-making about genomic testing in pediatrics. The framework relies on identifying the type of genomic testing, its intended role, and its anticipated implications to inform the scope of information delivered and the parents' role in decision-making (leading to shared decision-making along a continuum from clinician-guided to parent-guided). This type of framework rests on practices known to be standard in other complex decision-making but also integrates unique features of genomic testing and precision medicine. With the increasing prominence of genomics and precision medicine in pediatrics, with our communication and decision-making framework, we aim to guide clinicians to better support their pediatric patients and their parents in making informed, goal-concordant decisions throughout their care trajectory.

Interferon and B-cell Signatures Inform Precision Medicine in Lupus Nephritis

IntroductionCurrent therapeutic management of lupus nephritis (LN) fails to induce long-term remission in over 50% of patients, highlighting the urgent need for additional options. MethodsWe analysed differentially expressed genes in peripheral blood from active LN (n=41) and active non-renal lupus (n=62) patients versus healthy controls (n=497) from the European PRECISESADS project (NTC02890121), and dysregulated gene modules in a discovery (n=26) and a replication (n=15) set of active LN cases. ResultsReplicated gene modules qualified for correlation analyses with serological markers, and regulatory network and druggability analysis. Unsupervised co-expression network analysis revealed 20 dysregulated gene modules and stratified the active LN population into three distinct subgroups. These subgroups were characterised by low, intermediate, and high interferon (IFN) signatures, with differential dysregulation of the “B cell” and “plasma cells/immunoglobulins” modules. Drugs annotated to the IFN network included CC-motif chemokine receptor 1 inhibitors, programmed death-ligand 1 inhibitors, and irinotecan, while the anti-CD38 daratumumab and proteasome inhibitor bortezomib showed potential for counteracting the “plasma cells/immunoglobulins” transcriptomic signature. In silico analysis demonstrated the low-IFN subgroup to benefit from calcineurin inhibition and the intermediate-IFN subgroup from B cell targeted therapies. High-IFN patients exhibited greater anticipated response to anifrolumab while daratumumab appeared beneficial for the intermediate-IFN and high-IFN subgroups. ConclusionIn summary, IFN upregulation and B and plasma cell gene dysregulation patterns revealed three LN patient subgroups, which may not necessarily represent distinct disease phenotypes but rather phases of the inflammatory processes during a renal flare, providing a conceptual framework for precision medicine in LN.

From Strategy to Synergy: Paving Ontario's Proactive Path in Precision Medicine with Collaboration and Visionary Leadership.

Ontario is shifting to a Precision Medicine (PM) model, which emphasizes tailored patient care, an initiative reflected in the formation of Ontario Health Teams. However, this shift faces significant data governance, policy formulation, and technology integration hurdles. To overcome these barriers, we advocate for a comprehensive PM framework to orchestrate collaboration among healthcare providers, policymakers, and technologists. This framework enhances data management, propels digital health innovations, and uphold ethical standards in AI applications. Effective deployment of this framework is crucial for actualizing PM's promise in Ontario, potentially revolutionizing healthcare delivery.

Radiotheranostics in Oncology

This review discusses the urgent requirements in modern oncology to address the complexities associated with metastatic cancer, a condition that often lacks comprehensive understanding, particularly concerning receptor expression. The emerging concept of theranostics, which merges precise diagnostics and therapies within the precision medicine framework, underscores the need for further investigation. A central focus of this review is radiotheranostics, a versatile approach offering personalized diagnostic and therapeutic possibilities. It examines the intricate components of radiotheranostics, such as radionuclides, metal-complexing agents, and receptor-specific agents designed to target molecular markers linked to cancer selectively. The study explores potential applications of radiotheranostics, especially in scenarios where conventional treatments have limitations. Investigating patient acceptance and determining the optimal administration route are critical aspects that demand thorough examination to facilitate the integration of radiotheranostics into clinical practice. Furthermore, this review aims to reveal the potential of new ligands and isotopes to enhance the effectiveness of radiotheranostics. Molecular imaging, functioning at the molecular and cellular levels, constitutes a significant element of this study, delivering in-depth anatomical and functional insights. Recognizing the pivotal role of Cancer Stem Cells (CSCs) in precise cancer treatment, the review explores techniques for their identification and characterization within the framework of radiotheranostics. Despite its promise, radiotheranostics faces unique challenges from logistical constraints and regulatory intricacies, setting it apart from conventional chemotherapy. This review seeks to elucidate these challenges and provide potential solutions. In summary, this review responds to the crucial need for advancing our understanding and application of radiotheranostics, holding the potential to offer significant palliative measures, extend patient survival, and even provide cures, particularly for those with metastatic disease and suitable molecular targets.

Network‐based Genetic Subtyping for Target Prioritization and Drug Repositioning: The Artificial Intelligence for Alzheimer’s Disease (AI4AD) Consortium

AbstractBackgroundAlzheimer disease (AD) is a progressive neurodegenerative disease with complicated underlying disease mechanisms. The clinical and neuropathological heterogeneity of AD may be explained by specific disease mechanisms at a brain cell‐level. We postulate that the path to effective treatment of AD depends on identifying distinctive subtypes linking to specific disease mechanisms at brain cellular level.MethodWe developed a novel precision medicine framework using polygenic risk scores defined by co‐regulated cellular networks to stratify individuals into low and high‐risk groups (genetic subtypes) for brain cell‐type specific networks. We prioritized genes from the cell‐based networks using a graph‐based ranking (PageRank) algorithm. We investigated molecular signatures of the prioritized genes for the subtypes using autopsied brains from the Framingham Heart Study (FHS). We validated the signatures using isogenic APOE lines of human pluripotent stem cell (hiPSC)‐derived astrocytes. Existing drugs targeting for the genetic subtypes were identified using drug perturbation database and clustering algorithms using compound structures from the PubChem databaseResultWe identified six preserved and AD associated co‐expressed gene networks for astrocytes, oligodendrocytes, and oligodendrocyte progenitor cells. The top‐ranked astrocyte network was enriched with Parkinson’s disease, Alzheimer’s disease, mineral absorption, and ribosome pathways. The most important gene in the astrocyte network was the APOE gene, which its expression was increased in AD compared in control brains with elevated complement C4A level. We confirmed elevated expression of both APOE and C4A levels in APOE4 compared in APOE3 hiPSC‐derived astrocytes. We identified three drugs targeting the astrocyte subtype, all of them previously modulated expression of the prioritized genes in the drug perturbation database. Clustering algorithm identified multiple compounds targeting the prioritized genes in the astrocyte network with at least 80% similarity in structure.ConclusionThe newly established framework provided the potential implication in genetic subtype driven drug repositioning in AD. In future, we will develop novel graph neural network approaches to identify comprehensive genetic subtypes and drugs targeting the subtypes in collaboration with internal and external members from the AI4AD consortium.

The Impact of Clinical Factors, Vitamin B12 and Total Cholesterol on Severity of Anorexia Nervosa: A Multicentric Cross-Sectional Study

Severe forms of Anorexia Nervosa (AN) are characterized by medical complications, psychiatric comorbidity, and high mortality. This study investigated potential associations between clinical/biological factors and the severity of AN, measured by the Body Mass Index (BMI). Red and white blood cells, hemoglobin, platelets, iron, vitamins D and B12, folate, and total cholesterol were measured in a mixed sample of 78 inpatients and outpatients. Linear regressions and one-way analyses of variance (ANOVAs) were carried out to evaluate the relationship between BMI and clinical/biochemical variables. BMI was significantly lower in hospitalized patients (F = 4.662; p = 0.034) and in those under pharmacological treatment (F = 5.733; p = 0.019) or poly-therapy (F = 5.635; p = 0.021). Higher vitamin B12 (β = −0.556, p < 0.001), total cholesterol (β = −0.320, p = 0.027), and later age at onset (with a trend towards significance) (β = −0.376, p = 0.058) were associated with a lower BMI. Increased total cholesterol and vitamin B12, later age at onset, current pharmacological treatment, and poly-therapy might be distinctive in patients with a lower BMI. In clinical practice, these findings may contribute to the early identification of AN patients at higher risk of developing complicated or chronic forms of the disorder. Further studies on larger samples are needed to identify potential predictive factors of AN severity in the framework of precision medicine.

A neuroimaging-based precision medicine framework for depression

BackgroundSymptom-based diagnostic criteria of depression leads to notorious heterogeneity and subjectivity. MethodsThe study was conducted in two stages at two sites: development of a neuroimaging-based subtyping and precise repetitive transcranial magnetic stimulation (rTMS) strategy for depression at Center 1 and its clinical application at Center 2. Center 1 identified depression subtypes and subtype-specific rTMS targets based on amplitude of low frequency fluctuation (ALFF) in a cohort of 238 major depressive disorder patients and 66 healthy controls (HC). Subtypes were identified using a Gaussian Mixture Model, and subtype-specific rTMS targets were selected based on dominant brain regions prominently differentiating depression subtypes from HC. Subsequently, one classifier was employed and 72 hospitalized, depressed youths at Center 2 received two-week precise rTMS. MRI and clinical assessments were obtained at baseline, midpoint, and treatment completion for evaluation. ResultsTwo neuroimaging subtypes of depression, archetypal and atypical depression, were identified based on distinct frontal-posterior functional imbalance patterns as measured by ALFF. The dorsomedial prefrontal cortex was identified as the rTMS target for archetypal depression, and the occipital cortex for atypical depression. Following precise rTMS, ALFF alterations were normalized in both archetypal and atypical depressed youths, corresponding with symptom response of 90.00% in archetypal depression and 70.73% in atypical depression. ConclusionsA precision medicine framework for depression was developed based on objective neurobiomarkers and implemented with promising results, actualizing a subtyping-treatment-evaluation closed loop in depression. Future randomized controlled trials are warranted.

Personalised simulation of hemodynamics in cerebrovascular disease: lessons learned from a study of diagnostic accuracy.

Intracranial atherosclerotic disease (ICAD) poses a significant risk of subsequent stroke but current prevention strategies are limited. Mechanistic simulations of brain hemodynamics offer an alternative precision medicine approach by utilising individual patient characteristics. For clinical use, however, current simulation frameworks have insufficient validation. In this study, we performed the first quantitative validation of a simulation-based precision medicine framework to assess cerebral hemodynamics in patients with ICAD against clinical standard perfusion imaging. In a retrospective analysis, we used a 0-dimensional simulation model to detect brain areas that are hemodynamically vulnerable to subsequent stroke. The main outcome measures were sensitivity, specificity, and area under the receiver operating characteristics curve (ROC AUC) of the simulation to identify brain areas vulnerable to subsequent stroke as defined by quantitative measurements of relative mean transit time (relMTT) from dynamic susceptibility contrast MRI (DSC-MRI). In 68 subjects with unilateral stenosis >70% of the internal carotid artery (ICA) or middle cerebral artery (MCA), the sensitivity and specificity of the simulation were 0.65 and 0.67, respectively. The ROC AUC was 0.68. The low-to-moderate accuracy of the simulation may be attributed to assumptions of Newtonian blood flow, rigid vessel walls, and the use of time-of-flight MRI for geometric representation of subject vasculature. Future simulation approaches should focus on integrating additional patient data, increasing accessibility of precision medicine tools to clinicians, addressing disease burden disparities amongst different populations, and quantifying patient benefit. Our results underscore the need for further improvement of mechanistic simulations of brain hemodynamics to foster the translation of the technology to clinical practice.

Dynamic human exposure to airborne bacteria-associated antibiotic resistomes revealed by longitudinal personal monitoring data

Airborne antibiotic-resistant bacteria (ARB) can critically impact human health. We performed resistome profiling of 283 personal airborne exposure samples from 15 participants spanning 890 days and 66 locations. We found a greater diversity and abundance of airborne bacteria community and antibiotic resistomes in spring than in winter, and temperature contributed largely to the difference. A total of 1123 bacterial genera were detected, with 16 genera dominating. Of which, 7/16 were annotated as major antibiotic resistance gene (ARG) hosts. The participants were exposed to a highly dynamic collection of ARGs, including 322 subtypes conferring resistance to 18 antibiotic classes dominated by multidrug, macrolide-lincosamide-streptogramin, β-lactam, and fosfomycin. Unlike the overall community-level bacteria exposure, an extremely high abundance of specific ARG subtypes, including lunA and qacG, were found in some samples. Staphylococcus was the predominant genus in the bacterial community, serving as a primary bacterial host for the ARGs. The annotation of ARG-carrying contigs indicated that humans and companion animals were major reservoirs for ARG-carrying Staphylococcus. This study contextualized airborne antibiotic resistomes in the precision medicine framework through longitudinal personal monitoring, which can have broad implications for human health.

Voltage-gated sodium channels in genetic epilepsy: up and down of excitability.

The past two decades have witnessed a wide range of studies investigating genetic variants of voltage-gated sodium (NaV ) channels, which are involved in a broad spectrum of diseases, including several types of epilepsy. We have reviewed here phenotypes and pathological mechanisms of genetic epilepsies caused by variants in NaV α and β subunits, as well as of some relevant interacting proteins (FGF12/FHF1, PRRT2, and Ankyrin-G). Notably, variants of all these genes can induce either gain- or loss-of-function of NaV leading to either neuronal hyperexcitability or hypoexcitability. We present the results of functional studies obtained with different experimental models, highlighting that they should be interpreted considering the features of the experimental system used. These systems are models, but they have allowed us to better understand pathophysiological issues, ameliorate diagnostics, orientate genetic counseling, and select/develop therapies within a precision medicine framework. These studies have also allowed us to gain insights into the physiological roles of different NaV channels and of the cells that express them. Overall, our review shows the progress that has been made, but also the need for further studies on aspects that have not yet been clarified. Finally, we conclude by highlighting some significant themes of general interest that can be gleaned from the results of the work of the last two decades.

Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment.

Cancer related cognitive impairment (CRCI) is commonly associated with cancer and its treatments, yet the present binary diagnostic approach fails to capture the full spectrum of this syndrome. Cognitive function is highly complex and exists on a continuum that is poorly characterized by dichotomous categories. Advanced statistical methodologies applied to symptom assessments have demonstrated that there are multiple subclasses of CRCI. However, studies suggest that relying on symptom assessments alone may fail to account for significant differences in the neural mechanisms that underlie a specific cognitive phenotype. Treatment plans that address the specific physiologic mechanisms involved in an individual patient's condition is the heart of precision medicine. In this narrative review, we discuss how biotyping, a precision medicine framework being utilized in other mental disorders, could be applied to CRCI. Specifically, we discuss how neuroimaging can be used to determine biotypes of CRCI, which allow for increased precision in prediction and diagnosis of CRCI via biologic mechanistic data. Biotypes may also provide more precise clinical endpoints for intervention trials. Biotyping could be made more feasible with proxy imaging technologies or liquid biomarkers. Large cross-sectional phenotyping studies are needed in addition to evaluation of longitudinal trajectories, and data sharing/pooling is highly feasible with currently available digital infrastructures.

A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework.

Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.