Perspective Of Precision Medicine Research Articles

Who Requires Longer-term Anticoagulation Therapy for Venous Thromboembolism from the Perspective of Precision Medicine?

Who Requires Longer-term Anticoagulation Therapy for Venous Thromboembolism from the Perspective of Precision Medicine?

Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients

When modelling the dynamics of biomarkers in biomedical studies, it is essential to identify homogeneous clusters of patients and analyse them from a precision medicine perspective. This need has emerged as crucial and urgent during the COVID-19 pandemic: early understanding of symptoms and patient heterogeneity has significant implications for prevention, early diagnosis, effective management, and treatment. Additionally, biomarker progression may be associated with clinically relevant time-toevent data. Therefore, statistical models are necessary to gain insight into complex disease mechanisms by properly accounting for unobservable heterogeneity in patients while jointly modelling longitudinal and time-to-event data. In this study, we leverage the key features of Latent Class modelling and Bayesian Network approaches and propose a unified framework to (a) uncover homogeneous subgroups of patients concerning their longitudinal and survival data and (b) describe patient subgroups within a multivariate framework.

Tocilizumab Exerts Anti-Tumor Effects on Colorectal Carcinoma Cell Xenografts Corresponding to Expression Levels of Interleukin-6 Receptor.

The use of tocilizumab against the interleukin-6 receptor (IL-6R) has been demonstrated as inhibiting the progression of diverse cancers in vitro and in vivo. Nonetheless, evidence regarding the anti-tumor effects of tocilizumab on human colorectal carcinoma (CRC) corresponding to IL-6R expression levels remains scarce. To investigate the influence of IL-6R expression, SW480 and HT-29 cells inoculated subcutaneously into NU/NU mice were used as human CRC xenograft models with anti-IL-6R antibody (tocilizumab) therapy. The IL-6R expression levels, histology of CRC growth/invasiveness, and tumor growth-related signaling pathway were estimated by H&E and immunohistochemical staining. SW480 tumor cells with higher IL-6R expression levels showed better responsiveness in tocilizumab therapy than in the treated HT-29 group. Likewise, therapeutic effects of tocilizumab on the proliferative ability with mitotic index and Ki-67 expressions, invasiveness with MMP-9 proteinase expressions, and ERK 1/2 and STAT3 signaling transduction in the SW480 treatment group were superior to the HT-29 treatment group. In light of our results, IL-6R is the key indicator for the efficacy of tocilizumab treatment in CRC xenografts. From the perspective of precision medicine, tumor response to anti-IL-6R antibody therapy could be predicted on the basis of IL-6R expression levels. In this manner, tocilizumab may serve as a targeted and promising anti-CRC therapy.

Predictors of mental health outcomes of three refugee groups in an advocacy-based intervention: A precision medicine perspective.

Precision medicine is an area with great potential for mental health, but has made limited gains prognostically in predicting effective treatments. For refugees exposed to violence, culture may be a crucial factor in predicting treatment outcomes. For this study, 290 participants from three regions (Afghanistan, the Great Lakes region of Africa, and Iraq and Syria) participated in a randomized controlled trial of an advocacy-based intervention. Emotional distress symptoms were measured prior to intervention, midintervention (3 months), postintervention (6 months), and follow-up (6 months after the end of intervention). Number of traumatic events, resource access, social support, and English proficiency were tested for potential predictive effects on intervention outcome. Multilevel generalized linear models revealed that Afghans' (B = -0.259, SE = 0.108, p = .013), and Great Lakes Africans' (B = -0.116, SE = 0.057, p = .042) emotional distress symptoms improved as a function of the intervention, while Iraqis and Syrians showed no intervention effects. For Afghans, English proficiency (B = -0.453, SE = 0.157, p < .01) and social support (B = -0.179, SE = 0.086, p = .037) were most strongly correlated to emotional distress, while for Africans, resource access (B = -0.483, SE = 0.082, p < .001) and social support (B = -0.100, SE = 0.048, p = .040) were the strongest predictors of emotional distress. Response to advocacy-based interventions and active ingredients may be influenced by culture; findings have implications for refugees and precision medicine. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PP441 Development of novel therapeutic agents for pancreatic cancer from the perspective of precision medicine and cancer stem cell markers

PP441 Development of novel therapeutic agents for pancreatic cancer from the perspective of precision medicine and cancer stem cell markers

Telomere maintenance mechanism subtype reveals different immune activity in vestibular schwannoma

BackgroundThe immortality of cancer cells relies on maintaining the length of telomeres, which prevents cellular senescence and enables unlimited replication. However, little is currently known about telomerase activity and the alternative lengthening of telomeres (ALT) in vestibular schwannomas. In this study we aimed to elucidate the role that telomerase and ALTs play in vestibular schwannomas.MethodsTo address this gap, we conducted a study where we used the gene set variation analysis algorithm with bulk RNA-seq and single-cell RNA-seq to identify the characteristics of each group of patients with vestibular schwannomas, based on their telomere maintenance mechanism subtype.ResultsOur findings suggest that patients with relatively high ALT-like groups have a better prognosis than those with relatively high telomerase groups. Specifically, we found that the high telomerase group had relatively higher antigen-presenting cell (APC) activity than the high ALT like group. At the single-cell level, microglia, neutrophils, and fibroblasts showed high telomerase activity and relatively high APC activity compared to other cell types. In addition, Schwann cells in the group with low ALT levels exhibited elevated immune activity at the single-cell level.ConclusionThese results suggest that personalized drug therapy could be developed from the perspective of precision medicine for patients with relatively high telomerase activity and a high ALT-like group.

Precision Medicine: Disease Subtyping and Tailored Treatment.

The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

Long non-coding RNAs in bone metastasis: progresses and perspectives as potential diagnostic and prognostic biomarkers.

In a precision medicine perspective, among the biomarkers potentially useful for early diagnosis of cancers, as well as to define their prognosis and eventually to identify novel and more effective therapeutic targets, there are the long non-coding RNAs (lncRNAs). The term lncRNA identifies a class of non-coding RNA molecules involved in the regulation of gene expression that intervene at the transcriptional, post-transcriptional, and epigenetic level. Metastasis is a natural evolution of some malignant tumours, frequently encountered in patients with advanced cancers. Onset and development of metastasis represents a detrimental event that worsen the patient's prognosis by profoundly influencing the quality of life and is responsible for the ominous progression of the disease. Due to the peculiar environment and the biomechanical properties, bone is a preferential site for the secondary growth of breast, prostate and lung cancers. Unfortunately, only palliative and pain therapies are currently available for patients with bone metastases, while no effective and definitive treatments are available. The understanding of pathophysiological basis of bone metastasis formation and progression, as well as the improvement in the clinical management of the patient, are central but challenging topics in basic research and clinical practice. The identification of new molecular species that may have a role as early hallmarks of the metastatic process could open the door to the definition of new, and more effective, therapeutic and diagnostic approaches. Non-coding RNAs species and, particularly, lncRNAs are promising compounds in this setting, and their study may bring to the identification of relevant processes. In this review, we highlight the role of lncRNAs as emerging molecules in mediating the formation and development of bone metastases, as possible biomarkers for cancer diagnosis and prognosis, and as therapeutic targets to counteract cancer spread.

Secondary Cardiovascular Prevention after Acute Coronary Syndrome: Emerging Risk Factors and Novel Therapeutic Targets.

The control of cardiovascular risk factors, the promotion of a healthy lifestyle, and antithrombotic therapy are the cornerstones of secondary prevention after acute coronary syndrome (ACS). However, many patients have recurrent ischemic events despite the optimal control of traditional modifiable risk factors and the use of tailored pharmacological therapy, including new-generation antiplatelet and lipid-lowering agents. This evidence emphasizes the importance of identifying novel risk factors and targets to optimize secondary preventive strategies. Lipoprotein(a) (Lp(a)) has emerged as an independent predictor of adverse events after ACS. New molecules such as anti-PCSK9 monoclonal antibodies, small interfering RNAs, and antisense oligonucleotides can reduce plasma Lp(a) levels and are associated with a long-term outcome benefit after the index event. The inflammatory stimulus and the inflammasome, pivotal elements in the development and progression of atherosclerosis, have been widely investigated in patients with coronary artery disease. More recently, randomized clinical trials including post-ACS patients treated with colchicine and monoclonal antibodies targeting cytokines yielded promising results in the reduction in major cardiovascular events after an ACS. Gut dysbiosis has also raised great interest for its potential pathophysiological role in cardiovascular disease. This evidence, albeit preliminary and needing confirmation by larger population-based studies, suggests the possibility of targeting the gut microbiome in particularly high-risk populations. The risk of recurrent ischemic events after ACS is related to the complex interaction between intrinsic predisposing factors and environmental triggers. The identification of novel risk factors and targets is fundamental to customizing patient clinical management with a precision medicine perspective.

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis.

From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

Disentangling Clinical Profiles of Apathy in Behavioral Variant Frontotemporal Dementia.

Apathy is highly frequent in behavioral variant frontotemporal dementia (bvFTD). It is presumed to involve different pathophysiological mechanisms and neuroanatomical regions. We explored the hypothesis that subgroups showing distinct profiles of apathy and distinct patterns of atrophy within frontal lobes could be disentangled in bvFTD. Using data-driven clustering applied to 20 bvFTD patients, we isolated subgroups according to their profiles on the three subscales of the Dimensional Apathy Scale (DAS). We explored their apathy profiles and atrophy patterns. Apathy profiles were characterized through both subjective measures of apathy by questionnaires and measures including objective behavioral metrics. Atrophy patterns were obtained by voxel-based morphometry, contrasting each bvFTD subgroup with healthy controls (N = 16). By clustering based on DAS dimensions, we disentangled three subgroups of bvFTD patients, with distinct apathy profiles and atrophy patterns. One subgroup, which presented the smallest pattern of atrophy (including orbitofrontal cortex) with a right asymmetry, was characterized by high self-reported emotional and initiation apathy and by a self-initiation deficit reversible by external guidance. In other subgroups showing more diffuse bilateral atrophies extending to lateral prefrontal cortex, apathy was not reversible by external guidance and more difficulty to focus on goal-management was observed, especially in the subgroup with the largest atrophy and highest levels of executive apathy. Distinct clinical profiles of apathy, corresponding to distinct anatomical subtypes of bvFTD, were identified. These findings have implications for clinicians in a perspective of precision medicine as they could contribute to personalize treatments of apathy.

A re-appraisal of thrombogenesis in COVID-19, seen as a multiple Complex System

The aim of this essay is to re-consider the peculiar type of thrombogenesis observed in severe cases of COVID-19 infection, focusing on the multiple interconnected networks involved, such as inflammation, blood coagulation, fibrinolysis, and immune responses. These linked mechanisms can be assimilated to the “Complex Systems” (CS), that play a capital role in various domains: from physics to chemistry, biology and medicine, to social and behavioral sciences. CS are characterized by eliciting variable responses: their final results can be contradictory and often unpredictable. In fact, in severe COVID-19 various outcomes can occur, such as macro- and micro-thrombosis, vasculitis, hemorrhage, hyper and hypo fibrinolysis, distorted inflammatory and immune response, and others. The insight supplied by the CS theory in understanding thrombogenesis in COVID-19 can be useful in several ways. It recalls the importance of a “holistic” view of multiple patterns of signs, symptoms and biomarkers; stresses the added value of global versus mechanistic tests, particularly in coagulation and fibrinolysis; suggests building up small trials of selected patients in a perspective of precision medicine; discourages passive transfer of therapeutic choices from no- COVID to COVID patients; and finally indicates that some treatments, as the anti-inflammatory and the anti-coagulant ones, should be initiated as early as possible, so to avoid worsening of the condition by repetitive feedback and shortcut mechanisms.

Cluster Analysis of Home Polygraphic Recordings in Symptomatic Habitually-Snoring Children: A Precision Medicine Perspective.

(1) Background: Sleep-disordered breathing (SDB) is a frequent problem in children. Cluster analyses offer the possibility of identifying homogeneous groups within a large clinical database. The application of cluster analysis to anthropometric and polysomnographic measures in snoring children would enable the detection of distinctive clinically-relevant phenotypes; (2) Methods: We retrospectively collected the results of nocturnal home-based cardiorespiratory polygraphic recordings and anthropometric measurements in 326 habitually-snoring otherwise healthy children. K-medoids clustering was applied to standardized respiratory and anthropometric measures, followed by Silhouette-based statistics. Respiratory Disturbance Index (RDI) and oxygen desaturation index (≤3%) were included in determining the optimal number of clusters; (3) Results: Mean age of subjects was 8.1 ± 4.1 years, and 57% were males. Cluster analyses uncovered an optimal number of three clusters. Cluster 1 comprised 59.5% of the cohort (mean age 8.69 ± 4.14 years) with a mean RDI of 3.71 ± 3.23 events/hour of estimated sleep (e/ehSleep). Cluster 2 included 28.5% of the children (mean age 6.92 ± 3.43 years) with an RDI of 6.38 ± 3.92 e/ehSleep. Cluster 3 included 12% of the cohort (mean age 7.58 ± 4.73 years) with a mean RDI of 25.5 ± 19.4 e/ehSleep. Weight z-score was significantly lower in cluster 3 [−0.14 ± 1.65] than in cluster 2 [0.86 ± 1.78; p = 0.015] and cluster 1 [1.04 ± 1.78; p = 0.002]. Similar findings emerged for BMI z scores. However, the height z-score was not significantly different among the 3 clusters; (4) Conclusions: Cluster analysis of children who are symptomatic habitual snorers and are referred for clinical polygraphic evaluation identified three major clusters that differed in age, RDI, and anthropometric measures. An increased number of children in the cluster with the highest RDI had reduced body weight. We propose that the implementation of these approaches to a multicenter-derived database of home-based polygraphic recordings may enable the delineation of objective unbiased severity categories of pediatric SDB. Our findings could be useful for clinical implementation, formulation of therapeutic decision guidelines, clinical management, prevision of complications, and long-term follow-up.

Repositioning of Anti-Inflammatory Drugs for the Treatment of Cervical Cancer Sub-Types.

Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.

Treatment of Cognitive Impairment in Schizophrenia

Cognitive function in schizophrenia is one of the main elements significantly related to functional outcomes, accounting for approximately 25–50% of the variance in real-world functioning. Treatment approaches to cognitive dysfunctions include both pharmacological and psychosocial interventions. Second Generation Antipsychotics (SGAs) may partially improve cognitive dysfunction, due to their relatively high affinity for serotonin 5HT2A receptors. The effects of glutamatergic agents indicated benefits on cognition of a group of amino acids that act as glutamate agonists by binding to the glycine site on NMDA receptors. The administration of muscarinic antagonists potentiates cognitive impairments, while the α7 nicotinic acetylcholine receptors have been shown to play an important role in cognition with potential therapeutic applications in schizophrenia. Studies on drugs targeting neuroinflammation and oxidative stress emerged. Cognitive remediation has proved to be effective in improving cognitive dysfunctions and psychosocial functioning in people with schizophrenia, however there is still a limited understanding of how the putative active therapy ingredients contribute to changes in the brain and translate into improved real-world functioning. Cogrem exerts its maximal benefit when delivered in the context of other psychiatric rehabilitative interventions, but it is not yet clear what are the mechanisms of effectiveness of integrated treatment. It is important that future research shift from studies of group efficacy to individual efficacy of treatments, in the perspective of precision medicine. Issues related to individual predictors of effectiveness and of interactions between specific pharmacologic, specific cognitive remediation technique and individual patients’ characteristics should be furtherly addressed.DisclosureIn the last two years, Prof Vita received, directly or indirectly, support for clinical studies or trials, conferences, consultancies, Congress presentations, advisory boards from: Angelini, Boehringer Ingelheim, Innovapharma, Janssen-Cilag, Lundbeck,

Abstract A039: Transcriptomic analysis of canine osteosarcoma from a precision medicine perspective reveals limitations of differential gene expression studies

Abstract Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. Spontaneously occurring OSA in the dog shares remarkably similar clinical, histological, and molecular characteristics and therefore serves as an excellent model for the disease. The difficulty in managing OSA lies in its extreme genetic complexity, drug-resistance, and heterogeneity, making it improbable that a single-target treatment would be beneficial for the majority of affected individuals. Precision medicine seeks to fill this gap by addressing the intra- and inter-tumoral heterogeneity to improve patient outcome and survival. Characterization of differentially expressed genes (DEGs) provides insight into the phenotype and can be useful for informing appropriate therapies as well as development of novel treatments. To identify relevant DEGs, RNA profiles of primary tumor should be contrasted with that of normal tissue derived from the same cell-of-origin as the tumor prior to chemotherapy, as convoluted drug-resistant pathways make interpretation difficult. Traditional DEG analysis combines patient data to derive statistically inferred genes that are dysregulated in the group; however, the results from this approach are not necessarily consistent across individual patients, thus contradicting the basis of precision medicine. In this preliminary study, we use transcriptomic sequencing of RNA isolated from primary canine OSA tumor and patient-matched normal bone from seven dogs prior to chemotherapy to identify DEGs in the group. We then evaluate the universality of these changes in transcript levels among patients by deriving individual-level fold-change values using strict filtering parameters. The results from this study can be useful for reframing our perspective of transcriptomic analysis from a precision medicine perspective by identifying variations in DEGs among individuals. Citation Format: Rebecca L. Nance, Sara Cooper, Dmytro Starenki, Xu Wang, Maninder Sandey, Jey Koehler, Payal Agarwal, Brad Matz, Stephanie Lindley, Annette Smith, Ashley Smith, Noelle Bergman, Bruce F. Smith. Transcriptomic analysis of canine osteosarcoma from a precision medicine perspective reveals limitations of differential gene expression studies [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A039.

Transcriptomic Analysis of Canine Osteosarcoma from a Precision Medicine Perspective Reveals Limitations of Differential Gene Expression Studies.

Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. The difficulty in managing OSA lies in its extreme genetic complexity, drug resistance, and heterogeneity, making it improbable that a single-target treatment would be beneficial for the majority of affected individuals. Precision medicine seeks to fill this gap by addressing the intra- and inter-tumoral heterogeneity to improve patient outcome and survival. The characterization of differentially expressed genes (DEGs) unique to the tumor provides insight into the phenotype and can be useful for informing appropriate therapies as well as the development of novel treatments. Traditional DEG analysis combines patient data to derive statistically inferred genes that are dysregulated in the group; however, the results from this approach are not necessarily consistent across individual patients, thus contradicting the basis of precision medicine. Spontaneously occurring OSA in the dog shares remarkably similar clinical, histological, and molecular characteristics to the human disease and therefore serves as an excellent model. In this study, we use transcriptomic sequencing of RNA isolated from primary OSA tumor and patient-matched normal bone from seven dogs prior to chemotherapy to identify DEGs in the group. We then evaluate the universality of these changes in transcript levels across patients to identify DEGs at the individual level. These results can be useful for reframing our perspective of transcriptomic analysis from a precision medicine perspective by identifying variations in DEGs among individuals.

Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease.

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.

Three-dimensional models: a novel approach for lymphoma research.

Three dimensional (3D) "in vitro" models are progressively being applied to investigate tumor cell biology and the interaction of cancer cells with tumor microenvironment under conditions more similar and realistic to "in vivo" behavior than standard bidimensional (2D) cultures. In the last years, different methods have been developed to create spheroids and organoids and each technique has advantages and limitations also based on individual needs and cell types used. This review offers an overview of methodologies used for 3D systems: scaffold-free and scaffold-based methods up to bioreactors and organ-on-chip models. The principal goal for researchers is to select the 3D system that best suits their needs and that reflects the tumor model they want to study. A large chapter is dedicated to the application of these models to lymphomas' study, a neoplasm still little explored in the 3D field. These innovative and advanced models may represent new tools for cancer research and pre-clinical studies of new therapies in the perspective of precision medicine.

Network Analysis-Based Disentanglement of the Symptom Heterogeneity in Asian Patients with Schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics.

The symptom heterogeneity of schizophrenia is consistent with Wittgenstein’s analogy of a language game. From the perspective of precision medicine, this study aimed to estimate the symptom presentation and identify the psychonectome in Asian patients, using data obtained from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics. We constructed a network structure of the Brief Psychiatric Rating Scale (BPRS) items in 1438 Asian patients with schizophrenia. Furthermore, all the BPRS items were considered to be an ordered categorical variable ranging in value from 1–7. Motor retardation was situated most centrally within the BPRS network structure, followed by depressive mood and unusual thought content. Contrastingly, hallucinatory behavior was situated least centrally within the network structure. Using a community detection algorithm, the BPRS items were organized into positive, negative, and general symptom clusters. Overall, DSM symptoms were not more central than non-DSM symptoms within the symptom network of Asian patients with schizophrenia. Thus, motor retardation, which results from the unmet needs associated with current antipsychotic medications for schizophrenia, may be a tailored treatment target for Asian patients with schizophrenia. Based on these findings, targeting non-dopamine systems (glutamate, γ-aminobutyric acid) may represent an effective strategy with respect to precision medicine for psychosis.