Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data.

BACKGROUND: Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development. RESULTS: Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed. CONCLUSIONS: Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

Multiple Primary Cancers With Hematologic Malignancies and Germline Predisposition: A Case Series.

The term "multiple primary (MP) cancers" refers to the existence of more than one cancer in the same patient. The combination of MP cancers with hematological malignancies is relatively uncommon. In this study, we present five patients diagnosed with MP cancers concomitant with hematological malignancies. We comprehensively analyzed their clinical characteristics, cytogenetic profiles, and germline and somatic variants. As first primaries, two patients had solid cancer not followed by cytotoxic therapy and three had hematologic cancer, followed by cytotoxic therapy. The second primaries were all hematologic malignancies that did not meet the criteria for therapy-related myeloid neoplasm. Notably, two (40%) out of the five patients harbored pathogenic potential/presumed germline variants in cancer predisposition genes. Therefore, germline variant testing should be considered when MP cancers with hematological malignancies require consideration for related donor stem cell transplantation.

Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future.

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

Durvalumab with or without tremelimumab plus chemotherapy in HRR non-mutated, platinum-resistant ovarian cancer (KGOG 3045): A phase II umbrella trial.

AIM: We investigated the efficacy and safety of durvalumab (D) with or without tremelimumab (T) in addition to single-agent chemotherapy (CT) in patients with platinum-resistant recurrent ovarian cancer (PROC) lacking homologous recombination repair (HRR) gene mutations. PATIENTS AND METHODS: KGOG 3045 was an open-label, investigator-initiated phase II umbrella trial. Patients with PROC without HRR gene mutations who had received ≥2 prior lines of therapy were enrolled. Patients with high PD-L1 expression (TPS ≥25%) were assigned to arm A (D + CT), whereas those with low PD-L1 expression were assigned to arm B (D + T75 + CT). After completing arm B recruitment, patients were sequentially assigned to arms C (D + T300 + CT) and D (D + CT). RESULTS: Overall, 58 patients were enrolled (5, 18, 17, and 18 patients in arms A, B, C, and D, respectively). The objective response rates were 20.0, 33.3, 29.4, and 22.2%, respectively. Grade 3-4 treatment-related adverse events were observed in 20.0, 66.7, 47.1, and 66.7 of patients, respectively, but were effectively managed. Multivariable analysis demonstrated that adding T to D + CT improved progression-free survival (adjusted HR, 0.435; 95% CI, 0.229-0.824; P = 0.011). Favorable response to chemoimmunotherapy was associated with MUC16 mutation (P = 0.0214), high EPCAM expression (P = 0.020), high matrix remodeling gene signature score (P = 0.017), and low FOXP3 expression (P = 0.047). Patients showing favorable responses to D + T + CT exhibited significantly higher EPCAM expression levels (P = 0.008) and matrix remodeling gene signature scores (P = 0.031) than those receiving D + CT. CONCLUSIONS: Dual immunotherapy with chemotherapy showed acceptable response rates and tolerable safety in HRR non-mutated PROC, warranting continued clinical investigation.

Clinical Practice Recommendations for the Use of Next-Generation Sequencing in Patients with Solid Cancer: A Joint Report from KSMO and KSP.

In recent years, next-generation sequencing (NGS)-based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

External validation of the de novo stress urinary incontinence prediction model after pelvic organ prolapse surgery in Korean women: a retrospective cohort study.

BACKGROUND: De novo stress urinary incontinence (SUI) may develop following pelvic organ prolapse surgery. Performing prophylactic continence surgery may reduce the risk of de novo SUI and subsequent continence surgery; however, it may increase the risk of complications. Therefore, many surgeons try to identify women at high risk for de novo SUI and perform continence surgery selectively. Recently, a model for predicting the risk of de novo SUI after prolapse surgery was developed using data from the Outcomes following vaginal Prolapse repair and midUrethral Sling (OPUS) trial; its prediction accuracy was significantly better than that of the stress test alone. However, few studies have verified its prediction accuracy in discrete populations. The aim of this study was to externally validate the prediction model for de novo SUI after prolapse surgery in Korean women. METHODS: This retrospective cohort study included 320 stress-continent women who underwent prolapse surgery for pelvic organ prolapse quantification stage 2-4 anterior or apical prolapse and who completed a 1-year follow-up. Predicted probabilities by the de novo SUI online risk calculator were compared with observed outcomes and quantitated using the model's area under the curve and calibration plot. Subgroup analyses were also performed by the type of prolapse surgery. RESULTS: The de novo SUI prediction model showed moderate discrimination in our study cohort; area under the curve (95% confidence interval) = 0.73 (0.67-0.78) in the whole cohort, 0.69 (0.61-0.78) in women who underwent native tissue repair or colpocleisis, and 0.74 (0.65-0.82) in those who underwent sacrocolpopexy. Calibration curves demonstrated that the model accurately predicted the observed outcomes of de novo SUI in women who underwent native tissue repair or colpocleisis but underestimated outcomes in those who underwent sacrocolpopexy. The predicted probability cutoff points corresponding to an actual risk of 50% were 40% in women who underwent native tissue repair or colpocleisis and 30% in those who underwent sacrocolpopexy. CONCLUSIONS: The de novo SUI prediction model is acceptable for use in Korean women and may aid in shared decision-making regarding prophylactic continence procedure at the time of prolapse surgery.

Optimization of extraction condition for platycodin D from Platycodon grandiflorum root and verification of its biological activity.

Platycosides, major components of Platycodon grandiflorum (PG) extract, have been implicated in a wide range of biological effects. In particular, platycodin D (PD) is a well-known main bioactive compound of Platycosides. Despite the biological significance of PD, optimization of extract condition for PD from PG root has not been well investigated. Here, we established the optimum extraction condition as ethanol concentration of 0%, temperature of 50°C, and extraction time of 11 h to obtain PD-rich P. grandiflorum extract (PGE) by using response surface methodology (RSM) with Box-Behnken design (BBD). The 5.63 mg/g of PD was extracted from the PG root in optimum condition, and this result was close to the predicted PD content. To analyze the biological activity of PGE related to mucin production, we demonstrated the inhibitory effect of PGE on PMA-induced hyperexpression of MUC5AC as well as ERK activation, a signal mediator of MUC5AC expression. Moreover, we showed that PGE had expectorant activity in mice. These results indicated that PGE had sufficient functions as a potential mucoregulator and expectorant for treating diverse airway diseases. Additionally, we confirmed that PGE had antioxidant activity and inhibited LPS-induced proinflammatory cytokines, TNF-α, and IL-6. Taken together, PGE derived from novel optimizing conditions showed various biological effects, suggesting that PGE could be directly applied to the food industry as food material having therapeutic and preventive potential for human airway diseases.

The ReInforcement of adherence via self-monitoring app orchestrating biosignals and medication of RivaroXaban in patients with atrial fibrillation and co-morbidities: a study protocol for a randomized controlled trial (RIVOX-AF).

Background: Because of the short half-life of non-vitamin K antagonist oral anticoagulants (NOACs), consistent drug adherence is crucial to maintain the effect of anticoagulants for stroke prevention in atrial fibrillation (AF). Considering the low adherence to NOACs in practice, we developed a mobile health platform that provides an alert for drug intake, visual confirmation of drug administration, and a list of medication intake history. This study aims to evaluate whether this smartphone app-based intervention will increase drug adherence compared with usual care in patients with AF requiring NOACs in a large population. Methods: This prospective, randomized, open-label, multicenter trial (RIVOX-AF study) will include a total of 1,042 patients (521 patients in the intervention group and 521 patients in the control group) from 13 tertiary hospitals in South Korea. Patients with AF aged ≥19 years with one or more comorbidities, including heart failure, myocardial infarction, stable angina, hypertension, or diabetes mellitus, will be included in this study. Participants will be randomly assigned to either the intervention group (MEDI-app) or the conventional treatment group in a 1:1 ratio using a web-based randomization service. The intervention group will use a smartphone app that includes an alarm for drug intake, visual confirmation of drug administration through a camera check, and presentation of a list of medication intake history. The primary endpoint is adherence to rivaroxaban by pill count measurements at 12 and 24 weeks. The key secondary endpoints are clinical composite endpoints, including systemic embolic events, stroke, major bleeding requiring transfusion or hospitalization, or death during the 24 weeks of follow-up. Discussion: This randomized controlled trial will investigate the feasibility and efficacy of smartphone apps and mobile health platforms in improving adherence to NOACs. Trial registration: The study design has been registered in ClinicalTrial.gov (NCT05557123).

Genome-wide association study identifies novel susceptibilities to adult moyamoya disease.

Genome-wide association study has limited to discover single-nucleotide polymorphisms (SNPs) in several ethnicities. Here, we investigated an initial GWAS to identify genetic modifiers predicting with adult moyamoya disease (MMD) in Koreans. GWAS was performed in 216 patients with MMD and 296 controls using the large-scale Asian-specific Axiom Precision Medicine Research Array. A subsequent fine-mapping analysis was conducted to assess the causal variants associated with adult MMD. A total of 489,966 out of 802,688 SNPs were subjected to quality control analysis. Twenty-one SNPs reached a genome-wide significance threshold (p = 5 × 10-8) after pruning linkage disequilibrium (r2 < 0.8) and mis-clustered SNPs. Among these variants, the 17q25.3 region including TBC1D16, CCDC40, GAA, RNF213, and ENDOV genes was broadly associated with MMD (p = 3.1 × 10-20 to 4.2 × 10-8). Mutations in RNF213 including rs8082521 (Q1133K), rs10782008 (V1195M), rs9913636 (E1272Q), rs8074015 (D1331G), and rs9674961 (S2334N) showed a genome-wide significance (1.9 × 10-8 < p < 4.3 × 10-12) and were also replicated in the East-Asian populations. In subsequent analysis, RNF213 mutations were validated in a fine-mapping outcome (log10BF > 7). Most of the loci associated with MMD including 17q25.3 regions were detected with a statistical power greater than 80%. This study identifies several novel and known variations predicting adult MMD in Koreans. These findings may good biomarkers to evaluate MMD susceptibility and its clinical outcomes.

A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions.

Multimodal emotion recognition has gained much traction in the field of affective computing, human-computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

ITDetect: a method to detect internal tandem duplication of FMS-like tyrosine kinase (FLT3) from next-generation sequencing data with high sensitivity and clinical application.

Internal tandem duplication (ITD) of the FMS-like tyrosine kinase (FLT3) gene is associated with poor clinical outcomes in patients with acute myeloid leukemia. Although recent methods for detecting FLT3-ITD from next-generation sequencing (NGS) data have replaced traditional ITD detection approaches such as conventional PCR or fragment analysis, their use in the clinical field is still limited and requires further information. Here, we introduce ITDetect, an efficient FLT3-ITD detection approach that uses NGS data. Our proposed method allows for more precise detection and provides more detailed information than existing in silico methods. Further, it enables FLT3-ITD detection from exome sequencing or targeted panel sequencing data, thereby improving its clinical application. We validated the performance of ITDetect using NGS-based and experimental ITD detection methods and successfully demonstrated that ITDetect provides the highest concordance with the experimental methods. The program and data underlying this study are available in a public repository.

Rex: R-linked EXcel add-in for statistical analysis of medical and bioinformatics data.

BACKGROUND: Microsoft Excel has substantial functionalities for data management and analyses, and has been the most popular software in this field. However, in spite of Excel's user-friendly interface and functionality for data management, it provides very few functions for in-depth statistical analyses, which has limited its wider application for this purpose. OBJECTIVE: Here, we introduce Rex, an Excel add-in software implementing the powerful analytical and graphical functions of R within Excel. METHODS: Rex was implemented using three types of programming software: R, JavaScript, and Microsoft VB.Net. RESULTS: Rex provides a graphical user interface (GUI) through Excel, and statistical analysis can be conducted by pointing and clicking the menu without programming R. Rex covers a wide range of analyses from basic statistics to advanced analysis, including structural equation modeling, complex sampling design, and machine learning models, making it possible for researchers not skilled in using a command-line interface to conduct in-depth statistical analyses. Most Rex modules are available in a free version for non-commercial use, and it can be used for educational and public purposes. CONCLUSION: In this article, we introduce the framework and features of Rex with illustrative examples of its implementation.

Development, validation, and comparison of a nomogram based on radiologic findings for predicting malignancy in intraductal papillary mucinous neoplasms of the pancreas: An international multicenter study.

BACKGROUND: Although we previously proposed a nomogram to predict malignancy in intraductal papillary mucinous neoplasms (IPMN) and validated it in an external cohort, its application is challenging without data on tumor markers. Moreover, existing nomograms have not been compared. This study aimed to develop a nomogram based on radiologic findings and to compare its performance with previously proposed American and Korean/Japanese nomograms. METHODS: We recruited 3708 patients who underwent surgical resection at 31 tertiary institutions in eight countries, and patients with main pancreatic duct >10 mm were excluded. To construct the nomogram, 2606 patients were randomly allocated 1:1 into training and internal validation sets, and area under the receiver operating characteristics curve (AUC) was calculated using 10-fold cross validation by exhaustive search. This nomogram was then validated and compared to the American and Korean/Japanese nomograms using 1102 patients. RESULTS: Among the 2606 patients, 90 had main-duct type, 900 had branch-duct type, and 1616 had mixed-type IPMN. Pathologic results revealed 1628 low-grade dysplasia, 476 high-grade dysplasia, and 502 invasive carcinoma. Location, cyst size, duct dilatation, and mural nodule were selected to construct the nomogram. AUC of this nomogram was higher than the American nomogram (0.691 vs 0.664, P = .014) and comparable with the Korean/Japanese nomogram (0.659 vs 0.653, P = .255). CONCLUSIONS: A novel nomogram based on radiologic findings of IPMN is competitive for predicting risk of malignancy. This nomogram would be clinically helpful in circumstances where tumor markers are not available. The nomogram is freely available at http://statgen.snu.ac.kr/software/nomogramIPMN.

Self-Monitoring of Blood Pressure and Feed-back Using APP in TReatment of UnconTrolled Hypertension (SMART-BP): A Randomized Clinical Trial.

BACKGROUND AND OBJECTIVES: Self-monitoring of blood pressure (SMBP) is a reliable method used to assess BP accurately. However, patients do not often know how to respond to the measured BP value. We developed a mobile application-based feed-back algorithm (SMBP-App) for tailored recommendations. In this study, we aim to evaluate whether SMBP-App is superior to SMBP alone in terms of BP reduction and drug adherence improvement in patients with hypertension. METHODS: Self-Monitoring of blood pressure and Feed-back using APP in TReatment of UnconTrolled Hypertension (SMART-BP) is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App compared with SMBP alone. Patients with uncomplicated essential hypertension will be randomly assigned to the SMBP-App (90 patients) and SMBP alone (90 patients) groups. In the SMBP group, the patients will perform home BP measurement and receive the standard care, whereas in the SMBP-App group, the patients will receive additional recommendations from the application in response to the obtained BP value. Follow-up visits will be scheduled at 12 and 24 weeks after randomization. The primary endpoint of the study is the mean home systolic BP. The secondary endpoints include the drug adherence, the home diastolic BP, home and office BP. CONCLUSIONS: SMART-BP is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App. If we can confirm its efficacy, SMBP-App may be scaled-up to improve the treatment of hypertension. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04470284.

Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment.

BACKGROUND: Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies. METHODS: From three AML RNA-seq cohorts (BeatAML, LeuceGene, and TCGA; n = 451, 437, and 179, respectively), we constructed the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF). To demonstrate the significance of the BFSigs, we conducted modelling to predict response to BCL2 family inhibitors, clustering, and functional enrichment analysis. Cross-platform validity of BFSigs was also confirmed using NanoString technology in a separate cohort of 47 patients. RESULTS: We established BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins. Unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Interestingly, each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950). Lastly, we successfully confirmed the validity of BFSigs using NanoString technology. CONCLUSIONS: This study proposes BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML.

The telomere maintenance mechanism spectrum and its dynamics in gliomas.

BACKGROUND: The activation of the telomere maintenance mechanism (TMM) is one of the critical drivers of cancer cell immortality. In gliomas, TERT expression and TERT promoter mutation are considered to reliably indicate telomerase activation, while ATRX mutation and/or loss indicates an alternative lengthening of telomeres (ALT). However, these relationships have not been extensively validated in tumor tissues. METHODS: Telomerase repeated amplification protocol (TRAP) and C-circle assays were used to profile and characterize the TMM cross-sectionally (n = 412) and temporally (n = 133) across glioma samples. WES, RNA-seq, and NanoString analyses were performed to identify and validate the genetic characteristics of the TMM groups. RESULTS: We show through the direct measurement of telomerase activity and ALT in a large set of glioma samples that the TMM in glioma cannot be defined solely by the combination of telomerase activity and ALT, regardless of TERT expression, TERT promoter mutation, and ATRX loss. Moreover, we observed that a considerable proportion of gliomas lacked both telomerase activity and ALT. This telomerase activation-negative and ALT negative group exhibited evidence of slow growth potential. By analyzing a set of longitudinal samples from a separate cohort of glioma patients, we discovered that the TMM is not fixed and can change with glioma progression. CONCLUSIONS: This study suggests that the TMM is dynamic and reflects the plasticity and oncogenicity of tumor cells. Direct measurement of telomerase enzyme activity and evidence of ALT should be considered when defining TMM. An accurate understanding of the TMM in glioma is expected to provide important information for establishing cancer management strategies.

Effectiveness of Information and Communications Technology-Based Interventions for Obesity and Metabolic Syndrome.

The rapid increase in the obese population is a problem indicates the need for measures to prevent and treat obesity. Since the early 2000s, worldwide digital health has focused on obesity management. Information and communication technology (ICT)-based obesity intervention can be an efficient method for treating obesity and metabolic syndrome, has no time limitations, and is an inexpensive and easily accessible treatment modality for both physicians and patients. Previous studies have confirmed the effects of ICT-based interventions for obesity and metabolic syndrome management for behavioral improvement in lifestyle modification. In addition, ICT-based interventions in obese and metabolic syndrome patients are recommended as part of a comprehensive strategy for weight loss and maintenance. The Committee of IT-convergence Treatment of Metabolic Syndrome was established by the Korean Society for the Study of Obesity in 2021, and had been gathering theoretical and clinical evidence in digital therapeutics fields and developing new methods for managing obesity and metabolic syndrome. As part of this effort, if the "obesity management prototype" is commercialized, it will be available for convenient treatment of individuals with obesity and metabolic syndrome.

Laboratory information management system for COVID-19 non-clinical efficacy trial data.

BACKGROUND: As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. RESULTS: In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. CONCLUSIONS: This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.

Kernel-based hierarchical structural component models for pathway analysis.

MOTIVATION: Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex. RESULTS: To model complex effects including non-linear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models non-linear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies. AVAILABILITY AND IMPLEMENTATION: The HisCoM-Kernel software is freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/. The RNA-seq data underlying this article are available at https://xena.ucsc.edu/, and the others will be shared on reasonable request to the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.