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Abstract In complex systems, it’s crucial to uncover latent mechanisms and their context-dependent relationships. This is especially true in medical research, where identifying unknown cancer mechanisms and their impact on phenomena like drug resistance is vital. Directly observing these mechanisms is challenging due to measurement complexities, leading to an approach that infers latent mechanisms from observed variable distributions. Despite machine learning advancements enabling sophisticated generative models, their black-box nature complicates the interpretation of complex latent mechanisms. A promising method for understanding these mechanisms involves estimating latent factors through linear projection, though there’s no assurance that inferences made under specific conditions will remain valid across contexts. We propose a novel solution, suggesting data, even from systems appearing complex, can often be explained by sparse dependencies among a few common latent factors, regardless of the situation. This simplification allows for modeling that yields significant insights across diverse fields. We demonstrate this with datasets from finance, where we capture societal trends from stock price movements, and medicine, where we uncover new insights into cancer drug resistance through gene expression analysis.

ObjectiveThe purpose of this study was to predict the four cold–heat patterns in patients who have the subjective symptoms of the cold–heat pattern described in the International Classification of Diseases Traditional Medicine Conditions – Module 1 by applying a machine learning algorithm.MethodsSubjects were first-visit Kampo outpatients at six institutions who agreed to participate in this multicenter prospective observational study. The cold pattern model and the heat pattern model were created separately with 148 symptoms, body mass index, blood pressure (systolic and diastolic), age, and sex. Along with a single cold or heat pattern, the tangled heat/cold pattern is defined as being predicted by both cold and heat patterns, while the moderate (heat/cold) pattern is defined as being predicted by neither the cold pattern nor the heat pattern.ResultsWe included 622 participants (mean age ±standard deviation, 54.4 ± 16.9; with female 501). The accuracy, macro-recall, precision, and F1-score of a combination of the two prediction models were 96.7%, 93.2%, 85.6%, and 88.5% respectively. The important items were compatible with the definitions of the cold–heat pattern.ConclusionWe developed a prediction model on cold–heat patterns with data from patients whose subjective cold/heat-related symptoms matched the cold–heat pattern diagnosis by the physician.

Abstract Background Renal impairment is a predictor of coronavirus disease (COVID-19) severity. No studies have compared COVID-19 outcomes in patients with chronic kidney disease (CKD) and patients with impaired renal function without a prior diagnosis of CKD. This study aimed to identify the impact of pre-existing impaired renal function without CKD on COVID-19 outcomes. Methods This retrospective study included 3,637 patients with COVID-19 classified into three groups by CKD history and estimated glomerular filtration rate (eGFR) on referral: Group 1 (n = 2,460), normal renal function without a CKD history; Group 2 (n = 905), impaired renal function without a CKD history; and Group 3 (n = 272), history of CKD. We compared the clinical characteristics of these groups and assessed the effect of CKD and impaired renal function on critical outcomes (requirement for respiratory support with high-flow oxygen devices, invasive mechanical ventilation, or extracorporeal membrane oxygen, and death during hospitalization) using multivariable logistic regression. Results The prevalence of comorbidities (hypertension, diabetes, and cardiovascular disease) and incidence of inflammatory responses (white blood counts, and C-reactive protein, procalcitonin, and D-dimer levels) and complications (bacterial infection and heart failure) were higher in Groups 2 and 3 than that in Group 1. The incidence of critical outcomes was 10.8%, 17.7%, and 26.8% in Groups 1, 2, and 3, respectively. The mortality rate and the rate of requiring IMV support was lowest in Group 1 and highest in Group 3. Compared with Group 1, the risk of critical outcomes was higher in Group 2 (adjusted odds ratio [aOR]: 1.32, 95% confidence interval [CI]: 1.03–1.70, P = 0.030) and Group 3 (aOR: 1.94, 95% CI: 1.36–2.78, P

Objective This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients.Methods This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months.Results The incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p

Abstract The severity of chest X-ray (CXR) findings is a prognostic factor in patients with coronavirus disease 2019 (COVID-19). We investigated the clinical and genetic characteristics and prognosis of patients with worsening CXR findings during early hospitalization. We retrospectively included 1656 consecutive Japanese patients with COVID-19 recruited through the Japan COVID-19 Task Force. Rapid deterioration of CXR findings was defined as increased pulmonary infiltrates in ≥ 50% of the lung fields within 48 h of admission. Rapid deterioration of CXR findings was an independent risk factor for death, most severe illness, tracheal intubation, and intensive care unit admission. The presence of consolidation on CXR, comorbid cardiovascular and chronic obstructive pulmonary diseases, high body temperature, and increased serum aspartate aminotransferase, potassium, and C-reactive protein levels were independent risk factors for rapid deterioration of CXR findings. Risk variant at the ABO locus (rs529565-C) was associated with rapid deterioration of CXR findings in all patients. This study revealed the clinical features, genetic features, and risk factors associated with rapid deterioration of CXR findings, a poor prognostic factor in patients with COVID-19.

Abstract Background Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions. Methods This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions. Results The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59–19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60–8.76), IMV requirement (aOR 7.73, 95% CI 2.52–23.7), and mortality rate (aOR 6.46, 95% CI 1.87–22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36–9.52), older age (aOR 2.53, 95% CI 1.16–5.51), female sex (aOR 2.41, 95% CI 1.13–5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09–4.50) independently predicted persistent residual lung lesions. Conclusions AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.

Objectives: Although SARS-CoV-2 RNAemia has been reported to strongly impact patients with severe COVID-19, the clinical characteristics of patients with COVID-19 harboring detectable intracellular SARS-CoV-2 RNA remain unknown. Methods: We included adult patients who had developed COVID-19 between February and September 2020. Total white blood cells derived from the buffy coat of peripheral whole blood were used to detect SARS-CoV-2 RNA using the Illumina COVIDSeq test. We compared the clinical characteristics between patients with and without detected viral RNA (detected and undetected groups). Results: Among the 390 patients included, 17 harbored SARS-CoV-2 RNA in peripheral white blood cells. All 17 patients required oxygen support during the disease course and had higher intensive care unit admission (52.9% vs 28.9%, P = 0.035), mortality (17.7% vs 3.5%, P = 0.004), kidney dysfunction (severe, 23.5% vs 6.4%, P = 0.029), and corticosteroid treatment rates (76.5% vs 46.5%, P = 0.016) than those of patients in the undetected group. Conclusion: We propose that patients with circulating intracellular SARS-CoV-2 RNA in the peripheral blood exhibited the most severe disease course.

Objectives: COVID-19 was severe in the Delta variant-dominated epidemic wave (fifth wave) in Japan. The clinical characteristics and effectiveness of COVID-19 vaccination are not fully understood in the Omicron variant-dominated wave (sixth and seventh waves), especially in hospitalized patients. We investigated the relationship between vaccination and disease severity in the Omicron-dominated wave and compared these variant-dominated waves. Methods: The nationwide COVID-19 database (Japan COVID-19 Task Force) was used to compare clinical characteristics and critical outcomes in patients hospitalized with Delta (fifth, N = 735) vs Omicron-dominated waves (sixth, N = 495; seventh, N = 128). Results: Patients in the sixth and seventh waves had a lower incidence of critical outcomes and respiratory outcomes, and a higher incidence of bacterial infection, although the mortality rate did not differ significantly between waves. In the sixth and seventh waves, 138 (27.9%) and 29 (22.7%) patients with COVID-19 were unvaccinated, respectively. Multivariable analysis adjusted with previously reported factors revealed that the proportion of (1) critical outcomes and (2) respiratory outcomes decreased in a frequency-dependent manner. Thus, (1) (the number of vaccinations): 1-2 times: adjusted odds ratio (aOR) 0.37 (95% confidence interval [CI]; 0.20-0.69); 3-4 times: aOR 0.25 (95% CI; 0.11-0.58); and (2) 1-2 times: aOR 0.43 (95% CI; 0.27-0.66); 3-4 times: aOR 0.36 (95% CI; 0.21-0.60). Conclusions: Patients hospitalized with COVID-19 with Omicron infections showed a lower incidence of critical outcomes than those with Delta infections, and COVID-19 vaccination may contribute to preventing respiratory failure.

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Background Krebs von den Lungen-6 (KL-6) is a known biomarker for diagnosis and monitoring of interstitial lung diseases. However, the role of serum KL-6 and the mucin 1 (MUC1) variant (rs4072037) in COVID-19 outcomes remains to be elucidated. We aimed to evaluate the relationships among serum KL-6 levels, critical outcomes and the MUC1 variant in Japanese patients with COVID-19.Methods This is a secondary analysis of a multicentre retrospective study using data from the Japan COVID-19 Task Force collected from February 2020 to November 2021, including 2226 patients with COVID-19 whose serum KL-6 levels were measured. An optimal serum KL-6 level cut-off to predict critical outcomes was determined and used for multivariable logistic regression analysis. Furthermore, the relationship among the allele dosage of the MUC1 variant, calculated from single nucleotide polymorphism typing data of genome-wide association studies using the imputation method, serum KL-6 levels and COVID-19 critical outcomes was evaluated.Results Serum KL-6 levels were significantly higher in patients with COVID-19 with critical outcomes (511±442 U/mL) than those without (279±204 U/mL) (p

Abstract Background Although cases of respiratory bacterial infections associated with coronavirus disease 2019 (COVID-19) have often been reported, their impact on the clinical course remains unclear. Herein, we evaluated and analyzed the complication rates of bacterial infections, causative organisms, patient backgrounds, and clinical outcome in Japanese patients with COVID-19. Methods We performed a retrospective cohort study that included inpatients with COVID-19 from multiple centers participating in the Japan COVID-19 Taskforce (April 2020 to May 2021) and obtained demographic, epidemiological, and microbiological results and the clinical course and analyzed the cases of COVID-19 complicated by respiratory bacterial infections. Results Of the 1,863 patients with COVID-19 included in the analysis, 140 (7.5%) had respiratory bacterial infections. Community-acquired co-infection at COVID-19 diagnosis was uncommon (55/1,863, 3.0%) and was mainly caused by Staphylococcus aureus, Klebsiella pneumoniae and Streptococcus pneumoniae. Hospital-acquired bacterial secondary infections, mostly caused by Staphylococcus aureus, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia, were diagnosed in 86 patients (4.6%). Severity-associated comorbidities were frequently observed in hospital-acquired secondary infection cases, including hypertension, diabetes, and chronic kidney disease. The study results suggest that the neutrophil–lymphocyte ratio (> 5.28) may be useful in diagnosing complications of respiratory bacterial infections. COVID-19 patients with community-acquired or hospital-acquired secondary infections had significantly increased mortality. Conclusions Respiratory bacterial co-infections and secondary infections are uncommon in patients with COVID-19 but may worsen outcomes. Assessment of bacterial complications is important in hospitalized patients with COVID-19, and the study findings are meaningful for the appropriate use of antimicrobial agents and management strategies.

Abstract Myxofibrosarcoma (MFS) and undifferentiated sarcoma (US) have been considered as tumors of the same lineage based on genetic/epigenetic profiling. Although MFS shows a notably better prognosis than US, there are no clear criteria for distinguishing between them. Here, we examined 85 patients with MFS/US and found that tumors with infiltrative growth patterns tended to have more myxoid areas and higher local recurrence rates but fewer distant metastases and better overall survival. Morphologically characteristic sickle-shaped blood vessels, which tended to have fewer αSMA-positive cells, were also observed in these tumors, compared with normal vessels. Based on the incidence of these sickle-shaped blood vessels, we subdivided conventionally diagnosed US into two groups. This stratification was significantly correlated with metastasis and prognosis. RNA sequencing of 24 tumors (9 MFS and 15 US tumors) demonstrated that the proteasome, NF-kB, and VEGF pathways were differentially regulated among these tumors. Expression levels of KDR and NFATC4, which encode a transcription factor responsible for the neuritin-insulin receptor angiogenic signaling, were elevated in the sickle-shaped blood vessel-rich US tumors. These findings indicate that further analyses may help elucidate the malignant potential of MFS/US tumors as well as the development of therapeutic strategies for such tumors.

Objectives: Smoking and chronic obstructive pulmonary disease (COPD) are risk factors for severe COVID-19. However, limited literature exists on the effect of COPD and smoking on COVID-19 outcomes. This study examined the impact of smoking exposure in pack-years (PY) and COPD on COVID-19 outcomes among smokers in Japan. Methods: The study included 1266 smokers enrolled by the Japan COVID-19 task force between February 2020 and December 2021. PY and COPD status was self-reported by patients. Patients were classified into the non-COPD (n = 1151) and COPD (n = 115) groups; the non-COPD group was further classified into 30 PY (n = 361). The study outcome was the need for invasive mechanical ventilation (IMV). Results: The incidence of IMV increased with increasing PY and was highest in the COPD group (30 PY = 15.2%, COPD = 26.1%; P 30 PY and COPD groups through univariate (odds ratio [OR]: >30 PY = 2.11, COPD = 4.14) and multivariate (OR: >30 PY = 2.38; COPD = 7.94) analyses. Increasing PY number was also associated with increased IMV requirement in patients aged

Azacitidine, a DNA methylation inhibitor, is employed for the treatment of acute myeloid leukemia (AML). However, drug resistance remains a major challenge for effective azacitidine chemotherapy, though several studies have attempted to uncover the mechanisms of azacitidine resistance. With the aim to identify the mechanisms underlying acquired azacitidine resistance in cancer cell lines, we developed a computational strategy that can identify differentially regulated gene networks between drug-sensitive and -resistant cell lines by extending the existing method, differentially coexpressed gene sets (DiffCoEx). The technique specifically focuses on cell line-specific gene network analysis. We applied our method to gene networks specific to azacitidine sensitivity and identified differentially regulated gene networks between azacitidine-sensitive and -resistant cell lines. The molecular interplay between the metallothionein gene family, C19orf33, ELF3, GRB7, IL18, NRN1, and RBM47 were identified as differentially regulated gene network in drug resistant cell lines. The biological mechanisms associated with azacitidine and AML for the markers in the identified networks were verified through the literature. Our results suggest that controlling the identified genes (e.g., the metallothionein gene family) and “cellular response”-related pathways (“cellular response to zinc ion”, “cellular response to copper ion”, and “cellular response to cadmium ion”, where the enriched functional-related genes are MT2A, MT1F, MT1G, and MT1E) may provide crucial clues to address azacitidine resistance in patients with AML. We expect that our strategy will be a useful tool to uncover patient-specific molecular interplay that provides crucial clues for precision medicine in not only gastric cancer but also complex diseases.

Abstract Background We aimed to elucidate differences in the characteristics of patients with coronavirus disease 2019 (COVID-19) requiring hospitalization in Japan, by COVID-19 waves, from conventional strains to the Delta variant. Methods We used secondary data from a database and performed a retrospective cohort study that included 3261 patients aged ≥ 18 years enrolled from 78 hospitals that participated in the Japan COVID-19 Task Force between February 2020 and September 2021. Results Patients hospitalized during the second (mean age, 53.2 years [standard deviation {SD}, ± 18.9]) and fifth (mean age, 50.7 years [SD ± 13.9]) COVID-19 waves had a lower mean age than those hospitalized during the other COVID-19 waves. Patients hospitalized during the first COVID-19 wave had a longer hospital stay (mean, 30.3 days [SD ± 21.5], p

Abstract Background Respiratory symptoms are associated with coronavirus disease 2019 (COVID-19) outcomes. However, the impacts of upper and lower respiratory symptoms on COVID-19 outcomes in the same population have not been compared. The objective of this study was to characterize upper and lower respiratory symptoms and compare their impacts on outcomes of hospitalized COVID-19 patients. Methods This was a multicenter, retrospective cohort study; the database from the Japan COVID-19 Task Force was used. A total of 3314 COVID-19 patients were included in the study, and the data on respiratory symptoms were collected. The participants were classified according to their respiratory symptoms (Group 1: no respiratory symptoms, Group 2: only upper respiratory symptoms, Group 3: only lower respiratory symptoms, and Group 4: both upper and lower respiratory symptoms). The impacts of upper and lower respiratory symptoms on the clinical outcomes were compared. The primary outcome was the percentage of patients with poor clinical outcomes, including the need for oxygen supplementation via high-flow oxygen therapy, mechanical ventilation, and extracorporeal membrane oxygenation or death. Results Of the 3314 COVID-19 patients, 605, 1331, 1229, and 1149 were classified as Group 1, Group 2, Group 3, and Group 4, respectively. In univariate analysis, patients in Group 2 had the best clinical outcomes among all groups (odds ratio [OR]: 0.21, 95% confidence interval [CI]: 0.11–0.39), while patients in Group 3 had the worst outcomes (OR: 3.27, 95% CI: 2.43–4.40). Group 3 patients had the highest incidence of pneumonia, other complications due to secondary infections, and thrombosis during the clinical course. Conclusions Upper and lower respiratory tract symptoms had vastly different impacts on the clinical outcomes of COVID-19.

Abstract Background The clinical course of coronavirus disease (COVID-19) is diverse, and the usefulness of phenotyping in predicting the severity or prognosis of the disease has been demonstrated overseas. This study aimed to investigate clinically meaningful phenotypes in Japanese COVID-19 patients using cluster analysis. Methods From April 2020 to May 2021, data from inpatients aged ≥ 18 years diagnosed with COVID-19 and who agreed to participate in the study were collected. A total of 1322 Japanese patients were included. Hierarchical cluster analysis was performed using variables reported to be associated with COVID-19 severity or prognosis, namely, age, sex, obesity, smoking history, hypertension, diabetes mellitus, malignancy, chronic obstructive pulmonary disease, hyperuricemia, cardiovascular disease, chronic liver disease, and chronic kidney disease. Results Participants were divided into four clusters: Cluster 1, young healthy (n = 266, 20.1%); Cluster 2, middle-aged (n = 245, 18.5%); Cluster 3, middle-aged obese (n = 435, 32.9%); and Cluster 4, elderly (n = 376, 28.4%). In Clusters 3 and 4, sore throat, dysosmia, and dysgeusia tended to be less frequent, while shortness of breath was more frequent. Serum lactate dehydrogenase, ferritin, KL-6, d-dimer, and C-reactive protein levels tended to be higher in Clusters 3 and 4. Although Cluster 3 had a similar age as Cluster 2, it tended to have poorer outcomes. Both Clusters 3 and 4 tended to exhibit higher rates of oxygen supplementation, intensive care unit admission, and mechanical ventilation, but the mortality rate tended to be lower in Cluster 3. Conclusions We have successfully performed the first phenotyping of COVID-19 patients in Japan, which is clinically useful in predicting important outcomes, despite the simplicity of the cluster analysis method that does not use complex variables.

Objectives: This study aimed to identify the relationship between abnormal serum uric acid levels or a history of hyperuricemia and COVID-19 severity in the Japanese population. Methods: We included 1523 patients enrolled in the Japan COVID-19 Task Force cohort between February 2020 and May 2021. We compared the clinical characteristics, including co-morbidities, laboratory findings, and outcomes, particularly invasive mechanical ventilation (IMV), among patients with and without abnormal uric acid levels or a history of hyperuricemia. Results: Patients with high serum uric acid levels were older and had higher body weight and body mass index than those without. In addition, the multiple logistic regression analysis revealed a significant association between high serum uric acid levels or a history of hyperuricemia and an increased risk of IMV (odds ratio [OR] = 1.77; P = 0.03/OR = 1.56; P = 0.04). Moreover, patients with low uric acid levels on admission were also associated significantly with the requirement of IMV (OR = 5.09; P

Abstract Background Gene regulatory networks have garnered a large amount of attention to understand disease mechanisms caused by complex molecular network interactions. These networks have been applied to predict specific clinical characteristics, e.g., cancer, pathogenicity, and anti-cancer drug sensitivity. However, in most previous studies using network-based prediction, the gene networks were estimated first, and predicted clinical characteristics based on pre-estimated networks. Thus, the estimated networks cannot describe clinical characteristic-specific gene regulatory systems. Furthermore, existing computational methods were developed from algorithmic and mathematics viewpoints, without considering network biology. Results To effectively predict clinical characteristics and estimate gene networks that provide critical insights into understanding the biological mechanisms involved in a clinical characteristic, we propose a novel strategy for predictive gene network estimation. The proposed strategy simultaneously performs gene network estimation and prediction of the clinical characteristic. In this strategy, the gene network is estimated with minimal network estimation and prediction errors. We incorporate network biology by assuming that neighboring genes in a network have similar biological functions, while hub genes play key roles in biological processes. Thus, the proposed method provides interpretable prediction results and enables us to uncover biologically reliable marker identification. Monte Carlo simulations shows the effectiveness of our method for feature selection in gene estimation and prediction with excellent prediction accuracy. We applied the proposed strategy to construct gastric cancer drug-responsive networks. Conclusion We identified gastric drug response predictive markers and drug sensitivity/resistance-specific markers, AKR1B10, AKR1C3, ANXA10, and ZNF165, based on GDSC data analysis. Our results for identifying drug sensitive and resistant specific molecular interplay are strongly supported by previous studies. We expect that the proposed strategy will be a useful tool for uncovering crucial molecular interactions involved a specific biological mechanism, such as cancer progression or acquired drug resistance.

Abstract Background Obesity is reported to be a risk factor for severe disease in patients with coronavirus disease 2019 (COVID-19). However, there are no specific reports on the risk of severe disease according to body mass index (BMI) in Japan. Thus, this study aimed to investigate the effect of obesity stratified by BMI on the severity of COVID-19 in the general Japanese population. Methods From February 2020 to May 2021, 1 837 patients aged ≥18 years were enrolled in the Japan COVID-19 Task Force. Patients with known BMI and disease severity were analyzed. Severity was defined as critical if the patient was treated in the intensive care unit, required invasive mechanical ventilation, or died. Results Class 1 obesity (25.0 ≤ BMI

Genetic mechanisms influencing COVID-19 susceptibility are not well understood. Here, the authors analyzed whole blood RNA-seq data of 465 Japanese individuals with COVID-19, highlighting thousands of fine-mapped variants affecting expression and splicing of genes, as well as the presence of COVID-19 severity-interaction eQTLs.

The molecular heterogeneity of KMT2A-rearranged infant acute lymphoblastic leukemia (ALL) remains poorly characterised. Here, the authors perform multi-omics analysis for 84 ALL patients and suggest 5 distinct subgroups for risk stratification and personalised treatment.’

Summary: Bromodomain-containing protein 8 (BRD8) is a subunit of the NuA4/TIP60-histone acetyltransferase complex. Although BRD8 has been considered to act as a co-activator of the complex, its biological role remains to be elucidated. Here, we uncovered that BRD8 accumulates in colorectal cancer cells through the inhibition of ubiquitin-dependent protein degradation by the interaction with MRG domain binding protein. Transcriptome analysis coupled with genome-wide mapping of BRD8-binding sites disclosed that BRD8 transactivates a set of genes independently of TIP60, and that BRD8 regulates the expression of multiple subunits of the pre-replicative complex in concert with the activator protein-1. Depletion of BRD8 induced cell-cycle arrest at the G1 phase and suppressed cell proliferation. We have also shown that the bromodomain of BRD8 is indispensable for not only the interaction with histone H4 or transcriptional regulation but also its own protein stability. These findings highlight the importance of bromodomain as a therapeutic target.

Abstract Diamond‐Blackfan anaemia (DBA) shares clinical features with two recently reported sporadic cases of dyserythropoietic anaemia with a cryptic GATA1 splicing mutation (c.871‐24 C>T). We hypothesized that some patients clinically diagnosed with DBA but whose causative genes were unknown may carry the intronic GATA1 mutation. Here, we examined 79 patients in our DBA cohort, who had no detectable causative genes. The intronic GATA1 mutation was identified in two male patients sharing the same pedigree that included multiple cases with anaemia. Cosegregation of this mutation and disease in multiple family members provide evidence to support the pathogenicity of the intronic GATA1 mutation.

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

Biological condition-responsive gene network analysis has attracted considerable research attention because of its ability to identify pathways or gene modules involved in the underlying mechanisms of diseases. Although many condition-specific gene network identification methods have been developed, they are based on partial or incomplete gene regulatory network information, with most studies only considering the differential expression levels or correlations among genes. However, a single gene-based analysis cannot effectively identify the molecular interactions involved in the mechanisms underlying diseases, which reflect perturbations in specific molecular network functions rather than disorders of a single gene. To comprehensively identify differentially regulated gene networks, we propose a novel computational strategy called comprehensive analysis of differential gene regulatory networks (CIdrgn). Our strategy incorporates comprehensive information on the networks between genes, including the expression levels, edge structures and regulatory effects, to measure the dissimilarity among networks. We extended the proposed CIdrgn to cell line characteristic-specific gene network analysis. Monte Carlo simulations showed the effectiveness of CIdrgn for identifying differentially regulated gene networks with different network structures and scales. Moreover, condition-responsive network identification in cell line characteristic-specific gene network analyses was verified. We applied CIdrgn to identify gastric cancer and itsf chemotherapy (capecitabine and oxaliplatin) -responsive network based on the Cancer Dependency Map. The CXC family of chemokines and cadherin gene family networks were identified as gastric cancer-specific gene regulatory networks, which was verified through a literature survey. The networks of the olfactory receptor family with the ASCL1/FOS family were identified as capecitabine- and oxaliplatin sensitive -specific gene networks. We expect that the proposed CIdrgn method will be a useful tool for identifying crucial molecular interactions involved in the specific biological conditions of cancer cell lines, such as the cancer stage or acquired anticancer drug resistance.

Background and design: The coronavirus disease (COVID-19) pandemic is having a devastating effect worldwide. Host genome differences between populations may influence the severity of COVID-19.The Japan COVID-19 Task Force is conducting host genome analysis of hospitalized patients with COVID-19 from more than 70 institutions nationwide in Japan. This report describes the clinical characteristics of patients enrolled to date. Results: The median (interquartile range) age of the 1674 patients included in the analysis was 59 (45–71) years, and more than half of the patients (66.2%) were male. Less than half of the patients (41.2%) had severe disease. The case fatality rate was 3.2%. Conclusions: Since this is a hospital-based study, the number of severe cases was relatively high, but the case fatality rate was relatively low, when compared to that of other countries. In the future, we will continue to enroll patients and conduct genome analyses of patients with COVID-19.

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Langerhans cell histiocytosis (LCH) and acute myeloid leukemia (AML) are distinct entities of blood neoplasms, and the exact developmental origin of both neoplasms are considered be heterogenous among patients. However, reports of concurrent LCH and AML are rare. Herein we report a novel case of concurrent LCH and AML which shared same the driver mutations, strongly suggesting a common clonal origin.An 84-year-old female presented with cervical lymphadenopathy and pruritic skin rash on the face and scalp. Laboratory tests revealed pancytopenia with 13% of blasts, elevated LDH and liver enzymes, in addition to generalised lymphadenopathy and splenomegaly by computed tomography. Bone marrow specimens showed massive infiltration of MPO-positive myeloblasts, whereas S-100 and CD1a positive atypical dendritic cell-like cells accounted for 10% of the atypical cells on bone marrow pathology, suggesting a mixture of LCH and AML. A biopsy specimen from a cervical lymph node and the skin demonstrated the accumulation of atypical cells which were positive for S-100 and CD1a. LCH was found in lymph nodes, skin and bone marrow; AML was found in peripheral blood and bone marrow (AML was predominant compared with LCH in the bone marrow).Next generation sequencing revealed four somatic driver mutations (NRAS-G13D, IDH2-R140Q, and DNMT3A-F640fs/-I715fs), equally shared by both the lymph node and bone marrow, suggesting a common clonal origin for the concurrent LCH and AML. Prednisolone and vinblastine were initially given with partial response in LCH; peripheral blood blasts also disappeared for 3 months. Salvage chemotherapy with low dose cytarabine and aclarubicin were given for relapse, with partial response in both LCH and AML. She died from pneumonia and septicemia on day 384. Our case demonstrates a common cell of origin for LCH and AML with a common genetic mutation, providing evidence to support the proposal to classify histiocytosis, including LCH, as a myeloid/myeloproliferative malignancy.

Abstract Immune reactions in the tumor microenvironment are an important hallmark of cancer, and emerging immune therapies have been proven effective against several types of cancers. To investigate cancer genome-immune interactions and the role of immunoediting or immune escape mechanisms in cancer development, we analyzed 2834 whole genome and RNA sequencing datasets across 31 distinct tumor types with respect to key immunogenomic aspects and provided comprehensive immunogenomic profiles of pan-cancers. We found that selective copy number changes in immune-related genes may contribute to immune escape. Furthermore, we developed an index of the immunoediting history of each tumor sample based on the information of mutations in exonic regions and pseudogenes and evaluated the immunoediting history of each tumor. Our immuno-genomic analyses of pan-cancers have the potential to identify a subset of tumors with immunogenicity and diverse backgrounds or intrinsic pathways associated with their immune status and immunoediting history.

In chronic myeloid leukaemia (CML), the drivers of blast crisis and resistance to tyrosine kinase inhibitors are not fully characterised. Here, the authors analyse a cohort of CML samples with genomic technologies and find that at least one driver alteration is associated with progression and worse prognosis.

Lineage switches in acute leukemia occur rarely, and the underlying mechanisms are poorly understood. Herein, we report the case of an elderly patient with leukemia in which the leukemia started as B-cell acute lymphoblastic leukemia (B-ALL) and later changed to B- and T-cell mixed phenotype acute leukemia (MPAL) and acute myeloid leukemia (AML) during consecutive induction chemotherapy treatments. A 65-year-old woman was initially diagnosed with Philadelphia chromosome-negative B-ALL primarily expressing TdT/CD34/HLA-DR; more than 20% of the blasts were positive for CD19/CD20/cytoplasmic CD79a/cytoplasmic CD22/CD13/CD71.The blasts were negative for T-lineage markers and myeloperoxidase (MPO). Induction chemotherapy with the standard regimen for B-ALL resulted in primary induction failure. After the second induction chemotherapy regimen, the blasts were found to be B/T bi-phenotypic with additional expression of cytoplasmic CD3. A single course of clofarabine (the fourth induction chemotherapy regimen) dramatically reduced lymphoid marker levels. However, the myeloid markers (e.g., MPO) eventually showed positivity and the leukemia completely changed its lineage to AML. Despite subsequent intensive chemotherapy regimens designed for AML, the patient’s leukemia was uncontrollable and a new monoblastic population emerged. The patient died approximately 8 months after the initial diagnosis without experiencing stable remission. Several cytogenetic and genetic features were commonly identified in the initial diagnostic B-ALL and in the following AML, suggesting that this case should be classified as lineage switching leukemia rather than multiple simultaneous cancers (i.e., de novo B-ALL and de novo AML, or primary B-ALL and therapy-related myeloid neoplasm). A complex karyotype was persistently observed with a hemi-allelic loss of chromosome 17 (the location of the TP53 tumor suppressor gene). As the leukemia progressed, the karyotype became more complex, with the additional abnormalities. Sequential target sequencing revealed an increased variant allele frequency of TP53 mutation. Fluorescent in situ hybridization (FISH) revealed an increased number of mixed-lineage leukemia (MLL) genes, both before and after lineage conversion. In contrast, FISH revealed negativity for MLL rearrangements, which are well-known abnormalities associated with lineage switching leukemia and MPAL. To our best knowledge, this is the first reported case of acute leukemia presenting with lineage ambiguity and MLL gene amplification.

Abstract Background Nanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real-time. Through detecting the change of ion currency signals during a DNA/RNA fragment’s pass through a nanopore, genotypes are determined. Currently, the accuracy of nanopore basecalling has a higher error rate than the basecalling of short-read sequencing. Through utilizing deep neural networks, the-state-of-the art nanopore basecallers achieve basecalling accuracy in a range from 85% to 95%. Result In this work, we proposed a novel basecalling approach from a perspective of instance segmentation. Different from previous approaches of doing typical sequence labeling, we formulated the basecalling problem as a multi-label segmentation task. Meanwhile, we proposed a refined U-net model which we call UR-net that can model sequential dependencies for a one-dimensional segmentation task. The experiment results show that the proposed basecaller URnano achieves competitive results on the in-species data, compared to the recently proposed CTC-featured basecallers. Conclusion Our results show that formulating the basecalling problem as a one-dimensional segmentation task is a promising approach, which does basecalling and segmentation jointly.

Abstract Background Clear cell sarcoma of the kidney (CCSK) is the second most common pediatric renal tumor. Case A 2‐year‐old boy was diagnosed with CCSK, which relapsed four times until he yielded to the disease at the age of 7 years. To characterize the longitudinal genetic alterations occurring in the present case, we performed targeted‐capture sequencing by pediatric solid tumors panel (381 genes) for longitudinally sampled tumors, including autopsy samples of metastasis. Internal tandem duplication of BCOR (BCOR‐ITD) was the only truncal mutation, confirming the previously reported role of BCOR‐ITD in CCSK. Conclusion Acquisition of additional mutations along tumor relapses and detection of metastasis‐specific mutations were reminiscent of the tumor progression and therapeutic resistance of this case, leading to clonal selection and a dismal fate.

Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases; the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs), and contraindications (CIs). Incorporating these different clinical phenotypes of drug–disease associations (DDAs) can improve the prediction accuracy of the DDAs.Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor that supports drug repositioning by submitting known Is, SEs, and CIs for a target drug of interest. The dataset for Is, SEs, and CIs was extracted from PREDICT, SIDER, and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional restricted Boltzmann machine (RBM) enabling the identification of different phenotypes of drug–disease associations, including the prediction of as yet unknown DDAs in the input.Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the drug–disease association network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-nearest neighbors method (KNN, including drug-based KNN and disease-based KNN), Random Forest, and XGBoost. By conducting leave-one-drug-class-out cross-validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects, and contraindications, 7/10, 9/10, and 9/10 hit known drug–disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug–disease associations.

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.

Abstract Background Supercomputers have become indispensable infrastructures in science and industries. In particular, most state-of-the-art scientific results utilize massively parallel supercomputers ranked in TOP500. However, their use is still limited in the bioinformatics field due to the fundamental fact that the asynchronous parallel processing service of Grid Engine is not provided on them. To encourage the use of massively parallel supercomputers in bioinformatics, we developed middleware called Virtual Grid Engine, which enables software pipelines to automatically perform their tasks as MPI programs. Result We conducted basic tests to check the time required to assign jobs to workers by VGE. The results showed that the overhead of the employed algorithm was 246 microseconds and our software can manage thousands of jobs smoothly on the K computer. We also tried a practical test in the bioinformatics field. This test included two tasks, the split and BWA alignment of input FASTQ data. 25,055 nodes (2,000,440 cores) were used for this calculation and accomplished it in three hours. Conclusion We considered that there were four important requirements for this kind of software, non-privilege server program, multiple job handling, dependency control, and usability. We carefully designed and checked all requirements. And this software fulfilled all the requirements and achieved good performance in a large scale analysis.

Understanding the genomic features of dedifferentiated liposarcoma (DDLPS) is likely to uncover new options for management. Here, the authors reveal three prognostic groups, and highlight molecular markers associated with malignant transformation.

Read-depths (RDs) are frequently used in identifying structural variants (SVs) from sequencing data. For existing RD-based SV callers, it is difficult for them to determine breakpoints in single-nucleotide resolution due to the noisiness of RD data and the bin-based calculation. In this paper, we propose to use the deep segmentation model UNet to learn base-wise RD patterns surrounding breakpoints of known SVs. We integrate model predictions with an RD-based SV caller to enhance breakpoints in single-nucleotide resolution. We show that UNet can be trained with a small amount of data and can be applied both in-sample and cross-sample. An enhancement pipeline named RDBKE significantly increases the number of SVs with more precise breakpoints on simulated and real data. The source code of RDBKE is freely available at https://github.com/yaozhong/deepIntraSV.

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line’s (or patient’s) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.

Stepwise acquisition of mutations gives rise to myelodysplastic syndrome (MDS) in older adults. Here, the authors infer the clonal hierarchy of 1809 MDS patients, revealing insights into the evolution of dominant/secondary mutations and how these impact clinical phenotypes like leukemic progression and therapy response.

Abstract: Recent advances in the genetic understanding of acute myeloid leukemia (AML) have improved clinical outcomes in pediatric patients. However, ∼40% of patients with pediatric AML relapse, resulting in a relatively low overall survival rate of ∼70%. The objective of this study was to reveal the comprehensive genetic background of pediatric AML. We performed transcriptome analysis (RNA sequencing [RNA-seq]) in 139 of the 369 patients with de novo pediatric AML who were enrolled in the Japanese Pediatric Leukemia/Lymphoma Study Group AML-05 trial and investigated correlations between genetic aberrations and clinical information. Using RNA-seq, we identified 54 in-frame gene fusions and 1 RUNX1 out-of-frame fusion in 53 of 139 patients. Moreover, we found at least 258 gene fusions in 369 patients (70%) through reverse transcription polymerase chain reaction and RNA-seq. Five gene rearrangements were newly identified, namely, NPM1-CCDC28A, TRIP12-NPM1, MLLT10-DNAJC1, TBL1XR1-RARB, and RUNX1-FNBP1. In addition, we found rare gene rearrangements, namely, MYB-GATA1, NPM1-MLF1, ETV6-NCOA2, ETV6-MECOM, ETV6-CTNNB1, RUNX1-PRDM16, RUNX1-CBFA2T2, and RUNX1-CBFA2T3. Among the remaining 111 patients, KMT2A-PTD, biallelic CEBPA, and NPM1 gene mutations were found in 11, 23, and 17 patients, respectively. These mutations were completely mutually exclusive with any gene fusions. RNA-seq unmasked the complexity of gene rearrangements and mutations in pediatric AML. We identified potentially disease-causing alterations in nearly all patients with AML, including novel gene fusions. Our results indicated that a subset of patients with pediatric AML represent a distinct entity that may be discriminated from their adult counterparts. Based on these results, risk stratification should be reconsidered.

Mismatch repair (MMR)-deficient cancers are characterized by microsatellite instability (MSI) and hypermutation. Here authors reveal a mechanism by which replication stress induces MSI and associated induction of mutations in vitro.

Abstract Intraductal papillary mucinous neoplasm (IPMN) of pancreas has a high risk to develop into invasive cancer or co‐occur with malignant lesion. Therefore, it is important to assess its malignant risk by less‐invasive approach. Pancreatic juice cell‐free DNA (PJD) would be an ideal material in this purpose, but genetic biomarkers for predicting malignant risk from PJD are not yet established. We here performed deep exome sequencing analysis of PJD from 39 IPMN patients with or without malignant lesion. Somatic alterations and copy number alterations (CNAs) detected in PJD were compared with the histologic grade of IPMN to evaluate their potential as a malignancy marker. Somatic mutations of KRAS, GNAS, TP53, and RNF43 were commonly detected in PJD of IPMNs, but no association with the histologic grades of IPMN was found. Instead, mutation burden was positively correlated with the histologic grade (r = 0.427, P = 0.015). We also observed frequent copy number deletions in 17p13 (TP53) and amplifications in 7q21 and 8q24 (MYC) in PJDs. The amplifications in 7q21 and 8q24 were positively correlated with the histologic grade and most prevalent in the cases of invasive carcinoma (P = 0.002 and 7/11; P = 0.011 and 6/11, respectively). We concluded that mutation burden and CNAs detected in PJD may have potential to assess the malignant progression risk of IPMNs.

RAS pathway alterations have been implicated in the pathogenesis of various hematological malignancies. However, their clinical relevance in pediatric acute myeloid leukemia (AML) is not well characterized. We analyzed the frequency, clinical significance, and prognostic relevance of RAS pathway alterations in 328 pediatric patients with de novo AML. RAS pathway alterations were detected in 80 (24.4%) of 328 patients: NF1 (n=7, 2.1%), PTPN11 (n=15, 4.6%), CBL (n=6, 1.8%), NRAS (n=44, 13.4%), KRAS (n=12, 3.7%). Most of these alterations in the RAS pathway were mutually exclusive also together with other aberrations of signal transduction pathways such as FLT3-ITD (P=0.001) and KIT mutation (P=0.004). NF1 alterations were frequently detected in patients with complex karyotype (P=0.031) and were found to be independent predictors of poor overall survival (OS) in multivariate analysis (P=0.007). At least four of seven patients with NF1 alterations had biallelic inactivation. NRAS mutations were frequently observed in patients with CBFB-MYH11 and were independent predictors of favorable outcomes in multivariate analysis (OS, P=0.023; event-free survival [EFS], P=0.037). Patients with PTPN11 mutations more frequently received stem cell transplantation (P=0.035) and showed poor EFS than patients without PTPN11 mutations (P=0.013). Detailed analysis of RAS pathway alterations may enable a more accurate prognostic stratification of pediatric AML and may provide novel therapeutic molecular targets related to this signal transduction pathway.

[This corrects the article DOI: 10.1371/journal.pone.0241508.].

Neuroblastoma, the most common extracranial solid malignancy among children, originates from undifferentiated neural crest cells (NCC). Despite recent intensified treatment, high-risk patients still have a high mortality rate. To explore a new therapeutic strategy, we performed an integrated genomic and transcriptomic analysis of 30 high-risk neuroblastoma cases. Based on the expression profiling of RNA sequencing, neuroblastoma was classified into Mesenchymal (MES; n = 5) and Noradrenergic (ADRN; n = 25) clusters, as previously reported in the super-enhancer landscape. The expression patterns in MES-cluster cases were similar to normal adrenal glands, with enrichment in secretion-related pathways, suggesting chromaffin cell-like features built from NCC-derived Schwann cell precursors (SCPs). In contrast, neuron-related pathways were enriched in the ADRN-cluster, indicating sympathoblast features reported to originate from NCC but not via SCPs. Thus, MES- and ADRN-clusters were assumed to be corresponding to differentiation pathways through SCP and sympathoblast, respectively. ADRN-cluster cases were further classified into MYCN- and ATRX-clusters, characterized by genetic alterations, MYCN amplifications and ATRX alterations, respectively. MYCN-cluster cases showed high expression of ALDH18A1, encoding P5CS related to proline production. As reported in other cancers, this might cause reprogramming of proline metabolism leading to tumor specific proline vulnerability candidate for a target therapy of metabolic pathway. In ATRX-cluster, SLC18A2 (VMAT2), an enzyme known to prevent cell toxicity due to the oxidation of dopamine, was highly expressed and VMAT2 inhibitor (GZ-793A) represented significant attenuation of cell growth in NB-69 cell line (high SLC18A2 expression, no MYCN amplification) but not in IMR-32 cell line (MYCN amplification). In addition, the correlation of VMAT2 expression with metaiodobenzylguanidine (MIBG) avidity suggested a combination of VMAT2 inhibitor and MIBG radiation for a novel potential therapeutic strategy in ATRX-cluster cases. Thus, targeting the characteristics of unique neuroblastomas may prospectively improve prognosis.