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Table S1:Summary of baseline pathological diagnosis; Table S2:Noncompartmental Pharmacokinetic Summary Following Oral Administration of ralimetinib on Day 1 and on Day 14 (Cycle 1) - Capsule Formulation; Table S3: Noncompartmental Pharmacokinetic Summary Following Oral Administration of ralimetinib on Day 1 and on Day 14 (Cycle 1) - Tablet Formulation

Trial Profile

Purpose: p38 MAPK regulates the production of cytokines in the tumor microenvironment and enables cancer cells to survive despite oncogenic stress, radiotherapy, chemotherapy, and targeted therapies. Ralimetinib (LY2228820 dimesylate) is a selective small-molecule inhibitor of p38 MAPK. This phase I study aimed to evaluate the safety and tolerability of ralimetinib, as a single agent and in combination with tamoxifen, when administered orally to patients with advanced cancer.Experimental Design: The study design consisted of a dose-escalation phase performed in a 3+3 design (Part A; n = 54), two dose-confirmation phases [Part B at 420 mg (n = 18) and Part C at 300 mg (n = 8)], and a tumor-specific expansion phase in combination with tamoxifen for women with hormone receptor–positive metastatic breast cancer refractory to aromatase inhibitors (Part D; n = 9). Ralimetinib was administered orally every 12 hours on days 1 to 14 of a 28-day cycle.Results: Eighty-nine patients received ralimetinib at 11 dose levels (10, 20, 40, 65, 90, 120, 160, 200, 300, 420, and 560 mg). Plasma exposure of ralimetinib (Cmax and AUC) increased in a dose-dependent manner. After a single dose, ralimetinib inhibited p38 MAPK–induced phosphorylation of MAPKAP-K2 in peripheral blood mononuclear cells. The most common adverse events, possibly drug-related, included rash, fatigue, nausea, constipation, pruritus, and vomiting. The recommended phase II dose was 300 mg every 12 hours as monotherapy or in combination with tamoxifen. Although no patients achieved a complete response or partial response,19 patients (21.3%) achieved stable disease with a median duration of 3.7 months, with 9 of these patients on study for ≥6 cycles.Conclusions: Ralimetinib demonstrated acceptable safety, tolerability, and pharmacokinetics for patients with advanced cancer. Clin Cancer Res; 22(5); 1095–102. ©2015 AACR.

MotivationSingle-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis is often to distinguish cell types so they can be investigated separately. Researchers have recently developed several automated cell-type annotation tools, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data widely used in differential expression analysis. However, no current cell annotation method explicitly utilizes dropout information. Fully utilizing dropout information motivated this work.ResultsWe present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene’s marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using 14 real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods. Furthermore, because of its distinct modelling strategy, scAnnotate’s misclassified cells differ greatly from competitor methods. This suggests using scAnnotate together with other methods could further improve annotation accuracy.Availability and implementationWe implemented scAnnotate as an R package and made it publicly available from CRAN: https://cran.r-project.org/package=scAnnotate.Supplementary informationSupplementary data are available at Bioinformatics Advances online.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein–protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

Classification tree is a widely used machine learning method. It has multiple implementations as R packages, rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision, evtree is the best in Recall, F1-score and AUC, C5.0 prefers more complex trees, tree is consistently much faster than others, although its complexity is often higher than others.

MotivationSingle-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis is often to distinguish cell types so they can be investigated separately. Researchers have recently developed several automated cell type annotation tools, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data widely used in differential expression analysis. However, dropout information is not explicitly used by any current cell annotation method. Fully utilizing dropout information for cell type annotation motivated this work.ResultsWe present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene’s marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using fourteen real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods. Furthermore, because of its distinct modelling strategy, scAnnotate’s misclassified cells are very different from competitor methods. This suggests using scAnnotate together with other methods could further improve annotation accuracy.AvailabilityWe implemented scAnnotate as an R package and made it publicly available from CRAN.ContactXuekui Zhang: xuekui@uvic.ca and Li Xing: li.xing@math.usask.ca

Genome-wide association study (GWAS) is a popular approach to investigate relationships between genetic information and diseases. A number of associations are tested in a study and the results are often corrected using multiple adjustment methods. It is observed that GWAS studies suffer adequate statistical power for reliability. Hence, we document known models for reliability assessment using improved statistical power in GWAS analysis.

We study the impact of capital market openness on high‐frequency market quality in China. The Shanghai–Hong Kong Stock Connect program (SHHKConnect) opens China's stock market to foreign investors and offers a natural experiment to investigate this question. Using a difference‐in‐differences approach, we find that market liberalization leads to lower quoted spread, lower effective spread, lower market depth, and higher short‐term volatility. Our findings imply that opening the markets to more sophisticated foreign investors is associated with higher competition and more cross‐market arbitrage activities, narrowing the spread and reducing liquidity providers’ profits, but increasing the price impact and short‐term volatility of connected stocks.

Survival analysis is a technique to identify prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11,000 adult and >4,000 paediatric tumor cases with clinical outcomes and multi-omics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database, and offers three main advances: (a) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (b) survival analysis not only at the gene, but also the GS level; and (c) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Together, these analyses demonstrate cSurvival’s ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.Key pointsWe developed cSurvival, an advanced framework using clinical correlations to study biomarker interactions in cancers, with source code and curated datasets freely available for allcSurvival includes new algorithms to identify optimal cutoffs for two continuous predictors to stratify patients into risk groups, enabling for the first time joint analysis with two genomic predictors;cSurvival allows survival analysis at the gene set (GS) level with comprehensive and up-to-date GS librariesThe cSurvival pipeline integrates clinical outcomes and experimental cancer cell line data to generate synergistic biological insights and to mine for appropriate preclinical cell line toolscSurvival is built on a manually curated cancer outcomes database

Background Many countries have implemented lockdowns to reduce COVID-19 transmission. However, there is no consensus on the optimal timing of these lockdowns to control community spread of the disease. Here we evaluated the relationship between timing of lockdowns, along with other risk factors, and the growth trajectories of COVID-19 across 3,112 counties in the US. Methods We ascertained dates for lockdowns and implementation of various non-pharmaceutical interventions at a county level and merged these data with those of US census and county-specific COVID-19 daily cumulative case counts. We then applied a Functional Principal Component (FPC) analysis on this dataset to generate FPC scores, which were used as a surrogate variable to describe the trajectory of daily cumulative case counts for each county. We used machine learning methods to identify risk factors including the timing of lockdown that significantly influenced the FPC scores. Findings We found that the first eigen-function accounted for most (>92%) of the variations in the daily cumulative case counts. The impact of lockdown timing on the total daily case count of a county became significant beginning approximately 7 days prior to that county reporting at least 5 cumulative cases of COVID-19. Delays in lockdown implementation after this date led to a rapid acceleration of COVID-19 spread in the county over the first ~50 days from the date with at least 5 cumulative cases, and higher case counts across the entirety of the follow-up period. Other factors such as total population, median family income, Gini index, median age, and within-county mobility also had a substantial effect. When adjusted for all these factors, the timing of lockdowns was the most significant risk factor associated with the county-specific daily cumulative case counts. Interpretation Lockdowns are an effective way of controlling the spread of COVID-19 in communities. Significant delays in lockdown cause a dramatic increase in the cumulative case counts. Thus, the timing of the lockdown relative to the case count is an important consideration in controlling the pandemic in communities. Funding The study period is from June 2020 to July 2021. Dr. Xuekui Zhang is a Tier 2 Canada Research Chairs (Grant No. 950231363) and funded by Natural Sciences and Engineering Research Council of Canada (Grant No. RGPIN201704722). Dr. Li Xing is funded by Natural Sciences and Engineering Research Council of Canada (Grant Number: RGPIN 202103530). This research was enabled in part by support provided by WestGrid (www.westgrid.ca) and Compute Canada (www.computecanada.ca). The computing resource is provided by Compute Canada Resource Allocation Competitions #3495 (PI: Xuekui Zhang) and #1551 (PI: Li Xing). Dr. Don Sin is a Tier 1 Canada Research Chair in COPD and holds the de Lazzari Family Chair at the Heart Lung Innovation, Vancouver, Canada.

BackgroundCOVID-19 is a highly transmissible infectious disease that has infected over 122 million individuals worldwide. To combat this pandemic, governments around the world have imposed lockdowns. However, the impact of these lockdowns on the rates of COVID-19 transmission in communities is not well known. Here, we used COVID-19 case counts from 3,000+ counties in the United States (US) to determine the relationship between lockdown as well as other county factors and the rate of COVID-19 spread in these communities.MethodsWe merged county-specific COVID-19 case counts with US census data and the date of lockdown for each of the counties. We then applied a Functional Principal Component (FPC) analysis on this dataset to generate scores that described the trajectory of COVID-19 spread across the counties. We used machine learning methods to identify important factors in the county including the date of lockdown that significantly influenced the FPC scores.FindingsWe found that the first FPC score accounted for up to 92.81% of the variations in the absolute rates of COVID-19 as well as the topology of COVID-19 spread over time at a county level. The relation between incidence of COVID-19 and time at a county level demonstrated a hockey-stick appearance with an inflection point approximately 7 days prior to the county reporting at least 5 new cases of COVID-19; beyond this inflection point, there was an exponential increase in incidence. Among the risk factors, lockdown and total population were the two most significant features of the county that influenced the rate of COVID-19 infection, while the median family income, median age and within-county move also substantially affect COVID spread.InterpretationLockdowns are an effective way of controlling the COVID-19 spread in communities. However, significant delays in lockdown cause a dramatic increase in the case counts. Thus, the timing of the lockdown relative to the case count is an important consideration in controlling the pandemic in communities.Research in contextEvidence before this studyWe searched PubMed using the term “coronavirus”, OR “COVID-19”, OR “COVID-19 infection”, OR “SARS-CoV-2” combined with “Lockdown” or “sociodemographic factor” or “Vulnerability” for original articles published before March 18, 2021. Similar searches were done in medRxiv, Google Scholar, and Web of Science. Only papers published in English were reviewed. The most similar relevant works to our study were Acharya et al.1 and Karmakar et al.2, which investigated the associations between population-level social factors and COVID-19 incidence and mortality. Unlike our current study, which employed a longitudinal design, both of studies were cross-sectional in nature and thus fixed on a single time point. In addition, neither of these studies investigated the impact of lockdown measures on COVID-19 infection patterns. Another relevant study is Alfano et al.’s work3, which focused on the efficacy of lockdown on COVID-19 case rates. However, this study did not evaluate the timing of lockdown on this endpoint.Added value of this studyTo our knowledge, this is the first study to use functional principal component analysis (FPCA) to investigate COVID-19 infection trajectories (in a longitudinal manner) and their relationships with different sociodemographic factors and lockdown policy at a county level. The FPCA transformed a longitudinal vector with high-dimensions into a “single” surrogate variable, which retained 93% of the information. We used an advanced statistical model (segmented regression) to investigate the effects of lockdown on incidence of COVID-19 across the US. We found that the relationship had a “hockey stick” appearance with an inflection point at ∼7 days prior to a county reporting at least 5 cases of COVID-19. We also applied a machine learning model (i.e., elastic net) to explore joint effects of lockdown and other sociodemographic factors on COVID-19 infection patterns, which estimated the impact of each of factors, adjusted for each other.Implications of all the available evidenceOur study suggests that lockdown is an effective policy to reduce case counts of COVID-19 in communities; however, significant delays in its implementation result in exponential growth of COVID-19. The inflection point is approximately 7 days prior to a county reporting at least 5 cases of COVID-19. These data will help policy-makers to determine the optimal timing of lockdowns for their communities.

Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, the annotations of cell subtypes are often done manually, which is time-consuming and irreproducible. Garnett is a cell-type annotation software based the on elastic net method. Besides cell-type annotation, supervised machine learning methods can also be applied to predict other cell phenotypes from genomic data. Despite the popularity of such applications, there is no existing study to systematically investigate the performance of those supervised algorithms in various sizes of scRNA-seq data sets. Methods and Results: This study evaluates 13 popular supervised machine learning algorithms to classify cell phenotypes, using published real and simulated data sets with diverse cell sizes. The benchmark contained two parts. In the first part, we used real data sets to assess the popular supervised algorithms' computing speed and cell phenotype classification performance. The classification performances were evaluated using AUC statistics, F1-score, precision, recall, and false-positive rate. In the second part, we evaluated gene selection performance using published simulated data sets with a known list of real genes. Conclusion: The study outcomes showed that ElasticNet with interactions performed best in small and medium data sets. NB was another appropriate method for medium data sets. In large data sets, XGB works excellent. Ensemble algorithms were not significantly superior to individual machine learning methods. Adding interactions to ElasticNet can help, and the improvement was significant in small data sets., Comment: 21 pages, 4 figures, 1 table

As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (e.g. expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over 2 million hits by keyword searches on Google Scholar. However, how to properly incorporate the identified subsets/biomarkers to design clinical trials is not trivial and rarely discussed in the literature, which leads to a gap between research results and real-world drug development. To fill in this gap, we formulate the problem of clinical trial design into an optimization problem involving high-dimensional integration, and propose a novel computational solution based on Monte-Carlo and smoothing methods. Our method utilizes the modern techniques of General-Purpose computing on Graphics Processing Units for large-scale parallel computing. Compared to the standard method in three-dimensional problems, our approach is more accurate and 133 times faster. This advantage increases when dimensionality increases. Our method is scalable to higher-dimensional problems since the precision bound is a finite number not affected by dimensionality. Our software will be available on GitHub and CRAN, which can be applied to guide the design of clinical trials to incorporate the biomarker better. Although our research is motivated by the design of clinical trials, the method can be used widely to solve other optimization problems involving high-dimensional integration., Comment: 18 pages, 3 figures, 1 table

Genome-wide association study (GWAS) is a popular approach to investigate relationships between genetic information and diseases. A number of associations are tested in a study and the results are often corrected using multiple adjustment methods. It is observed that GWAS studies suffer adequate statistical power for reliability. Hence, we document known models for reliability assessment using improved statistical power in GWAS analysis.

Summary Background The signaling protein p38 mitogen-activated protein kinase (MAPK) regulates the tumor cell microenvironment, modulating cell survival, migration, and invasion. This phase 1 study evaluated the safety of p38 MAPK inhibitor LY3007113 in patients with advanced cancer to establish a recommended phase 2 dose. Methods In part A (dose escalation), LY3007113 was administered orally every 12 h (Q12H) at doses ranging from 20 mg to 200 mg daily on a 28-day cycle until the maximum tolerated dose (MTD) was reached. In part B (dose confirmation), patients received MTD. Safety, pharmacokinetics, pharmacodynamics, and tumor response data were evaluated. Results MTD was 30 mg Q12H. The most frequent treatment-related adverse events (>10%) were tremor, rash, stomatitis, increased blood creatine phosphokinase, and fatigue. Grade ≥ 3 treatment-related adverse events included upper gastrointestinal haemorrhage and increased hepatic enzyme, both occurring at 40 mg Q12H and considered dose-limiting toxicities. LY3007113 exhibited an approximately dose-proportional increase in exposure and time-independent pharmacokinetics after repeated dosing. Maximal inhibition (80%) of primary biomarker MAPK-activated protein kinase 2 in peripheral blood mononuclear cells was not reached, and sustained minimal inhibition (60%) was not maintained for 6 h after dosing to achieve a biologically effective dose (BED). The best overall response in part B was stable disease in 3 of 27 patients. Conclusions The recommended phase 2 dosage of LY3007113 was 30 mg Q12H. Three patients continued treatment after the first radiographic assessment, and the BED was not achieved. Further clinical development of this compound is not planned as toxicity precluded achieving a biologically effective dose.

Motivation: HIV is difficult to treat because its virus mutates at a high rate and mutated viruses easily develop resistance to existing drugs. If the relationships between mutations and drug resistances can be determined from historical data, patients can be provided personalized treatment according to their own mutation information. The HIV Drug Resistance Database was built to investigate the relationships. Our goal is to build a model using data in this database, which simultaneously predicts the resistance of multiple drugs using mutation information from sequences of viruses for any new patient. Results: We propose two variations of a stacking algorithm which borrow information among multiple prediction tasks to improve multivariate prediction performance. The most attractive feature of our proposed methods is the flexibility with which complex multivariate prediction models can be constructed using any univariate prediction models. Using cross-validation studies, we show that our proposed methods outperform other popular multivariate prediction methods. Availability: An R package will be made available., Comment: 15 pages, 5 figures

Purpose: p38 MAPK regulates the production of cytokines in the tumor microenvironment and enables cancer cells to survive despite oncogenic stress, radiotherapy, chemotherapy, and targeted therapies. Ralimetinib (LY2228820 dimesylate) is a selective small-molecule inhibitor of p38 MAPK. This phase I study aimed to evaluate the safety and tolerability of ralimetinib, as a single agent and in combination with tamoxifen, when administered orally to patients with advanced cancer. Experimental Design: The study design consisted of a dose-escalation phase performed in a 3+3 design (Part A; n = 54), two dose-confirmation phases [Part B at 420 mg (n = 18) and Part C at 300 mg (n = 8)], and a tumor-specific expansion phase in combination with tamoxifen for women with hormone receptor–positive metastatic breast cancer refractory to aromatase inhibitors (Part D; n = 9). Ralimetinib was administered orally every 12 hours on days 1 to 14 of a 28-day cycle. Results: Eighty-nine patients received ralimetinib at 11 dose levels (10, 20, 40, 65, 90, 120, 160, 200, 300, 420, and 560 mg). Plasma exposure of ralimetinib (Cmax and AUC) increased in a dose-dependent manner. After a single dose, ralimetinib inhibited p38 MAPK–induced phosphorylation of MAPKAP-K2 in peripheral blood mononuclear cells. The most common adverse events, possibly drug-related, included rash, fatigue, nausea, constipation, pruritus, and vomiting. The recommended phase II dose was 300 mg every 12 hours as monotherapy or in combination with tamoxifen. Although no patients achieved a complete response or partial response,19 patients (21.3%) achieved stable disease with a median duration of 3.7 months, with 9 of these patients on study for ≥6 cycles. Conclusions: Ralimetinib demonstrated acceptable safety, tolerability, and pharmacokinetics for patients with advanced cancer. Clin Cancer Res; 22(5); 1095–102. ©2015 AACR.

Glioblastoma is a type of glioma with a relatively higher degree of malignancy that may result in severe intracranial hypertension and focal symptoms. Surgery is the preferred treatment modality. Combination therapy including radiotherapy, chemotherapy, gene therapy, immunotherapy and targeted therapy have also been employed. However, due to the invasiveness and pathogenesis of the disease, such treatments do not yield satisfactory outcomes. The aim of the present study was to examine the expression of microRNA (miR)-184 in Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) signaling pathway in the mechanism of glioblastoma formation, thus providing a new basis for the mechanism of glioblastoma induction. The LN18 cell line was employed in the present study. After undergoing thawing, culturing and passaging processes, the cells were divided into the set control group, miR-184 mimic group (transfer miR-184 simulator) and miR-184 group. The expression of miR-184 was detected using quantitative polymerase chain reaction. An MTT assay was used to detect the proliferation ability of glioma cells, and clone formation ability was also detected. The cell scratch and invasion assays were used to identify the cell invasion ability. Western blotting was performed to detect the expression level of p-JAK2 and p-STAT3 proteins. The results showed that compared to the control group, the expression of miR-184 in the miR-184 mimic group increased. Cell proliferation, as well as clone formation and invasion ability were enhanced. The number of cells penetrating septum, as well as the expression of p-JAK2 and p-STAT3 proteins were increased. Differences were statistically significant (P