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The 2-in-1 adaptive design (Chen et al. 2018) allows seamless expansion of an ongoing Phase 2 trial into a Phase 3 trial to expedite a drug development program. Under a mild assumption expected to generally hold in practice, as Slepian's lemma guarantees, the trial can be tested at the full alpha level with or without expansion, sacrificing no statistical power. The endpoint used for expansion decisions can be the same as or different from the primary endpoints, and there is no restriction on the expansion threshold. Due to its flexibility and robustness, it has drawn immediate attention from academic researchers and industry practitioners. The design has been substantially extended in the literature and successfully implemented in multiple trials. Group sequential methods are a cornerstone in trial monitoring. A preliminary investigation (Chen, Li, and Deng) suggests that it can be naturally incorporated into the 2-in-1 design without providing formal mathematical proof. In this article, we fill the gap by providing a sufficient condition that is expected to generally hold in practice to unlock the full potential of the 2-in-1 design and pave the way for its broader applications. In practice, the condition can be verified with trial data as needed using simulation studies per the FDA guideline on adaptive designs. We also discuss a special case that guarantees the validity without the need for any simulation checking. [ABSTRACT FROM AUTHOR]

The shape of the relationship between a continuous exposure variable and a binary disease variable is often central to epidemiologic investigations. This article investigates a number of issues surrounding inference and the shape of the relationship. Presuming that the relationship can be expressed in terms of regression coefficients and a shape parameter, we investigate how well the shape can be inferred in settings which might typify epidemiologic investigations and risk assessment. We also consider a suitable definition of the median effect of exposure, and investigate how precisely this can be inferred. This is done both in the case of using a model acknowledging uncertainty about the shape parameter and in the case of ignoring this uncertainty and using a two‐step method, where in step one we transform the predictor and in step two we fit a simple logistic model with transformed predictor. All these investigations require a family of exposure‐disease relationships indexed by a shape parameter. For this purpose, we employ a family based on the Box–Cox transformation. [ABSTRACT FROM AUTHOR]

Background: The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice. Methods: In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data. Results: We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival. Conclusion: In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment. [ABSTRACT FROM AUTHOR]

Background: Macrophages and monocytes orchestrate inflammatory processes in the lungs. However, their role in the pathogenesis of chronic obstructive pulmonary disease (COPD), an inflammatory condition, is not well known. Here, we determined the characteristics of these cells in lungs of COPD patients and identified novel therapeutic targets. Methods: We analyzed the RNA sequencing (scRNA-seq) data of explanted human lung tissue from COPD (n = 18) and control (n = 28) lungs and found 16 transcriptionally distinct groups of macrophages and monocytes. We performed pathway and gene enrichment analyses to determine the characteristics of macrophages and monocytes from COPD (versus control) lungs and to identify the therapeutic targets, which were then validated using data from a randomized controlled trial of COPD patients (DISARM). Results: In the alveolar macrophages, 176 genes were differentially expressed (83 up- and 93 downregulated; Padj < 0.05, |log2FC| > 0.5) and were enriched in downstream biological processes predicted to cause poor lipid uptake and impaired cell activation, movement, and angiogenesis in COPD versus control lungs. Classical monocytes from COPD lungs harbored a differential gene set predicted to cause the activation, mobilization, and recruitment of cells and a hyperinflammatory response to influenza. In silico, the corticosteroid fluticasone propionate was one of the top compounds predicted to modulate the abnormal transcriptional profiles of these cells. In vivo, a fluticasone–salmeterol combination significantly modulated the gene expression profiles of bronchoalveolar lavage cells of COPD patients (p < 0.05). Conclusions: COPD lungs harbor transcriptionally distinct lung macrophages and monocytes, reflective of a dysfunctional and hyperinflammatory state. Inhaled corticosteroids and other compounds can modulate the transcriptomic profile of these cells in patients with COPD. [ABSTRACT FROM AUTHOR]

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 and implementation An R package is being developed. In the meantime, R code can be requested by email. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: MNase-Seq and ChIP-Seq have evolved as popular techniques to study chromatin and histone modification. Although many tools have been developed to identify enriched regions, software tools for nucleosome positioning are still limited. We introduce a flexible and powerful open-source R package, PING 2.0, for nucleosome positioning using MNase-Seq data or MNase– or sonicated– ChIP-Seq data combined with either single-end or paired-end sequencing. PING uses a model-based approach, which enables nucleosome predictions even in the presence of low read counts. We illustrate PING using two paired-end datasets from Saccharomyces cerevisiae and compare its performance with nucleR and ChIPseqR.Availability: PING 2.0 is available from the Bioconductor website at http://bioconductor.org. It can run on Linux, Mac and Windows.Contact: rgottard@fhcrc.orgSupplementary Information: Supplementary material is available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Dementia is characterized as a decline in cognitive function, including memory, language and problem-solving abilities. In this paper, we conducted a Genome-Wide Association Study (GWAS) using data from the electronic Medical Records and Genomics (eMERGE) network. This study has two aims, (1) to investigate the genetic mechanism of dementia and (2) to discuss multiple p-value thresholds used to address multiple testing issues. Using the genome-wide significant threshold ( p ≤ 5 × 10 − 8 ), we identified four SNPs. Controlling the False Positive Rate (FDR) level below 0.05 leads to one extra SNP. Five SNPs that we found are also supported by QQ-plot comparing observed p-values with expected p-values. All these five SNPs belong to the TOMM40 gene on chromosome 19. Other published studies independently validate the relationship between TOMM40 and dementia. Some published studies use a relaxed threshold ( p ≤ 1 × 10 − 5 ) to discover SNPs when the statistical power is insufficient. This relaxed threshold is more powerful but cannot properly control false positives in multiple testing. We identified 13 SNPs using this threshold, which led to the discovery of extra genes (such as ATP10A-DT and PTPRM). Other published studies reported these genes as related to brain development or neuro-development, indicating these genes are potential novel genes for dementia. Those novel potential loci and genes may help identify targets for developing new therapies. However, we suggest using them with caution since they are discovered without proper false positive control. [ABSTRACT FROM AUTHOR]

Paralytic shellfish toxins (PST) in shellfish products have led to severe risks to human health. To monitor the risk, the Canadian Shellfish Sanitation Program has been collecting longitudinal PST measurements in blue mussel (Mytilus edulis) and soft-shell clam (Mya arenaria) samples in six coastal provinces of Canada. The spatial distributions of major temporal variation patterns were studied via Functional Principal Component Analysis. Seasonal increases in PST contamination were found to vary the most in terms of magnitude along the coastlines, which provides support for location-specific management of the time-sensitive PST contamination. In British Columbia, the first functional principal component (FPC1) indicated the variance among the magnitudes, while FPC2 indicated the seasonality of the PST levels. The temporal variations tended to be positively correlated with the abundance of dianoflagellates Alexandrium spp., and negatively with precipitation and inorganic nutrients. These findings indicate the underlying mechanism of PST variation in various geographical settings. In New Brunswick, Prince Edward, and Nova Scotia, the top FPCs indicated that the PST contamination differed mostly in the seasonal increase of the PST level during summer. • Multi-decade Paralytic Shellfish Toxins (PST) in Canada were analyzed. • Shellfish harvesting sites differed the most in seasonal elevation of PST levels. • The spring-to-autumn period of increased PST was found in BC, QC, NB, and NS. • Rainfall, Alexandrium spp., nitrate/nitrite, and phosphate correlated with PST. • Higher seasonal increases tended to occur at locations of strong water mixing. [ABSTRACT FROM AUTHOR]

Survival analysis is a technique for identifying prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11 000 adult and >4000 pediatric tumor cases with clinical outcomes and multiomics 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: (i) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (ii) survival analysis not only at the gene, but also the GS level; and (iii) 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. Altogether, 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. [ABSTRACT FROM AUTHOR]

Under continuous, glucose-limited conditions, budding yeast exhibit robust metabolic cycles associated with major oscillations of gene expression. How such fluctuations are linked to changes in chromatin status is not well understood. Here we examine the correlated genome-wide transcription and chromatin states across the yeast metabolic cycle at unprecedented temporal resolution, revealing a 'just-in-time supply chain' by which components from specific cellular processes such as ribosome biogenesis become available in a highly coordinated manner. We identify distinct chromatin and splicing patterns associated with different gene categories and determine the relative timing of chromatin modifications relative to maximal transcription. There is unexpected variation in the chromatin modification and expression relationship, with histone acetylation peaks occurring with varying timing and 'sharpness' relative to RNA expression both within and between cycle phases. Chromatin-modifier occupancy reveals subtly distinct spatial and temporal patterns compared to those of the modifications themselves. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (eg, expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over two million hits by keyword searches on Google Scholar. However, designing clinical trials that utilize the discovered uncertain subsets/biomarkers is not trivial and rarely discussed in the literature. This 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 a published 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 of our estimated study power is a finite number not affected by dimensionality. To design clinical trials incorporating the potential biomarkers, users can use our software "DesignCTPB". This software can be found on Github and will be available as an R package on CRAN. 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. [ABSTRACT FROM AUTHOR]

Epidemiologic and animal studies have shown that exposure to particulate matter air pollution (PM) is a risk factor for the development of atherosclerosis. Whether PM-induced lung and systemic inflammation is involved in this process is not clear. We hypothesized that PM exposure causes lung and systemic inflammation, which in turn leads to vascular endothelial dysfunction, a key step in the initiation and progression of atherosclerosis. New Zealand White rabbits were exposed for 5 days (acute, total dose 8 mg) and 4 wk (chronic, total dose 16 mg) to either PM smaller than 10 μm (PM[sub10]) or saline intratracheally. Lung inflammation was quantified by morphometry; systemic inflammation was assessed by white blood cell and platelet counts and serum interleukin (lL)-6, nitric oxide, and endothelin levels. Endothelial dysfunction was assessed by vascular response to acetylcholine (ACh) and sodium nitroprusside (SNP). PM[sub10] exposure increased lung macrophages (P < 0.02), macrophages containing particles (P < 0.001), and activated macrophages (P < 0.006). PM[sub10] increased serum IL-6 levels in the first 2 wk of exposure (P < 0.05) but not in weeks 3 or 4. PM10 exposure reduced ACh-related relaxation of the carotid artery with both acute and chronic exposure, with no effect on SNP-induced vasodilatation. Serum IL-6 levels correlated with macrophages containing particles (P = 0.043) and ACh-induced vasodilatation (P = 0.014 at week 1, P = 0.021 at week 2). Exposure to PM[sub10] caused lung and systemic inflammation that were both associated with vascular endothelial dysfunction. This suggests that PM-induced lung and systemic inflammatory responses contribute to the adverse vascular events associated with exposure to air pollution. [ABSTRACT FROM AUTHOR]

Background: There is controversy about whether therapy with inhaled corticosteroids (ICSs) modifies the natural history of COPD, characterized by an accelerated decline in FEV(1).Methods: The Inhaled Steroids Effect Evaluation in COPD (ISEEC) study is a pooled study of patient-level data from seven long-term randomized controlled trials of ICS vs placebo lasting >/= 12 months in patients with moderate-to-severe COPD. We have previously reported a survival benefit for ICS therapy in COPD patients using ISEEC data. We aimed to determine whether the regular use of ICSs vs placebo improves FEV(1) decline in COPD patients, and whether this relationship is modified by gender and smoking.Results: There were 3,911 randomized participants (29.2% female) in this analysis. In the first 6 months after randomization, ICS use was associated with a significant mean (+/- SE) relative increase in FEV(1) of 2.42 +/- 0.19% compared with placebo (p < 0.01), which is quantifiable in absolute terms as 42 mL in men and 29 mL in women over 6 months. From 6 to 36 months, there was no significant difference between placebo and ICS therapy in terms of FEV(1) decline (-0.01 +/- 0.09%; p = 0.86). The initial treatment effect was dependent on smoking status and gender. Smokers who continued to smoke had a smaller increase in FEV(1) during the first 6 months than did ex-smokers. Female ex-smokers had a larger increase in FEV(1) with ICS therapy than did male ex-smokers.Conclusions: We conclude that in COPD in the first 6 months of treatment, ICS therapy is more effective in ex-smokers than in current smokers with COPD in improving lung function, and women may have a bigger response to ICSs than men. However, it seems that after 6 months, ICS therapy does not modify the decline in FEV(1) among those who completed these randomized clinical trials. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approach by showing better controls of false discovery rate (FDR) and higher sensitivity. We re-analyzed two real studies to identifying SNPs associated with severe bortezomib-induced peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The original analysis in the literature failed to identify SNPs after FDR adjustment. Our proposed method not only detected the reported SNPs after FDR adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has been reported to be related to MM in another study. [ABSTRACT FROM AUTHOR]