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MicroRNAs (miRNAs) are small endogenous non-coding functional RNAs that post-transcriptionally regulate gene expression. They play essential roles in nearly all biological processes including cell development and differentiation, DNA damage repair, cell death as well as intercellular communication. They are highly involved in cancer, acting as tumor suppressors and/or promoters to modulate cell proliferation, epithelial-mesenchymal transition and tumor invasion and metastasis. Recent studies have shown that more than half of miRNAs are located within protein-coding or non-coding genes. Intragenic miRNAs and their host genes either share the promoter or have independent transcription. Meanwhile, miRNAs work as partners or antagonists of their host genes by fine-tuning their target genes functionally associated with host genes. This review outlined the complicated relationship between intragenic miRNAs and host genes. Focusing on miRNAs known as oncogenes or tumor suppressors in specific cancer types, it studied co-expression relationships between these miRNAs and host genes in the cancer types using TCGA data sets, which validated previous findings and revealed common, tumor-specific and even subtype-specific patterns. These observations will help understand the function of intragenic miRNAs and further develop miRNA therapeutics in cancer. [ABSTRACT FROM AUTHOR]

Background: Our preclinical work revealed tumour hypoxia induces homologous recombination deficiency (HRD), increasing sensitivity to Poly (ADP-ribose) polymerase inhibitors. We aimed to induce tumour hypoxia with ramucirumab thereby sensitising tumours to olaparib. Patients and methods: This multi-institution single-arm Phase 1/2 trial enrolled patients with metastatic gastroesophageal adenocarcinoma refractory to ≥1 systemic treatment. In dose escalation, olaparib was evaluated at escalating dose levels with ramucirumab 8 mg/kg day 1 in 14-day cycles. The primary endpoint of Phase 1 was the recommended Phase 2 dose (RP2D), and in Phase 2 the primary endpoint was the overall response rate (ORR). Results: Fifty-one patients received ramucirumab and olaparib. The RP2D was olaparib 300 mg twice daily with ramucirumab 8 mg/kg. In evaluable patients at the RP2D the ORR was 6/43 (14%) (95% CI 4.7–25.6). The median progression-free survival (PFS) was 2.8 months (95% CI 2.3–4.2) and median overall survival (OS) was 7.3 months (95% CI 5.7–13.0). Non-statistically significant improvements in PFS and OS were observed for patients with tumours with mutations in HRD genes. Conclusions: Olaparib and ramucirumab is well-tolerated with efficacy that exceeds historical controls with ramucirumab single agent for gastric cancer in a heavily pre-treated patient population. [ABSTRACT FROM AUTHOR]

Adapter trimming is an essential step for analyzing small RNA sequencing data, where reads are generally longer than target RNAs ranging from 18 to 30 bp. Most adapter trimming tools require adapter information as input. However, adapter information is hard to access, specified incorrectly, or not provided with publicly available datasets, hampering their reproducibility and reusability. Manual identification of adapter patterns from raw reads is labor-intensive and error-prone. Moreover, the use of randomized adapters to reduce ligation biases during library preparation makes adapter detection even more challenging. Here, we present FindAdapt, a Python package for fast and accurate detection of adapter patterns without relying on prior information. We demonstrated that FindAdapt was far superior to existing approaches. It identified adapters successfully in 180 simulation datasets with diverse read structures and 3,184 real datasets covering a variety of commercial and customized small RNA library preparation kits. FindAdapt is stand-alone software that can be easily integrated into small RNA sequencing analysis pipelines. [ABSTRACT FROM AUTHOR]

Background: Treatment switching, also called crossover, is common in clinical trials because of ethical concerns or other reasons. When it occurs and the primary objective is to identify treatment effects, the most widely used intention-to-treat analysis may lead to underpowered trials. Here, we presented an approach to preview power reductions and to estimate sample sizes required to achieve the desired power when treatment switching occurs in the intention-to-treat analysis. Methods: We proposed a simulation-based approach and developed an R package to perform power and sample sizes estimation in clinical trials with treatment switching. Results: We simulated a number of randomized trials incorporating treatment switching and investigated the impact of the relative effectiveness of the experimental treatment to the control, the switching probability, the switching time, and the deviation between the assumed and the real distributions for the survival time on power reductions and sample sizes estimation. The switching probability and the switching time are key determinants for significant power decreasing and thus sample sizes surging to maintain the desired power. The sample sizes required in randomized trials absence of treatment switching vary from around four-fifths to one-seventh of the sample sizes required in randomized trials allowing treatment switching as the switching probability increases. The power reductions and sample sizes increase with the decrease of switching time. Conclusions: The simulation-based approach not only provides a preview for power declining but also calculates the required sample size to achieve an expected power in the intention-to-treat analysis when treatment switching occurs. It will provide researchers and clinicians with useful information before randomized controlled trials are conducted. [ABSTRACT FROM AUTHOR]

Purpose/Objectives: To identify factors associated with diagnostic delay after an incomplete or abnormal mammogram among women participating in a state mammography screening program.Research Approach: Retrospective case-control design using bivariate and multivariate logistic regression analyses to explore the associations between age, race, ethnicity, marital status, breast cancer history, and self-reported breast symptoms and delay.Setting: A statewide program of free screening mammography for women who are under- or uninsured.Participants: 11,460 women enrolled in a free, statewide screening program from 2002-2006.Methodologic Approach: Using the Tennessee Breast and Cervical Cancer Screening Program database, further analyses were conducted.Main Research Variables: The outcome measure was delay in completion of all diagnostic tests and was defined as women who did not complete testing within 60 days.Findings: Thirty-seven percent of women required follow-up, and of a subset used in the analysis, 30% experienced delay of more than 60 days. Controlling for marital status, age, and breast cancer history, women who experienced delay were more likely to be African American versus Caucasian (odds ratio [OR] = 1.45, 95% confidence interval [CI] = 1.13, 1.85) or Hispanic (OR = 0.72, 95% CI = 0.55, 0.93) and to have self-reported breast symptoms (OR = 1.50, 95% CI = 1.27, 1.77).Conclusions: In a sample of women with low income needing mammography follow-up, delay was associated with three intrapersonal variables, potentially reducing the effectiveness of mammography screening for women who were African American, or Hispanic, or had self-reported breast symptoms.Interpretation: Nurses providing cancer screening examinations are uniquely positioned to assess the knowledge, beliefs, and resources of women using the program and to navigate women through barriers to completion. Knowledge of factors associated with delay is valuable for planning interventions and allocating program resources. [ABSTRACT FROM AUTHOR]

Purpose: Although concurrent chemoradiotherapy can now achieve demonstrated long-term survival in patients with locally advanced non–small-cell lung cancer (LANSCLC), it is difficult to predict which patients will benefit most from this therapeutic approach. Studies have suggested that local control, and the response to therapy, may be linked to improved survival; however, detailed analysis of the impact of tumor response to chemoradiotherapy on survival has not been thoroughly reported. Therefore, we sought to determine the impact of the response rate on survival for patients who were treated with combined modality therapy for LANSCLC. Methods and Materials: We reviewed the data from 116 patients enrolled between 1994 and 1997 in three trials investigating paclitaxel-based concurrent chemoradiotherapy for LANSCLC. Tumor size measurements were assessed immediately before and 2 months after completion of combined modality therapy to determine the response and to calculate the percentage of decrease in tumor size. Results: Patients with a response (complete or partial) had an improved 4-year overall survival rate compared with patients with no response (stable or progressive disease; 21.1% vs. 3.3%, p <0.0001) in the 109 assessable patients. Progression-free survival also improved significantly with response. An analysis of the percentage of decrease in tumor size vs. survival was performed (n = 74) using Cox proportion model analysis. After combined modality therapy, a 20%, 40%, 60%, 80%, and 100% decrease in tumor size conferred a 39%, 63%, 78%, 86%, and 92% reduction in risk of death compared with a 0% decrease in tumor size (p <0.0001). Conclusion: The response by conventional response criteria correlated strongly with improved overall survival and progression-free survival and an increasing percentage of decrease in tumor size resulted in a reduction in the risk of death. Additional investigation of the degree of response as a factor predictive of improved therapeutic efficacy, translating into improved survival, is warranted. [Copyright &y& Elsevier]

Summary: Purpose:: For patients with locally advanced non-small cell lung cancer (LANSCLC), concurrent chemotherapy/radiation therapy (RT) has become the standard of care. Three multi-institutional phase II studies with paclitaxel-based chemotherapeutic regimen given concurrently with RT for patients with LANSCLC were performed from March of 1994 to May of 1997. We sought to determine mature data from this database of patients, as well as to perform analysis of a cohort of patients who have achieved long term survival (LTS) when treated with this regimen. Patients and methods:: Database of these patients was analyzed retrospectively upon longer follow up, with median follow up for the three studies being 498 days (range 11–2905 days, average 780 days). Weight loss limitation for the three studies was liberal: weight loss <10% and <15% 3 months preceding diagnosis (LUN-27 and LUN-63, respectively), and no weight loss limitation for LUN-56. Results:: The 4-year overall survival (OS) for the three trials was 16.3%, and 2-year progression free survival (PFS) was 25.7%. Statistical analysis of the long term survivors (OS>4 years) was performed, and performance status (PS) was found to be a significant factor predictive of LTS. PS of 0 compared to 1 yielded a 2.5-fold increased likelihood of LTS (p =.04). There was also a trend (p =.067) for responders (complete or partial response) to yield a five-fold likelihood of LTS compared to non-responders (stable or progressive disease). Conclusion:: Our results support the efficacy of combined modality therapy (CMT) for patients with LANSCLC even despite our more liberal weight loss eligibility criteria. Furthermore, our analysis indicates that LTS is more likely to be achievable in patients with PS=0 compared to 1 when treated with CMT for LANSCLC. [Copyright &y& Elsevier]

Background and purpose: Tumor hypoxia and anemia have been linked to poor overall survival and local control in carcinomas of both the uterine cervix and the head and neck for patients undergoing radiotherapy. Little is known about the effect of these factors on the outcome of NSCLC patients with locally advanced disease undergoing chemoradiation therapy.Materials and methods: To examine the impact of anemia in stage III NSCLC patients undergoing combined modality therapy, we reviewed our database from three sequential trials of concurrent weekly paclitaxel +/− carboplatin and radiation therapy. 115 pts from 17 institutions were enrolled from 4/94 to 5/97. In this series, hemoglobin values were collected before treatment and weekly during radiotherapy. We correlated baseline Hb and Hb changes during chemoradiation treatment with overall survival. Results: Although pretreatment anemia was common (30% Hb=110–130 g/l, 16% Hb <110 g/l), log-rank analyses of presenting Hb, average Hb and minimum Hb during treatment were not statistically significantly predictive of survival. However in a Cox model, declining hemoglobin during chemoradiation had a statistically significant impact on survival.Conclusions: These analyses reveal that a decline in hemoglobin during chemoradiation for stage III NSCLC has a statistically significant correlation with overall survival. Strategies maximizing hemoglobin during combined modality therapy need further exploration in well-planned randomized trials. [Copyright &y& Elsevier]

Examines the roles of primary care physicians and specialists in the medical care of children with serious heart disease. Study of a convenience sample of parents, primary care physicians and pediatric cardiologists of 92 children with serious heart disease; Parents' low level of confidence in the ability of primary care physicians to meet their child's medical care needs.

Identifies the symptoms associated with neurocardiogenic syncope, syncope due to ventricular tachycardia and syncope resulting from atrioventricular (AV) block. Comparison of historical features of syncope; Predictors of syncope due to AV block or ventricular tachycardia versus neurocardiogenic syncope; Predictors of syncope due to ventricular tachycardia versus AV block.

Normalization and batch correction are critical steps in processing single-cell RNA sequencing (scRNA-seq) data, which remove technical effects and systematic biases to unmask biological signals of interest. Although a number of computational methods have been developed, there is no guidance for choosing appropriate procedures in different scenarios. In this study, we assessed the performance of 28 scRNA-seq noise reduction procedures in 55 scenarios using simulated and real datasets. The scenarios accounted for multiple biological and technical factors that greatly affect the denoising performance, including relative magnitude of batch effects, the extent of cell population imbalance, the complexity of cell group structures, the proportion and the similarity of nonoverlapping cell populations, dropout rates and variable library sizes. We used multiple quantitative metrics and visualization of low-dimensional cell embeddings to evaluate the performance on batch mixing while preserving the original cell group and gene structures. Based on our results, we specified technical or biological factors affecting the performance of each method and recommended proper methods in different scenarios. In addition, we highlighted one challenging scenario where most methods failed and resulted in overcorrection. Our studies not only provided a comprehensive guideline for selecting suitable noise reduction procedures but also pointed out unsolved issues in the field, especially the urgent need of developing metrics for assessing batch correction on imperceptible cell-type mixing. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA-seq normalization is an essential step to correct unwanted biases caused by sequencing depth, capture efficiency, dropout, and other technical factors. Existing normalization methods primarily reduce biases arising from sequencing depth by modeling count-depth relationship and/or assuming a specific distribution for read counts. However, these methods may lead to over or under-correction due to presence of technical biases beyond sequencing depth and the restrictive assumption on models and distributions. Results We present scKWARN, a K ernel W eighted A verage R obust N ormalization designed to correct known or hidden technical confounders without assuming specific data distributions or count-depth relationships. scKWARN generates a pseudo expression profile for each cell by borrowing information from its fuzzy technical neighbors through a kernel smoother. It then compares this profile against the reference derived from cells with the same bimodality patterns to determine the normalization factor. As demonstrated in both simulated and real datasets, scKWARN outperforms existing methods in removing a variety of technical biases while preserving true biological heterogeneity. Availability and Implementation scKWARN is freely available at https://github.com/cyhsuTN/scKWARN. [ABSTRACT FROM AUTHOR]

Background: Tocilizumab, an IL-6 receptor antagonist, can be used to treat cytokine release syndrome (CRS), with observed improvements in a coronavirus disease 2019 (COVID-19) case series.Research Question: The goal of this study was to determine if tocilizumab benefits patients hospitalized with COVID-19.Study Design and Methods: This observational study of consecutive COVID-19 patients hospitalized between March 10, 2020, and March 31, 2020, and followed up through April 21, 2020, was conducted by chart review. Patients were treated with tocilizumab using an algorithm that targeted CRS. Survival and mechanical ventilation (MV) outcomes were reported for 14 days and stratified according to disease severity designated at admission (severe, ≥ 3 L supplemental oxygen to maintain oxygen saturation > 93%). For tocilizumab-treated patients, pre/post analyses of clinical response, biomarkers, and safety outcomes were assessed. Post hoc survival analyses were conducted for race/ethnicity.Results: Among the 239 patients, median age was 64 years; 36% and 19% were black and Hispanic, respectively. Hospital census increased exponentially, yet MV census did not. Severe disease was associated with lower survival (78% vs 93%; P < .001), greater proportion requiring MV (44% vs 5%; P < .001), and longer median MV days (5.5 vs 1.0; P = .003). Tocilizumab-treated patients (n = 153 [64%]) comprised 90% of those with severe disease; 44% of patients with nonsevere disease received tocilizumab for evolving CRS. Tocilizumab-treated patients with severe disease had higher admission levels of high-sensitivity C-reactive protein (120 vs 71 mg/L; P < .001) and received tocilizumab sooner (2 vs 3 days; P < .001), but their survival was similar to that of patients with nonsevere disease (83% vs 91%; P = .11). For tocilizumab-treated patients requiring MV, survival was 75% (95% CI, 64-89). Following tocilizumab treatment, few adverse events occurred, and oxygenation and inflammatory biomarkers (eg, high-sensitivity C-reactive protein, IL-6) improved; however, D-dimer and soluble IL-2 receptor (also termed CD25) levels increased significantly. Survival in black and Hispanic patients, after controlling for age, was significantly higher than in white patients (log-rank test, P = .002).Interpretation: A treatment algorithm that included tocilizumab to target CRS may influence MV and survival outcomes. In tocilizumab-treated patients, oxygenation and inflammatory biomarkers improved, with higher than expected survival. Randomized trials must confirm these findings. [ABSTRACT FROM AUTHOR]

Summary Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene–disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data. Availability and implementation A demo PheWAS - ME application is publicly available at https://prod.tbilab.org/phewas%5fme/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas%5fme%5finfo/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity%5fexplorer). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Simple Summary: Small RNA sequencing has been widely used for characterizing the landscape of small non-coding RNAs—the most abundant cargo in extracellular vesicles (EVs). Here, we performed a systematic assessment of the quality, technical, and potential biological biases introduced by different EV isolation methods and the enrichment of specific, small RNA biotypes in EVs. The findings in this study guide the quality control of EV small RNA-seq and the selection of EV isolation techniques and enhance the interpretation of small RNA contents and the preferential loading of specific RNA biotypes into EVs. Motivation: Extracellular vesicles (EVs) are produced and released by most cells and are now recognized to play a role in intercellular communication through the delivery of molecular cargo, including proteins, lipids, and RNA. Small RNA sequencing (small RNA-seq) has been widely used to characterize the small RNA content in EVs. However, there is a lack of a systematic assessment of the quality, technical biases, RNA composition, and RNA biotypes enrichment for small RNA profiling of EVs across cell types, biofluids, and conditions. Methods: We collected and reanalyzed small RNA-seq datasets for 2756 samples from 83 studies involving 55 with EVs only and 28 with both EVs and matched donor cells. We assessed their quality by the total number of reads after adapter trimming, the overall alignment rate to the host and non-host genomes, and the proportional abundance of total small RNA and specific biotypes, such as miRNA, tRNA, rRNA, and Y RNA. Results: We found that EV extraction methods varied in their reproducibility in isolating small RNAs, with effects on small RNA composition. Comparing proportional abundances of RNA biotypes between EVs and matched donor cells, we discovered that rRNA and tRNA fragments were relatively enriched, but miRNAs and snoRNA were depleted in EVs. Except for the export of eight miRNAs being context-independent, the selective release of most miRNAs into EVs was study-specific. Conclusion: This work guides quality control and the selection of EV isolation methods and enhances the interpretation of small RNA contents and preferential loading in EVs. [ABSTRACT FROM AUTHOR]

Engineering News-Record (ENR) , a professional magazine in the field of global construction engineering, publishes Construction Cost Index (CCI) every month. Cost estimators and contractors assess projects, arrange budgets and prepare bids by forecasting CCI. However, fluctuations and uncertainties of CCI cause irrational estimations now and then. This paper aims at achieving more accurate predictions of CCI based on a network approach in which time series is firstly converted into a visibility graph and future values are forecasted relied on link prediction. According to the experimental results, the proposed method shows satisfactory performance since the error measures are acceptable. Compared with other methods, the proposed method is easier to implement and is able to forecast CCI with less errors. It is convinced that the proposed method is efficient to provide considerably accurate CCI predictions, which will make contributions to the construction engineering by assisting individuals and organizations in reducing costs and making project schedules. [ABSTRACT FROM AUTHOR]

Patients diagnosed with lung cancer (LC) exhibit increased susceptibility to SARS-CoV-2 infection. Rodilla et al. monitor the levels of plasma anti-nucleocapsid antibodies within a cohort of fully vaccinated LC patients and reveal that the actual infection rate is nearly twice the documented rate, indicating a significant prevalence of unreported cases. [ABSTRACT FROM AUTHOR]

High throughput genomic assays empower us to study the entire human genome in short time with reasonable cost. Formalin fixed-paraffin-embedded (FFPE) tissue processing remains the most economical approach for longitudinal tissue specimen storage. Therefore, the ability to apply high throughput genomic applications to FFPE specimens can expand clinical assays and discovery. Many studies have measured the accuracy and repeatability of data generated from FFPE specimens using high throughput genomic assays. Together, these studies demonstrate feasibility and provide crucial guidance for future studies using FFPE specimens. Here, we summarize the findings of these studies and discuss the limitations of high throughput data generated from FFPE specimens across several platforms that include microarray, high throughput sequencing, and NanoString. [ABSTRACT FROM AUTHOR]

• Treatments are limited in early-stage resectable NSCLC with targetable alterations. • In this setting, long overall survival follow-up can delay regulatory approvals. • Clinically relevant and validated surrogate endpoints can accelerate trial readout. • Further prospective validation data are needed for new surrogate primary endpoints. • A collaborative effort is needed to allow rapid assessment of targeted treatments. While the discovery of oncogenic driver mutations has personalized the metastatic non-small cell lung cancer (NSCLC) treatment landscape with effective targeted therapies, implementation of new treatments in resectable NSCLC has been limited due to the long follow-up needed for overall survival (OS). Until recently, treatment for patients with early-stage resectable NSCLC has been limited to perioperative chemotherapy, which provides modest benefits. However, the regulatory acceptance of two surrogate endpoints for OS has allowed recent approval of both adjuvant osimertinib and atezolizumab, providing patients with new treatment options to improve outcomes. In phase 3 oncology trials, OS has historically been viewed as the gold-standard efficacy measure, but disease-free survival and event-free survival (EFS) are now validated surrogate endpoints for OS in clinical trials and should be considered when mature OS data is unavailable. Another potential surrogate endpoint in the adjuvant NSCLC setting is circulating tumor DNA (ctDNA)-based minimal residual disease (MRD), although prospective validation is needed. For neoadjuvant targeted therapies, EFS, major pathologic response and ctDNA-based MRD are potential surrogate endpoints. To fully translate the success of the personalized treatment advances in the metastatic setting to earlier-stage disease, prospective validation studies of these potential surrogate endpoints that can accelerate the evaluation of drug efficacy are needed. A collaborative effort is also needed from all clinical and regulatory parties to collate surrogate endpoint data for large-scale validation. In this review we discuss the trends in surrogate endpoints used in oncology trials, with a focus on considerations for selecting appropriate primary endpoints in early-stage resectable EGFR-mutant NSCLC, an area of unmet need for novel treatment options. [ABSTRACT FROM AUTHOR]

Background Seborrheic keratoses ( SK) are the most common cutaneous neoplasm in humans. Given their numbers, a squamous cell carcinoma in situ ( SCCIS) arising from an SK would not be unexpected, although this occurrence has not been widely reported. Objective This study compares the immune status of patients with a SCCIS arising contiguously from SK ( SK/ SCCIS) and the number of daily medications taken by these patients. The biopsy tissue was evaluated for the presence of human papillomavirus DNA. Methods Over a 5-year period at our institution, 13 cases of SK/ SCCIS were diagnosed. For each case, age- and sex-matched control patients with a histologically diagnosed unremarkable SK were randomly selected. The number of patients taking immunosuppressing medications was determined from each group and compared statistically. In addition, polymerase chain reaction analysis of the 13 SK/ SCCIS biopsies was performed. Results Five of the 13 study patients took immunosuppressive medications compared to none of the 39 control patients ( P = 0.0005). None of the biopsies demonstrated the presence of human papillomavirus DNA by polymerase chain reaction. Conclusions Immunocompromised patients with SKs appear to carry a greater risk of these otherwise innocuous lesions developing an SCCIS, although given the small number of patients evaluated, further research into this possible association is warranted. [ABSTRACT FROM AUTHOR]

Motivation Intracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand–receptor interactions. Although computational methods have been developed to infer cell type-specific ligand–receptor interactions from one single-cell transcriptomics profile, there is lack of approaches considering ligand and receptor simultaneously to identifying dysregulated interactions across conditions from multiple single-cell profiles. Results We developed scLR, a statistical method for examining dysregulated ligand–receptor interactions between two conditions. scLR models the distribution of the product of ligands and receptors expressions and accounts for inter-sample variances and small sample sizes. scLR achieved high sensitivity and specificity in simulation studies. scLR revealed important cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection, and activated TGF-β signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis. Availability and implementation scLR is freely available at https://github.com/cyhsuTN/scLR. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

We previously found that short-term treatment (week 8 to 12 after injury) with high-dose angiotensin receptor blocker (ARB) induced the regression of existing glomerulosclerosis in 5/6 nephrectomy rats. We therefore assessed the effects of long-term intervention with ARB vs. nonspecific antihypertensives in this study. Adult rats underwent 5/6 nephrectomy and renal biopsy 8 weeks later. The rats were then divided into three groups with equivalent renal function and glomerular sclerosis and treated with high-dose losartan (ARB), nonspecific antihypertensive triple-therapy (TRX), or left untreated (Control) until week 30. We found that blood pressure, serum creatinine levels, and glomerulosclerosis were lower at sacrifice in ARB and TRX vs. Control. Only ARB reduced proteinuria and maintained the density of WT-1-positive podocytes. Glomerular tufts showed more double-positive cells for CD44, a marker of activated parietal epithelial cells, and synaptopodin after ARB vs. TRX or Control. ARB treatment reduced aldosterone levels. ARB-treated rats had significantly improved survival when compared with TRX or Control. We conclude that both long-term ARB and triple-therapy ameliorate progression, but do not sustain the regression of glomerulosclerosis. ARB resulted in the superior preservation of podocyte integrity and decreased proteinuria and aldosterone, linked to increased survival in the uremic environment. [ABSTRACT FROM AUTHOR]

Abstract: Quantitative imaging has emerged as a leading priority on the imaging research agenda, yet clinical radiology has traditionally maintained a skeptical attitude toward numerical measurement in diagnostic interpretation. To gauge the extent to which quantitative reporting has been incorporated into routine clinical radiology practice, and to offer preliminary baseline data against which the evolution of quantitative imaging can be measured, we obtained all clinical computed tomography (CT) and magnetic resonance imaging (MRI) reports from two randomly selected weekdays in 2011 at a single mixed academic-community practice and evaluated those reports for the presence of quantitative descriptors. We found that 44% of all reports contained at least one “quantitative metric” (QM), defined as any numerical descriptor of a physical property other than quantity, but only 2% of reports contained an “advanced quantitative metric” (AQM), defined as a numerical parameter reporting on lesion function or composition, excluding simple size and distance measurements. Possible reasons for the slow translation of AQMs into routine clinical radiology reporting include perceptions that the primary clinical question may be qualitative in nature or that a qualitative answer may be sufficient; concern that quantitative approaches may obscure important qualitative information, may not be adequately validated, or may not allow sufficient expression of uncertainty; the feeling that “gestalt” interpretation may be superior to quantitative paradigms; and practical workflow limitations. We suggest that quantitative imaging techniques will evolve primarily as dedicated instruments for answering specific clinical questions requiring precise and standardized interpretation. Validation in real-world settings, ease of use, and reimbursement economics will all play a role in determining the rate of translation of AQMs into broad practice. [Copyright &y& Elsevier]

Expansion of Gr-1+/CD11b+ myeloid derived suppressor cells (MDSCs) is governed by the presence of increasingly metastatic, malignant primary tumors. Metastasis, not the primary tumor, is often the cause of mortality. This study sought to fully characterize the MDSC proteome in response to metastatic and non-metastatic mammary tumors using label-free mass spectrometry shotgun proteomics in a mouse model with tumor cell lines, 67NR and 4T1, derived from the same tumor. 67NR cells form only primary mammary tumors, whereas 4T1 cells readily metastasize to the lungs, lymph nodes, and blood. Overall analysis identified a total of 2825 protein groups with a 0.78% false discovery rate. Of the 2814 true identifications, 43 proteins were exclusive to the 67NR group, 153 were exclusive to the 4T1 group, and 2618 were shared. Among the shared cohort, 26 proteins were increased and 31 were decreased in the metastatic 4T1 cohort compared to non-metastatic 67NR controls after filtering. MDSCs selectively express proteins involved in the c-glutamyl transferase, glutathione synthase pathways, CREB transcription factor signaling, and other pathways involved in platelet aggregation, as well as lipid and amino acid metabolism, in response to highly metastatic 4T1 tumors. Cell cycle regulation dominated protein pathways and ontological groups of the 67NR non-metastatic group. Not only does this study provide a starting point to identify potential biomarkers of metastasis expressed by MDSCs; it identifies critical pathways that are unique to non-metastatic and metastatic conditions. Therapeutic interventions aimed at these pathways in MDSC may offer a new route to control malignancy and metastasis. [ABSTRACT FROM AUTHOR]

Studies involving longitudinal binary responses are widely applied in the health and biomedical sciences research and frequently analyzed by generalized estimating equations (GEE) method. This article proposes an alternative goodness-of-fit test based on the nonparametric smoothing approach for assessing the adequacy of GEE fitted models, which can be regarded as an extension of the goodness-of-fit test of le Cessie and van Houwelingen ( Biometrics 1991; 47:1267-1282). The expectation and approximate variance of the proposed test statistic are derived. The asymptotic distribution of the proposed test statistic in terms of a scaled chi-squared distribution and the power performance of the proposed test are discussed by simulation studies. The testing procedure is demonstrated by two real data. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Microarray technology can be used to assess simultaneously global changes in expression of mRNA or genomic DNA copy number among thousands of genes in different biological states. In many cases, it is desirable to determine if altered patterns of gene expression correlate with chromosomal abnormalities or assess expression of genes that are contiguous in the genome. We describe a method, differential gene locus mapping (DIGMAP), which aligns the known chromosomal location of a gene to its expression value deduced by microarray analysis. The method partitions microarray data into subsets by chromosomal location for each gene interrogated by an array. Microarray data in an individual subset can then be clustered by physical location of genes at a subchromosomal level based upon ordered alignment in genome sequence. A graphical display is generated by representing each genomic locus with a colored cell that quantitatively reflects its differential expression value. The clustered patterns can be viewed and compared based on their expression signatures as defined by differential values between control and experimental samples. In this study, DIGMAP was tested using previously published studies of breast cancer analyzed by comparative genomic hybridization (CGH) and prostate cancer gene expression profiles assessed by cDNA microarray experiments. Analysis of the breast cancer CGH data demonstrated the ability of DIGMAP to deduce gene amplifications and deletions. Application of the DIGMAP method to the prostate data revealed several carcinoma-related loci, including one at 16q13 with marked differential expression encompassing 19 known genes including 9 encoding metallothionein proteins. We conclude that DIGMAP is a powerful computational tool enabling the coupled analysis of microarray data with genome location. [Copyright &y& Elsevier]

Instability of repetitive sequences is a hallmark of human cancer, and its enhancement has been linked to oxidative stress. Malondialdehyde is an endogenous product of oxidative stress that reacts with guanine to form the exocyclic adduct, pyrimido[1,2α]purin-10(3H)-one (M[sub1]G). We used site-specifically rnodified single- and double-stranded vectors to investigate the mutagenic potential of MIG in bacteria and mammalian cells. M[sub1]G induced frameshift mutations (-1 and -2) when positioned in a reiterated (CpG)[sub4] sequence bur not when positioned in a nonreiterated sequence in Escherichia coli and in COS-7 cells. The frequency of frameshift mutations was highest when M[sub1]G was placed at the third G in the sequence. M[sub1]G induced base pair substitutions at comparable frequencies in both sequence contexts in COS-7 cells. These studies indicate that M[sub1]G, an endogenously generated product of oxidative stress, induces sequence-dependent frameshift mutations and base pair substitutions in bacteria and in mammalian cells. This finding suggests a potential role for the M[sub1]G lesion in the induction of mutations commonly associated with human diseases. [ABSTRACT FROM AUTHOR]

Vascular access blood flow monitoring reduces access morbidity and costs. Background. Vascular access morbidity results in suboptimal patient outcomes and costs more than $8000 per patient-year at risk, representing approximately 15% of total Medicare expenditures for ESRD patients annually. In recent years, the rate of access thrombosis has improved following the advent of vascular access blood flow monitoring (VABFM) programs to identify and treat stenosis prior to thrombosis. To define further both the clinical and financial impact of such programs, we used the ultrasound dilution method to study the effects of VABFM on thrombosis-related morbid events and associated costs, compared with both dynamic venous pressure monitoring (DVPM) and no monitoring (NM) in arteriovenous fistulas (AVF) and grafts. Methods. A total of 132 chronic hemodialysis patients were followed prospectively for three consecutive study phases (I, 11 months of NM; II, 12 months of DVPM; III, 10 months of VABFM). All vascular access-related information (thrombosis rate, hospitalization, angiogram, angioplasty, access surgery, thrombectomy, catheter placement, missed treatments) was collected during the three study periods. Results. During the three study phases, graft thrombosis rate was reduced from 0.71 (phase I), to 0.67 (phase II), to 0.16 (phase III) events per patient-year at risk (P < 0.001 phase III vs. phases I and II). Similarly, hospital days, missed treatments, and catheter use related to thrombotic events were significantly reduced during phase III compared to phases I and II. Hospital days related to vascular access morbidity and adjusted for patient-year at risk were 1.8, 1.6, and 0.4 and missed dialysis treatments were 0.98, 0.86, and 0.26 treatments per patient-year at risk for phases I, II, and III, respectively (P < 0.001 for phase III vs. phases I and II). Catheter use was also significantly reduced during phases II and III, from 0.29 (phase I) to 0.17 and further to... [ABSTRACT FROM AUTHOR]

Background: Robust and reliable prognosis prediction models have not been developed and validated for Asian patients with breast cancer, a rapidly growing yet understudied population in the United States.Methods: We used longitudinal data from the Shanghai Breast Cancer Survival Study, a population-based prospective cohort study (n = 5042), to develop prediction models for 5- and 10-year disease-free survival (DFS) and overall survival (OS). The initial models considered age at diagnosis, tumor grade, tumor size, number of positive nodes, TNM stage, chemotherapy, tamoxifen therapy, and estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status. We then evaluated whether the addition of modifiable lifestyle factors (physical activity, soy isoflavones intake, and postdiagnostic weight change) improved the models. All final models have been validated internally and externally in the National Cancer Database when applicable.Results: Our final models included age at diagnosis, tumor grade, tumor size, number of positive nodes, TNM stage, chemotherapy, tamoxifen therapy, ER status, PR status, 6-month postdiagnostic weight change, interaction between ER status and tamoxifen therapy, and interaction between age and TNM stage. The internal validation yielded C-statistics of 0.76, 0.74, 0.78, and 0.75 for 5-year DFS, 10-year DFS, 5-year OS, and 10-year OS, respectively. The external validation yielded C-statistics of 5- and 10-year OS both at 0.78 for Chinese ethnicity, 0.79 for East Asian ethnicity, and 0.75 and 0.76 for all ethnic groups combined.Conclusion: We developed prediction models for breast cancer prognosis from a large prospective study. Our prognostic models performed very well in women from the United States-particularly in Asian American women-and demonstrated high prediction accuracy and generalizability. [ABSTRACT FROM AUTHOR]

Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles. Author summary: Analyses of gene set differential coexpression often shed light on molecular mechanisms underlying phenotypes and diseases. However, results from conceptually similar individual studies are often inconsistent and underpowered to reach definitive conclusions. We provide an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles. We established the usefulness of MetaGSCA via case studies of chronic kidney disease and non-small cell lung cancer, and applied it to a pan-cancer analysis of 11 cancer types. We further demonstrated the tool with 100 randomly generated gene sets and showed excellent specificity, indicating low false positive rates. [ABSTRACT FROM AUTHOR]

Ductal carcinoma in situ (DCIS) is the most common type of pre-invasive breast cancer diagnosed in women. Because the majority of DCIS cases are unlikely to progress to invasive breast cancer, many women are over-treated for DCIS. By understanding the molecular basis of early stage breast cancer progression, we may identify better prognostic factors and design treatments tailored specifically to the predicted outcome of DCIS. Chemokines are small soluble molecules with complex roles in inflammation and cancer progression. Previously, we demonstrated that CCL2/CCR2 chemokine signaling in breast cancer cell lines regulated growth and invasion through p42/44MAPK and SMAD3 dependent mechanisms. Here, we sought to determine the clinical and functional relevance of CCL2/CCR2 signaling proteins to DCIS progression. Through immunostaining analysis of DCIS and IDC tissues, we show that expression of CCL2, CCR2, phospho-SMAD3 and phospho-p42/44MAPK correlate with IDC. Using PDX models and an immortalized hDCIS.01 breast epithelial cell line, we show that breast epithelial cells with high CCR2 and high CCL2 levels form invasive breast lesions that express phospho-SMAD3 and phospho-p42/44MAPK. These studies demonstrate that increased CCL2/CCR2 signaling in breast tissues is associated with DCIS progression, and could be a signature to predict the likelihood of DCIS progression to IDC. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Healthcare Under Sanctions in Iraq: An Elective Experience" by M. Akunjee and A. Ali.

Background: Acute exacerbation (AE) of COPD may be accompanied by the deterioration of cardiovascular comorbidities, as evidenced by the increased incidence of acute cardiovascular events.Research Question: The goal of this study was to determine whether preceding AE might be associated with mortality of cardiovascular events.Study Design and Methods: Using a health insurance research database in Taiwan, patients with COPD were identified who experienced first-time acute myocardial infarction (AMI; n = 26,442), ischemic stroke (n = 54,959), and intracranial hemorrhage (ICH; n = 14,893) over a 13-year period. In each cohort, 4,356, 6,655, and 1,727 patients, respectively, had been hospitalized for AE within the previous year prior to the index cardiovascular events, and patients with COPD but without hospitalization for AEs constituted the control subjects. ORs of 90-day mortality and hazard ratios (HRs) of overall mortality during follow-up in relation to hospitalization for an AE and the frequency of hospitalization for AEs (ie, 1 and ≥ 2 hospitalizations for AEs) were estimated with adjustment for potential confounders.Results: Hospitalization for an AE was independently associated with 90-day mortality of AMI (OR, 1.33; 95% CI, 1.24-1.43), ischemic stroke (OR, 1.46; 95% CI, 1.36-1.56), and ICH (OR, 1.19; 95% CI, 1.06-1.32). Hospitalization for an AE was associated with overall mortality of AMI (HR, 1.23; 95% CI, 1.19-1.27), ischemic stroke (HR, 1.29; 95% CI, 1.26-1.33), and ICH (HR, 1.19; 95% CI, 1.13-1.26). In addition, compared with control subjects, patients with more frequent hospitalizations for AEs exhibited significant trends at higher risk of 90-day and overall mortality of AMI, ischemic stroke, and ICH. Finally, these results were consistent with propensity score matching-based estimates.Interpretation: Preceding hospitalization for AEs is associated with 90-day and overall mortality of cardiovascular events in COPD. [ABSTRACT FROM AUTHOR]

Background: Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness.Methods: In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing.Findings: Of 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53-2·21), male sex (1·63, 1·07-2·48), smoking status (former smoker vs never smoked: 1·60, 1·03-2·47), number of comorbidities (two vs none: 4·50, 1·33-15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11-7·18), active cancer (progressing vs remission: 5·20, 2·77-9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79-4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07-0·84) or the US-Midwest (0·50, 0·28-0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality.Interpretation: Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.Funding: American Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research. [ABSTRACT FROM AUTHOR]

Objectives Patients with RA and SLE have an excess cardiovascular risk. We aimed to evaluate outcomes of acute cardiovascular events in these patients. Methods Using a nationwide database of Taiwan, we identified adult patients who experienced first-time acute myocardial infarction (n = 191 008), intracranial haemorrhage (n = 169 923) and ischaemic stroke (n = 486 890) over a 13-year period. Odds ratios (ORs) of in-hospital mortality and hazard ratios (HRs) of overall mortality and adverse outcomes during long-term follow-up in relation to RA and SLE were estimated with adjustment for potential confounders. Results In each cohort, 748, 410 and 1419 patients had established RA; 256, 292 and 622 patients had SLE. Among acute myocardial infarction patients, RA and SLE were associated with in-hospital mortality (RA: OR 1.61, 95% CI 1.33, 1.95; SLE: OR 2.31, 95% CI 1.62, 3.28) and overall mortality. Additionally, RA (HR 1.28, 95% CI 1.18, 1.38) and SLE (HR 1.46, 95% CI 1.27, 1.69) increased the risk of major adverse cardiac events. After intracranial haemorrhage, patients with RA and SLE had higher risks of in-hospital mortality (RA: OR 1.61, 95% CI 1.26, 2.06; SLE: OR 3.00, 95% CI 2.33, 3.86) and overall mortality. After ischaemic stroke, RA and SLE increased in-hospital mortality (RA: OR 1.45, 95% CI 1.15, 1.83; SLE: OR 2.18, 95% CI 1.57, 3.02), overall mortality and recurrent cerebrovascular events (RA: HR 1.10, 95% CI 1.002, 1.21; SLE: HR 1.31, 95% CI 1.14, 1.51), among which ischaemic stroke (HR 1.39, 95% CI 1.19, 1.62) was more likely to recur in SLE patients. Conclusion Both RA and SLE are consistently associated with adverse outcomes following acute cardiovascular events, highlighting the necessity of integrated care for affected patients. [ABSTRACT FROM AUTHOR]

Objective: To determine whether kidney stone history is associated with adverse outcomes after percutaneous coronary intervention (PCI). Kidney stone formers have an increased risk of developing coronary artery disease; however, whether these patients have worse cardiac outcomes is unknown.Materials and Methods: We identified adult patients who underwent first-time PCI in Vanderbilt University Medical Center (VUMC) Synthetic Derivative from 2008 to 2016 (n = 11,289) and in a nationwide database of Taiwan (NHIRD) from 2005 to 2012 (n = 155,762). Odds ratios (ORs) of 30-day in-hospital mortality and hazard ratios (HRs) of 1-year and 3-year adverse outcomes associated with kidney stone history were estimated using a propensity score approach.Results: Overall, 294 and 12,286 stone formers undergoing PCI were identified in the VUMC and NHIRD, respectively. After matching, stone formers at VUMC were at higher risks of 30-day in-hospital mortality (OR 2.79, 95% CI 1.15-6.69) and 1-year (HR 1.59, 95% CI 1.13-2.24) and 3-year (HR 1.36, 95% CI 1.02-1.81) myocardial infarction. In the NHIRD, kidney stone history was associated with 1-year (HR 1.12, 95% CI 1.03-1.21) and 3-year (HR 1.14, 95% CI 1.06-1.22) myocardial infarction. In a sensitivity analysis, stone formers undergoing kidney stone surgery were marginally associated with 30-day in-hospital mortality (OR 1.21, 95% CI 0.99-1.48) and were associated with 3-year myocardial infarction (HR 1.13, 95% CI 1.02-1.25).Conclusion: Kidney stone history is associated with poorer cardiac outcomes after PCI. Improving secondary cardiac prevention strategies after PCI may be necessary for patients with a history of kidney stone disease. [ABSTRACT FROM AUTHOR]

The article reports factors including income, costs and healthcare utilization that influence surgical choices in patients with early stage breast cancer. Topics discussed include mastectomy and breast conservation surgery (BCS) with radiation being a common treatment, and only mastectomy being more common in women with lower income levels.

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution. [ABSTRACT FROM AUTHOR]

Summary Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers. Availability and implementation scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies. [ABSTRACT FROM AUTHOR]

Background: Enhancers are distal cis-regulatory elements that control gene expression. Despite an increasing appreciation of the importance of enhancers in cellular function and disease, our knowledge of enhancer-regulated transcription is very limited. Nascent RNA sequencing technologies, such as global nuclear run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq), not only provide a direct and reliable measurement of enhancer activity, but also allow for quantifying transcription of enhancers and target genes simultaneously, making these technologies extremely useful for exploring enhancer-mediated regulation. Results: Nascent RNA sequencing analysis (NRSA) provides a comprehensive view of enhancer-mediated gene regulation. NRSA not only outperforms existing methods for enhancer identification, but also enables annotation and quantification of active enhancers, and prediction of their target genes. Furthermore, NRSA identifies functionally important enhancers by integrating 1) nascent transcriptional changes in enhancers and their target genes and 2) binding profiles from regulator(s) of interest. Applied to wildtype and histone deacetylase 3 (Hdac3) knockout mouse livers, NRSA showed that HDAC3 regulates RNA polymerase recruitment through both proximal (promoter) and distal (enhancer) regulatory elements. Integrating ChIP-seq with PRO-seq data, NRSA prioritized enhancers based on their potential contribution to mediating HDAC3 regulation. Conclusions: NRSA will greatly facilitate the usage of nascent RNA sequencing techniques and accelerate the study of enhancer-mediated regulation. [ABSTRACT FROM AUTHOR]

Background: One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. Results: To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference's distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/. Conclusions: RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. [ABSTRACT FROM AUTHOR]

Purpose The promise of precision oncology is that identification of genomic alterations will direct the rational use of molecularly targeted therapy. This approach is particularly applicable to neoplasms that are resistant to standard cytotoxic chemotherapy, like T-cell leukemias and lymphomas. In this study, we tested the feasibility of targeted next-generation sequencing in profiles of diverse T-cell neoplasms and focused on the therapeutic utility of targeting activated JAK1 and JAK3 in an index case. Patients and Methods Using Foundation One and Foundation One Heme assays, we performed genomic profiling on 91 consecutive T-cell neoplasms for alterations in 405 genes. The samples were sequenced to high uniform coverage with an Illumina HiSeq and averaged a coverage depth of greater than 500× for DNA and more than 8M total pairs for RNA. An index case of T-cell prolymphocytic leukemia (T-PLL), which was analyzed by targeted next-generation sequencing, is presented. T-PLL cells were analyzed by RNA-seq, in vitro drug testing, mass cytometry, and phospho-flow. Results One third of the samples had genomic aberrations in the JAK-STAT pathway, most often composed of JAK1 and JAK3 gain-of-function mutations. We present an index case of a patient with T-PLL with a clonal JAK1 V658F mutation that responded to ruxolitinib therapy. After relapse developed, an expanded clone that harbored mutant JAK3 M511I and downregulation of the phosphatase, CD45, was identified. We demonstrate that the JAK missense mutations were activating, caused pathway hyperactivation, and conferred cytokine hypersensitivity. Conclusion These results underscore the utility of profiling occurrences of resistance to standard regimens and support JAK enzymes as rational therapeutic targets for T-cell leukemias and lymphomas. [ABSTRACT FROM AUTHOR]

We hypothesize that the relative mitochondria copy number (MTCN) can be estimated by comparing the abundance of mitochondrial DNA to nuclear DNA reads using high throughput sequencing data. To test this hypothesis, we examined relative MTCN across 13 breast cancer cell lines using the RT-PCR based NovaQUANT Human Mitochondrial to Nuclear DNA Ratio Kit as the gold standard. Six distinct computational approaches were used to estimate the relative MTCN in order to compare to the RT-PCR measurements. The results demonstrate that relative MTCN correlates well with the RT-PCR measurements using exome sequencing data, but not RNA-seq data. Through analysis of copy number variants (CNVs) in The Cancer Genome Atlas, we show that the two nuclear genes used in the NovaQUANT assay to represent the nuclear genome often experience CNVs in tumor cells, questioning the accuracy of this gold-standard method when it is applied to tumor cells. [ABSTRACT FROM AUTHOR]

Background: Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness.Methods: A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables.Results: From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI.Conclusions: The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to study whether cancer cells possess distinguishing metabolic features compared with surrounding normal cells, such as increased glutamine uptake. Given this, quantitative measures of glutamine uptake may reflect critical processes in oncology. Approximately, 10 % of patients with colorectal cancer (CRC) express BRAF V600E , which may be actionable with selective BRAF inhibitors or in combination with inhibitors of complementary signaling axes. Non-invasive and quantitative predictive measures of response to these targeted therapies remain poorly developed in this setting. The primary objective of this study was to explore 4-[18F]fluoroglutamine (4-[18F]F-GLN) positron emission tomography (PET) to predict response to BRAFV600E-targeted therapy in preclinical models of colon cancer.Procedures: Tumor microarrays from patients with primary human colon cancers (n = 115) and CRC liver metastases (n = 111) were used to evaluate the prevalence of ASCT2, the primary glutamine transporter in oncology, by immunohistochemistry. Subsequently, 4-[18F]F-GLN PET was evaluated in mouse models of human BRAF V600E -expressing and BRAF wild-type CRC.Results: Approximately 70 % of primary colon cancers and 53 % of metastases exhibited positive ASCT2 immunoreactivity, suggesting that [18F]4-F-GLN PET could be applicable to a majority of patients with colon cancer. ASCT2 expression was not associated selectively with the expression of mutant BRAF. Decreased 4-[18F]F-GLN predicted pharmacological response to single-agent BRAF and combination BRAF and PI3K/mTOR inhibition in BRAF V600E -mutant Colo-205 tumors. In contrast, a similar decrease was not observed in BRAF wild-type HCT-116 tumors, a setting where BRAFV600E-targeted therapies are ineffective.Conclusions: 4-[18F]F-GLN PET selectively reflected pharmacodynamic response to BRAF inhibition when compared with 2-deoxy-2[18F]fluoro-D-glucose PET, which was decreased non-specifically for all treated cohorts, regardless of downstream pathway inhibition. These findings illustrate the utility of non-invasive PET imaging measures of glutamine uptake to selectively predict response to BRAF-targeted therapy in colon cancer and may suggest further opportunities to inform colon cancer clinical trials using targeted therapies against MAPK activation. [ABSTRACT FROM AUTHOR]