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Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification. [ABSTRACT FROM AUTHOR]

Background: The initial wave of the Covid‐19 pandemic has hit Italy, and Lombardy in particular, with violence, forcing to reshape all hospitals' activities; this happened even in pediatric hospitals, although the young population seemed initially spared from the disease. "Vittore Buzzi" Children's Hospital, which is a pediatric/maternal hospital located in Milan (Lombardy Region), had to stop elective procedures—with the exception of urgent/emergent ones—between February and May 2020 to leave space and resources to adults' care. We describe the challenges of reshaping the hospital's identity and structure, and restarting pediatric surgery and anesthesia, from May on, in the most hit area of the world, with the purpose to avoid and contain infections. Both patients and caregivers admitted to hospital have been tested for Sars‐CoV‐2 in every case. Methods: Observational cohort study via review of clinical charts of patients undergoing surgery between 16th May and 30th September 2020, together with SARS‐CoV ‐2 RT‐PCR testing outcomes, and comparison to same period surgeries in 2019. Results: An increase of approximately 70% in pediatric surgeries (OR 1.68 [1.33‐2.13], P <.001) and a higher increase in the number of surgeries were reported (OR 1.75 (1.43‐2.15), P <.001). Considering only urgent procedures, a significant difference in the distribution of the type of surgery was observed (Chi‐squared P‐value <.001). Sars‐CoV‐2‐positive patients have been 0.8% of total number; 14% of these was discovered through caregiver's positivity. Conclusion: We describe our pathway for safe pediatric surgery and anesthesia and the importance of testing both patient and caregiver. [ABSTRACT FROM AUTHOR]

Background: The increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots. Results: We analyzed the Ras superfamily exploiting the known driver mutations of the trio K-N-HRAS, identifying new putative driver mutations and genes belonging to less known members of the Rho, Rab and Rheb subfamilies. Furthermore, we applied the same concept to a list of known and candidate driver genes, and observed that low confidence genes show similar patterns of mutation compared to high confidence genes of the same protein family. Conclusions: LowMACA is a software for the identification of gain-of-function mutations in putative oncogenic families, increasing the amount of information on functional domains and their possible role in cancer. In this context LowMACA emphasizes the role of genes mutated at low frequency otherwise undetectable by classical single gene analysis. LowMACA is an R package available at http://www.bioconductor.org/packages/release/bioc/html/LowMACA.html. It is also available as a GUI standalone downloadable at: https://cgsb.genomics.iit.it/wiki/projects/LowMACA [ABSTRACT FROM AUTHOR]

A key challenge in the analysis of cancer genomes is the identification of driver genes from the vast number of mutations present in a cohort of patients. DOTS-Finder is a new tool that allows the detection of driver genes through the sequential application of functional and frequentist approaches, and is specifically tailored to the analysis of few tumor samples. We have identified driver genes in the genomic data of 34 tumor types derived from existing exploratory projects such as The Cancer Genome Atlas and from studies investigating the usefulness of genomic information in the clinical settings. DOTS-Finder is available at https://cgsb.genomics.iit.it/wiki/projects/DOTS-Finder/. [ABSTRACT FROM AUTHOR]

Aims Materials and Methods Results Conclusions To develop a clinical risk model to identify individuals at higher risk of developing new‐onset diabetes and who might benefit more from weight loss pharmacotherapy.A total of 21 143 patients without type 2 diabetes at baseline from two TIMI clinical trials of stable cardiovascular patients were divided into a derivation (~2/3) and validation (~1/3) cohort. The primary outcome was new‐onset diabetes. Twenty‐seven candidate risk variables were considered, and variable selection was performed using multivariable Cox regression. The final model was evaluated for discrimination and calibration, and for its ability to identify patients who experienced a larger benefit from the weight loss medication lorcaserin in terms of risk of new‐onset diabetes.During a median (interquartile range) follow‐up of 2.3 (1.8–2.7) years, new‐onset diabetes occurred in 1013 patients (7.7%). The final model included five independent predictors (glycated haemoglobin, fasting glucose, age, body mass index, and triglycerides/high‐density lipoprotein). The clinical risk model showed good discrimination (Harrell's C‐indices 0.802, 95% confidence interval [CI] 0.788–0.817 and 0.807, 95% CI 0.788–0.826) in the derivation and validation cohorts. The calibration plot demonstrated adequate calibration (2.5‐year area under the curve was 81.2 [79.1–83.5]). While hazard ratios for new‐onset diabetes with a weight‐loss therapy were comparable across risk groups (annual risks of <1%, 1%–5%, and >5%), there was a sixfold gradient in absolute risk reduction from lowest to highest risk group (p = 0.027).The developed clinical risk model effectively predicts new‐onset diabetes, with potential implications for personalized patient care and therapeutic decision making. [ABSTRACT FROM AUTHOR]

Motivation Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking. Results We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design. Availability and implementation The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter , the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Cellular mRNA levels originate from the combined action of multiple regulatory processes, which can be recapitulated by the rates of pre-mRNA synthesis, pre-mRNA processing and mRNA degradation. Recent experimental and computational advances set the basis to study these intertwined levels of regulation. Nevertheless, software for the comprehensive quantification of RNA dynamics is still lacking. Results: INSPEcT is an R package for the integrative analysis of RNA- and 4sU-seq data to study the dynamics of transcriptional regulation. INSPEcT provides gene-level quantification of these rates, and a modeling framework to identify which of these regulatory processes are most likely to explain the observed mRNA and pre-mRNA concentrations. Software performance is tested on a synthetic dataset, instrumental to guide the choice of the modeling parameters and the experimental design. [ABSTRACT FROM AUTHOR]

The analyses carried out using two different bioinformatics pipelines (SomaticSniper and MuTect) on the same set of genomic data from 133 Acute Myeloid Leukemia (AML) patients, sequenced inside the Cancer Genome Atlas project, gave discrepant results. We subsequently tested these two variant-calling pipelines on 20 leukemia samples from our series (19 primary AMLs and one secondary AML). By validating many of the predicted somatic variants (variant allele frequencies ranging from 100% to 5%), we observed significantly different calling efficiencies. In particular, despite relatively high specificity, sensitivity was poor in both pipelines resulting in a high rate of false negatives. Our findings raise the possibility that landscapes of AML genomes might be more complex than previously reported and characterized by the presence of hundreds of genes mutated at low variant allele frequency, suggesting that the application of genome sequencing to the clinic requires a careful and critical evaluation. We think that improvements in technology and workflow standardization, through the generation of clear experimental and bioinformatics guidelines, are fundamental to translate the use of Next generation sequencing from research to the clinic and to transform genomic information into better diagnosis and outcomes for the patient. [ABSTRACT FROM AUTHOR]