Study design requirements for RNA sequencing-based breast cancer diagnostics

Sequencing-based molecular characterization of tumors provides information required for individualized cancer treatment. There are well-defined molecular subtypes of breast cancer that provide improved prognostication compared to routine biomarkers. However, molecular subtyping is not yet implemented in routine breast cancer care. Clinical translation is dependent on subtype prediction models providing high sensitivity and specificity. In this study we evaluate sample size and RNA-sequencing read requirements for breast cancer subtyping to facilitate rational design of translational studies. We applied subsampling to ascertain the effect of training sample size and the number of RNA sequencing reads on classification accuracy of molecular subtype and routine biomarker prediction models (unsupervised and supervised). Subtype classification accuracy improved with increasing sample size up to N = 750 (accuracy = 0.93), although with a modest improvement beyond N = 350 (accuracy = 0.92). Prediction of routine biomarkers achieved accuracy of 0.94 (ER) and 0.92 (Her2) at N = 200. Subtype classification improved with RNA-sequencing library size up to 5 million reads. Development of molecular subtyping models for cancer diagnostics requires well-designed studies. Sample size and the number of RNA sequencing reads directly influence accuracy of molecular subtyping. Results in this study provide key information for rational design of translational studies aiming to bring sequencing-based diagnostics to the clinic.NonePublishe

MicroRNAs Modulate the Dynamics of the NF-κB Signaling Pathway

BACKGROUND: NF-κB, a major transcription factor involved in mammalian inflammatory signaling, is primarily involved in regulation of response to inflammatory cytokines and pathogens. Its levels are tightly regulated since uncontrolled inflammatory response can cause serious diseases. Mathematical models have been useful in revealing the underlying mechanisms, the dynamics, and other aspects of regulation in NF-κB signaling. The recognition that miRNAs are important regulators of gene expression, and that a number of miRNAs target different components of the NF-κB network, motivate the incorporation of miRNA regulated steps in existing mathematical models to help understand the quantitative aspects of miRNA mediated regulation. METHODOLOGY/PRINCIPAL FINDINGS: In this study, two separate scenarios of miRNA regulation within an existing model are considered. In the first, miRNAs target adaptor proteins involved in the synthesis of IKK that serves as the NF-κB activator. In the second, miRNAs target different isoforms of IκB that act as NF-κB inhibitors. Simulations are carried out under two different conditions: when all three isoforms of IκB are present (wild type), and when only one isoform (IκBα) is present (knockout type). In both scenarios, oscillations in the NF-κB levels are observed and are found to be dependent on the levels of miRNAs. CONCLUSIONS/SIGNIFICANCE: Computational modeling can provide fresh insights into intricate regulatory processes. The introduction of miRNAs affects the dynamics of the NF-κB signaling pathway in a manner that depends on the role of the target. This "fine-tuning" property of miRNAs helps to keep the system in check and prevents it from becoming uncontrolled. The results are consistent with earlier experimental findings

Highlights from the 16th International Society for Computational Biology Student Council Symposium 2020.

In this meeting overview, we summarise the scientific program and organisation of the 16th International Society for Computational Biology Student Council Symposium in 2020 (ISCB SCS2020). This symposium was the first virtual edition in an uninterrupted series of symposia that has been going on for 15 years, aiming to unite computational biology students and early career researchers across the globe. [Abstract copyright: Copyright: © 2021 Cuypers WL et al.

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Vorhersage der subzellulären Lokalisation von Proteinen mittels der Zugänglichkeit von Aminosäuren

The proteins perform their functions in associated cellular locations. Therefore, subcellular location is a key-feature in the functional characterization of proteins. The experimental methods of determining a protein's subcellular location are costly, time consuming, error prone and can not cope with exponentially growing genomic and proteomic data. Therefore, computational prediction of protein subcellular location is a major effort in bioinformatics research. Subcellular location of a protein can be predicted either from its sequence by identifying the targeting peptide and motifs, or by homology to proteins of known location. Another approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations. This is an especially useful approach if motif and homology information are missing. In this study, we expand this approach taking into account amino acid composition at different levels of amino acid exposure. Through careful selection and data integration we created a high quality dataset of proteins with known structure and location. The members of three subcellular location categories were considered: nuclear, cytoplasmic and extracellular, plus the extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttle between nucleus and cytoplasm. We explored the relationship between residue exposure and protein subcellular location. The analysis demonstrated that amino acids at different levels of exposure have signal about the location of proteins. For the classification purpose we applied a novel approach of two stage classification. At stage one, multiple Support Vector Machines (SVMs) were trained to score eukaryotic protein sequences for membership to each location class. In stage two, an artificial neural network (ANN) was used to propose a category from the scores assigned to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62%. The algorithm is implemented as the web server 'NYCE'. We compared the performance of NYCE against other state-of-the-art subcellular location prediction tools. The comparison revealed the fact that 'NYCE' performs reasonably well compared to other tools, though using a limited set of information. A major challenge of protein subcellular location prediction methods based on homology is that there are very similar proteins that act in different subcellular locations. Using pairs of paralog proteins experimentally known to be in different locations, we demonstrated that our algorithm can evaluate proteins independently of their homology. NYCE can discern proteins in different locations even if they share high levels of identity whereas other tools fail to do so.Proteine können ihre Funktion nur in bestimmten intrazellulären Kompartimenten erfüllen, deshalb ist die subzelluläre Lokalisation ein wichtiges Merkmal der funktionellen Charakterisierung von Proteinen. Die experimentellen Methoden zur Bestimmung der subzellulären Lokalisation von Proteinen sind teuer, zeitintensiv, fehleranfällig und können nicht mit der exponentiell anwachsenden Menge an genomischen und proteomischen Daten mithalten. Deshalb ist die computergestützte Vorhersage der intrazellulären Lokalisation von Proteinen ein wichtiges Ziel der bioinformatischen Forschung. Die Lokalisation eines Proteins kann entweder aus dessen Sequenz vorhergesagt werden oder durch das Heranziehen homologer Proteine deren Lokalisation schon bekannt ist. Ein anderer, komplementärer Ansatz nutzt die Aminosäurezusammensetzung von verschieden lokalisierten Proteinen. In dieser Arbeit erweitern wir diesen Ansatz, indem wir die Aminnosäurezusammensetzung in Zusammenhang damit betrachten, wie gut die Aminosäuren aufgrund der Proteinstruktur von außen zugänglich sind. Es wurden vier Kategorien der subzellulären Lokalisation in die Untersuchungen einbezogen: nukleär, zytoplasmatisch, extrazellulär und nukleo-zytoplasmatisch. Wir haben einen qualitativ hochwertigen Datensatz zusammengestellt, der Proteine mit bekannter Struktur und Lokalisation enthält und den Zusammenhang zwischen der Zugänglichkeit der Aminosäuren und der subzellulären Lokalisation des Proteins untersucht. Diese Analyse zeigte, dass Aminosäuren mit verschiedenen Zugänglichkeiten zur Vorhersage der Lokalisation von Proteinen genutzt werden können. Zum Zweck der Klassifizierung haben wir einen neuartigen Ansatz, basierend auf einer zweistufigen Klassifizierung, verwendet. In der ersten Stufe werden Support Vector Machines trainiert, die Wahrscheinlichkeit der Zugehörigkeit (Score) für alle Klassen anhand der Proteinsequenzen zu berechnen. Die zweite Stufe, ein künstliches neuronales Netzwerk, wird benutzt um eine Kategorie auf der Grundlage der vorher berechneten Scores für die vier möglichen Lokalisationen vorzuschlagen. Diese Methode erreicht eine Präzision von 68% wenn auf 3D-Strukturen basierende Werte für die Zugänglichkeit der Aminosäuren benutzt werden. Die Kalibrierung der Methode mithilfe theoretisch berechneter Werte für die Zugänglichkeit der Aminosäuren ermöglicht eine Klassifizierung der Proteine ohne 3D-Information mit einer Präzision von 62%. Der Algorithmus wurde als der Webserver “NYCE” implementiert. Ein Vergleich von “NYCE” mit anderen modernen Vorhersageprogrammen zeigte eine gute Leistung. Ein großes Problem der auf Homologie basierenden Vorhersageprogramme ist die Existenz von Proteinen mit sehr ähnlicher Sequenz aber unterschiedlicher subzellulärer Lokalisation. Anhand paraloger Proteine, welche unterschiedliche Lokalisation aufweisen, konnten wir zeigen dass “NYCE” - im Gegensatz zu anderen Vorhersageprogrammen - zwischen Proteinen mit großer Sequenzähnlichkeit aber verschiedener Lokalisation unterscheiden kann. Unser Ansatz kann in Zukunft für die Vorhersage der Lokalisation von Proteinen in anderen Kompartimenten und in nicht-eukaryotischen Organismen nützlich sein. Wir erwarten, dass solch eine Erweiterung unserer Methode durch die wachsende Anzahl von in Datenbanken verfügbaren Proteinstrukturen und Proteinen mit experimentell bestätigter Lokalisation erleichtert wird

Novel subtypes of NPM1-mutated AML with distinct outcome

Acute myeloid leukemia (AML) is heterogeneous with one common subtype recognized by the presence of recurrent mutation of nucleophosmin-1 (NPM1). Emerging evidence indicates that within NPM1 mutated AML there is variation in outcome which challenges how best to characterize and treat the individual patient. Our recent findings show that there are two distinct (primitive and committed) subtypes within NPM1 mutated AML patients. These subtypes exhibit specific molecular characteristics, disease differentiation states, patient survival, and differential drug responses

Expression levels of long non-coding RNAs are prognostic for AML outcome

Background: Long non-coding RNA (lncRNA) expression has been implicated in a range of molecular mechanisms that are central in cancer. However, lncRNA expression has not yet been comprehensively characterized in acute myeloid leukemia (AML). Here, we assess to what extent lncRNA expression is prognostic of AML patient overall survival (OS) and determine if there are indications of lncRNA-based molecular subtypes of AML. Methods: We performed RNA sequencing of 274 intensively treated AML patients in a Swedish cohort and quantified lncRNA expression. Univariate and multivariate time-to-event analysis was applied to determine association between individual lncRNAs with OS. Unsupervised statistical learning was applied to ascertain if lncRNA-based molecular subtypes exist and are prognostic. Results: Thirty-three individual lncRNAs were found to be associated with OS (adjusted p value < 0.05). We established four distinct molecular subtypes based on lncRNA expression using a consensus clustering approach. LncRNA-based subtypes were found to stratify patients into groups with prognostic information (p value < 0.05). Subsequently, lncRNA expression-based subtypes were validated in an independent patient cohort (TCGA-AML). LncRNA subtypes could not be directly explained by any of the recurrent cytogenetic or mutational aberrations, although associations with some of the established genetic and clinical factors were found, including mutations in NPM1, TP53, and FLT3. Conclusion: LncRNA expression-based four subtypes, discovered in this study, are reproducible and can effectively stratify AML patients. LncRNA expression profiling can provide valuable information for improved risk stratification of AML patients