19 research outputs found
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.
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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
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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