Prospective observational study for the prediction of adverse events of triplet chemotherapy for pancreatic cancer and colorectal cancer by genetic polymorphisms.
Ontology highlight
ABSTRACT:
DISEASE(S): Pancreatic Cancer And Colorectal Cancer
PROVIDER: 2657405 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description: Not available
| S-EPMC5968021 | biostudies-literature
Project description:The inclusion of a genetic risk score (GRS) can modify the risk prediction of coronary artery disease (CAD), providing an advantage over the use of traditional models. The predictive value of the genetic information on the recurrence of major adverse cardiovascular events (MACE) remains controversial. A total of 33 genetic variants previously associated with CAD were genotyped in 1587 CAD patients from the GENEMACOR study. Of these, 18 variants presented an hazard ratio >1, so they were selected to construct a weighted GRS (wGRS). MACE discrimination and reclassification were evaluated by C-Statistic, Net Reclassification Index and Integrated Discrimination Improvement methodologies. After the addition of wGRS to traditional predictors, the C-index increased from 0.566 to 0.572 (p=0.0003). Subsequently, adding wGRS to traditional plus clinical risk factors, this model slightly improved from 0.620 to 0.622 but with statistical significance (p=0.004). NRI showed that 17.9% of the cohort was better reclassified when the primary model was associated with wGRS. The Kaplan-Meier estimator showed that, at 15-year follow-up, the group with a higher number of risk alleles had a significantly higher MACE occurrence (p=0.011). In CAD patients, wGRS improved MACE risk prediction, discrimination and reclassification over the conventional factors, providing better cost-effective therapeutic strategies.
| S-EPMC8201463 | biostudies-literature
Project description:BackgroundPrediction of inpatients with community-acquired pneumonia (CAP) at high risk for severe adverse events (SAEs) requiring higher-intensity treatment is critical. However, evidence regarding prediction rules applicable to all patients with CAP including those with healthcare-associated pneumonia (HCAP) is limited. The objective of this study is to develop and validate a new prediction system for SAEs in inpatients with CAP.MethodsLogistic regression analysis was performed in 1334 inpatients of a prospective multicenter study to develop a multivariate model predicting SAEs (death, requirement of mechanical ventilation, and vasopressor support within 30 days after diagnosis). The developed ALL-COP-SCORE rule based on the multivariate model was validated in 643 inpatients in another prospective multicenter study.ResultsThe ALL-COP SCORE rule included albumin (< 2 g/dL, 2 points; 2-3 g/dL, 1 point), white blood cell (< 4000 cells/μL, 3 points), chronic lung disease (1 point), confusion (2 points), PaO2/FIO2 ratio (< 200 mmHg, 3 points; 200-300 mmHg, 1 point), potassium (≥ 5.0 mEq/L, 2 points), arterial pH (< 7.35, 2 points), systolic blood pressure (< 90 mmHg, 2 points), PaCO2 (> 45 mmHg, 2 points), HCO3- (< 20 mmol/L, 1 point), respiratory rate (≥ 30 breaths/min, 1 point), pleural effusion (1 point), and extent of chest radiographical infiltration in unilateral lung (> 2/3, 2 points; 1/2-2/3, 1 point). Patients with 4-5, 6-7, and ≥ 8 points had 17%, 35%, and 52% increase in the probability of SAEs, respectively, whereas the probability of SAEs was 3% in patients with ≤ 3 points. The ALL-COP SCORE rule exhibited a higher area under the receiver operating characteristic curve (0.85) compared with the other predictive models, and an ALL-COP SCORE threshold of ≥ 4 points exhibited 92% sensitivity and 60% specificity.ConclusionsALL-COP SCORE rule can be useful to predict SAEs and aid in decision-making on treatment intensity for all inpatients with CAP including those with HCAP. Higher-intensity treatment should be considered in patients with CAP and an ALL-COP SCORE threshold of ≥ 4 points.Trial registrationThis study was registered with the University Medical Information Network in Japan, registration numbers UMIN000003306 and UMIN000009837.
| S-EPMC8753951 | biostudies-literature
Project description:Proteogenomics approaches often struggle with the distinction between right and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for spectral matching in a proteogenomic context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator post-processing tool for protein databases constructed from RNA-seq and ribosome profiling analyses. The presented results provide evidence that this approach enhances the peptide identification power in a proteogenomic setting and in the meantime they lead to the validation of new proteoforms with elevated stringency. In this online repository, we submitted the conventional proteomic search results with MaxQuant against the custom nanopore RNA-seq-based search space. All other results can be found in the supplemental materials of the manuscript, in SRA (sequencing data) or under ProteomeXChange Project PXD011353 (as this is original data from a previuos paper).
2021-03-26 | PXD022280 | Pride
Project description:BackgroundDisease-related single nucleotide polymorphisms (SNPs) based genetic risk score (GRS) has been proven to provide independent inherited risk other than family history in multiple cancer types.AimTo evaluate the potential of GRS in the prediction of pancreatic cancer risk.MethodsIn this case-control study (254 cases and 1200 controls), we aimed to evaluate the association between GRS and pancreatic ductal adenocarcinoma (PDAC) risk in the Chinese population. The GRS was calculated based on the genotype information of 18 PDAC-related SNPs for each study subject (personal genotyping information of the SNPs) and was weighted by external odd ratios (ORs).ResultsGRS was significantly different in cases and controls (1.96 ± 3.84 in PDACs vs 1.09 ± 0.94 in controls, P < 0.0001). Logistic regression revealed GRS to be associated with PDAC risk [OR = 1.23, 95% confidence interval (CI): 1.13-1.34, P < 0.0001]. GRS remained significantly associated with PDAC (OR = 1.36, 95%CI: 1.06-1.74, P = 0.015) after adjusting for age and sex. Further analysis revealed an association of increased risk for PDAC with higher GRS. Compared with low GRS (< 1.0), subjects with high GRS (2.0) were 99% more likely to have PDAC (OR: 1.99, 95%CI: 1.30-3.04, P = 0.002). Participants with intermediate GRS (1.0-1.9) were 39% more likely to have PDAC (OR: 1.39, 95%CI: 1.03-1.84, P = 0.031). A positive trend was observed (P trend = 0.0006).ConclusionGRS based on PDAC-associated SNPs could provide independent information on PDAC risk and may be used to predict a high risk PDAC population.
| S-EPMC7304113 | biostudies-literature
Project description:BackgroundThis study was aimed to investigate the correlation between CYP2C19 and ABCB1 polymorphisms and the recurrence of ischemic cardiovascular adverse events in patients with coronary artery disease treated with clopidogrel.MethodsA total of 168 patients with coronary heart disease who underwent PCI operation and received clopidogrel treatment were enrolled. Dual antiplatelet therapy was applied to the treatment of patients for 2 years. Thromboelastography was used to test the efficiency of blood coagulation. Polymerase chain reaction (PCR) was used to detect CYP2C19 and ABCB1 3435CT polymorphisms. One-year follow-up visit was carried out to record the incidence of cardiovascular adverse events after drug-eluting stent implantation was inset.ResultsFollow-up visit results suggested that the patients with high on-treatment platelet reactivity (HPR) had a higher recurrence rate of cardiovascular adverse events after PCI operation and clopidogrel treatment. Gene polymorphism testing results indicated that patients with CYP2C19*3 had a significantly higher incidence of HPR, whereas CYP2C19*2 and ABCB1 3435CT were not significantly correlated with HPR. Multivariable logistic regression analysis showed that CYP2C19*3 might be an independent predictive factor of post-PCI HPR. In addition, CYP2C19*3 as well as post-PCI HPR could function as independent predictive factors of cardiovascular adverse events.ConclusionCYP2C19*3 polymorphism could be an important predictive factor of HPR and ischemic cardiovascular adverse events after clopidogrel treatment.
| S-EPMC6816974 | biostudies-literature
Project description:Identifying genetic factors associated with the development of adverse events might allow screening before vaccinia virus administration. Two independent clinical trials of the smallpox vaccine (Aventis Pasteur) were conducted in healthy, vaccinia virus-naive adult volunteers. Volunteers were assessed repeatedly for local and systemic adverse events (AEs) associated with the receipt of vaccine and underwent genotyping for 1,442 singlenucleotide polymorphisms (SNPs). In the first study, 36 SNPs in 26 genes were associated with systemic AEs (P <or= .05); these 26 genes were tested in the second study. In the final analysis, 3 SNPs were consistently associated with AEs in both studies. The presence of a nonsynonymous SNP in the methylenetetrahydrofolate reductase (MTHFR)gene was associated with the risk ofAEin both trials (odds ratio [OR], 2.3 [95% confidence interval {CI}, 1.1-5.2] [P = .04] and OR, 4.1 [95% CI, 1.4 -11.4] [P<.01]). Two SNPs in the interferon regulatory factor-1 (IRF1) gene were associated with the risk of AE in both sample sets (OR, 3.2 [95% CI, 1.1-9.8] [P = .03] andOR, 3.0 [95% CI, 1.1- 8.3] [P = .03]). Genetic polymorphisms in genes expressing an enzyme previously associated with adverse reactions to a variety of pharmacologic agents (MTHFR) and an immunological transcription factor (IRF1) were associated with AEs after smallpox vaccination in 2 independent study samples.
| S-EPMC2746083 | biostudies-literature
Project description:MotivationAdverse drug reactions (ADRs) are a central consideration during drug development. Here we present a machine learning classifier to prioritize ADRs for approved drugs and pre-clinical small-molecule compounds by combining chemical structure (CS) and gene expression (GE) features. The GE data is from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset that measured changes in GE before and after treatment of human cells with over 20 000 small-molecule compounds including most of the FDA-approved drugs. Using various benchmarking methods, we show that the integration of GE data with the CS of the drugs can significantly improve the predictability of ADRs. Moreover, transforming GE features to enrichment vectors of biological terms further improves the predictive capability of the classifiers. The most predictive biological-term features can assist in understanding the drug mechanisms of action. Finally, we applied the classifier to all >20 000 small-molecules profiled, and developed a web portal for browsing and searching predictive small-molecule/ADR connections.Availability and implementationThe interface for the adverse event predictions for the >20 000 LINCS compounds is available at http://maayanlab.net/SEP-L1000/ CONTACT: avi.maayan@mssm.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4965635 | biostudies-literature
Project description:BackgroundWe sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak.MethodsSingle-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed event ratio (E/O). Discrimination was assessed by C-statistics (AUC).ResultsIn the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (E/O: 1.01, AUC: 0.76); for critical illness (E/O 1.03, AUC: 0.79); and for death (E/O: 1.63, AUC=0.93). Among 30 predictors, the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate.ConclusionsCoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.
| S-EPMC7325189 | biostudies-literature
Project description:Ritodrine, a tocolytic ?2-agonist, has been used extensively in Europe and Asia despite its safety concerns. This study was designed to identify associations between ?2-adrenergic receptor (ADRB2) polymorphisms and adverse drug events (ADEs) in patients with preterm labor treated with ritodrine.This follow-up study was prospectively conducted at Ewha Womans University Mokdong Hospital in Korea. Five single nucleotide polymorphisms (SNPs) of the ADRB2 gene (rs1042713, rs1042714, rs1042717, rs1042718, and rs1042719) were analyzed in 186 pregnant women with preterm labor. Patients with the AA genotype of rs1042717 had significantly lower incidence of ADEs compared to those with the G allele (p = 0.009). In multivariate analysis, one of the predictors of ADEs was the maximum infusion rate of ritodrine (AOR 4.47, 95% CI 1.31-15.25). Rs1042719 was also a significant factor for ritodrine-induced ADEs. The CC genotype carriers had 78% decreased risk of ADEs compared to those with other genotypes.This study demonstrates that ADEs induced by ritodrine are associated with ADRB2 gene polymorphisms, as well as the infusion rate of ritodrine in pregnant women with preterm labor.
| S-EPMC5683230 | biostudies-literature