Hepatitis C elimination in the Netherlands (CELINE): Study protocol for nationwide retrieval of lost to follow-up patients with chronic hepatitis C

Background The Netherlands has a low hepatitis C virus (HCV) prevalence, estimated at 0.16%. Previous studies have shown that up to 30% of the diagnosed HCV population in the Netherlands has been lost to follow-up (LTFU). Retrieval of these patients could halt progression of liver disease in infected patients, reduce the number of infected individuals and limit HCV transmission. Several regional Dutch retrieval projects have already been executed, which demonstrated that retrieval is feasible. Therefore, we initiated a nationwide retrieval project, aiming to achieve microelimination in previously diagnosed but LTFU patients with chronic HCV through retrieval. Methods Laboratory records will be used to identify possible patients with chronic hepatitis C, defined as either a positive most recent HCV RNA or positive HCV antibodies without known RNA result. Reviewing patient records and obtaining current contact information from municipality databases will identify LTFU patients who ar

Loss to follow-up in the hepatitis C care cascade: A substantial problem but opportunity for micro-elimination

Since the advent of direct-acting antivirals, elimination of hepatitis C viral (HCV) infections seems within reach. However, studies on the HCV cascade of care show suboptimal progression through each step for all patient groups. Loss to follow-up (LTFU) is a major issue and is a barrier to HCV elimination. This review summarizes the scale of the LTFU problem and proposes a micro-elimination approach. Retrieving LTFU patients and re-engaging them with care again has shown to be feasible in the Netherlands. Micro-elimination through retrieval can contribute to reaching the World Health Organization's viral hepatitis elimination targets by 2030

Improved high-dimensional prediction with Random Forests by the use of co-data

Background: Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary 'co-data' can be used to improve the performance of a Random Forest in such a setting. Results: Co-data are incorporated in the Random Forest by replacing the uniform sampling probabilities that are used to draw candidate variables by co-data moderated sampling probabilities. Co-data here are defined as any type information that is available on the variables of the primary data, but does not use its response labels. These moderated sampling probabilities are, inspired by empirical Bayes, learned from the data at hand. We demonstrate the co-data moderated Random Forest (CoRF) with two examples. In the first example we aim to predict the presence of a lymph node metastasis with gene expression data. We demonstrate how a set of external p-values, a gene signature, and the correlation between gene expression and DNA copy number can improve the predictive performance. In the second example we demonstrate how the prediction of cervical (pre-)cancer with methylation data can be improved by including the location of the probe relative to the known CpG islands, the number of CpG sites targeted by a probe, and a set of p-values from a related study. Conclusion: The proposed method is able to utilize auxiliary co-data to improve the performance of a Random Forest

Loss to follow-up in the hepatitis C care cascade: A substantial problem but opportunity for micro-elimination

Since the advent of direct-acting antivirals, elimination of hepatitis C viral (HCV) infections seems within reach. However, studies on the HCV cascade of care show suboptimal progression through each step for all patient groups. Loss to follow-up (LTFU) is a major issue and is a barrier to HCV elimination. This review summarizes the scale of the LTFU problem and proposes a micro-elimination approach. Retrieving LTFU patients and re-engaging them with care again has shown to be feasible in the Netherlands. Micro-elimination through retrieval can contribute to reaching the World Health Organization's viral hepatitis elimination targets by 2030

Direct-Acting Antiviral Treatment for Hepatitis C Genotypes Uncommon in High-Income Countries: A Dutch Nationwide Cohort Study (vol 8, ofab006, 2021)

Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Hepatitis B virus RNA decline without concomitant viral antigen decrease is associated with a low probability of sustained response and hepatitis B surface antigen loss

Background: Serum hepatitis B virus (HBV) RNA may reflect intrahepatic HBV replication. Novel anti-viral drugs have shown potent HBV RNA decline without concomitant hepatitis B surface antigen (HBsAg) decrease. How this relates to off-treatment response is yet unclear. Aim: To study the degree of on-treatment viral antigen decline among patients with pronounced HBV RNA decrease in relation to off-treatment sustained response and HBsAg loss. Methods: HBV RNA, HBsAg and hepatitis B core-related antigen (HBcrAg) were quantified in patients with chronic hepatitis B who participated in two randomised controlled trials of peginterferon-based therapy. Sustained response (HBV DNA 2 log HBV RNA decline or >1 log decline resulting in an undetectable value at on-treatment week 24), stratified by concomitant HBsAg decline (1 log). Results: We enrolled 279 patients; 176 were hepatitis B e antigen (HBeAg)-positive, and 103 were HBeAg-negative. Sustained response was achieved in 20.4% of patients. At on-treatment week 24, HBV RNA response was associated with higher

tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities

Background: To determine which changes in the host cell genome are crucial for cervical carcinogenesis, a longitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genome-wide manner. Four cell lines affected with either HPV16 or HPV18 were assayed at 8 sequential time points for gene expression (mRNA) and gene copy number (DNA) using high-resolution microarrays. Available methods for temporal differential expression analysis are not designed for integrative genomic studies. Results: Here, we present a method that allows for the identification of differential gene expression associated with DNA copy number changes over time. The temporal variation in gene expression is described by a generalized linear mixed model employing low-rank thin-plate splines. Model parameters are estimated with an empirical Bayes procedure, which exploits integrated nested Laplace approximation for fast computation. Iteratively, posteriors of hyperparameters and model parameters are estimated. The empirical Bayes procedure shrinks multiple dispersion-related parameters. Shrinkage leads to more stable estimates of the model parameters, better control of false positives and improvement of reproducibility. In addition, to make estimates of the DNA copy number more stable, model parameters are also estimated in a multivariate way using triplets of features, imposing a spatial prior for the copy number effect.Conclusion: With the proposed method for analysis of time-course multilevel molecular data, more profound insight may be gained through the identification of temporal differential expression induced by DNA copy number abnormalities. In particular, in the analysis of an integrative oncogenomics study with a time-course set-up our method finds genes previously reported to be involved in cervical carcinogenesis. Furthermore, the proposed method yields improvements in sensitivity, specificity and reproducibility compared to existing methods. Finally, the proposed method is able to handle count (RNAseq) data from time course experiments as is shown on a real data set

Epithelial-to-mesenchymal transition is a prognostic marker for patient outcome in advanced stage HNSCC patients treated with chemoradiotherapy

Contains fulltext : 220434.pdf (Publisher’s version ) (Open Access)BACKGROUND: The prognosis of patients with HPV-negative advanced stage head and neck squamous cell carcinoma (HNSCC) remains poor. No prognostic markers other than TNM staging are routinely used in clinic. Epithelial-to-mesenchymal transition (EMT) has been shown to be a strong prognostic factor in other cancer types. The purpose of this study was to determine the role of EMT in HPV-negative HNSCC outcomes. METHODS: Pretreatment tumor material from patients of two cohorts, totalling 174 cisplatin-based chemoradiotherapy treated HPV-negative HNSCC patients, was RNA-sequenced. Seven different EMT gene expression signatures were used for EMT status classification and generation of HNSCC-specific EMT models using Random Forest machine learning. RESULTS: Mesenchymal classification by all EMT signatures consistently enriched for poor prognosis patients in both cohorts of 98 and 76 patients. Uni- and multivariate analyses show important HR of 1.6-5.8, thereby revealing EMT's role in HNSCC outcome. Discordant classification by these signatures prompted the generation of an HNSCC-specific EMT profile based on the concordantly classified samples in the first cohort (cross-validation AUC > 0.98). The independent validation cohort confirmed the association of mesenchymal classification by the HNSCC-EMT model with poor overall survival (HR = 3.39, p < 0.005) and progression free survival (HR = 3.01, p < 0.005) in multivariate analysis with TNM. Analysis of an additional HNSCC cohort from PET-positive patients with metastatic disease prior to treatment further supports this relationship and reveals a strong link of EMT to the propensity to metastasize. CONCLUSIONS: EMT in HPV-negative HNSCC co-defines patient outcome after chemoradiotherapy. The generated HNSCC-EMT prediction models can function as strong prognostic biomarkers

The netherlands is on track to meet the world health organization hepatitis C elimination targets by 2030

Background: The Netherlands strives for hepatitis C virus (HCV) elimination, in accordance with the World Health Organization targets. An accurate estimate when HCV elimination will be reached is elusive. We have embarked on a nationwide HCV elimination project (CELINE) that allowed us to harvest detailed data on the Dutch HCV epidemic. This study aims to provide a well‐supported timeline towards HCV elimination in The Netherlands. Methods: A previously published Markov model was used, adopting published data and unpublished CELINE project data. Two main scenarios were devised. In the Status Quo scenario, 2020 diagnosis and treatment levels remained constant in subsequent years. In the Gradual Decline scenario, an annual decrease of 10% in both diagnoses and treatments was implemented, starting in 2020. WHO incidence target was disregarded, due to low HCV incidence in The Netherlands (≤5 per 100,000). Results: Following the Status Quo and Gradual Decline scenarios, The Netherlands would meet WHO’s elimination targets by 2027 and 2032, respectively. From 2015 to 2030, liver‐related mortality would be reduced by 97% in the Status Quo and 93% in the Gradual Decline scenario. Compared to the Status Quo scenario, the Gradual Decline scenario would result in 12 excess cases of decompensated cirrhosis, 18 excess cases of hepatocellular carcinoma, and 20 excess cases of liverrelated death from 2020-2030. Conclusions: The Netherlands is on track to reach HCV elimination by 2030. However, it is vital that HCV elimination remains high on the agenda to ensure adequate numbers of patients are being diagnosed and treated.</p