In this thesis the role of anti-nuclear antibodies to function as biomarkers in systemic sclerosis has been evaluated. Respectively, the heterogeneity of the disease, the need for biomarkers and... Show moreIn this thesis the role of anti-nuclear antibodies to function as biomarkers in systemic sclerosis has been evaluated. Respectively, the heterogeneity of the disease, the need for biomarkers and the role of auto-antibodies to function as such, with specific attention for anti-topoisomerase I, have been outlined in this thesis. Show less
Doorn, C.L.R. van; Eckold, C.; Ronacher, K.; Ruslami, R.; Veen, S. van; Lee, J.S.; ... ; TANDEM Consortium 2022
Background Globally, the tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients.... Show moreBackground Globally, the tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients. Treatment success is lower among people with diabetes mellitus (DM). Predicting treatment outcome early after diagnosis, especially in TB-DM patients, would allow early treatment adaptation for individuals and may improve global TB control.Methods Samples were collected in a longitudinal cohort study of adult TB patients from South Africa (n = 94) and Indonesia (n = 81), who had concomitant DM (n = 59), intermediate hyperglycaemia (n = 79) or normal glycaemia/no DM (n = 37). Treatment outcome was monitored, and patients were categorized as having a good (cured) or poor (failed, recurrence, died) outcome during treatment and 12 months follow-up. Whole blood transcriptional profiles before, during and at the end of TB treatment were characterized using unbiased RNA-Seq and targeted gene dcRT-MLPA.Findings We report differences in whole blood transcriptome profiles, which were observed before initiation of treatment and throughout treatment, between patients with a good versus poor TB treatment outcome. An eight-gene and a 22-gene blood transcriptional signature distinguished patients with a good TB treatment outcome from patients with a poor TB treatment outcome at diagnosis (AUC = 0.815) or two weeks (AUC = 0.834) after initiation of TB treatment, respectively. High accuracy was obtained by cross-validating this signature in an external cohort (AUC = 0.749).Interpretation These findings suggest that transcriptional profiles can be used as a prognostic biomarker for treatment failure and success, even in patients with concomitant DM. Copyright (C) 2022 The Authors. Published by Elsevier B.V. Show less
Background: The aim of this study was to develop a prediction model for 10-year overall survival (OS) after resection of colorectal liver metastasis (CRLM) based on patient, tumour and treatment... Show moreBackground: The aim of this study was to develop a prediction model for 10-year overall survival (OS) after resection of colorectal liver metastasis (CRLM) based on patient, tumour and treatment characteristics.Methods: Consecutive patients after complete resection of CRLM were included from two centres (1992-2019). A prediction model providing 10-year OS probabilities was developed using Cox regression analysis, including KRAS, BRAF and histopathological growth patterns. Discrimination and calibration were assessed using cross-validation. A web-based calculator was built to predict individual 10-year OS probabilities.Results: A total of 4112 patients were included. The estimated 10-year OS was 30% (95% CI 29 -32). Fifteen patient, tumour and treatment characteristics were independent prognostic factors for 10-year OS; age, gender, location and nodal status of the primary tumour, disease-free interval, number and diameter of CRLM, preoperative CEA, resection margin, extrahepatic disease, KRAS and BRAF mutation status, histopathological growth patterns, perioperative systemic chemotherapy and hepatic arterial infusion pump chemotherapy. The discrimination at 10-years was 0.73 for both centres. A simplified risk score identified four risk groups with a 10-year OS of 57%, 38%, 24%, and 12%.Conclusions: Ten-year OS after resection of CRLM is best predicted with a model including 15 patient, tumour, and treatment characteristics. The web-based calculator can be used to inform patients. This model serves as a benchmark to determine the prognostic value of novel biomarkers. (C) 2022 The Author(s). Published by Elsevier Ltd. Show less
This thesis focusses on the further unravelling of one of the mechanisms involved in developing Parkinson's disease: the GBA1 gene, encoding the lysosomal enzyme GCase. Several questions are... Show moreThis thesis focusses on the further unravelling of one of the mechanisms involved in developing Parkinson's disease: the GBA1 gene, encoding the lysosomal enzyme GCase. Several questions are addressed: How prevalent are mutations in this gene in the Netherlands and does it affect disease onset (chapter 2 and 5)? What methodological challenges accompany the sequencing of this gene (chapter 3 and 4)? What biomarkers may be used in clinical trials targeting GCase (chapter 6)? And what are the effects of the novel GCase activator LTI-291, when first administered to healthy volunteers (chapter 7) and to GBA-PD patients (chapter 8)? Show less
The studies described in this thesis contribute to the identification of biomarkers for risk stratification in systemic sclerosis (SSc). Luckily, nowadays many SSc prospective cohorts have been set... Show moreThe studies described in this thesis contribute to the identification of biomarkers for risk stratification in systemic sclerosis (SSc). Luckily, nowadays many SSc prospective cohorts have been set up worldwide which allows high-quality research. Given the rarity and the heterogeneity of the disease, relatively large cohorts are needed to draw valuable conclusions. For the studies described in the current thesis, I was able to incorporate data from the Leiden prospective SSc cohort and data from other prospective cohorts in Europe, which made it possible to strengthen the data. In this final chapter, I summarize the main findings of the studies presented in this thesis, put our findings in a broader perspective, discuss future perspectives and formulate research questions that are relevant to assess in the years ahead of us. Show less
Immune checkpoint inhibitors targeting the programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) axis have been remarkably successful in inducing tumor remissions in... Show moreImmune checkpoint inhibitors targeting the programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) axis have been remarkably successful in inducing tumor remissions in several human cancers, yet a substantial number of patients do not respond to treatment. Because this may be partially due to the mechanisms giving rise to high PD-L1 expression within a patient, it is highly relevant to fully understand these mechanisms. In this study, we conduct a bioinformatic analysis to quantify the relative importance of transcription factor (TF) activity, microRNAs (miRNAs) and mutations in determining PD-L1 (CD274) expression at mRNA level based on data from the Cancer Genome Atlas. To predict individual CD274 levels based on TF activity, we developed multiple linear regression models by taking the expression of target genes of the TFs known to directly target PD-L1 as independent variables. This analysis showed that IRF1, STAT1, NFKB and BRD4 are the most important regulators of CD274 expression, explaining its mRNA levels in 90-98% of the patients. Because the remaining patients had high CD274 levels independent of these TFs, we next investigated whether mutations associated with increased CD274 mRNA levels, and low levels of miRNAs associated with negative regulation of CD274 expression could cause high CD274 levels in these patients. We found that mutations or miRNAs offered an explanation for high CD274 levels in 81-100% of the underpredicted patients. Thus, CD274 expression is largely explained by TF activity, and the remaining unexplained cases can largely be explained by mutations or low miRNA abundance. Show less
Background: Antibodies against mycobacterial proteins are highly specific, but lack sensitivity, whereas cytokines have been shown to be sensitive but not very specific in the diagnosis of... Show moreBackground: Antibodies against mycobacterial proteins are highly specific, but lack sensitivity, whereas cytokines have been shown to be sensitive but not very specific in the diagnosis of tuberculosis (TB). We assessed combinations between antibodies and cytokines for diagnosing TB. Methods: Immuoglubulin (Ig) A and IgM antibody titres against selected mycobacterial antigens including Apa, NarL, Rv3019c, PstS1, LAM, "Kit 1" (MTP64 and Tpx)", and "Kit 2" (MPT64, Tpx and 19 kDa) were evaluated by ELISA in plasma samples obtained from individuals under clinical suspicion for TB. Combinations between the antibody titres and previously published cytokine responses in the same participants were assessed for diagnosing active TB. Results: Antibody responses were more promising when used in combination (AUC of 0.80), when all seven antibodies were combined. When anti-"Kit 1"-IgA levels were combined with five host cytokine biomarkers, the AUC increased to 97% (92-100%) with a sensitivity of 95% (95% CI, 73-100%), and specificity of 88.5% (95% CI, 68.7-97%) achieved after leave-one-out cross validation. Conclusion: When used in combination, IgA titres measured with ELISA against multiple Mycobacterium tuberculosis antigens may be useful in the diagnosis of TB. However, diagnostic accuracy may be improved if the antibodies are used in combination with cytokines. Show less
Kuipers, S.; Overmars, L.M.; Es, B. van; Bresser, J. de; Bron, E.E.; Hoefer, I.E.; ... ; Haitjema, S. 2022
Biological processes underlying cerebral small vessel disease (cSVD) are largely unknown. We hypothesized that identification of clusters of inter-related bood-based biomarkers that are associated... Show moreBiological processes underlying cerebral small vessel disease (cSVD) are largely unknown. We hypothesized that identification of clusters of inter-related bood-based biomarkers that are associated with the burden of cSVD provides leads on underlying biological processes. In 494 participants (mean age 67.6 +/- 8.7 years; 36% female; 75% cardiovascular diseases; 25% reference participants) we assessed the relation between 92 blood-based biomarkers from the OLINK cardiovascular III panel and cSVD, using cluster-based analyses. We focused particularly on white matter hyperintensities (WMH). Nineteen biomarkers individually correlated with WMH ratio (r range: 0.16-0.27, Bonferroni corrected p-values <0.05), of which sixteen biomarkers formed one biomarker cluster. Pathway analysis showed that this biomarker cluster predominantly reflected coagulation processes. This cluster related also significantly to other cSVD manifestations (lacunar infarcts, microbleeds, and enlarged perivascular spaces), which supports generalizability beyond WMHs. To study possible causal effects of biological processes reflected by the cluster we performed a mediation analysis that showed a mediation effect of the cluster on the relation between age and WMH ratio (proportion mediated 17%), and hypertension and WMH-volume (proportion mediated 21%). In conclusion, we identified a cluster of blood-based biomarkers reflecting coagulation, that is related to manifestations of cSVD, corroborating involvement of coagulation abnormalities in the etiology of cSVD. Show less
Whitehouse, D.P.; Monteiro, M.; Czeiter, E.; Vande Vyvere, T.; Valerio, F.; Ye, Z.; ... ; CENTER-TBI Participants Investigat 2022
Background We aimed to understand the relationship between serum biomarker concentration and lesion type and volume found on computed tomography (CT) following all severities of TBI.Methods... Show moreBackground We aimed to understand the relationship between serum biomarker concentration and lesion type and volume found on computed tomography (CT) following all severities of TBI.Methods Concentrations of six serum biomarkers (GFAP, NFL, NSE, S100B, t-tau and UCH-L1) were measured in samples obtained <24 hours post-injury from 2869 patients with all severities of TBI, enrolled in the CENTER-TBI prospective cohort study (NCT02210221). Imaging phenotypes were defined as intraparenchymal haemorrhage (IPH), oedema, subdural haematoma (SDH), extradural haematoma (EDH), traumatic subarachnoid haemorrhage (tSAH), diffuse axonal injury (DAI), and intraventricular haemorrhage (IVH). Multivariable polynomial regression was performed to examine the association between biomarker levels and both distinct lesion types and lesion volumes. Hierarchical clustering was used to explore imaging phenotypes; and principal component analysis and k-means clustering of acute biomarker concentrations to explore patterns of biomarker clustering.Findings 2869 patient were included, 68% (n=1946) male with a median age of 49 years (range 2-96). All severities of TBI (mild, moderate and severe) were included for analysis with majority (n=1946, 68%) having a mild injury (GCS 13-15). Patients with severe diffuse injury (Marshall III/IV) showed significantly higher levels of all measured biomarkers, with the exception of NFL, than patients with focal mass lesions (Marshall grades V/VI). Patients with either DAI+IVH or SDH+IPH+tSAH, had significantly higher biomarker concentrations than patients with EDH. Higher biomarker concentrations were associated with greater volume of IPH (GFAP, S100B, t-tau;adj r2 range:0.48-0.49; p<0.05), oedema (GFAP, NFL, NSE, t-tau, UCH-L1;adj r2 range:0. 44-0.44; p<0.01), IVH (S100B;adj r2 range:0.48-0.49; p<0.05), Unsupervised k-means biomarker clustering revealed two clusters explaining 83.9% of variance, with phenotyping characteristics related to clinical injury severity.Interpretation Interpretation: Biomarker concentration within 24 hours of TBI is primarily related to severity of injury and intracranial disease burden, rather than pathoanatomical type of injury. Copyright (C) 2021 The Author(s). Published by Elsevier B.V. Show less
