The Use of Molecular Analyses in Voided Urine for the Assessment of Patients with Hematuria

Introduction:Patients presenting with painless hematuria form a large part of the urological patient population. In many cases, especially in younger patients, the cause of hematuria is harmless. Nonetheless, hematuria could be a symptom of malignant disease and hence most patients will be subject to cystoscopy. In this study, we aimed to develop a prediction model based on methylation markers in combination with clinical variables, in order to stratify patients with high risk for bladder cancer.Material and Methods:Patients (n=169) presenting with painless hematuria were included. 54 patients were diagnosed with bladder cancer. In the remaining 115 patients, the cause of hematuria was non-malignant. Urine samples were collected prior to cystoscopy. Urine DNA was analyzed for methylation of OSR1, SIM2, OTX1, MEIS1 and ONECUT2. Methylation percentages were calculated and were combined with clinical variables into a logistic regression model.Results:Logistic regression analysis based on the five methylation markers, age, gender and type of hematuria resulted in an area under the curve (AUC) of 0.88 and an optimism corrected AUC of 0.84 after internal validation by bootstrapping. Using a cut-off value of 0.307 allowed stratification of patients in a low-risk and high-risk group, resulting in a sensitivity of 82% (44/54) and a specificity of 82% (94/115). Most aggressive tumors were found in patients in the high-risk group. The addition of cytology to the prediction model, improved the AUC from 0.88 to 0.89, with a sensitivity and specificity of 85% (39/46) and 87% (80/92), retrospectively.Conclusions:This newly developed prediction model could be a helpful tool in risk stratification of patients presenting with painless hematuria. Accurate risk prediction might result in less extensive examination of low risk patients and thereby, reducing patient burden and costs. Further validation in a large prospective patient cohort is necessary to prove the true clinical value of this model

Modeling Personalized Adjuvant TreaTment in EaRly stage coloN cancer (PATTERN)

Aim To develop a decision model for the population-level evaluation of strategies to improve the selection of stage II colon cancer (CC) patients who benefit from adjuvant chemotherapy. Methods A Markov cohort model with a one-month cycle length and a lifelong time horizon was developed. Five health states were included; diagnosis, 90-day mortality, death other causes, recurrence and CC death. Data from the Netherlands Cancer Registry were used to parameterize the model. Transition probabilities were estimated using parametric survival models including relevant clinical and pathological covariates. Subsequently, biomarker status was implemented using external data. Treatment effect was incorporated using pooled trial data. Model development, data sources used, parameter estimation, and internal and external validation are described in detail. To illustrate the use of the model, three example strategies were evaluated in which allocation of treatment was based on (A) 100% adherence to the Dutch guidelines, (B) observed adherence to guideline recommendations and (C) a biomarker-driven strategy. Results Overall, the model showed good internal and external validity. Age, tumor growth, tumor sidedness, evaluated lymph nodes, and biomarker status were included as covariates. For the example strategies, the model predicted 83, 87 and 77 CC deaths after 5 years in a cohort of 1000 patients for strategies A, B and C, respectively. Conclusion This model can be used to evaluate strategies for the allocation of adjuvant chemotherapy in stage II CC patients. In future studies, the model will be used to estimate population-level long-term health gain and cost-effectiveness of biomarker-based selection strategies.Financial support for this study was provided by a grant from ZonMw (Grant number: 848015007). ZonMw had no role in designing the study, interpreting the data, writing the manuscript, and publishing the report

Modeling Personalized Adjuvant TreaTment in EaRly stage coloN cancer (PATTERN)

Aim: To develop a decision model for the population-level evaluation of strategies to improve the selection of stage II colon cancer (CC) patients who benefit from adjuvant chemotherapy. Methods: A Markov cohort model with a one-month cycle length and a lifelong time horizon was developed. Five health states were included; diagnosis, 90-day mortality, death other causes, recurrence and CC death. Data from the Netherlands Cancer Registry were used to parameterize the model. Transition probabilities were estimated using parametric survival models including relevant clinical and pathological covariates. Subsequently, biomarker status was implemented using external data. Treatment effect was incorporated using pooled trial data. Model development, data sources used, parameter estimation, and internal and external validation are described in detail. To illustrate the use of the model, three example strategies were evaluated in which allocation of treatment was based on (A) 100% adherence to the Dutch guidelines, (B) observed adherence to guideline recommendations and (C) a biomarker-driven strategy. Results: Overall, the model showed good internal and external validity. Age, tumor growth, tumor sidedness, evaluated lymph nodes, and biomarker status were included as covariates. For the example strategies, the model predicted 83, 87 and 77 CC deaths after 5 years in a cohort of 1000 patients for strategies A, B and C, respectively. Conclusion: This model can be used to evaluate strategies for the allocation of adjuvant chemotherapy in stage II CC patients. In future studies, the model will be used to estimate population-level long-term health gain and cost-effectiveness of biomarker-based selection strategies

DNA methylation based biomarkers in colorectal cancer: A systematic review

Since genetic and epigenetic alterations influence the development of colorectal cancer (CRC), huge potential lies in the use of DNA methylation as biomarkers to improve the current diagnosis, screening, prognosis and treatment prediction. Here we performed a systematic review on DNA methylation-based biomarkers published in CRC, and discussed the current state of findings and future challenges. Based on the findings, we then provide a perspective on future studies. Genome-wide studies on DNA methylation revealed novel biomarkers as well as distinct subgroups that exist in CRC. For diagnostic purposes, the most independently validated genes to study further are VIM, SEPT9, ITGA4, OSM4, GATA4 and NDRG4. These hypermethylated biomarkers can even be combined with LINE1 hypomethylation and the performance of markers should be examined in comparison to FIT further to find sensitive combinations. In terms of prognostic markers, myopodin, KISS1, TMEFF2, HLTF, hMLH1, APAF1, BCL2 and p53 are independently validated. Most prognostic markers published lack both a multivariate analysis in comparison to clinical risk factors and the appropriate patient group who will benefit by adjuvant chemotherapy. Methylation of IGFBP3, mir148a and PTEN are found to be predictive markers for 5-FU and EGFR therapy respectively. For therapy prediction, more studies should focus on finding markers for chemotherapeutic drugs as majority of the patients would benefit. Translation of these biomarkers into clinical utility would require large-scale prospective cohorts and randomized clinical trials in future. Based on these findings and consideration we propose an avenue to introduce methylation markers into clinical practice in near future. For future studies, multi-omics profiling on matched tissue and non-invasive cohorts along with matched cohorts of adenoma to carcinoma is indispensable to concurrently stratify CRC and find novel, robust biomarkers. Moreover, future studies should examine the timing and heterogeneity of methylation as well as the difference in methylation levels between epithelial and stromal tissue

Selective separation, detection of zotepine and mass spectral characterization of degradants by LCâMS/MS/QTOF

A simple, precise, accurate stability-indicating gradient reversed-phase high-performance liquid chromatographic (RPâHPLC) method was developed for the quantitative determination of zotepine (ZTP) in bulk and pharmaceutical dosage forms in the presence of its degradation products (DPs). The method was developed using Phenomenex C18 column (250 mmÃ4.6 mm i.d., 5 µm) with a mobile phase containing a gradient mixture of solvents, A (0.05% trifluoroacetic acid (TFA), pH=3.0) and B (acetonitrile). The eluted compounds were monitored at 254 nm; the run time was within 20.0 min, in which ZTP and its DPs were well separated, with a resolution of >1.5. The stress testing of ZTP was carried out under acidic, alkaline, neutral hydrolysis, oxidative, photolytic and thermal stress conditions. ZTP was found to degrade significantly in acidic, photolytic, thermal and oxidative stress conditions and remain stable in basic and neutral conditions. The developed method was validated with respect to specificity, linearity, limit of detection, limit of quantification, accuracy, precision and robustness as per ICH guidelines. This method was also suitable for the assay determination of ZTP in pharmaceutical dosage forms. The DPs were characterized by LCâMS/MS and their fragmentation pathways were proposed. Keywords: Zotepine, Stability-indicating RPâHPLC method, Characterization, ESI-Q-TOF-MS, Bulk drugs and formulation

Stratification based on methylation of TBX2 and TBX3 into three molecular grades predicts progression in patients with pTa-bladder cancer

The potential risk of recurrence and progression in patients with non-muscle-invasive bladder cancer necessitates followup by cystoscopy. The risk of progression to muscle-invasive bladder cancer is estimated based on the European Organisation of Research and Treatment of Cancer score, a combination of several clinicopathological variables. However, pathological assessment is not objective and reproducibility is insufficient. The use of molecular markers could contribute to the estimation of tumor aggressiveness. We recently demonstrated that methylation of GATA2, TBX2, TBX3, and ZIC4 genes could predict progression in Ta tumors. In this study, we aimed to validate the markers in a large patient set using DNA from formalin-fixed and paraffin-embedded tissue. PALGA: the Dutch Pathology Registry was used for patient selection. We included 192 patients with pTaG1/2 bladder cancer of whom 77 experienced progression. Methylation analysis was performed and log-rank analysis was used to calculate the predictive value of each methylation marker for developing progression over time. This analysis showed better progression-free survival in patients with low methylation rates compared with the patients with high methylation rates for all markers (P<0.001) during a followup of ten-years. The combined predictive effect of the methylation markers was analyzed with the Cox-regression method. In this analysis, TBX2, TBX3, and ZIC4 were independent predictors of progression. On the basis of methylation status of TBX2 and TBX3, patients were divided into three new molecular grade groups. Survival analysis showed that only 8% of patients in the low molecular grade group progressed within 5 years. This was 29 and 63% for the intermediate-and high-molecular grade groups. In conclusion, this new molecular-grade based on the combination of TBX2 and TBX3 methylation is an excellent marker for predicting progression to muscle-invasive bladder cancer in patients with primary pTaG1/2 bladder cancer

The Use of Molecular Analyses in Voided Urine for the Assessment of Patients with Hematuria

Introduction:Patients presenting with painless hematuria form a large part of the urological patient population. In many cases, especially in younger patients, the cause of hematuria is harmless. Nonetheless, hematuria could be a symptom of malignant disease and hence most patients will be subject to cystoscopy. In this study, we aimed to develop a prediction model based on methylation markers in combination with clinical variables, in order to stratify patients with high risk for bladder cancer.Material and Methods:Patients (n=169) presenting with painless hematuria were included. 54 patients were diagnosed with bladder cancer. In the remaining 115 patients, the cause of hematuria was non-malignant. Urine samples were collected prior to cystoscopy. Urine DNA was analyzed for methylation of OSR1, SIM2, OTX1, MEIS1 and ONECUT2. Methylation percentages were calculated and were combined with clinical variables into a logistic regression model.Results:Logistic regression analysis based on the five methylation markers, age, gender and type of hematuria resulted in an area under the curve (AUC) of 0.88 and an optimism corrected AUC of 0.84 after internal validation by bootstrapping. Using a cut-off value of 0.307 allowed stratification of patients in a low-risk and high-risk group, resulting in a sensitivity of 82% (44/54) and a specificity of 82% (94/115). Most aggressive tumors were found in patients in the high-risk group. The addition of cytology to the prediction model, improved the AUC from 0.88 to 0.89, with a sensitivity and specificity of 85% (39/46) and 87% (80/92), retrospectively.Conclusions:This newly developed prediction model could be a helpful tool in risk stratification of patients presenting with painless hematuria. Accurate risk prediction might result in less extensive examination of low risk patients and thereby, reducing patient burden and costs. Further validation in a large prospective patient cohort is necessary to prove the true clinical value of this model