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

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

A 3-Plex Methylation Assay Combined with the FGFR3 Mutation Assay Sensitively Detects Recurrent Bladder Cancer in Voided Urine

Purpose: DNA methylation is associated with bladder cancer and these modifications could serve as useful biomarkers. FGFR3 mutations are present in 60% to 70% of non-muscle invasive bladder cancer (NMIBC). Low-grade bladder cancer recurs in more than 50% of patients. The aim of this study is to determine the sensitivity and specificity of a urine assay for the diagnosis of recurrences in patients with a previous primary NMIBC G1/G2 by using cystoscopy as the reference standard. Experimental Design: We selected eight CpG islands (CGI) methylated in bladder cancer from our earlier genome-wide study. Sensitivity of the CGIs for recurrences detection was investigated on a test set of 101 preTUR urines. Specificity was determined on 70 urines from healthy males aged more than 50 years. A 3-plex assay for the best combination was developed and validated on an independent set of 95 preTUR, recurrence free, and nonmalignant urines (n = 130). Results: The 3-plex assay identified recurrent bladder cancer in voided urine with a sensitivity of 74% in the validation set. In combination with the FGFR3 mutation assay, a sensitivity of 79% was reached (specificity of 77%). Sensitivity of FGFR3 and cytology was 52% and 57%, respectively. Conclusion: The combination of methylation and FGFR3 assays efficiently detects recurrent bladder cancer without the need for stratification of patients regarding methylation/mutation status of the primary tumor. We conclude that the sensitivity of this combination is in the same range as cystoscopy and paves the way for a subsequent study that investigates a modified surveillance protocol consisting of the urine test followed by cystoscopy only when the urine test is positive. (C)2013 AACR

The association between SYK mRNA expression and known CRC mutations.

<p>Mutation rates in the MATCH cohort (n = 240)(a); differences in mRNA expression of <i>SYK(T)</i> (b), <i>SYK(S)</i> (c) and <i>SYK(L)</i> in the MATCH cohort; mutation rates in the TCGA (n = 108)(e); differences in mRNA expression of <i>SYK(T)</i> in the TCGA cohort (f).</p

High mRNA expression of splice variant <i>SYK</i> short correlates with hepatic disease progression in chemonaive lymph node negative colon cancer patients

<div><p>Objective</p><p>Overall and splice specific expression of Spleen Tyrosine Kinase (<i>SYK</i>) has been posed as a marker predicting both poor and favorable outcome in various epithelial malignancies. However, its role in colorectal cancer is largely unknown. The aim of this study was to explore the prognostic role of <i>SYK</i> in three cohorts of colon cancer patients.</p><p>Methods</p><p>Total messenger RNA (mRNA) expression of <i>SYK</i>, <i>SYK(T)</i>, and mRNA expression of its two splice variants <i>SYK</i> short <i>(S)</i> and <i>SYK</i> long <i>(L)</i> were measured using quantitative reverse transcriptase (RT-qPCR) in 240 primary colon cancer patients (n = 160 patients with chemonaive lymph node negative [LNN] and n = 80 patients with adjuvant treated lymph node positive [LNP] colon cancer) and related to microsatellite instability (MSI), known colorectal cancer mutations, and disease-free (DFS), hepatic metastasis-free (HFS) and overall survival (OS). Two independent cohorts of patients with respectively 48 and 118 chemonaive LNN colon cancer were used for validation.</p><p>Results</p><p>Expression of <i>SYK</i> and its splice variants was significantly lower in tumors with MSI, and in <i>KRAS</i> wild type, <i>BRAF</i> mutant and <i>PTEN</i> mutant tumors. In a multivariate Cox regression analysis, as a continuous variable, increasing <i>SYK(S)</i> mRNA expression was associated with worse HFS (Hazard Ratio[HR] = 1.83; 95% Confidence Interval[CI] = 1.08–3.12; p = 0.026) in the LNN group, indicating a prognostic role for <i>SYK(S)</i> mRNA in patients with chemonaive LNN colon cancer. However, only a non-significant trend between <i>SYK(S)</i> and HFS in one of the two validation cohorts was observed (HR = 4.68; 95%CI = 0.75–29.15; p = 0.098).</p><p>Conclusion</p><p>In our cohort, we discovered <i>SYK(S)</i> as a significant prognostic marker for HFS for patients with untreated LNN colon cancer. This association could however not be confirmed in two independent smaller cohorts, suggesting that further extensive validation is needed to confirm the prognostic value of <i>SYK(S)</i> expression in chemonaive LNN colon cancer.</p></div