Personalized Bladder Cancer Management

Bladder cancer incidence in the Netherlands has increased over 1.5-fold since the 1990s, with currently over 7.000 new cases each year in the Netherlands. Over two thirds of new patients are diagnosed with NMIBC, the remaining one third of patients are diagnosed with MIBC. The 5-year survival of NMIBC patients is very good (>90%), however recurrence rates are high (>50%) and the disease may progress to MIBC. Consequently, NMIBC necessitates long and costly surveillance. On the other hand, the prognosis of MIBC is very poor; the 5-year survival rate is around 60% for stage T2 and only 6% for stage T4 disease 6. Treatment options for MIBC are limited and the survival rates have not improved much over the past 20 years, although recently introduced immune therapies present promising results. The high incidence and recurrence rate of NMIBC, together with the poor survival rate of MIBC and the immense costs make bladder cancer a serious public health problem. The general aim of this thesis was to evaluate and validate the use of (epi)genetic biomarkers in personalized bladder cancer management; including diagnosis of primary disease, prognosis of disease, tools for treatment allocation and their use in individualized surveillance regimens

A reported 20-gene expression signature to predict lymph node-positive disease at radical cystectomy for muscle-invasive bladder cancer is clinically not applicable

Background Neoadjuvant chemotherapy (NAC) for muscle-invasive bladder cancer (MIBC) provides a small but significant survival benefit. Nevertheless, controversies on applying NAC remain because the limited benefit must be weight against chemotherapy-related toxicity and the delay of definitive local treatment. Therefore, there is a clear clinical need for tools to guide treatment decisions on NAC in MIBC. Here, we aimed to validate a previously reported 20-gene expression signature that predicted lymph node-positive disease at radical cystectomy in clinically node-negative MIBC patients, which would be a justification for upfront chemotherapy. Methods We studied diagnostic transurethral resection of bladder tumors (dTURBT) of 150 MIBC patients (urothelial carcinoma) who were subsequently treated by radical cystectomy and pelvic lymph node dissection. RNA was isolated and the expression level of the 20 genes was determined on a qRT-PCR platform. Normalized Ct values were used to calculate a risk score to predict the presence of node-positive disease. The Cancer Genome Atlas (TCGA) RNA expression data was analyzed to subsequently validate the results. Results In a univariate regression analysis, none of the 20 genes significantly correlated with nodepositive disease. The area under the curve of the risk score calculated by the 20-gene expression signature was 0.54 (95% Confidence Interval: 0.44-0.65) versus 0.67 for the model published by Smith et al. Node-negative patients had a significantly lower tumor grade at TURBT (p = 0.03), a lower pT stage (p<0.01) and less frequent lymphovascular invasion (13% versus 38%, p<0.01) at radical cystectomy than node-positive patients. In addition, in the TCGA data, none of the 20 genes was differentially expressed in node-negative versus node-positive patients. Conclusions We conclude that a 20-gene expression signature developed for nodal staging of MIBC at radical cystectomy could not be validated on a qRT-PCR platform in a large cohort of dTURBT specimens

Urinary peptide panel for prognostic assessment of bladder cancer relapse

Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently needed to guide clinical intervention. As a follow-up to the previously published study on CE-MSbased urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation towards BC patients with longitudinal data. Profling datasets of BC patients with follow-up information regarding the relapse status were investigated. The peptidomics dataset (n=98) was split into training and test set. Cox regression was utilized for feature selection in the training set. Investigation of the entire training set at the single peptide level revealed 36 peptides being strong independent prognostic markers of disease relapse. Those features were further integrated into a Random Forest-based model evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort, showing high signifcance in BC relapse prognosis [HR=5.76, p-value=0.0001, c-index=0.64]. Urinary peptide profles integrated into a prognostic model allow for quantitative risk assessment of BC relapse highlighting the need for its incorporation in prospective studies to establish its value in the clinical management of BC