Introduction
Non-muscle invasive bladder cancer (NMIBC) represents approximately 75% of newly diagnosed
bladder cancers (BCa) in western countries (1). Patients with NMIBC have a relatively
favorable prognosis, with ten-years cancer-specific survival (CSS) rates varying between
75% and 100%, depending on tumor grade (2). Nevertheless, despite adequate therapy,
patients with NMIBC have a life-long risk of disease recurrence and, more importantly,
of progression to muscle-invasive bladder cancer (MIBC) (3). While cancer recurrence
mainly impacts our patients’ quality of life and the economical burden of the disease,
progression to MIBC represents a dramatic event, significantly lowering survival probability
and calling for intensified therapy such as radical cystectomy (4). Indeed, patients
harboring a NMIBC that eventually progress to MIBC have a worse survival probability
compared to a patient who presents with a primary MIBC (5). Because of these reasons,
predicting both disease recurrence and progression is of fundamental importance to
accurately stratify patients into personalized risk groups for selection of the appropriate
treatment strategy, which can range from variable follow up scheduling, from adjuvant
intravesical therapy schemes to radical surgery. A personalized assessment of the
biologic potential and clinical behavior of NMIBC in every specific patient could
allow for an improvement of oncologic outcomes and smart allocation of resources.
Status quo in the prediction of outcomes in NMIBC
Currently, risk-stratification of patients with NMIBC is based on patients’ characteristics
and tumor-related features. Based on tumor stage, grade, presence of carcinoma in
situ (CIS), tumor size, tumor number and previous recurrence rate, the European Organization
for Research and Treatment of Cancer (EORTC) risk tables stratify patients into low,
intermediate and high risk for each disease recurrence and progression (6). Since
these risk tables were built using clinical trial data of patients treated in previous
decades before the wide spread use of BCG immunotherapy and re-TUR, their predictive
accuracy is limited in contemporary patients. The Spanish Urological Club for Oncological
Treatment (CUETO) group tried to overcome these limitations by including only patients
treated with BCG and by adding additional features to the model such as age and gender
(7). However, the discrimination of even this nomogram remains unsatisfactory when
tested in external validation cohorts (8). Both tools exhibited poor discrimination
for both disease recurrence and progression (0.60 and 0.66, and 0.52 and 0.62, for
the EORTC and CUETO models, respectively), underlying the need for better tools incorporating
more powerful predictors of oncologic behavior in order to improve NMIBC risk-stratification
and therapy.
One hope is to fill the “missing information” by integrating biomarkers that reflect
the biological behavior of the cells and its host thereby increasing the capture of
the tumors personality. To date, several urinary, blood and tissue markers have been
developed and tested with the aim to improving prediction of outcomes and helping
with selection, thereby moving a step forward towards the era of personalized medicine.
However, due to their suboptimal performances, their role remains, as of today, still
limited and none of them is currently recommended by expert guidelines for daily clinical
practice (9).
Urinary biomarkers have been used to predict short to intermediate term oncological
outcomes as well as response to BCG. A positive fluorescence in situ hybridization
(FISH) assay, for example, performed at different time points during BCG therapy,
was associated with either disease persistence or recurrence (10,11). Kamat et al.
found that a positive FISH both at 6 and 12 weeks resection on BCG therapy can identify
patients at higher risk of disease recurrence and progression (12). While promising,
validations of these findings are still pending.
Blood-based biomarkers measuring systemic inflammatory response such as the neutrophil-to-lymphocyte
ratio (NLR) and the C-reactive protein (CRP) have also been evaluated as predictors
of oncological outcomes in NMIBC. Their integration into a model for the prediction
of disease recurrence and progression led to an increase in the discrimination of
the model (13). These biomarkers are interesting as they may be able to help patients
most likely to benefit from systemic immunotherapy such as check-point inhibitors.
A growing body of literature shows that several genes and proteins related to different
pathways are not only involved in bladder carcinogenesis but also in its clinical
behavior. Consequently, several tissue biomarkers have been tested in a multiphased
systematic approach (14). Even if multiple biomarkers, such as cell-cycle markers
as well as Ki-67, FGFR3, cadherins, surviving as well as immune and inflammation-related
biomarkers have shown to predict NMIBC outcomes, their prognostic value remains suboptimal
with only few of them having prospective validation study phases (15-20). Recently,
van Kessel et al. prospectively tested a panel of tissue biomarkers comparing their
performance to current clinicopathological characteristics for risk-stratification
(21). Fresh frozen tumor samples from 1,239 patients with primary or recurrent NMIBC
were analyzed for GATA2, TBX2, TBX3 and ZIC4 methylation and FGFR3, TERT, PIK3CA and
RAS mutation status. Overall, wild type FGFR3 and methylation of GATA2 and TBX3 were
significantly associated with disease progression; the addition of these selected
markers to the EORTC risk stratification model increased its accuracy and was able
to identify a subset of patients at very high risk for tumor progression. This is
probably clinically the most significant finding of this study, as one of the major
controversies in NMIBC management is to identify the patients who are most likely
to benefit from intensified therapy such as combination systemic therapy or early
radical cystectomy.
Conclusions
The search for an ideal biomarker in NMIBC is still ongoing. Given the variable and
rich mutation landscape, branched evolution and intratumor heterogeneity of the disease,
it is unlikely that a single biomarker is able to address the diverse needs of clinicians.
Conversely, biomarkers panels integrating multiple complementary pathways involved
in the process of interest (diagnosis, staging, prognosis, and/or prediction) could
represent a breakthrough for patients’ risk stratification and treatment selection.
Several new biomarkers, probably linked to novel therapies such as PD-L1 expression,
will soon enter in clinical practice helping drive a precision medicine approach to
BCa. We are slowly but steadily moving towards the era of personalized medicine with
biomarkers being the traffic light and/or the target of the personalized medicine
voyage in NMIBC.