










































Bladder Cancer Tissue-Based Biomarkers
Francesco Soria,1 Marta Sanchez-Carbayo,2 Natalya Benderska-Söder,3  
Bernd J. Schmidz-Dräger,3,4 Stefania Zamboni,5 Marco Moschini,6 Anirban P. Mitra,7 Yair Lotan8

1 Division of Urology, Department of Surgical Sciences, Torino School of Medicine, Torino, Italy 2 Translational Oncology Laboratory, Lucio Lascaray Research Center, 
University of the Basque Country, Vitoria-Gasteiz, Spain 3 Urologie 24, Nuremberg, Germany 4 Department of Urology and Pediatric Urology, Friedrich-Alexander 
University, Erlangen, Germany 5 Urology Unit, ASST Spedali Civili, Brescia, Italy; Department of Medical and Surgical Specialties, Radiological Science and Public Health, 
University of Brescia, Italy 6 Klinik für Urologie, Luzerner Kantonsspital, Lucerne, Switzerland 7Institute of Urology, University of Southern California, Los Angeles, 
United States 8 Department of Urology, University of Texas Southwestern Medical Center, Dallas, United States

Abstract

This review aims to provide a practical update regarding the current role of tissue-based biomarkers in bladder cancer. 
Their prognostic and predictive role both in non-muscle-invasive (NMIBC) and in muscle-invasive disease (MIBC) 
has been reviewed with particular focus to their use in clinical practice. 

In summary, the literature on the prediction of disease recurrence in NMIBC is inconclusive, and there is little 
information on prediction of response to intravesical bacillus Calmette-Guérin (BCG). 

Concerning disease progression, external prospective validation studies suggest that FGFR3 mutation status and 
gene signatures may improve models that are based only on clinicopathologic information. 

In MIBC, tissue-based biomarkers are increasingly important, since they may predict the response to systemic 
chemotherapy and immunotherapy. In particular, the advent of molecular characterization promises to revolutionize 
the paradigm of decision-making in the treatment of MIBC. Molecular subtyping has been shown to improve the 
prediction of pathological stage at RC and to predict the response to systemic chemotherapy and immunotherapy. 
However, external and prospective validations are warranted to confirm these preliminary findings. 

Several different tissue-based biomarkers such as PD-1/PD-L1 expression, tumor mutational burden, and the 
analysis of tumor microenvironment, may in future play a role in selecting patients for systemic immunotherapy. 
However, to date, no pretreatment recommendations can be definitively made on the basis of any molecular 
predictors.

In conclusion, despite the potential of tissue-based biomarkers, their use in bladder cancer should be limited to 
experimental settings.

Introduction

In recent years, there have been significant innovations in the treatment of bladder cancer (BCa). While most 
treatments are standardized, we are transitioning from the era of “one size fits all” into the era of “precision medicine,” 
in which treatments are personalized and tailored according to the particular characteristics of each patient and 
tumor. Biomarkers play an undeniable role in this setting, allowing patient risk stratification, predicting response to 
treatments, and paving the way for targeted therapies. In this non-systematic review, the technical aspects of tissue-
based biomarkers, as well as their current role in terms of clinical utility, both in non-muscle invasive (NMIBC) and 
muscle-invasive bladder cancer (MIBC), are reviewed. 

Key Words Competing Interests Article Information

Bladder cancer, tissue-based biomarkers, 
prognosis, prediction, immunotherapy

None declared. Received on July 16, 2020 
Accepted on October 18, 2020

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Technical Aspects of Tissue-Based 
Biomarkers in Bladder Cancer
Several technical aspects should be considered when 
work ing with tissue specimens. First, there are 
important differences between using fresh, frozen, or 
formalin-fixed material. The use of fresh or fresh-frozen 
tissue, for example, enriches the quality of RNA-derived 
material as compared with formalin-fixed tissue[1]. 
Conversely, DNA is usually more stable, and unless long 
nucleic acids are required, DNA of sufficient quality for 
analysis can be obtained for the majority of the PCR-
based methods in fresh, fresh-frozen, or formalin-fixed 
material.

Second, it is critical to consider that BCa is extremely 
heterogeneous. This is particularly evident in MIBC, 
in which tissue heterogeneity might be extremely high, 
with differential ratios between tumor and stromal cells 
among paraffin blocks, leading to very different results 
if microdissection is not performed. This may lead to 
significant differences when staining sections from 
different blocks. However, even when microdissection 
is performed, intratumoral heterogeneity may currently 
play a role in limiting the use of tissue-based biomarkers 
in BCa. 

Third, sample handling is very important to preserve 
the quality of the specimen. In frozen material, it is 
important that freezing is performed within 30 minutes 
of the specimen’s removal. When working with paraffin-
embedded tumors it is important that specimens are 
maintained no longer than 24 hours in formalin for 
fixation and that blocks are well orientated to be cut 
within that time[2,3].

Finally, the use of controls is mandatory. A comparison 
with normal urothelium should always be performed in 
any experiment at the DNA, RNA, or protein level[4]. 
This is not always possible for BCa because of the field 
effect of carcinogenesis so adjacent “normal” may be best 
alternative control.

Tissue-Based Biomarkers in Non-Muscle 
Invasive Bladder Cancer
In general, prognostic information for patients with 
NMIBC is highly desirable, because guidance for 
further treatment and follow-up is urgently needed. 
So far, this information relies exclusively on clinico-
pathological parameters[5–8]. Since there is a broad 
range of recurrence and progression rates even when 
applying European Association of Urology (EAU) or 
American Urology Association (AUA) risk stratification, 
there is need to refine prediction of rates to help inform 
treatment and surveillance decisions.  Key prognostic 
biomarkers for disease recurrence and progression in 
NMIBC are listed in Table 1 and are briefly described 
below.

Prognostic markers for disease recurrence
The p53 tumor suppressor gene is probably the first 
molecular alteration that has been extensively studied, 
but there have been conf licting results regarding 
association with prognosis[9–14]. Using p53 as a 
single marker has issues due to multiple cell cycle 
regulators that may have overlapping roles. Considering 
the biological diversity of NMIBC along with its 
intratumoral heterogeneity, the research focus in tissue 
marker research has shifted from the investigation 
of single alterations to consideration of combined 
alterations, including gene classifiers for discrimination 
between recurrent and non-recurrent NMIBC, with 
promising results[13,15–17]. Nevertheless, a large 
international retrospective validation study including 
404 patients investigating a 26-gene signature found no 
association with tumor recurrence[18]. 

FGFR3 mutations have been correlated with the 
prognosis of patients with NMIBC. A retrospective 
multicentre study investigated the prognostic potential 
of FGFR3 status and 3 molecular markers (MIB-1, 
P53, and P27kip1) showed that the combination of 
FGFR3 and MIB-1 was able to independently predict 
disease recurrence[12]. More recently, dysregulation of 
several miRNAs has been suggested to predict tumor 
recurrence[19–21]; however, validation and prospective 
assessment are lacking. 

In summary, the role of molecular markers in the 
prognostication of disease recurrence in NMIBC 
seems limited, not only for technical reasons but also 
because  clinical parameters (eg, multiplicity, tumor size, 
incomplete TUR) have a substantial effect on this event 
and mitigate the impact of biomarkers[12]. Furthermore, 
most patients with NMIBC, especially those with high-
risk disease, undergo treatment, and response to therapy 
will impact the likelihood of recurrence significantly.

Abbreviations 
AUA American Urological Association
BCa bladder cancer
CSM cancer-specific mortality
EAU European Association of Urology
MIBC muscle invasive disease
NAC neoadjuvant chemotherapy
NMIBC non-muscle invasive bladder cancer
RC radical cystectomy

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TABLE 1. 

Prognostic biomarkers in non-muscle invasive bladder cancer

Molecular 
pathway

Biomarker(s) Method n Results Year Reference

Cell cycle 
regulation

Rb IHC 74 No association with progression 1996 96

P21 IHC
207 No association with progression 2000 97

244 No association with recurrence 1999 98

P27 IHC 61
No association with recurrence 

and progression
2013 99

Ki-76 IHC 61
No association with recurrence 

and progression
2013 99

Cell death 
pathways

p53

IHC 69
Overexpression predicts 

disease progression
1995 9

IHC 104
Overexpression predicts 

disease recurrence
1997 10

IHC 286
Overexpression alone predicts disease 

progression, but not in combination  
with FGFR3 mutation

2003 12

IHC 83
Overexpression predicts disease 

recurrence and progression
2007 13

rtPCR 105 No prognostic value 2007 14

Bcl-2
IHC 100 No association with recurrence 1998 100

IHC 93 No association with recurrence 2000 101

Cell growth 
signaling

FGFR3 rtPCR 286
Mutation associated with 
higher recurrence-free and 
progression-free survival

2003 12

erbB2 
(HER2)

IHC 88
Association with recurrence 

and progression
2015 102

rtPCR 141
Association with recurrence 

and progression
2015 103

rtPCR 34 Association with progression 2017 104

Survivin IHC

233 Association with progression 2016 105

115
Association with recurrence 

and progression
2015 106

283
Association with progression 

and survival
2012 107

Angiogenesis 
markers

VEGF IHC

185 No association with recurrence 1999 108

140
No association with recurrence 

and progression
2005 109 

HIF-1 α IHC 140 No association with recurrence and progression 2005 109

Immune 
markers

PD-L1 rtPCR 296
Association with recurrence, progression, 

and survival
2018 110

IHC: immunohistochemistry

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Prognostic markers for disease progression
Progression of disease is defined as a recurrence with 
a worsening stage or grade of disease. p53 alteration is 
one of the first and certainly most frequently studied 
markers in this context. Most of these studies, including 
a combined analysis of 23 studies[22], reported a 
correlation between p53 overexpression and tumor 
progression. However, as p53 alterations are closely 
related to tumor grade, stage, and other molecular 
changes, the independent prognostic value of this 
parameter remains a matter of controversy[11–13]. 
Immunohistochemical p53 overexpression has also been 
tested in combination with other alterations, frequently 
related to cell cycle regulation. In  one prospective study 
every patient with high-grade NMIBC underwent 
immunohistochemical staining for 5 biomarkers (p21, 
p27, p53, KI-67, and cyclin E1) no differences were found 
in progression or survival based on the number of 
altered markers[23].

A molecular grading based on the combination of 
FGFR3 mutation together with MIB-1 expression is 
significantly associated with disease progression. A large 
prospective study of 1239 patients from the same group 
demonstrated that molecular grading based on FGFR3 
mutational status and methylation of GATA2 was able to 
improve the EAU NMIBC risk score in predicting tumor 
progression[24].  

The development of gene classifiers and subtyping 
using microarrays is another option for combining 
molecular information[25,26]. In a large prospective 
Scandinavian-based trial of 1224 patients, Dyrskjøt et 
al. demonstrated that the results of a 12-gene real-time 
qualitative PCR assay yielded independent prognostic 
information on tumor progression[27]. Nevertheless, 
with a 66% sensitivity and specificity to predict tumor 
progression as a stand-alone assay, it becomes obvious 
that, at this stage, information obtained by molecular 
markers is not sufficient and needs to be integrated with 
established clinicopathologic variables.

Predictive markers for response to  
intravesical therapy
Various tissue-based biomarkers have been evaluated for 
prediction of response to intravesical bacillus Calmette-
Guérin (BCG) therapy. To date, the best evidence 
comes from a validation study based on 2 Nordic 
multicenter trials comparing treatment with BCG 
and other intravesical adjuvant therapies[28]. In this 
report, ezrin, CK20, and Ki-67 have been analyzed in a 
tissue microarray: unfortunately, none of the variables 
correlated with disease recurrence, and only tumor 
multifocality was associated with disease progression. 

Several studies demonstrated a clinical utility in 
combining gene expression signatures with clinico-
pathologic features[29]. Pietzak et al. demonstrated that 
patients with NMIBC had a high prevalence of alterations 
to DNA damage repair genes and that mutations in 
ARID1A are associated with an increased risk of recurrence 
following BCG therapy[30]. Moreover, total mutational 
burden has been associated with disease progression in 
a small retrospective study of 25 patients treated with 
BCG[31].

Following the advent of immunotherapy with 
checkpoint inhibitors also in the NMI setting (recently, 
according to the results of the Keynote-057 trial[32], 
the use of pembrolizumab in patients with BCG-
unresponsive Cis has been approved by the FDA), the 
role of potential immune markers (ie, PD-1 and PD-L1 
mRNA expression) to predict response to BCG has been 
investigated with promising results. However, it should 
be underlined that these findings need to be externally 
validated before they could be considered for clinical 
practice. Finally, in the context of immunological 
markers, Pichler et al. studied the association between 
recurrence-free survival and the count of CD4, GATA3, 
tumor-associated macrophages, Tregs, and T-bet+ 
T cells in the malignant tissue samples prior to BCG 
therapy[33]. They found that CD4+ and GATA3+ T cells 
were predictors of prolonged recurrence-free survival, 
while the predictors of shorter recurrence-free survival 
were TAMs, Tregs, and T-bet+ T cells.

Tissue-Based Biomarkers in Muscle- 
invasive Bladder Cancer
Prediction of Oncological Outcomes
The standard treatment for patients with MIBC is radical 
cystectomy (RC) with neoadjuvant chemotherapy 
(NAC). However, despite the administration of adequate 
therapy and the recent development of new treatment 
strategies such as trimodal therapy (TMT) or targeted 
t herapies, MIBC remains an aggressive disease 
characterized by a generally unfavorable prognosis 
[34,35]. There are significant challenges in accurately 
staging the disease and insufficient ability using clinical/
pathological factors alone to predict recurrence and 
progression, and an inability to predict response to 
systemic therapies and radiotherapy.  The consequence is 
that many patients are either undertreated, overtreated, 
or given therapies that are unlikely to benefit the patient.

Understanding the molecular pathology and biology 
of BCa could be useful to improve patients’ stratification 
and decision-making. Recently, several reports have 
focused their attention on molecular biomarkers as 

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diagnostic and prognostic tools in MIBC, although their 
application in clinical practice remains, to date, unclear.

Prediction of disease stage at radical 
cystectomy
An accurate prediction of disease stage at diagnosis is 
of fundamental importance to risk-stratification and 
to select patients for neoadjuvant systemic therapies. 
Understaging disease after initial TURBT is over 
40% despite examination under anesthesia and cross 
sectional imaging[36]. As such improving staging at 
diagnosis is important to appropriately treat patients.  
Several tissue-based biomarkers have been investigated 
for this purpose and have been integrated into predictive 
models. Mitra et al. firstly developed a pre-cystectomy 
decision model to predict pathological upstaging and 
oncological outcomes in cT2 patients undergoing RC[37]. 
This model was based on clinicopathologic variables 
such as the preoperative presence of hydronephrosis, 
evidence of deep muscolaris propria invasion and LVI as 
well as tumor growth pattern and count. Subsequently, 
Shariat et al. tested the accuracy of a preoperative panel 
of tissue-based biomarkers (p53, p21, p27, Ki67, and 
cyclin E1); the number of altered biomarkers was able 
to predict T-stage upstaging but not T- and/or N-stage 
upstaging; however, the accuracy of the model in the 
prediction of the T-stage upstaging was low (62%)[38]. 
Recently, a genomic subtyping classifier was used to 
evaluate pathological upstaging in a multi-institutional 
cohort of patients with cT1-T2 BCa treated with RC[39]. 
Luminal tumors showed a lower rate of upstaging to 
non-organ confined disease compared to non-luminal 
ones (34% versus 51%). Pending external validation, 
molecular characterization promises to transform the 
paradigm of BCa risk-stratification, thus paving the way 
to an even more personalized approach.

Prediction of oncological outcomes after 
radical cystectomy alone
There has been an interest in predicting the likelihood of 
recurrence in patients who underwent RC alone. These 
patients may benefit from adjuvant therapies such as 
chemotherapy[40] and multiple trials are evaluating the 
value of adjuvant checkpoint inhibitors. Key biomarkers 
evaluated for prediction of oncological outcomes are 
listed in Table 2. 

Currently, p53 is the most studied prognostic 
biomarker in patients treated with RC, with conflicting 
results reported in the literature. In patients with BCa 
confined to the bladder, p53 has been associated with 
progression and survival, independently of tumor grade, 
stage, and lymph node status[41].

Human epidermal growth factor receptor 2 (HER2) 
is a tyrosine kinase transmembrane receptor involved 

in cycle cell regulation and cell proliferation. Its 
overexpression was associated with adverse pathological 
features at RC but its relationship with long-term 
oncological outcomes remains controversial[42,43]. 

The retinoblastoma protein (R B1) is a tumor 
suppressor gene, which acts as a negative regulator of cell 
cycle progression and has been proved to be dysregulated 
in several cancers. The loss of RB1 expression is 
an adverse prognostic biomarker in MIBC[44,45]. 
Inactivating RB1 mutation results in a lower expression 
of FGFR3 levels and is associated with worse cancer-
specific mortality (CSM)[46]. 

Survivin, an inhibitor of apoptosis, was found to 
be associated with disease recurrence (HR 1.7, P = 
0.04), CSM (HR 1.7, P = 0.03), and all-cause mortality 
(HR 1.7, P = 0.04) in 222 consecutive patients treated 
with RC after accounting for the effects of standard 
prognosticators[47]. These results have been externally 
validated and survivin status has been incorporated in 
a nomogram for the prediction of outcomes in patients 
with pT1-3N0M0 disease: the addition of survivin 
improved the accuracy of the model over standard 
clinicopathologic features for prediction of disease 
recurrence and CSM.

Despite these findings, the role of these biomarkers 
in clinical practice as single markers remains limited, 
mainly due to their unsatisfactory accuracy. Models 
based on the assessment of multiple markers (p53, 
p21, RB, cyclin E1, and p27) showed higher predictive 
accuracy compared to those based on single markers 
[48–50]. A prospective study of 216 patients treated with 
RC who underwent immunohistochemical staining 
for p53, p21, p27, cyclin E1, and Ki-67 found that in a 
multivariable model adjusting for the effects of standard 
prognosticators, only LVI and number of altered 
biomarkers were independent predictors of recurrence 
and CSM[51].  

As a lready before mentioned, BCa molecular 
characterization is acquiring increasing importance 
in the prediction of prognosis in patients with MIBC. 
Recently, a consensus classification has been provided. 
Compared to luminal papillary tumors that were 
taken as reference, luminal non-specified and stroma-
rich tumors showed similar outcomes while luminal 
unstable, basal/squamous, and neuroendocrine-like 
subtypes were associated with worse survival, with the 
latter representing the class with the worst prognosis 
(HR 2.18, P < 0.05). Moreover, this classification, besides 
providing a promising tool for risk-stratification, 
suggests possible therapeutic implications such as those 
related to targeted therapies, thereby representing a 
milestone for MIBC classification. 

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Prediction of response to neoadjuvant 
chemotherapy
Key biomarkers investigated for prediction of response 
to NAC are listed in Table 3.

Cisplatin acts as an alkylating agent and interferes 
with DNA replication and gene transcription. This 
DNA-damage is repaired by 2 pathways: the first 
includes BRCA1, BRCA2, and RADS51 genes while the 
second involves the nucleotide excision repair NER, 
and includes several genes such as ERCC1-5, CDK7, 
DDB1-2, XPA. Alteration of these pathways has been 
suggested to affect the response to cisplatin-based 
chemotherapy.  The breast cancer susceptibility gene 1 

(BRCA1) modulates chemoresistance encoding a nuclear 
protein that responds to DNA damage with several 
different mechanisms. Patients with low/intermediate 
BRCA1 levels were found to have a significantly higher 
pathological response at RC compared to patients with 
high BRCA1 levels (66% versus 22%, P = 0.01)[52]. The 
excision repair cross-complementing 1 (ERCC1) is 
involved in DNA repair and DNA recombination: it 
was found associated with cisplatin resistance different 
tumors[53–55], whereas its role in BCa remains 
debated[56]. Genomic alterations in the DNA repair-
associated genes ATM, RB1, and FANCC were found 
to be predictors of response (87% sensitivity, 100% 
specificity) and better OS after MVAC chemotherapy for 

TABLE 2. 

Prognostic biomarkers after radical cystectomy alone in muscle-invasive bladder cancer

Molecular 
pathway

Biomarker(s) Method n Results Year Reference

Cell cycle 
regulation

Rb IHC 38
Association with  

cancer-specific mortality
2012 45

P21 IHC 692
Association with disease 

recurrence and cancer-specific 
mortality

2010 111

Cell death 
pathways

p53

IHC 692
Association with disease 

recurrence and cancer-specific 
mortality

2010 112

IHC 243
Association with disease 

recurrence and cancer-specific 
mortality

1994 41

erbB2 (HER2)

IHC 354
No association with 

oncological outcomes
2010 42

IHC 198
Association with disease 

recurrence and cancer-specific 
mortality

2016 43

Survivin IHC 222
Association with disease 

recurrence and overall 
mortality

2007 47

Angiogenesis 
markers

VEGF IHC 286
Association with  

cancer-specific mortality
2007 113

Markers of tumor 
cell invasion

E-Cadherin IHC 25 Association with survival 1993 114

MMPs IHC 54
Association with disease 

recurrence
2003 115

Molecular 
markers

Luminal unstable

Transcriptome 
analysis

1750
Association with worse 

survival compared to luminal 
papillary subtype

2019 61Basal/squamous

Neuroendocrine-like

IHC: immunohistochemistry

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MIBC[57,58]. Regarding the ability of p53 mutation to 
predict response to NAC, conflicting results have been 
reported[58,59]. 

Recent studies evaluated the role of molecular profiles 
for decision-making and counseling of patients treated 
with NAC and RC. While there is some evidence that 
different tumor subtypes (basal, luminal, p53-like) are 
associated with different patterns of response to NAC 
[60], in the recently developed international consensus 
about the molecular classification of MIBC[61] no 
significant association between the consensus classes 
and oncologic outcomes in patients treated with NAC 
was found. 

Prediction of response to systemic 
chemotherapy
Several cell cycle regulators and markers of proliferation 
have been evaluated as predictors of chemotherapy 
response[62,63]. Alongside this, a combination of 
regulatory RNAs and transcription factors has shown 
to be predictive in metastatic BCa patients treated with 
cisplatin-based therapy[64]. However, there is yet to be a 
clinically validated role for them as predictive markers.

The most promising class of predictive markers thus 
far for chemotherapeutic response is represented by 
those involved in DNA damage detection and repair 

(ie, BRCA-1, BRCA-2, RAD51, PAR, PARP1, ERCC1, 
ERCC2, and RR M1). In a study of patients with 
advanced or metastatic urothelial cancer receiving 
platinum-based palliative chemotherapy, 341 genes 
including 34 DNA damage response (DDR)-associated 
genes were evaluated[65]. Patients with DDR gene 
alterations had significantly longer progression-free 
and OS than patients with wild-type DDR genes. 
In the setting of advanced urothelial carcinoma, 
overexpression of ERCC1, RAD51, and PAR has been 
correlated with worse survival for patients treated with 
first-line platinum combination chemotherapy[66,67].

Aberrations of growth factors and their associated 
tyrosine kinase receptors can result in an abnormal 
increase in the rate of transduction of growth signals, 
thereby leading to uncontrolled cellular proliferation 
and tumor formation. Such kinases are the targets of 
several new systemic therapies in oncology. Several 
tyrosine kinase inhibitors have been tried in BCa 
including lapatinib (inhibits EGFR and HER2/neu 
pathways), and pazopanib (inhibits FGF, PDGF, and 
VEGF pathways). While these drugs appear to have 
limited activity in BCa, the possibility of biomarker 
enrichment for response has been assessed with mixed 
results[68–72].

TABLE 3. 

Biomarkers associated with response to neoadjuvant chemotherapy

Molecular 
pathway

Biomarker(s) Method Drug(s) n Results Year Reference

Cellular efflux CTR1 IHC Cis 47
Association with 

pathologic response
2016 116

Cell death 
pathways

p53
IHC  MVAC 111 Association with survival 1995 59

IHC MVAC 44
Does not predict 

pathologic response
2014 58

Bcl-2 IHC Cis + radiotherapy 51
Low levels associated 
with better prognosis

2000 117

DNA repair

BRCA-1 rtPCR Cis 57
Low/intermediate levels 

predict pathologic 
response

2011 52

ERCC1 IHC Cis 38
Does not predict 

pathologic response but 
predicts survival

2013 118

ERCC2 rtPCR Cis 50
Predicts pathologic 

response 
2014 119

Molecular markers Basal subtype
Transcriptome 

analysis
Cis 343

Association with better 
survival

2019 120

MVAC: methotrexate, vinblastine, doxorubicin, cisplatin, Cis: cisplatin-based, IHC: immunohistochemistry

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Several other factors have been assessed for their 
ability to predict chemotherapy response, including 
immunological markers[73], germline and somatic DNA 
mutations[74,75], as well as drug transport genes[62,76]. 
Several of these factors are summarized in Table 4.

Prediction of response to systemic 
immunotherapy
The advent of systemic immunot herapy in t he 
management of advanced BCa represents a quantum 
leap over the last few years, especially in patients 

refractory to cisplatin-based therapies. Several immune 
checkpoint inhibitors have shown promising activity, 
including agents targeting PD-1 receptor and its ligand 
PD-L1, and cytotoxic T-lymphocyte antigen 4 (CTLA-
4). While these developments are promising, a majority 
of patients still do not respond to treatment[77–84], 
resulting in a significant financial burden and potential 
treatment-related side effects. This highlights the need 
for appropriate biomarkers to aid in selecting patients 
who are most likely to benefit from checkpoint targeting 
therapy. While several biomarkers have been explored 

TABLE 4. 

Biomarkers associated with systemic chemotherapy and immunotherapy response

Molecular 
pathway

Biomarker(s) Method Drug(s) n Results Year Reference

Cell cycle and 
proliferation

Cyclin D1 IHC Cis 63
Overexpression 

predicts better chemo 
response

2016 63

CCDN1 FISH Cis 63
Does not predict 
chemo response

2016 63

Ki-67 IHC CMV, MVAC 99
Does not predict 
chemo response

1998 62

miRNAs rtPCR MVAC, GC 83

Increased miR-21, 
miR-372 and E2F1 
associated with 

chemo response and 
survival

2013 64

Cell death 
pathways

p53

IHC
MC, MEC CMV, 

MVAC
83

Overexpression 
predicts improved 

survival in 
chemoresistant 

patients

1999 121

IHC CMV, MVAC 99
Does not predict 
chemo response

1998 62

IHC CISCA, MVAC 25
Overexpression 

associated with worse 
response

1998 122

IHC MVAC 114
Does not predict 
chemo response 
(phase III RCT)

2011 123

Bcl-2

IHC Cis 51

Low expression 
predicts better 

response to 
chemoradiation

2000 117

IHC CISCA, MVAC 25
Overexpression 

associated with worse 
response

1998 122

MVAC: methotrexate, vinblastine, doxorubicin, cisplatin, MC: methotrexate, cisplatin, CMV: cisplatin, methotrexate, vinblastine 
MVEC: methotrexate, vinblastine, epirubicin, cisplatin, MEC: methotrexate, epirubicin, cisplatin, GC: gemcitabine, cisplatin 
GCT: gemcitabine, cisplatin, paclitaxel, Cis: cisplatin, CISCA: cisplatin, doxorubicin, cyclophosphamide, IHC: immunohistochemistry

continued on page 61

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in the context of these clinical trials, no pretreatment 
recommendations can be definitively made at this point 
based on any molecular predictors, since a significant 
proportion of patients do respond to treatment 
despite testing negative for a biomarker. Nevertheless, 
biomarker-based selection for immunotherapy remains 
an area of active interest that is likely to further develop 
in the years to come. 

PD-L1 ex pression has been associated w it h 
higher tumor grade, worse outcomes, and decreased 
postoperative survival[85,86]. In the IMvigor210 trial, 
a higher PD-L1 expression score was associated with a 

higher response rate[77]. In contrast, the CheckMate 
275 trial showed meaningful responses to nivolumab, 
irrespective of PD-L1 expression levels[78]. Lack of 
standardized testing and evaluation of PD-L1 may 
be partia lly responsible for these discrepancies. 
Additionally, PD-L1 expression has been variously 
assessed: on tumor-infiltrating immune cells in the 
IMvigor210 trial[77], on tumor cells in the CheckMate 
275 trial[78], and on both tumor cells and immune 
cells in the durvalumab trial[84]. Furthermore, there 
are variations in percentage cutoffs used to define the 
high and low expression. Finally, PD-L1 expression is 

continued on page 62

TABLE 4. 

Biomarkers associated with systemic chemotherapy and immunotherapy response

Molecular 
pathway

Biomarker(s) Method Drug(s) n Results Year Reference

DNA repair

BRCA-1

IHC Cis 104
Does not predict 
chemo response

2015 66

rtPCR GC, GCT 57
Does not predict 
chemo response

2007 67

BRCA-2 IHC Cis 104
Does not predict 
chemo response

2015 66

RAD51 IHC Cis 104
Overexpression 

associated with worse 
survival

2015 66

PAR IHC Cis 104
Overexpression 

associated with worse 
survival

2015 66

PARP1 IHC Cis 104
Does not predict 
chemo response

2015 66

ERCC1

IHC Cis 104
Overexpression 

associated with worse 
survival

2015 66

rtPCR GC, GCT 57
Overexpression 

associated with worse 
survival

2007 67

RRM1 rtPCR GC, GCT 57
Does not predict 
chemo response

2007 67

Drug resistance

MDR1 rtPCR MVEC 108
Overexpression 
associated with 
inferior outcome

2010 76

P-glycoprotein
(MDR1)

IHC CMV, MVAC 99
Does not predict 
chemo response

1998 62

Caveolin-1 rtPCR GC, GCT 57
Does not predict 
chemo response

2007 67

MVAC: methotrexate, vinblastine, doxorubicin, cisplatin, MC: methotrexate, cisplatin, CMV: cisplatin, methotrexate, vinblastine 
MVEC: methotrexate, vinblastine, epirubicin, cisplatin, MEC: methotrexate, epirubicin, cisplatin, GC: gemcitabine, cisplatin 
GCT: gemcitabine, cisplatin, paclitaxel, Cis: cisplatin, CISCA: cisplatin, doxorubicin, cyclophosphamide, IHC: immunohistochemistry

, Cont’d

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dynamic, and a single biopsy is unlikely to provide a 
complete assessment of status for the entire duration 
of disease. Therefore, evaluation of the predictive value 
of PD-L1 positivity is difficult, and correlations with 
response to treatment or survival vary between trials.

Despite these discrepancies, both pembrolizumab 
and atezolizumab are approved by the FDA and the 
EMA for first-line treatment in cisplatin-ineligible 
patients only in case of positive PD-L1 status on the 
basis of unpublished results from ongoing phase  II 
trials. Patients with negative PD-L1 expression should be 

treated with chemotherapy-based combinations.

Exploratory analyses from the cisplatin pre-treated 
arm of the IMvigor210 trial showed that molecular 
subtypes were independently associated with response 
to atezolizumab treatment[77]. PD-L1 immune cell 
prevalence was highly enriched in the basal subtype 
versus the luminal subtype (60% versus 23%, P < 0.001). 
Response to atezolizumab occurred in all subtypes 
but was significantly higher in luminal cluster II than 
in other subtypes[77]. Conversely, in CheckMate 275, 
the basal-1 subtype had the highest proportion of 

continued on page 63

TABLE 4. 

Biomarkers associated with systemic chemotherapy and immunotherapy response

Molecular 
pathway

Biomarker(s) Method Drug(s) n Results Year Reference

Cell growth 
signaling

FGFR3 WES Pazopanib 3
Mutation associated 
with partial response

2016 68

erbB2 (HER2)

WES Pazopanib 3
Mutation associated 
with better response

2016 68

IHC Lapatinib 116
Does not predict 
chemo response

2017 69

IHC Lapatinib 34
Does not predict 
chemo response

2009 70

HER1 IHC Lapatinib 116
Does not predict 
chemo response

2017 69

EGFR IHC Lapatinib 34
Overexpression 
associated with 

response
2009 70

VEGF

Serum Sunitinib 26
Does not predict 
chemo response

2014 71

Serum, IHC Pazopanib 18
Does not predict 
chemo response

2013 72

HIF1 IHC Pazopanib 18
Does not predict 
chemo response

2013 72

DNA markers Germline SNPs

Microarray Cabazitaxel 45
SNPs predicted chemo 
response and toxicity

2016 74

Microarray Cis 210
SNPs predicted chemo 

response
2013 75

Immune markers IL-8 Luminex xMAP Sunitinib 38
Underexpression 

associated with better 
time to progression

2011 73

Other Metallothionein IHC CMV, MVAC 99
Overexpression 

associated with worse 
survival

1998 62

MVAC: methotrexate, vinblastine, doxorubicin, cisplatin, MC: methotrexate, cisplatin, CMV: cisplatin, methotrexate, vinblastine 
MVEC: methotrexate, vinblastine, epirubicin, cisplatin, MEC: methotrexate, epirubicin, cisplatin, GC: gemcitabine, cisplatin 
GCT: gemcitabine, cisplatin, paclitaxel, Cis: cisplatin, CISCA: cisplatin, doxorubicin, cyclophosphamide, IHC: immunohistochemistry

, Cont’d

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TABLE 4. 

Biomarkers associated with systemic chemotherapy and immunotherapy response

Molecular 
pathway

Biomarker(s) Method Drug(s) n Results Year Reference

Immune markers PD-L1

IHC Atezolizumab 310

Overexpression 
associated with 

increased response 
to systemic 

immunotherapy

2016 77

IHC Nivolumab 265
Does not predict 

response to systemic 
immunotherapy

2017 78

IHC Pembrolizumab 114

Overexpression 
associated with 

increased response 
to neoadjuvant 
immunotherapy

2020 86

Molecular 
markers

Luminal cluster II
Transcriptome 

analysis
Atezolizumab 310

Better response 
compared to other 

subtypes
2016 77

Basal-1
Transcriptome 

analysis
Nivolumab 265

Better response 
compared to other 

subtypes
2017 78

MVAC: methotrexate, vinblastine, doxorubicin, cisplatin, MC: methotrexate, cisplatin, CMV: cisplatin, methotrexate, vinblastine 
MVEC: methotrexate, vinblastine, epirubicin, cisplatin, MEC: methotrexate, epirubicin, cisplatin, GC: gemcitabine, cisplatin 
GCT: gemcitabine, cisplatin, paclitaxel, Cis: cisplatin, CISCA: cisplatin, doxorubicin, cyclophosphamide, IHC: immunohistochemistry

, Cont’d

responders[78]. These discrepancies may be partially 
attributable to the fact that both trials allowed biopsy 
specimens from the primary tumor, lymph nodes, 
or metastatic lesions for subtyping, which may lead 
to inaccurate tumor classif ication. Until further 
details emerge, molecular classification may not be a 
reproducible predictive biomarker for immunotherapy.

The role of neoadjuvant therapy with checkpoint 
inhibitors is also gaining interest.  The PURE-01 study 
evaluated the activity of preoperative pembrolizumab 
(NCT02736266) administered in T2-4aN0M0 MIBC 
patients[87]. In total, 114 patients were enrolled, and the 
pT0 rate was 37% (95% CI 28 to 46) while pT ≤ 1 rate 
was 55% (95% CI 46 to 65). On multivariable analysis, 
tumor mutationa l burden and PD-L1 combined 
positive score were associated with both the pT0 and 
the pT ≤  1 response, regardless of tumor histology. A 
separate study using RNA sequencing found that the 
Immune190 signature was significant for complete 
response on multivariable logistic regression analyses in 
PURE-01, but not in a cohort of patients who underwent 
NAC and RC[88]. Hallmark signatures for interferon 
gamma (IFNγ; OR 1.11, P = 0.004) and IFNα response 
(OR 1.07, P = 0.006) were also associated with complete 
response for PURE-01, but not for NAC (IFNγ: OR 

0.99, P = 0.9 and IFNα: OR 0.99, P = 0.8). Basal subtypes 
(across classifications) with higher Immune190 scores 
showed 100% 2-year progression-free survival after 
pembrolizumab therapy. 

High mutational load may be associated with better 
response to immunotherapy[77]. However, there is 
currently no standardized definition of mutation burden 
relative to the depth of sequencing performed. Targeted 
sequencing panels may also not adequately cover gene 
fusions, truncations, and translocations. Further, 
germline variants may not be silenced by informatics 
techniques that f ilter common germline single-
nucleotide polymorphisms. These challenges currently 
limit the use of tumor mutational burden as a predictive 
biomarker for immunotherapy.

Finally, the tumor microenvironment may play a role 
in predicting response to therapy[89]. CheckMate 275 
found that the highest CXCL9 or CXCL10 expression 
was observed in nivolumab responders[78]. The same 
findings were reported by analyzing the cohort of 
cisplatin-pretreated patients of the IMvigor210 trial[77]. 
Immune markers investigated for the prediction of 
response to systemic immunotherapy are summarized 
in Table 4.

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TABLE 5. 

Biomarkers associated with radiotherapy response

Molecular 
pathway

Biomarker(s)
Chemoradiation 

regimen
n Results Year Reference

Cell proliferation Ki-67

RT 59.4 Gy + cisplatin 70
Higher Ki-67 

associated with 
higher CR

2000 124

RT 40 Gy + cisplatin 94
Higher Ki-67 

associated with 
higher CR

2015 125

RT 40.5 Gy (median) + 
cisplatin

62
No association with 

response
2004 126

Cell death 
pathways

Apoptotic index RT 59.4 Gy + cisplatin 70
Higher index 

associated with 
higher CR

2000 124

Bax/Bcl-2 ratio
RT 40.5 Gy (median) + 

cisplatin
62

Higher ratio 
associated with 

higher CR
2004 126

DNA repair

ERCC1
RT 40-66 Gy + cisplatin 

or nedaplatin
22

Expression loss 
associated with 

higher CR
2011 127

ERCC1, XRCC1
RT 48.6 Gy (median) + 

cisplatin
157

Positive expression 
associated with 

improved survival
2013 128

MRE11 RT 55 Gy 179
High expression 
associated with 

improved survival
2010 129

DDR alterations RT or chemoradiation 48

Presence of 
alterations 

associated with 
trend to improved 
recurrence-free 

survival

2016 130

Cell growth 
signaling

erbB2

RT 40 Gy + cisplatin + 
other agents

55
Positivity associated 

with lower CR
2005 131

RT 40 Gy + cisplatin 119
Positivity associated 

with lower CR
2014 132

RT 64.8 Gy + paclitaxel 
with (group 1: erbB2+) 

or without trastuzumab 
(group 2: erbB2-)

66
CR rates, 72% for 

group 1 and 68% for 
group 2

2017 133

Other

Molecular 
subtype

RT 40 Gy + cisplatin 118

CR rates, 
52%/45%/15% 

for GU/SCC-like/
urobasal

2018 134

Hsp60 RT 40 Gy + cisplatin 54
Positivity associated 

with better 
response

2007 135

RT: radiotherapy, CR: complete response, DDR: DNA damage response, GU: genomically unstable, SCC: squamous cell cancer

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Prediction of response to radiotherapy
In t he moder n era, rad iot herapy is genera l ly 
administered in the context of organ preservation 
therapy in BCa. With careful patient selection, 
TMT yields oncological outcomes and quality of life 
comparable to RC in MIBC[90–92]. However, those 
who do not achieve complete response may undergo 
salvage cystectomy, with unfavorable oncological 
outcomes[93,94]. It is therefore imperative to carefully 
select patients who may be the optimal candidates for 
TMT. Several studies have looked at biomarkers that 
can predict response to TMT; the logic for evaluation 
of these biomarkers is generally based on their ability to 
predict response to the radiotherapy aspect of TMT. 

Severa l dif ferent biomarkers such as cellu lar 
proliferation markers (ie, Ki-67), cell cycle regulators 
(ie, p53, Bcl-2, Bax, Bad), cell growth factors (ie, HER2/
neu), and DNA damage repairs genes have been studied 
for this purpose. However, because of conflicting results 
between studies or lack of external validations, none of 
these markers is currently available for clinical practice. 
Important biomarkers that are predictive of response to 
radiotherapy in the context of TMT are listed in Table 5.

Tissue-based biomarkers and target therapies
Biomarkers are important not only because of their 
ability to predict outcomes or response to therapy but 
also, and more importantly, because they could act as 
potential targets for biomarkers-directed therapies.

This is the case of erdafitinib, the first FDA-approved 
oral pan-fibroblast growth factor receptor (FGFR) kinase 
inhibitor that binds to 4 FGFRs (FGFR-1 to -4), leading 
to decreased cell signaling and cellular apoptosis. The 
efficacy of erdafitinib (Balversa) has been tested in 99 
patients with advanced or metastatic urothelial cancer 
progressing after at least one cycle of chemotherapy with 
FGFR2 or FGFR3 alterations[95]. The overall response 
was 40%, with 3% of patients experiencing complete 
response and 37% experiencing partial response. Of 

note, response was observed also in patients previously 
treated with systemic immunotherapy (response rate of 
59% in this subgroup of patients). 

Following these promising results, FGFR3 inhibitors 
(eg, infigratinib) are currently under investigation in the 
adjuvant setting after RC (EudraCT 2019-003248-63).

Conclusions
Reviewing the literature on the utility of tissue-

based biomarkers in BCa through the last 2 decades, it 
appears obvious that the focus of research has moved  
from immunohistochemical analysis and tumor-related 
phenotypic changes to the analysis of genetic alterations. 
Furthermore, a trend towards marker combinations and 
genetic classifiers, mostly combining these findings with 
clinical parameters, is observed. 

In summary, the literature on the prediction of 
disease recurrence in NMIBC is inconclusive, and little 
information is available for prediction of response to 
intravesical BCG. Concerning disease progression, 
external prospective validation studies suggest that 
mutational FGFR3 status and gene signatures may 
improve models on the basis of clinicopathologic 
information. 

In MIBC, tissue-based biomarkers are increasing 
their importance since they may predict the response 
to systemic chemotherapy and immunotherapy. The 
advent of molecular characterization carries the promise 
to revolutionize the paradigm of decision-making 
in the treatment of MIBC, especially in these years 
characterized by the advent of systemic immunotherapy. 

Prospective studies in well-defined patient cohorts 
and with clinically meaningful endpoints are needed 
for retrieving definitive conclusions about the utility 
of tissue-based biomarkers in BCa. Until then, their 
role, despite their promising value, should be limited to 
experimental settings.

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71SIUJ.ORG SIUJ  •  Volume 2, Number 1  •  January 2021

Bladder Cancer Tissue-Based Biomarkers

http://siuj.org



