Clinical and molecular prognostic and predictive biomarkers in clear cell renal cell cancer
Anna M Czarnecka*,1, Wojciech Kukwa2, Anna Kornakiewicz1,3, Fei Lian4 & Cezary Szczylik1
1Department of Oncology with Laboratory of Molecular Oncology, Military Institute of Medicine, 04-141 Warsaw, Poland 2Department of Otolaryngology, Czerniakowski Hospital, Medical University of Warsaw, Warsaw, Poland 3Postgraduate School of Molecular Medicine, Medical University of Warsaw, Warsaw, Poland 4Department of Urology, Emory School of Medicine, Atlanta, GA, USA
ABSTRACT The natural history of clear cell renal cell cancer is highly unpredictable with various progressors and with populations where small renal masses may be accompanied by metastatic disease. Currently, there is a critical need to determine patient risk and optimize treatment regimes. For these patients, molecular markers may offer significant information in terms of prognostic and predictive values, as well as determination of valid therapeutic targets. Until now, only a few of the many identified clear cell renal cell cancer biomarkers have been clinically validated in large cohorts. And only several biomarkers are integrated in predictive or prognostic models. Therefore, a large cohesive effort is required to advance the field of clear cell renal cell cancer prognostic biomarkers through systematic discovery, verification, validation and clinical implementation.
A biomarker is an objective measure of normal biological processes, pathogenic processes or pharmaco logic responses to therapeutic intervention [1]. The main goal is to refine prediction of tumor progression, pharmacotherapy responsiveness and cancer-specific overall survival (OS) [2]. The biological behavior of renal cell cancer (RCC) is unpredictable by histology alone. Current accepted prognostic factors are tumor stage and the histological differentiation grade. For metastatic clear cell renal cell cancer (ccRCC) treatment with nephrectomy, multiple factors are predictive, such as age, gender, time from nephrectomy to metastases, location, tumor thrombus, histology, TNM staging, nuclear grade and many more. For patients treated with sunitinib, the Memorial Sloan–Kettering Cancer Center (MSKCC) prognostic model incorporates corrected serum calcium, number of metastatic sites, hemoglobin (Hb) level, prior nephrectomy, presence of lung and liver, Eastern Cooperative Oncology Group performance status (ECOG-PS), thrombocytosis, time from diagnosis to treatment, alkaline phosphatase and lactate dehydrogenase (LDH) level. On the other hand, the Mayo Clinic metastases-free survival scoring system combines tumor stage, regional lymph node status, tumor size, Fuhrman nuclear grade and the presence of tumor necrosis [3,4].
Prognostic biomarkers provide information on the likely course of the cancer disease in an untreated individual. Such markers identify cancer patients who are at high risk for metastatic relapse, potential candidates for adjuvant treatment, and stratify different risks of outcome (e.g., recurrence of disease, progression-free survival [PFS], OS). The presence or absence of such can be useful for treatment selection, but does not predict the response to this treatment (Tables 1 & 2). Prognostic biomarkers can be separated in two groups: one that gives information on recurrence in patients who receive cura-tive treatment, and one that correlates with the duration of PFS in patients with metastatic disease.
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Table 1. Prognostic biomarkers in clear cell renal cell cancer under investigation over last 5 years.
Prognostic indicator of poor survival
O/D Gene function Patients in trial (n)
RFS DSS OS Method Maximal follow-up (months)
Year Patients’ country
Ref.
BAP1 D Deubiquitinase 559 NN p < 0.0001 p = 0.001 IHC 183 2013 USA [8]
CAIX† D Hydration of carbon dioxide, hypoxia response
63,100 † † † IHC 120 2008 multiple [7,9–10]
Caspase 7 D Cell cycle regulation
120 NN p = 0.032 p = 0.046 IHC 2013 Brazil [11]
CAV1 O Cell cycle progression
289 NN p = 0.001 p = 0.001 IHC 132 2011 Germany [12]
CD105 O Angiogenesis 63 p = 0.01 NN NN IHC 140 2012 Canada [13]
CD133† D Apical plasma membrane organization
99 p = 0.01 p = 0.003 NN IHC 60 2012 Brazil [14]
CD151 O Cell development, growth and motility
489 p = 0.04 p = 0.334 NN IHC 169 2011 Korea [15]
CD95 O Apoptosis 519 NN p = 0.02 p = 0.04 IHC 200 2011 Germany [16]
COX I O Arachidonic acid peroxidase
196 p < 0.001 NN NN qRT-PCR 115 2013 China [17]
CXCR4 O Chemotaxia 240 NN p = 0.151 NN IHC 60 2011 Italy [18]
DUSP-9 D Mitosis, cell differentiation
46 NN NN p = 0.023 qRT-PCR, IHC 125 2011 China [19]
EMMPRIN O Angiogenesis 50 NN NN p = 0.0345 IHC 120 2013 Japan [20]
ET-2 D Vasoactive peptide
289 p = 0.03 NN NN IHC 96 2012 USA [21]
EZH2 O Gene silencer 520 NN p = 0.025 NN IHC 190 2010 Germany [22]
EZH2 O 103 NN p = 0.049 NN IHC 105 2013 China [23]
FAK O Migration, adhesion, Invasion, angiogenesis, proliferation
57 NN p = 0.006 NN IHC 200 2012 UK [24]
FER O Cell-cell adhesion, Cytoskeleton regulation
206 NN NN p < 0.001 qRT-PCR 120 2013 China [25]
GST-α D Detoxification of xenobiotics
119 NN p = 0.011 NN IHC, WB 120 2011 UK [26]
HLA-E O Immune response 38 p = 0.015 NN NN qRT-PCR, IHC 105 2012 Czech Republic
[27]
HLA-G O Immune response 38 p = 0.04 NN NN qRT-PCR, IHC 105 2012 Czech Republic
[27]
Jagged1 O Ligand for multiple Notch receptors
129 NN p = 0.028 p = 0.035 WB, IHC, qRT-PCR
80 2010 China [28]
miR-127, miR-145, miR-126
O NN 77 p = 0.014, p = 0.05, p = 0.015
NN NN qRT-PCR, TaqMan Arrays
56 2012 Czech Republic
[29]
p27 D Regulator of the cell cycle
68 NN p = 0.011 p = 0.002 IHC 100 2010 Italy [30]
†The markers have contradictory data available.D: Downregulation; DSS: Disease-specific survival; IB: Immunoblotting; IHC: Immunohistochemistry; NN: Not known; ND: No data; O: Overexpression; OS: Overall survival; qRT-PCR: Quantitative real-time PCR; RFS: Recurrence-free survival; WB: Western blot.
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Patients may be offered postsurgical treatment (adjuvant treatment); many ‘prognostic’ studies include patients who received systemic anticancer treatment, which can influence the natural dis-ease course. By contrast, a predictive biomarker is a biologic factor that provides information on the likely benefit from treatment (either in terms of tumor shrinkage or patient survival). Such predictive factors can be used to identify subpopulations of patients who are most likely to benefit from a given therapy (Table 2), and make it possible to select the therapy with the highest likelihood of efficacy to the individual patient. In summary, prognostic factors define the effects of patient or tumor characteristics on the patient outcome, whereas predictive factors define the effect of treatment on the tumor [5–7].
Here, we review the current field of biomark-ers for RCC and address potential challenges in biomarker development. We summarize biomarker candidates and analyze multima-rker studies as predictive tools for patients with advanced ccRCC (Tables 1 & 2). In this analy-sis, expression of particular genes in relation to Fuhrman nuclear grade, in relation to pathologic stage or in primary versus metastatic sites will
not be considered, since such analyses have lim-ited applicability of biomarkers as independent predictive or prognostic markers. Analysis is con-fined to molecular expression in ccRCC in direct relation to outcome only including OS, PFS and disease-specific survival (DSS). Our analysis confirms that most molecular biomarkers are associated with other well-established clinical and/or pathological characteristics of ccRCC [4]. Each patient in our review analysis underwent nephrectomy. DFS was defined as from the date of radical nephrectomy to the date that tumor distant metastasis was confirmed. OS was inves-tigated and defined as from the date of radical nephrectomy to the date of death or censored at the date of last follow-up. Deaths attributable to causes other than the tumor were excluded. To exclude bias in the study, only reports with no treatment indicated were included. When no data on patient treatment were described, the assumption of no treatment (tyrosine kinase inhibitor [TKI], immunotherapy) was made.
●● Molecular prognostic markers in ccRCCFor localized ccRCC, conventional clinicopatho-logical variables such as TNM stage, ECOG-PS
Table 1. Prognostic biomarkers in clear cell renal cell cancer under investigation over last 5 years (cont.).
Prognostic indicator of poor survival
O/D Gene function Patients in trial (n)
RFS DSS OS Method Maximal follow-up (months)
Year Patients’ country
Ref.
PAI-1 O Serine protease and MMP inhibitor
172 NN p < 0.001 NN IHC 233 2010 Norway [31]
PBMR1 O SWI/SNF complex subunit
213 p = 0.048 p = 0.017 NN IHC 213 2013 Brazil [32]
PDCD4 D Apoptosis 98 NN NN p = 0.008 IHC, WB 62 2012 China [33]
Pirh2 O Ubiquitin-protein ligase
35 p = 0.003 NN p = 0.001 qRT-PCR, IHC 80 2013 China [34]
TIMP-3 O T-cell regulation, immune response
137 NN p = 0.007 NN IHC, WB 120 2013 China [35]
β-catenin O Cell–cell adhesion, Wnt signaling
278 NN p = 0.01 p = 0.03 182 2013 Germany [36]
Eg5 O Bipolar spindle formation (mitosis)
164 p = 0.003 NN NN IHC 80 2013 China [37]
FoxM1 O Mitosis, angiogenesis
87 p = 0.007 NN p = 0.008 IHC 70 2013 China [38]
O 83 NN NN p < 0.001 qRT-PCR, IHC, WB
120 2012 China [39,40]
†The markers have contradictory data available.D: Downregulation; DSS: Disease-specific survival; IB: Immunoblotting; IHC: Immunohistochemistry; NN: Not known; ND: No data; O: Overexpression; OS: Overall survival; qRT-PCR: Quantitative real-time PCR; RFS: Recurrence-free survival; WB: Western blot.
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and Fuhrman nuclear grade provide prognostic information, but cannot be used for accurate prediction of disease recurrence or progression [9]. Prognostic biomarkers can offer superior pre-dictive capacity when compared with previously published clinical parameters and nomograms (Table 1). In a classic study by Motzer et al. [52], they proposed a prognostic system based on a retrospective study of 670 RCC patients. In this study, pre-treatment risk factors were identified as low Karnofsky performance status (<80), high serum LDH, high corrected serum calcium, low Hb and absence of prior nephrectomy. Motzer Scale patients are categorized into three risks: low (no risk factors), intermediate (one or two risk factors) or high (three or more risk factors), with median survivals of 20, 10 and 4 months, respectively. The Motzer Scale continues to be used clinically [52]. It is worth noting that LDH is involved in anaerobic glycolysis and is regulated by the PI3K/Akt/mTOR-containing complex 1 as well as tumor hypoxia and necro-sis, which are known genetic factors involved in ccRCC development [53].
More recently, Choi. et al. have described erythrocyte sedimentation rate (ESR) and ane-mia as independent predictors of survival based on 1226 ccRCC patients. Cutoff points for high ESR was more than 22 mm/h for males and more than 29 mm/h for females. Anemia evaluation was based on definition of Hb and hematocrit (Hct) levels were as follows: Hb: 13.6–17.4 g dl−1 for males and 11.2–14.8 g dl−1 for females and Hct in the range of 40.4–51.3% for males and in the range of 31.8–43.8% for females. This study demonstrated patients who
had anemia before surgery – defined by either low Hb or Hct – had significantly worse 5-year cancer-specific survival (CSS) and OS rates when compared with those without anemia. Patients with high ESR levels had significantly poorer OS, CSS and OCS rates compared with patients with low ESR [54]. Another clinical bio-chemical parameter – preoperative serum high-sensitivity C-reactive protein – is also an inde-pendent prognostic factor for localized ccRCC at the threshold of 0.75 mg/dl [55,56]. At the same time for the population of patients treated with TKIs (including sunitinib, sorafenib, pazopanib and axitinib) and mTOR inhibitor (everolimus, temsirolimus), classical clinical prognostic mark-ers are the presence of bone metastases, prior cytokine treatment, MSKCC score, ECOG-PS and first-line PFS longer than 6 months. Among factors analyzed in multivariate analysis, some have been confirmed as independent prognostic factor, including first-line treatment PFS above 6 months, second-line treatment (TKI vs mTOR inhibitor) and MSKCC score [57–61].
One of most widely investigated markers in ccRCC field is CAIX. In early studies, low CAIX expression predicted worse outcome. Cutoff at 85% CAIX staining was an independent prog-nostic factor for poor survival [62]. More recently, high intratumoral CAIX expression verified by immunohistochemistry (IHC) corresponded to 70% decreased risk from ccRCC-specific death with 2-year ccRCC survival of 80 ± 5 versus 54 ± 12% (p = 0.001) when compared with low expression [63]. In a detailed analysis, high CAIX expression is associated with absence of nodal involvement (p = 0.0001), low Fuhrman grade
Table 2. Biomarker panels in clear cell renal cell cancer under investigation over last 5 years.
Biomarkers in panel Significant correlation (p < 0.05) Ref.
(p = 0.02), small tumor sizes (p = 0.01), longer PFS (p = 0.037) and DSS (p = 0.001). Next, it was shown that low CAIX expression correlates with higher tumor stage, grade, lymph node involvement, distant metastases, shorter survival and poor response to immunotherapy [7,10].
Moreover, decreased CAIX expression was reported as an independent prognostic indica-tor of poor survival in patients with metastatic ccRCC. CAIX status was a predictor of poor outcome with a hazard ratio of 3.1, almost double that of T stage, ECOG-PS or Fuhrman grade. Low CAIX expression is also useful in identifying a subset of high-risk patients with localized disease (T ≥3, or Fuhrman grade ≥2). Subsequently, in a prospective study, the risk of death for patients with low CAIX expression was 3.9-fold greater than those with high expression. On multivariate analysis, low CAIX expression increased the risk of death by 2.5-fold, approach-ing statistical significance as an independent pre-dictor of DSS [9,63]. In another study looking at blood samples from 216 ccRCC patients, CAIX serum level significantly correlated with DFS, CSS and OS [56].
Recently, multivariate analysis revealed that overexpression of CAIX is an independent favorable prognostic factor in patients treated with both targeted therapies and/or cytokines (p < 0.001) [64]. In all the studies, more than 95% of ccRCCs cases had high and homogeneous lev-els of CAIX expression detected by reverse tran-scription PCR (RT-PCR) and IHC. Monoclonal antibody G250 was shown to have high affinity for the CAIX antigen and using this antibody with iodine 131 radioimmunoscintigraphy and 18F-fluorodeoxyglucose PET was shown to detect primary ccRCC and metastatic ccRCC. A total of 78% of RCC cases responding to IL-2 had high CAIX expression in primary tumors.
Patients with high CAIX expression and VHL gene mutation had the most favorable prognosis (86% with 2-year survival); patients with VHL mutation or high level of CAIX expression had intermediate prognosis (69%); patients without VHL mutation and low CAIX expression had the poorest prognosis (45%) [63]. Another genetic–biochemical correlation was shown between CAIX and VEGF expression. Multivariate analysis has defined CAIX-VEGF co-expression as an independent prognostic fac-tor of DSS (p = 0.0002) in patients who undergo radical nephrectomy for ccRCC [65]. One of the latest studies of CAIX evaluated prognostic value
of CAIX expression level in 730 patients treated surgically between 1990 and 1999. In 2012, with 247 patients still under observation, the median follow-up was 13.8 years. It was shown that low CAIX expression was associated with increased risk of ccRCC death (hazard ratio: 1.62; p.62; ratio expression in the tumor was associated with an increased ccRCC death or recurrence with distant metastases development after adjusting for nuclear grade or necrosis) [66]. Finally, low CAIX expression was not statistically associated with patient death or distant metastases after adjustment for nuclear grade and tumor necrosis. Since current studies on CAIX significance in primary and metastatic ccRCC show conflicting conclusions, further investigation on CAIX as a prognostic tool such as a large prospective trial is recommended [66].
HIF-1α is a major regulator of cell response to low oxygen (hypoxic) conditions. It is a transcription factor promoting expression of VEGF, CAIX and many other genes. Patients with high HIF-1α expression (>35% expression) have significantly worse survival than patients with low expression and expression is an impor-tant independent prognostic factor for patients with metastatic ccRCC [67]. It was also shown that cytoplasmic HIF-1α is highly expressed in high-grade ccRCC [68]. HIF-1α was analyzed by IHC in a cohort of 176 patients. Analysis showed that elevated expression correlates with pT stage (p = 0.009) and DSS (p ≤ 0.025) and is an independent biomarker of early progres-sion in ccRCC [42]. This observation was also relevant for ccRCC with sarcomatoid differen-tiation. HIF-1α overexpression was shown to be predictive of shorter PFS (p = 0.023) and DSS (p = 0.035), respectively [69]. In another study of 168 ccRCC patients, high HIF-1α expression accompanied with low HIF-2α was a predictor of shorter OS compared with low HIF-1α and low HIF-2α expression (p = 0.04) [70]. Other biomarkers correlated with HIF-1α expression are VEGFs and Ki-67. High co-expression of VEGF-A or VEGF-C with HIF-1α in ccRCC is a biomarker of worse prognosis. High nuclear HIF-1α and perimembranous VEGF-C expres-sion were associated with lower nuclear grade, lower pT stage and better prognosis. At the same time, cytoplasmic sequestration of HIF-1α and VEGF-A or VEGF-C are biomarkers of low 5-year survival rate in univariate analysis [71].
VHL is a gene on chromosome 3 that acts as a major tumor suppressor in ccRCC. Mutations
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Figure 1. HIF-1 signaling pathway in clear cell renal cell cancer cells.
MT1-MMP
FIH
EPO
GLUT-1
GADPH
IGF2
Leptin
Hexokinase 1,2
VEGF – A
VEGF – D
VEGF – C
Potential biomaker for clinical validation Inhibition
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Molecular nodesdownstream HIF 1 α
Other nodes of HIF 1 α signailing network
Legend
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PDGF-β
TGF-β
Hormone/enzymatic pathways
Angiogenetic pathways
Proliferation/cell cycle regulation
HIF1 α
TGF-α
Cyclin
p27p21
p53
Tumor invasion
UPAR
MET
MMP2
Min3
CAIX
Enolase
AMF
Katepsin
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in the VHL gene are a common finding in spo-radic ccRCC, and a large body of evidence sup-ports the role of VHL, and VHL dysfunction is assumed to be an important biomarker of disease prognosis (Figure 1). The first studies published over 10 years ago have shown VHL mutations and promoter hypermethylation events signifi-cantly correlated with tumor grade. Next, it was shown that VHL mutations that disabled pVHL expression were biomarkers of poor CSS; how-ever, other clinical trials have shown no correla-tion between VHL mutational status and prog-nosis [72]. In another study, patients with tumors harboring VHL mutations had better prognosis than those without. In this study of 100 patients who underwent radical nephrectomy, PFS and DSS were longer, but no data on postsurgery treatment were analyzed, and 46% of patients had metastases at time of nephrectomy. The
presence of VHL mutations was associated with the absence of nodal or distant metastases and a favorable ECOG-PS at the time of ccRCC diag-nosis [63]. Currently, it is not clear that ccRCC associated with VHL gene mutation has spe-cific prognostic value or if it is just a diagnostic biomarker for ccRCC [72].
●● Prognostic biomarker panels in ccRCCMolecular prognostic marker panels in ccRCCThe cumulative number of aberrantly expressed biomarkers correlates with patient outcomes with higher specificity due to multifactorial abnor-malities that are responsible for cancer develop-ment and progression. The first biomarker panel study included HIF-α, VEGF, CAIX, mTOR, survivin, B7-H1, p53, p21, matrix metallopro-teinases 1–3 (MMPs), IGF, Ki-67, vimentin and fascin. The analysis of staining intensity
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Figure 2. mTOR signaling pathway in clear cell renal cell cancer cells.
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and expression levels of the mTOR pathway has proven its importance in ccRCC progno-sis (Figure 2) [41], which remain in concordance with mTOR inhibitor activity in ccRCC treat-ment [60,73–74]. More recent analysis focused on phosphorylated-S6, phosphorylated-mTOR, phosphorylated-AKT, HIF-1α, Raptor, phos-phatase and tensin homolog (PTEN), phospho-inositide 3-kinase (PI3K) and phosphorylated 4E-binding protein-1. A tissue microarray study
of 419 nonmetastatic ccRCC patients demon-strated patients with high and intermediate marker scores had significantly increased risk of dying from ccRCC compared with those who had a low marker score (p = 0.008). A high marker score was an independent prognostic factor of faster disease recurrence (p = 0.01). On their scale, a higher number of altered mTOR pathway genes were associated with greater risk score [41]. A similar study of mTOR pathway
Cell membraneIGFR1
IRS1
PIK3
PIP3
p27FoxO3a
FoxO1SKP2
mTORC2
mTOR Deptor mLST8
mSIN1 RictorAMPK
p53PLD2
S6K1
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Cap-dependent translation
c-mycNucleus
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PRAS40Raptor
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PTEN
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Raf
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PDK1
Potential biomarker for clinical validation Inhibition
Direct activation
Indirect activation
Other nodes of mTOR signailling network
Legend
mLST8
Protor1
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Figure 3. Glucose metabolism regulation by signaling pathways in clear cell renal cell cancer cells.
GlucoseGLUT 1
IGFR 1
IRS1
PIK3
PDK1
S6K1
PTEN
AMPK
p53
MTOR C1
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Phosphoenolopyruvate
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1,3 diphosphoglycerate
DHAPPGAL
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PKM2
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PFK1
HK2
LDHA
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Akt
HIF1α
PLD2
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members including PTEN, phos-AKT, phos-S6 and 4EBP1 complemented with AKT, c-MYC, p27 and HIF-1α revealed that HIF-1α and phos-S6 overexpression are independent predictors of DSS and PFS in primary ccRCC in a multivariate analysis model (Figure 3). Moreover, expression of phos-AKT correlated with OS (p = 0.0326), but not with DSS [42].
A recent study of SIRT1, DBC1, p53 and AR in 200 ccRCC patients revealed expression of SIRT1, P53, DBC1 and AR significantly corre-lated with each other and all of them predicted shorter OS, RFS and DSS [43]. Next, a panel of biomarkers including programmed cell death 1 ligand 1 (PD-L1/B7-H1), survivin and Ki-67 in 634 ccRCC cases was analyzed. The analy-sis revealed that each biomarker was indepen-dently associated with ccRCC-specific death after adjusting for the remaining two. Patients
with high expression of those genes are five-times more likely to die from ccRCC compared with low expression patients [44]. Another molecular IHC signature of ccRCC stratifying patient out-come came from a study of 170 patients and biomarker panel including: CAIX, CAXII, chemokine receptor 3 (CXCR3), gelsolin, Ki-67, vimentin, epithelial cellular adhesion molecule (EpCAM), p21, p27, p53, pD6, PTEN, HIF-1α, pAkt, VEGF-A, VEGF-C, VEGF-D, VEGFR-1, VEGFR-2 and VEGFR-3. Association with DFS was statistically significant for multiple markers, but VEGF-A expression alone was not an inde-pendent prognostic factor, whereas Ki-67, P53, epithelial VEGFR-1, endothelial VEGFR-1 and epithelial VEGF-D are independent prognostic factors. These markers alone were better prog-nostic markers than clinicopathologic variables. VEGF-D and epithelial/endothelial VEGFR-1
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were also independent predictors of outcome [45]. Subsequent IHC analysis of a panel of molecu-lar markers involved in the process of epithelial-mesenchymal including E-cadherin, clusterin, Twist and vimentin, revealed them as factors associated with shorter RFS [46]. Tissue micro-array analysis has proven that CXCR4, CXCR7 and SDF-1 overexpression predicts poor progno-sis in ccRCC patients. In multivariate analysis, high CXCR4, CXCR7 and SDF-1 expression significantly correlated with poor OS and RFS independent of gender, age, American Joint Committee on Cancer stage, lymph node sta-tus, metastasis, histologic variant and Fuhrman grade [47].
Another IHC study analyzed expression of 12 proteins: Aurora-A, Bcl-2, Bcl-xL, clusterin, HSP27, HSP70, HSP90, Ki-67, MMP-2 and -9, p53, and VEFG in a cohort of 153 patients. Only Ki-67 expression was independently related to disease recurrence, but significant differences in RFS were found only when clusters of biomark-ers were analyzed together for two or three risk factors [48]. A similar tissue microarray of 170 ccRCC cases covered 29 markers including: CAIX, CAXII, CXCR3, gelsolin, Ki-67, vimen-tin, EpCAM, p21, p27, p53, pS6, PTEN, HIF-1alpha, pAkt, VEGF-A, VEGF-C, VEGF-D, VEGFR-1, VEGFR-2 and VEGFR-3. Out of 29 biomarkers, overexpression of six – Ki-67, p53, nuclear p21, endothelial VEGFR-1, epithelial VEGFR-1 and epithelial VEGF-D – were cor-related with shorter DFS [49]. In another analysis, proteomic profiling revealed that the 29 proteins were differentially expressed (12 overexpressed and 17 underexpressed) in metastatic versus pri-mary ccRCC. Further subanalysis showed that protein expression profiles specific for metastatic ccRCC (high profilin-1 [Pfn1], 14-3-3 zeta/delta [14-3-3ζ/δ] and galectin-1 [Gal-1]) can distin-guish aggressive from nonaggressive ccRCC cases, and overexpression of Pfn1 is associated with poor OS [50]. A larger study of 46 epithelial-mesenchymal transition related genes revealed that patients with low CXCR4, vimentin, fibronectin and TWIST1 have better outcomes. CXCR4 and vimentin upregulation was further found to be independent prognostic markers for poor CSS [51].
Furthermore, next-generation deep sequenc-ing technology can enable genome-wide expres-sion profiling and discovery of novel biomarkers including miRNA . Five miRNAs discriminated between nonrecurrent versus recurrent and
metastatic disease, whereas 12 uniquely distin-guished nonrecurrent versus metastatic disease. The biological relevance of candidate novel miRNAs is unknown at present [75].
●● Molecular predictive biomarkers in ccRCCTargeted agents including TKIs – sunitinib, sorafenib, axitinib, pazopanib; mTOR inhibi-tors – everolimus and temsirolimus; and anti-VEGF-targeted antibody – bevacizumab have significantly improved ccRCC patients’ outcome [76–79]. Treatment guidelines are now shifting toward achieving long-term treatment with OS extended with each line of therapy. Increased OS may also be achieved through selected use of targeted agents in particular patients, optimal side effects management and appropriate drug dosing [6,59]. To achieve this goal, factors that impact treatment effectiveness and tolerability need to be found and considered. Identification of predictive factors that could be used in the era of targeted therapy is urgently needed. New pre-dictive biomarkers are constantly under inves-tigation, including research into large, Phase III clinical trials. In ccRCC, this approach is even more important, as antiangiogenic drugs act on nonmalignant – endothelial – cells, and therefore the genetic background of patients may play a crucial role in determining the efficacy of ccRCC treatment [80]. Recent molecular experi-ments are likely to change treatment guidelines of ccRCC in next decade [59]. And an increasing incidence of ccRCC has prompted investigators even more to search for new biomarkers that can be of high importance in ccRCC treatment out-come prediction.
Predictive markers of response to therapy are increasingly important in advanced ccRCC due to the expanding number of treatment options in recent years [79,81–82]. Different types of potential predictive markers may include clini-cal (toxicity-based), serum (biochemical), tis-sue (IHC and PCR) and radiologic biomark-ers (RECIST). Clinical factors are commonly used in overall prognostic-predictive models of ccRCC, but have limited utility. Correlations between development of particular toxicities and response to therapy have been noted, such as the correlation between hypertension and response to angiogenesis-targeted therapy. Serum and tis-sue biomarkers will be covered in detail later, but factors such as serum LDH and circulating cytokines show promise in this regard. Finally, baseline or early treatment radiographic studies
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may have predictive ability for longer-term effi-cacy, with most studies to date focusing on func-tional imaging modalities such as ultrasound, dynamic contrast-enhanced, MRI or positron emission tomography scans [83–85].
Predictive biomarkers have developed in par-allel with new therapies. In December 2005, the US FDA approved sorafenib as a new treat-ment for ccRCC, based on the trial proving that sorafenib delayed disease progression in patients with advanced RCC whose cancer recurred or persisted despite previous immunotherapy [86]. In the clinical setting, ESR kinetics were shown to be significant predictive markers of PFS in ccRCC patients treated with sorafenib as sec-ond-line therapy. This parameter was defined as a continuously rising ESR level of three con-secutive rises at least 10% each or three rises that involved an increase of 50% over the nadir ESR level. Median PFS was 27 months in the decreased ESR group, but 12 months in the stable ESR group, and only 6 months in the increased ESR group [87].
In the Phase III TARGET trial, multiple potential biomarkers of sorafenib treatment response were evaluated. The analysis covered VEGF, soluble VEGF receptor 2 (sVEGFR-2), CAIX, tissue inhibitor of metalloproteinase 1 (TIMP-1), and Ras (p21) genes expression, and VHL tumor gene sequence analysis. Blood sam-ples were analyzed for biomarker levels with ELISA assays and DNA was extracted from for-malin-fixed, paraffin-embedded tumor samples. Placebo-treated individuals were selected as the control cohort. Results of analysis suggested that patients with elevated VEGF (>75th percentile) may experience greater benefit from sorafenib treatment (in terms of PFS) than those with low VEGF, but that sVEGFR-2, CAIX, TIMP-1, Ras p21 and VHL mutational status revealed no relationship between these biomarkers and sorafenib benefit [88].
Predictive biomarkers were also developed for the most widely used first-line TKI – sunitinib. At first, normal C-reactive protein levels were shown as an independent predictive indicator for patients with advanced ccRCC treated – sunitinib significantly correlated with objective responses (p = 0.002) and significantly longer PFS (p = 0.036) [89]. In a small trial, ccRCC tissue microarrays from primary tumor speci-mens of 42 patients with metastatic disease treated with sunitinib were evaluated for selected markers related to angiogenesis. In this report,
immunostaining of HIF-1α, CA9, Ki67, CD31, pVEGFR1, VEGFR-1 and -2, pPDGFR-α and -β expression was significantly associated with sunitinib response after 9 months. Among those biomarkers, expression of HIF-1α, CA9, CD34, VEGFR1 and -3 and PDGRFα was associated with PFS and OS, and high membrane CA9 staining was an independent prognostic factor for longer OS in multivariate analysis [90]. On the other hand, VEGF and VEGFR polymor-phisms were shown to affect clinical outcome in terms of OS in patients on sunitinib for first-line therapy. The patients with VEGF A/TT poly-morphism of SNP rs833061, CC polymorphism of SNP rs699947, CC polymorphism of SNP rs2010963 and SNP rs307821, and VEGR3 CG polymorphism of SNP rs6877011 all seemed to have a shorter OS when treated with first-line sunitinib (p = 0.0001) [91,92].
Furthermore, polymorphisms in genes involved in sunitinib pharmacokinetics may pre-dict response to sunitinib treatment including SNP rs1128503 ABCB1 (ATP-binding cassette transporter; p = 0.027) and SNP rs4073054 in NR1/3 (glutamate NMDA receptor; p = 0.025) [92]. Finally VEGF soluble isoforms VEGF(121) and VEGF(165) were associated with positive response to sunitinib (p = 0.04) and for tumors overexpressing VEGF(121) and VEGF(165), the probability of response was as high as 81 and 90%, respectively in terms of partial response and stable disease [93]. At the same time, the 20S core subunit of protease complex circu-lating serum levels was found to be lower in patients responding to sunitinib treatment than in patients with stable disease and progressive disease with a cutoff of 7.24 μg ml-1. Moreover, the risk of death from ccRCC increased by 21% by each 1 μg ml-1 increase in 20S proteasome serum levels (p < 0.0001) [94].
In another study, apolipoprotein A2 (ApoA2) and serum amyloid α (SAA) were found to be independent predictive and prognostic factors in interferon-treated and TKI-treated ccRCC patients. A SAA level more than 71 ng/ml was defined as the cutoff value for PFS and OS pre-diction in this cohort of patients [95]. Serum was analyzed for miRNA expression using microar-rays and 28 of 287 total miRNAs were related to poor response – 23 were related to prolonged response to sunitinib treatment in an obser-vational prospective study of 44 patients. In the poor response group, median PFS was 3.5 months and the OS was 8.5 months, whereas in
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the prolonged response group median PFS was 14 and OS was 24 months. Identified miRNAs were involved in regulating multiple pathways, including angiogenesis, p53, Ras, PDGF and apoptosis [96].
A large trial by Choueiri et al. examined CAIX and pathological features of the tumor as predictors of outcome in patients with meta-static ccRCC treated with anti-VEGFs: suni-tinib, sorafenib, valatinib and bevacizumab. No association was found between CAIX expression and metastatic site number, sites of metastases (except bony metastases), and tumor responsive-ness to sorafenib or sunitinib. Only in patients receiving sorafenib, an association of high CAIX expression with tumor shrinkage was seen [97]. For bevacizumab alone, limited data are availa-ble. Pre-treatment total circulating VEGF-A was found to be prognostic for OS in the population treated with bevacizumab from the AVOREN trial [98]. Complementary data came from the analysis of VEGF receptor 1 (VEGFR1, FLT1) polymorphisms. Populations of patients from AViTA and AVOREN trials were screened and SNP rs9582036 and rs7993418 were found to be significantly associated with OS in the bevaci-zumab-treated group (p = 0.00014), but no such association was observed in placebo-treated indi-viduals. AC/CC polymorphism carriers exhib-ited longer survival relative to AA carriers. SNP rs9582036 also correlated with PFS, but SNP rs7993418 was found to be a synonymous (shift) SNP affecting tyrosine 1213 in the TK domain and leading to increased VEGFR1 expression and downstream signaling. This SNP rs7993418 correlated significantly with PFS (p = 0.033), but not OS (p = 0.78) in the bevacizumab group in AVOREN [99].
Clinical trial VEG102616 included a bio-marker search for patients treated with pazo-panib. First, VHL gene inactivation (mutation and/or methylation), HIF-1α, HIF-2α and HIF-1α expression were analyzed. VHL gene status, HIF-1α and HIF-2α protein levels did not correlate with either objective response rate or PFS [100]. At the same time, IL-6, IL-8, HGF, TIMP-1 and E-selectin were screened for predic-tion of clinical outcome. Correlation between blood serum levels of IL-6, IL-8, VEGF, osteo-pontin, E-selectin and HGF with treatment response (continuous tumor shrinkage) and PFS in patients treated with pazopanib was found primarily. In the Phase III validation trial, a cohort of patients treated with pazopanib who
had high concentrations of IL-8, osteopontin, HGF and TIMP-1 had shorter PFS. Moreover, patients with high concentrations of IL-6 obtained relative PFS benefit from pazopanib treatment compared with placebo (p = 0.009), while standard clinical classifications including ECOG, Motzer and Heng criteria were not pre-dictive of increased PFS [101].
In a Phase III randomized trial with 404 high-risk patients with RCC treated with tem-sirolimus (TORC1 inhibitor) or IFN-α – includ-ing 203 patients treated with IFN-α and 201 patients treated with temsirolimus – LDH was evaluated as a predictive biomarker. The median OS for patients treated with temsirolimus was 10.6 months and LDH was a statistically sig-nificant prognostic factor (p < 0.001) for OS. Patients with LDH more than 1 × upper limit of normal compared with those ≤1 × upper limit of normal had shorter OS [53].
A recent study has described the predictive and prognostic value of serum CAIX in ccRCC patients treated with targeted therapy. In this study, high serum CAIX level was associated with significantly shorter OS (p = 0.0136) in the group treated with temsirolimus or beva-cizumab. Serum CAIX levels were lower in responders (64.7 ± 104.7 pg/ml) than nonre-sponders (108.2 ± 203.8 pg/ml), with no statis-tically significant difference (p = 0.366). There was no association between serum CAIX and clinical parameters including ECOG-PS (p = 0.367) or Motzer classification (p = 0.431) [102]. Also no biomarker predictive value was found in another study of patient subgroup from the Phase III global advanced RCC (ARCC) trial. This study was conducted to determine if intratumoral expression of PTEN and HIF1-α correlate with temsirolimus treatment efficacy when compared with IFN-α. There was no correlation between PTEN and HIF1-α expres-sion and treatment effect with respect to OS, PFS or objective response rate in this group of patients [103].
ConclusionBiomarker discovery in ccRCC may be divided into three fields: detection, prognosis and diagnostics. A need for better understand-ing of underlying ccRCC molecular pathways prompted the authors to analyze potential roles of molecular biomarkers for patient prognosis and outcome. The recent identification and incorporation of molecular markers in the
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prognosis and prediction of ccRCC patients are expected to play an increasing role in the future. Biomarkers may give providers powerful tools enabling understanding pathology of RCC, its epidemiology, and also rendering randomized clinical trials, screening and diagnosis and prog-nosis in clinical practice.
We found that additional efforts are needed in identifying biomarkers associated with clinical outcome to VEGF agents in metastatic ccRCC. A key to success in development of such biomark-ers would be the ability to pre-select patients who would experience clinical benefit from particular therapies due to molecular pre-analysis of can-cer tissue and as a result match an appropriate drug for appropriate patients. In line with this challenge, several molecular biomarkers have been investigated with the purpose of guiding therapy. The analysis of VEGF, -soluble VEGF receptors (sVEGFRs), CAIX, HIF-1α and VHL sequencing and expression of the tumor gave the most promising results; nevertheless, more work still needs to be done to impact patient care. With that, genome-wide screening for novel prognostic biomarkers seems inevitable, such as through high-throughput whole-genome and exome sequencing, or further analysis of TKI molecular pathways.
Biomarker analyses might be useful for treat-ment resistance risk stratification. It is highly beneficial to select patients who would ben-efit from standard TKI treatment and those should be treated by nonstandard therapeutic approaches [6,60–61,104]. Such analyses may fuel the rational development of new targeted thera-pies. The cumulative PSF and OS of ccRCC patients are still not satisfactory despite current therapeutic advances. Hence, there is urgent need to identify biomarkers that guide treatment. New predictive biomarkers can be interrogated more easily with high-throughput methods [59]. Currently, multiple potential diagnostic, predic-tive and prognostic molecular biomarkers have been identified in ccRCC, but none has been approved and incorporated into routine clini-cal practice. It is expected that these validated biomarkers would enhance RCC management guidelines while enabling individualized treat-ment. These RCC biomarkers require prospec-tive validation in large appropriately designed Phase III randomized trials. It may be expected that in the future, validated biomarkers would be incorporated in management guidelines in RCC and enable tailored individualized treatment.
This in turn is expected to improve clinical outcomes in terms of PFS and OS [105,106].
Future perspectiveLong-term survival in ccRCC may be achieved through optimal sequencing of targeted thera-pies including both TKIs and mTOR inhibitors. Optimal sequencing should be accompanied by optimal dosing, adverse event management, treatment prolongation and compliance sup-port. Recent advances in the identification of prognostic and predictive biomarkers high-light the potential for personalizing treatment for ccRCC. Nevertheless, no biomarker has reached level I evidence in clinical practice. The above-described biomarkers (i.e., VEGRF, mTOR, HIF, CAIX) are useful in certain pop-ulations of RCC patients, but the expression and activity of certain biological molecules dif-fer significantly between patients and targeted therapy used. Biomarker application may not only potentiate the ability to choose appropri-ate targeted therapies for appropriate patients, but also cost-effective screening of high-risk patients (candidate for neoadjuvant treatment? Intensive screening?), identification of aggressive cancers among small renal masses (candidates for nephrectomy and intensive screening?), early detection of recurrences postoperatively, rational selection of patients for immunotherapy and pos-sibly patient risk profiles (candidates for dose reduction? alternative treatment schedule?). It is becoming evident that ccRCC genomic data should be analyzed and correlated with genome-wide and miRNA expression profiling data. Future research should focus on exploration of new biomarkers with greater potency than those now characterized. These goals may be achieved by comparison of gene libraries and proteins with abnormal expression in ccRCC in relation to normal renal tissue. Identified and selected molecules can be a clue to clarify the molecular mechanisms responsible for devel-opment, progression and prognosis of ccRCC. The incorporation of novel molecular biomark-ers into well-defined staging systems such as UCLA Integrated Staging System, Bioscore or SSIGN and correlation with WHO classifica-tion, TNM system and Fuhrman grading criteria and finally with prognostic model for patients with metastatic disease developed at the MSKCC is required. Overall, more application-focused development of biomarkers with prospective assessment through clinical trials is required.
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Financial & competing interests disclosureThis research was supported by National Science Centre OPUS project No UMO-2011/01/B /NZ5/02822 (2011–2014). C Szczylik received consulting fees and lec-ture fees from Pfizer, Bayer HealthCare, Astellas, GlaxoSmithKline and Novartis. AM Czarnecka received lecture fees from Pfizer, GlaxoSmithKline, Novartis,
Merck, Vipharm and Roche. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or finan-cial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.
● Carbonic anhydrase may be used as a prognostic biomarker in ccRCC patients.
● VHL gene and HIF-1α are correlated with overall survival and disease-specific survival.
● Other markers require prospective validation in Phase III clinical trials.
Treatment biomarkers
● Vascular endothelial growth factor polymorphisms, serum levels and intratumoral expression levels may be used to assess the likelihood of pharmacotherapy responsiveness.
● IL-8 is a prognostic biomarker of pazopanib treatment.
Future perspective
● More effort is needed to validate markers currently under investigation.
● The correlation of novel molecular biomarkers with Memorial Sloan–Kettering Cancer Center and Mayo Clinic prognostic models is required.
ReferencesPapers of special note have been highlighted as:• of interest; •• of considerable interest
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