Predictive molecular biomarkers to guide clinical decision making in kidney cancer: current progress and future challenges.

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Review

Predictive molecular biomarkers to guide clinical decision making in kidney cancer: current progress and future challenges Expert Rev. Mol. Diagn. 15(5), 631–646 (2015)

Jason Yongsheng Chan1, Yukti Choudhury2 and Min-Han Tan*1,2 1 Department of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Drive, Singapore, Singapore 2 Institute of Bioengineering and Nanotechnology, 31 Biopolis Way, #04-01, Singapore 138669, Singapore *Author for correspondence: Tel.: +65 6824 7110 Fax: +65 6478 9080 [email protected]

Although the past decade has seen a surfeit of new targeted therapies for renal cell carcinoma (RCC), no predictive molecular biomarker is currently used in routine clinical practice to guide personalized therapy as a companion diagnostic. Many putative biomarkers have been suggested, but none have undergone rigorous validation. There have been considerable advances in the biological understanding of RCC in recent years, with the development of accompanying molecular diagnostics that with additional validation, may be helpful for routine clinical decision making. In this review, we summarize the current understanding of predictive biomarkers in RCC management and also highlight upcoming developments of interest in biomarker research for personalizing RCC diagnostics and therapeutics. KEYWORDS: angiogenesis . biomarker . cytokine . immunotherapy . renal cell carcinoma

Epithelial renal cell carcinoma (RCC) is a heterogeneous disease with diverse histological subtypes, including clear cell, papillary, chromophobe, translocation and collecting duct tumors [1]. The clinico-pathological behavior of RCC is unpredictable, ranging from indolent localized tumors to aggressive metastatic disease. Up to one-third of patients present with metastatic disease, and a similar proportion of those with localized disease eventually relapse with distant metastases following curative surgery [2]. Inactivation of the von Hippel–Lindau (VHL) gene occurs in the majority of sporadic clear cell RCC (ccRCC), resulting in the accumulation of hypoxia-inducible factor (HIF) a and up-regulation of its downstream proangiogenic factors, including VEGF. Currently, there are eight drugs approved by the US FDA for the treatment of metastatic RCC. These include five agents targeting either VEGF (sunitinib, sorafenib, pazopanib, axitinib) or its receptor (VEGFR; bevacizumab, used in conjunction with IFN-a), two mTOR inhibitors informahealthcare.com

10.1586/14737159.2015.1032261

(temsirolimus, everolimus) and recombinant IL-2. Current treatment selection strategies are largely dependent on clinical efficacy of these agents based on survival data reported in clinical trials, as well as on criteria for prognostic risk, such as those developed by the Memorial Sloan-Kettering Cancer Center [3]. Prognostic markers are able to evaluate patient’s risk of certain outcomes and can be useful for patient selection for treatment. However, their utility is limited by the inability to directly foresee response to specific therapy. Predictive markers, on the other hand, can provide information on whether or not there will be any response to specific therapy [4,5]. Despite major efforts to characterize the molecular landscape of RCC, our understanding of the clinical biology of RCC remains inadequate, and there is at present no molecular biomarker that has been integrated into daily clinical practice to rationally guide the selection of therapy in individual patients. Both tumor-specific biomarkers (e.g., tumorspecific proteins or genes) as well as

2015 Informa UK Ltd

ISSN 1473-7159

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Review

Chan, Choudhury & Tan

host-specific biomarkers (e.g., single nucleotide polymorphisms [SNPs]) may predict response to anti-angiogenic tyrosine kinase inhibitors (TKI), which are known to not only target host endothelial and other stroma cells but also exert direct antiproliferative effects on tumor cells [6,7]. To date, relatively few studies have identified molecular biomarkers that reliably predict therapeutic response in metastatic RCC, and none have been reliably validated in independent large prospective studies (summarized in TABLE 1 & FIGURE 1). In a recent systematic review of biomarkers for anti-angiogenic therapy in RCC for example, none of the biomarkers satisfied level I evidence [8]. Perhaps more importantly, the discovery of predictive biomarkers may provide insight into drug resistance mechanisms (and vice versa). In the clinic, drug resistance occurs either as a primary (intrinsic) phenomenon of unresponsiveness or manifest as a secondary (acquired) event in response to treatment. The knowledge of molecular biology and mechanisms of resistance has been proposed to be necessary in the development of predictive biomarkers. Alterations of relevant tumor genes or proteins targeted by specific agents, differences in alternative pathways that mediate resistance, as well as variations in host genomics may be hypothesized to guide therapy [9,10]. The significance of predictive biomarkers in this context has been recently reviewed and is beyond the scope of this review [11]. Notably, other excellent reviews have similarly discussed the roles of molecular genetics [12] and pharmacogenomics [13] on the development of novel therapeutics in RCC, and we encourage the reader to refer to them. In the contemporary treatment of metastatic RCC, informative biomarkers predictive of therapeutic response will be of paramount importance. Notwithstanding the wide range of novel drugs available to the clinician, treatment response rates are highly variable and prognosis remains dismal. The challenge then lies in identifying the drug which will most likely benefit the patient, while avoiding unnecessary toxicities. In this review, we summarize our current understanding of predictive molecular biomarkers in the modern management of RCC, and highlight upcoming developments in biomarker research that may unlock the key to personalized RCC diagnostics and therapeutics.

responses for those who achieve complete remission [14–16]. However, IL-2 administration can be associated with substantial toxicity and requires specialized care. There is thus a need to identify pre-therapeutic parameters that can predict the likelihood of response. Immunohistochemical biomarkers

For patients who may be candidates for high dose IL-2 therapy, one potential predictive molecular biomarker is carbonic anhydrase IX (CAIX) (encoded by CA9), a protein implicated in regulating cellular proliferation in response to hypoxia. In a retrospective cohort, Bui et al. showed that 94% of ccRCC expressed CAIX protein on immunohistochemistry (IHC). The study further demonstrated that high CAIX protein expression (>85% of cells stained) in nephrectomy specimens was associated with greater likelihood of response to high dose IL-2 therapy. Overall response rate to IL-2 was superior in the group with high CAIX expression (27%) compared with the group with low expression (14%). In addition, all complete responders to IL-2 treatment had high CAIX expression [17]. Using the same parameters, Atkins et al. [18] subsequently showed that 78% of IL-2 responding patients displayed high CAIX expression compared with 51% of non-responding patients. In a separate retrospective cohort of patients with metastatic ccRCC who underwent nephrectomy followed by treatment with IL-2-based regimens, the predictive and prognostic utility of CAIX expression, rather than its polymorphisms, was demonstrated [19]. In a Korean study, Kim et al. [20] again showed the predictive value of high CAIX expression, as well as COX-2 expression, to IL-2-based therapy. In these studies, survival outcomes were significantly better in patients with high CAIX expression [17–20]. Despite these positive studies, the ‘Select’ trial conducted within a prospective patient cohort enriched for ccRCC histology and low/intermediate risk scores was unable to validate CAIX expression as a predictive marker. This unexpected result, however, may possibly be due to differences in patient selection and/or tumor sample processing [21]. Interestingly, exploratory analysis from the trial demonstrated that treatment response was positively associated with tumor programmed death-ligand 1 (PD-L1) expression [21].

Review criteria

The MEDLINE database was systematically searched for publications on predictive biomarkers and RCC using keywords of ‘renal cell carcinoma’, ‘biomarker’, ‘sunitinib’, ‘sorafenib’, ‘pazopanib’, ‘axitinib’, ‘bevacizumab’ and ‘interleukin-2’. Articles published up to 25 December 2014 were retrieved. The search was limited to articles published in English. Preclinical studies and studies not focusing on molecular biomarkers were excluded. Citation lists of all retrieved papers were screened for additional relevant publications.

Circulating & protein biomarkers

Besides studies on CAIX, a proteomic approach using protein chip arrays and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry revealed an 11-peak signature to be an independent predictor of high dose IL-2 response, albeit with a limited accuracy of 72% [22]. Sabatino et al. [23] demonstrated that in a mixed cohort of patients with either metastatic RCC or melanoma receiving high dose IL-2 therapy, high pretreatment levels of serum VEGF and fibronectin were correlated with lack of clinical response and poor overall survival (OS).

Predictive biomarkers of efficacy Immunotherapy with high dose IL-2

Sunitinib

High dose IL-2 therapy induces major response rates in 20% of patients with metastatic RCC, and is associated with durable

Sunitinib is an oral multi-targeted TKI that is approved for treatment of metastatic RCC in the first-line setting. In

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Predictive biomarkers for kidney cancer

Review

Table 1. Summary of predictive biomarkers in kidney cancer. Prior systemic treatment

n

Method

Biomarker

Outcome measures

Ref.

Naı¨ve

86

IHC

CAIX

ORR: high vs low expression, 27 vs 14% DSS: high vs low expression, 24.8 vs 5.5 months

[17]

Naı¨ve

66

IHC

CAIX

ORR: responders vs non-responders, 78 vs 51% OS: high vs low expression, 40 vs 0% at 5 years

[18]

Naı¨ve

43

IHC

CAIX

ORR: high vs low expression, 37 vs 8% OS: high vs low expression, 25.5 vs 8.5 months

[19]

Naı¨ve

62

IHC

CAIX

ORR: responders vs non-responders, 83 vs 55% PFS: high vs low expression, 6.5 vs 2.1 months

[20]

IHC

COX-2

ORR: responders vs non-responders, 94 vs 72% PFS: high vs low expression, 6.9 vs 3.1 months OS: high vs low expression, 40.4 vs 20.2 months

PD-L1

ORR: high vs low expression, 50 vs 19% PFS: high vs low expression, 33 vs 6% at 3 years

[21]

ORR: 11-peak signature had a 72% prediction accuracy

[22] [23]


IL-2

Naı¨ve

113

IHC

Naı¨ve

56

Proteomics

Naı¨ve

11

Blood

VEGF

ORR: lower baseline levels in responders OS: low vs high levels, 23.3 vs 13 months

Blood

Fibronectin

ORR: lower baseline levels in responders OS: low vs high levels, 23.3 vs 11 months

Sunitinib Naı¨ve

61

IHC

CAIX

ORR: high vs low expression, 66 vs 17%

[37]

Naı¨ve (64%) Cytokine (26%) Sorafenib (10%)

42

IHC

CAIX

OS: high vs low expression, 45 vs 19.5 months ORR: higher expression in responders

[36]

IHC

HIF-1A Ki67 CD31 pVEGFR-1 VEGFR-1 VEGFR-2 pPDGFR-A pPDGFR-B

ORR: higher expression in responders

Unknown

40

IHC

VEGFR-2

PFS: longer with high VEGFR-2 expression

[39]

Naı¨ve

58

IHC

VEGFR-2


[40]

Naı¨ve

23

IHC

pVEGFR-2

PFS: high vs low expression, 15.8 vs 23.4 months OS: high vs low expression, 25.9 vs 27.6 months

[41]

Naı¨ve

62

IHC

CXCR4

ORR: lower expression in responders PFS: longer with low expression

[42]

Naı¨ve

20

IHC

IL-8

ORR: lower expression in responders

[43]

Naı¨ve

67

IHC

HIF-2A

ORR: higher expression in responders OS: longer in high expression

[38]

IHC

PDGFR-B


IHC

VEGFR-3

PFS: longer with high expression

IHC

VEGF-A

OS: longer with low expression

mRNA

EGLN

OS: longer with low mRNA content

CAIX: Carbonic anhydrase IX; DSS: Disease-specific survival; HIF: Hypoxia-inducible factor; IHC: Immunohistochemistry; LDH: Lactate dehydrogenase; MMP: Matrix metalloproteinase; ORR: Objective response rate; OS: Overall survival; PD-L1: Programmed death-ligand 1; PFS: Progression-free survival; SNP: Single nucleotide polymorphism; TIMP: Tissue inhibitor of metalloproteinase; TKI: Tyrosine kinase inhibitor; TTP: Time to progression; VHL: von Hippel–Lindau.

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Table 1. Summary of predictive biomarkers in kidney cancer (cont.). Prior systemic treatment

n

Method

Biomarker

Outcome measures

Ref.

Cytokine (100%)

63

Blood

VEGF sVEGFR-2 sVEGFR-3

ORR: greater post-treatment change in responders

[26]

Naı¨ve (55%) Cytokine (45%)

42

Blood

VEGF

ORR + SD: lesser post-treatment change in responders PFS: lesser vs greater change, 367 vs 134 days

[27]

Naı¨ve (51%) Cytokine (52%) Cytokine and antiangiogenic (7%)

43

Blood

Ang-2

ORR: greater post-treatment change in responders

[28]

Cytokine (52%) Bevacizumab (100%)

61

Blood

sVEGFR-3

ORR: lower baseline levels in responders PFS: high vs low levels, 19.4 vs 36.7 weeks

[30]

Blood

VEGF-C

ORR: lower baseline levels in responders PFS: high vs low levels, 21.9 vs 46.1 weeks

Naı¨ve

33

Blood

sVEGFR-3

OS: high vs low baseline levels, 23.3 months vs not reached

Blood

IL-8

OS: high vs low baseline levels, 23.3 months vs not reached

Blood

VEGF

PFS: high vs low baseline levels, 4.7 vs 11.2 months

Blood

NGAL

PFS: high vs low baseline levels, 3.4 vs 8.2 months

Blood

TNF-a

ORR + SD: lower baseline levels in responders OS: high vs low levels, 7 vs 20 months

Blood

MMP-9

ORR + SD: lower baseline levels in responders TTP: high vs low levels, 3 vs 8 months


Sunitinib (cont.)

[32]

[29]

Naı¨ve (1%) Unspecified (99%)

85

Naı¨ve (17%) Cytokine (48%) Bevacizumab (19%) Unspecified (16%)

31

Naı¨ve

52

Blood

MMP-9 TIMP-2

ORR: higher baseline MMP-9/TIMP-2 ratio in non-responders

[34]

Naı¨ve

292

Blood

MMP-2

ORR: higher baseline levels in responders

[33]

Blood

Ang-2

ORR: lower baseline levels in responders

IHC

HIF-1A

PFS: high vs low expression, 30 vs 42 weeks

[31]

Naı¨ve (15%) Cytokine/other TKI (85%)

13

Blood

GM-CSF

ORR: higher baseline levels in responders Treatment received: 62% sunitinib, 8% sorafenib, 30% axitinib

[35]

Cytokine (100%)

23

IHC

VEGF

ORR: higher differential expression between tumor center and margins in responders

[45]

mRNA

VEGF

ORR: higher transcript expression of isoforms 121 and 165 in responders OS: longer with greater ratio of VEGF isoform 121 and165

Naı¨ve

44

miRNA

Naı¨ve

26

miRNA

Naı¨ve

20

miRNA

miRNA expression signatures predictive of PFS and OS using different models

[47]

miR-141

PFS: longer with high expression

[48]

miR-942 miR-628-5p miR-133a miR-484

TTP: Shorter with high expression OS: Shorter with high expression

[49]


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Review


n

Method

Biomarker

Outcome measures

Ref.


48

RNA

CXCL5 EFNA5 EMCN LAMB3 PLG PRAME RARRES1 SLC6A19

Eight-gene expression signature predictive of ORR and cancer-specific survival

[104]


123

DNA

VHL mutation

ORR: mutant vs wild-type, 52 vs 31% † Treatment received: 51% sunitinib, 23% sorafenib, 4% bevacizumab, 12% axitinib

[71]

Naı¨ve

101

SNP

VEGFR-3 rs307826

PFS: AA vs AG, 13.7 vs 3.6 months

[51]

SNP

VEGFR-3 rs307821

PFS: GG vs GT, 13.7 vs 6.7 months

SNP

VEGFR-2 rs1870377

OS: TT vs AA/AT, 9.4 vs 16.3 months

SNP

CYP3A5 rs776746

PFS: AG/AA vs GG, not reached vs 9.3 months

SNP

NR1I3 CAT haplotype

PFS: present vs absent, 8.0 vs 13.3 months

SNP

ABCB1 TCG haplotype

PFS: present vs absent, 15.2 vs 8.4 months

SNP

VEGF-A rs833061

PFS: CC/CT vs TT, 17 vs 4 months OS: CC/CT vs TT, 38 vs 10 months

SNP

VEGF-A rs2010963

PFS: GG vs CG vs CC, 18 vs 8 vs 2 months OS: GG vs CG vs CC, 31 vs 36 vs 9 months

SNP

VEGFR-3 rs68877011

PFS: CC vs CG, 12 vs 4 months OS: CC vs CG, 36 vs 17 months

SNP

ABCB1 rs1128503

PFS: TT vs CC/CT, 8 vs 19 months OS: TT vs CC/CT, 21 vs 34 months

SNP

NR1/2 rs2276707

PFS: TT vs CC/CT, 7 vs 18 months

SNP

NR1/3 rs4073054

PFS: TT vs GG/TG, 12 vs 21 months OS: TT vs GG/TG, 40 vs 47 months

SNP

NR1/3 rs2307424

OS: CC vs CT/TT, 42 vs 23 months

SNP

VEGFR-3 rs307821

PFS: GG vs GT/TT, 18 vs 10 months OS: GG vs GT/TT, 29 vs 34 months

SNP

VEGFR-3 rs307826

OS: AA vs AG/GG, 31 vs 22 months

SNP

FGFR-2 rs2981582

PFS: TT vs CC, 7.5 vs 14 months

SNP

VEGFR-1 rs9582036

PFS: CC vs AA/AC, 10 vs 18 months OS: CC vs AA/AC, 14 vs 31 months

SNP

VEGFR-1 rs9554320

PFS: AA vs CC/CA, 12 vs 21 months OS: AA vs CC/CA, 22 vs 34 months

SNP

VEGF rs3025039 VEGFR-2 rs2305948

OS: shorter with rs3025039-CC and rs2305948-GG combination genotype


Sunitinib (cont.)

Naı¨ve (59%) Cytokine (33%) Anti-angiogenic (3%) Both cytokine and anti-angiogenic (5%)

136

Naı¨ve

84

Cytokine (unknown %)

Naı¨ve (83%)

88

91

Cytokine (27%)

Naı¨ve (37%) Cytokine (48%) TKI (24%)

63

[50]

[52]

[54]

[55]

[53]



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n

Method

Biomarker

Outcome measures

Ref.

Cytokine (82%) Naı¨ve (18%)

708

Blood

VEGF

OS: longer with lower baseline levels PFS: high vs low, 73 vs 42% benefit from sorafenib over placebo

[57]

Naı¨ve

69

Blood

VEGF sVEGFR-2 Osteopontin colIV TRAIL CAIX

Signature positive: sorafenib vs sorafenib + IFN-a, PFS 7.7 vs 3.9 months Signature negative: sorafenib vs sorafenib + IFN-a, PFS 3.7 vs 11.1 months

[59]

Cytokine (unknown %)

94

IHC

CAIX

Tumor shrinkage: high vs low expression,

[61]

Naı¨ve

21

IHC

CAIX

ORR: high vs low expression, 62 vs 0%

[37]

Naı¨ve

40

IHC

pAKT

PFS: shorter with high expression OS: shorter with high expression

[60]

IHC

AKT

OS: shorter with low expression


Sorafenib

13 vs +9%

Cytokine (100%)

17

IHC

c-KIT

ORR + SD: positive vs negative, 75 vs 25% OS: positive vs negative, 92 vs 44 weeks

[63]

Cytokine (100%)

45

IHC

PDGFR-A

PFS: shorter with high expression

[62]

559

Blood

IL-6

PFS: high vs low baseline levels, 69 vs 45% benefit from pazopanib over placebo

[68]

Blood

IL-8

PFS: high vs low baseline levels, 31.3 vs 49.1 weeks

Blood

Osteopontin


Blood

HGF


Blood

TIMP-1


Pazopanib Cytokine (40%) Naı¨ve (60%)

TKI (100%) MTOR-inhibitor (71%) Cytokine (7%) Bevacizumab (8%)

28

Blood

HGF VEGF IL-6 IL-8 Soluble IL-2R

ORR: greater post-treatment decrease in responders

[70]

Cytokine (39%) Naı¨ve (61%)

397

SNP

IL-8 rs1126647

PFS: AA vs TT, 48 vs 27 weeks

[67]

SNP

IL-8 rs4073

PFS: TT vs AA, 49 vs 32 weeks

SNP

HIF-1A rs11549467

PFS: GG vs AG, 44 vs 20 weeks ORR: GG vs AG, 43 vs 30%

SNP

NR1I2 rs3814055

ORR: CC vs TT, 50 vs 37%

SNP

VEGF-A rs833061

ORR: TT vs CC, 51 vs 33%

SNP

VEGF-A rs699947

ORR: CC vs AA, 51 vs 33%

SNP

VEGF-A rs2010963

ORR: CC vs GG, 48 vs 35%

Blood

sVEGFR-2

ORR: greater vs lesser post-treatment decrease, 64.5 vs 37.5% PFS: greater vs lesser decrease, 12.9 vs 9.2 months

Axitinib Cytokine (100%)

64

[74]


636



Review


n

Method

Biomarker

Outcome measures

37

Protein

AMPK

PFS: longer with high expression OS: longer with high expression

Protein

CCNB1

PFS: shorter with high expression OS: shorter with high expression

Protein

Phospho-AKT Phospho-S6

OS: shorter with high expression

Protein

PTEN protein

OS: longer with high expression

Ref.

Bevacizumab


Naı¨ve

RNA

[78]

Cell cycle control and p53 signaling gene expression profiles correlated with PFS

Naı¨ve

384

Blood

VEGF-A

PFS (low baseline VEGF-A group): bevacizumab + IFN-a vs IFN-a alone, 12.9 vs 7.4 months

[77]

Naı¨ve

110

SNP

VEGFR-1 rs7993418 VEGFR-1 rs9554316 VEGFR-1 rs9513070

PFS: shorter for variant allele carriers in the bevacizumab group

[76]

Unknown

20

IHC

Phospho-S6

ORR: high vs low/intermediate expression, 36 vs 11% OS: high vs low expression, 17.3 vs 9.1 months

[82]

Naı¨ve

404

Blood

LDH

OS (high baseline LDH group): temsirolimus vs IFN-a, 6.9 vs 4.2 months

[80]

Temsirolimus


Phase III randomized controlled trials, sunitinib has been shown to improve both progression-free survival (PFS) as well as OS, compared with IFN-a [24,25]. Circulating biomarkers

In patients with metastatic ccRCC treated with sunitinib, plasma levels of VEGF, placental growth factor, sVCAM-1 increased post-treatment, levels of sVEGFR-2 and 3, PDGF, Ang-2 and sTie2 decreased, while those of sICAM-1 and vWF remained stable [26–28]. Significantly larger post-treatment changes in plasma levels of VEGF, sVEGFR-2, and sVEGFR-3 levels were observed in those exhibiting tumor response compared with those exhibiting disease stability or progression [26]. Using other outcome measures however, fold-increase in plasma VEGF was instead found to be significantly lower in patients that obtained clinical benefit (objective response and stable disease) as compared with patients that demonstrated disease progression [27]. Yet in another study, only reduction in circulating Ang-2 levels post-treatment was positively correlated with decrease in tumor burden. Otherwise, post-treatment change in plasma VEGF, sVCAM-1, and sTie-2 showed no correlation with sunitinib response [28]. Several biomarkers measured at baseline before treatment were correlated with treatment response or survival outcomes. Lower VEGF-A levels correlated with better outcome in some studies [29] but not others [27–31]. Lower levels of VEGF-C [30], informahealthcare.com

sVEGFR-3 [30,32], and IL-8 [32] were associated with superior outcomes. Lower Ang-2 and higher matrix metalloproteinase (MMP)-2 levels were associated with tumor response [33], whereas lower NGAL levels correlated with better PFS [29]. Levels of TNF-a and MMP-9 were significantly higher in nonresponders and significantly associated with reduced OS and time-to-progression, respectively [31]. A latter study demonstrated higher MMP-9 and lower tissue inhibitor of metalloproteinase (TIMP)-2 levels in responders, with a high MMP-9 to TIMP-2 ratio associated with PFS [34]. In a more recent study on patients receiving anti-angiogenic therapy (mostly sunitinib, as well as sorafenib and axitinib), plasma GM-CSF level was shown to be significantly higher in patients with response, compared with those with stable or progressive disease [35] sVEGFR-2, PDGF [27], placental growth factor [30], ICAM-1, stromal cell-derived factor 1, brain-derived neurotrophic factor [31], sICAM-1, vWF [28], MMP-2 and TIMP-1 [34] were all not correlated with outcome in response to sunitinib. Immunohistochemical biomarkers

In terms of protein expression on IHC, expression of CAIX, HIF-1a, pVEGFR-1, VEGFR-1, VEGFR-2, pPDGFR-A, pPDGFR-B, Ki67 and CD31 in primary tumors were significantly associated with response to sunitinib treatment [36]. In multivariate analysis, high CAIX expression showed positive 637

Review


Host biomarkers - SNPs Angiogenic factors VEGF 52,53,67 VEGFR -155,76/2 50,53/3 51,52,54 FGFR-2 54/HIF-1A67, IL-8 67

Tumor biomarkers

Drug metabolism CYP3A5 50 NR1/2 54,67, NR1/3 50,54


DNA VHL mutation71

Drug transporters ABCB150,54

RNA EGLN 38, VEGF 45 8 gene signature104: CXCL5, EFNA5, EMCN, LAMB3, PLG, PRAME, RARRES1, SLC6A19 miRNA signatures47–49 Cell cycle/p53 signaling profiles78

Circulating biomarkers Cytokines TNF -α31, TRAIL59, GM-CSF35 IL-668,70, IL-832,68,70, IL-2R70

Protein CAIX17–20,36,37,61, COX-220, PD-L121 HIF-1A33,36/2A38, Ki6736, CD3136 VEGF38,45, VEGFR-136/236,39–41/338 PDGFR-A36,62/B36,38 CXCR442, IL-843 AKT60,78, pS678,82 c-KIT63 AMPK78, CCNB178, PTEN78 Proteomic signatures22

Angiogenic factors VEGF23,26,27,29,30,57,59,70,77 VEGFR-226,59,74/326,30,32 CAIX59, HGF68,70 MMP-233, MMP-931,34 TIMP-168, TIMP-234 NGAL29, Fibronectin23, Ang-228,33 Osteopontin59,68, colIV59 Others LDH80

Figure 1. Predictive biomarker discovery in kidney cancer. Several biomarkers have been shown to correlate with therapeutic efficacy in kidney cancer. Generally, these biomarkers have been derived from tumor tissue (DNA, RNA and protein), circulating angiogenic factors and/or cytokine factors, as well as from host single nucleotide polymorphisms (SNPs). CAIX: Carbonic anhydrase IX; HIF: Hypoxia-inducible factor; LDH: Lactate dehydrogenase; MMP: Matrix metalloproteinase; TIMP: Tissue inhibitor of metalloproteinase; VHL: von Hippel–Lindau.

correlation with OS [36]. The correlation of tumor response with high CAIX expression was observed in another retrospective study [37], but unfortunately could not be validated in a prospective cohort [38]. High HIF-1a expression correlated with longer PFS in one study [33] but the association with tumor response or survival outcome was not observed by others [37,38]. High expression of HIF-2a was shown to be associated with better objective response and longer OS [38]. The potential predictive value of VEGFR-2 was also demonstrated in other studies [39,40]. Terakawa et al. [39] showed that strong VEGFR-2 expression correlated with improved PFS, whereas You et al. [40] showed that VEGFR-2 expression was associated significantly with initial and best overall responses to sunitinib. del Puerto-Nevado et al. [41], however, showed that tumor stromal expression of pVEGFR-2, but not VEGFR-2, correlated with PFS and OS. High VEGFR-3, PDGFR-B and VEGF-A expression were associated with longer PFS, improved response and shorter OS, respectively [38], but no correlation with response or survival outcome was seen in several other studies [36,39–41]. In other studies, high CXCR4 [42] and high IL-8 expression [43] correlated with poor response. No correlation with any outcome was found for VEGFR-1 [38,39]. 638

Similarly, no predictive value of PDGFR-A and CD31 were suggested in other studies [39,41]. In another exploratory study, none of the biomarkers (CD31, FGF-2, MET, pS6K, CD3, CD45, FOXP3, PDL-1) examined via IHC predicted early disease progression [44]. Genetic biomarkers

Paule et al. [45] analyzed a transcript panel consisting of VEGF isoforms 121, 165 and 189, VEGFR-1, VEGFR-2, PDGF-A, PDGF-B, PDGFR-A, PDGFR-B, FGF-2, HIF-1A, CXCR4, uPA, uPA-R, PAI-1, LYVE-1. Among transcripts analyzed, only the levels of VEGF isoforms 121 and 165 were associated with response to sunitinib. Furthermore, a greater ratio of VEGF isoforms 121 to 165 was significantly associated with improved OS. One study demonstrated that in such patients, high EGLN3 mRNA content may be associated with shorter OS [38]. miRNA are a class of small non-coding RNAs involved in multiple biological processes, and their abnormal expression has been implicated in oncogenesis. It has been suggested that oncogenic miRNAs may contribute to regulation of RCC biology, possibly through their effect on cancer stem cells, and specific miRNA signatures may be useful biomarkers to guide diagnosis, Expert Rev. Mol. Diagn. 15(5), (2015)



Review

risk assessment and therapy [46]. However, there is rather limited information about the role of miRNAs as predictive biomarkers of treatment efficacy. In a prospective study, Ga´mez-Pozo et al. [47] demonstrated that miRNA expression signatures, measured in peripheral blood, may stratify patients with advanced RCC according to their survival response to first-line therapy with sunitinib. In other studies, miR-141 was significantly downregulated in tumors of poor responders to sunitinib, as compared with good responders (defined as progressive disease not within 6 months after initiating therapy) [48]. High expression levels of miR-942, miR-628-5p, miR-133a and miR-484 were significantly associated with decreased time to progression and OS [49].

independent retrospective validation cohort of patients with metastatic ccRCC. Both PFS and OS were associated significantly with VEGFR-3 rs307821, ABCB1 rs1128503 and NR1/3 rs4073054. PFS alone was associated with FGFR-2 rs2981582 and NR1/2 rs2276707, whereas OS alone was associated with VEGFR-3 rs307826 and NR1/3 rs2307424. SNPs for ABCB1 (rs1045642, rs2032582), IL-8 (rs4073, rs1126647), CYP3A5 rs776746, NR1/ 2 rs3814055, NR1/3 rs2307418, HIF-1A rs11549467, PGDFR-A rs35597368 and VEGFR-2 rs1870377 were not found to correlate with outcome in this study. In another study, they also showed that VEGFR-1 rs9582036 and rs9554320 (but not rs7993418, rs9554316, rs9513070) were correlated with PFS and OR [55].

SNPs

Sorafenib Circulating biomarkers

In a retrospective multi-center cohort study, van der Veldt et al. examined 30 SNPs in 11 genes, including ABCB1 (rs1045642, rs1128503, rs2032582), ABCG2 (rs2231142, rs2231137, rs2622604, -15622C/T), NR1I2 (rs3814055, rs2276706, rs6785049, rs2276707, rs1054190, rs1054191), NR1I3 (rs2307424, rs2307418, rs4073054), VEGFR-2 (rs2071559, rs1531289, rs7692791, rs2305948, rs1870377), VEGFR-3 rs307826, PDGFR-A (rs35597368, rs1800810, rs1800813, rs1800812), FLT-3 rs1933437, CYP1A1 rs1048943, CYP1A2 rs762551 and CYP3A5 rs776746. The study reported prolonged PFS in association with CYP3A5 rs776746 polymorphism, as well as with NR1I3 CAT and ABCB1 TCG haplotypes, whereas the VEGFR-2 rs1870377 polymorphism was shown to be associated with OS [50]. In a prospective study on patients with treatment-naive metastatic ccRCC receiving sunitinib, 16 SNPs in nine genes were investigated, including VEGFR-2 (rs2305948, rs1870377), VEGFR-3 (rs307826, rs448012, rs307821), PDGFR-A (rs35597368), VEGF-A (rs2010963, rs699947, rs1570360), IL-8 (rs1126647), CYP3A4 (rs2740574), CYP3A5 (rs776746), ABCB1 (rs1045642, rs1128503, rs2032582) and ABCB2 (rs2231142). Two missense polymorphisms in VEGFR-3 (rs307826 and rs307821) were associated with reduced PFS but not OS [51]. Scartozzi et al. [52] investigated several SNPs of VEGF-A (rs2010963, rs25648, rs3025039, rs699947, rs833061), VEGF-C (rs4604006, rs7664413), VEGFR-1 (rs664393, rs7993418), VEGFR-2 (rs1870377, rs2071559, rs2305948, rs7667298) and VEGFR-3 (rs307805, rs6877011, rs307822). They showed that VEGF-A (rs833061, rs2010963) and VEGFR-3 rs68877011 were significantly correlated with PFS, whereas VEGF-A rs833061 and VEGFR-3 rs68877011 were independent factors in OS. Kim et al. [53] showed that the combination of VEGF rs3025039 and VEGFR-2 2305948 genotypes was found to be associated with OS. Individual SNPs for VEGF (rs699947, rs35569394, rs833061, rs1570360, rs2010963, rs3025039) and VEGFR-2 (rs2305948, rs1870377), however, were not found to correlate with clinical outcome. No significant correlation with any outcome between VEGF-A (rs699947 and rs1570360) and VEGFR-3 (rs448012, rs307826 and rs307821) was demonstrated in one study [33]. Beuselinck et al. [54] attempted to replicate the association of 16 key SNPs in 10 genes with sunitinib outcome in an informahealthcare.com

Sorafenib is an orally active multi-kinase inhibitor with effects on tumor-cell proliferation and angiogenesis. In the TARGET Phase III study of second-line treatment with sorafenib in patients with metastatic cytokine-refractory ccRCC [56], baseline pre-treatment serum VEGF level was evaluated as a potential molecular biomarker. In this study, sorafenib was favorable in terms of objective response and PFS. VEGF levels were found to be inversely correlated with both PFS and OS. In addition, although benefit from sorafenib was found in patients with both high and low VEGF levels, exploratory analysis suggested that those with higher levels above the 75th percentile derived more benefit from sorafenib over placebo [57]. Within the same cohort, similar analyses of baseline pre-treatment plasma levels of sVEGFR-2, CAIX, TIMP-1 and Ras p21, as well as their magnitude of change post-treatment, however, revealed no predictive relation with sorafenib benefit [58]. In patients with metastatic ccRCC, who were randomized between first-line sorafenib alone or in combination with IFN-a therapy in a Phase II trial, Zurita et al. [59] screened a total of 52 plasma cytokine and angiogenic factors and identified a predictive sixmarker signature (osteopontin, VEGF, CAIX, collagen IV, sVEGFR-2 and TRAIL) that correlated with PFS benefit from sorafenib treatment alone, whereas those without the signature may instead benefit from combination therapy with IFN-a. Immunohistochemical biomarkers

In subsequent analyses, elevated phospho-AKT expression on IHC correlated inversely with PFS and OS [60]. In retrospective studies, high tumor CAIX expression on IHC correlated with superior response in patients with metastatic ccRCC treated with sorafenib [37,61], whereas high PDGFR-A expression correlated with shorter PFS [62]. In patients with metastatic sarcomatoid RCC treated with sunitinib following failure of cytokine therapy, positive c-Kit expression was associated with superior disease control rate and OS [63]. Pazopanib

Pazopanib is a multi-targeted TKI which efficacy in both cytokine-pretreated and treatment-naı¨ve patients with locally advanced or metastatic ccRCC has been demonstrated in the 639

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VEG105192 Phase III study. Compared with placebo, pazopanib was superior in terms of PFS and response rates [64,65]. Using patient samples derived from this cohort as well as from an earlier Phase II study [66], several exploratory biomarker studies were performed [67–69].


SNPs

Xu et al. evaluated 27 functional polymorphisms within 13 genes and showed that pazopanib was more effective in terms of objective response and PFS for carriers of wild-type HIF-1A rs11549467 GG genotype than in those with variant AG genotype. Individuals with wild-type IL-8 rs1126647 or rs4073 genotype demonstrated superior PFS than if they carried variants. Conversely, improved response was observed in patients carrying variant VEGF-A rs699947 CC, rs833061 TT or rs2010963 CC, as well as wild-type NR1I2 rs3814055 CC genotypes [67]. Circulating biomarkers

Using a stepwise approach to plasma cytokine and angiogenic factor profiling, Tran et al. [68] demonstrated that pre-treatment IL-8, osteopontin, HGF and TIMP-1 levels were inversely associated with PFS, and high IL-6 concentrations were predictive of greater relative PFS benefit from pazopanib compared with placebo. In patients receiving third-line pazopanib, responders had lower post-treatment levels of HGF, VEGF, IL-6, IL-8 and soluble IL-2R compared with non-responders [70]. Pre-treatment levels of these plasma proteins did not correlate with tumor response. Immunohistochemical & genetic biomarkers

Choueiri et al. [69] showed that mutation or methylation status of VHL, immunohistochemical expression of HIF-1a and HIF-2a, as well as HIF-1A transcriptional signatures were not correlated with objective response or PFS to pazopanib. This is in contrast to their earlier report on patients with metastatic ccRCC who received anti-angiogenic therapy with sunitinib, sorafenib, axitinib or bevacizumab, in which the presence of VHL loss of function mutations (frameshift, nonsense, splice and in-frame deletions/insertions) was an independent predictor of treatment response (52% in mutant compared with 31% in wild-type VHL) [71]. In another exploratory study, none of the biomarkers (CD31, FGF-2, MET, pS6K, CD3, CD45, FOXP3, PDL-1) examined via IHC predicted early disease progression [44]. Axitinib

Axitinib is an oral small molecule TKI with potent and selective inhibitory effects on VEGFR-1, 2 and 3. The AXIS Phase III study evaluated axitinib and sorafenib in the secondline treatment of metastatic ccRCC and demonstrated a significant PFS benefit with axitinib (8.3 vs 5.7 months) [72,73]. In a Phase II study in Japanese patients with cytokine-refractory metastatic ccRCC, levels of sVEGFR-1, 2 and 3, as well as sKIT were shown to decrease during axitinib therapy, whereas levels of VEGF increased. Only the change of sVEGFR-2 levels correlated with clinical activity of axitinib. As compared to 640

those with smaller decreases, patients with greater decrement in plasma sVEGFR-2 displayed significantly higher objective response rates (64.5 vs 37.5%) and longer PFS (12.9 vs 9.2 months) to axitinib therapy [74]. Bevacizumab SNPs

Bevacizumab is a recombinant humanized monoclonal antibody that blocks angiogenesis by inhibiting VEGF-A. The AVOREN Phase III study compared patients with previously untreated metastatic ccRCC who received IFN-a with bevacizumab or placebo, and demonstrated an additional 4.8 months PFS benefit with the addition of bevacizumab [75]. Using patient DNA from the AVOREN study, Lambrechts et al. [76] investigated 138 SNPs in the VEGF pathway and showed that the VEGFR-1 rs7993418, rs9554316 and rs9513070 SNPs correlated significantly with PFS within the bevacizumab group. SNPs of PLGF, VEGF A-D, VEGFR-2, HIF-1A, HIF-2A, FIH-1, VHL, EGLN 1-3 were not shown to impact on survival. In another cohort of patients derived from the AVOREN study, pre-treatment plasma VEGF-A levels showed an inverse prognostic correlation in terms of OS. Specifically, within the patient group with low VEGF-A levels, PFS was 12.9 months with the addition of bevacizumab, compared with 7.4 months with treatment using IFN-a alone [77]. Protein & genetic biomarkers

In a Phase II trial consisting of 37 metastatic ccRCC patients on bevacizumab therapy with or without erlotinib, nephrectomy material was used for reverse-phase protein array profiling and microarray-based gene expression profiling. Among 36 candidate proteins, high AMPK protein levels were associated with longer OS and PFS, whereas high CCNB1 levels were associated with shorter OS and PFS. Phospho-AKT and phospho-S6 expression levels were inversely correlated with OS, whereas PTEN expression levels were positively correlated with OS. Differential expression of several genes involved in cell cycle control and p53 signaling were also associated with PFS. These results, however, were mainly exploratory in view of the small patient cohort and heterogeneous treatment arms [78]. mTOR inhibitors

Temsirolimus is a specific inhibitor of the mTOR kinase that has an established role in treatment-naive metastatic RCC patients with poor prognosis. Based on the results of the pivotal ARCC Phase III study against IFN-a, temsirolimus was shown to improve both PFS (5.5 vs 3.1 months) and OS (10.9 vs 7.3 months) [79]. In a retrospective analysis of results from the ARCC trial, elevated pre-treatment serum lactate dehydrogenase (LDH) levels was a significant marker of poor prognosis. Interestingly, OS benefit from first-line treatment with temsirolimus relative to IFN-a (6.9 vs 4.2 months) was observed only in patients with high pre-treatment serum LDH levels, but not in those with normal LDH levels, supporting its role as both a prognostic and predictive biomarker [80]. Analysis Expert Rev. Mol. Diagn. 15(5), (2015)



of baseline PTEN and HIF-1 levels on IHC, however, did not predict differential response to temsirolimus over IFN-a [81]. In an earlier study, Cho et al. [82] performed an exploratory analysis of 20 patients who had participated in a randomized Phase II study evaluating temsirolimus and showed that phospho-S6 expression was positively correlated with response and OS benefit to temsirolimus. Phospho-AKT, CAIX and PTEN expression, as well as VHL mutational status, were not found to be potentially predictive. Future predictive molecular biomarkers Clinical application of gene expression profiling

Through the use of microarray technology, earlier studies identified detailed, reproducible gene expression patterns that showed robust correlation with the major histopathological subtypes of RCC [83–86]. Several reports also suggested that at least two molecularly distinct forms of ccRCC existed and that gene expression profiling may aid in identifying patient groups with divergent prognostic outcomes [87–93]. Recently, the discovery of novel prognostic gene expression signatures using systematic unbiased approaches and clinically applicable platforms, followed by their validation in large retrospective cohorts across geographical boundaries, has reinvigorated the development of gene expression-based risk prediction tools. Brannon et al. [94,95] identified two subtypes of ccRCC (ccA and ccB) using 110 genes of divergent biological pathways and showed in a retrospective cohort, that this inherent molecular profile was predictive of survival outcome independently of clinical predictors including stage, grade and performance status. Importantly, distinct from other geneexpression-based profiles [96–101], the ccB signature was the only independent prognostic biomarker for the prediction of poor CSS when validated in The Cancer Genome Atlas consortium cohort of 350 ccRCC patients [102]. To adapt this classification to the clinical setting, Brooks et al. [103] derived ClearCode34 – a 34-gene risk predictor, from the ccA/ccB profile and showed that this simplified tool was able to stratify localized ccRCC into good and poor risk subtypes, correlating with recurrence-free survival, CSS and OS. None of these gene expression signatures however had been demonstrated to be predictive biomarkers. Recently, Choudhury et al. [104] developed an eight gene (CXCL5, EFNA5, EMCN, LAMB3, PLG, PRAME, RARRES1 and SLC6A19) quantitative PCR assay for the classification of localized ccRCC into two subtypes prognostic for CSS using formalin-fixed tissue. This practical assay was validated both internally and externally in large retrospective cohorts. In addition, in patients with metastatic ccRCC, this assay may be predictive of clinical benefit from TKI therapy, with a threefold improvement for CSS in the subgroup with good prognosis [104]. The predictive effect on survival outcome continued to hold for patients receiving TKI in first-line setting and when survival time from initiation of TKI treatment was considered, as well as after patient stratification by Memorial SloanKettering Cancer Center risk criteria. Taken together, these results encourage the development of simplified and convenient informahealthcare.com

Review

molecular diagnostic tools based on gene expression profiles that may aid clinical decision making. Future immunotherapy

In the contemporary palliative treatment of metastatic RCC, targeted agents have largely replaced cytokine therapy as the standard of care. Although overall response rates using targeted agents are generally slightly higher than that with IL-2, complete responses remain uncommon, and unlike IL-2 therapy, durable remissions are the exception [105,106]. As immunotherapy with high dose IL-2 is the only form of treatment known to produce durable responses, it remains a viable option for selected patients with excellent performance status. The modulation of T-cell activation and immune signaling via monoclonal antibody blockade of cytotoxic T-lymphocyteassociated protein 4, programmed death 1 (PD-1), and its ligand (PD-L1) have most recently been in the limelight as a form of ‘targeted’ immunotherapy [107]. Unlike the relatively more toxic predecessor IL-2, these novel agents are more tolerable, and have been demonstrated to induce responses in heavily pre-treated RCC [108]. Thompson et al. [109,110] demonstrated that two-thirds of ccRCC had PD-L1 expression and those with high expression had worse CSS. More recently, increased PD-L1 expression was associated with shorter OS in patients receiving TKIs [111]. With the discovery of PD-1 or PD-L1 inhibitors, tumor PD-1 or PD-L1 expression may predict favorable response to these novel agents, and in turn dramatically alter the prognostic outlook for this group of aggressive cancers. However, as encouraging as these emerging biomarkers may seem, their clinical utility still awaits the development of standardized and prospectively validated assays. An earlier Phase I study in cancers including metastatic RCC, advanced melanoma, non– small-cell lung cancer and colorectal cancer have demonstrated that response to the PD-1 inhibitor nivolumab does not always corroborate with PD-L1 expression [108]. Several technical issues, such as defining the appropriate cut-off for expression, or selecting the cell type to measure expression (whether tumor or infiltrating inflammatory cells), remains to be resolved. In a recent randomized Phase II trial that evaluated nivolumab in patients with metastatic RCC similarly demonstrated modest objective response rates of approximately 20%. In addition, although response to treatment was higher in patients with greater PD-L1 expression at 5% cut-off, those with lower PD-L1 expression also showed significant responses [112]. Predictive biomarkers in non-ccRCC subtypes

Thus far, the predictive molecular biomarkers described in this review are derived predominantly from studies on patient with ccRCC, unless otherwise stated. Apart from a small study on patients with metastatic sarcomatoid RCC treated with sunitinib demonstrating an association of positive c-Kit expression with superior disease control rate and OS [63], only one other study on papillary RCC was published. In this Phase II trial on patients with papillary RCC receiving foretinib, a multi-kinase inhibitor targeting MET and VEGFR-2, individuals with germline MET 641

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mutations were significantly more likely to experience a partial response than those without (50 vs 9%) [113]. Future studies will need to be performed on uncommon subtypes of RCC to uncover predictive biomarkers specific to them.


Expert commentary

We have summarized the results obtained from studies of biomarkers predictive of cytokine therapy and targeted agents in metastatic RCC. At a glance, results seem inconclusive, and none of the predictive biomarkers examined to date have been reliably validated in large prospective studies, several of which are ongoing. Logistical issues surrounding biomarker development have been succinctly summarized previously [114]. Challenges with tissue collection, handling and storage, problems with gathering clinical data, as well as assessment of laboratory data may all contribute to conflicting results. As has been recently suggested for markers for bone turnover, in the use of biomarkers for longitudinal follow-up and monitoring, the concept of ‘critical difference’ or ‘least significant change’ – defined as the smallest change in consecutive results within the same patient which is not due to chance, must be reached before asserting the difference between the two results is clinically significant [115,116]. Such variables must be clearly defined before biomarkers may be used as reliable companion diagnostic tools. Contributions to the inconsistencies may also be due to differences in methodologies or the presence of intratumor genomic heterogeneity. The latter has been demonstrated using spatially separated samples from primary and metastatic tissue [117], as well as with single cells obtained from tumor and adjacent normal kidney [118]. Nonetheless, given that metastatic RCC remains one of two solid tumor types for which durable

complete response and apparent cure can be achieved with high dose IL-2 therapy for metastatic RCC, it remains a key model for predictive biomarker research. The discovery of these biomarkers amidst the complex molecular landscape of renal cancer will be the challenge as we move toward next generation diagnostics and therapeutics. Five-year view

In the 5 years to come, current molecular biomarkers in RCC will require additional development to demonstrate utility in the clinical setting. The correlation of several SNPs with therapeutic outcomes has been reproduced, but it is not clear that the effect size, sensitivity or specificity is sufficiently high to justify routine clinical utilization. Circulating molecular biomarkers are certainly of interest, especially with the advent of next-generation sequencing. Multi-gene quantitative PCR assays for predicting response to TKI will also require additional validation. Future treatment selection strategies should be expected to be based on predictive models built upon both clinical variables as well as molecular biomarkers. Financial & competing interests disclosure

M-H Tan and Y Choudhury have filed for patents for molecular diagnostics in renal cell carcinoma. M-H Tan has received research funding from Pfizer. This work is supported by the Institute of Bioengineering and Nanotechnology, Biomedical Research Council (Diagnostics Grant), and Agency for Science, Technology and Research, Singapore. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Key issues .

Renal cell carcinoma is a heterogeneous disease with unpredictable clinico-pathological behavior.

.

Conventional treatment options with IL-2, anti-angiogenic agents or mTOR inhibitors are limited by efficacy and/or toxicity issues.

.

In contemporary clinical practice, no predictive biomarker exists to rationally guide individual therapy.

.

Several putative biomarkers have been shown to correlate with therapeutic efficacy, although none have been rigorously validated.

.

Recently, clinically applicable PCR-based assays have identified a practical number of genes capable of risk-stratifying patients with renal cell carcinoma, and in addition, may predict their clinical benefit from tyrosine kinase inhibitors.

.

These results encourage the development of simplified and convenient molecular diagnostic toolkits that may aid routine clinical decision making.

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