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Potential Role of Genetic Markers in the Management of Kidney Cancer
EURURO-4776; No. of Pages 8 EUROPEAN UROLOGY XXX (2012) XXX–XXX
available at www.sciencedirect.com journal homepage: www.europeanurology.com
Collaborative Review – Kidney Cancer
Potential Role of Genetic Markers in the Management of Kidney Cancer Kerstin Junker a,*, Vincenzo Ficarra b, Eugene D. Kwon c,d, Bradley C. Leibovich c, R. Houston Thompson c, Egbert Oosterwijk e a
Clinic of Urology and Pediatric Urology, Saarland University Medical Center and Saarland University Faculty of Medicine, Homburg, Germany; b Department
of Oncological and Surgical Sciences, Urologic Unit, University of Padua, Padua, Italy; c Department of Urology, Mayo Clinic College of Medicine, Rochester, MN, USA; d Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA; e Experimental Urology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
Article info
Abstract
Article history: Accepted September 14, 2012 Published online ahead of print on September 24, 2012
Context: Kidney cancer is not a single entity but comprises a number of different types of cancer that occur in the kidney including renal cell tumours as the most common type. Four major renal cell tumour subtypes can be distinguished based on morphologic and genetic characteristics. To individualise therapy and to improve the prognosis in patients with renal cell tumours, accurate subtyping, definition of individual course of disease, and the prediction of therapy response are necessary. Objective: To discuss the potential role of genetic markers in the management of kidney cancer. Evidence acquisition: A Medline search was conducted to identify original articles, review articles, and editorials addressing the role of genetic alterations in kidney cancer management. Keywords included kidney neoplasms, genetics, SNP, gene expression, miRNA, classification, diagnosis, drug therapy, prognosis, and therapy. The articles with the highest level of evidence were identified and critically reviewed. This review is the result of an interactive peer-reviewing process by an expert panel of co-authors. Evidence synthesis: Each subtype is characterised by specific genetic, epigenetic, and expression patterns that potentially can be used to subclassify renal cell tumours in cases of ambivalent histopathology. Molecular signatures and single alterations in primary tumours are associated with aggressiveness and prognosis. Germline polymorphisms in specific genes encoding for metabolizing enzymes, efflux transporters, and drug targets seem to be associated with toxicity and response in patients receiving targeted therapy. Conclusions: Significant advances have been achieved in the molecular analysis of renal cancer. Validation of findings is greatly needed to implement genetic markers in the management of renal cancer. This should lead to improved diagnosis, prognosis, and personalised therapy in this heterogeneous disease. # 2012 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Keywords: Kidney cancer Genetic markers Gene expression
* Corresponding author. Clinic of Urology and Pediatric Urology, Saarland University Medical Center and Saarland University Faculty of Medicine, Kirrbergerstrasse, 66424 Homburg/Saar, Germany. Tel. +49 6841 1614734; Fax: +49 6841 1624795. E-mail address: [email protected] (K. Junker).
1.
Introduction
Renal tumours account for 2–3% of all malignant diseases in adults. The vast majority of renal cancers (>90%) are various forms of renal cell carcinoma (RCC) originating from
different parts of the nephron. RCC is the seventh most common cancer in men and the ninth most common in women. Incidence worldwide is about 209 000 new cases per year with 102 000 deaths per year [1]. The incidence of all stages of this cancer has increased over the
0302-2838/$ – see back matter # 2012 European Association of Urology. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eururo.2012.09.040
Please cite this article in press as: Junker K, et al. Potential Role of Genetic Markers in the Management of Kidney Cancer. Eur Urol (2012), http://dx.doi.org/10.1016/j.eururo.2012.09.040
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Table 1 – Hereditary syndromes of kidney cancer: chromosomal localisation, affected genes and pathways, and clinical features Syndrome
Chromosome
Gene/protein
Potential pathway
Birt-Hogg-Dube´
17p
Folliculin
mTOR
Hereditary leiomyomatosis renal cell carcinoma Hereditary papillary renal carcinoma Tuberous sclerosis
1q
Krebs cycle/HIF1
7q
Fumarate hydratase c-Met
9q/16p
TSC1/TSC2
Hepatocyte growth factor/MET mTOR
Von Hippel-Lindau
3p
VHL
HIF1
Clinical features Chromophobe, oncocytic, hybrid, ccRCC, pulmonary cysts/pneumothorax, fibrofolliculoma Type II papillary RCC; uterine and skin leiomyoma Multifocal, bilateral type 1 papillary RCC Bilateral, multifocal angiomyolipoma, ccRCC, seizures, mental retardation, angiofibroma, cardiac rhabdomyoma, retinal hamartoma ccRCC, hemangioblastoma, retinal angioma, pheochromocytoma
mTOR = mammalian target of rapamycin; ccRCC = clear cell renal cell carcinoma; HIF1 = hypoxia inducible factor 1; RCC = renal cell carcinoma.
last several years. RCCs can be subdivided based on their morphologic appearance [2]. Genetic analyses have shown that these subtypes are characterised by different chromosomal alterations supporting the idea that each subtype represent a distinct tumour entity with a different tumour biology. Several autosomal dominant syndromes characterised by the development of RCC have been described that has greatly aided in the identification of the genes involved in the development of RCC (Table 1). Seven different kidney cancer genes have been identified thus far. Despite the clear demonstration that different histologic subtypes of RCC demonstrate unique pathogenesis and genetic alterations, the impact of histology on prognosis remains controversial. In view of specific genomic alterations associated with a specific RCC subtype, implementation of genetic markers in the management of RCC might become possible and helpful in the near future like they are in other tumour entities including breast, colorectal, and lung cancer. 2.
Evidence acquisition
A Medline search was conducted to identify original articles, review articles, and editorials addressing genetic alterations in kidney cancer concerning diagnosis, prognostic evaluation, and prediction of therapy response. Keywords included kidney neoplasms, genetics, SNP, gene expression, miRNA, classification, diagnosis, drug therapy, prognosis, and therapy. Links to related articles and cross-reading of citations in related articles were surveyed. This review is the result of an interactive peer-reviewing process by the panel of co-authors. 3.
Evidence synthesis
3.1.
Diagnosis
3.1.1.
Genomic alterations
various syndromes is beyond the scope of this review, but Table 1 shows the clinical and molecular characteristics of currently well-characterised hereditary RCC syndromes. 3.1.1.2. Sporadic renal cell carcinoma. Specific chromosomal aberrations have been well documented in the various recognised renal cancer subtypes (see Table 2). The most common RCC type is clear cell RCC (ccRCC), characterised by a loss of 3p in >90% of cases [3,4]. In 1993 the VHL tumour suppressor gene involved in the development of ccRCC was identified and is localised on 3p25-p26 [5]. Biallelic inactivation, for example, by loss of one 3p arm and mutations on the second chromosome 3, is a key event in the tumour development of ccRCC. In addition, gain of 5q and losses of 6p, 9, 10, 14, and 18 occur frequently in ccRCC [3,4]. These losses are often associated with tumour progression. Gains of chromosomes 7 and 17 are typical features of papillary RCC (pRCC) [6]. Additional gains of chromosomes 3, 12, 16, and 20 are more frequent in tumour progression. Over the last 10 yr, it was hypothesised that two distinct types of pRCC exist with different histopathologic features and chromosomal aberrations. Gains of chromosomes 7 and 17 occur in both pRCC subtypes, whereas loss of chromosomes 3p, 6q, and 9p, as well as gains of 1q, 2, and 8q, can be found more often or exclusively in type 2 pRCC [7–9]. However, clear histopathologic criteria have to be defined, and further genetic studies are necessary in correlation with histopathologic as well as clinical data. Chromophobe RCC (chRCC) is characterised by the loss of chromosomes 1, 2, 6, 10, 13, 17, and 21. Oncocytomas, a benign tumour entity, are characterised by the loss of chromosome 1 in most cases
Table 2 – Most frequent subtypes of renal cell tumours: frequency and chromosomal alterations Subtype
3.1.1.1. Hereditary renal cell carcinoma. Although hereditary forms of RCC represent <5% of cases, they have played a pivotal role in understanding and characterising the molecular pathways involved in sporadic RCC. Each hereditary syndrome is characterised by a unique molecular alteration, and therefore genetic analysis can be performed to confirm a differential diagnosis. A detailed synopsis of the
Clear cell RCC Papillary RCC Chromophobe RCC Oncocytoma
Frequency, % 70–75 10–15 5–7 5
Chromosomal alterations S3p, +5q, S9, S10, S14 +7, +17, +3, +12, +20 Combination of S1, S2, S6, S10, S13, S17, S21 S1, S14, t(11,n)
RCC = renal cell carcinoma. Bold type indicates most frequent alterations.
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or by translocations involving chromosome 11. There are fewer published reviews on rare forms of renal tumours such as Bellini duct carcinomas and so-called TFE3translocation tumours. The current understanding of chromosomal alterations was derived from classical karyotyping and comparative genomic hybridisation (CGH). In the last several years, array-based single nucleotide polymorphism (SNP) techniques have allowed a more detailed analysis of copy number alterations and the loss of heterogeneity. Most studies confirmed the genetic patterns of each RCC subtype [10,11]. However, only a few studies examined the smallest overlapping regions as identified by CGH or genes commonly altered in RCC. Based on the known genetic alterations of different subtypes, it is possible to classify these tumours in cases when histopathologic features are not clear. For this purpose, fluorescent in situ hybridization (FISH) has been investigated as a tool for routine diagnosis and in determining histologic subtype [12,13]. Using centromere probes for chromosomes 1, 2, 6, 9, 7, and 17 as well as region-specific probes for chromosome 3p on isolated cell nuclei, Sanjmyatav et al. [12] were able to classify RCCs with high accuracy. Brunelli et al. [14], who used paraffin sections, obtained similar results. One especially clinically relevant application would be the ability of FISH to differentiate between chRCCs and oncocytomas [14]. However, based on an analysis of 73 oncocytomas and 20 chRCCs, Dvorakova et al. concluded that FISH cannot be used as a differential diagnostic modality due to overlapping genetic alterations [15]. Further studies need to address the relevance of specific chromosomal losses in a lower percentage of analysed cells for the diagnosis and prognosis of patients with oncocytomas. Histopathologic classification on small samples is very challenging, and genetic analysis may be able to improve the diagnosis of biopsies. CGH analyses of ex vivo fineneedle aspiration biopsies reached a correct diagnosis in 75% of the investigated samples [16]. A very high diagnostic accuracy was achieved in ccRCC (93.5%) and pRCC (100%). ChRCC was correctly diagnosed in 62.5%. The lower rate could be the result of hyperploid karyotypes leading to the misinterpretations of gains and losses by CGH. Similar to the differential diagnosis of larger specimens, application of FISH may be of interest. Two studies demonstrated that FISH on core biopsies can aid in the differential diagnosis of RCC. The accuracy on postoperatively obtained samples was 94% using FISH and 86% by histology alone [17]. Chyhrai et al. performed FISH on preoperatively obtained biopsies and reached 95.5% diagnostic fidelity compared with 90.5% by histology alone [18]. These results suggest that genetic analysis and especially multicolour FISH (mFISH) can help to improve diagnostics in uncertain cases. It can also help to identify rare RCC subtypes like TFE3-translocation tumours that are often overlooked because of similarities with ccRCC or pRCC [19,20]. Although mFISH appears to offer the potential to improve diagnostic accuracy in RCC, standardised technical procedures and defined cut-off values need to be
3
established before mFISH can be implemented as a standard diagnostic procedure. 3.1.2.
Gene expression studies
A global view of the gene expression patterns and deregulated pathways may provide a more accurate picture of renal cancer including its clinical behaviour. However, the function and role of most of these genes in tumour development are unknown, and some may even be bystander genes that play no role in tumourigenesis. Nevertheless, these signatures may serve as effective biomarkers because of their unique differential expression patterns. 3.1.2.1. Gene expression profiling. Several expression profiling studies have reported the ability of gene expression patterns to distinguish between histologic subtypes of RCC, such as conventional ccRCCs, papillary type 1 and type 2 carcinomas, chromophobe carcinomas, oncocytomas, and urothelial carcinoma of the renal pelvis [21–24]. The findings overlap with the results obtained with CGH and FISH. More interestingly, the outcome of the gene expression profiling studies has identified a number of new potential diagnostic markers. Examples are glutathione S-transferase a highly expressed in ccRCC18 and a-methylacyl-CoA racemase in pRCC [25,26]. Their discriminatory power has been confirmed in a number of independent studies (eg, Allory et al. [27]), and as such are useful in the differential diagnosis of RCC. Substantial efforts have been made to find prognostic biomarkers with the aid of expression profiling. Although the outcome differs substantially from the general one-gene one-biomarker approach, implementation of multiplex biomarkers has not been achieved. Independent replication of microarray-derived predictive gene signatures has also proven to be difficult. One aspect that needs attention is interethnic variability. Most studies are based on white populations, but RCC incidence rates in individuals of African American descent are higher and survival rates are lower compared with all other races, whereas Asian/Pacific Islander patients have lower incidence rates and higher survival rates than all the other ethnicities [28]. Response to treatment and frequency of severe toxicity are also related to ethnic origin, most likely due to different genetic backgrounds [29]. These ethnic differences need to be considered when assessing molecular biomarkers [30]. 3.1.2.2. Gene profiling and next-generation sequencing. Deep sequencing technologies whereby the complete coding sequence of the tumours is determined are now within an affordable price range. The data set generated by nextgeneration sequencing is much richer and detailed than data sets derived from expression analyses, for example [31–34]. Exome sequencing has already revealed that in addition to VHL mutations, alterations in chromatin remodelling play a major role in ccRCC. As a result of these studies, histone-lysine N-methyltransferase SETD2, one of the chromatin remodelling genes, is now recognised as an important driver gene in ccRCC [31]. This gene had not been
Please cite this article in press as: Junker K, et al. Potential Role of Genetic Markers in the Management of Kidney Cancer. Eur Urol (2012), http://dx.doi.org/10.1016/j.eururo.2012.09.040
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implemented as a tumour suppressor gene in ccRCC before. The importance of SETD2 was confirmed in subsequent studies that also demonstrated remarkable heterogeneity within ccRCC tumours [33]. The exome sequencing studies also revealed the existence of hypoxic and nonhypoxic tumours [31]. Whether these gene expression patterns correlate with clinical behaviour is currently unknown. It has been suggested that intratumour heterogeneity and sample bias may explain the difficulties in the validation of biomarkers [33]. Although multiregion sequencing revealed substantial intratumour heterogeneity [33], exome sequencing at the single-cell level did not reveal any significant clonal subpopulations within a tumour [32]. Clearly, the lack of overlapping results in different whole genome expression studies most likely is due to the inherent tumour heterogeneity, and stromal differences present a major challenge, making the applicability of exome sequencing for biomarker purposes doubtful at this time. Nevertheless, it may possibly lead to the development of reliable multiplex biomarker signatures that correlate complex tumour gene expression patterns with clinical behaviour that can be used to personalise treatment. 3.1.3.
Epigenetic alterations
Accumulating data obtained during the last 10–15 yr suggest that not only genetic but also epigenetic alterations play an important role in tumour development and progression. This is of special interest in RCC because large-scale sequencing analysis revealed that candidate tumour suppressor genes are mutated in <10% of tumours with exception of the VHL gene and PBRM1 [31,34]. 3.1.3.1. DNA methylation and histone modifications. DNA hypermethylation of CpG islands results in silencing of tumour suppressor genes. At present, only a few studies have focussed on the frequency and importance of DNA methylation in RCC to identify subtype-specific methylation profiles. Morris et al. identified nine genes with frequent promoter methylation in primary tumours (80% ccRCC) [35]. In another study, Arai et al. analysed 17 samples of non-ccRCC including pRCC and chRCC as well as oncocytomas in comparison with 51 ccRCCs using bacterial artificial chromosome (BAC) array-based microarrays [36]. Unsupervised hierarchical clustering analysis clustered papillary type 1 and 2 RCCs into different subclasses [7]. ChRCC and oncocytomas were grouped together. But based on 21 BAC clones, a differentiation between these two subtypes was possible. The ccRCCs showed a distinct methylation pattern. The number of analysed cases was small in both studies. Further studies have to substantiate whether DNA methylation patterns can be useful in the classification of RCC. However, methylation studies will offer important insights into gene expression regulation processes and possibly provide the basis of new therapies using methylation-modifying agents such as DNA methyltransferase inhibitors. Two studies indicated that histone remodelling and chromatin remodelling may play an important role in
ccRCC. Inactivating mutations in three genes encoding enzymes involved in histone modification were reported [31]. The investigators were able to cluster ccRCC into tumours with a hypoxic and nonhypoxic gene signature. Whether these signatures correlate with a particular clinical behaviour remains to be established. In another study, the investigators identified mutations in PBRM1 in >40% of ccRCC [34]. PBRM1 is involved in chromatin remodelling, and the fact that this gene is truncated in many ccRCCs suggests that aberrant chromatin biology plays a major role in ccRCC. Therefore, it is highly likely that biomarkers correlated with epigenetic events can be identified. 3.1.3.2. MicroRNA expression. MicroRNAs (miRNAs) are endogenous short 20–22 nucleotides in length noncoding RNAs that regulate gene expression processes of many genes, and currently >1500 human miRNAs are known (http://www. mirbase.org/index.shtml). MiRNA profiling in kidney cancers revealed that miR-28, miR-185, miR-7-2, and let-7f-2 were significantly upregulated in cancer compared with normal kidney [37]. Juan et al. also found nine upregulated miRNAs (miR-34b, miR-224, miR-142-3p, miR-185, miR-34a, miR-21, miR-155, miR-210, and miR-592) that could distinguish RCC from normal kidney [38]. Whether this will become a valuable tool for diagnostic means is doubtful. It does show, however, that on the miRNA level, RCC and normal kidney tissue also differ substantially, and this difference may be exploitable for differential diagnosis, prognostic, or therapeutic means. Several groups recently described specific miRNA profiles that can differentiate between the four most common renal cell tumour entities [39–41]. In contrast to mRNA expression profiles, no more than 30 miRNAs were sufficient for subclassification. The number could be further reduced to differentially diagnose two different subtypes. However, there is little overlap among three published studies: three miRNAs (miR-143, miR-145, hsa-miR-126) differentiating between ccRCC and pRCC and one miRNA (miR-200b) differentiating between chRCC and oncocytomas. The different results can be caused by unequal case numbers included in the studies, the differences in starting material and the types of microarrays, and the bioinformatics used for analysis. In the largest series of 94 tumours, Youssef et al. developed a classification system allowing the definition of the subtype in a maximum of four steps comparing relative expression of defined miRNA pairs [39]. The accuracy was 90% by cross-validation. Independent validation studies are needed to verify that this method can be used to classify RCC types. Concluding from these results, miRNAs seem to be very promising type of tumour biomarkers for the future because of their high stability in fresh, frozen, and formalin-fixed paraffin-embedded samples. 3.1.4.
Blood biomarkers
Biomarkers detectable in blood samples would be of great value. Because circulating DNA and RNA molecules can be detected in blood as free molecules or in exosomes, DNA isolated from plasma or serum samples from preoperative
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RCC patients were tested by microsatellite analysis of genes known to be involved in RCC. Sensitivity in preoperative samples reached 56–87% depending on the number of analysed microsatellites [42,43]. Gang et al. showed that the integrity of cell-free DNA can be used as a diagnostic marker for ccRCC [44]. A more tumour-specific study was published by de Martino et al. analysing DNA-promoter methylation of RASSF1A, VHL, PTGS2, and p16 [45]. They found a high specificity for all genes but a low sensitivity (50%). MiRNAs represent an interesting source for serum or plasma analysis. The first data on circulating miRNA were published by Wulfken et al, who identified 36 miRNAs with upregulation in serum and tumour tissue of RCC patients [46]. In a multicentre cohort of patients, sensitivity and specificity reached 77.4% and 37.6%, respectively. Clearly the sensitivity of this assay needs to be improved before circulating DNA/RNA can be used as a biomarker. It would be interesting to investigate circulating DNA/RNA levels in relation to therapy to serve in therapy monitoring. 3.2.
Prognosis
Historically, ccRCC was thought to behave more aggressively compared with pRCC and chRCC, highlighting the need for accurate subtyping [47]. Subsequently, a multi-institutional collaboration combined data from eight international centres and compared overall survival for patients with ccRCC, pRCC, and chRCC [48]. The results from Patard et al. suggested that in a multivariable analysis, histology did not remain an independent prognostic variable [48]. More recently, the group from Memorial Sloan-Kettering reported their experience in 1863 patients with ccRCC, pRCC, and chRCC, demonstrating that patients with ccRCC were significantly more likely to develop metastatic disease or to die from RCC even after adjusting for widely accepted factors influencing prognosis [49]. The Mayo Clinic reported their experience with 3062 patients with ccRCC, pRCC, and chRCC, again suggesting that histologic subtype is an independent predictor of cancerspecific death with ccRCC patients experiencing the worst prognosis [50]. Taken together, most of the available literature suggests that prognosis is worse for ccRCC patients, and no major difference is appreciated among pRCC and chRCC patients [51]. Independent of the subtype, metastasis is still frequent in RCC patients and defines their clinical outcome. Several nomograms have been developed to stratify patients in risk groups, mainly based on clinical and histopathologic parameters [52]. Nomograms for patients with nonmetastatic disease have reached an accuracy of approximately 90%, but models for patients with metastatic disease, particularly for patients treated with tyrosine kinase inhibitors (TKIs), are still inaccurate [53,54]. New prognostic markers are necessary to predict the risk of metastasis and the prognosis to define individual follow-up and therapy. 3.2.1.
Genomic alterations
The first CGH results revealed that genomic alterations could be associated with the development of metastasis in
5
patients with ccRCC. In addition to a higher number of genomic alterations, especially of chromosomal losses, specific alterations like the loss of chromosomes 9p, 10, 14q, and 18 as well as gains of 17 were associated with the development of metastasis and progression-free survival (PFS) [55,56]. Further studies using microarray techniques and FISH confirmed these data and identified new regions associated with metastasis and outcome such as gains on 1q, 7q, 8q, 12q, and 20q [57–59]. These results highlight the complexity of the metastatic process and the involvement of multiple genes. Although it appears that mFISH might be useful to stratify patients based on specific genomic alterations in the primary tumour [60], a better definition of genes involved in the metastatic process is necessary, and their clinical value and the best combination of these candidate regions have to be defined in prospective studies. Only limited data are available for pRCC, and these suggest that pRCC type 2 with 3p and 9p loss characterise an aggressive phenotype with the development of lymph node and distant metastasis as well as a shorter survival time [7,8]. 3.2.2.
Epigenetic alterations
3.2.2.1. DNA methylation. Until now only some data on DNA
methylation and prognosis of RCC patients have been published. Zhang et al. found a correlation of DLEC1 (tumour suppressor gene on 3p22.3) promoter methylation and advanced tumour stage as well as grading [60]. Methylation of miRNA genes miR-9-1 and miR-9-3 is significantly associated with recurrence and a nearly 30-mo decrease in recurrence-free survival time [61]. In a genomewide methylation study, Morris et al. found a significant correlation between SCUBE3 DNA methylation and an increased risk of cancer death or relapse [35]. 3.2.2.2. MicroRNA expression. MiRNAs are not only involved in tumour development but also in tumour progression including metastasis. Two published studies attempted to correlate miRNA expression with metastatic potential using global miRNA profiling by microarrays. Heinzelmann et al. compared primary ccRCC with and without development of metastasis and identified several miRNAs that are downregulated in metastatic primary tumours [62]. Khella et al. analysed distant metastases with primary tumours and found a distinct miRNA signature in metastases. Some of the primary tumour samples clustered together with the distant metastasis, suggesting that these primary tumours have a metastasis-specific signature [63]. Although both studies tested a different hypothesis, the collective data suggest that miR-10b, miR-29a, and miR-30a characterise the metastatic potential of primary tumours. Osanto et al. performed a genomewide miRNA expression analysis in ccRCC by deep sequencing and identified a miRNA panel that discriminates nonrecurrent, recurrent, and metastatic disease [64]. Because miRNAs can be easily detected and quantified in blood, serum assays based on these metastasis-associated miRNAs may be of value. In addition, Lin et al. identified 12 SNPs in miRNA-related genes that are significantly
Please cite this article in press as: Junker K, et al. Potential Role of Genetic Markers in the Management of Kidney Cancer. Eur Urol (2012), http://dx.doi.org/10.1016/j.eururo.2012.09.040
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associated with recurrence or survival [65]. They found a cumulative effect of multiple SNPs with recurrence. Taking together, more studies in larger patient cohorts are necessary to validate the potential value of miRNAs as prognostic biomarkers.
tumour heterogeneity and ethnicity needs to be addressed, and validation of findings is greatly needed to implement genetic markers in the management of RCC. This should lead to improved diagnosis, prognosis, and personalised therapy in this heterogeneous disease.
3.3.
Author contributions: Kerstin Junker had full access to all the data in the
Therapy
study and takes responsibility for the integrity of the data and the
Treatment options for mRCC have improved substantially over the past 5 yr, partly due to our increased understanding of the genetic events underlying RCC as described earlier. Vascular endothelial growth factor and the mammalian target of rapamycin pathways have been recognised as fundamental to the biology of RCC. This has led to the implementation of various antiangiogenic drugs (sunitinib, sorafenib, pazopanib, axitinib, everolimus, and bevacizumab plus interferon-a) for the treatment of RCC. With the advent of the new treatment possibilities, biomarker research aiming at personalising treatment of mRCC patients has intensified. Because treatment response and toxicity are (partly) derivatives of underlying interpatient genetic variability, pharmacogenetic studies have addressed whether a relation exists between a particular genotype and the pharmacokinetics and pharmacodynamics of these new treatment modalities, mainly in patients treated with sunitinib. In exploratory studies it was shown that germline polymorphisms in specific genes encoding for metabolizing enzymes, efflux transporters, and drug targets are associated with sunitinib-related toxicities and that genetic polymorphisms in three genes involved in sunitinib pharmacokinetics were associated with PFS in sunitinibtreated mRCC patients [66,67]. The investigators suggested that, ‘‘In the future, genetic variants may be added to the current prognostic criteria, enabling physicians to predict benefit from sunitinib in individual patients.’’ In an independent prospective study, correcting for multiple testing, polymorphisms in VEGFR3 and CYP3A5*1 (one of the metabolizing enzymes) appeared to be able to define a subset of mRCC patients with decreased sunitinib response and tolerability [68]. Finally, eight positive associations were observed in a study where relations between genotype and pazopanib efficacy were studied [69]. Collectively the data suggest that patients with a particular genotype may have reduced clinical benefit when treated with a certain TKI, and if confirmed, these genetic variants could provide the basis to personalise RCC treatment. Data on specific genetic alterations in tumour tissues are rare and controversial. These studies are mainly focussed only on the analysis of targeted pathways. Further global studies of genomic and expression alterations could lead to new predictive biomarkers.
accuracy of the data analysis. Study concept and design: Junker, Oosterwijk. Acquisition of data: Junker, Oosterwijk, Thompson. Analysis and interpretation of data: Junker, Oosterwijk, Thompson, Ficarra, Leibovich, Kwon. Drafting of the manuscript: Junker, Oosterwijk, Thompson, Leibovich, Kwon. Critical revision of the manuscript for important intellectual content: Junker, Oosterwijk, Thompson, Ficarra, Kwon. Statistical analysis: None. Obtaining funding: None. Administrative, technical, or material support: None. Supervision: None. Other (specify): None. Financial disclosures: Kerstin Junker certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None. Funding/Support and role of the sponsor: None.
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4.
Conclusions
Significant advances have been achieved in the molecular analysis of RCC. Particularly the recognition of the unique molecular signature of the different RCC subtypes has been impressive. However, molecular diagnosis or biomarkers have not yet reached the clinical arena. The importance of
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available at www.sciencedirect.com journal homepage: www.europeanurology.com
Collaborative Review – Kidney Cancer
Potential Role of Genetic Markers in the Management of Kidney Cancer Kerstin Junker a,*, Vincenzo Ficarra b, Eugene D. Kwon c,d, Bradley C. Leibovich c, R. Houston Thompson c, Egbert Oosterwijk e a
Clinic of Urology and Pediatric Urology, Saarland University Medical Center and Saarland University Faculty of Medicine, Homburg, Germany; b Department
of Oncological and Surgical Sciences, Urologic Unit, University of Padua, Padua, Italy; c Department of Urology, Mayo Clinic College of Medicine, Rochester, MN, USA; d Department of Immunology, Mayo Clinic College of Medicine, Rochester, MN, USA; e Experimental Urology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
Article info
Abstract
Article history: Accepted September 14, 2012 Published online ahead of print on September 24, 2012
Context: Kidney cancer is not a single entity but comprises a number of different types of cancer that occur in the kidney including renal cell tumours as the most common type. Four major renal cell tumour subtypes can be distinguished based on morphologic and genetic characteristics. To individualise therapy and to improve the prognosis in patients with renal cell tumours, accurate subtyping, definition of individual course of disease, and the prediction of therapy response are necessary. Objective: To discuss the potential role of genetic markers in the management of kidney cancer. Evidence acquisition: A Medline search was conducted to identify original articles, review articles, and editorials addressing the role of genetic alterations in kidney cancer management. Keywords included kidney neoplasms, genetics, SNP, gene expression, miRNA, classification, diagnosis, drug therapy, prognosis, and therapy. The articles with the highest level of evidence were identified and critically reviewed. This review is the result of an interactive peer-reviewing process by an expert panel of co-authors. Evidence synthesis: Each subtype is characterised by specific genetic, epigenetic, and expression patterns that potentially can be used to subclassify renal cell tumours in cases of ambivalent histopathology. Molecular signatures and single alterations in primary tumours are associated with aggressiveness and prognosis. Germline polymorphisms in specific genes encoding for metabolizing enzymes, efflux transporters, and drug targets seem to be associated with toxicity and response in patients receiving targeted therapy. Conclusions: Significant advances have been achieved in the molecular analysis of renal cancer. Validation of findings is greatly needed to implement genetic markers in the management of renal cancer. This should lead to improved diagnosis, prognosis, and personalised therapy in this heterogeneous disease. # 2012 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Keywords: Kidney cancer Genetic markers Gene expression
* Corresponding author. Clinic of Urology and Pediatric Urology, Saarland University Medical Center and Saarland University Faculty of Medicine, Kirrbergerstrasse, 66424 Homburg/Saar, Germany. Tel. +49 6841 1614734; Fax: +49 6841 1624795. E-mail address: [email protected] (K. Junker).
1.
Introduction
Renal tumours account for 2–3% of all malignant diseases in adults. The vast majority of renal cancers (>90%) are various forms of renal cell carcinoma (RCC) originating from
different parts of the nephron. RCC is the seventh most common cancer in men and the ninth most common in women. Incidence worldwide is about 209 000 new cases per year with 102 000 deaths per year [1]. The incidence of all stages of this cancer has increased over the
0302-2838/$ – see back matter # 2012 European Association of Urology. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eururo.2012.09.040
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Table 1 – Hereditary syndromes of kidney cancer: chromosomal localisation, affected genes and pathways, and clinical features Syndrome
Chromosome
Gene/protein
Potential pathway
Birt-Hogg-Dube´
17p
Folliculin
mTOR
Hereditary leiomyomatosis renal cell carcinoma Hereditary papillary renal carcinoma Tuberous sclerosis
1q
Krebs cycle/HIF1
7q
Fumarate hydratase c-Met
9q/16p
TSC1/TSC2
Hepatocyte growth factor/MET mTOR
Von Hippel-Lindau
3p
VHL
HIF1
Clinical features Chromophobe, oncocytic, hybrid, ccRCC, pulmonary cysts/pneumothorax, fibrofolliculoma Type II papillary RCC; uterine and skin leiomyoma Multifocal, bilateral type 1 papillary RCC Bilateral, multifocal angiomyolipoma, ccRCC, seizures, mental retardation, angiofibroma, cardiac rhabdomyoma, retinal hamartoma ccRCC, hemangioblastoma, retinal angioma, pheochromocytoma
mTOR = mammalian target of rapamycin; ccRCC = clear cell renal cell carcinoma; HIF1 = hypoxia inducible factor 1; RCC = renal cell carcinoma.
last several years. RCCs can be subdivided based on their morphologic appearance [2]. Genetic analyses have shown that these subtypes are characterised by different chromosomal alterations supporting the idea that each subtype represent a distinct tumour entity with a different tumour biology. Several autosomal dominant syndromes characterised by the development of RCC have been described that has greatly aided in the identification of the genes involved in the development of RCC (Table 1). Seven different kidney cancer genes have been identified thus far. Despite the clear demonstration that different histologic subtypes of RCC demonstrate unique pathogenesis and genetic alterations, the impact of histology on prognosis remains controversial. In view of specific genomic alterations associated with a specific RCC subtype, implementation of genetic markers in the management of RCC might become possible and helpful in the near future like they are in other tumour entities including breast, colorectal, and lung cancer. 2.
Evidence acquisition
A Medline search was conducted to identify original articles, review articles, and editorials addressing genetic alterations in kidney cancer concerning diagnosis, prognostic evaluation, and prediction of therapy response. Keywords included kidney neoplasms, genetics, SNP, gene expression, miRNA, classification, diagnosis, drug therapy, prognosis, and therapy. Links to related articles and cross-reading of citations in related articles were surveyed. This review is the result of an interactive peer-reviewing process by the panel of co-authors. 3.
Evidence synthesis
3.1.
Diagnosis
3.1.1.
Genomic alterations
various syndromes is beyond the scope of this review, but Table 1 shows the clinical and molecular characteristics of currently well-characterised hereditary RCC syndromes. 3.1.1.2. Sporadic renal cell carcinoma. Specific chromosomal aberrations have been well documented in the various recognised renal cancer subtypes (see Table 2). The most common RCC type is clear cell RCC (ccRCC), characterised by a loss of 3p in >90% of cases [3,4]. In 1993 the VHL tumour suppressor gene involved in the development of ccRCC was identified and is localised on 3p25-p26 [5]. Biallelic inactivation, for example, by loss of one 3p arm and mutations on the second chromosome 3, is a key event in the tumour development of ccRCC. In addition, gain of 5q and losses of 6p, 9, 10, 14, and 18 occur frequently in ccRCC [3,4]. These losses are often associated with tumour progression. Gains of chromosomes 7 and 17 are typical features of papillary RCC (pRCC) [6]. Additional gains of chromosomes 3, 12, 16, and 20 are more frequent in tumour progression. Over the last 10 yr, it was hypothesised that two distinct types of pRCC exist with different histopathologic features and chromosomal aberrations. Gains of chromosomes 7 and 17 occur in both pRCC subtypes, whereas loss of chromosomes 3p, 6q, and 9p, as well as gains of 1q, 2, and 8q, can be found more often or exclusively in type 2 pRCC [7–9]. However, clear histopathologic criteria have to be defined, and further genetic studies are necessary in correlation with histopathologic as well as clinical data. Chromophobe RCC (chRCC) is characterised by the loss of chromosomes 1, 2, 6, 10, 13, 17, and 21. Oncocytomas, a benign tumour entity, are characterised by the loss of chromosome 1 in most cases
Table 2 – Most frequent subtypes of renal cell tumours: frequency and chromosomal alterations Subtype
3.1.1.1. Hereditary renal cell carcinoma. Although hereditary forms of RCC represent <5% of cases, they have played a pivotal role in understanding and characterising the molecular pathways involved in sporadic RCC. Each hereditary syndrome is characterised by a unique molecular alteration, and therefore genetic analysis can be performed to confirm a differential diagnosis. A detailed synopsis of the
Clear cell RCC Papillary RCC Chromophobe RCC Oncocytoma
Frequency, % 70–75 10–15 5–7 5
Chromosomal alterations S3p, +5q, S9, S10, S14 +7, +17, +3, +12, +20 Combination of S1, S2, S6, S10, S13, S17, S21 S1, S14, t(11,n)
RCC = renal cell carcinoma. Bold type indicates most frequent alterations.
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or by translocations involving chromosome 11. There are fewer published reviews on rare forms of renal tumours such as Bellini duct carcinomas and so-called TFE3translocation tumours. The current understanding of chromosomal alterations was derived from classical karyotyping and comparative genomic hybridisation (CGH). In the last several years, array-based single nucleotide polymorphism (SNP) techniques have allowed a more detailed analysis of copy number alterations and the loss of heterogeneity. Most studies confirmed the genetic patterns of each RCC subtype [10,11]. However, only a few studies examined the smallest overlapping regions as identified by CGH or genes commonly altered in RCC. Based on the known genetic alterations of different subtypes, it is possible to classify these tumours in cases when histopathologic features are not clear. For this purpose, fluorescent in situ hybridization (FISH) has been investigated as a tool for routine diagnosis and in determining histologic subtype [12,13]. Using centromere probes for chromosomes 1, 2, 6, 9, 7, and 17 as well as region-specific probes for chromosome 3p on isolated cell nuclei, Sanjmyatav et al. [12] were able to classify RCCs with high accuracy. Brunelli et al. [14], who used paraffin sections, obtained similar results. One especially clinically relevant application would be the ability of FISH to differentiate between chRCCs and oncocytomas [14]. However, based on an analysis of 73 oncocytomas and 20 chRCCs, Dvorakova et al. concluded that FISH cannot be used as a differential diagnostic modality due to overlapping genetic alterations [15]. Further studies need to address the relevance of specific chromosomal losses in a lower percentage of analysed cells for the diagnosis and prognosis of patients with oncocytomas. Histopathologic classification on small samples is very challenging, and genetic analysis may be able to improve the diagnosis of biopsies. CGH analyses of ex vivo fineneedle aspiration biopsies reached a correct diagnosis in 75% of the investigated samples [16]. A very high diagnostic accuracy was achieved in ccRCC (93.5%) and pRCC (100%). ChRCC was correctly diagnosed in 62.5%. The lower rate could be the result of hyperploid karyotypes leading to the misinterpretations of gains and losses by CGH. Similar to the differential diagnosis of larger specimens, application of FISH may be of interest. Two studies demonstrated that FISH on core biopsies can aid in the differential diagnosis of RCC. The accuracy on postoperatively obtained samples was 94% using FISH and 86% by histology alone [17]. Chyhrai et al. performed FISH on preoperatively obtained biopsies and reached 95.5% diagnostic fidelity compared with 90.5% by histology alone [18]. These results suggest that genetic analysis and especially multicolour FISH (mFISH) can help to improve diagnostics in uncertain cases. It can also help to identify rare RCC subtypes like TFE3-translocation tumours that are often overlooked because of similarities with ccRCC or pRCC [19,20]. Although mFISH appears to offer the potential to improve diagnostic accuracy in RCC, standardised technical procedures and defined cut-off values need to be
3
established before mFISH can be implemented as a standard diagnostic procedure. 3.1.2.
Gene expression studies
A global view of the gene expression patterns and deregulated pathways may provide a more accurate picture of renal cancer including its clinical behaviour. However, the function and role of most of these genes in tumour development are unknown, and some may even be bystander genes that play no role in tumourigenesis. Nevertheless, these signatures may serve as effective biomarkers because of their unique differential expression patterns. 3.1.2.1. Gene expression profiling. Several expression profiling studies have reported the ability of gene expression patterns to distinguish between histologic subtypes of RCC, such as conventional ccRCCs, papillary type 1 and type 2 carcinomas, chromophobe carcinomas, oncocytomas, and urothelial carcinoma of the renal pelvis [21–24]. The findings overlap with the results obtained with CGH and FISH. More interestingly, the outcome of the gene expression profiling studies has identified a number of new potential diagnostic markers. Examples are glutathione S-transferase a highly expressed in ccRCC18 and a-methylacyl-CoA racemase in pRCC [25,26]. Their discriminatory power has been confirmed in a number of independent studies (eg, Allory et al. [27]), and as such are useful in the differential diagnosis of RCC. Substantial efforts have been made to find prognostic biomarkers with the aid of expression profiling. Although the outcome differs substantially from the general one-gene one-biomarker approach, implementation of multiplex biomarkers has not been achieved. Independent replication of microarray-derived predictive gene signatures has also proven to be difficult. One aspect that needs attention is interethnic variability. Most studies are based on white populations, but RCC incidence rates in individuals of African American descent are higher and survival rates are lower compared with all other races, whereas Asian/Pacific Islander patients have lower incidence rates and higher survival rates than all the other ethnicities [28]. Response to treatment and frequency of severe toxicity are also related to ethnic origin, most likely due to different genetic backgrounds [29]. These ethnic differences need to be considered when assessing molecular biomarkers [30]. 3.1.2.2. Gene profiling and next-generation sequencing. Deep sequencing technologies whereby the complete coding sequence of the tumours is determined are now within an affordable price range. The data set generated by nextgeneration sequencing is much richer and detailed than data sets derived from expression analyses, for example [31–34]. Exome sequencing has already revealed that in addition to VHL mutations, alterations in chromatin remodelling play a major role in ccRCC. As a result of these studies, histone-lysine N-methyltransferase SETD2, one of the chromatin remodelling genes, is now recognised as an important driver gene in ccRCC [31]. This gene had not been
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implemented as a tumour suppressor gene in ccRCC before. The importance of SETD2 was confirmed in subsequent studies that also demonstrated remarkable heterogeneity within ccRCC tumours [33]. The exome sequencing studies also revealed the existence of hypoxic and nonhypoxic tumours [31]. Whether these gene expression patterns correlate with clinical behaviour is currently unknown. It has been suggested that intratumour heterogeneity and sample bias may explain the difficulties in the validation of biomarkers [33]. Although multiregion sequencing revealed substantial intratumour heterogeneity [33], exome sequencing at the single-cell level did not reveal any significant clonal subpopulations within a tumour [32]. Clearly, the lack of overlapping results in different whole genome expression studies most likely is due to the inherent tumour heterogeneity, and stromal differences present a major challenge, making the applicability of exome sequencing for biomarker purposes doubtful at this time. Nevertheless, it may possibly lead to the development of reliable multiplex biomarker signatures that correlate complex tumour gene expression patterns with clinical behaviour that can be used to personalise treatment. 3.1.3.
Epigenetic alterations
Accumulating data obtained during the last 10–15 yr suggest that not only genetic but also epigenetic alterations play an important role in tumour development and progression. This is of special interest in RCC because large-scale sequencing analysis revealed that candidate tumour suppressor genes are mutated in <10% of tumours with exception of the VHL gene and PBRM1 [31,34]. 3.1.3.1. DNA methylation and histone modifications. DNA hypermethylation of CpG islands results in silencing of tumour suppressor genes. At present, only a few studies have focussed on the frequency and importance of DNA methylation in RCC to identify subtype-specific methylation profiles. Morris et al. identified nine genes with frequent promoter methylation in primary tumours (80% ccRCC) [35]. In another study, Arai et al. analysed 17 samples of non-ccRCC including pRCC and chRCC as well as oncocytomas in comparison with 51 ccRCCs using bacterial artificial chromosome (BAC) array-based microarrays [36]. Unsupervised hierarchical clustering analysis clustered papillary type 1 and 2 RCCs into different subclasses [7]. ChRCC and oncocytomas were grouped together. But based on 21 BAC clones, a differentiation between these two subtypes was possible. The ccRCCs showed a distinct methylation pattern. The number of analysed cases was small in both studies. Further studies have to substantiate whether DNA methylation patterns can be useful in the classification of RCC. However, methylation studies will offer important insights into gene expression regulation processes and possibly provide the basis of new therapies using methylation-modifying agents such as DNA methyltransferase inhibitors. Two studies indicated that histone remodelling and chromatin remodelling may play an important role in
ccRCC. Inactivating mutations in three genes encoding enzymes involved in histone modification were reported [31]. The investigators were able to cluster ccRCC into tumours with a hypoxic and nonhypoxic gene signature. Whether these signatures correlate with a particular clinical behaviour remains to be established. In another study, the investigators identified mutations in PBRM1 in >40% of ccRCC [34]. PBRM1 is involved in chromatin remodelling, and the fact that this gene is truncated in many ccRCCs suggests that aberrant chromatin biology plays a major role in ccRCC. Therefore, it is highly likely that biomarkers correlated with epigenetic events can be identified. 3.1.3.2. MicroRNA expression. MicroRNAs (miRNAs) are endogenous short 20–22 nucleotides in length noncoding RNAs that regulate gene expression processes of many genes, and currently >1500 human miRNAs are known (http://www. mirbase.org/index.shtml). MiRNA profiling in kidney cancers revealed that miR-28, miR-185, miR-7-2, and let-7f-2 were significantly upregulated in cancer compared with normal kidney [37]. Juan et al. also found nine upregulated miRNAs (miR-34b, miR-224, miR-142-3p, miR-185, miR-34a, miR-21, miR-155, miR-210, and miR-592) that could distinguish RCC from normal kidney [38]. Whether this will become a valuable tool for diagnostic means is doubtful. It does show, however, that on the miRNA level, RCC and normal kidney tissue also differ substantially, and this difference may be exploitable for differential diagnosis, prognostic, or therapeutic means. Several groups recently described specific miRNA profiles that can differentiate between the four most common renal cell tumour entities [39–41]. In contrast to mRNA expression profiles, no more than 30 miRNAs were sufficient for subclassification. The number could be further reduced to differentially diagnose two different subtypes. However, there is little overlap among three published studies: three miRNAs (miR-143, miR-145, hsa-miR-126) differentiating between ccRCC and pRCC and one miRNA (miR-200b) differentiating between chRCC and oncocytomas. The different results can be caused by unequal case numbers included in the studies, the differences in starting material and the types of microarrays, and the bioinformatics used for analysis. In the largest series of 94 tumours, Youssef et al. developed a classification system allowing the definition of the subtype in a maximum of four steps comparing relative expression of defined miRNA pairs [39]. The accuracy was 90% by cross-validation. Independent validation studies are needed to verify that this method can be used to classify RCC types. Concluding from these results, miRNAs seem to be very promising type of tumour biomarkers for the future because of their high stability in fresh, frozen, and formalin-fixed paraffin-embedded samples. 3.1.4.
Blood biomarkers
Biomarkers detectable in blood samples would be of great value. Because circulating DNA and RNA molecules can be detected in blood as free molecules or in exosomes, DNA isolated from plasma or serum samples from preoperative
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RCC patients were tested by microsatellite analysis of genes known to be involved in RCC. Sensitivity in preoperative samples reached 56–87% depending on the number of analysed microsatellites [42,43]. Gang et al. showed that the integrity of cell-free DNA can be used as a diagnostic marker for ccRCC [44]. A more tumour-specific study was published by de Martino et al. analysing DNA-promoter methylation of RASSF1A, VHL, PTGS2, and p16 [45]. They found a high specificity for all genes but a low sensitivity (50%). MiRNAs represent an interesting source for serum or plasma analysis. The first data on circulating miRNA were published by Wulfken et al, who identified 36 miRNAs with upregulation in serum and tumour tissue of RCC patients [46]. In a multicentre cohort of patients, sensitivity and specificity reached 77.4% and 37.6%, respectively. Clearly the sensitivity of this assay needs to be improved before circulating DNA/RNA can be used as a biomarker. It would be interesting to investigate circulating DNA/RNA levels in relation to therapy to serve in therapy monitoring. 3.2.
Prognosis
Historically, ccRCC was thought to behave more aggressively compared with pRCC and chRCC, highlighting the need for accurate subtyping [47]. Subsequently, a multi-institutional collaboration combined data from eight international centres and compared overall survival for patients with ccRCC, pRCC, and chRCC [48]. The results from Patard et al. suggested that in a multivariable analysis, histology did not remain an independent prognostic variable [48]. More recently, the group from Memorial Sloan-Kettering reported their experience in 1863 patients with ccRCC, pRCC, and chRCC, demonstrating that patients with ccRCC were significantly more likely to develop metastatic disease or to die from RCC even after adjusting for widely accepted factors influencing prognosis [49]. The Mayo Clinic reported their experience with 3062 patients with ccRCC, pRCC, and chRCC, again suggesting that histologic subtype is an independent predictor of cancerspecific death with ccRCC patients experiencing the worst prognosis [50]. Taken together, most of the available literature suggests that prognosis is worse for ccRCC patients, and no major difference is appreciated among pRCC and chRCC patients [51]. Independent of the subtype, metastasis is still frequent in RCC patients and defines their clinical outcome. Several nomograms have been developed to stratify patients in risk groups, mainly based on clinical and histopathologic parameters [52]. Nomograms for patients with nonmetastatic disease have reached an accuracy of approximately 90%, but models for patients with metastatic disease, particularly for patients treated with tyrosine kinase inhibitors (TKIs), are still inaccurate [53,54]. New prognostic markers are necessary to predict the risk of metastasis and the prognosis to define individual follow-up and therapy. 3.2.1.
Genomic alterations
The first CGH results revealed that genomic alterations could be associated with the development of metastasis in
5
patients with ccRCC. In addition to a higher number of genomic alterations, especially of chromosomal losses, specific alterations like the loss of chromosomes 9p, 10, 14q, and 18 as well as gains of 17 were associated with the development of metastasis and progression-free survival (PFS) [55,56]. Further studies using microarray techniques and FISH confirmed these data and identified new regions associated with metastasis and outcome such as gains on 1q, 7q, 8q, 12q, and 20q [57–59]. These results highlight the complexity of the metastatic process and the involvement of multiple genes. Although it appears that mFISH might be useful to stratify patients based on specific genomic alterations in the primary tumour [60], a better definition of genes involved in the metastatic process is necessary, and their clinical value and the best combination of these candidate regions have to be defined in prospective studies. Only limited data are available for pRCC, and these suggest that pRCC type 2 with 3p and 9p loss characterise an aggressive phenotype with the development of lymph node and distant metastasis as well as a shorter survival time [7,8]. 3.2.2.
Epigenetic alterations
3.2.2.1. DNA methylation. Until now only some data on DNA
methylation and prognosis of RCC patients have been published. Zhang et al. found a correlation of DLEC1 (tumour suppressor gene on 3p22.3) promoter methylation and advanced tumour stage as well as grading [60]. Methylation of miRNA genes miR-9-1 and miR-9-3 is significantly associated with recurrence and a nearly 30-mo decrease in recurrence-free survival time [61]. In a genomewide methylation study, Morris et al. found a significant correlation between SCUBE3 DNA methylation and an increased risk of cancer death or relapse [35]. 3.2.2.2. MicroRNA expression. MiRNAs are not only involved in tumour development but also in tumour progression including metastasis. Two published studies attempted to correlate miRNA expression with metastatic potential using global miRNA profiling by microarrays. Heinzelmann et al. compared primary ccRCC with and without development of metastasis and identified several miRNAs that are downregulated in metastatic primary tumours [62]. Khella et al. analysed distant metastases with primary tumours and found a distinct miRNA signature in metastases. Some of the primary tumour samples clustered together with the distant metastasis, suggesting that these primary tumours have a metastasis-specific signature [63]. Although both studies tested a different hypothesis, the collective data suggest that miR-10b, miR-29a, and miR-30a characterise the metastatic potential of primary tumours. Osanto et al. performed a genomewide miRNA expression analysis in ccRCC by deep sequencing and identified a miRNA panel that discriminates nonrecurrent, recurrent, and metastatic disease [64]. Because miRNAs can be easily detected and quantified in blood, serum assays based on these metastasis-associated miRNAs may be of value. In addition, Lin et al. identified 12 SNPs in miRNA-related genes that are significantly
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associated with recurrence or survival [65]. They found a cumulative effect of multiple SNPs with recurrence. Taking together, more studies in larger patient cohorts are necessary to validate the potential value of miRNAs as prognostic biomarkers.
tumour heterogeneity and ethnicity needs to be addressed, and validation of findings is greatly needed to implement genetic markers in the management of RCC. This should lead to improved diagnosis, prognosis, and personalised therapy in this heterogeneous disease.
3.3.
Author contributions: Kerstin Junker had full access to all the data in the
Therapy
study and takes responsibility for the integrity of the data and the
Treatment options for mRCC have improved substantially over the past 5 yr, partly due to our increased understanding of the genetic events underlying RCC as described earlier. Vascular endothelial growth factor and the mammalian target of rapamycin pathways have been recognised as fundamental to the biology of RCC. This has led to the implementation of various antiangiogenic drugs (sunitinib, sorafenib, pazopanib, axitinib, everolimus, and bevacizumab plus interferon-a) for the treatment of RCC. With the advent of the new treatment possibilities, biomarker research aiming at personalising treatment of mRCC patients has intensified. Because treatment response and toxicity are (partly) derivatives of underlying interpatient genetic variability, pharmacogenetic studies have addressed whether a relation exists between a particular genotype and the pharmacokinetics and pharmacodynamics of these new treatment modalities, mainly in patients treated with sunitinib. In exploratory studies it was shown that germline polymorphisms in specific genes encoding for metabolizing enzymes, efflux transporters, and drug targets are associated with sunitinib-related toxicities and that genetic polymorphisms in three genes involved in sunitinib pharmacokinetics were associated with PFS in sunitinibtreated mRCC patients [66,67]. The investigators suggested that, ‘‘In the future, genetic variants may be added to the current prognostic criteria, enabling physicians to predict benefit from sunitinib in individual patients.’’ In an independent prospective study, correcting for multiple testing, polymorphisms in VEGFR3 and CYP3A5*1 (one of the metabolizing enzymes) appeared to be able to define a subset of mRCC patients with decreased sunitinib response and tolerability [68]. Finally, eight positive associations were observed in a study where relations between genotype and pazopanib efficacy were studied [69]. Collectively the data suggest that patients with a particular genotype may have reduced clinical benefit when treated with a certain TKI, and if confirmed, these genetic variants could provide the basis to personalise RCC treatment. Data on specific genetic alterations in tumour tissues are rare and controversial. These studies are mainly focussed only on the analysis of targeted pathways. Further global studies of genomic and expression alterations could lead to new predictive biomarkers.
accuracy of the data analysis. Study concept and design: Junker, Oosterwijk. Acquisition of data: Junker, Oosterwijk, Thompson. Analysis and interpretation of data: Junker, Oosterwijk, Thompson, Ficarra, Leibovich, Kwon. Drafting of the manuscript: Junker, Oosterwijk, Thompson, Leibovich, Kwon. Critical revision of the manuscript for important intellectual content: Junker, Oosterwijk, Thompson, Ficarra, Kwon. Statistical analysis: None. Obtaining funding: None. Administrative, technical, or material support: None. Supervision: None. Other (specify): None. Financial disclosures: Kerstin Junker certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None. Funding/Support and role of the sponsor: None.
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