Int. J. Cancer: 125, 474–482 (2009) ' 2009 UICC
SHORT REPORT Gene signatures of pulmonary metastases of renal cell carcinoma reflect the disease-free interval and the number of metastases per patient Daniela Wuttig1*, Barbara Baier2, Susanne Fuessel1, Matthias Meinhardt3, Alexander Herr4, Christian Hoefling1, Marieta Toma3, Marc-Oliver Grimm1, Axel Meye1, Axel Rolle2 and Manfred P. Wirth1 1 Department of Urology, Dresden University of Technology, Dresden, Germany 2 Department of Thoracic and Vascular Surgery, Center for Pneumology, Thoracic and Vascular Surgery, Coswig Specialized Hospital, Coswig, Germany 3 Institute of Pathology, Dresden University of Technology, Dresden, Germany 4 Institute of Clinical Genetics, Dresden University of Technology, Dresden, Germany Our understanding of metastatic spread is limited and molecular mechanisms causing particular characteristics of metastasis are largely unknown. Herein, transcriptome-wide expression profiles of a unique cohort of 20 laser-resected pulmonary metastases (Mets) of 18 patients with clear-cell renal cell carcinoma (RCC) were analyzed to identify expression patterns associated with two important prognostic factors in RCC: the disease-free interval (DFI) after nephrectomy and the number of Mets per patient. Differentially expressed genes were identified by comparing early (DFI 9 months) and late (DFI 5 years) Mets, and Mets derived from patients with few (8) and multiple (16) Mets. Early and late Mets could be separated by the expression of genes involved in metastasis-associated processes, such as angiogenesis, cell migration and adhesion (e.g., PECAM1, KDR). Samples from patients with multiple Mets showed an elevated expression of genes associated with cell division and cell cycle (e.g., PBK, BIRC5, PTTG1) which indicates that a high number of Mets might result from an increased growth potential. Minimal sets of genes for the prediction of the DFI and the number of Mets per patient were identified. Microarray results were confirmed by quantitative PCR by including nine further pulmonary Mets of RCC. In summary, we showed that subgroups of Mets are distinguishable based on their expression profiles, which reflect the DFI and the number of Mets of a patient. To what extent the identified molecular factors contribute to the development of these characteristics of metastatic spread needs to be analyzed in further studies. ' 2009 UICC Key words: kidney microarrays
cancer;
lung
metastases;
oligonucleotide
In numerous tumour types, the development of metastases (Mets) causes the patients’ death. The median survival of renal cell carcinoma (RCC) patients amounts to merely 6 to 12 months after Mets have been diagnosed.1 RCC is the urological cancer with the highest percentage of tumour-related deaths2 because metastasis occurs in about 60% of the patients.3 The preferential localization of RCC Mets is the lung.1 In contrast to the emerging development of molecular-based therapies for RCC in the last few years,3 molecular prognostic markers are still missing. Despite the knowledge of several molecular factors involved in metastatic spread like angiogenesis, cell adhesion, invasion or migration,4,5 little is known about specific characteristics of this complex process. These are, for example, primary-dependent site-specific metastasis,6 varying dormancy periods of Mets originating from the same primary tumour entity causing disease-free intervals (DFI) ranging from several months to many years, or the differing number of Mets in patients with the same primary tumour. Knowing the molecular fundamentals of these phenomena would support the prognosis of patients’ outcome and facilitate the decision for an appropriate therapy regime, particularly in RCC where the DFI and the number of Mets are important predictors of clinical outcome.7,8 So far, most microarray studies on RCC metastasis comprise metastatic and nonmetastatic primary RCC.9,10 Only a few studies include Mets of RCC to identify expression patterns associated with metastatic spread11–13 since fresh-frozen metastatic Publication of the International Union Against Cancer
lesions are rare because of their restricted surgical treatment. However, these studies have never addressed the particular characteristics of metastatic spread mentioned above. RCC patients early diagnosed or with multiple Mets have an extremely with poor prognosis.7,8,14 In the present study, we investigated if this aggressive behaviour is reflected at the gene expression profile of these Mets. Therefore, we performed transcriptome-wide expression analyses on a homogenous cohort of pulmonary RCC Mets obtained by a new resection technique.14 Material and methods Tissue specimens Renal cell carcinoma lung Mets were removed by 1,318-nm laser resection.14 One-half of each tissue was snap-frozen in liquid nitrogen, while the other half was embedded in paraffin. Paraffinembedded tissues were histologically evaluated to ensure a complete resection in the healthy lung tissue. Twenty cryo-preserved pulmonary Mets from 18 patients were analyzed by oligonucleotide microarrays. For validation of the array data by quantitative PCR nine additional samples obtained from nine patients were included. All patients underwent nephrectomy for clear-cell RCC, showed clinically no other distant Mets before diagnosis of lung Mets, received no immune- or immunechemotherapy (except one patient) and had no other primary tumours. Pulmonary Mets were diagnosed by the attending urologist or the general practitioner by CT or MRT. After pulmonary metastasectomy, patients were generally monitored every 3 months for 2 years, afterwards semi-annually for 3 years and annually thereafter. Clinical and follow-up data of the patients are shown in Table I. Tissue collection and investigation was approved by the ethics committee of the Dresden University of Technology and informed consent was obtained from each patient. RNA isolation and microarray processing Cryo-sections of Mets (4 lm) were made after apparent nonmalignant lung tissue had been removed. Representative H&E-stained sections were histologically re-evaluated to ensure a clear-cell histology and a tumour cell amount of 70%. Tissue sections were homogenized using QiaShredders (Qiagen, Hilden, Germany).
Additional Supporting Information may be found in the online version of this article. Abbreviations: CP, crossing point; DFI, disease-free interval after nephrectomy; FDR, false discovery rate; KNN, k-nearest neighbouring; Mets, metastases; RCC, renal cell carcinoma; WV, weighted voting. *Correspondence to: Department of Urology, Faculty of Medicine, Dresden University of Technology, Fetscherstr. 74, 01307 Dresden, Germany. Fax: 149-351-458-5771. E-mail:
[email protected] Received 9 June 2008; Accepted after revision 8 January 2009 DOI 10.1002/ijc.24353 Published online 10 February 2009 in Wiley InterScience (www.interscience. wiley.com).
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GENE SIGNATURES OF PULMONARY RENAL CELL CARCINOMA METASTASES TABLE I – CLINICO-PATHOLOGICAL DATA OF THE PATIENTS Sample ID
Classification
Gender
Age at Nephrectomy (yr)
Tissue Samples for Microarray and qPCR Analyses Met 1 A2, B1 M Met 2 B3 M Met 3 A1, B1 M Met 4 B2 F Met 5 B2 M Met 6 B2 M Met 7 B1 M Met 8 A2, B1 M Met 9 A2, B2 M Met 11 B2 M Met 12 A2, B1 M Met 13 A2, B1 M Met 14 A1, B2 M Met 15 A1, B3 F Met 16 A1, B2 F Met 17 A1, B2 F Met 18 B1 M Met 20 A1, B3 M Met 23 B2 F Met 252 A3, B2 M Median M:F 5 3:1 Tissue Samples for qPCR Analyses Met 10 A4, B4 M Met 19 A4, B4 M Met 21 B4 M Met 22 A4, B4 M Met 24 B4 M Met 26 A4, B4 F Met 27 A4, B4 F Met 28 A4, B4 M Met 29 A4, B4 F Median M:F 5 2:1 Median (All Patients) M:F 5 2.6:1
TNM Stage and Grading of Primary Tumour
DFI (mo)
65 62 58 74 69 69 49 66 63 59 50 58 59 49 58 58 64 42 73 57 59
T2 N0 M0 G3 pT2 pN0 M0 G2 pT3 pN0 M0 G1 pT3 pN0 M0 G1 pT3 N0 M0 G2 pT3 N0 M0 G2 pT3 N0 M0 G2 pT3 pN0 M1 pT2 Nx M1 G2 T2 N0 M0 G2 pT2 pNx M1 G3 pT3 pN0 M1 G3 NA pT2 N0 M0 NA NA pT3 pN0 Mx G2 pT3a N0 M0 G1 pT3 pN0 M0 G3 NA
1 15 95 12 38 38 10 2 9 31 1 1 60 156 90 90 81 114 11 127 23
72 62 58 42 60 67 68 52 59 60 59
NA NA pT2 pN0 M0 G2 NA NA pT3 N0 M1 G3 pT1 pN0 M0 G3 NA NA
1 150 21 2 13 2 66 174 132 21 26
No. of Mets
44 10 18 4 3 3 16 24 1 3 24 37 1 6 8 8 >80 2 2 5 6 6 17 7 8 2 49 8 3 1 7 6.5
Status
DOD DOD DOD AWD ANED ANED DOD AWD NA ANED AWD NA ANED AWD AWD AWD NA DOD AWD ANED ANED AWD AWD AWD ANED DOD ANED NA NA
Clinoco-pathological Data of the Mets Subgroups (Median Values) Group
Designation
Gender
Age at Nephrectomy (yr)
DFI (mo)
No. of Mets
A1 (n 5 6) A2 (n 5 5) B1 (n 5 7) B2 (n 5 10)
Late Metastases Early Metastases Multiple Metastases Few Metastases
M:F 5 1:1 M:F 5 5:0 M:F 5 7:0 M:F 5 1.5:1
58 63 58 61
92.5 1 2 38
7 24 24 3
Tissue sample IDs and the age at nephrectomy, as far as known the TNM stage and grading of the primary tumour, the DFI, the number (no.) of lung Mets and the current status of the corresponding patients are shown for samples used for microarray and qPCR analyses, respectively. The DFI was consistently defined as the time period from nephrectomy to resection of the investigated Mets. Mets 5 and 6 as well as 16 and 17 originated from the same patients, respectively. For calculation of median values, these Mets have been regarded as individual samples (biological replicates). Groups investigated in microarray analyses are designated: A1 5 ‘‘late Mets’’, A2 5 ‘‘early Mets’’, A3 5 microarray validation group for ‘‘A1 vs. A2’’, B1 5 ‘‘multiple Mets’’, B2 5 ‘‘few Mets’’, B3 5 microarray validation sample for ‘‘B1 vs. B2’’. Nine further samples were exclusively used for qPCR analyses (A4 5 validation samples for ‘‘A1 vs. A2’’, B4 5 validation samples for ‘‘B1 vs. B2’’). The third part of the table shows the clinico-pathological data of the groups compared in the microarray analyses (n, number of samples). ANED, alive with no evidence of disease; AWD, alive with disease/patient have developed new metastases; DOD, dead of disease; NA, no data available. 1 This patient had a DFI of 8 months (another single lung metastasis, which had been resected), but the metastasis investigated herein was diagnosed 11 months after nephrectomy.–2This patient received chemotherapy before metastasectomy. However, exclusion of this patient’s tissue sample did not actually alter the results.
RNA was isolated with the RNeasy Mini Kit (Qiagen) with additional DNA digestion (RNase-Free DNase Set, Qiagen). RNA quality and quantity were determined with the Bioanalyzer 2100 (Agilent Technologies, Boeblingen, Germany). Only tissue samples with an RNA integrity number 4.5 were included in microarray processing. Biotin-labeled cRNA was synthesized from total RNA (2.0– 2.5 lg) using the One-Cycle Target Labeling and Control Reagents Kit (Affymetrix, Santa Clara, CA) according to the manufacturer’s specifications. Twenty micrograms (adjusted concentration) of cRNA were fragmented and hybridized to HG-U133 Plus 2.0 Arrays (Affymetrix). Arrays were washed, stained (GeneChip Fluidic Station, Affymetrix) and scanned (GCS3000 7G, Affymetrix) following the manufacturer’s instructions (for sample and array quality see Supporting Information).
Data analyses Data preprocessing and calculation of expression signals Raw data was preprocessed by global background correction and quantile normalization (RMAExpress 4.115). Expression signals were calculated based on a PM-only model and log2-transformed using RMAExpress 4.1. The raw and preprocessed data have been deposited in NCBI’s Gene Expression Omnibus (GSE14378). Identification of metastasis-associated genes by meta-analysis For the comparison of expression profiles of Mets generated in different studies a meta-analysis procedure was applied using the Bioconductor package RankProd.16–18 It provides a nonparametric permutation test based on the determination of consistently highly ranked genes by fold-change among the different datasets,
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adjusted to multiple testing by estimating the false positive rate (corresponds with the false discovery rate, FDR). As other metaanalysis procedures, this software performs a two-class comparison what requires samples of both classes in each dataset to estimate inter-study variations. Therefore, beside Mets we included in our own dataset the expression data of four primary clear-cell RCC obtained from patients with different DFI (Supporting Information) generated in our lab using HG-U133 Plus 2.0 arrays, which was processed as described above (RMAExpress 4.1). We compared this data on Mets and primary RCC with log2-transformed expression values of 22 primary clear-cell RCC and 10 RCC Mets generated using HG-U133A arrays by Jones et al.11 and provided as expression matrix (calculated with dCHIP software).11 Data from Jones et al.11 and our own study was filtered to obtain only probe sets included in both, HG-U133 A and HGU133 Plus 2.0 arrays. Further 16 probe sets with missing values for one or several samples in the data of Jones et al. were excluded from the analyses. To minimize the amount of data input, the distribution of signal intensities (log2) for each array was first standardized by median centering of the data and division by the standard deviation resulting in a new standard deviation of one for each array. Afterwards, the probe sets with max/ min > |1.585| (max/min > 3 on nonlogarithmized scale) were selected. Finally, the RankProd software (RPadvance) was run to identify differences between Mets and primary tumours (FDR < 0.05). Identification of differentially expressed genes in Mets subgroups Genes were defined to be differentially expressed between subgroups of Mets if one or several probe sets representing these genes showed a statistically significant difference of |0.848| on the log scale (1.8-fold change) between the average expression levels of the compared groups. Statistical significance was determined by a two-sided permutation-based t test adjusted to multiple testing (Benjamini and Hochberg’s FDR < 0.05) using GenePattern 3.0.19 Unsupervised clustering Complete linkage hierarchical clustering (Euclidian distance) and principal component analysis (covariance value) were performed after log2-transformed data was mean centered and divided by standard deviation using Genesis 1.7.2.20 Determination of over-represented biological processes Biological processes were defined to be over-represented in a list of probe sets if the list showed a statistically significant enrichment (FDR < 0.05) of these processes compared with all probe sets on the HG-U133 Plus 2.0 array. Enrichment statistics was calculated by a modified Fisher´s exact test corrected for multiple testing by FDR (Benjamini and Hochberg) using the DAVID Bioinformatics Resources.21,22 Supervised clustering K-nearest neighbouring (KNN; cosine correlation, mean-based) and weighted voting (WV; mean-based) were implemented for supervised learning using GeneCluster 2.0.19 Generation of prediction models Prediction models were generated by identifying a minimal set of genes fulfilling a correct leave-1-out cross validation (construct a classifier using all but one sample for training; test on that sample; repeat for all samples) using KNN or WV (GeneCluster 2.0). Statistical significance was examined with Fisher’s exact test (GeneCluster 2.0). Genes were used whose differential expression was verified by two independent mathematical algorithms: GCOS 1.4 (Affymetrix; log2, FDR < 0.05) and dCHIP 1.323 (PM-only, outlier detection, p < 0.05). In these algorithms, fold-change cutoffs (see Results) were adjusted to obtain a similar number of differentially expressed genes as with RMAExpress procedure.
Validation of array data by quantitative PCR RNA was transcribed into cDNA using SuperScript II RNase HReverse Transcriptase (Invitrogen, Karlsruhe, Germany) and random hexamer primers (GE Healthcare, Munich, Germany). Quantitative PCR (qPCR) was performed using TaqMan Gene Expression Assays (BCMP11: Hs00411286_m1, CEACAM6: Hs00366002_m1, GALNTL2: Hs00365065_m1, HSPG2: Hs01078536_m1, PECAM1: Hs00169777_m1, PTTG1: Hs00851754_u1, TBP: Hs00427620_m1) and the TaqMan Gene Expression Mastermix (both Applied Biosystems, Foster City, CA) according to the manufacturer’s specifications. TATA box binding protein (TBP) was used for normalization as its expression showed only a low coefficient of variation (4%) among all samples in the microarray data. Crossing points (CP) were measured within two independent experiments (coefficient of variation 2.0%) and the mean value was used for further calculations. Relative transcript levels were calculated relatively by the DDCP method.24 As reference a fictitious sample with DCP 5 0 was used. Results and discussion Because fresh-frozen metastatic lesions of RCC are hardly available, studies on such tissues are limited so far. We characterized a homogenous cohort of 20 pulmonary Mets of clear-cell RCC regarding its transcriptome-wide expression profiles. First of all, we addressed the question if the expression profiles of this cohort of pulmonary Mets are representative for distant Mets of RCC. Comparing the expression profiles of these 20 lung Mets and of 10 clear-cell RCC Mets (three bone, one brain, five lung, one lymph node) generated in another study11 with the profiles of the corresponding primary RCC using a meta-analysis procedure resulted in 810 differentially expressed and therefore, potentially metastasis-associated genes (Supporting Information). Because these results are based on the expression data of both studies, the high number of identified genes indicates the molecular similarity of the Mets of both studies and therefore, demonstrates the representativeness of the RCC Mets analyzed in this study. Among the identified genes are the matrix metallopeptidases MMP7 and MMP9, the chemokine receptor CXCR3, the differentiation marker MME (CD10), the apoptosis inhibitor BCL2 or the cell-surface protein CD44 for which an association to progression of RCC has already been described.25 Seventeen of the identified metastasis-associated genes belong to a 94-gene signature of lung Mets identified in breast cancer Mets26 and 12 of them have also been found in a previously published metastatic signature of 128 genes discovered on adenocarcinomas,12 further demonstrating similarities of RCC Mets investigated in this study to metastatic lesions of other tumours and localizations. Further on, pulmonary Mets were classified into subgroups according to DFI and number of lung Mets of the patient, respectively (Table I), and expression patterns associated with these characteristics were identified. Molecular patterns distinguish late and early Mets Based on studies describing a significantly shorter disease-specific survival of RCC patients with a short ( 2 years.8 Nevertheless, we would suggest a biological role of an activated metastatic potential in ‘‘late Mets’’. For this intention, we examined the expression patterns of Mets of patients with a ‘‘medium DFI’’ (DFI 5 11–38 months; n 5 8; Table II). A prediction algorithm (KNN) based on the 414 probe sets of the 306 differentially
expressed genes classified these pulmonary Mets (except Met 2) to the ‘‘late Mets’’ (DFI 5 years) group. This molecular similarity to ‘‘late Mets’’ indicates the existence of significant molecular differences between Mets developed synchronously or metachronously to the primary tumour. Combined with the detected expression differences this fact indicates that Mets which start their growth when the primary tumour is still present and able to metastasize by itself have a lower metastatic activity than Mets whose growth-onset occurs when the primary tumour is already removed. The latter ones might develop a higher metastatic potential right from the start of their growth, because the primary tumour does no longer exist as a source of metastatic spread. Because the identified overall expression patterns are not in accordance to the clinical observation of a better outcome of patients with late Mets, there must be other molecular factors determining varying dormancy periods which were not clearly detected by analyzing metastatic lesions. Cell cycle arrest, blocked angiogenesis or an effective immune response are discussed as possible reasons for the dormancy of cancer cells.33 As described above, we identified some genes that might be associated with the inhibition of tumour cell proliferation as a supposable reason for dormancy of Mets (PTGER3, SFTPC, TSPAN7). However, so far, mechanisms causing the escape from the dormant status are unknown. Possibly, tumour cells which are dormant for years need a longer time to adapt to the changed microenvironment.33 Only a few genes identified to be associated with DFI in this study are included in published Mets-associated gene signatures.9,11,12,34 Presumably, this is because of the fact that these studies focussed on the identification of genes associated with metastatic spread in general, whereas we discovered signatures distinguishing Mets subgroups. Molecular patterns are associated with varying numbers of Mets We further compared Mets of patients with ‘‘multiple’’ (16) and ‘‘few Mets’’ (8) because clinical analyses showed a survival
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GENE SIGNATURES OF PULMONARY RENAL CELL CARCINOMA METASTASES TABLE III – THE TOP TEN UP- AND DOWN-REGULATED GENES WHICH ARE DIFFERENTIALLY EXPRESSED IN ‘‘MULTIPLE’’ VS. ‘‘FEW METS’’ Rank
Gene Symbol
Literature Search
FC
p value
FDR
No Yes No No No Yes Yes
11.4 4.6 3.1 2.7 1.9 4.3 2.4
0.004 0.006 0.002 0.008 0.002 0.002 0.004
0.047 0.047 0.044 0.050 0.044 0.044 0.047
No No
No No
2.3 2.6
0.008 0.004
0.050 0.047
fl
No
No
2.3
0.008
0.050
fl fl No No No › ›
Yes No No No No No No
No No No No No Yes Yes
215.3 24.2 23.1 29.4 27.2 26.5 28.7
0.002 0.004 0.002 0.006 0.004 0.006 0.004
0.044 0.047 0.044 0.047 0.047 0.047 0.047
No › ›
No No No
No Yes No
24.8 26.4 22.8
0.006 0.004 0.004
0.047 0.047 0.047
Gene Title
Top 10 Up-regulated Genes 1 GPR64 G protein-coupled receptor 64 2 RASGEF1A RasGEF domain family, member 1A 3 PBK PDZ binding kinase 4 E2F8 E2F transcription factor 8 5 ARNTL2 Aryl hydrocarbon receptor nuclear translocator-like 2 6 HOXA10 Homeobox A10 7 CDKN2A Cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 8 DLGAP5 Discs, large (Drosophila) homolog-associated protein 5 9 CDKN3 Cyclin-dependent kinase inhibitor 3 (CDK2-associated dual specificity phosphatase) 10 HOXD11 Homeobox D11 Top 10 Down-regulated Genes 1 SCGB1A1 Secretoglobin, family 1A, member 1 (uteroglobin) 2 NKX2-1 NK2 homeobox 1 3 TMEM178 Transmembrane protein 178 4 SCGB3A2 Secretoglobin, family 3A, member 2 5 SFTA2 Surfactant associated 2 6 AGR3 Breast cancer membrane protein 11 7 CEACAM6 Carcinoembryonic antigen-related cell adhesion molecule 6 (nonspecific cross reacting antigen) 8 CXCL17 Chemokine (C-X-C motif) ligand 17 9 AQP4 Aquaporin 4 10 RAB25 RAB25, member RAS oncogene family
Tumour
RCC
No › › fl fl fl fl
No No No No No No Yes
› ›
MS
The top 10 annotated up- and down-regulated genes in ‘‘multiple’’ compared to ‘‘few Mets’’ determined by a mean-based t test using GeneCluster 2.0 are shown. Results of literature search concerning the identified genes are briefly demonstrated. Literature studies were carried out as abstract search using the NCBI homepage (www.ncbi.nlm.nih.gov; PubMed, Gene). Fold-changes of expression (FC 5 2Dlog; represented as 21/FC for down-regulated genes) are shown. The p values and the FDR indicate the statistical significance of the expression differences in the compared groups. Tumour, association with tumour progression and/or differential expression in tumours/tumour cells compared to nonmalignant tissue/cells (›, fl, up- or down-regulation); RCC, association with RCC; MS, involvement in metastatic spread. TABLE IV – GENE SIGNATURES FOR PREDICTION OF THE DFI AND THE NUMBER OF METS PER PATIENT Gene Symbol
Literature Search
Gene Title
Tumour
RCC
Prediction of ‘‘late Mets’’ vs. ‘‘early Mets’’ HSPG2 Heparan sulfate proteoglycan 2 ICAM2 Intercellular adhesion molecule 2 PECAM1 Platelet/endothelial cell adhesion molecule (CD31 antigen) SFTPC Surfactant, pulmonary-associated protein C TPM2 Tropomyosin 2 (beta) TSPAN7 Tetraspanin 7
› › › fl › No
No No Yes No No No
Prediction of ‘‘multiple Mets’’ vs. ‘‘few Mets’’ AGR3 Breast cancer membrane protein 11 E2F8 E2F transcription factor 8 EHF Ets homologous factor GPR64 G protein-coupled receptor 64 PBK PDZ binding kinase RASGEF1A RasGEF domain family, member 1A SCGB1A1 Secretoglobin, family 1A, member 1 (uteroglobin) SCGB3A2 Secretoglobin, family 3A, member 2 SFTPG Surfactant associated 2 NKX2-1 NK2 homeobox 1 TMEM178 Transmembrane protein 178
› fl › No › › fl No No fl No
No No No No No No Yes No No No No
FC
p value
FDR
Yes No Yes No No No
2.8 2.6 3.0 17.6 2.6 6.5
0.002 0.002 0.002 0.002 0.008 0.002
0.022 0.022 0.022 0.022 0.049 0.022
Yes No No No No Yes Yes No No No No
26.5 2.7 24.2 11.4 3.1 4.6 215.3 29.4 27.2 24.2 23.1
0.006 0.008 0.008 0.004 0.002 0.006 0.002 0.006 0.004 0.004 0.002
0.047 0.050 0.050 0.047 0.044 0.047 0.044 0.047 0.047 0.047 0.044
MS
The six-gene (identified using 11 samples) and 11-gene signatures (identified using 17 samples) for the prediction of the DFI and the number of Mets, respectively, are shown. Results of literature search concerning the identified genes are briefly demonstrated. Literature studies were carried out as abstract search using the NCBI homepage (www.ncbi.nlm.nih.gov; PubMed, Gene). Fold-changes of expression (FC 5 2Dlog; represented as 21/FC for down-regulated genes) are shown. The p values and FDR indicate the statistical significance of the expression differences in the compared groups. Tumour, association with tumour progression and/or differential expression in tumours/tumour cells compared to nonmalignant tissue/cells (›, fl, up- or down-regulation); RCC, association with RCC; MS, involvement in metastatic spread.
benefit for patients with a limited number of Mets.7,14 As the 1,318-nm laser allowed the complete resection of all palpable lung Mets of the patients included in this study (except patient with Met 18, Table I), the number of Mets was defined as the number of all resected pulmonary Mets.
By comparing Mets obtained from patients with ‘‘multiple’’ and ‘‘few Mets’’ (Table I: B1 vs. B2), 135 genes (163 probe sets) were identified to be differentially expressed (Supporting Information; Fig. 1). Fifty genes (37%) were up-regulated and 85 (63%) downregulated. The top 10 up- and down-regulated genes are listed in
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FIGURE 2 – Validation of microarray results by qPCR. The figure shows the expression of the six genes quantified by qPCR for validation of expression patterns associated with a varying DFI (a) or number of Mets (b). The upper images represent gene expression values of the initial sample cohorts. In the lower images, expression values of independent samples are included. For comparison, the corresponding results of the microarray analyses are given. Fold changes represent the ratios of mean values ‘‘late vs. early Mets’’ and ‘‘multiple vs. few Mets’’ (array data: FC52Dlog, mean FC of all probe sets). Statistical significance of the expression differences measured by qPCR was determined by a two-sided heteroscedastic t test (Microsoft Excel) based on the log2-transformed expression values. Boxplots show the median value (solid lines) and values within the 25th and 75th percentiles, respectively. Error bars indicate the minimum and maximum expression value, respectively. Statistically significant expression differences between the initial sample cohorts detected by microarray analyses were completely confirmed by qPCR. For five of the six genes these expression differences were also statistically significant when including additional samples. (n, number of samples).
GENE SIGNATURES OF PULMONARY RENAL CELL CARCINOMA METASTASES
Table III. Among them are genes known to be involved in progression of tumours or in metastatic spread like RASGEF1A, AGR3 or CEACAM6, but also genes which have never been described to be related to tumour formation like GRP64. Their highly significant differential expression in the compared groups indicates their role in RCC progression and metastatic disease. Gene Ontology enrichment analyses showed an over-representation of cell division and mitotic cell cycle among the 163 probe sets of the 135 differentially expressed genes. These genes, such as the mitotic kinase PBK or the cell cycle regulators BIRC5, PTTG1 and CKS2, are positive regulators of these processes and up-regulated in patients with ‘‘multiple Mets’’. The apoptosis inhibitor survivin (BIRC5) is a well-known predictor of poor outcome in many tumour types including RCC.35 PTTG1 (securin), which is involved in sister chromatid separation and possesses tumourigenic activity, was also identified to be metastasis-associated in other microarray studies.11,12 Additionally, literature search showed a functional relation to or a differential expression in human cancers or tumour cells for 79 (59%) out of the 135 genes (Supporting Information). Regarding the up- or down-regulation, 60% of these tumour-associated genes were regulated in ‘‘multiple Mets’’ in the same direction as described for cancer. These observations indicate the existence of a rather tumour-like expression pattern in ‘‘multiple Mets’’ and are, therefore, in accordance with a worse outcome of disease of the corresponding patients, who have a decreased survival compared with patients with a limited number of Mets (up to six or nine, respectively).7,14 Surprisingly, Gene Ontology analysis showed no over-representation of metastasis-associated processes like angiogenesis. Thus, differences in the number of Mets might result from varying growth potential rather than from different metastatic features. A higher cell division activity of tumour cells may facilitate growth of Mets in a patient more frequently and therefore, might result in a higher number of Mets. Gene signatures of Mets have a predictive potential We further identified minimal sets of genes sufficient for classifying the samples to the appropriate subgroup of Mets. For this purpose, we generated prediction models based on genes whose differential expressions were verified by two further mathematical algorithms (GCOS, dCHIP). These genes are most likely to have predictive potential and presumably to be functionally involved in the development of the investigated metastatic characteristics. Among the 55 genes (74 probe sets), whose differential expression was confirmed using dCHIP {log (fold-change) > |1.1|} and GCOS {log (fold-change) > |1.6|} by comparing ‘‘late’’ and ‘‘early Mets’’, six genes (Table IV) were sufficient for a correct leave-1out cross validation (p 5 0.002, WV). Furthermore, this gene signature classified one additional sample (Met 25, Table I) correctly (‘‘late Mets’’), which initially was not included in the analyses. Based on the 35 genes (45 probe sets), whose differential expression in the ‘‘multiple Mets’’ compared with the ‘‘few Mets’’ group was confirmed by dCHIP {log (fold-change) > |1.0|} and GCOS {log (fold-change) |1.8|}, an 11-gene signature (Table IV) sufficient for a correct leave-1-out cross validation (p < 0.001) was identified using KNN. Furthermore, this gene signature was able to classify two additional samples (Met 15, Met 20, Table I) correctly (‘‘few Mets’’). A third sample (Met 2), originating from a patient with 10 Mets, was assigned to the ‘‘multiple Mets’’
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group. This is concordant with our previous results on 328 patients (34% with kidney cancer), which showed a significant decrease in survival rate for patients with >9 lung Mets.14 Based on the observation that patients with multiple Mets frequently show an early growth-onset of Mets, we further compared Mets from patients who had ‘‘late and few Mets’’ (Table I: A1 1 B2, A3 1 B2, A1 1 B3; n 5 6) and ‘‘early and multiple Mets’’ (Table I: A2 1 B1; n 5 4), respectively. The 444 differentially expressed genes (581 probe sets) include 35 of the 135 and 229 of the 306 genes identified to be differentially expressed in the ‘‘number of Mets’’ and ‘‘DFI’’ comparisons, respectively, and therefore, comprise molecular features of both characteristics. Inclusion of the remaining samples (n 5 10) demonstrated that, in principle, these 444 genes are able to distinguish Mets of patients with ‘‘late and few Mets’’ and ‘‘early and multiple Mets’’ (Supporting Information). In summary, these results demonstrate the predictive potential of the identified expression patterns in Mets, individually as well as in combination. Furthermore, the correct classification of one and three additional samples, respectively, confirm the validity of the identified expression patterns. Validation For validation of the microarray results, the expression of six genes was quantified by qPCR. These genes were identified to be differentially expressed exclusively based on RMAExpress software (PTTG1, GALNTL2), by all analysis procedures (CEACAM6) or were included in the predictive gene signatures (AGR3, HSPG2, PECAM1). qPCR analysis of these six genes in the initial sample cohorts (Table I: A1 vs. A2, B1 vs. B2) completely confirmed the results of the microarray analyses (Fig. 2). Statistically significant expression differences were further confirmed for five of the six genes on eight (‘‘DFI’’ comparison; Table I: A3, A4) and 12 (‘‘number of Mets’’ comparison; Table I: B3, B4) additional lung Mets. Although this independent sample cohort is relatively small due to the hardly availability of the sample material, the results confirm the validity of the microarray results. Conclusion and perspectives The present study describes differences in gene expression associated with particular metastatic characteristics, namely varying DFI and different numbers of Mets, identified in a unique homogeneous cohort of lung Mets, and represents the predictive potential of the identified gene signatures. It needs further investigation to identify those genes which are already differentially expressed in matched primary tumours and therefore, represent promising targets for prognostic purposes predicting the DFI and the metastatic burden of a patient. Functional studies on selected genes will investigate their role in RCC progression and metastatic spread as well as their suitability as potential therapeutic targets. Acknowledgements The authors thank the Dr. Robert-Pfleger-Stiftung for kindly supporting this study, Dr. M. Grosser for the technical support of the microarray analyses and Dr. K. Kraemer for critically reviewing the manuscript.
References 1. 2. 3. 4.
Drucker BJ. Renal cell carcinoma: current status and future prospects. Cancer Treat Rev 2005;31:536–45. Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ. Cancer statistics, 2007. CA Cancer J Clin 2007;57:43–66. Lam JS, Leppert JT, Belldegrun AS, Figlin RA. Novel approaches in the therapy of metastatic renal cell carcinoma. World J Urol 2005;23: 202–12. Kopfstein L, Christofori G. Metastasis: cell-autonomous mechanisms versus contributions by the tumor microenvironment. Cell Mol Life Sci 2006;63:449–68.
5.
6. 7.
Neal CP, Garcea G, Doucas H, Manson MM, Sutton CD, Dennison AR, Berry DP. Molecular prognostic markers in resectable colorectal liver metastases: a systematic review. Eur J Cancer 2006;42: 1728–43. Fokas E, Engenhart-Cabillic R, Daniilidis K, Rose F, An HX. Metastasis: the seed and soil theory gains identity. Cancer Metastasis Rev 2007;26:705–15. Hofmann HS, Neef H, Krohe K, Andreev P, Silber RE. Prognostic factors and survival after pulmonary resection of metastatic renal cell carcinoma. Eur Urol 2005;48:77–81.
482 8. 9.
10.
11.
12. 13.
14.
15. 16. 17.
18. 19. 20. 21. 22.
WUTTIG ET AL.
van der Poel HG, Roukema JA, Horenblas S, van Geel AN, Debruyne FM. Metastasectomy in renal cell carcinoma: A multicenter retrospective analysis. Eur Urol 1999;35:197–203. Kosari F, Parker AS, Kube DM, Lohse CM, Leibovich BC, Blute ML, Cheville JC, Vasmatzis G. Clear cell renal cell carcinoma: gene expression analyses identify a potential signature for tumor aggressiveness. Clin Cancer Res 2005;11:5128–39. Takahashi M, Rhodes DR, Furge KA, Kanayama H, Kagawa S, Haab BB, Teh BT. Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification. Proc Natl Acad Sci USA 2001;98:9754–9. Jones J, Otu H, Spentzos D, Kolia S, Inan M, Beecken WD, Fellbaum C, Gu X, Joseph M, Pantuck AJ, Jonas D, Libermann TA. Gene signatures of progression and metastasis in renal cell cancer. Clin Cancer Res 2005;11:5730–9. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet 2003;33:49–54. Boer JM, Huber WK, Sultmann H, Wilmer F, von Heydebreck A, Haas S, Korn B, Gunawan B, Vente A, Fuzesi L, Vingron M, Poustka A. Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31,500-element cDNA array. Genome Res 2001;11:1861–70. Rolle A, Pereszlenyi A, Koch R, Richard M, Baier B. Is surgery for multiple lung metastases reasonable? A total of 328 consecutive patients with multiple-laser metastasectomies with a new 1318-nm. Nd:YAG laser. J Thorac Cardiovasc Surg 2006;131:1236–42. Software available at: http://rmaexpress.bmbolstad.com (by Bolstad B; released Jan 30, 2006). Hong F, Breitling R. A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics 2008;24:374–82. Hong F, Breitling R, McEntee CW, Wittner BS, Nemhauser JL, Chory J. RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics 2006;22: 2825–7. Manual available at: http://www.bioconductor.org/packages/bioc/ vignettes/RankProd/inst/doc/RankProd.pdf (by Houg F, Wittner B; released Oct 22, 2008). Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP. GenePattern 2.0. Nat Genet 2006;38:500–1. Sturn A, Quackenbush J, Trajanoski Z. Genesis: cluster analysis of microarray data. Bioinformatics 2002;18:207–8. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: Database for annotation, visualization and integrated discovery. Genome Biol 2003;4:P3 (Epub Apr 3, 2003). Huang da W, Sherman BT, Tan Q, ir J, iu D, ryant D, uo Y, tephens R, aseler MW, ane HC, Lempicki RA. DAVID Bioinformatics
23. 24. 25. 26. 27.
28.
29.
30. 31.
32.
33. 34.
35.
Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 2007; 35:W169–75. Available at: www.dchip.org. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001;25:402–8. PubMed abstract and Gene search at: www.ncbi.nlm.nih.gov. Minn AJ, Gupta GP, Siegel PM, Bos PD, Shu W, Giri DD, Viale A, Olshen AB, Gerald WL, Massague J. Genes that mediate breast cancer metastasis to lung. Nature. 2005;436:518–24. Shoji Y, Takahashi M, Kitamura T, Watanabe K, Kawamori T, Maruyama T, Sugimoto Y, Negishi M, Narumiya S, Sugimura T, Wakabayashi K. Downregulation of prostaglandin E receptor subtype EP3 during colon cancer development. Gut 2004;53:1151–8. Bandyopadhyay S, Zhan R, Chaudhuri A, Watabe M, Pai SK, Hirota S, Hosobe S, Tsukada T, Miura K, Takano Y, Saito K, Pauza ME, et al. Interaction of KAI1 on tumor cells with DARC on vascular endothelium leads to metastasis suppression. Nat Med 2006;12: 933–8. Li R, Todd NW, Qiu Q, Fan T, Zhao RY, Rodgers WH, Fang HB, Katz RL, Stass SA, Jiang F. Genetic deletions in sputum as diagnostic markers for early detection of stage I non-small cell lung cancer. Clin Cancer Res 2007;13:482–7. Sanjmyatav J, Schubert J, Junker K. Comparative study of renal cell carcinoma by CGH, multicolor-FISH and conventional cytogenic banding analysis. Oncol Rep 2005;14:1183–7. Wilhelm M, Veltman JA, Olshen AB, Jain AN, Moore DH, Presti JC, Jr., Kovacs G, Waldman FM. Array-based comparative genomic hybridization for the differential diagnosis of renal cell cancer. Cancer Res 2002;62:957–60. Berois N, Blanc E, Ripoche H, Mergui X, Trajtenberg F, Cantais S, Barrois M, Dessen P, Kagedal B, Benard J, Osinaga E, Raguenez G. ppGalNAc-T13: a new molecular marker of bone marrow involvement in neuroblastoma. Clin Chem 2006;52:1701–12. Aguirre-Ghiso JA. Models, mechanisms and clinical evidence for cancer dormancy. Nat Rev Cancer 2007;7:834–46. Sultmann H, von Heydebreck A, Huber W, Kuner R, Buness A, Vogt M, Gunawan B, Vingron M, Fuzesi L, Poustka A. Gene expression in kidney cancer is associated with cytogenetic abnormalities, metastasis formation, and patient survival. Clin Cancer Res 2005;11: 646–55. Parker AS, Kosari F, Lohse CM, Houston Thompson R, Kwon ED, Murphy L, Riehle DL, Blute ML, Leibovich BC, Vasmatzis G, Cheville JC. High expression levels of survivin protein independently predict a poor outcome for patients who undergo surgery for clear cell renal cell carcinoma. Cancer 2006;107:37–45.
