Abstract
Background/Aim: The treatment of advanced clear cell renal cell carcinoma (ccRCC) is based on stratification of patients according to prognosis (favorable, intermediate, and poor). The aim of the study was to improve prognostication by biomarkers involved in angiogenesis. Patients and Methods: The study group consisted of 20 patients who underwent surgery for ccRCC. Gene expression analysis was peformed on a set of matched (primary tumor, metastasis, n=20+20) FFPE tissue samples. An additional analysis was done on expression data of 606 patients obtained from the TCGA Kidney Clear Cell Carcinoma (KIRC) database. Quantitative estimation of mRNA of selected genes (TaqMan human Angiogenesis Array, 97 genes) was performed by a real-time RT-PCR method with TaqMan® arrays. Results: Using the Cox regression model, 4 genes (PDGFB, FGF4, EPHB2 and BAI1) were identified whose expression was related to progression-free interval (PFI). Further analysis using the Kaplan Meier method conclusively revealed the relationship of BAI1 expression to prognosis (both datasets). Patients with higher BAI1 expression had significantly shorter PFI and overall survival. Conclusion: We showed that tumor tissue BAI1 expression level is a prognostic marker in ccRCC. Therefore, this gene might be involved in a prognostic panel to improve scoring systems on which the management of metastatic ccRCC patients is based.
Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cancer and belongs the most frequent urological malignancies worldwide. About one third of cases are diagnosed at advanced stages with 5-year survival rates of 12% (1). The Czech Republic belongs to countries with the highest incidence (29.0 per 100,000 in 2017) and mortality (9.7 per 100,000 in 2017) rate for renal cell carcinoma worldwide (2).
ccRCC can be one of the clinical manifestations of von Hippel-Lindau (VHL) disease resulting from changes of the von Hippel Lindau (VHL) tumor suppressor gene. Metastatic ccRCC is a highly angiogenic tumor with mutations, deletion or methylation of VHL, the most frequent genetic hallmark of ccRCC. The second most common mutated gene in ccRCC after VHL is PBRM1, a component of the chromatin remodelling complex (3). Additionally to genetics, many lifestyle, dietary and environmental factors have been associated with ccRCC. The major established risk factors include hypertension, cigarette smoking and obesity (4). Genetic alterations result in overexpression of VEGF. Therefore, oncologic treatment is based on targeting the angiogenesis vascular endothelial growth factor (VEGF/VEGFR) pathway by monoclonal antibodies or tyrosine kinase inhibitors (TKIs) and immunotherapies aiming to block immune checkpoints (5).
Patients with ccRCC are stratified into groups of favorable, intermediate, and poor prognosis based on the Memorial Sloan Kettering Cancer Center (MSKCC) risk stratification model. Risk scores are obtained from 5 clinical and laboratory parameters as performance status, time from diagnosis to start of therapy, hemoglobin level, serum lactate dehydrogenase and calcium concentration (6, 7). Recently, efforts are being made to improve prognostication of patients by including specific molecular biomarkers (3, 8, 9). Voss et al. from the Memorial Sloan Kettering Cancer Center proposed to involve the mutation status of three genes (BAP1, PBRM1, and TP53) as genomic biomarkers of ccRCC (10).
Precise patient stratification should help personalize the treatment algorithms to match individual patient profiles according to the current approach of preventive, predictive, personalized (3P) medicine (11). For most prevalent solid tumors as colorectal cancer and breast cancer, there exist multigene panels for predicting clinical outcome and treatment response (ColoPrint, MammaPrint and Oncotype DX) based on the assessment of expression of certain genes on the mRNA level (12, 13). Regarding ccRCC, currently there is no clinically used multigene mRNA expression panel for predicting outcome and treatment response, however, there are promising results on the identification genes which could be a part of such panel (14, 15).
The aim of our study was to evaluate the prognostic potential of mRNA expression of 97 genes involved in angiogenesis (TaqMan human Angiogenesis Array) in patients with metastatic ccRCC to improve decision about the treatment. The study was performed on a set of matched (primary tumor, metastasis, n=20+20) formalin-fixed, paraffin-embedded (FFPE) tissue samples and using the The Cancer Genome Atlas (TCGA) (16) dataset (n=606) to confirm the findings.
Patients and Methods
Patients. The study was approved by the Ethics Committee of the Faculty Hospital in Pilsen (approval from the date 2016/01/06). It was a retrospective study. All patients signed an informed consent for the use of their biological samples for the assessment of tumor markers. The study group consisted of 20 patients who underwent surgery for ccRCC at the Department of Urology of the University Hospital in Pilsen between December 2007 and October 2017. Each diagnosis of ccRCC was verified by a pathologist. The stage of disease was determined using the TNM system of the Union for International Cancer Control (UICC, 7th edition) (17). Metastases sites were located in lungs (7 cases), suprarenal glands (5 cases), vertebrae (4 cases), colon (2 cases), brain (2 cases), spleen (2 cases) and retroperitoneal adipose tissue (1 case). Treatment of 17 out of 20 metastatic ccRCC patients was based on sunitinib (Sutent) administered in cycles consisting of 4 weeks at a dose of 50 mg daily followed by a 2-week rest period (schedule 4/2), continuing until progression. Two patients were treated by sorafenib (Nexavar) administered at a total daily dose of 800 mg continuously until progression. One patient received pazopanib (Votrient) at a dose of 600 mg daily continuously until progression. The evaluation of treatment response was based on Response Evaluation Criteria in Solid Tumors (RECIST) (18). The detailed characteristics of patients are provided in Table I. We validated gene expression results obtained on our group of patients in additional datasets from TCGA public database available on https://www.cancer.gov/tcga. Characteristic of 606 patients from TCGA Kidney Clear Cell Carcinoma (KIRC) dataset is shown in Table II. The patients underwent the treatment based on the surgery, pharmaceutical therapy or radiation therapy. After identification of BAI1 as a prognostic marker in ccRCC patients, due to comparison of prognostic significance of BAI1 expression in ccRCC and other tumor types, where the involvement of BAI1 in pathogenesis is more understood, we performed survival analysis based on BAI1 expression also for glioblastoma and lung cancer adenocarcinoma patients. As a cohort of patients, we used TCGA Glioblastoma (GBM) dataset (166 patients) and TCGA Lung Adenocarcinoma (LUAD) dataset (567 patients).
Characteristics of patients in the study (at the time of ccRCC diagnosis).
Characteristics of patients of the TCGA Kidney Clear Cell Carcinoma (KIRC) dataset.
Tissue samples and RNA isolation. Biopsy tissue samples were processed by standard laboratory techniques at Department of Pathology of the University Hospital in Pilsen. FFPE tissue samples were stored at room temperature until use. Paraffin sections (4-μm thick) were stained with hematoxylin and eosin (H&E), microscopically verified by pathologists, and examined in order to identify sites with primary cancer cells, metastasis and sites of adjacent non-cancerous epithelial tissue suitable for dissection. Areas selected for expression analysis were highlighted manually. Matched samples of primary tumor, adjacent normal tissue and metastasis were analyzed. Total RNA was extracted from FFPE sections following macrodissection of tissue of interest using the RNeasy FFPE Kit (Qiagen, Hilden, Germany) as described previously (19). The 15-μm sections corresponded to H&E representatives, on which the areas for macrodissection were highlighted.
RT Real time PCR. Quantitative estimation of mRNA of selected genes (TaqMan human Angiogenesis Array, 97 genes including 3 reference genes, catalog number: 4378710, Thermo Fisher Scientific, Foster City, CA, USA) was performed by a real-time RT-PCR method with TaqMan® array card block on QuantStudio™ 7 Flex Real-Time PCR System (Thermo Fisher Scientific) according to manufacturer’s protocol, which also includes the list of all assessed 97 genes. Briefly, reverse transcription was performed on 50 ng of total RNA with SuperScript IV VILO Master Mix (Thermo Fisher Scientific) and random hexamers as primers. The qPCR reactions started with incubation at 50°C for 2 min and followed by 10 min at 92°C. The amplification was carried out in 40 two-step cycles (95 °C for 1 s and 60°C for 20 s. ROX passive reference dye was used for normalization of interwell variations. GAPDH, HPRT and β-actin were used as reference genes. Gene expression of particular sample was only evaluated if the expression of all three housekeeping genes was present. The results are presented as normalized values (2–ΔΔCt algorithm) using the geometric mean of quantifications (Ct) of the three reference genes (20).
Statistical analysis. The statistical analysis was performed using the R software package. The TCGA datasets were browsed using the Xena tool (21). Downloaded raw data were also analyzed using R. Essential descriptive statistics for all variables of interest were prepared based on the clinical and pathological data of the patients. Cox regression model was applied for the evaluation of prognostic significance. Kaplan-Meier survival curves for progression-free interval (PFI) and overall survival (OS) were generated.
Results
We performed a search for prognostic biomarkers of ccRCC patients treated with antiangiogenic targeted therapy by evaluating the relationship of tissue expression of genes involved in angiogenesis. We evaluated the relationship of primary tumor and metastasis tissue expression of 94 genes to PFI. The evaluation of PFI was based on the length of the time period of treatment administration, which continued if the patient responded to treatment, i.e., the patient was in the condition of complete response (CR), partial response (PR), stable disease (SD) according to the RECIST criteria. Based on this analysis, we identified 4 genes (BAI1, PDGFB, FGF4 and EPHB2) whose expression was related to PFI (Table III) using the Cox regression model. For these genes we observed that higher level of expression was associated with shorter PFI.
Relation of gene expression to progression-free interval (PFI) by the Cox regression model.
Further, Kaplan-Meier survival curves were generated for these genes. There was a statistically significant difference in time to progression between the patients with low and high BAI1 expression in ccRCC metastatic tissue (Figure 1A). The patients with low BAI1 expression had a better prognosis. In the case of BAI1 expression in primary tumor tissue, the statistical significance was not recorded (Figure 1B).
Kaplan-Meier curves showing the relation of BAI1 metastasis (A) and primary tumor (B) tissue expression level to progression-free survival in clear cell renal cell carcinoma (ccRCC) patients. Patients with high BAI1 expression had the worst outcome.
We validated the relationship of BAI1 expression in ccRCC primary tumor tissue to prognosis in a group of 606 ccRCC patients whose data were available in the public TCGA Kidney Clear Cell Carcinoma (KIRC) database. Patients with higher BAI1 expression had significantly shorter OS and PFI (Figure 2). The data on gene expression in metastatic tissue were not available.
Relation of BAI1 expression to overall survival (A) and progression-free survival (B) in TCGA Kidney Clear Cell Carcinoma (KIRC) group of patients (Kaplan-Meier curves). Patients with high BAI1 expression had a significantly shorter both overall survival and progression-free interval.
As BAI1 is a known tumor suppressor in many cancer types and our results in ccRCC patients presented above are contradictory to this proposed function, we explored the relationship of BAI1 expression to prognosis also in other types of cancers. Herein, we present the relation of BAI1 to prognosis in glioblastoma (expression data from TCGA Glioblastoma (GBM) database) and lung adenocarcinoma (expression data from TCGA Lung Adenocarcinoma (LUAD) database). Using the Xena software tool we found no relation of BAI1 expression to patients’ outcome in glioblastoma. In lung adenocarcinoma, it was recorded that patients with lower BAI1 expression had significantly shorter overall survival (OS) and progression-free interval (PFI) (Figure 3).
Kaplan-Meier curves showing the relation of BAI1 expression with overall survival (OS) and progression-free survival (PFI) in TCGA Glioblastoma (GBM) group of patients (A, B) and in TCGA Lung Adenocarcinoma (LUAD) group of patients (C, D). There is no relation of BAI1 expression to patient outcome in glioblastoma. In lung adenocarcinoma it was recorded that patients with lower BAI1 expression had significantly shorter both OS and PFI.
Discussion
In the management of ccRCC patients, precise patient stratification on the basis of prognostic algorithm helps personalize the treatment to match patient profiles (personalized medicine). According to the American Society of Clinical Oncology (ASCO) and European Society for Medical Oncology (ESMO) guidelines (22, 23), stratification of patients with advanced ccRCC into groups of favorable, intermediate, and poor prognosis is important for selecting the appropriate treatment regimen. Efforts have been made to find new molecular prognostic biomarkers to improve the currently used Memorial Sloan Kettering Cancer Center (MSKCC) scoring system. This could be achieved by employing advanced molecular technologies (7).
Based on the tissue expression of 97 genes involved in angiogenesis (TaqMan human Angiogenesis Array), by applying the Cox regression model, we identified 4 genes (PDGF-B, FGF4, EPHB2 and BAI1) that were related to PFI. In our set of tissue samples we recorded that high expression levels of BAI1, PDGF-B, FGF4, and EPHB2 were associated with shorter PFI.
Platelet-derived growth factor-B (PDGF-B) is responsible for proliferation and migration of cells during vascular maturation. Wang et al. in 2015 showed that high expression of PDGF-B was associated with significantly decreased risk of ccRCC mortality (24). However, Shim et al. in their study did not observe any relation of PDGF-B expression to prognosis in ccRCC (25). Taken together with our results, prognostic significance of PDGF-B remains unclear. Fibroblast growth factor 4 (FGF4) has been reported to induce epithelial-mesenchymal transition (EMT) in lung adenocarcinoma (26). According to our knowledge, there are no published data showing relation of FGF4 to prognosis of ccRCC. EphB2 is a member of large family of receptor tyrosine kinases with roles in cell motility, EMT and angiogenesis. It appears prognostic in multiple malignancies, especially colorectal cancers (27). Ghatalia et al. showed differential expression of EphB2 between primary tumor and metastasis of ccRCC with higher expression in metastases (28).
Using Kaplan-Meier analysis applied on genes identified by the Cox regression model and external validation on publically available expression data (TCGA KIRC database), we identified the BAI1 as a promising prognostic marker in ccRCC patients with the potential to be incorporated in the scoring system.
Brain-specific angiogenesis inhibitor 1 (BAI1) also known as adhesion G protein-coupled receptor B1 (ADGRB1) was isolated and characterized for the first time as a target gene transactivated by P53 (29). Today we know it belongs to the adhesion G-protein coupled receptors (aGPCRs) subfamily of seven transmembrane spanning receptors (7TMRs) (30, 31).
There is a high expression of BAI1 in the brain, however it is also expressed in other tissues like kidney or lungs. It has been described that BAI1 is involved in neuronal synaptogenesis and process of phagocytosis (32). It was found that BAI1 plays a role as a tumor suppressor in glioblastoma (33). BAI1 was named for the ability of its inhibiting angiogenesis in glioblastomas. Liu et al. observed its tumor suppressing effect in lung cancer cells (34). These results indicating the tumor suppressing function of BAI suggest that high levels of BAI1 must be associated with better patient outcomes. This corresponds to the results we obtained from the public databases on lung adenocarcinoma patients, where a high level of BAI1 expression meant a better prognosis (Figure 3C and D). In the case of glioblastoma, we revealed no relationship of BAI1 expression to prognosis (Figure 3A and B).
In renal cancer, in 2007 Kudo et al. published a work on transgenic mice concluding that the transfer of the BAI1 gene can suppress the tumor growth via inhibition of angiogenesis (35). Another study on renal cancer and BAI1 by Izutsu et al. was published in 2011. The authors observed significant decrease in BAI1 mRNA in renal cell carcinoma tissue compared to normal kidney tissue in a group of 47 renal cell carcinoma patients. Additional to that, they detected lower expression in advanced renal cell carcinoma than in localized renal cell carcinoma (36).
However, in our study on ccRCC patients both data obtained from our group (20 patients) and data from the TCGA database (606 patients) independently showed that the patients with high BAI1 expression in ccRCC tumor tissue had worse outcome in terms of both shorter OS and PFI. This suggests more complex involvement of BAI1 in cancerogenesis. At this point it is necessary to mention that the majority of patients with ccRCC undergoes treatment based on antiangiogenic therapeutics. Of course, there is a question on how much BAI1 is involved in angiogenesis and probably also in other processes being in relation to the effect of antiangiogenic treatment.
Until now, there were some gene expression panels for the assessment of prognosis of ccRCC patients proposed (none of them included BAI1). Liu et al. constructed a panel containing a cluster of 10 metabolism-related genes (ALDH6A1, FBP1, HAO2, TYMP, PSAT1, IL4I1, P4HA3, HK3, CPT1B, and CYP26A1) (37). Pan et al. identified differentially expressed genes involved in the metastasis of ccRCC and proposed a 5-gene (OTX1, MATN4, PI3, ERVV-2, and NFE4) panel predicting progression and prognosis (15).
In conclusion, we showed that tumor tissue BAI1 expression may serve as a prognostic marker in ccRCC. This gene might be involved in a prognostic panel to improve scoring systems as MSKCC on which the management of metastatic ccRCC patients is based. Gene expression panels are a promising tool on how to individualize patients’ treatment as part of personalized medicine.
Acknowledgements
This work was supported by the grant of Ministry of Health of the Czech Republic–Conceptual Development of Research Organization (Faculty Hospital in Pilsen-FNPl, 00669806) and by the Charles University Research Fund (Progres Q39). The results published here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
Footnotes
Authors’ Contributions
Study design: Travnicek, Hora, Topolcan, Pesta; Methodology: Pesta, Kulda, Windrichova; Investigation: Windrichova, Rezackova, Houfkova, Macanova, Nestorova, Hes, Bendova; Data processing: Travnicek, Kulda, Polivka; Manuscript writing: Pesta, Kulda, Polivka; Funding acquisition: Travnicek, Topolcan. All Authors reviewed and approved the manuscript.
Conflicts of Interest
The Authors declare no conflicts of interest.
- Received June 3, 2021.
- Revision received July 9, 2021.
- Accepted July 13, 2021.
- Copyright © 2021 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.
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