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Mehra R, Nallandhighal S, Cotta B, Knuth Z, Su F, Kasputis A, Zhang Y, Wang R, Cao X, Udager AM, Dhanasekaran SM, Cieslik MP, Morgan TM, Salami SS. Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma. JCO Precis Oncol 2024; 8:e2300565. [PMID: 38810179 DOI: 10.1200/po.23.00565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/11/2024] [Accepted: 03/15/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease-specific survival (DSS), and overall survival (OS) in two validation data sets (combined n = 761). RESULTS After quality control, the discovery cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up of 26 and 36 months, respectively. We developed a 15-gene (15G) signature, which was independently associated with worse DFS and DSS (DFS: hazard ratio [HR], 11.08 [95% CI, 4.9 to 25.1]; DSS: HR, 9.67 [95% CI, 3.4 to 27.7]) in a multivariable model adjusting for clinicopathologic parameters (including stage, size, grade, and necrosis [SSIGN] score and Memorial Sloan Kettering Cancer Center nomogram) and mxCCP score. The 15G signature was also independently associated with worse DFS and DSS in both validation data sets (Validation A [n = 382], DFS: HR, 2.6 [95% CI, 1.6 to 4.3]; DSS: HR, 3 [95% CI, 1.4 to 6.1] and Validation B (n = 379), DFS: HR, 2.1 [95% CI, 1.2 to 3.6]; OS: HR, 3 [95% CI, 1.6 to 5.7]) adjusting for clinicopathologic variables and mxCCP score. CONCLUSION We developed and validated a novel 15G prognostic signature to improve risk stratification of patients with ccRCC. Pending further validation, this signature has the potential to facilitate optimal treatment allocation.
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Affiliation(s)
- Rohit Mehra
- University of Michigan Rogel Cancer Center, Ann Arbor, MI
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Pathology, Michigan Medicine, Ann Arbor, MI
| | | | | | - Zayne Knuth
- Department of Urology, Michigan Medicine, Ann Arbor, MI
| | - Fengyun Su
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Pathology, Michigan Medicine, Ann Arbor, MI
| | - Amy Kasputis
- Department of Urology, Michigan Medicine, Ann Arbor, MI
| | - Yuping Zhang
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Pathology, Michigan Medicine, Ann Arbor, MI
| | - Rui Wang
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Pathology, Michigan Medicine, Ann Arbor, MI
| | - Xuhong Cao
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Pathology, Michigan Medicine, Ann Arbor, MI
- Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI
| | - Aaron M Udager
- University of Michigan Rogel Cancer Center, Ann Arbor, MI
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Pathology, Michigan Medicine, Ann Arbor, MI
| | - Saravana M Dhanasekaran
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Pathology, Michigan Medicine, Ann Arbor, MI
| | - Marcin P Cieslik
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Pathology, Michigan Medicine, Ann Arbor, MI
- Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Todd M Morgan
- University of Michigan Rogel Cancer Center, Ann Arbor, MI
- Department of Urology, Michigan Medicine, Ann Arbor, MI
| | - Simpa S Salami
- University of Michigan Rogel Cancer Center, Ann Arbor, MI
- Michigan Center for Translational Pathology, Michigan Medicine, Ann Arbor, MI
- Department of Urology, Michigan Medicine, Ann Arbor, MI
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Wang K, Dong L, Li S, Liu Y, Niu Y, Li G. CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model. BMC Cancer 2024; 24:148. [PMID: 38291357 PMCID: PMC10826073 DOI: 10.1186/s12885-024-11870-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND We aimed to identify preoperative predictors of aggressive pathology for cT1 solid renal cell carcinoma (RCC) by combining clinical features with qualitative and quantitative CT parameters, and developed a nomogram model. METHODS We conducted a retrospective study of 776 cT1 solid RCC patients treated with partial nephrectomy (PN) or radical nephrectomy (RN) between 2018 and 2022. All patients underwent four-phase contrast-enhanced CT scans and the CT parameters were obtained by two experienced radiologists using region of interest (ROI). Aggressive pathology was defined as patients with nuclear grade III-IV; upstage to pT3a; type II papillary renal cell carcinoma (pRCC), collecting duct or renal medullary carcinoma, unclassified RCC or sarcomatoid/rhabdoid features. Univariate and multivariate logistic analyses were used to determine significant predictors and develop the nomogram model. To evaluate the accuracy and clinical utility of the nomogram model, we used the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis (DCA), risk stratification, and subgroup analysis. RESULTS Of the 776 cT1 solid RCC patients, 250 (32.2%) had aggressive pathological features. The interclass correlation coefficient (ICC) of CT parameters accessed by two reviewers ranged from 0.758 to 0.982. Logistic regression analyses showed that neutrophil-to-lymphocyte ratio (NLR), distance to the collecting system, CT necrosis, tumor margin irregularity, peritumoral neovascularity, and RER-NP were independent predictive factors associated with aggressive pathology. We built the nomogram model using these significant variables, which had an area under the curve (AUC) of 0.854 in the ROC curve. CONCLUSIONS Our research demonstrated that preoperative four-phase contrast-enhanced CT was critical for predicting aggressive pathology in cT1 solid RCC, and the constructed nomogram was useful in guiding patient treatment and postoperative follow-up.
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Affiliation(s)
- Keruo Wang
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Liang Dong
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Songyang Li
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Yaru Liu
- Department of Pulmonary & Critical Care Medicine, 8th Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yuanjie Niu
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
| | - Gang Li
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
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Wang K, Wang G, Liu Y, Dong L, Niu Y, Li G. Tumor margin irregularity degree is an important preoperative predictor of adverse pathology for clinical T1/2 renal cell carcinoma and the construction of predictive model. World J Urol 2024; 42:64. [PMID: 38289390 DOI: 10.1007/s00345-023-04698-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 10/30/2023] [Indexed: 02/01/2024] Open
Abstract
PURPOSE To explore the critical role of the tumor margin irregularity degree (TMID) of renal tumors in predicting adverse pathology of patients with clinical T1/2 (cT1/2) renal cell carcinoma (RCC). METHODS A total of 821 patients with cT1/2 RCC undergoing nephrectomy in the Second Hospital of Tianjin Medical University between January 2017 and December 2020 were reviewed. The tumor margin irregularity (TMI) was classified into renal mass with locally raised protrusion and smooth margin called 'lobular', sharply and unsmooth nodular margin called 'spiculation', blurred margins between tumor and renal parenchyma or a completely irregular and non-elliptical shape. The ratio between the number of irregular cross-sections (X) and the number of total cross-sections from top to bottom occupied (Y) was defined as TMID (X/Y). The logistic regression was performed to determine the independent predictors of adverse pathology, and the Kaplan-Meier curve and log-rank test were used to analyze the survival outcomes. RESULTS Among 821 cT1/2 RCC patients, 245 (29.8%) had adverse pathology. The results of the univariate and multivariate logistic regressions showed that the age, tumor size, hemoglobin, and TMID were the independent predictors of adverse pathology. Incorporation of TMID could increase the discrimination of the predictive model with the area under curve (AUC) of ROC curves increasing from 0.725 to 0.808. Patients with adverse pathology or higher TMID both had significantly shorter recurrence-free survival (RFS). CONCLUSION The nomogram model incorporated with TMID for predicting adverse pathology could increase its discrimination, calibration, and clinical application values, compared with the models without TMID.
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Affiliation(s)
- Keruo Wang
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, China
| | - Guixin Wang
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, China
| | - Yaru Liu
- Department of Emergency, The Second Hospital of Tianjin Medical University, Tianjin Medical University, Tianjin, 300211, China
| | - Liang Dong
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, China
| | - Yuanjie Niu
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
| | - Gang Li
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
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Vasudev NS, Scelo G, Glennon KI, Wilson M, Letourneau L, Eveleigh R, Nourbehesht N, Arseneault M, Paccard A, Egevad L, Viksna J, Celms E, Jackson SM, Abedi-Ardekani B, Warren AY, Selby PJ, Trainor S, Kimuli M, Cartledge J, Soomro N, Adeyoju A, Patel PM, Wozniak MB, Holcatova I, Brisuda A, Janout V, Chanudet E, Zaridze D, Moukeria A, Shangina O, Foretova L, Navratilova M, Mates D, Jinga V, Bogdanovic L, Kovacevic B, Cambon-Thomsen A, Bourque G, Brazma A, Tost J, Brennan P, Lathrop M, Riazalhosseini Y, Banks RE. Application of Genomic Sequencing to Refine Patient Stratification for Adjuvant Therapy in Renal Cell Carcinoma. Clin Cancer Res 2023; 29:1220-1231. [PMID: 36815791 PMCID: PMC10068441 DOI: 10.1158/1078-0432.ccr-22-1936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 10/12/2022] [Accepted: 01/10/2023] [Indexed: 02/24/2023]
Abstract
PURPOSE Patients with resected localized clear-cell renal cell carcinoma (ccRCC) remain at variable risk of recurrence. Incorporation of biomarkers may refine risk prediction and inform adjuvant treatment decisions. We explored the role of tumor genomics in this setting, leveraging the largest cohort to date of localized ccRCC tissues subjected to targeted gene sequencing. EXPERIMENTAL DESIGN The somatic mutation status of 12 genes was determined in 943 ccRCC cases from a multinational cohort of patients, and associations to outcomes were examined in a Discovery (n = 469) and Validation (n = 474) framework. RESULTS Tumors containing a von-Hippel Lindau (VHL) mutation alone were associated with significantly improved outcomes in comparison with tumors containing a VHL plus additional mutations. Within the Discovery cohort, those with VHL+0, VHL+1, VHL+2, and VHL+≥3 tumors had disease-free survival (DFS) rates of 90.8%, 80.1%, 68.2%, and 50.7% respectively, at 5 years. This trend was replicated in the Validation cohort. Notably, these genomically defined groups were independent of tumor mutational burden. Amongst patients eligible for adjuvant therapy, those with a VHL+0 tumor (29%) had a 5-year DFS rate of 79.3% and could, therefore, potentially be spared further treatment. Conversely, patients with VHL+2 and VHL+≥3 tumors (32%) had equivalent DFS rates of 45.6% and 35.3%, respectively, and should be prioritized for adjuvant therapy. CONCLUSIONS Genomic characterization of ccRCC identified biologically distinct groups of patients with divergent relapse rates. These groups account for the ∼80% of cases with VHL mutations and could be used to personalize adjuvant treatment discussions with patients as well as inform future adjuvant trial design.
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Affiliation(s)
- Naveen S. Vasudev
- Leeds Institute of Medical Research at St James's, University of Leeds, St James's University Hospital, Leeds, United Kingdom
| | - Ghislaine Scelo
- World Health Organisation (WHO), International Agency for Research on Cancer (IARC), The Genomic Epidemiology Branch, Lyon, France
| | - Kate I. Glennon
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
- Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Michelle Wilson
- Leeds Institute of Medical Research at St James's, University of Leeds, St James's University Hospital, Leeds, United Kingdom
| | - Louis Letourneau
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
| | - Robert Eveleigh
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
| | - Nazanin Nourbehesht
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
- Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Madeleine Arseneault
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
| | - Antoine Paccard
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
| | - Lars Egevad
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Juris Viksna
- Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia
| | - Edgars Celms
- Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia
| | - Sharon M. Jackson
- Leeds Institute of Medical Research at St James's, University of Leeds, St James's University Hospital, Leeds, United Kingdom
| | - Behnoush Abedi-Ardekani
- World Health Organisation (WHO), International Agency for Research on Cancer (IARC), The Genomic Epidemiology Branch, Lyon, France
| | - Anne Y. Warren
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, United Kingdom
| | - Peter J. Selby
- Leeds Institute of Medical Research at St James's, University of Leeds, St James's University Hospital, Leeds, United Kingdom
| | - Sebastian Trainor
- Leeds Institute of Medical Research at St James's, University of Leeds, St James's University Hospital, Leeds, United Kingdom
| | - Michael Kimuli
- Pyrah Department of Urology, Leeds Teaching Hospitals NHS Trust, Lincoln Wing, St James's University Hospital, Leeds, United Kingdom
| | - Jon Cartledge
- Pyrah Department of Urology, Leeds Teaching Hospitals NHS Trust, Lincoln Wing, St James's University Hospital, Leeds, United Kingdom
| | - Naeem Soomro
- Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | | | - Poulam M. Patel
- Division of Cancer & Stem Cells, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Magdalena B. Wozniak
- World Health Organisation (WHO), International Agency for Research on Cancer (IARC), The Genomic Epidemiology Branch, Lyon, France
| | - Ivana Holcatova
- Charles University in Prague, First Faculty of Medicine, Institute of Hygiene and Epidemiology, Prague, Czech Republic
| | | | - Vladimir Janout
- Faculty of Health Sciences, Palacky University, Olomouc, Czech Republic
| | - Estelle Chanudet
- World Health Organisation (WHO), International Agency for Research on Cancer (IARC), The Genomic Epidemiology Branch, Lyon, France
| | - David Zaridze
- N.N. Blokhin National Medical Research Centre of Oncology, Moscow, Russian Federation
| | - Anush Moukeria
- N.N. Blokhin National Medical Research Centre of Oncology, Moscow, Russian Federation
| | - Oxana Shangina
- N.N. Blokhin National Medical Research Centre of Oncology, Moscow, Russian Federation
| | - Lenka Foretova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Marie Navratilova
- Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Dana Mates
- National Institute of Public Health, Bucuresti, Romania
| | - Viorel Jinga
- Carol Davila University of Medicine and Pharmacy, Prof. Dr. Th. Burghele Clinical Hospital, Bucharest, Romania
| | - Ljiljana Bogdanovic
- Institute of Pathology, School of Medicine Belgrade, University of Belgrade, Belgrade, Serbia
| | - Bozidar Kovacevic
- Institute of Pathology and Forensic Medicine, Military Medical Academy, Belgrade, Serbia
| | - Anne Cambon-Thomsen
- Institut National de la Santé et de la Recherche Médicale (INSERM) and Université Toulouse III Paul Sabatier (UPS), Toulouse, France
| | - Guillaume Bourque
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
- Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Alvis Brazma
- European Bioinformatics Institute, European Molecular Biology Laboratory, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Jörg Tost
- Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie Francois Jacob, University Paris Saclay, Evry, France
| | - Paul Brennan
- World Health Organisation (WHO), International Agency for Research on Cancer (IARC), The Genomic Epidemiology Branch, Lyon, France
| | - Mark Lathrop
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
- Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Yasser Riazalhosseini
- Victor Philip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Québec, Canada
- Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Rosamonde E. Banks
- Leeds Institute of Medical Research at St James's, University of Leeds, St James's University Hospital, Leeds, United Kingdom
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Molecular Characterization of Clear Cell Renal Cell Carcinoma Reveals Prognostic Significance of Epithelial-mesenchymal Transition Gene Expression Signature. Eur Urol Oncol 2021; 5:92-99. [PMID: 34840106 DOI: 10.1016/j.euo.2021.10.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/17/2021] [Accepted: 10/31/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND There is an ongoing need to develop prognostic biomarkers to improve the management of clear cell renal cell carcinoma (ccRCC). OBJECTIVE To leverage enriched pathways in ccRCC to improve risk-stratification. DESIGN, SETTING, AND PARTICIPANTS We retrospectively identified two complementary discovery cohorts of patients with ccRCC who underwent (1) radical nephrectomy (RNx) with inferior vena cava tumor thrombectomy (patients = 5, samples = 24) and (2) RNx for localized disease and developed recurrence versus no recurrence (n = 36). Patients with localized ccRCC (M0) in The Cancer Genome Atlas (TCGA, n = 386) were used for validation. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS A differential expression gene (DEG) analysis was performed on targeted RNA next-generation sequencing data from both discovery cohorts. Using TCGA for validation, Kaplan-Meier survival analysis and multivariable Cox proportional hazard testing were utilized to investigate the prognostic impact of DEGs, cell cycle proliferation (CCP), and a novel epithelial-mesenchymal transition (EMT) score on progression-free (PFS) and disease-specific (DSS) survival. RESULTS AND LIMITATIONS In the discovery cohorts, we observed overexpression of WT1 and CCP genes in the tumor thrombus versus the primary tumor, as well as in patients with recurrence versus those without recurrence. A hallmark pathway analysis demonstrated enrichment of the EMT- and CCP-related pathways in patients with high WT1 expression in the TCGA (validation) ccRCC cohort. CCP and EMT scores were derived in the validation cohort, which was stratified into four risk groups using Youden Index cut points: CCPlow/EMTlow, CCPlow/EMThigh, CCPhigh/EMTlow, and CCPhigh/EMThigh. The CCPhigh/EMThigh risk group was associated with the worst PFS and DSS (both p < 0.001). In a multivariable analysis, CCPhigh/EMThigh was independently associated with poor PFS and DSS (hazard ratio = 4.6 and 10.3, respectively; p < 0.001). CONCLUSIONS We demonstrate the synergistic prognostic impact of EMT in tumors with a high CCP score. Our novel EMT score has the potential to improve risk stratification and provide potential novel therapeutic targets. PATIENT SUMMARY Genes involved in epithelial-mesenchymal transition provides important prognostic information for patients with clear cell renal cell carcinoma.
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Mano R, Duzgol C, Ganat M, Goldman DA, Blum KA, Silagy AW, Walasek A, Sanchez A, DiNatale RG, Marcon J, Kashan M, Becerra MF, Benfante NE, Coleman JA, Kattan MW, Russo P, Akin O, Ostrovnaya I, Hakimi AA. Somatic mutations as preoperative predictors of metastases in patients with localized clear cell renal cell carcinoma - An exploratory analysis. Urol Oncol 2021; 39:791.e17-791.e24. [PMID: 34580025 DOI: 10.1016/j.urolonc.2021.08.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 06/20/2021] [Accepted: 08/16/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Recurrent genomic alterations in clear cell renal cell carcinoma (ccRCC) have been associated with treatment outcomes; however, current preoperative predictive models do not include known genetic predictors. We aimed to explore the value of common somatic mutations in the preoperative prediction of metastatic disease among patients treated for localized ccRCC. MATERIALS AND METHODS After obtaining institutional review board approval, data of 254 patients with localized ccRCC treated between 2005 and 2015 who underwent genetic sequencing was collected. The mutation status of VHL, PBRM1, SETD2, BAP1 and KDM5C were evaluated in the nephrectomy tumor specimen, which served as a proxy for biopsy mutation status. The Raj et al. preoperative nomogram was used to predict the 12-year metastatic free probability (MFP). The study outcome was MFP; the relationship between MFP and mutation status was evaluated with Cox-regression models adjusting for the preoperative nomogram variables (age, gender, incidental presentation, lymphadenopathy, necrosis, and size). RESULTS The study cohort included 188 males (74%) and 66 females (26%) with a median age of 58 years. VHL mutations were present in 152/254 patients (60%), PBRM1 in 91/254 (36%), SETD2 in 32/254 (13%), BAP1 in 19/254 (8%), and KDM5C in 19/254 (8%). Median follow-up for survivors was 8.1 years. Estimated 12-year MFP was 70% (95% CI: 63%-75%). On univariable analysis SETD2 (HR: 3.30), BAP1 (HR: 2.44) and PBRM1 (HR: 1.78) were significantly associated with a higher risk of metastases. After adjusting for known preoperative predictors in the existing nomogram, SETD2 mutations remained associated with a higher rate of metastases after nephrectomy (HR: 2.09, 95% CI: 1.19-3.67, P = 0.011). CONCLUSION In the current exploratory analysis, SETD2 mutations were significant predictors of MFP among patients treated for localized ccRCC. Our findings support future studies evaluating genetic alterations in preoperative renal biopsy samples as potential predictors of treatment outcome.
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Affiliation(s)
- Roy Mano
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, Tel-Aviv Sourasky Medical Center, Sackler School of Medicine, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Cihan Duzgol
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Maz Ganat
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Surgery, Division of Urologic Oncology, Englewood Health, Englewood, NJ
| | - Debra A Goldman
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kyle A Blum
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, University of Texas Health Science Center at Houston, Houston, TX
| | - Andrew W Silagy
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Surgery, University of Melbourne, Austin Hospital, Melbourne, Australia
| | - Aleksandra Walasek
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Alejandro Sanchez
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Division of Urology, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| | - Renzo G DiNatale
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Julian Marcon
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Mahyar Kashan
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, SUNY Downstate Medical Center, Brooklyn, NY
| | - Maria F Becerra
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, Miller School of Medicine, University of Miami, Miami, FL
| | - Nicole E Benfante
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Jonathan A Coleman
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Michael W Kattan
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH
| | - Paul Russo
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Irina Ostrovnaya
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - A Ari Hakimi
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
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Chakiryan NH, Kimmel GJ, Kim Y, Johnson JO, Clark N, Hajiran A, Chang A, Aydin AM, Zemp L, Katende E, Chahoud J, Ferrall-Fairbanks MC, Spiess PE, Francis N, Fournier M, Dhillon J, Park JY, Wang L, Mulé JJ, Altrock PM, Manley BJ. Geospatial Cellular Distribution of Cancer-Associated Fibroblasts Significantly Impacts Clinical Outcomes in Metastatic Clear Cell Renal Cell Carcinoma. Cancers (Basel) 2021; 13:cancers13153743. [PMID: 34359645 PMCID: PMC8345222 DOI: 10.3390/cancers13153743] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/12/2021] [Accepted: 07/22/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Cancer-associated fibroblasts (CAFs) are highly prevalent cells in the clear cell renal cell carcinoma (ccRCC) tumor immune microenvironment. CAFs are thought to potentiate tumor proliferation primarily through paracrine interactions, as evidenced by laboratory-based studies. We sought to corroborate these findings using surgically removed tissue samples from 96 patients with metastatic ccRCC and associate geospatial relationships between CAFs and rapidly proliferating tumor cells with survival outcomes. We found that CAFs exhibited more geospatial clustering with proliferating tumor cells than with dying tumor cells, and patients whose samples exhibited higher tumor cell proliferation had worse overall survival and were more likely to be resistant to systemic tyrosine-kinase-inhibiting targeted therapies. Immunotherapy resistance was not associated with the geospatial metrics measured in this analysis. Overall, these findings suggest that close proximity to CAFs potentiates tumor cell proliferation, worsening survival and conferring resistance to targeted therapies. Abstract Cancer-associated fibroblasts (CAF) are highly prevalent cells in the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). CAFs exhibit a pro-tumor effect in vitro and have been implicated in tumor cell proliferation, metastasis, and treatment resistance. Our objective is to analyze the geospatial distribution of CAFs with proliferating and apoptotic tumor cells in the ccRCC tumor microenvironment and determine associations with survival and systemic treatment. Pre-treatment primary tumor samples were collected from 96 patients with metastatic ccRCC. Three adjacent slices were obtained from 2 tumor-core regions of interest (ROI) per patient, and immunohistochemistry (IHC) staining was performed for αSMA, Ki-67, and caspase-3 to detect CAFs, proliferating cells, and apoptotic cells, respectively. H-scores and cellular density were generated for each marker. ROIs were aligned, and spatial point patterns were generated, which were then used to perform spatial analyses using a normalized Ripley’s K function at a radius of 25 μm (nK(25)). The survival analyses used an optimal cut-point method, maximizing the log-rank statistic, to stratify the IHC-derived metrics into high and low groups. Multivariable Cox regression analyses were performed accounting for age and International Metastatic RCC Database Consortium (IMDC) risk category. Survival outcomes included overall survival (OS) from the date of diagnosis, OS from the date of immunotherapy initiation (OS-IT), and OS from the date of targeted therapy initiation (OS-TT). Therapy resistance was defined as progression-free survival (PFS) <6 months, and therapy response was defined as PFS >9 months. CAFs exhibited higher cellular clustering with Ki-67+ cells than with caspase-3+ cells (nK(25): Ki-67 1.19; caspase-3 1.05; p = 0.04). The median nearest neighbor (NN) distance from CAFs to Ki-67+ cells was shorter compared to caspase-3+ cells (15 μm vs. 37 μm, respectively; p < 0.001). Multivariable Cox regression analyses demonstrated that both high Ki-67+ density and H-score were associated with worse OS, OS-IT, and OS-TT. Regarding αSMA+CAFs, only a high H-score was associated with worse OS, OS-IT, and OS-TT. For caspase-3+, high H-score and density were associated with worse OS and OS-TT. Patients whose tumors were resistant to targeted therapy (TT) had higher Ki-67 density and H-scores than those who had TT responses. Overall, this ex vivo geospatial analysis of CAF distribution suggests that close proximity clustering of tumor cells and CAFs potentiates tumor cell proliferation, resulting in worse OS and resistance to TT in metastatic ccRCC.
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Affiliation(s)
- Nicholas H. Chakiryan
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
- Correspondence: ; Tel.: +1-813-745-3208; Fax: +1-813-745-8494
| | - Gregory J. Kimmel
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (G.J.K.); (M.C.F.-F.); (P.M.A.)
| | - Youngchul Kim
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Joseph O. Johnson
- Analytic Microcopy Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Noel Clark
- Tissue Core Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Ali Hajiran
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Andrew Chang
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Ahmet M. Aydin
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Logan Zemp
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Esther Katende
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Jad Chahoud
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Meghan C. Ferrall-Fairbanks
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (G.J.K.); (M.C.F.-F.); (P.M.A.)
| | - Philippe E. Spiess
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Natasha Francis
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Michelle Fournier
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
| | - Jasreman Dhillon
- Department of Pathology, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA;
| | - Jong Y. Park
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA;
| | - Liang Wang
- Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - James J. Mulé
- Immunology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Philipp M. Altrock
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (G.J.K.); (M.C.F.-F.); (P.M.A.)
| | - Brandon J. Manley
- Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (A.H.); (A.C.); (A.M.A.); (L.Z.); (E.K.); (J.C.); (P.E.S.); (N.F.); (M.F.); (B.J.M.)
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A 25-year perspective on evaluation and understanding of biomarkers in urologic cancers. Urol Oncol 2021; 39:602-617. [PMID: 34315659 DOI: 10.1016/j.urolonc.2021.06.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 12/15/2022]
Abstract
The past 25 years have witnessed an explosion of investigative attempts to identify clinically useful biomarkers which can have meaningful impacts for patients with urologic cancers. However, in spite of the enormous amount of research aiming to identify markers with the hope of impacting patient care, only a handful have proven to have true clinical utility. Improvements in targeted imaging, pan-omics evaluation, and genetic sequencing at the tissue and single-cell levels have yielded many potential targets for continued biomarker investigation. This article, as one in this series for the 25th Anniversary Issue of Urologic Oncology: Seminars and Original Investigations, serves to give a perspective on our progress and failures over the past quarter-century in our highest volume urologic cancers: prostate, bladder, and kidney cancers.
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Liu H, Tang K, Chen Z, Li Z, Meng X, Xia D. Comparison and development of preoperative systemic inflammation markers-based models for the prediction of unfavorable pathology in newly diagnosed clinical T1 renal cell carcinoma. Pathol Res Pract 2021; 225:153563. [PMID: 34371466 DOI: 10.1016/j.prp.2021.153563] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/18/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND We sought to investigate the preoperative risk factors associated with the unfavorable pathology (UP) of clinical T1 (cT1) renal lesions. The aims of this study were to develop and compare several novel models capable of accurately identifying those patients at high risk of harboring occult adverse histopathological characteristics. METHODS The clinical parameters and preoperative laboratory test results from 1281 cT1 renal cell carcinomas (RCCs) patients who underwent partial nephrectomy (PN) or radical nephrectomy (RN) were collected. The data was randomly split into training (70%) and testing (30%) datasets. We performed univariable and multivariable logistic regression analyses for significant predictors and, subsequently, constructed predictive models based on those significant risk factors. Receiver operating characteristic (ROC) analysis was used to determine the model with the highest discrimination power with corresponding area under the curve (AUC). Calibration curves were plotted and decision curve analyses (DCAs) were applied to explore clinical net benefit. RESULTS UP was identified in 21.1% (n = 270), 21.0% (n = 188) and 21.3% (n = 82) patients in the total population, training cohort and validation cohort, respectively. R.E.N.A.L. (radius, exophytic/endophytic properties, nearness of tumor to collecting system or sinus, anterior/posterior, location relative to the polar lines) nephrometry score, tumor size, neutrophil-to-lymphocyte ratio (NLR) and albumin-to-globulin ratio (AGR) were independent predictors of UP. Among those predictive models, the model that consisted of tumor size, hemoglobin, NLR and AGR performs best according to the highest AUC of 0.70 and the highest net benefit. When tumor histology was added to the biomarker-based model, including tumor size, hemoglobin, NLR and AGR, the AUC improved from 0.60 to 0.63 in the validation cohort. CONCLUSIONS In this analytical model study, our findings verified that systemic inflammation response markers showed high potential for identifying UP. Our biomarker-based models well predicted occult aggressive histopathological characteristics among patients with cT1 renal lesions, and the use of models may be greatly beneficial to urologists in tailoring precise management and therapy for patients. Robust validation is warranted prior to adoption into clinical practice.
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Affiliation(s)
- Hailang Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Kun Tang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Zhiqiang Chen
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
| | - Ding Xia
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
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