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Dani KA, Rich JM, Kumar SS, Cen H, Duddalwar VA, D’Souza A. Comprehensive Systematic Review of Biomarkers in Metastatic Renal Cell Carcinoma: Predictors, Prognostics, and Therapeutic Monitoring. Cancers (Basel) 2023; 15:4934. [PMID: 37894301 PMCID: PMC10605584 DOI: 10.3390/cancers15204934] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/30/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Challenges remain in determining the most effective treatment strategies and identifying patients who would benefit from adjuvant or neoadjuvant therapy in renal cell carcinoma. The objective of this review is to provide a comprehensive overview of biomarkers in metastatic renal cell carcinoma (mRCC) and their utility in prediction of treatment response, prognosis, and therapeutic monitoring in patients receiving systemic therapy for metastatic disease. METHODS A systematic literature search was conducted using the PubMed database for relevant studies published between January 2017 and December 2022. The search focused on biomarkers associated with mRCC and their relationship to immune checkpoint inhibitors, targeted therapy, and VEGF inhibitors in the adjuvant, neoadjuvant, and metastatic settings. RESULTS The review identified various biomarkers with predictive, prognostic, and therapeutic monitoring potential in mRCC. The review also discussed the challenges associated with anti-angiogenic and immune-checkpoint monotherapy trials and highlighted the need for personalized therapy based on molecular signatures. CONCLUSION This comprehensive review provides valuable insights into the landscape of biomarkers in mRCC and their potential applications in prediction of treatment response, prognosis, and therapeutic monitoring. The findings underscore the importance of incorporating biomarker assessment into clinical practice to guide treatment decisions and improve patient outcomes in mRCC.
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Affiliation(s)
- Komal A. Dani
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA;
| | - Joseph M. Rich
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA;
| | - Sean S. Kumar
- Eastern Virginia Medical School, Norfolk, VA 23507, USA;
- Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Harmony Cen
- University of Southern California, Los Angeles, CA 90033, USA;
| | - Vinay A. Duddalwar
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA;
- Institute of Urology, University of Southern California, Los Angeles, CA 90033, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Anishka D’Souza
- Department of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
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Varghese B, Cen S, Zahoor H, Siddiqui I, Aron M, Sali A, Rhie S, Lei X, Rivas M, Liu D, Hwang D, Quinn D, Desai M, Vaishampayan U, Gill I, Duddalwar V. Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma. Eur J Radiol Open 2022; 9:100440. [PMID: 36090617 PMCID: PMC9460152 DOI: 10.1016/j.ejro.2022.100440] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/25/2022] [Accepted: 08/30/2022] [Indexed: 01/26/2023] Open
Abstract
Objectives To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC). Methods Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively. Results Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively. Conclusions CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC.
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Affiliation(s)
- Bino Varghese
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA,Correspondence to: Keck Medical Center of USC, University of Southern California, Norris Topping Tower 4417, Los Angeles, CA 90033, USA.
| | - Steven Cen
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Haris Zahoor
- Keck School of Medicine, Department of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Imran Siddiqui
- Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, CA, USA
| | - Manju Aron
- Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, CA, USA
| | - Akash Sali
- Homi Bhabha Cancer Hospital, Department of Pathology, Sangrur, Punjab, India
| | - Suhn Rhie
- Keck School of Medicine, Department of Molecular Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiaomeng Lei
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Marielena Rivas
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Derek Liu
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Darryl Hwang
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - David Quinn
- Keck School of Medicine, Department of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mihir Desai
- Keck School of Medicine, Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Ulka Vaishampayan
- Rogel Cancer Center, Urologic Oncology Clinic, University of Michigan, Ann Arbor, MI, USA
| | - Inderbir Gill
- Keck School of Medicine, Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
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Roviello G, Gambale E, Giorgione R, Santini D, Stellato M, Fornarini G, Rebuzzi SE, Basso U, Bimbatti D, Doni L, Nesi G, Bersanelli M, Buti S, De Giorgi U, Galli L, Sbrana A, Conca R, Carella C, Naglieri E, Pignata S, Procopio G, Antonuzzo L. Effect of systemic therapies or best supportive care after disease progression to both nivolumab and cabozantinib in metastatic renal cell carcinoma: The Meet-Uro 19BEYOND study. Cancer Med 2022; 11:3084-3092. [PMID: 35312175 PMCID: PMC9385587 DOI: 10.1002/cam4.4681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/16/2022] [Accepted: 02/24/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Nivolumab and cabozantinib are currently approved agents in metastatic renal cell carcinoma (mRCC) but there are no data available for patients progressing to both treatments. The aim of this study was to compare active therapeutic options and best supportive care (BSC) after progression to nivolumab and cabozantinib in mRCC. METHODS In this retrospective study, we selected 50 patients from eight Italian centers. The primary endpoint of the study was the overall survival (OS) of patients on active treatment versus BSC. Secondary endpoints were the progression-free survival (PFS) and objective response rate (ORR). The efficacy of active therapy was also investigated. RESULTS After progression to both nivolumab and cabozantinib, 57.1% of patients were given active treatment (mainly everolimus and sorafenib) while 42.9% received BSC. The median OS was 13 months (95% CI: 4-NR) in actively treated patients and 3 months (95% CI: 2-4) in BSC patients (p = 0.001). Patients treated with sorafenib had better disease control than those treated with everolimus (stable disease: 71.4% vs. 16.7%, progression disease: 14.3% vs. 58.3%; p = 0.03), with no significant differences in PFS (5 and 3 months, 95% CI: 1-6 vs. 2-5; p = 0.6) and OS (12 and 4 months, 95% CI: 3-NR vs. 2-NR; p = 0.2). CONCLUSION After treatment with both nivolumab and cabozantinib, the choice of a safe active systemic therapy offered better outcomes than BSC.
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Affiliation(s)
| | | | | | - Daniele Santini
- Department of Medical Oncology, University Campus Bio-Medico, Rome, Italy
| | - Marco Stellato
- Department of Medical Oncology, University Campus Bio-Medico, Rome, Italy
| | - Giuseppe Fornarini
- Medical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino di Genova, Genoa, Italy
| | - Sara Elena Rebuzzi
- Medical Oncology Unit, San Paolo General Hospital, Savona, Italy.,Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genova, Genoa, Italy
| | - Umberto Basso
- MedicalOncology Unit 1, Istituto Oncologico Veneto IOV IRCCS, Padua, Italy
| | - Davide Bimbatti
- MedicalOncology Unit 1, Istituto Oncologico Veneto IOV IRCCS, Padua, Italy
| | - Laura Doni
- Medical Oncology Unit, Careggi University Hospital, Florence, Italy
| | - Gabriella Nesi
- Department of Health Sciences, University of Florence, Florence, Italy
| | | | - Sebastiano Buti
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Ugo De Giorgi
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Italy
| | - Luca Galli
- Medical Oncology Unit 2, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Andrea Sbrana
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Raffaele Conca
- Division of Medical Oncology, Department of Onco-Hematology, IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture, Italy
| | | | | | - Sandro Pignata
- Department of Urology and Gynecology, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples, Italy
| | - Giuseppe Procopio
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Lorenzo Antonuzzo
- Medical Oncology Unit, Careggi University Hospital, Florence, Italy.,Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
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