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Destefanis N, Fiano V, Milani L, Vasapolli P, Fiorentino M, Giunchi F, Lianas L, Del Rio M, Frexia F, Pireddu L, Molinaro L, Cassoni P, Papotti MG, Gontero P, Calleris G, Oderda M, Ricardi U, Iorio GC, Fariselli P, Isaevska E, Akre O, Zelic R, Pettersson A, Zugna D, Richiardi L. Cohort profile: the Turin prostate cancer prognostication (TPCP) cohort. Front Oncol 2023; 13:1242639. [PMID: 37869094 PMCID: PMC10587560 DOI: 10.3389/fonc.2023.1242639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/18/2023] [Indexed: 10/24/2023] Open
Abstract
Introduction Prostate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research. Methods The TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study's prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks. Results The cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage. Discussion This study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa.
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Affiliation(s)
- Nicolas Destefanis
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Valentina Fiano
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Lorenzo Milani
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Paolo Vasapolli
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Michelangelo Fiorentino
- DIMEC Department of Medicine and Surgery, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Luca Lianas
- Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy
| | - Mauro Del Rio
- Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy
| | - Francesca Frexia
- Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy
| | - Luca Pireddu
- Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia), Pula, Italy
| | - Luca Molinaro
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Paola Cassoni
- Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | | | - Paolo Gontero
- Urology Unit, Department of Surgical Sciences, University of Turin, Molinette Hospital, Turin, Italy
| | - Giorgio Calleris
- Urology Unit, Department of Surgical Sciences, University of Turin, Molinette Hospital, Turin, Italy
| | - Marco Oderda
- Urology Unit, Department of Surgical Sciences, University of Turin, Molinette Hospital, Turin, Italy
| | | | | | - Piero Fariselli
- Computational Biomedicine Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Elena Isaevska
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Olof Akre
- Department of Molecular Medicine and Surgery, Section of Urology, Karolinska Institutet, Stockholm, Sweden
| | - Renata Zelic
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Department of Pelvic Cancer, Karolinska University Hospital, Stockholm, Sweden
| | - Andreas Pettersson
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Daniela Zugna
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
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