A novel algorithm to implement the molecular classification according to the new ESGO/ESTRO/ESP 2020 guidelines for endometrial cancer. Issue 8 (22nd June 2022)

• Record Type: Journal Article
• Authors: Betella, Ilaria; Fumagalli, Caterina; Rafaniello Raviele, Paola; Schivardi, Gabriella; De Vitis, Luigi Antonio; Achilarre, Maria Teresa; Aloisi, Alessia; Garbi, Annalisa; Maruccio, Matteo; Zanagnolo, Vanna; Aletti, Giovanni; Guerini-Rocco, Elena; Mariani, Andrea; Maggioni, Angelo; Barberis, Massimo; Colombo, Nicoletta; Multinu, Francesco
• Is Part Of: International journal of gynecological cancer. Volume 32:Issue 8(2022)
• Page Start: 993
• Page End: 1000
• Publication Date: 2022-06-22
• Subjects: Endometrial Neoplasms -- Pathology; Generative organs, Female -- Cancer -- Periodicals; 616.99465
• DOI: 10.1136/ijgc-2022-003480
• Languages: English
• ISSNs: 1048-891X

Abstract

Abstract : Objective: To compare the risk class attribution with molecular classification unknown to those with molecular classification known, according to the European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology (ESGO/ESTRO/ESP) 2020 guidelines on endometrial cancer, with a focus on risk group migration. Additionally, to evaluate the capability of a novel molecular analysis algorithm to reduce the number of required tests. Methods: We conducted a retrospective study including all consecutive patients with endometrial cancer undergoing surgery and comprehensive molecular analyses between April 2019 and December 2021. Molecular analyses including immunohistochemistry for p53 and mismatch repair (MMR) proteins, and DNA sequencing for POLE exonuclease domain were performed to classify tumors as POLE-mutated (POLE), MMR-deficient (MMR-d), p53 abnormal (p53abn), or non-specific molecular profile (NSMP). The two risk classifications of the ESGO/ESTRO/ESP 2020 guidelines were compared to estimate the proportion of patients in which the molecular analysis was able to change the risk class attribution. We developed a novel algorithm where the molecular analyses are reserved only for patients in whom incorporation of the molecular classification could change the risk class attribution. Results: A total of 278 patients were included. Molecular analyses were successful for all cases, identifying the four subgroups: 27 (9.7%) POLE, 77 (27.7%) MMR-d, 49 (17.6%) p53abn, and 125 (45.0%) NSMP. Comparison of risk class attribution between the two classification systems demonstrated discordance in the risk class assignment in 19 (6.8%, 95% CI 4.2% to 10.5%) cases. The application of our novel algorithm would have led to a reduction in the number of POLE sequencing tests by 67% (95% CI 61% to 73%) and a decrease of p53 immunohistochemistry by 27% (95% CI 22% to 33%), as compared with the application of molecular classification to all patients. Conclusion: Molecular categorization of endometrial cancer allows the reallocation of a considerable proportion of patients in a different risk class. Furthermore, the application of our algorithm enables a reduction in the number of required tests without affecting the risk classification.