RT Journal Article
SR Electronic
T1 A novel algorithm to implement the molecular classification according to the new ESGO/ESTRO/ESP 2020 guidelines for endometrial cancer
JF International Journal of Gynecologic Cancer
JO Int J Gynecol Cancer
FD BMJ Publishing Group Ltd
SP 993
OP 1000
DO 10.1136/ijgc-2022-003480
VO 32
IS 8
A1 Ilaria Betella
A1 Caterina Fumagalli
A1 Paola Rafaniello Raviele
A1 Gabriella Schivardi
A1 Luigi Antonio De Vitis
A1 Maria Teresa Achilarre
A1 Alessia Aloisi
A1 Annalisa Garbi
A1 Matteo Maruccio
A1 Vanna Zanagnolo
A1 Giovanni Aletti
A1 Elena Guerini-Rocco
A1 Andrea Mariani
A1 Angelo Maggioni
A1 Massimo Barberis
A1 Nicoletta Colombo
A1 Francesco Multinu
YR 2022
UL http://ijgc.bmj.com/content/32/8/993.abstract
AB 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.Data are available upon reasonable request.