Risk Reclassification of Patients with Endometrial Cancer Based on Tumor Molecular Profiling: First Real World Data

Autor: Christoph Grimm, Leonhard Müllauer, Sarah Moling, Stephan Polterauer, Felicitas Oberndorfer, Alina Sturdza, Stefanie Aust, Alexander Reinthaller, Leonie Annika Hagelkruys, Richard Schwameis
Rok vydání: 2020
Předmět:
Zdroj: Journal of Personalized Medicine
Journal of Personalized Medicine, Vol 11, Iss 48, p 48 (2021)
Volume 11
Issue 1
ISSN: 2075-4426
Popis: Recently, guidelines for endometrial cancer (EC) were released that guide treatment decisions according to the tumors&rsquo
molecular profiles. To date, no real-world data regarding the clinical feasibility of molecular profiling have been released. This retrospective, monocentric study investigated the clinical feasibility of molecular profiling and its potential impact on treatment decisions. Tumor specimens underwent molecular profiling (testing for genetic alterations, (immune-)histological examination of lymphovascular space invasion (LVSI), and L1CAM) as part of the clinical routine and were classified according to the European Society for Medical Oncology (ESMO) classification system and to an integrated molecular risk stratification. Shifts between risk groups and potential treatment alterations are described. A total of 60 cases were included, of which twelve were excluded (20%), and eight of the remaining 48 were not characterized (drop-out rate of 16.7%). Molecular profiling revealed 4, 6, 25, and 5 patients with DNA polymerase-epsilon mutation, microsatellite instability, no specific molecular profile, and TP53 mutation, respectively. Three patients had substantial LVSI, and four patients showed high L1CAM expression. Molecular profiling took a median of 18.5 days. Substantial shifts occurred between the classification systems: four patients were upstaged, and 19 patients were downstaged. Molecular profiling of EC specimens is feasible in a daily routine, and new risk classification systems will change treatment decisions substantially.
Databáze: OpenAIRE