Defining a mutational signature for endometrial cancer screening and early detection Publication date: August 2019 Source: Cancer Epidemiology, Volume 61 Author(s): Laura Costas, Luis Palomero, Yolanda Benavente, Magdalena Guardiola, Jon Frias-Gomez, Miquel Ángel Pavón, Maite Climent, José Manuel Martinez, Marc Barahona, Mónica Salinas, Marta Pineda, Ilaria Bianchi, Jaume Reventós, Gabriel Capellà, Mireia Diaz, August Vidal, Josep Maria Piulats, Jordi Ponce, Joan Brunet, Francesc Xavier Bosch Abstract Introduction The current availability of genomic information represents an opportunity to develop new strategies for early detection of cancer. New molecular tests for endometrial cancer may improve performance and failure rates of histological aspirate-based diagnosis, and provide promising perspectives for a potential screening scenario. However, the selection of relevant biomarkers to develop efficient strategies can be a challenge. Materials and methods We developed an algorithm to identify the largest number of patients with endometrial cancer using the minimum number of somatic mutations based on The Cancer Genome Atlas (TCGA) dataset. Results The algorithm provided the number of subjects with mutations (sensitivity) for a given number of biomarkers included in the signature. For instance, by evaluating the 50 most representative point mutations, up to 81.9% of endometrial cancers can be identified in the TCGA dataset. At gene level, a 92.9% sensitivity can be obtained by interrogating five genes. Discussion We developed a computational method to aid in the selection of relevant genomic biomarkers in endometrial cancer that can be adapted to other cancer types or diseases.