Defining a mutational signature for endometrial cancer screening and early detection. (August 2019)

• Record Type: Journal Article
• Authors: Costas, Laura; Palomero, Luis; Benavente, Yolanda; Guardiola, Magdalena; Frias-Gomez, Jon; Pavón, Miquel Ángel; Climent, Maite; Martinez, José Manuel; Barahona, Marc; Salinas, Mónica; Pineda, Marta; Bianchi, Ilaria; Reventós, Jaume; Capellà, Gabriel; Diaz, Mireia; Vidal, August; Piulats, Josep Maria; Ponce, Jordi; Brunet, Joan; Bosch, Francesc Xavier; Matias-Guiu, Xavier; Alemany, Laia; de Sanjosé, Silvia; Aytés, Álvaro
• Is Part Of: Cancer epidemiology. Volume 61(2019:Aug.)
• Page Start: 129
• Page End: 132
• Publication Date: 2019-08
• Subjects: Endometrial cancer -- Early detection -- Screening -- Genomics -- Biomarkers -- Algorithm; Cancer -- Epidemiology -- Periodicals; Cancer -- Prevention -- Periodicals; Cancer -- Diagnosis -- Periodicals; Carcinogenesis -- Periodicals; 616.994005
• DOI: 10.1016/j.canep.2019.06.003
• Languages: English
• ISSNs: 1877-7821

Abstract

Highlights: The selection of relevant biomarkers to develop efficient secondary prevention strategies for cancer can be a challenge. Computational methods may aid in the selection of relevant genomic biomarkers in endometrial cancer. A signature of 5 point mutations only identified 35.4% of all endometrial cancers. By evaluating the top 50 most representative point mutations, up to 81.9% can be identified in the TCGA dataset. 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.