1. Prognostic classification of endometrial cancer using a molecular approach based on a twelve-gene NGS panel
- Author
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Ignacio A. Romero, Cristina Zorrero, Carmen Illueca, Raquel López-Reig, Antonio Fernandez-Serra, José Antonio López-Guerrero, Zaida García-Casado, and Andres Poveda
- Subjects
0301 basic medicine ,Oncology ,medicine.medical_specialty ,lcsh:Medicine ,Article ,03 medical and health sciences ,Prognostic markers ,0302 clinical medicine ,Endometrial cancer ,Mutation Rate ,Prognostic classification ,Artificial Intelligence ,Internal medicine ,Cancer genome ,Overall survival ,medicine ,Humans ,lcsh:Science ,Gene ,Multidisciplinary ,Molecular medicine ,business.industry ,lcsh:R ,Amplicon ,medicine.disease ,Prognosis ,Endometrial Neoplasms ,030104 developmental biology ,030220 oncology & carcinogenesis ,Mutation ,Microsatellite ,Female ,Microsatellite Instability ,lcsh:Q ,business - Abstract
Endometrial Cancer (EC) is one of the most common malignancies in women in developed countries. Molecular characterization of different biotypes may improve clinical management of EC. The Cancer Genome Atlas (TCGA) project has revealed four prognostic EC subgroups: POLE, MSI; Copy Number Low (CNL) and Copy Number High (CNH). The goal of this study was to develop a method to classify tumors in any of the four EC prognostic groups using affordable molecular techniques. Ninety-six Formalin-Fixed Paraffin-embedded (FFPE) samples were sequenced following a NGS TruSeq Custom Amplicon low input (Illumina) protocol interrogating a multi-gene panel. MSI analysis was performed by fragment analysis using eight specific microsatellite markers. A Random Forest classification algorithm (RFA), considering NGS results, was developed to stratify EC patients into different prognostic groups. Our approach correctly classifies the EC patients into the four TCGA prognostic biotypes. The RFA assigned the samples to the CNH and CNL groups with an accuracy of 0.9753 (p
- Published
- 2019