Building flexible and robust analysis frameworks for molecular subtyping of cancers - Pedersen, Christina Bligaard; Campos, Benito; Rene, Lasse; Wegener, Helene Scheel; Krishnan, Neeraja M.; Panda, Binay; Vitting‐Seerup, Kristoffer; Rossing, Maria; Bagger, Frederik Otzen; Olsen, Lars Rønn (Molecular oncology, 2024)
Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in‐depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer. We provide a generally applicable roadmap for implementing data and bioinformatics tools for robust inference of cancer subtypes in a clinical setting, both from raw and processed RNA sequencing data and on a per‐sample basis. We apply the resulting framework to the PAM50 and CIT breast cancer classifiers.