An Improved, Assay Platform Agnostic, Absolute Single Sample Breast Cancer Subtype Classifier.

ABSTRACT: While intrinsic molecular subtypes provide important biological classification of breast cancer, the subtype assignment of individuals is influenced by assay technology and study cohort composition. We sought to develop a platform-independent absolute single-sample subtype classifier based on a minimal number of genes. Pairwise ratios for subtype-specific differentially expressed genes from un-normalized expression data from 432 breast cancer (BC) samples of The Cancer Genome Atlas (TCGA) were used as inputs for machine learning. The subtype classifier with the fewest number of genes and maximal classification power was selected during cross-validation. The final model was evaluated on 5816 samples from 10 independent studies profiled with four different assay platforms. Upon cross-validat

SUBMITTER: Seo MK 

PROVIDER: S-EPMC7761033 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature