Specifically, we compared the prognostic powers of (a) the kernel machine learning method for genome-wide aggregation of mRNA transcripts; (b) pre-specified prognostic signatures, including the metagene signatures15 and the ESTIMATE immune signatures6 developed across multiple cancer types; (c) the PAM50 breast cancer classifier21 or the MammaPrint signature22 that predicts distant metastasis for early stage breast cancer; (d) LGG subtypes defined by IDH1 mutation and co-deletion of chromosome 1p/19q; and (e) algorithmically selecting mRNA transcripts by L1 penalized Cox regression (LASSO)16. The gene discussed is IDH1; the disease is breast cancer.