Figure 2. Predictive Power of Molecular Features and Feature Classes.

A, Relative information content of different feature categories as shown by the Cohen κ metric as a measure of overall accuracy. Black diamonds represent the accuracy of a classifier built for each feature category as indicated; open circles represent the accuracy on incrementally adding feature categories (top to bottom). Mutations encompass hotspots and non-hotspots. B, Relative importance of different feature categories in different cancer types. Circle size represents the mean contribution of the features in each category to accurate predictions in each cancer type. C, Selected individual features for predicting breast cancer and non–small cell lung cancer (NSCLC) in the study cohort and their relative contribution. Informative features driving correct predictions in all tumor types are shown in eFigure 1 in the Supplement. D, Different features contributing to tumor type predictions in BRAF V600E-mutant colorectal cancer, melanoma, and thyroid cancer, establishing the value of feature interactions to inform tumor type prediction in a cohort of patients that nevertheless share a common molecular alteration. CNA indicates copy number alterations; MMR, mismatch repair; VUS, variants of unknown significance.