Fig. 3: Griffin enables accurate cancer detection.

Receiver operator characteristic (ROC) curves for logistic regression classification of cancer vs. healthy controls in three cohorts. Logistic regression was performed on the top PCA components which explained 80% of the variance in the features (central coverage, mean coverage, and amplitude) extracted from nucleosome profiles around 30,000 TFBSs for each of 270 TFs. ROC and area under the ROC curve (AUC) performance is shown for each disease stage. The number of cancer samples (Ca) is indicated for each stage. Each ROC curve also includes all healthy controls (H) from that cohort. 95% confidence intervals (CI) were obtained from 1000 bootstrap iterations. a Performance for DELFI cohort³⁸ consisting of plasma samples for 208 early-stage cancers and 215 healthy controls. b Comparison of the performance in the DELFI cohort before and after GC correction using Griffin. Samples are the same as in a. Boxplots indicate median, interquartile range (IQR), whiskers for 1.5× IQR, and outliers. c Performance of the LUCAS cohort⁴⁵ consisting of plasma from 129 lung cancer patients and 158 healthy patients. d Performance of the LUCAS validation cohort⁴⁵ consisting of plasma for 46 lung cancers and 385 healthy controls. For each dataset, performance is shown for both the original low pass (1–2×) WGS and ultra-low pass (0.1×) WGS generated by in-silico downsampling. Source data are provided as a Source Data file.