Fig. 3: Burden analysis of somatic single nucleotide variants (SNVs) and INDELs.

A Somatic mutation burden in different cancer types and in non-cancer CBS controls (Ai), distribution of mutation numbers across genes (Aii) and consequences of mutations (Aiii). In (Ai), each circle corresponds to a different plasma sample. Bi Scores quantifying mutation burden imbalances in the chromosome arms of each cancer plasma sample compared to the non-cancer plasma CBS controls. In each sample, each circle corresponds to a different chromosome arm. Red circles indicate a difference in the somatic mutations burden of the chromosome arm in relation to the same arm in the CBS controls. Bii Integrated somatic mutation scores over all chromosome arms in each plasma sample. A red circle indicates a higher mutation burden in the corresponding sample, when compared to the CBS controls. We identified correctly 32 out of 61 cancer plasma samples (sensitivity 52.5%). C Integrated somatic mutation scores against cancer stage and type (Ci) and moderate correlation between median integrated somatic mutation score and stage (Cii); Spearman’s r = 50%). D In silico validation of somatic mutation analysis at increasing ctDNA fractions. At each ctDNA fraction, we simulated 1000 controls and 1000 cancer plasma samples using actual non-cancer and cancer plasma samples as templates (see ‘Methods‘). The area under the receiver operating characteristic (ROC) curve (AUC) is 74% at ctDNA fraction 1%. CTRL CBS controls (n = 9 subjects); CRC colorectal (n = 36 subjects), OES oesophageal (n = 8 subjects), PNCR pancreatic (n = 6 subjects), RNL renal (n = 5 subjects), OVR ovarian (n = 4 subjects), BST breast (n = 2 subjects). For each boxplot in (Ai, Ci, D), the box bounds, and centre correspond to the 25th, 50th (median) and 75th percentiles of the data in each corresponding group, and the whiskers extend to 1.5 times the interquartile range (IQR) above and below the box bounds. Source data is provided as a source data file.