Extended Data Fig. 6: Effect of bioinformatic analysis pipeline on variant calling.

a–d, Comparison of observed mutational count per sample for SNVs (a), MNVs (b), indels (c) and SVs (d) on 24 patient samples analysed by the PCAWG and HMF pipelines. The PCAWG pipeline was found to have a 43% lower sensitivity for indels (which is based on a consensus calling), 18% lower for SVs (based on a different algorithm) and 6% lower for MNVs (only includes MNVs involving two nucleotides), with nearly the same sensitivity for SNVs. e, f, Cumulative distribution function plot for each tumour type (the number of independent patients per category is provided) of coverage and pipeline-adjusted mutational load for SNVs and MNVs (e) and indels and SVs (f). Mutational loads as shown in Fig. 1 were adjusted for the sensitivity effects caused by differences in sequencing depth coverage (Extended Data Fig. 4) and analysis pipeline differences (a–d). After this correction, the TMB between primary and metastatic cohorts across all variant types are much more comparable (e, f), which indicates that technical differences do contribute to the reported mutational load differences between primary and metastatic tumours. Prostate cancer is the most notable exception, with approximately twice the TMB in all variant classes, although more subtle differences, potentially driven by biology, can also be observed for other tumour and mutation types. For cancer types that are comparable with the PCAWG cohort, the equivalent PCAWG numbers are shown by dotted lines. The median for each cohort is shown by a horizontal line.