Extended Data Fig. 2: Inference of SNV and SV mutational signatures by WGS.

a, Landscape of copy number gains and losses in the MSK SPECTRUM cohort (n = 40) and the HGSOC samples in the metacohort (n = 170). The fraction of tumour samples with gains or losses is shown on they-axis, calculated in 500-kb genomic bins shown on thex-axis.b, First panel, Oncoprint of selected somatic and germline mutations per patient and cohort-wide prevalence by MSK-IMPACT. Patients are grouped by mutational signature. Second panel, heatmap of standardized probabilities for genomic features used to infer mutational signature subtypes from WGS using MMCTM. Features used for inference (in rows) are grouped into single nucleotide variant (SNV) and structural variation (SV) features. SV features include duplications (S-Dup, M-Dup, L-Dup), deletions (S-Del, L-Del), unclustered and clustered foldback inversions (FBI/Inv, Clust-FBI), clustered rearrangements (Clust-SV) and translocations (Tr). Third panel, standardized probabilities for SNV, indel and SV features used by HRDetect. Fourth panel, standardized probabilities forBRCA1- andBRCA2-like signatures used by CHORD.c, UMAP representation of SNV and SV features in the MSK SPECTRUM cohort (n = 40) and the HGSOC/TNBC metacohort (n = 309), coloured by signature strata. Patient identifiers of SPECTRUM cases are highlighted.d, Ranked SNV and SV feature importance in the classification of signature strata. Violin plots show permutation-based importance estimates over randomly shuffled signature strata.e, Paired violin plot of SNV and SV signature probabilities estimated by MMCTM, showing a comparison between the MSK SPECTRUM cohort and the HGSOC/TNBC metacohort.f使用日志,19号染色体拷贝数₂ratio (y-axis) for individual genomic bins (x-axis), coloured by the copy number state. A chromosome ideogram highlights the region of interest in chromosome 19. TheCCNE1locus is marked with a dashed line. Patients are annotated to show whetherCCNE1amplification was predicted by WGS. Only patients with a focalCCNE1amplification called by WGS were included.g, Violin plot of single-cell expression of oncogenes in scRNA-seq, stratified by oncogene copy number in site-matched WGS.h, Correlation between log₂CN change in oncogenes profiled by WGS and mean expression in cancer cells based on matched scRNA-seq from CD45⁻samples. Spearman’s rank correlation coefficient for the linear fit is shown. Patients are coloured by mutational signature, and those highlighted have high-level amplifications detected by WGS. Ind,eandg, box plots show the median, top and bottom quartiles; whiskers correspond to 1.5× IQR.