Fig. 5: Architecture of clonal evolution associates with survival outcomes.

a Schematized workflow for modeling clonal architecture in a cohort of WES patients. Briefly, a deep-sequenced cohort was assembled and analyzed using PyClone to generate robust clonal populations and cellular prevalences. These cellular prevalence estimates were then leveraged to model the temporal acquisition of mutations and clonal architecture using ClonEvol. b Heatmap summarizing PyClone results of the per-patient cellular prevalence for the most common clonal genotypes. Each column is an individual patient sample grouped by hierarchical clustering based on similarity in clonal patterns. For patients with multiple mutations in the same gene, only the mutation with the largest cancer cell fraction (CCF) is shown. c Correlation between mutation burden and the number of unique clones derived from PyClone in the de novo cohort (Kruskal–Wallis p = 1.6e−33; n = 409 patients). Points are colored by broad clonal evolution architecture as determined by ClonEvol (blue = branched evolution; gray = linear evolution). For each distribution, the boxplot represents the boundaries for the first and third quartiles with a line at each median; whiskers delimit the highest data point below the third quartile +1.5× the interquartile distance and the lowest data point above the first quartile −1.5× the interquartile distance. d Kaplan–Meier plot showing the association between higher median mutational burden per clone (red curve) and worse outcomes in de novo patients (two-sided log-rank test). e Kaplan–Meier plot showing the association of improved outcomes in patients exhibiting branched evolutionary architecture (blue curve = branched evolution; gray curve = linear evolution; two-sided log-rank test). f Forrest plot depicting univariate Cox proportional-hazards ratios for various aspects of the clonal architecture analyses. g Forrest plot depicting univariate Cox proportional-hazards ratios for clonal and mutational burden risk stratification based on linear or branched architecture. h Schematic depicting the different genetic and clinical features associated with evolutionary architecture. Source data are provided as a Source Data file.