Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning
Fig 5
(A) Transfer learning approach utilizing ‘renovated’ pre-trained neural networks for alternative evolutionary inference tasks in tumour cellular populations. TEMULATOR is an alternative cancer evolution simulator that generates synthetic tumour sequencing data by deterministically initiating subclones at user specified fitnesses and time points [38]. (B) Pre-trained models provide significant reductions in testing loss, over non-pretrained models, when updating neural network weights on reduced dataset size of 500,000 synthetic VAF distributions (~1.25% of the total dataset size used to originally train TumE). (C) TumE transfer (TumE-T) effectively recovers evolutionary parameters from TEMULATOR simulations (75–200x mean sequencing depth, 100% tumor purity) with mean and median percentage errors less than 10% in all cases. A full description of performance across variable sequencing depths, mutation rates, and subclone frequencies is provided in S23 Fig. (D) We find consistency between the subclone cellular fraction estimated by TumE-T and the subclone frequency (cellular fraction / 2) estimates generated from TumE, indicating nearly identical tasks are easily transferred through pre-training. (E) Per genome per division mutation rate estimates in 88 WES and WGS samples from von Loga et al. [33] (MMR-GE = mismatch deficient repair gastro-esophageal cancer), Griffith et al. [31] (AML = acute myeloid leukemia), and PCAWG [11]. (F) Subclone fitness (1+s) estimates (relative growth rate advantage of subclone over background population) and (G) subclone emergence time estimates in 36 tumour biopsies identified with 1 subclone in the PCAWG data. Subclone fitness and emergence time estimates were scaled to a final tumour population size of 1010 cells, similar to [7]. PCAWG sample identifiers are provided on the x-axis.