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Inspired by previous work showing that cancer genome evolution is deterministic bit.ly/44sAYBG bit.ly/4d3MBBb we built a model that uses mutation rate and selection estimates to predict the probability of future driver amp/dels, given a tumour genome as input 2/

Simple model? Seemingly so. But estimating mutation rate and selection coefficients from tumour DNA alone, especially for DNA copy number, is tough! We faced a number of challenges: 3/

Challenge 1: amp/del rates cannot be readily measured from an input genome. Solution: approximate using a steady-state probability of locus-specific copy number change over tumour lifetime. We adapted our previous CIN signatures (CX bit.ly/42SVA3i) for this 4/

Challenge 2: growth rates of mutant vs non-mutant cells (and thus selection) cannot be easily determined in a clinical context. Solution: approximate selection coeffs using driver amp/del frequency at a population-level (supported by recent work showing s≈fβ) 5/

Challenge 3: forecasting in a clinical setting. Solution: binarise predictions and only use standard genomic test data as input. We designed guidelines to apply and (if needed) train the model + optimize thresholds for binary risk classification (high vs low) 6/

We trained this model for 241 drivers using 7,880 TCGA samples across 33 tumour types. But do these models work? 7/

First, we tested performance on two independent cohorts: PCAWG: 2,114 primaries; HMF: 4,784 metastases. 147 of the 241 models showed AUC > 0.7 across both datasets 8/

10/6/25, 2:15 pm0 reposts0 likes

Next we tested longitudinal pairs forecasting at the early time point (before driver amp) and testing at the latter. In prostate, we predicted AR amp (linked to ADT resistance) in pretreatment samples. In NSCLC, we predicted HIST1H3B amp (exclusive to metastases) in primaries 9/

Encouraging right? We then applied our approach to two clinical scenarios where forecasting specific genetic changes might unlock new clinical opportunities: risk stratification of low-grade glioma (LGG) and anticipation of osimertinib resistance in lung cancer 10/

Currently, LGGs are classified into 4 WHO risk groups. CDK4/PDGFRA amps and CDKN2A dels are linked with poor prognosis but under utilised. Forecasting these facilitates a risk upgrade of 9% of IDHmut-non-codel cases while maintaining median survival times across WHO groups 11/