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Flu vaccines are chosen by predicting which virus strains will dominate next season. The same forecasting maths could predict which resistance mutation a patient's tumour will develop next.
Evolutionary forecasting methods from influenza and bacterial resistance estimate the fitness of circulating variants from their frequency trajectories. Applied to longitudinal ctDNA and to population-level databases of resistance under each drug, they could give per-patient probabilities of specific next-step mechanisms. Forecasts would be scored prospectively, as in weather forecasting, to build calibrated models.
Shares Tumour heterogeneity and clonal evolution, Drug resistance (primary and acquired), Osimertinib, Acquired resistance to every therapy.
Shares Drug resistance (primary and acquired), Acquired resistance to every therapy, Liquid biopsy (ctDNA).
Shares Tumour heterogeneity and clonal evolution, Osimertinib, Acquired resistance to every therapy, Liquid biopsy (ctDNA).
Shares Tumour heterogeneity and clonal evolution, Drug resistance (primary and acquired), Acquired resistance to every therapy.
Shares Tumour heterogeneity and clonal evolution, Targeted therapy roadmap: imatinib → designed for resistance → the undruggable drivers fall, Osimertinib, Acquired resistance to every therapy.
Shares Tumour heterogeneity and clonal evolution, Drug resistance (primary and acquired), Acquired resistance to every therapy.
Shares Drug resistance (primary and acquired), Acquired resistance to every therapy, Liquid biopsy (ctDNA).
Shares Targeted therapy roadmap: imatinib → designed for resistance → the undruggable drivers fall, Acquired resistance to every therapy, Liquid biopsy (ctDNA).