{"entity":{"id":"idea-bio1-evolution-forecasting","kind":"idea","name":"Forecast the next resistance mutation like the weather","aka":[],"tldr":"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.","summary":"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.","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (Tumour heterogeneity and clonal evolution): Gerlinger et al., Intratumor heterogeneity and branched evolution (NEJM 2012)","url":"https://doi.org/10.1056/NEJMoa1113205"}],"tags":[],"related":[],"cancers":[],"sections":[],"technologies":["liquid-biopsy","ai-drug-design"],"targets":[],"drugs":["osimertinib","sotorasib"],"companies":[],"institutions":[],"pathways":[],"terms":["resistance"],"trials":[],"people":[],"bottlenecks":["b-tumor-heterogeneity","b-resistance","b-ai-validation"],"keyPapers":[],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"A forecasting model trained on longitudinal ctDNA from patients on a given targeted drug predicts the dominant resistance mechanism at progression with calibrated accuracy well above the population base rate.","rationale":"Resistance mechanisms under a given drug are strongly constrained (a handful of routes dominate for osimertinib, alectinib, or sotorasib), and their early emergence is visible in serial plasma. Predictability is what makes pre-emptive combination possible.","test":"Train on serial plasma from completed trials, publish locked forecasts for a prospective cohort, and score them against observed progression biopsies; a Brier score better than base rate confirms.","maturity":"speculative","actor":"data","cost":"small","horizonYears":3},"route":"/ideas/idea-bio1-evolution-forecasting/","neighbours":{"technology":[{"id":"ai-drug-design","kind":"technology","name":"AI-driven drug & target discovery","route":"/technologies/ai-drug-design/"},{"id":"liquid-biopsy","kind":"technology","name":"Liquid biopsy (ctDNA)","route":"/technologies/liquid-biopsy/"}],"drug":[{"id":"osimertinib","kind":"drug","name":"Osimertinib","route":"/drugs/osimertinib/"},{"id":"sotorasib","kind":"drug","name":"Sotorasib","route":"/drugs/sotorasib/"}],"term":[{"id":"resistance","kind":"term","name":"Drug resistance (primary and acquired)","route":"/terms/resistance/"}],"bottleneck":[{"id":"b-resistance","kind":"bottleneck","name":"Acquired resistance to every therapy","route":"/bottlenecks/b-resistance/"},{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"},{"id":"b-tumor-heterogeneity","kind":"bottleneck","name":"Tumour heterogeneity and clonal evolution","route":"/bottlenecks/b-tumor-heterogeneity/"}],"roadmap":[{"id":"targeted-therapy-roadmap","kind":"roadmap","name":"Targeted therapy roadmap: imatinib → designed for resistance → the undruggable drivers fall","route":"/roadmaps/targeted-therapy-roadmap/"}]}}