# Forecast the next resistance mutation like the weather

Source: https://onco.cc/ideas/idea-bio1-evolution-forecasting/  
OnCo record `idea-bio1-evolution-forecasting` (Idea). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

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.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- 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.
- Proposed 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

## Sources

- Bottleneck evidence (Tumour heterogeneity and clonal evolution): Gerlinger et al., Intratumor heterogeneity and branched evolution (NEJM 2012): https://doi.org/10.1056/NEJMoa1113205

## Connected records

- technologies: [AI-driven drug & target discovery](https://onco.cc/technologies/ai-drug-design/), [Liquid biopsy (ctDNA)](https://onco.cc/technologies/liquid-biopsy/)
- drugs: [Osimertinib](https://onco.cc/drugs/osimertinib/), [Sotorasib](https://onco.cc/drugs/sotorasib/)
- terms: [Drug resistance (primary and acquired)](https://onco.cc/terms/resistance/)
- bottlenecks: [Acquired resistance to every therapy](https://onco.cc/bottlenecks/b-resistance/), [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/), [Tumour heterogeneity and clonal evolution](https://onco.cc/bottlenecks/b-tumor-heterogeneity/)
- roadmaps: [Targeted therapy roadmap: imatinib → designed for resistance → the undruggable drivers fall](https://onco.cc/roadmaps/targeted-therapy-roadmap/)

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