On 19 August 2026 a cancer therapy manufactured for a single person beat the standard of care in a Phase 3 trial. This is what that took, what it did not prove, and why the next bottleneck is compute.
Treat a tumour and you do not kill it evenly — you clear space for whichever subclone was already immune. A targeted drug selects for drug resistance. Immune pressure selects for antigen loss. They fail in different directions, which is the whole argument for combining them.
That is easy to assert and hard to believe until you watch it happen. So the model is on this site, running, and you can drive it.
Pick one of thirteen cancers, each carrying its own mutational burden and immune infiltration. Combine seven treatment modalities across three dosing schedules. Then watch three thousand cells sort themselves into sensitive, drug-resistant and immune-escaped — and find out, in months, whether your protocol cleared it, contained it, or bred something worse.
Whether any of it works depends on how much there is to aim at. Mutational burden, by tumour type:
Intismeran autogene plus Keytruda met both recurrence-free and distant metastasis-free survival in 1,137 patients with resected stage IIB–IV melanoma. It is the first Phase 3 win for an individualised neoantigen therapy, and the first time anything has beaten Keytruda alone in this setting. Both of those are real firsts.
What has not been published is how large the effect is. No hazard ratio, no p-value. Overall survival is explicitly not mature. The number the market moved on is a number nobody has seen.
Every dose is manufactured for one person, from that person's tumour. Five of these six steps are laboratory work with known answers. One is a prediction problem — and it is the reason this is a computing story.
The question a pipeline is usually asked is does this peptide bind this HLA? The question that decides whether a patient benefits is different: what evidence is there that this exact target matters? Nine modelled candidates below — the positions are illustrative, the reasoning attached to each is real. Pick one.
The open ecosystem for neoantigen selection already exists and is already in clinical use. It is the right place for any honest effort to start — by integrating, not replacing.
Griffith Lab, WashU. Modular suite covering SNVs, indels, fusions, splicing, vector design and human review. Over 164,000 PyPI downloads.
Open-source MHC class I binding predictor built on allele-specific neural networks.
Mount Sinai. Raw tumour/normal reads in, vaccine contents out. In use in three clinical trials — NCT02721043, NCT03223103, NCT03359239.