Bo Wang@BoWang87
Everyone is talking about personalized mRNA cancer vaccines.
I want to share two recent Nature papers that cut through the excitement and reveal something the viral posts aren't telling you: the approach works — but only in patients whose immune system actually responds to the vaccine. In the PDAC trial, that was half.
Papers:
— TNBC-MERIT trial (Nature 2026): nature.com/articles/s4158…
— PDAC 3-year follow-up (Nature 2025): nature.com/articles/s4158…
Here's the exact number that explains why.
The PDAC trial: at 3.2 years median follow-up, vaccine responders had median recurrence-free survival that was never reached. Non-responders: 13.4 months. HR = 0.14. The T cell memory is real — some clones are projected to persist for over a decade.
The TNBC trial: 10 of 14 patients remained relapse-free at 5 years. One patient has been in remission for over 6 years, with neoantigen-specific T cells still circulating at ~2% of her CD8 repertoire.
So what separates responders from non-responders?
Across both trials: only 41 of 251 neoantigens actually triggered a T cell response. That's 16%.
Each vaccine encodes up to 20 neoantigens — the algorithm's best guess at which tumor mutations will be immunogenic. Most don't work. Half the PDAC patients didn't respond — not because they couldn't mount an immune response (they responded fine to concurrent COVID vaccines) — but because their selected neoantigens happened to miss.
This is the core unsolved problem: predicting, from sequence alone, which mutations will produce peptides that a specific patient's immune system will actually recognize.
It sounds like an MHC binding problem. It isn't. Tools like NetMHCpan handle binding affinity reasonably well. What they miss is the full causal chain:
1. Proteasomal processing — will the protein actually be cleaved into this exact peptide?
2. TAP transport — will it reach the ER for MHC loading?
3. HLA-peptide stability — across the patient's specific HLA alleles (10,000+ variants in the population)
4. T cell repertoire availability — has central tolerance already deleted the clones that would recognize it?
5. Tumor clonal architecture — is this mutation in every tumor cell, or just 30%? Targeting subclonal neoantigens leaves most of the tumor untouched.
Every step is a filter. Current prediction stops at step one.
Compounding everything: average manufacturing time in the TNBC trial was 69 days (range: 34–125) from sample to vaccine release. For pancreatic cancer, where non-responders recur at 13.4 months post-surgery, that's not a footnote. It's a window closing.
The good news: the T cell biology is sound. The mRNA platform works. The immunology is spectacular — when it works.
The bottleneck is the first step: choosing which 20 neoantigens go in the vaccine. Get that prediction right, and the responder rate moves.
This is where AI in cancer immunotherapy has to go next. Not mRNA design. Not LNP formulation. Immunogenicity prediction — integrating mutation calling, HLA typing, T cell repertoire sequencing, and single-cell tumor expression simultaneously, as a causal inference problem, not a binding affinity lookup.
We don't have a model that does this well. That's the gap.
