Mikhail Shugay

N.B. VDJdb is up and running after a planned server upgrade, it can be now accessed at vdjdb.com, the old vdjdb.cdr3.net URL redirects there and should be considered obsolete. If you are using VDJdb web API please update accordingly.

immunoPETE: A DNA-based integrated B-cell and T-cell receptor profiling platform biorxiv.org/content/10.648… #biorxiv_immuno

⭐️Wow 👉🏼published in @Nature 👉🏼“Thymic health and immunotherapy outcomes in patients with cancer.” ⭐️Across melanoma, breast, & renal cancers, the signal is clear: thymic health is a pan‑cancer, #tumoragnostic determinant of immunotherapy efficacy. ⭐️I think there are huge implications for patient stratification, treatment timing, & immune‑rejuvenating strategies in #PrecisionMedicine here @OncoAlert nature.com/articles/s4158…

The thymus shrinks as we get older, so is it actually doing anything useful? This study (nature.com/articles/s4158…) measured thymic health based on CT scans, and found: 1. Better thymic health is associated with longer survival

Overcoming T cell tolerance to tumor self-antigens through catch-bond engineering @ScienceMagazine @Stanford science.org/doi/10.1126/sc…

🚀 Deadline Extended! The submission deadline for tutorials & demos at AIRR Community Meeting VIII is now April 15, 2026 — matching the abstract deadline for oral presentations & posters. 📅 June 8–11, 2026 📍 Yale University (New Haven, USA) + Virtual 🔗 tinyurl.com/airrcmeeting8

Happy to share the work of Aisha Shah about training predictors of TCR-epitope recognition with unpaired TCRa + TCRb sequences: biorxiv.org/content/10.648….

Ultra-high-throughput IGH genotyping of 25 global populations reveals population-biased allelic diversity and homozygous V and D gene deletions @ImmunityCP cell.com/immunity/fullt… @NillaKH @karolinskainst

Experimental immunologists in the era of artificial intelligence dlvr.it/TRZ4tQ #immunology

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.

stVCR: spatiotemporal dynamics of single cells nature.com/articles/s4159…

I'm excited to announce some major updates to our ProteinEBM paper with Chenxi Ou and @sokrypton!

A statistical physics framework that models peptidomes across species shows that self and nonself peptides are nearly one and the same, implying that the immune system benefits by targeting antigens near those represented in the organism’s own proteome. go.aps.org/40tAuIl

Studying TCR and BCR opens the door to understanding how cells divide post engaging with antigens, which is key for a proper immune response. Read more in our recent review jitc.bmj.com/content/14/3/e… @IcahnMountSinai @TischCancer @jitcancer

A new scRNAseq analysis in human atherosclerotic plaques. I think the largest to-date (n=46). The use of scRNAseq to deconvolute bulk RNAseq in 656 plaques and then link single-cell phenotypes to clinical outcomes is one of the first efforts of its kind in the field.

Interpretable inflammation landscape of circulating immune cells | Nature Medicine nature.com/articles/s4159…

Congrats Todd & team @Bruskolab @puneet021192 Large-scale TCR repertoire profiling from peripheral blood reveals type 1 diabetes signatures linked to high-risk HLA alleles science.org/doi/10.1126/sc… @ScienceAdvances @UF @UniOslo

⁉️ Are we there yet (on immunogenic peptide predictions)?- No bit.ly/4k2Kp09

Highlights importance of AIRR-C recommendations for germline reporting sciencedirect.com/science/articl…

Always good to know your IGLV germline

Comprehensive assessment of the possibility to build accurate predictors of pathogen exposure with T-cell receptor repertoires of 1000+ donors with known COVID19 status. Using both alpha and beta chains, HLA haplotypes, fixing batch effects and validating using two large independent cohorts link.springer.com/article/10.118…