Thanks! It’s all of Genomics England >90k WGS samples but only ~15k of which are from tumours

And a final thanks to our funders without which none of this work would have been possible: @cancerresearchuk.org, @wellcometrust.bsky.social and Rosetrees as well as most importantly all the patients & participants.

A huge thanks to my co-authors, especially my PI Prof. Nicholas McGranahan for guiding all of this and @charlesswanton.bsky.social who gave his support throughout. Thanks also to @genomicsengland.bsky.social , @cruk-cityoflondon.bsky.social , UCL Cancer Institute, and @crick.ac.uk

Finally, we directly compared the prognostic value of circulating vs. tumour infiltrating T cells in cancer patients
🔹 Circulating T cells were more prognostic than infiltrating
🔹 Circulating B cells were also prognostic, but only non-class-switched IgM/D B cells contributed to this signal.

ImmuneLENS also enables the study of tumour microenvironment and cancer evolution.
🧬 In the 100KGP cohort we identified driver mutations enriched in T cell–replete tumours vs. T cell–depleted tumours, suggesting that different cancer drivers may have immune-suppressive or immune-evasive roles.

In blood samples, ImmuneLENS scores reflect circulating T cell fractions, a proxy for the neutrophil-to-lymphocyte ratio.
🔹 In cancer patients, circulating T cell fractions were significantly lower than in healthy individuals.
🔹 We also observed notable sex-based differences.

We applied ImmuneLENS to the 100,000 Genomes Project (100KGP) from Genomics England —a WGS cohort lacking orthogonal immune data—creating an important new resource for researchers.

🚨 Now, you can investigate immune content in this or any other WGS dataset!

ImmuneLENS follows the same principles but improves the model to better reflect V(D)J recombination, leveraging the uniform coverage of WGS, it:
✅ Measures T and B cell fractions
✅ Detects class switching in B cells
✅ Predicts V and J segment usage
📢 Now Available: github.com/McGranahanLa...