Sohrab Salehi

✨ Excited to share new work in @NatureGenet, with next-door neighbor @SohrabShah! Our labs – led by @nceglia; @GreedyApple; Zach Sethna; and @elhanati_yuval - applied information theory and probabilistic modeling to the question of how T cell dynamics changes in response to immunotherapy, particularly coupling between a T cell's T cell receptor (TCR) and its phenotypic state. T cell responses are critical to cancer immunotherapies. Their behavior is often strongly influenced by their TCR, which directly engages with antigens presented on the cell surface. How clonal the sequenced TCR repertoire is in a dataset has been used to understand response – TCRs that recognize an antigen multiply – but its often a noisy measure, and we wanted to extend this logic to single-cell data in an eqiposed way. Information theory offers a solution. The specific question of how information is propagated between the TCR and its phenotypic state also becomes a general way to probe genotype-phenotype mapping. The mutual information between receptor and state - that is how much knowing about one tells you information about the other - and how that changes can then be used to probe therapeutic response. Given data sparsity this becomes a problem in probabilistic inference – which was addressed here via a cool hierarchical Bayesian framework led by @GreedyApple & @nceglia. A flux in phenotype and stronger mutual information post-treatment was associated with response to immunotherapy. In non-responders, therapy did not improve poor communication between TCR and phenotype. Hence information dynamics can be used to measure response. We are already collectively using this approach across many, many projects. We use variants to analyze vaccine data with @TheVinodLab at the Olayan Center for Cancer Vaccines @MSKCancerCenter, in multiple projects with @break_cancer, throughout the Halvorsen Center @CompOncMSK, and elsewhere. It was a lot of fun and we hope you enjoy! Code link included! 👇 👇 👇 nature.com/articles/s41588-0…