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A community machine learning challenge to predict the effects of gene perturbations on T cell differentiation for cancer immunotherapy

GSE327731 Mus musculus Expression profiling by high throughput sequencing 4 samples Submitted 2026/04/15 Platform GPL19057
Summary
Perturbations of genes with functional importance in T cells could be used to change the distribution of CD8 T cell states to enhance anti-tumor functions for cancer immunotherapies. We launched a world-wide computational challenge to predict the effects of gene perturbations and to devise objective functions for prioritizing gene perturbations that lead to desired T-cell state distributions. We supported the challenge by generating a single-cell Perturb-seq dataset profiling the effect of knocking out 73 individual expert-defined genes in T cells transferred into a mouse melanoma model. We compared the top algorithms developed by participants, and found that performance was primarily determined by the prior data used for gene feature representation, with perturbational data derived features, proving most effective. Experimental validation of the top 61 genes nominated by the algorithms revealed that perturbation of Ndufv2 and Dimt1 reached the defined objective and biased differentiation toward desired states.
Published in
A community machine learning challenge to predict the effects of gene perturbations on T cell differentiation for cancer immunotherapy
Zhang J, Schwartz MA, Mutaher M et al. · bioRxiv : the preprint server for biology 2026 · PMID 42239045 · doi:10.64898/2026.05.21.726863
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Also filed as BioProject PRJNA1452654 and SRA study SRP691246. Searching any of these in the dataset finder brings you back here.

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