biostudies-literatureWang ZJNCI NIH HHS390-404https://www.ebi.ac.uk/biostudies/studies/S-EPMC11922765biostudies-literatureUnknown9(3)Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for colNature biomedical engineeringIdentifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles.PMC11922765R21 CA284221Farooq ASThomson MWXu AMChen YJBhargava AWang ZJfalseIdentifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles.Cancer progression can be slowed down or halted via the activation of either endogenous or engineered T cells and their infiltration of the tumour microenvironment. Here we describe a deep-learning model that uses large-scale spatial proteomic profiles of tumours to generate minimal tumour perturbations that boost T-cell infiltration. The model integrates a counterfactual optimization strategy for the generation of the perturbations with the prediction of T-cell infiltration as a self-supervised machine learning problem. We applied the model to 368 samples of metastatic melanoma and colorectal cancer assayed using 40-plex imaging mass cytometry, and discovered cohort-dependent combinatorial perturbations (CXCL9, CXCL10, CCL22 and CCL18 for melanoma, and CXCR4, PD-1, PD-L1 and CYR61 for col2025-01-01T00:00:00Z2025 Mar2025-04-05T12:38:35.927Z2025-04-05T12:38:35.927ZS-EPMC119227654004481910.1038/s41551-025-01357-0