{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang ZJ"],"funding":["NCI NIH HHS"],"pagination":["390-404"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11922765"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(3)"],"pubmed_abstract":["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 col"],"journal":["Nature biomedical engineering"],"pubmed_title":["Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles."],"pmcid":["PMC11922765"],"funding_grant_id":["R21 CA284221"],"pubmed_authors":["Farooq AS","Thomson MW","Xu AM","Chen YJ","Bhargava A","Wang ZJ"],"additional_accession":[]},"is_claimable":false,"name":"Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles.","description":"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 col","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-05T12:38:35.927Z","creation":"2025-04-05T12:38:35.927Z"},"accession":"S-EPMC11922765","cross_references":{"pubmed":["40044819"],"doi":["10.1038/s41551-025-01357-0"]}}