{"doi":"10.1038/s41551-025-01357-0","title":"Identifying perturbations that boost T-cell infiltration into tumours via counterfactual learning of their spatial proteomic profiles","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 colorectal cancer) that support T-cell infiltration across patient cohorts, as confirmed via in vitro experiments. Leveraging counterfactual-based predictions of spatial omics data may aid the design of cancer therapeutics.","journal":"Nature Biomedical Engineering","year":2025,"id":512030,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9504,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1370889,"name":"Ali Farooq","orcid":"0000-0003-2947-0914","position":1,"is_corresponding":false},{"id":1370890,"name":"Y. Q. Chen","orcid":"0009-0009-0992-8976","position":2,"is_corresponding":false},{"id":1370891,"name":"Aman Bhargava","orcid":"0000-0002-3347-0602","position":3,"is_corresponding":false},{"id":109710,"name":"Alexander M. Xu","orcid":"0000-0003-4877-4358","position":4,"is_corresponding":false},{"id":275169,"name":"Matt Thomson","orcid":"0000-0003-1021-1234","position":5,"is_corresponding":false},{"id":1370888,"name":"Zitong Jerry Wang","orcid":"0000-0001-8008-7318","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":null,"created_at":"2026-07-19T02:48:01.269605Z","pmid":"40044819","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}