Immunotherapy drug target identification using machine learning and patient-derived tumour explant validation
Year: 2026
Type: article
Abstract: Immunotherapy has revolutionized cancer treatment, yet only a minority of individuals respond clinically, necessitating alternative strategies that can benefit these patients. Novel immuno-oncology targets may achieve this through bypassing resistance mechanisms to standard therapies. We introduce Mining Immunotherapy Drug tArgetS (MIDAS), a multimodal graph neural network system for immuno-oncology target discovery. MIDAS leverages gene interactions, multi-omic patient profiles, immune cell biology, antigen processing, disease associations and phenotypic consequences of genetic perturbations. It generalizes to time-sliced data, outcompetes state-of-the-art baselines (including OpenTargets) and ranks approved targets above those in clinical development. Moreover, MIDAS recovers immunotherapy-response-associated genes in unseen patients, thereby capturing immunotherapy response determinants. Interpretability analyses reveal a reliance on autoimmunity, regulatory networks and immuno-oncology pathways. Functionally perturbing oncostatin M–oncostatin M receptor signalling, a proposed MIDAS target, in TRACERx melanoma-patient-derived explants yielded reduced dysfunctional CD8 + T cells, which associate with immunotherapy response, and reduced CCL4 levels. Furthermore, oncostatin M and oncostatin M receptor expression is associated with altered T cell and macrophage profiles in bulk transcriptomic data from patient samples. These data are consistent with a role for oncostatin M–oncostatin M in modulating the tumour microenvironment towards immunosuppressive, tumour-promoting phenotypes. Our results present a machine learning framework for analysing multimodal data for immuno-oncology target discovery.
Source: Nature Machine Intelligence
Authors: Marcellus Augustine, Nuno Rocha Nene, Hongchang Fu, Christopher L. Pinder, Lorena Ligammari, Alexander P. Simpson, Irene Sanz-Fernández, Krupa Thakkar, Danwen Qian, Evelyn Fitzsimons, Benjamin S. Simpson, Roberto Vendramin, Andrea Castro, Heather Niederer, Samra Turajlic, Sergio A. Quezada, Nicholas McGranahan, Chris Watkins, Charles Swanton, Kevin Litchfield
Institutions: The Francis Crick Institute, University College London, Royal Marsden NHS Foundation Trust, Royal Marsden Hospital, University of Manchester, Cancer Research UK Manchester Institute, Cancer Research UK, University of London, Royal Holloway University of London, University College Hospital
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
OpenAlex ID: W7161573884