Immunotherapy drug target identification using machine learning and patient-derived tumour explant validation
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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 LitchfieldAbstract
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.
Journal details
Journal
Nature Machine Intelligence
Volume
8
Issue number
5
Pages
670-689
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10.1038/s42256-026-01201-3
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Europe PubMed Central
42206145
Pubmed
42206145
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The publication was previously a preprint.
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