The concept of a proximity antigen sits at an important intersection of tumour immunology, spatial biology, and machine learning, and appreciating why this intersection matters requires stepping back to consider how the immune system normally decides what to attack. Cytotoxic T lymphocytes and other immune effector cells do not survey cells in the abstract; they interrogate surfaces. Peptides presented by major histocompatibility complex molecules, along with a constellation of co-stimulatory and co-inhibitory proteins, form the molecular interface through which immune cells sample the internal state of every nucleated cell in the body. A tumour cell that displays a peptide derived from a mutated protein, or from an aberrantly expressed self protein, can in principle be recognised as abnormal. The difficulty, which has occupied the field for decades, is that most peptides displayed on tumour cells are also displayed, at lower abundance or in different contexts, on healthy tissue. The search for antigens that are genuinely tumour-specific, rather than merely tumour-enriched, has therefore been one of the central challenges of cancer immunotherapy.
Traditional antigen discovery has relied heavily on bulk omics. RNA sequencing of tumour biopsies identifies transcripts that are elevated in tumours relative to normal tissues, and mass spectrometry of immunopeptidomes identifies the peptides actually presented on human leukocyte antigen molecules. These approaches have been enormously productive, yielding the tumour-associated antigens that underpin therapeutic cancer vaccines, bispecific antibodies, and adoptive cell therapies. Yet they share a structural blind spot: they measure abundance, not arrangement. A transcript that is highly expressed in a tumour may also be expressed in a vital healthy tissue, and a peptide that appears abundant in a dissociated tumour sample may, in the intact tissue, be presented only on stromal cells rather than on the malignant compartment itself. Dissociation destroys the spatial relationships that the immune system, operating in intact tissue, would encounter.
Proximity labelling technologies emerged precisely to address this limitation. By fusing an engineered enzyme, such as a promiscuous biotin ligase or a peroxidase, to a protein of interest, researchers can covalently tag molecules that come within a few tens of nanometres of that protein in living cells. The tagged molecules can then be enriched and identified by mass spectrometry, producing a snapshot of the local molecular neighbourhood rather than the global composition of the cell. Applied to tumour biology, proximity labelling offers a way to ask what molecules physically congregate around a given marker protein, information that bulk profiling cannot provide. The microenvironment of a membrane protein, its partners, its neighbours in the plasma membrane, and the proteins trafficked alongside it, constitutes a layer of biological organisation that is invisible to standard transcriptomics and only partially accessible to conventional proteomics.
The challenge that arises once proximity data are generated is computational. A proximity labelling experiment produces a network-like structure: bait proteins, their tagged neighbours, the abundances of those neighbours, and the connections among them across conditions and cell states. Representing this as a simple list discards most of its meaning. Graph-structured data demand graph-aware analysis, and this is where modern graph learning methods become relevant. Graph neural networks and related architectures are designed to learn representations of nodes in a network that incorporate information from their local neighbourhoods, so that the identity of a protein is encoded not only by its own properties but by the company it keeps. In biological settings, this inductive bias is often exactly right: function in cellular systems is relational, and proteins that occupy similar network positions frequently share functional roles even when their sequences are unrelated.
Applying graph learning to proximity data in the tumour context creates an opportunity to formalise an intuition that immunologists have long held informally. An antigen that is safe to target therapeutically is not simply one that is absent from healthy tissue in a bulk measurement; it is one whose presentation is unlikely to occur on healthy cells under the conditions the immune system will actually encounter. Proximity provides a proxy for this contextual specificity. A candidate antigen that is consistently found in the immediate molecular neighbourhood of validated tumour markers, and that participates in the same spatial programmes as known malignant-surface proteins, carries more evidence of tumour association than a candidate identified by expression alone. Graph learning allows this evidence to be aggregated systematically across many candidates and many data modalities, rather than assessed one protein at a time by expert curation.
The therapeutic stakes of this problem are considerable. CAR T cell therapy, which engineers a patient’s T cells to recognise a surface antigen, has produced remarkable outcomes in haematological malignancies where a truly tumour-restricted target such as CD19 exists. Solid tumours have proven far more resistant, and a major reason is the absence of comparable target antigens. The antigens that are available on solid tumours, such as HER2, EGFR, or B7-H3, are frequently shared with essential healthy tissues, and targeting them produces on-target off-tumour toxicity that can be dose-limiting or fatal. The field has responded with a variety of engineering strategies, including logic-gated CARs that require two antigens to be present simultaneously, tunable affinity receptors that respond only to high antigen density, and adaptor systems that allow dosing control. All of these strategies depend on knowing which combinations of antigens are jointly specific to tumours, and this is a question about spatial co-organisation that proximity-guided approaches are well positioned to answer.
There is also a deeper immunological rationale for thinking in terms of proximity. The immune synapse itself is a proximity phenomenon: a T cell commits to killing only after sustained engagement with a target cell, integrating signals from many receptor-ligand interactions across the contact interface. Antigens that cluster together on the tumour surface may be recognised more effectively than antigens presented in isolation, because multivalent engagement strengthens T cell receptor signalling and can overcome the inhibitory signals that tumours deploy. Conversely, an antigen that is spatially segregated from co-stimulatory context may be immunologically silent even if it is abundant. Understanding the spatial organisation of tumour antigens therefore has implications not only for target selection but for predicting the quality of the immune response that targeting will provoke.
The tumour microenvironment adds further layers of complexity that proximity-aware methods are suited to capture. Tumours are not homogeneous masses of malignant cells; they are ecosystems containing fibroblasts, endothelial cells, macrophages, T cells, and extracellular matrix, all arranged in structured architectures that vary between patients and between regions of the same tumour. A candidate antigen expressed by tumour-associated fibroblasts, for example, might be attractive for stromal targeting strategies but inappropriate for direct tumour-cell killing. Distinguishing the malignant compartment from the reactive stromal compartment requires information about which proteins co-localise with which, and dissociated single-cell methods, while powerful, lose the tissue architecture that defines these compartments. Proximity labelling performed in intact systems, combined with graph-based inference, offers a route to recovering some of this architectural information from molecular data.
From a machine learning perspective, the tumour antigen problem illustrates a broader trend in computational biology: the shift from classification of individual entities to inference over relational structures. Early bioinformatics treated each gene or protein as an independent feature, and predictive models were built on expression vectors. The realisation that biological molecules operate in networks led to a family of methods that propagate information across interaction graphs, including network propagation algorithms, random walk approaches, and eventually graph neural networks. Each generation of methods has expanded the kinds of questions that can be asked. Where earlier approaches could ask whether a protein is connected to known disease genes, graph learning can ask whether a protein occupies a network position characteristic of disease-relevant molecules, a subtler and often more robust criterion. In the antigen discovery setting, this means the model can learn what the neighbourhood of a validated tumour antigen looks like and then score uncharacterised candidates by the similarity of their neighbourhoods.
Validation remains the essential counterweight to computational prediction, and the history of antigen discovery offers sobering lessons about candidates that looked compelling in silico but failed in vivo. A predicted proximity antigen must survive several successive tests: confirmation that the protein is genuinely presented on the tumour cell surface by human leukocyte antigen molecules, demonstration that healthy tissues lack comparable presentation, evidence that T cells capable of recognising the presented peptide exist in patients, and finally evidence that engaging those T cells produces tumour killing without tissue damage. Each of these tests is demanding, and attrition between stages is high. Computational methods that incorporate proximity information improve the front end of this pipeline by enriching the candidate list for molecules likely to pass the later stages, which is a meaningful advance even though no computational prediction can substitute for experimental validation.
The timing of this work within the broader technological landscape is notable. Spatial transcriptomics and spatial proteomics have matured rapidly, multiplexed imaging now routinely profiles dozens of proteins in intact tissue sections, and proximity labelling has been adapted to an expanding range of model systems. Meanwhile, immunopeptidomics has improved in sensitivity to the point where thousands of presented peptides can be catalogued from limited clinical material. The convergence of these technologies with graph-based machine learning creates conditions in which antigen discovery can become less dependent on serendipity. Historically, many successful tumour antigens were found through patient-specific approaches, such as isolating tumour-infiltrating lymphocytes and identifying their targets, which is powerful but slow and individualised. A systematic, proximity-informed discovery framework aims to generalise this process, producing catalogues of candidate antigens that can serve the broader population of patients.
There are also implications beyond T cell therapy. Antibody-drug conjugates require surface antigens with sufficient differential expression and internalisation behaviour, and the proximity context of an antigen can inform predictions about its trafficking and its accessibility to circulating antibodies. Bispecific molecules that bridge tumour cells and T cells depend on pairs of antigens whose joint expression pattern is tumour-restricted, and proximity data speak directly to co-localisation. Even vaccine design benefits, since peptide antigens that arise in the context of a tumour-specific molecular programme are more likely to elicit responses that discriminate tumour from self. In each of these therapeutic modalities, the fundamental question is the same: which molecular features distinguish the tumour cell surface in its intact context, and how confidently can that distinction be made for a given patient population.
As the field moves forward, the integration of proximity-guided graph learning into antigen discovery pipelines will likely be judged by a practical standard: whether the candidates it surfaces translate into therapies with wider therapeutic windows than those discovered by expression-based approaches alone. The conceptual contribution, however, may prove equally durable. By treating the tumour cell surface as a structured, relational system rather than a list of abundances, this line of work aligns the computational representation of tumour biology with the way the immune system itself reads that biology, through contact, context, and the molecular neighbourhoods in which every antigen is embedded.
Subject of Research: Proximity-guided graph learning reveals tumour-associated proximity antigens
Article Title: Proximity-guided graph learning reveals tumour-associated proximity antigens
Article References: Scandore, C., Malone, C. F., May, C. K., de Regt, A. K., Guernsey, J., Ma, H., Dephoure, N., Setter, B., Howell, R. A., Johnson, K. R., Farr, C. L., Romero, S., Vignale, L., Vittum, T., Dawson, E., Habtetsion, T., Nardi, F., Woodruff, B., Mathay, M., … Fadeyi, O. O. (2026). Proximity-guided graph learning reveals tumour-associated proximity antigens. Nature. https://doi.org/10.1038/s41586-026-11003-7
Image Credits: AI Generated
DOI: 10.1038/s41586-026-11003-7
Keywords: Proximity-guided, graph, learning, reveals, tumour-associated, proximity, antigens, scientific research
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Nathaniel Bowman. (September 11, 2026). Proximity-guided graph learning reveals tumour-associated proximity antigens. Scienmag. https://scienmag.com/proximity-guided-graph-learning-reveals-tumour-associated-proximity-antigens/
Nathaniel Bowman. “Proximity-guided graph learning reveals tumour-associated proximity antigens.” Scienmag, 11 September 2026, https://scienmag.com/proximity-guided-graph-learning-reveals-tumour-associated-proximity-antigens/. Accessed 11 September 2026.
Nathaniel Bowman. “Proximity-guided graph learning reveals tumour-associated proximity antigens.” Scienmag. September 11, 2026. https://scienmag.com/proximity-guided-graph-learning-reveals-tumour-associated-proximity-antigens/
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Tags: antigenscancer antigen discoverygraphimmune cell surface interrogationimmunopeptidomicslearningmachine learning in immunologyproximityProximity-guidedproximity-guided graph learningrevealsScientific Researchspatial biology and cancerspatial biology in cancerspatial transcriptomics in cancertumor-specific antigenstumour immunologytumour microenvironment analysistumour-associatedTumour-associated proximity antigens
