Introduction

Computational models are becoming part of how cancer is studied, alongside laboratory and clinical work. AI methods, including foundation models and generative models, are starting to capture aspects of the molecular and spatial organisation of tumours from large, multimodal datasets. Efforts to simulate how tumours respond to perturbation and to predict individual patient outcomes are active but harder, and results have not yet been shown to hold at the scale and reliability that clinical use requires. Validation, generalization across cohorts, interpretability, and integration with experiments and trials remain open problems.

The EACR Conference on Cancer Biology: Virtual Systems and Beyond will bring together computational and experimental scientists and clinicians to assess what current models can and cannot do, where they add value in discovery and care, and what is needed to move from representation toward reliable simulation and prediction. The goal is a realistic account of where these approaches help, where they fall short, and what comes next.

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Target audience:

Basic, computational, and translational cancer scientists, clinicians, and machine learning researchers, both senior and early-career, from academia and industry, who work on or want a broad overview of computational modelling, AI, and virtual systems in cancer biology and their translation to the clinic.

Topics to be covered:  

• Single-cell and spatial omics in cancer: technologies, computational methods, and biological insight
• Computational and digital pathology: deep learning for histology and image-based biomarkers
• Multimodal integration across imaging, pathology, genomics, and clinical data
• Foundation models, representation learning, and generative modeling for cancer biology
• Modeling cellular perturbation and treatment response for precision oncology
• Cancer genomics, tumour evolution, heterogeneity, and the tumour microenvironment

Scientific Programme Committee:  

• Raza Ali (Co-Chair), CRUK Cambridge Institute, UK
• Charlotte Bunne (Co-Chair), EPFL, Switzerland
• Marianna Rapsomaniki, Swiss Biomedical Data Science Center, University of Lausanne,Switzerland
• Eytan Ruppin, Cedars Sinai, US

Lay summary of the conference topic:  

Every cancer is different, and a treatment that helps one patient may not help another. Researchers are now using artificial intelligence to build "virtual" models of cancer: computer systems that learn from large amounts of biological and clinical data to capture how tumours are organised and how they grow, to uncover new vulnerabilities and to understand how tumours respond to different therapies. These systems aim to run experiments and test treatments on a computer before moving to the laboratory or the clinic. This conference brings together the researchers building these tools and developing cutting-edge measurement technology. The shared goal is to understand each person's cancer more precisely, select better treatments, and ultimately improve outcomes for people living with cancer.

Register for the Cancer Biology: Virtual Systems & Beyond conference now.

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Image from 2026 conference at the Centro Congressi Giovanni XXIII, Bergamo, Italy