Blog Post – Insights from IO & Biomarker Summit 2026: The Evolving Immuno-oncology Landscape

The IO & Biomarker Summit, held in London, UK from 16-17 March, brought together a diverse group of stakeholders across the oncology landscape, including academic researchers, biotech innovators, and clinical development experts, to explore the latest advances in immuno-oncology and biomarker science.

We were pleased to contribute to this year’s program through a capabilities presentation delivered by Karthikeyan Devaraju, Senior Scientist, R&D and Cell & Gene Therapy, highlighting integrated scientific approaches supporting immuno-oncology research across the development lifecycle.

Alongside this, the summit provided valuable perspectives from across the field, with discussions and presentations reflecting the rapid pace of innovation in areas such as cell and gene therapy, biomarker development, and AI-driven drug discovery.

Explore our key scientific insights, along with highlights from our presentation.

Oncology R&D is increasingly characterized by its diversity. Discussions throughout the summit reflected a field that is no longer dominated by a single therapeutic approach, but instead driven by a convergence of technologies, including:

• Cell and gene therapies (such as CAR-T and related approaches)
• Antibody-based therapeutics, including bispecifics and antibody-drug conjugates
• Biomarker-driven strategies to guide patient selection and monitor response
• Novel drug delivery platforms, particularly mRNA and lipid nanoparticle systems.

This breadth underscores a key shift: progress in oncology is being enabled not by isolated breakthroughs, but by the integration of multiple scientific disciplines working in parallel.

Immuno-oncology remains a central pillar of innovation, with ongoing efforts focused on improving both the precision and performance of existing therapies.

Emerging approaches are addressing known challenges in current treatment modalities, including:

• Enhancing tumor targeting and killing efficiency
• Reducing off-target effects and improving safety profiles
• Expanding applicability beyond highly personalized treatments.

New targeting strategies, such as next-generation T-cell engagers, are being explored alongside refinements in CAR-based therapies. Together, these developments reflect a broader effort to optimize immune-mediated responses while maintaining control over toxicity and variability.

Artificial intelligence is playing an increasingly important role in shaping the future of drug development. Across multiple presentations, AI-driven approaches were highlighted as a means to:

• Accelerate early-stage discovery and candidate selection
• Enable more rational design of antibodies and other therapeutics
• Reduce development timelines through improved predictive modelling.

These advances are already contributing to a measurable shift in R&D timelines, with drug development cycles becoming significantly shorter than in previous decades.

At the same time, there is a clear recognition that AI must be applied thoughtfully. Its effectiveness depends on the quality of underlying data and the expertise used to interpret outputs, reinforcing the continued importance of scientific oversight in an increasingly data-driven environment.

Innovations in drug delivery are also reshaping the therapeutic landscape. In particular, lipid nanoparticle (LNP) and mRNA-based systems are emerging as powerful alternatives to traditional viral vectors. These platforms offer several potential advantages, including:

• Improved safety profiles
• Greater flexibility in design and application
• Scalability for broader clinical use.

Their growing adoption reflects a wider trend toward developing delivery mechanisms that can support both precision and accessibility, particularly in the context of cell and gene therapies.

Biomarkers continue to play a critical role in advancing oncology research, enabling more informed decision-making across the development lifecycle.

From early discovery through to clinical application, there is an increasing emphasis on:

• Identifying predictive biomarkers to guide patient stratification
• Monitoring pharmacokinetic and pharmacodynamic responses
• Leveraging large datasets to uncover new biological insights.

As the volume and complexity of data grow, the ability to effectively analyze and interpret this information is becoming a key differentiator in the development of next-generation therapies.