The advances are astounding. A personalized cancer vaccine developed with messenger RNA (mRNA) technology extends the survival of melanoma patients. It trains patients’ immune systems to recognize and attack unique mutated proteins found only on their tumor cells. Another new mRNA drug, Daraxonrasib, doubles the life expectancy of pancreatic cancer patients.
These breakthroughs result from longstanding cooperation between US and European researchers and firms.
Start with the basic research. In 2005, Hungarian Katalin Karikó and American Drew Weissman at the University of Pennsylvania showed that modifying the nucleoside building blocks of synthetic mRNA could reduce unwanted inflammatory responses while improving protein production. Their work helped make therapeutic mRNA viable and later earned them the 2023 Nobel Prize.
The technology then moved across the Atlantic.
Germany’s BioNTech pursued mRNA and individualized cancer immunotherapies long before the COVID-19 pandemic. When the pandemic arrived, BioNTech partnered with US pharma giant Pfizer to develop and distribute the COVID vaccine.
Cancer represents a more complex biological challenge than COVID. mRNA delivers genetic instructions rather than targeting a single fixed protein. Those instructions can be changed without rebuilding an entirely new manufacturing architecture for every disease. Researchers must first determine what biological instructions to encode. That is where advances in sequencing, computational biology, and AI are important.
A patient’s tumor can contain hundreds or thousands of mutations. A fraction of them produce neoantigens (a kind of protein) that the immune system can recognize. Computational pipelines and machine-learning models help researchers sort through that complexity. They identify mutations, estimate which protein fragments a patient’s body will flag for their immune system to see, and prioritize the candidates most likely to provoke a useful T-cell response.
The marriage of AI and biology is changing the economics and speed of biological research. Google DeepMind released AlphaGenome Atlas in September 2026, which contains predictions for the molecular effects of roughly nine billion possible single-letter changes in the human genome. Anthropic announced a new life-sciences research group and laboratory in the same month after Anthropic’s Claude helped identify previously uncharacterized enzymes. OpenAI also introduced GPT-Rosalind, a specialized model built for biology, drug discovery, medicinal chemistry, protein engineering, genomics, and translational research.
Predictions still require experimental and clinical validation. Tumors evolve. Immune responses vary between patients. Models remain constrained by the quality and diversity of the data used to train them.
Admittedly, AI and mRNA will not vanquish all cancers. BioNTech recently announced that it would terminate a Phase II trial of its personalized mRNA therapy for high-risk colorectal cancer.
But computational tools speed up the search for promising targets. The key new tool is CRISPR-Cas9, which emerged from work led by American Jennifer Doudna at the University of California, Berkeley, and Frenchwoman Emmanuelle Charpentier, whose research unfolded across several European institutions. Their collaboration produced a programmable method of genome editing and earned them the 2020 Nobel Prize. With CRISPR, researchers can sequence a tumor, model its biology, identify candidate targets, encode selected instructions, test the resulting therapy, and refine the process as new data becomes available.
Taken together, CRISPR, mRNA, advanced sequencing, and AI make biology programmable and, effectively, an engineering problem, with computation helping scientists navigate its complexity.
CRISPR and mRNA illustrate the power of sustained transatlantic collaboration. American and European scientific talent, research capacity, capital, computational expertise, clinical infrastructure, manufacturing capabilities, and regulatory institutions reinforce one another and create an innovation system capable of moving discoveries from the laboratory into clinical development and, ultimately, to patients.
As AI and biology become more deeply integrated, these connections will prove crucial. COVID-19 showed the strategic value of this architecture under emergency conditions. Scientific findings moved rapidly between laboratories. Companies combined capabilities across borders. Regulators evaluated large datasets under compressed timelines. Manufacturers expanded production across several continents.
That capacity matters as China becomes competitive across the biotech value chain. China’s role is expanding beyond manufacturing into drug discovery and platform development. American and European pharmaceutical companies increasingly license and develop Chinese discoveries.
The latest cancer breakthroughs show what the transatlantic system can do when those capabilities work together. As AI and biology converge, the ability to connect discovery with scale may become one of the US and Europe’s most consequential technology advantages.
Elly Rostoum is a Resident Senior Fellow with the Tech Policy Program at the Center for European Policy Analysis (CEPA).
Tech 2030
A Roadmap for Europe-US Tech Cooperation
