From Operations to Experiment Design: The Hidden Science of Precision Oncology

A blog from UMVI Translational Research Manager Camilla Cucinotta, who recently presented at the Urological Cancers Symposium on 15 September 2026.

When we think about scientific discovery in precision oncology, we often focus on what happens at the end of the pipeline.

The sequencing. The computational analysis. The publication. That is where science becomes visible. But I would argue that many of the most important scientific decisions happen much earlier. Before a single dataset exists.

Which patient should be sampled? When in the treatment journey should we collect tissue?  How should tissues be collected and stored, for a long? Which biopsy is biologically informative? Which assays are most appropriate to answer the question? How do we connect molecular data back to meaningful clinical outcomes?

These decisions are sometimes described as 'just' operations, logistics, or study coordination but they are not simply organisational choices. They define the experiment and clinical trial. They determine what biology we can observe, what questions we can answer, and ultimately whether a trial can generate meaningful insight.

Modern precision oncology gives us extraordinary tools. We can measure tumour biology at unprecedented depth. But the value of those measurements depends on the decisions made before the measurement happens. A beautifully analysed dataset cannot compensate for a poorly designed sampling strategy; the most advanced assay cannot recover information from a sample that was never collected and the most sophisticated analysis cannot answer a biological question that was never built into the study. This is where translational science sits, not between science and operations but within science itself.

It is the discipline of connecting clinical questions, biological hypotheses, sample strategy, technologies and analysis into one coherent experiment. Because the trial is not defined when the data are analysed, the trial is defined much earlier. At the moment we decide: what biology we want to see.

In conclusion, we often think of operations and the people in operations as the infrastructure around science. But in reality, the choices we every day make around patients, samples, assays, study design, data integration are not separate from the study, they define the study. Because discovery doesn't begin when we analyse the data, it begins when we decide what data are worth generating.