"From Molecule to Medicine" — Using Target Actionability Reviews to Bridge Data and Drug Development

 Most research stories end with a publication. The best ones end with a treatment.

Between those two endpoints lies a long and difficult journey: from a statistically significant finding in a patient cohort, through mechanistic validation, to a druggable target, through preclinical models, to a clinical trial. It's a journey that requires not just good science, but an organised, evidence-based case for why this particular target, in this particular cancer, deserves the investment of a drug development programme.

R2's Target Actionability Review (TAR) module is a tool built specifically to support that case-building process — and it is unlike almost anything else in a genomics platform.

A TAR is a manually curated, structured literature review focused on a single gene target in a single cancer context. It brings together evidence from multiple domains: the genomic prevalence of alterations in the target, the functional evidence linking it to disease biology, the availability of existing drugs or drug candidates that modulate it, the preclinical and clinical data on those compounds, and the clinical and biological rationale for pursuing this target in this disease. It is, in essence, a living document that answers the question: should we develop a drug against this target?

In R2, published TARs can be browsed and explored by anyone — they're a valuable secondary resource for researchers who want to quickly orient themselves to the therapeutic landscape around a gene they've just identified as clinically relevant. You arrive from a Kaplan-Meier result showing that high expression of your target predicts poor survival, and within minutes you're reading a structured evidence synthesis of everything the field knows about drugging it.

For groups embedded in clinical trial consortia or precision medicine programmes, R2 also provides a TAR creation module — a structured interface for building new reviews, collecting and annotating evidence, and maintaining a collaborative literature database. This turns the informal process of "reading around a target" into a reproducible, shareable scientific artefact.

It's a reminder that R2 was built not just as a research tool, but as a translational one — designed from the start with the knowledge that the data it hosts is ultimately in service of patients, not just papers.

This is Part of an ongoing series on the R2 Genomics Analysis and Visualization Platform, developed at Amsterdam UMC. All analyses can be freely performed at r2.amc.nl. Full tutorials at r2-tutorials.readthedocs.io.