"Who Survives, and Why?" — Using Kaplan-Meier Analysis to Link Gene Expression to Patient Outcomes

 There's a moment in every cancer biologist's research when the question shifts from what is this gene doing? to does it actually matter for patients?

That shift used to require a statistician, a clinical dataset, and a lot of back-and-forth. R2 collapses all of that into a single afternoon.

The Kaplan-Meier module in R2 is built for exactly this question. A Kaplan-Meier curve is one of medicine's most powerful visual tools — it shows you, over time, what fraction of patients in a group are still alive (or relapse-free). When you split patients into two groups — say, high versus low expression of your favourite gene — and their survival curves diverge dramatically, you feel it in your stomach. That divergence is a signal.

Here's how a typical session goes. You've been studying a receptor gene that you suspect is linked to poor prognosis. You open R2, select a neuroblastoma dataset with survival annotation, and navigate to the Kaplan-Meier module. You choose to separate patients by your gene's expression level, and R2 automatically finds the most statistically meaningful cut-off point — a feature called the Kaplan Scan, which saves you from the temptation of cherry-picking a threshold yourself.

The curve appears. Patients with high expression of your gene trend towards worse overall survival. The log-rank p-value sits at 0.003. You stare at it for a moment.

Then you get greedy (in the best way). R2 lets you combine two tracks to create subgroups — so you split by both your gene's expression and tumour stage. Now you're asking: among high-stage patients, does my gene's expression still add prognostic information on top of what we already know from staging? The answer, in your case, is yes. And that's a finding.

For the more statistically adventurous, R2 also offers Cox regression analysis, which lets you assess hazard ratios and control for confounding variables. But even without going that deep, the basic Kaplan scan is genuinely powerful — and genuinely accessible.

You walk away not just with a curve, but with a biological hypothesis that now has patient-level support behind it.

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.