By the early 2000s, researchers at Dana-Farber and elsewhere knew that a certain protein appeared in many tumors taken from lung cancer patients. Based on that data, doctors started treating those patients with a drug that inhibited that protein. Unfortunately, for reasons that weren’t clear at the time, most of the patients didn’t respond — but there was a small percentage of patients, about one out of ten, who did. People called it a "Lazarus-like effect."

So how can doctors know which of their patients is Lazarus? How can they know which ones will respond to a drug and which ones won’t? In other words, how can they interpret data so it makes sense? That's the subject of this episode of Unraveled about precision medicine and the EGFR discovery. It's an episode that begins with a riddle and ends with a roadmap.