Why don’t cancer medicines work the same for everyone?

Cancer treatment has gotten more precise over time, as doctors first classified the disease by where it began in the body and, more recently, by the mutations found inside cancer cells to help find the right drugs to treat it.

But why can two people with seemingly similar cancers respond so differently to the same medication? Microsoft researcher Lorin Crawford thinks the answer lies in how tumors actually behave, not just how they’re categorized.

A study by Crawford and his team, published today in Nature Methods, marks an important step in helping AI understand how individual cells act and interact with their environment as researchers harness the technology’s power to spot patterns traditional approaches may miss.

The research is part of Project Ex Vivo, a collaboration between Microsoft and the Broad Institute with support from the Dana-Farber Cancer Institute. The group’s work aims to make cell behavior part of how cancer is categorized and treated, helping combat one of the leading causes of death worldwide by more successfully matching therapies to patients.

“The complexity of the disease is scientifically a very interesting one, but also one where you can have almost immediate impact,” Crawford says. “It feels like I’m doing something that’s larger than me. Any single finding seems like a step forward in some way.”

Looking beyond mutations

Part of the challenge with cancer research is that scientists can lose key signals when they test drugs outside the body. Ex vivo models — cancer cells grown in labs, including mini-tumors called organoids — don’t always match what’s happening inside a person. That means a medication that looks promising in a petri dish can fall short in a patient.

The Project Ex Vivo team focuses on “cell state” — how cancer cells behave and respond to their surroundings. Cell states can influence which treatments a tumor is sensitive to, how quickly resistance to drugs develops and how aggressive the disease becomes.

A statistician in a wet lab