Maintaining Tissue Sample Quality Might Reduce Medical Errors : Shots - Health News The goal is to customize treatments for cancer and other diseases to a patient's own biology. But something as simple as failing to take care of tissue samples en route to the lab can derail that.

Precision Medical Treatments Have A Quality Control Problem

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In the high-tech world of precision medicine, precision may depend on the orderly who's pushing a cart down the hospital halls. There might be, for example, tissue samples on that cart on the way to the pathology lab. And if they end up at the lab in poor shape, that has serious implications for patients and for scientists who plan to use that material to develop new tests and treatments. NPR's Richard Harris reports.

RICHARD HARRIS, BYLINE: Consider the story of a test that's commonly used to find the best treatment for breast cancer patients. About a decade ago, pathologists realized that the test was wrong about 20 percent of the time. As a result, some women were getting the wrong treatment. And it turns out it wasn't a flaw with the test itself. A big problem was that tissue sat out for too long before being tested. Doctors established guidelines in 2007 to assure that breast cancer samples are processed correctly, but pathologist Carolyn Compton says a decade later, that's still the only test that has such strict guidelines.

CAROLYN COMPTON: Why are breast cancer patients special?

HARRIS: Compton, at Arizona State University, has been pressuring her colleagues to improve tissue handling standards for all types of cancer. This is not just a question about making sure each and every patient gets the most appropriate treatment. It's also important to gather and preserve specimens with care because they often end up being preserved in huge collections called biobanks. And those collections are a foundation for the field of precision medicine.

COMPTON: I don't see how we're going to get precision medicine at the end of the day when everything under the hood is so imprecise.

HARRIS: To flush out this story, we meet at the exhibit hall at the annual meeting of the College of American Pathologists.

COMPTON: Maybe some of the things that we're looking at will validate what I'm saying. And I'm just going to tell Richard Friedberg to come join us.

HARRIS: Friedberg has just finished his term as president of the College of American Pathologists, and he shares Compton's concern about the lack of standards for handling biological samples. For example, he says you can't tell just by looking at a tube of blood whether it's OK.

RICHARD FRIEDBERG: And if it was left on a window sill and hit a hundred degrees, a lot of things change.

HARRIS: Like Compton, Friedberg is concerned about how this lack of standardization will affect research once these samples are sent to biobanks and used by scientists for their studies in precision medicine.

FRIEDBERG: We need to be sure that the stuff that they're looking at is valid, accurate, reliable, reproducible, all of those parameters that are hallmarks of good quality laboratory medicine.

HARRIS: And to what extent is that the case?

FRIEDBERG: I don't know.

HARRIS: The scary part to him is that you can still run a test and still get a readout.

FRIEDBERG: But if it's garbage in, it's garbage out.

HARRIS: We walk over to the display of some of the remarkable technology now sweeping the world of precision medicine, including gene sequencing machines that cost hundreds of thousands of dollars. Compton says people think these machines are invincible. They aren't.

COMPTON: You can't make up for a bad sample. So even with all of this technological magic, you can't turn straw into gold with this machine.

HARRIS: And results from pathology labs are not even the biggest source of error in the growing field of precision medicine. Researchers are combining that data with information gleaned from electronic medical records, and there's no question that those records are riddled with mistakes. But not everyone in the new world of precision medicine is so concerned about these quality control issues.

ATUL BUTTE: So I am not a believer in garbage-in, garbage-out at all.

HARRIS: Atul Butte is the director of the Institute of Computational Health Sciences at the University of California at San Francisco. And he sits in a chair endowed by someone who made himself rich with big data, Mark Zuckerberg. Error-filled data doesn't really bother Butte.

BUTTE: I know that no one scientist, no one clinician or pathologist is perfect. But I would rather take 10 or a hundred, you know, so-called mediocre data sets and figure out what's in common there than to take one from someone who says they're perfect at doing this kind of measurement.

HARRIS: True, it's easier to find real things in clean data. But in the real world, he says, data are always full of errors. So when you find something in noisy data, it's more useful in real-world settings. In Butte's view, it's far more important to make lots of noisy data available and to as many scientists as possible.

BUTTE: To me, I really want to see a world where I don't just see five companies or 10 companies or 20 companies working in a particular area. I want a thousand drug companies. I want 10,000 drug companies.

HARRIS: Even if that were to happen, discoveries based on noisy data often turn out to be just plain wrong, so this enterprise will generate tons of false leads. And the most expensive and most time-consuming part of research is finding out whether something that looks great in the lab actually works in people. Richard Harris, NPR News.


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