Giving Compass' Take:

• Diane Mapes explains why Fred Hutch is pursuing AI technology to capture EGFR and ALK mutations in patients’ records to improve cancer care. 

• How can funders support the development of technology like this? What resources do you have at your disposable to support this type of effort? 

• Learn how to find and fund scientific research. 

When rural Minnesota music teacher Shelly Engfer-Triebenbach started getting winded while doing the Hokey Pokey with her students, she knew something was wrong. She went to a doctor who gave her antibiotics for what was initially diagnosed as pneumonia, but days later learned that the shortness of breath was due to stage 4 non-small cell lung cancer.

She was 40 years old. The average age of lung cancer patients at diagnosis is 70.

“That didn’t set right with me,” said Engfer-Triebenbach, now 46. “I sought four other opinions before I settled on a doctor who said, ‘You’re too young to have this disease. You fit the criteria of an ALK mutation.’”

Engfer-Triebenbach’s first oncologist had tested her for ALK and EGFR, two common mutations that drive non-small cell lung cancer (among others) and told her she was negative for both. Her second oncologist suggested she be retested at a different lab then started her on a chemotherapy.

“I didn’t have a lot of hope at the time,” she said. “If you don’t have those mutations, you’re lucky to live a year. My oncologist sent in my test, started me on a second line of chemo, which was really awful, then three weeks later said, ‘You’re not going to believe this. You’re ALK positive.’ It was like winning the lung cancer lottery. There are so many other things available. It was a total life-changer.”

Targeted oral therapies for these two common mutations — ALK, short for anaplastic lymphoma kinase, and EGFR, short for epidermal growth factor receptor — have been life-changers for many patients. So much so, that they’re now offered as a first-line treatment for patients diagnosed with stage 4 non-small cell lung cancer.

But not all lung cancer patients are being tested and, as in the case of Engfer-Treibenbach, some test results can be flawed.

That’s why researchers at Fred Hutchinson Cancer Research Center in Seattle are road-testing a big data workaround, using a type of artificial intelligence called natural language processing, or NLP, to delve into the pathology reports of patients with metastatic non-small cell lung cancer to find these two treatable mutations. The results of their first study were published in the Journal of Clinical Cancer Informatics; additional studies are already underway.

“We’re trying to develop an AI method to capture EGFR and ALK mutations in patients’ records,” said lead author Dr. Bernardo Goulart, a researcher and lung cancer oncologist with Fred Hutch and its clinical care partner Seattle Cancer Care Alliance.

“It’s all about helping patients and clinicians move forward with advancing targeted therapies and personalized medicine,” he continued. “We know these patients should be treated up front with specific oral targeted therapies that are highly effective and less toxic than chemotherapy and recommended by all the guidelines. We wanted to know, in real practice, if this patient population was getting timely access to these oral drugs and what their outcomes were. We also wanted to know whether patients were getting tested.”

Read the full article about AI to improve cancer care by Diane Mapes at Fred Hutchinson Cancer Research Center.