Genetic information collected from seemingly healthy tissue near lung tumors may be a better predictor of whether cancer will come back after treatment than analysis of the tumors themselves, according to new research led by NYU Langone Health and its Perlmutter Cancer Center.
The new study focuses on lung adenocarcinoma, a cancer that forms in alveolar epithelial cells and accounts for about a third of all lung cancers in the United States, according to the U.S. Centers for Disease Control and Prevention. Most patients are cured if tumors are surgically removed early in the disease's progression, but residual cancer cells regrow in about 30% of cases and can lead to death.
For the study, the research team collected almost 300 tumor and healthy tissue samples from lung cancer patients. The study investigators then sequenced the RNA from each sample and fed these data, along with whether or not recurrence occurred within five years of surgery, into an artificial intelligence algorithm. This program used a technique called "machine learning" to build mathematical models that estimated recurrence risk.
Related Stories"Immunotherapy, which bolsters the body's immune defenses, might therefore help combat tumor growth before it becomes visible to traditional methods of detection," added study co-senior author and cancer biologist Aristotelis Tsirigos, PhD. Funding for the study was provided by National Institutes of Health grants R37CA244775 and U01CA214195. Further support was provided by the American Association for Cancer Research Grant and the Roche Access to Distinguished Scientists Programme.
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