An odor-based test that sniffs out vapors emanating from blood samples was able to distinguish between benign and pancreatic and ovarian cancer cells with up to 95% accuracy, according to a new study from researchers across Penn.
The findings suggest that the Penn-developed tool—which uses artificial intelligence and machine learning to decipher the mixture of volatile organic compounds (VOCs) emitting off cells in blood plasma samples—could serve as a noninvasive approach to screen for harder-to-detect cancers, such as pancreatic and ovarian.
Co-authors include Erica L. Carpenter, director of the Circulating Tumor Material Laboratory and research assistant professor in the Perelman School of Medicine, Janos Tanyi, an assistant professor of obstetrics and gynecology, and Cynthia Otto, director of the Penn Vet Working Dog Center and professor at the School of Veterinary Medicine.
The results were presented at the annual American Society of Clinical Oncology meeting in early June by A. T. Charlie Johnson, the Rebecca W. Bushnell Professor of physics and astronomy in Penn’s School of Arts & Sciences.
“It’s an early study but the results are very promising,” Johnson says. “The data shows we can identify these tumors at both advanced and the earliest stages, which is exciting. If developed appropriately for the clinical setting, this could potentially be a test that’s done on a standard blood draw that may be part of your annual physical.”
The electronic olfaction—“e-nose”—system is equipped with nanosensors calibrated to detect the composition of VOCs, which all cells emanate. Previous studies from the researchers demonstrated that VOCs released from tissue and plasma from ovarian cancer patients are distinct from those released from samples of patients with benign tumors.
Read more at Penn Medicine News.