By profiling the metabolic processes of individual tumor cells on a chip, researchers at the Institute for Systems Biology found they could predict treatment responses among patients with non-small cell lung cancer.
In addition, by revealing the molecular basis responsible for triggering different metabolic states, the researchers described potential avenues for new treatments for patients unlikely to respond to chemotherapy or targeted drugs.
Using single-cell metabolic cytometry and fluorescent probes, they found a wide variety of tumor cells based on different phenotypes. Two of those types in particular—whether cells are directly engaged in breaking down glucose, as well as those that are actively producing energy within the mitochondria—were linked to patient therapy response and survival out to five to seven months.
In samples taken from 32 patients with adenocarcinoma of the lung, those that had a higher ratio of cells engaged in glycolysis were associated with elevated expressions of immune checkpoint ligands and the receptor tyrosine kinase AXL, a potential therapeutic target. The researchers’ work was published in the journal Nature Communications.
“Our metabolic assay can provide unique information complementary to tumor genetics and other clinical factors for improved cancer diagnostics,” said corresponding author Wei Wei, an assistant professor at ISB in Seattle. “For example, tumor genetics can identify whether the patients are bearing targetable driver oncogene mutations and thus segregate patients into various chemo- and targeted therapy regimens.”
“Our metabolic assay can further reveal whether patients are likely—or unlikely—to benefit from the standard chemo- or targeted therapies identified by tumor genetics,” Wei said in a statement. “This is important particularly for newly diagnosed patients who may benefit from such predictions prior to the onset of therapy.”
The researchers—in collaboration with laboratories at UCLA, UC Riverside, Fudan University and Shanghai Jiao Tong University—say the assay could potentially be performed in any clinical lab equipped with a fluorescence imaging system, with the entire protocol taking about 20 minutes from sample processing to metabolic phenotyping. They are now working to validate their findings in a larger patient cohort, before translating it to the clinic.
“The notion of precision cancer medicine has been mostly driven by tumor genomics,” Wei said. “Except for PET imaging, functional assays are rarely used as diagnostic tools for clinical decision-making.”
“Our results highlight the promise of using cellular metabolic functions to address some of the most challenging questions in cancer diagnostics, namely predicting the diverse therapy responses for patients with similar tumor genetics in order to match the right patient with the right therapy,” he said.