Algorithm identifies risk-stratifying glioblastoma tumor cells

"A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses," Rebecca Ihrie, PhD, and Jonathan Irish, PhD, associate professors in the department of cell and developmental biology at Vanderbilt University, and colleagues wrote. "We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival." Ihrie and Irish told Healio what prompted this research, implications of the findings and what future research should entail. Question: What prompted this research? Ihrie: Cancers are now being studied using single-cell approaches, through which we can learn about the presence and abundance of different subsets of cells within the sample.