As large multi-cancer datasets become more important for predicting who may benefit from cancer drugs, a new approach better accounts for potentially overlooked variation.
New research from the University of Michigan Rogel Cancer Center aims to improve anti-cancer drug response predictions by teasing apart and allowing for simultaneous examination of differences across multiple cancer types as well as within individual types. The findings appear in PLOS Computational Biology.
“It’s like the old argument about nature versus nurture,” says study co-senior author Jun Li, a professor of human genetics, and associate chair for research of computational medicine and bioinformatics. “Obviously both contribute. The questions we set out to answer were: How much does each contribute? And can we use that information to make predictions that would be useful into the clinic?”
The study, led by former U-M postdoctoral fellow John Lloyd, who had worked closely with Li and co-senior author Sofia Merajver, used MEK inhibitor response as a proof-of-concept example, and drew on two public datasets, each containing several hundred patient-derived cancer cell lines.
The analysis — which included mRNA expression, point mutations and copy number variations — found that while predictions of drug response were highly accurate when comparing one cancer type as a whole to another cancer type, the predictions only held up for about five of 10 cancer types when looking at that cancer type on its own.