A research team led by engineers at the University of Pennsylvania and Northwestern University scientists has created a new synthetic biology approach, or a “QR code for cancer cells,” to follow tumor cells over time, finding there are meaningful differences in why a cancer cell dies or survives in response to anti-cancer therapies.
What fate cancer cells choose after months of therapy is “entirely predictable” based on seemingly small, yet important, differences that appear even before treatment begins. The researchers also discovered the reason is not genetics, contrary to beliefs held in the field. The findings are published in Nature.
The study outlined the team’s new technology platform that developed a QR code for each of the millions of cells for scientists to find and use later—much like tagging swans in a pond. The QR code directs researchers to a genome-wide molecular makeup of these cells and provides information about how they’ve reacted to cancer treatment.
“We think this work stands to really change how we think about therapy resistance,” says Arjun Raj, co-senior author and professor in bioengineering in the School of Engineering and Applied Science. “Rather than drug-resistant cells coming in just one flavor, we show that even in highly controlled conditions, different ‘flavors’ can emerge, raising the possibility that each of these flavors may need to be treated individually.”
Using the interdisciplinary team, the scientists put the before-and-after cloned cells through a whole genome sequencing pipeline to compare the populations and found no systematic underlying genetic mutations to investigate the hypothesis. Raj and and his team helped develop the QR code framework, FateMap, that could identify each unique cell that seemed to develop resistance to drug therapy. “Fate” refers to whether a cell dies or survives (and if so, how), and the scientists “map” the cells across their lifespan, prior to and following anti-cancer therapy. FateMap is the result of work from several research institutions, and it applies an amalgamation of concepts spanning several disciplines, including synthetic biology, genome engineering, bioinformatics, machine learning, and thermodynamics.
Read more at Penn Engineering Today.
