Dusting for Cancer’s Protein "Fingerprint"
Even before researchers finished sequencing the human genome, many shifted their focus to proteomics, the study of the proteins encoded in that sequence. Understanding how proteins work and how to manipulate them could provide new ways to diagnose and treat disease. This summer, proteomics took an important step toward medical application when the National Cancer Institute and the U.S. Food and Drug Administration began using proteomic tools as part of human trials for new cancer treatments.
In the three-year program, researchers will use tissue from biopsies to study how patients’ proteomic “fingerprints”-profiles of the proteins in particular cells-change during treatment. “This is the first time proteomics is being used during clinical trials with actual biopsy material,” says the FDA’s Emanuel Petricoin, codirector of the program. It’s also the first time researchers will be able to follow health-related changes in a patient’s protein profile over time. “I think it’s a great idea,” says Joshua LaBaer, director of the Institute of Proteomics at Harvard Medical School.
But it’s an ambitious idea as well, LaBaer cautions. “I’m worried the technology is not mature enough, and a lot of stuff will be missed,” he says. Indeed, detecting and analyzing these fingerprints is no easy task. Using a laser dissection device, the researchers extract cancerous, precancerous and normal cells from a tissue sample; special “protein chips” (see “Protein Chips,” TR May 2001) are then used to identify hundreds of proteins within each cell. Computers compare such fingerprints from dozens of cell types and hundreds of patients, looking for patterns associated with disease, remission and drug toxicity.
“Right now we aren’t making clinical decisions-we aren’t yet telling oncologists to change therapy,” Petricoin says. In two to three years, though, proteomic tests could be used to guide treatment, alerting a doctor when a drug is causing a toxic reaction, for example, before significant damage is done.
Keep Reading
Most Popular
Large language models can do jaw-dropping things. But nobody knows exactly why.
And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.
The problem with plug-in hybrids? Their drivers.
Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.
Google DeepMind’s new generative model makes Super Mario–like games from scratch
Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.
How scientists traced a mysterious covid case back to six toilets
When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.