New Sensors Could Make Cancer Detection Much Better

Researchers have developed biosensors with advanced sleuthing skills and the technology may revolutionize cancer detection and monitoring.

This article was written by Georgia Tech and originally published by Futurity.

The tiny detectives can identify key biological markers using logical reasoning inspired by the “AND” function in computers—like, when you need your username and password to log in. And unlike traditional biosensors comprised of genetic materials—cells, bits of DNA—these are made of manufactured molecules.

These new biosensors are more precise and simpler to manufacture, reducing the number of false positives and making them more practical for clinical use. And because the sensors are cell-free, there’s a reduced risk for immunogenic side effects.

“We think the accuracy and simplicity of our biosensors will lead to accessible, personalized, and effective treatments, ultimately saving lives,” says Gabe Kwong, associate professor and endowed chair in the Wallace H. Coulter biomedical engineering department at Georgia Tech, who led the study, which appears in Nature Nanotechnology.

Solving problems with current sensors

The researchers set out to address the limitations in current biosensors for cancer, like the ones designed for CAR-T cells to allow them to recognize tumor cells. These advanced biosensors are made of genetic material, and there is growing interest to reduce the potential for off-target toxicity by using Boolean “AND-gate” computer logic. That means they’re designed to release a signal only when two specific conditions are met.

“Traditionally, these biosensors involve genetic engineering using cell-based systems, which is a complex, time-consuming, and expensive process,” says Kwong.

So, his team developed biosensors made of iron oxide nanoparticles and special molecules called cyclic peptides. Synthesizing nanomaterials and peptides is a simpler, less costly process than genetic engineering, according to Kwong, “which means we can likely achieve large-scale, economical production of high-precision biosensors.”

Getting super specific

Biosensors detect cancer signals and track treatment progress by turning biological signals into readable outputs for doctors. With AND-gate logic, two distinct inputs are required for an output.

Accordingly, the researchers engineered cyclic peptides—small amino acid chains—to respond only when they encounter two specific types of enzymes, proteases called granzyme B (secreted by the immune system) and matrix metalloproteinase (from cancer cells). The peptides generate a signal when both proteases are present and active.

Think of a high-security lock that needs two unique keys to open. In this scenario, the peptides are the lock, activating the sensor signal only when cancer is present and being confronted by the immune system.

“Our peptides allow for greater accuracy in detecting cancer activity,” says lead author Anirudh Sivakumar, a postdoctoral researcher in Kwong’s Laboratory for Synthetic Immunity. “It’s very specific, which is important for knowing when immune cells are targeting and killing tumor cells.”

In animal studies, the biosensors successfully distinguished between tumors that responded to a common cancer treatment called immune checkpoint blockade therapy—ICBT, which enhances the immune system—from tumors that resisted treatment.

During these tests, the sensors also demonstrated their ability to avoid false signals from other, unrelated health issues, such as when the immune system confronted a flu infection in the lungs, away from the tumor.

“This level of specificity can be game changing,” Kwong says. “Imagine being able to identify which patients are responding to the therapy early in their treatment. That would save time and improve patient outcomes.”

Additional researchers from the University of California, Riverside and Georgia Tech contributed to the work.

Funding for the work came from the Petit Institute for Bioengineering and Bioscience, the National Science Foundation, and National Institutes of Health.

Source: Georgia Tech