Engineers from the Massachusetts Institute of Technology (MIT) have designed a diagnostic system to make lung cancer screening more accessible globally.
The new technology could replace or supplement low-dose computed tomography (CT), the current gold standard for diagnosing lung cancer.
Based on nanosensors, sensors produce signals when detecting cancer-linked proteins in the lungs, which accumulate in the urine, where they can be detected with a simple paper test strip.
Researchers created two formulations of their particles: one that can be aerosolised and another delivered with a nebuliser, a dry powder delivered using an inhaler.
Consisting of polymer nanoparticles coated with a DNA barcode, particles are absorbed into the tissue in the lungs, where they may encounter any proteases, which are overactive in tumours.
Overactive proteases in the human body help cancer cells escape and cleave DNA barcodes from the sensors to allow barcodes to circulate the bloodstream and eventually accumulate in the urine.
The urine test can detect up to four different DNA barcodes that identify different types of proteases and can be read 20 minutes after the sample is obtained.
Using genetically engineered mice with lung tumours similar to those seen in humans, researchers administered sensors 7.5 weeks after the tumours started to form.
Researchers measured the levels of 20 different sensors using a machine learning algorithm and identified a combination of four sensors that were predicted to give accurate diagnostic results.
They then tested this combination in the mouse model and found that it could accurately detect early-stage lung tumours.
Researchers now plan to analyse human biopsy samples to see whether their sensor panels can detect human cancers.
“Around the world, cancer is going to become more… prevalent in low- and middle-income countries” and “accessibility to this kind of technology could have a big impact,” said Sangeeta Bhatia, the John and Dorothy Wilson professor of health sciences and technology, and of electrical engineering and computer science at MIT.
