There were an estimated 20 million new cancer cases in 2022, with more than 35 million new cases predicted by 2050.
The new diagnosis is based on nanosensors that can be administered by inhaler or nebulizer. When sensors in the lungs encounter cancer-related proteins, they emit a signal that accumulates in the urine and can be detected with a simple paper test strip.
This technology could replace or complement the current gold standard for lung cancer diagnosis, low-dose computed tomography (CT). This could have serious implications for low- and middle-income countries without widespread access to CT scanners, the researchers said.
"Globally, cancer is more common in low- and middle-income countries. "The epidemiology of lung cancer globally is that it's driven by pollution and smoking, so we know this is a condition where access to technology can have a big impact," said Sangeeta Bhatia, the John and Dorothy Wilson Professor. in Health Sciences and Technology, Electrical and Computer Engineering from MIT and is a member of the MIT Koch Institute for Integrative Cancer Research and the Institute of Engineering and Medical Sciences.
Bhatia is the lead author of the paper published in the journal Science Advances. MIT researcher Qian Zhong and former MIT postdoc Edward Tan are lead authors of the study.
To help diagnose lung cancer as early as possible, the U.S. The Preventive Services Task Force recommends annual CT scans for heavy smokers over 50 years of age. However, not everyone in this target group undergoes these scans, and the high false-positive rate of scans can lead to unnecessary and invasive tests.
Bhatia has been developing nanosensors to diagnose cancer and other diseases for the past decade, and in this study, he and his colleagues explored the possibility of using them as a cheaper alternative to lung cancer CT scans. These sensors consist of polymer nanoparticles coated with reporters, such as DNA barcodes.
These reporters are released from the particles when the sensors encounter enzymes called proteases, which are often overactive in tumors. These reporters eventually accumulate in the urine and are excreted from the body.
Previous versions of the sensor, which target other tumor sites such as the liver and ovaries, were designed for intravenous administration. For lung cancer diagnosis, the researchers wanted to develop an inhalable version that would facilitate deployment in low-resource settings.
"When we developed this technology, our goal was to address the resources and gap in early detection of lung cancer by providing a way to not only detect cancer with high specificity and sensitivity, but also lower barriers to access." -Zhong says.
To achieve this, the researchers created two particle formulations: a solution that can be delivered by spray and nebulizer, and a dry powder that can be administered by inhaler. Once the particles reach the lungs, they are absorbed into the tissues and encounter proteolytic enzymes. Human cells can express hundreds of different proteases, some of which become overactive in tumors and cleave proteins in the extracellular matrix, helping cancer cells to leave their original site.
These cancerous proteases cleave the DNA barcode on the sensor, allowing the barcode to circulate in the blood until it is excreted in the urine.
In previous versions of this technology, researchers used mass spectrometry to analyze urine samples and identify DNA barcodes. However, mass spectrometry requires equipment that may not be available in resource-poor regions, so for this option, researchers have developed a lateral flow test that can detect barcodes using paper test strips.
The researchers designed the strip to detect four different DNA barcodes, each indicating the presence of a different protease. Urine samples require no pretreatment or processing and results can be read approximately 20 minutes after collection.
"We pushed this test to be useful in low-resource situations, so the idea was to write the pattern on paper, without processing or amplifying it. Go to the right place and read it in 20 minutes," says Bhatia. Accurate diagnosis
The researchers tested the diagnostic system in mice that were genetically engineered to develop lung tumors identical to those in humans. The sensor was implanted 7.5 weeks after tumor formation, which corresponds to stage 1 or 2 cancer in humans. In the first series of experiments on mice, the researchers measured the levels of 20 different sensors for different proteases.
To analyze these results, the researchers used machine learning algorithms to determine the combination of four sensors expected to provide the most accurate diagnostic results. They then tested this combination in a mouse model and found that it could accurately detect lung tumors at an early stage.
Use in humans may require multiple sensors for an accurate diagnosis, but this could be accomplished using multiple pieces of paper, each detecting four different DNA barcodes, the researchers said. The researchers now plan to analyze human biopsy samples to see if the sensory panel they used also works to detect tumors in humans. In the long term, they hope to conduct clinical trials in human patients.
A company called Sunbird Bio has conducted phase 1 trials of a similar sensor developed in Bhatia's lab to diagnose liver cancer and a type of hepatitis called nonalcoholic steatohepatitis (NASH).
In parts of the world where access to CT scans is limited, this technology could provide a significant improvement in lung cancer screening, especially since results can be obtained in a single visit. "The idea is that you can go in and get an answer if they need follow-up and get patients with early-stage injuries into the system so they can get curative surgery or life-saving drugs," says Bhatia.