Lung Cancer
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Researchers at Massachusetts Institute of Technology (MIT) have developed a nanoparticle-based approach that allows the early diagnosis of lung cancer through a simple urine test. The strategy detects biomarkers resulting from the interaction of peptide-coated nanoparticles with disease-associated proteases in the tumor microenvironment. Experiments in two different mouse models of lung cancer showed that the urine test could detect tumors as small as 2.8 mm3. The researchers hope that this type of noninvasive diagnosis could reduce the number of false positives associated with an existing test method, and help to detect more tumors in the early stages of the disease.

“If you look at the field of cancer diagnostics and therapeutics, there’s a renewed recognition of the importance of early cancer detection and prevention,” said study lead Sangeeta Bhatia, PhD, who is the John and Dorothy Wilson professor of health sciences and technology and electrical engineering and computer science, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science. “We really need new technologies that are going to give us the capability to see cancer when we can intercept it and intervene early.” Bhatia and colleagues report on development of the test in Science Translational Medicine, in a paper titled, “Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling.”

nanoparticles to detect lung cancer
MIT engineers have developed nanoparticles that can be delivered to the lungs, where tumor-associated proteases cut peptides on the surface of the particles, releasing reporter molecules. Those reporters can be detected by a urine test.

Lung cancer is the most common cause of cancer-related death (25.3%) in the United States the authors wrote, and has a “dismal” five-year survival rate of 18.6%. Early detection is key, as the five-year survival rates are 6- to 13-fold higher in patients whose tumors are detected before they spread to distal sites in the body. People in the United States who are at high risk of developing lung cancer, such as heavy smokers, are routinely screened using low-dose computed tomography (LDCT), which can detect tumors in the lungs. However, this test has an extremely high rate of false positives, as it also picks up benign nodules in the lungs. There is then a “considerable burden of complications incurred during unnecessary follow-up procedures,” the investigators stated, and the method isn’t routinely used in other countries. “As a result of these complications, screening by LDCT has not been widely adopted outside of the United States, and there is “an urgent need to develop diagnostic tests that increase the effectiveness of lung cancer screening.”

The approach taken by the MIT researchers is based on the use of what they call “activity-based sensors” that monitor for disease and intensify disease-associated signals, which can then be detected in urine. “Activity-based nanosensors leverage dysregulated protease activity to overcome the insensitivity of previous biomarker assays, amplifying disease-associated signals generated in the tumor microenvironment and providing a concentrated urine-based readout,” the team explained.

Bhatia’s lab has for several years been developing such nanoparticles that can detect cancer by interacting with proteases. These enzymes help tumor cells to escape their original locations by cutting through proteins of the extracellular matrix. To find the cancer-associated proteases Bhatia created nanoparticles coated with peptides that are targeted by the cancer-related proteases. The particles accumulate at tumor sites, where the peptides are cleaved, releasing biomarkers that can then be detected in a urine sample.

The Bhatia lab has previously developed sensors for colon and ovarian cancer, and in their new study, the researchers applied the technology to lung cancer, which kills about 150,000 people in the United States every year. They project that the test could be applied to confirm cancer in patients who have had a positive CT scan. These patients would commonly undergo a biopsy or other invasive test to search for lung cancer, but in some cases, this procedure can cause complications, so a noninvasive follow-up test could be useful to determine which patients actually need a biopsy, Bhatia said.

“The CT scan is a good tool that can see a lot of things,” she said. “The problem with it is that 95% of what it finds is not cancer, and right now you have to biopsy too many patients who test positive.”

To customize their sensors for lung cancer, the researchers analyzed data in The Cancer Genome Atlas, and identified proteases that are abundant in lung cancer. They created a panel of 14 peptide-coated nanoparticles that could interact with these enzymes.

The researchers then tested the sensors in two different genetic mouse models, “driven by either Kras/Trp53 (KP) mutations, or Eml4-Alk (EA) fusion,” that spontaneously develop lung cancer. To help prevent background noise that could come from other organs or the bloodstream, the researchers injected the particles directly into the animals’ airways. The researchers carried out their diagnostic test using the sensors at 5 weeks, 7.5 weeks, and 10.5 weeks after tumor growth began. To make the diagnoses more accurate, they used machine learning to train an algorithm to distinguish between data from mice that had tumors and from mice that did not.

Using this approach, the researchers found that they could accurately detect tumors in one of the mouse models as early as 7.5 weeks, when the tumors were only 2.8 mm3, on average. In the other strain of mice, tumors could be detected at 5 weeks. The sensors’ success rate was also comparable to or better than the success rate of CT scans performed at the same time points.

“Intrapulmonary administration of the nanosensors to a Kras- and Trp53-mutant lung adenocarcinoma mouse model confirmed the role of metalloproteases in lung cancer and enabled accurate detection of localized disease, with 100% specificity and 81% sensitivity,” they reported. “Furthermore, this approach generalized to an alternative autochthonous model of lung adenocarcinoma, where it detected cancer with 100% specificity and 95% sensitivity and was not confounded by lipopolysaccharide-driven lung inflammation.”

Importantly, the sensors could distinguish between early-stage cancer and noncancerous inflammation of the lungs, a common condition in smokers, and one of the reasons that CT scans produce so many false positives. “Activity-based nanosensors may have clinical utility as a rapid, safe, and cost-effective follow-up to LDCT, reducing the number of patients referred for invasive testing,” the authors concluded. “With further optimization and validation studies, activity-based nanosensors may one day provide an accurate, noninvasive, and radiation-free strategy for lung cancer testing.”

The authors acknowledged that their study was carried out in mouse models, which do not fully recapitulate human disease, and there were other study limitations that will need to be addressed. Clinical trials will be needed to fully validate the use of activity-based nanosensors for detecting lung cancer and distinguishing malignant from benign and extrapulmonary disease, they pointed out.

Bhatia envisions that the nanoparticle sensors could be used as a noninvasive diagnostic for people who get a positive result on a screening test, potentially eliminating the need for a biopsy. For use in humans, her team is working on a form of the particles that could be inhaled as a dry powder or through a nebulizer. Another possible application is using the sensors to monitor how well lung tumors respond to treatment, such as drugs or immunotherapies. “A great next step would be to take this into patients who have known cancer, and are being treated, to see if they’re on the right medicine,” Bhatia said.

She is also working on a version of the sensor that could be used to distinguish between viral and bacterial forms of pneumonia, which could help doctors to determine which patients need antibiotics and may even provide complementary information to nucleic acid tests like those being developed for COVID-19. Glympse Bio, a company co-founded by Bhatia, is also working on developing this approach to replace biopsy in the assessment of liver disease.

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