Two studies show the accuracy of perception-based systems in detecting disease biomarkers without needing molecular recognition elements, such as antibodies
Researchers from multiple academic and research institutions have collaborated to develop a non-conventional machine learning-based technology for identifying and measuring biomarkers to detect ovarian cancer without the need for molecular identification elements, such as antibodies.
Traditional clinical laboratory methods for detecting biomarkers of specific diseases require a “molecular recognition molecule,” such as an antibody, to match with each disease’s biomarker. However, according to a Lehigh University news release, for ovarian cancer “there’s not a single biomarker—or analyte—that indicates the presence of cancer.
“When multiple analytes need to be measured in a given sample, which can increase the accuracy of a test, more antibodies are required, which increases the cost of the test and the turnaround time,” the news release noted.
Unveiled in two sequential studies, the new method for detecting ovarian cancer uses machine learning to examine spectral signatures of carbon nanotubes to detect and recognize the disease biomarkers in a very non-conventional fashion.
Perception-based Nanosensor Array for Detecting Disease
In the Science Advances paper, the researchers described their development of “a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids.
“Perception-based machine learning (ML) platforms, modeled after the complex olfactory system, can isolate individual signals through an array of relatively nonspecific receptors. Each receptor captures certain features, and the overall ensemble response is analyzed by the neural network in our brain, resulting in perception,” the researchers wrote.
“This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements,” the researchers concluded.
In the Nature Biomedical Engineering paper, the researchers described a fined-tuned toolset that could accurately differentiate ovarian cancer biomarkers from biomarkers in individuals who are cancer-free.
“Here we show that a ‘disease fingerprint’—acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects—detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best [clinical laboratory] screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography,” the researchers wrote.
“We demonstrated that a perception-based nanosensor platform could detect ovarian cancer biomarkers using machine learning,” said Yoona Yang, PhD, a postdoctoral research associate in Lehigh’s Department of Chemical and Biomolecular Engineering and co-first author of the Science Advances article, in the news release.
How Perception-based Machine Learning Platforms Work
According to Yang, perception-based sensing functions like the human brain.
“The system consists of a sensing array that captures a certain feature of the analytes in a specific way, and then the ensemble response from the array is analyzed by the computational perceptive model. It can detect various analytes at once, which makes it much more efficient,” Yang said.
“SWCNTs have unique optical properties and sensitivity that make them valuable as sensor materials. SWCNTS emit near-infrared photoluminescence with distinct narrow emission bands that are exquisitely sensitive to the local environment,” the researchers wrote in Science Advances.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD, Head of the Cancer Nanotechnology Laboratory at Memorial Sloan Kettering Cancer Center and Associate Professor in the Department of Pharmacology at Weill Cornell Medicine of Cornell University, in the Lehigh University news release.
“If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins,” he added.
The researchers put their technology to practical test in the second study. The wanted to learn if it could differentiate symptomatic patients with high-grade ovarian cancer from cancer-free individuals.
The research team used 269 serum samples. This time, nanotubes were bound with a specific molecule providing “an extra signal in terms of data and richer data from every nanotube-DNA combination,” said Anand Jagota PhD, Professor, Bioengineering and Chemical and Biomolecular Engineering, Lehigh University, in the news release.
This year, 19,880 women will be diagnosed with ovarian cancer and 12,810 will die from the disease, according to American Cancer Society data. While more research and clinical trials are needed, the above studies are compelling and suggest the possibility that one day clinical laboratories may detect ovarian cancer faster and more accurately than with current methods.
For example, radical prostatectomy is the removal of the entire prostate gland and surrounding tissues. It is one of the primary treatments for malignant cancer. Failure to remove all the cancer tissue during the procedure typically leads to poor clinical outcomes, including tumor reoccurrence and subsequent increased risk of metastasis and death.
Currently, intraoperative frozen-section analysis of the prostate is the most common intraoperative method for real-time analysis of surgical margins. But research into CLI may provide surgeons with an additional strategy for reducing positive surgical margins.
“Our objective was to assess the feasibility and accuracy of Cerenkov luminescence imaging (CLI) for assessment of surgical margins intraoperatively during radical prostatectomy,” they wrote.
According to the Essen researchers, the “single-center” study “included 10 patients with high-risk primary prostate cancer. 68Ga-PSMA PET scans were performed followed by radical prostatectomy and intraoperative CLI of the excised prostate. CLI images were analyzed postoperatively to determine regions of interest based on signal intensity, and tumor-to-background ratios were calculated. CLI tumor margin assessment was performed by analyzing elevated signals at the surface of the intact prostate images.
“To determine accuracy, tumor margin status as detected by CLI was compared to postoperative histopathology. Tumor cells were successfully detected on the incised prostate CLI images and confirmed by histopathology. Three patients had positive surgical margins, and in two of the patients, elevated signal levels enabled correct identification on CLI. Overall, 25 out of 35 CLI regions of interest proved to visualize tumor signaling according to standard histopathology,” the Essen researchers concluded.
The research showed that CLI can accurately assess surgical margins during radical prostatectomy. This first in vivo research of the technique was conducted over a 17-month period between 2018 and 2019.
The researchers found that two of three patients who had positive surgical margins were correctly identified using CLI images. Overall, 25 of 35 CLI regions of interest successfully visualized tumor signaling, which is a result in line with standard histopathology. The one positive surgical margin CLI missed had group 3 prostate cancer at the surgical margin.
Essen Study Finds CLI Results in ‘Higher than Expected’ False Positives
The Essen University Hospital’s CLI feasibility study also revealed the technique resulted in a higher-than-expected number of false positives, with 10 of 35 regions of interest showing “elevated signal levels without histopathologic evidence of PC tissue at the resection margin.” Most of the false positives occurred at the prostate base.
The Essen study authors speculated that the presence of radioactive tracer in the urinary bladder and other factors may explain the false positive rate. They suggested that, “Further optimization of the CLI protocol, or the use of lower-energy imaging tracers such as 18F-PSMA, is required to reduce false-positives.”
The researchers called for a larger study to assess CLI’s diagnostic performance.
Boris A. Hadaschik, PhD, Director of the Clinic for Urology at Essen University Hospital, added, “Radical prostatectomy could achieve significantly higher accuracy and oncological safety, especially in patients with high-risk prostate cancer, through the intraoperative use of radioligands that specifically detect prostate cancer cells. In the future, a targeted resection of lymph node metastases could also be performed in this way. This new imaging combines urologists and nuclear medicine specialists in the local treatment of patients with prostate cancer.”
Because of the high reoccurrence rate of prostate cancer in men, surgical pathologists will find this potential new strategy for reducing positive surgical margins a welcomed advancement, but additional investigation will be needed to ensure its promise can be realized.