Meet Audrey Zheng, 17, who developed a pancreatic cancer test
A talented high school student has pioneered a cutting-edge bioengineering method for the early detection of pancreatic cancer. By utilizing magnetic beads to isolate tumor-derived particles from blood samples, her clinical trials demonstrated remarkable accuracy. Her extraordinary research has secured her a spot as a national finalist in a prestigious science competition, offering a promising…
Seventeen-year-old Audrey Zheng, a student from Pennsylvania, has created a novel method to detect early-stage pancreatic cancer using blood samples. The groundbreaking technique utilizes magnetic beads to capture tumor-derived particles present in the bloodstream. Audrey, a senior at North Allegheny Senior High School in Wexford, successfully identified 87 percent of pancreatic ductal adenocarcinoma patients in clinical tests, while correctly excluding the disease in 87.5 percent of non-cancerous individuals.
Her innovative approach earned her a spot as a national finalist in the 2026 Regeneron Science Talent Search, a prestigious competition for high school seniors in science and mathematics.
Pancreatic cancer often goes undetected until it has already spread, as it does not typically present clear symptoms in its early stages. Existing screening methods, such as standard imaging and general protein biomarkers, struggle to identify small, localized tumors. Audrey focused on extracellular vesicles, tiny membrane-enclosed sacs released by cells into the bloodstream.
These vesicles carry molecular markers, including proteins and genetic information, that can provide insights into cellular health. To differentiate cancer-related vesicles from healthy ones, Audrey developed a mixture of magnetic nanospheres coated with three specific antibodies. These antibodies target cancer-related surface molecules found on tumor-derived vesicles.
After the antibodies bind to the cancer vesicles, a magnetic field separates the targeted particles from the healthy biological material in the blood sample.
Further analysis of the isolated vesicles revealed genetic mutations, particularly in the KRAS gene, which is linked to more than 90 percent of pancreatic ductal adenocarcinoma cases. By examining the DNA within the targeted vesicles for these mutations, the diagnostic system achieved high sensitivity and specificity. The two-step process first identified cancer-related vesicles through their surface markers and then verified the presence of cancer-causing mutations in their genetic material.
This dual confirmation significantly improved the test's reliability. Early detection of pancreatic ductal adenocarcinoma is crucial, as the five-year survival rate is below 13 percent, largely due to the disease's late diagnosis. Detecting the cancer at early stages (Stage I or II) can greatly enhance the chances of long-term survival through surgery.
Beyond her scientific pursuits, Audrey Zheng is actively involved in leadership roles at North Allegheny High School. She is co-president of the math and physics club and captain of the school's tennis team. Audrey also has a strong commitment to community service, having founded The Food Lounge Pittsburgh, a non-profit organization that has organized over 50 volunteer events to support regional food banks and community kitchens.
Looking ahead, Audrey is adapting her nanosphere technology to create a portable sensor capable of quickly detecting common food allergens in field settings. Her groundbreaking pancreatic cancer diagnostic project was showcased at the Society for Science Public Day exhibition in Washington, D.C., where 40 Regeneron Science Talent Search finalists presented their research to scientific panels and the public.
Audrey's work exemplifies how student-led research can combine bioengineering, molecular biology, and nanotechnology to tackle significant medical challenges, paving the way for more rapid and minimally invasive cancer detection methods in the future.
Written by urgent.news from Times of India's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.