Researchers Develop New Blood and Urine Tests to Detect Lung Cancer

Researchers are advancing simpler and more accessible lung cancer detection methods through blood and urine tests. These new approaches range from optical DNA fingerprinting and CRISPR-powered light sensors to microchip cell isolation and AI-designed molecular sensors, aiming to spot early warning signs and monitor treatment efficacy without invasive biopsies.

Optical DNA Fingerprinting and CRISPR Light Sensors in Blood Testing

Lung cancer claims more lives worldwide than any other malignancy, yet success depends largely on catching the disease before symptoms appear. Traditional diagnostics like low-dose computed tomography screening can reduce mortality, but they remain costly and frequently yield benign findings that trigger unnecessary biopsies. To address these hurdles, researchers are developing liquid biopsy techniques that identify cancer traces circulating in the bloodstream.

At Tel Aviv University, investigators created an optical method that avoids complex DNA sequencing. Instead of sequencing, the technique attaches light-emitting molecules to cancer-related changes in cell-free DNA extracted from blood samples. The resulting pattern is scanned on a specialized chip and read by a computational model resembling a QR code.

Meanwhile, scientists at Shenzhen University constructed an advanced light-based sensor combining DNA nanostructures, quantum dots, and CRISPR gene editing technology. Described in research published in Scitechdaily, the sensor relies on second harmonic generation (SHG) to detect extremely low concentrations of biomarkers like miR-21 in human serum.

Microchip Technology for Tracking Treatment Efficacy

Detecting the disease is only part of the clinical challenge; clinicians also need reliable ways to determine whether an administered therapy is actually working. Researchers at the University of Michigan demonstrated that a specialized microchip can track treatment response from a routine blood draw by the four-week mark.

Microscopically thin sheets of graphene oxide are coated with antibodies that recognize cancer-specific surface markers, allowing the device to concentrate sparse cells from blood samples.

Current methods often require waiting weeks or months for CT scans to show measurable changes in tumor size, during which patients may endure ineffective therapies and adverse effects.

AI-Designed Peptides and Urine-Based Screening Paths

Targeting Proteases with CleaveNet

Beyond blood-based cellular capture and optical assays, researchers at MIT and Microsoft Research turned to artificial intelligence to design molecular sensors. Published in MIT, the study introduces an AI model named CleaveNet that designs short proteins, or peptides, targeted by enzymes called proteases.

Proteases are frequently overactive in cancer cells as they cut through extracellular matrix proteins. By coating nanoparticles with these AI-designed peptides, researchers created sensors that release detectable signals when encountering cancer-linked proteases anywhere in the body. These signals can potentially be collected and measured using a simple paper strip via a urine test performed at home.

“We’re focused on ultra-sensitive detection in diseases like the early stages of cancer, when the tumor burden is small, or early on in recurrence after surgery.”

Comparative Overview of Emerging Detection Platforms

Platform / Technology	Primary Mechanism	Sample Type	Key Institution
Optical DNA Chip	Fluorescent labeling and optical scanning of cell-free DNA	Blood	Tel Aviv University
CRISPR-SHG Sensor	Cas12a cleavage of DNA-anchored quantum dots	Blood / Serum	Shenzhen University
GO Chip	Graphene oxide sheets with antibodies trapping circulating cells	Blood	University of Michigan
CleaveNet AI Sensors	Peptide cleavage by overactive cancer proteases	Urine	MIT and Microsoft Research

While these diverse platforms—from optical chemical fingerprinting to AI-guided peptide design—demonstrate high sensitivity in laboratory and clinical evaluations, translating them into routine clinical practice will require further validation in larger patient populations and miniaturization of optical equipment for bedside or home deployment.