No two people have the exact same fingerprints; their characteristic loops, whorls and arches form distinct patterns that can tell even identical twins apart. Likewise, biomolecules like proteins used to distinguish between cancer cells and healthy tissues have unique ‘fingerprints’ that can be picked up using an analytical technique called Raman spectroscopy (RS).
In the context of ovarian cancer, RS could have an edge over gold standard approaches such as the CA125 blood test and histological methods. However, because it requires complex and expensive equipment, RS isn’t feasible across many clinical settings. Moreover, conventional RS is not sensitive enough to detect low biomarker concentrations, which reduces its effectiveness for early-stage ovarian cancer detection.
Malini Olivo, who leads the Translational Biophotonics Laboratory at A*STAR Skin Research Labs (A*SRL) where she is a Distinguished Principal Scientist, collaborated with Mahesh Choolani from the National University of Singapore, to develop a next-generation RS-based ovarian cancer diagnostic technology.
The team zeroed in on haptoglobin (Hp): a protein biomarker which collaborators had previously found in ovarian cyst fluids, detectable even in patients with early-stage ovarian cancer. Based on those findings, the team developed a miniaturised and portable Raman-based system capable of rapidly measuring even trace levels of Hp.
“It doesn’t require invasive procedures or contrast agents, making it patient-friendly,” said Olivo, who referred to the new platform as a ‘game-changer’. The technology was designed to only detect a specific spectral band at the 1500 to 1700 cm-1 wavelength which occurs in the presence of Hp. This strategy markedly lowered the cost and complexity of the innovative diagnostic compared to traditional RS.
Despite being simplified, the tool boasted 100 percent sensitivity and 85 percent specificity when distinguishing between benign and malignant tumours in validation tests using samples from ovarian cancer patients. According to Olivo, this means that the new RS platform can outperform CA125 blood tests by delivering fewer false positives. Moreover, providing real-time diagnoses makes it more valuable to cancer surgeons than histopathology tests that take weeks to provide answers.
“This can significantly impact cancer treatment strategies, particularly for cancers with vague symptoms or those in need of intraoperative assessments, ultimately contributing to better patient care and outcomes across various cancer types,” concluded Olivo.
The research group is currently integrating automation into their RS diagnostic workflow to further streamline sample preparation and boost clinical usability. They are also incorporating machine learning tools to enhance the accuracy of the innovative new technology, which they have patented and are working towards commercialising.
The A*STAR-affiliated researchers contributing to this research are from A*STAR Skin Research Labs (A*SRL).