OCRF-7: A multivariate index assay

OCRF-FUNDED RESEARCH PROJECT

OCRF-7: A MULTIVARIATE INDEX ASSAY

Institution: The University of Queensland

Lead Researcher: Associate Professor Carlos Salomon-Gallo

Funding Awarded: $570,383

Subsequent Collaboration to OCRF Funding MRFF $2.6M over 4 years

Year/s: 2018-2025

Research Pillar: Early Detection

Project Description:

Associate Professor Salomon Gallo seeks to develop a world-first exosome-based ovarian cancer screening test. Exosomes are small particles released by cells that convey important information to parent cells, and therefore can hold information important to the early detection of ovarian cancer. Named in recognition of the OCRF’s initial funding, the OCRF-7 algorithm is at the centre of the Associate Professor Salomon Gallo’s early detection project. The team are collaborating with INOVIQ technologies for further validation.

“The use of a scalable exosome isolation tool such as INOVIQ’s EXO-NET product is critical to enable the commercialisation of routine exosome-based tests that can be used in pathology laboratories worldwide.”

Associate Professor Carlos Salomon Gallo

Expected/Achieved Outcomes:

Knowledge Building: Knowledge Building: In an independent 500-sample study, Associate Professor Salomon Gallo showed that the OCRF-7 algorithm of multiple biomarkers achieves over 90% accuracy in detecting ovarian cancer in stages 1 and 2. A sign of the project’s promise, INOVIQ committed to providing its innovative EXO-NET technology in-kind.

In December 2022 it was announced that INOVIQ and Associate Professor Salomon Gallo’s team had validated the use of EXO-NET technology and the OCRF-7 platform in the OC97 study. This study evaluated extracellular vesicles in the plasma of women with early-stage ovarian cancer, with benign ovarian masses and healthy women. When the biomarkers were combined in the OCRF-7 algorithm they achieved an AUC rating of 98%, with sensitivity at 92% and specificity at 92%. This study validated the use of EXO-NET with the OCRF-7 algorithm for diagnostics.