Background
INOVIQ is using its EXO-NET® exosome isolation technology to isolate exosomes from biofluids, discover novel exosomal biomarkers and develop more accurate and reliable diagnostics for cancer. Our first exosome diagnostic, the EXO-OC screening test, is in development for the early detection of ovarian cancer. Currently, there is no approved screening test for ovarian cancer, resulting in a critical unmet need for an accurate and reliable test for earlier detection of ovarian cancer to enable timely intervention and help save women's lives.
Objective
The purpose of this biomarker validation study was to validate our previously discovered ovarian cancer biomarkers and diagnostic performance in an independent patient cohort of over 530 plasma samples from ovarian cancer patients across all-stages, benign disease and healthy women.
Methods
In this case-control study, exosomes were isolated from more than 500 blood samples, using INOVIQ’s EXO-NET® on a fully-automated high-throughput robotic platform. Exosome ovarian cancer protein biomarkers, previously identified in the OC97 study, were measured using targeted mass spectrometry (multiple reaction monitoring, MRM) performed by The University of Queensland’s (UQ) Centre for Extracellular Vesicle Nanomedicine. All targeted biomarkers were identified in ovarian cancer samples and their diagnostic performance was confirmed using ROC curve analysis and multivariate modelling. EXO-NET-isolated exosomes also enabled the identification of additional informative cancer biomarkers.
Results
When these high performing EXO-NET-isolated biomarkers were combined in 10-fold cross validated machine learning algorithms overall test accuracy exceeded 94%. When test specificity was set at 96%, sensitivity was 92% for all stages of disease and 91% for Stage I alone.
Figure 1: Receiver Operating Characteristic Curve for the EXO-OC test. The EXO-OC test combine EXO-NET isolated ovarian cancer biomarkers using a cross-validated machine learning algorithm. The area under the curve (AUC) = 0.98, indicative of very high accuracy.
Conclusion
The results obtained in this study are outstanding and evidence the robustness and reproducibility of EXO-NET-isolated ovarian cancer biomarkers that deliver high diagnostic performance in multivariate algorithms. This biomarker panel showed exceptional performance in detecting early-stage ovarian cancer (Stages I), where accurate diagnosis is most critical for improving patient outcomes. Early detection enables timely intervention, which is crucial for increasing survival rates and reducing disease progression.
Further development and optimisation of the EXO-OC blood test will be required including analytical validation on a commercial MRM instrument platform that can be routinely used in pathology laboratories worldwide and robust clinical studies to commercialise the test as an LDT or IVD in an accredited clinical laboratory.
Abstract 5582: Early detection of ovarian cancer: An accurate high-throughput extracellular vesicle test
Authors
Carlos Salomon, Andrew Lai, Dominic Guanzon, Shayna Sharma, Katherin Scholz-Romero, Melissa Razo, Amanda Barnard, Mahesh Choolani, Carlos Palma, Ramin Khanabdali, Sunil Lakhani, Jermaine Coward, Leearne Hinch, Kaltin Ferguson, Lewis Perrin, Rohan Lourie, Anna DeFazio, John Hooper, Gregory Rice.
Background
The high mortality of Ovarian cancer (OC) has been attributed to late-stage diagnosis and the lack of an effective early detection strategy, particularly for asymptomatic women. In this study, we developed and validated a high-throughput OC detection test based on plasma extracellular vesicle (EV)-associated biomarkers.
Methods
A case-control study was conducted to evaluate blood-borne EV-associated ovarian cancer biomarkers, including miRNAs, proteins, lncRNAs, miscRNAs, MtrRNAs, MttRNAs, rRNAs, scaRNAs, snRNAs, and tRNAs. Protein and RNA biomarkers were identified by mass spectrometry and RNA sequencing, respectively. Training (n=453) and independent test (n=471) sample sets were used to develop and validate a multivariate index assay (MIA). The MIA was further validated using a high-throughput, pathology laboratory compatible, EV isolation platform (EXO-NET) and two independent sample cohorts (n=97 and n=532). The classification accuracy, sensitivity and specificity of the MIA was compared to that of CA125 levels.
Results
Discovery and Training phases - more than 100,000 EV-associated biomarkers were identified from 453 EV samples. The classification performance of these biomarkers was assessed using machine learning algorithms. EV-associated protein and miRNA biomarkers delivered the highest performing classifiers and, therefore, were used in subsequent MIA development and training. During the training phase, multivariate classification algorithms were validated using a 10-fold cross-validation method. The highest performing classifiers for EV-associated protein and miRNA, at specificity of 98%, achieved sensitivities of 90% and 82%, respectively. Validation phase: Locked classification algorithms (i.e. MIAs) were validated using two independent sample cohorts and reported classification accuracies of 92-98%, significantly outperforming CA-125 (CE = 62%, p<0.001). Automated high-throughput MIA – All stages OC: the best performing automated high-throughput MIA demonstrated an overall sensitivity of 92% (95% CI, 75–96%) and specificity of 93% (95% CI, 86–96%) for all stages of OC, Positive Predictive Value of 95% (CI, 93-96%) and Negative Predictive Value of 80% (CI, 76-89%) at 98% specificity (n=532). Stage I OC: Importantly, the MIA displayed a sensitivity of 90% (95% CI, 76–100%) and specificity of 96% (95% CI, 40%–99%) for stage I OC. While CA125 have an overall sensitivity for all stages of OC of 61% (95% CI, 53–69%), with a sensitivity of 44% for stage I (95% CI, 28–62%).
Conclusion
In this study we report the development and validation of an accurate, automated high-throughput EV-based test for early detection of ovarian cancer. The test delivers significant improvements in sensitivity and specificity compared to CA-125, especially in detecting early-stage OC.
