SUMMARY: The American Cancer Society’s estimates that in 2022, about 62,210 people will be diagnosed with pancreatic cancer and 49,830 people will die of the disease, 19,880 women will receive a new diagnosis of ovarian cancer and about 12,810 women will die of the disease, and about 81,800 new cases of bladder cancer will be diagnosed in 2022 and about 17,100 patients will die of the disease. These three cancer types are estimated to account for approximately 80,000 deaths in the US in 2022. Detecting cancer at early stages can significantly increase survival rates and outcomes.
Several multi-cancer early detection tests are being developed that involve blood-based circulating cell-free tumor DNA (cfDNA) in the plasma, to track hundreds of patient-specific mutations, to detect Minimal Residual Disease (MRD) , as well as detection of abnormal methylation patterns, followed by machine learning approaches, to differentiate between cancer and non-cancer, for detecting clinically significant, late-stage (III and IV) cancers. Early detection of cancer however is the key to improving survival. This is particularly relevant for certain cancer types. Pancreatic Ductal AdenoCarcinoma (PDAC) is one of the deadliest cancers, and a leading cause of all cancer-related deaths in the United States, and is typically detected when the disease is advanced. However, when detected at Stage I, survival rates can be as high as 80%. Ovarian cancer is often detected when the disease is advanced and the 5-year survival rates are less than 30%, but can be as high as 93% when detected early. The same holds true for metastatic bladder cancer, with 5-year survival rates of only 6%, whereas while detected when the tumor is still localized to the bladder wall inner layer results in a 5-year survival rate of 96%. Even though serum CA19-9 is intended as an aid in the management of patients with confirmed pancreatic cancer for serial monitoring of their response to therapy and disease progression, it is not recommended by the FDA for screening, as it may be elevated in several benign conditions. Similarly, serum CA-125 is FDA approved for use in monitoring patients with ovarian cancer for disease persistence and recurrence, but is not recommended to screen for ovarian cancer. Currently, there are few general screening strategies to detect asymptomatic, early-stage PDAC, ovarian, or bladder cancer and there is therefore a significant unmet need in this patient group.
Exosomes are 30-150 nm-sized Extracellular Vesicles (EVs) secreted by multiple different cell types and ejected by tumors into the bloodstream. They mediate intercellular signaling by transferring mRNAs and microRNAs between distant cells and tissues and therefore carry functional protein biomarkers representing the tumor proteome. Exosomes represent one potential approach for more sensitive detection of cancer-related biomarkers from blood.
The researchers in this study used an Alternating Current Electrokinetic (ACE)-based platform (Verita™ System) to efficiently isolate EVs from soluble contaminants such as cells, small proteins, or other vesicles from patient samples, and then measured the concentrations of associated protein biomarkers (“EV proteins”) present in the purified EV samples from our case-control study subjects. The researchers chose this platform over the current gold standard ultracentrifugation method, which the authors felt was inefficient and not suitable for point-of-care applications. Artificial Intelligence machine-learning algorithm developed by the researchers, enabled detection of early-stage pancreatic, ovarian, and bladder cancers.
In this case-control pilot study, 139 pathologically confirmed Stage I and II cancer cases representing pancreatic, ovarian, or bladder patients were compared with 184 control subjects, using the Verita™ System. The Extracellular Vesicles (EVs) isolated using this technology, were consistent with the presence of Exosomes, in accordance with the International Society for Extracellular Vesicles (ISEV) 2018 guidelines. The researchers selected a panel of 13 Extracellular Vesicle (EV) proteins along with age, a known cofactor in cancer. In order to simulate a real-world screening scenario, all cancer cases were treatment-naïve and to ensure that these were early-stage patients, the histopathologic staging was confirmed using the American Joint Commission on Cancer (AJCC) guidelines. The median age of the cancer cases was 60 years and 63.3% of the overall cancer cases were Stage I, with the remaining 36.7% at Stage II. The median age of the control group was 57 years and had no known history of cancer, autoimmune diseases, neurodegenerative disorders or diabetes mellitus.
When the overall cancer case cohort was compared with the control individuals using the EV protein biomarker test, the average sensitivity was 71.2%, at a specificity of 99.5%. When considered across all the three cancers studied, EV protein biomarker test using this technology demonstrated similar sensitivities of 70.5% and 72.5% for Stage I and II patients, respectively. This new technology detected 95.5% of Stage I pancreatic cancers, 73.1% of pathologic Stage IA lethally aggressive serous ovarian adenocarcinomas and 43.8% in bladder cancer, demonstrating the potential value of this platform for detection of early stage cancers. The lower sensitivity for detecting early stage bladder cancer may be due to high molecular and histologic heterogeneity of bladder tumors.
It was concluded from this study that blood-based EV protein detection test has potential clinical value for early cancer detection and the use of Verita™ platform resulted in the accurate detection of early stage pancreatic, ovarian, or bladder cancer. The authors added that mortality from pancreatic cancer which will soon become the second leading cause of cancer mortality in the U.S., can be greatly reduced if this study results are validated.
Early-stage multi-cancer detection using an extracellular vesicle protein-based blood test. Hinestrosa, J.P., Kurzrock, R., Lewis, J.M. et al. Commun Med 2, 29 (2022). https://doi.org/10.1038/s43856-022-00088-6.
