(370h) Using Nanoparticle-Gated Electrokinetic Membrane Sensors and Janus Diffusivity Sensors for Quantifying Disease-Related Nanovesicles in Biofluids

Lipoproteins and Extracellular vesicles (EVs) found in plasma, typically 6-200 nm in size, provide us with information essential for more accurate diagnosis than the current state-of-the-art factors. Diseases such as cancer and cardiovascular diseases, while detrimental, are high preventable, curable, or manageable, provided early screening could be done with > 90% 5-year survival rate in early stage cancer. The current screening process is often invasive, uncomfortable, and potentially harmful and essentially requires whereabouts/location of the tumor. As cancer treatments become more advanced, the need for more robust early screening methods, such as detecting cancer cell biomarkers, becomes apparent. Advancing the field of diagnostic technology has the potential to have profound impact on how we screen for and treat cancer.

Moreover, tumor cells often overexpress specific markers on their surface which aid in tumorigenesis, angiogenesis, and eventually metastasis. Detection of these disease-specific protein biomarkers remains the most promising non-invasive early screening technique. While performing biopsy of such cells lies in the invasive category and requires one to know about the possibility of a tumor or observe some degree of abnormality through commonly used techniques such as mammography and colonoscopy, one can achieve the same using EVs found in plasma that reflect the proteomic signature of their parent cells. A subset of EV know as Exosomes, once considered to be a disposal mechanism for cells, have recently been identified as a strategy to identify and detect the biomarkers of diseased cells. During biogenesis, the exosomes are formed using a portion of the cell’s lipid bilayer which is laced with copies of cancer-specific membrane proteins. This is especially important for detecting type of cancer as the exosomes secreted by tumor will contain both the overexpressed and the tissue-specific protein. Additionally, lipoproteins carry surface proteins such as PON1 that provides a significant portion of the atheroprotective properties, and thus prevents cardiovascular disease.

The bottlenecks associated with the measurement of these particles rely on several factors. Firstly, the gold standard for protein measurement, Enzymatic Immunoassays (ELISA), are incompatible with lipids due to redox interference from lipid peroxides on these particles. At the same time, flow cytometry-based methods cannot be extended to such small vesicles. Secondly, ELISA and other commonly used methods lack the sensitivity (>pM) to assess these particles accurately (~fM-pM concentration), requiring more sensitive methods. Thirdly, there can be significant competition from the soluble versions of these proteins or non-targets, which requires us to have a large dynamic range to operate away from sensor saturation. Lastly, the detection method should be fast (<1 hour) and cheap (~$1) so that everyone can afford these tests – perhaps even as an annual or semi-annual test. This would ensure we can detect the disease early, start the treatment early (>85 percent survival for stage I cancer detection) and use these markers to monitor the efficacy of treatment, remission, and recurrence. We have developed Charge-gated Anion Exchange Membrane (AEM)-based Electrokinetic sensors and Janus particle-based diffusometry sensors that are very sensitive (~fM), cheap (~$1), fast (<1 hour), and have a very large dynamic range and is less susceptible to environmental factors.

We have currently validated our strategy of detecting lipid-protein assemblies as well as our technology by assessing cardiovascular risk, detecting glioblastoma and colorectal cancers non-invasively with ongoing studies that include pancreatic, breast and lung cancers. We were able to achieve an impressive AUC ~ 0.99 with PON1-HDL in distinguishing Coronary Artery Disease patients from healthy controls and an AUC > 0.95 for cancer patients (Colorectal and Glioblastoma) from healthy controls. We compared our platform to the state-of-the-art methods used by healthcare providers for the cardiovascular risk assessment, outperforming Cholesterol tests, Apolipoprotein levels, HDL-P levels (AUC~0.65) that accurately predicted CAD in about 60-70% of the patients compared to our platform that predicted >95% of the CAD patients and controls accurately. Identification of cancer and cardiovascular diseases and their associated risks early enough can significantly improve the lives of about 50% of world population that ultimately fall to these diseases. Our platforms can potentially be the annual test for cancer that are highly accurate in diagnosing cancer based on our pilot study of Colorectal Cancer and Glioblastoma.

Publications:

1. Kumar, S.; Maniya, N.; Wang, C.; Senapati, S.; Chang, H.-C., Quantifying PON1 on HDL with nanoparticle-gated electrokinetic membrane sensor for accurate cardiovascular risk assessment. Nature Communications 2023, 14 (1), 557.