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Early detection of cancer remains a significant challenge. Existing liquid biopsy approaches to detect early-stage cancer show promise but suffer significant limitations including the abundance and stability of the analyte, large sample requirements, and insufficient specificity to distinguish biomarkers as originating from benign or cancerous cells. Extracellular vesicles (EVs) from cancer cells exhibit both stability and abundance in human biofluids which makes them an attractive target for early-cancer detection. Measurement of multiple, colocalized cancer-associated biomarkers on single EVs may improve upon existing methods for the non-invasive detection of cancer. To demonstrate this, we developed Mercy Halo™, an immunoassay designed to capture and detect cancer-specific biomarkers on the surface of EVs derived from cancer cells. Our platform shows promise for the sensitive and specific detection of early-stage ovarian tumors in human plasma, outperforming a single biomarker (CA125), for the detection of stage I and II high-grade serous ovarian cancer (HGSOC).

Noninvasive detection of nonalcoholic steatohepatitis (NASH), the progressive form of nonalcoholic fatty liver disease, promises to improve patient screening, accelerate drug trials, and reduce health care costs. On the basis of protease dysregulation of the biological pathways of fibrotic NASH, we developed the Glympse Bio Test System (GBTS) for multiplexed quantification of liver protease activity. GBTS-NASH comprises a mixture of 19 mass-barcoded PEGylated peptides that is administered intravenously and senses liver protease activity by releasing mass-barcoded reporters into urine for analysis by mass spectrometry. To identify a protease signature of NASH, transcriptomic analysis of 355 human liver biopsies identified a 13-protease panel that discriminated clinically relevant NASH ≥F2 fibrosis from F0-F1 with high classification accuracy across two independent patient datasets. We screened 159 candidate substrates to identify a panel of 19 peptides that exhibited high activity for our 13-protease panel. In the choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) mouse model, binary classifiers trained on urine samples discriminated fibrotic NASH from simple steatosis and healthy controls across a range of nondisease conditions and indicated disease regression upon diet change [area under receiver operating characteristics (AUROCs)0.97]. Using a hepatoprotective triple combination treatment (FXR agonist, ACC and ASK1 inhibitors) in a rat model of NASH, urinary classification distinguished F0-F1 from ≥F2 animals and indicated therapeutic response as early as 1 week on treatment (AUROCs0.91). Our results support GBTS-NASH to diagnose fibrotic NASH via an infusion of peptides, monitor changes in disease severity, and indicate early treatment response.

Introduction: Screening for lung cancer (LC), the leading cause of cancer deaths, with helical computerized tomography lowers mortality but uptake is poor. Investigations into new approaches such as using circulating tumor cells and circulating tumor DNA for LC detection have soared in the last decade. However, the low abundance of these targets has limited the performance of these approaches as screening tools. We hypothesize that co-localization of biomarkers on the surface of individual extracellular vesicles (EVs), which are shed into the circulation by cancer cells, may lead to development of a blood test for early stage LC. We evaluated the potential of our approach in detecting early stage LC in clinical samples. Methods: EVs were purified from plasma using size-exclusion chromatography and immunoaffinity capture, and biomarkers co-localized on the EV surface were detected with proximity ligation qPCR. We used antibody combinations comprising 1 capture antibody and 2 oligonucleotide-tagged detection antibodies, recognizing 1, 2 or 3 unique biomarkers. We evaluated this approach by testing plasma samples from early stage I/II lung adenocarcinoma (LUAD) patients (15 smokers, 19 non-smokers), late stage III/IV LUAD patients (16 smokers, 18 non-smokers), and healthy donors (34 smokers, 33 non-smokers). Samples were from one vendor, processed using a standardized protocol. LUAD samples were sourced from a cancer research center and healthy samples from a primary care facility. PCR cycle threshold (Ct) values were generated for each combination and data was evaluated using univariate analysis. Results: Combinations recognizing 3 biomarkers were better in detecting all stages of LUAD (AUC=0.83, 95% CI 0.77-0.90), as compared to combinations recognizing 2 biomarkers (AUC=0.71, 95% CI 0.63-0.80) or 1 biomarker (AUC=0.50, 95% CI 0.35-0.55), demonstrating greater accuracy with an increasing number of co-localized biomarkers. In detecting LUAD (all stages) at a specificity of 0.80 (95% CI 0.69-0.88), sensitivity improved as the number of co-localized biomarkers increased from 1 (0.08, 95% CI 0.03-0.18) to 2 (0.60, 95% CI 0.48-0.72) to 3 (0.76, 95% CI 0.65-0.86). In detecting early stage I/II LUAD, the most effective combination used 3 biomarkers (STn, MUC1, CEACAM6) and had a sensitivity of 0.56 (95% CI 0.38-0.73). Conclusions: These preliminary data highlight the potential of detecting biomarkers co-localized on the surface of single EVs as an effective tool for early stage LC detection, and the benefit of using 3 biomarkers simultaneously. Despite inherent challenges associated with commercial samples, our finding that detection of co-localized EV surface biomarkers distinguished LUAD is promising. Additional studies with LC cohorts beyond LUAD are underway to refine combinations and independently validate our assay for early stage LC detection. Citation Format: Daniel P. Salem, Laura T. Bortolin, Sanchari Banerjee, Kelly M. Biette, Delaney M. Byrne, Anthony D. Couvillon, Peter A. Duff, Jonian Grosha, Daniel Gusenleitner, MacKenzie Sadie King, Christopher R. Sedlak, Ibukunoluwapo O. Zabroski, Karen Copeland, Emily S. Winn-Deen, Eric K. Huang, Christine D. Berg, Joseph C. Sedlak. Preliminary results for a novel single extracellular vesicle assay for early lung cancer: The power of co-localized detection of surface biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2232.

Introduction: Ovarian cancer (OC) is one of the deadliest cancers, with 314,000 new cases and 207,000 deaths globally in 2020. Serum CA125 has been explored as an OC biomarker for the past 40 years, but lacks sensitivity for early stage OC and is not recommended for screening average-risk, asymptomatic women. We hypothesize that co-localization of biomarkers on the surface of individual extracellular vesicles (EVs), which are shed into the circulation by cancer cells, may lead to development of a blood test for early stage OC. We evaluated the potential of our approach in detecting early stage OC in clinical samples. Methods: We isolated EVs using size-exclusion chromatography and immunoaffinity capture, and detected biomarkers co-localized on the surface of individual EVs with proximity ligation qPCR. Using this approach, we evaluated 49 antibody combinations recognizing 2 or more biomarkers. Each combination consisted of 1 capture antibody and 2 oligonucleotide-tagged detection antibodies. We tested plasma samples from women with early stage I/II high-grade serous ovarian carcinoma (HGSOC)(n=18; 48-80 yr, med 57) and late stage HGSOC (n=24; 37-80 yr, med 54). HGSOC samples were sourced from 2 commercial vendors. Controls comprised samples from women with benign ovarian masses (n=26; 23-76 yr, med 39.5) sourced from a single vendor, and samples prospectively collected by Mercy from healthy women with no cancer history (n=24; 22-72 yr, med 52.5). PCR cycle threshold (Ct) values were measured for each of 49 combinations and data was evaluated using univariate analysis. Performance was compared to plasma CA125 measured at Mercy by commercial ELISA. Results: 8 of 49 combinations distinguished all stages of HGSOC relative to benign and healthy controls with AUCs ranging from 0.86 (95% CI 0.78-0.94) to 0.95 (95% CI 0.90-1.00), comparable to CA125 with an AUC of 0.87 (95% CI 0.79-0.95). One of the most effective combinations (STn, BST2, MUC1) had a sensitivity of 0.78 (95% CI 0.52-0.94) at a specificity of 0.96 (95% CI 0.87-0.99) in detecting early stage HGSOC. This combination also detected HGSOC in 6 of 11 women (3 early stage, 3 late stage) with normal CA125 (< 25 U/mL) and correctly classified 7 of 8 women with benign masses and high CA125 (> 25 U/mL). Conclusions: These preliminary data suggest that co-localization of surface biomarkers in single EVs may provide an effective means to identify women with early stage HGSOC, including those with normal CA125, while avoiding false positives in women with benign masses and high CA125. Despite the inherent challenges associated with commercial samples, our finding that several combinations detected early stage HGSOC is promising. Statistically powered studies with curated repository specimens are underway to refine combinations and independently validate our assay for early stage OC detection. Citation Format: Laura T. Bortolin, Daniel P. Salem, Sanchari Banerjee, Kelly M. Biette, Delaney M. Byrne, Anthony D. Couvillon, Peter A. Duff, Jonian Grosha, MacKenzie Sadie King, Christopher R. Sedlak, Ibukunoluwapo O. Zabroski, Claire Alexander, Karen Copeland, Daniel Gusenleitner, Emily S. Winn-Deen, Eric K. Huang, Bo R. Rueda, Joseph Charles Sedlak. Preliminary results for a novel single extracellular vesicle assay for early stage ovarian cancer: The power of co-localized detection of surface biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3390.

5542 Background: Detection of cancer with improved discrimination compared to current blood tests could be achieved using an approach that assesses extracellular vesicles (EVs). This approach should have high sensitivity (se) because of EVs abundance in blood and high specificity (sp) by assaying EVs with multiple cancer-related protein and glycosylation epitopes (PGEs) co-localized on their surfaces. We are developing a platform technology that detects multiple cancer-related PGEs co-localized on the same EV using immunoaffinity-capture and proximity-ligation qPCR. In this study, we compare the performance of this technology vs plasma CA125 for correctly categorizing early-stage high-grade serous ovarian cancer (HGSOC) vs healthy/benign ovarian tumors (OT). Methods: We evaluated our EV-based platform technology using 7 PGE combinations to discriminate HGSOC from benign adnexal masses. We first derived a prediction model on a retrospectively collected cohort of 42 HGSOC and 26 benign OT samples from 2 commercial vendors and 24 healthy controls (HC) using a machine-learning algorithm. We validated this model on an independent cohort [89 HGSOC: Stage I (17), II (35), III (37); 192 benign OT] from university-associated biobanks and 124 HC. We also assessed the assay’s performance in plasma from 87 women with off-target cancers and 42 women with inflammatory conditions from commercial vendors. For each sample, we also measured CA125 levels using a commercial ELISA. Results: The prediction model distinguishes HGSOC from benign and HC with an AUC of 0.965 (95% CI 0.93-0.99), with 89.9% (0.82-0.95) se at 98% sp. For stage I/II HGSOC, the model achieves an AUC of 0.942 (0.9-0.99), with 84.6% (0.72-0.93) se at 98% sp. By comparison, CA125 achieves an AUC of 0.875 (0.81-0.94) and 44.2% (0.3-0.59) se at 98% sp. Direct comparison of CA125 and our model shows a significant difference at 98% sp for both all and stage I/II HGSOC (McNemar p-val < 0.001). When comparing HGSOC to HC, there is no significant difference between our model and CA125 (p-val = 1.0). There is a significant difference when comparing patients with all stage and stage I/II HGSOC to patients with benign OT (p-val < 0.001). Our assay had 1 false positive and CA125 had 3 false positives out of 42 inflammatory cases. Conclusions: These preliminary data suggest our platform technology for detecting PGEs co-localized on individual EVs may detect all stages of HGSOC from plasma with high se at a very high sp. Our assay may improve on CA125 by distinguishing stage I/II HGSOC from benign OT and could have clinical utility for both early detection and surgical referral recommendation for benign and malignant OT. While the diverse cohorts in this study may present challenges in interpretation, the reproducibility in an independent cohort is encouraging and supports further investigation using cases and controls from well-defined cohort studies.