Ramachandran, Prasanna

22 Background: Glycosylation plays a pivotal role in host immune response during cancer development. Recently, we have reported specific changes in blood-based glycoforms for the early detection of advanced adenomas (AAs) and colorectal cancer (CRC). This study aims to identify glycoproteomic biomarkers associated with various polyp types, as glycosylation abnormalities are linked to neoplastic transformation, and CRC risk varies by polyp histology. Methods: Samples from an observational study (NCT05445570) were used to identify three distinct histologic polyp groups: adenomatous (AP), hyperplastic (HP), or serrated (SP). Combining liquid-chromatography/mass-spectrometry and artificial-intelligence powered data processing, our glycoproteomic profiling platform assessed glycopeptide (GP) and non-glycosylated peptide quantification transitions from the serum samples. We used multi-nominal response logit models to examine the relationship between polyp types and biomarkers. An a priori biomarker panel was interjected as exposures in the model. Each marker is assessed independently for inclusion into our final model. The final model utilized biomarkers found to have a significant association with the presence of AP, SP, or HPs along with age, sex, and BMI. Results: We identified 1,035 subjects with distinct polyp histologies and 1,300 polyp-free controls. We found a significant increase in relative risk (RR) for polyp presence in patients who were: >60 years (AP RR 1.40), male (AP RR 1.74), or had a BMI> 25. From the GP biomarker panel, 15 GPs were associated with risk of polyp presence; however, only 7 GP markers were significantly associated after adjusting for age, sex, and BMI. Two biomarkers were found to have a protective effect for the risk of an adenomatous polyp (RR 0.37; RR 0.39). Three biomarkers increased the risk of an adenomatous polyp (RR 1.31; RR 1.42; RR 1.98). One biomarker was found to increase the risk of a hyperplastic polyp (RR 1.42). Two biomarkers were found to increase the risk of serrated polyps (RR 1.67; RR 2.08) and one biomarker was found to decrease the risk of serrated polyps (RR 0.77). Conclusions: This is a novel exploratory study using GP biomarkers as it relates to risk of polyp presence by distinct histology. The use of these biomarkers allows us to harness the power of glycobiology in the liquid biopsy domain for AAs/CRC detection. The exact function of these GP markers during the development of different polyp types needs to be further explored through future studies.

763 Background: Non-invasive biomarkers with high sensitivity and specificity would be of great value for patients with Pancreatic Ductal Adenocarcinoma (PDAC). This would aid in early detection and serve other purposes that 'liquid biopsies' are being explored in. Besides circulating tumor DNA (ctDNA) and methylation markers, glycosylation markers hold great potential promise. We developed a novel workflow using high-resolution quantification of site-specific protein glycosylation by liquid chromatography and tandem mass spectrometry to evaluate the clinical utility of glycoprotemics signature for patients with PDAC. Methods: Serum samples from newly diagnosed PDAC patients and controls were obtained from a commercial biobank (Indivumed, Hamburg, Germany), and a panel of 504 glycan motifs, representing 73 previously reported proteomic markers, was determined. Age-adjusted generalized linear regression models were used to evaluate the differential abundance of each marker, and stepwise variable selection was used for model construction. Results: We analyzed 45 PDAC and 136 control samples. PDAC patients (60% male) had a mean age of 67 (±11) years. with 4.4%, 71.1%, 4.4%, and 20% at stage 1, 2, 3,4, respectively. Controls were women with benign histology after pelvic mass surgery; with a mean age of 61 (±11) years. Twenty-six glycoproteomic markers showed statistically highly significant differential abundance among cases and controls (p<1e-4 each) and were highly reproducible(Pearson’s r > 0.85), which were glycoforms in proteins that have previously been found to be associated with PDAC. Fourteen of these markers displayed >0.8 area under the curve of the receiver operating characteristic (AUC). Multivariate logistic regression modeling with backward selection yielded a classification model with an AUC of 0.94 (95% CI: 0.89-0.99), sensitivity of 91% (95% CI: 75-97%) and specificity of 86% (95% CI: 81-92%). Conclusions: Circulating glycoproteomic biomarkers may be useful in the early detection and clinical management of PDAC patients; offering a new platform to explore and validate.