Colorectal cancer (CRC) remains a leading cause of cancer-related deaths despite being the most preventable and treatable forms of cancer when caught early through screening. There is an unmet need for novel screening approaches with improved accuracy, less invasiveness, and reduced costs. In recent years, evidence has accumulated around particular biological events that happen during the adenoma-to-carcinoma transition, especially focusing on precancerous immune responses in the colonic crypt. Protein glycosylation plays a central role in driving those responses, and recently, numerous reports have been published on how aberrant protein glycosylation both in colonic tissue and on circulating glycoproteins reflects these precancerous developments. The complex field of glycosylation, which exceeds complexity of proteins by several orders of magnitude, can now be studied primarily because of the availability of new high-throughput technologies such as mass spectrometry and artificial intelligence-powered data processing. This has now opened new avenues for studying novel biomarkers for CRC screening. This review summarizes the early events taking place from the normal colon mucosa toward adenoma and adenocarcinoma formation and associated critical protein glycosylation phenomena, both on the tissue level and in the circulation. These insights will help establish an understanding in the interpretation of novel CRC detection modalities that involve high-throughput glycomics.

69 Background: Colorectal cancer (CRC) remains a leading cancer despite current screening modalities. Precancerous lesions, or Advanced Adenomas (AA), commonly precede invasive cancer development by years. Newer technologies use circulating tumor DNA and/or proteins for CRC detection but have not been able to effectively detect AA. Aberrant protein glycosylation is associated with (pre-)malignant lesions. To detect glycoproteome profiles associated with the occurrence of AA, we studied serum glycoproteins in AA/CRC. Methods: A novel platform combining liquid-chromatography/mass-spectrometry (LC-MS) and artificial-intelligence (AI)-powered data processing allowing high resolution, high throughput glycoproteomic profiling was used to identify glycoprotein biomarkers in peripheral blood. Samples were sourced from biorepositories and included patients diagnosed with CRC, AA, ulcerative colitis (UC) and controls. The samples were split into a training (50%) and a hold-out testing set (50%) for the development of a machine learning (ML)-based multivariable predictive model. Statistical analysis was performed on normalized data to identify biomarkers differentiating AAs and different stages of CRC from controls. Results: We studied 563 patient samples: 196 controls (mean age 51.7; 52% female); 32 AA (mean age 68.6; 53% female); 247 CRC (mean age 65.6; 50% female) and 88 UC (mean age 44.1; 47% female). There were 250 differentially abundant (FDR < 0.05) glycopeptides/peptides when comparing CRC and AA samples with healthy and UC controls. A subset was assessed, generating a six (6) biomarker ML classification model. This model was applied to the hold-out test and achieved an overall sensitivity of 91.4% and specificity of 91.8% for predicting AA/CRC versus healthy/UC with an area under the receiver operating characteristic of 0.962. AA and CRC separately were predicted with a sensitivity of 84.4% and 92.8%, respectively, relative to healthy/UC with sensitivities for CRC stage 1/2 and stage 3/4 being 91.2% and 93.2%, respectively). Conclusions: Glycoproteomic serum profiles accurately detect precancerous AA in addition to CRC and offer a new approach to effective CRC screening. We will have completed an interim analysis of a large prospective observational study at the time of the meeting. Clinical trial information: NCT05445570 . [Table: see text]

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