Use of glycoproteome profiles to detect advanced adenomas and colorectal cancer.

Authors

null

Khushbu Desai

InterVenn Biosciences, South San Francisco, CA;

Khushbu Desai , Alan Mitchell , Ankita Shah , Dharini Chandrasekar , Gege Xu , Klaus Lindpaintner , Dan Serie , Tillman E. Pearce , Daniel Hommes

Organizations

InterVenn Biosciences, South San Francisco, CA; , InterVenn Biosciences, Redwood City, CA;

Research Funding

Pharmaceutical/Biotech Company
Intervenn Biosciences

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.

Performance of the multivariable classifier on the hold-out test set.

ComparisonAccuracySensitivitySpecificityPPVNPVF1
CRC/APL vs Healthy/UC Controls0.9150.9140.9180.9630.8210.938
CRC vs Healthy/UC Controls0.9240.9280.9180.9570.8670.942
APL vs Healthy/UC Controls0.8970.8440.9180.7940.940.818
CRC Stage 1/2 vs Healthy/UC Controls0.9160.9120.9180.8160.9630.861
CRC Stage 3/4 vs Healthy/UC Controls0.9260.9320.9180.9460.8970.939

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Abstract Details

Meeting

2023 ASCO Gastrointestinal Cancers Symposium

Session Type

Poster Session

Session Title

Poster Session C: Cancers of the Colon, Rectum, and Anus

Track

Colorectal Cancer,Anal Cancer

Sub Track

Prevention, Screening, and Hereditary Cancers

Clinical Trial Registration Number

NCT05445570

Citation

J Clin Oncol 41, 2023 (suppl 4; abstr 69)

DOI

10.1200/JCO.2023.41.4_suppl.69

Abstract #

69

Poster Bd #

D6

Abstract Disclosures

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