Authors: Alan Mitchell, Apoorva Srinivasan, Gege Xu, Rachel Rice, Alyn Castellanos, Ranjan Bhadra, Danie Serie, Xin Cong, Klaus Lindpaintner
Published: 2022-06-06
DOI: 10.1200/jco.2022.40.16_suppl.e12545
Source: Full article
e12545 Background: Breast cancer is the most common cancer among women worldwide.Traditional methods of cancer detection such as tissue biopsy are invasive, costly, time consuming and not amenable for repetition. As a result, minimally invasive liquid biopsies, especially blood-based biomarkers show potential value for breast cancer risk prediction and early detection. In this study, we investigated the use of serum glycoproteins circulating in blood to identify a panel of potential prognostic markers that may aid in predicting breast cancer in women. Methods: We applied a novel platform for characterizing blood glycoproteomic biomarkers, combining liquid-chromatography/mass spectrometry (LC-MS) with artificial intelligence/neural networks (AI-NN) to analyze serum samples from 279 breast cancer patients (median age 56 years, with stage 0-4 N’s: 1 / 83 / 114 / 56 / 25) and 102 healthy control samples (median age 52 years). A panel of 596 serum glycosylated and non-glycosylated peptides, representing 71 serum proteins, were analyzed. Age-adjusted differential expression analysis for 596 normalized biomarkers were performed to evaluate statistically significant differential abundances using an FDR q-value of 0.05 as a cutoff. Using the top differentially expressed markers as input, a LASSO penalized logistic regression model with 5-fold repeated cross validation was applied to identify the top biomarkers contributing to the separation between healthy controls and breast cancer patients. Results: We identified 243 out of 596 markers that were differentially expressed (FDR <<0.05) between breast cancer samples and healthy controls. Out of those, 11 markers were obtained as the top predictors in classifying breast cancer patients and healthy controls. The classification algorithm yielded an accuracy of 94% (95.9% sensitivity, 88.7% specificity) and an AUC of 0.983 on the training set. This classifier was validated on an independent test set with 30% of the subjects, yielding an accuracy of 93% (96.4% sensitivity, 83.9% specificity) and an AUC of 0.974. Test sensitivity was high across stages, at 96% / 90% / 95% / 90% in stages 1-4, respectively. Conclusions: Based on the results, we conclude that circulating glycoproteins in serum may be useful in screening applications in breast cancer, and strongly demonstrates the utility of glycoprotein profiles as a powerful non-invasive diagnostic tool.