Personalized Autoantibody Profiling Distinguishes Early-stage Breast Cancer from Benign Disease

Abstract Background Early and accurate detection of breast cancer and differentiation from benign breast disease remains a substantial challenge, with about 70% of diagnostic breast biopsies having no malignant findings. Tumor-associated Autoantibodies represent the immune system’s response to a neoplasm and are a promising biomarker group for the early diagnosis of breast cancer by liquid biopsy. Methods In this study, we quantified the IgM and IgG titers to 525 Tumor Associated Antigens in a prospectively-collected cohort of 50 serum samples from donors with benign breast disease and donors with early-stage breast cancer. The considerable number of antibodies analyzed enabled us to account for variations in individual immune profiles through z- score normalization of each donor’s total antibody distribution. Differentially expressed antibodies were identified using Mann-Whitney U tests ( p < 0.05) and fold-change analysis (fold-change > ± 1.2). For each donor, we calculated the total number of “high-titer” antibodies, defined as antibodies with relative concentrations > 3 SD above the cohort mean. Logistic regression classifiers were then built using differentially expressed biomarkers and high-titer antibody counts to distinguish benign breast disease from breast cancer. Results We identified 25 differentially expressed antibodies between the benign and cancer groups. A down-selected panel of eight antibodies demonstrated good performance in a logistic regression classifier to distinguish benign disease from invasive carcinomas (AUC-ROC = 0.83 ± 0.14). High-titer antibody analysis revealed that the benign group had a higher prevalence of donors with elevated IgG immune response, and donors displayed antibody signatures unique to their individual disease pathway. Conclusions This study identifies an eight-antibody panel with promising diagnostic potential to distinguish benign breast disease from early-stage breast cancer. The z-score normalization approach and analysis of individual donors’ high-titer antibody profiles represent a novel approach towards personalized cancer immunology. This study provides encouraging preliminary evidence supporting the promise of tumor-associated autoantibody profiling for distinguishing benign and malignant breast disease, warranting future studies in larger cohorts.