Autoantibodies as potential biomarkers for breast cancer

Li Zhong , Kun Ge , Jin-chi Zu , Long-hua Zhao , Wei-ke Shen , Jian-fei Wang , Xiao-gang Zhang , Xu Gao , Wanping Hu , Yun Yen and Kemp H Kernstine

Breast Cancer Research 2008, 10:R40doi:10.1186/bcr2091


Published:	6 May 2008

Abstract (provisional)

Introduction

Currently only a limited number of tumor markers for breast cancer are available. Antibodies to tumor-associated proteins may expand the number of available tumor markers for breast cancer and be used together in a serum profile to enhance sensitivity and specificity.

Methods

In this study, we interrogated a breast cancer cDNA T7 phage library for tumor-associated proteins using biopan enrichment techniques with sera from normal and breast cancer patients. The enrichment of tumor-associated proteins after biopanning was tested using plaque-lift assay and immunochemical detection. The putative tumor-associated phage clones were collected for PCR and sequencing analysis. Unique and ORF phage-expressed proteins were then used to develop phage protein enzyme-linked immunosorbent assays (ELISAs) to measure corresponding autoantibodies using 87 breast cancer patients and 87 normal serum samples. Logistic regression model and leave-one-out validation were used to evaluate predictive accuracies with single marker as well as combined markers. Identities of those selected proteins were revealed through sequence BLAST.

Results

We harvested 100 putative tumor-associated phage clones after biopan enrichment. Sequencing analysis revealed that six phage proteins were in-frame and unique. Antibodies to these six phage-expressed proteins were measured by ELISAs and results showed that three of the phage clones had statistical significance in discriminating patients from normals. BLAST results of the three proteins showed great matches to ASB-9, SERAC1, and RELT. Measurements of the three predictive phage proteins were combined in a logistic regression model that achieved 80% sensitivity and 100% specificity in prediction of sample status, whereas leave-one-out validation achieved 77.0% sensitivity and 82.8% specificity among 87 patient and 87 control samples. ROC curve analysis and leave-one-out method both showed that combined measurements of the three antibodies were more predictive of disease than any of the single antibodies studied, underscoring the importance of identifying multiple potential markers.

Conclusions

For breast cancer, serum autoantibody profiling is a promising approach for early detection and diagnosis. Rather than one, a panel of autoantibodies appears preferable to achieve superior accuracy. Further refinements will need to be made to further improve the accuracy. Once refined the assay must be applied to a prospective patient population to demonstrate applicability.