Breast cancer is a highly prevalent
malignancy that shows improved outcomes with earlier diagnosis. Current screening and monitoring methods have improved survival rates, but the limitations of these approaches have led to the investigation of
biomarker evaluation to improve early diagnosis and treatment monitoring. The
enzyme-linked
immunosorbent assay (ELISA) is a specific and robust technique ideally suited for the quantification of
protein biomarkers from blood or its constituents. The continued clinical relevancy of this assay format will require overcoming specific technical challenges, including the ultra-sensitive detection of trace
biomarkers and the circumventing of potential assay interference due to the expanding use of
monoclonal antibody (mAb)
therapeutics. Approaches to increasing the sensitivity of ELISA have been numerous and include employing more sensitive substrates, combining ELISA with the polymerase chain reaction (PCR), and incorporating nanoparticles as shuttles for detection
antibodies and
enzymes. These modifications have resulted in substantial boosts in the ability to detect extremely low levels of
protein biomarkers, with some systems reliably detecting
antigen at sub-femtomolar concentrations. Extensive utilization of mAb
therapies in oncology has presented an additional contemporary challenge for ELISA, particularly when both therapeutic and assay
antibodies target the same
protein antigen. Resolution of issues such as
epitope overlap and steric hindrance requires a rational approach to the design of diagnostic
antibodies that takes advantage of modern antibody generation pipelines,
epitope binning techniques and computational methods to strategically target
biomarker epitopes. This review discusses technical strategies in ELISA implemented to date and their feasibility to address current constraints on sensitivity and problems with interference in the clinical setting. The impact of these recent advancements will depend upon their transformation from research laboratory protocols into facile, reliable detection systems that can ideally be replicated in point-of-care devices to maximize utilization and transform both the diagnostic and therapeutic monitoring landscape.