Dr Rimm on the Utility and Shortcomings of HER2 IHC Testing in Breast Cancer

“Not only is it impossible for pathologists to tell a [HER2] 0+ from [a] 1+, but the machines that make the stain can [also be a] variable as well and can add to the problem of telling 0 plus from 1 plus.”

David Rimm, MD, PhD, Anthony N. Brady Professor of Pathology, professor, medicine, Medical Oncology, Yale School of Medicine; director, Physician Scientist Training Program, Pathology Research, director, Tissue Microarray Facility, director, Yale Pathology Tissue Services, Pathology, Yale Cancer Center, discusses current HER2 immunohistochemistry (IHC) assays that are used in breast cancer disease classification.

One key challenge with diagnostic stains, particularly companion diagnostics, lies in the need for standardization between laboratories, Rimm begins. Historically, there has been no effective way to standardize results across laboratories, leading to significant variability, according to Rimm. The assumption that using the same autostainer and reagents yields consistent results has been debunked, he reports. Recent findings demonstrated that staining intensity could differ up to 4- or 5-fold between labs, even those using identical equipment and reagents, Rimm emphasizes. This inconsistency is especially problematic for HER2 IHC testing, particularly in the low-expression category, he says. A HER2 result categorized as “low” or 1+ in one lab might be undetectable, or HER2 0+, in another, leading to discrepancies, he notes.

To address this issue, a new quality control tool has been developed, he continues. This slide, which includes antigens bound to glass beads, provides a standardized measure of HER2 detection that can be used to produce consistent results across laboratories, he says. By including reference spots representing various antigen concentrations, laboratories can assess the performance of their staining systems, Rimm explains. For instance, if a lab fails to detect a specific spot that corresponds to a known detection threshold, the tool signals the need to recalibrate or rerun the batch to ensure proper functionality, he notes.

Although this approach advances the accuracy of machine-based staining, it does not solve variability in pathologist interpretations, Rimm concludes, adding that addressing both machine and human factors remains critical to improving diagnostic accuracy.