Quantitative Whole Slide Assessment of Tumor-Infiltrating CD8-Positive Lymphocytes in ER-Positive Breast Cancer in Relation to Clinical Outcome.

There is a need for a reliable and reproducible quantification of the immune infiltrate within the heterogeneous microenvironment of tumors in order to support therapy selection in oncology. Here we present an automated, modular method for whole-slide image analysis of the spatial distribution of tumor-infiltrating CD8-positive lymphocytes. The method uses a deep learning tissue-type classification algorithm on the hematoxylin eosin (HE) stained tissue section to identify the central tumor (CT) and invasive margin (IM) of the tumor. A CD8-positive cell detection algorithm using a deep learning-based nucleus detection is applied to a sequential immunohistochemistry (IHC)-stained tissue section. Image registration then allows obtaining IHC-derived CD8 scores for the HE-derived CT and the IM, respectively. Both, the mean and the standard deviation of the spatial CD8-positive density distributions were determined for the CT and IM in a cohort of post-menopausal, estrogen receptor-positive invasive breast cancer patients who received adjuvant tamoxifen therapy. Spatial density distributions were found to be highly heterogeneous. In contrast to previous studies, CD8 density in the IM and CT correlated positively with clinical outcome. However, statistical significance was only achieved for the standard deviation of the CD8 density distribution. We hypothesize that this is due to the positive contribution of local high-density areas. The IM/CT density ratio did not correlate with outcome. In view of the clinical relevance of our finding, we would like to encourage a study with a larger cohort. Our modular pipeline approach allows a robust and objective scoring of CD8 infiltrate based on routine pathology staining and should contribute to clinical adoption of computational pathology.

A Novel Dual Antibody Staining Assay to Measure Estrogen Receptor Transcriptional Activity.

Activity of the canonical estrogen receptor (ER) pathway is equivalent to functional activity of the nuclear ER transcription factor. Monoclonal antibodies (MoAbs) that identify nuclear ER in cells and tissue samples are frequently used to assess ER transcriptional activity, however, it remains unclear if this approach is sufficiently predictive of ER pathway activity. This study uses ER-positive breast cancer cell lines (MCF7 and T47D) in which ER transcriptional activity was quantified using an mRNA-based ER pathway activity assay. The relationship between ER activity and nuclear ER staining with ER MoAbs was then investigated. Confirming earlier findings, the results show that while the presence of ER in the cell nucleus is a prerequisite for ER activity, it is not predictive of ER transcriptional activity. There were remarkable differences in the behaviours of the antibodies used in the study. EP1 and 1D5 showed reduced nuclear staining when ER was transcriptionally active, while staining with H4624 was independent of ER activity. To improve discrimination between active and inactive nuclear ER based on ER staining, a method was developed which consists of dual ER MoAb immunofluorescent staining, followed by generation of a digital image with a standard digital pathology scanner. Then a cell nucleus detection algorithm and per cell calculation of the nuclear H4624/EP1 fluorescence intensity ratio was applied, where a high H4624/EP1 ratio predicts an active ER pathway. With this method, the EP1 and 1D5 antibodies are interchangeable. We hypothesize that the transcriptional activation of ER hides the epitope recognized by MoAbs EP1 and 1D5, while H4624 binds an ER epitope that remains accessible during ER pathway activation. The method described in this study should add substantial value to the assessment of ER pathway activity for biomedical research and diagnostics.