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.