Background Advances in the molecular understanding of carcinogenesis and immunology have transformed the development of targeted anticancer drugs addressing both the tumor and the immune system. The success of these therapies depends on the target associated signal transduction and/or interaction of different types of cells. Using our unique, clinical trial-ready mIF workflow, we demonstrate how custom multiplexed immunofluorescence (mIF) and digital image analysis offer an innovative method to categorize Her-2 expressing breast cancers by evaluating the tumor cells and the tumor microenvironment.

Methods We have optimized a multiplex immunofluorescence assay on Lunaphore Comet™ for use with human control tissues and FFPE solid tumor specimens. Among others, the mIF panel included standard immune cell markers CD3, CD4, CD8, FoxP3, CD56, CD20, CD68, CD11c, PD-L1, PD-1 and CD45. Pan cytokeratin, α-SMA and FAP-α were used to delineate the tumor area and investigate reactivity of the surrounding stroma. Diagnostic and selected Her-2 associated signal transduction markers included E-Cadherin, ER, PgR, PTEN, phospho-ERK1/2, phospho-AKT/PKB, ki67, CyclinD1, p27, EGFR, Her-2, phospho-Her-2, Her-3, and phospho-Her-3. Following a rigorous and standardized approach to mIF panel establishment and validation, we established accuracy by comparing the final mIF to single plex bright field immunohistochemistry as the ‘ground truth’. Furthermore, each individual target was independently validated for specificity regarding elution efficacy and epitope stability to repeated antibody elution. Automated image analysis and cellular phenotyping followed a workflow of custom Visiopharm™ apps. The tissue was segmented into tumor and non-tumor regions of interest by manual annotation.

Results Additional parameters like TNM classification were also taken into consideration for data analysis after image analysis. Statistical analyses employing unsupervised hierarchical 2D clustering helped us gain significant insights in Her-2 downstream signaling and breast cancer biology.

Conclusions For each patient population, the cluster analysis revealed the potential for sub-classification by means of molecular marker expression patterns, independent of clinical features. In the nearby future, protein expression analyses like the herein presented work will empower physicians to make more informed decisions ultimately leading to a more personalized and effective therapy.