Abstract 2776: Sequential immunohistochemistry and computational image analysis for a deeper characterization of tumor-infiltrating myeloid cells
Authors: Tumedei Maria Maddalena , Piccinini Filippo , Bulgarelli Jenny , Guidoboni Massimo , Limarzi Francesco , Vergani Barbara , Leone Biagio Eugenio , Puccetti Maurizio , Bravaccini Sara , Martinelli Giovanni , Ibrahim Toni , Tazzari Marcella 
Published: July 2021

Tumor-infiltrating macrophages (TIMs) often outnumber other immune cells and across different tumor histologies have been linked to resistance to therapies, including immunotherapy. Despite the huge steps headed in the characterization of these cells, their phenotyping and quantification within the tumor micro-environment (TME) still remain challenging. Multiplex Immunohistochemistry/Immunofluorescence (mIHC/IF) technologies, which allow the simultaneous detection of multiple markers on a single tissue section have empowered the approach to study the TME, allowing the analysis of co-expressing molecules and the spatial relationship between distinct immune and non-immune cells. However, this technique well performed for lymphoid cells and nuclear markers, needs to be optimized for TIMs, often characterized by an elongated shape and an enlarged cytoplasm. mIHC by iterative immunostaining on a single paraffin-embedded (FFPE) tissue slide followed by whole slide digital pathology analysis was performed. Different TIM-related antibodies were used, including: CD68, CD206, CD163, CD14, MS4A4A, HLA-DR. Moreover, an array of checkpoint molecules (B7-H3, VISTA, TIM-3, PD-L1), an inflammation-related marker (STING), a pan-leukocyte marker (CD45) and lymphoid related markers (CD56, CD8, CD3, CD4) have been also validated. Together with the staining procedure, we developed computational pipelines for the processing of the whole-slide images. To perform a precise ascription of the staining intensity, we used a customized CellProfiler pipeline for segmenting single cells after using the Color Deconvolution ImageJ plugin for the generation of combined pseudo-fluorescent images. This segmentation pipeline permits the conversion of microscopy data into CSV files reporting the single-cell feature values (e.g. staining intensity and reciprocal position). With this approach IHC images can be visualized in user-friendly multi-parameter cytometric-like outputs allowing a comprehensive in situ TIM quantification and phenotyping. Nonetheless, our approach does not require specialized equipment and makes usage of commonly reagents, allowing its easy translation also in real-life clinical practice for the tailoring of therapeutic choices. In conclusion, in this study we discuss critical aspects and computational tools for data visualization and analysis of mIHC TIM data together with results obtained in different clinical setting (melanoma, breast cancer and lymphoma). We believe that this approach can be useful for a deeper characterization of the TME.

Citation Format: Maria Maddalena Tumedei, Filippo Piccinini, Jenny Bulgarelli, Massimo Guidoboni, Francesco Limarzi, Barbara Vergani, Biagio Eugenio Leone, Maurizio Puccetti, Sara Bravaccini, Giovanni Martinelli, Toni Ibrahim, Marcella Tazzari. Sequential immunohistochemistry and computational image analysis for a deeper characterization of tumor-infiltrating myeloid cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2776.