Computer-assisted image analysis of the tumor microenvironment on an oral tongue squamous cell carcinoma tissue microarray

Sangjune Laurence Lee; Michael Cabanero; Martin Hyrcza; Marcus Butler; Fei-Fei Liu; Aaron Hansen; Shao Hui Huang; Ming-Sound Tsao; Yuyao Song; Lin Lu; Wei Xu; Douglas B Chepeha; David P Goldstein; Ilan Weinreb; Scott V Bratman

doi:10.1016/j.ctro.2019.05.001

Computer-assisted image analysis of the tumor microenvironment on an oral tongue squamous cell carcinoma tissue microarray

Clin Transl Radiat Oncol. 2019 May 18:17:32-39. doi: 10.1016/j.ctro.2019.05.001. eCollection 2019 Jul.

Authors

Sangjune Laurence Lee¹, Michael Cabanero^{2

3}, Martin Hyrcza^{2

3}, Marcus Butler^{3

4}, Fei-Fei Liu^{1

3}, Aaron Hansen^{3

4}, Shao Hui Huang^{1

3}, Ming-Sound Tsao^{2

3}, Yuyao Song^{3

5}, Lin Lu^{3

5}, Wei Xu^{3

5}, Douglas B Chepeha^{3

6}, David P Goldstein^{3

6}, Ilan Weinreb^{2

3}, Scott V Bratman^{1

3

7}

Affiliations

¹ Department of Radiation Oncology, University of Toronto, Canada.
² Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada.
³ Princess Margaret Cancer Centre, University Health Network, Canada.
⁴ Department of Medical Oncology, University of Toronto, Canada.
⁵ Department of Biostatistics, University of Toronto, Canada.
⁶ Department of Otolaryngology, University of Toronto, Canada.
⁷ Department of Medical Biophysics, University of Toronto, Canada.

Abstract

Oral tongue squamous cell carcinoma (OTSCC) displays variable levels of immune cells within the tumor microenvironment. The quantity and localization of tumor infiltrating lymphocytes (TILs), specific functional TIL subsets (e.g., CD8+), and biomarker-expressing cells (e.g., PD-L1+) may have prognostic and predictive value. The purpose of this study was to evaluate the robustness and utility of computer-assisted image analysis tools to quantify and localize immunohistochemistry-based biomarkers within the tumor microenvironment on a tissue microarray (TMA). We stained a 91-patient OTSCC TMA with antibodies targeting CD3, CD4, CD8, FOXP3, IDO, and PD-L1. Cell populations were segmented into epithelial (tumor) or stromal compartments according to a mask derived from a pan-cytokeratin stain. Definiens Tissue Studio was used to enumerate marker-positive cells or to quantify the staining intensity. Automated methods were validated against manual tissue segmentation, cell count, and stain intensity quantification. Univariate associations of cell count and stain intensity with smoking status, stage, overall survival (OS), and disease-free survival (DFS) were determined. Our results revealed that the accuracy of automated tissue segmentation was dependent on the distance of the tissue section from the cytokeratin mask and the proportion of the tissue containing tumor vs. stroma. Automated and manual cell counts and stain intensities were highly correlated (Pearson coefficient range: 0.46-0.90; p < 0.001). Within this OTSCC cohort, smokers had significantly stronger PD-L1 stain intensity and higher numbers of CD3+, CD4+ and FOXP3+ TILs. In the subset of patients who had received adjuvant radiotherapy, a higher number of CD8+ TILs was associated with inferior OS and DFS. Taken together, this proof-of-principle study demonstrates the robustness and utility of computer-assisted image analysis for high-throughput assessment of multiple IHC markers on TMAs, with potential implications for studies on prognostic and predictive biomarkers.