Independent Prognostic Value of Intratumoral Heterogeneity and Immune Response Features by Automated Digital Immunohistochemistry Analysis in Early Hormone Receptor-Positive Breast Carcinoma.

Dovile Zilenaite,Renaldas Augulis,Arvydas Laurinavicius,Valerijus Ostapenko,Benoit Plancoulaine,Allan Rasmusson,Aida Laurinaviciene,Justinas Besusparis

doi:10.3389/fonc.2020.00950

Dovile Zilenaite, Renaldas Augulis + Show 6 more

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https://doi.org/10.3389/fonc.2020.00950

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Abstract

Immunohistochemistry (IHC) for ER, PR, HER2, and Ki67 is used to predict outcome and therapy response in breast cancer patients. The current IHC assessment, visual or digital, is based mostly on global biomarker expression levels in the tissue sample. In our study, we explored the prognostic value of digital image analysis of conventional breast cancer IHC biomarkers supplemented with their intratumoral heterogeneity and tissue immune response indicators. Surgically excised tumor samples from 101 female patients with hormone receptor-positive breast cancer (HRBC) were stained for ER, PR, HER2, Ki67, SATB1, CD8, and scanned at 20x. Digital image analysis was performed using the HALO™ platform. Subsequently, hexagonal tiling was used to compute intratumoral heterogeneity indicators for ER, PR and Ki67 expression. Multiple Cox regression analysis revealed three independent predictors of the patient's overall survival: Haralick's texture entropy of PR (HR = 0.19, p = 0.0005), Ki67 Ashman's D bimodality (HR = 3.0, p = 0.01), and CD8+SATB1+ cell density in tumor tissue (HR = 0.32, p = 0.02). Remarkably, the PR and Ki67 intratumoral heterogeneity indicators were prognostically more informative than the rates of their expression. In particular, a distinct non-linear relationship between the rate of PR expression and its intratumoral heterogeneity was observed and revealed a non-linear prognostic effect of PR expression. The independent prognostic significance of CD8+SATB1+ cells infiltrating the tumor could indicate their role in anti-tumor immunity. In conclusion, we suggest that prognostic modeling, based entirely on the computational image-based IHC biomarkers, is possible in HRBC patients. The intratumoral heterogeneity and immune response indicators outperformed both conventional breast cancer IHC and clinicopathological variables while markedly increasing the power of the model.

Highlights

Gene expression profiling studies have identified several subtypes of breast cancer (BC) distinguished by the expression of hormone receptor, cell proliferation, and human epidermal growth factor receptor 2 (HER2) genes [1,2,3]
In 2002, Camp et al [27] developed a set of algorithms for automated quantification of protein expression with pixel-based tissue segmentation using fluorescent labels for cytokeratin and α-catenin. They found that automated analysis of estrogen receptors (ER) has better reproducibility and Abbreviations: ASCO, the American Society of Clinical Oncology; AshD, Ashman’s D; CAP, the College of American Pathologists; CI, confidence intervals; d, density; DIA, digital image analysis; ER, estrogen receptor; G, histological grade; HER2, human epidermal growth factor receptor 2; HR, hazard ratio; HRBC, hormone receptor-positive breast cancer; IHC, immunohistochemistry; LR, likelihood ratio; OS, overall survival; PD-1, programmed death 1; PD-L1, programmed death-ligand 1; pN, lymph node metastasis status; progesterone receptors (PR), progesterone receptor; pT, tumor invasion stage; S, stroma compartment; SATB1, special ATrich sequence-binding protein 1; T, tumor compartment; Tumor-infiltrating lymphocytes (TIL), tumor-infiltrating lymphocytes; tissue microarrays (TMA), tissue microarray; TME, tumor microenvironment
In this study of hormone receptor-positive BC (HRBC), we explored the prognostic value of IHC, performed on full-face surgical excision slides, by combining DIA data from multiple IHC biomarkers, along with their intratumoral heterogeneity indicators and tissue immune response properties, in the context of conventional clinicopathologic variables

Summary

INTRODUCTION

Gene expression profiling studies have identified several subtypes of breast cancer (BC) distinguished by the expression of hormone receptor, cell proliferation, and human epidermal growth factor receptor 2 (HER2) genes [1,2,3]. In 2002, Camp et al [27] developed a set of algorithms for automated quantification of protein expression with pixel-based tissue segmentation using fluorescent labels for cytokeratin and α-catenin They found that automated analysis of ER has better reproducibility and Abbreviations: ASCO, the American Society of Clinical Oncology; AshD, Ashman’s D; CAP, the College of American Pathologists; CI, confidence intervals; d, density; DIA, digital image analysis; ER, estrogen receptor; G, histological grade; HER2, human epidermal growth factor receptor 2; HR, hazard ratio; HRBC, hormone receptor-positive breast cancer; IHC, immunohistochemistry; LR, likelihood ratio; OS, overall survival; PD-1, programmed death 1; PD-L1, programmed death-ligand 1; pN, lymph node metastasis status; PR, progesterone receptor; pT, tumor invasion stage; S, stroma compartment; SATB1, special ATrich sequence-binding protein 1; T, tumor compartment; TIL, tumor-infiltrating lymphocytes; TMA, tissue microarray; TME, tumor microenvironment. The independent prognostic role of CD8+SATB1+ TIL is suggestive of the potential utility of this biomarker in the context of cancer immunotherapy

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