Overexpression of the orphan nuclear receptor NR2F6 is associated with improved survival across molecular subgroups in endometrial cancer patients.

INTRODUCTION: NR2F6 (nuclear receptor subfamily 2 group F member 6, also called Ear-2) is known to be an orphan nuclear receptor that has been characterized as an intracellular immune checkpoint in effector T cells and, therefore, may control tumor development and growth. The prognostic impact of NR2F6 in endometrial cancers is evaluated in this study. MATERIALS AND METHODS: Expression analysis of NR2F6 in 142 endometrial cancer patients was performed by immunohistochemistry of primary paraffin­embedded tumor samples. Staining intensity of positive tumor cells was automatically assessed semi-quantitatively, and results were correlated with clinicopathological characteristics and survival. RESULTS: Forty five of 116 evaluable samples (38.8%) showed an overexpression of NR2F6. This leads to an improvement of the overall survival (OS) and progression-free survival (PFS). In NR2F6-positive patients, the estimated mean OS was 156.9 months (95% confidence interval (CI) 143.1-170.7) compared to 106.2 months in NR2F6-negative patients (95% CI 86.2-126.3; p = 0.022). The estimated PFS differed by 63 months (152 months (95% CI 135.7-168.4) vs. 88.3 months (95% CI 68.5-108.0), p = 0.002). Furthermore, we found significant associations between NR2F6 positivity, MMR status, and PD1 status. A multivariate analysis suggests NR2F6 to be an independent factor influencing the OS (p = 0.03). CONCLUSION: In this study, we could demonstrate that there is a longer progression-free and overall survival for NR2F6-positive patients with endometrial cancer. We conclude that NR2F6 might play an essential role in endometrial cancers. Further studies are required to validate its prognostic impact.

Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression.

OBJECTIVE: Quantifying protein expression in immunohistochemically stained histological slides is an important tool for oncologic research. The use of computer-aided evaluation of IHC-stained slides significantly contributes to objectify measurements. Manual digital image analysis (mDIA) requires a user-dependent annotation of the region of interest (ROI). Others have built-in machine learning algorithms with automated digital image analysis (aDIA) and can detect the ROIs automatically. We aimed to investigate the agreement between the results obtained by aDIA and those derived from mDIA systems. METHODS: We quantified chromogenic intensity (CI) and calculated the positive index (PI) in cohorts of tissue microarrays (TMA) using mDIA and aDIA. To consider the different distributions of staining within cellular sub-compartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas. RESULTS: Within all cohorts, we were able to show a high correlation between mDIA and aDIA for the CI (p<0.001) along with high agreement for the PI. Moreover, we were able to show that the cell detections of the programs were comparable as well and both proved to be reliable when compared to manual counting. CONCLUSION: mDIA and aDIA show a high correlation in acquired IHC data. Both proved to be suitable to stratify patients for evaluation with clinical data. As both produce the same level of information, aDIA might be preferable as it is time-saving, can easily be reproduced, and enables regular and efficient output in large studies in a reasonable time period.