Prognostic Integrated Image-Based Immune and Molecular Profiling in Early-Stage Endometrial Cancer.

Nanda Horeweg,Katarzyna Kedzierska,Carien L Creutzberg,Jan J Jobsen,Hans W Nijman,Elzbieta M Van Der Steen-Banasik,Marco De Bruyn,Ellen Stelloo,Ludy C.H.W Lutgens,Michelle Osse,Annechien Plat,Tjalling Bosse,Remi A Nout,Kirsten D Mertz,Viktor H Koelzer,Ina M Jürgenliemk-Schulz,Vincent T Smit,David N Church,Alicia León-Castillo

doi:10.1158/2326-6066.cir-20-0149

Abstract

Optimum risk stratification in early-stage endometrial cancer combines clinicopathologic factors and the molecular endometrial cancer classification defined by The Cancer Genome Atlas (TCGA). It is unclear whether analysis of intratumoral immune infiltrate improves this. We developed a machine-learning, image-based algorithm to quantify density of CD8+ and CD103+ immune cells in tumor epithelium and stroma in 695 stage I endometrioid endometrial cancers from the PORTEC-1 and -2 trials. The relationship between immune cell density and clinicopathologic/molecular factors was analyzed by hierarchical clustering and multiple regression. The prognostic value of immune infiltrate by cell type and location was analyzed by univariable and multivariable Cox regression, incorporating the molecular endometrial cancer classification. Tumor-infiltrating immune cell density varied substantially between cases, and more modestly by immune cell type and location. Clustering revealed three groups with high, intermediate, and low densities, with highly significant variation in the proportion of molecular endometrial cancer subgroups between them. Univariable analysis revealed intraepithelial CD8+ cell density as the strongest predictor of endometrial cancer recurrence; multivariable analysis confirmed this was independent of pathologic factors and molecular subgroup. Exploratory analysis suggested this association was not uniform across molecular subgroups, but greatest in tumors with mutant p53 and absent in DNA mismatch repair-deficient cancers. Thus, this work identified that quantification of intraepithelial CD8+ cells improved upon the prognostic utility of the molecular endometrial cancer classification in early-stage endometrial cancer.

Highlights

Endometrial cancer is the most common gynecologic malignancy in developed countries [1, 2]
We show that machine-learning, image-based quantification of intraepithelial CD8þ cells refines prognostication in earlystage endometrioid endometrial cancer beyond clinicopathologic and molecular factors
The discordance with our results is currently unexplained, but may relate to the methodology used for immune cell localization, statistical analysis, or the smaller, nontrial population of mixed histotypes and stages used in their study

Summary

Introduction

Endometrial cancer is the most common gynecologic malignancy in developed countries [1, 2]. This used whole-exome sequencing to define four endometrial cancer subgroups with differing biology and clinical outcome: (i) POLE ultramutated (POLEmut), defined by pathogenic mutations within the DNA polymerase epsilon catalytic subunit (POLE) exonuclease domain; (ii) DNA mismatch repair deficient (MMRd); (iii) TP53 mutant/somatic copy number alteration (SCNA) high; and (iv) an SCNA-low group lacking these other genomic alterations, and often referred to as no specific molecular profile (NSMP) Approximation of this molecular classification using surrogate markers is feasible in clinical practice [16] and improves upon prognostication provided by Integrated Immune-Molecular Profiling in Endometrial Cancer clinicopathologic factors [11]. We examined this in a more homogenous population of stage I endometrioid endometrial cancers from two large randomized clinical trials

Methods

Results

Conclusion