A clinically applicable molecular-based classification for endometrial cancers

Abstract

Background: Classification of endometrial carcinomas (ECs) by morphologic features is inconsistent, and yields limited prognostic and predictive information. A new system for classification based on the molecular categories identified in The Cancer Genome Atlas is proposed.

Methods: Genomic data from the Cancer Genome Atlas (TCGA) support classification of endometrial carcinomas into four prognostically significant subgroups; we used the TCGA data set to develop surrogate assays that could replicate the TCGA classification, but without the need for the labor-intensive and cost-prohibitive genomic methodology. Combinations of the most relevant assays were carried forward and tested on a new independent cohort of 152 endometrial carcinoma cases, and molecular vs clinical risk group stratification was compared.

Results: Replication of TCGA survival curves was achieved with statistical significance using multiple different molecular classification models (16 total tested). Internal validation supported carrying forward a classifier based on the following components: mismatch repair protein immunohistochemistry, POLE mutational analysis and p53 immunohistochemistry as a surrogate for 'copy-number' status. The proposed molecular classifier was associated with clinical outcomes, as was stage, grade, lymph-vascular space invasion, nodal involvement and adjuvant treatment. In multivariable analysis both molecular classification and clinical risk groups were associated with outcomes, but differed greatly in composition of cases within each category, with half of POLE and mismatch repair loss subgroups residing within the clinically defined 'high-risk' group. Combining the molecular classifier with clinicopathologic features or risk groups provided the highest C-index for discrimination of outcome survival curves.

Conclusions: Molecular classification of ECs can be achieved using clinically applicable methods on formalin-fixed paraffin-embedded samples, and provides independent prognostic information beyond established risk factors. This pragmatic molecular classification tool has potential to be used routinely in guiding treatment for individuals with endometrial carcinoma and in stratifying cases in future clinical trials.

Figures

Figure 1

Kaplan–Meier survival analyses and log-rank statistics of eight possible models for pragmatic molecular classification of endometrial cancers applied to the Vancouver cohort (n=143). Overall survival (OS), disease-specific survival (DSS) and recurrence-free survival (RFS) are shown for each model and molecular subgroups are distinguished by colour (POLE (blue), MMR IHC abn (yellow), p53 wt (green) and p53 abn (red)). Model 8 is outlined in red and is the model that was used for subsequent univariate and multivariate analysis, was combined with either European Society of Medical Oncologists clinical risk groups or pathological parameters.

Figure 1

Kaplan–Meier survival analyses and log-rank statistics of eight possible models for pragmatic molecular classification of endometrial cancers applied to the Vancouver cohort (n=143). Overall survival (OS), disease-specific survival (DSS) and recurrence-free survival (RFS) are shown for each model and molecular subgroups are distinguished by colour (POLE (blue), MMR IHC abn (yellow), p53 wt (green) and p53 abn (red)). Model 8 is outlined in red and is the model that was used for subsequent univariate and multivariate analysis, was combined with either European Society of Medical Oncologists clinical risk groups or pathological parameters.

Figure 2

Harrell's C-Index for Models 1 to 8, ESMO clinical risk group, and combined molecular and risk groups or pathologic parameters as applied to the Vancouver cohort (n=143). A C-index of 0.5 (dotted line) indicates that the model has no discriminative ability and a C-index of 1 indicates that a model perfectly distinguishes between those who have an event and those who do not. The pragmatic model chosen to move forward with is outlined in red. Also outlined are the indices for the molecular classifier combined with clinical risk groups or pathological parameters, suggesting an improved ability to discriminate outcomes when taken together.

Figure 3

Favoured pragmatic model for molecular classification of endometrial cancers (Model 8 in Figures 1 and 2). Selection was based on survival analyses, C-index, anticipated clinical benefit in order of testing, and cost and accessibility of methods.

Figure 4

Cross-tabulation of clinicopathologic risk groups (ESMO) with molecular classification by proposed model: MMR IHC/POLE mut/p53 IHC. Approximately half of the POLE and MMR IHC abn molecular subgroups are noted to include cases that would be designated as ‘high risk' by traditional clinical risk group stratification. The p53 abn molecular subgroup includes ∼25% ‘low' and ‘intermediate' risk cases who would usually be designated to receive minimal (e.g., vaginal brachytherapy) or no therapy. Although both molecular subgroups and clinical risk groups were associated with outcomes, they may identify different women with EC.