The implementation of a commercially available multi-gene profile test for breast cancer characterization in a department of pathology: what have we learned from the first 100 cases?

Comparison of risk assessment in 1652 early ER positive, HER2 negative breast cancer in a real-world data set: classical pathological parameters vs. 12-gene molecular assay (EndoPredict)

BACKGROUND

Risk assessment on the molecular level is important in predictive pathology to determine the risk of metastatic disease for ERpos, HER2neg breast cancer. The gene expression test EndoPredict (EP) was trained and validated for prediction of a 10-year risk of distant recurrence to support therapy decisions regarding endocrine therapy alone or in combination with chemotherapy. The EP test provides the 12-gene Molecular Score (MS) and the EPclin-Score (EPclin), which combines the molecular score with tumor size and nodal status. In this project we investigated the correlation of 12-gene MS and EPclin scores with classical pathological markers.

METHODS

EndoPredict-based gene expression profiling was performed prospectively in a total of 1652 patients between 2017 and 2020. We investigated tumor grading and Ki67 cut-offs of 20% for binary classification as well as 10% and 30% for three classes (low, intermediate, high), based on national and international guidelines.

RESULTS

410 (24.8%) of 1652 patients were classified as 12-gene MS low risk and 626 (37.9%) as EPclin low risk. We found significant positive associations between 12-gene MS and grading (p < 0.001), EPclin and grading (p = 0.001), 12-gene MS and Ki67 (p < 0.001), and EPclin and Ki67 (p < 0.001). However, clinically relevant differences between EP test results, Ki67 and tumor grading were observed. For example, 118 (26.3%) of 449 patients with Ki67 > 20% were classified as low risk by EPclin. Same differences were seen comparing EP test results and tumor grading.

CONCLUSION

In this study we could show that EP risk scores are distributed differentially among Ki67 expression groups, especially in Ki67 low and high tumors with a substantial proportion of patients with EPclin high risk results in Ki67 low tumors and vice versa. This suggests that classical pathological parameters and gene expression parameters are not interchangeable, but should be used in combination for risk assessment.

Combined Simplified Molecular Classification of Gastric Adenocarcinoma, Enhanced by Lymph Node Status: An Integrative Approach

Simple Summary

In this study, we present a simple but comprehensive molecular analysis of gastric carcinoma. The two major existing classification schemes show some discrepancies and are highly technically demanding, which makes them hardly feasible in daily diagnostic routines. Our workflow is based on simple and commercially available technology and provides a potential consensus approach by integrating the two major classification schemes. Furthermore, our approach allows the molecular subtypes to be assigned to different prognostic groups. We are convinced that our approach may help to better understand the molecular mechanisms of this worldwide health burden and that it could pave the way for new therapeutic targets.

Abstract

Gastric adenocarcinoma (GAC) is a heterogeneous disease and at least two major studies have recently provided a molecular classification for this tumor: The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ARCG). Both classifications quote four molecular subtypes, but these subtypes only partially overlap. In addition, the classifications are based on complex and cost-intensive technologies, which are hardly feasible for everyday practice. Therefore, simplified approaches using immunohistochemistry (IHC), in situ hybridization (ISH) as well as commercially available next generation sequencing (NGS) have been considered for routine use. In the present study, we screened 115 GAC by IHC for p53, MutL Homolog 1 (MLH1) and E-cadherin and performed ISH for Epstein–Barr virus (EBV). In addition, sequencing by NGS for TP53 and tumor associated genes was performed. With this approach, we were able to define five subtypes of GAC: (1) Microsatellite Instable (MSI), (2) EBV-associated, (3) Epithelial Mesenchymal Transition (EMT)-like, (4) p53 aberrant tumors surrogating for chromosomal instability and (5) p53 proficient tumors surrogating for genomics stable cancers. Furthermore, by considering lymph node metastasis in the p53 aberrant GAC, a better prognostic stratification was achieved which finally allowed us to separate the GAC highly significant in a group with poor and good-to-intermediate prognosis, respectively. Our data show that molecular classification of GAC can be achieved by using commercially available assays including IHC, ISH and NGS. Furthermore, we present an integrative workflow, which has the potential to overcome the uncertainty resulting from discrepancies from existing classification schemes.