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

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.

Surgeon–Pathologist Team Approach Dramatically Affects Lymph Nodes Detection and Improves Patients’ Short-Term Outcome

“…Interestingly, we found a better survival in the study group patients (85% vs. 66%; p = 0.03). This finding might be explained by the higher number of LNs removed, that not only reduced the mechanism of metastasis but also allowed for a precise staging and therapy [ 39 ].…”

Helicobacter pylori cytotoxin-associated gene A virulence and its association with the epithelial-mesenchymal transition in gastric cancer

“…Besides, other, less frequently used classifications of GC were also introduced. Goseki et al classification focuses on the tendency to form glandular structures and the presence of intracellular mucus in cancer cells [21]. However, its prognostic value is questioned, and scientific research is often contradictory to its usefulness [22][23][24][25][26].…”

Deep learning based on hematoxylin–eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma

“…pembrolizumab), with overall response rates ranging from 85% to 100% [5]. Several attempts have been made to classify GC into molecular subgroups based on tissue staining techniques, without using molecular sequencing methods: EBV cases can be identified using EBV-encoded RNA (EBER) in situ hybridization, and MSI can be detected using immunohistochemistry (IHC; with up to four antibodies: anti-MLH1, anti-PMS2, anti-MSH2, anti-MSH6) or fragment length analysis [5][6][7][8][9][10][11][12]. However, the distinction between GS and CIN is less clear.…”

“…However, the distinction between GS and CIN is less clear. For these categories, decreased expression of E‐cadherin [8], Lauren morphology [7], or mutational status of TP53 [11] are used for differentiation. However, comparison of these methods with genomic data is lacking, so their trustworthiness is unknown.…”