Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Abstract

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

Keywords: TP53; classification; gastric cancer; immune; mesenchymal; microsatellite instability; molecular; prognosis; tumor mutational burden.

Figure 1

Algorithm for the classification of cases, according to the Asian Cancer Research Group system [43]. ACRG: Asian Cancer Research Group; MSI: microsatellite instability.

Figure 2

Lei’s classification. Molecular characteristics of GC types [156].

Figure 3

Wang’s classification (2021). Clinicopathological and molecular features of GC types [157].

Figure 4

Algorithm for the categorization of cases according to the HOPE classification [161]. TMB: tumor mutational burden; EMT: epithelial-mesenchymal transition.

Figure 5

HOPE classification. Clinicopathological and molecular characteristics of GC types [161].

Figure 6

Wang’s classification (2014). Molecular features of GC types [162]. MSI: microsatellite instability; MSS-EBV: stable Epstein-Barr virus-positive; MSS: stable.

Figure 7

Cheong’s classification. Clinicopathological and molecular features of GC types [164].

Figure 8

Zhou’s classification. Clinicopathological and molecular characteristics of GC types [165]. TMB: tumor mutational burden.

Figure 9

Lin’s classification. Clinicopathological and molecular features of GC types [168]. dMMR: deficient mismatch repair; MSI: microsatellite instability; TMB: tumor mutational burden; EMT: epithelial-mesenchymal transition.

Figure 10

Wu’s classification. Clinicopathological and molecular characteristics of GC types [169]. TMB: tumor mutational burden.

Figure 11

Wei’s classification. Clinical and molecular characteristics of GC types [171]. TMB: tumor mutational burden.

Figure 12

Weng’s classification. Clinicopathological and molecular features of GC types [172]. MSI: microsatellite instability; CIMP: CpG island methylator phenotype.