Implementing an On-Slide Molecular Classification of Gastric Cancer: A Tissue Microarray Study

Abstract

Background and Objectives: Gastric cancer (GC) is one of the most commonly diagnosed cancers and the fourth cause of cancer death worldwide. Personalised treatment improves GC outcomes. A molecular classification is needed to choose the appropriate therapy. A classification that uses on-slide biomarkers and formalin-fixed and paraffin-embedded (FFPE) tissue is preferable to comprehensive genomic analysis. In 2016, Setia and colleagues proposed an on-slide classification; however, this is not in widespread use. We propose a modification of this classification that has six subgroups: GC associated with Epstein-Barr virus (GC EBV+), GC with mismatch-repair deficiency (GC dMMR), GC with epithelial-mesenchymal transformation (GC EMT), GC with chromosomal instability (GC CIN), CG that is genomically stable (GC GS) and GC not otherwise specified (GC NOS). This classification also has a provision for biomarkers for current or emerging targeted therapies (Her2, PD-L1 and Claudin18.2). Here, we assess the implementation and feasibility of this inclusive working classification. Materials and Methods: We constructed a tissue microarray library from a cohort of 79 resection cases from FFPE tissue archives. We used a restricted panel of on-slide markers (EBER, MMR, E-cadherin, beta-catenin and p53), defined their interpretation algorithms and assigned each case to a specific molecular subtype. Results: GC EBV(+) cases were 6%, GC dMMR cases were 20%, GC EMT cases were 14%, GC CIN cases were 23%, GC GS cases were 29%, and GC NOS cases were 8%. Conclusions: This working classification uses markers that are widely available in histopathology and are easy to interpret. A diagnostic subgroup is obtained for 92% of the cases. The proportion of cases in each subgroup is in keeping with other published series. Widescale implementation appears feasible. A study using endoscopic biopsies is warranted.

Keywords: Claudin18.2; E-cadherin; EBER; Her2; MMR; PD-L1; beta-catenin; gastric cancer; molecular classification; p53.

Figure 1

Hierarchical classification. On-slide biomarkers are assessed sequentially; the micrographs framed with a red line highlight the critical test results. Tumours are placed into the GC EBV(+) group if EBER is positive (strong and diffuse blue nuclear staining in the micrograph framed with a red line); if EBER is negative, MMR markers are assessed, and if found to be deficient, tumours are classified as dMMR (in the tiles framed with a red line, please note absence of MLH-1 nuclear staining in the tumour glands compared with the presence of staining of MSH-2); if MMR proficient, E-cadherin and β-catenin are next assessed, and if expression is aberrent, tumours are classified as GC EMT (this is exemplified in the tiles framed with a red line, which show diffuse absence of these two biomarkers as compared with the tumours in the other categories that show strong membrane expression); lastly, if EMT marker expression is normal, tumours are assessed for p53 mutation; if a mutant pattern expression is detected, they are classified as GC CIN (see strong and diffuse nuclear staining in >80% of the tumour cells in the micrograph framed with a red line); if wild-type p53 expression is detected, they are classified as GC GS (note focal nuclear staining with variable intensity in some of the tumour in the last tile framed with a red line). All microphotographs taken at ×20 magnification using a digital microscope.

Figure 2

Predictive on-slide biomarkers. Examples of positive and negative cases of the three predictive biomarkers assessed, photographed at ×400 magnification. The case positive for Her2 IHC shows the presence of complete membrane staining of strong intensity in the TCs; the case positive for Her2 DDISH shows the presence of large clusters of amplification (denoted by silver black dots) in each TC; the case positive for PD-L1 shows membrane staining in the TCs as well as some staining in the ICs; the case positive for CLDN18.2 shows strong membrane staining in most TCs.

Figure 3

Molecular classification of our GC study cohort. The pie chart indicates the relative prevalence of each molecular subtype; the bar graphs indicate the prevalence of specific predictive biomarkers (Her2, PD-L1 and Claudin18.2—CLDN18.2) in each of the molecular subtypes.

Figure 4

Molecular subgroups and their aggressiveness. This is an indicative diagram considering published papers [2,4,5,6,7,8]. GC EBV(+) and GC dMMR are the two less-aggressive subtypes, while GC EMT is the most-aggressive subtype. Availability of effective treatment may alter outcomes and, therefore, the prognosis.