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

• Published in: Cancers 2023-12, Vol.16 (1), p.55
• Main Authors: Costache, Simona, de Havilland, Rebecca, Diaz McLynn, Sofia, Sajin, Maria, Baltan, Adelina, Wedden, Sarah, D'Arrigo, Corrado
• Format: Article
• Language: English
• Subjects: Analysis; Biomarkers; Biopsy; Cancer; Cancer therapies; Care and treatment; Classification; E-cadherin; Epstein-Barr virus; ErbB-2 protein; Gastric cancer; Gene amplification; Genomic analysis; Genomic instability; Genotype & phenotype; Health aspects; Histopathology; Medical research; Oncology, Experimental; PD-L1 protein; Stomach cancer; Tumor proteins; Tumors; β-Catenin
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers16010055

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. : 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. : 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%. : 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.