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

Simple Summary: Personalised cancer treatment improves outcome for patients but requires that each individual tumour is classified using molecular tools. Extensive genomic studies for each individual patients are out of reach for the majority of the population, therefore, pathologists need to apply readily available tests to achieve molecular classification for all cancer patients. This paper describes how gastric cancer can be classified using on slide tests already used by histopathologists. Using this classification, oncologists can select more effective treatment for each patient. 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. [ABSTRACT FROM AUTHOR]

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