Version 1
: Received: 15 June 2023 / Approved: 16 June 2023 / Online: 16 June 2023 (05:46:59 CEST)
How to cite:
Costache, S.; De Havilland, R.; Diaz McLynn, S.; Sajin, M.; Wedden, S.; D'Arrigo, C. Implementing an On-Slide Molecular Classification of Gastric Cancer: A Tissue Microarray Study. Preprints2023, 2023061178. https://doi.org/10.20944/preprints202306.1178.v1
Costache, S.; De Havilland, R.; Diaz McLynn, S.; Sajin, M.; Wedden, S.; D'Arrigo, C. Implementing an On-Slide Molecular Classification of Gastric Cancer: A Tissue Microarray Study. Preprints 2023, 2023061178. https://doi.org/10.20944/preprints202306.1178.v1
Costache, S.; De Havilland, R.; Diaz McLynn, S.; Sajin, M.; Wedden, S.; D'Arrigo, C. Implementing an On-Slide Molecular Classification of Gastric Cancer: A Tissue Microarray Study. Preprints2023, 2023061178. https://doi.org/10.20944/preprints202306.1178.v1
APA Style
Costache, S., De Havilland, R., Diaz McLynn, S., Sajin, M., Wedden, S., & D'Arrigo, C. (2023). Implementing an On-Slide Molecular Classification of Gastric Cancer: A Tissue Microarray Study. Preprints. https://doi.org/10.20944/preprints202306.1178.v1
Chicago/Turabian Style
Costache, S., Sarah Wedden and Corrado D'Arrigo. 2023 "Implementing an On-Slide Molecular Classification of Gastric Cancer: A Tissue Microarray Study" Preprints. https://doi.org/10.20944/preprints202306.1178.v1
Abstract
Background and Objectives: Gastric cancer (GC) is one of the most commonly diagnosed cancer 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 et al. 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 functional loss of p53 due to mutation (GC p53m), CG with intact p53 (GC p53wt) and GC not otherwise specified (GC NOS). This classification also has 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 p53m cases were 23%, GC p53wt 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.
Medicine and Pharmacology, Pathology and Pathobiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
