Implementing an integrated molecular classification for gastric cancer from endoscopic biopsies using on-slide tests.

The availability of more effective biological therapy can improve outcomes of gastric cancer (GC), but most patients do not have access to personalized treatment. GC molecular classification helps identify patients suitable for specific therapies and provides useful prognostic information. To date, only a small number of patients have access to molecular classification. We proposed a working molecular classification that can be delivered using on-slide tests available in most histopathology laboratories. We used eight on-slide tests [in situ hybridization (ISH) for Epstein-Barr virus-encoded small ribonucleic acid (EBER) and immunohistochemistry (IHC) for MutL homolog 1 (MLH1), PMS1 homolog 2 (PMS2), MutS homolog 2 (MSH2), MutS homolog 6 (MSH6), E-cadherin, ß-catenin and p53] to classify GC into one of six categories: GC associated with Epstein-Barr virus (GC-EBV), GC mismatch repair deficient (GC-dMMR), GC with epithelial-mesenchymal transition (GC-EMT), GC with chromosomal instability (GC-CIN), GC genomically stable (GC-GS) and GC not otherwise specified (GC-NOS)∕indeterminate. The classification has provision also for current and future on-slide companion diagnostic (CDx) tests necessary to select specific biological therapies and, as proof of principle, in this study we used three CDx tests currently required for the management of GC [human epidermal growth factor receptor 2 (Her2), programmed cell death-ligand 1 (PD-L1) 22C3 and Claudin18.2 (CLDN18.2)]. This paper describes the necessary tissue pathways and laboratory workflow and assesses the feasibility of using this classification prospectively on small endoscopic biopsies of gastric and gastroesophageal junction adenocarcinoma. This work demonstrates that such molecular classification can be implemented in the context of a histopathology diagnostic routine with little impact on turnaround times and laboratory capacity. The widespread adoption of a molecular classification for GC will help refine prognosis and guide the choice of more appropriate biological therapy for these patients.

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

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.

Tissue Selection for PD-L1 Testing in Triple Negative Breast Cancer (TNBC).

Atezolizumab in combination with nab-paclitaxel has been introduced for the treatment of locally advanced or recurrent triple negative breast cancer (TNBC). Patient selection relies on the use of immunohistochemistry using a specific monoclonal PD-L1 antibody (clone SP142) in a tightly controlled companion diagnostic test (CDx) with a defined interpretative algorithm. Currently there are no standardized recommendations for selecting the optimal tissue to be tested and there is limited data to support decision making, raising the possibility that tissue selection may bias test results. We compared PD-L1 SP142 assessment in a collection of 73 TNBC cases with matched core biopsies and excision samples. There was good correlation between PD-L1-positive core biopsy and subsequent excision, but we found considerable discrepancy between PD-L1 negative core biopsy and matched excision, with a third of cases found negative on core biopsies converting to positive upon examination of the excision tissue. In view of these findings, we developed a workflow for the clinical testing of TNBC for PD-L1 and implemented it in a central referral laboratory. We present audit data from the clinical PD-L1 testing relating to 2 years of activities, indicating that implementation of this workflow results in positivity rates in our population of TNBC similar to those of IMpassion130 clinical trial. We also developed an online atlas with a precise numerical annotation to aid pathologists in the interpretation of PD-L1 scoring in TNBC.