BackgroundOesophageal (OeC) and gastric (GC) cancer patients are treated with similar multimodal therapy and have poor survival. There remains an urgent clinical need to identify biomarkers to individualise patient management and improve outcomes. Therapy with immune checkpoint inhibitors has shown promising results in other cancers. Proposed biomarkers to predict potential response to immune checkpoint inhibitors include DNA mismatch repair (MMR) and/or Epstein–Barr virus (EBV) status. The aim of this study was to establish and compare EBV status and MMR status in large multi-centre series of OeC and GC.MethodsEBV was assessed by EBV-encoded RNA (EBER) in situ hybridisation and MMR protein expression by immunohistochemistry (IHC) in 988 OeC and 1213 GC from multiple centres. In a subset of OeC, microsatellite instability (MSI) was tested in parallel with MMR IHC.ResultsFrequency of MMR deficiency (MMRdef) and MSI was low in OeC (0.8% and 0.6%, respectively) compared with GC (10.3%). None of the OeCs were EBER positive in contrast to 4.8% EBER positive GC. EBV positive GC patients were younger (p = 0.01), more often male (p = 0.001) and had a better overall survival (p = 0.012). MMRdef GC patients were older (p = 0.001) and showed more often intestinal-type histology (p = 0.022).ConclusionsThis is the largest study to date indicating that EBV and MMRdef do not play a role in OeC carcinogenesis in contrast to GC. The potential clinical usefulness of determining MMRdef/EBV status to screen patients for eligibility for immune-targeting therapy differs between OeC and GC patients.

AimsNeoadjuvant chemotherapy (NAC) remains an important therapeutic option for advanced oesophageal cancer (OC). Pathological tumour regression grade (TRG) may offer additional information by directing adjuvant treatment and/or follow‐up but its clinical value remains unclear. We analysed the prognostic value of TRG and associated pathological factors in OC patients enrolled in the Medical Research Council (MRC) OE02 trial.Methods and resultsHistopathology was reviewed in 497 resections from OE02 trial participants randomised to surgery (S group; n = 244) or NAC followed by surgery [chemotherapy plus surgery (CS) group; n = 253]. The association between TRG groups [responders (TRG1–3) versus non‐responders (TRG4–5)], pathological lymph node (LN) status and overall survival (OS) was analysed. One hundred and ninety‐five of 253 (77%) CS patients were classified as ‘non‐responders’, with a significantly higher mortality risk compared to responders [hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.05–2.24, P = 0.026]. OS was significantly better in patients without LN metastases irrespective of TRG [non‐responders HR = 1.87, 95% CI = 1.33–2.63, P < 0.001 versus responders HR = 2.21, 95% CI = 1.11–4.10, P = 0.024]. In multivariate analyses, LN status was the only independent factor predictive of OS in CS patients (HR = 1.93, 95% CI = 1.42–2.62, P < 0.001). Exploratory subgroup analyses excluding radiotherapy‐exposed patients (n = 48) showed similar prognostic outcomes.ConclusionLymph node status post‐NAC is the most important prognostic factor in patients with resectable oesophageal cancer, irrespective of TRG. Potential clinical implications, e.g. adjuvant treatment or intensified follow‐up, reinforce the importance of LN dissection for staging and prognostication.

ObjectiveEndoscopic mucosal biopsies of primary gastric cancers (GCs) are used to guide diagnosis, biomarker testing and treatment. Spatial intratumoural heterogeneity (ITH) may influence biopsy-derived information. We aimed to study ITH of primary GCs and matched lymph node metastasis (LNmet).DesignGC resection samples were annotated to identify primary tumour superficial (PTsup), primary tumour deep (PTdeep) and LNmet subregions. For each subregion, we determined (1) transcriptomic profiles (NanoString ‘PanCancer Progression Panel’, 770 genes); (2) next-generation sequencing (NGS, 225 gastrointestinal cancer-related genes); (3) DNA copy number profiles by multiplex ligation-dependent probe amplification (MLPA, 16 genes); and (4) histomorphological phenotypes.ResultsNanoString profiling of 64 GCs revealed no differences between PTsup1 and PTsup2, while 43% of genes were differentially expressed between PTsup versus PTdeep and 38% in PTsup versus LNmet. Only 16% of genes were differently expressed between PTdeep and LNmet. Several genes with therapeutic potential (eg IGF1, PIK3CD and TGFB1) were overexpressed in LNmet and PTdeep compared with PTsup. NGS data revealed orthogonal support of NanoString results with 40% mutations present in PTdeep and/or LNmet, but not in PTsup. Conversely, only 6% of mutations were present in PTsup and were absent in PTdeep and LNmet. MLPA demonstrated significant ITH between subregions and progressive genomic changes from PTsup to PTdeep/LNmet.ConclusionIn GC, regional lymph node metastases are likely to originate from deeper subregions of the primary tumour. Future clinical trials of novel targeted therapies must consider assessment of deeper subregions of the primary tumour and/or metastases as several therapeutically relevant genes are only mutated, overexpressed or amplified in these regions.

The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cancer-specific death from digital images of immunohistochemically (IHC) stained tissue microarrays (TMAs). Specifically, we evaluated a cohort of 248 gastric cancer patients using convolutional neural networks (CNNs) in an end-to-end weakly supervised scheme independent of subjective pathologist input. To account for the time-to-event characteristic of the outcome data, we developed new survival models to guide the network training. In addition to the standard H&E staining, we investigated the prognostic value of a panel of immune cell markers (CD8, CD20, CD68) and a proliferation marker (Ki67). Our CNN-derived risk scores provided additional prognostic value when compared to the gold standard prognostic tool TNM stage. The CNN-derived risk scores were also shown to be superior when systematically compared to cell density measurements or a CNN score derived from binary 5-year survival classification, which ignores time-to-event. To better understand the underlying biological mechanisms, we qualitatively investigated risk heat maps for each marker which visualised the network output. We identified patterns of biological interest that were related to low risk of cancer-specific death such as the presence of B-cell predominated clusters and Ki67 positive sub-regions and showed that the corresponding risk scores had prognostic value in multivariate Cox regression analyses (Ki67&CD20 risks: hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.15-1.89, p = 0.002; CD20&CD68 risks: HR = 1.33, 95% CI = 1.07-1.67, p = 0.009). Our study demonstrates the potential additional value that deep learning in combination with a panel of IHC markers can bring to the field of precision oncology.