This study investigates the association of PD-L1 expression and immune cell infiltrates and their impact on clinical outcome, in addition to their overlap with microsatellite instability (MSI), HER2 and ATM molecular subgroups of gastric cancer (GC). PD-L1 membrane expression on tumour cells (TC) and infiltrating immune cells (IC), CD3 + T-lymphocytes, CD8+ cytotoxic T-cells, ATM and HER2 were assessed by immunohistochemistry (IHC) in the ACRG (Asian Cancer Research Group) GC cohort (N = 380). EBV status was determined using in situ hybridization and MSI status was performed using PCR and MLH1 IHC. The PD-L1 segment was associated with increased T-cell infiltrates, while the MSI-high segment was enriched for PD-L1, CD3, and CD8. Multivariate analysis confirmed PD-L1 positivity, high CD3 and high CD8 as independent prognostic factors for both disease-free survival and overall survival (all p < 0.05). Patients with MSI-high tumours had better overall survival by both univariate and multivariate analysis. The ATM-low and HER2-high subgroups differed markedly in their immune profile; the ATM-low subgroups enriched for MSI, PD-L1 positivity and CD8 + T-cells, while the HER2 segment was enriched for MSS, with no enrichment for immune markers. Hence, we demonstrate a molecular profiling approach that can divide GC into four molecular subgroups, namely ATM-low, HER2-high, PD-L1 positive and MSI-high with differing levels of immune infiltrates and prognostic significance which may help to stratify patients for response to targeted therapies.

BackgroundThere has been a dramatic increase in T cell receptor (TCR) sequencing spurred, in part, by the widespread adoption of this technology across academic medical centers and by the rapid commercialization of TCR sequencing. While the raw TCR sequencing data has increased, there has been little in the way of approaches to parse the data in a biologically meaningful fashion. The ability to parse this new type of 'big data' quickly and efficiently to understand the T cell repertoire in a structurally relevant manner has the potential to open the way to new discoveries about how the immune system is able to respond to insults such as cancer and infectious diseases.

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Lung squamous cell carcinoma (LUSC) is a type of lung cancer with a dismal prognosis that lacks adequate therapies and actionable targets. This disease is characterized by a sequence of low and high-grade preinvasive stages with increasing probability of malignant progression. Increasing our knowledge about the biology of these premalignant lesions (PMLs) is necessary to design new methods of early detection and prevention, and to identify the molecular processes that are key for malignant progression. To facilitate this research, we have designed XTABLE, an open-source application that integrates the most extensive transcriptomic databases of PMLs published so far. With this tool, users can stratify samples using multiple parameters and interrogate PML biology in multiple manners, such as two and multiple group comparisons, interrogation of genes of interests and transcriptional signatures. Using XTABLE, we have carried out a comparative study of the potential role of chromosomal instability scores as biomarkers of PML progression and mapped the onset of the most relevant LUSC pathways to the sequence of LUSC developmental stages. XTABLE will critically facilitate new research for the identification of early detection biomarkers and acquire a better understanding of the LUSC precancerous stages.