Cancer is a leading cause of death worldwide. Understanding the functional mechanisms associated with metabolic reprogramming, which is a typical feature of cancer cells, is key to effective therapy. CD38, primarily a NAD + glycohydrolase and ADPR cyclase, is a multifunctional transmembrane protein whose abnormal overexpression in a variety of tumor types is associated with cancer progression. It is linked to VEGFR2 mediated angiogenesis and immune suppression as it favors the recruitment of suppressive immune cells like Tregs and myeloid-derived suppressor cells, thus helping immune escape. CD38 is expressed in M1 macrophages and in neutrophil and T cell-mediated immune response and is associated with IFNγ-mediated suppressor activity of immune responses. Targeting CD38 with anti-CD38 monoclonal antibodies in hematological malignancies has shown excellent results. Bearing that in mind, targeting CD38 in other nonhematological cancer types, especially carcinomas, which are of epithelial origin with specific anti-CD38 antibodies alone or in combination with immunomodulatory drugs, is an interesting option that deserves profound consideration.

(1) Abnormally increased expression of claudin-6 in gastric cancer is considered a prognostic marker of the chromosomal unstable molecular subtype. However, a detailed molecular profile analysis of differentially expressed genes and affected pathways associated with claudin-6 increased (Cldn6high) expression has not been assessed. (2) The TCGA Stomach Adenocarcinoma Pan-Cancer Atlas Data was evaluated using Cytoscape’s Gene Mania, MCODE, and Cytohubba bioinformatic software. (3) 96.88% of Cldn6high gastric cancer tumors belonging to the chromosomal unstable molecular subtype are associated with a worse prognosis. Cldn6expression coincided with higher mutations in TP53, MIEN1, STARD3, PGAP3, and CCNE1 genes compared to Cldn6low expression. In Cldn6high cancers, 1316 genes were highly expressed. Cholesterol metabolism was the most affected pathway as APOA1, APOA2, APOH, APOC2, APOC3, APOB-100, LDL receptor-related protein 1/2, Sterol O-acyltransferase, STARD3, MAGEA-2, -3, -4, -6, -9B, and -12 genes were overexpressed in Cldn6high gastric cancers; interestingly, APOA2 and MAGEA9b were identified as top hub genes. Functional enrichment of DEGs linked HNF-4α and HNF-1α genes as highly expressed in Cldn6high gastric cancer. (4) Our results suggest that APOA2 and MAGEA9b could be considered as prognostic markers for Cldn6high gastric cancers.

Background Immune interactions in the tumor microenvironment (TME) are a major factor in deciding the fate of immunotherapy. Heterogeneity and molecular subtypes, especially in solid tumors like gastric cancer (GC), involve substantial modification in TME. Hence understanding the immune cell populations infiltrating the TME of different molecular subtypes will help to develop more effective and targeted therapies Methods CBioPortal 1,2 was used to extract and analyze The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma Pan-Cancer Atlas Data (STAD). Immune Metagene signatures from previous publications 3 were used to analyze the immune infiltration landscape in different GC molecular subtypes-chromosomal instability (CIN)(n=223), microsatellite instable (MSI) (n=73), genomically stable (GS)(n=50), Epstein-Barr virus (EBV) associated (n=30). GraphPad Prism9 and MS Excel were used to analyze the data and generate the figures. Results We analyzed the infiltration landscape of Natural Killer (NK) cells, T cells ( CD4 + , CD8 + , Th1, Th2, Th17), immune suppressive Myeloid-derived suppressor cells (MDSC), and Regulatory T cells (Tregs) in GC molecular subtypes. The results showed that CD8 + cytotoxic T cells and CD4 + helper T cells were highly downregulated in the GS subtype in comparison to other molecular subtypes (figure 1). Cytokine secreting T helper cell subsets, Th1, Th2, and Th17 demonstrate a dissimilar infiltration pattern among subtypes (figure 2). Th1 cells were highly expressed in the EBV subtype as compared to Th1 infiltration in the other subtypes (figure 3). GS and EBV both depict higher infiltration of Th2 cells and lower infiltration of Th17 cells in comparison to CIN and MSI subtypes (figure 2). GS subtype along with EBV has a superior infiltration of immune suppressive MDSC and Tregs (figure 3). Both cytotoxic NK 56dim and cytokine secreting immune modulating NK 56bright cells were the lowest infiltrating the GS molecular subtype (figure 4). Intriguingly, NK 56bright infiltration markers don't differ significantly between subtypes. It was worth observing that in general, there is more infiltration of NK 56dim over NK 56bright cells in GC patients (figure 4). Conclusions TME of the GS molecular subtype of GC contains less cytotoxic cells (NK56 Dim and CD8 + T) and higher infiltration of immune suppressive cells (Tregs and MDSCs). Abstract 171 Figure 1 Immune cell infiltration landscape of T cells ( CD4+, CD8+) cells by GC molecular subtypes. Statistical significance was confirmed using one-way ANOVA, P-Value,**** <0.

BackgroundClaudin-6 (CLDN6) differentially overexpressed in Gastric Cancer (GC) is associated with poor prognosis and survival of patients. Uncovering affected pathways and genes associated with CLDN6 in GC can help in the identification of novel prognostic targets.MethodsCBioPortal1 2 was used to extract and analyze The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma Pan-Cancer Atlas Data (STAD). FunRich tool3 and Gene Ontology molecular signature database of Gene Set Enrichment Analysis (GSEA) were used for functional enrichment. CBioPortal was used to identify differentially expressed genes between groups. Graph pad Prism 8 was used to generate the graphics.ResultsTCGA STAD PanCancer Atlas data was analyzed in terms of alterations in CLDN6. 34% of the GC samples (141 samples) were assigned to the CLDN6 alteration group while the rest of the samples (299) were assigned to the non-CLDN6 alteration group (figure 1). Major alterations of CLDN6 in GC included shallow deletion, diploid, gain, and differentially expressed mRNA (figure 2). Differentially overexpressed genes in the CLDN6 group with log-ratio cutoff≥1 were used for functional enrichment in different biological pathway categories (figure 3); 18.1% of genes were associated with the transport of small molecules through the membrane, 14.6% mediated transmembrane transport and 12.5% were associated with lipid metabolism (figure 4). The Gene ontology molecular signature database of GSEA also confirmed that these genes were involved in lipid metabolism, transport activity, and epithelium development processes (table 1). Moreover, GC samples with CLDN6 alterations have higher mutations in p53 signaling (29% in TP53, 7% in CDKN2A) gene signature (figure 5) over samples in the non-CLDN6 alteration group (figure 6). Similarly, genes related with cell cycle control like CCNE1, MYC, SRC, and STAT3 showed higher mutations in the CLDN6 alteration group while JAK1 and E2F8 showed lower mutations than non-CLDN6 GC samples (figure 7). These observations indicate that CLDN6 in GC affects the transport of small molecules and lipid metabolism and that it is associated to tumors with higher mutations in p53 and cell cycle-related genes.Abstract 14 Table 1Functional enrichment of differentially expressed genes (log-ratio ≥1) in CLDN6 group GC samples in Gene ontology molecular signature database of GSEAAbstract 14 Figure 1Distribution of GC samples and patients of TCGA STAD data between CLDN6 and non CLDN6 alteration groupsAbstract 14 Figure 2Major alterations in CLDN6 in GC (TCGA STAD data)Abstract 14 Figure 3Volcano plot of differentially expressed genes in CLDN6 and non CLDN6 groups. Log10 p-Value cutoff ≥2Abstract 14 Figure 4Functional enrichment of differentially expressed genes (log-ratio ≥1) in biological process categoryAbstract 14 Figure 5Overall mutations in several important gene signatures involved in cancer between CLDN6 and non-CLDN6 GC samplesAbstract 14 Figure 6Total mutations in p53 signaling genesAbstract 14 Figure 7Total mutations in genes involved in cell cycle control between CLDN6 and non-CLDN6 GC samplesConclusionsCLDN6 alterations in GC affect the cell cycle and p53 signaling pathways with higher mutations in TP53, CDKN2A, CCNE1, MYC, SRC, and STAT3 genes.AcknowledgementsThis research was supported by CONACYT CVU grant 871712.ReferencesCerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2:401–4.Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6:l1.Pathan M, Keerthikumar S, Ang C-S, Gangoda L, Quek CYJ, Williamson NA, et al. FunRich: An open access standalone functional enrichment and interaction network analysis tool. PROTEOMICS 2015;15:2597–601.