Mutations Defining Patient Cohorts With Elevated PD-L1 Expression in Gastric Cancer

Biomarker-oriented chemo-immunotherapy for advanced gastric cancer

The biomarker-oriented chemo-immunotherapy is useful and promising in the development of new anticancer agents, since the responders can be enriched by selecting patients with biomarkers. Compared to colorectal and lung cancers, the development of biomarker-driven molecular-targeted therapeutics for gastric cancers has been straggled. However, several new biomarkers in gastric cancers have been discovered and clinical trials in enrichment design with certain biomarkers have been conducted. Therefore, there are currently several treatment options to treat gastric cancer patients based on individual biomarker-oriented strategies. In the present review, we describe the useful biomarkers in gastric cancer, with focusing on HER2, PD-L1, and Claudin18.2, in relation to their clinical significance and associated targeted agents.

Predictive Panel for Immunotherapy in Low-Grade Glioma

BACKGROUND

The main treatment of low-grade glioma (LGG) is still surgical resection followed by radiotherapy and/or chemotherapy, which has certain limitations, including side effects and drug resistance. Immunotherapy is a promising treatment for LGG, but it is generally hindered by the tumor microenvironment with the limited expression of tumor antigens.

METHODS

We integrated RNA sequencing data sets and clinical information and conducted consistent cluster analysis to explore the most suitable patients for immune checkpoint therapy. Gene set enrichment analysis, UMAP analysis, mutation correlation analysis, TIMER analysis, and TIDE analysis were used to identify the immune characteristics of 3 immune subtypes and the feasibility of 5 antigens as immune checkpoint markers.

RESULTS

We analyzed the isolation and mutation of homologous recombination repair genes (HRR) of the 3 immune subtypes, and the HRR genes of the 3 subtypes were obviously segregated. Among them, the IS2 subtype has a large number of HRR gene mutations, which increases the immunogenicity of tumors-this is consistent with the results of tumor mutation load analysis of 3 immune subtypes. Then we evaluated the immune cell infiltration of immune subtypes and found that IS2 and IS3 subtypes were rich in immune cells. It is worth noting that there are many Treg cells and NK cells in the IS1 subtype. In addition, when analyzing the immune checkpoint gene expression of the 3 subtypes, we found that they were upregulated most in IS2 subtypes compared with other subtypes. Then when we further confirmed the role of immune-related genes in LGG; through TIDE analysis and TISIDB analysis, we obtained 5 markers that can predict the efficacy of ICB in patients with LGG. In addition, we confirmed that they were associated with poor prognosis through survival analysis.

CONCLUSIONS

We obtained 3 reliable immune subtypes, and patients with the IS2 subtype are suitable for immunotherapy, in which NAMPT, SLC11A1, TNC, VIM, and SPP1 are predictive panel markers for ICB in the LGG group. Our findings provide a rationale for immunotherapy selection and prediction of patient prognosis in LGG patients.

KIF15 knockdown inhibits colorectal cancer proliferation and migration through affecting the ubiquitination modification of NRAS

As one of the most common malignancies, colorectal cancer (CRC) requires a thorough understanding of the mechanisms that promote its development and the discovery of new therapeutic targets. In this study, immunohistochemical staining confirmed significantly higher expression levels of KIF15 in CRC. qPCR and western blot results demonstrated the effective suppression of KIF15 mRNA and protein expression by shKIF15. Downregulation of KIF15 inhibited the proliferation and migration of CRC cells while promoting apoptosis. In addition, evidence from the xenograft experiments in nude mice demonstrated that KIF15 knockdown also suppressed tumor growth. Through bioinformatics analysis, the downstream molecular NRAS and Rac signaling pathway associated with KIF15 were identified. KIF15 knockdown was found to inhibit NRAS expression and disrupt Rac signaling pathway. Moreover, WB and Co-IP assays revealed that KIF15 reduced the ubiquitination modification of NRAS protein by interacting with the E3 ligase MDM2, thereby enhancing NRAS protein stability. Functionally, NRAS knockdown was shown to inhibit cell proliferation and migration. In conclusion, KIF15 promoted CRC progression by regulating NRAS expression and Rac signaling pathway.

Identification of a Two-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival in Diffuse-Type Gastric Cancer

BACKGROUND

It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice.

METHODS

We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15).

RESULTS

A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset.

CONCLUSIONS

We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.

SLC11A1 associated with tumor microenvironment is a potential biomarker of prognosis and immunotherapy efficacy for colorectal cancer

Colorectal cancer (CRC) is one of the most lethal cancers of the digestive system. The tumor microenvironment (TME) plays a central role in the initiation and development of CRC. However, little is known about the modulation mechanism of the TME in CRC. In our study, we attempted to identify a biomarker related to the TME modulation that could serve as a potential prognostic biomarker for CRC. We identified differentially expressed genes between the ImmuneScore high/low and StromalScore high/low groups. Using univariate COX regression analysis and hub gene analysis (cytoHubba), SLC11A1 was identified as the only candidate gene for subsequent analysis. CIBERSORT, EPIC, MCPcounter, and immunogenic cell death were performed to evaluate the effect of SLC11A1 on the TME. We also collected samples and performed Real-time quantitative PCR to verify the expression levels of SLC11A1 in CRC and adjacent normal tissues. The IMvigor210 cohort, TIDE score, and immunophenoscore (IPS) were used to analyze the association between SLC11A1 and immunotherapy efficacy. SLC11A1 was highly expressed in CRC tissues compared with its expression in normal colorectal tissues and was associated with poor prognosis and advanced clinicopathological stages. Gene set enrichment analysis showed that TGF-β pathways, JAK-STAT pathways, and angiogenesis were significantly enriched in the high-SLC11A1 group. Single-cell analysis validated the correlation between SLC11A1 and the TME. Using CIBERSORT, EPIC, and MCPcounter algorithms, we found that there was more macrophage and fibroblast infiltration in the SLC11A1 high-expression group. Meanwhile, high-SLC11A1 patients had lower IPS scores, higher TIDE scores, and fewer immunotherapy benefits than those of low-SLC11A1 patients. In conclusion, SLC11A1 plays a crucial role in the TME and could serve as a potential biomarker for poor prognosis and immunotherapy efficacy in CRC.

From Anti-HER-2 to Anti-HER-2-CAR-T Cells: An Evolutionary Immunotherapy Approach for Gastric Cancer

Current Therapeutic modalities provide no survival advantage to gastric cancer (GC) patients. Targeting the human epidermal growth factor receptor-2 (HER-2) is a viable therapeutic strategy against advanced HER-2 positive GC. Antibody-drug conjugates, small-molecule tyrosine kinase inhibitors (TKIs), and bispecific antibodies are emerging as novel drug forms that may abrogate the resistance to HER-2-specific drugs and monoclonal antibodies. Chimeric antigen receptor-modified T cells (CAR-T) targeting HER-2 have shown considerable therapeutic potential in GC and other solid tumors. However, due to the high heterogeneity along with the complex tumor microenvironment (TME) of GC that often leads to immune escape, the immunological treatment of GC still faces many challenges. Here, we reviewed and discussed the current progress in the research of anti-HER-2-CAR-T cell immunotherapy against GC.

Integrative Pan-Cancer Analysis of KIF15 Reveals Its Diagnosis and Prognosis Value in Nasopharyngeal Carcinoma

Background

KIF15 plays a vital role in many biological processes and has been reported to influence the occurrence and development of certain human cancers. However, there are few systematic evaluations on the role of KIF15 in human cancers, and the role of KIF15 in the diagnosis and prognosis of nasopharyngeal carcinoma (NPC) also remains unexplored. Therefore, this study aimed to conduct a pan-cancer analysis of KIF15 and evaluate its diagnostic and prognostic potential in NPC.

Methods

The expression pattern, prognostic value, molecular function, tumor mutation burden, microsatellite instability, and immune cell infiltration of KIF15 were examined based on public databases. Next, the diagnostic value of KIF15 in NPC was analyzed using the Gene Expression Omnibus (GEO) database and immunohistochemistry (IHC). Kaplan–Meier curves, Cox regression analyses, and nomograms were used to evaluate the effects of KIF15 expression on NPC prognosis. Finally, the effect of KIF15 on NPC was explored by in vitro experiments.

Results

The expression of KIF15 was significantly upregulated in 20 out of 33 cancer types compared to adjacent normal tissue. Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG) analysis showed that KIF15 could participate in several cancer-related pathways. The increased expression level of KIF15 was correlated with worse clinical outcomes in many types of human cancers. Additionally, KIF15 expression was related to cancer infiltration of immune cells, tumor mutation burden, and microsatellite instability. In the analysis of NPC, KIF15 was significantly upregulated based on the GEO database and immunohistochemistry. A high expression of KIF15 was negatively associated with the prognosis of patients with NPC. A nomogram model integrating clinical characteristics and KIF15 expression was established, and it showed good predictive ability with an area under the curve value of 0.73. KIF15 knockdown significantly inhibited NPC cell proliferation and migration.

Conclusions

Our findings revealed the important and functional role of KIF15 as an oncogene in pan-cancer. Moreover, high expression of KIF15 was found in NPC tissues, and was correlated with poor prognosis in NPC. KIF15 may serve as a potential therapeutic target in NPC treatment.

The Highly Expressed IFIT1 in Nasopharyngeal Carcinoma Enhances Proliferation, Migration, and Invasion of Nasopharyngeal Carcinoma Cells

In this study, we aimed to identify potential targets modulating the progression of nasopharyngeal carcinoma (NPC) using integrated bioinformatics analysis and functional assays. Differentially expressed genes (DEGs) between NPC and normal tissues samples were obtained from publicly availably microarray datasets (GSE68799, GSE34573, and GSE53819) in the Gene Expression Omnibus (GEO) database. The bioinformatics analysis identified 49 common DEGs from three GEO datasets, which were mainly enriched in cytokine/chemokine pathways and extracellular matrix organization pathway. Further protein-protein interaction network analysis identified 11 hub genes from the 49 DEGs. The 11 hub genes were significantly up-regulated in the NPC tissues when compared to normal tissues by analyzing the Oncomine database. The 8 hub genes including COL5A1, COL7A1, COL22A1, CXCL11, IFI44L, IFIT1, RSAD2, and USP18 were significantly up-regulated in the NPC tissues when compared to normal tissues by using the Oncomine database. Further validation studies showed that IFIT1 was up-regulated in the NPC cells. Knockdown of IFI1T1 suppressed the proliferation, migration, and invasion of NPC cells; while IFIT1 overexpression promoted the proliferation, migration, and invasion of NPC cells. In conclusion, a total of 49 DEGs and 11 hub genes in NPC using the integrated bioinformatics analysis. IFIT1 was up-regulated in the NPC cells lines, and IFIT1 may act as an oncogene by promoting NPC cell proliferation, migration, and invasion.

Correlation of PIK3CA mutation with programmed death ligand-1 (PD-L1) expression and their clinicopathological significance in colorectal cancer

Background

The prognostic significance of PIK3CA mutations in colorectal cancer (CRC) remains controversial. Recently, an association between programmed death ligand-1 (PD-L1) and PIK3CA mutations has been reported. The study presented here was conducted to investigate the effect of PIK3CA mutations on the prognosis of CRC patients and the association between PIK3CA mutations and PD-L1.

Methods

PIK3CA mutations were analyzed by targeted next-generation sequencing using formalin-fixed paraffin-embedded specimens from 224 primary CRC patients. PD-L1 expression was evaluated by immunohistochemical staining.

Results

PIK3CA mutations and PD-L1 expression were detected in 21.4% and 10.3% of CRC patients, respectively. PIK3CA mutations were significantly correlated with right-side colon cancer (P=0.011) and were correlated inversely with lymph node metastasis (P=0.026), distant metastasis (P=0.047), and high TNM stage (P=0.036). In univariate analysis, PIK3CA mutations were correlated with longer relapse-free survival in CRC patients. PD-L1 expression was correlated significantly with PIK3CA mutations (P<0.001).

Conclusions

PIK3CA mutations were associated with favorable prognostic factors, longer relapse-free survival, and expression of PD-L1. Further investigation is needed to identify whether PIK3CA mutations are a good prognostic factor. Additionally, further studies are needed to understand the mechanisms behind the correlation between PIK3CA mutations and PD-L1 expression.

The Positive and Negative Immunoregulatory Role of B7 Family: Promising Novel Targets in Gastric Cancer Treatment

Gastric cancer (GC), with a heterogeneous nature, is the third leading cause of death worldwide. Over the past few decades, stable reductions in the incidence of GC have been observed. However, due to the poor response to common treatments and late diagnosis, this cancer is still considered one of the lethal cancers. Emerging methods such as immunotherapy with immune checkpoint inhibitors (ICIs) have transformed the landscape of treatment for GC patients. There are presently eleven known members of the B7 family as immune checkpoint molecules: B7-1 (CD80), B7-2 (CD86), B7-H1 (PD-L1, CD274), B7-DC (PDCD1LG2, PD-L2, CD273), B7-H2 (B7RP1, ICOS-L, CD275), B7-H3 (CD276), B7-H4 (B7x, B7S1, Vtcn1), B7-H5 (VISTA, Gi24, DD1α, Dies1 SISP1), B7-H6 (NCR3LG1), B7-H7 (HHLA2), and Ig-like domain-containing receptor 2 (ILDR2). Interaction of the B7 family of immune-regulatory ligands with the corresponding receptors resulted in the induction and inhibition of T cell responses by sending co-stimulatory and co-inhibitory signals, respectively. Manipulation of the signals provided by the B7 family has significant potential in the management of GC.

Identification of hub genes associated with prognosis, diagnosis, immune infiltration and therapeutic drug in liver cancer by integrated analysis

Background

Liver cancer is one of the most common cancers and causes of cancer death worldwide. The objective was to elucidate novel hub genes which were benefit for diagnosis, prognosis, and targeted therapy in liver cancer via integrated analysis.

Methods

GSE84402, GSE101685, and GSE112791 were filtered from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified by using the GEO2R. The GO and KEGG pathway of DEGs were analyzed in the DAVID. PPI and TF network of the DEGs were constructed by using the STRING, TRANSFAC, and Harmonizome. The relationship between hub genes and prognoses in liver cancer was analyzed in UALCAN based on The Cancer Genome Atlas (TCGA). The diagnostic value of hub genes was evaluated by ROC. The relationship between hub genes and tumor-infiltrate lymphocytes was analyzed in TIMER. The protein levels of hub genes were verified in HPA. The interaction between the hub genes and the drug were identified in DGIdb.

Results

In total, 108 upregulated and 60 downregulated DEGs were enriched in 148 GO terms and 20 KEGG pathways. The mRNA levels and protein levels of CDK1, HMMR, PTTG1, and TTK were higher in liver cancer tissues compared to normal tissues, which showed excellent diagnostic and prognostic value. CDK1, HMMR, PTTG1, and TTK were positively correlated with tumor-infiltrate lymphocytes, which might involve tumor immune response. The CDK1, HMMR, and TTK had close interaction with anticancer agents.

Conclusions

The CDK1, HMMR, PTTG1, and TTK were hub genes in liver cancer; hence, they might be potential biomarkers for diagnosis, prognosis, and targeted therapy of liver cancer.

Harnessing Genomic Stress for Antitumor Immunity

Significance: Genomic instability, a hallmark of cancer, renders cancer cells susceptible to genomic stress from both endogenous and exogenous origins, resulting in the increased tendency to accrue DNA damage, chromosomal instability, or aberrant DNA localization. Apart from the cell autonomous tumor-promoting effects, genomic stress in cancer cells could have a profound impact on the tumor microenvironment. Recent Advances: Recently, it is increasingly appreciated that harnessing genomic stress could provide a promising strategy to revive antitumor immunity, and thereby offer new therapeutic opportunities in cancer treatment. Critical Issues: Genomic stress is closely intertwined with antitumor immunity via mechanisms involving the direct crosstalk with DNA damage response components, upregulation of immune-stimulatory/inhibitory ligands, release of damage-associated molecular patterns, increase of neoantigen repertoire, and activation of DNA sensing pathways. A better understanding of these mechanisms will provide molecular basis for exploiting the genomic stress to boost antitumor immunity. Future Directions: Future research should pay attention to the heterogeneity between individual cancers in the genomic instability and the associated immune response, and how to balance the toxicity and benefit by specifying the types, potency, and treatment sequence of genomic stress inducer in therapeutic practice. Antioxid. Redox Signal. 34, 1128-1150.

Distinct genomic landscapes of gastroesophageal adenocarcinoma depending on PD-L1 expression identify mutations in RAS-MAPK pathway and TP53 as potential predictors of immunotherapy efficacy

BACKGROUND

The impact of molecular alterations on programmed death-ligand 1 (PD-L1) combined positive score (CPS) is not well studied in gastroesophageal adenocarcinomas (GEAs). We aimed to characterize genomic features of tumors with different CPSs in GEAs.

PATIENTS AND METHODS

Genomic alterations of 2518 GEAs were compared in three groups (PD-L1 CPS ≥ 10, high; CPS = 1-9, intermediate; CPS < 1, low) using next-generation sequencing. We assessed the impact of gene mutations on the efficacy of immune checkpoint inhibitors (ICIs) and tumor immune environment based on the Memorial Sloan Kettering Cancer Center and The Cancer Genome Atlas databases.

RESULTS

High, intermediate, and low CPSs were seen in 18%, 54% and 28% of GEAs, respectively. PD-L1 positivity was less prevalent in women and in tissues derived from metastatic sites. PD-L1 CPS was positively associated with mismatch repair deficiency/microsatellite instability-high, but independent of tumor mutation burden distribution. Tumors with mutations in KRAS, TP53, and RAS-mitogen-activated protein kinase (MAPK) pathway were associated with higher PD-L1 CPSs in the mismatch repair proficiency and microsatellite stability (pMMR&MSS) subgroup. Patients with RAS-MAPK pathway alterations had longer overall survival (OS) from ICIs compared to wildtype (WT) patients [27 versus 13 months, hazard ratio (HR) = 0.36, 95% confidence interval (CI): 0.19-0.7, P = 0.016] and a similar trend was observed in the MSS subgroup (P = 0.11). In contrast, patients with TP53 mutations had worse OS from ICIs compared to TP53-WT patients in the MSS subgroup (5 versus 21 months, HR = 2.39, 95% CI: 1.24-4.61, P = 0.016).

CONCLUSIONS

This is the largest study to investigate the distinct genomic landscapes of GEAs with different PD-L1 CPSs. Our data may provide novel insights for patient selection using mutations in TP53 and RAS-MAPK pathway and for the development of rational combination immunotherapies in GEAs.

Druggable targets meet oncogenic drivers: opportunities and limitations of target-based classification of tumors and the role of Molecular Tumor Boards

The therapeutic landscape of cancer is changing rapidly due to the growing number of approved drugs capable of targeting specific genetic alterations. This aspect, together with the development of noninvasive methods for the assessment of somatic mutations in the peripheral blood of patients, generated a growing interest toward a new tumor-agnostic classification system based on ‘predictive’ biomarkers. The current review article discusses this emerging alternative approach to the classification of cancer and its implications for the selection of treatments. It is suggested that different types of cancers sharing the same molecular profiles could benefit from the same targeted drugs. Although recent clinical trials have demonstrated that this approach cannot be generalized, there are also specific examples that demonstrate the clinical utility of this alternative vision. In this rapidly evolving scenario, a multidisciplinary approach managed by institutional Molecular Tumor Boards is fundamental to interpret the biological and clinical relevance of genetic alterations and the complexity of their relationship with treatment response.

•

The identification of oncogenic drivers offers the opportunity to develop target-specific drugs.

•

The inhibition of crucial pathways realizes the principle of druggable target to exploit cancer vulnerability.

•

The approval of new anticancer agents based on target-based concept represents a paradigm shift in cancer therapy.

•

However, only few drugs have been approved so far on an agnostic basis and the concept of biomarker cannot be generalized.

•

Tumor Molecular Boards will have an increasing role in the identification of new therapeutic options in selected patients.

Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis

While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.

muTarget: A platform linking gene expression changes and mutation status in solid tumors

Large oncology repositories have paired genomic and transcriptomic data for all patients. We used these data to perform two independent analyses: to identify gene expression changes related to a gene mutation and to identify mutations altering the expression of a selected gene. All data processing steps were performed in the R statistical environment. RNA-sequencing and mutation data were acquired from The Cancer Genome Atlas (TCGA). The DESeq2 algorithm was applied for RNA-seq normalization, and transcript variants were annotated with AnnotationDbi. MuTect2-identified somatic mutation data were utilized, and the MAFtools Bioconductor program was used to summarize the data. The Mann-Whitney U test was used for differential expression analysis. The established database contains 7876 solid tumors from 18 different tumor types with both somatic mutation and RNA-seq data. The utility of the approach is presented via three analyses in breast cancer: gene expression changes related to TP53 mutations, gene expression changes related to CDH1 mutations and mutations resulting in altered progesterone receptor (PGR) expression. The breast cancer database was split into equally sized training and test sets, and these data sets were analyzed independently. The highly significant overlap of the results (chi-square statistic = 16 719.7 and P < .00001) validates the presented pipeline. Finally, we set up a portal at http://www.mutarget.com enabling the rapid identification of novel mutational targets. By linking somatic mutations and gene expression, it is possible to identify biomarkers and potential therapeutic targets in different types of solid tumors. The registration-free online platform can increase the speed and reduce the development cost of novel personalized therapies.

Construction and Analysis of the Tumor-Specific mRNA-miRNA-lncRNA Network in Gastric Cancer

Weighted correlation network analysis (WGCNA) is a statistical method that has been widely used in recent years to explore gene co-expression modules. Competing endogenous RNA (ceRNA) is commonly involved in the cancer gene expression regulation mechanism. Some ceRNA networks are recognized in gastric cancer; however, the prognosis-associated ceRNA network has not been fully identified using WGCNA. We performed WGCNA using datasets from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) to identify cancer-associated modules. The criteria of differentially expressed RNAs between normal stomach samples and gastric cancer samples were set at the false discovery rate (FDR) < 0.01 and |fold change (FC)| > 1.3. The ceRNA relationships obtained from the RNAinter database were examined by both the Pearson correlation test and hypergeometric test to confirm the mRNA–lncRNA regulation. Overlapped genes were recognized at the intersections of genes predicted by ceRNA relationships, differentially expressed genes, and genes in cancer-specific modules. These were then used for univariate and multivariate Cox analyses to construct a risk score model. The ceRNA network was constructed based on the genes in this model. WGCNA-uncovered genes in the green and turquoise modules are those most associated with gastric cancer. Eighty differentially expressed genes were observed to have potential prognostic value, which led to the identification of 12 prognosis-related mRNAs (KIF15, FEN1, ZFP69B, SP6, SPARC, TTF2, MSI2, KYNU, ACLY, KIF21B, SLC12A7, and ZNF823) to construct a risk score model. The risk genes were validated using the GSE62254 and GSE84433 datasets, with 0.82 as the universal cutoff value. 12 genes, 12 lncRNAs, and 35 miRNAs were used to build a ceRNA network with 86 dysregulated lncRNA–mRNA ceRNA pairs. Finally, we developed a 12-gene signature from both prognosis-related and tumor-specific genes, and then constructed a ceRNA network in gastric cancer. Our findings may provide novel insights into the treatment of gastric cancer.

Current status of immune checkpoint inhibitors for gastric cancer

Recent breakthrough results from immune checkpoint inhibitors (ICI) have paved the way to a new era of cancer immunotherapy. In particular, inhibition of programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) axis with ICI including nivolumab and pembrolizumab has been emerging as a novel treatment strategy for advanced gastric cancers (GC). In a meta-analysis for anti-PD-1/PD-L1 therapy in GC, the objective response rate was 12.0% and the disease control ratio was 34.7%. The ICI treatment in GC provided modest survival benefit and especially, anti-PD-1 treatment could improve the 12-month and 18-month overall survival rate and prolonged the duration of the response. Moreover, it is likely that anti-PD-1/PD-L1 therapy is more effective in subgroups with microsatellite instability-high, Epstein-Barr virus-positive or high mutation burden in advanced GC. The next steps for developing ICI in GC are mainly two challenges as follows. First is the identification of accurate biomarkers that can predict the response to ICI. The second challenge is the clinical development of combinatorial approaches to maximize the efficacy of ICI. In this review, recent advances in ICI for GC are discussed from a viewpoint of translational aspect including biomarkers and tumor microenvironment, and from a viewpoint of clinical aspects including combination therapies.

Multi-layered control of PD-L1 expression in Epstein-Barr virus-associated gastric cancer

Gastric cancer (GC) is the fifth most common cancer worldwide. In approximately 10% of GC cases, cancer cells show ubiquitous and monoclonal Epstein-Barr virus (EBV) infection. A significant feature of EBV-associated GC (EBVaGC) is high lymphocytic infiltration and high expression of immune checkpoint proteins, including programmed death-ligand 1 (PD-L1). This highlights EBVaGC as a strong candidate for immune checkpoint blockade therapy. Indeed, several recent studies have shown that EBV positivity in GC correlates with positive response to programmed cell death protein 1 (PD-1)/PD-L1 blockade therapy. Understanding the mechanisms that control PD-L1 expression in EBVaGC can indicate new predictive biomarkers for immunotherapy, as well as therapeutic targets for combination therapy. Various mechanisms have been implicated in PD-L1 expression regulation, including structural variations, post-transcriptional control, oncogenic activation of intrinsic signaling pathways, and increased sensitivity to extrinsic signals. This review provides the most recent updates on the multilayered control of PD-L1 expression in EBVaGC.

Pik3ca mutations significantly enhance the growth of SHH medulloblastoma and lead to metastatic tumour growth in a novel mouse model

Medulloblastoma (MB) is the most frequent malignant brain tumour in children with a poor outcome. Divided into four molecular subgroups, MB of the Sonic hedgehog (SHH) subgroup accounts for approximately 25% of the cases and is driven by mutations within components of the SHH pathway, such as its receptors PTCH1 or SMO. A fraction of these cases additionally harbour PIK3CA mutations, the relevance of which is so far unknown. To unravel the role of Pik3ca mutations alone or in combination with a constitutively activated SHH signalling pathway, transgenic mice were used. These mice show mutated variants within Smo, Ptch1 or Pik3ca genes in cerebellar granule neuron precursors, which represent the cellular origin of SHH MB. Our results show that Pik3ca mutations alone are insufficient to cause developmental alterations or to initiate MB. However, they significantly accelerate the growth of Shh MB, induce tumour spread throughout the cerebrospinal fluid, and result in lower survival rates of mice with a double Pik3caH1047R/SmoM2 or Pik3caH1047R/Ptch1 mutation. Therefore, PIK3CA mutations in SHH MB may represent a therapeutic target for first and second line combination treatments.

High PD-L1 expression in gastric cancer (GC) patients and correlation with molecular features

BACKGROUND

The programmed death receptor ligand 1 (PD-L1) immunohistochemistry (IHC) 22C3 pharmDx assay is a widely used selection method for pembrolizumab treatment in gastric cancer (GC) patients, especially in the U.S. The present study investigated the relationship between PD-L1 expression and the clinical features, molecular markers, and molecular subtypes of GC.

METHODS

PD-L1 expression was assessed based on combined positive score (CPS) using PD-L1 IHC 22C3 pharmDx in the Asian Cancer Research Group (ACRG) GC cohort (N = 300), which has been previously genomically profiled. PD-L1 positivity was defined as PD-L1 CPS ≥ 1. The association between PD-L1 expression and clinical features, tumor burden, and molecular subtypes (ACRG and The Cancer Genome Atlas [TCGA]) was analyzed.

RESULTS

Of the 300 tumors, 178 (59.3 %) had PD-L1 CPS ≥ 1 and 122 (40.7 %) had PD-L1 CPS < 1. PD-L1 CPS ≥ 1 was significantly associated with stage I tumor (P = 0.022), high microsatellite instability (MSI-H) (P < 0.001), Epstein-Barr virus (EBV) positivity (P = 0.008), and positive Helicobacter pylori status (P = 0.001). PD-L1 CPS ≥ 1 was observed in 96/193 (49.7 %) EBV-negative/microsatellite stable (MSS) tumors. In gene expression profiling, PD-L1 CPS was highly correlated with mutational load (P < 0.001) as well as EBV (P < 0.001) and MSI subtypes (P < 0.001); 27/300 (9%) GC patients had a very high PD-L1 (≥ 20) score (MSI-H, n = 10; EBV, n = 6; and non-EBV/MSS, n = 11). OS was longer in patients with PD-L1 CPS ≥ 1 tumors than in those with PD-L1 CPS < 1 tumors (median OS not reached vs. 40 months; P = 0.008; log-rank test).

CONCLUSIONS

PD-L1 is expressed in 59.3 % of GC patients and is associated with MSI and EBV positivity. These results provide a basis for identifying GC patients who may benefit from anti-PD-1/PD-L1 therapy.

Molecular Classification of Gastric Cancer among Alaska Native People

Gastric cancer is an aggressive and heterogeneous malignancy that often varies in presentation and disease among racial and ethnic groups. The Alaska Native (AN) people have the highest incidence and mortality rates of gastric cancer in North America. This study examines molecular markers in solid tumor samples from eighty-five AN gastric adenocarcinoma patients using next-generation sequencing, immunohistochemistry, and in situ hybridization analysis. AN patients have a low mutation burden with fewer somatic gene mutations in their tumors compared to other populations, with the most common mutation being TP53. Epstein-Barr virus (EBV) was associated with 20% of AN gastric cancers, which is higher than the world average of 10%. The inflammation marker, cyclooxygenase-2 (COX-2), is highly expressed in patients with the lowest survival rates. Mismatch repair deficiency was present in 10% of AN patients and was associated with patients who were female, 50 years or older, gene mutations, and tumors in the distal stomach. Program death-ligand 1 (PD-L1) was expressed in 14% of AN patients who were more likely to have MMR deficiency, EBV-associated gastric cancers, and mutations in the PIK3CA gene, all of which have been linked to clinical response to PD-1 inhibitors. These studies suggest a portion of AN gastric cancer patients could be candidates for immunotherapy. Overall, this study highlights future avenues of investigation for clinical and translational studies, so that we can improve early detection and develop more effective treatments for AN patients.

Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use.

Positive correlation between programmed death ligand-1 and p53 in triple-negative breast cancer

Purpose

Tumors with high mutation load tend to have a stronger immune response in some tumors. The correlation between expression of programmed death ligand-1 (PD-L1), a biomarker of immune response in tumors, and p53, accepted as the most frequently mutated gene in many cancers, in triple-negative breast cancer (TNBC) has not been fully investigated in cancer patients.

Materials and methods

132 cases of TNBC and 32 cases of non-TNBC paraffin-embedded tissue sections were selected to detect the expression of PD-L1 and p53 by immunohistochemistry, and results were correlated with clinical data and survival outcomes. The staining of PD-L1 in tumor cells (TCs) and tumor-associated immune cells (TAICs) was assessed separately.

Results

Strong positive correlations were observed between expression of p53 and PD-L1 both in TCs (r=0.338, P=0.000) and TAICs (r=0.186, P=0.033). The same positive correlation was found in the expression of PD-L1 in TCs and TAICs (r=0.764, P=0.000). Like p53 (P=0.024), positive rate of PD-L1 in TCs was significantly higher in TNBC than in non-TNBC (P=0.02). PD-L1 and p53 in TCs staining were significantly associated with histological grade, tumor size and Ki67 index (P<0.05). PD-L1 in TCs staining was also associated with lymphatic metastasis status (P=0.000). However, PD-L1 in TAICs was only related to histological grade in statistically (P=0.012). Kaplan–Meier survival analysis showed that positive groups of p53, PD-L1 in TCs and TAICs had a worse overall survival and a worse progression-free survival as compared with the negative groups, but marginal significance was found only in overall survival of PD-L1 in TCs and TAICs, and progression-free survival of PD-L1 in TAICs (P=0.074, 0.097, 0.068, respectively).

Conclusion

Our findings suggest that positive correlation between p53 and PD-L1 in TNBC and the higher expression rates are closely correlated with some key prognostic factors and worse survival outcomes. These findings would lay the foundation for further study on the relationship of p53 and PD-L1 and the combination of mutated p53 inhibitors and PD-1/PD-L1 antibodies in TNBC.