H. pylori-induced NF-κB-PIEZO1-YAP1-CTGF axis drives gastric cancer progression and cancer-associated fibroblast-mediated tumour microenvironment remodelling

BACKGROUND

Gastric cancer (GC) is one of the most common tumours in East Asia countries and is associated with Helicobacter pylori infection. H. pylori utilizes virulence factors, CagA and VacA, to up-regulate pro-inflammatory cytokines and activate NF-κB signaling. Meanwhile, the PIEZO1 upregulation and cancer-associated fibroblast (CAF) enrichment were found in GC progression. However, the mechanisms of PIEZO1 upregulation and its involvement in GC progression have not been fully elucidated.

METHODS

The CAF enrichment and clinical significance were investigated in animal models and primary samples. The expression of NF-κB and PIEZO1 in GC was confirmed by immunohistochemistry staining, and expression correlation was analysed in multiple GC datasets. GSEA and Western blot analysis revealed the YAP1-CTGF axis regulation by PIEZO1. The stimulatory effects of CTGF on CAFs were validated by the co-culture system and animal studies. Patient-derived organoid and peritoneal dissemination models were employed to confirm the role of the PIEZO1-YAP1-CTGF cascade in GC.

RESULTS

Both CAF signature and PIEZO1 were positively correlated with H. pylori infection. PIEZO1, a mechanosensor, was confirmed as a direct downstream of NF-κB to promote the transformation from intestinal metaplasia to GC. Mechanistic studies revealed that PIEZO1 transduced the oncogenic signal from NF-κB into YAP1 signaling, a well-documented oncogenic pathway in GC progression. PIEZO1 expression was positively correlated with the YAP1 signature (CTGF, CYR61, and c-Myc, etc.) in primary samples. The secreted CTGF by cancer cells stimulated the CAF infiltration to form a stiffened collagen-enrichment microenvironment, thus activating PIEZO1 to form a positive feedback loop. Both PIEZO1 depletion by shRNA and CTGF inhibition by Procyanidin C1 enhanced the efficacy of 5-FU in suppressing the GC cell peritoneal metastasis.

CONCLUSION

This study elucidates a novel driving PIEZO1-YAP1-CTGF force, which opens a novel therapeutic avenue to block the transformation from precancerous lesions to GC. H. pylori-NF-κB activates the PIEZO1-YAP1-CTGF axis to remodel the GC microenvironment by promoting CAF infiltration. Targeting PIEZO1-YAP1-CTGF plus chemotherapy might serve as a potential therapeutic option to block GC progression and peritoneal metastasis.

Whole-genome profiling of nasopharyngeal carcinoma reveals viral-host co-operation in inflammatory NF-κB activation and immune escape

Interplay between EBV infection and acquired genetic alterations during nasopharyngeal carcinoma (NPC) development remains vague. Here we report a comprehensive genomic analysis of 70 NPCs, combining whole-genome sequencing (WGS) of microdissected tumor cells with EBV oncogene expression to reveal multiple aspects of cellular-viral co-operation in tumorigenesis. Genomic aberrations along with EBV-encoded LMP1 expression underpin constitutive NF-κB activation in 90% of NPCs. A similar spectrum of somatic aberrations and viral gene expression undermine innate immunity in 79% of cases and adaptive immunity in 47% of cases; mechanisms by which NPC may evade immune surveillance despite its pro-inflammatory phenotype. Additionally, genomic changes impairing TGFBR2 promote oncogenesis and stabilize EBV infection in tumor cells. Fine-mapping of CDKN2A/CDKN2B deletion breakpoints reveals homozygous MTAP deletions in 32-34% of NPCs that confer marked sensitivity to MAT2A inhibition. Our work concludes that NPC is a homogeneously NF-κB-driven and immune-protected, yet potentially druggable, cancer.

WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts

Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modelling distribution of background mutation counts, existing statistical methods are often underpowered to discriminate cancer-driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER, http://grass.cgs.hku.hk/limx/witer or KGGSeq,http://grass.cgs.hku.hk/limx/kggseq/), to detect cancer-driver genes showing an excess of somatic mutations. By fitting the distribution of background mutation counts properly, this approach works well even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in most tested cancers. Applying this approach, we estimated 229 driver genes in 26 different types of cancers. In silico validation confirmed 78% of predicted genes as likely known drivers and many other genes as very likely new drivers for corresponding cancers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects and rescue of more genes missed by alternative tools in moderate or small samples.

Assessment of programmed cell death ligand-1 expression by 4 diagnostic assays and its clinicopathological correlation in a large cohort of surgical resected non-small cell lung carcinoma

Immune checkpoint blockade targeting the PD-1/PD-L1 axis has recently demonstrated efficacy and promise in cancer treatment. Appropriate biomarker selection is therefore essential for improving treatment efficacy. However, the establishment of PD-L1 assay in pathology laboratories is complicated by the presence of multiple testing platforms using different scoring systems. Here we assessed the PD-L1 expression in 713 consecutive non-small cell lung carcinomas by four commercially available PD-L1 immunohistochemical assays, namely, 22C3, 28-8, SP142 and SP263. The analytical performances of the four assays and diagnostic performances across clinically relevant cutoffs were evaluated. The prevalence of PD-L1 (22C3) expression was 21% with a ≥50% cutoff and 56% with a ≥1% cutoff. High PD-L1 expression (using a ≥50% cutoff) was significantly associated with male sex (P = 0.001), ever smoking history (P < 0.001), squamous cell carcinoma (P = 0.001), large cell carcinoma (P < 0.001), lymphoepithelioma-like carcinoma (P = 0.006), sarcomatoid carcinoma (P < 0.001), mutant KRAS (P = 0.005) and wild-type EGFR (P = 0.003). Elevated PD-L1 expression was also significantly associated with shorter survival in patients with adenocarcinoma (log-rank P = 0.026) and remained an independent prognostic factor by multivariable analysis. Among the four assays, 22C3, 28-8 and SP263 were highly concordant for tumor cell scoring. With a cutoff of ≥50% (i.e., the threshold for first-line patient selection), inter-rater agreement was high among the three assays with percentage agreement >97%. In conclusion, three PD-L1 assays showed good analytical performance and a high agreement with each other, but not all cases were correctly classified using the same clinical cutoff. Further studies comparing the predictive value of these assays are required to address the interchangeability of these assays for clinical use.

FGF18, a prominent player in FGF signaling, promotes gastric tumorigenesis through autocrine manner and is negatively regulated by miR-590-5p

Fibroblast growth factors (FGFs) and their receptors are significant components during fundamental cellular processes. FGF18 plays a distinctive role in modulating the activity of both tumor cells and tumor microenvironment. This study aims to comprehensively investigate the expression and functional role of FGF18 in gastric cancer (GC) and elucidate its regulatory mechanisms. The upregulation of FGF18 was detected in seven out of eleven (63.6%) GC cell lines. In primary GC samples, FGF18 was overexpressed in genomically stable and chromosomal instability subtypes of GC and its overexpression was associated with poor survival. Knocking down FGF18 inhibited tumor formation abilities, induced G1 phase cell cycle arrest and enhanced anti-cancer drug sensitivity. Expression microarray profiling revealed that silencing of FGF18 activated ATM pathway but quenched TGF-β pathway. The key factors that altered in the related signaling were validated by western blot and immunofluorescence. Meanwhile, treating GC cells with human recombinant FGF18 or FGF18-conditioned medium accelerated tumor growth through activation of ERK-MAPK signaling. FGF18 was further confirmed to be a direct target of tumor suppressor, miR-590-5p. Their expressions showed a negative correlation in primary GC samples and more importantly, re-overexpression of FGF18 partly abolished the tumor-suppressive effect of miR-590-5p. Our study not only identified that FGF18 serves as a novel prognostic marker and a therapeutic target in GC but also enriched the knowledge of FGF-FGFR signaling during gastric tumorigenesis.

Inheritance-mode specific pathogenicity prioritization (ISPP) for human protein coding genes

MOTIVATION

Exome sequencing studies have facilitated the detection of causal genetic variants in yet-unsolved Mendelian diseases. However, the identification of disease causal genes among a list of candidates in an exome sequencing study is still not fully settled, and it is often difficult to prioritize candidate genes for follow-up studies. The inheritance mode provides crucial information for understanding Mendelian diseases, but none of the existing gene prioritization tools fully utilize this information.

RESULTS

We examined the characteristics of Mendelian disease genes under different inheritance modes. The results suggest that Mendelian disease genes with autosomal dominant (AD) inheritance mode are more haploinsufficiency and de novo mutation sensitive, whereas those autosomal recessive (AR) genes have significantly more non-synonymous variants and regulatory transcript isoforms. In addition, the X-linked (XL) Mendelian disease genes have fewer non-synonymous and synonymous variants. As a result, we derived a new scoring system for prioritizing candidate genes for Mendelian diseases according to the inheritance mode. Our scoring system assigned to each annotated protein-coding gene (N = 18 859) three pathogenic scores according to the inheritance mode (AD, AR and XL). This inheritance mode-specific framework achieved higher accuracy (area under curve  = 0.84) in XL mode.

CONCLUSION

The inheritance-mode specific pathogenicity prioritization (ISPP) outperformed other well-known methods including Haploinsufficiency, Recessive, Network centrality, Genic Intolerance, Gene Damage Index and Gene Constraint scores. This systematic study suggests that genes manifesting disease inheritance modes tend to have unique characteristics.

AVAILABILITY AND IMPLEMENTATION

ISPP is included in KGGSeq v1.0 (http://grass.cgs.hku.hk/limx/kggseq/), and source code is available from (https://github.com/jacobhsu35/ISPP.git).

CONTACT

mxli@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online.

Meta-analysis of genome-wide association studies identifies two loci associated with circulating osteoprotegerin levels

Osteoprotegerin (OPG) is involved in bone homeostasis and tumor cell survival. Circulating OPG levels are also important biomarkers of various clinical traits, such as cancers and atherosclerosis. OPG levels were measured in serum or in plasma. In a meta-analysis of genome-wide association studies in up to 10 336 individuals from European and Asian origin, we discovered that variants >100 kb upstream of the TNFRSF11B gene encoding OPG and another new locus on chromosome 17q11.2 were significantly associated with OPG variation. We also identified a suggestive locus on chromosome 14q21.2 associated with the trait. Moreover, we estimated that over half of the heritability of OPG levels could be explained by all variants examined in our study. Our findings provide further insight into the genetic regulation of circulating OPG levels.

Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies

Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ~22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.

A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases

Exome sequencing strategy is promising for finding novel mutations of human monogenic disorders. However, pinpointing the casual mutation in a small number of samples is still a big challenge. Here, we propose a three-level filtration and prioritization framework to identify the casual mutation(s) in exome sequencing studies. This efficient and comprehensive framework successfully narrowed down whole exome variants to very small numbers of candidate variants in the proof-of-concept examples. The proposed framework, implemented in a user-friendly software package, named KGGSeq (http://statgenpro.psychiatry.hku.hk/kggseq), will play a very useful role in exome sequencing-based discovery of human Mendelian disease genes.

Post-genome wide association studies and functional analyses identify association of MPP7 gene variants with site-specific bone mineral density

Our previous genome-wide association study (GWAS) in a Hong Kong Southern Chinese population with extreme bone mineral density (BMD) scores revealed suggestive association with MPP7, which ranked second after JAG1 as a candidate gene for BMD. To follow-up this suggestive signal, we replicated the top single-nucleotide polymorphism rs4317882 of MPP7 in three additional independent Asian-descent samples (n= 2684). The association of rs4317882 reached the genome-wide significance in the meta-analysis of all available subjects (P(meta)= 4.58 × 10(-8), n= 4204). Site heterogeneity was observed, with a larger effect on spine than hip BMD. Further functional studies in a zebrafish model revealed that vertebral bone mass was lower in an mpp7 knock-down model compared with the wide-type (P= 9.64 × 10(-4), n= 21). In addition, MPP7 was found to have constitutive expression in human bone-derived cells during osteogenesis. Immunostaining of murine MC3T3-E1 cells revealed that the Mpp7 protein is localized in the plasma membrane and intracytoplasmic compartment of osteoblasts. In an assessment of the function of identified variants, an electrophoretic mobility shift assay demonstrated the binding of transcriptional factor GATA2 to the risk allele 'A' but not the 'G' allele of rs4317882. An mRNA expression study in human peripheral blood mononuclear cells confirmed that the low BMD-related allele 'A' of rs4317882 was associated with lower MPP7 expression (P= 9.07 × 10(-3), n= 135). Our data suggest a genetic and functional association of MPP7 with BMD variation.

Risk prediction of complex diseases from family history and known susceptibility loci, with applications for cancer screening

Risk prediction based on genomic profiles has raised a lot of attention recently. However, family history is usually ignored in genetic risk prediction. In this study we proposed a statistical framework for risk prediction given an individual's genotype profile and family history. Genotype information about the relatives can also be incorporated. We allow risk prediction given the current age and follow-up period and consider competing risks of mortality. The framework allows easy extension to any family size and structure. In addition, the predicted risk at any percentile and the risk distribution graphs can be computed analytically. We applied the method to risk prediction for breast and prostate cancers by using known susceptibility loci from genome-wide association studies. For breast cancer, in the population the 10-year risk at age 50 ranged from 1.1% at the 5th percentile to 4.7% at the 95th percentile. If we consider the average 10-year risk at age 50 (2.39%) as the threshold for screening, the screening age ranged from 62 at the 20th percentile to 38 at the 95th percentile (and some never reach the threshold). For women with one affected first-degree relative, the 10-year risks ranged from 2.6% (at the 5th percentile) to 8.1% (at the 95th percentile). For prostate cancer, the corresponding 10-year risks at age 60 varied from 1.8% to 14.9% in the population and from 4.2% to 23.2% in those with an affected first-degree relative. We suggest that for some diseases genetic testing that incorporates family history can stratify people into diverse risk categories and might be useful in targeted prevention and screening.

GATES: a rapid and powerful gene-based association test using extended Simes procedure

The gene has been proposed as an attractive unit of analysis for association studies, but a simple yet valid, powerful, and sufficiently fast method of evaluating the statistical significance of all genes in large, genome-wide datasets has been lacking. Here we propose the use of an extended Simes test that integrates functional information and association evidence to combine the p values of the single nucleotide polymorphisms within a gene to obtain an overall p value for the association of the entire gene. Our computer simulations demonstrate that this test is more powerful than the SNP-based test, offers effective control of the type 1 error rate regardless of gene size and linkage-disequilibrium pattern among markers, and does not need permutation or simulation to evaluate empirical significance. Its statistical power in simulated data is at least comparable, and often superior, to that of several alternative gene-based tests. When applied to real genome-wide association study (GWAS) datasets on Crohn disease, the test detected more significant genes than SNP-based tests and alternative gene-based tests. The proposed test, implemented in an open-source package, has the potential to identify additional novel disease-susceptibility genes for complex diseases from large GWAS datasets.

Novel sib pair selection strategy increases power in quantitative association analysis

Quantitative-trait association studies have been widely used in search for genetic loci for complex traits in recent years. Yet, fiscal constraints still prohibit many on-going research projects from recruiting a large number of individuals for genotyping to reach a desired level of statistical power. Accordingly, in this article, we describe a novel sib pair sampling strategy for genotyping in QTL association studies. With the use of phenotypic scores (and IBD allele-sharing probabilities if available), the genetic effect of a biallelic additive trait locus can be properly modelled within the maximum-likelihood variance components framework proposed by Fulker et al. (Am J Hum Genet 64(1):259-267, 1999) and sib pairs can be rank-ordered by use of informativeness indices. The performance of our method was investigated using simulation. The power of our approach was shown to be higher when compared with other phenotypic selection schemes. An R-script implementing all the selection approaches (including the traditional phenotype-based ones) used in the simulation is available at http://statgen.hku.hk/jshkwan .

Depression in college: depressive symptoms and personality factors in Beijing and Hong Kong college freshmen

OBJECTIVES

The present study investigated the 2-week prevalence of depressive symptoms in college freshmen from Beijing and Hong Kong. The relationship between depression and 3 personality factors in these college freshmen was analyzed.

METHOD

Center for Epidemiologic Studies Depression Scale (CES-D), Eysenck Personality Questionnaire-Neuroticism, Rosenberg Self-esteem Scale, and Frost Multidimensional Perfectionism Scale were administered to 988 Beijing and 802 Hong Kong Chinese college freshmen.

RESULTS

Approximately 24.8% of freshmen in Beijing had scores on the CES-D exceeding 16, whereas 8.9% reported scores of 25 or higher. There was no sex difference in prevalence in Beijing. Approximately 43.9% of freshmen in Hong Kong had scores on the CES-D exceeding 16, whereas 17.6% reported scores of 25 or higher. The prevalence is significantly different between sexes in Hong Kong, with approximately 36.1% of men having scores of 16 or higher and 13.4% having scores of 25 or higher and approximately 50.7% of women having scores of 16 or higher and 21.3% having scores of 25 or higher. High neuroticism, concern over mistakes, doubts about actions, low self-esteem, and poor organization were associated with current depressive symptoms in both sites.

CONCLUSION

The higher prevalence of current depressive symptoms in college freshmen in Hong Kong suggests that their mental health is not as satisfactory as that of their counterparts in Beijing. The strong relationship between certain personality features and current depressive symptoms is similar in both regions. Personality differences in the 2 sites explain only part, but not all, of the difference in depressive symptoms between the 2 sites.

Combining functional and linkage disequilibrium information in the selection of tag SNPs

We have developed an online program, WCLUSTAG, for tag SNP selection that allows the user to specify variable tagging thresholds for different SNPs. Tag SNPs are selected such that a SNP with user-specified tagging threshold C will have a minimum R2 of C with at least one tag SNP. This flexible feature is useful for researchers who wish to prioritize genomic regions or SNPs in an association study.

AVAILABILITY

The online WCLUSTAG program is available at http://bioinfo.hku.hk/wclustag/