Association of genetic polymorphisms with survival of pancreatic ductal adenocarcinoma patients

The PANcreatic Disease ReseArch (PANDoRA) consortium: Ten years' experience of association studies to understand the genetic architecture of pancreatic cancer

Pancreatic cancer has an incidence that almost matches its mortality. Only a small number of risk factors and 33 susceptibility loci have been identified. so Moreover, the relative rarity of pancreatic cancer poses significant hurdles for research aimed at increasing our knowledge of the genetic mechanisms contributing to the disease. Additionally, the inability to adequately power research questions prevents small monocentric studies from being successful. Several consortia have been established to pursue a better understanding of the genetic architecture of pancreatic cancers. The Pancreatic disease research (PANDoRA) consortium is the largest in Europe. PANDoRA is spread across 12 European countries, Brazil and Japan, bringing together 29 basic and clinical research groups. In the last ten years, PANDoRA has contributed to the discovery of 25 susceptibility loci, a feat that will be instrumental in stratifying the population by risk and optimizing preventive strategies.

Cuprotosis-Related Genes: Predicting Prognosis and Immunotherapy Sensitivity in Pancreatic Cancer Patients

Based on TCGA, GTEx, and TIMER databases and various bioinformatics analysis methods, the potential biological roles of cuprotosis-related genes in pancreatic cancer were deeply explored, and a predictive model for pancreatic cancer patients was constructed. We downloaded the RNA-Seq data and clinicopathological and predictive data of 179 pancreatic cancer tissues and 332 adjacent normal tissues from TCGA and GTEx databases. The differential expression of cuprotosis-related genes in pancreatic cancer tissue and adjacent normal tissue was analyzed, and the LASSO regression algorithm was used to construct a prediction model and verify the validity of the model prediction. Based on the LASSO regression algorithm, a predictive model composed of three genes LIPT1, LIAS, and DLAT was screened. The corresponding survival curves showed that the constructed prediction model could significantly distinguish the prognosis of pancreatic cancer patients, and the prognosis of patients in the high-risk group was worse (P = 0.00557). The ROC curve showed that the area under the curve of the predictive model for predicting the 4-, 5-, and 6-year survival rates in pancreatic cancer was 0.816, 0.836, and 0.956, respectively. The AUC value of this risk model was significantly higher than 0.7, which could more accurately predict the prognosis of pancreatic cancer patients. This study determined a risk-scoring model of cuprotosis-related genes, which can provide an essential basis for judging the prognosis of pancreatic cancer patients.

Bioinformatic and experimental analyses of key biomarkers in pancreatic cancer

The present study aimed to screen the key genes in pancreatic cancer and to explore the pathogenesis of pancreatic cancer. A total of three expression profiling datasets (GSE28735, GSE16515 and GSE15471) associated with pancreatic cancer were retrieved from the public gene chip database. The differentially expressed genes (DEGs) were screened by GEO2R and subjected to Gene Ontology (GO) and signaling pathway enrichment analysis. Furthermore, a protein interaction network was constructed. The GEPIA online database was used to screen for genes that affect the prognosis of pancreatic cancer. Finally, cell functional experiments were performed on the selected key genes. A total of 72 DEGs were identified, including 52 upregulated and 20 downregulated genes. Enrichment analysis revealed roles of the DEGs in endodermal cell differentiation, cell adhesion, extracellular matrix-receptor interaction and PI3K-Akt signaling pathway. In total, 10 key nodal genes were identified, including integrin subunit α 2 (ITGA2), ITGB6 and collagen α 1 chain 1. Through survival analysis, two genes with an impact on the prognosis of pancreatic cancer were identified, namely ITGA2 and ITGB6. Silencing of ITGB6 in a pancreatic cancer cell line significantly suppressed cell proliferation and induced cell cycle arrest at G2/M phase. The identified key genes and signaling pathways may help to deepen the understanding of the molecular mechanisms involved in pancreatic cancer and provide a theoretical basis to develop novel therapies.

Catenin Alpha-2 Mutation Changes the Immune Microenvironment in Lung Adenocarcinoma Patients Receiving Immune Checkpoint Inhibitors

Background: Lung cancer has always been the most prevalent cancer. Lung adenocarcinoma (LUAD) is the most common lung cancer subtype and has a high tumor mutation rate. In addition to KRAS, EGFR, ALK, HER2, ROS1, and BRAF, which are known to have high mutation rates, we discovered some new mutated genes, such as catenin alpha-2 (CTNNA2), in LUAD patients treated with immune checkpoint inhibitors (ICIs). These mutant genes are potential therapeutic targets for LUAD.

Methods: We analyzed a cohort of LUAD patients with somatic mutation and survival data in the Cancer Genome Atlas (TCGA) database and a cohort of LUAD patients receiving immune checkpoint inhibitors with clinical data and whole-exome sequencing (WES) mutation data to evaluate the role of CTNNA2 gene mutation in LUAD. In addition, CIBERSORT was used to analyze the immune characteristics of CTNNA2 wild-type patients and CTNNA2 mutant-type patients, and gene set enrichment analysis (GSEA) was employed for pathway enrichment analysis. The results were verified by downloading data regarding the drug sensitivity of LUAD cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Results: We found that CTNNA2 mutation was associated with longer overall survival (OS) in LUAD patients. Analysis of the cohort from the Cancer Genome Atlas showed that patients with CTNNA2 mutation had more tumor neoantigens and a greater tumor mutation burden (TMB). Through further analysis of the tumor immune microenvironment, we found that in LUAD patients with CTNNA2 mutations, the gene expression levels of chemokine C-X-C motif chemokine 9 (CXCL9) and granzyme B (GZMB) were elevated, and the gene expression level of inhibitory receptor killer cell immunoglobulin-like receptor 2DL1 (KIR2DL1) was significantly reduced. These alterations might affect gene expression in macrophages, NK cells, and mast cell markers. In addition, LUAD patients with CTNNA2 mutation had a significantly increased number of mutations in DNA damage response (DDR) genes. The drug susceptibility results and gene set enrichment analysis showed that after CTNNA2 mutation occurred, changes were found in the DNA damage response pathway, the phosphoinositide 3-kinase (PI3K) pathway and others, indicating that CTNNA2 mutation can regulate the activation of PI3K and DDR pathways.

Conclusion: Our findings provide novel insights into the underlying pathogenesis of LUAD. CTNNA2 mutation can change the immune microenvironment, thereby improving patient prognosis. The results also suggest that CTNNA2 may become a new biomarker and therapeutic target for LUAD in the future.

Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence

MOTIVATION

The evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers' attention.

METHODS

In this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).

RESULTS

This method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some "dark genes" that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective.

Lack of association of CD44-rs353630 and CHI3L2-rs684559 with pancreatic ductal adenocarcinoma survival

Although pancreatic ductal adenocarcinoma (PDAC) survival is poor, there are differences in patients' response to the treatments. Detection of predictive biomarkers explaining these differences is of the utmost importance. In a recent study two genetic markers (CD44-rs353630 and CHI3L2-rs684559) were reported to be associated with survival after PDAC resection. We attempted to replicate the associations in 1856 PDAC patients (685 resected with stage I/II) from the PANcreatic Disease ReseArch (PANDoRA) consortium. We also analysed the combined effect of the two genotypes in order to compare our results with what was previously reported. Additional stratified analyses considering TNM stage of the disease and whether the patients received surgery were also performed. We observed no statistically significant associations, except for the heterozygous carriers of CD44-rs353630, who were associated with worse OS (HR = 5.01; 95% CI 1.58-15.88; p = 0.006) among patients with stage I disease. This association is in the opposite direction of those reported previously, suggesting that data obtained in such small subgroups are hardly replicable and should be considered cautiously. The two polymorphisms combined did not show any statistically significant association. Our results suggest that the effect of CD44-rs353630 and CHI3L2-rs684559 cannot be generalized to all PDAC patients.

Novel Biomarkers in the Diagnosis of Benign and Malignant Gastrointestinal Diseases

BACKGROUND

Various noninvasive biomarkers have been used in the diagnosis, prognosis, and treatment of different gastrointestinal (GI) diseases for years. Novel technological developments and profound perception of molecular processes related to GI diseases over the last decade have allowed researchers to evaluate genetic, epigenetic, and many other potential molecular biomarkers in different diseases and clinical settings. Here, we present a review of recent and most relevant articles in order to summarize major findings on novel biomarkers in the diagnosis of benign and malignant GI diseases.

SUMMARY

Genetic variations, noncoding RNAs (ncRNAs), cell-free DNA (cfDNA), and microbiome-based biomarkers have been extensively analyzed as potential biomarkers in benign and malignant GI diseases. Multiple single-nucleotide polymorphisms have been linked with a number of GI diseases, and these observations are further being used to build up disease-specific genetic risk scores. Micro-RNAs and long ncRNAs have a large potential as noninvasive biomarkers in the management of inflammatory bowel diseases and GI tumors. Altered microbiome profiles were observed in multiple GI diseases, but most of the findings still lack translational clinical application. As of today, cfDNA appears to be the most potent biomarker for early detection and screening of GI cancers. Key Messages: Novel noninvasive molecular biomarkers show huge potential as useful tools in the diagnostics and management of different GI diseases. However, the use of these biomarkers in real-life clinical practice still remains limited, and further large studies are needed to elucidate the ultimate role of these potential noninvasive clinical tools.

Genetic variability of the ABCC2 gene and clinical outcomes in pancreatic cancer patients

Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis, caused by various factors, such as the aggressiveness of the disease, the limited therapeutic options and the lack of early detection and risk markers. The ATP binding cassette subfamily C member 2 (ABCC2) protein plays a critical role in response to various drugs and is differentially expressed in gemcitabine sensitive and resistant cells. Moreover, single nucleotide polymorphisms (SNPs) in the gene have been associated with differential outcomes and prognosis in several tumour types. The aim of this study was to investigate the possible association between SNPs in the ABCC2 gene and overall survival (OS) in PDAC patients. We analysed 12 polymorphisms, including tagging-SNPs covering all the genetic variability of the ABCC2 gene and genotyped them in 1415 PDAC patients collected within the Pancreatic Disease ReseArch (PANDoRA) consortium. We tested the association between ABCC2 SNPs and PDAC OS using Cox proportional hazard models. We analysed PDAC patients dividing them by stage and observed that the minor alleles of three SNPs showed an association with worse OS [rs3740067: hazard ratio (HR) = 3.29, 95% confidence interval (CI) = 1.56-6.97, P = 0.002; rs3740073: HR = 3.11, 95% CI = 1.52-6.38, P = 0.002 and rs717620: HR = 2.90, 95% CI = 1.41-5.95, P = 0.004, respectively] in stage I patients. In patients with more advanced PDAC, we did not observe any statistically significant association. Our results suggest that rs3740067, rs3740073 and rs717620 could be promising prognostic markers in stage I PDAC patients.

Identification and Validation of a Biomarker Signature in Patients With Resectable Pancreatic Cancer via Genome-Wide Screening for Functional Genetic Variants

Importance

Surgery currently offers the only chance for a cure in pancreatic ductal adenocarcinoma (PDAC), but it carries a significant morbidity and mortality risk and results in varying oncologic outcomes. At present, to our knowledge, there are no tests available before surgical resection to identify tumors with an aggressive biological phenotype that could guide personalized treatment strategies.

Objective

Identification of noninvasive genetic biomarkers that could direct therapy in patients whose cases are amenable to pancreatic cancer resection.

Design, Setting, and Participants

This multicenter study combined a prospective European cohort of patients with PDAC who underwent pancreatic resection (from University Hospital of Zurich, Zurich, Switzerland; Cantonal Hospital of Winterthur, Winterthur, Switzerland; and University Clinic of Ulm, Ulm, Germany) with data from the Cancer Genome Atlas database in the United States, which includes prospectively registered patients with PDAC. A genome-wide screening for functional single-nucleotide polymorphisms (SNPs) that affect PDAC survival was conducted using the European cohort for identification and the Cancer Genome Atlas cohort for validation. We used Cox proportional hazards models to screen for high-frequency polymorphic variants that are associated with allelic differences in tumor-associated survival and either result in an altered protein structure and function or reside in known regulatory noncoding genomic regions. The false-discovery rate method was applied for multiple hypothesis-testing corrections. Data analysis occurred from November 2017 to May 2018.

Exposures

Pancreatic resection.

Main Outcomes and Measures

Tumor-associated survival.

Results

A total of 195 patients in the European cohort were included, as well as 136 patients in the Cancer Genome Atlas cohort (overall median [range] age, 66 [19-87] years; 156 [47.1%] were women, and 175 [52.9%] were men). Two SNPs in noncoding, functional regions of genes that regulate cancer progression, invasion, and metastasis were identified (CHI3L2 SNP rs684559 and CD44 SNP rs353630). These were associated with survival after PDAC resection; patients who carry the risk alleles at 1 of both SNP loci had a 2.63-fold increased risk for tumor-associated death compared with those with protective genotypes (hazard ratio for survival, 0.38 [95% CI, 0.27-0.53]; P = 1.0 × 10-8).

Conclusions and Relevance

The identified polymorphisms may serve as a noninvasive biomarker signature of prospective survival after pancreatic resection that is readily available at the time of PDAC diagnosis. This signature can be used to identify a subset of high-risk patients with PDAC with very low survival probability who might be eligible for inclusion in clinical trials of new therapeutic strategies, including neoadjuvant chemotherapy protocols. In addition, the biological knowledge about these SNPs could help guide the development of individualized genomic strategies for PDAC therapies.

Genetic determinants of telomere length and risk of pancreatic cancer: A PANDoRA study

Telomere deregulation is a hallmark of cancer. Telomere length measured in lymphocytes (LTL) has been shown to be a risk marker for several cancers. For pancreatic ductal adenocarcinoma (PDAC) consensus is lacking whether risk is associated with long or short telomeres. Mendelian randomization approaches have shown that a score built from SNPs associated with LTL could be used as a robust risk marker. We explored this approach in a large scale study within the PANcreatic Disease ReseArch (PANDoRA) consortium. We analyzed 10 SNPs (ZNF676-rs409627, TERT-rs2736100, CTC1-rs3027234, DHX35-rs6028466, PXK-rs6772228, NAF1-rs7675998, ZNF208-rs8105767, OBFC1-rs9420907, ACYP2-rs11125529 and TERC-rs10936599) alone and combined in a LTL genetic score ("teloscore", which explains 2.2% of the telomere variability) in relation to PDAC risk in 2,374 cases and 4,326 controls. We identified several associations with PDAC risk, among which the strongest were with the TERT-rs2736100 SNP (OR = 1.54; 95%CI 1.35-1.76; p = 1.54 × 10-10 ) and a novel one with the NAF1-rs7675998 SNP (OR = 0.80; 95%CI 0.73-0.88; p = 1.87 × 10-6 , ptrend = 3.27 × 10-7 ). The association of short LTL, measured by the teloscore, with PDAC risk reached genome-wide significance (p = 2.98 × 10-9 for highest vs. lowest quintile; p = 1.82 × 10-10 as a continuous variable). In conclusion, we present a novel genome-wide candidate SNP for PDAC risk (TERT-rs2736100), a completely new signal (NAF1-rs7675998) approaching genome-wide significance and we report a strong association between the teloscore and risk of pancreatic cancer, suggesting that telomeres are a potential risk factor for pancreatic cancer.

Small Molecules in Rare Tumors: Emerging Role of MicroRNAs in GIST

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of gastrointestinal tract. GISTs have very different clinical phenotypes and underlying molecular characteristics that are not yet completely understood. microRNAs (miRNAs) have been shown to participate in carcinogenesis pathways through post-transcriptional regulation of gene expression in different tumors. Over the last years emerging evidence has highlighted the role of miRNAs in GISTs. This review provides an overview of original research papers that analyze miRNA deregulation patterns, functional role, diagnostic, therapeutic and prognostic implications in GIST as well as provides directions for further research in the field.

Genetic polymorphisms associated with pancreatic cancer survival: a genome-wide association study

Previous findings on the association of genetic factors and pancreatic cancer survival are limited and inconsistent. In a two-stage study, we analyzed the existing genome-wide association study dataset of 868 pancreatic cancer patients from MD Anderson Cancer Center in relation to overall survival using Cox regression. Top hits were selected for replication in another 820 patients from the same institution using the Taqman genotyping method. Functional annotation, pathway analysis and gene expression analysis were conducted using existing software and databases. We discovered genome-wide significant associations of patient survival with three imputed SNPs which, in complete LD (r²  = 1), were intronic SNPs of the PAIP2B (rs113988120) and DYSF genes (rs112493246 and rs138529893) located on Chromosome 2. The variant alleles were associated with a 3.06-fold higher risk of death [95% confidence interval (CI) = 2.10-4.47, p=6.4 × 10-9] after adjusting for clinical factors. Eleven SNPs were tested in the replication study and the association of rs113988120 with survival was confirmed (hazard ratio: 1.57, 95% CI: 1.13-2.20,  p=0.008). In silico analysis found rs1139988120 might lead to altered motif. This locus is in LD (D' = 0.77) with three eQTL SNPs near or belong to the NAGK and MCEE genes. According to The Cancer Genome Atlas data and our previous RNA-sequencing data, the mRNA expression level of PAIP2B but not NAGK, MCEE or DYSF was significantly lower in pancreatic tumors than in normal adjacent tissues. Additional validation efforts and functional studies are warranted to demonstrate whether PAIP2B is a novel tumor suppressor gene and a potential therapeutic target for pancreatic cancer.