Application of genetic risk score for in-stent restenosis of second- and third-generation drug-eluting stents in geriatric patients.

BACKGROUND: The second-and third-generation drug-eluting stents (DESs) in-stent restenosis (ISR) genetic risk score (GRS) model has been previously validated. However, the model has not been validated in geriatric patients. Therefore, we conducted this study to test the feasibility of the DES-ISR GRS model in geriatric patients with coronary artery disease (CAD) in Taiwan. METHODS: We conducted a retrospective, single-center cohort study and included geriatric patients (age ≥ 65 years) with CAD and second-or third-generation DES(s) deployment. Patients undergoing maintenance dialysis were excluded. ISR was defined as ≥ 50% luminal narrowing on the follow-up coronary arteriography. The DES-ISR GRS model included five selected exonic single-nucleotide polymorphisms (SNPs): CAMLG, GALNT2, C11orf84, THOC5, and SAMD11. The GRS was defined as the sum of the five selected SNPs for the risk allele. RESULTS: We enrolled 298 geriatric patients from January 2010 and December 2019 in this study. After propensity score matching, there were 192 geriatric patients with CAD in the final analysis, of which 32 patients had ISR. Patients were divided into two groups based on their GRS values: low (0-2) and high (≥ 3) GRS. A high GRS was significantly associated with DES-ISR in geriatric patients. CONCLUSION: Those geriatric patients with a high GRS had significantly higher second-or third-generation DES ISR rates. The five SNP-derived DES-ISR GRS model could provide genetic information for interventional cardiologists to treat geriatric patients with CAD. TRIAL REGISTRATION: The primary study protocol was registered with clinicaltrials.org. with registration number: NCT03877614; on March 15, 2019. ( http://clinicaltrials.gov/ct2/show/NCT03877614 ).

Deciphering genes associated with diffuse large B-cell lymphoma with lymphomatous effusions: A mutational accumulation scoring approach.

INTRODUCTION: Earlier studies have shown that lymphomatous effusions in patients with diffuse large B-cell lymphoma (DLBCL) are associated with a very poor prognosis, even worse than for non-effusion-associated patients with stage IV disease. We hypothesized that certain genetic abnormalities were associated with lymphomatous effusions, which would help to identify related pathways, oncogenic mechanisms, and therapeutic targets. METHODS: We compared whole-exome sequencing on DLBCL samples involving solid organs (n = 22) and involving effusions (n = 9). We designed a mutational accumulation-based approach to score each gene and used mutation interpreters to identify candidate pathogenic genes associated with lymphomatous effusions. Moreover, we performed gene-set enrichment analysis from a microarray comparison of effusion-associated versus non-effusion-associated DLBCL cases to extract the related pathways. RESULTS: We found that genes involved in identified pathways or with high accumulation scores in the effusion-based DLBCL cases were associated with migration/invasion. We validated expression of 8 selected genes in DLBCL cell lines and clinical samples: MUC4, SLC35G6, TP53BP2, ARAP3, IL13RA1, PDIA4, HDAC1 and MDM2, and validated expression of 3 proteins (MUC4, HDAC1 and MDM2) in an independent cohort of DLBCL cases with (n = 31) and without (n = 20) lymphomatous effusions. We found that overexpression of HDAC1 and MDM2 correlated with the presence of lymphomatous effusions, and HDAC1 overexpression was associated with the poorest prognosis.  CONCLUSION: Our findings suggest that DLBCL associated with lymphomatous effusions may be associated mechanistically with TP53-MDM2 pathway and HDAC-related chromatin remodeling mechanisms.

Genetic risk model for in-stent restenosis of second-and third-generation drug-eluting stents.

The new generation, i.e., second- and third-generation, drug-eluting stents (DESs) remain a risk of in-stent restenosis (ISR). We evaluated the power of a genetic risk score (GRS) model to identify high-risk populations for new generation DES ISR. We enrolled patients with coronary artery disease (CAD) treated with new generations DESs by a single-center cohort study in Taiwan and evaluated their genetic profile. After propensity score matching, there were 343 patients and 153 patients in the derivation and validation cohorts, respectively. Five selected single-nucleotide polymorphisms (SNPs), i.e., SNPs in CAMLG, GALNT2, C11orf84, THOC5, and SAMD11, were included to calculate the GRS for new generation DES ISR. In the derivation and the validation cohorts, patients with a GRS greater than or equal to 3 had significantly higher new generation DES ISR rates. We provide biological information for interventional cardiologists prior to percutaneous coronary intervention by specific five SNP-derived GRS.

AIJ: joint test for simultaneous detection of imprinting and non-imprinting allelic expression imbalance.

Epigenetics is the study of heritable changes in gene expression or cellular phenotype caused by mechanisms other than changes in the underlying DNA sequence. Genomic imprinting is an epigenetically regulated process by which imprinted genes are expressed in a parent-of-origin-specific manner. It can be confounded with a phenomenon, allelic expression imbalance (AEI), which, in this paper, refers to asymmetric expression of the two alleles of a heterozygous subject at a single nucleotide polymorphism not caused by imprinting (non-imprinting AEI). Since existing methods in the literature are not amenable to distinguishing imprinting from non-imprinting AEI for data without replicates, we propose AIJ, a joint test for simultaneous detection of imprinting and non-imprinting AEI that accounts for potential confounding using RNA-seq data based on a reciprocal cross design. Through a simulation study, we show that AIJ is more powerful compared to two frequently used methods that do not account for confounding. To illustrate the practical utility of AIJ, we applied the method to a mouse dataset and identified genes with the imprinting effect and/or non-imprinting AEI phenomenon, with some already confirmed in an existing database. The results are also largely consistent with a study on human data for a set of orthologous genes, affirming earlier conclusion in the literature that non-imprinting AEI events are evolutionarily conserved.

PP2A Deficiency Enhances Carcinogenesis of Lgr5+ Intestinal Stem Cells Both in Organoids and In Vivo.

In most cancers, cellular origin and the contribution of intrinsic and extrinsic factors toward transformation remain elusive. Cell specific carcinogenesis models are currently unavailable. To investigate cellular origin in carcinogenesis, we developed a tumorigenesis model based on a combination of carcinogenesis and genetically engineered mouse models. We show in organoids that treatment of any of three carcinogens, DMBA, MNU, or PhIP, with protein phosphatase 2A (PP2A) knockout induced tumorigenesis in Lgr5+ intestinal lineage, but not in differentiated cells. These transformed cells increased in stem cell signature, were upregulated in EMT markers, and acquired tumorigenecity. A mechanistic approach demonstrated that tumorigenesis was dependent on Wnt, PI3K, and RAS-MAPK activation. In vivo combination with carcinogen and PP2A depletion also led to tumor formation. Using whole-exome sequencing, we demonstrate that these intestinal tumors display mutation landscape and core driver pathways resembling human intestinal tumor in The Cancer Genome Atlas (TCGA). These data provide a basis for understanding the interplay between extrinsic carcinogen and intrinsic genetic modification and suggest that PP2A functions as a tumor suppressor in intestine carcinogenesis.

A non-threshold region-specific method for detecting rare variants in complex diseases.

A region-specific method, NTR (non-threshold rare) variant detection method, was developed-it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.

Methods for identifying differentially methylated regions for sequence- and array-based data.

DNA methylation is one of the most important epigenetic mechanisms, and participates in the pathogenic processes of many diseases. Differentially methylated regions (DMRs) in the genome have been reported and implicated in a number of different diseases, tissues and cell types, and are associated with gene expression levels. Therefore, identification of DMRs is one of the most critical and fundamental issues in dissecting the disease etiologies. Based on bisulfite conversion, advances in sequence- and array-based technologies have helped investigators study genome-wide DNA methylation. Many methods have been developed to detect DMRs, and they have revolutionized our understanding of DNA methylation and provided new insights into its role in diverse biological functions. According to data and region types, we discuss various methods in detecting DMRs, their utility and limitations comprehensively. We recommend using a few of the methods in the same data and region type to detect DMRs because they could be complementary to one another.