Prognostic Value of Drug Targets Predicted Using Deep Bioinformatic Analysis of m6A-Associated lncRNA-Based Pancreatic Cancer Model Characteristics and Its Tumour Microenvironment

The role of N6-methyladenosine (m6A)-associated long-stranded non-coding RNA (lncRNA) in pancreatic cancer is unclear. Therefore, we analysed the characteristics and tumour microenvironment in pancreatic cancer and determined the value of m6A-related lncRNAs for prognosis and drug target prediction. An m6A-lncRNA co-expression network was constructed using The Cancer Genome Atlas database to screen m6A-related lncRNAs. Prognosis-related lncRNAs were screened using univariate Cox regression; patients were divided into high- and low-risk groups and randomised into training and test groups. In the training group, least absolute shrinkage and selection operator (LASSO) was used for regression analysis and to construct a prognostic model, which was validated in the test group. Tumor mutational burden (TMB), immune evasion, and immune function of risk genes were analysed using R; drug sensitivity and potential drugs were examined using the Genomics of Drug Sensitivity in Cancer database. We screened 129 m6A-related lncRNAs; 17 prognosis-related m6A-related lncRNAs were obtained using multivariate analysis and three m6A-related lncRNAs (AC092171.5, MEG9, and AC002091.1) were screened using LASSO regression. Survival rates were significantly higher (p < 0.05) in the low-risk than in the high-risk group. Risk score was an independent predictor affecting survival (p < 0.001), with the highest risk score being obtained by calculating the c-index. The TMB significantly differed between the high- and low-risk groups (p < 0.05). In the high- and low-risk groups, mutations were detected in 61 of 70 samples and 49 of 71 samples, respectively, with KRAS, TP53, and SMAD4 showing the highest mutation frequencies in both groups. A lower survival rate was observed in patients with a high versus low TMB. Immune function HLA, Cytolytic activity, and Inflammation-promoting, T cell co-inhibition, Check-point, and T cell co-stimulation significantly differed in different subgroups (p < 0.05). Immune evasion scores were significantly higher in the high-risk group than in the low-risk group. Eight sensitive drugs were screened: ABT.888, ATRA, AP.24534, AG.014699, ABT.263, axitinib, A.443654, and A.770041. We screened m6A-related lncRNAs using bioinformatics, constructed a prognosis-related model, explored TMB and immune function differences in pancreatic cancer, and identified potential therapeutic agents, providing a foundation for further studies of pancreatic cancer diagnosis and treatment.

Can the tumor-agnostic evaluation of MSI/MMR status be the common denominator for the immunotherapy treatment of patients with several solid tumors?

Alterations in short-repetitive DNA sequences, known as microsatellite instability (MSI), can reflect deficiencies in Mismatch Repair (MMR) system which represents a major player in DNA integrity maintenance. The incidence of MSI-H/dMMR has been shown to be variable depending on the tumor type. Several studies confirmed that dMMR/MSI status, although less frequent than PD-L1 expression, may better predict response to immune-checkpoint inhibitors (ICIs) in patients with solid tumors. In October 2016, the FDA granted pembrolizumab as breakthrough therapy for the treatment of non-CRC, MSI-H/dMMR tumors, providing, for the first time, a tumor-agnostic indication. In the next future, the tissue-agnostic evaluation of MSI-H/dMMR could become the common denominator for the immunotherapy treatment of patients with different advanced solid tumors, in order to select patient subgroups which may benefit from this therapy. In this Review we provided an overview of the main clinical studies describing the association between MSI-H/dMMR tumors and immunotherapy response.

Integrating genomic correlation structure improves copy number variations detection

MOTIVATION

Copy number variation plays important roles in human complex diseases. The detection of copy number variants (CNVs) is identifying mean shift in genetic intensities to locate chromosomal breakpoints, the step of which is referred to as chromosomal segmentation. Many segmentation algorithms have been developed with a strong assumption of independent observations in the genetic loci, and they assume each locus has an equal chance to be a breakpoint (i.e. boundary of CNVs). However, this assumption is violated in the genetics perspective due to the existence of correlation among genomic positions, such as linkage disequilibrium (LD). Our study showed that the LD structure is related to the location distribution of CNVs, which indeed presents a non-random pattern on the genome. To generate more accurate CNVs, we proposed a novel algorithm, LDcnv, that models the CNV data with its biological characteristics relating to genetic dependence structure (i.e. LD).

RESULTS

We theoretically demonstrated the correlation structure of CNV data in SNP array, which further supports the necessity of integrating biological structure in statistical methods for CNV detection. Therefore, we developed the LDcnv that integrated the genomic correlation structure with a local search strategy into statistical modeling of the CNV intensities. To evaluate the performance of LDcnv, we conducted extensive simulations and analyzed large-scale HapMap datasets. We showed that LDcnv presented high accuracy, stability and robustness in CNV detection and higher precision in detecting short CNVs compared to existing methods. This new segmentation algorithm has a wide scope of potential application with data from various high-throughput technology platforms.

AVAILABILITY AND IMPLEMENTATION

https://github.com/FeifeiXiaoUSC/LDcnv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A super scalable algorithm for short segment detection

In many applications such as copy number variant (CNV) detection, the goal is to identify short segments on which the observations have different means or medians from the background. Those segments are usually short and hidden in a long sequence, and hence are very challenging to find. We study a super scalable short segment (4S) detection algorithm in this paper. This nonparametric method clusters the locations where the observations exceed a threshold for segment detection. It is computationally efficient and does not rely on Gaussian noise assumption. Moreover, we develop a framework to assign significance levels for detected segments. We demonstrate the advantages of our proposed method by theoretical, simulation, and real data studies.

Updated risk factors to inform early pancreatic cancer screening and identify high risk patients

Pancreatic adenocarcinoma (PDAC) is associated with poor clinical outcomes and incomplete responses to conventional therapy. Therefore, there is an unmet clinical need to better understand the predisposing factors for pancreatic cancer in hopes of providing early screening to high-risk patients. While select risk factors such as age, race, and family history, or predisposing syndromes are unavoidable, there are several new and established risk factors that allow for intervention, namely by counseling patients to make the appropriate lifestyle modifications. Here, we discuss the best-studied risk factors for PDAC such as tobacco use and chronic pancreatitis, as well as newly emerging risk factors including select nutritional deficits, bacterial infections, and psychosocial factors. As several of these risk factors appear to be additive or synergistic, by understanding their relationships and offering coordinated, multidisciplinary care to high-risk patients, it may be possible to reduce pancreatic cancer incidence and improve clinical outcomes through early detection.

Moving the Target on the Optimal Adjuvant Strategy for Resected Pancreatic Cancers: A Systematic Review with Meta-Analysis

Combination regimens have shown superiority over single agents in the adjuvant treatment of resected pancreatic cancer (PC), but there are no data supporting definition of the best regimen. This work aimed to compare the efficacy and safety of mFOLFIRINOX, gemcitabine+capecitabine, and gemcitabine+nab/paclitaxel in PC patients. A meta-analysis was performed for direct comparison between trials comparing combination regimens and gemcitabine monotherapy. Subsequently, an indirect comparison was made between trials investigating the efficacy and safety of mFOLFIRINOX, gemcitabine+capecitabine, and gemcitabine+nab/paclitaxel because of the same control arm (gemcitabine). A total of three studies met the selection criteria and were included in our indirect comparison. Indirect comparisons for efficacy outcomes showed a benefit in terms of DFS (disease-free survival)/EFS (event-free survival)/RFS (relapse-free survival) for both mFOLFIRINOX versus gemcitabine+capecitabine (HR 0.69, 95% CI 0.52–0.91) and versus gemcitabine+nab/paclitaxel (HR 0.67, 95% CI 0.50–0.90). No significant advantage was registered for OS (overall survival). Indirect comparisons for safety showed an increase in terms of G3-5 AEs (with the exception of neutropenia) for mFOLFIRINOX versus gemcitabine+capecitabine (RR 1.24, 95% CI 1.03–1.50), while no significant differences were observed versus gemcitabine+nab/paclitaxel. According to our results, mFOLFIRINOX is feasible and manageable and could represent a first option for fit PC resected patients.

modSaRa: a computationally efficient R package for CNV identification

Summary

Chromosomal copy number variation (CNV) refers to a polymorphism that a DNA segment presents deletion or duplication in the population. The computational algorithms developed to identify this type of variation are usually of high computational complexity. Here we present a user-friendly R package, modSaRa, designed to perform copy number variants identification. The package is developed based on a change-point based method with optimal computational complexity and desirable accuracy. The current version of modSaRa package is a comprehensive tool with integration of preprocessing steps and main CNV calling steps.

Availability and Implementation

modSaRa is an R package written in R, C ++ and Rcpp and is now freely available for download at http://c2s2.yale.edu/software/modSaRa .

Contact

heping.zhang@yale.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Germline copy number variations are associated with breast cancer risk and prognosis

Breast cancer is one of the most common cancers among women, and susceptibility is explained by genetic, lifestyle and environmental components. Copy Number Variants (CNVs) are structural DNA variations that contribute to diverse phenotypes via gene-dosage effects or cis-regulation. In this study, we aimed to identify germline CNVs associated with breast cancer susceptibility and their relevance to prognosis. We performed whole genome CNV genotyping in 422 cases and 348 controls using Human Affymetrix SNP 6 array. Principal component analysis for population stratification revealed 84 outliers leaving 366 cases and 320 controls of Caucasian ancestry for association analysis; CNVs with frequency > 10% and overlapping with protein coding genes were considered for breast cancer risk and prognostic relevance. Coding genes within the CNVs identified were interrogated for gene- dosage effects by correlating copy number status with gene expression profiles in breast tumor tissue. We identified 200 CNVs associated with breast cancer (q-value < 0.05). Of these, 21 CNV regions (overlapping with 22 genes) also showed association with prognosis. We validated representative CNVs overlapping with APOBEC3B and GSTM1 genes using the TaqMan assay. Germline CNVs conferred dosage effects on gene expression in breast tissue. The candidate CNVs identified in this study warrant independent replication.

Genome-wide association study identified copy number variants associated with sporadic colorectal cancer risk

Background

Multiple single nucleotide polymorphisms (SNPs) have been associated with colorectal cancer (CRC) risk. The role of structural or copy number variants (CNV) in CRC, however, remained unclear. We investigated the role of CNVs in patients with sporadic CRC.

Methods

A genome-wide association study (GWAS) was performed on 1000 Singapore Chinese patients aged 50 years or more with no family history of CRC and 1000 ethnicity-matched, age-matched and gender-matched healthy controls using the Affymetrix SNP 6 platform. After 16 principal component corrections, univariate and multivariate segmentations followed by association testing were performed on 1830 samples that passed quality assurance tests.

Results

A rare CNV region (CNVR) at chromosome 14q11 (OR=1.92 (95% CI 1.59 to 2.32), p=2.7e-12) encompassing CHD8, and common CNVR at chromosomes 3q13.12 (OR=1.54 (95% CI 1.33 to 1.77), p=2.9e-9) and 12p12.3 (OR=1.69 (95% CI 1.41 to 2.01), p=2.8e-9) encompassing CD47 and RERG/ARHGDIB, respectively, were significantly associated with CRC risk. CNV loci were validated in an independent replication panel using an optimised copy number assay. Whole-genome expression data in matched tumours of a subset of cases demonstrated that copy number loss at CHD8 was significantly associated with dysregulation of several genes that perturb the Wnt, TP53 and inflammatory pathways.

Conclusions

A rare CNVR at 14q11 encompassing the chromatin modifier CHD8 was significantly associated with sporadic CRC risk. Copy number loss at CHD8 altered expressions of genes implicated in colorectal tumourigenesis.

Whole-genome sequencing reveals oncogenic mutations in mycosis fungoides

The pathogenesis of mycosis fungoides (MF), the most common cutaneous T-cell lymphoma (CTCL), is unknown. Although genetic alterations have been identified, none are considered consistently causative in MF. To identify potential drivers of MF, we performed whole-genome sequencing of MF tumors and matched normal skin. Targeted ultra-deep sequencing of MF samples and exome sequencing of CTCL cell lines were also performed. Multiple mutations were identified that affected the same pathways, including epigenetic, cell-fate regulation, and cytokine signaling, in MF tumors and CTCL cell lines. Specifically, interleukin-2 signaling pathway mutations, including activating Janus kinase 3 (JAK3) mutations, were detected. Treatment with a JAK3 inhibitor significantly reduced CTCL cell survival. Additionally, the mutation data identified 2 other potential contributing factors to MF, ultraviolet light, and a polymorphism in the tumor suppressor p53 (TP53). Therefore, genetic alterations in specific pathways in MF were identified that may be viable, effective new targets for treatment.

Modified screening and ranking algorithm for copy number variation detection

MOTIVATION

Copy number variation (CNV) is a type of structural variation, usually defined as genomic segments that are 1 kb or larger, which present variable copy numbers when compared with a reference genome. The screening and ranking algorithm (SaRa) was recently proposed as an efficient approach for multiple change-points detection, which can be applied to CNV detection. However, some practical issues arise from application of SaRa to single nucleotide polymorphism data.

RESULTS

In this study, we propose a modified SaRa on CNV detection to address these issues. First, we use the quantile normalization on the original intensities to guarantee that the normal mean model-based SaRa is a robust method. Second, a novel normal mixture model coupled with a modified Bayesian information criterion is proposed for candidate change-point selection and further clustering the potential CNV segments to copy number states. Simulations revealed that the modified SaRa became a robust method for identifying change-points and achieved better performance than the circular binary segmentation (CBS) method. By applying the modified SaRa to real data from the HapMap project, we illustrated its performance on detecting CNV segments. In conclusion, our modified SaRa method improves SaRa theoretically and numerically, for identifying CNVs with high-throughput genotyping data.

AVAILABILITY AND IMPLEMENTATION

The modSaRa package is implemented in R program and freely available at http://c2s2.yale.edu/software/modSaRa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Germline copy number variation in the YTHDC2 gene: does it have a role in finding a novel potential molecular target involved in pancreatic adenocarcinoma susceptibility?

OBJECTIVE

The vast majority of pancreatic cancers occurs sporadically. The discovery of frequent variations in germline gene copy number can significantly influence the expression levels of genes that predispose to pancreatic adenocarcinoma. We prospectively investigated whether patients with sporadic pancreatic adenocarcinoma share specific gene copy number variations (CNVs) in their germline DNA.

PATIENTS AND METHODS

DNA samples were analyzed from peripheral leukocytes from 72 patients with a diagnosis of sporadic pancreatic adenocarcinoma and from 60 controls using Affymetrix 500K array set. Multiplex ligation-dependent probe amplification (MLPA) assay was performed using a set of self-designed MLPA probes specific for seven target sequences.

RESULTS

We identified a CNV-containing DNA region associated with pancreatic cancer risk. This region shows a deletion of 1 allele in 36 of the 72 analyzed patients but in none of the controls. This region is of particular interest since it contains the YTHDC2 gene encoding for a putative DNA/RNA helicase, such protein being frequently involved in cancer susceptibility. Interestingly, 82.6% of Sicilian patients showed germline loss of one allele.

CONCLUSIONS

Our results suggest that the YTHDC2 gene could be a potential candidate for pancreatic cancer susceptibility and a useful marker for early detection as well as for the development of possible new therapeutic strategies.