iWhale: a computational pipeline based on Docker and SCons for detection and annotation of somatic variants in cancer WES data

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data

BACKGROUND

High-throughput sequencing (HTS) offers unprecedented opportunities for the discovery of causative gene variants in multiple human disorders including cancers, and has revolutionized clinical diagnostics. However, despite more than a decade of use of HTS-based assays, extracting relevant functional information from whole-exome sequencing (WES) data remains challenging, especially for non-specialists lacking in-depth bioinformatic skills.

RESULTS

To address this limitation, we developed Var∣Decrypt, a web-based tool designed to greatly facilitate WES data browsing and analysis. Var∣Decrypt offers a wide range of gene and variant filtering possibilities, clustering and enrichment tools, providing an efficient way to derive patient-specific functional information and to prioritize gene variants for functional analyses. We applied Var∣Decrypt on WES datasets of 10 acute erythroid leukemia patients, a rare and aggressive form of leukemia, and recovered known disease oncogenes in addition to novel putative drivers. We additionally validated the performance of Var∣Decrypt using an independent dataset of ~ 90 multiple myeloma WES, recapitulating the identified deregulated genes and pathways, showing the general applicability and versatility of Var∣Decrypt for WES analysis.

CONCLUSION

Despite years of use of WES in human health for diagnosis and discovery of disease drivers, WES data analysis still remains a complex task requiring advanced bioinformatic skills. In that context, there is a need for user-friendly all-in-one dedicated tools for data analysis, to allow biologists and clinicians to extract relevant biological information from patient datasets. Here, we provide Var∣Decrypt (trial version accessible here: https://vardecrypt.com/app/vardecrypt ), a simple and intuitive Rshiny application created to fill this gap. Source code and detailed user tutorial are available at https://gitlab.com/mohammadsalma/vardecrypt .

Molecular profiling of primary uveal melanoma: results of a Polish cohort

There is no published data regarding the molecular alterations of Polish patients with primary uveal melanoma. We performed whole exome sequencing of 20 primary uveal melanomas (UMs), 10 metastasizing and 10 non-metastasizing cases to identify significant molecular alterations. We detected mutations and copy number variants in the BAP1 gene in 50% (10 cases) of the cases. GNA11 mutations were detected in 50% (10 cases) including nine p.Q209L and one p.R183C. GNAQ mutations gene were detected in 40% (8 cases) and all were p.Q209P. SF3B1, EIF1AX, PLCB4 , and PALB2 mutations were detected in one case each. Genetic aberrations of FBXW7 were detected in 55% of cases, with copy number loss of 10 and missense mutation in one. Gain or loss of copy number was observed in 60%, 60%, and 10% of cases in MYC, MLH1 , and CDKN2A genes, respectively. BAP1 and GNAQ tumor suppressor genes are more often mutated in UM with metastasis, while GNA11 mutations are more frequently detected in non-metastasizing tumors. MYC copy gain was present twice as frequently (80% versus 40%) in cases with versus those without metastases. BAP1 mutation correlated with worse overall survival; while GNA11 mutation and CDKN2A loss correlated with better and worse progression-free survival, respectively. We have confirmed BAP1 prognostic potential and documented frequent MYC amplification in metastasizing cases. Although GNA11 mutation and CDKN2A loss significantly correlated with progression-free survival in our study, our sample size is small. The prognostic significance of GNAQ/GNA11 mutation and CDKN2A loss would require further investigation.

Construction of a 5-methylcytosine-Related Molecular Signature to Inform the Prognosis and Immunotherapy of Lung Squamous Cell Carcinoma

BACKGROUND

Methylation of cytosine residues resulting in 5-methylcytosine (5-mC) is an important epigenetic modification associated with tumorigenesis. The present study explored the relationship between methylation, prognosis, and immunotherapy of patients suffering from lung squamous cell carcinoma (LUSC).

METHODS

RNA sequencing data and corresponding clinical information were downloaded, and preprocessed, and unsupervised consistent cluster analysis was used to identify 5-mC-related clusters and gene clusters. 5-mC scores were calculated using principal component analysis, and a Boruta algorithm was used to evaluate the relationship between tumor mutation burden (TMB), immune checkpoint inhibitor response, and prognosis of individual LUSC patients.

RESULTS

: Two 5-mC clusters and three gene clusters with different prognoses were identified. Patients with higher 5-mC scores showed worse prognoses, which was confirmed in multiple cohorts. Some immune-related biological functions and pathways were enriched in the high-5-mC score subtype.

CONCLUSION

The 5-mC score is a potential biomarker independent of TMB, which can be a decisive factor regarding immune treatment responses. Further, patients with low 5-mC scores may respond better to immunotherapy. The 5-mC score can thus be used as a potential biomarker for the prognosis of LUSC patients and their response to immunotherapy.

A Novel Ferroptosis-Related LncRNA Pair Prognostic Signature Predicts Immune Landscapes and Treatment Responses for Gastric Cancer Patients

Background: The construction of ferroptosis-related lncRNA prognostic models in malignancies has been an intense area of research recently. However, most of the studies focused on the exact expression of lncRNAs and had limited application values. Herein, we aim to establish a novel prognostic model for gastric cancer (GC) patients and discuss its correlation with immune landscapes and treatment responses.

Methods: The present study retrieved transcriptional data of GC patients from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed ferroptosis-related lncRNAs between tumor and normal controls of GC samples. Based on a new method of cyclically single pairing, we constructed a 0 or 1 matrix of ferroptosis-related lncRNA pairs (FRLPs). A risk score signature consisting of 10 FRLPs was established using multi-step Cox regression analysis. Next, we performed a series of systematic analyses to investigate the association of the FRLP model and tumor microenvironment, biological function, and treatment responses. An alternative model to the FRLP risk score signature, the gene set score (GS) model was also constructed, which could represent the former when lncRNA expression was not available.

Results: We established a novel prognostic signature of 10 ferroptosis-related lncRNA pairs. High-risk patients in our risk score model were characterized by high infiltration of immune cells, upregulated carcinogenic and stromal activities, and heightened sensitivity to a wide range of anti-tumor drugs, whereas low-risk patients were associated with better responses to methotrexate treatment and elevated immunotherapeutic sensitivity. The practicability of the FRLP risk score model was also validated in two independent microarray datasets downloaded from Gene Expression Omnibus (GEO) using the GS model. Finally, two online dynamic nomograms were built to enhance the clinical utility of the study.

Conclusion: In this study, we developed a ferroptosis-related lncRNA pair-based risk score model that did not rely on the exact lncRNA expression level. This novel model might provide insights for the accurate prediction and comprehensive management for GC patients.

Comprehensive analysis of the prognostic value and immune implications of the TTK gene in lung adenocarcinoma: a meta-analysis and bioinformatics analysis

Background

High expression levels of the TTK gene are closely related to tumor occurrence and poor prognosis, as confirmed by some studies. Our study explored the prognosis of lung adenocarcinoma (LUAD) patients with different TTK levels and the possible pathological mechanism of TTK in LUAD.

Methods

We extensively searched literature databases and high-throughput sequencing databases and included relevant literature or datasets in the meta-analysis according to the inclusion and exclusion criteria. Hazard ratios (HRs) and 95% confidence intervals (CIs) related to TTK expression were calculated, publication bias was assessed, and sensitivity tests were performed. We also compared the relationship between cancer immune infiltrating cells and tumor mutation burden (TMB) in patients with different TTK expression levels via bioinformatics analysis. The half maximal inhibitory concentration (IC50) of some chemotherapeutic and targeted therapy drugs were calculated. The potential biological functions or pathways associated with different TTK expression levels were determined by gene set enrichment analysis (GSEA).

Results

The meta-analysis revealed that higher TTK expression level was significantly associated with poor prognosis in LUAD patients, both in overall survival (OS) and progression-free survival (PFS). The expression level of TTK was significantly correlated with presence of some immune cells and TMB. Tumors with higher TTK expression levels were mostly enriched for the cell cycle, DNA replication and homologous recombination pathways. In addition, patients with different TTK expression levels were differently sensitive to some antitumor drugs.

Conclusion

TTK may be a promising prognostic biomarker for LUAD and is worthy of further investigation.

Clinical significance of circulating tumor cells and cell-free DNA in pediatric rhabdomyosarcoma

Liquid biopsy analysis represents a powerful and noninvasive tool to uncover biomarkers for disseminated disease assessment and longitudinal monitoring of patients. Herein, we explored the value of circulating and disseminated tumor cells (CTC and DTC, respectively) and cell‐free DNA (cfDNA) in pediatric rhabdomyosarcoma (RMS). Peripheral blood and bone marrow samples were analyzed to detect and enumerate CTC and DTC, respectively. We used the epithelial cellular adhesion molecule (EpCAM)‐based CellSearch platform coupled with an automatic device to collect both EpCAM‐positive and EpCAM‐low/negative CTCs. The standard assay was implemented, including the mesenchymal marker desmin. For selected cases, we molecularly profiled primary tumors and liquid biopsy biomarkers using whole‐exome sequencing and droplet digital PCR, respectively. RMS patients with metastatic disease had a significantly higher number of CTCs compared to those with localized disease, whereas DTCs were detected independently of disease presentation. The use of the desmin marker remarkably increased the identification of CTCs and DTCs in RMS samples. Of note, CTC clusters were detected in RMS patients with disseminated disease. Further, cfDNA and CTC molecular features closely reflected the molecular makeup of primary tumors and informed of disease course.

Applying immune-related lncRNA pairs to construct a prognostic signature and predict the immune landscape of stomach adenocarcinoma

Background: Long noncoding RNAs (lncRNAs) are associated with the survival of cancer patients. We constructed an immune-related lncRNA (irlncRNA) pair signature for stomach adenocarcinoma (STAD).Research design and methods: irlncRNAs were identified via coexpression analysis with immune-related genes. Differentially expressed irlncRNAs (DEirlncRNAs) were paired. Least absolute shrinkage and selection operator (LASSO) and multivariate Cox proportional hazards regression methods were used to construct the signature. We calculated the area under the receiver operating characteristic (ROC) curve and determined the best cutoff value according to the Akaike information criterion (AIC). Patients were divided into high - and low-risk groups, and differences in immune cell infiltration, tumor mutation burden (TMB) and drug treatment effects between the groups were explored according to the risk score.Results: An 8-irlncRNA-pair signature was constructed and proven to be a strong prognosis predictor in STAD patients through external verification. Moreover, the risk score was identified as an independent prognostic factor. There were significant differences in immune cell infiltration and the response to several drug treatments between patients with high and low risk scores, and the risk score was negatively correlated with TMB.Conclusions: The signature consisting of 8 irlncRNA pairs showed good prognostic predictive value.

DIVIS: Integrated and Customizable Pipeline for Cancer Genome Sequencing Analysis and Interpretation

Next-generation sequencing (NGS) has drastically enhanced human cancer research, but diverse sequencing strategies, complicated open-source software, and the identification of massive numbers of mutations have limited the clinical application of NGS. Here, we first presented GPyFlow, a lightweight tool that flexibly customizes, executes, and shares workflows. We then introduced DIVIS, a customizable pipeline based on GPyFlow that integrates read preprocessing, alignment, variant detection, and annotation of whole-genome sequencing, whole-exome sequencing, and gene-panel sequencing. By default, DIVIS screens variants from multiple callers and generates a standard variant-detection format list containing caller evidence for each sample, which is compatible with advanced analyses. Lastly, DIVIS generates a statistical report, including command lines, parameters, quality-control indicators, and mutation summary. DIVIS substantially facilitates complex cancer genome sequencing analyses by means of a single powerful and easy-to-use command. The DIVIS code is freely available at https://github.com/niu-lab/DIVIS, and the docker image can be downloaded from https://hub.docker.com/repository/docker/sunshinerain/divis.