Integrative annotation of variants from 1092 humans: application to cancer genomics

Advancing targeted therapies in pancreatic cancer: Leveraging molecular abberrations for therapeutic success

Pancreatic cancer is one of the most deadly with poor prognosis and overall survival rate due to the dense stroma in the tumors which often is challenging for the delivery of drug to penetrate deep inside the tumor bed and usually results in the progression of cancer. The conventional treatment such as chemotherapy, radiotherapy or surgery shows a minimal benefit in the survival due to the drug resistance, poor penetration, less radiosensitivity or recurrence of tumor. There is an urgent demand to develop molecular-level targeted therapies to achieve therapeutic efficacy in the pancreatic ductal adenocarcinoma (PDAC) patients. The precision oncology focuses on the unique attributes of the patient such as epigenome, proteome, genome, microbiome, lifestyle and diet habits which contributes to promote oncogenesis. The targeted therapy helps to target the mutated proteins responsible for controlling growth, division and metastasis of tumor in the cancer cells. It is very important to consider all the attributes of the patient to provide the suitable personalized treatment to avoid any severe side effects. In this review, we have laid emphasis on the precision medicine; the utmost priority is to improve the survival of cancer patients by targeting molecular mutations through transmembrane proteins, inhibitors, signaling pathways, immunotherapy, gene therapy or the use of nanocarriers for the delivery at the tumor site. It will become beneficial therapeutic window to be considered for the advanced stage pancreatic cancer patients to prolong their survival rate.

Polymorphisms within autophagy-related genes as susceptibility biomarkers for pancreatic cancer: A meta-analysis of three large European cohorts and functional characterization

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with patients having unresectable or metastatic disease at diagnosis, with poor prognosis and very short survival. Given that genetic variation within autophagy-related genes influences autophagic flux and susceptibility to solid cancers, we decided to investigate whether 55,583 single nucleotide polymorphisms (SNPs) within 234 autophagy-related genes could influence the risk of developing PDAC in three large independent cohorts of European ancestry including 12,754 PDAC cases and 324,926 controls. The meta-analysis of these populations identified, for the first time, the association of the BIDrs9604789 variant with an increased risk of developing the disease (ORMeta = 1.31, p = 9.67 × 10-6). We also confirmed the association of TP63rs1515496 and TP63rs35389543 variants with PDAC risk (OR = 0.89, p = 6.27 × 10-8 and OR = 1.16, p = 2.74 × 10-5). Although it is known that BID induces autophagy and TP63 promotes cell growth, cell motility and invasion, we also found that carriers of the TP63rs1515496G allele had increased numbers of FOXP3+ Helios+ T regulatory cells and CD45RA+ T regulatory cells (p = 7.67 × 10-4 and p = 1.56 × 10-3), but also decreased levels of CD4+ T regulatory cells (p = 7.86 × 10-4). These results were in agreement with research suggesting that the TP63rs1515496 variant alters binding sites for FOXA1 and CTCF, which are transcription factors involved in modulating specific subsets of regulatory T cells. In conclusion, this study identifies BID as new susceptibility locus for PDAC and confirms previous studies suggesting that the TP63 gene is involved in the development of PDAC. This study also suggests new pathogenic mechanisms of the TP63 locus in PDAC.

Noncoding variation near UBE2E2 orchestrates cardiometabolic pathophenotypes through polygenic effectors

Mechanisms underpinning signals from genome-wide association studies remain poorly understood, particularly for noncoding variation and for complex diseases such as type 2 diabetes mellitus (T2D) where pathogenic mechanisms in multiple different tissues may be disease driving. One approach is to study relevant endophenotypes, a strategy we applied to the UBE2E2 locus where noncoding single nucleotide variants (SNVs) are associated with both T2D and visceral adiposity (a pathologic endophenotype). We integrated CRISPR targeting of SNV-containing regions and unbiased CRISPR interference (CRISPRi) screening to establish candidate cis-regulatory regions, complemented by genetic loss of function in murine diet-induced obesity or ex vivo adipogenesis assays. Nomination of a single causal gene was complicated, however, because targeting of multiple genes near UBE2E2 attenuated adipogenesis in vitro; CRISPR excision of SNV-containing noncoding regions and a CRISPRi regulatory screen across the locus suggested concomitant regulation of UBE2E2, the more distant UBE2E1, and other neighborhood genes; and compound heterozygous loss of function of both Ube2e2 and Ube2e1 better replicated pathological adiposity and metabolic phenotypes compared with homozygous loss of either gene in isolation. This study advances a model whereby regulatory effects of noncoding variation not only extend beyond the nearest gene but may also drive complex diseases through polygenic regulatory effects.

Melanoma-specific mutation hotspots in distal, non-coding, promoter-interacting regions implicate novel candidate driver genes

BACKGROUND

To develop targeted treatments, it is crucial to identify the full spectrum of genetic drivers in melanoma, including those in non-coding regions. However, recent efforts to explore non-coding regions have primarily focused on gene-adjacent elements such as promoters and non-coding RNAs, leaving intergenic distal regulatory elements largely unexplored.

METHODS

We used Hi-C chromatin contact data from melanoma cells to map distal, non-coding, promoter-interacting regulatory elements genome-wide in melanoma. Using this "promoter-interaction network", alongside whole-genome sequence and gene expression data from the Pan Cancer Analysis of Whole Genomes, we developed multivariate linear regression models to identify distal somatic mutation hotspots that affect promoter activity.

RESULTS

We identified eight recurrently mutated hotspots that are novel, melanoma-specific, located in promoter-interacting distal regulatory elements, alter transcription factor binding motifs, and affect the expression of genes (e.g., HSPB7, CLDN1, ADCY9 and FDXR) previously implicated as tumour suppressors/oncogenes in various cancers.

CONCLUSIONS

Our study suggests additional non-coding drivers beyond the well-characterised TERT promoter in melanoma, offering new insights into the disruption of complex regulatory networks by non-coding mutations that may contribute to melanoma development. Furthermore, our study provides a framework for integrating multiple levels of biological data to uncover cancer-specific non-coding drivers.

Oncogenic Enhancers in Leukemia

Although the study of leukemogenesis has traditionally focused on protein-coding genes, the role of enhancer dysregulation is becoming increasingly recognized. The advent of high-throughput sequencing, together with a better understanding of enhancer biology, has revealed how various genetic and epigenetic lesions produce oncogenic enhancers that drive transformation. These aberrations include translocations that lead to enhancer hijacking, point mutations that modulate enhancer activity, and copy number alterations that modify enhancer dosage. In this review, we describe these mechanisms in the context of leukemia and discuss potential therapeutic avenues to target these regulatory elements. Significance: Large-scale sequencing projects have uncovered recurrent gene mutations in leukemia, but the picture remains incomplete: some patients harbor no such aberrations, whereas others carry only a few that are insufficient to bring about transformation on their own. One of the missing pieces is enhancer dysfunction, which only recently has emerged as a critical driver of leukemogenesis. Knowledge of the various mechanisms of enhancer dysregulation is thus key for a complete understanding of leukemia and its causes, as well as the development of targeted therapies in the era of precision medicine.

Getting to know ovarian cancer: Focusing on the effect of LncRNAs in this cancer and the effective signaling pathways

This article undertakes a comprehensive investigation of ovarian cancer, examining the complex nature of this challenging disease. The main focus is on understanding the role of long non-coding RNAs (lncRNAs) in the context of ovarian cancer (OC), and their regulatory functions in disease progression. Through extensive research, the article identifies specific lncRNAs that play significant roles in the intricate molecular processes of OC. Furthermore, the study examines the signaling pathways involved in the development of OC, providing a detailed comprehension of the underlying molecular mechanisms. By connecting lncRNA dynamics with signaling pathways, this exploration not only advances our understanding of ovarian cancer but also reveals potential targets for therapeutic interventions. The findings open up opportunities for targeted treatments, highlighting the importance of personalized approaches in addressing this complex disease and driving progress in ovarian cancer research and treatment strategies.

Allele-specific binding (ASB) analyzer for annotation of allele-specific binding SNPs

BACKGROUND

Allele-specific binding (ASB) events occur when transcription factors (TFs) bind more favorably to one of the two parental alleles at heterozygous single nucleotide polymorphisms (SNPs). Evidence suggests that ASB events could reveal the impact of sequence variations on TF binding and may have implications for the risk of diseases.

RESULTS

Here we present ASB-analyzer, a software platform that enables the users to quickly and efficiently input raw sequencing data to generate individual reports containing the cytogenetic map of ASB SNPs and their associated phenotypes. This interactive tool thereby combines ASB SNP identification, biological annotation, motif analysis, phenotype associations and report summary in one pipeline. With this pipeline, we identified 3772 ASB SNPs from thirty GM12878 ChIP-seq datasets and demonstrated that the ASB SNPs were more likely to be enriched at important sites in TF-binding domains.

CONCLUSIONS

ASB-analyzer is a user-friendly tool that enables the detection, characterization and visualization of ASB SNPs. It is implemented in Python, R and bash shell and packaged in the Conda environment. It is available as an open-source tool on GitHub at https://github.com/Liying1996/ASBanalyzer .

Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data

Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.

Two germline mutations can serve as genetic susceptibility screening makers for a lung adenocarcinoma family

OBJECTIVES

Lung cancer is the most common form of cancer and the leading cause of cancer death. For familial lung cancer, identification of causing genetic factors is essential for prevention and control of non-lung cancer in carriers.

MATERIALS AND METHODS

We studied two generations of a family with suspected inherited lung cancer susceptibility. Four individuals in this family had lung adenocarcinoma. To identify the gene(s) that cause the lung cancer in this pedigree, we extracted DNA from the peripheral blood of four cancer individuals and blood from three cancer-free family members as the control and performed whole-genome sequencing. Our filtering strategy includes, assessment of allele frequency, functional affection on amino acids, mutation accumulation, phased blocks and evolution analysis towards the alterations.

RESULTS

We identified two possible mutations, including PLEKHM2 (D134N) and MCC (R448Q) in all affected family members but did not found in the control group. Then, we performed a genetic susceptibility screening for 10 non-lung cancer relatives and found two individuals with PLEKHM2 (D134N) mutation, two with MCC (R448Q) mutation and one carrying both mutations. 3 carriers performed LDCT scan and 2 of them carried MCC (R448Q) also had ground-glass opacity (GGO) lesion in their lung.

CONCLUSION

Our data suggested that WGS together with our filtering strategy was successful in identifying PLEKHM2 (D134N) and MCC (R448Q) as the possible driver mutations in this family. Genetic susceptibility screening of non-lung cancer carriers will be a useful approach to prevent and control lung cancer in families with high-risk for the disease.

The Role of Long Noncoding Ribonucleic Acids in Glioblastoma: What the Neurosurgeon Should Know

A significant proportion of the human transcriptome, long noncoding RNAs (lncRNAs) play pivotal roles in several aspects of glioblastoma (GBM) pathophysiology including proliferation, invasion, radiation and temozolomide resistance, and immune modulation. The majority of lncRNAs exhibit tissue- and tumor-specific expression, lending them to be attractive targets for therapeutic translation. In recent years, unprecedented progress has been made toward our understanding of lncRNA in GBM. In this review, we discuss the function of lncRNAs, including specific lncRNAs that have critical roles in key aspects of GBM pathophysiology, and potential clinical relevance of lncRNAs for patients with GBM.

Unified views on variant impact across many diseases

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.

Identification of Driver Epistatic Gene Pairs Combining Germline and Somatic Mutations in Cancer

Cancer arises from the complex interplay of various factors. Traditionally, the identification of driver genes focuses primarily on the analysis of somatic mutations. We describe a new method for the detection of driver gene pairs based on an epistasis analysis that considers both germline and somatic variations. Specifically, the identification of significantly mutated gene pairs entails the calculation of a contingency table, wherein one of the co-mutated genes can exhibit a germline variant. By adopting this approach, it is possible to select gene pairs in which the individual genes do not exhibit significant associations with cancer. Finally, a survival analysis is used to select clinically relevant gene pairs. To test the efficacy of the new algorithm, we analyzed the colon adenocarcinoma (COAD) and lung adenocarcinoma (LUAD) samples available at The Cancer Genome Atlas (TCGA). In the analysis of the COAD and LUAD samples, we identify epistatic gene pairs significantly mutated in tumor tissue with respect to normal tissue. We believe that further analysis of the gene pairs detected by our method will unveil new biological insights, enhancing a better description of the cancer mechanism.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Emerging role of oncogenic long noncoding RNA as cancer biomarkers

The view of long noncoding RNAs as nonfunctional "garbage" has been definitely outdated by the large body of evidence indicating this class of ncRNAs as "golden junk", especially in precision oncology. Indeed, in light of their oncogenic role and the higher expression in multiple cancer types compared with paired adjacent tissues, the clinical interest for lncRNAs as diagnostic and/or prognostic biomarkers has been rapidly increasing. The emergence of large-scale sequencing technologies, their subsequent diffusion even in small research and clinical centers, the technological advances for the detection of low-copy lncRNAs in body fluids, coupled to the huge reduction of operating costs, have nowadays made possible to rapidly and comprehensively profile them in multiple tumors and large cohorts. In this review, we first summarize some relevant data about the oncogenic role of well-studied lncRNAs having a clinical relevance. Then, we focus on the description of their potential use as diagnostic/prognostic biomarkers, including an updated overview about licensed patents or clinical trials on lncRNAs in oncology.

WMDS.net: a network control framework for identifying key players in transcriptome programs

MOTIVATION

Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes.

RESULTS

To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall.

AVAILABILITY AND IMPLEMENTATION

https://github.com/chaofen123/WMDS.net.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrated multi-dimensional analysis highlights DHCR7 mutations involving in cholesterol biosynthesis and contributing therapy of gastric cancer

BACKGROUND

Genetic background plays an important role in the occurrence and development of gastric cancer (GC). With the application of genome-wide association study (GWAS), an increasing number of tumor susceptibility genes in gastric cancer have been discovered. While little of them can be further applicated in clinical diagnosis and treatment due to the lack of in-depth analysis.

METHODS

A GWAS of peripheral blood leukocytes from GC patients was performed to identify and obtain genetic background data. In combination with a clinical investigation, key SNP mutations and mutated genes were screened. Via in vitro and in vivo experiments, the function of the mutated gene was verified in GC. Via a combination of molecular function studies and amino acid network analysis, co-mutations were discovered and further identified as potential therapeutic targets.

RESULTS

At the genetic level, the G allele of rs104886038 in DHCR7 was a protective factor identified by the GWAS. Clinical investigation showed that patients with the rs104886038 A/G genotype, age ≥ 60, smoking ≥ 10 cigarettes/day, heavy drinking and H. pylori infection were independent risk factors for GC, with odds ratios of 12.33 (95% CI, 2.10 ~ 72.54), 20.42 (95% CI, 2.46 ~ 169.83), and 11.39 (95% CI, 1.82 ~ 71.21), respectively. Then molecular function studies indicated that DHCR7 regulated cell proliferation, migration, and invasion as well as apoptosis resistance via cellular cholesterol biosynthesis pathway. Further amino acid network analysis based on the predicted structure of DHCR7 and experimental verification indicated that rs104886035 and rs104886038 co-mutation reduced the stability of DHCR7 and induced its degradation. DHCR7 mutation suppressed the malignant behaviour of GC cells and induced apoptosis via inhibition on cell cholesterol biosynthesis.

CONCLUSION

In this work, we provided a comprehensive multi-dimensional analysis strategy which can be applied to in-depth exploration of GWAS data. DHCR7 and its mutation sites identified by this strategy are potential theratic targets of GC via inhibition of cholesterol biosynthesis.

Development of bioinformatics and multi-omics analyses in organoids

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].

Identifying driver modules based on multi-omics biological networks in prostate cancer

The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi-omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors' method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.

Disease-associated human genetic variation through the lens of precursor and mature RNA structure

Disease-associated variants (DAVs) are commonly considered either through a genomic lens that describes variant function at the DNA level, or at the protein function level if the variant is translated. Although the genomic and proteomic effects of variation are well-characterized, genetic variants disrupting post-transcriptional regulation is another mechanism of disease that remains understudied. Specific RNA sequence motifs mediate post-transcriptional regulation both in the nucleus and cytoplasm of eukaryotic cells, often by binding to RNA-binding proteins or other RNAs. However, many DAVs map far from these motifs, which suggests deeper layers of post-transcriptional mechanistic control. Here, we consider a transcriptomic framework to outline the importance of post-transcriptional regulation as a mechanism of disease-causing single-nucleotide variation in the human genome. We first describe the composition of the human transcriptome and the importance of abundant yet overlooked components such as introns and untranslated regions (UTRs) of messenger RNAs (mRNAs). We present an analysis of Human Gene Mutation Database variants mapping to mRNAs and examine the distribution of causative disease-associated variation across the transcriptome. Although our analysis confirms the importance of post-transcriptional regulatory motifs, a majority of DAVs do not directly map to known regulatory motifs. Therefore, we review evidence that regions outside these well-characterized motifs can regulate function by RNA structure-mediated mechanisms in all four elements of an mRNA: exons, introns, 5' and 3' UTRs. To this end, we review published examples of riboSNitches, which are single-nucleotide variants that result in a change in RNA structure that is causative of the disease phenotype. In this review, we present the current state of knowledge of how DAVs act at the transcriptome level, both through altering post-transcriptional regulatory motifs and by the effects of RNA structure.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

The 1000 Genomes Project (1kGP) is the largest fully open resource of whole-genome sequencing (WGS) data consented for public distribution without access or use restrictions. The final, phase 3 release of the 1kGP included 2,504 unrelated samples from 26 populations and was based primarily on low-coverage WGS. Here, we present a high-coverage 3,202-sample WGS 1kGP resource, which now includes 602 complete trios, sequenced to a depth of 30X using Illumina. We performed single-nucleotide variant (SNV) and short insertion and deletion (INDEL) discovery and generated a comprehensive set of structural variants (SVs) by integrating multiple analytic methods through a machine learning model. We show gains in sensitivity and precision of variant calls compared to phase 3, especially among rare SNVs as well as INDELs and SVs spanning frequency spectrum. We also generated an improved reference imputation panel, making variants discovered here accessible for association studies.

DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)-to-gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET's predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation-specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2

Current somatic mutation callers are biased against repetitive regions, preventing the identification of potential driver alterations in these loci. We developed a mutation caller for repetitive regions, and applied it to study repetitive non protein-coding genes in more than 2200 whole-genome cases. We identified a recurrent mutation at position c.28 in the gene encoding the snRNA U2. This mutation is present in B-cell derived tumors, as well as in prostate and pancreatic cancer, suggesting U2 c.28 constitutes a driver candidate associated with worse prognosis. We showed that the GRCh37 reference genome is incomplete, lacking the U2 cluster in chromosome 17, preventing the identification of mutations in this gene. Furthermore, the 5′-flanking region of WDR74, previously described as frequently mutated in cancer, constitutes a functional copy of U2. These data reinforce the relevance of non-coding mutations in cancer, and highlight current challenges of cancer genomic research in characterizing mutations affecting repetitive genes.

Whole-genome landscape of adult T-cell leukemia/lymphoma

Adult T-cell leukemia/lymphoma (ATL) is an aggressive neoplasm immunophenotypically resembling regulatory T cells, associated with human T-cell leukemia virus type-1. Here, we performed whole-genome sequencing (WGS) of 150 ATL cases to reveal the overarching landscape of genetic alterations in ATL. We discovered frequent (33%) loss-of-function alterations preferentially targeting the CIC long isoform, which were overlooked by previous exome-centric studies of various cancer types. Long but not short isoform-specific inactivation of Cic selectively increased CD4+CD25+Foxp3+ T cells in vivo. We also found recurrent (13%) 3'-truncations of REL, which induce transcriptional upregulation and generate gain-of-function proteins. More importantly, REL truncations are also common in diffuse large B-cell lymphoma, especially in germinal center B-cell-like subtype (12%). In the non-coding genome, we identified recurrent mutations in regulatory elements, particularly splice sites, of several driver genes. In addition, we characterized the different mutational processes operative in clustered hypermutation sites within and outside immunoglobulin/T-cell receptor genes and identified the mutational enrichment at the binding sites of host and viral transcription factors, suggesting their activities in ATL. By combining the analyses for coding and noncoding mutations, structural variations, and copy number alterations, we discovered 56 recurrently altered driver genes, including 11 novel ones. Finally, ATL cases were classified into 2 molecular groups with distinct clinical and genetic characteristics based on the driver alteration profile. Our findings not only help to improve diagnostic and therapeutic strategies in ATL, but also provide insights into T-cell biology and have implications for genome-wide cancer driver discovery.

Somatic mutations enriched in cis-regulatory elements affect genes involved in embryonic development and immune system response in neuroblastoma

Noncoding cis-regulatory variants have gained interest as cancer drivers, yet progress in understanding their significance is hindered by the numerous challenges and limitations of variant prioritization. To overcome these limitations, we focused on active cis-regulatory elements (aCRE) in order to design a customized panel for the deep sequencing of 56 neuroblastoma tumor and normal DNA sample pairs. In order to search for driver mutations, aCREs were defined by reanalysis of H3K27ac ChiP-seq peaks in 25 neuroblastoma cell lines. These regulatory genomic regions were tested for an excess of somatic mutations and assessed for statistical significance using a global approach that accounted for chromatin accessibility and replication timing. Additional validation was provided by whole genome sequence analysis of 151 neuroblastomas. Analysis of Hi-C data determined the presence of candidate target genes interacting with mutated regions. An excess of somatic mutations in aCREs of diverse genes were identified, including IPO7, HAND2, and ARID3A. CRISPR-Cas9 editing was utilized to assess the functional consequences of mutations in the IPO7 aCRE. Patients with noncoding mutations in aCREs showed inferior overall and event-free survival independent of age at diagnosis, stage, risk stratification, and MYCN status. Expression of aCRE-interacting genes correlated strongly with negative prognostic markers and low survival rates. Moreover, a convergence between the biological functions of aCRE target genes and transcription factors with mutated binding motifs was associated with embryonic development and immune system response. Overall, this strategy enabled the identification of somatic mutations in regulatory elements that collectively promote neuroblastoma tumorigenesis.

Molecular Mechanisms of lncRNAs in the Dependent Regulation of Cancer and Their Potential Therapeutic Use

Deep whole genome and transcriptome sequencing have highlighted the importance of an emerging class of non-coding RNA longer than 200 nucleotides (i.e., long non-coding RNAs (lncRNAs)) that are involved in multiple cellular processes such as cell differentiation, embryonic development, and tissue homeostasis. Cancer is a prime example derived from a loss of homeostasis, primarily caused by genetic alterations both in the genomic and epigenetic landscape, which results in deregulation of the gene networks. Deregulation of the expression of many lncRNAs in samples, tissues or patients has been pointed out as a molecular regulator in carcinogenesis, with them acting as oncogenes or tumor suppressor genes. Herein, we summarize the distinct molecular regulatory mechanisms described in literature in which lncRNAs modulate carcinogenesis, emphasizing epigenetic and genetic alterations in particular. Furthermore, we also reviewed the current strategies used to block lncRNA oncogenic functions and their usefulness as potential therapeutic targets in several carcinomas.

Analysis of the Batch Effect Due to Sequencing Center in Population Statistics Quantifying Rare Events in the 1000 Genomes Project

The 1000 Genomes Project (1000G) is one of the most popular whole genome sequencing datasets used in different genomics fields and has boosting our knowledge in medical and population genomics, among other fields. Recent studies have reported the presence of ghost mutation signals in the 1000G. Furthermore, studies have shown that these mutations can influence the outcomes of follow-up studies based on the genetic variation of 1000G, such as single nucleotide variants (SNV) imputation. While the overall effect of these ghost mutations can be considered negligible for common genetic variants in many populations, the potential bias remains unclear when studying low frequency genetic variants in the population. In this study, we analyze the effect of the sequencing center in predicted loss of function (LoF) alleles, the number of singletons, and the patterns of archaic introgression in the 1000G. Our results support previous studies showing that the sequencing center is associated with LoF and singletons independent of the population that is considered. Furthermore, we observed that patterns of archaic introgression were distorted for some populations depending on the sequencing center. When analyzing the frequency of SNPs showing extreme patterns of genotype differentiation among centers for CEU, YRI, CHB, and JPT, we observed that the magnitude of the sequencing batch effect was stronger at MAF < 0.2 and showed different profiles between CHB and the other populations. All these results suggest that data from 1000G must be interpreted with caution when considering statistics using variants at low frequency.

CTCF binding modulates UV damage formation to promote mutation hot spots in melanoma

Somatic mutations in DNA-binding sites for CCCTC-binding factor (CTCF) are significantly elevated in many cancers. Prior analysis has suggested that elevated mutation rates at CTCF-binding sites in skin cancers are a consequence of the CTCF-cohesin complex inhibiting repair of UV damage. Here, we show that CTCF binding modulates the formation of UV damage to induce mutation hot spots. Analysis of genome-wide CPD-seq data in UV-irradiated human cells indicates that formation of UV-induced cyclobutane pyrimidine dimers (CPDs) is primarily suppressed by CTCF binding but elevated at specific locations within the CTCF motif. Locations of CPD hot spots in the CTCF-binding motif coincide with mutation hot spots in melanoma. A similar pattern of damage formation is observed at CTCF-binding sites in vitro, indicating that UV damage modulation is a direct consequence of CTCF binding. We show that CTCF interacts with binding sites containing UV damage and inhibits repair by a model repair enzyme in vitro. Structural analysis and molecular dynamic simulations reveal the molecular mechanism for how CTCF binding modulates CPD formation.

The Expression Patterns and Prognostic Value of the Proteasome Activator Subunit Gene Family in Gastric Cancer Based on Integrated Analysis

Increasing evidence supports that proteasome activator subunit (PSME) genes play an indispensable role in multiple tumors. The diverse expression patterns, prognostic value, underlying mechanism, and the role in the immunotherapy of PSME genes in gastric cancer (GC) have yet to be fully elucidated. We systematically demonstrated the functions of these genes in GC using various large databases, unbiased in silico approaches, and experimental validation. We found that the median expression levels of all PSME genes were significantly higher in GC tissues than in normal tissues. Our findings showed that up-regulated PSME1 and PSME2 expression significantly correlated with favorable overall survival, post-progression survival, and first progression survival in GC patients. The expression of PSME1 and PSME2 was positively correlated with the infiltration of most immune cells and the activation of anti-cancer immunity cycle steps. Moreover, GC patients with high PSME1 and PSME2 expression have higher immunophenoscore and tumor mutational burden. In addition, a receiver operating characteristic analysis suggested that PSME3 and PSME4 had high diagnostic performance for distinguishing GC patients from healthy individuals. Moreover, our further analysis indicated that PSME genes exert an essential role in GC, and the present study indicated that PSME1 and PSME2 may be potential prognostic markers for enhancing survival and prognostic accuracy in GC patients and may even act as potential biomarkers for GC patients indicating a response to immunotherapy. PSME3 may serve as an oncogene in tumorigenesis and may be a promising therapeutic target for GC. PSME4 had excellent diagnostic performance and could serve as a good diagnostic indicator for GC.

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.

Exploring absent protein function in yeast: assaying post translational modification and human genetic variation

Yeast is a valuable eukaryotic model organism that has evolved many processes conserved up to humans, yet many protein functions, including certain DNA and protein modifications, are absent. It is this absence of protein function that is fundamental to approaches using yeast as an in vivo test system to investigate human proteins. Functionality of the heterologous expressed proteins is connected to a quantitative, selectable phenotype, enabling the systematic analyses of mechanisms and specificity of DNA modification, post-translational protein modifications as well as the impact of annotated cancer mutations and coding variation on protein activity and interaction. Through continuous improvements of yeast screening systems, this is increasingly carried out on a global scale using deep mutational scanning approaches. Here we discuss the applicability of yeast systems to investigate absent human protein function with a specific focus on the impact of protein variation on protein-protein interaction modulation.

A computational and structural analysis of germline and somatic variants affecting the DDR mechanism, and their impact on human diseases

DNA-Damage Response (DDR) proteins are crucial for maintaining the integrity of the genome by identifying and repairing errors in DNA. Variants affecting their function can have severe consequences since failure to repair damaged DNA can result in cells turning cancerous. Here, we compare germline and somatic variants in DDR genes, specifically looking at their locations in the corresponding three-dimensional (3D) structures, Pfam domains, and protein–protein interaction interfaces. We show that somatic variants in metastatic cases are more likely to be found in Pfam domains and protein interaction interfaces than are pathogenic germline variants or variants of unknown significance (VUS). We also show that there are hotspots in the structures of ATM and BRCA2 proteins where pathogenic germline, and recurrent somatic variants from primary and metastatic tumours, cluster together in 3D. Moreover, in the ATM, BRCA1 and BRCA2 genes from prostate cancer patients, the distributions of germline benign, pathogenic, VUS, and recurrent somatic variants differ across Pfam domains. Together, these results provide a better characterisation of the most recurrent affected regions in DDRs and could help in the understanding of individual susceptibility to tumour development.

Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

Advances and challenges in quantitative delineation of the genetic architecture of complex traits

Background

Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.

Results

This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.

Conclusion

GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.

Co-occurrence and mutual exclusivity: what cross-cancer mutation patterns can tell us

Cancer is the dysregulated proliferation of cells caused by acquired mutations in key driver genes. The most frequently mutated driver genes promote tumorigenesis in various organisms, cell types, and genetic backgrounds. However, recent cancer genomics studies also point to the existence of context-dependent driver gene functions, where specific mutations occur predominately or even exclusively in certain tumor types or genetic backgrounds. Here, we review examples of co-occurring and mutually exclusive driver gene mutation patterns across cancer genomes and discuss their underlying biology. While co-occurring driver genes typically activate collaborating oncogenic pathways, we identify two distinct biological categories of incompatibilities among the mutually exclusive driver genes depending on whether the mutated drivers trigger the same or divergent tumorigenic pathways. Finally, we discuss possible therapeutic avenues emerging from the study of incompatible driver gene mutations.

Rare variants discovery by extensive whole-genome sequencing of the Han Chinese population in Taiwan: Applications to cardiovascular medicine

Introduction

A population-specific genomic reference is important for research and clinical practice, yet it remains unavailable for Han Chinese (HC) in Taiwan.

Objectives

We report the first whole genome sequencing (WGS) database of HC (1000 Taiwanese genome (1KTW-WGS)) and demonstrate several applications to cardiovascular medicine.

Methods

Whole genomes of 997 HC were sequenced to at least 30X depth. A total of 20,117 relatively healthy HC individuals were genotyped using a customized Axiom GWAS array. We performed a genome-wide genotype imputation technique using IMPUTE2.

Results

We identified 26.7 million single-nucleotide variants (SNVs) and 4.2 million insertions-deletions. Of the SNVs, 16.1% were novel relative to dbSNP (build 152), and 34.2% were novel relative to gnomAD. A total of 18,450 healthy HC individuals were genotyped using a customized Genome-Wide Association Study (GWAS) array. We identified hypertension-associated variants and developed a hypertension prediction model based on the correlation between the WGS data and GWAS data (combined clinical and genetic models, AUC 0.887), and also identified 3 novel hyperlipidemia-associated variants. Each individual carried an average of 16.42 (SD = 3.72) disease-causing variants. Additionally, we established an online SCN5A (an important cardiac gene) database that can be used to explore racial differences. Finally, pharmacogenetics studies identified HC population-specific SNVs in genes (CYP2C9 and VKORC1) involved in drug metabolism and blood clotting.

Conclusion

This research demonstrates the benefits of constructing a population-specific genomic reference database for precision medicine.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

Correlated Evolution of Large DNA Fragments in the 3D Genome of Arabidopsis thaliana

In eukaryotes, the three-dimensional (3D) conformation of the genome is far from random, and this nonrandom chromatin organization is strongly correlated with gene expression and protein function, which are two critical determinants of the selective constraints and evolutionary rates of genes. However, whether genes and other elements that are located close to each other in the 3D genome evolve in a coordinated way has not been investigated in any organism. To address this question, we constructed chromatin interaction networks (CINs) in Arabidopsis thaliana based on high-throughput chromosome conformation capture data and demonstrated that adjacent large DNA fragments in the CIN indeed exhibit more similar levels of polymorphism and evolutionary rates than random fragment pairs. Using simulations that account for the linear distance between fragments, we proved that the 3D chromosomal organization plays a role in the observed correlated evolution. Spatially interacting fragments also exhibit more similar mutation rates and functional constraints in both coding and noncoding regions than the random expectations, indicating that the correlated evolution between 3D neighbors is a result of combined evolutionary forces. A collection of 39 genomic and epigenomic features can explain much of the variance in genetic diversity and evolutionary rates across the genome. Moreover, features that have a greater effect on the evolution of regional sequences tend to show higher similarity between neighboring fragments in the CIN, suggesting a pivotal role of epigenetic modifications and chromatin organization in determining the correlated evolution of large DNA fragments in the 3D genome.

Clonal expansion in non-cancer tissues

Cancer is a clonal disorder derived from a single ancestor cell and its progenies that are positively selected by acquisition of 'driver mutations'. However, the evolution of positively selected clones does not necessarily imply the presence of cancer. On the contrary, it has become clear that expansion of these clones in phenotypically normal or non-cancer tissues is commonly seen in association with ageing and/or in response to environmental insults and chronic inflammation. Recent studies have reported expansion of clones harbouring mutations in cancer driver genes in the blood, skin, oesophagus, bronchus, liver, endometrium and bladder, where the expansion could be so extensive that tissues undergo remodelling of an almost entire tissue. The presence of common cancer driver mutations in normal tissues suggests a strong link to cancer development, providing an opportunity to understand early carcinogenic processes. Nevertheless, some driver mutations are unique to normal tissues or have a mutation frequency that is much higher in normal tissue than in cancer, indicating that the respective clones may not necessarily be destined for evolution to cancer but even negatively selected for carcinogenesis depending on the mutated gene. Moreover, tissues that are remodelled by genetically altered clones might define functionalities of aged tissues or modified inflammatory processes. In this Review, we provide an overview of major findings on clonal expansion in phenotypically normal or non-cancer tissues and discuss their biological significance not only in cancer development but also in ageing and inflammatory diseases.

Mutational processes in cancer preferentially affect binding of particular transcription factors

Protein binding microarrays provide comprehensive information about the DNA binding specificities of transcription factors (TFs), and can be used to quantitatively predict the effects of DNA sequence variation on TF binding. There has also been substantial progress in dissecting the patterns of mutations, i.e., the "mutational signatures", generated by different mutational processes. By combining these two layers of information we can investigate whether certain mutational processes tend to preferentially affect binding of particular classes of TFs. Such preferential alterations of binding might predispose to particular oncogenic pathways. We developed and implemented a method, termed "Signature-QBiC", that integrates protein binding microarray data with the signatures of mutational processes, with the aim of predicting which TFs’ binding profiles are preferentially perturbed by particular mutational processes. We used Signature-QBiC to predict the effects of 47 signatures of mutational processes on 582 human TFs. Pathway analysis showed that binding of TFs involved in NOTCH1 signaling is strongly affected by the signatures of several mutational processes, including exposure to ultraviolet radiation. Additionally, toll-like-receptor signaling pathways are also vulnerable to disruption by this exposure. This study provides a novel overview of the effects of mutational processes on TF binding and the potential of these processes to activate oncogenic pathways through mutating TF binding sites.

Whole-genome characterization of lung adenocarcinomas lacking the RTK/RAS/RAF pathway

RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(-) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(-) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(-) cases are required to understand this important LUAD subset.

CNCDatabase: a database of non-coding cancer drivers

Most mutations in cancer genomes occur in the non-coding regions with unknown impact on tumor development. Although the increase in the number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies making it difficult to understand their roles in tumorigenesis of different cancer types. We have developed CNCDatabase, Cornell Non-coding Cancer driver Database (https://cncdatabase.med.cornell.edu/) that contains detailed information about predicted non-coding drivers at gene promoters, 5′ and 3′ UTRs (untranslated regions), enhancers, CTCF insulators and non-coding RNAs. CNCDatabase documents 1111 protein-coding genes and 90 non-coding RNAs with reported drivers in their non-coding regions from 32 cancer types by computational predictions of positive selection using whole-genome sequences; differential gene expression in samples with and without mutations; or another set of experimental validations including luciferase reporter assays and genome editing. The database can be easily modified and scaled as lists of non-coding drivers are revised in the community with larger whole-genome sequencing studies, CRISPR screens and further experimental validations. Overall, CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations in human cancers.

Functional Screening Techniques to Identify Long Non-Coding RNAs as Therapeutic Targets in Cancer

Simple Summary

Long non-coding RNAs (lncRNAs) are a recently discovered class of molecules in the cell, with potential to be utilized as therapeutic targets in cancer. A number of lncRNAs have been described to play important roles in tumor progression and drive molecular processes involved in cell proliferation, apoptosis or invasion. However, the vast majority of lncRNAs have not been studied in the context of cancer thus far. With the advent of CRISPR/Cas genome editing, high-throughput functional screening approaches to identify lncRNAs that impact cancer growth are becoming more accessible. Here, we review currently available methods to study hundreds to thousands of lncRNAs in parallel to elucidate their role in tumorigenesis and cancer progression.

Abstract

Recent technological advancements such as CRISPR/Cas-based systems enable multiplexed, high-throughput screening for new therapeutic targets in cancer. While numerous functional screens have been performed on protein-coding genes to date, long non-coding RNAs (lncRNAs) represent an emerging class of potential oncogenes and tumor suppressors, with only a handful of large-scale screens performed thus far. Here, we review in detail currently available screening approaches to identify new lncRNA drivers of tumorigenesis and tumor progression. We discuss the various approaches of genomic and transcriptional targeting using CRISPR/Cas9, as well as methods to post-transcriptionally target lncRNAs via RNA interference (RNAi), antisense oligonucleotides (ASOs) and CRISPR/Cas13. We discuss potential advantages, caveats and future applications of each method to provide an overview and guide on investigating lncRNAs as new therapeutic targets in cancer.

NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis

Background

Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues.

Results

We developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma–Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods.

Conclusion

NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

Long non-coding RNAs in ovarian cancer: expression profile and functional spectrum

Long non-coding RNAs (lncRNAs), initially recognized as byproducts of the transcription process, have been proven to play crucial modulatory roles in preserving overall homoeostasis of cells and tissues. Furthermore, aberrant levels of these transcripts have been shown to contribute many diseases, including cancer. Among these, many aspects of ovarian cancer biology have been found to be regulated by lncRNAs, including cancer initiation, progression and dissemination. In this review, we summarize recent studies to highlight the various roles of lncRNAs in ovary in normal and pathological conditions, immune system, diagnosis, prognosis, and therapy. We address lncRNAs that have been extensively studied in ovarian cancer and their contribution to cellular dynamics.

LncRNAs in Cancer: From garbage to Junk

Sequencing-based transcriptomics has significantly redefined the concept of genome complexity, leading to the identification of thousands of lncRNA genes identification of thousands of lncRNA genes whose products possess transcriptional and/or post-transcriptional regulatory functions that help to shape cell functionality and fate. Indeed, it is well-established now that lncRNAs play a key role in the regulation of gene expression through epigenetic and posttranscriptional mechanims. The rapid increase of studies reporting lncRNAs alteration in cancers has also highlighted their relevance for tumorigenesis. Herein we describe the most prominent examples of well-established lncRNAs having oncogenic and/or tumor suppressive activity. We also discuss how technical advances have provided new therapeutic strategies based on their targeting, and also report the challenges towards their use in the clinical settings.

Prediction of genome-wide effects of single nucleotide variants on transcription factor binding

Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated ‘gainability’ and ‘disruptability’ scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.

Using whole-exome sequencing and protein interaction networks to prioritize candidate genes for germline cutaneous melanoma susceptibility

Although next-generation sequencing has demonstrated great potential for novel gene discovery, confirming disease-causing genes after initial discovery remains challenging. Here, we applied a network analysis approach to prioritize candidate genes identified from whole-exome sequencing analysis of 98 cutaneous melanoma patients from 27 families. Using a network propagation method, we ranked candidate genes by their similarity to known disease genes in protein-protein interaction networks and identified gene clusters with functional connectivity. Using this approach, we identified several new candidate susceptibility genes that warrant future investigations such as NGLY1, IL1RN, FABP2, PRKDC, and PROSER2. The propagated network analysis also allowed us to link families that did not have common underlying genes but that carried variants in genes that interact on protein-protein interaction networks. In conclusion, our study provided an analysis perspective for gene prioritization in the context of genetic heterogeneity across families and prioritized top potential candidate susceptibility genes in our dataset.

Genetics of extreme human longevity to guide drug discovery for healthy ageing

Ageing is the greatest risk factor for most common chronic human diseases, and it therefore is a logical target for developing interventions to prevent, mitigate or reverse multiple age-related morbidities. Over the past two decades, genetic and pharmacologic interventions targeting conserved pathways of growth and metabolism have consistently led to substantial extension of the lifespan and healthspan in model organisms as diverse as nematodes, flies and mice. Recent genetic analysis of long-lived individuals is revealing common and rare variants enriched in these same conserved pathways that significantly correlate with longevity. In this Perspective, we summarize recent insights into the genetics of extreme human longevity and propose the use of this rare phenotype to identify genetic variants as molecular targets for gaining insight into the physiology of healthy ageing and the development of new therapies to extend the human healthspan.