Global analysis of somatic structural genomic alterations and their impact on gene expression in diverse human cancers

Major impacts of widespread structural variation on sorghum

Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype-phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.

Accurate identification of structural variations from cancer samples

Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the Cancer Optical Mapping for detecting Structural Variations (COMSV) method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

Genome-wide Detection of Chimeric Transcripts in Early-stage Non-small Cell Lung Cancer

BACKGROUND/AIM

Lung cancer remains the main culprit in cancer-related mortality worldwide. Transcript fusions play a critical role in the initiation and progression of multiple cancers. Treatment approaches based on specific targeting of discovered driver events, such as mutations in EGFR, and fusions in NTRK, ROS1, and ALK genes led to profound improvements in clinical outcomes. The formation of chimeric proteins due to genomic rearrangements or at the post-transcriptional level is widespread and plays a critical role in tumor initiation and progression. Yet, the fusion landscape of lung cancer remains underexplored.

MATERIALS AND METHODS

We used the JAFFA pipeline to discover transcript fusions in early-stage non-small cell lung cancer (NSCLC). The set of detected fusions was further analyzed to identify recurrent events, genes with multiple partners and fusions with high predicted oncogenic potential. Finally, we used a generalized linear model (GLM) to establish statistical associations between fusion occurrences and clinicopathological variables. RNA sequencing was used to discover and characterize transcript fusions in 270 NSCLC samples selected from the Glans-Look specimen repository. The samples were obtained during the early stages of disease prior to the initiation of chemo- or radiotherapy.

RESULTS

We identified a set of 792 fusions where 751 were novel, and 33 were recurrent. Four of the 33 recurrent fusions were significantly associated with clinicopathological variables. Several of the fusion partners were represented by well-established oncogenes ERBB4, BRAF, FGFR2, and MET.

CONCLUSION

The data presented in this study allow researchers to identify, select, and validate promising candidates for targeted clinical interventions.

Definition and Prognostic Value of Ph-like and IKZF1plus Status in Children With Down Syndrome and B-cell Precursor Acute Lymphoblastic Leukemia

Children with Down syndrome have an augmented risk for B-cell acute lymphoblastic leukemia (DS-ALL), which is associated with lower survival than in non-DS-ALL. It is known that cytogenetic abnormalities common in childhood ALL are less frequent in DS-ALL, while other genetic aberrancies (ie, CRLF2 overexpression and IKZF1 deletions) are increased. A possible cause for the lower survival of DS-ALL that we herewith evaluated for the first time was the incidence and prognostic value of the Philadelphia-like (Ph-like) profile and the IKZF1plus pattern. These features have been associated with poor outcome in non-DS ALL and therefore introduced in current therapeutic protocols. Forty-six out of 70 DS-ALL patients treated in Italy from 2000 to 2014 displayed Ph-like signature, mostly characterized by CRLF2 (n = 33) and IKZF1 (n = 16) alterations; only 2 cases were positive for ABL-class or PAX5-fusion genes. Moreover, in an Italian and German joint cohort of 134 DS-ALL patients, we observed 18% patients positive for IKZF1plus feature. Ph-like signature and IKZF1 deletion were associated with poor outcome (cumulative incidence of relapse: 27.7 ± 6.8% versus 13 ± 7%; P = 0.04 and 35.2 ± 8.6% versus 17 ± 3.9%; P = 0.007, respectively), which further worsens when IKZF1 deletion was co-occurring with P2RY8::CRLF2, qualifying for the IKZF1plus definition (13/15 patients had an event of relapse or treatment-related death). Notably, ex vivo drug screening revealed sensitivity of IKZF1plus blasts for drugs active against Ph-like ALL such as Birinapant and histone deacetylase inhibitors. We provided data in a large setting of a rare condition (DS-ALL) supporting that these patients, not associated with other high-risk features, need tailored therapeutic strategies.

Long-read sequencing identifies novel structural variations in colorectal cancer

Structural variations (SVs) are a key type of cancer genomic alterations, contributing to oncogenesis and progression of many cancers, including colorectal cancer (CRC). However, SVs in CRC remain difficult to be reliably detected due to limited SV-detection capacity of the commonly used short-read sequencing. This study investigated the somatic SVs in 21 pairs of CRC samples by Nanopore whole-genome long-read sequencing. 5200 novel somatic SVs from 21 CRC patients (494 SVs / patient) were identified. A 4.9-Mbp long inversion that silences APC expression (confirmed by RNA-seq) and an 11.2-kbp inversion that structurally alters CFTR were identified. Two novel gene fusions that might functionally impact the oncogene RNF38 and the tumor-suppressor SMAD3 were detected. RNF38 fusion possesses metastasis-promoting ability confirmed by in vitro migration and invasion assay, and in vivo metastasis experiments. This work highlighted the various applications of long-read sequencing in cancer genome analysis, and shed new light on how somatic SVs structurally alter critical genes in CRC. The investigation on somatic SVs via nanopore sequencing revealed the potential of this genomic approach in facilitating precise diagnosis and personalized treatment of CRC.

Targeting the PI3K/AKT/mTOR and RAF/MEK/ERK pathways for cancer therapy

The PI3K/AKT/mTOR and RAF/MEK/ERK pathways are commonly activated by mutations and chromosomal translocation in vital targets. The PI3K/AKT/mTOR signaling pathway is dysregulated in nearly all kinds of neoplasms, with the component in this pathway alternations. RAF/MEK/ERK signaling cascades are used to conduct signaling from the cell surface to the nucleus to mediate gene expression, cell cycle processes and apoptosis. RAS, B-Raf, PI3K, and PTEN are frequent upstream alternative sites. These mutations resulted in activated cell growth and downregulated cell apoptosis. The two pathways interact with each other to participate in tumorigenesis. PTEN alterations suppress RAF/MEK/ERK pathway activity via AKT phosphorylation and RAS inhibition. Several inhibitors targeting major components of these two pathways have been supported by the FDA. Dozens of agents in these two pathways have attracted great attention and have been assessed in clinical trials. The combination of small molecular inhibitors with traditional regimens has also been explored. Furthermore, dual inhibitors provide new insight into antitumor activity. This review will further comprehensively describe the genetic alterations in normal patients and tumor patients and discuss the role of targeted inhibitors in malignant neoplasm therapy. We hope this review will promote a comprehensive understanding of the role of the PI3K/AKT/mTOR and RAF/MEK/ERK signaling pathways in facilitating tumors and will help direct drug selection for tumor therapy.

The pseudogenes of eukaryotic translation elongation factors (EEFs): Role in cancer and other human diseases

The eukaryotic translation elongation factors (EEFs), i.e. EEF1A1, EEF1A2, EEF1B2, EEF1D, EEF1G, EEF1E1 and EEF2, are coding-genes that play a central role in the elongation step of translation but are often altered in cancer. Less investigated are their pseudogenes. Recently, it was demonstrated that pseudogenes have a key regulatory role in the cell, especially via non-coding RNAs, and that the aberrant expression of ncRNAs has an important role in cancer development and progression. The present review paper, for the first time, collects all that published about the EEFs pseudogenes to create a base for future investigations. For most of them, the studies are in their infancy, while for others the studies suggest their involvement in normal cell physiology but also in various human diseases. However, more investigations are needed to understand their functions in both normal and cancer cells and to define which can be useful biomarkers or therapeutic targets.

DriverFuse: An R package for analysis of next-generation sequencing datasets to identify cancer driver fusion genes

We developed the DriverFuse package to integrate orthogonal data types such as Structural Variants (SV) and Copy Number Variations (CNV) to characterize fusion genes in cancer datasets. A fusion gene is reported as a driver or passenger fusion gene, based on mapping SV and CNV profiles. DriverFuse generates a fusion plot of fusion genes with their mapping SV, CNV profile, domain architecture and classification of its role in cancer. The analysis facilitates discrimination of driver fusions from passenger fusions. To demonstrate the utility of DriverFuse, we analyzed two datasets, one each for CCLE (Cancer Cell Line Encyclopedia) for lung cancer and HCC1395BL for breast cancer. The analysis validates the driver fusion genes that are already reported for the datasets. Thus, DriverFuse is a valuable tool for studying the driver fusion genes in cancers, enabling the identification of recurrent complex rearrangements that provide intuitive insights into disease driver events.

Meerkat: An Algorithm to Reliably Identify Structural Variations and Predict Their Forming Mechanisms

Next-generation sequencing technologies have been widely used to query genetic variants in normal individuals as well as in those with diseases. Large-scale structural variations are a common source of genetic diversity in human population, and some of them have significant contributions to the etiology of diseases. However, the detection of large-scale structural variations from sequencing data remains challenging. Here, we describe Meerkat-an algorithm which can reliably detect structural variations from Illumina short-read sequencing data at basepair resolution. A unique feature of Meerkat is that it can infer the variant forming mechanisms based on the DNA content and features at the breakpoints.

Fusion transcripts and their genomic breakpoints in polyadenylated and ribosomal RNA-minus RNA sequencing data

BACKGROUND

Fusion genes are typically identified by RNA sequencing (RNA-seq) without elucidating the causal genomic breakpoints. However, non-poly(A)-enriched RNA-seq contains large proportions of intronic reads that also span genomic breakpoints.

RESULTS

We have developed an algorithm, Dr. Disco, that searches for fusion transcripts by taking an entire reference genome into account as search space. This includes exons but also introns, intergenic regions, and sequences that do not meet splice junction motifs. Using 1,275 RNA-seq samples, we investigated to what extent genomic breakpoints can be extracted from RNA-seq data and their implications regarding poly(A)-enriched and ribosomal RNA-minus RNA-seq data. Comparison with whole-genome sequencing data revealed that most genomic breakpoints are not, or minimally, transcribed while, in contrast, the genomic breakpoints of all 32 TMPRSS2-ERG-positive tumours were present at RNA level. We also revealed tumours in which the ERG breakpoint was located before ERG, which co-existed with additional deletions and messenger RNA that incorporated intergenic cryptic exons. In breast cancer we identified rearrangement hot spots near CCND1 and in glioma near CDK4 and MDM2 and could directly associate this with increased expression. Furthermore, in all datasets we find fusions to intergenic regions, often spanning multiple cryptic exons that potentially encode neo-antigens. Thus, fusion transcripts other than classical gene-to-gene fusions are prominently present and can be identified using RNA-seq.

CONCLUSION

By using the full potential of non-poly(A)-enriched RNA-seq data, sophisticated analysis can reliably identify expressed genomic breakpoints and their transcriptional effects.

Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers

Read-depths (RDs) are frequently used in identifying structural variants (SVs) from sequencing data. For existing RD-based SV callers, it is difficult for them to determine breakpoints in single-nucleotide resolution due to the noisiness of RD data and the bin-based calculation. In this paper, we propose to use the deep segmentation model UNet to learn base-wise RD patterns surrounding breakpoints of known SVs. We integrate model predictions with an RD-based SV caller to enhance breakpoints in single-nucleotide resolution. We show that UNet can be trained with a small amount of data and can be applied both in-sample and cross-sample. An enhancement pipeline named RDBKE significantly increases the number of SVs with more precise breakpoints on simulated and real data. The source code of RDBKE is freely available at https://github.com/yaozhong/deepIntraSV.

Attacking the PI3K/Akt/mTOR signaling pathway for targeted therapeutic treatment in human cancer

Cancer is the second leading cause of human death globally. PI3K/Akt/mTOR signaling is one of the most frequently dysregulated signaling pathways observed in cancer patients that plays crucial roles in promoting tumor initiation, progression and therapy responses. This is largely due to that PI3K/Akt/mTOR signaling is indispensable for many cellular biological processes, including cell growth, metastasis, survival, metabolism, and others. As such, small molecule inhibitors targeting major kinase components of the PI3K/Akt/mTOR signaling pathway have drawn extensive attention and been developed and evaluated in preclinical models and clinical trials. Targeting a single kinase component within this signaling usually causes growth arrest rather than apoptosis associated with toxicity-induced adverse effects in patients. Combination therapies including PI3K/Akt/mTOR inhibitors show improved patient response and clinical outcome, albeit developed resistance has been reported. In this review, we focus on revealing the mechanisms leading to the hyperactivation of PI3K/Akt/mTOR signaling in cancer and summarizing efforts for developing PI3K/Akt/mTOR inhibitors as either mono-therapy or combination therapy in different cancer settings. We hope that this review will facilitate further understanding of the regulatory mechanisms governing dysregulation of PI3K/Akt/mTOR oncogenic signaling in cancer and provide insights into possible future directions for targeted therapeutic regimen for cancer treatment, by developing new agents, drug delivery systems, or combination regimen to target the PI3K/Akt/mTOR signaling pathway. This information will also provide effective patient stratification strategy to improve the patient response and clinical outcome for cancer patients with deregulated PI3K/Akt/mTOR signaling.

Next Generation Sequencing Technology in the Clinic and Its Challenges

Simple Summary

Precise identification and annotation of mutations are of utmost importance in clinical oncology. Insights of the DNA sequence can provide meaningful knowledge to unravel the underlying genetics of disease. Hence, tailoring of personalized medicine often relies on specific genomic alteration for treatment efficacy. The aim of this review is to highlight that sequencing harbors much more than just four nucleotides. Moreover, the gradual transition from first to second generation sequencing technologies has led to awareness for choosing the most appropriate bioinformatic analytic tools based on the aim, quality and demand for a specific purpose. Thus, the same raw data can lead to various results reflecting the intrinsic features of different datamining pipelines.

Abstract

Data analysis has become a crucial aspect in clinical oncology to interpret output from next-generation sequencing-based testing. NGS being able to resolve billions of sequencing reactions in a few days has consequently increased the demand for tools to handle and analyze such large data sets. Many tools have been developed since the advent of NGS, featuring their own peculiarities. Increased awareness when interpreting alterations in the genome is therefore of utmost importance, as the same data using different tools can provide diverse outcomes. Hence, it is crucial to evaluate and validate bioinformatic pipelines in clinical settings. Moreover, personalized medicine implies treatment targeting efficacy of biological drugs for specific genomic alterations. Here, we focused on different sequencing technologies, features underlying the genome complexity, and bioinformatic tools that can impact the final annotation. Additionally, we discuss the clinical demand and design for implementing NGS.

A pediatric brain tumor atlas of genes deregulated by somatic genomic rearrangement

The global impact of somatic structural variants (SSVs) on gene expression in pediatric brain tumors has not been thoroughly characterised. Here, using whole-genome and RNA sequencing from 854 tumors of more than 30 different types from the Children's Brain Tumor Tissue Consortium, we report the altered expression of hundreds of genes in association with the presence of nearby SSV breakpoints. SSV-mediated expression changes involve gene fusions, altered cis-regulation, or gene disruption. SSVs considerably extend the numbers of patients with tumors somatically altered for critical pathways, including receptor tyrosine kinases (KRAS, MET, EGFR, NF1), Rb pathway (CDK4), TERT, MYC family (MYC, MYCN, MYB), and HIPPO (NF2). Compared to initial tumors, progressive or recurrent tumors involve a distinct set of SSV-gene associations. High overall SSV burden associates with TP53 mutations, histone H3.3 gene H3F3C mutations, and the transcription of DNA damage response genes. Compared to adult cancers, pediatric brain tumors would involve a different set of genes with SSV-altered cis-regulation. Our comprehensive and pan-histology genomic analyses reveal SSVs to play a major role in shaping the transcriptome of pediatric brain tumors.

Comprehensive Assessment of Copy Number Alterations Uncovers Recurrent AIFM3 and DLK1 Copy Gain in Medullary Thyroid Carcinoma

Simple Summary

Medullary thyroid cancer (MTC) is often discovered in its advanced stage. Although a rare disease, advanced MTC cases have poor prognosis and the treatment is often palliative. Several studies have reported the existence of an association between copy number alterations (CNAs) burden and cancer progression. Moreover, the accumulation of broad CNAs, which contribute to intra-tumor heterogeneity, might be required for immune evasion. The identification of the recurrent CNAs associated with tumor phenotype aided in discovering new therapeutics options in several cancer types. To our knowledge, CNA is not well characterized in MTC. We analyzed recurrent focal CNAs on MTC. Our analysis provides a novel insight on MTC biology and may help in uncovering novel potential therapeutic targets.

Abstract

Medullary thyroid carcinoma (MTC) is a malignant tumor originating from thyroid C-cells that can occur either in sporadic (70–80%) or hereditary (20–30%) form. In this study we aimed to identify recurrent copy number alterations (CNA) that might be related to the pathogenesis or progression of MTC. We used Affymetrix SNP array 6.0 on MTC and paired-blood samples to identify CNA using PennCNV and Genotyping Console software. The algorithms identified recurrent copy number gains in chromosomes 15q, 10q, 14q and 22q in MTC, whereas 4q cumulated losses. Coding genes were identified within CNA regions. The quantitative PCR analysis performed in an independent series of MTCs (n = 51) confirmed focal recurrent copy number gains encompassing the DLK1 (14q32.2) and AIFM3 (22q11.21) genes. Immunohistochemistry confirmed AIFM3 and DLK1 expression in MTC cases, while no expression was found in normal thyroid tissues and few MTC samples were found with normal copy numbers. The functional relevance of CNA was also assessed by in silico analysis. CNA status correlated with protein expression (DLK1, p = 0.01), tumor size (DLK1, p = 0.04) and AJCC staging (AIFM3p = 0.01 and DLK1p = 0.05). These data provide a novel insight into MTC biology, and suggest a common CNA landscape, regardless of if it is sporadic or hereditary MTC.

Chromosome structural variation in tumorigenesis: mechanisms of formation and carcinogenesis

With the rapid development of next-generation sequencing technology, chromosome structural variation has gradually gained increased clinical significance in tumorigenesis. However, the molecular mechanism(s) underlying this structural variation remain poorly understood. A search of the literature shows that a three-dimensional chromatin state plays a vital role in inducing structural variation and in the gene expression profiles in tumorigenesis. Structural variants may result in changes in copy number or deletions of coding sequences, as well as the perturbation of structural chromatin features, especially topological domains, and disruption of interactions between genes and their regulatory elements. This review focuses recent work aiming at elucidating how structural variations develop and misregulate oncogenes and tumor suppressors, to provide general insights into tumor formation mechanisms and to provide potential targets for future anticancer therapies.

Systematic investigation of promoter substitutions resulting from somatic intrachromosomal structural alterations in diverse human cancers

One of the ways in which genes can become activated in tumors is by somatic structural genomic rearrangements leading to promoter swapping events, typically in the context of gene fusions that cause a weak promoter to be substituted for a strong promoter. While identifiable by whole genome sequencing, limited availability of this type of data has prohibited comprehensive study of the phenomenon. Here, we leveraged the fact that copy number alterations (CNAs) arise as a result of structural alterations in DNA, and that they may therefore be informative of gene rearrangements, to pinpoint recurrent promoter swapping at a previously intractable scale. CNA data from nearly 9500 human tumors was combined with transcriptomic sequencing data to identify several cases of recurrent activating intrachromosomal promoter substitution events, either involving proper gene fusions or juxtaposition of strong promoters to gene upstream regions. Our computational screen demonstrates that a combination of CNA and expression data can be useful for identifying novel fusion events with potential driver roles in large cancer cohorts.

A Practical Guide for Structural Variation Detection in the Human Genome

Profiling genetic variants-including single nucleotide variants, small insertions and deletions, copy number variations, and structural variations (SVs)-from both healthy individuals and individuals with disease is a key component of genetic and biomedical research. SVs are large-scale changes in the genome and involve breakage and rejoining of DNA fragments. They may affect thousands to millions of nucleotides and can lead to loss, gain, and reshuffling of genes and regulatory elements. SVs are known to impact gene expression and potentially result in altered phenotypes and diseases. Therefore, identifying SVs from the human genomes is particularly important. In this review, I describe advantages and disadvantages of the available high-throughput assays for the discovery of SVs, which are the most challenging genetic alterations to detect. A practical guide is offered to suggest the most suitable strategies for discovering different types of SVs including common germline, rare, somatic, and complex variants. I also discuss factors to be considered, such as cost and performance, for different strategies when designing experiments. Last, I present several approaches to identify potential SV artifacts caused by samples, experimental procedures, and computational analysis. © 2020 Wiley Periodicals LLC.

Identification of prognostic biomarkers of hepatocellular carcinoma via long noncoding RNA expression and copy number alterations

Aim: This study aimed to identify long noncoding RNAs (lncRNAs) with potential to be prognostic biomarkers of hepatocellular carcinoma (HCC) by analyzing copy number alterations (CNAs). Methods: RNA Sequencing data of 369 HCC patients was downloaded from The Cancer Genome Atlas database and analyzed with a series of systematic bioinformatics methods. Results: LncRNA-CNA association analysis revealed that many lncRNAs were located in sites frequently amplified or deleted. Three upregulated lncRNAs (LINC00689, SNHG20 and MAFG-AS1) with copy amplification and one downregulated lncRNA TMEM220-AS1 with copy deletion were associated with poor prognosis of HCC. Conclusion: This study reveals that differentially expressed lncRNAs correlate with CNAs in HCC. Moreover, the differentially expressed lncRNAs and their copy amplification/deletions could be promising prognostic biomarkers of HCC.

Dysregulation of cancer genes by recurrent intergenic fusions

Background

Gene fusions have been studied extensively, as frequent drivers of tumorigenesis as well as potential therapeutic targets. In many well-known cases, breakpoints occur at two intragenic positions, leading to in-frame gene-gene fusions that generate chimeric mRNAs. However, fusions often occur with intergenic breakpoints, and the role of such fusions has not been carefully examined.

Results

We analyze whole-genome sequencing data from 268 patients to catalog gene-intergenic and intergenic-intergenic fusions and characterize their impact. First, we discover that, in contrast to the common assumption, chimeric oncogenic transcripts—such as those involving ETV4, ERG, RSPO3, and PIK3CA—can be generated by gene-intergenic fusions through splicing of the intervening region. Second, we find that over-expression of an upstream or downstream gene by a fusion-mediated repositioning of a regulatory sequence is much more common than previously suspected, with enhancers sometimes located megabases away. We detect a number of recurrent fusions, such as those involving ANO3, RGS9, FUT5, CHI3L1, OR1D4, and LIPG in breast; IGF2 in colon; ETV1 in prostate; and IGF2BP3 and SIX2 in thyroid cancers.

Conclusion

Our findings elucidate the potential oncogenic function of intergenic fusions and highlight the wide-ranging consequences of structural rearrangements in cancer genomes.

A genome-wide survey of copy number variations reveals an asymmetric evolution of duplicated genes in rice

Background

Copy number variations (CNVs) are an important type of structural variations in the genome that usually affect gene expression levels by gene dosage effect. Understanding CNVs as part of genome evolution may provide insights into the genetic basis of important agricultural traits and contribute to the crop breeding in the future. While available methods to detect CNVs utilizing next-generation sequencing technology have helped shed light on prevalence and effects of CNVs, the complexity of crop genomes poses a major challenge and requires development of additional tools.

Results

Here, we generated genomic and transcriptomic data of 93 rice (Oryza sativa L.) accessions and developed a comprehensive pipeline to call CNVs in this large-scale dataset. We analyzed the correlation between CNVs and gene expression levels and found that approximately 13% of the identified genes showed a significant correlation between their expression levels and copy numbers. Further analysis showed that about 36% of duplicate pairs were involved in pseudogenetic events while only 5% of them showed functional differentiation. Moreover, the offspring copy mainly contributed to the expression levels and seemed more likely to become a pseudogene, whereas the parent copy tended to maintain the function of ancestral gene.

Conclusion

We provide a high-accuracy CNV dataset that will contribute to functional genomics studies and molecular breeding in rice. We also showed that gene dosage effect of CNVs in rice is not exponential or linear. Our work demonstrates that the evolution of duplicated genes is asymmetric in both expression levels and gene fates, shedding a new insight into the evolution of duplicated genes.

The genomic and epigenomic evolutionary history of papillary renal cell carcinomas

Intratumor heterogeneity (ITH) and tumor evolution have been well described for clear cell renal cell carcinomas (ccRCC), but they are less studied for other kidney cancer subtypes. Here we investigate ITH and clonal evolution of papillary renal cell carcinoma (pRCC) and rarer kidney cancer subtypes, integrating whole-genome sequencing and DNA methylation data. In 29 tumors, up to 10 samples from the center to the periphery of each tumor, and metastatic samples in 2 cases, enable phylogenetic analysis of spatial features of clonal expansion, which shows congruent patterns of genomic and epigenomic evolution. In contrast to previous studies of ccRCC, in pRCC, driver gene mutations and most arm-level somatic copy number alterations (SCNAs) are clonal. These findings suggest that a single biopsy would be sufficient to identify the important genetic drivers and that targeting large-scale SCNAs may improve pRCC treatment, which is currently poor. While type 1 pRCC displays near absence of structural variants (SVs), the more aggressive type 2 pRCC and the rarer subtypes have numerous SVs, which should be pursued for prognostic significance.

Pan-Cancer Analysis Reveals the Diverse Landscape of Novel Sense and Antisense Fusion Transcripts

Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.

Mining Prognostic Biomarkers of Hepatocellular Carcinoma Based on Immune-Associated Genes

This research aims to investigate the immune-associated gene signature from databases to improve the prognostic value in hepatocellular carcinoma (HCC) by multidimensional methods using various bioinformatic methods. Fifty-one immune-associated genes were mined out, which were associated with clinical characters through univariate and multivariate Cox regression analyses, and 51 immune-associated genes could be well-divided HCC samples into high-risk and low-risk clusters. Next, we performed least absolute shrinkage and selection operator (LASSO) Cox regression method to reveal 18 immune-associated genes' signature and calculate risk score of each gene for receiver operating characteristic (ROC) analysis. Comparing with low-risk cluster, high-risk cluster had higher risk score with unfavorable prognosis. Then, multivariate Cox regression analysis showed that risk score of 18 immune-associated genes' signature was associated with tumor invasion and tumor-node-metastasis (TNM) stage. ROC analysis indicated combined TNM stage, and risk score performed more sensitive and specific than single TNM stage or risk score in survival prediction. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis found that the pathways enriched in tumorigenesis were related to risk score, and those pathways could separate HCC samples into high and low clusters. In addition, the survival prediction of 18 immune-associated genes' signature was well validated in independent test data set, external data set, and Real-time Quantitative PCR (RT-qPCR) experiment. The 18 immune-associated genes' signature was constructed, which could be used in effective prediction of HCC prognosis.

Effect of Diphtheria Toxin-Based Gene Therapy for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a major global malignancy, responsible for >90% of primary liver cancers. Currently available therapeutic options have poor performances due to the highly heterogeneous nature of the tumor cells; recurrence is highly probable, and some patients develop resistances to the therapies. Accordingly, the development of a novel therapy is essential. We assessed gene therapy for HCC using a diphtheria toxin fragment A (DTA) gene-expressing plasmid, utilizing a non-viral hydrodynamics-based procedure. The antitumor effect of DTA expression in HCC cell lines (and alpha-fetoprotein (AFP) promoter selectivity) is assessed in vitro by examining HCC cell growth. Moreover, the effect and safety of the AFP promoter-selective DTA expression was examined in vivo using an HCC mice model established by the hydrodynamic gene delivery of the yes-associated protein (YAP)-expressing plasmid. The protein synthesis in DTA transfected cells is inhibited by the disappearance of tdTomato and GFP expression co-transfected upon the delivery of the DTA plasmid; the HCC cell growth is inhibited by the expression of DTA in HCC cells in an AFP promoter-selective manner. A significant inhibition of HCC occurrence and the suppression of the tumor marker of AFP and des-gamma-carboxy prothrombin can be seen in mice groups treated with hydrodynamic gene delivery of DTA, both 0 and 2 months after the YAP gene delivery. These results suggest that DTA gene therapy is effective for HCC.

Gene Therapy for Liver Cancers: Current Status from Basic to Clinics

The liver is a key organ for metabolism, protein synthesis, detoxification, and endocrine function, and among liver diseases, including hepatitis, cirrhosis, malignant tumors, and congenital disease, liver cancer is one of the leading causes of cancer-related deaths worldwide. Conventional therapeutic options such as embolization and chemotherapy are not effective against advanced-stage liver cancer; therefore, continuous efforts focus on the development of novel therapeutic options, including molecular targeted agents and gene therapy. In this review, we will summarize the progress toward the development of gene therapies for liver cancer, with an emphasis on recent clinical trials and preclinical studies.

Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods

BACKGROUND

Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly.

RESULTS

We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes.

CONCLUSION

The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.

Global impact of somatic structural variation on the DNA methylome of human cancers

Background

Genomic rearrangements exert a heavy influence on the molecular landscape of cancer. New analytical approaches integrating somatic structural variants (SSVs) with altered gene features represent a framework by which we can assign global significance to a core set of genes, analogous to established methods that identify genes non-randomly targeted by somatic mutation or copy number alteration. While recent studies have defined broad patterns of association involving gene transcription and nearby SSV breakpoints, global alterations in DNA methylation in the context of SSVs remain largely unexplored.

Results

By data integration of whole genome sequencing, RNA sequencing, and DNA methylation arrays from more than 1400 human cancers, we identify hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of a somatic structural variant (SSV) breakpoint is recurrently associated with altered expression or DNA methylation, respectively, independently of copy number alterations. CGIs with SSV-associated increased methylation are predominantly promoter-associated, while CGIs with SSV-associated decreased methylation are enriched for gene body CGIs. Rearrangement of genomic regions normally having higher or lower methylation is often involved in SSV-associated CGI methylation alterations. Across cancers, the overall structural variation burden is associated with a global decrease in methylation, increased expression in methyltransferase genes and DNA damage response genes, and decreased immune cell infiltration.

Conclusion

Genomic rearrangement appears to have a major role in shaping the cancer DNA methylome, to be considered alongside commonly accepted mechanisms including histone modifications and disruption of DNA methyltransferases.

The landscape of genomic copy number alterations in colorectal cancer and their consequences on gene expression levels and disease outcome

Aneuploidy, the unbalanced state of the chromosome content, represents a hallmark of most solid tumors, including colorectal cancer. Such aneuploidies result in tumor specific genomic imbalances, which emerge in premalignant precursor lesions. Moreover, increasing levels of chromosomal instability have been observed in adenocarcinomas and are maintained in distant metastases. A number of studies have systematically integrated copy number alterations with gene expression changes in primary carcinomas, cell lines, and experimental models of aneuploidy. In fact, chromosomal aneuploidies target a number of genes conferring a selective advantage for the metabolism of the cancer cell. Copy number alterations not only have a positive correlation with expression changes of the majority of genes on the altered genomic segment, but also have effects on the transcriptional levels of genes genome-wide. Finally, copy number alterations have been associated with disease outcome; nevertheless, the translational applicability in clinical practice requires further studies. Here, we (i) review the spectrum of genetic alterations that lead to colorectal cancer, (ii) describe the most frequent copy number alterations at different stages of colorectal carcinogenesis, (iii) exemplify their positive correlation with gene expression levels, and (iv) discuss copy number alterations that are potentially involved in disease outcome of individual patients.

Improved detection of gene fusions by applying statistical methods reveals oncogenic RNA cancer drivers

The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce Data-Enriched Efficient PrEcise STatistical fusion detection (DEEPEST), an algorithm that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling 10-fold fewer false-positive fusions in nontransformed human tissues. We leverage the increased precision of DEEPEST to discover fundamental cancer biology. Namely, 888 candidate oncogenes are identified based on overrepresentation in DEEPEST calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs, demonstrating a previously unappreciated prevalence and potential for function. DEEPEST also reveals a high enrichment for fusions involving oncogenes in cancers, including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. Specific protein domains are enriched in DEEPEST calls, indicating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.

Integrative analysis of genomic and transcriptomic characteristics associated with progression of aggressive thyroid cancer

Anaplastic thyroid cancer (ATC) and advanced differentiated thyroid cancers (DTCs) show fatal outcomes, unlike DTCs. Here, we demonstrate mutational landscape of 27 ATCs and 86 advanced DTCs by massively-parallel DNA sequencing, and transcriptome of 13 ATCs and 12 advanced DTCs were profiled by RNA sequencing. TERT, AKT1, PIK3CA, and EIF1AX were frequently co-mutated with driver genes (BRAF^V600E and RAS) in advanced DTCs as well as ATC, but tumor suppressors (e.g., TP53 and CDKN2A) were predominantly altered in ATC. CDKN2A loss was significantly associated with poor disease-specific survival in patients with ATC or advanced DTCs, and up-regulation of CD274 (PD-L1) and PDCD1LG2 (PD-L2). Transcriptome analysis revealed a fourth molecular subtype of thyroid cancer (TC), ATC-like, which hardly reflects the molecular signatures in DTC. Furthermore, the activation of JAK-STAT signaling pathway could be a potential druggable target in RAS-positive ATC. Our findings provide insights for precision medicine in patients with advanced TCs.

Integrative analysis of genomic and transcriptomic characteristics associated with progression of aggressive thyroid cancer

Anaplastic thyroid cancer (ATC) and advanced differentiated thyroid cancers (DTCs) show fatal outcomes, unlike DTCs. Here, we demonstrate mutational landscape of 27 ATCs and 86 advanced DTCs by massively-parallel DNA sequencing, and transcriptome of 13 ATCs and 12 advanced DTCs were profiled by RNA sequencing. TERT, AKT1, PIK3CA, and EIF1AX were frequently co-mutated with driver genes (BRAF^V600E and RAS) in advanced DTCs as well as ATC, but tumor suppressors (e.g., TP53 and CDKN2A) were predominantly altered in ATC. CDKN2A loss was significantly associated with poor disease-specific survival in patients with ATC or advanced DTCs, and up-regulation of CD274 (PD-L1) and PDCD1LG2 (PD-L2). Transcriptome analysis revealed a fourth molecular subtype of thyroid cancer (TC), ATC-like, which hardly reflects the molecular signatures in DTC. Furthermore, the activation of JAK-STAT signaling pathway could be a potential druggable target in RAS-positive ATC. Our findings provide insights for precision medicine in patients with advanced TCs.

Anaplastic thyroid cancer (ATC) and advanced differentiated thyroid cancers (DTCs) come with a dismal prognosis. Here, Yoo and colleagues reveal the genomic and transcriptomic landscape of ATC and DTC, highlighting potential therapeutic vulnerabilities.

Whole-Genome Sequencing in Cancer

Genome sequencing of cancer has fundamentally advanced our understanding of the underlying biology of this disease, and more recently has provided approaches to characterize and monitor tumors in the clinic, guiding and evaluating treatment. Although cancer research is relying more on whole-genome characterization, the clinical application of genomics is largely limited to targeted sequencing approaches, tailored to capture specific clinically relevant biomarkers. However, as sequencing costs reduce, and the tools to effectively analyze complex and large-scale data improve, the ability to effectively characterize whole genomes at scale in a clinically relevant time frame is now being piloted. This ability effectively blurs the line between clinical cancer research and the clinical management of the disease. This leads to a new paradigm in cancer management in which real-time analysis of an individual's disease can have a rapid and lasting impact on our understanding of how clinical practices need to change to exploit novel therapeutic rationales. In this article, we will discuss how whole-genome sequencing (WGS), often combined with transcriptome analysis, has been used to understand cancer and how this approach is uniquely positioned to provide a comprehensive view of an evolving disease in response to therapy.

Modeling double strand break susceptibility to interrogate structural variation in cancer

BACKGROUND

Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs).

RESULTS

We used experimentally quantified DSB frequencies in cell lines with matched chromatin and sequence features to derive the first quantitative genome-wide models of DSB susceptibility. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type-specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumors, many SV-enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation and are therefore credible targets of positive selection in tumors. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel coldspot regions appear to be subject to purifying selection in tumors and are enriched for active promoters and enhancers.

CONCLUSIONS

We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumors.

Breaking point: the genesis and impact of structural variation in tumours

Somatic structural variants undoubtedly play important roles in driving tumourigenesis. This is evident despite the substantial technical challenges that remain in accurately detecting structural variants and their breakpoints in tumours and in spite of our incomplete understanding of the impact of structural variants on cellular function. Developments in these areas of research contribute to the ongoing discovery of structural variation with a clear impact on the evolution of the tumour and on the clinical importance to the patient. Recent large whole genome sequencing studies have reinforced our impression of each tumour as a unique combination of mutations but paradoxically have also discovered similar genome-wide patterns of single-nucleotide and structural variation between tumours. Statistical methods have been developed to deconvolute mutation patterns, or signatures, that recur across samples, providing information about the mutagens and repair processes that may be active in a given tumour. These signatures can guide treatment by, for example, highlighting vulnerabilities in a particular tumour to a particular chemotherapy. Thus, although the complete reconstruction of the full evolutionary trajectory of a tumour genome remains currently out of reach, valuable data are already emerging to improve the treatment of cancer.

Discovery of New Fusion Transcripts in a Cohort of Pediatric Solid Cancers at Relapse and Relevance for Personalized Medicine

We hypothetized that pediatric cancers would more likely harbor fusion transcripts. To dissect the complexity of the fusions landscape in recurrent solid pediatric cancers, we conducted a study on 48 patients with different relapsing or resistant malignancies. By analyzing RNA sequencing data with a new in-house pipeline for fusions detection named ChimComp, followed by verification by real-time PCR, we identified and classified the most confident fusion transcripts (FTs) according to their potential biological function and druggability. The majority of FTs were predicted to affect key cancer pathways and described to be involved in oncogenesis. Contrary to previous descriptions, we found no significant correlation between the number of fusions and mutations, emphasizing the particularity to study pre-treated pediatric patients. A considerable proportion of FTs containing tumor suppressor genes was detected, reflecting their importance in pediatric cancers. FTs containing non-receptor tyrosine kinases occurred at low incidence and predominantly in brain tumors. Remarkably, more than 30% of patients presented a potentially druggable high-confidence fusion. In conclusion, we detected new oncogenic FTs in relapsing pediatric cancer patients by establishing a robust pipeline that can be applied to other malignancies, to detect and prioritize experimental validation studies leading to the development of new therapeutic options.

A Pan-Cancer Compendium of Genes Deregulated by Somatic Genomic Rearrangement across More Than 1,400 Cases

A systematic cataloging of genes affected by genomic rearrangement, using multiple patient cohorts and cancer types, can provide insight into cancer-relevant alterations outside of exomes. By integrative analysis of whole-genome sequencing (predominantly low pass) and gene expression data from 1,448 cancers involving 18 histopathological types in The Cancer Genome Atlas, we identified hundreds of genes for which the nearby presence (within 100 kb) of a somatic structural variant (SV) breakpoint is associated with altered expression. While genomic rearrangements are associated with widespread copy-number alteration (CNA) patterns, approximately 1,100 genes-including overexpressed cancer driver genes (e.g., TERT, ERBB2, CDK12, CDK4) and underexpressed tumor suppressors (e.g., TP53, RB1, PTEN, STK11)-show SV-associated deregulation independent of CNA. SVs associated with the disruption of topologically associated domains, enhancer hijacking, or fusion transcripts are implicated in gene upregulation. For cancer-relevant pathways, SVs considerably expand our understanding of how genes are affected beyond point mutation or CNA.

FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer

Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.

Oncogenic Signaling Pathways in The Cancer Genome Atlas

Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.

TERT structural rearrangements in metastatic pheochromocytomas

Pheochromocytomas (PC) and paragangliomas (PGL) are endocrine tumors for which the genetic and clinicopathological features of metastatic progression remain incompletely understood. As a result, the risk of metastasis from a primary tumor cannot be predicted. Early diagnosis of individuals at high risk of developing metastases is clinically important and the identification of new biomarkers that are predictive of metastatic potential is of high value. Activation of TERT has been associated with a number of malignant tumors, including PC/PGL. However, the mechanism of TERT activation in the majority of PC/PGL remains unclear. As TERT promoter mutations occur rarely in PC/PGL, we hypothesized that other mechanisms - such as structural variations - may underlie TERT activation in these tumors. From 35 PC and four PGL, we identified three primary PCs that developed metastases with elevated TERT expression, each of which lacked TERT promoter mutations and promoter DNA methylation. Using whole genome sequencing, we identified somatic structural alterations proximal to the TERT locus in two of these tumors. In both tumors, the genomic rearrangements led to the positioning of super-enhancers proximal to the TERT promoter, that are likely responsible for the activation of the normally tightly repressed TERT expression in chromaffin cells.