Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc Natl Acad Sci U S A. 2011;108:E1128-E1136. [PMID: 22065754 DOI: 10.1073/pnas.1110574108]

Multimodal analysis of cfDNA methylomes for early detecting esophageal squamous cell carcinoma and precancerous lesions

Detecting early-stage esophageal squamous cell carcinoma (ESCC) and precancerous lesions is critical for improving survival. Here, we conduct whole-genome bisulfite sequencing (WGBS) on 460 cfDNA samples from patients with non-metastatic ESCC or precancerous lesions and matched healthy controls. We develop an expanded multimodal analysis (EMMA) framework to simultaneously identify cfDNA methylation, copy number variants (CNVs), and fragmentation markers in cfDNA WGBS data. cfDNA methylation markers are the earliest and most sensitive, detectable in 70% of ESCCs and 50% of precancerous lesions, and associated with molecular subtypes and tumor microenvironments. CNVs and fragmentation features show high specificity but are linked to late-stage disease. EMMA significantly improves detection rates, increasing AUCs from 0.90 to 0.99, and detects 87% of ESCCs and 62% of precancerous lesions with >95% specificity in validation cohorts. Our findings demonstrate the potential of multimodal analysis of cfDNA methylome for early detection and monitoring of molecular characteristics in ESCC.

NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model

Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing," the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD's performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD .

Quinoxaline-Based Anti-Schistosomal Compounds Have Potent Anti-Malarial Activity

The human pathogens Plasmodium and Schistosoma are each responsible for over 200 million infections annually, being particularly problematic in low- and middle-income countries. There is a pressing need for new drug targets for these diseases, driven by emergence of drug-resistance in Plasmodium and the overall dearth of new drug targets for Schistosoma. Here, we explored the opportunity for pathogen-hopping by evaluating a series of quinoxaline-based anti-schistosomal compounds for activity against P. falciparum. We identified compounds with low nanomolar potency against 3D7 and multidrug-resistant strains. Evolution of resistance using a mutator P. falciparum line revealed a low propensity for resistance. Only one of the series, compound 22, yielded resistance mutations, including point mutations in a non-essential putative hydrolase pfqrp1, as well as copy-number amplification of a phospholipid-translocating ATPase, pfatp2, a potential target. Notably, independently generated CRISPR-edited mutants in pfqrp1 also showed resistance to compound 22 and a related analogue. Moreover, previous lines with pfatp2 copy-number variations were similarly less susceptible to challenge with the new compounds. Finally, we examined whether the predicted hydrolase activity of PfQRP1 underlies its mechanism of resistance, showing that both mutation of the putative catalytic triad and a more severe loss of function mutation elicited resistance. Collectively, we describe a compound series with potent activity against two important pathogens and their potential target in P. falciparum.

On the core segmentation algorithms of copy number variation detection tools

Shotgun sequencing is a high-throughput method used to detect copy number variants (CNVs). Although there are numerous CNV detection tools based on shotgun sequencing, their quality varies significantly, leading to performance discrepancies. Therefore, we conducted a comprehensive analysis of next-generation sequencing-based CNV detection tools over the past decade. Our findings revealed that the majority of mainstream tools employ similar detection rationale: calculates the so-called read depth signal from aligned sequencing reads and then segments the signal by utilizing either circular binary segmentation (CBS) or hidden Markov model (HMM). Hence, we compared the performance of those two core segmentation algorithms in CNV detection, considering varying sequencing depths, segment lengths and complex types of CNVs. To ensure a fair comparison, we designed a parametrical model using mainstream statistical distributions, which allows for pre-excluding bias correction such as guanine-cytosine (GC) content during the preprocessing step. The results indicate the following key points: (1) Under ideal conditions, CBS demonstrates high precision, while HMM exhibits a high recall rate. (2) For practical conditions, HMM is advantageous at lower sequencing depths, while CBS is more competitive in detecting small variant segments compared to HMM. (3) In case involving complex CNVs resembling real sequencing, HMM demonstrates more robustness compared with CBS. (4) When facing large-scale sequencing data, HMM costs less time compared with the CBS, while their memory usage is approximately equal. This can provide an important guidance and reference for researchers to develop new tools for CNV detection.

Unlocking the power of nanomedicine: the future of nutraceuticals in oncology treatment

Cancer, an intricate and multifaceted disease, is characterized by the uncontrolled proliferation of cells that can lead to serious health complications and ultimately death. Conventional therapeutic strategies mainly target rapidly dividing cancer cells, but often indiscriminately harm healthy cells in the process. As a result, there is a growing interest in exploring novel therapies that are both effective and less toxic to normal cells. Herbs have long been used as natural remedies for various diseases and conditions. Some herbal compounds exhibit potent anti-cancer properties, making them potential candidates for nutraceutical-based treatments. However, despite their promising efficacy, there are considerable limitations in utilizing herbal preparations due to their poor solubility, low bioavailability, rapid metabolism and excretion, as well as potential interference with other medications. Nanotechnology offers a unique platform to overcome these challenges by encapsulating herbal compounds within nanoparticles. This approach not only increases solubility and stability but also enhances the cellular uptake of nutraceuticals, allowing for controlled and targeted delivery of therapeutic agents directly at tumor sites. By harnessing the power of nanotechnology-enabled therapy, this new frontier in cancer treatment presents an opportunity to minimize toxicity while maximizing efficacy. In conclusion, this manuscript provides compelling evidence for integrating nanotechnology with nutraceuticals derived from herbal sources to optimize cancer therapy outcomes. We explore the roadblocks associated with traditional herbal treatments and demonstrate how nanotechnology can help circumvent these issues, paving the way for safer and more effective cancer interventions in future oncological practice.

Mapping the genomic landscape of multidrug resistance in Plasmodium falciparum and its impact on parasite fitness

Drug-resistant Plasmodium falciparum parasites have swept across Southeast Asia and now threaten Africa. By implementing a P. falciparum genetic cross using humanized mice, we report the identification of key determinants of resistance to artemisinin (ART) and piperaquine (PPQ) in the dominant Asian KEL1/PLA1 lineage. We mapped k13 as the central mediator of ART resistance in vitro and identified secondary markers. Applying bulk segregant analysis, quantitative trait loci mapping using 34 recombinant haplotypes, and gene editing, our data reveal an epistatic interaction between mutant PfCRT and multicopy plasmepsins 2/3 in mediating high-grade PPQ resistance. Susceptibility and parasite fitness assays implicate PPQ as a driver of selection for KEL1/PLA1 parasites. Mutant PfCRT enhanced susceptibility to lumefantrine, the first-line partner drug in Africa, highlighting a potential benefit of opposing selective pressures with this drug and PPQ. We also identified that the ABCI3 transporter can operate in concert with PfCRT and plasmepsins 2/3 in mediating multigenic resistance to antimalarial agents.

Tracing cancer evolution and heterogeneity using Hi-C

Chromosomal rearrangements can initiate and drive cancer progression, yet it has been challenging to evaluate their impact, especially in genetically heterogeneous solid cancers. To address this problem we developed HiDENSEC, a new computational framework for analyzing chromatin conformation capture in heterogeneous samples that can infer somatic copy number alterations, characterize large-scale chromosomal rearrangements, and estimate cancer cell fractions. After validating HiDENSEC with in silico and in vitro controls, we used it to characterize chromosome-scale evolution during melanoma progression in formalin-fixed tumor samples from three patients. The resulting comprehensive annotation of the genomic events includes copy number neutral translocations that disrupt tumor suppressor genes such as NF1, whole chromosome arm exchanges that result in loss of CDKN2A, and whole-arm copy-number neutral loss of homozygosity involving PTEN. These findings show that large-scale chromosomal rearrangements occur throughout cancer evolution and that characterizing these events yields insights into drivers of melanoma progression.

Analysis of copy number variations and possible candidate genes in spontaneous abortion by copy number variation sequencing

Introduction

Embryonic chromosomal abnormalities represent a major causative factor in early pregnancy loss, highlighting the importance of understanding their role in spontaneous abortion. This study investigates the potential correlation between chromosomal abnormalities and spontaneous abortion using copy number variation sequencing (CNV-seq), a Next-Generation Sequencing (NGS) technology.

Methods

We analyzed Copy Number Variations (CNVs) in 395 aborted fetal specimens from spontaneous abortion patients by CNV-seq. And collected correlated data, including maternal age, gestational week, and Body Mass Index (BMI), and analyzed their relationship with the CNVs.

Results

Out of the 395 cases, 67.09% of the fetuses had chromosomal abnormalities, including numerical abnormalities, structural abnormalities, and mosaicisms. Maternal age was found to be an important risk factor for fetal chromosomal abnormalities, with the proportion of autosomal trisomy in abnormal karyotypes increasing with maternal age, while polyploidy decreased. The proportion of abnormal karyotypes with mosaic decreased as gestational age increased, while the frequency of polyploidy and sex chromosome monosomy increased. Gene enrichment analysis identified potential miscarriage candidate genes and functions, as well as pathogenic genes and pathways associated with unexplained miscarriage among women aged below or over 35 years old. Based on our study, it can be inferred that there is an association between BMI values and the risk of recurrent miscarriage caused by chromosomal abnormalities.

Discussion

Overall, these findings provide important insights into the understanding of spontaneous abortion and have implications for the development of personalized interventions for patients with abnormal karyotypes.

Mapping the genomic landscape of multidrug resistance in Plasmodium falciparum and its impact on parasite fitness

Drug-resistant Plasmodium falciparum parasites have swept across Southeast Asia and now threaten Africa. By implementing a P. falciparum genetic cross using humanized mice, we report the identification of key determinants of resistance to artemisinin (ART) and piperaquine (PPQ) in the dominant Asian KEL1/PLA1 lineage. We mapped k13 as the central mediator of ART resistance and identified secondary markers. Applying bulk segregant analysis, quantitative trait loci mapping and gene editing, our data reveal an epistatic interaction between mutant PfCRT and multicopy plasmepsins 2/3 in mediating high-grade PPQ resistance. Susceptibility and parasite fitness assays implicate PPQ as a driver of selection for KEL1/PLA1 parasites. Mutant PfCRT enhanced susceptibility to lumefantrine, the first-line partner drug in Africa, highlighting a potential benefit of opposing selective pressures with this drug and PPQ. We also identified that the ABCI3 transporter can operate in concert with PfCRT and plasmepsins 2/3 in mediating multigenic resistance to antimalarial agents.

Mitigating the risk of antimalarial resistance via covalent dual-subunit inhibition of the Plasmodium proteasome

The Plasmodium falciparum proteasome constitutes a promising antimalarial target, with multiple chemotypes potently and selectively inhibiting parasite proliferation and synergizing with the first-line artemisinin drugs, including against artemisinin-resistant parasites. We compared resistance profiles of vinyl sulfone, epoxyketone, macrocyclic peptide, and asparagine ethylenediamine inhibitors and report that the vinyl sulfones were potent even against mutant parasites resistant to other proteasome inhibitors and did not readily select for resistance, particularly WLL that displays covalent and irreversible binding to the catalytic β2 and β5 proteasome subunits. We also observed instances of collateral hypersensitivity, whereby resistance to one inhibitor could sensitize parasites to distinct chemotypes. Proteasome selectivity was confirmed using CRISPR/Cas9-edited mutant and conditional knockdown parasites. Molecular modeling of proteasome mutations suggested spatial contraction of the β5 P1 binding pocket, compromising compound binding. Dual targeting of P. falciparum proteasome subunits using covalent inhibitors provides a potential strategy for restoring artemisinin activity and combating the spread of drug-resistant malaria.

Optimization of 2,3-Dihydroquinazolinone-3-carboxamides as Antimalarials Targeting PfATP4

There is an urgent need to populate the antimalarial clinical portfolio with new candidates because of resistance against frontline antimalarials. To discover new antimalarial chemotypes, we performed a high-throughput screen of the Janssen Jumpstarter library against the Plasmodium falciparum asexual blood-stage parasite and identified the 2,3-dihydroquinazolinone-3-carboxamide scaffold. We defined the SAR and found that 8-substitution on the tricyclic ring system and 3-substitution of the exocyclic arene produced analogues with potent activity against asexual parasites equivalent to clinically used antimalarials. Resistance selection and profiling against drug-resistant parasite strains revealed that this antimalarial chemotype targets PfATP4. Dihydroquinazolinone analogues were shown to disrupt parasite Na⁺ homeostasis and affect parasite pH, exhibited a fast-to-moderate rate of asexual kill, and blocked gametogenesis, consistent with the phenotype of clinically used PfATP4 inhibitors. Finally, we observed that optimized frontrunner analogue WJM-921 demonstrates oral efficacy in a mouse model of malaria.

Dysregulated ceRNA network modulated by copy number variation-driven lncRNAs in breast cancer: A comprehensive analysis

Breast cancer is a malignancy harmful to physical and mental health in women, with quite high mortality. Copy number variations (CNVs) are vital factors affecting the progression of breast cancer. Detecting CNVs in breast cancer to predict the prognosis of patients has become a promising approach to accurate treatment in recent years. The differential analysis was performed on CNVs of long noncoding RNAs (lncRNAs) as well as the expression of lncRNAs, microRNAs (miRNAs) and mRNAs in normal tissue and breast tumor tissue based on The Cancer Genome Atlas (TCGA) database. The CNV-driven lncRNAs were identified by the Kruskal-Wallis test. Meanwhile, a competitive endogenous RNA (ceRNA) network regulated by CNV-driven lncRNA was constructed. As the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed, the mRNAs in the dysregulated ceRNA network were mainly enriched in the biological functions and signaling pathways, including the Focal Adhesion-PI3K-Akt-mTOR-signaling pathway, the neuronal system, metapathway biotransformation Phase I and II and blood circulation, etc. The relationship between the CNVs of five lncRNAs and their gene expression in the ceRNA network was analyzed via a chi-square test, which confirmed that except for LINC00243, the expression of four lncRNAs was notably correlated with the CNVs. The survival analysis revealed that only the copy number gain of LINC00536 was evidently related to the poor prognosis of patients. The CIBERSORT algorithm showed that five lncRNAs were correlated with the abundance of immune cell infiltration and immune checkpoints. In a word, by analyzing CNV-driven lncRNAs and the ceRNA network regulated by these lncRNAs, this study explored the mechanism of breast cancer and provided novel insights into new biomarkers.

State of the Art: ctDNA in Upper Gastrointestinal Malignancies

Circulating tumor DNA (ctDNA) has emerged as a promising non-invasive source to characterize genetic alterations related to the tumor. Upper gastrointestinal cancers, including gastroesophageal adenocarcinoma (GEC), biliary tract cancer (BTC) and pancreatic ductal adenocarcinoma (PADC) are poor prognostic malignancies, usually diagnosed at advanced stages when no longer amenable to surgical resection and show a poor prognosis even for resected patients. In this sense, ctDNA has emerged as a promising non-invasive tool with different applications, from early diagnosis to molecular characterization and follow-up of tumor genomic evolution. In this manuscript, novel advances in the field of ctDNA analysis in upper gastrointestinal tumors are presented and discussed. Overall, ctDNA analyses can help in early diagnosis, outperforming current diagnostic approaches. Detection of ctDNA prior to surgery or active treatment is also a prognostic marker that associates with worse survival, while ctDNA detection after surgery is indicative of minimal residual disease, anticipating in some cases the imaging-based detection of progression. In the advanced setting, ctDNA analyses characterize the genetic landscape of the tumor and identify patients for targeted-therapy approaches, and studies show variable concordance levels with tissue-based genetic testing. In this line, several studies also show that ctDNA serves to follow responses to active therapy, especially in targeted approaches, where it can detect multiple resistance mechanisms. Unfortunately, current studies are still limited and observational. Future prospective multi-center and interventional studies, carefully designed to assess the value of ctDNA to help clinical decision-making, will shed light on the real applicability of ctDNA in upper gastrointestinal tumor management. This manuscript presents a review of the evidence available in this field up to date.

Genetic diagnostic yields of 354 Chinese ASD children with rare mutations by a pipeline of genomic tests

Purpose: To establish an effective genomic diagnosis pipeline for children with autism spectrum disorder (ASD) for its genetic etiology and intervention. Methods: A cohort of 354 autism spectrum disorder patients were obtained from Beijing Children's Hospital, Capital Medical University. Peripheral blood samples of the patients were collected for whole genome sequencing (WGS) and RNA sequencing (RNAseq). Sequencing data analyses were performed for mining the single nucleotide variation (SNV), copy number variation (CNV) and structural variation (SV). Sanger sequencing and quantitative PCR were used to verify the positive results. Results: Among 354 patients, 9 cases with pathogenic/likely pathogenic copy number variation and 10 cases with pathogenic/likely pathogenic single nucleotide variations were detected, with a total positive rate of 5.3%. Among these 9 copy number variation cases, 5 were de novo and 4 were inherited. Among the 10 de novo single nucleotide variations, 7 were previously unreported. The pathological de novo mutations account for 4.2% in our cohort. Conclusion: Rare mutations of copy number variations and single nucleotide variations account for a relatively small proportion of autism spectrum disorder children, which can be easily detected by a genomic testing pipeline of combined whole genome sequencing and RNA sequencing. This is important for early etiological diagnosis and precise management of autism spectrum disorder with rare mutations.

CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data

Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation, we introduce CNAViz, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAViz allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel single-cell DNA sequencing data from the same samples, we show that by using CNAViz, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling.

Copy-number aberrations (CNAs) are large genetic alterations that are pervasive in cancer and, therefore, have been the focus of several cancer research studies. Copy-number segmentation is a key step in the process of CNA identification, which consist in partitioning the genome into genomic segments with the same copy-number state. However, segmentation is challenging and the limitations of current segmentation algorithms lead to inaccuracies in the characterization of CNAs. In this paper, we introduce CNAViz, an interactive web-based tool that enables the user to edit segmentation solutions and overcome current limitations. We demonstrate the ability of a user to use CNAViz to improve segmentation solutions on both simulated and real data, analyzing six published bulk DNA sequencing samples from three breast cancer patients. Finally, we demonstrate that these improvements in segmentation solutions improve accuracy in downstream copy-number calling, enabling more accurate analyses of intra-tumor heterogeneity.

Starfish infers signatures of complex genomic rearrangements across human cancers

Complex genomic rearrangements (CGRs) are common in cancer and are known to form via two aberrant cellular structures-micronuclei and chromatin bridges. However, which of these mechanisms is more relevant to CGR formation in cancer and whether there are other undiscovered mechanisms remain unknown. Here we developed a computational algorithm, 'Starfish', to analyze 2,014 CGRs from 2,428 whole-genome-sequenced (WGS) tumors and discovered six CGR signatures based on their copy number and breakpoint patterns. Extensive benchmarking showed that our CGR signatures are highly accurate and biologically meaningful. Three signatures can be attributed to known biological processes-micronuclei- and chromatin-bridge-induced chromothripsis and circular extrachromosomal DNA. Over half of the CGRs belong to the remaining three signatures, not reported previously. A unique signature, which we named 'hourglass chromothripsis', with localized breakpoints and a low amount of DNA loss, is abundant in prostate cancer. Hourglass chromothripsis is associated with mutant SPOP, which may induce genome instability.

A G358S mutation in the Plasmodium falciparum Na+ pump PfATP4 confers clinically-relevant resistance to cipargamin

Diverse compounds target the Plasmodium falciparum Na+ pump PfATP4, with cipargamin and (+)-SJ733 the most clinically-advanced. In a recent clinical trial for cipargamin, recrudescent parasites emerged, with most having a G358S mutation in PfATP4. Here, we show that PfATP4G358S parasites can withstand micromolar concentrations of cipargamin and (+)-SJ733, while remaining susceptible to antimalarials that do not target PfATP4. The G358S mutation in PfATP4, and the equivalent mutation in Toxoplasma gondii ATP4, decrease the sensitivity of ATP4 to inhibition by cipargamin and (+)-SJ733, thereby protecting parasites from disruption of Na+ regulation. The G358S mutation reduces the affinity of PfATP4 for Na+ and is associated with an increase in the parasite's resting cytosolic [Na+]. However, no defect in parasite growth or transmissibility is observed. Our findings suggest that PfATP4 inhibitors in clinical development should be tested against PfATP4G358S parasites, and that their combination with unrelated antimalarials may mitigate against resistance development.

The genomic landscape of cholangiocarcinoma reveals the disruption of post-transcriptional modifiers

Molecular variation between geographical populations and subtypes indicate potential genomic heterogeneity and novel genomic features within CCA. Here, we analyze exome-sequencing data of 87 perihilar cholangiocarcinoma (pCCA) and 261 intrahepatic cholangiocarcinoma (iCCA) cases from 3 Asian centers (including 43 pCCAs and 24 iCCAs from our center). iCCA tumours demonstrate a higher tumor mutation burden and copy number alteration burden (CNAB) than pCCA tumours, and high CNAB indicates a poorer pCCA prognosis. We identify 12 significantly mutated genes and 5 focal CNA regions, and demonstrate common mutations in post-transcriptional modification-related potential driver genes METTL14 and RBM10 in pCCA tumours. Finally we demonstrate the tumour-suppressive role of METTL14, a major RNA N6-adenosine methyltransferase (m6A), and illustrate that its loss-of-function mutation R298H may act through m6A modification on potential driver gene MACF1. Our results may be valuable for better understanding of how post-transcriptional modification can affect CCA development, and highlight both similarities and differences between pCCA and iCCA.

Cholangiocarcinoma is a heterogenous group of cancers, with large genetic variation seen within subtypes. Here, the authors find 12 significantly mutated genes and 5 focal CNA regions were found in perihilar cholangiocarcinoma, and identified METTL14 to have a potential tumour suppressive role.

Circulating Tumor DNA: An Emerging Tool in Gastrointestinal Cancers

Circulating tumor DNA (ctDNA) is tumor-derived fragmented DNA in the bloodstream that has come from primary or metastatic cancer sites. Neoplasm-specific genetic and epigenetic abnormalities are increasingly being identified through liquid biopsy: a novel, minimally invasive technique used to isolate and analyze ctDNA in the peripheral circulation. Liquid biopsy and other emerging ctDNA technologies represent a paradigm shift in cancer diagnostics because they allow for the detection of minimal residual disease in patients with early-stage disease, improve risk stratification, capture tumor heterogeneity and genomic evolution, and enhance ctDNA-guided adjuvant and palliative cancer therapy. Moreover, ctDNA can be used to monitor the tumor response to neoadjuvant and postoperative therapy in patients with metastatic disease. Using clearance of ctDNA as an endpoint for escalation/de-escalation of adjuvant chemotherapy for patients considered to have high-risk disease has become an important area of research. The possibility of using ctDNA as a surrogate for treatment response-including for overall survival, progression-free survival, and disease-free survival-is an attractive concept; this surrogate will arguably reduce study duration and expedite the development of new therapies. In this review, we summarize the current evidence on the applications of ctDNA for the diagnosis and management of gastrointestinal tumors. Gastrointestinal cancers-including tumors of the esophagus, stomach, colon, liver, and pancreas-account for one-quarter of global cancer diagnoses and contribute to more than one-third of cancer-related deaths. Given the prevalence of gastrointestinal malignancies, ctDNA technology represents a powerful tool to reduce the global burden of disease.

Elevated MACC1 Expression in Colorectal Cancer Is Driven by Chromosomal Instability and Is Associated with Molecular Subtype and Worse Patient Survival

Metastasis-Associated in Colon Cancer 1 (MACC1) is a strong prognostic biomarker inducing proliferation, migration, invasiveness, and metastasis of cancer cells. The context of MACC1 dysregulation in cancers is, however, still poorly understood. Here, we investigated whether chromosomal instability and somatic copy number alterations (SCNA) frequently occurring in CRC contribute to MACC1 dysregulation, with prognostic and predictive impacts. Using the Oncotrack and Charité CRC cohorts of CRC patients, we showed that elevated MACC1 mRNA expression was tightly dependent on increased MACC1 gene SCNA and was associated with metastasis and shorter metastasis free survival. Deep analysis of the COAD-READ TCGA cohort revealed elevated MACC1 expression due to SCNA for advanced tumors exhibiting high chromosomal instability (CIN), and predominantly classified as CMS2 and CMS4 transcriptomic subtypes. For that cohort, we validated that elevated MACC1 mRNA expression correlated with reduced disease-free and overall survival. In conclusion, this study gives insights into the context of MACC1 expression in CRC. Increased MACC1 expression is largely driven by CIN, SCNA gains, and molecular subtypes, potentially determining the molecular risk for metastasis that might serve as a basis for patient-tailored treatment decisions.

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.

Multiomic analysis identifies CPT1A as a potential therapeutic target in platinum-refractory, high-grade serous ovarian cancer

Resistance to platinum compounds is a major determinant of patient survival in high-grade serous ovarian cancer (HGSOC). To understand mechanisms of platinum resistance and identify potential therapeutic targets in resistant HGSOC, we generated a data resource composed of dynamic (±carboplatin) protein, post-translational modification, and RNA sequencing (RNA-seq) profiles from intra-patient cell line pairs derived from 3 HGSOC patients before and after acquiring platinum resistance. These profiles reveal extensive responses to carboplatin that differ between sensitive and resistant cells. Higher fatty acid oxidation (FAO) pathway expression is associated with platinum resistance, and both pharmacologic inhibition and CRISPR knockout of carnitine palmitoyltransferase 1A (CPT1A), which represents a rate limiting step of FAO, sensitize HGSOC cells to platinum. The results are further validated in patient-derived xenograft models, indicating that CPT1A is a candidate therapeutic target to overcome platinum resistance. All multiomic data can be queried via an intuitive gene-query user interface (https://sites.google.com/view/ptrc-cell-line).

Current status of structural variation studies in plants

Structural variations (SVs) including gene presence/absence variations and copy number variations are a common feature of genomes in plants and, together with single nucleotide polymorphisms and epigenetic differences, are responsible for the heritable phenotypic diversity observed within and between species. Understanding the contribution of SVs to plant phenotypic variation is important for plant breeders to assist in producing improved varieties. The low resolution of early genetic technologies and inefficient methods have previously limited our understanding of SVs in plants. However, with the rapid expansion in genomic technologies, it is possible to assess SVs with an ever-greater resolution and accuracy. Here, we review the current status of SV studies in plants, examine the roles that SVs play in phenotypic traits, compare current technologies and assess future challenges for SV studies.

CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data

Copy number variation (CNV), is defined as repetitions or deletions of genomic segments of 1 Kb to 5 Mb, and is a major trigger for human disease. The high-throughput and low-cost characteristics of next-generation sequencing technology provide the possibility of the detection of CNVs in the whole genome, and also greatly improve the clinical practicability of next-generation sequencing (NGS) testing. However, current methods for the detection of CNVs are easily affected by sequencing and mapping errors, and uneven distribution of reads. In this paper, we propose an improved approach, CNV-MEANN, for the detection of CNVs, involving changing the structure of the neural network used in the MFCNV method. This method has three differences relative to the MFCNV method: (1) it utilizes a new feature, mapping quality, to replace two features in MFCNV, (2) it considers the influence of the loss categories of CNV on disease prediction, and refines the output structure, and (3) it uses a mind evolutionary algorithm to optimize the backpropagation (neural network) neural network model, and calculates individual scores for each genome bin to predict CNVs. Using both simulated and real datasets, we tested the performance of CNV-MEANN and compared its performance with those of seven widely used CNV detection methods. Experimental results demonstrated that the CNV-MEANN approach outperformed other methods with respect to sensitivity, precision, and F1-score. The proposed method was able to detect many CNVs that other approaches could not, and it reduced the boundary bias. CNV-MEANN is expected to be an effective method for the analysis of changes in CNVs in the genome.

Lineage-defined leiomyosarcoma subtypes emerge years before diagnosis and determine patient survival

Leiomyosarcomas (LMS) are genetically heterogeneous tumors differentiating along smooth muscle lines. Currently, LMS treatment is not informed by molecular subtyping and is associated with highly variable survival. While disease site continues to dictate clinical management, the contribution of genetic factors to LMS subtype, origins, and timing are unknown. Here we analyze 70 genomes and 130 transcriptomes of LMS, including multiple tumor regions and paired metastases. Molecular profiling highlight the very early origins of LMS. We uncover three specific subtypes of LMS that likely develop from distinct lineages of smooth muscle cells. Of these, dedifferentiated LMS with high immune infiltration and tumors primarily of gynecological origin harbor genomic dystrophin deletions and/or loss of dystrophin expression, acquire the highest burden of genomic mutation, and are associated with worse survival. Homologous recombination defects lead to genome-wide mutational signatures, and a corresponding sensitivity to PARP trappers and other DNA damage response inhibitors, suggesting a promising therapeutic strategy for LMS. Finally, by phylogenetic reconstruction, we present evidence that clones seeding lethal metastases arise decades prior to LMS diagnosis.

Plasmodium falciparum K13 mutations in Africa and Asia impact artemisinin resistance and parasite fitness

The emergence of mutant K13-mediated artemisinin (ART) resistance in Plasmodium falciparum malaria parasites has led to widespread treatment failures across Southeast Asia. In Africa, K13-propeller genotyping confirms the emergence of the R561H mutation in Rwanda and highlights the continuing dominance of wild-type K13 elsewhere. Using gene editing, we show that R561H, along with C580Y and M579I, confer elevated in vitro ART resistance in some African strains, contrasting with minimal changes in ART susceptibility in others. C580Y and M579I cause substantial fitness costs, which may slow their dissemination in high-transmission settings, in contrast with R561H that in African 3D7 parasites is fitness neutral. In Cambodia, K13 genotyping highlights the increasing spatio-temporal dominance of C580Y. Editing multiple K13 mutations into a panel of Southeast Asian strains reveals that only the R561H variant yields ART resistance comparable to C580Y. In Asian Dd2 parasites C580Y shows no fitness cost, in contrast with most other K13 mutations tested, including R561H. Editing of point mutations in ferredoxin or mdr2, earlier associated with resistance, has no impact on ART susceptibility or parasite fitness. These data underline the complex interplay between K13 mutations, parasite survival, growth and genetic background in contributing to the spread of ART resistance.

Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs

Cancer genomes often harbor hundreds of somatic DNA rearrangement junctions, many of which cannot be easily classified into simple (e.g., deletion) or complex (e.g., chromothripsis) structural variant classes. Applying a novel genome graph computational paradigm to analyze the topology of junction copy number (JCN) across 2,778 tumor whole-genome sequences, we uncovered three novel complex rearrangement phenomena: pyrgo, rigma, and tyfonas. Pyrgo are "towers" of low-JCN duplications associated with early-replicating regions, superenhancers, and breast or ovarian cancers. Rigma comprise "chasms" of low-JCN deletions enriched in late-replicating fragile sites and gastrointestinal carcinomas. Tyfonas are "typhoons" of high-JCN junctions and fold-back inversions associated with expressed protein-coding fusions, breakend hypermutation, and acral, but not cutaneous, melanomas. Clustering of tumors according to genome graph-derived features identified subgroups associated with DNA repair defects and poor prognosis.

Whole genome sequencing reveals a frameshift mutation and a large deletion in YY1AP1 in a girl with a panvascular artery disease

Background

Rare diseases are pathologies that affect less than 1 in 2000 people. They are difficult to diagnose due to their low frequency and their often highly heterogeneous symptoms. Rare diseases have in general a high impact on the quality of life and life expectancy of patients, which are in general children or young people. The advent of high-throughput sequencing techniques has improved diagnosis in several different areas, from pediatrics, achieving a diagnostic rate of 41% with whole genome sequencing (WGS) and 36% with whole exome sequencing, to neurology, achieving a diagnostic rate between 47 and 48.5% with WGS. This evidence has encouraged our group to pursue a molecular diagnosis using WGS for this and several other patients with rare diseases.

Results

We used whole genome sequencing to achieve a molecular diagnosis of a 7-year-old girl with a severe panvascular artery disease that remained for several years undiagnosed. We found a frameshift variant in one copy and a large deletion involving two exons in the other copy of a gene called YY1AP1. This gene is related to Grange syndrome, a recessive rare disease, whose symptoms include stenosis or occlusion of multiple arteries, congenital heart defects, brachydactyly, syndactyly, bone fragility, and learning disabilities. Bioinformatic analyses propose these mutations as the most likely cause of the disease, according to its frequency, in silico predictors, conservation analyses, and effect on the protein product. Additionally, we confirmed one mutation in each parent, supporting a compound heterozygous status in the child.

Conclusions

In general, we think that this finding can contribute to the use of whole genome sequencing as a diagnosis tool of rare diseases, and in particular, it can enhance the set of known mutations associated with different diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00328-1.

Novel Antimalarial Tetrazoles and Amides Active against the Hemoglobin Degradation Pathway in Plasmodium falciparum

Malaria control programs continue to be threatened by drug resistance. To identify new antimalarials, we conducted a phenotypic screen and identified a novel tetrazole-based series that shows fast-kill kinetics and a relatively low propensity to develop high-level resistance. Preliminary structure-activity relationships were established including identification of a subseries of related amides with antiplasmodial activity. Assaying parasites with resistance to antimalarials led us to test whether the series had a similar mechanism of action to chloroquine (CQ). Treatment of synchronized Plasmodium falciparum parasites with active analogues revealed a pattern of intracellular inhibition of hemozoin (Hz) formation reminiscent of CQ's action. Drug selections yielded only modest resistance that was associated with amplification of the multidrug resistance gene 1 (pfmdr1). Thus, we have identified a novel chemical series that targets the historically druggable heme polymerization pathway and that can form the basis of future optimization efforts to develop a new malaria treatment.

Statistical Considerations on NGS Data for Inferring Copy Number Variations

The next-generation sequencing (NGS) technology has revolutionized research in genetics and genomics, resulting in massive NGS data and opening more fronts to answer unresolved issues in genetics. NGS data are usually stored at three levels: image files, sequence tags, and alignment reads. The sizes of these types of data usually range from several hundreds of gigabytes to several terabytes. Biostatisticians and bioinformaticians are typically working with the aligned NGS read count data (hence the last level of NGS data) for data modeling and interpretation.To horn in on the use of NGS technology, researchers utilize it to profile the whole genome to study DNA copy number variations (CNVs) for an individual subject (or patient) as well as groups of subjects (or patients). The resulting aligned NGS read count data are then modeled by proper mathematical and statistical approaches so that the loci of CNVs can be accurately detected. In this book chapter, a summary of most popularly used statistical methods for detecting CNVs using NGS data is given. The goal is to provide readers with a comprehensive resource of available statistical approaches for inferring DNA copy number variations using NGS data.

Systematic analysis of enhancer regulatory circuit perturbation driven by copy number variations in malignant glioma

Background: Enhancers are emerging regulatory regions controlling gene expression in diverse cancer types. However, the functions of enhancer regulatory circuit perturbations driven by copy number variations (CNVs) in malignant glioma are unclear. Therefore, we aimed to investigate the comprehensive enhancer regulatory perturbation and identify potential biomarkers in glioma.

Results: We performed a meta-analysis of the enhancer centered regulatory circuit perturbations in 683 gliomas by integrating CNVs, gene expression, and transcription factors (TFs) binding. We found widespread CNVs of enhancers during glioma progression, and CNVs were associated with the perturbations of enhancer activities. In particular, the degree of perturbations for amplified enhancers was much greater accompanied by the glioma malignant progression. In addition, CNVs and enhancers cooperatively regulated the expressions of cancer-related genes. Genome-wide TF binding profiles revealed that enhancers were pervasively regulated by TFs. A network-based analysis of TF-enhancer-gene regulatory circuits revealed a core TF-gene module (58 interactions including seven genes and 14 TFs) that was associated survival of patients with glioma (p < 0.001). Finally, we validated this prognosis-associated TF-gene regulatory module in an independent cohort. In summary, our analyses provided new molecular insights for enhancer-centered transcriptional perturbation in glioma therapy.

Conclusion: Integrative analysis revealed enhancer regulatory perturbations in glioma and also identified a network module that was associated with patient survival, thereby providing novel insights into enhancer-centered cancer therapy.

Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data

Copy-number aberrations (CNAs) and whole-genome duplications (WGDs) are frequent somatic mutations in cancer but their quantification from DNA sequencing of bulk tumor samples is challenging. Standard methods for CNA inference analyze tumor samples individually; however, DNA sequencing of multiple samples from a cancer patient has recently become more common. We introduce HATCHet (Holistic Allele-specific Tumor Copy-number Heterogeneity), an algorithm that infers allele- and clone-specific CNAs and WGDs jointly across multiple tumor samples from the same patient. We show that HATCHet outperforms current state-of-the-art methods on multi-sample DNA sequencing data that we simulate using MASCoTE (Multiple Allele-specific Simulation of Copy-number Tumor Evolution). Applying HATCHet to 84 tumor samples from 14 prostate and pancreas cancer patients, we identify subclonal CNAs and WGDs that are more plausible than previously published analyses and more consistent with somatic single-nucleotide variants (SNVs) and small indels in the same samples.

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.

CONDEL: Detecting Copy Number Variation and Genotyping Deletion Zygosity from Single Tumor Samples Using Sequence Data

Characterizing copy number variations (CNVs) from sequenced genomes is a both feasible and cost-effective way to search for driver genes in cancer diagnosis. A number of existing algorithms for CNV detection only explored part of the features underlying sequence data and copy number structures, resulting in limited performance. Here, we describe CONDEL, a method for detecting CNVs from single tumor samples using high-throughput sequence data. CONDEL utilizes a novel statistic in combination with a peel-off scheme to assess the statistical significance of genome bins, and adopts a Bayesian approach to infer copy number gains, losses, and deletion zygosity based on statistical mixture models. We compare CONDEL to six peer methods on a large number of simulation datasets, showing improved performance in terms of true positive and false positive rates, and further validate CONDEL on three real datasets derived from the 1000 Genomes Project and the EGA archive. CONDEL obtained higher consistent results in comparison with other three single sample-based methods, and exclusively identified a number of CNVs that were previously associated with cancers. We conclude that CONDEL is a powerful tool for detecting copy number variations on single tumor samples even if these are sequenced at low-coverage.

CONY: A Bayesian procedure for detecting copy number variations from sequencing read depths

Copy number variations (CNVs) are genomic structural mutations consisting of abnormal numbers of fragment copies. Next-generation sequencing of read-depth signals mirrors these variants. Some tools used to predict CNVs by depth have been published, but most of these tools can be applied to only a specific data type due to modeling limitations. We develop a tool for copy number variation detection by a Bayesian procedure, i.e., CONY, that adopts a Bayesian hierarchical model and an efficient reversible-jump Markov chain Monte Carlo inference algorithm for whole genome sequencing of read-depth data. CONY can be applied not only to individual samples for estimating the absolute number of copies but also to case-control pairs for detecting patient-specific variations. We evaluate the performance of CONY and compare CONY with competing approaches through simulations and by using experimental data from the 1000 Genomes Project. CONY outperforms the other methods in terms of accuracy in both single-sample and paired-samples analyses. In addition, CONY performs well regardless of whether the data coverage is high or low. CONY is useful for detecting both absolute and relative CNVs from read-depth data sequences. The package is available at https://github.com/weiyuchung/CONY.

Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations

Copy number variation (CNV) is a type of genomic/genetic variation that plays an important role in phenotypic diversity, evolution, and disease susceptibility. Next generation sequencing (NGS) technologies have created an opportunity for more accurate detection of CNVs with higher resolution. However, efficient and precise detection of CNVs remains challenging due to high levels of noise and biases, data heterogeneity, and the "big data" nature of NGS data. Sequence coverage (readcount) data are mostly used for detecting CNVs, specially for whole exome sequencing data. Readcount data are contaminated with several types of biases and noise that hinder accurate detection of CNVs. In this work, we introduce a novel preprocessing pipeline for reducing noise and biases to improve the detection accuracy of CNVs in heterogeneous NGS data, such as cancer whole exome sequencing data. We have employed several normalization methods to reduce readcount's biases that are due to GC content of reads, read alignment problems, and sample impurity. We have also developed a novel efficient and effective smoothing approach based on Taut String to reduce noise and increase CNV detection power. Using simulated and real data we showed that employing the proposed preprocessing pipeline significantly improves the accuracy of CNV detection.

HiNT: a computational method for detecting copy number variations and translocations from Hi-C data

The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that combines chromatin conformation capture with high-throughput sequencing. However, structural variations often yield features that can be mistaken for chromosomal interactions. Here, we describe a computational method HiNT (Hi-C for copy Number variation and Translocation detection), which detects copy number variations and interchromosomal translocations within Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT outperforms existing methods on both simulated and real data. We also show that Hi-C can supplement whole-genome sequencing in structure variant detection by locating breakpoints in repetitive regions.

The Antimalarial Natural Product Salinipostin A Identifies Essential α/β Serine Hydrolases Involved in Lipid Metabolism in P. falciparum Parasites

Salinipostin A (Sal A) is a potent antiplasmodial marine natural product with an undefined mechanism of action. Using a Sal A-derived activity-based probe, we identify its targets in the Plasmodium falciparum parasite. All of the identified proteins contain α/β serine hydrolase domains and several are essential for parasite growth. One of the essential targets displays a high degree of homology to human monoacylglycerol lipase (MAGL) and is able to process lipid esters including a MAGL acylglyceride substrate. This Sal A target is inhibited by the anti-obesity drug Orlistat, which disrupts lipid metabolism. Resistance selections yielded parasites that showed only minor reductions in sensitivity and that acquired mutations in a PRELI domain-containing protein linked to drug resistance in Toxoplasma gondii. This inability to evolve efficient resistance mechanisms combined with the non-essentiality of human homologs makes the serine hydrolases identified here promising antimalarial targets.

A systematic evaluation of copy number alterations detection methods on real SNP array and deep sequencing data

BACKGROUND

The Copy Number Alterations (CNAs) are discovered to be tightly associated with cancers, so accurately detecting them is one of the most important tasks in the cancer genomics. A series of CNAs detection methods have been proposed and new ones are still being developed. Due to the complexity of CNAs in cancers, no CNAs detection method has been accepted as the gold standard caller. Several evaluation works have made attempts to reveal typical CNAs detection methods' performance. Limited by the scale of evaluation data, these different comparison works don't reach a consensus and the researchers are still confused on how to choose one proper CNAs caller for their analysis. Therefore, it needs a more comprehensive evaluation of typical CNAs detection methods' performance.

RESULTS

In this work, we use a large-scale real dataset from CAGEKID consortium to evaluate total 12 typical CNAs detection methods. These methods are most widely used in cancer researches and always used as benchmark for the newly proposed CNAs detection methods. This large-scale dataset comprises of SNP array data on 94 samples and the whole genome sequencing data on 10 samples. Evaluations are comprehensively implemented in current scenarios of CNAs detection, which include that detect CNAs on SNP array data, on sequencing data with tumor and normal matched samples and on sequencing data with single tumor sample. Three SNP based methods are firstly ranked. Subsequently, the best SNP based method's results are used as benchmark to compare six matched samples based methods and three single tumor sample based methods in terms of the preprocessing, recall rate, Jaccard index and segmentation characteristics.

CONCLUSIONS

Our survey thoroughly reveals 12 typical methods' superiority and inferiority. We explain why methods show specific characteristics from a methodological standpoint. Finally, we present the guiding principle for choosing one proper CNAs detection method under specific conditions. Some unsolved problems and expectations are also addressed for upcoming CNAs detection methods.

Joint detection of germline and somatic copy number events in matched tumor-normal sample pairs

MOTIVATION

Whole-genome sequencing (WGS) of tumor-normal sample pairs is a powerful approach for comprehensively characterizing germline copy number variations (CNVs) and somatic copy number alterations (SCNAs) in cancer research and clinical practice. Existing computational approaches for detecting copy number events cannot detect germline CNVs and SCNAs simultaneously, and yield low accuracy for SCNAs.

RESULTS

In this study, we developed TumorCNV, a novel approach for jointly detecting germline CNVs and SCNAs from WGS data of the matched tumor-normal sample pair. We compared TumorCNV with existing copy number event detection approaches using the simulated data and real data for the COLO-829 melanoma cell line. The experimental results showed that TumorCNV achieved superior performance than existing approaches.

AVAILABILITY AND IMPLEMENTATION

The software TumorCNV is implemented using a combination of Java and R, and it is freely available from the website at https://github.com/yongzhuang/TumorCNV.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Dysregulated Competitive Endogenous RNA Networks Driven by Copy Number Variations in Malignant Gliomas

Gliomas represent 80% of malignant brain tumors. Because of the high heterogeneity, the oncogenic mechanisms in gliomas are still unclear. In this study, we developed a new approach to identify dysregulated competitive endogenous RNA (ceRNA) interactions driven by copy number variation (CNV) in both lower-grade glioma (LGG) and glioblastoma multiforme (GBM). By analyzing genome and transcriptome data from The Cancer Genome Atlas (TCGA), we first found out the protein coding genes and long non-coding RNAs (lncRNAs) significantly affected by CNVs and further determined CNV-driven dysregulated ceRNA interactions by a customized pipeline. We obtained 13,776 CNV-driven dysregulated ceRNA pairs (including 3,954 mRNAs and 306 lncRNAs) in LGG and 262 pairs (including 221 mRNAs and 11 lncRNAs) in GBM, respectively. Our results showed that most of the ceRNA interactions were weakened by CNVs in both LGG and GBM, and many CNV-driven genes shared the same ceRNAs in the dysregulated ceRNA networks. Functional analysis indicated that the CNV-driven ceRNA network involved in some important mechanisms of tumorigenesis, such as cell cycle, p53 signaling pathway and TGF-beta signaling pathway. Further investigation of the ceRNA pairs in the communities from the dysregulated ceRNA network revealed more detailed biological functions related to the oncogenesis of malignant gliomas. Moreover, by exploring the association of CNV-driven ceRNAs with prognosis and histological subtype, we found that the copy number status of MTAP, KLHL9, and ELAVL2 related to the overall survival in LGG and showed high correlation with histological subtype. In conclusion, this study provided new insight into the molecular mechanisms and clinical biomarkers in gliomas.

The Draft Genome of Kochia scoparia and the Mechanism of Glyphosate Resistance via Transposon-Mediated EPSPS Tandem Gene Duplication

Increased copy number of the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene confers resistance to glyphosate, the world's most-used herbicide. There are typically three to eight EPSPS copies arranged in tandem in glyphosate-resistant populations of the weed kochia (Kochia scoparia). Here, we report a draft genome assembly from a glyphosate-susceptible kochia individual. Additionally, we assembled the EPSPS locus from a glyphosate-resistant kochia plant by sequencing select bacterial artificial chromosomes from a kochia bacterial artificial chromosome library. Comparing the resistant and susceptible EPSPS locus allowed us to reconstruct the history of duplication in the structurally complex EPSPS locus and uncover the genes that are coduplicated with EPSPS, several of which have a corresponding change in transcription. The comparison between the susceptible and resistant assemblies revealed two dominant repeat types. Additionally, we discovered a mobile genetic element with a FHY3/FAR1-like gene predicted in its sequence that is associated with the duplicated EPSPS gene copies in the resistant line. We present a hypothetical model based on unequal crossing over that implicates this mobile element as responsible for the origin of the EPSPS gene duplication event and the evolution of herbicide resistance in this system. These findings add to our understanding of stress resistance evolution and provide an example of rapid resistance evolution to high levels of environmental stress.

An enhanced genetic model of colorectal cancer progression history

BACKGROUND

The classical genetic model of colorectal cancer presents APC mutations as the earliest genomic alterations, followed by KRAS and TP53 mutations. However, the timing and relative order of clonal expansion and other types of genomic alterations, such as genomic rearrangements, are still unclear.

RESULTS

Here, we perform comprehensive bioinformatic analysis to dissect the relative timing of somatic genetic alterations in 63 colorectal cancers with whole-genome sequencing data. Utilizing allele fractions of somatic single nucleotide variants as molecular clocks while accounting for the presence of copy number changes and structural alterations, we identify key events in the evolution of colorectal tumors. We find that driver point mutations, gene fusions, and arm-level copy losses typically arise early in tumorigenesis; different mechanisms act on distinct genomic regions to drive DNA copy changes; and chromothripsis-clustered rearrangements previously thought to occur as a single catastrophic event-is frequent and may occur multiple times independently in the same tumor through different mechanisms. Furthermore, our computational approach reveals that, in contrast to recent studies, selection is often present on subclones and that multiple evolutionary models can operate in a single tumor at different stages.

CONCLUSION

Combining these results, we present a refined tumor progression model which significantly expands our understanding of the tumorigenic process of human colorectal cancer.

GPA: A Microbial Genetic Polymorphisms Assignments Tool in Metagenomic Analysis by Bayesian Estimation

Identifying antimicrobial resistant (AMR) bacteria in metagenomics samples is essential for public health and food safety. Next-generation sequencing (NGS) technology has provided a powerful tool in identifying the genetic variation and constructing the correlations between genotype and phenotype in humans and other species. However, for complex bacterial samples, there lacks a powerful bioinformatic tool to identify genetic polymorphisms or copy number variations (CNVs) for given genes. Here we provide a Bayesian framework for genotype estimation for mixtures of multiple bacteria, named as Genetic Polymorphisms Assignments (GPA). Simulation results showed that GPA has reduced the false discovery rate (FDR) and mean absolute error (MAE) in CNV and single nucleotide variant (SNV) identification. This framework was validated by whole-genome sequencing and Pool-seq data from Klebsiella pneumoniae with multiple bacteria mixture models, and showed the high accuracy in the allele fraction detections of CNVs and SNVs in AMR genes between two populations. The quantitative study on the changes of AMR genes fraction between two samples showed a good consistency with the AMR pattern observed in the individual strains. Also, the framework together with the genome annotation and population comparison tools has been integrated into an application, which could provide a complete solution for AMR gene identification and quantification in unculturable clinical samples. The GPA package is available at https://github.com/IID-DTH/GPA-package.

SurVIndel: improving CNV calling from high-throughput sequencing data through statistical testing

MOTIVATION

Structural variations (SV) are large scale mutations in a genome; although less frequent than point mutations, due to their large size they are responsible for more heritable differences between individuals. Two prominent classes of SVs are deletions and tandem duplications. They play important roles in many devastating genetic diseases, such as Smith-Magenis syndrome, Potocki-Lupski syndrome and Williams-Beuren syndrome.Since paired-end whole genome sequencing data has become widespread and affordable, reliably calling deletions and tandem duplications has been a major target in bioinformatics; unfortunately, the problem is far from being solved, since existing solutions often offer poor results when applied to real data.

RESULTS

We developed a novel caller, SurVIndel, which focuses on detecting deletions and tandem duplications from paired next-generation sequencing data. SurVIndel uses discordant paired reads, clipped reads as well as statistical methods. We show that SurVIndel outperforms existing methods on both simulated and real biological datasets.

AVAILABILITY

SurVIndel is available at https://github.com/Mesh89/SurVIndel.

Characterization and evolutionary dynamics of complex regions in eukaryotic genomes

Complex regions in eukaryotic genomes are typically characterized by duplications of chromosomal stretches that often include one or more genes repeated in a tandem array or in relatively close proximity. Nevertheless, the repetitive nature of these regions, together with the often high sequence identity among repeats, have made complex regions particularly recalcitrant to proper molecular characterization, often being misassembled or completely absent in genome assemblies. This limitation has prevented accurate functional and evolutionary analyses of these regions. This is becoming increasingly relevant as evidence continues to support a central role for complex genomic regions in explaining human disease, developmental innovations, and ecological adaptations across phyla. With the advent of long-read sequencing technologies and suitable assemblers, the development of algorithms that can accommodate sample heterozygosity, and the adoption of a pangenomic-like view of these regions, accurate reconstructions of complex regions are now within reach. These reconstructions will finally allow for accurate functional and evolutionary studies of complex genomic regions, underlying the generation of genotype-phenotype maps of unprecedented resolution.

SM-RCNV: a statistical method to detect recurrent copy number variations in sequenced samples

BACKGROUND

Copy number variation (CNV) is an important form of genomic structural variation and is linked to dozens of human diseases. Using next-generation sequencing (NGS) data and developing computational methods to characterize such structural variants is significant for understanding the mechanisms of diseases.

OBJECTIVE

The objective of this study is to develop a new statistical method of detection recurrent CNVs across multiple samples from genomic sequences.

METHODS

A statistical method is carried out to detect recurrent CNVs, referred to as SM-RCNV. This method uses a statistic associated with each location by combining the frequency of variation at one location across whole samples and the correlation among consecutive locations. The weights of the frequency and correlation are trained using real datasets with known CNVs. P-value is assessed for each location on the genome by permutation testing.

RESULTS

Compared with six peer methods, SM-RCNV outperforms the peer methods under receiver operating characteristic curves. SM-RCNV successfully identifies many consistent recurrent CNVs, most of which are known to be of biological significance and associated with diseased genes. The validation rate of SM-RCNV in the CEU call set and YRI call set with Database of Genomic Variants are 258/328 (79%) and (157/309) 51%, respectively.

CONCLUSION

SM-RCNV is a well-grounded statistical framework for detecting recurrent CNVs from multiple genomic sequences, providing valuable information to study genomes in human diseases. The source code is freely available at https://sourceforge.net/projects/sm-rcnv/ .

Allele-Specific Expression Analysis in Cancer Using Next-Generation Sequencing Data

Allele-specific expression arises when transcriptional activity at the different alleles of a gene differs considerably. Although extensive research has been carried out to detect and characterize this phenomenon, the landscape of allele-specific expression in cancer is still poorly understood. In this chapter, we describe a fast and reliable analysis pipeline to study allele-specific expression in cancer using next-generation sequencing data. The pipeline provides a gene-level analysis approach that exploits paired germline DNA and tumor RNA sequencing data and benefits from parallel computation resources when available.