Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.

Disease Modeling on Tumor Organoids Implicates AURKA as a Therapeutic Target in Liver Metastatic Colorectal Cancer

BACKGROUND & AIMS

Patient-derived tumor organoids recapitulate the characteristics of colorectal cancer (CRC) and provide an ideal platform for preclinical evaluation of personalized treatment options. We aimed to model the acquisition of chemotolerance during first-line combination chemotherapy in metastatic CRC organoids.

METHODS

We performed next-generation sequencing to study the evolution of KRAS wild-type CRC organoids during adaptation to irinotecan-based chemotherapy combined with epidermal growth factor receptor (EGFR) inhibition. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated 9 protein (Cas9)-editing showed the specific effect of KRAS^G12D acquisition in drug-tolerant organoids. Compound treatment strategies involving Aurora kinase A (AURKA) inhibition were assessed for their capability to induce apoptosis in a drug-persister background. Immunohistochemical detection of AURKA was performed on a patient-matched cohort of primary tumors and derived liver metastases.

RESULTS

Adaptation to combination chemotherapy was accompanied by transcriptomic rather than gene mutational alterations in CRC organoids. Drug-tolerant cells evaded apoptosis and up-regulated MYC (c-myelocytomatosis oncogene product)/E2F1 (E2 family transcription factor 1) and/or interferon-α-related gene expression. Introduction of KRAS^G12D further increased the resilience of drug-persister CRC organoids against combination therapy. AURKA inhibition restored an apoptotic response in drug-tolerant KRAS-wild-type organoids. In dual epidermal growth factor receptor (EGFR)- pathway blockade-primed CRC organoids expressing KRAS^G12D, AURKA inhibition augmented apoptosis in cases that had acquired increased c-MYC protein levels during chemotolerance development. In patient-matched CRC cohorts, AURKA expression was increased in primary tumors and derived liver metastases.

CONCLUSIONS

Our study emphasizes the potential of patient-derived CRC organoids in modeling chemotherapy tolerance ex vivo. The applied therapeutic strategy of dual EGFR pathway blockade in combination with AURKA inhibition may prove effective for second-line treatment of chemotolerant CRC liver metastases with acquired KRAS mutation and increased AURKA/c-MYC expression.

Fanconi anemia and homologous recombination gene variants are associated with functional DNA repair defects in vitro and poor outcome in patients with advanced head and neck squamous cell carcinoma

Mutations in Fanconi Anemia or Homologous Recombination (FA/HR) genes can cause DNA repair defects and could therefore impact cancer treatment response and patient outcome. Their functional impact and clinical relevance in head and neck squamous cell carcinoma (HNSCC) is unknown. We therefore questioned whether functional FA/HR defects occurred in HNSCC and whether they are associated with FA/HR variants. We assayed a panel of 29 patient-derived HNSCC cell lines and found that a considerable fraction is hypersensitive to the crosslinker Mitomycin C and PARP inhibitors, a functional measure of FA/HR defects. DNA sequencing showed that these hypersensitivities are associated with the presence of bi-allelic rare germline and somatic FA/HR gene variants. We next questioned whether such variants are associated with prognosis and treatment response in HNSCC patients. DNA sequencing of 77 advanced stage HNSCC tumors revealed a 19% incidence of such variants. Importantly, these variants were associated with a poor prognosis (p = 0.027; HR = 2.6, 1.1–6.0) but favorable response to high cumulative cisplatin dose. We show how an integrated in vitro functional repair and genomic analysis can improve the prognostic value of genetic biomarkers. We conclude that repair defects are marked and frequent in HNSCC and are associated with clinical outcome.

Detection of aneuploidy in patients with cancer through amplification of long interspersed nucleotide elements (LINEs)

Aneuploidy is a feature of most cancer cells, and a myriad of approaches have been developed to detect it in clinical samples. We previously described primers that could be used to amplify ∼38,000 unique long interspersed nucleotide elements (LINEs) from throughout the genome. Here we have developed an approach to evaluate the sequencing data obtained from these amplicons. This approach, called Within-Sample AneupLoidy DetectiOn (WALDO), employs supervised machine learning to detect the small changes in multiple chromosome arms that are often present in cancers. We used WALDO to search for chromosome arm gains and losses in 1,677 tumors and in 1,522 liquid biopsies of blood from cancer patients or normal individuals. Aneuploidy was detected in 95% of cancer biopsies and in 22% of liquid biopsies. Using single-nucleotide polymorphisms within the amplified LINEs, WALDO concomitantly assesses allelic imbalances, microsatellite instability, and sample identification. WALDO can be used on samples containing only a few nanograms of DNA and as little as 1% neoplastic content and has a variety of applications in cancer diagnostics and forensic science.

Homoeologous exchanges cause extensive dosage-dependent gene expression changes in an allopolyploid crop

Structural variation is a major source of genetic diversity and an important substrate for selection. In allopolyploids, homoeologous exchanges (i.e. between the constituent subgenomes) are a very frequent type of structural variant. However, their direct impact on gene content and gene expression had not been determined. Here, we used a tissue-specific mRNA-Seq dataset to measure the consequences of homoeologous exchanges (HE) on gene expression in Brassica napus, a representative allotetraploid crop. We demonstrate that expression changes are proportional to the change in gene copy number triggered by the HEs. Thus, when homoeologous gene pairs have unbalanced transcriptional contributions before the HE, duplication of one copy does not accurately compensate for loss of the other and combined homoeologue expression also changes. These effects are, however, mitigated over time. This study sheds light on the origins, timing and functional consequences of homeologous exchanges in allopolyploids. It demonstrates that the interplay between new structural variation and the resulting impacts on gene expression, influences allopolyploid genome evolution.

WISExome: a within-sample comparison approach to detect copy number variations in whole exome sequencing data

In clinical genetics, detection of single nucleotide polymorphisms (SNVs) as well as copy number variations (CNVs) is essential for patient genotyping. Obtaining both CNV and SNV information from WES data would significantly simplify clinical workflow. Unfortunately, the sequence reads obtained with WES vary between samples, complicating accurate CNV detection with WES. To avoid being dependent on other samples, we developed a within-sample comparison approach (WISExome). For every (WES) target region on the genome, we identified a set of reference target regions elsewhere on the genome with similar read frequency behavior. For a new sample, aberrations are detected by comparing the read frequency of a target region with the distribution of read frequencies in the reference set. WISExome correctly identifies known pathogenic CNVs (range 4 Kb–5.2 Mb). Moreover, WISExome prioritizes pathogenic CNVs by sorting them on quality and annotations of overlapping genes in OMIM. When comparing WISExome to four existing CNV detection tools, we found that CoNIFER detects much fewer CNVs and XHMM breaks calls made by other tools into smaller calls (fragmentation). CODEX and CLAMMS seem to perform more similar to WISExome. CODEX finds all known pathogenic CNVs, but detects much more calls than all other methods. CLAMMS and WISExome agree the most. CLAMMS does, however, miss one of the known CNVs and shows slightly more fragmentation. Taken together, WISExome is a promising tool for genome diagnostics laboratories as the workflow can be solely based on WES data.

Close correlation of copy number aberrations detected by next-generation sequencing with results from routine cytogenetics in acute myeloid leukemia

High throughput sequencing approaches, including the analysis of exomes or gene panels, are widely used and established to detect tumor-specific sequence variants such as point mutations or small insertions/deletions. Beyond single nucleotide resolution, sequencing data also contain information on changes in sequence coverage between samples and thus allow the detection of somatic copy number alterations (CNAs) representing gain or loss of genomic material in tumor cells arising from aneuploidy, amplifications, or deletions. To test the feasibility of CNA detection in sequencing data we analyzed the exomes of 25 paired leukemia/remission samples from acute myeloid leukemia (AML) patients with well-defined chromosomal aberrations, detected by conventional chromosomal analysis and/or molecular cytogenetics assays. Thereby, we were able to confirm chromosomal aberrations including trisomies, monosomies, and partial chromosomal deletions in 20 out of 25 samples. Comparison of CNA detection using exome, custom gene panel, and SNP array analysis showed equivalent results in five patients with variable clone size. Gene panel analysis of AML samples without matched germline control samples resulted in confirmation of cytogenetic findings in 18 out of 22 cases. In all cases with discordant findings, small clone size (<33%) was limiting for CNA detection. We detected CNAs consistent with cytogenetics in 83% of AML samples including highly correlated clone size estimation (R = 0.85), while six out of 65 cytogenetically normal AML samples exhibited CNAs apparently missed by routine cytogenetics. Overall, our results show that high throughput targeted sequencing data can be reliably used to detect copy number changes in the dominant AML clone. © 2016 Wiley Periodicals, Inc.

CopywriteR: DNA copy number detection from off-target sequence data

Current methods for detection of copy number variants (CNV) and aberrations (CNA) from targeted sequencing data are based on the depth of coverage of captured exons. Accurate CNA determination is complicated by uneven genomic distribution and non-uniform capture efficiency of targeted exons. Here we present CopywriteR, which eludes these problems by exploiting 'off-target' sequence reads. CopywriteR allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. CopywriteR outperforms existing methods and constitutes a widely applicable alternative to available tools.

Quantitative analysis of differences in copy numbers using read depth obtained from PCR-enriched samples and controls

BACKGROUND

Next-generation sequencing (NGS) is rapidly becoming common practice in clinical diagnostics and cancer research. In addition to the detection of single nucleotide variants (SNVs), information on copy number variants (CNVs) is of great interest. Several algorithms exist to detect CNVs by analyzing whole genome sequencing data or data from samples enriched by hybridization-capture. PCR-enriched amplicon-sequencing data have special characteristics that have been taken into account by only one publicly available algorithm so far.

RESULTS

We describe a new algorithm named quandico to detect copy number differences based on NGS data generated following PCR-enrichment. A weighted t-test statistic was applied to calculate probabilities (p-values) of copy number changes. We assessed the performance of the method using sequencing reads generated from reference DNA with known CNVs, and we were able to detect these variants with 98.6% sensitivity and 98.5% specificity which is significantly better than another recently described method for amplicon sequencing. The source code (R-package) of quandico is licensed under the GPLv3 and it is available at https://github.com/reineckef/quandico .

CONCLUSION

We demonstrated that our new algorithm is suitable to call copy number changes using data from PCR-enriched samples with high sensitivity and specificity even for single copy differences.

Dynamics of copy number variation in host races of the pea aphid

Copy number variation (CNV) makes a major contribution to overall genetic variation and is suspected to play an important role in adaptation. However, aside from a few model species, the extent of CNV in natural populations has seldom been investigated. Here, we report on CNV in the pea aphid Acyrthosiphon pisum, a powerful system for studying the genetic architecture of host-plant adaptation and speciation thanks to multiple host races forming a continuum of genetic divergence. Recent studies have highlighted the potential importance of chemosensory genes, including the gustatory and olfactory receptor gene families (Gr and Or, respectively), in the process of host race formation. We used targeted resequencing to achieve a very high depth of coverage, and thereby revealed the extent of CNV of 434 genes, including 150 chemosensory genes, in 104 individuals distributed across eight host races of the pea aphid. We found that CNV was widespread in our global sample, with a significantly higher occurrence in multigene families, especially in Ors. We also observed a decrease in the gene probability of being completely duplicated or deleted (CDD) with increase in coding sequence length. Genes with CDD variants were usually more polymorphic for copy number, especially in the P450 gene family where toxin resistance may be related to gene dosage. We found that Gr were overrepresented among genes discriminating host races, as were CDD genes and pseudogenes. Our observations shed new light on CNV dynamics and are consistent with CNV playing a role in both local adaptation and speciation.

Exome sequence read depth methods for identifying copy number changes

Copy number variants (CNVs) play important roles in a number of human diseases and in pharmacogenetics. Powerful methods exist for CNV detection in whole genome sequencing (WGS) data, but such data are costly to obtain. Many disease causal CNVs span or are found in genome coding regions (exons), which makes CNV detection using whole exome sequencing (WES) data attractive. If reliably validated against WGS-based CNVs, exome-derived CNVs have potential applications in a clinical setting. Several algorithms have been developed to exploit exome data for CNV detection and comparisons made to find the most suitable methods for particular data samples. The results are not consistent across studies. Here, we review some of the exome CNV detection methods based on depth of coverage profiles and examine their performance to identify problems contributing to discrepancies in published results. We also present a streamlined strategy that uses a single metric, the likelihood ratio, to compare exome methods, and we demonstrated its utility using the VarScan 2 and eXome Hidden Markov Model (XHMM) programs using paired normal and tumour exome data from chronic lymphocytic leukaemia patients. We use array-based somatic CNV (SCNV) calls as a reference standard to compute prevalence-independent statistics, such as sensitivity, specificity and likelihood ratio, for validation of the exome-derived SCNVs. We also account for factors known to influence the performance of exome read depth methods, such as CNV size and frequency, while comparing our findings with published results.

An evaluation of copy number variation detection tools from whole-exome sequencing data

Copy number variation (CNV) has been found to play an important role in human disease. Next-generation sequencing technology, including whole-genome sequencing (WGS) and whole-exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well-known WES-based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome-based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well-known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.

Future of Clinical Genomics in Pediatric Oncology

The somatic genomic alterations in pediatric cancers to some extent overlap with those seen in adult cancers, but the exact distribution throughout the genome and the types and frequency of alterations differ. The ultimate goal of genomic research in children, as with adults, is translation to the clinic to achieve more accurate diagnosis, more precise risk stratification, and more effective, less toxic therapy. The genomic features of pediatric malignancies and pediatric-specific issues in clinical investigation may make translating genomic discoveries to the clinic more difficult. However, through large-scale molecular profiling of pediatric tumors, continued coordinated efforts to evaluate novel therapies in the pediatric population, thoughtful phase II and III trial design, and continued drug development, genomically based therapies will become more common in the pediatric oncology clinic in the future.

A survey of copy-number variation detection tools based on high-throughput sequencing data

Copy-number variation (CNV) is a major class of genomic variation with potentially important functional consequences in both normal and diseased populations. Remarkable advances in development of next-generation sequencing (NGS) platforms provide an unprecedented opportunity for accurate, high-resolution characterization of CNVs. In this unit, we give an overview of available computational tools for detection of CNVs and discuss comparative advantages and disadvantages of different approaches.