Analysis of Epstein-Barr Virus Genomes and Expression Profiles in Gastric Adenocarcinoma.

Epstein-Barr virus (EBV) is a causative agent of a variety of lymphomas, nasopharyngeal carcinoma (NPC), and ∼9% of gastric carcinomas (GCs). An important question is whether particular EBV variants are more oncogenic than others, but conclusions are currently hampered by the lack of sequenced EBV genomes. Here, we contribute to this question by mining whole-genome sequences of 201 GCs to identify 13 EBV-positive GCs and by assembling 13 new EBV genome sequences, almost doubling the number of available GC-derived EBV genome sequences and providing the first non-Asian EBV genome sequences from GC. Whole-genome sequence comparisons of all EBV isolates sequenced to date (85 from tumors and 57 from healthy individuals) showed that most GC and NPC EBV isolates were closely related although American Caucasian GC samples were more distant, suggesting a geographical component. However, EBV GC isolates were found to contain some consistent changes in protein sequences regardless of geographical origin. In addition, transcriptome data available for eight of the EBV-positive GCs were analyzed to determine which EBV genes are expressed in GC. In addition to the expected latency proteins (EBNA1, LMP1, and LMP2A), specific subsets of lytic genes were consistently expressed that did not reflect a typical lytic or abortive lytic infection, suggesting a novel mechanism of EBV gene regulation in the context of GC. These results are consistent with a model in which a combination of specific latent and lytic EBV proteins promotes tumorigenesis.IMPORTANCE Epstein-Barr virus (EBV) is a widespread virus that causes cancer, including gastric carcinoma (GC), in a small subset of individuals. An important question is whether particular EBV variants are more cancer associated than others, but more EBV sequences are required to address this question. Here, we have generated 13 new EBV genome sequences from GC, almost doubling the number of EBV sequences from GC isolates and providing the first EBV sequences from non-Asian GC. We further identify sequence changes in some EBV proteins common to GC isolates. In addition, gene expression analysis of eight of the EBV-positive GCs showed consistent expression of both the expected latency proteins and a subset of lytic proteins that was not consistent with typical lytic or abortive lytic expression. These results suggest that novel mechanisms activate expression of some EBV lytic proteins and that their expression may contribute to oncogenesis.

A Screen for Epstein-Barr Virus Proteins That Inhibit the DNA Damage Response Reveals a Novel Histone Binding Protein.

To replicate and persist in human cells, linear double-stranded DNA (dsDNA) viruses, such as Epstein-Barr virus (EBV), must overcome the host DNA damage response (DDR) that is triggered by the viral genomes. Since this response is necessary to maintain cellular genome integrity, its inhibition by EBV is likely an important factor in the development of cancers associated with EBV infection, including gastric carcinoma. Here we present the first extensive screen of EBV proteins that inhibit dsDNA break signaling. We identify the BKRF4 tegument protein as a DDR inhibitor that interferes with histone ubiquitylation at dsDNA breaks and recruitment of the RNF168 histone ubiquitin ligase. We further show that BKRF4 binds directly to histones through an acidic domain that targets BKRF4 to cellular chromatin and is sufficient to inhibit dsDNA break signaling. BKRF4 transcripts were detected in EBV-positive gastric carcinoma cells (AGS-EBV), and these increased in lytic infection. Silencing of BKRF4 in both latent and lytic AGS-EBV cells (but not in EBV-negative AGS cells) resulted in increased dsDNA break signaling, confirming a role for BKRF4 in DDR inhibition in the context of EBV infection and suggesting that BKRF4 is expressed in latent cells. BKRF4 was also found to be consistently expressed in EBV-positive gastric tumors in the absence of a full lytic infection. The results suggest that BKRF4 plays a role in inhibiting the cellular DDR in latent and lytic EBV infection and that the resulting accumulation of DNA damage might contribute to development of gastric carcinoma.IMPORTANCE Epstein-Barr virus (EBV) infects most people worldwide and is causatively associated with several types of cancer, including ∼10% of gastric carcinomas. EBV encodes ∼80 proteins, many of which are believed to manipulate cellular regulatory pathways but are poorly characterized. The DNA damage response (DDR) is one such pathway that is critical for maintaining genome integrity and preventing cancer-associated mutations. In this study, a screen for EBV proteins that inhibit the DDR identified BKRF4 as a DDR inhibitor that binds histones and blocks their ubiquitylation at the DNA damage sites. We also present evidence that BKRF4 is expressed in both latent and lytic forms of EBV infection, where it downregulates the DDR, as well as in EBV-positive gastric tumors. The results suggest that BKRF4 could contribute to the development of gastric carcinoma through its ability to inhibit the DDR.

CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads.

SUMMARY: Sequence comparison of genetic material between known and unknown organisms plays a crucial role in genomics, metagenomics and phylogenetic analysis. The emerging long-read sequencing technologies can now produce reads of tens of kilobases in length that promise a more accurate assessment of their origin. To facilitate the classification of long and short DNA sequences, we have developed a Python package that implements a new sequence classification model that we have demonstrated to improve the classification accuracy when compared with other state of the art classification methods. For the purpose of validation, and to demonstrate its usefulness, we test the combined sequence similarity score classifier (CSSSCL) using three different datasets, including a metagenomic dataset composed of short reads. AVAILABILITY AND IMPLEMENTATION: Package's source code and test datasets are available under the GPLv3 license at https://github.com/oicr-ibc/cssscl. CONTACT: ivan.borozan@oicr.on.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification.

MOTIVATION: Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized. RESULTS: Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Because the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures, we show that our model significantly improves the classification accuracy over the current composition- and alignment-based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences. AVAILABILITY AND IMPLEMENTATION: All the datasets and the code used in this study are freely available at https://collaborators.oicr.on.ca/vferretti/borozan_csss/csss.html. CONTACT: ivan.borozan@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hepatic cell-type specific gene expression better predicts HCV treatment outcome than IL28B genotype.

BACKGROUND & AIMS: Cell-type specific expression patterns of hepatic interferon-stimulated genes (ISGs) and single nucleotide polymorphisms (SNPs) near the IL28B gene are associated with response to interferon-based therapy in patients with chronic hepatitis C virus (HCV) infection. It is not known how the IL28B genotype influences the ISG expression pattern and which is a better predictor of treatment response. METHODS: Patients at the Toronto Western Hospital Liver Centre with known outcome to interferon-based treatment for HCV infection were evaluated. Analysis included hepatic gene expression profile using complementary DNA microarrays, genotype at the IL28B SNP rs12979860, and immunostaining for human myxovirus A protein 1 (MxA) in hepatocytes and macrophages. RESULTS: The level of ISG immunostaining in hepatic macrophages correlated inversely with that of hepatocytes and was strongly associated with treatment outcome. Gene expression profiles and the IL28B genotype were associated with treatment response, but only absence of MxA staining in macrophages accurately predicted nonresponse to treatment. The positive predictive value (PPV) of the IL28B genotype was 94% and the negative predictive value (NPV) was 51% (n = 209). For messenger RNA expression, the PPV was 94% and the NPV was 54% (n = 65). For detection of MxA in macrophages, the PPV was 60% and the NPV was 98% (n = 110). Of 53 patients with undetectable macrophage MxA staining, only one had a sustained virologic response. IL28B genotype was strongly associated with cell-type specific staining for MxA. There was a stepwise increase in macrophage staining and decrease in hepatocyte staining from the TT (lack of response) to CC SNP (associated with response) in IL28B. By logistic regression, after controlling for the presence of macrophage MxA staining, the IL28B genotype was no longer associated with treatment response. CONCLUSIONS: The cell-type-specific expression pattern of ISGs varies among patients with different IL28B genotypes and is a strong predictor of response to interferon-based treatment.

CaPSID: a bioinformatics platform for computational pathogen sequence identification in human genomes and transcriptomes.

BACKGROUND: It is now well established that nearly 20% of human cancers are caused by infectious agents, and the list of human oncogenic pathogens will grow in the future for a variety of cancer types. Whole tumor transcriptome and genome sequencing by next-generation sequencing technologies presents an unparalleled opportunity for pathogen detection and discovery in human tissues but requires development of new genome-wide bioinformatics tools. RESULTS: Here we present CaPSID (Computational Pathogen Sequence IDentification), a comprehensive bioinformatics platform for identifying, querying and visualizing both exogenous and endogenous pathogen nucleotide sequences in tumor genomes and transcriptomes. CaPSID includes a scalable, high performance database for data storage and a web application that integrates the genome browser JBrowse. CaPSID also provides useful metrics for sequence analysis of pre-aligned BAM files, such as gene and genome coverage, and is optimized to run efficiently on multiprocessor computers with low memory usage. CONCLUSIONS: To demonstrate the usefulness and efficiency of CaPSID, we carried out a comprehensive analysis of both a simulated dataset and transcriptome samples from ovarian cancer. CaPSID correctly identified all of the human and pathogen sequences in the simulated dataset, while in the ovarian dataset CaPSID's predictions were successfully validated in vitro.

Molecular and Pathology Features of Colorectal Tumors and Patient Outcomes Are Associated with Fusobacterium nucleatum and Its Subspecies animalis.

BACKGROUND: Fusobacterium nucleatum (F. nucleatum) activates oncogenic signaling pathways and induces inflammation to promote colorectal carcinogenesis. METHODS: We characterized F. nucleatum and its subspecies in colorectal tumors and examined associations with tumor characteristics and colorectal cancer-specific survival. We conducted deep sequencing of nusA, nusG, and bacterial 16s rRNA genes in tumors from 1,994 patients with colorectal cancer and assessed associations between F. nucleatum presence and clinical characteristics, colorectal cancer-specific mortality, and somatic mutations. RESULTS: F. nucleatum, which was present in 10.3% of tumors, was detected in a higher proportion of right-sided and advanced-stage tumors, particularly subspecies animalis. Presence of F. nucleatum was associated with higher colorectal cancer-specific mortality (HR, 1.97; P = 0.0004). This association was restricted to nonhypermutated, microsatellite-stable tumors (HR, 2.13; P = 0.0002) and those who received chemotherapy [HR, 1.92; confidence interval (CI), 1.07-3.45; P = 0.029). Only F. nucleatum subspecies animalis, the main subspecies detected (65.8%), was associated with colorectal cancer-specific mortality (HR, 2.16; P = 0.0016), subspecies vincentii and nucleatum were not (HR, 1.07; P = 0.86). Additional adjustment for tumor stage suggests that the effect of F. nucleatum on mortality is partly driven by a stage shift. Presence of F. nucleatum was associated with microsatellite instable tumors, tumors with POLE exonuclease domain mutations, and ERBB3 mutations, and suggestively associated with TP53 mutations. CONCLUSIONS: F. nucleatum, and particularly subspecies animalis, was associated with a higher colorectal cancer-specific mortality and specific somatic mutated genes. IMPACT: Our findings identify the F. nucleatum subspecies animalis as negatively impacting colorectal cancer mortality, which may occur through a stage shift and its effect on chemoresistance.

Cell-type specific gene expression signature in liver underlies response to interferon therapy in chronic hepatitis C infection.

BACKGROUND & AIMS: Chronic hepatitis C virus (CHC) infection is treated with interferon/ribavirin, but only a subset of patients respond. Treatment nonresponders have marked pretreatment up-regulation of a subset of interferon stimulated genes (ISGs) in their livers, including ISG15. We here study how the nonresponder gene expression phenotype is influenced by clinical factors and uncover the cellular basis of the phenotype through ISG15 protein expression. METHODS: Seventy-eight CHC patients undergoing treatment were classified by clinical (gender, viral genotype, viral load, treatment outcome) and histologic (inflammation, fibrosis) factors and subjected to gene expression profiling on their pretreatment liver biopsies. An analysis of variance model was used to study the influence of individual factors on gene expression. ISG15 immunohistochemistry was performed on a subset of 31 liver biopsy specimens. RESULTS: One hundred twenty-three genes were differentially expressed in the 78 CHC livers when compared with 20 normal livers (P < .001; fold change, > or =1.5-fold). Of genes influenced by a single factor, genotype (1 vs 2/3) influenced more genes (17) than any other variable; when treatment outcome was included in the analysis, this became the predominant influence (24 genes), and the effect of genotype was diminished. Treatment response was linked to cell-specific activation patterns: ISG15 protein up-regulation was more pronounced in hepatocytes in treatment nonresponders but in Kuppfer cells in responders. CONCLUSIONS: Genotype is a surrogate marker for the nonresponder phenotype. This phenotype manifests as differential gene expression and is driven by activation of different cell types: hepatocytes in treatment nonresponders and macrophages in treatment responders.

The landscape of viral associations in human cancers.

Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, for which whole-genome and-for a subset-whole-transcriptome sequencing data from 2,658 cancers across 38 tumor types was aggregated, we systematically investigated potential viral pathogens using a consensus approach that integrated three independent pipelines. Viruses were detected in 382 genome and 68 transcriptome datasets. We found a high prevalence of known tumor-associated viruses such as Epstein-Barr virus (EBV), hepatitis B virus (HBV) and human papilloma virus (HPV; for example, HPV16 or HPV18). The study revealed significant exclusivity of HPV and driver mutations in head-and-neck cancer and the association of HPV with APOBEC mutational signatures, which suggests that impaired antiviral defense is a driving force in cervical, bladder and head-and-neck carcinoma. For HBV, HPV16, HPV18 and adeno-associated virus-2 (AAV2), viral integration was associated with local variations in genomic copy numbers. Integrations at the TERT promoter were associated with high telomerase expression evidently activating this tumor-driving process. High levels of endogenous retrovirus (ERV1) expression were linked to a worse survival outcome in patients with kidney cancer.

Landscape of somatic single nucleotide variants and indels in colorectal cancer and impact on survival.

Colorectal cancer (CRC) is a biologically heterogeneous disease. To characterize its mutational profile, we conduct targeted sequencing of 205 genes for 2,105 CRC cases with survival data. Our data shows several findings in addition to enhancing the existing knowledge of CRC. We identify PRKCI, SPZ1, MUTYH, MAP2K4, FETUB, and TGFBR2 as additional genes significantly mutated in CRC. We find that among hypermutated tumors, an increased mutation burden is associated with improved CRC-specific survival (HR = 0.42, 95% CI: 0.21-0.82). Mutations in TP53 are associated with poorer CRC-specific survival, which is most pronounced in cases carrying TP53 mutations with predicted 0% transcriptional activity (HR = 1.53, 95% CI: 1.21-1.94). Furthermore, we observe differences in mutational frequency of several genes and pathways by tumor location, stage, and sex. Overall, this large study provides deep insights into somatic mutations in CRC, and their potential relationships with survival and tumor features.

Intake of Dietary Fruit, Vegetables, and Fiber and Risk of Colorectal Cancer According to Molecular Subtypes: A Pooled Analysis of 9 Studies.

Protective associations of fruits, vegetables, and fiber intake with colorectal cancer risk have been shown in many, but not all epidemiologic studies. One possible reason for study heterogeneity is that dietary factors may have distinct effects by colorectal cancer molecular subtypes. Here, we investigate the association of fruit, vegetables, and fiber intake with four well-established colorectal cancer molecular subtypes separately and in combination. Nine observational studies including 9,592 cases with molecular subtypes for microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and somatic mutations in BRAF and KRAS genes, and 7,869 controls were analyzed. Both case-only logistic regression analyses and polytomous logistic regression analyses (with one control set and multiple case groups) were used. Higher fruit intake was associated with a trend toward decreased risk of BRAF-mutated tumors [OR 4th vs. 1st quartile = 0.82 (95% confidence interval, 0.65-1.04)] but not BRAF-wildtype tumors [1.09 (0.97-1.22); P difference as shown in case-only analysis = 0.02]. This difference was observed in case-control studies and not in cohort studies. Compared with controls, higher fiber intake showed negative association with colorectal cancer risk for cases with microsatellite stable/MSI-low, CIMP-negative, BRAF-wildtype, and KRAS-wildtype tumors (P trend range from 0.03 to 3.4e-03), which is consistent with the traditional adenoma-colorectal cancer pathway. These negative associations were stronger compared with MSI-high, CIMP-positive, BRAF-mutated, or KRAS-mutated tumors, but the differences were not statistically significant. These inverse associations for fruit and fiber intake may explain, in part, inconsistent findings between fruit or fiber intake and colorectal cancer risk that have previously been reported. SIGNIFICANCE: These analyses by colorectal cancer molecular subtypes potentially explain the inconsistent findings between dietary fruit or fiber intake and overall colorectal cancer risk that have previously been reported.

Exaggerated up-regulation of tumor necrosis factor alpha-dependent apoptosis in the older mouse liver following reperfusion injury: targeting liver protective strategies to patient age.

Although it is becoming increasingly common to accept livers from older donors for transplantation, old livers are more damaged by hepatic ischemia and reperfusion injury (HIRI) than young livers. We hypothesized that this age-related susceptibility to HIRI is due to increased hepatocellular apoptosis driven by tumor necrosis factor alpha (TNFalpha). Young (6-week-old) and old (60-week-old) mice underwent 60 minutes of hepatic ischemia and increasing periods of reperfusion. TNFalpha was determined by enzyme-linked immunosorbent assay. Liver injury (enzyme release), apoptosis (terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-digoxigenin nick-end labeling staining, cytochrome C release, and caspase activation), and necrosis (hematoxylin and eosin staining) were assessed. We assessed the impact of apoptosis by blocking TNFalpha production or effect (pentoxifylline and TNFalpha receptor knockout), inhibiting apoptotic pathways (caspase inhibition), or imposing a hepatic protective strategy [glucose infusion with ischemic preconditioning (Glc/PC)]. In comparison with young livers, old livers subjected to HIRI had more pronounced liver aspartate aminotransferase release (6200 versus 3900 U/L, P = 0.02), necrosis (45% versus 25%, P = 0.03), and apoptosis with increased 30-minute TNFalpha release (19.02 versus 10.62 pg/mg, P = 0.03). Eliminating TNFalpha production reversed the effect of age, as did inhibition of apoptotic pathways with caspase inhibition. Glc/PC of old mice attenuated TNFalpha release (9.56 versus 19.02 pg/mg, P = 0.001) and age-related exaggerated HIRI and improved survival (60% versus 0%). In conclusion, the age-related susceptibility to HIRI is driven by an exaggerated induction of TNFalpha-dependent hepatocellular apoptosis. Targeting the apoptotic cascade has implications for the older donor liver population.

Epstein-Barr virus BORF2 inhibits cellular APOBEC3B to preserve viral genome integrity.

The apolipoprotein B messenger RNA editing enzyme, catalytic polypeptide-like (APOBEC) family of single-stranded DNA (ssDNA) cytosine deaminases provides innate immunity against virus and transposon replication1-4. A well-studied mechanism is APOBEC3G restriction of human immunodeficiency virus type 1, which is counteracted by a virus-encoded degradation mechanism1-4. Accordingly, most work has focused on retroviruses with obligate ssDNA replication intermediates and it is unclear whether large double-stranded DNA (dsDNA) viruses may be similarly susceptible to restriction. Here, we show that the large dsDNA herpesvirus Epstein-Barr virus (EBV), which is the causative agent of infectious mononucleosis and multiple cancers5, utilizes a two-pronged approach to counteract restriction by APOBEC3B. Proteomics studies and immunoprecipitation experiments showed that the ribonucleotide reductase large subunit of EBV, BORF26,7, binds APOBEC3B. Mutagenesis mapped the interaction to the APOBEC3B catalytic domain, and biochemical studies demonstrated that BORF2 stoichiometrically inhibits APOBEC3B DNA cytosine deaminase activity. BORF2 also caused a dramatic relocalization of nuclear APOBEC3B to perinuclear bodies. On lytic reactivation, BORF2-null viruses were susceptible to APOBEC3B-mediated deamination as evidenced by lower viral titres, lower infectivity and hypermutation. The Kaposi's sarcoma-associated herpesvirus homologue, ORF61, also bound APOBEC3B and mediated relocalization. These data support a model where the genomic integrity of human γ-herpesviruses is maintained by active neutralization of the antiviral enzyme APOBEC3B.

MAID : an effect size based model for microarray data integration across laboratories and platforms.

BACKGROUND: Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions. RESULTS: Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model (MAID) can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different laboratories and platforms. The model uses two standardized indices, the standard effect size score for experiments with two groups of data, and a new standardized index that measures the difference in gene expression between treatment and control groups for one sample data with replicate arrays. The statistical significance of treatment effect across studies for each gene is determined by appropriate permutation methods depending on the type of data integrated. We apply our method to three different expression datasets from two different laboratories generated using three different array platforms and two different experimental designs. Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models. We also show that genes found to be significant using our data integration method are of direct biological relevance to the three experiments integrated. CONCLUSION: High-throughput genomics data provide a rich and complex source of information that could play a key role in deciphering intricate molecular networks behind disease. Here we propose an extension of the traditional effect size model to allow the integration of as many array experiments as possible with the aim of increasing the statistical power for identifying differentially expressed genes.

Gene expression profiling of early primary biliary cirrhosis: possible insights into the mechanism of action of ursodeoxycholic acid.

OBJECTIVES: Primary biliary cirrhosis (PBC) is a poorly understood disease, both in terms of its pathogenesis and the mechanism of action of its most common treatment, ursodeoxycholic acid (UDCA). We used gene expression profiling to compare liver tissue from treatment-naïve and UDCA-treated patients in order to outline some of the molecular changes associated with PBC and its treatment. PATIENTS AND EXPERIMENTAL DESIGN: Liver biopsy specimens from non-cirrhotic, treatment-naïve (n=11) patients were compared with biopsies from UDCA-treated patients (n=20) and with 10 normal, healthy female controls. Gene expression was determined using a 19K cDNA microarray. In order to determine whether the observed changes in gene expression levels were specific to PBC or generic to liver damage overall, PBC samples were also compared with chronically diseased [48 hepatitis C virus (HCV), 18 hepatitis B virus (HBV)] and acutely stressed liver tissue (25 liver biopsies taken after reperfusion of liver transplant grafts). RESULTS: We found a gene signature specific to PBC (P

Association of Distinct Mutational Signatures With Correlates of Increased Immune Activity in Pancreatic Ductal Adenocarcinoma.

IMPORTANCE: Outcomes for patients with pancreatic ductal adenocarcinoma (PDAC) remain poor. Advances in next-generation sequencing provide a route to therapeutic approaches, and integrating DNA and RNA analysis with clinicopathologic data may be a crucial step toward personalized treatment strategies for this disease. OBJECTIVE: To classify PDAC according to distinct mutational processes, and explore their clinical significance. DESIGN, SETTING, AND PARTICIPANTS: We performed a retrospective cohort study of resected PDAC, using cases collected between 2008 and 2015 as part of the International Cancer Genome Consortium. The discovery cohort comprised 160 PDAC cases from 154 patients (148 primary; 12 metastases) that underwent tumor enrichment prior to whole-genome and RNA sequencing. The replication cohort comprised 95 primary PDAC cases that underwent whole-genome sequencing and expression microarray on bulk biospecimens. MAIN OUTCOMES AND MEASURES: Somatic mutations accumulate from sequence-specific processes creating signatures detectable by DNA sequencing. Using nonnegative matrix factorization, we measured the contribution of each signature to carcinogenesis, and used hierarchical clustering to subtype each cohort. We examined expression of antitumor immunity genes across subtypes to uncover biomarkers predictive of response to systemic therapies. RESULTS: The discovery cohort was 53% male (n = 79) and had a median age of 67 (interquartile range, 58-74) years. The replication cohort was 50% male (n = 48) and had a median age of 68 (interquartile range, 60-75) years. Five predominant mutational subtypes were identified that clustered PDAC into 4 major subtypes: age related, double-strand break repair, mismatch repair, and 1 with unknown etiology (signature 8). These were replicated and validated. Signatures were faithfully propagated from primaries to matched metastases, implying their stability during carcinogenesis. Twelve of 27 (45%) double-strand break repair cases lacked germline or somatic events in canonical homologous recombination genes-BRCA1, BRCA2, or PALB2. Double-strand break repair and mismatch repair subtypes were associated with increased expression of antitumor immunity, including activation of CD8-positive T lymphocytes (GZMA and PRF1) and overexpression of regulatory molecules (cytotoxic T-lymphocyte antigen 4, programmed cell death 1, and indolamine 2,3-dioxygenase 1), corresponding to higher frequency of somatic mutations and tumor-specific neoantigens. CONCLUSIONS AND RELEVANCE: Signature-based subtyping may guide personalized therapy of PDAC in the context of biomarker-driven prospective trials.

Evaluation of alignment algorithms for discovery and identification of pathogens using RNA-Seq.

Next-generation sequencing technologies provide an unparallelled opportunity for the characterization and discovery of known and novel viruses. Because viruses are known to have the highest mutation rates when compared to eukaryotic and bacterial organisms, we assess the extent to which eleven well-known alignment algorithms (BLAST, BLAT, BWA, BWA-SW, BWA-MEM, BFAST, Bowtie2, Novoalign, GSNAP, SHRiMP2 and STAR) can be used for characterizing mutated and non-mutated viral sequences--including those that exhibit RNA splicing--in transcriptome samples. To evaluate aligners objectively we developed a realistic RNA-Seq simulation and evaluation framework (RiSER) and propose a new combined score to rank aligners for viral characterization in terms of their precision, sensitivity and alignment accuracy. We used RiSER to simulate both human and viral read sequences and suggest the best set of aligners for viral sequence characterization in human transcriptome samples. Our results show that significant and substantial differences exist between aligners and that a digital-subtraction-based viral identification framework can and should use different aligners for different parts of the process. We determine the extent to which mutated viral sequences can be effectively characterized and show that more sensitive aligners such as BLAST, BFAST, SHRiMP2, BWA-SW and GSNAP can accurately characterize substantially divergent viral sequences with up to 15% overall sequence mutation rate. We believe that the results presented here will be useful to researchers choosing aligners for viral sequence characterization using next-generation sequencing data.

Hepatic gene expression and prediction of therapy response in chronic hepatitis C patients.

Chronic hepatitis C (HCV) infection remains a major health problem worldwide. The current standard of care is a combination of pegylated interferon-alpha and ribavirin. Considering the length of antiviral therapy, as well as its side effects and costs, accurate prediction of treatment response prior to initiation of treatment is critical. In addition to viral, demographic and environmental factors, host genetic diversity is believed to contribute to the spectrum of clinical outcomes of chronic HCV. The development of high-throughput technologies provides opportunities to define patterns of gene expression that are associated with certain disease outcomes and/or response to therapy. This article reviews genomics-based predictors of pre-treatment response to antiviral therapy.