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.

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.

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.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

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

The APOBEC family of single-stranded (ss)DNA cytosine deaminases provides innate immunity against virus and transposon replication^1–4. A well-studied mechanism is APOBEC3G restriction of HIV-1, which is counteracted by a virus-encoded degradation mechanism^1–4. Accordingly, most work has focused on retroviruses with obligate ssDNA replication intermediates and it is unclear whether large double-stranded (ds)DNA 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. The large subunit of the EBV ribonucleotide reductase, BORF2^6,7, bound to APOBEC3B in proteomics studies and immunoprecipitation experiments. 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. Upon lytic reactivation, BORF2-null viruses were susceptible to APOBEC3B-mediated deamination as evidenced by lower viral titers, lower infectivity, and hypermutation. The Kaposi’s sarcoma herpesvirus (KSHV) homolog, ORF61, also bound APOBEC3B and mediated relocalization. These data support a model in which the genomic integrity of human γ-herpesviruses is maintained by active neutralization of the antiviral enzyme APOBEC3B.

Abstract 1286: Targeted deep sequencing of colorectal tumor tissues to study associations of tumor subtypes with germline genetic, lifestyle, and environmental risk factors

The Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) in collaboration with the Colorectal Cancer Family Registry (CCFR) aims to identify genetic variants and environmental risk factors that impact colorectal cancer (CRC). Over 30 studies from North America, Europe, and Australia participate in GECCO. These studies have collected clinical, epidemiological, and survival data, as well as blood and tumor biospecimens, for over 80,000 CRC cases and controls.

The current study aims to conduct targeted deep sequencing of tumors and matching normal DNA to identify recurrent and novel somatic and germline variants in 4,200 CRC cases. To achieve this goal, an AmpliSeq targeted sequencing panel of 1.12 Mbp was constructed to sequence the coding regions of 190 significantly mutated genes identified from whole exome sequencing datasets generated by the Nurses’ Health Study and Health Professional’s Follow-up Study, and The Cancer Genome Atlas. The panel also covers coding regions of 15 genes with germline high penetrance mutations in CRC, 54 regions to detect CRC-related copy number alterations (CNAs), and microsatellite and homopolymer repeat regions to identify defective DNA mismatch repair. Primers were also included to detect Fusobacterium nucleatum DNA in tumor biopsies, as F. nucleatum is thought to promote CRC carcinogenesis.

Sequencing of the DNA libraries on Illumina HiSeq 2500 produced a mean coverage of greater than 500X for tumor DNA and 100X for normal DNA, with >85% of the bases covered at the target at 50x. So far, targeted sequencing of >1,500 DNA samples from CRC tumors and normal tissues has identified recurrent and novel somatic mutations, germline genetic variants, and hypermutation status of the tumors due to defective DNA mismatch repair or pathogenic mutations in the POLE gene. Targeted sequencing has also allowed quantification of the F. nucleatum DNA in tumor biopsies; the results were validated by a multiplex QPCR assay.

At the AACR annual meeting, we will present targeted sequencing results generated from the first two GECCO-participating studies (n=1,300 cases). These data will be valuable for future association testing of somatic mutations, CNAs, hypermutation status, and F. nucleatum with germline genetic variants, lifestyle, and environmental risk factors and survival. This large study will allow development of better strategies for diagnosis, treatment, and prevention of CRC.

Citation Format: Syed H. Zaidi, Wei Sun, Jeroen Huyghe, Catherine S. Grasso, Quang Trinh, Charles Connolly, Amy French, Jasmine Mu, Marios Giannakis, Eve Shinbrot, Ivan Borozan, Michael J. Quist, Hermann Brenner, Daniel Buchanan, Peter Campbell, Andrew Chan, Jenny Chang-Claude, Vincent Ferretti, Charles Fuchs, Andrea Gsur, Marc Gunter, Tabitha Harrison, Michael Hoffmeister, Wen-Yi Huang, Paul Krzyzanowski, Stephen Lee, Mathieu Lemire, Jessica Miller, Danielle Pasternack, Cherie Teney, Elaine Mardis, Polly Newcomb, Lincoln Stein, Lee Timms, David Wheeler, Christina Yung, Niha Zubair, Levi Garraway, Shuji Ogino, Li Hsu, Steven Gallinger, Stephen Thibodeau, Thomas Hudson, Ulrike Peters. Targeted deep sequencing of colorectal tumor tissues to study associations of tumor subtypes with germline genetic, lifestyle, and environmental risk factors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1286. doi:10.1158/1538-7445.AM2017-1286

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.

Abstract 5221: Linking the molecular profile of colorectal tumors to germline genetic and environmental risk factors

The completion of The Cancer Genome Atlas (TCGA) project for colorectal cancer (CRC) has enabled a new, focused phase of sequencing tumor samples for which genome-wide genetic, epidemiological, clinical and lifestyle data have been collected. By combining somatic mutational profiles with these aforementioned data, we may identify and report effective prevention and treatment approaches for a broader population of individuals. The advent of targeted deep sequencing approaches using DNA obtained from formalin fixed paraffin embedded tissues has made possible the genetic characterization of the large numbers of patients needed to make such an effort relevant.

As a first step, we describe a custom gene panel designed from large-scale studies for targeted deep sequencing, and its application to over 4,200 CR tumors, collected by the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). This study is designed to identify recurrent somatic mutations and copy number alterations (CNAs) for association testing with germline genetic and lifestyle and environmental risk factors for CRC, and thereby identify relevant approaches to impact cancer prevention.

The targeted CRC panel includes 205 genes. Genes were primarily selected as significantly mutated genes identified from the Nurses’ Health Study and Health Professional's Follow-up Study (n = 700), and TCGA (n = 525). We also included 15 genes associated with high penetrance germline mutations and augmented the list to include genes in pathways of somatically altered genes, identified by literature review, from public databases and known to be associated with loss of heterozygosity. For these 205 genes, amplification primers were designed to include all coding regions of transcripts that are listed in the UCSC Genome Browser.

For regions with CNAs, the TCGA dataset was analyzed to include regions with greater than or equal to 2 copy focal gains or losses that were found in more than 4 or 3 tumors, respectively. Candidate genes in regions with significant CNAs from the TCGA CRC publication (Nature 2012) were also included. For CNAs, 6 to 12 amplicons were designed for each of the 56 selected regions (32 gains and 24 losses).

Additional target regions include: 1) 25 microsatellite loci previously used to identify defective DNA mismatch repair and 212 homoploymer repeats. These include microsatellite loci recommended by the NCI Consensus Panel for identifying MSI; 2) amelogenin (for gender); and 3) Fusobacterium to detect a putative CRC-associated pathogen in tumor biopsies.

At the AACR annual meeting, we expect to present results for deep sequencing (∼1,000X) of the first 1,000 CR tumors, including any preliminarily identified pathways and subtypes that may provide the basis for association testing with germline genetic and lifestyle and environmental risk factors needed for inferring better approaches to prevention and treatment of CRC.

Citation Format: Syed H. Zaidi, Catie Grasso, Jasmine Mu, Eve Shinbrot, Marios Giannakis, Charles Connolly, Ivan Borozan, Hermann Brenner, Peter Campbell, Andrew Chan, Jenny Chang-Claude, Mengmeng Du, Vincent Ferretti, Amy French, Charles Fuchs, Steven Gallinger, Levi Garraway, Andrea Gsur, Marc Gunter, Tabitha Harrison, Michael Hoffmeister, Li Hsu, Wen-Yi Huang, Jeroen Huyghe, Mathieu Lemire, Elaine Mardis, John McPherson, Polly Newcomb, Lincoln Stein, Wei Sun, Lee Timms, Quang Trinh, David Wheeler, Christina Yung, Niha Zubair, Shuji Ogino, Stephen Thibodeau, Ulrike Peters, Thomas Hudson. Linking the molecular profile of colorectal tumors to germline genetic and environmental risk factors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5221.

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.

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.

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.

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.

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.

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.

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<or=0.012), containing biologically plausible genes (221 genes with adjusted P</=0.05). Differences in the expression of selected genes were confirmed using real-time polymerase chain reaction. When gene expression from non-cirrhotic UDCA-treated (n=20) and UDCA-naïve liver tissue was compared, we found a striking downregulation of a number of genes involved in protein biosynthetic pathways.

CONCLUSIONS

These studies highlight the genes associated with both treatment-naïve and UDCA-treated PBC, and suggest that the effects of UDCA are mediated, at least in part, via a modulation of protein biosynthesic pathways.

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.

Genome-wide analysis of substrate specificities of the Escherichia coli haloacid dehalogenase-like phosphatase family

Haloacid dehalogenase (HAD)-like hydrolases are a vast superfamily of largely uncharacterized enzymes, with a few members shown to possess phosphatase, beta-phosphoglucomutase, phosphonatase, and dehalogenase activities. Using a representative set of 80 phosphorylated substrates, we characterized the substrate specificities of 23 soluble HADs encoded in the Escherichia coli genome. We identified small molecule phosphatase activity in 21 HADs and beta-phosphoglucomutase activity in one protein. The E. coli HAD phosphatases show high catalytic efficiency and affinity to a wide range of phosphorylated metabolites that are intermediates of various metabolic reactions. Rather than following the classical "one enzyme-one substrate" model, most of the E. coli HADs show remarkably broad and overlapping substrate spectra. At least 12 reactions catalyzed by HADs currently have no EC numbers assigned in Enzyme Nomenclature. Surprisingly, most HADs hydrolyzed small phosphodonors (acetyl phosphate, carbamoyl phosphate, and phosphoramidate), which also serve as substrates for autophosphorylation of the receiver domains of the two-component signal transduction systems. The physiological relevance of the phosphatase activity with the preferred substrate was validated in vivo for one of the HADs, YniC. Many of the secondary activities of HADs might have no immediate physiological function but could comprise a reservoir for evolution of novel phosphatases.

Gene expression profiling of acute liver stress during living donor liver transplantation

During liver transplantation, the donor graft is subjected to a number of acute stresses whose molecular basis is not well-understood. The effects of surgical stress, preservation and reperfusion injury were studied in 24 consecutive living donor liver transplant (LDLT) operations. Liver biopsies were taken early in the donor operation (OPENING), after transection of the donor liver (PRECLAMP) and following implantation of the graft (post hepatic artery, [PHA]); these were evaluated for histology, tissue glutathione content and gene expression using a 19K-human cDNA microarray. LDLT was associated with an ischemia/reperfusion injury, with accumulation of small numbers of neutrophils and decreased glutathione in the PHA biopsies. Following reperfusion, the expression of 129 genes increased and 106 genes decreased when compared to OPENING levels (> or <2-fold, p < 0.01). By real-time PCR a subset of 25 genes was verified (15 increased, 10 decreased). These genes were similarly altered in another condition of acute liver stress (the response to brain-death), but not in three chronic liver disease states (HCV, HBV and PBC). This study has identified a set of genes whose expression is altered in acute, but not chronic, liver stress, likely to play a central role in the pathogenesis of acute liver injury of liver transplantation.

Hepatic gene expression discriminates responders and nonresponders in treatment of chronic hepatitis C viral infection

BACKGROUND & AIMS

Pegylated interferon (IFN)-alpha plus ribavirin is the most effective treatment of chronic hepatitis C but has unpleasant side effects and high costs. A large proportion of patients do not respond to therapy for reasons that are unclear. We used gene expression profiling to investigate the molecular basis for treatment failure.

METHODS

Expression profiling was performed on percutaneous needle liver biopsy specimens taken before therapy. Gene expression levels were compared among 15 nonresponder, 16 responder, and 20 normal liver biopsy specimens. Differential gene expression was confirmed using real-time polymerase chain reaction.

RESULTS

We identified 18 genes whose expression differed significantly between all responders and all nonresponders (P < .005). Many of these 18 genes are IFN sensitive and 2 (ISG15/USP18) are linked in a novel IFN-regulatory pathway, suggesting a possible rationale for treatment resistance. Using a number of independent classifier analyses, an 8-gene subset accurately predicted treatment response for 30 of 31 patients. The classifier analyses were applicable to patients with genotype 1 infection and were not correlated with viral load, disease activity, or fibrosis.

CONCLUSIONS

Hepatic gene expression profiling identified consistent differences in patients who subsequently fail treatment with pegylated IFN-alpha plus ribavirin: up-regulation of a specific set of IFN-responsive genes predicts nonresponse to exogenous therapy. These data may be of use in predicting clinical responses to treatment.