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.

A user guide for the online exploration and visualization of PCAWG data.

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user's guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

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.

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.

Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API.

The NCI Genomic Data Commons (GDC) was launched in 2016 and makes available over 4 petabytes (PB) of cancer genomic and associated clinical data to the research community. This dataset continues to grow and currently includes over 14,500 patients. The GDC is an example of a biomedical data commons, which collocates biomedical data with storage and computing infrastructure and commonly used web services, software applications, and tools to create a secure, interoperable, and extensible resource for researchers. The GDC is (i) a data repository for downloading data that have been submitted to it, and also a system that (ii) applies a common set of bioinformatics pipelines to submitted data; (iii) reanalyzes existing data when new pipelines are developed; and (iv) allows users to build their own applications and systems that interoperate with the GDC using the GDC Application Programming Interface (API). We describe the GDC API and how it has been used both by the GDC itself and by third parties. Cancer Res; 77(21); e15-18. ©2017 AACR.

The NCI Genomic Data Commons as an engine for precision medicine.

The National Cancer Institute Genomic Data Commons (GDC) is an information system for storing, analyzing, and sharing genomic and clinical data from patients with cancer. The recent high-throughput sequencing of cancer genomes and transcriptomes has produced a big data problem that precludes many cancer biologists and oncologists from gleaning knowledge from these data regarding the nature of malignant processes and the relationship between tumor genomic profiles and treatment response. The GDC aims to democratize access to cancer genomic data and to foster the sharing of these data to promote precision medicine approaches to the diagnosis and treatment of cancer.

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.

Clinical genomics information management software linking cancer genome sequence and clinical decisions.

Using sequencing information to guide clinical decision-making requires coordination of a diverse set of people and activities. In clinical genomics, the process typically includes sample acquisition, template preparation, genome data generation, analysis to identify and confirm variant alleles, interpretation of clinical significance, and reporting to clinicians. We describe a software application developed within a clinical genomics study, to support this entire process. The software application tracks patients, samples, genomic results, decisions and reports across the cohort, monitors progress and sends reminders, and works alongside an electronic data capture system for the trial's clinical and genomic data. It incorporates systems to read, store, analyze and consolidate sequencing results from multiple technologies, and provides a curated knowledge base of tumor mutation frequency (from the COSMIC database) annotated with clinical significance and drug sensitivity to generate reports for clinicians. By supporting the entire process, the application provides deep support for clinical decision making, enabling the generation of relevant guidance in reports for verification by an expert panel prior to forwarding to the treating physician.

Feasibility of real time next generation sequencing of cancer genes linked to drug response: results from a clinical trial.

The successes of targeted drugs with companion predictive biomarkers and the technological advances in gene sequencing have generated enthusiasm for evaluating personalized cancer medicine strategies using genomic profiling. We assessed the feasibility of incorporating real-time analysis of somatic mutations within exons of 19 genes into patient management. Blood, tumor biopsy and archived tumor samples were collected from 50 patients recruited from four cancer centers. Samples were analyzed using three technologies: targeted exon sequencing using Pacific Biosciences PacBio RS, multiplex somatic mutation genotyping using Sequenom MassARRAY and Sanger sequencing. An expert panel reviewed results prior to reporting to clinicians. A clinical laboratory verified actionable mutations. Fifty patients were recruited. Nineteen actionable mutations were identified in 16 (32%) patients. Across technologies, results were in agreement in 100% of biopsy specimens and 95% of archival specimens. Profiling results from paired archival/biopsy specimens were concordant in 30/34 (88%) patients. We demonstrated that the use of next generation sequencing for real-time genomic profiling in advanced cancer patients is feasible. Additionally, actionable mutations identified in this study were relatively stable between archival and biopsy samples, implying that cancer mutations that are good predictors of drug response may remain constant across clinical stages.

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.

Germline EPHB2 receptor variants in familial colorectal cancer.

Familial clustering of colorectal cancer occurs in 15-20% of cases, however recognized cancer syndromes explain only a small fraction of this disease. Thus, the genetic basis for the majority of hereditary colorectal cancer remains unknown. EPHB2 has recently been implicated as a candidate tumor suppressor gene in colorectal cancer. The aim of this study was to evaluate the contribution of EPHB2 to hereditary colorectal cancer. We screened for germline EPHB2 sequence variants in 116 population-based familial colorectal cancer cases by DNA sequencing. We then estimated the population frequencies and characterized the biological activities of the EPHB2 variants identified. Three novel nonsynonymous missense alterations were detected. Two of these variants (A438T and G787R) result in significant residue changes, while the third leads to a conservative substitution in the carboxy-terminal SAM domain (V945I). The former two variants were found once in the 116 cases, while the V945I variant was present in 2 cases. Genotyping of additional patients with colorectal cancer and control subjects revealed that A438T and G787R represent rare EPHB2 alleles. In vitro functional studies show that the G787R substitution, located in the kinase domain, causes impaired receptor kinase activity and is therefore pathogenic, whereas the A438T variant retains its receptor function and likely represents a neutral polymorphism. Tumor tissue from the G787R variant case manifested loss of heterozygosity, with loss of the wild-type allele, supporting a tumor suppressor role for EPHB2 in rare colorectal cancer cases. Rare germline EPHB2 variants may contribute to a small fraction of hereditary colorectal cancer.

Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24.

Using a multistage genetic association approach comprising 7,480 affected individuals and 7,779 controls, we identified markers in chromosomal region 8q24 associated with colorectal cancer. In stage 1, we genotyped 99,632 SNPs in 1,257 affected individuals and 1,336 controls from Ontario. In stages 2-4, we performed serial replication studies using 4,024 affected individuals and 4,042 controls from Seattle, Newfoundland and Scotland. We identified one locus on chromosome 8q24 and another on 9p24 having combined odds ratios (OR) for stages 1-4 of 1.18 (trend; P = 1.41 x 10(-8)) and 1.14 (trend; P = 1.32 x 10(-5)), respectively. Additional analyses in 2,199 affected individuals and 2,401 controls from France and Europe supported the association at the 8q24 locus (OR = 1.16, trend; 95% confidence interval (c.i.): 1.07-1.26; P = 5.05 x 10(-4)). A summary across all seven studies at the 8q24 locus was highly significant (OR = 1.17, c.i.: 1.12-1.23; P = 3.16 x 10(-11)). This locus has also been implicated in prostate cancer.