Pan-Cancer Analysis Of Whole Genomes Research Articles

33 PAN-cancer whole genome sequencing reveals patterns of subclonal mutations, signature changes and selection

IntroductionDuring their development, tumour cells accumulate somatic mutations, structural variants and copy number alterations (CNAs). Driver events facilitate clonal expansions and lead to intra-tumour heterogeneity (ITH). While ITH is an important therapeutic challenge, its degree among different cancer types is largely unknown.Material and methodsThe pan-cancer analysis of whole genomes (PCAWG) enabled us to characterise ITH in an unprecedented set of 2778 tumour samples representing 36 histologically distinct cancer types. We applied six CNA callers and eleven subclonal reconstruction algorithms to integrate their solutions into robust consensus copy number profiles and subclonal reconstructions.Results and discussionsOur analysis revealed pervasive ITH in all examined cancer types. We found at least one subclone in 96.7% of the 1801 samples for which we had statistical power to detect subclones. In addition, we find that the average proportions of subclonal point mutations, indels, SVs and CNAs are highly variable across cancer types. These observations suggest distinct evolutionary narratives of each histological cancer type.Analysis of dN/dS ratios shows clear signs of positive selection within both clonal and subclonal mutations. We also identified subclonal mutations in driver genes that are recurrently hit and we found a significant enrichment of subclonal mutations in genes responsible for chromatin regulation. More than 5% of tumours contain driver mutations in genes for which specific treatment is available only in subclones, indicating the importance of assessing the clonality of targeted mutations for clinical decisions.Mutational signatures in the analysed samples show changes in activity over the course of tumour development. Characteristic carcinogen signatures, e.g. UV light exposure in melanomas, make less contributions to subclonal than clonal mutations, while APOBEC-induced mutagenesis has increased activity during the subclonal phase.ConclusionThe absence of a detectable driver mutation in a majority of subclones suggests that late tumour development is frequently driven by CNAs or genomic rearrangements, or that a significant number of late drivers have yet to be identified. We found that selection is widespread and likely the rule rather than the exception and we identified differential activity of mutational signatures, reflecting successive waves of subclonal expansion.

PO-303 Germline determinants of the somatic mutation landscape in 2642 cancer genomes

IntroductionCancers develop through somatic mutations; however, germline genetic variation contributes to cancer risk via diverse mechanisms including by modulating mutational processes.Material and methodsWithin the Pan Cancer Analysis of Whole Genomes (PCAWG) project, we discovered and phased 88 million single nucleotide variants, short insertions/deletions, and large structural variants in whole genomes from 2642 cancer patients, and employed this resource to investigate germline determinants of somatic mutation across 39 cancer types.Results and discussionsWe describe over 100 germline L1 retrotransposons mediating somatic retrotransposition activity in cancer. Furthermore, rare damaging germline mutations in genes involved in DNA repair, DNA replication, and cell cycle associate with a variety of somatic mutation processes. We implicate mutations in the DNA glycosylase MBD4 with an elevated rate of C>T mutations at CpG dinucleotides, resulting in the genetic modulation of a widespread mutational process. Genome-wide association analysis reveals common genetic variation within the APOBEC3 gene cluster modulating mutations attributed to APOBEC cytidine deaminases in multiple cancer types. Analysis of somatic structural variation additionally exposed complex rearrangement patterns including duplications and template insertion cycles in BRCA1-deficient cancers.ConclusionOur study underscores the notable impact rare and common germline variants have on cancer mutational landscapes.

4 Patterns of clustered mutational processes: Pan-Cancer analysis of chromothripsis, chromoplexy and kataegis

IntroductionSome mutational processes can generate multiple mutations in a single event, leading to substantial reconfiguration of the genome. Three such processes have been described: (i) chromothripsis, in which tens to hundreds of dsDNA breaks occur simultaneously, clustered on one or a few chromosomes, with near-random stitching together of fragments; (ii) chromoplexy, in which repair of co-occurring dsDNA breaks, typically on different chromosomes, results in shuffled chains of rearrangements; and (iii) kataegis, a focal hypermutation process leading to clustered nucleotide substitutions, biassed towards a single DNA strand.Material and methodsWe characterised the Pan-Cancer Analysis of Whole Genomes 2778 cancer genomes for chromothripsis, chromoplexy and kataegis. We refined existing methods and developed novel algorithms for detecting these events, and time their occurrence during tumour evolution.Results and discussionsKataegis was observed in 45.3% of all cancers, particularly in lung squamous cell carcinoma and bladder cancer. The events mostly bore the footprint of APOBEC cytidine deaminases (86.9%). Unexpectedly, 5.3% involved clusters of T>N mutations, often in a TpT or CpT context in tumours of the gastrointestinal tract. In lymphoid tumours, we find hotspots of clustered mutations indicative of off-target activity of AID and Pol. Chromoplexy was observed in 454 samples, frequently involving disease-specific genes. Prostate cancer chains involved TMPRSS2 and ERG. Multiple complex chains were also seen in thyroid cancer, affecting thyroid cancer genes such as IGF2BP3, BRAF and THADA. We identified chromothripsis in 238 samples, most often sarcoma and melanoma. The events manifested in rather different patterns and frequency across tumour types. In stomach cancers, the events were typically limited to a single chromosome, whereas they often involved multiple chromosomes in sarcoma and glioblastoma. Overall, kataegis occurs late during tumour evolution, contributing substantially to subclonal diversification. In contrast, chromothripsis and chromoplexy tended to be clonal. We find early clonal chromothripsis, leading to promoter hijacking by TERT, in a subset of chromophobe kidney cancers. Chromothripsis also occurs early in evolution of several melanomas, enabling rapid amplification of CCND1 with frequent co-involvement of other cancer genes.ConclusionClustered mutational events are widespread across tumour types and can generate multiple drivers as well as enable rapid subclonal diversification.

Abstract 378: The Cancer Genome Collaboratory

Abstract The Cancer Genome Collaboratory is an academic compute cloud designed to enable computational research on the world’s largest and most comprehensive cancer genome dataset, the International Cancer Genome Consortium (ICGC). The ICGC is on target to categorize the genomes of 25,000 tumors by 2018. A subproject of ICGC, the PanCancer Analysis of Whole Genomes (PCAWG) alone has generated over 800TB of harmonized sequence alignments, variants and interpreted data from over 2,800 cancer patients. A dataset of this size requires months to download and significant resources to store and process. By making the ICGC data available in cloud compute form in the Collaboratory, researchers can bring their analysis methods to the cloud, yielding benefits from the high availability, scalability and economy offered by cloud services, avoiding a large investment in static compute resources and essentially eliminating the time needed to download the data. To facilitate the computational analysis on the ICGC data, the Collaboratory has developed software solutions that are optimized for typical cancer genomics workloads, including well tested and accurate genome aligners and somatic variant calling pipelines. We have developed a simple to use, but fast and secure, data transfer tool that imports genomic data from cloud object storage into the user’s compute instances. Because a growing number of cancer datasets have restrictions on their storage locations, it is important to have software solutions that are interoperable across multiple cloud environments. We have successfully demonstrated interoperability across The Cancer Genome Atlas (TCGA) dataset hosted at University of Chicago’s Bionimbus Protected Data Cloud, the ICGC dataset hosted at the Collaboratory, and ICGC datasets stored in the Amazon Web Services (AWS) S3 storage. Lastly, we have developed a non-intrusive user authorization system that allows the Collaboratory to authenticate against the ICGC Data Access Compliance Office (DACO) when researchers require access to controlled tier data. We anticipate that our software solutions will be implemented on additional commercial and academic clouds. The Collaboratory is actively growing, with a target hardware infrastructure of over 3000 CPU cores and 15 petabytes of raw storage. As of November 2016, the Collaboratory holds information on 2,000 ICGC PCAWG donors (500TB total). We anticipate expanding the Collaboratory to host the entire ICGC dataset of 25,000 donors (approximately 5PB) and to extend its data management and analysis facilities across multiple clouds. During the current closed beta phase, the Collaboratory has been successfully utilized by multiple research groups, most notably PCAWG project researchers who analyzed thousands of genomes at scale over a few weeks’ time. The Collaboratory will open to the public during the second quarter of 2017. We invite cancer researchers to learn more about our cloud resources at cancercollaboratory.org, and apply for access to the Collaboratory. Citation Format: Christina K. Yung, George L. Mihaiescu, Bob Tiernay, Junjun Zhang, Francois Gerthoffert, Andy Yang, Jared Baker, Guillaume Bourque, Paul C. Boutros, Bartha M. Knoppers, BF Francis Ouellette, Cenk Sahinalp, Sohrab P. Shah, Vincent Ferretti, Lincoln D. Stein. The Cancer Genome Collaboratory [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 378. doi:10.1158/1538-7445.AM2017-378

Abstract 389: Integrating diverse transcriptomic alterations to identify cancer-relevant genes

Abstract Introduction: We present a novel method to identify cancer driver genes that jointly examines any number of diverse transcriptomic alterations with the goal to uncover highly recurrent and heterogeneous patterns in 1190 samples across 26 cancer types as part of the PanCancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC). Motivation: Previous pan-cancer genomic studies have focused on the analysis of somatic mutations as the driver of phenotypic changes. Here, we propose a method to integrate a wide variety of RNA and DNA changes to redefine the concept of driver events and account for the transcriptome’s role in tumorigenesis. PTK2 provides a motivating example, since it has many RNA alterations that correlate with patient survival, such as overexpression, exon-skips, and alternative promoter usage. In our analysis, we integrate an unprecedented amount of various alterations including gene fusions, RNA editing, alternative splicing, expression outliers, alternative promoters, allele specific expression, and somatic mutations. This enables us to also identify mutually exclusive (MutE) and co-occurring (CoO) patterns between different types of alterations within a gene. Methods: Our method has 3 main strengths: flexibility to handle any number or type of alteration, sensitivity to different frequencies of alterations so rare events are not lost in the recurrence analysis, and diversity of ranking such that genes with multiple alterations are prioritized. Our method is summarized in two steps: 1) Identify genes that are both recurrently and heterogeneously altered across many samples by calculating a rank-based score for each gene. 2) Identify MutE and CoO patterns between alteration types for the genes identified in the previous step. To ensure that alterations were comparable, we applied a thresholding model to binarize all alterations for gene-sample pairs, allowing us to account for the properties of the different modalities involved. Step 1 of our method calculates a score for each gene that takes into account: 1) the number of alterations to a gene across all samples, 2) the rarity of each alteration, and 3) how many types of alterations are observed per gene. The score is then used to rank the genes and top genes are considered for MutE and CoO analyses. Results: Our top 100 ranked genes were highly enriched for cancer census genes (adjusted p-value: 2.06e-9), indicating that we identify cancer relevant genes. Our top five ranked cancer census genes were IGF2, ERBB2, RARA, CREBBP, and ARID1A; all of which had at least 4 of 7 possible alterations, showing our scoring method prioritizes genes with diverse alterations. We also found that alternative promoter usage and alternative splicing were highly co-occurring alterations, with PTK2 having the highest co-occurrence between them. In summary, we propose a new method to analyze various RNA disruptions and show it can yield new insights beyond genomic variation. Citation Format: Natalie R. Davidson, PanCancer Analysis of Whole Genomes 3 (PCAWG-3) for ICGC, Alvis Brazma, Angela N. Brooks, Claudia Calabrese, Nuno A. Fonseca, Jonathan Goke, Yao He, Xueda Hu, Andre Kahles, Kjong-Van Lehmann, Fenglin Liu, Gunnar Rätsch, Siliang Li, Roland F. Schwarz, Mingyu Yang, Zemin Zhang, Fan Zhang, Liangtao Zheng. Integrating diverse transcriptomic alterations to identify cancer-relevant genes [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 389. doi:10.1158/1538-7445.AM2017-389

Abstract 377: International Cancer Genome Consortium (ICGC)

Abstract The International Cancer Genome Consortium (ICGC) was established to bring together researchers from around the globe to comprehensively analyze the genomic, transcriptomic, and epigenomic changes in 50 different tumor types or subtypes that are of clinical and societal importance across the globe (International network of cancer genome projects. Nature 464, 993-998 (15 April 2010)). As of November 2016, the ICGC has received commitments from researchers and funding organizations in Asia, Australia, Europe, North America and South America for 103 project teams in 17 jurisdictions to study more than 25,000 tumor genomes. Processed data is available via the Data Coordination Centre (http://dcc.icgc.org) based at the Ontario Institute for Cancer Research and is updated semi-annually. The August 2016 release (Version 22) in total comprises data from more than 16,000 cancer donors spanning 70 projects and 21 tumor sites. The Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the ICGC and The Cancer Genome Atlas (TCGA) is coordinating analysis of more than 2,600 cancer genomes, with the extensive use of cloud computing. Because of the very large size of the pan-cancer dataset, with 5,000 whole genome sequences, PCAWG is using a distributed compute cloud environment (generated by computing centres in the USA, Europe and Asia) that meets the project’s technical requirements and the bioethical framework of ICGC and its member projects. Each genome is being characterized through a suite of standardized algorithms, including alignment to the reference genome, uniform quality assessment, and the calling of multiple classes of somatic mutations. Scientists participating in the research projects of PCAWG are addressing a series of fundamental questions about cancer biology and evolution based on these data. The first phase of ICGC, which is slated for completion in 2018, has focused on developing extensive catalogs of tumor genomic information. The proposed second phase, ICGCmed, will link genomics to clinical information and health, including lifestyle, patient history, response to therapies, and underlying causes of disease, for a broad spectrum of cancers, including preneoplastic lesions, early cancers and metastases. The goal will be to accelerate the movement of genomic information into the clinic to guide prevention, early detection, diagnosis, and prognosis, and provide the information needed to match a patient’s disease to the most effective combinations of therapy. The ICGC develops policies and quality control criteria to help harmonize the work of member projects located in different jurisdictions. Data produced by ICGC projects are made rapidly and freely available to qualified researchers around the world via the data cloud and through the ICGC Data Coordination Center at (http://dcc.icgc.org). More information can be found on www.icgc.org. Citation Format: Jennifer L. Jennings, Lincoln D. Stein, Fabien Calvo. International Cancer Genome Consortium (ICGC) [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 377. doi:10.1158/1538-7445.AM2017-377

Abstract SY10-02: Pan-cancer study of recurrent and heterogeneous RNA aberrations and association with whole-genome variants

Abstract Whole-exome sequencing studies have transformed our understanding of recurrent somatic mutations that contribute to cancer pathogenesis; however, these studies limit our ability to identify cancer-associated mutations to those that cause protein-coding changes. To more comprehensively catalogue cancer-associated gene alterations, we have extensively characterized tumor transcriptomes from 1,220 donors with matched whole-genome sequence data to identify recurrent RNA-level aberrations. Specifically, we created a unified RNA-Seq analysis pipeline including sequence alignment and quality control and subsequently identified gene alterations through outlier detection from estimated gene expression levels, alternative splicing, alternative transcription starts, and allele-specific expression and through identified RNA-edited sites and gene fusions. Our data represent an extensive catalog of RNA aberrations for each gene across 27 cancer types. We have also tested for genetic associations with these RNA phenotypes. Using an integrative analysis approach, we have mapped genome-wide cis and trans effects on individual RNA phenotypes, considering both common germline variants as well as somatic SNVs in gene promoters, enhancers, and intronic and other regions. Many of the regulatory associations we identify are not accessible by exome sequencing, underlining the importance of whole-genome sequence data. Utilizing this RNA-centric view, we have identified genes that are recurrently altered, yet have not been previously characterized as cancer genes or identified through DNA-level driver gene analysis. To identify further supporting evidence that these recurrent alterations are potential drivers, we identified genes with mutually exclusive RNA-level alterations. Our findings reveal new insights into selective advantages of somatic changes and molecular mechanisms of cancer. This work is by the Transcriptome Working Group of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and authors are listed in alphabetical order. Citation Format: Samirkumar Amin, Philip Awadalla, Andrew Biankin, Paul Boutros, Alvis Brazma, Angela Norie Brooks, Claudia Calabrese, David Chang, Aurélien Chateigner, Ken Chen, Zechen Chong, Brian Craft, Chad Creighton, Deniz Demircioğlu, Nuno Fonseca, Milana Frenkel-Morgenstern, Gad Getz, Jonathan Göke, Mary Goldman, Liliana Greger, Syed Haider, Yao He, Katherine Hoadley, Yuan Ji, Andre Kahles, Ekta Khurana, Jan Korbel, Kjong Lehmann, Han Liang, Fenglin Liu, Maximillian Marin, Matthew Meyerson, Akinyemi Ojesina, Francis Ouellette, Chandra Pedamallu, Marc Perry, Gunnar Rätsch, Roland Schwarz, Yuichi Shiraishi, Cameron Soulette, Oliver Stegle, Patrick Tan, Alfonso Valencia, Linda Xiang, Christina Yung, Junjun Zhang, Fan Zhang, Zemin Zhang, Jingchun Zhu. Pan-cancer study of recurrent and heterogeneous RNA aberrations and association with whole-genome variants [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 SY10-02. doi:10.1158/1538-7445.AM2017-SY10-02

Abstract 3588: Computational method to identify non-coding cancer drivers

Abstract Cancer is caused by mutations in driver genes - yet ~25% patients do not show any mutations in known drivers. ~99% somatic mutations obtained from whole-genome sequencing localize to regions that do not code for proteins and many of these non-coding mutations regulate gene expression. Although variants in protein-coding regions have received the majority of attention, numerous studies have now noted the importance of non-coding variants in cancer - for example, mutations in the promoter of the TERT gene. Identification of functional non-coding variants that drive tumor growth remains a challenge and a bottleneck for the use of whole-genome sequencing in the clinic. Cancer drivers are generally identified by the high frequency at which their mutations occur across patients. However, mutation rate is highly heterogeneous across the genome and many non-driver elements show higher mutation frequency than others, such as regions bound by transcription factors (TFs) in melanoma or regions replicating late during cell division in colon cancer. We have developed a novel computational approach to predict non-coding cancer drivers that differentiates between regions highly mutated due to positive selection (true drivers) vs those due to mutation rate heterogeneity (false positives). This method integrates the signals of high functional impact of variants with the recurrence of variants across multiple tumor samples to identify the elements that show more and higher functional impact mutations than expected randomly. Our method controls for multiple covariates of mutation rates, such as replication timing, histone modification marks and open chromatin regions to identify the non-coding cancer drivers. The functional impact is predicted using the FunSeq scheme that uses the properties of ENCODE elements (including conservation, TF motif disruption and network properties) within a weighted scoring scheme to predict deleteriousness of non-coding variants. I will present the details of this method and discuss the ongoing efforts to analyze ~2,900 tumor whole-genomes in the ‘Pan-Cancer Analysis of Whole Genomes, PCAWG’ consortium. Citation Format: Eric Minwei Liu, Priyanka Dhingra, Alexander M. Fundichely, Ekta Khurana. Computational method to identify non-coding cancer drivers [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 3588. doi:10.1158/1538-7445.AM2017-3588

The Dockstore: enabling modular, community-focused sharing of Docker-based genomics tools and workflows

As genomic datasets continue to grow, the feasibility of downloading data to a local organization and running analysis on a traditional compute environment is becoming increasingly problematic. Current large-scale projects, such as the ICGC PanCancer Analysis of Whole Genomes (PCAWG), the Data Platform for the U.S. Precision Medicine Initiative, and the NIH Big Data to Knowledge Center for Translational Genomics, are using cloud-based infrastructure to both host and perform analysis across large data sets. In PCAWG, over 5,800 whole human genomes were aligned and variant called across 14 cloud and HPC environments; the processed data was then made available on the cloud for further analysis and sharing. If run locally, an operation at this scale would have monopolized a typical academic data centre for many months, and would have presented major challenges for data storage and distribution. However, this scale is increasingly typical for genomics projects and necessitates a rethink of how analytical tools are packaged and moved to the data. For PCAWG, we embraced the use of highly portable Docker images for encapsulating and sharing complex alignment and variant calling workflows across highly variable environments. While successful, this endeavor revealed a limitation in Docker containers, namely the lack of a standardized way to describe and execute the tools encapsulated inside the container. As a result, we created the Dockstore ( https://dockstore.org), a project that brings together Docker images with standardized, machine-readable ways of describing and running the tools contained within. This service greatly improves the sharing and reuse of genomics tools and promotes interoperability with similar projects through emerging web service standards developed by the Global Alliance for Genomics and Health (GA4GH).

The Dockstore: enabling modular, community-focused sharing of Docker-based genomics tools and workflows.

As genomic datasets continue to grow, the feasibility of downloading data to a local organization and running analysis on a traditional compute environment is becoming increasingly problematic. Current large-scale projects, such as the ICGC PanCancer Analysis of Whole Genomes (PCAWG), the Data Platform for the U.S. Precision Medicine Initiative, and the NIH Big Data to Knowledge Center for Translational Genomics, are using cloud-based infrastructure to both host and perform analysis across large data sets. In PCAWG, over 5,800 whole human genomes were aligned and variant called across 14 cloud and HPC environments; the processed data was then made available on the cloud for further analysis and sharing. If run locally, an operation at this scale would have monopolized a typical academic data centre for many months, and would have presented major challenges for data storage and distribution. However, this scale is increasingly typical for genomics projects and necessitates a rethink of how analytical tools are packaged and moved to the data. For PCAWG, we embraced the use of highly portable Docker images for encapsulating and sharing complex alignment and variant calling workflows across highly variable environments. While successful, this endeavor revealed a limitation in Docker containers, namely the lack of a standardized way to describe and execute the tools encapsulated inside the container. As a result, we created the Dockstore ( https://dockstore.org), a project that brings together Docker images with standardized, machine-readable ways of describing and running the tools contained within. This service greatly improves the sharing and reuse of genomics tools and promotes interoperability with similar projects through emerging web service standards developed by the Global Alliance for Genomics and Health (GA4GH).

Abstract LB-068: Classification of cancer by synthesis of rare non-coding mutations within recurrent epigenetic and protein networks

Abstract Cancers are mutationally heterogeneous, resulting in multiple subtypes with different clinical outcomes and responses to treatment. Newly available data from the Pan-Cancer Analysis of Whole Genomes (PCAWG) working group have revealed vast numbers of mutations located in non-coding regions of the human genome. However, few of these mutations are recurrent and their functions are poorly characterized. Here, we synthesize all mutations identified in a patient, both coding and non-coding, in the context of prior knowledge of epigenetic architecture and molecular networks. Non-coding mutations are selected using a functional segmentation of the genome defined by the ENCODE, Roadmap, and FANTOM consortia; these are then aggregated with coding mutations using enhancer-gene (eQTL, HiC, Roadmap, linkage) and gene-gene functional interactions relevant to human cancer. This approach enables stratification of 756 patients from 22 cancer types into biologically and clinically informative subtypes, based on highly recurrent alteration of networks mediating cell adhesion, DNA damage checkpoint and apoptosis, HLA class I, PI3K signaling, telomere maintenance, or p53 signaling. Non-coding mutations (e.g. to enhancers of IKBKB, IL1A, and MALT1) are unified with coding mutations (e.g. in BTRC, ATM, and the PPAR subunits) within common networks, providing valuable prognostic power beyond the baseline survival rate of the tissue. We find that most of the relevant non-coding elements are affected in only one patient, with the recurrent signal becoming clear only after aggregation within epigenetic networks. Citation Format: Wei Zhang, Trey Ideker, the Pan-Cancer Analysis of Whole Genomes Working Group on Regulatory drivers and Pathways/Networks. Classification of cancer by synthesis of rare non-coding mutations within recurrent epigenetic and protein networks. [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 LB-068.

Abstract 130: International Cancer Genome Consortium (ICGC)

Abstract The International Cancer Genome Consortium (ICGC) was established to bring together researchers from around the globe to comprehensively analyze the genomic, transcriptomic, and epigenomic changes in 50 different tumour types or subtypes that are of clinical and societal importance across the globe (International network of cancer genome projects. Nature 464, 993-998 (15 April 2010)). As of November 2015, the ICGC has received commitments from researchers and funding organizations in Asia, Australia, Europe, North America and South America for 89 project teams in 17 jurisdictions to study more than 25,000 tumour genomes. Processed data is available via the Data Coordination Centre (http://dcc.icgc.org) based at the Ontario Institute for Cancer Research and is updated semi-annually. The November 2015 release (Version 20) includes datasets from 66 ICGC projects. In total, ICGC data release 20 comprises data from 14,767 cancer genomes. The Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the ICGC and The Cancer Genome Atlas (TCGA) is coordinating analysis of more than 2,800 cancer genomes, with the extensive use of cloud computing. Because of the very large size of the pan-cancer dataset, with 5,000 whole genome sequences, PCAWG is using a distributed compute cloud environment (generated by computing centres in the USA, Europe and Asia) that meets the project's technical requirements and the bioethical framework of ICGC and its member projects. Each genome is being characterized through a suite of standardized algorithms, including alignment to the reference genome, uniform quality assessment, and the calling of multiple classes of somatic mutations. Scientists participating in the research projects of PCAWG are addressing a series of fundamental questions about cancer biology and evolution based on these data. The first phase of ICGC, which is slated for completion in 2018, has focused on developing extensive catalogs of tumour genomic information. The proposed second phase, ICGCmed, will link genomics to clinical information and health, including lifestyle, patient history, response to therapies, and underlying causes of disease, for a broad spectrum of cancers, including preneoplastic lesions, early cancers and metastases. The goal will be to accelerate the movement of genomic information into the clinic to guide prevention, early detection, diagnosis, and prognosis, and provide the information needed to match a patient's disease to the most effective combinations of therapy. The ICGC develops policies and quality control criteria to help harmonize the work of member projects located in different jurisdictions. Data produced by ICGC projects are made rapidly and freely available to qualified researchers around the world via the data cloud and through the ICGC Data Coordination Center at (http://dcc.icgc.org). More information can be found on www.icgc.org. Citation Format: Jennifer L. Jennings, Thomas J. Hudson. International Cancer Genome Consortium (ICGC). [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 130.

Abstract 2988: International cancer genome consortium (ICGC)

Abstract The International Cancer Genome Consortium (ICGC) was established to bring together researchers from around the globe to comprehensively analyze the genomic, transcriptomic, and epigenomic changes in 50 different tumour types or subtypes that are of clinical and societal importance across the globe (International network of cancer genome projects. Nature 464, 993-998 (15 April 2010)). As of December 2014, the ICGC has received commitments from researchers and funding organizations in Asia, Australia, Europe, North America and South America for 77 project teams in 17 jurisdictions to study more than 25,000 tumour genomes. More than 27,000 tumours that meet ICGC requirements for consent have been obtained and research pathologists have approved over 27,000 samples as meeting ICGC standards. Processed data is available via the Data Coordination Centre (http://dcc.icgc.org) based at the Ontario Institute for Cancer Research and is updated quarterly. The data release (Version 17) in October 2014 included datasets from 42 ICGC members. The ICGC Data Portal has been improved to provide enhanced support to the research community browsing this vast dataset. The ICGC and The Cancer Genome Atlas (TCGA) PanCancer Analysis of Whole Genomes (PCAWG) project is committing more than 2,500 tumour-normal whole genome pairs from 23 different tumour types to uniform alignment and variant calling. More than 600 researchers will contribute to the interpretation of the data and writing of papers that will focus on specific analyses covering: Identification of driver mutations outside coding regions, including those that affect cis-regulatory regions and non-coding RNAs; and identification of gene regulatory pathways altered by non-coding mutations. Because the projected size of the pan-cancer dataset for 5,000 WGS is very large, PCAWG is using a distributed compute cloud environment (at different computing centres in the USA, Europe and Asia) that meet technical requirements of the project and the bioethical framework of ICGC and its member projects. More information can be found on www.icgc.org. Citation Format: Jennifer L. Jennings, Thomas J. Hudson. International cancer genome consortium (ICGC). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2988. doi:10.1158/1538-7445.AM2015-2988

Management of primary malignant brain tumours.

<h3>Abstract</h3> The International Cancer Genome Consortium (ICGC)’s Pan-Cancer Analysis of Whole Genomes (PCAWG) project aimed to categorize somatic and germline variations in both coding and non-coding regions in over 2,800 cancer patients. To provide this dataset to the research working groups for downstream analysis, the PCAWG Technical Working Group marshalled ~800TB of sequencing data from distributed geographical locations; developed portable software for uniform alignment, variant calling, artifact filtering and variant merging; performed the analysis in a geographically and technologically disparate collection of compute environments; and disseminated high-quality validated consensus variants to the working groups. The PCAWG dataset has been mirrored to multiple repositories and can be located using the ICGC Data Portal. The PCAWG workflows are also available as Docker images through Dockstore enabling researchers to replicate our analysis on their own data.

The Epping jaundice outbreak: a 24 year follow up.

The aim was to trace 84 cases of jaundice that occurred following accidental ingestion of methylene dianiline (MDA) in Epping in 1965, and to look at long term health effects. The original case notes of the cases were used to identify the patients. Subsequent tracing procedures included local general practitioners, the Central NHS Registry, electoral rolls, and company records. This was a community based survey. The health status of 68 (81%) of the group was established with 18 deaths. Of the 50 cases known to be alive, 58% completed a health questionnaire. The causes of death were unremarkable except for one case of carcinoma of the biliary tract. Two surviving cases had suffered retinal pathology. Four other surviving cases had had a further, perhaps unrelated, episode of jaundice. Although the dose and route of administration in the epidemic differed from occupational exposure, this follow up study a generation on provides little, if any, evidence of long term health sequelae. Nevertheless, in the absence of well documented exposure and health effects data, such accidental poisonings with proven animal carcinogens warrant long term follow up. The identified cohort will be the subject of further study.