Pan-Cancer Analysis Of Whole Genomes Project Research Articles

Pan-cancer analysis of whole-genome doubling and its association with patient prognosis

BackgroundWhole-genome doubling (WGD) is a common mutation in cancer. Various studies have suggested that WGD is associated with a poor prognosis in cancer. However, the detailed association between WGD occurrence and prognosis remains unclear. In this study, we aimed to elucidate the mechanism by which WGD affects prognosis using sequencing data from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and The Cancer Genome Atlas.MethodsWhole-genome sequencing data of 23 cancer types were downloaded from PCAWG project. We defined the WGD event in each sample using the WGD status annotated using PCAWG. We used MutationTimeR to predict the relative timings of mutations and loss of heterozygosity (LOH) in WGD, thus evaluating their association with WGD. We also analyzed the association between WGD-associated factors and patient prognosis.ResultsWGD was associated with several factors, e.g., length of LOH regions. Survival analysis using WGD-associated factors revealed that longer LOH regions and LOH in chr17 were associated with poor prognosis in samples with WGD (WGD samples) and samples without WGD (nWGD samples). In addition to these two factors, nWGD samples showed that the number of mutations in tumor suppressor genes was associated with prognosis. Moreover, we explored the genes associated with prognosis in both samples separately.ConclusionThe prognosis-related factors in WGD samples differed significantly compared with those in nWGD samples. This study emphasizes the need for different treatment strategies for WGD and nWGD samples.

Discovery of novel RNA transcripts for endometrial cancer with copy-number high alterations using pan-cancer analysis of whole genomes (PCAWG) endometrial cancer dataset (275)

<b>Objectives:</b> Uterine serous carcinoma is an aggressive type of endometrial cancer that has a propensity for metastatic spread and disproportionately affects Black women. Several new drugs are being evaluated in clinical trials for uterine serous carcinoma, including trastuzumab, pembrolizumab - lenvatinib combination, and adavosertib. However, additional treatment options are greatly needed. The Cancer Genome Atlas (TCGA) endometrial cancer project has shown that endometrial cancer can be broadly divided into four molecular classifications. Within the four molecular subtypes, the copy-number high group consists of high-grade endometrioid carcinoma and uterine serous carcinoma, which are characterized by frequent <i>TP53</i> mutation and somatic copy number alterations. To better understand the biology of this aggressive subset of endometrial cancer, we have performed bioinformatic analysis using the dataset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and TCGA. <b>Methods:</b> To better understand the biology of uterine serous carcinoma, we sought to identify novel or poorly characterized genes using the PCAWG dataset. We analyzed the RNA sequencing data from the TCGA endometrial cancer dataset, focusing on the copy-number high group. Genes that are highly expressed in the copy-number high endometrial cancer were identified by comparing the gene expression profiles between the copy-number high endometrial cancer dataset to genes expressed in normal endometrium obtained from the PCAWG project. Survival outcomes for genes of interest were evaluated using Kaplan-Meier Plotter, containing survival data extracted from the TCGA endometrial cancer dataset. <b>Results:</b> RNA transcripts that were significantly upregulated in copy-number high endometrial cancer were identified. Three of the most significantly upregulated transcripts were claudin-6 (<i>CLDN6</i>), cancer-testis antigen <i>MAGEA9B</i>, and long non-coding RNA <i>LINC01833</i>. Kaplan-Meier analysis of overall survival showed that high <i>CLDN6</i> expression was associated with worse overall survival when compared to low <i>CLDN6</i> expression (HR: 2.04 in White patients, p=0.0037; HR: 7.35 in Black patients, p=0.0002). High expression of <i>MAGEA9B</i> was also associated with worse overall survival than low <i>MAGEA9B</i> expression (HR: 1.78 in White patients, p=0.022; HR: 5.69 in Black patients, p=0.000033). In contrast, high expression of long non-coding RNA <i>LINC02159</i> was not associated with significant alteration in overall survival (HR: 1.28 in White patients, p=0.34, HR: 0.52 in Black patients, p=0.17). <b>Conclusions:</b> Integrated genomic analysis of the TCGA endometrial cancer dataset led to the identification of several novels, previously uncharacterized RNA transcripts that are associated with poor prognosis in copy-number high endometrial cancer. Further characterization of these transcripts in uterine serous carcinoma will improve our understanding of the biology of this disease and lead to novel therapeutic options.

Genomic and Molecular Analyses Identify Molecular Subtypes of Pancreatic Cancer Recurrence

Genomic and Molecular Analyses Identify Molecular Subtypes of Pancreatic Cancer Recurrence

Combined burden and functional impact tests for cancer driver discovery using DriverPower

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. 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 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

Pathway and network analysis of more than 2500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

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