Seven Bridges Genomics (United States)

More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data reside. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse.See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Pot et al., p. 1396

Abstract A direct way to spot structural features that are universally shared among proteins is to find analogues from simpler condensed matter systems. In the current study, the feasibility of creating ensembles of artificial structures that can automatically reproduce a large number of geometrical and topological descriptors of globular proteins is investigated. Towards this aim, a simple cubic (SC) arrangement is shown to provide the best background lattice after a careful analysis of the residue packing trends from 210 globular proteins. It is shown that a minimalistic set of rules imposed on this lattice is sufficient to generate structures that can mimic real proteins. In the proposed method, 210 such structures are generated by randomly removing residues (beads) from clusters that have a SC lattice arrangement such that all the generated structures have single connected components. Two additional sets are prepared from the initial structures via random relaxation and a reverse Monte Carlo simulated annealing algorithm, which targets the average radial distribution function (RDF) of 210 globular proteins. The initial and relaxed structures are compared to real proteins via RDF, bond orientational order parameters and several descriptors of network topology. Based on these features, results indicate that the structures generated with 40% occupancy closely resemble real residue networks. The structure generation mechanism automatically produces networks that are in the same topological class as globular proteins and reproduce small-world characteristics of high clustering and small shortest path lengths. Most notably, the established correspondence rules out icosahedral order as a relevant structural feature for residue networks in contrast to other amorphous systems where it is an inherent characteristic. The close correspondence is also observed in the vibrational characteristics as computed from the Anisotropic Network Model, therefore hinting at a non-superficial link between the proteins and the defect laden cubic crystalline order.

Abstract We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data.The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI’s Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized.The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

Abstract SummaryWe developed the STAAR WDL workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the workflow description language (WDL) allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate, and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline.Availability and implementationThe workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR_workflow.

Abstract We created the PDX Network (PDXNet) Portal to provide an intuitive way for researchers to explore and understand the models, sequencing data, and bioinformatics workflows generated by NCI's PDXNet consortium for research access (https://portal.pdxnetwork.org/). The portal also provides metrics for PDXNet's activities, data processing protocols, and training materials for processing PDX data. The PDXNet Portal highlights model and data resources that include 216 new models across 29 cancer types. The most prevalent cancers represented in the PDX model dataset include invasive breast carcinoma (30.6%), melanoma (18.1%), and adenocarcinoma (14.4%). PDXNet teams have provided 2263 sequencing files from 356 samples across 204 patients, comprising whole exome (82.9%) and RNA seq files (17.1%). The most prevalent cancers represented in the PDXNet sequencing data set include Breast Pleural Effusion (27.2%), Breast Poorly Differentiated (12.5%), and Lung Adenocarcinoma (9.6%). The portal also provides access to 9492 sequencing files across 78 disease types that include 2594 samples across 463 patients uploaded from the NCI Patient-Derived Model Repository. The dataset includes both whole exomes (52.8%) and RNA seq (47.2%) data. The PDMR samples include PDX (82.7%), primary tumor (5.7%), normal germline (5.5), organoid culture (3.2), and Mixed Tumor Culture (2.9). The PDMR dataset also has multiple passages: P0 (21.8%), P1(39.5%), P2 (25.6%), and P3 (8.5%). These models and data resources support ten PDXNet Pilot activities, multiple publications, and international collaborations. PDXNet has also developed a set of 13 robust, validated, and standardized workflows for processing PDXNet whole-exome and RNA seq data. Collectively, these workflows allow for the standardized processing of PDX and complementary human tissues (normal and tumor). Our plan is to continuously update the model and data lists on the PDX portal as resources are generated. We expect that the PDXNet generated models, scheduled to grow to 1000 new models by 2022, will support multi-agent treatment studies, determination of mechanisms of sensitivity and resistance, and pre-clinical trials for example through the COMBO-MATCH program. The robust standard workflows currently processing all PDX sequencing data may also facilitate harmonizing data across studies. Lastly, we expect that the generated sequencing data will support computational approaches for studying cancer evolution and the mechanisms underlying cancer treatments. Citation Format: Soner Koc, Mike Lloyd, Steven Neuhauser, Javad Noodbakhsh, Anuj Srivastava, Xing Yi Woo, Ryan Jeon, Jeffrey Grover, Sara Seepo, Christian Frech, Jack DiGiovanna, PDXNet Consortium, Yvonne A. Evard, Tiffany Wallace, Jeffrey Moscow, James H. Doroshow, Nicholas Mitsuade, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis Carvarjal-Carmona, Alana Welm, Bryan Welm, Michael T. Lewis, Govindan Ramaswamy, Li Ding, Shunquang Li, Meenherd Herlyn, Mike Davies, Jack Roth, Funda Meric-Bernstam, Peter Robinson, Carol J. Bult, Brandi Davis-Dusenbery, Dennis A. Dean, Jeffrey H. Chuang. Advancing PDX research through model, data, and bioinformatics with the PDXNet Portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3017.

For Zenodo archiving purposes.

Abstract Over 200 chemicals have been associated with altered mammary gland development and cancer in rodent studies, yet the molecular mechanisms by which this diverse set of chemicals affect the breast remain poorly understood. We examined gene expression profiles of eight known and suspected mammary toxicants using the TempO-Seq platform and a targeted gene expression panel, Breast Carcinogen Screen (BCScreen). The BCScreen panel has 500 genes with at least 30 genes strongly associated with each of the 14 biologic processes relevant to breast cancer and mammary gland development. We compared gene expression profiles in MCF-7 cells treated for 24hrs with four concentrations (1 nM to 10 uM) of the following chemicals: three estrogenic chemicals (genistein, bisphenol A, butyl benzyl phthalate); the selective estrogen receptor modulator, tamoxifen (TAM); 1,4 benzoquinone (BQ), a genotoxic metabolite of the rodent mammary carcinogen benzene; perfluorooctanoic acid (PFOA), a persistent pollutant that alters mammary gland development; and two closely related perfluorinated chemicals that have not been evaluated for mammary toxicity/effects (PFHXA, PFNA). We identified expression patterns activated by mammary toxicants with known ER agonist activity (genistein, BPA, BBP). Some of these genes were suppressed by PFOA and BQ and TAM, mammary toxicants with antagonist or no known direct ER activity. The known weak estrogens genistein, BPA, and BBP had similar gene expression profiles and were enriched for genes associated with cell cycle and DNA repair, particularly at higher doses. Notably, PFOA, Tam, and BQ had greater gene expression changes at lowest (1nM) vs. higher doses and were enriched for genes associated with cell cycle. For context, serum PFOA levels in the general US population are generally five to ten times higher than the 1 nM dose where we observed these changes (i.e., > 0.4 micrograms/L). PFOA profiles were enriched for several transcription factors including E2F1 and E2F4, which regulate cell cycle genes and important processes in mammary development and tumor progression. Utilizing the BCScreen gene panel, we have identified mechanistic targets relevant to breast cancer that are perturbed by mammary toxicants. Citation Format: Vanessa Y. De La Rosa, Erik Lehnert, Marko Milanovic, Chris Vulpe, Ruthann A. Rudel. Targeted expression profiling identifies mechanisms of potential breast carcinogens [abstract]. In: Proceedings of the AACR Special Conference on Environmental Carcinogenesis: Potential Pathway to Cancer Prevention; 2019 Jun 22-24; Charlotte, NC. Philadelphia (PA): AACR; Can Prev Res 2020;13(7 Suppl): Abstract nr A52.

Structural variations (SV) are genomic aberrations where large sections (longer than 50 nucleotides) of a chromosome are deleted, duplicated, inserted, inverted or translocated. Accurate detection of SVs is important, as SVs are linked to both genetic diseases (autism, segmental uniparental disomy) and cancer (acting both on tumor suppressor deactivation and oncogene activation). It is therefore critical when researching these diseases that SV calls are made in a reliable and reproducible manner, however, the multitude of SV callers available makes selecting the appropriate one challenging for many researchers. Here we present a benchmarking study comparing commonly used SV callers. To do this, we first tested three commonly used SV data simulators (BAMSurgeon, VarSim and SCNVSim) and used them to create several synthetic somatic SV datasets. We then benchmarked 9 current SV callers (Manta, SvABA, novoBreak, GRIDSS, TARDIS, CNVnator, WHAM GRAPHENING, Lumpy, Delly) using both the synthetic SV datasets and real data (HG001 and HG002). Evaluation of SV calling methods was performed with a novel approach which clusters variants based on SV breakpoint proximity and breakend orientation. Additionally, we explored the potential for increasing sensitivity and precision by combining the results of multiple callers. We compared our developed method SBG Conseca SV with other available consensus methods (MetaSV, SURVIVOR and Parliament2). In this study we show that combining results of multiple callers increases overall F-score. All of the benchmarked tools are available on the NCI Cancer Genomics Cloud platform which can be accessed at www.cancergenomicscloud.com. $300 in pilot funding is available to allow researchers to run these tools on data from The Cancer Genome Atlas, or their private data.

Abstract Aberrant alternative splicing is emerging as a frequent and important process in cancer. Splice aberrations are mainly associated with missense mutations within genes encoding splice factors involved in 3′-splice site recognition. SF3B1, U2AF1, SRSF2 and ZRSR2 are the most frequently mutated genes encoding splicing factors in tumors. Interestingly, these mutations are mutually exclusive and each leads to a distinct aberrant splice pattern. Based on RNA-seq analyses of 74 primary tumors of uveal melanoma, we previously defined the aberrant splice pattern of SF3B1 hotspot mutations. In order to further characterize the catalogue of pathogenic mutations in SF3B1 that induce the aberrant splice pattern, we used a Sequence Bloom Tree (SBT) constructed from RNA-seq data for a total of 11,350 different tumors from the TCGA (Collaborative project with Seven Bridges Cancer Genome Cloud) to rapidly screen for samples consistent with the aberrant splice pattern. Based on this analysis, 75 tumors from different tissues of origin presented with the mutant-SF3B1 pattern, including 70 tumors harboring SF3B1 mutations: 48 cases of the hotspot mutations (21 tumors with SF3B1R625, 6 with SF3B1K666 and 21 with SF3B1K700) and 22 cases harbored non-hotspot SF3B1 mutations. No mutation or deletion in SF3B1 was detected in 5 tumors. Taking into account that the threshold of scores defining the significant presence of the aberrant splice pattern varies between tumor types, we proceeded to an in cellulo validation of a set of newly described mutations of SF3B1 and confirmed the induction of the aberrant splice pattern. Overall, our study highlights the potential of splice patterns as a biomarker in absence of detectable splice related mutations and sheds light on an efficient, large-scale computational approach to screen clinical cases for splice patterns. Citation Format: Samar Alsafadi, Dorine Bellanger, Michele Cornella, Erik Lehnert, Sergio Roman-Roman, Marc-Henri Stern, Tatiana Popova. Comprehensive detection and analysis of mutant-SF3B1 splice pattern [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3387.