Yichao Sun

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

The cBioPortal enables integration, visualization, and analysis of multidimensional cancer genomic and clinical data. The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Estradiol promotes pentose phosphate pathway addiction and cell survival via reactivation of Akt in mTORC1 hyperactive cells.

Lymphangioleiomyomatosis (LAM) is a female-predominant interstitial lung disease that can lead to respiratory failure. LAM cells typically have inactivating TSC2 mutations, leading to mTORC1 activation. The gender specificity of LAM suggests that estradiol contributes to disease development, yet the underlying pathogenic mechanisms are not completely understood. Using metabolomic profiling, we identified an estradiol-enhanced pentose phosphate pathway signature in Tsc2-deficient cells. Estradiol increased levels of cellular NADPH, decreased levels of reactive oxygen species, and enhanced cell survival under oxidative stress. Mechanistically, estradiol reactivated Akt in TSC2-deficient cells in vitro and in vivo, induced membrane translocation of glucose transporters (GLUT1 or GLUT4), and increased glucose uptake in an Akt-dependent manner. 18F-FDG-PET imaging demonstrated enhanced glucose uptake in xenograft tumors of Tsc2-deficient cells from estradiol-treated mice. Expression array study identified estradiol-enhanced transcript levels of glucose-6-phosphate dehydrogenase (G6PD), the rate-limiting enzyme of the pentose phosphate pathway. Consistent with this, G6PD was abundant in xenograft tumors and lung metastatic lesions of Tsc2-deficient cells from estradiol-treated mice. Molecular depletion of G6PD attenuated estradiol-enhanced survival in vitro, and treatment with 6-aminonicotinamide, a competitive inhibitor of G6PD, reduced lung colonization of Tsc2-deficient cells. Collectively, these data indicate that estradiol promotes glucose metabolism in mTORC1 hyperactive cells through the pentose phosphate pathway via Akt reactivation and G6PD upregulation, thereby enhancing cell survival under oxidative stress. Interestingly, a strong correlation between estrogen exposure and G6PD was also found in breast cancer cells. Targeting the pentose phosphate pathway may have therapeutic benefit for LAM and possibly other hormonally dependent neoplasms.

EVfold.org: Evolutionary Couplings and Protein 3D Structure Prediction

Recently developed maximum entropy methods infer evolutionary constraints on protein function and structure from the millions of protein sequences available in genomic databases. The EVfold web server (at EVfold.org) makes these methods available to predict functional and structural interactions in proteins. The key algorithmic development has been to disentangle direct and indirect residue-residue correlations in large multiple sequence alignments and derive direct residue-residue evolutionary couplings (EVcouplings or ECs). For proteins of unknown structure, distance constraints obtained from evolutionarily couplings between residue pairs are used to de novo predict all-atom 3D structures, often to good accuracy. Given sufficient sequence information in a protein family, this is a major advance toward solving the problem of computing the native 3D fold of proteins from sequence information alone. Availability EVfold server at http://evfold.org/ Contact evfoldtest@gmail.com Abbreviations DI direct information EC evolutionary coupling EV evolutionary MSA multiple sequence alignment PLM pseudo-likelihood maximization PPV positive predictive value (number of true positives divided by the sum of true and false positives) TM-score template modeling score

Abstract 5277: The cBioPortal for cancer genomics and its application in precision oncology

The cBioPortal for Cancer Genomics provides intuitive visualization and analysis of complex cancer genomics data. The public site (http://cbioportal.org/) is accessed by more than 1,500 researchers per day, and there are now dozens of local instances of the software that host private data sets at cancer centers around the globe. We have recently released the software under an open source license, making it free to use and modify by anybody. The software and detailed documentation are available at https://github.com/cBioPortal/cbioportal. We are now establishing a multi-institutional software development network, which will coordinate and drive the future development of the software and associated data pipelines. This group will focus on four main areas: 1. New analysis and visualization features, including: a. Improved support for cross-cancer queries and cohort comparisons. b. Enhanced clinical decision support for precision oncology, including an improved patient view with knowledge base integration, patient timelines and improved tools for visualizing tumor evolution. 2. New data pipelines, including support for new genomic data types and streamlined pipelines for TCGA and the International Cancer Genome Consortium (ICGC). 3. Software architecture and performance improvements. 4. Community engagement: Documentation, user support, and training. This coordinated effort will help to further establish the cBioPortal as the software of choice in cancer genomics research, both in academia and the pharmaceutical industry. Furthermore, as the sequencing of tumor samples has entered clinical practice, we are expanding the features of the software so that it can be used for precision medicine at cancer centers. In particular, clean, web-accessible, interactive clinical reports integrating multiple sources of genome variation and clinical annotation over time has potential to improve clinical action beyond current text-based molecular reports. By making complex genomic data easily interpretable and linking it to information about drugs and clinical trials, the cBioPortal software has the potential to facilitate the use of genomic data in clinical decision making. Citation Format: Jianjiong Gao, James Lindsay, Stuart Watt, Istemi Bahceci, Pieter Lukasse, Adam Abeshouse, Hsiao-Wei Chen, Ino de Bruijn, Benjamin Gross, Dong Li, Ritika Kundra, Zachary Heins, Jorge Reis-Filho, Onur Sumer, Yichao Sun, Jiaojiao Wang, Qingguo Wang, Hongxin Zhang, Priti Kumari, M. Furkan Sahin, Sander de Ridder, Fedde Schaeffer, Kees van Bochove, Ugur Dogrusoz, Trevor Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for cancer genomics and its application in precision oncology. [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 5277.

Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced extensive mass spectrometry based proteomics data for selected breast, colon and ovarian tumors from The Cancer Genome Atlas (TCGA). We have incorporated the CPTAC proteomics data into the cBioPotal to support easy exploration and integrative analysis of these proteomic datasets in the context of the clinical and genomics data from the same tumors. cBioPortal is an open source platform for exploring, visualizing, and analyzing multi-dimensional cancer genomics and clinical data. The public instance of the cBioPortal (http://cbioportal.org/) hosts more than 100 cancer genomics studies including all of the data from TCGA. Its biologist-friendly interface provides many rich analysis features, including a graphical summary of gene-level data across multiple platforms, correlation analysis between genes or other data types, survival analysis, and network visualization. Here, we present the integration of the CPTAC mass spectrometry based proteomics data into the cBioPortal, consisting of 77 breast, 95 colorectal, and 174 ovarian tumors that already have been profiled by TCGA for mutations, copy number alterations, gene expression, and DNA methylation. As a result, the CPTAC data can now be easily explored and analyzed in the cBioPortal in the context of clinical and genomics data. By integrating CPTAC data into cBioPortal, limitations of TCGA proteomics array data can be overcome while also providing a user-friendly web interface, a web API and an R client to query the mass spectrometry data together with genomic, epigenomic, and clinical data.

Abstract 2607: The cBioPortal for Cancer Genomics: an open source platform for accessing and interpreting complex cancer genomics data in the era of precision medicine

The cBioPortal for Cancer Genomics is an open-access portal (http://cbioportal.org) that enables interactive, exploratory analysis of large-scale cancer genomics data. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including cohort and patient-level visualization, mutation visualization, survival analysis, enrichment analysis, and network analysis. The user interface is user-friendly, responsive, and makes genomic data easily accessible to translational scientists, biologists, and clinicians. The cBioPortal is a fully open source platform. All code is available on GitHub (https://github.com/cBioPortal/) under GNU Affero GPL license. The code base is maintained by multiple groups, including Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve, an open source bioinformatics company based in the Netherlands. More than 30 academic centers as well as multiple pharmaceutical and biotech companies maintain private instances of the cBioPortal. This includes the recently launched cBioPortal instance at the NCI Genomic Data Commons (https://cbioportal.gdc.nci.nih.gov/), and two large cBioPortal instances hosting genomic and clinical data at MSK and DFCI, supporting the MSK-IMPACT and DFCI Profile projects, two of the largest clinical sequencing efforts in the world. Our multi-institutional software team has accelerated the progress of evolving the core architectural technologies and developing new features to keep pace with the rapidly advancing fields of cancer genomics and precision cancer medicine. For example, we have integrated multi-platform genomics data with extensive clinical data including patient demographics, treatment history, and survival data. We have also developed a patient-centric view that visualizes both clinical and genomic data with annotation from OncoKB knowledge base. In the next few years, the development team will focus on the following areas: (1) Implementing major architectural changes to ensure future scalability and performance. (2) New features to support precision medicine, including (i) improved integration of knowledge base annotation, (ii) enhanced visualization of patient timeline, drug response, and tumor evolution, (iii) new patient similarity metrics, (iv) improved support for immunogenomics and immunotherapy, and (v) new visualization and analysis features for understanding response to therapy. (3) New analysis and target discovery features for large cohorts, including (i) supporting user-defined virtual cohort by selecting samples from multiple studies, and (ii) comparison of genomic or clinical characteristics of two or more selected cohorts. (4) Expanding community outreach, user support and training, and documentation. Citation Format: Jianjiong Gao, Ersin Ciftci, Pichai Raman, Pieter Lukasse, Istemi Bahceci, Adam Abeshouse, Hsiao-Wei Chen, Ino de Bruijn, Benjamin Gross, Zachary Heins, Ritika Kundra, Aaron Lisman, Angelica Ochoa, Robert Sheridan, Onur Sumer, Yichao Sun, Jiaojiao Wang, Manda Wilson, Hongxin Zhang, James Xu, Andy Dufilie, Priti Kumari, James Lindsay, Anthony Cros, Karthik Kalletla, Fedde Schaeffer, Sander Tan, Sjoerd van Hagen, Jorge Reis-Filho, Kees van Bochove, Ugur Dogrusoz, Trevor Pugh, Adam Resnick, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for Cancer Genomics: an open source platform for accessing and interpreting complex cancer genomics data in the era of precision medicine [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 2607. doi:10.1158/1538-7445.AM2017-2607

Abstract 4271: The cBioPortal for Cancer Genomics as a clinical decision support tool

As sequencing of tumor samples is entering clinical practice, there is an urgent need for new tools that facilitate the interpretation of sequence data so that they can effectively inform treatment decisions. To this end, we are evolving the cBioPortal for Cancer Genomics into a clinical decision support tool. The cBioPortal is a web-based visualization and analysis engine that makes complex cancer genomics data accessible to a wide range of cancer researchers and clinicians. To transition the cBioPortal towards use in clinical practice, we have recently developed the following new functions: 1. Filtering of oncogenic mutations: By using information about the known or likely oncogenic effects of specific mutations, the portal can now filter out passenger events and highlight known and potentially druggable drivers. 2. Clinical timelines: Compact and interactive visualization of a patient9s clinical and treatment history 3. Image support: Interactive visualization of histology images 4. Support for multiple tumor samples from a single patient: The portal can display genomic differences and similarities of different samples (e.g. multiple sites of the same tumor, recurrence, metastasis, post-treatment) in the context of a patient9s treatment history. 5. Tumor clonality: Assessment of a tumor9s purity and clonality / heterogeneity 6. Targeted treatment suggestions: The portal matches drugs to drug targets that are altered in the patient9s tumor. 7. Clinical trial matching: Based on a patient9s clinical history and the genomic alterations found in the tumor, the cBioPortal will identify relevant clinical trials. By making complex genomic data easily interpretable and linking it to information about drugs and clinical trials, the system has the potential to facilitate the proper use of genomic data in clinical decision making. We are constantly improving the software and will initially focus most strongly on the matching of patients to clinical trials. We are sharing the software with other hospitals, where it can be used by oncologists and tumor boards. Citation Format: JianJiong Gao, B. Arman Aksoy, Benjamin Gross, Gideon Dresdner, Yichao Sun, S. Onur Sumer, Chris Sander, Nikolaus Schultz. The cBioPortal for Cancer Genomics as a clinical decision support tool. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4271. doi:10.1158/1538-7445.AM2014-4271