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Supplementary figure legends. Supplementary methods, including additional filtering and heuristics for sensitivity data processing and ACME analysis and additional methods for western blotting and immunostaining. Supplementary Table S8. Small molecules used in validation studies. Supplementary Table S9. CCLs used in the validation studies. Supplementary Table S10. Clarification of growth media. Supplementary Table S11. Significant synergistic and antagonistic combinations in LS513 cells.

Supplemental Table S1. Small-molecule Informer Set Description of the small-molecule informer set used in the sensitivity profiling experiment, including protein target or activity. Supplemental Table S2.Cancer cell-line panel Description of the cancer cell lines profiled in this experiment; for a clarification of growth media compositions, see Supplemental Table S10. Supplemental Table S3. Area-under-sensitivity-curve values Area-under-sensitivity-curve (AUC) values for each compound-cell line pair using the indices provided in Supplemental Table S1 and Supplemental Table S2. Supplemental Table S4. Compound target annotations. Compound target annotations used for ACME analysis of the compound dendrogram. Supplemental Table S5. Cellular feature annotations Cellular feature annotations used for ACME analysis of the cell-line dendrogram. Supplemental Table S6. Cell-line mutation annotations Mutation annotations of the cancer cell lines used in ACME analysis of the cell-line dendrogram. Supplemental Table S7. ACME results table Results of ACME analysis of sensitivity profiling data.

Supplemental Figure S1. Further characterization of the small-molecule Informer Set and comparisons between CTRP v1 and v2. Supplemental Figure S2. Cellular features of the CCL panel. Supplemental Figure S3. Details on ACME analysis described in this paper. Supplemental Figure S4. ACME identifies clinically relevant associations between small-molecule sensitivity and cancer cell features. Supplemental Figure S5. Using ACME to investigate small-molecule mechanism of action. Supplemental Figure S6. Microtubule regrowth assay in NCIH661 cells. Supplemental Figure S7. Combined IGF1R and ALK inhibition in ALK overexpressed neuroblastoma. Supplemental Figure S8. Using ACME to inform combination screening.

Over the last two decades, research in biology has become increasingly demanding of compute resources. For example, gene expression analysis has evolved from single gene assays containing a small number of values, to single-cell RNA sequencing experiments producing matrices representing millions of individual cells, each with tens of thousands of transcript expression values. Biological researchers with many years of study in their fields now need access to high performance compute (HPC) clusters to analyze their data. However the use and programming of HPC systems is a specialized skill that requires training outside the scope of what most biologists receive. GenePattern, www.genepattern.org, is a gateway providing implicit access to HPC systems for biologists and other non-programming scientists. It includes access to hundreds of tools for the analysis and visualization of multiple genomic data types. GenePattern has a web-based interface to provide easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research. In recognition of the success of the electronic notebook, we have also released the GenePattern Notebook Environment, which extends the power of GenePattern in a Jupyter-based environment. The publicly available GenePattern servers currently support thousands of users and up to tens of thousands of analyses each month. To handle the scope and scale of modern genomic analyses, the GenePattern gateway uses a cloud hybrid model where analyses are distributed to public cloud HPC systems such as AWS Batch as well as academic HPC clusters such as the Expanse cluster at the San Diego Supercomputer Center. Management of jobs and the distribution of jobs to HPC are handled by the GenePattern server, allowing the biologists to focus on their data and its analysis. To manage configuration of the analyses across disparate HPC systems, GenePattern has embraced container-based systems. This has allowed GenePattern to support over 250 different analyses written in multiple programming languages. Here again GenePattern uses a hybrid model where different container systems including Docker and Singularity can be used, and in some case

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. Still, inferring biological processes requires additional post hoc statistics and annotation for interpretation of features learned from software packages developed for NMF implementation. Here, we aim to introduce a suite of computational tools that implement NMF and provide methods for accurate, clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations, and open questions in the field is followed by three vignettes for the Bayesian NMF algorithm CoGAPS (Coordinated Gene Activity across Pattern Subsets). Each vignette will demonstrate NMF analysis to quantify cell state transitions in public domain single-cell RNA-sequencing (scRNA-seq) data of malignant epithelial cells in 25 pancreatic ductal adenocarcinoma (PDAC) tumors and 11 control samples. The first uses PyCoGAPS, our new Python interface for CoGAPS that we developed to enhance runtime of Bayesian NMF for large datasets. The second vignette steps through the same analysis using our R CoGAPS interface, and the third introduces two new cloud-based, plug-and-play options for running CoGAPS using GenePattern Notebook and Docker. By providing Python support, cloud-based computing options, and relevant example workflows, we facilitate user-friendly interpretation and implementation of NMF for single-cell analyses.

Summary With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab., Graphical abstract, Highlights • Analysis of omics data from different spaceflight studies presents unique challenges • A standardized pipeline for RNA-seq analysis eliminates data processing variation • The GeneLab RNA-seq pipeline includes QC, trimming, mapping, quantification, and DGE • Space-relevant data processed with this pipeline are available at genelab.nasa.gov, Omics; Space Sciences

As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The electronic analysis notebook has recently emerged as a versatile tool for this purpose, allowing scientists to combine the scientific exposition with the code that runs the analysis, creating a single “research narrative” document. The Jupyter Notebook system has become the de facto standard notebook environment in data science and genomic analysis. However, the Jupyter environment requires familiarity with programming to run analyses, and even text must be formatted using a programming-style language.To extend notebook capabilities to researchers at all levels of programming expertise, we developed the GenePattern Notebook environment, which integrates Jupyter with the hundreds of genomic tools available through the GenePattern platform. This tool allows scientists to develop, share, collaborate on, and publish their notebooks, requiring only a web browser. Investigators can design their in-silico experiments, refine workflows, launch compute-intensive analyses on cloud-based and high-performance compute resources, and publish results that others can adopt to reproduce the original analyses and modify for their own work.GenePattern Notebook provides: (1) Access to a wide range of genomic analyses within a notebook. Hundreds of analyses are available, from machine learning techniques such as clustering, classification, and dimension reduction, to omic-specific methods for gene expression analysis, proteomics, flow cytometry, sequence variation analysis, pathway analysis, and others. (2) A library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. To support the growing role of single-cell analysis, we have recently released single-cell RNA-seq preprocessing, cluster harmonization, pseudotime, and RNA velocity notebooks. (3) Notebook enhancements. A rich text editor allows scientists to format text as they would in a word processor. A user interface-building tool allows notebook developers to wrap code so it is displayed as a web form, with only the necessary inputs exposed. (4) Publication and collaborative editing. Authors can publish their notebooks, where they are then made available on the community section of the notebook workspace, where other scientists may copy, run, and edit their own version.The GenePattern Notebook environment is freely available at http://genepattern-notebook.org. Citation Format: Michael M. Reich, Thorin Tabor, Edwin Juarez, Alexander Wenzel, Ted Liefeld, Barbara Hill, David Eby, Forrest Kim, Helga Thorvaldsdóttir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebook: An integrative analytical environment for cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1903.

The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data. Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes. RESULTS To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms. CONCLUSION Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data. journal article 2020 May imported Supported by the National Cancer Institute (NCI) Informatics Technology for Cancer Research (R21CA209940 [O.G., T.F.B., J.P.M., N.P.], U01CA214846 [V.C.], U01CA214846 Collaborative Set-aside [O.G., A.M.K., V.C., N.P.], U24CA194107 [J.P.M.], U24CA220341 [J.P.M.], U24CA180922 [B.J.H., N.P., A. Regev]), NCI (R01CA215072 [A.M.K.], U01CA217851 [O.G.], U01CA199241 [O.G.], Stanford CTD2 [O.G.]), National Institute of Allergy and Infectious Diseases (R03AI131066 [T.F.B., N.P.]), and National Institute of Biomedical Imaging and Bioengineering (R01EB020527 [O.G.]). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Summary Here, we describe a protocol combining functional metrics with genomic data to elucidate drivers of within-cell-type heterogeneity via the phenotype-to-genotype link. This technique involves using fluorescence tagging to label and isolate cells grown in 3D culture, enabling high-throughput enrichment of phenotypically defined cell subpopulations by fluorescence-activated cell sorting. We then perform a validated phenotypically supervised single-cell analysis pipeline to reveal unique functional cell states, including genes and pathways that contribute to cellular heterogeneity and were undetectable by unsupervised analysis. For complete details on the use and execution of this protocol, please refer to Chen et al. (2020)., Graphical abstract, Highlights • Sorting of cells by the phenotype from 3D culture is achieved through photoconversion • The photolabeling technique is adaptable to other systems, cells, and phenotypes • Phenotypically supervised analysis reveals novel insights into cellular heterogeneity • A GenePattern notebook facilitates phenotypically supervised scRNAseq analysis, Here, we describe a protocol combining functional metrics with genomic data to elucidate drivers of within-cell-type heterogeneity via the phenotype-to-genotype link. This technique involves using fluorescence tagging to label and isolate cells grown in 3D culture, enabling high-throughput enrichment of phenotypically defined cell subpopulations by fluorescence-activated cell sorting. We then perform a validated phenotypically supervised single-cell analysis pipeline to reveal unique functional cell states, including genes and pathways that contribute to cellular heterogeneity and were undetectable by unsupervised analysis.

As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The electronic analysis notebook has recently emerged as an effective and versatile tool for this purpose, allowing scientists to combine the scientific exposition – text, images, and multimedia – with the actual code that runs the analysis, creating a single “research narrative” document. The Jupyter Notebook system has become the de facto standard notebook environment in data science and genomic analysis. However, the Jupyter environment requires familiarity with a programming language to run analyses, and even text must be formatted using a programming-style language. To extend notebook capabilities to the needs of researchers at all levels of programming expertise, we developed the GenePattern Notebook environment, which integrates Jupyter's capabilities with the hundreds of genomic tools available through the GenePattern platform. This tool allows scientists to develop, share, collaborate on, and publish their notebooks, requiring only a web browser. In this environment, investigators can design their in-silico experiments, perform and refine analyses, launch compute-intensive analyses on cloud-based and high-performance compute resources, and publish their results as electronic notebooks that other scientists can adopt to reproduce the original analyses and modify for their own work. GenePattern Notebook provides: (1) Access to a wide range of genomic analyses within a notebook. Hundreds of analyses are available, from machine learning techniques such as clustering, classification, and dimension reduction, to omic-specific methods for gene expression analysis, proteomics, flow cytometry, sequence variation analysis, pathway analysis, and others. (2) A library of featured genomic analysis notebooks is provided. These include templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. (3) Notebook enhancements. A rich text editor allows scientists to enter and format text as they would in a word processor. A user interface-building tool allows notebook developers to wrap their code so it is displayed as a web form, with only the necessary inputs exposed. Users of the notebook are presented with a simplified display that allows them to run the analyses without needing to interact with the code behind them. (4) Publication and collaborative editing. To make a notebook public, an author selects the “publish” feature and adds descriptive information. The notebook is then made available on the community section of the notebook workspace. An author can include a web link to a public notebook in a publication, and users who follow the link will see a read-only version of the notebook, with the option to log in to the workspace, where they can run, copy, and edit their own version. The GenePattern Notebook environment is freely available at http://genepattern-notebook.org. Citation Format: Michael M. Reich, Thorin Tabor, Ted Liefeld, Edwin Juarez, Barbara Hill, Helga Thorvaldsdottir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebook: An integrative analytical environment for cancer research [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3207.

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets.

A major bottleneck in network-based analyses of genomic data is quantitatively comparing biological signal in different networks and to identifying the optimal network dataset to answer a particular biological question. Towards these aims, we developed a unified web platform 9Broad Institute Web Platform for Genome Networks (GeNets)9, where users can compare biological signal of networks, and execute, store, and share network analyses. We designed a machine learningmachine-learning algorithm (Quack) which), which uses topological features to can quantify the overall and pathway-specific biological signals in networks, thus enabling users to choose the optimal network dataset for their analyses. We illustrated a typical workflow using GeNets to identify interesting autism candidate genes in the network that, when compared to four other networks, best recapitulates established neurodevelopmental pathway information. GeNets is a scalable, general and uniquely enabling computational framework for analyzing, managing and sharing analyses of genetic datasets using heterogeneous functional genomics networks, for example, from single-cell transcriptional analyses.

Integrative analysis of multiple data types to address complex biomedical questions requires the use of multiple software tools in concert and remains an enormous challenge for most of the biomedical research community. Here we introduce GenomeSpace (http://www.genomespace.org), a cloud-based, cooperative community resource. Seeded as a collaboration of six of the most popular genomics analysis tools, GenomeSpace now supports the streamlined interaction of 20 bioinformatics tools and data resources. To facilitate the ability of non-programming users’ to leverage GenomeSpace in integrative analysis, it offers a growing set of ‘recipes’, short workflows involving a few tools and steps to guide investigators through high utility analysis tasks.

Summary Interactive analysis notebook environments promise to streamline genomics research through interleaving text, multimedia, and executable code into unified, sharable, reproducible "research narratives." However, current notebook systems require programming knowledge, limiting their wider adoption by the research community. We have developed the GenePattern Notebook environment (http://www.genepattern-notebook.org), to our knowledge the first system to integrate the dynamic capabilities of notebook systems with an investigator-focused, easy-to-use interface that provides access to hundreds of genomic tools without the need to write code.

As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, the need has become more urgent for a software environment that supports the entire “idea to dissemination” cycle of an integrative cancer genomics analysis. Such a system would need to provide access to a large number of analysis tools without the need for programming, be sufficiently flexible to accommodate the practices of non-programming biologists as well as experienced bioinformaticians, and would provide a way for researchers to encapsulate their work into a single executable document including not only the analytical workflow but also the associated descriptive text, graphics, and supporting research. To address these needs, we have developed GenePattern Notebook, based on the GenePattern environment for integrative genomics and the Jupyter Notebook system. GenePattern Notebook unites the phases of in silico research - experiment design, analysis, and publication - into a single interface. GenePattern Notebook presents a familiar lab notebook format that allows researchers to build a record of their work by creating cells containing text, graphics, and executable analyses. Researchers add, delete, and modify cells as the research evolves, supporting the initial research phases of prototyping and collaborative analysis. When an analysis is ready for publication, the same document that was used in the design and analysis phases becomes a research narrative that interleaves text, graphics, data, and executable analyses. The online notebook format allows researchers to explain the analytical and scientific considerations of each step in any level of detail, promoting reproducibility and adoption. GenePattern Notebook features are designed to help nonprogramming users. We have developed additional cell types allowing users to select analyses, specify inputs, navigate results, send result files to new analyses, and create richly formatted text, all without the need for programming. The GenePattern Notebook Environment is also a platform for open science and reproducible research. Authors can elect to publish a notebook, making it visible to all users, who can then create a copy and use it to reproduce the author’s results or adapt it to their own work. The repository provides links to public notebooks that can be used in a publication as permalinks. Researchers can invite collaborators to work together on a notebook prior to publication. A free, cloud-based GenePattern Notebook workspace is available at http://www.genepattern-notebook.org, where researchers can develop, share, and publish notebook documents. We have provided a collection of template notebooks that walk users through various genomic and machine learning analyses, and are collaborating with research laboratories to create integrative cancer genomics notebooks. Note: This abstract was not presented at the meeting. Citation Format: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov. The GenePattern Notebook environment for reproducible cancer research [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 5106.

SummaryThe high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene β-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage.

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, the need has become more urgent for a software environment that supports the entire “idea to dissemination” cycle of an integrative cancer genomics analysis. Such a system would need to provide access to a large number of analysis tools without the need for programming, be sufficiently flexible to accommodate the practices of non-programming biologists as well as experienced bioinformaticians, and would provide a way for researchers to encapsulate their work into a single “executable document” including not only the analytical workflow but also the associated descriptive text, graphics, and supporting research. To address these needs, we have developed GenePattern Notebook, based on the GenePattern environment for integrative genomics and the Jupyter Notebook system. GenePattern Notebook unites the phases of in silico research - experiment design, analysis, and publication - into a single interface. GenePattern Notebook presents a familiar lab notebook format that allows researchers to build a record of their work by creating “cells” containing text, graphics, and executable analyses. Researchers add, delete, and modify cells as the research evolves, supporting the initial research phases of prototyping and collaborative analysis. When an analysis is ready for publication, the same document that was used in the design and analysis phases becomes a research narrative that interleaves text, graphics, data, and executable analyses. The online notebook format allows researchers to explain the analytical and scientific considerations of each step in any level of detail, promoting reproducibility and adoption. Notebooks can also be shared between researchers for collaborative development. GenePattern Notebook features are designed to help nonprogramming users create and adapt notebooks. We have developed additional cell types allowing users to choose analyses, specify inputs, navigate results, send result files to new analyses, and create richly formatted text, all without the need for programming. A free online GenePattern Notebook workspace is available at http://www.genepattern-notebook.org, where researchers can develop, share, and publish notebook documents. We have provided a collection of template notebooks that walk users through various genomic and machine learning analyses, and are collaborating with cancer research laboratories to create integrative cancer genomics notebooks. Citation Format: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov. The GenePattern Notebook environment for reproducible cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2279.

Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with ~19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters, and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.

Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2). Significance: We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses. Cancer Discov; 5(11); 1210–23. ©2015 AACR. See related commentary by Gray and Mills, p. 1130. This article is highlighted in the In This Issue feature, p. 1111

The World-Wide Web provides a globally distributed communication framework that is essential for almost all scientific collaboration, including bioinformatics. However, several limits and inadequacies have become apparent, one of which is the inability to programmatically identify locally named objects that may be widely distributed over the network. This shortcoming limits our ability to integrate multiple knowledgebases, each of which gives partial information of a shared domain, as is commonly seen in bioinformatics. The Life Science Identifier (LSID) and LSID Resolution System (LSRS) provide simple and elegant solutions to this problem, based on the extension of existing internet technologies. LSID and LSRS are consistent with next-generation semantic web and semantic grid approaches. This article describes the syntax, operations, infrastructure compatibility considerations, use cases and potential future applications of LSID and LSRS. We see the adoption of these methods as important steps toward simpler, more elegant and more reliable integration of the world's biological knowledgebases, and as facilitating stronger global collaboration in biology.

As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, the need has become more urgent for a software environment that supports the entire “idea to dissemination” cycle of an integrative cancer genomics analysis. Such a system would need to provide access to a large number of analysis tools without the need for programming, be sufficiently flexible to accommodate the practices of non-programming biologists as well as experienced bioinformaticians, and would provide a way for researchers to encapsulate their work into a single “executable document” including not only the analytical workflow but also the associated descriptive text, graphics, and supporting research. To address these needs, we have developed GenePattern Notebook, based on the GenePattern environment for integrative genomics and the Jupyter Notebook system. GenePattern Notebook unites the phases of in silico research – experiment design, collaborative analysis, and publication – into a single interface. GenePattern Notebook presents a familiar lab-notebook format that allows researchers to build a record of their work by creating “cells” containing text, graphics, or executable analyses. Researchers add, delete, and modify cells as the research evolves, supporting the initial research phases of prototyping and collaborative analysis. When an analysis is ready for publication, the same document that was used in the design and analysis phases becomes a research narrative that interleaves text, graphics, data, and executable analyses, serving as the complete, reproducible, in silico methods section for a publication. GenePattern Notebook features are designed to make it easy for nonprogramming users to create and adapt notebooks. We have developed new cell types that allow users to choose analyses, specify input parameters and datasets, navigate results, send result files to new analyses, and create richly formatted text, all without the need for programming. We have released a freely available online GenePattern Notebook workspace, http://notebook.genepattern.org, where researchers can develop and publish notebook documents. We have provided a collection of template notebooks that walk users through various machine learning analyses, and are collaborating with cancer research laboratories to create integrative cancer genomics notebooks as well. Notebook topics in development include characterization of intratumoral heterogeneity from single cell RNA-Seq data, effective clinical interpretation of comprehensive genomic profiling from whole exome sequencing of a patient’s tumor and germ line samples, and identification of master regulators/transcription factors associated with the downstream transcriptional effects associated with the activation of an oncogene. Citation Format: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov. GenePattern Notebook: an environment for reproducible cancer research [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 2588. doi:10.1158/1538-7445.AM2017-2588

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.

Modern genomic analysis often requires workflows incorporating multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows between them. One such tool is Cytoscape 3, a popular application that enables analysis and visualization of graph-oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over 850 times since the release of its first version in September, 2013.

Background Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research. Results In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines. Conclusions GenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.

Multiple myeloma is an incurable malignancy of plasma cells, and its pathogenesis is poorly understood. Here we report the massively parallel sequencing of 38 tumour genomes and their comparison to matched normal DNAs. Several new and unexpected oncogenic mechanisms were suggested by the pattern of somatic mutation across the data set. These include the mutation of genes involved in protein translation (seen in nearly half of the patients), genes involved in histone methylation, and genes involved in blood coagulation. In addition, a broader than anticipated role of NF-κB signalling was indicated by mutations in 11 members of the NF-κB pathway. Of potential immediate clinical relevance, activating mutations of the kinase BRAF were observed in 4% of patients, suggesting the evaluation of BRAF inhibitors in multiple myeloma clinical trials. These results indicate that cancer genome sequencing of large collections of samples will yield new insights into cancer not anticipated by existing knowledge., Multiple Myeloma Research Foundation

available in PMC 2010 August 18., A powerful way to discover key genes with causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here we present high-resolution analyses of somatic copy-number alterations (SCNAs) from 3,131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across several cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-κΒ pathway. We show that cancer cells containing amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend on the expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in several cancer types., National Institutes of Health (U.S.) (Dana-Farber/Harvard Cancer Center and Pacific Northwest Prostate Cancer SPOREs, P50CA90578), National Institutes of Health (U.S.) (Dana-Farber/Harvard Cancer Center and Pacific Northwest Prostate Cancer SPOREs, R01CA109038)), National Institutes of Health (U.S.) (Dana-Farber/Harvard Cancer Center and Pacific Northwest Prostate Cancer SPOREs, R01CA109467), National Institutes of Health (U.S.) (Dana-Farber/Harvard Cancer Center and Pacific Northwest Prostate Cancer SPOREs, P01CA085859), National Institutes of Health (U.S.) (Dana-Farber/Harvard Cancer Center and Pacific Northwest Prostate Cancer SPOREs, P01CA 098101), National Institutes of Health (U.S.) (Dana-Farber/Harvard Cancer Center and Pacific Northwest Prostate Cancer SPOREs, K08CA122833)

Since the Life Science Identifier (LSID) data identification and access standard made its official debut in late 2004, several organizations have begun to use LSIDs to simplify the methods used to uniquely name, reference and retrieve distributed data objects and concepts. In this review, the authors build on introductory work that describes the LSID standard by documenting how five early adopters have incorporated the standard into their technology infrastructure and by outlining several common misconceptions and difficulties related to LSID use, including the impact of the byte identity requirement for LSID-identified objects and the opacity recommendation for use of the LSID syntax. The review describes several shortcomings of the LSID standard, such as the lack of a specific metadata standard, along with solutions that could be addressed in future revisions of the specification.

Comprehensive genomic characterization of cancer is proceeding at a rapidly accelerating pace, mainly due to the expanded use of massively parallel sequencing. Despite the promise of cancer genomics, many cancer drugs still fail in the clinic due to nonresponsive patients and this translates into a significant unmet medical need. Accurate predictions of which patients are more likely to respond to drugs in development could speed clinical trials and personalize treatments. Here we propose the use of a compendium of experimentally tractable cancer model systems, ∼1000 human genomically-annotated cancer cell lines (at the level of gene expression, DNA copy number alterations and mutations), coupled with pharmacological profiling, to systematically link genetic and transcriptional features to drug response. This resource, the Cancer Cell Line Encyclopedia (CCLE), is available online at www.broadinstitute.org/ccle. Through computational predictive modeling we have both rediscovered molecular features that predict response to several drugs and also uncovered a number of novel potential biomarkers of sensitivity and resistance to targeted agents and chemotherapy drugs. For instance, we have found that response to topoisomerase 1 inhibitors seem to be driven by expression of a single gene. We have also observed that tissue lineage is a key predictor for sensitivity to certain compounds, providing rationale for clinical trials of these drugs in particular cancer types. Our cell line-based platform provides a valuable tool for the development of personalized cancer medicine, revealing critical tumor dependencies and helping to stratify patients for clinical trials. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5455. doi:10.1158/1538-7445.AM2011-5455

The Cancer Cell Line Encyclopedia represents a collaborative effort to assemble a comprehensive resource of human cancer models for basic and translational research. The CCLE aims to contain high-density SNP microarray data, gene expression microarray data and selected cancer gene mutation data for approximately 1000 human cancer cell lines spanning many tumor types. In addition, we are assessing sensitivity of some of these cell lines using a series of pharmacological compounds that represent both conventional cytotoxic and targeted agents. Another goal of the CCLE collaboration involves systematic integration of the genomic and pharmacologic datasets in order to identify putative targets of prevalent genetic alterations as well as predictors of pharmacologic sensitivity and resistance. The availability of high-quality data across hundreds of cell lines markedly enhances the statistical power to discover genetic alterations involved in carcinogenesis and molecular predictors of pharmacologic vulnerability. We are assembling systematic algorithms that identify genetic predictors of sensitivity or resistance to particular pharmacological compounds. Toward this end, we integrated a sensitivity dataset for 28 compounds profiled against more than 500 cell lines with all genomic data available in the CCLE. Gene expression, DNA copy-number and loss of heterozygosity values were combined with critical oncogene mutations and genotype information as inputs to multifaceted prediction models for pharmacological sensitivity, the accuracies of which were assessed using cross-validations. Two complementary paths were followed in order to predict for the sensitivity of cancer cell lines to pharmacological compounds. In a categorical classification-based model, we have used the Naïve Bayes algorithm to find the most significant features predictive of the sensitive or resistant status of the cell lines to each of the compounds. A second path that we followed is a regression-based machine learning approach, called the Elastic Net, where the goal is to predict a continuous value representing the sensitivity of each cell line, such as the GI50. For both approaches, we show examples of the results that we get for some compounds in the collection, such as AZD6244 (MEK), PHA-665752 (MET), Nutlin-3 (MDM2). We detail the performances of the prediction models as well as the most significant genetic determinants of sensitivity to these inhibitors. Several previously unappreciated genomic predictors of response or intrinsic resistance to targeted anticancer agents have been identified. Our results suggest that this integrative approach applied to a robust cancer cell line collection such as the CCLE has considerable power to discover novel associations that augment ongoing basic research into cancer biology and drug discovery. Citation Information: Clin Cancer Res 2010;16(14 Suppl):PR4.

The Cancer Cell Line Encyclopedia (CCLE) represents a collaborative effort to assemble a comprehensive resource of human cancer models for basic and translational research. Thus far, the CCLE contains high-density SNP array data, gene expression microarray data and selected cancer gene mutation data for approximately 1,000 human cancer cell lines spanning many tumor types. Additionally, we are assessing the sensitivity of these same cell lines using a series of pharmacological compounds that represent both conventional cytotoxic and targeted agents. Another goal of the CCLE collaboration involves systematic integration of the genomic and pharmacologic datasets in order to identify putative targets of prevalent genetic alterations as well as predictors and modifiers of pharmacologic sensitivity and resistance. The availability of high-quality data generated by uniform criteria across hundreds of cell lines markedly enhances the statistical power to discover genetic alterations involved in carcinogenesis and molecular predictors of pharmacologic vulnerability. As proof of principle, we have carried out systematic nomination of putative targets of genetic alterations using integrative analyses. Here, significant regions of genomic gains and losses have been linked to expression and mutation data to find significant correlations at both single-gene and pathway levels. We have also begun to assemble systematic algorithms that identify genetic predictors of sensitivity or resistance to particular pharmacological compounds, taking advantage of the fact that the CCLE is a comprehensive resource with extensive genomic characterization. Toward this end, we integrated a preliminary sensitivity dataset for 28 compounds accurately profiled against more than 400 cell lines with all genomic data available in the CCLE. To enhance the robustness of our method, we reduced the number of significant genomic features for each cell line to a number that allows properly determined prediction of sensitivity. Expression data was converted to cell line-specific readouts of gene set expression; and DNA gains and losses are reduced to statistically significant regions using the GISTIC algorithm. These values were combined with critical oncogene mutations as inputs to a multifaceted prediction model for pharmacological sensitivity, the accuracy of which was assessed using cross-validation. Our results suggest that this integrative approach applied to a robust cancer cell line collection has considerable power to discover novel associations that augment ongoing basic research into cancer biology and drug discovery. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 105.

Cancer genome characterization efforts such as The Cancer Genome Atlas project are rapidly improving our knowledge of tumor genetic alterations. With the expanded use of massively parallel sequencing, the catalogue of known genetic alterations in cancer is expected to expand at an accelerating rate. In this context, the emphasis is shifting towards systematic identification of the genes and pathways targeted by recurrent genetic alterations, their functional impact in tumor biology, and the resulting cellular dependencies that might be exploited therapeutically. Anticipating the need for a companion resource to systematically probe tumor biology armed with cancer genomics knowledge, we have assembled a compendium of experimentally tractable cancer model systems consisting of ∼1000 human cancer cell lines and performed extensive genomic analysis (at the level of gene expression, DNA copy number and mutations) coupled with pharmacological profiling. This resource, which we call the Cancer Cell Line Encyclopedia (CCLE), is being used not only to identify the putative targets of prevalent genetic alterations, but also to systematically link the presence or absence of certain genetic alterations to drug sensitivity or resistance. To date, we have identified several previously unappreciated genomic predictors of response or intrinsic resistance to targeted anticancer agents. For instance, through integrative analysis, we have discovered additional mechanisms that may underlie sensitivity to MET inhibitors, beyond amplification of the MET receptor, highlighting the fact that response prediction in the clinic may require assessment of multiple variables. We have also broadened the potential relevance of known predictive biomarkers that might provide a rationale for future genotype-driven clinical trials. As an example, we have expanded on existing knowledge of resistance to receptor tyrosine kinase (RTK) inhibitors, showing that the presence of RAS mutations may predict lack of response to a broad spectrum of RTK inhibitors in addition to EGFR inhibitors. This work demonstrates that pharmacological profiling of large, genomically-annotated cancer model systems may uncover new tumor dependencies as well as positive and negative predictors of drug response. The results of this study are being made publicly available at a CCLE online portal, with the hope they will become a valuable resource for the cancer community to propel translation of the knowledge generated through in vitro integrative genomics into personalized cancer medicine. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2620.

Current integrative genomics projects are enabling new discoveries in cancer research through the ability to combine multiple modalities of data, e.g. gene expression, copy number, RNAi, exon resequencing, and epigenetics. However, the tools to access and analyze this data have traditionally been out of the reach of clinicians and research biologists, due to the widely distributed nature of the available data and the significant learning curve required to use the analytical tools. This problem is particularly relevant to the multiple myeloma research community because of the heterogeneity of genomic aberrations underlying this disease and lack of a central repository of multi-modal multiple myeloma data. To address these problems, the Broad Institute has created a pilot Multiple Myeloma Genomics Portal, http://www.broad.mit.edu/mmgp, which serves as an interface between biologists (and clinical investigators), analytical tools, and multiple myeloma datasets. The Portal provides access to a number of advanced gene expression, gene set enrichment, and copy number analyses and visualizations within an easy to use Web interface. These analyses can be performed on the datasets hosted on the Portal, which include previously published curated, high quality genomic multiple myeloma datasets as well as a new reference collection of multiple myeloma samples. The Portal is continuously updated with new datasets, data types, and analytical capabilities as they become available. The Portal’s accessibility allows it to serve as a significant venue for investigators from other fields, engaging a broader range of investigators in exploring these data, and its design is readily adaptable to integrative genomics studies of other cancer types.

The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative is a three-year program to analyze tumor tissue from hundreds of multiple myeloma (MM) patients via gene expression profiling (GEP), comparative genomic hybridization (aCGH), and exon re-sequencing. In addition, RNAi knockdown of selected genes in MM tumor cell lines is being evaluated to identify potential new targets. All genomic data generated is scheduled for placement in an open-access Multiple Myeloma Genomics Portal pre-publication and in near real-time (www.broad.mit.edu/mmgp). Additionally, samples are also destined for drug validation and correlative science on clinical protocols as this study moves forward. This comprehensive project is spearheaded by the MMRC and conducted via collaboration with the Eli and Edythe L. Broad Institute of MIT and Harvard, the Translational Genomics Research Institute (TGen), Mayo Clinic Arizona, and The Dana-Farber Cancer Center. The study is supported by the collection from member institutions of the MMRC of bone marrow aspirates and matched peripheral blood samples from over 1000 patients. Specific genomic technologies that are currently being employed across this sample set include GEP using Affymetrix Human Genome U133A 2.0 Plus Arrays, and, in parallel, efforts to identify regions of genomic gain and loss are using Agilent Human Genome CGH arrays. In contrast to other large-scale genomic projects based on exon-sequencing of targeted gene sets, this project will be the first to perform genome-scale single molecule sequencing (SMS) of DNA from patient specimens. Results will be targeted against candidate classes of genes (e.g. kinases, phosphatases, known oncogenes and tumor suppressors), and genes from GEP or within candidate regions of copy gain or loss identified by the aCGH experiments. Mutations will be further validated in an independent set of patient specimens. Finally we will attempt to identify points of vulnerability of MM through systematic loss-of-function screens in myeloma cell lines using high-throughput RNA interference (using both shRNA and siRNA platforms). Importantly, data generated from this genomics initiative will ultimately be made public pre-publication through the established MMRC Multiple Myeloma Genomics Portal. Data from all aspects of this project (sample collection and analyte isolation, GEP, aCGH, SMS, RNAi and bioinformatics) will be described in this presentation. The power of this study is the comprehensive collection of gene expression, CGH, and genome sequencing on a single reference set of clinically annotated samples. The addition of RNAi screens makes this a very important and unique data resource, which we hope will help expedite the discovery of novel targeted agents for MM scientific community.