Genetic and transcriptional evolution alters cancer cell line drug response.

Human cancer cell lines are the workhorse of cancer research. Although cell lines are known to evolve in culture, the extent of the resultant genetic and transcriptional heterogeneity and its functional consequences remain understudied. Here we use genomic analyses of 106 human cell lines grown in two laboratories to show extensive clonal diversity. Further comprehensive genomic characterization of 27 strains of the common breast cancer cell line MCF7 uncovered rapid genetic diversification. Similar results were obtained with multiple strains of 13 additional cell lines. Notably, genetic changes were associated with differential activation of gene expression programs and marked differences in cell morphology and proliferation. Barcoding experiments showed that cell line evolution occurs as a result of positive clonal selection that is highly sensitive to culture conditions. Analyses of single-cell-derived clones demonstrated that continuous instability quickly translates into heterogeneity of the cell line. When the 27 MCF7 strains were tested against 321 anti-cancer compounds, we uncovered considerably different drug responses: at least 75% of compounds that strongly inhibited some strains were completely inactive in others. This study documents the extent, origins and consequences of genetic variation within cell lines, and provides a framework for researchers to measure such variation in efforts to support maximally reproducible cancer research.

GeNets: a unified web platform for network-based genomic analyses.

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 high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals.

Understanding the principles governing mammalian gene regulation has been hampered by the difficulty in measuring in vivo binding dynamics of large numbers of transcription factors (TF) to DNA. Here, we develop a high-throughput Chromatin ImmunoPrecipitation (HT-ChIP) method to systematically map protein-DNA interactions. HT-ChIP was applied to define the dynamics of DNA binding by 25 TFs and 4 chromatin marks at 4 time-points following pathogen stimulus of dendritic cells. Analyzing over 180,000 TF-DNA interactions we find that TFs vary substantially in their temporal binding landscapes. This data suggests a model for transcription regulation whereby TF networks are hierarchically organized into cell differentiation factors, factors that bind targets prior to stimulus to prime them for induction, and factors that regulate specific gene programs. Overlaying HT-ChIP data on gene-expression dynamics shows that many TF-DNA interactions are established prior to the stimuli, predominantly at immediate-early genes, and identified specific TF ensembles that coordinately regulate gene-induction.

Genetic modifiers of EGFR dependence in non-small cell lung cancer.

Lung adenocarcinomas harboring activating mutations in the epidermal growth factor receptor (EGFR) represent a common molecular subset of non-small cell lung cancer (NSCLC) cases. EGFR mutations predict sensitivity to EGFR tyrosine kinase inhibitors (TKIs) and thus represent a dependency in NSCLCs harboring these alterations, but the genetic basis of EGFR dependence is not fully understood. Here, we applied an unbiased, ORF-based screen to identify genetic modifiers of EGFR dependence in EGFR-mutant NSCLC cells. This approach identified 18 kinase and kinase-related genes whose overexpression can substitute for EGFR in EGFR-dependent PC9 cells, and these genes include seven of nine Src family kinase genes, FGFR1, FGFR2, ITK, NTRK1, NTRK2, MOS, MST1R, and RAF1. A subset of these genes can complement loss of EGFR activity across multiple EGFR-dependent models. Unbiased gene-expression profiling of cells overexpressing EGFR bypass genes, together with targeted validation studies, reveals EGFR-independent activation of the MEK-ERK and phosphoinositide 3-kinase (PI3K)-AKT pathways. Combined inhibition of PI3K-mTOR and MEK restores EGFR dependence in cells expressing each of the 18 EGFR bypass genes. Together, these data uncover a broad spectrum of kinases capable of overcoming dependence on EGFR and underscore their convergence on the PI3K-AKT and MEK-ERK signaling axes in sustaining EGFR-independent survival.

Quantitative visualization of alternative exon expression from RNA-seq data.

MOTIVATION: Analysis of RNA sequencing (RNA-Seq) data revealed that the vast majority of human genes express multiple mRNA isoforms, produced by alternative pre-mRNA splicing and other mechanisms, and that most alternative isoforms vary in expression between human tissues. As RNA-Seq datasets grow in size, it remains challenging to visualize isoform expression across multiple samples. RESULTS: To help address this problem, we present Sashimi plots, a quantitative visualization of aligned RNA-Seq reads that enables quantitative comparison of exon usage across samples or experimental conditions. Sashimi plots can be made using the Broad Integrated Genome Viewer or with a stand-alone command line program. AVAILABILITY AND IMPLEMENTATION: Software code and documentation freely available here: http://miso.readthedocs.org/en/fastmiso/sashimi.html

Visualizing biological data-now and in the future.

Methods and tools for visualizing biological data have improved considerably over the last decades, but they are still inadequate for some high-throughput data sets. For most users, a key challenge is to benefit from the deluge of data without being overwhelmed by it. This challenge is still largely unfulfilled and will require the development of truly integrated and highly useable tools.

Leveraging electronic health records to support chronic disease management: the need for temporal data views.

BACKGROUND: The ageing population worldwide is increasingly acquiring multiple chronic diseases. The complex management of chronic diseases could be improved with electronic health records (EHRs) tailored to chronic disease care, but most EHRs in use today do not adequately support longitudinal data management. A key aspect of chronic disease management is that it takes place over long periods, but the way that most EHRs display longitudinal data makes it difficult to trend changes over time and slows providers as they review each patient's unique course. METHODS: We present five clinical scenarios illustrating longitudinal data needs in complex chronic disease management. These scenarios may function as example cases for software development. OUTPUTS: For each scenario, we describe and illustrate improvements in temporal data views. Two potential solutions are visualisation for numerical data and disease-oriented text summaries for non-numerical data. CONCLUSIONS: We believe that development and widespread implementation of improved temporal data views in EHRs will improve the efficiency and quality of chronic disease management in primary care.

Design and user experience testing of a polygenic score report: a qualitative study of prospective users.

BACKGROUND: Polygenic scores-which quantify inherited risk by integrating information from many common sites of DNA variation-may enable a tailored approach to clinical medicine. However, alongside considerable enthusiasm, we and others have highlighted a lack of standardized approaches for score disclosure. Here, we review the landscape of polygenic score reporting and describe a generalizable approach for development of a polygenic score disclosure tool for coronary artery disease. METHODS: We assembled a working group of clinicians, geneticists, data visualization specialists, and software developers. The group reviewed existing polygenic score reports and then designed a two-page mock report for coronary artery disease. We then conducted a qualitative user-experience study with this report using an interview guide focused on comprehension, experience, and attitudes. Interviews were transcribed and analyzed for themes identification to inform report revision. RESULTS: Review of nine existing polygenic score reports from commercial and academic groups demonstrated significant heterogeneity, reinforcing the need for additional efforts to study and standardize score disclosure. Using a newly developed mock score report, we conducted interviews with ten adult individuals (50% females, 70% without prior genetic testing experience, age range 20-70 years) recruited via an online platform. We identified three themes from interviews: (1) visual elements, such as color and simple graphics, enable participants to interpret, relate to, and contextualize their polygenic score, (2) word-based descriptions of risk and polygenic scores presented as percentiles were the best recognized and understood, (3) participants had varying levels of interest in understanding complex genomic information and therefore would benefit from additional resources that can adapt to their individual needs in real time. In response to user feedback, colors used for communicating risk were modified to minimize unintended color associations and odds ratios were removed. All 10 participants expressed interest in receiving a polygenic score report based on their personal genomic information. CONCLUSIONS: Our findings describe a generalizable approach to develop a polygenic score report understandable by potential patients. Although additional studies are needed across a wider spectrum of patient populations, these results are likely to inform ongoing efforts related to polygenic score disclosure within clinical practice.