The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.
ABSTRACT The analysis of ‘omic data depends heavily on machine-readable information about protein interactions, modifications, and activities. Key resources include protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. Software systems that read primary literature can potentially extend and update such resources while reducing the burden on human curators, but machine-reading software systems have a high error rate. Here we describe an approach to precisely assemble molecular mechanisms at scale using natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies overlaps and redundancies in information extracted from published papers and pathway databases and uses probability models to reduce machine reading errors. INDRA enables the automated creation of high-quality, non-redundant corpora for use in data analysis and causal modeling. We demonstrate the use of INDRA in extending protein-protein interaction databases and explaining co-dependencies in the Cancer Dependency Map.
Abstract Characterizing the molecular effects of targeted therapies is an important step towards understanding and predicting drug efficacy in cancer. In this work, we used the L1000 assay developed at the Broad Institute to measure the transcriptional response of six breast cancer cell lines to more than 100 different targeted drugs, many of whom are in clinical trials. We focused on inhibitors targeting the most important signaling kinases such as PI3K, AKT or MAPK, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs). With two time points and six doses, the dataset contains more than 8000 unique perturbations. We clustered the perturbations that elicited a significant response (37% of measured perturbations when using p<0.05) according to their gene expression profile and obtained 23 clusters. The perturbation significance was correlated with inhibitor dose, but no cluster was biased toward a particular concentration. Clusters were generally time point specific: the transcriptional responses at 3 hours differed significantly from the 24 hour ones. Some clusters contained perturbations from multiple cell lines whereas others were cell line specific. In particular, responses to CDK inhibitors were similar across most cell lines and showed a down-regulation of genes related to the cell cycle. On the other hand, cell lines responded differently to PI3K/AKT and MAPK inhibitors as illustrated by clusters specific to each cell line and pathway. Interestingly, the perturbations induced by RTK (e.g. EGFR, MET, ALK) and non-RTK (e.g. SRC, ABL, BTK) inhibitors, clustered with either the PI3K/AKT or the MAPK inhibitors depending on the cell line. Thus the transcriptional response allowed us to identify differences in pathway connectivity between cell lines, in particular which RTK connects to the PI3K/AKT pathway or the MAPK one. In parallel to the transcriptional response, we measured the growth inhibition after three days of treatment. We found diverse phenotypic responses that are not necessarily related to the strength of the transcriptional signature. In particular, we identified cases where inhibitors had little effect on growth, yet induced a significant transcriptional response. These cases suggest possible adaptation mechanisms to the inhibitors that may lead to drug resistance. We followed these cases experimentally and identified cell line-specific resistance mechanisms. Finally, we used the directionality of the transcriptional response in the mRNA space to guide co-drugging strategies. By testing if targeting parallel pathways is more potent than inhibiting twice the same pathway, we derived a systematic approach to design synergetic combinations. Because of the differential responses across cell lines, we were able to find combinations that are potent only in a specific cell line. This approach is a step toward the design of co-drugging strategies with differential effect and large therapeutic windows. In conclusion, our measurements of expression signatures and cellular phenotype across thousands of perturbations and six breast cancer cell lines allowed differential analyses that complement the C-MAP strategy based on consensus signatures. With our approach, we found connections between drugs based on transcriptional responses and paved the way for systematic design and analyses of drug synergies. Citation Format: Marc Hafner, Mario Niepel, Qiaonan Duan, Xiaodong Lu, Aravind Subramanian, Avi Ma'ayan, Peter K. Sorger. Transcriptional landscape of drug response guides the design of specific and potent drug combinations. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR16.
Abstract Homologous recombination (HR)-deficient cancers are sensitive to poly-ADP ribose polymerase inhibitors (PARPi), which have shown clinical efficacy in the treatment of high-grade serous cancers (HGSC). However, the majority of patients will relapse, and acquired PARPi resistance is emerging as a pressing clinical problem. Here we generated seven single-cell clones with acquired PARPi resistance derived from a PARPi-sensitive TP53−/− and BRCA1−/− epithelial cell line generated using CRISPR/Cas9. These clones showed diverse resistance mechanisms, and some clones presented with multiple mechanisms of resistance at the same time. Genomic analysis of the clones revealed unique transcriptional and mutational profiles and increased genomic instability in comparison with a PARPi-sensitive cell line. Clonal evolutionary analyses suggested that acquired PARPi resistance arose via clonal selection from an intrinsically unstable and heterogenous cell population in the sensitive cell line, which contained preexisting drug-tolerant cells. Similarly, clonal and spatial heterogeneity in tumor biopsies from a clinical patient with BRCA1-mutant HGSC with acquired PARPi resistance was observed. In an imaging-based drug screening, the clones showed heterogenous responses to targeted therapeutic agents, indicating that not all PARPi-resistant clones can be targeted with just one therapy. Furthermore, PARPi-resistant clones showed mechanism-dependent vulnerabilities to the selected agents, demonstrating that a deeper understanding on the mechanisms of resistance could lead to improved targeting and biomarkers for HGSC with acquired PARPi resistance. Significance: This study shows that BRCA1-deficient cells can give rise to multiple genomically and functionally heterogenous PARPi-resistant clones, which are associated with various vulnerabilities that can be targeted in a mechanism-specific manner.
