Query: chemical-genetic interaction map

title: Linking tumor mutations to drug responses via a quantitative chemical genetic interaction map.
abstract: There is an urgent need in oncology to link molecular aberrations in tumors with therapeutics that can be administered in a personalized fashion. One approach identifies synthetic lethal genetic interactions or dependencies that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated a systematic and quantitative chemical genetic interaction map that charts the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model complex cellular contexts. Applying this dataset to triple negative breast cancer, we report clinically actionable interactions with the MYC oncogene, including resistance to AKT PI3K pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic lethal manner, providing new drug and biomarker pairs for clinical investigation. This scalable approach enables the prediction of drug responses from patient data and can accelerate the development of new genotype directed therapies.  Determining how the plethora of genomic abnormalities that exist within a given tumor cell affects drug responses remains a major challenge in oncology. Here, we develop a new mapping approach to connect cancer genotypes to drug responses using engineered isogenic cell lines and demonstrate how the resulting dataset can guide clinical interrogation.

title: A chemical genetic interaction map of small molecules using high throughput imaging in cancer cells
abstract: Small molecules often affect multiple targets, elicit off target effects, and induce genotype specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high content screening and image analysis to measure effects of 1,280 pharmacologically active compounds on complex phenotypes in isogenic cancer cell lines which harbor activating or inactivating mutations in key oncogenic signaling pathways. Using multiparametric chemical genetic interaction analysis, we observed phenotypic gene drug interactions for more than 193 compounds, with many affecting phenotypes other than cell growth. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC) which enables exploration of drug mode of action, detection of potential off target effects, and the generation of hypotheses on drug combinations and synergism. For example, we demonstrate that MEK inhibitors amplify the viability effect of the clinically used anti alcoholism drug disulfiram and show that the EGFR inhibitor tyrphostin AG555 has off target activity on the proteasome. Taken together, this study demonstrates how combining multiparametric phenotyping in different genetic backgrounds can be used to predict additional mechanisms of action and to reposition clinically used drugs.

title: Abstract PR07: Identifying factors mediating response and resistance to chemotherapy through a chemical genetic interaction map
abstract: Nearly every cancer patient, especially those with highly lethal and aggressive tumor types, is treated with cytotoxic chemotherapy. Currently, the selection of chemotherapy is based on average responses over a large number of patients. This belies our understanding of DNA repair that has demonstrated that the inability of a tumor cell to properly repair particular types of DNA damage has a dramatic influence on cell survival. Here, we report the generation of a quantitative chemical genetic interaction map to chart the influence of knockdown of 625 genes on sensitivity to 30 FDA approved chemotherapeutic agents in breast epithelial cells. The resulting map highlights key genes which, when mutated or deleted in tumor cells, can dramatically induce sensitivity or resistance to particular DNA damaging agents. Interrogation of this map reveals new DNA repair factors that are recurrently deleted in breast and ovarian cancers, provides a platform for prediction of cancer cell line responses from genomic data and provides the basis for the prioritization of new drug combinations. Our data reveal that the loss of ARID1A, a key component of the SWI/SNF chromatin remodeling complex, drives resistance to cisplatin as well as PARP inhibition. Mechanistically, we uncover that ARID1A functions as a suppressor of DNA repair by homologous recombination (HR) and that its loss via mutation or deletion rescues HR in a BRCA1 independent fashion. Together, our data indicates a plethora of potential biomarkers for chemotherapy response and provides an opportunity for computational integration of chemical genetic networks with patient genomic data to predict optimal chemotherapeutic regimes. This abstract is also being presented as Poster A14.  Hsien Ming Hu, Sourav Bandyopadhyay. Identifying factors mediating response and resistance to chemotherapy through a chemical genetic interaction map [abstract]  Tumor Development and Therapeutic Response; 2016 Nov 2 5; Montreal, QC, Canada. Philadelphia (PA) AACR;

title: Correction: Linking Tumor Mutations to Drug Responses via a Quantitative Chemical Genetic Interaction Map.

title: Abstract A02: Expanding tumor chemical genetic interaction map using next generation cancer models
abstract: The development of new cancer therapeutics requires sufficient genetic and phenotypic diversity of cancer models. Current collections of human cancer cell lines are limited and for many rare cancer types, zero models exist that are broadly available. Here, we report results from the pilot phase of the Cancer Cell Line Factory (CCLF) project that aims to overcome this obstacle by systematically creating next generation in vitro cancer models from adult and pediatric cancer patients9 specimens and making these models broadly available. We first developed a workflow of laboratory, genomics and informatics tools that make it possible to systematically compare published ex vivo culture conditions for each individual tumor to enable the scientific community to iterate towards disease specific culture recipes. Based on sample volume and rarity, 4 100 conditions were applied to each sample and all data was captured in a custom Laboratory Information Management System to enhance subsequent predictions. We developed a $150, 5 day turnaround genomics panel to validate cultures based on genomics. Importantly, we show that tumor genomics can be retained in such patient derived models and tumor genomics are generally stable across 20 passages. Since the inception of this project, we have processed over 600 patient cancer specimens from 450 patients across 16 tumor types and report the successful generation of over 100 genomically characterized adult and pediatric cancer and normal models. We next hypothesized that novel patient derived cell models could be used to enhance dependency predictions. To do so, we tested 72 cell lines against the informer set of 440 compounds developed by the Broad Cancer Target Discovery and Development (CTD2) Center. We show that generating cell lines and testing their sensitivities within 3 months is feasible and the high throughput drug responses are reproducible. Moreover, to strengthen relationships between drug sensitivities and cellular features, we compared results with recently published data on the identical compounds tested against 860 existing cell lines. With this approach, we show that many chemical genetic interaction vulnerabilities can be rapidly assessed. Importantly, adding more cancer models with the dimensions of quantity and diversity increases the predictive power of chemical genetic interaction map. We are currently evaluating these drug sensitivity predictors for novel co dependencies. Overall, our proof of concept framework demonstrates initial feasibility of rapidly generating cancer models at scale and expanding the chemical genetic interaction map to identify new cancer vulnerability.  Yuen Yi (Moony) Tseng, Andrew Hong, Shubhroz Gill, Paula Keskula, Srivatsan Raghavan, Jaime Cheah, Aviad Tsherniak, Francisca Vazquez, Sahar Alkhairy, Anson Peng, Abeer Sayeed, Rebecca Deasy, Peter Ronning, Philip Kantoff, Levi Garraway, Mark Rubin, Calvin Kuo, Sidharth Puram, Adi Gazdar, Nikhil Wagle, Adam Bass, Keith Ligon, Katherine Janeway, David Root, Stuart Schreiber, Paul Clemons, Todd Golub, William Hahn, Jesse Boehm. Expanding tumor chemical genetic interaction map using next generation cancer models [abstract]  Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4 7, 2017; San Diego, CA. Philadelphia (PA) AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A02.

title: Abstract B48: Identification of novel drug interactions with MYC via a quantitative chemical genetic interaction map
abstract: There is an urgent need in oncology to link molecular aberrations in tumors with therapeutics that can be administered in a personalized fashion. One approach identifies synthetic lethal genetic interactions or emergent dependencies that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated an unbiased and quantitative chemical genetic interaction map that measures the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model more complex cellular contexts. Applied to triple negative breast cancer, we report clinically actionable interactions with the MYC oncogene including resistance to AKT/PI3K pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic lethal manner, providing new drug and biomarker pairs for clinical investigation. This scalable approach enables the prediction of drug responses from patient data and can be used to accelerate the development of new genotype directed therapies.  Alicia Y. Zhou, Maria M. Martins, Alexandra Corella, Dai Horiuchi, Christina Yau, Taha Rakshandehroo, John D. Gordan, Rebecca S. Levin, Jeff Johnson, John Jascur, Mike Shales, Antonio Sorrentino, Jaime Cheah, Paul A. Clemons, Alykhan Shamji, Stuart L. Schreiber, Nevan J. Krogan, Kevan M. Shokat, Frank McCormick, Andrei Goga, Sourav Bandyopadhyay. Identification of novel drug interactions with MYC via a quantitative chemical genetic interaction map.  From Biology to Therapy; Jan 7 10, 2015; La Jolla, CA. Philadelphia (PA) AACR;

title: Chemical genetic interaction landscape of mono (2 ethylhexyl) phthalate using chemogenomic profiling in yeast.
abstract: Integration of chemical genetic interaction data with biological functions provides a mechanistic understanding of how toxic compounds affect cells. Mono (2 ethylhexyl) phthalate (MEHP) is an active metabolite of di (2 ethylhexyl) phthalate (DEHP) a commonly used plasticizer. MEHP adversely affects human health causing hepatotoxicity and reproductive toxicity. How MEHP affects cellular physiology is not fully understood. We utilized a genome wide competitive fitness based assay called 'chemogenomic profiling' to determine the genetic interaction map of MEHP in Saccharomyces cerevisiae. Gene Ontology enrichment analysis of 218 genes that provide resistance to MEHP indicated that MEHP affects seven cellular processes namely: (1) cellular amino acid biosynthetic process, (2) sterol biosynthetic process, (3) cellular transport, (4) transcriptional and translational regulation, (5) protein glycosylation, (6) cytokinesis and cell morphogenesis and (7) ionic homeostasis. We show that MEHP protects yeast cells from membrane perturbing agents such as amphotericin B, dihydrosphingosine and phytosphingosine. Moreover, we also demonstrate that MEHP compromises the integrity of the yeast plasma membrane and cell wall. Our work provides a basis for further investigation of MEHP toxicity in humans.

title: A Quantitative Chemotherapy Genetic Interaction Map Reveals Factors Associated with PARP Inhibitor Resistance
abstract: Chemotherapy is used to treat most cancer patients, yet our understanding of factors that dictate response and resistance to such drugs remains limited. We report the generation of a quantitative chemical genetic interaction map in human mammary epithelial cells charting the impact of the knockdown of 625 genes related to cancer and DNA repair on sensitivity to 29 drugs, covering all classes of chemotherapy. This quantitative map is predictive of interactions maintained in other cell lines, identifies DNA repair factors, predicts cancer cell line responses to therapy, and prioritizes synergistic drug combinations. We identify that ARID1A loss confers resistance to PARP inhibitors in cells and ovarian cancer patients and that loss of GPBP1 causes resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map helps navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage.

title: MOSAIC: a chemical genetic interaction data repository and web resource for exploring chemical modes of action
abstract: Chemical genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high throughput platform for mapping chemical genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode of action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes of action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation MOSAIC is available at http:/mosaic.cs.umn.edu. Contact hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu. Supplementary information Supplementary data are available at Bioinformatics online.

title: MOSAIC: a chemical genetic interaction data repository and web resource for exploring chemical modes of action
abstract: Chemical genomic approaches that map interactions between compounds and genetic perturbations offer a promising new strategy for annotation of uncharacterized bioactive compounds. We recently developed a new high throughput platform for mapping chemical genetic interactions in yeast that can be scaled to screen large compound collections, which has been applied to generate chemical genetic (CG) profiles for more than 13,000 compounds. When integrated with existing yeast genetic interaction network data, these CG interaction profiles can enable the prediction of modes of action for previously uncharacterized compounds as well as the discovery of new effects for approved drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, genes, or biological processes (GO terms) for direct chemical genetic interactions or target predictions. MOSAIC also provides direct access to chemical structural information of screened molecules and the ability to search for compounds sharing structural similarity. This resource will be of interest both to chemical biologists interested in discovering new probes with specific modes of action as well as computational biologists interested in analyzing chemical genetic interaction networks. Availability MOSAIC is available at http:/mosaic.cs.umn.edu.