Chemical-genetic Interaction Map Research Articles

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

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

A Quantitative Chemotherapy Genetic Interaction Map Reveals Factors Associated with PARP Inhibitor Resistance

SUMMARYChemotherapy 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.

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 patients' 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. Citation Format: 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]. In: Proceedings of the AACR Precision Medicine Series: 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.

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. Citation Format: Hsien-Ming Hu, Sourav Bandyopadhyay. Identifying factors mediating response and resistance to chemotherapy through a chemical-genetic interaction map [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr PR07.

A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells.

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.

Abstract PR15: Functional analysis of diverse oncogenic driver mutations using an isogenic cell line library identifies novel drug responses and alterations in metabolism

Abstract There is an urgent need in oncology to link molecular aberrations in tumors with altered cellular behaviors, such as metabolic derangements, and to identify novel therapeutics for cancer treatment. We have sought to identify synthetic-lethal genetic interactions 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 PI3K/AKT pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic-lethal manner. These studies provide new drug and biomarker pairs for clinical investigation. We have also performed global metabolomics analysis in a subset of the isogenic cell lines demonstrating alterations in metabolic pathways that are shared across multiple oncogenes, as well as those that are distinct to specific oncogenic drivers. 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. Citation Format: Maria M. Martins, Alicia Y. Zhou, 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 Schreiber, Stuart Schreiber, Nevan J. Krogan, Kevan M. Shokat, Kevan M. Shokat, Frank McCormick, Daniel Nomura, Sourav Bandyopadhyay, Andrei Goga. Functional analysis of diverse oncogenic driver mutations using an isogenic cell line library identifies novel drug responses and alterations in metabolism. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR15.

Abstract PR07: Functional analysis of diverse oncogenic driver mutations using an isogenic cell line library identifies novel drug responses and alterations in metabolism

Abstract There is an urgent need in oncology to link molecular aberrations in tumors with altered cellular behaviors, such as metabolic derangements, and to identify novel therapeutics for cancer treatment. We have sought to identify synthetic-lethal genetic interactions 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 PI3K/AKT pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic-lethal manner. These studies provide new drug and biomarker pairs for clinical investigation. We have also performed global metabolomics analysis in a subset of the isogenic cell lines demonstrating alterations in metabolic pathways that are shared across multiple oncogenes, as well as those that are distinct to specific oncogenic drivers. 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. This abstract is also presented as a poster at the Translation of the Cancer Genome conference. Citation Format: Maria M. Martins, Alicia Y. Zhou, 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 Schreiber, Stuart Schreiber, Nevan J. Krogan, Kevan M. Shokat, Kevan M. Shokat, Frank McCormick, Daniel Nomura, Sourav Bandyopadhyay, Andrei Goga. Functional analysis of diverse oncogenic driver mutations using an isogenic cell line library identifies novel drug responses and alterations in metabolism. [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 PR07.

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. Citation Format: 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. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr B48.

Abstract B44: A systems approach combining genomics, advocacy, and emerging novel therapeutics to address triple-negative breast cancer (TNBC) outcomes disparities

Abstract Background: Genomic analyses of patient tumors have unearthed an overwhelming number of recurrent somatic alterations in genes that have dramatic effects on tumor biology, patient drug responses, and clinical outcomes. In one study, high-grade triple negative breast cancer (TNBC) accounts for 34% of breast cancers in African American women versus 21% in white women. African American women have biologically more aggressive disease, independent of social determinants, and suffer the highest mortality rates. In advanced TNBC, a poor prognosis subtype, there is an urgent need to translate this emerging patient genomic data into new therapeutic paradigms. Objectives: Our study focuses on emerging compounds that are already approved (i.e., Dasatinib) or in testing for human use and we expect that this work will serve as a prelude to one or more clinical trials in TNBC. We seek to determine if the treatment of metastatic TNBC recurrence with more targeted genotype-specific agents could improve the outcomes/survival of all women in this particularly aggressive poor prognosis subset, including African American women. Methods: To guide the development of genotype-specific therapies in TNBC, we have established an isogenic cell-line drug screen that measures the impact of gene activation on a panel of emerging, clinically relevant compounds targeting a variety of cancer pathways. 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. We believe that this approach can identify core synthetic lethal interactions, which underlie drug sensitivity and can be used as a foundation to identify patient populations that will selectively respond to drug treatments. Results: Using our systems approach, our interaction map highlights both known and novel connections between oncogene activation and drug responses and provides a modular roadmap for the exploration of synthetic lethal relationships. 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. Ensuring that the voice of the patient is represented in our scientific inquiry, advocacy has played a significant role in the development and realization of this project. Aligning experiential and professionalized expertise, trained advocates explore relentless challenges and opportunities for moving the science forward. Conclusion: A novel systems biology approach that uses module maps of oncogenes and emerging therapeutics can define synthetic-lethal interactions and actionable therapeutics to help decrease TNBC outcomes/survival disparities in African American women. Citation Format: 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 Schreiber, Nevan J. Krogan, Kevan M. Shokat, Frank McCormick, Susan Samson, Andrei Goga, Sourav Bandyopadhyay. A systems approach combining genomics, advocacy, and emerging novel therapeutics to address triple-negative breast cancer (TNBC) outcomes disparities. [abstract]. In: Proceedings of the Seventh AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 9-12, 2014; San Antonio, TX. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2015;24(10 Suppl):Abstract nr B44.

Linking tumor mutations to drug responses via a quantitative chemical-genetic interaction map.

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.