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Supplementary Table S2. Drugs and their concentrations used in the isogenic drug screen.

Supplementary Figure 1. Distribution of gene alterations in Breast TCGA and verification of expression of MCF10A cells. Supplementary Figure 2. Analysis of the MCF10A drug screen. Supplementary Figure 3. Significance of overlap between interactions found in this study and in the CGP. Supplementary Figure 4. Response of isogenic engineered cells to dasatinib. Supplementary Figure 5. Dasatinib sensitivity of CML versus AML cancer cell lines. Supplementary Figure 6. Verification of LYN knockdown via siRNA and response of LYN T319I to dasatinib. Supplementary Figure 7. Co-­�expression of MYC and LYN in breast cancer cell lines.

Background There is great interest in utilizing nanoparticles to improve drug delivery to central nervous system (CNS) tumors, but the biologic features underlying successful delivery at the tumor site are not known. METHODS We developed a pooled screening assay to investigate cellular features predictive of nanoparticle delivery and screened 35 fluorescent nanoparticle formulations against 488 pooled cancer cell lines with DNA barcodes. The nanoparticle library was comprised of nonlethal liposomal or polymeric cores with varied surface functionality, including natural and synthetic polymers. Cells were profiled using fluorescence-activated cell sorting and barcodes sequenced and deconvolved to generate an association score for each nanoparticle-cell line pair. RESULTS Of 488 cancer cell lines, 22 cancer cell lineages (tissues of origin) were included. There were 35 cell lines derived from CNS tumors; of these 22 were classified as astrocytoma or glioblastoma, one as oligodendroglioma, and 2 as medulloblastoma. Of CNS tumor cell lines, 40% were from female patients and average age at cell line derivation was 52 years; TP53 hotspot mutations were detected in 25/35 lines, PTEN hotspot mutations in 15/25 lines. Using unsupervised hierarchical clustering by nanoparticle association profiles, CNS tumor lines did not cluster together by lineage, subtype, or mutation status but rather by baseline expression of genes involved in nanocarrier trafficking. Nanoparticle association profiles were heterogeneous among the CNS tumor lines for most formulations, though CNS tumor lines had high association with polystyrene formulations with respect to other lineages. Select CNS tumor lines had high uptake of liposomes coated with the natural polymers fucoidan, alginate, hyaluronic acid and chondroitin sulfate; these trends were further probed in a non-pooled screen of 5 additional cell lines. Conclusions Uptake of liposomal and polymeric nanoparticles is heterogeneous across CNS tumor lines, but can be predicted using baseline gene expression.

Macrophages are plastic and, in response to different local stimuli, can polarize toward multi-dimensional spectrum of phenotypes, including the pro-inflammatory M1-like and the anti-inflammatory M2-like states. Using a high-throughput phenotypic screen in a library of ~4000 FDA-approved drugs, bioactive compounds and natural products, we find ~300 compounds that potently activate primary human macrophages toward either M1-like or M2-like state, of which ~30 are capable of reprogramming M1-like macrophages toward M2-like state and another ~20 for the reverse repolarization. Transcriptional analyses of macrophages treated with 34 non-redundant compounds identify both shared and unique targets and pathways through which the tested compounds modulate macrophage activation. One M1-activating compound, thiostrepton, is able to reprogram tumor-associated macrophages toward M1-like state in mice, and exhibit potent anti-tumor activity. Our compound-screening results thus help to provide a valuable resource not only for studying the macrophage biology but also for developing therapeutics through modulating macrophage activation., Macrophages may polarize into different states with distinct regulatory functions for inflammation. Here the authors perform high-throughput in vitro screening of a library of ~4000 compounds to identify those with specific effects on human macrophage polarization, while RNAseq helps uncover the targets and pathways mediating these effects.

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. Significance: 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. Cancer Discov; 5(2); 154–67. ©2014 AACR. This article is highlighted in the In This Issue feature, p. 97

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.

Ongoing pre-clinical efforts aim to deploy genome-scale CRISPR/Cas9 technology and large collections of small molecules to catalog maps of cancer vulnerabilities at scale. However, such efforts in pediatric and rare cancers have lagged behind comparable efforts in more common cancer types due to the dearth of cell models. Here, we present an update from our “Cancer Cell Line Factory” project on efforts to overcome key laboratory and biologistics challenges precluding progress in pediatric and rare cancers. This effort, now in it’s 3rd year, represents an industry scale pipeline aiming to generate, characterize and share novel cancer models of many tumor types with the scientific community. Overall, we have processed 1153 samples from 818 patients across over 16 cancer types through this pipeline with a 28% success rate overall, including over 350 patient samples from rare and pediatric cancers. To optimize conditions for each tumor type, we have systematically compared published methods including (1) next-generation 2-dimension, (2) organoid and (3) standard approaches and have captured all information with a data management system that should enhance the ability to predict optimal ex vivo propagation conditions for future samples. Among the successful cell models verified already as part of this effort, we have generated a series of over 30 unique pediatric and rare cancer models, many of which represent the first of their kind. We screened these and other models against a library of highly annotated 440 small molecules that were previously tested against 860 existing cancer cell lines. Our results suggest that dependency data generated with novel next-generation cell cultures is potentially backwards-compatible with existing small molecule dependency datasets. Furthermore, we tested the novel Broad Institute Drug Repurposing library consisting of 4100 approved therapeutics, or those under investigation for any disease, against the first cell line models of several of these rare next generation models including angioimmunoblastic T-cell lymphoma and renal medullary carcinoma, leading to several novel drug repurposing hypotheses for rare cancers. Given these proof-of-concept studies, in partnership with the Rare Cancer Research Foundation, we launched an online matchmaking platform to connect patients with rare cancers to available research studies, facilitate online consent and provide biologistics support to enable fresh tissue donation to support cancer model generation from any clinical site in the United States. We will present results from this novel direct-to-patient approach to facilitate the generation of even larger numbers of next generation models from rare and pediatric cancers, propelling the generation of pre-clinical dependency maps of these tumors for the scientific community. Citation Format: Yuen-Yi Tseng, Andrew Hong, Paula Keskula, Shubhroz Gill, Jaime Cheah, Grigoriy Kryukov, Aviad Tsherniak, Francisca Vazquez, Glenn Cowley, Sahar Alkhairy, Coyin Oh, Anson Peng, Rebecca Deasy, Abeer Sayeed, Peter Ronning, Samuel Ng, Steven Corsello, Corrie Painter, David Sandak, Levi Garraway, Mark Rubin, Calvin Kuo, Sidharth Puram, David Weinstock, Adam Bass, Nikhil Wagle, Keith Ligon, Katherine Janeway, David Root, Stuart Schreiber, Paul Clemons, Aly Shamji, Aly Shamji, William Hahn, Todd Golub, Jesse Boehm. Accelerating prediction of pediatric and rare cancer vulnerabilities using next-generation cancer models [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 1953. doi:10.1158/1538-7445.AM2017-1953

Loss-of-function screening using RNAi technologies over the past decade and more recently with CRISPR-Cas9 technologies have been applied to well-established cancer models. We asked if minimally passaged cancer models would tolerate such screening modalities, particularly perturbations focused on actionable drug targets. We have established a patient derived model, CLF-PED-015-T, as a proof of concept to test this question. After validating that the cell line retains the major genomic, transcriptomic and tumorigenic properties of the tissue it was derived from, we then performed systematic genetic screens using both CRISPR-Cas9 and RNAi to identify potentially actionable vulnerabilities. We then overlapped this with pharmacologic screens. We identified dependencies to CDK4 and XPO1 that spanned all three screens. These dependencies have subsequently validated in an in vivo model. These results suggest use of such technologies at early stages of patient derived model development is feasible. This abstract is also being presented as Poster B14. Citation Format: Andrew L. Hong, Yuen-Yi Tseng, Glenn Cowley, Oliver Jonas, Jaime Cheah, Mihir Doshi, Bryan Kynnap, Coyin Oy, Paula Keskula, Gregory Kryukov, Michael Cima, Robert Langer, Stuart Schreiber, David Root, Jesse Boehm, William Hahn. Integration of CRISPR-Cas9, RNAi and pharmacologic screens identify actionable targets in a rare cancer. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr PR04.

The mapping of cancer genomes is rapidly approaching completion. The genomic information encoded by individual patients’ tumors should, in principle, provide a guide for predicting dependencies, but our ability to do so is suboptimal. The challenge stems from the absence of clinical data relating genotypes with dependencies since most cancer mutations are rare and our arsenal of cancer drugs is incomplete. If it was possible to build a preclinical ‘cancer dependency map’ at a scale that captured the genomic diversity of cancer (for instance, models of all genotypes tested for genetic and small-molecule dependencies), it should be feasible to improve dependency predictions. New technologies (e.g. CRISPR/Cas9 libraries) make such an effort now feasible. However, we lack a sufficient diversity of cancer models derived directly from patient samples to reflect the genetic diversity of cancer and the ability to systematically create functional data for each cancer patient to expand the map. In an attempt to overcome these obstacles, we have established an industry-scale pipeline to generate new cancer models directly from patient samples, a “Cancer Cell Line Factory”. We have processed over 620 samples from 400 patients across 16 cancer types through this pipeline with a 25% success rate overall. To optimize conditions for each tumor type, we have systematically compared published cell line generation methods with standard approaches and captured all information with a data management system that will enhance the ability to predict optimal ex vivo propagation conditions for future samples. In all, we report the successful derivation of over 100 new genomically confirmed cancer and normal cell lines, including a series of unique pediatric cancer models derived from rare tumors. We hypothesized that novel patient-derived cultures could be used to enhance dependency predictions. To test this hypothesis, we tested dependencies of 65 of these novel cultures against an identical set of 440 small molecules that were previously tested against 860 existing cancer cell lines. Our results suggest that dependency data generated with novel cell cultures is potentially backwards-compatible with existing small molecule dependency datasets. Finally, we demonstrate proof-of-concept that such new models can successfully used in CRISPR-Cas9 screens and integrate results with small molecule sensitivities to uncover CDK4 and XPO1 dependencies in a rare pediatric undifferentiated sarcoma. In aggregate, these proof-of-concept studies demarcate a path by which pre-clinical dependency maps may enhance clinical dependency predictions from genomic data alone. Citation Format: Yuen-Yi Tseng, Andrew Hong, Paula Keskula, Shubhroz Gill, Jaime Cheah, Grigoriy Kryukov, Aviad Tsherniak, Francisca Vazquez, Glenn Cowley, Coyin Oh, Anson Peng, Abeer Sayeed, Rebecca Deasy, Peter Ronning, Philip Kantoff, Levi Garraway, Mark Rubin, Calvin Kuo, Sidharth Puram, Adi Gazdar, Filemon Dela Cruz, Adam Bass, Nikhil Wagle, Keith Ligon, Katherine Janeway, David Root, Stuart Schreiber, Paul Clemons, Aly Shamji, William Hahn, Todd Golub, Jesse S. Boehm. Accelerating prediction of tumor vulnerabilities using next-generation cancer models. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4367.

Background: Collaborative team science provides a starting point for comprehensive change, and advocates have a unique and important role developing and engaging in transdisciplinary collaboratives that focus on new questions and new possibilities to advance the science of ethnic and medically underserved health care disparities. Participating in four areas : 1) research and programmatic support, 2) education and outreach, 3) policy and strategy, and 4) representation and advisory, the UCSF Breast Science Advocacy Core (BSAC) Program, a volunteer affiliate of the Breast Oncology Program (BOP), one of ten multidisciplinary research programs under the umbrella of the UCSF Helen Diller Comprehensive Cancer Center promotes a transformative, transdisciplinary, integrated environment to study the biological basis of the diseases that comprise breast cancer; to define the risk of developing or progressing with specific types of breast cancer; to develop novel interventions that work locally and globally to reduce morbidity and mortality from breast cancer and its treatment; and to leverage new collaborative research, education, and mentoring/training opportunities that address cancer outcome disparities. Advocates involved in KOMEN, DOD, PCORI, AND CBCRP funded research and training grants apply four core principles that forge synergy with NCI Advocacy Research Working Group Recommendations: 1)strategic innovation, 2)collaborative execution, 3)evidence based decision-making, and 4) ethical codes of conduct. Embracing transdisciplinary professionalism, researchers and advocates build on their track record as shared value partners committed to furthering the collective impact of science advocacy exchange (SAE). Study Objectives: 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. A growing body of evidence has shown that African American women have biologically more aggressive disease, independent of social determinants, and suffer the highest mortality rates. While biological breakthroughs of the last decade have greatly advanced our understanding of cancer, in advanced TNBC, a poor prognosis subtype, there is an urgent need to translate this evolving patient genomic data into new therapeutic paradigms. Our study focuses on the intersection of synthetic lethal approaches, MYC driven human cancers, and immunotherapy as an “innovation agenda”. A distinct MYC vision highlights how overexpression is associated with aggressive outcomes and poor patient outcomes, and synthetic lethal strategies to target MYC (CDK inhibitors, PIM2, as well as the PDI immune pathways) have potential for addressing outcome disparities In African American Women with Triple Negative Breast Cancer (TNBC). Key Findings: We have developed a screening technique that can be used to rapidly and accurately identify potential synthetic lethal interactions in TNBC. This platform utilizes an isogenic cell line system that we have developed to model oncogene activation in TNBC. A growing body of evidence has shown that: 1) Quantitative approach maps genotype-specific drug responses in isogenic cells 2) Systematic discovery of biomarkers for cancer drugs under clinical investigation 3) Clinically actionable synthetic lethal interaction between MYC and dasatinib is discovered 4) Mechanism of dasatinib action through inhibition of LYN kinase is described Key Take-Away Message: The inclusion of advocates in convergent science settings remind academic stakeholders that research is there to benefit the patient as they attempt to spark innovation, democratize science, and support smarter interventions that expedite the incredible potential of future investments in bioscience within disparities arenas. Citation Format: Susan Samson, Alicia Y. Zhou, Maria Martins, Alexandra Corella, Dai Horiuchi, Christina Yau, Taha Rakshandehroo, John Gordan, Rebecca Levin, Jeff Johnson, John Jascur, Mike Shales, Antonio Sorrentino, Jaime Cheah, Paul Clemens, Alykhan Shamji, Stuart Schreiber, Nevan Krogan, Kevan Shokat, Frank McCormick, Sourav Bandyopadhyay, Andrei Goga. Genomics, advocacy, and emerging therapeutics to address triple-negative breast cancer (TNBC) outcome disparities. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr C88.

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.

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.

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.

SummaryPostsynaptic AMPA receptor (AMPAR) trafficking mediates some forms of synaptic plasticity that are modulated by NMDA receptor (NMDAR) activation and N-ethylmaleimide sensitive factor (NSF). We report that NSF is physiologically S-nitrosylated by endogenous, neuronally derived nitric oxide (NO). S-nitrosylation of NSF augments its binding to the AMPAR GluR2 subunit. Surface insertion of GluR2 in response to activation of synaptic NMDARs requires endogenous NO, acting selectively upon the binding of NSF to GluR2. Thus, AMPAR recycling elicited by NMDA neurotransmission is mediated by a cascade involving NMDA activation of neuronal NO synthase to form NO, leading to S-nitrosylation of NSF which is thereby activated, enabling it to bind to GluR2 and promote the receptor’s surface expression.

Introduction: Glioblastoma is the most malignant and deleterious brain tumor. Average patient survival is less than 14 months and there is no effective cure. Glioma cell lines, grown in serum, have been used to explore new therapeutic approaches, but they are poor representatives of primary tumors. At the genomic level, they exhibit many allelic imbalances and mutations that are likely associated with long-term passages and selective pressure to fit an artificial environment. At the gene expression level, they do not clearly recapitulate expression patterns seen in human glioblastomas. Finally, when these cell lines are injected in animal models tend to form “ball”-like masses rather than infiltrating tumors. Recently, it has been found that “neurosphere” cells derived from glioblastomas cultured in cytokines without serum more closely mirror the phenotype and genotype of primary tumors. Experimental procedures: We are therefore pursuing a systematic analysis of genetic and non-genetic features that correlate with sensitivity to each of 480 small molecules in 40 neurosphere cell lines grown in the absence of serum. Here we present initial results from 12 neurospheres and nine traditional glioblastoma cell lines. We considered mutations, copy number alterations, and gene expression. In order to determine the representation of cancer stem cells in these populations, we also interrogated the cell surface markers CD44, CD15, and CD133 by flow citometry and the neural stem cell markers Nestin, Olig2, Sox2 and lineage differentiation markers GFAP, Tuj1, and O4 by Western blots. Summary: We find that copy number profiles of the neurosphere lines resemble glioblastomas more closely than do established cells lines growing in serum. Gene expression profiles denoted that cell lines don't recapitulate accurately molecular signatures previously defined in GBMs. Neurospheres were classified mainly as Proneuronal or Mesenchymal subtypes, in change cell lines growing in serum displayed more heterogeneous profiles being difficult assign to previously defined subtypes. We also find that neurosphere lines are more sensitive to a variety of small molecules than are traditional cell lines. Among the neurospheres, those lines that grow in suspension or as sphereoids tend to exhibit heterogeneity in expression of CD44/CD133/CD15, whereas neurospheres that grow attached to the plate tend to homogenously express CD44 alone. The suspension lines are also more sensitive to a variety of small molecules. Statement: These results indicate the feasibility of large-scale small molecule screening in neurosphere cell lines, and allowed us correlate with genomic and non-genomic determinants in a more accurate glioblastoma cell line model. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A103. Citation Format: Ruben Ferrer-Luna, Shakti Ramkissoon, Jaime Cheah, Rebecca Lamothe, Steven Schumacher, Alykhan Shamji, Paul Clemons, David Reardon, Patrick Wen, Stuart Schreiber, Keith Ligon, Rameen Beroukhim. High-throughput genomic and chemical screening in glioblastoma cell lines. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A103.

Abstract 571 Despite advances in the treatment of multiple myeloma (MM), this disease remains incurable and novel therapeutic strategies are urgently needed. Ideal strategies would overcome resistance factors from the bone-marrow microenvironment (niche) since a variety of inhibitors are rendered less effective by bone-marrow stromal cells (BMSCs) of the MM niche (McMillin et al., Nat Med. 2010 Apr;16(4):483–9). Drug discovery often entails a target-based approach but identifying targets in MM is challenging because of its complex genome and multiple niche interactions. We used a chemical biology approach in which small-molecule inhibitors of MM cells, grown within their niche, are first identified and then used to discover targets within MM or its niche. These compounds also serve as leads for future drug discovery. To model myeloma/niche interactions, we chose an MM cell line MOLP5 that has an obligate dependence on BMSCs to maintain viability. Small-molecule inhibitors were identified by screening ∼25,000 structurally diverse small molecules on GFP-labeled MOLP5 cells co-cultured with primary BMSCs derived from hip replacement samples. MOLP5 growth inhibition was measured by quantifying GFP(+) cells with automated high-throughput microscopy. About 800 hits were counter-screened on BMSCs alone to exclude non-specifically toxic compounds. The remaining 182 MOLP5-selective inhibitors were then tested on 2 other GFP-labeled MM cell-lines, MM1S and INA6, in the presence or absence of BMSCs to exclude compounds that are less effective in the presence of BMSCs. The 64 compounds that overcome BMSC resistance were tested on CD34+ human hematopoietic progenitors to prioritize compounds with selectivity between MM and normal blood cells. The 8 compounds that met these criteria fell into 3 categories: 1) compounds with equal activity in the presence or absence of BMSCs (overcome stromal resistance); 2) compounds with selectivity for BMSC-dependent MOLP5 cells (block stromal viability factors); and 3) compounds with increased activity in the presence of BMSCs (enhance stromal inhibitory factors). Because most efficacious clinical compounds like bortezomib act like compounds in category 1, compound BRD9876 was chosen from this category for mechanistic studies. Gene-expression profiling of BRD9876-treated MM1S cells suggested possible links to mitotic arrest and cell cycle analyses revealed a rapid accumulation of cells in the G2/M phase. Treated cells were stained for the mitotic spindle protein α-tubulin and found to exhibit an aberrant mono-astral mitotic phenotype, reminiscent of the kinesin-5 (Eg5; KIF11) inhibitor monastrol. This was encouraging because a kinesin-5 inhibitor ARRY-520 has shown promising durable responses in multiple myeloma (Shah et al, ASH Annual Meeting 2011; Abstract 1860). To determine if BRD9876 was a kinesin-5 inhibitor, a BRD9876-resistant sub-line of MM1S was developed and the kinesin-5 gene sequenced. BRD9876-resistant cells have a novel kinesin-5 mutation (Y104C) at a site that is distant from the monastrol-binding pocket. Most kinesin-5 inhibitors in clinical development bind the monastrol pocket, and the BRD9876-resistant cells were not cross-resistant to one such inhibitor, ispinesib, suggesting a distinct mode of kinesin-5 inhibition by BRD9876. To identify biomarkers of sensitivity to BRD9876, quantitative dose/response measurements in 98 genetically characterized cell lines (Schreiber & co-workers, submitted) comprising a subset of the Cancer Cell Line Encyclopedia (CCLE) were analyzed. Unbiased analyses correlating genetic features with sensitivity revealed that mutations in the mitotic regulator WEE1 were associated with sensitivity to BRD9876. Validation studies comparing WEE1 mutant to wild-type cell lines confirmed enhanced sensitivity of mutant cells to both BRD9876 and ispinesib suggesting that WEE1 mutations could be a useful biomarker for different kinesin-5 inhibitors. In contrast, co-treatment of WEE1 WT cells with sub-toxic concentrations of the WEE1 inhibitor MK1775 led to marked enhancement of BRD9876 activity but had little effect on ispinesib activity, suggesting a unique synergistic relationship between WEE1 inhibitors and BRD9876. In summary, niche-based screening in multiple myeloma has revealed a novel therapeutic candidate and can complement other drug-discovery approaches against this disease. Disclosures: Ebert: Celgene: Consultancy; Genoptix: Consultancy. Raje:Onyx: Consultancy; Celgene: Consultancy; Millennium: Consultancy; Acetylon: Research Funding; Amgen: Research Funding; Eli-Lilly: Research Funding.

The structure of l‐amino acid oxidase (LAAO) from Calloselasma rhodostoma has been determined to 2.0 A resolution in the presence of two ligands: citrate and o ‐aminobenzoate (AB). The protomer consists of three domains: an FAD‐binding domain, a substrate‐binding domain and a helical domain. The interface between the substrate‐binding and helical domains forms a 25 A long funnel, which provides access to the active site. Three AB molecules are visible within the funnel of the LAAO–AB complex; their orientations suggest the trajectory of the substrate to the active site. The innermost AB molecule makes hydrogen bond contacts with the active site residues, Arg90 and Gly464, and the aromatic portion of the ligand is situated in a hydrophobic pocket. These contacts are proposed to mimic those of the natural substrate. Comparison of LAAO with the structure of mammalian d‐amino acid oxidase reveals significant differences in their modes of substrate entry. Furthermore, a mirror‐symmetrical relationship between the two substrate‐binding sites is observed which facilitates enantiomeric selectivity while preserving a common arrangement of the atoms involved in catalysis.