Abstract 1383: A CRISPR-Cas9 tiling screen to identify functional domains within DNMT1 and/or DNMT3B that can be targeted for therapeutic intervention in AML

DNA methylation is an epigenetic mechanism that regulates gene expression during many stages of development, including genomic imprinting, stem cell regulation, and X-chromosome inactivation. Moreover, aberrant DNA methylation patterns, characterized by genome-wide hypomethylation and promoter-specific hypermethylation, are a prominent feature of cancer. Methylation of DNA at the 5-position of cytosines is mediated by the DNA methyltransferase (DNMT) protein family, which regulates both maintenance methylation (DNMT1) and de novo methylation (DNMT3A and DNMT3B). Loss-of-function mutations of DNMT3A have been identified in hematological malignancies including acute myeloid leukemia (AML), where DNMT3A is mutated in approximately 25% of known cases.

Published reports suggest the existence of a synthetic lethal interaction between DNMT3A and DNMT1/3B. To further study this potential genetic interaction, we are performing a CRISPR-Cas9 tiling screen to identify functional domains within DNMT1 and/or DNMT3B that are synthetic lethal with DNMT3A. We generated a lentiviral library containing 777 and 421 single guide RNAs (sgRNAs) that tile the coding region of DNMT1 and DNMT3B, respectively and performed viability screens in AML cell lines that are either wild-type or mutant for DNMT3A. This screen was designed to identify in-frame alterations within functional domains that lead to effects on cell viability. Next generation sequencing of sgRNAs identified three functional domains of DNMT1 which, when mutated, leads to decreases in cell viability. Current efforts are focused on verifying the essentiality of these functional domains using CRISPR-Cas9-based approaches as well as mutagenesis by integrated tiles (MITE)-seq analyses.

Citation Format: Balpreet Bhogal, Barbara Weir, Ramona Crescenzo, Min Chul Kwon, Ulrike Philippar, Ricardo Attar, Glenn Cowley, David Pocalyko. A CRISPR-Cas9 tiling screen to identify functional domains within DNMT1 and/or DNMT3B that can be targeted for therapeutic intervention in AML [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1383.

Abstract 1953: Accelerating prediction of pediatric and rare cancer vulnerabilities using next-generation cancer models

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

Abstract B43: Towards a Cancer Dependency Map

This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR02) of the Conference Proceedings.

Citation Format: Aviad Tsherniak, Francisca Vazquez, Barbara Weir, Philip Montgomery, Glenn Cowley, Stanley Gill, Gregory Kryukov, Sasha Pantel, Will Harrington, Mike Burger, Robin Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Levi Garraway, Jesse Boehm, David Root, Todd Golub, William Hahn. Towards a Cancer Dependency Map. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr B43.

Abstract PR02: Towards a Cancer Dependency Map

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 acquired cancer dependencies. Unfortunately, while the success of precision cancer genomics hinges on the decoding of such dependencies, we lack the ability to predict dependencies for most individual tumors. 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.

A comprehensive Cancer Dependency Map comprised of a catalog of genetic and small molecule vulnerabilities across a diverse set of cancers, along with robust statistical models able to predict these vulnerabilities from molecular and genomic features, would provide a roadmap of targets ripe for therapeutic development and would help reveal the mechanisms underlying the emergence of these vulnerabilities.

Here, we report progress in creating a Cancer Dependency Map consisting of the following components: 1) Systematic genetic perturbation (RNAi/CRISPR) of over 600 cancer cell models representing a wide range of human cancers and cell lineages using massively parallel genome scale loss-of-function screens. 2) Computational segregation of on- from off-target effects of RNAi enabling the discovery of outlier dependencies. 3) Predictive modeling to discover biomarkers for each dependency.

Our results demonstrate that our analytical approach (DEMETER) that models both gene and miRNA-based seed sequence effects effectively segregates on- from off-target effects of shRNAs. We discover 768 preferential dependencies whose suppression decreases viability at a level greater than six standard deviations in at least one of 503 cancer models and 105 such dependencies each present in at least 15 models. We find that 95% of the cancer models screened are strongly sensitive to the suppression of at least one of these dependencies, and that many models have common dependencies so that all models harbor at least one six-sigma dependency out of a set of only 76. Using a custom random forest based predictive modeling framework (ATLANTIS), we discover predictive biomarkers for hundreds of dependencies. These include known and novel vulnerabilities specified by somatic oncogenic alterations, overexpression of genes that specify lineage and differentiation, copy-number driven essentiality, and loss of functionally redundant paralogs.

These observations provide a rigorous computational and experimental foundation for the creation of a comprehensive Cancer Dependency Map. Subsampling and projection analyses suggest that over 10,000 genomically characterized cancer cell models will be needed to achieve this important goal.

This abstract is also being presented as Poster B43.

Citation Format: Aviad Tsherniak, Francisca Vazquez, Barbara Weir, Philip Montgomery, Glenn Cowley, Stanley Gill, Gregory Kryukov, Sasha Pantel, Will Harrington, Mike Burger, Robin Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Levi Garraway, Jesse Boehm, David Root, Todd Golub, William Hahn. Towards a Cancer Dependency Map. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr PR02.

Abstract B44: Emerging targets from Cancer Dependency Map v0.1

Precision Cancer Medicine requires the identification of vulnerabilities linked to genetic features of tumors. Recent studies utilizing highly annotated small molecule collections to assess dependencies across hundreds of genomically annotated cell lines have demonstrated the potential for such large-scale preclinical “Dependency Map” projects. We have undertaken a complementary approach using genetic perturbation tools (RNAi and CRISPR-Cas9 based loss-of-function viability screens), to systematically catalog preferential genetic dependencies and markers that predict response. These efforts are providing a foundation for the discovery of novel targets poised for early therapeutic discovery projects together with patient populations that may be enriched for responders to such therapies.

Here, we present results from our initial Cancer Dependency Map consisting of RNAi loss-of-function screens across 503 cell lines, including both solid and hematopoietic tumors. We discovered 43 genes whose mutation or copy number creates a cancer dependency (oncogene addiction) including a novel dependency on the small GTPase, GNAI2 in Diffuse large B-cell Lymphoma. We discovered 142 genes in which elevated levels of expression create a dependency (gene addiction), a group of genes highly enriched for master regulator transcription factors such as SOX10, SPDEF, PAX8 and HNF1B. We discovered 474 genes for which hemizygous copy number creates a dependency (CYCLOPS genes), a group of genes highly enriched for members of macromolecular protein complexes including the spliceosome and proteasome. Finally, we discovered 171 genes that become a dependency when a redundant functional paralog is lost in cancer cells (redundant essentials). We demonstrate the mechanistic basis behind one such redundant essential dependency relationship in which promoter methylation of the UBB ubiquitin gene eliminates a compensatory mechanism leading to a novel vulnerability on the suppression of the UBC ubiquitin gene.

These observations begin to provide an initial census, categorization and prioritization of robust cancer dependencies and support the potential impact for expanding early efforts to develop dependency maps of cancer.

Citation Format: Francisca Vazquez, Aviad Tsherniak, Barbara Weir, Phil Montgomery, Glenn Cowley, Stanley Gill, Gregory Kryukov, Sasha Pantel, Will Harrington, Mike Burger, Robin Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Levi Garraway, Jesse Boehm, David Root, Todd Golub, William Hahn. Emerging targets from Cancer Dependency Map v0.1. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr B44.

Abstract B26: Accelerating prediction of tumor vulnerabilities using next-generation cancer models

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 650 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 cultures could be used to enhance dependency predictions. To do so, we tested 65 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 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 are able to identify many known drug dependencies in these novel models and exhibit the consistency sensitivities compared to existing cell lines. We are also evaluating drug sensitivity predictors for novel dependencies. Overall, our proof-of-concept framework demonstrates initial feasibility of rapidly generating cancer models and assessing drug sensitivities at scale.

Citation Format: Yuen-Yi Tseng, Paula Keskula, Andrew L. Hong, Shubhroz Gill, Jaime H. Cheah, Gregory V. Kryukov, Aviad Tsherniak, Francisca Vazquez, Glenn Cowley, Coyin Oh, Anson Peng, Abeer Sayeed, Rebecca Deasy, Peter Ronning, Philip Kantoff, Levi Garraway, Mark A. Rubin, Calvin Kuo, Sidharth Puram, Adi Gazdar, Filemon S. Dela Cruz, Jr., Adam Bass, Jr., Nikhil Wagle, Keith L. Ligon, Katherine Janeway, David Root, Stuart L. Schreiber, Paul A. Clemons, Aly Shamji, William C. Hahn, Todd R. Golub, Jesse S. Boehm. Accelerating prediction of tumor vulnerabilities using next-generation cancer models. [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 B26.

Abstract PR04: Integration of CRISPR-Cas9, RNAi and pharmacologic screens identify actionable targets in a rare cancer

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.

Abstract 4367: Accelerating prediction of tumor vulnerabilities using next-generation cancer models

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.

Abstract PR03: Prmt1 and Prmt1-dependent translation initiation are critical vulnerabilities of osteosarcoma

Osteosarcoma (OS) remains a challenging clinical entity for which targeted therapy is lacking. The frequent mutations of p53 and Rb in OS would be anticipated to create a genetic context in which specific vulnerabilities might exist. To discover genetic vulnerabilities in OS, we conducted high throughput shRNA-based screens in vitro and in vivo using p53/Rb-null OS cells and identified Prmt1, a protein arginine methyltransferase, among other factors as critical to growth/survival of OS cells from genetically engineered mice or human tumors. Indeed, depletion of Prmt1 in OS cells using shRNAs and a Prmt1-specific inhibitor leads to growth arrest and death in vitro. In vivo, Prmt1 inhibition impairs xenograft engraftment and proliferation. Moreover, deletion of Prmt1 in p53/Rb-null osteoblast progenitors using Cre/Lox-based technology significantly inhibits OS initiation and progression in mice, while normal bone development is largely unaffected. In rescue experiments, we find that enzymatically inactive Prmt1 cannot restore proliferation of Prmt1-depleted cells, indicating that the enzymatic activity of Prmt1 is essential for tumorigenicity. To gain mechanistic insights into the molecular functions of Prmt1, we characterized the Prmt1-associated arginine-methylome and downstream targets of Prmt1 using a SILAC-based quantitative proteomics approach. This innovative technique identified many candidate Prmt1-methylated substrates representing various molecular pathways including RNA processing, transcription and translation. In particular, we have shown that loss of Prmt1 leads to a decrease in arginine methylation of members of the eIF4F translation initiation complex, thereby disrupting their physical association and inhibiting translation. Consistent with these findings, we observed that OS cells are sensitive to inhibition of eIF4G, a major component of the eIF4F translation initiation complex, further exposing an additional OS vulnerability to translation inhibition that could be exploited therapeutically. Taken together, our findings implicate a role of Prmt1 in initiation and maintenance of OS and suggest that Prmt1-mediated effects on translation initiation are responsible for tumor proliferation and survival. Based on our findings, we propose that targeted therapy directed to inhibition of Prmt1 and its associated pathways represents a novel and promising therapeutic strategy for OS.

This abstract is also presented as Poster A17.

Citation Format: Jessie Hao-Ru Hsu, Benjamin Hubbell-Engler, Guillaume Adelmant, Jennifer Perry, Glenn Cowley, Jarrod Marto, Stuart H. Orkin. Prmt1 and Prmt1-dependent translation initiation are critical vulnerabilities of osteosarcoma. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Pediatric Cancer Research: From Mechanisms and Models to Treatment and Survivorship; 2015 Nov 9-12; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(5 Suppl):Abstract nr PR03.

Gene sets identified with oncogene cooperativity analysis regulate in vivo growth and survival of leukemia stem cells

Leukemia stem cells (LSCs) represent a biologically distinct subpopulation of myeloid leukemias, with reduced cell cycle activity and increased resistance to therapeutic challenge. To better characterize key properties of LSCs, we employed a strategy based on identification of genes synergistically dysregulated by cooperating oncogenes. We hypothesized that such genes, termed "cooperation response genes" (CRGs), would represent regulators of LSC growth and survival. Using both a primary mouse model and human leukemia specimens, we show that CRGs comprise genes previously undescribed in leukemia pathogenesis in which multiple pathways modulate the biology of LSCs. In addition, our findings demonstrate that the CRG expression profile can be used as a drug discovery tool for identification of compounds that selectively target the LSC population. We conclude that CRG-based analyses provide a powerful means to characterize the basic biology of LSCs as well as to identify improved methods for therapeutic targeting.

Abstract B20: A genome-scale RNA interference screen for resistance mechanisms to BRAF inhibition in melanoma

Mutation of the serine/threonine protein kinase BRAF is observed in approximately 50% of melanomas. This leads to enhanced activation of the kinase, stimulation of the MAPK pathway and aberrant cell proliferation and tumorigenesis. Small molecule inhibitors such as vemurafenib (PLX4032) block BRAF-mediated cell proliferation and recent clinical trials show an overall response rate of about 50%. Evidently, some patients do not respond to the drug or go on to develop resistance while on treatment. Therefore, we designed an RNA interference screen to identify loss of function events that could drive resistance to growth-inhibitory concentrations of PLX4720. We used a pooled lentiviral shRNA library consisting of approximately 90,000 hairpins targeting over 16,500 genes. shRNA-infected cells were split into two groups, one treated with DMSO, the other treated with PLX4720. Following a period in culture, the abundance of each hairpin was assessed by PCR amplification of barcoded hairpin DNA followed by massively parallel paired-end sequencing. RIGER was used to rank the individual hairpins and identify candidate genes that were required for survival of A375 cells in the presence of DMSO, compared to an early time point control. Strikingly, BRAF emerged as the most depleted and therefore most essential gene, validating our screening approach. We then identified those hairpins that were enriched in the presence of PLX4720 compared to DMSO. A small number of genes were identified as candidate ‘resistance suppressors’, which when knocked down, conferred a survival advantage to BRAF inhibitor-treated cells. Validation of these genes in secondary assays and characterization of the resistance mechanism(s) will be presented. By integrating these findings with other genomic and functional studies we aim to identify clinically relevant genetic events that cause RAF-inhibitor resistance in melanoma.

Abstract PR5: Treatment of ovarian cancer with targeted tumor-penetrating siRNA nanocomplexes

Whole-genome analysis of cancer samples is identifying many potential therapeutic targets, by virtue of their being frequently mutated or functionally essential in specific types of cancer. However, we lack efficient ways to test the therapeutic benefit of modulating targets in vivo. RNAi offers one potential solution; however, approaches to deliver siRNA in vivo have been challenging due to their susceptibility to serum nucleases, endosomal entrapment, and stimulation of innate immunity. Furthermore, nanoparticle- and antibody-based siRNA delivery approaches have historically suffered from limited tumor penetration and low transvascular transit, thereby limiting the applicability of parenchymal siRNA targets. Here we describe a tumor penetrating nanocomplex (TPN) comprised of siRNA complexed to a tandem tumor-penetrating and membrane-translocating peptide, which enables the homing of siRNA deep into tumor parenchyma. Upon complexation with siRNA, the resulting nanocomplex is stable, non-immunostimulatory, displays homing peptides in a multivalent fashion that increases their binding avidity and delivers siRNA to the cytosol of tumor cells through receptor-specific interactions and membrane translocation. Upon systemic administration into mice, this nanocomplex penetrates into the parenchyma of metastatic peritoneal tumors and silences target genes in cells of interest in a receptor-specific manner. We employed TPNs in vivo to evaluate ID4, a novel candidate oncogene in ovarian cancer, which we identified by combining genome-scale RNAi screening of cancer cell lines with genome-scale sequence analysis of patient tumors. We show that treatment of tumor-bearing mice with ID4-specific TPNs suppresses tumor growth and significantly improved survival. These findings provide a framework for the identification, credentialing, and understanding of novel cancer targets as well as validating a specific therapeutic target in ovarian cancer.

This abstract is also presented as Poster B2.

Citation Format: Yin Ren, Hiu Wing Cheung, Ronny Drapkin, David Root, Justin Lo, Valentina Fogal, Erkki Ruoslahti, William Hahn, Sangeeta Bhatia, Geoffrey von Maltzahn, Amit Agrawal, Glenn Cowley, Barbara Weir, Jesse Boehm, Pablo Tamayo, Jill Mesirov, Alison Karst. Treatment of ovarian cancer with targeted tumor-penetrating siRNA nanocomplexes [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer; 2012 Jan 8-11; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(2 Suppl):Abstract nr PR5.

Abstract 4972: High-throughput functional profiling of dedifferentiated liposarcoma cell lines

We previously profiled several adult soft-tissue sarcoma subtypes using sequencing, copy number analysis, and gene expression arrays. This high-throughput genetic profiling revealed dozens of candidate genes deserving of further functional validation.

To prioritize genes, we performed a pooled shRNA screen to identify essential genes in liposarcoma. Dedifferentiated liposarcoma (DDLS) served as a model for this approach as multiple robust cell lines are available. Five DDLS cell lines (DDLS8817, LPS141, FU-DDLS-1, RDD8107, and LP6) were infected with a pool of 54,020 lentiviral shRNAs targeting ∼11,000 genes (median 5 shRNAs per gene) and passaged for 16 doublings. DNA was extracted and hairpin sequences were PCR amplified and hybridized to a custom microarray that interrogates all shRNAs in the pool. shRNAs were ranked according to their differential abundance between early and late passages. Genes corresponding to the most significantly depleted shRNAs are presumed to affect cell proliferation and are candidate oncogenes.

We compared the pattern of hairpin depletion in the DDLS cell lines to a panel of over 60 cell lines representing other common cancers screened with the same pooled shRNA library. The 5 DDLS cell lines clustered into 3 distinct groups. Only one gene appeared to be essential in all 3 groups: WWTR1 (TAZ1), a master-regulator of adipocyte differentiation. Twenty-eight genes appeared essential in at least two DDLS groups including MDM2 and ZBTB2, both reported to negatively regulate the tumor suppressor gene TP53. Low-throughput in vitro experiments have confirmed that WWTR1 knockdown using two shRNA clones inhibits LPS141 proliferation by 78 and 75% after 4 days as compared to scramble (p=0.004 and 0.06 respectively).

Our complementary genome-scaled functional screen has confirmed a known oncogene in DDLS and revealed additional candidate genes that appear to have a role in proliferation. Further integration of existing datasets may nominate additional genes essential for liposarcoma pathogenesis.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4972. doi:10.1158/1538-7445.AM2011-4972

Abstract A23: An integrated platform for the functional annotation of the cancer genome

The systematic characterization of mutations in cancer genomes through efforts such as The Cancer Genome Atlas will lead to a comprehensive list of alterations associated with particular cancers. A powerful complementary approach is to comprehensively characterize the functional basis of cancer, by identifying the genes essential for growth and related phenotypes in different cancer cells. Such information would be particularly valuable for identifying potential drug targets. The recent development of an efficient, robust approach to perform genome-scale pooled shRNA screens now permits the highly parallel identification of essential genes in cancer cells in a cost-effective manner. We have initiated a project to identify essential genes in 300 cancer cell lines representing a diverse range of lineages and genotypes. In each screen the abundance of 55,000 shRNA constructs targeting 11,000 genes is monitored in quadruplicate at the completion of 16 population doublings via hybridization of half-hairpin barcodes to a custom Affymetrix microarray. We have developed multiple complementary approaches for the analysis of this screening data at the shRNA level and at the gene level. shRNA level analytical tools include signal to noise and fold depletion metrics to identify individual shRNA constructs whose abundance at the completion of the experiment discriminates two classes of cell lines (e.g., KRASmut vs. KRASwt). Gene level analytical tools include RIGER, a gene-set enrichment analysis (GSEA)-based non-parametric algorithm which treats the 5 shRNA constructs targeting a given gene as a set and assesses bias of each gene-shRNA set as showing evidence of depletion during the experiment. Using these tools, we have begun to systematically identify known and novel anti-cancer drug targets via the integration of these functional screening results with corresponding structural cancer genomic data derived from both the screened cell lines and from known alterations in tumor samples. To facilitate this analysis, each of the screened cell lines has undergone comprehensive molecular characterization (DNA copy number, RNA expression, OncoMap high-throughput mutation profiling) to identify the genomic alterations harbored in its genome. Our preliminary data suggests that this integrated approach is efficient at pinpointing molecular targets that not only include genes altered in cancer genomes but additionally include genes exhibiting a synthetic lethal relationship with an oncogenic driver mutation (e.g., KRAS).We are validating candidate molecular targets using both loss-of-function and gain-of-function secondary screens. To facilitate these gain-of-function screens, we are creating a library of human open reading frames (ORFs) by sequencing and transferring the Human ORFeome collection, developed by the Center for Cancer Systems Biology at the Dana-Farber Cancer Institute, from Gateway Entry vectors into lentiviral expression vectors. This integrated platform for the unbiased, systematic functional annotation of the cancer genome represents an opportunity to identify molecular targets at genome-scale.

Citation Information: Cancer Res 2009;69(23 Suppl):A23.

Gene expression profiling and copy number analysis to identify predictive molecular markers in breast cancer: Successful use of formalin fixed paraffin embedded tissue (FFPE)

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Background: FFPE is a valuable and widely available resource for translational research which to date has been under-used due to technical limitations. Improvement in technology has enabled genome-wide analysis of FFPE samples. We have assessed gene expression and copy number changes in the same cohort of breast cancers to identify markers or pathways important in prediction of treatment response. Methods: FFPE tissues from patients treated with neoadjuvant adriamycin/cyclophosphamide followed by taxanes in a clinical study were used. Gene expression profiling was assessed using the cDNA mediated annealing selection and ligation assay using the cancer panel which assess 502 genes (DASL assay, Illumina). Data was analysed using BeadStudio software. Copy number changes were assessed using the Molecular inversion probe assay with the 50K SNP panel (Affymetrix, California) and analysed using Nexus software (Biodiscovery). Results: Gene expression profiling was carried out on 44 samples. 12/44 (27%) patients had a pathological complete response (pCR) following chemotherapy. Significant differential expression of genes between pCR and non-pCR cancers were shown. TNFRSF5, CTSD, BCL3, ARNT, BIRC3, TGFBR1, MLLT6, and EVI2A were over-expressed and COL18A1, FGF12, IGFBP1 and NOTCH4 which were down-regulated in cancers that have a pCR (p ≤ 0.01). Copy number changes were assessed in 33 samples and comparison of copy number changes in pCR vs. non-pCR showed gains in regions 6q22, 21q21, 4p14, 4q21, 4p14, and loss at 11q11 (p ≤ 0.01). Three regions containing microRNA coding sequences, mir130a (11q11) mir142 (17q23) and mir21 (17q23) showed significant loss among pCR tumours (p < 0.05). Conclusions: This feasibility study shows that FFPE can be used for gene expression and copy number analysis which is a useful tool for the discovery of predictive markers for treatment response in neoadjuvant treatment trials. The role of TNFRSF5, microRNA 21/130a/142, and 11q11 loss should be further investigated as predictive markers of response to chemotherapy.

[Table: see text]

Interstitial laser photocoagulation and interstitial photodynamic therapy of normal lung parenchyma in the pig

Interstitial laser photocoagulation (ILP) and interstitial photodynamic therapy (PDT) involve delivery of light to lesions in solid organs using thin fibres passed through needles inserted percutaneously under image guidance. In ILP, the laser energy heats the tissue, whereas in PDT it activates a previously administered photosensitising agent. This study looks at their potential for treating localised, small, peripheral lung cancers in patients unsuitable for surgery. Experiments were undertaken on nine normal pigs, up to four fibres being inserted into the lung parenchyma percutaneously under X-ray guidance (ILP: 2-3 W, 1000 q/fibre, from 805 nm diode laser, PDT, 100-200 J/fibre from 652 nm diode laser at 50-100 W, 3 days after 0.15 mg/kg mTHPC). Animals were killed from 3 days to 3 months later and the treated areas examined macroscopically and microscopically. Both techniques were well tolerated, producing well-defined, localised lesions, typically 3.5 x 2 x 2 cm using four fibres. Histology showed thermal coagulative necrosis after ILP and haemorrhagic necrosis after PDT. Early small haematomas and late cavitation were sometimes seen after ILP, but not after PDT. PDT lesions healed with preservation of larger arteries and bronchi in the treated area. A few small pneumothoraces were seen which resolved spontaneously, probably related to the chest wall puncture. It was concluded that ILP and PDT lesions of a size large enough to cover a small tumour can be made safely in the lung parenchyma, although healing was better after PDT. Pilot clinical studies with both techniques are now justified on carefully selected patients.