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Functional genomics has held great promise for mapping cancer dependencies and identifying new therapeutic targets in cancer cell lines. However, this remains a major challenge for prostate cancer (PCa) due to the lack of cancer cell models and efficient optimization of pooled genetic perturbation screens. The Broad Cancer Cell Line Factory (CCLF) is attempting to fulfill this unmet need by generating novel prostate cancer cell models and uncovering prostate specific lineage dependencies. Encouragingly, Dr. Yu Chen at Memorial Sloan Kettering Cancer Center has developed several 3D cancer organoids from advanced prostate cancer. Although the success rate needs improvement for primary PCa, over 12 long-term models have been established by this group. In collaboration with Chen Lab, we aim to expand the prostate cell model collection by using PCa organoids and patient-derived normal cells. We can then apply these models for genetic perturbation profiling to unravel prostate lineage specific dependencies. Many historical cancer cell lines and cancer organoids were not compatible with genome-scale CRISPR screening due to the slow cellular growth. Here we first optimized the media conditions in these PCa organoids by using empirical media screening assay conditions through our HYBRID technology. The HYBRID technology allows for the systemic evaluation of 64 media conditions at once, so that we can perform RNAseq analysis to identify the conditions most physiologically resembling the PCa environment with the maximum doubling time. We have further developed our high throughput screening strategies for organoid culture using an optimized protocol to study 3D growth patterns in a 96-well format using the Incucyte S3 System. Next, to map out all possible dependencies, we are testing the feasibility of a pooled genome scale CRISPR screen. In our preliminary assay development results, we demonstrated the reproducible viral infectibility in several organoids ranging from 40-70% infection rate. We anticipate that these genome-wide CRISPR data in PCa organoid and normal prostate cell models can be integrated and analyzed with 4 previous PCa cell lines screened in the Broad Dependency Map to point out more prostate lineage specific genetic vulnerabilities and possible therapeutic targets. Citation Format: Adel Attari, Sean Misek, Pinar Eser, Alexa Yeagley, Yu Chen, Jesse Boehm, Yuen-Yi (Moony) Tseng. Genome-scale CRISPR screen finds prostate lineage specific dependencies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2975.

Precision cancer medicine is based on the ability to predict the dependencies of a given tumor from its molecular makeup. These dependencies can be exploited with targeted, cytotoxic and/or immunity-inducing therapeutics. Ongoing efforts to perform genomic and cellular analyses on clinically annotated patient tumors are powerful, but bounded to existing therapies and focused cohorts. Here, we describe how living tumor material is increasingly being used in the generation of a systematic laboratory-based functional map of cancer dependencies (a 'Cancer Dependency Map'). In particular, we emphasize the important contributions of long-term cell models, emerging uses for short-term cell models and future potential for 'alpha cultures' that are mere hours or days from the cancer patient. Collecting research-grade cancer dependency data with each of these model formats could pave the way to ensure that the Map increasingly reflects all tumors. The integration of clinical genomics and preclinical functional genomics data should provide a powerful research platform to improve the accuracy of precision medicine predictions.

Precision cancer medicine is based on the ability to predict the dependencies of a given tumor from its molecular makeup. Despite successes in multiple common cancers, such prediction remains challenging for the majority of rare and understudied tumors given the absence of laboratory model systems in which to discover and/or validate therapeutic hypotheses. Here, we describe our efforts to address this challenge systematically with the ultimate goal of making it possible to learn how to predict ex vivo growth requirements for cancer samples based on technical, clinical and genomic properties of the starting tumor material. Over the last 5 years, we have processed nearly 2,000 tumor biospecimens and created over 375 genomically-confirmed patient-derived cell lines, organoids and neurosphere cultures, with >10% of these representing rare cancers. To make this possible, we have implemented three key workflows including (1) direct-to-patient sample sourcing, (2) a tissue cryopreservation and genomic credentialing system to ensure quality prior to model creation, and (3) a systematic empirical approach to screening rich medias and variations on organoid technologies ex vivo (HYBRID). We have begun performing genome-wide CRISPR viability screens in these cultures as part of our larger activities to generate a systematic laboratory-based functional map of cancer dependencies (a ‘Cancer Dependency Map'). The novel organoid, spheroid and cell line models created as part of this effort are being made publically available to the scientific community. Looking ahead, as the barriers to culturing rare tumors are overcome, we expect that preclinical functional genomics data will be useful for difficult-to-treat tumors without existing molecularly guided standard-of-care regimens. Citation Format: Yuen-Yi Tseng, Mushriq AI-Jazrawe, Rebecca Deasy, Paula Keskula, Grace Johnson, Andrew Hong, Priya Chatterji, Francisca Vasquez, Adam Bass, Barbara Van Hare, David Sandak, Keith Ligon, Jesse Boehm. Cancer Cell Line Factory: A systematic approach to create next-generation cancer model at scale [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3453.

Precision cancer medicine is based on the ability to predict the dependencies of a given tumor from its molecular makeup. Despite successes in multiple common cancers, such prediction remains challenging for the majority of rare and understudied tumors, given the absence of laboratory model systems in which to discover and/or validate therapeutic hypotheses. Here, we describe our efforts to address this challenge systematically with the ultimate goal of making it possible to learn how to predict ex vivo growth requirements for cancer samples based on technical, clinical, and genomic properties of the starting tumor material. Over the last 5 years, we have processed nearly 2,000 tumor biospecimens and created over 375 genomically confirmed patient-derived cell lines, organoids, and neurosphere cultures, with >10% of these representing rare cancers. To make this possible, we have implemented three key workflows including (1) direct-to-patient sample sourcing, (2) a tissue cryopreservation and genomic credentialing system to ensure quality prior to model creation, and (3) a systematic empirical approach to screening rich media and variations on organoid technologies ex vivo (HYBRID). We have begun performing genome-wide CRISPR viability screens in these cultures as part of our larger activities to generate a systematic laboratory-based functional map of cancer dependencies (a “Cancer Dependency Map”). The novel organoid, spheroid, and cell line models created as part of this effort are being made publicly available to the scientific community. Looking ahead, as the barriers to culturing rare tumors are overcome, we expect that preclinical functional genomics data will be useful for difficult-to-treat tumors without existing molecularly guided standard-of-care regimens. This abstract is also being presented as Poster A06. Citation Format: Yuen-Yi Tseng, Paula Keskula, Rebecca Deasy, Andrew Hong, Priya Chatterji, Francisca Vazquez, Adam Bass, Barbara Van Hare, David Sandak, Keith Ligon, Jesse Boehm. A systematic approach to create patient-derived models of rare tumors [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr PR09.

Over the last several decades, multiple recurrent chromosomal amplifications and deletions have been detected in a large number of cancers. These regions of amplification and deletion can encompass a few to several hundred genes. Determining which of these genes is causing the outgrowth of the cancer is difficult. Complicating the analysis is the fact that several genes within the affected chromosomal region may cooperate to promote tumorigenesis. In this protocol we describe a method of chromosomal engineering in mice that allows modeling of chromosomal duplications and deficiencies. This method faithfully recapitulates several aspects of chromosomal loss and gain in human cancers and can reveal cancer drivers difficult to identify by other means.

Patient-derived xenografts (PDXs) have become a prominent model for studying human cancer in vivo. The underlying assumption is that PDXs faithfully represent the genomic features of primary tumors, retaining their molecular characteristics throughout propagation. However, the genomic stability of PDXs during passaging has not yet been evaluated systematically. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We found that new CNAs accumulated quickly, such that within four passages an average of 12% of the genome was affected by newly acquired CNAs. Selection for preexisting minor clones was a major contributor to these changes, leading to both gains and losses of CNAs. The rate of CNA acquisition in PDX models was correlated with the extent of both aneuploidy and genetic heterogeneity observed in primary tumors of the same tissue. However, the specific CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients, suggesting that PDX tumors are subjected to distinct selection pressures compared to those that exist in human hosts. Specifically, several recurrent CNAs observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Finally, we found that the genomic stability of PDX models also affected their responses to chemotherapy and targeted drugs. Our findings thus highlight the need to couple the timing of PDX molecular characterization to that of drug testing experiments. These results suggest that while PDX models are powerful tools, they should be used with caution.

Plasticity of the cell state has been proposed to drive resistance to multiple classes of cancer therapies, thereby limiting their effectiveness. A high-mesenchymal cell state observed in human tumours and cancer cell lines has been associated with resistance to multiple treatment modalities across diverse cancer lineages, but the mechanistic underpinning for this state has remained incompletely understood. Here we molecularly characterize this therapy-resistant high-mesenchymal cell state in human cancer cell lines and organoids and show that it depends on a druggable lipid-peroxidase pathway that protects against ferroptosis, a non-apoptotic form of cell death induced by the build-up of toxic lipid peroxides. We show that this cell state is characterized by activity of enzymes that promote the synthesis of polyunsaturated lipids. These lipids are the substrates for lipid peroxidation by lipoxygenase enzymes. This lipid metabolism creates a dependency on pathways converging on the phospholipid glutathione peroxidase (GPX4), a selenocysteine-containing enzyme that dissipates lipid peroxides and thereby prevents the iron-mediated reactions of peroxides that induce ferroptotic cell death. Dependency on GPX4 was found to exist across diverse therapy-resistant states characterized by high expression of ZEB1, including epithelial-mesenchymal transition in epithelial-derived carcinomas, TGFβ-mediated therapy-resistance in melanoma, treatment-induced neuroendocrine transdifferentiation in prostate cancer, and sarcomas, which are fixed in a mesenchymal state owing to their cells of origin. We identify vulnerability to ferroptic cell death induced by inhibition of a lipid peroxidase pathway as a feature of therapy-resistant cancer cells across diverse mesenchymal cell-state contexts.

Patient-derived xenografts (PDXs) have become a prominent cancer model system, as they are presumed to faithfully represent the genomic features of primary tumors. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We observed rapid accumulation of CNAs during PDX passaging, often due to selection of pre-existing minor clones. CNA acquisition in PDXs was correlated with the tissue-specific levels of aneuploidy and genetic heterogeneity observed in primary tumors. However, the particular CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients. Several CNAs recurrently observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Importantly, the genomic stability of PDXs was associated with their response to chemotherapy and targeted drugs. These findings have important implications for PDX-based modeling of human cancer.

Identifying therapeutic targets in rare cancers remains challenging due to the paucity of established models to perform preclinical studies. As a proof-of-concept, we developed a patient-derived cancer cell line, CLF-PED-015-T, from a paediatric patient with a rare undifferentiated sarcoma. Here, we confirm that this cell line recapitulates the histology and harbours the majority of the somatic genetic alterations found in a metastatic lesion isolated at first relapse. We then perform pooled CRISPR-Cas9 and RNAi loss-of-function screens and a small-molecule screen focused on druggable cancer targets. Integrating these three complementary and orthogonal methods, we identify CDK4 and XPO1 as potential therapeutic targets in this cancer, which has no known alterations in these genes. These observations establish an approach that integrates new patient-derived models, functional genomics and chemical screens to facilitate the discovery of targets in rare cancers., Identifying therapeutic targets in rare cancers is challenging due to the lack of relevant pre-clinical models. Here, the authors generate a cancer cell line from a paediatric patient with a rare undifferentiated sarcoma and through functional genomics and chemical screens identified CDK4 and XPO1 as potential therapeutic targets in this cancer.

Renal medullary carcinomas (RMCs) are thought to be driven by the loss of tumor suppressor, SMARCB1. These rare kidney cancers carry a very poor prognosis and primarily affect African American adolescents and young adults with sickle cell trait. From two patients with RMC, we have identified by whole-genome sequencing mechanisms of SMARCB1 loss (e.g., inactivating fusion events involving SMARCB1). We developed in vitro models of primary and relapsed metastatic disease. We performed biochemical and functional studies to conclusively show that RMC is dependent on loss of SMARCB1, similar to rhabdoid tumors and atypical teratoid/rhabdoid tumors. Furthermore, we performed small-molecule screens, pooled CRISPR-Cas9 knockout, and RNAi suppression screens focused on druggable cancer targets. Integration of these orthogonal methods identifies a core set of targets that may provide a rational approach to therapeutic targeting for this rare kidney cancer and other SMARCB1-deficient cancers. Citation Format: Andrew L. Hong, Yuen-Yi Tseng, Bryan D. Kynnap, Mihir B. Doshi, Jeremiah Wala, Gabriel Sandoval, Alanna J. Church, Elizabeth Mullen, Cigall Kadoch, Charles W.M. Roberts, Rameen Beroukhim, Jesse S. Boehm, William C. Hahn. Modeling renal medullary carcinomas identifies druggable vulnerabilities in SMARCB1-deficient cancers [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr B18.

Patient-derived xenografts (PDXs) have become a prominent cancer model system, as they are presumed to faithfully represent the genomic features of primary tumors. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We observed rapid accumulation of CNAs during PDX passaging, often due to selection of pre-existing minor clones. CNA acquisition in PDXs was correlated with the tissue-specific levels of aneuploidy and genetic heterogeneity observed in primary tumors. However, the particular CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients. Several CNAs recurrently observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Importantly, the genomic stability of PDXs was associated with their response to chemotherapy and targeted drugs. These findings have important implications for PDX-based modeling of human cancer. Citation Format: Uri Ben-David, Gavin Ha, Yuen-Yi Tseng, Noah F. Greenwald, Coyin Oh, Juliann Shih, James M. McFarland, Bang Wong, Jesse S. Boehm, Rameen Beroukhim, Todd R. Golub. Patient-derived xenografts undergo mouse-specific tumor evolution [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 1028.

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

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.

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.

Of pediatric solid tumors, as many as 10% of tumors are categorized as rare. Many of these rare tumors lack standard effective known therapy. The ability to identify vulnerabilities for many rare tumors has been significantly limited by the lack of in vitro and in vivo models. Furthermore, current approaches to study such vulnerabilities are usually limited to a specific compound or target. Our objectives were 1) to develop a platform to collect tumor samples and generate in vitro models and 2) to develop systematic and orthogonal approaches focused on currently known druggable cancer targets to identify vulnerabilities in these difficult to treat cancers. We have developed a proof of concept cell line from a patient who succumbed to progressive undifferentiated sarcoma treated on an aggressive multi-therapy regimen. This cell line, in its early passages, has novel gene fusions that match that of the primary tumor. Furthermore, even at early passages, this cell line was amenable to high throughput functional screens. Using a targeted pooled shRNA screen (employing matched seed controls) and an analogous CRISPR screen we identified dependencies to XPO1 and CDK4. In parallel, compounds against these targets were identified in a small molecule compound screen. These targetable dependencies were further validated in vivo with a micro-dosing device. These observations identify new targets in this rare malignancy. Furthermore, this suggests that the interrogation of patient derived cell lines facilitates the identification of testable therapeutic approaches. Citation Format: Andrew L. Hong, Glenn S. Cowley, Yuen-Yi Tseng, Jaime H. Cheah, Oliver Jonas, Mihir B. Doshi, Bryan D. Kynnap, Coyin Oh, Stephanie Meyer, Paul Clemons, Michael Burger, Francisca Vazquez, Barbara Weir, Gregory V. Kryukov, Alanna Church, Alma Imamovic, Aviad Tsherniak, Craig Bielski, Brian Crompton, Elizabeth Mullen, Charles Roberts, Carlos Rodriguez-Galindo, Katherine A. Janeway, Kimberly Stegmaier, Paul van Hummelen, Robert Langer, Levi A. Garraway, Stuart L. Schreiber, David E. Root, Jesse S. Boehm, William C. Hahn. Developing a functional genomics platform to interrogate rare pediatric cancers. [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 B38.

With the comprehensive analysis of cancer genomes approaching completion, the research community stands poised to rapidly advance genome-guided therapeutic hypotheses into clinical settings. However, for the vast majority of cancer patients, existing knowledge of the function(s) of the newly discovered mutant genes harbored by their tumor is incomplete or non-existent since most cancer mutations are exceedingly rare. As a result, we now have long lists of candidate alleles but a paucity of targets whose biology is sufficiently well understood to guide therapeutics. Here we present an interim progress report on a pilot effort aiming to create a generalizable framework to systematically map the molecular consequences of cancer variants at scale (Target Accelerator). First, we created an efficient pipeline to generate cancer variants and generated an initial library of 1300 mutant cDNA clones corresponding to variants in lung cancer and diffuse large B-cell lymphoma as well as those nominated by “pan-cancer” computational analyses. Second, we established an industry-scale, next-generation pipeline to generate new cancer models (Cell Line Factory) directly from patient samples. We have leveraged this pipeline to process over 330 samples from 208 patients across 16 cancer types, with over 60% growing through at least 5 population doublings. We show that tumor genomics can be retained in such patient-derived models and that drug testing to discover clinically validated dependencies within 3 months is feasible. In addition, we use combinatorial molecular barcoding to rapidly generate a panel of pathway-primed human tumorigenesis models that are suitable for massively parallel multiplexed tumorigenesis assays in vivo (TumorPlex). We hypothesized that this integrated framework could be utilized to generate meaningful functional hypotheses from cancer variants of unknown significance in a high-throughput manner. To test this hypothesis, we introduced over 1000 cancer mutations into cell models and created gene expression signatures together with phenotypic data. In lung cancer, we show that the mutational impact of mutant alleles with known and unknown functions can be rapidly assessed by comparing signatures of wild-type and mutant alleles. We show that this generalizable approach, which does not require prior knowledge, can place variants of unknown significance into dominant gain-of-function and loss-of-function categories. As a complementary approach, we have used TumorPlex assays to test the tumorigenic potential of 550 mutant alleles nominated by Pan-Cancer computational analyses and discovered unexpected variants in the KRAS, AKT1, MAP2K1, ERBB2, PIK3CB, NFE2L2, FAM200A and POT1 genes as being potently tumorigenic. These proof-of-concept studies demonstrate initial feasibility of mapping cancer variant function at scale. Importantly, they demarcate a path by which mapping variant function and predicting vulnerabilities might soon be possible on a patient-by-patient basis, achieving the promise of precision functional genomics. Citation Format: Alice H. Berger, Eejung Kim, Angela Brooks, Nina Ilic, Yashaswi Shrestha, Yuen-Yi Tseng, Xiaoyun Wu, Lihua Zou, Atanas Kamburov, Xiaoping Yang, Cong Zhu, Paula Keskula, Sara Seepo, Andrew Hong, Philip Kantoff, Keith L. Ligon, Levi A. Garraway, John G. Doench, David E. Root, Matthew Meyerson, William C. Hahn, Gad Getz, Todd R. Golub, Jesse S. Boehm. Towards precision functional genomics via next-generation functional mapping of cancer variants. [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 PR07.

Copy number gain of the 8q24.21 chromosomal region have been a prominent mutation in many human cancers and are associated with poor prognosis. The well-characterized MYC oncogene, which resides in the 8q24.21 region, has been, conventionally, the attractive candidate driving these cancers. However, MYC is often co-gained with an adjacent ”gene desert” region. This “gene desert” often encompasses the long non-coding RNA gene PVT1, the CCDC26 gene candidate, and the GSDMC gene. Whether low copy number gain of one or more of these genes drives neoplasia is not known. We used chromosome engineering in mice to develop strains containing an extra copy of 1) Myc, 2) Pvt1, Ccdc26, Gsdmc, and 3) Myc, Pvt1, Ccdc26, Gsdmc. When rat Neu was introduced into these three strains to test for changes in latency and penetrance in mammary tumor development, only the mice with an extra copy of Myc, Pvt1, Ccdc26, Gsdmc, (but not those with an extra copy of Myc or Pvt1,Ccdc26,Gsdmc), developed adenocarcinomas with reduced latency, suggesting that while an additional copy of the Myc gene failed to measurably advance cancer, it may co-operate with Pvt1, Ccdc26 or Gsdmc to promote cancer. Si-RNA mediated knockdown of Pvt1/PVT1 reduced the proliferation rates in the mouse mammary tumors, as well as in two human breast cell lines with 8q24 amplification (SK-BR-3 and MDA-MB-231). Silencing of PVT1 markedly decreased MYC protein levels, while no effect was seen in MYC transcript levels, suggesting a PVT1-dependence of MYC protein even in high MYC- amplification cancer cells. Analysis of the TCGA and Progenetix databases suggested that 18% of 45,922 human tumors queried harbor MYC copy number increases, and in >99% of MYC-copy-increase cancers, copy number of PVT1 was co-increased .Tissue microarray analysis using in situ hybridization for PVT1 and immunohistochemistry for MYC revealed that PVT1 RNA and MYC protein expression correlated in primary human tumors. These data together suggested that PVT1 can potentiate MYC in human cancer. CRISPR mediated deletion of PVT1 in colorectal cancer cell line HCT116 either partially or fully ablated tumor formation in xenografts and significantly reduced MYC protein levels by ~50%. We propose that the dependence of high MYC protein levels on PVT1 lncRNA provides a much-needed therapeutic target against MYC protein, which has been refractory to small molecule inhibition. Citation Format: Yuen-Yi Tseng, Anindya Bagchi. Long noncoding RNA PVT1 potentiates MYC in human cancers with 8q24 gain. [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 PR01.

Gain of 8q24, harboring the avian myelocytomatosis viral oncogene homolog (MYC), is a frequent mutation in cancers. Although MYC is the usual suspect in these cancers, the role of other co-gained loci remains mostly unknown. We have recently found that MYC partners with the adjacent long non-coding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1), which stabilizes MYC protein and potentiates its activity.