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BACKGROUND: Primary and metastatic prostate cancers have low mutation rates and recurrent alterations in a small set of genes, enabling targeted sequencing of prostate cancer-associated genes as an efficient approach to characterizing patient samples (compared to whole-exome and whole-genome sequencing). For example, targeted sequencing provides a flexible, rapid, and cost-effective method for genomic assessment of patient-derived cell lines to evaluate fidelity to initial patient tumor samples. METHODS: We developed a prostate cancer-specific targeted next-generation sequencing (NGS) panel to detect alterations in 62 prostate cancer-associated genes as well as recurring gene fusions with ETS family members, representing the majority of common alterations in prostate cancer. We tested this panel on primary prostate cancer tissues and blood biopsies from patients with metastatic prostate cancer. We generated patient-derived cell lines from primary prostate cancers using conditional reprogramming methods and applied targeted sequencing to evaluate the fidelity of these cell lines to the original patient tumors. RESULTS: The prostate cancer-specific panel identified biologically and clinically relevant alterations, including point mutations in driver oncogenes and ETS family fusion genes, in tumor tissues from 29 radical prostatectomy samples. The targeted panel also identified genomic alterations in cell-free DNA and circulating tumor cells from patients with metastatic prostate cancer, and in standard prostate cancer cell lines. We used the targeted panel to sequence our set of patient-derived cell lines; however, no prostate cancer-specific mutations were identified in the tumor-derived cell lines, suggesting preferential outgrowth of normal prostate epithelial cells. CONCLUSIONS: We evaluated a prostate cancer-specific targeted NGS panel to detect common and clinically relevant alterations (including ETS family gene fusions) in prostate cancer. The panel detected driver mutations in a diverse set of clinical samples of prostate cancer, including fresh-frozen tumors, cell-free DNA, circulating tumor cells, and cell lines. Targeted sequencing of patient-derived cell lines highlights the challenge of deriving cell lines from primary prostate cancers and the importance of genomic characterization to credential candidate cell lines. Our study supports that a prostate cancer-specific targeted sequencing panel provides an efficient, clinically feasible approach to identify genetic alterations across a spectrum of prostate cancer samples and cell lines.

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Purpose:Novel targeted therapeutics have transformed the care of subsets of patients with cancer. In pediatric malignancies, however, with simple tumor genomes and infrequent targetable mutations, there have been few new FDA-approved targeted drugs. The cyclin-dependent kinase (CDK)4/6 pathway recently emerged as a dependency in Ewing sarcoma. Given the heightened efficacy of this class with targeted drug combinations in other cancers, as well as the propensity of resistance to emerge with single agents, we aimed to identify genes mediating resistance to CDK4/6 inhibitors and biologically relevant combinations for use with CDK4/6 inhibitors in Ewing.Experimental Design:We performed a genome-scale open reading frame (ORF) screen in 2 Ewing cell lines sensitive to CDK4/6 inhibitors to identify genes conferring resistance. Concurrently, we established resistance to a CDK4/6 inhibitor in a Ewing cell line.Results:The ORF screen revealed IGF1R as a gene whose overexpression promoted drug escape. We also found elevated levels of phospho-IGF1R in our resistant Ewing cell line, supporting the relevance of IGF1R signaling to acquired resistance. In a small-molecule screen, an IGF1R inhibitor scored as synergistic with CDK4/6 inhibitor treatment. The combination of CDK4/6 inhibitors and IGF1R inhibitors was synergistic in vitro and active in mouse models. Mechanistically, this combination more profoundly repressed cell cycle and PI3K/mTOR signaling than either single drug perturbation.Conclusions:Taken together, these results suggest that IGF1R inhibitors activation is an escape mechanism to CDK4/6 inhibitors in Ewing sarcoma and that dual targeting of CDK4/6 inhibitors and IGF1R inhibitors provides a candidate synergistic combination for clinical application in this disease.

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Wilms tumor (WT) is the most common renal malignancy of childhood. Despite improvements in the overall survival, relapse occurs in ~15% of patients with favorable histology WT (FHWT). Half of these patients will succumb to their disease. Identifying novel targeted therapies in a systematic manner remains challenging in part due to the lack of faithful preclinical in vitro models. We established ten short-term patient-derived WT cell lines and characterized these models using low-coverage whole genome sequencing, whole exome sequencing and RNA-sequencing, which demonstrated that these ex-vivo models faithfully recapitulate WT biology. We then performed targeted RNAi and CRISPR-Cas9 loss-of-function screens and identified the nuclear export genes (XPO1 and KPNB1) as strong vulnerabilities. We observed that these models are sensitive to nuclear export inhibition using the FDA approved therapeutic agent, selinexor (KPT-330). Selinexor treatment of FHWT suppressed TRIP13 expression, which was required for survival. We further identified in vitro and in vivo synergy between selinexor and doxorubicin, a chemotherapy used in high risk FHWT. Taken together, we identified XPO1 inhibition with selinexor as a potential therapeutic option to treat FHWTs and in combination with doxorubicin, leads to durable remissions in vivo.

The cellular processes that govern tumor resistance to immunotherapy remain poorly understood. To gain insight into these processes, here we perform a genome-scale CRISPR activation screen for genes that enable human melanoma cells to evade cytotoxic T cell killing. Overexpression of four top candidate genes (CD274 (PD-L1), MCL1, JUNB, and B3GNT2) conferred resistance in diverse cancer cell types and mouse xenografts. By investigating the resistance mechanisms, we find that MCL1 and JUNB modulate the mitochondrial apoptosis pathway. JUNB encodes a transcription factor that downregulates FasL and TRAIL receptors, upregulates the MCL1 relative BCL2A1, and activates the NF-κB pathway. B3GNT2 encodes a poly-N-acetyllactosamine synthase that targets >10 ligands and receptors to disrupt interactions between tumor and T cells and reduce T cell activation. Inhibition of candidate genes sensitized tumor models to T cell cytotoxicity. Our results demonstrate that systematic gain-of-function screening can elucidate resistance pathways and identify potential targets for cancer immunotherapy.

Clear-cell carcinomas (CCCs) are a histological group of highly aggressive malignancies commonly originating in the kidney and ovary. CCCs are distinguished by aberrant lipid and glycogen accumulation and are refractory to a broad range of anti-cancer therapies. Here we identify an intrinsic vulnerability to ferroptosis associated with the unique metabolic state in CCCs. This vulnerability transcends lineage and genetic landscape, and can be exploited by inhibiting glutathione peroxidase 4 (GPX4) with small-molecules. Using CRISPR screening and lipidomic profiling, we identify the hypoxia-inducible factor (HIF) pathway as a driver of this vulnerability. In renal CCCs, HIF-2α selectively enriches polyunsaturated lipids, the rate-limiting substrates for lipid peroxidation, by activating the expression of hypoxia-inducible, lipid droplet-associated protein (HILPDA). Our study suggests targeting GPX4 as a therapeutic opportunity in CCCs, and highlights that therapeutic approaches can be identified on the basis of cell states manifested by morphological and metabolic features in hard-to-treat cancers., Clear-cell carcinomas are aggressive tumours characterised by high accumulation of lipids and glycogen. Here, the authors report that these cancers have a common vulnerability to GPX4 inhibition-induced ferroptosis and using CRISPR screen and lipodomic profiling, they identify HIF-2α- HILPDA axis promotes ferroptosis via enrichment of PUFA lipids.

Synthetic lethality-an interaction between two genetic events through which the co-occurrence of these two genetic events leads to cell death, but each event alone does not-can be exploited for cancer therapeutics1. DNA repair processes represent attractive synthetic lethal targets, because many cancers exhibit an impairment of a DNA repair pathway, which can lead to dependence on specific repair proteins2. The success of poly(ADP-ribose) polymerase 1 (PARP-1) inhibitors in cancers with deficiencies in homologous recombination highlights the potential of this approach3. Hypothesizing that other DNA repair defects would give rise to synthetic lethal relationships, we queried dependencies in cancers with microsatellite instability (MSI), which results from deficient DNA mismatch repair. Here we analysed data from large-scale silencing screens using CRISPR-Cas9-mediated knockout and RNA interference, and found that the RecQ DNA helicase WRN was selectively essential in MSI models in vitro and in vivo, yet dispensable in models of cancers that are microsatellite stable. Depletion of WRN induced double-stranded DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models required the helicase activity of WRN, but not its exonuclease activity. These findings show that WRN is a synthetic lethal vulnerability and promising drug target for MSI cancers.

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.

In vitro cancer cultures, including three-dimensional organoids, typically contain exclusively neoplastic epithelium but require artificial reconstitution to recapitulate the tumor microenvironment (TME). The co-culture of primary tumor epithelia with endogenous, syngeneic tumor-infiltrating lymphocytes (TILs) as a cohesive unit has been particularly elusive. Here, an air-liquid interface (ALI) method propagated patient-derived organoids (PDOs) from >100 human biopsies or mouse tumors in syngeneic immunocompetent hosts as tumor epithelia with native embedded immune cells (T, B, NK, macrophages). Robust droplet-based, single-cell simultaneous determination of gene expression and immune repertoire indicated that PDO TILs accurately preserved the original tumor T cell receptor (TCR) spectrum. Crucially, human and murine PDOs successfully modeled immune checkpoint blockade (ICB) with anti-PD-1- and/or anti-PD-L1 expanding and activating tumor antigen-specific TILs and eliciting tumor cytotoxicity. Organoid-based propagation of primary tumor epithelium en bloc with endogenous immune stroma should enable immuno-oncology investigations within the TME and facilitate personalized immunotherapy testing.

SUMMARYKidney cancers are characterized by extensive metabolic reprogramming and resistance to a broad range of anti-cancer therapies. By interrogating the Cancer Therapeutics Response Portal compound sensitivity dataset, we show that cells of clear-cell renal cell carcinoma (ccRCC) possess a lineage-specific vulnerability to ferroptosis that can be exploited by inhibiting glutathione peroxidase 4 (GPX4). Using genome-wide CRISPR screening and lipidomic profiling, we reveal that this vulnerability is driven by the HIF-2α–HILPDA pathway by inducing a polyunsaturated fatty acyl (PUFA)-lipid-enriched cell state that is dependent on GPX4 for survival and susceptible to ferroptosis. This cell state is developmentally primed by the HNF-1β–1-Acylglycerol-3-Phosphate O-Acyltransferase 3 (AGPAT3) axis in the renal lineage. In addition to PUFA metabolism, ferroptosis is facilitated by a phospholipid flippase TMEM30A involved in membrane topology. Our study uncovers an oncogenesis-associated vulnerability, delineates the underlying mechanisms and suggests targeting GPX4 to induce ferroptosis as a therapeutic opportunity in ccRCC.HIGHLIGHTSccRCC cells exhibit strong susceptibility to GPX4 inhibition-induced ferroptosisThe GPX4-dependent and ferroptosis-susceptible state in ccRCC is associated with PUFA-lipid abundanceThe HIF-2α–HILPDA axis promotes the selective deposition of PUFA-lipids and ferroptosis susceptibilityAGPAT3 selectively synthesizes PUFA-phospholipids and primes renal cells for ferroptosis

Purpose: Novel targeted therapeutics have transformed the care of subsets of patients with cancer. In pediatric malignancies, however, with simple tumor genomes and infrequent targetable mutations, there have been few new FDA-approved targeted drugs. The cyclin-dependent kinase (CDK)4/6 pathway recently emerged as a dependency in Ewing sarcoma. Given the heightened efficacy of this class with targeted drug combinations in other cancers, as well as the propensity of resistance to emerge with single agents, we aimed to identify genes mediating resistance to CDK4/6 inhibitors and biologically relevant combinations for use with CDK4/6 inhibitors in Ewing. Experimental Design: We performed a genome-scale open reading frame (ORF) screen in 2 Ewing cell lines sensitive to CDK4/6 inhibitors to identify genes conferring resistance. Concurrently, we established resistance to a CDK4/6 inhibitor in a Ewing cell line. Results: The ORF screen revealed IGF1R as a gene whose overexpression promoted drug escape. We also found elevated levels of phospho-IGF1R in our resistant Ewing cell line, supporting the relevance of IGF1R signaling to acquired resistance. In a small-molecule screen, an IGF1R inhibitor scored as synergistic with CDK4/6 inhibitor treatment. The combination of CDK4/6 inhibitors and IGF1R inhibitors was synergistic in vitro and active in mouse models. Mechanistically, this combination more profoundly repressed cell cycle and PI3K/mTOR signaling than either single drug perturbation. Conclusions: Taken together, these results suggest that IGF1R inhibitors activation is an escape mechanism to CDK4/6 inhibitors in Ewing sarcoma and that dual targeting of CDK4/6 inhibitors and IGF1R inhibitors provides a candidate synergistic combination for clinical application in this disease.

The mammalian genome contains thousands of loci that transcribe long noncoding RNAs (lncRNAs)1-3, some of which are known to play critical roles in diverse cellular processes4-7. LncRNA loci can contribute to cellular regulation through a variety of mechanisms: while some encode RNAs that act non-locally (in trans)6,8, emerging evidence indicates that many lncRNA loci act locally (in cis)—for example, through functions of the lncRNA promoter, the process of lncRNA transcription, or the lncRNA transcript itself in regulating the expression of nearby genes7,9-11. Despite their potentially important roles, it remains challenging to identify functional lncRNA loci and distinguish among these and other mechanisms. To address these challenges, we developed a genome-scale CRISPR-Cas9 activation screen targeting more than 10,000 lncRNA transcriptional start sites to identify noncoding loci that influence a phenotype of interest. We found 11 novel lncRNA loci that, upon recruitment of an activator, each mediate BRAF inhibitor resistance in melanoma. We investigated potential local and non-local mechanisms at these candidate loci and found that most appear to regulate nearby genes. Detailed analysis of one candidate, termed EMICERI, revealed that its transcriptional activation results in dosage-dependent activation of four neighboring protein-coding genes, one of which confers the resistance phenotype. Our screening and characterization approach provides a CRISPR toolkit to systematically discover functions of noncoding loci and elucidate their diverse roles in gene regulation and cellular function.

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 angiomyolipoma is a kidney tumor in the perivascular epithelioid (PEComa) family that is common in patients with Tuberous Sclerosis Complex (TSC) and Lymphangioleiomyomatosis (LAM) but occurs rarely sporadically. Though histologically benign, renal angiomyolipoma can cause life-threatening hemorrhage and kidney failure. Both angiomyolipoma and LAM have mutations in TSC2 or TSC1. However, the frequency and contribution of other somatic events in tumor development is unknown. We performed whole exome sequencing in 32 resected tumor samples (n = 30 angiomyolipoma, n = 2 LAM) from 15 subjects, including three with TSC. Two germline and 22 somatic inactivating mutations in TSC2 were identified, and one germline TSC1 mutation. Twenty of 32 (62%) samples showed copy neutral LOH (CN-LOH) in TSC2 or TSC1 with at least 8 different LOH regions, and 30 of 32 (94%) had biallelic loss of either TSC2 or TSC1. Whole exome sequencing identified a median of 4 somatic non-synonymous coding region mutations (other than in TSC2/TSC1), a mutation rate lower than nearly all other cancer types. Three genes with mutations were known cancer associated genes (BAP1, ARHGAP35 and SPEN), but they were mutated in a single sample each, and were missense variants with uncertain functional effects. Analysis of sixteen angiomyolipomas from a TSC subject showed both second hit point mutations and CN-LOH in TSC2, many of which were distinct, indicating that they were of independent clonal origin. However, three tumors had two shared mutations in addition to private somatic mutations, suggesting a branching evolutionary pattern of tumor development following initiating loss of TSC2. Our results indicate that TSC2 and less commonly TSC1 alterations are the primary essential driver event in angiomyolipoma/LAM, whereas other somatic mutations are rare and likely do not contribute to tumor development., Author Summary We performed comprehensive genome analysis of a kidney tumor called angiomyolipoma. These tumors are known to develop in most individuals who have Tuberous Sclerosis Complex (TSC) and those who have sporadic lymphangioleiomyomatosis (LAM), and are seen rarely in the general population. In these angiomyolipomas, we found consistent involvement of the TSC2 and TSC1 genes that are known to cause TSC, but very few (

Copy number gain of the 8q24 region including the v-myc avian myelocytomatosis viral oncogene homolog (MYC) oncogene has been observed in many different cancers and is associated with poor outcomes. While the role of MYC in tumor formation has been clearly delineated, we have recently shown that co-operation between adjacent long non-coding RNA plasmacytoma variant transcription 1 (PVT1) and MYC is necessary for tumor promotion. Chromosome engineered mice containing an increased copy of Myc-Pvt1 (Gain Myc-Pvt1) accelerates mammary tumors in MMTV-Neu mice, while single copy increase of each is not sufficient. In addition, mammary epithelium from the Gain Myc-Pvt1 mouse show precancerous phenotypes, notably increased DNA replication, elevated -H2AX phosphorylation and increased ductal branching. In an attempt to capture the molecular signatures in pre-cancerous cells we utilized RNA sequencing to identify potential targets of supernumerary Myc-Pvt1 cooperation in mammary epithelial cells. In this extra view we show that an extra copy of both Myc and Pvt1 leads to increased levels of Rspo1, a crucial regulator of canonical β-catenin signaling required for female development. Human breast cancer tumors with high levels of MYC transcript have significantly more PVT1 transcript and RSPO1 transcript than tumors with low levels of MYC showing that the murine results are relevant to a subset of human tumors. Thus, this work identifies a key mechanism in precancerous and cancerous tissue by which a main player in female differentiation is transcriptionally activated by supernumerary MYC and PVT1, leading to increased premalignant features, and ultimately to tumor formation.

BACKGROUND: Tumors involving the central nervous system (CNS) include over 200 primary and metastatic subtypes with major clinical impact. Research on CNS neoplasms has been hampered by the lack of appropriate models for many subtypes. We established a robust workflow and systematic culturing approach to create cancer cell line models from all adult and pediatric CNS cancer patients and here report the results of these ongoing efforts. METHODS: Tumor samples from consented patients with CNS cancers were systematically collected from 2008–18. Tumors were grown in different media and substrates and growth verified by >5 passages. Genomic verification was performed using NGS analysis (focused SNV and CNA) and expression profiling assays. RESULTS: We attempted to generate cell line models from >1500 consented brain tumor patients at the Brigham and Women’s and Boston Children’s Hospital (IRB-10–417) under the DFCI Living Tissue Bank Program as well as within the Broad Institute Cancer Cell Line Factory (CCLF) Project and the Leuven Living Tissue Bank (IRB-S59804, Belgium). 120 different tumor types, including high and low-grade brain tumors of both adult and pediatric origin, were evaluated for in vitro growth in >2000 culturing attempts. The success rate of growing high-grade tumors (i.e. glioma and of other origin) was robustly high (~50%), while the growth verification rate for low-grade tumors remained low (150 novel model systems covering >20 primary and metastatic diagnoses. CONCLUSION: Patient-derived cell lines may be created at scale from primary and metastatic CNS tumors to support pre-clinical cancer research but technological improvements will be required to culture even more tumor types.

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.

We investigated the effect of C-reactive protein (CRP) and sodium azide (NaN(3)) on endothelial Cx43 gap junctions. Human aortic endothelial cells (HAEC) were treated with (a) detoxified CRP, (b) detoxified dialyzed CRP, (c) detoxified dialyzed CRP plus NaN(3), (d) NaN(3), or (e) dialyzed NaN(3). The concentration of CRP in all preparations was fixed to 25 microg/ml and that of NaN(3) in the preparations of (c) to (e) was equivalent to that contained in the 25 microg/ml CRP purchased commercially. The results showed that both the expression of Cx43 protein and gap junctional communication function post-48-h incubation were reduced and inhibited by the detoxified CRP, NaN(3), or detoxified dialyzed CRP plus NaN(3), but not by the detoxified dialyzed CRP or dialyzed NaN(3). Reverse transcription-polymerase chain reaction analysis of cells treated for 72 h also showed a pattern of transcriptional regulation essentially the same as that for the proteins. We concluded that CRP does not have a significant effect on Cx43 gap junctions of HAEC, but NaN(3) inhibited the viability of cells and downregulate their junctions.

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.

Renal medullary carcinoma is a rare kidney cancer that is primarily seen in adolescent and young adult African American patients with sickle cell trait. Prognosis is poor and treatment options are limited. We have developed several cell line models that recapitulate the primary and relapsed metastatic samples from a patient who succumbed to this disease. We have confirmed by whole exome sequencing that our models have sickle cell trait and loss of heterozygosity of the SMARCB1 loci, both hallmarks of this disease. By RNA-sequencing, we see a lack of SMARCB1 transcription. We have further shown dependency of our models to SMARCB1 re-expression thus suggesting that this cancer is indeed driven by loss of SMARCB1 at a functional level. We performed pooled CRISPR-Cas9 and RNAi loss of function screens and a small molecule screen focused on druggable cancer targets based on our previous work in parallel to a genome-wide pooled CRISPR-Cas9 loss of function screen. Integrating these complementary and orthogonal methods, we identified a number of targets for further validation. These targets, when combined may provide a rational approach to therapeutic targeting for this rare kidney cancer. Citation Format: Andrew L. Hong, Yuen-Yi Tseng, Bryan D. Kynnap, Mihir B. Doshi, Gabriel Sandoval, Coyin Oh, Abeer Sayeed, Gill Shubhroz, Alanna J. Church, Paula Keskula, Anson Peng, Paul A. Clemons, Aviad Tsherniak, Francisca Vazquez, Carlos Rodriguez-Galindo, Katherine A. Janeway, Levi A. Garraway, Stuart L. Schreiber, David E. Root, Elizabeth Mullen, Kimberly Stegmaier, Cigall Kadoch, Charles W.M. Roberts, Jesse S. Boehm, William C. Hahn. Identification of Druggable Targets through Functional Multi-Omics in Renal Medullary Carcinoma [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 B17.

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.

Though gain of 8q24.21 is a common mutation in human cancers, its functional annotation is limited to studying myelocytomatosis (MYC), the prominent oncogene in the amplicon. However, MYC is co-gained with an adjacent long non-coding RNA gene plasmacytoma variant translocation 1 (PVT1), the CCDC26 gene candidate and gasdermin C GSDMC. Whether copy number gain of one or more of these genes drives neoplasia is not known. We developed chromosome engineered mouse strains with an extra copy of 1) Myc, 2) Pvt1, Ccdc26, Gsdmc, and 3) Myc, Pvt1, Ccdc26, Gsdmc. When rat Neu was introduced to test the change in the latency in mammary tumor development, only the mice with an extra copy of Myc, Pvt1, Ccdc26, Gsdmc (but not with an extra copy of Myc or Pvt1, Ccdc26, Gsdmc) developed adenocarcinomas with reduced latency, suggesting that while an extra copy of 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 tumors, and in SK-BR-3 and MDA-MB-231, two human breast cancer cell lines with 8q24 amplification. Ablation of PVT1 markedly decreased MYC protein levels, suggesting a PVT1-dependence of MYC protein in MYC amplified cancer cells. Analysis of the cancer genome atlas suggested 99% of tumors with increased MYC copy-number also harbor co-gain of PVT1. Tissue microarray analysis revealed that PVT1 RNA and MYC protein expression are correlated in primary human tumors. These data suggests that PVT1 can potentiate MYC in human cancers. CRISPR mediated deletion of PVT1 in colorectal cell line HCT116 impaired tumor formation in xenografts, and significantly reduced MYC levels. We further confirmed the oncogenic potential of PVT1 in cancers even without supernumerary 8q24. Additionally, we have identified the putative functional domain of PVT1 which confers its oncogenic potential. We propose that the dependence of high levels of MYC on lncRNA PVT1 provides a much-needed therapeutic window against MYC protein, known to be refractory to small molecule inhibition. Citation Format: Kojiro Tashiro, Robert Klink, Jon Zetterval, Veronica Diedrich, Yuen-Yi Tseng, Anindya Bagchi. Insights into the oncogenic basis of long non-coding RNA PVT1 in human cancer. [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 2663.

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.