Targeting TRIP13 in favorable histology Wilms tumor with nuclear export inhibitors synergizes with doxorubicin

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 remains challenging in part due to the lack of faithful preclinical in vitro models. Here we establish twelve patient-derived WT cell lines and demonstrate that these models faithfully recapitulate WT biology using genomic and transcriptomic techniques. We then perform loss-of-function screens to identify the nuclear export gene, XPO1, as a vulnerability. We find that the FDA approved XPO1 inhibitor, KPT-330, suppresses TRIP13 expression, which is required for survival. We further identify synergy between KPT-330 and doxorubicin, a chemotherapy used in high-risk FHWT. Taken together, we identify XPO1 inhibition with KPT-330 as a potential therapeutic option to treat FHWTs and in combination with doxorubicin, leads to durable remissions in vivo.

Radical Collaboration: Reimagining Cancer Team Science

SUMMARY

Here, we define a future of cancer team science adopting "radical collaboration"-in which six "Hallmarks of Cancer Collaboration" are utilized to propel cancer teams to reach new levels of productivity and impact in the modern era. This commentary establishes a playbook for cancer team science that can be readily adopted by others.

Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal

BACKGROUND

Hundreds of functional genomic screens have been performed across a diverse set of cancer contexts, as part of efforts such as the Cancer Dependency Map, to identify gene dependencies-genes whose loss of function reduces cell viability or fitness. Recently, large-scale screening efforts have shifted from RNAi to CRISPR-Cas9, due to superior efficacy and specificity. However, many effective oncology drugs only partially inhibit their protein targets, leading us to question whether partial suppression of genes using RNAi could reveal cancer vulnerabilities that are missed by complete knockout using CRISPR-Cas9. Here, we compare CRISPR-Cas9 and RNAi dependency profiles of genes across approximately 400 matched cancer cell lines.

RESULTS

We find that CRISPR screens accurately identify more gene dependencies per cell line, but the majority of each cell line's dependencies are part of a set of 1867 genes that are shared dependencies across the entire collection (pan-lethals). While RNAi knockdown of about 30% of these genes is also pan-lethal, approximately 50% have selective dependency patterns across cell lines, suggesting they could still be cancer vulnerabilities. The accuracy of the unique RNAi selectivity is supported by associations to multi-omics profiles, drug sensitivity, and other expected co-dependencies.

CONCLUSIONS

Incorporating RNAi data for genes that are pan-lethal knockouts facilitates the discovery of a wider range of gene targets than could be detected using the CRISPR dataset alone. This can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

Abstract 5324: Drug repurposing and genetic screening strategies for effective treatment discovery in soft-tissue sarcomas

Treatment advances for soft-tissue sarcomas have been slow. This is, in part, due to their rarity (accounting for 0.7% of all cancers) and heterogeneity (over 50 different diseases fall under this category). Moreover, preclinical models are scarce, often exhibiting slow growth kinetics, which limits their study by large genetic and pharmacological libraries. Here, we present an update on our efforts to harness the power of patient-partnered research to create a platform for rare cancer drug target discovery as a broadly available community resource. We developed a patient-partnered tissue donation pipeline to enable patients anywhere in the United States to participate and piloted our approach for CTNNB1-driven desmoid tumors. To overcome challenges in tissue heterogeneity during ex vivo culture, we optimized a multiplexed sequencing protocol to quantitatively track changes in tumor cell fraction across hundreds of media formulations. Following this strategy, we were able to verify and expand three cell lines that preserve the CTNNB1 mutations at high purity. To identify potential therapeutics, we completed a 6,750-drug repurposing screen, at 2.5uM in duplicate, in two verified cell line models. After extensive quality control assessments and data integration steps to leverage the power of other large scale drug screens, we selected 263 compounds for follow-up based on potency, selectivity, and association with molecular features associated with desmoid tumors. Approximately 70% of selected compounds were validated by an 8-point, 2-fold dilution, dose-response format with a top concentration of 10uM. Of the confirmed active compounds, 80 showed a strong pattern of selectivity, 20 are FDA approved drugs and 13 investigational compounds show a statistical association with CTNNB1 hotspot mutation status or transcriptomic features associated with desmoid tumors. To prioritize potential therapeutic targets, we tested an efficient CRISPR/Cas9 all-in-one library design. The reduction of the CRISPR/Cas9 library size was achieved via multiple gene- and guide-level strategies, which enables statistically powered gene essentiality interrogation in slow-growing patient-derived models. We tested several plating and infection parameters and developed an optimized pipeline for the rapid introduction of this library into early patient-derived samples. Established cell lines of mesenchymal and non-mesenchymal origin, which have previously been tested by genome-wide libraries, were used to control for library and lineage effects. We are developing a biologist-friendly web portal that will enable the research community to easily interact with models and data produced by this effort. Our study provides evidence that a systematic patient-powered approach can facilitate discovery of therapeutic hypotheses for these understudied diseases.

Citation Format: Mushriq Al-Jazrawe, Kathryn Cebula, Elisabeth A. Abeyta, Haley S. Curtis, Jane K. McIninch, Jaime H. Cheah, James Berstler, Lisa Miller, James Neiswender, Lisa Brenan, Mike Burger, Francisca Vazquez, Jesse S. Boehm. Drug repurposing and genetic screening strategies for effective treatment discovery in soft-tissue sarcomas. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5324.

Cell Painting predicts impact of lung cancer variants

Most variants in most genes across most organisms have an unknown impact on the function of the corresponding gene. This gap in knowledge is especially acute in cancer, where clinical sequencing of tumors now routinely reveals patient-specific variants whose functional impact on the corresponding genes is unknown, impeding clinical utility. Transcriptional profiling was able to systematically distinguish these variants of unknown significance as impactful vs. neutral in an approach called expression-based variant-impact phenotyping. We profiled a set of lung adenocarcinoma-associated somatic variants using Cell Painting, a morphological profiling assay that captures features of cells based on microscopy using six stains of cell and organelle components. Using deep-learning-extracted features from each cell's image, we found that cell morphological profiling (cmVIP) can predict variants' functional impact and, particularly at the single-cell level, reveals biological insights into variants that can be explored at our public online portal. Given its low cost, convenient implementation, and single-cell resolution, cmVIP profiling therefore seems promising as an avenue for using non-gene specific assays to systematically assess the impact of variants, including disease-associated alleles, on gene function.

Sparse dictionary learning recovers pleiotropy from human cell fitness screens

In high-throughput functional genomic screens, each gene product is commonly assumed to exhibit a singular biological function within a defined protein complex or pathway. In practice, a single gene perturbation may induce multiple cascading functional outcomes, a genetic principle known as pleiotropy. Here, we model pleiotropy in fitness screen collections by representing each gene perturbation as the sum of multiple perturbations of biological functions, each harboring independent fitness effects inferred empirically from the data. Our approach (Webster) recovered pleiotropic functions for DNA damage proteins from genotoxic fitness screens, untangled distinct signaling pathways upstream of shared effector proteins from cancer cell fitness screens, and predicted the stoichiometry of an unknown protein complex subunit from fitness data alone. Modeling compound sensitivity profiles in terms of genetic functions recovered compound mechanisms of action. Our approach establishes a sparse approximation mechanism for unraveling complex genetic architectures underlying high-dimensional gene perturbation readouts.

Implementation of a prostate cancer-specific targeted sequencing panel for credentialing of patient-derived cell lines and genomic characterization of patient samples

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 (CTCs) 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, CTCs, 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.

PI3K activation allows immune evasion by promoting an inhibitory myeloid tumor microenvironment

BACKGROUND

Oncogenes act in a cell-intrinsic way to promote tumorigenesis. Whether oncogenes also have a cell-extrinsic effect on suppressing the immune response to cancer is less well understood.

METHODS

We use an in vivo expression screen of known cancer-associated somatic mutations in mouse syngeneic tumor models treated with checkpoint blockade to identify oncogenes that promote immune evasion. We then validated candidates from this screen in vivo and analyzed the tumor immune microenvironment of tumors expressing mutant protein to identify mechanisms of immune evasion.

RESULTS

We found that expression of a catalytically active mutation in phospho-inositol 3 kinase (PI3K), PIK3CA c.3140A>G (H1047R) confers a selective growth advantage to tumors treated with immunotherapy that is reversed by pharmacological PI3K inhibition. PIK3CA H1047R-expression in tumors decreased the number of CD8⁺ T cells but increased the number of inhibitory myeloid cells following immunotherapy. Inhibition of myeloid infiltration by pharmacological or genetic modulation of Ccl2 in PIK3CA H1047R tumors restored sensitivity to programmed cell death protein 1 (PD-1) checkpoint blockade.

CONCLUSIONS

PI3K activation enables tumor immune evasion by promoting an inhibitory myeloid microenvironment. Activating mutations in PI3K may be useful as a biomarker of poor response to immunotherapy. Our data suggest that some oncogenes promote tumorigenesis by enabling tumor cells to avoid clearance by the immune system. Identification of those mechanisms can advance rational combination strategies to increase the efficacy of immunotherapy.

Chronos: a cell population dynamics model of CRISPR experiments that improves inference of gene fitness effects

CRISPR loss of function screens are powerful tools to interrogate biology but exhibit a number of biases and artifacts that can confound the results. Here, we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of cell proliferation dynamics after CRISPR gene knockout. We test Chronos on two pan-cancer CRISPR datasets and one longitudinal CRISPR screen. Chronos generally outperforms competitors in separation of controls and strength of biomarker associations, particularly when longitudinal data is available. Additionally, Chronos exhibits the lowest copy number and screen quality bias of evaluated methods. Chronos is available at https://github.com/broadinstitute/chronos .

Weekly SARS-CoV-2 screening of asymptomatic kindergarten to grade 12 students and staff helps inform strategies for safer in-person learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in K-12 schools was rare during in 2020-2021; few studies included Centers for Disease Control and Prevention (CDC)-recommended screening of asymptomatic individuals. We conduct a prospective observational study of SARS-CoV-2 screening in a mid-sized suburban public school district to evaluate the incidence of asymptomatic coronavirus disease 2019 (COVID-19), document frequency of in-school transmission, and characterize barriers and facilitators to asymptomatic screening in schools. Staff and students undergo weekly pooled testing using home-collected saliva samples. Identification of >1 case in a school prompts investigation for in-school transmission and enhancement of safety strategies. With layered mitigation measures, in-school transmission even before student or staff vaccination is rare. Screening identifies a single cluster with in-school staff-to-staff transmission, informing decisions about in-person learning. The proportion of survey respondents self-reporting comfort with in-person learning before versus after implementation of screening increases. Costs exceed $260,000 for assays alone; staff and volunteers spend 135-145 h per week implementing screening.

Gene Fusions Create Partner and Collateral Dependencies Essential to Cancer Cell Survival

Gene fusions frequently result from rearrangements in cancer genomes. In many instances, gene fusions play an important role in oncogenesis; in other instances, they are thought to be passenger events. Although regulatory element rearrangements and copy number alterations resulting from these structural variants are known to lead to transcriptional dysregulation across cancers, the extent to which these events result in functional dependencies with an impact on cancer cell survival is variable. Here we used CRISPR-Cas9 dependency screens to evaluate the fitness impact of 3,277 fusions across 645 cell lines from the Cancer Dependency Map. We found that 35% of cell lines harbored either a fusion partner dependency or a collateral dependency on a gene within the same topologically associating domain as a fusion partner. Fusion-associated dependencies revealed numerous novel oncogenic drivers and clinically translatable alterations. Broadly, fusions can result in partner and collateral dependencies that have biological and clinical relevance across cancer types. SIGNIFICANCE: This study provides insights into how fusions contribute to fitness in different cancer contexts beyond partner-gene activation events, identifying partner and collateral dependencies that may have direct implications for clinical care.

Abstract 96: Gene fusions create partner and collateral dependencies that are essential to cancer cell survival

Gene fusions frequently result from rearrangements in cancer genomes. In many instances, they play an important role in oncogenesis; in other instances, they are thought to be passenger events. Regulatory element rearrangements and copy number alterations resulting from these structural variants (SVs) lead to transcriptional dysregulation across cancers, though the extent to which these events result in functional dependencies with an impact on cancer cell survival is variable. We demonstrated enrichment of fusion partner genes and “collateral genes”, or genes in the same topologically associating domains (TADs) as fusion partners, among CRISPR-Cas9 dependencies across 209 cell lines with whole-genome sequencing data. We subsequently used CRISPR-Cas9 dependency screens to evaluate the fitness impact of 3,277 fusions across 645 cell lines with RNAseq data from the Cancer Dependency Map (DepMap). We found that 35% of cell lines harbored either a fusion partner dependency or a collateral dependency. CRISPR-Cas9 loss-of-function screening identified fusion-associated dependencies in 20 of 35 (57%) COSMIC fusions from our dataset. Fusion-associated dependencies revealed numerous novel oncogenic drivers and clinically translatable alterations. We observed several instances of intrachromosomal FOXR1 fusions associated with FOXR1 as a strong dependency, occurring independently in osteosarcoma, lung adenocarcinoma, and bladder carcinoma cell lines; these dependencies were validated through in vitro CRISPR-Cas9 knockout. FOXR1 fusions leading to FOXR1 overexpression were seen in an independent cohort of clinical tumor samples, speaking to the clinical relevance of this finding. FGFR3, a targetable kinase, was the key dependency in t(4;14) multiple myeloma cells that harbored an IgH-NSD2 fusion. We also showed that the implications of fusion-associated dependencies extended beyond two-dimensional cell line space, exemplified by dependency on EML4 in the context of a THADA-MTA3 fusion persisting in spheroid models. Broadly, fusions can result in partner and collateral dependencies that have biological and clinical relevance across cancer types.

Citation Format: Riaz Gillani, Bo Kyung A. Seong, Jett Crowdis, Jake R. Conway, Neekesh V. Dharia, Saif Alimohamed, Brian J. Haas, Kyuho Han, Jihye Park, David Liu, Felix Dietlein, Meng Xiao He, Alma Imamovic, Clement Ma, Michael C. Bassik, Jesse S. Boehm, Francisca Vazquez, Alexander Gusev, Katherine A. Janeway, James M. McFarland, Kimberly Stegmaier, Eliezer M. Van Allen. Gene fusions create partner and collateral dependencies that are essential to cancer cell survival [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 96.

The power of parent scientists

Parent scientists lead a journey to bring surveillance severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing to public schools across the state of Massachusetts and beyond.

A first-generation pediatric cancer dependency map

Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR-Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.

Global computational alignment of tumor and cell line transcriptional profiles

Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines.

Synthetic Lethal Interaction between the ESCRT Paralog Enzymes VPS4A and VPS4B in Cancers Harboring Loss of Chromosome 18q or 16q

Few therapies target the loss of tumor suppressor genes in cancer. We examine CRISPR-SpCas9 and RNA-interference loss-of-function screens to identify new therapeutic targets associated with genomic loss of tumor suppressor genes. The endosomal sorting complexes required for transport (ESCRT) ATPases VPS4A and VPS4B score as strong synthetic lethal dependencies. VPS4A is essential in cancers harboring loss of VPS4B adjacent to SMAD4 on chromosome 18q and VPS4B is required in tumors with co-deletion of VPS4A and CDH1 (E-cadherin) on chromosome 16q. We demonstrate that more than 30% of cancers selectively require VPS4A or VPS4B. VPS4A suppression in VPS4B-deficient cells selectively leads to ESCRT-III filament accumulation, cytokinesis defects, nuclear deformation, G2/M arrest, apoptosis, and potent tumor regression. CRISPR-SpCas9 screening and integrative genomic analysis reveal other ESCRT members, regulators of abscission, and interferon signaling as modifiers of VPS4A dependency. We describe a compendium of synthetic lethal vulnerabilities and nominate VPS4A and VPS4B as high-priority therapeutic targets for cancers with 18q or 16q loss.

Neggers, Paolella, and colleagues identify the ATPases VPS4A and VPS4B as selective vulnerabilities and potential therapeutic targets in cancers harboring loss of chromosome 18q or 16q. In VPS4B-deficient cancers, VPS4A suppression leads to ESCRT-III dysfunction, nuclear deformation, and abscission defects. Moreover, ESCRT proteins and interferons can modulate dependency on VPS4A.

Abstract PO-011: Synthetic lethal interaction between the ESCRT paralog enzymes VPS4A and VPS4B in SMAD4 or CDH1-deleted cancers

Somatic copy number alterations that result in loss of tumor suppressor gene function are important drivers of tumorigenesis. However, few existing therapeutic options to target oncogenic processes evoked by tumor suppressor gene inactivation exist. The discovery of synthetic lethal interactions with genetic drivers of cancer may yield new therapeutic strategies with cancer selective potential. We examined genome-scale CRISPR-SpCas9 and RNA interference screens to uncover new synthetic lethal vulnerabilities associated with the loss of common tumor suppressor genes (TSGs). The ATPases Vacuolar protein sorting 4 homolog A (VPS4A) and B (VPS4B) scored as strong synthetic lethal dependencies, with VPS4A selectively essential in cancers harboring loss of VPS4B adjacent to SMAD4 and VPS4B required in tumors with co-deletion of VPS4A and CDH1 (encoding E-cadherin). VPS4B resides 12.3 Mb away from the SMAD4 TSG on chromosome 18q and is lost in approximately 33% of all cancers, suggesting broad clinical applicability. Moreover, VPS4B is commonly lost in pancreatic cancer due to the frequent loss of SMAD4, highlighting VPS4A represents a promising target for this deadly cancer. VPS4A and VPS4B function as AAA ATPases forming a multimeric protein complex within the endosomal sorting complex required for transport (ESCRT) pathway to regulate membrane remodeling in a range of cellular processes. VPS4A suppression in cells with VPS4B/SMAD4 loss led to accumulation of ESCRT-III filaments, cytokinesis defects, nuclear deformation and micronucleation, which ultimately resulted in G2/M cell cycle arrest and apoptosis. Furthermore, upon VPS4A suppression, we observed potent in vivo tumor regression, which led to extended survival, in mouse subcutaneous xenograft models utilizing a pancreatic or rhabdomyosarcoma cancer cell line harboring VPS4B loss. CRISPR-SpCas9 screening and integrative genomic analysis revealed other ESCRT members, regulators of abscission and interferon signaling as modifiers of VPS4A dependency. Using the most comprehensive available CRISPR-SpCas9 and RNA-interference screening datasets to date, we provide a compendium of synthetic lethal vulnerabilities with TSG loss and credential VPS4A as a new and promising therapeutic target in cancers with VPS4B/SMAD4 deletion.

Citation Format: Jasper E. Neggers, Brenton R. Paolella, Adhana Asfaw, Michael V. Rothberg, Thomas A. Skipper, Radha L. Kalekar, Michael J. Krill-Burger, Neekesh V. Dharia, Guillaume Kugener, Adam D. Durbin, Annan Yang, Nancy Dumont, Yvonne Y. Li, Brian M. Wolpin, Federica Piccioni, David E. Root, Jesse S. Boehm, Andrew D. Cherniack, Aviad Tsherniak, Andrew L. Hong, William C. Hahn, Kimberly Stegmaier, Todd R. Golub, Francisca Vazquez, Andrew J. Aguirre. Synthetic lethal interaction between the ESCRT paralog enzymes VPS4A and VPS4B in SMAD4 or CDH1-deleted cancers [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PO-011.

Abstract LB-053: VPS4A is a synthetic lethal target in VPS4B-deficient cancers due to co-deletion with SMAD4

Somatic copy number alterations that result in loss of tumor suppressor gene function are important drivers of tumorigenesis. However, few existing therapeutic options to target oncogenic processes evoked by tumor suppressor gene inactivation exist. The discovery of synthetic lethal interactions with genetic drivers of cancer may yield new therapeutic strategies with cancer selective potential. We examined genome-scale CRISPR-SpCas9 and RNA interference screens to uncover new synthetic lethal vulnerabilities associated with the loss of common tumor suppressor genes (TSGs).

Vacuolar protein sorting 4 homolog A (VPS4A) scored as a strong, selective dependency in cancer cells with genomic loss of the SMAD4 tumor suppressor due to co-deletion of VPS4A's paralog gene, VPS4B. VPS4B resides 12.3 Mb away from the SMAD4 TSG on chromosome 18q and is lost in approximately 33% of all cancers, suggesting broad clinical applicability. VPS4A and VPS4B function as AAA ATPases forming a multimeric protein complex within the endosomal sorting complex required for transport (ESCRT) pathway to regulate membrane remodeling in a range of cellular processes. VPS4A suppression in cells with VPS4B/SMAD4 loss led to accumulation of ESCRT-III filaments, cytokinesis defects, nuclear deformation and micronucleation, which ultimately resulted in G2/M cell cycle arrest and apoptosis. Furthermore, upon VPS4A suppression, we observerd potent in vivo tumor regression, which led to extended survival, in mouse subcutaneous xenograft models with human cancer cell lines harboring VPS4B loss. Finally, genome-scale CRISPR-SpCas9 loss-of-function screening revealed other ESCRT pathway members and regulators of cellular abscission as modifiers of VPS4A dependency.

Using the most comprehensive available CRISPR-SpCas9 and RNA-interference screening datasets to date, we provide a compendium of synthetic lethal vulnerabilities with TSG loss and credential VPS4A as a new and promising therapeutic target in cancers with VPS4B/SMAD4 deletion.

Citation Format: Jasper E. Neggers, Brenton R. Paolella, Adhana Asfaw, Michael V. Rothberg, Thomas A. Skipper, Radha L. Kalekar, John M. Krill-Burger, Andrew L. Hong, Guillaume Kugener, Jeremie Kalfon, Annan Yang, Chen Yuan, Nancy Dumont, Alfredo Gonzalez, Mai Abdusamad, Yvonne Y. Li, Liam F. Spurr, Westley W. Wu, Federica Piccioni, Brian M. Wolpin, David E. Root, Jesse S. Boehm, Andrew D. Cherniack, Aviad Tsherniak, Todd R. Golub, Francisca Vazquez, Andrew J. Aguirre. VPS4A is a synthetic lethal target in VPS4B-deficient cancers due to co-deletion with SMAD4 [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 LB-053.

Abstract LB-100: Systematic target prioritization and validation from genome-scale loss-of-function screens in large panels of human cancer cell lines

Despite its increasing success and revolutionary impact on clinical oncology, Precision Cancer Medicine still has major roadblocks before it becomes applicable to a large proportion of patients. One such roadblock is the limited number of therapeutic targets available. Indeed, for the vast majority of cancer patients, we either do not know what their specific vulnerabilities are or do not have strategies to precisely target their vulnerabilities. In the Cancer Dependency Map Project (DepMap) at the Broad Institute, we aim to overcome these limitations through the use of genome-scale loss-of-function screens in a large panel of cancer cell lines combined with systematic molecular characterization of these cell lines. To date, we have conducted viability screens with genome-wide RNAi and CRISPR/Cas9 libraries on > 800 cell lines, all of which have also been comprehensively profiled with various omics approaches. In order to systematically identify and prioritize potential therapeutic targets, we created an analytical framework that uses a multifaceted approach to score gene dependencies based on the information extracted from screening outcomes, predictive models of sensitivity from all the genetic and molecular information, and the use of priors. To reproducibly validate the nominated targets, we also developed a toolbox of standardized assays that include confirmation of cell viability effects with orthogonal reagents/read-outs and efficient testing for in vivo efficacy across multiple cancer models. Using this approach, we have identified and validated several promising targets, including the WRN DNA helicase that is selectively essential in cancers with microsatellite instability (MSI). The data, framework, and toolbox developed here can inform the nomination and advancement of promising targets for drug development for Precision Cancer Medicine.

Citation Format: Tsukasa Shibue, John M. Krill-Burger, Brenton R. Paolella, Benjamin Gaeta, Adhana Asfaw, Joshua M. Dempster, James M. McFarland, David E. Root, Jesse S. Boehm, Aviad Tsherniak, William C. Hahn, Francisca Vazquez. Systematic target prioritization and validation from genome-scale loss-of-function screens in large panels of human cancer cell lines [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 LB-100.

Abstract B10: Rhabdomyosarcoma requires MYC family genomic events to pathogenically subvert core-regulatory circuitry

Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood. Despite multimodality therapy and trials of molecularly targeted agents, limited improvements in overall survival have been realized for patients with high-risk disease. Thus, we aimed to determine the landscape of tumor-specific gene dependencies that underlie tumorigenesis in RMS and therefore provide a valuable group of targets for the development of novel therapeutics. Using unbiased genome-scale CRISPR-Cas9 approaches, we identified a set of RMS-specific tumor dependencies involved in tumor cell growth and survival. RMS dependencies were enriched for nucleic acid binding proteins, including transcription factors (TFs). We then used genome-wide chromatin-immunoprecipitation coupled to high-throughput sequencing analysis to demonstrate that a small number of essential TFs—MYCN, MYOD1, TCF12, SOX8, ZEB2, ZNF217, and SIX1—are members of the transcriptional core regulatory circuitry (CRC) that maintains the malignant cell state of RMS. Both c-MYC and MYCN were associated with gene and enhancer copy number increases in cell lines and primary tumors and represented strong dependencies in the RMS cell lines screened. c-MYC and MYCN function to similarly invade and regulate the CRC in respectively dependent cells. To disable the CRC, we tested A485, an inhibitor of the histone acetyltransferase enzymes involved in the establishment of super-enhancer elements that are associated with high level expression of the CRC factors. A485 caused a reversible and rapid loss of CRC factor and c-MYC and/or MYCN expression, and prolonged treatment resulted in cell cycle arrest, differentiation, and apoptosis in vitro and in vivo. This phenotype is rescued by exogenous re-expression of either c-MYC or MYCN in a manner insensitive to A485, indicating a mechanism by which these genes subvert a myogenic CRC to produce an oncogenic fate. This study defines a common set of critical dependency genes in RMS and identifies key genomic events surrounding the c-MYC and MYCN loci that lead to elevated expression and tumorigenesis.

Citation Format: Adam D. Durbin, Guillaume Kugener, Mark W. Zimmerman, Chuan Yan, Neekesh V. Dharia, Elizabeth S. Frank, Xiang Chen, Ken N. Ross, Brenton Paolella, Michael Krill-Burger, David E. Root, Jesse S. Boehm, Francisca Vazquez, Andrew L. Hong, Aviad Tsherniak, David M. Langenau, William C. Hahn, Todd R. Golub, Brian J. Abraham, Richard A. Young, A. Thomas Look, Kimberly Stegmaier. Rhabdomyosarcoma requires MYC family genomic events to pathogenically subvert core-regulatory circuitry [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B10.

The Cancer Dependency Map enables drug mechanism-of-action investigations

How do small molecules exert their effects in mammalian cells? This seemingly simple question continues to represent one of the fundamental challenges of modern translational science and as such has long been the subject of intense scientific scrutiny. In their recent study, Garnett and colleagues (Gonçalves et al, 2020) demonstrate proof-of-concept for a new way to attack this problem systematically for Oncology drugs, by identifying correlated CRISPR- and drug-killing profiles in the Cancer Dependency Map dataset.

Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling

Anti-cancer uses of non-oncology drugs have occasionally been found, but such discoveries have been serendipitous. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. We used PRISM, a molecular barcoding method, to screen drugs against cell lines in pools. An unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines in a manner predictable from the cell lines' molecular features. Our findings include compounds that killed by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing depended on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which killed cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, which killed via the multi-drug resistance protein ABCB1. The PRISM drug repurposing resource (https://depmap.org/repurposing) is a starting point to develop new oncology therapeutics, and more rarely, for potential direct clinical translation.

Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling

Anti-cancer uses of non-oncology drugs have occasionally been found, but such discoveries have been serendipitous. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. We used PRISM, a molecular barcoding method, to screen drugs against cell lines in pools. An unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines in a manner predictable from the cell lines' molecular features. Our findings include compounds that killed by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing depended on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which killed cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, which killed via the multi-drug resistance protein ABCB1. The PRISM drug repurposing resource (https://depmap.org/repurposing) is a starting point to develop new oncology therapeutics, and more rarely, for potential direct clinical translation.

Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets

Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyze data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger Institutes. Despite significant differences in experimental protocols and reagents, we find that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one data set are recovered in the other. Through further analysis and replication experiments at each institute, we show that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings.

Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States

The systematic sequencing of the cancer genome has led to the identification of numerous genetic alterations in cancer. However, a deeper understanding of the functional consequences of these alterations is necessary to guide appropriate therapeutic strategies. Here, we describe Onco-GPS (OncoGenic Positioning System), a data-driven analysis framework to organize individual tumor samples with shared oncogenic alterations onto a reference map defined by their underlying cellular states. We applied the methodology to the RAS pathway and identified nine distinct components that reflect transcriptional activities downstream of RAS and defined several functional states associated with patterns of transcriptional component activation that associates with genomic hallmarks and response to genetic and pharmacological perturbations. These results show that the Onco-GPS is an effective approach to explore the complex landscape of oncogenic cellular states across cancers, and an analytic framework to summarize knowledge, establish relationships, and generate more effective disease models for research or as part of individualized precision medicine paradigms.

Abstract 2948: Novel cell line barcoding method reveals tepoxalin as a selective drug against MDR1-high tumor cells

Drug repurposing for cancer therapy has seen notable success, such as the reintroduction of thalidomide. To discover new indications, all existing drugs known to be safe should be systematically evaluated for anti-cancer properties. However, given cost and throughput considerations, phenotypic screens are typically limited along the cell line (e.g., NCI-60 cell lines) or compound dimension (e.g., testing only several hundred cancer drugs). To identify new repurposing opportunities at scale, we tested more than 4,600 existing drugs for cytotoxicity against 578 diverse cancer cell lines using PRISM, a recently developed multiplexed cell viability assay. High gene expression of the ABCB1 transporter (MDR1/p-glycoprotein) was strongly correlated with resistance to numerous approved cancer therapeutics including taxanes, anthracyclines, vinca alkaloids, and proteasome inhibitors. Surprisingly, we identified a single drug, tepoxalin, with the opposite profile: selective activity against MDR1-high cancer cell lines.

Tepoxalin, an oral cyclooxygenase inhibitor, is FDA-approved for treatment of osteoarthritis in dogs and was previously found to be safe in human trials. To evaluate for alternative mechanism of action and genomic mediators of tepoxalin resistance, we conducted pooled genome-wide CRISPR-Cas9 modifier screens in the LS1034 colorectal cancer cell line. The CRISPR knockout screen revealed ABCB1 as the top enriched gene mediating tepoxalin resistance, while the CRISPR activation screen revealed ABCB1 as the top depleted gene. Overexpression of ABCB1 in ovarian cancer cell lines also sensitized to tepoxalin killing. Small molecule combination testing demonstrated rescue of tepoxalin killing by known ABCB1 inhibitors. Future studies will include biophysical characterization of the interaction between tepoxalin and the ABCB1 protein as well as in vivo efficacy testing. Our results demonstrate that tepoxalin or related derivatives may be useful in the treatment of chemotherapy-resistant colorectal cancer, ovarian cancer, lymphoma, and other malignancies.

Citation Format: Steven M. Corsello, Ryan D. Spangler, Rohith T. Nagari, Mustafa Kocak, Jordan Rossen, Patrick O'Hearn, Jennifer Roth, Alfredo Gonzalez, Nancy Dumont, John Doench, Jesse S. Boehm, Francisca Vazquez, Aviad Tsherniak, Todd R. Golub. Novel cell line barcoding method reveals tepoxalin as a selective drug against MDR1-high tumor cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2948.

A GPX4-dependent cancer cell state underlies the clear-cell morphology and confers sensitivity to ferroptosis

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.

Renal medullary carcinomas depend upon SMARCB1 loss and are sensitive to proteasome inhibition

Renal medullary carcinoma (RMC) is a rare and deadly kidney cancer in patients of African descent with sickle cell trait. We have developed faithful patient-derived RMC models and using whole-genome sequencing, we identified loss-of-function intronic fusion events in one SMARCB1 allele with concurrent loss of the other allele. Biochemical and functional characterization of these models revealed that RMC requires the loss of SMARCB1 for survival. Through integration of RNAi and CRISPR-Cas9 loss-of-function genetic screens and a small-molecule screen, we found that the ubiquitin-proteasome system (UPS) was essential in RMC. Inhibition of the UPS caused a G2/M arrest due to constitutive accumulation of cyclin B1. These observations extend across cancers that harbor SMARCB1 loss, which also require expression of the E2 ubiquitin-conjugating enzyme, UBE2C. Our studies identify a synthetic lethal relationship between SMARCB1-deficient cancers and reliance on the UPS which provides the foundation for a mechanism-informed clinical trial with proteasome inhibitors.

Renal medullary carcinoma (RMC for short) is a rare type of kidney cancer that affects teenagers and young adults. These patients are usually of African descent and carry one of the two genetic changes that cause sickle cell anemia. RMC is an aggressive disease without effective treatments and patients survive, on average, for only six to eight months after their diagnosis.

Recent genetic studies found that most RMC cells have mutations that prevent them from producing a protein called SMARCB1. SMARCB1 normally acts as a so-called tumor suppressor, preventing cells from becoming cancerous. However, it was not clear whether RMCs always have to lose SMARCB1 if they are to survive and grow.

Often, diseases are studied using laboratory-grown cells and tissues that have certain features of the disease. No such models had been created for RMC, which has slowed efforts to understand how the disease develops and find new treatments for it. Hong et al. therefore worked with patients to develop new lines of cells that can be used to study RMC in the laboratory. These RMC cells started dying when they were given copies of the SMARCB1 gene, which supports the theory that RMCs have to lose SMARCB1 in order to grow.

Hong et al. then used a set of genetic reagents that can suppress or delete genes that are targeted by drugs, and followed this by testing a range of drugs on the RMC cells. Drugs and genetic reagents that reduced the activity of the proteasome – the structure inside cells that gets rid of old or unwanted proteins – caused the RMC cells to die. These proteasome inhibitor drugs also killed other kinds of cancer cells with SMARCB1 mutations.

Proteasome inhibitors are already used to treat different types of cancer. Potentially, a clinical trial could be run to see if they will treat patients whose cancers lack SMARCB1. Further work is also needed to determine the exact link between SMARCB1 and the proteasome.

Organoid Modeling of the Tumor Immune Microenvironment

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.

A Combination CDK4/6 and IGF1R Inhibitor Strategy for Ewing Sarcoma

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.

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens ( https://depmap.org/R2-D2 ). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens (https://depmap.org/R2-D2). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

Integrated analyses of multiple large-scale screenings can be complicated by batch effects and technical artefacts. McFarland et al. introduce DEMETER2, a hierarchical model coupled with model-based normalization, which allows the assessment of differential dependencies across genes and cell lines.

Abstract B18: Modeling renal medullary carcinomas identifies druggable vulnerabilities in SMARCB1-deficient cancers

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.

Abstract 2352: Defining a pediatric cancer dependency map

Many children with metastatic or recurrent pediatric solid tumors continue to have poor survival, and there is an immense need to identify novel therapeutic approaches. Moreover, these cancers typically have simple genomes with limited known druggable molecular events. In order to discover new vulnerabilities in pediatric solid tumors, we have performed genome-scale CRISPR-Cas9 loss-of-function screening and deep “omic” characterization in over 60 pediatric cancer cell lines to date, including neuroblastoma, medulloblastoma, Ewing sarcoma, malignant rhabdoid tumor and rhabdomyosarcoma lines, to begin defining a pediatric cancer dependency map. Global analyses of the pediatric dependency landscape have identified emerging classes of pediatric cancers, including epigenetic-driven, aberrant transcription factor-driven and receptor tyrosine kinase-driven malignancies. For example, the preferential dependencies identified in a subset of neuroblastoma, which has aberrantly high expression of the transcription factor MYCN, are highly enriched for an interconnected network of genes annotated to have transcription factor activity. In addition to the global evaluation, we have developed methods and tools for prioritizing targets for further validation within a cancer type. These tools computationally integrate the pediatric dependency data across multiple datasets to identify categories of genetic dependencies that are especially strong hits or enriched hits in a specific pediatric malignancy. As an example, the intersection of MYCN-amplified neuroblastoma specific dependencies and H3-lysine 27 acetylation (H3K27ac) profiling across MYCN-amplified neuroblastoma allowed us to identify a transcriptional core regulatory circuit (CRC) that may drive the malignant state. Furthermore, targeting transcription with the BRD4 inhibitor JQ1 and CDK7 inhibitor THZ1 caused synergistic killing of neuroblastoma cells suggesting a novel therapeutic approach to treating this disease. Thus, defining a comprehensive pediatric cancer dependency map and developing the methods and tools to prioritize vulnerabilities in different cancer types will allow us to discover both novel biology and new therapeutic opportunities in childhood malignancies.

Citation Format: Neekesh V. Dharia, Clare Malone, Amanda Balboni Iniguez, Lillian Guenther, Liying Chen, Gabriela Alexe, Adam D. Durbin, Mark W. Zimmerman, Andrew Hong, Pratiti Bandopadhayay, Mariella G. Filbin, Thomas Howard, Brenton Paolella, Iris Fung, Josephine Lee, Phil Montgomery, John M. Krill-Burger, Brian J. Abraham, Jennifer Roth, David E. Root, Richard A. Young, A. Thomas Look, Rameen Beroukhim, Jesse S. Boehm, William C. Hahn, Todd R. Golub, Aviad Tsherniak, Francisca Vazquez, Kimberly Stegmaier. Defining a pediatric cancer dependency map [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 2352.

Abstract 1028: Patient-derived xenografts undergo mouse-specific tumor evolution

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.

Abstract LB-225: Liquid biopsies identify trunk mutations and reflect multiple tumors in a patient

Introduction: Precision medicine approaches to guide therapy selection require routine sampling of tumors. However, tumor biopsies are not always accessible and may be confounded by spatial heterogeneity. Liquid biopsies, including analysis of cell-free DNA (cfDNA), present a non-invasive alternative which may reflect multiple tumors in the body. Previous studies have demonstrated exome-wide concordance between single-site tumor biopsies and cfDNA, but little is known about how cfDNA reflects multiple lesions within a patient. Here we sought to determine how cfDNA reflects the body-wide tumor phylogeny, which will inform the use of cfDNA for cancer precision medicine.

Methods: We identified 20 patients with pancreatic cancer who had undergone rapid autopsy. We then screened cfDNA tumor fraction and performed whole-exome sequencing of cfDNA and multiple tumor biopsies for 3 patients with cfDNA tumor fraction >10%. We inferred the tumor phylogeny and then developed a statistical approach to deconvolute the contributions to cfDNA from tumor phylogenetic nodes. Finally, we determined whether shared trunk mutations could be detected in cfDNA and tumor biopsies.

Results: For each patient, we found mutations shared between all sites and cfDNA, including putative driver mutations. We found mutations which were clonal in multiple regions were detectable in cfDNA, whereas mutations private to individual sites were never clonal in cfDNA. Through our deconvolution analysis, we found that cfDNA could not be modeled as a simple linear combination of individual sites, but rather that cfDNA represented multiple nodes in the inferred phylogeny. For two pancreatic adenocarcinoma patients, the inferred ancestor of the metastases had high estimated contribution (>70%) to cfDNA, while the ancestors of the primaries had lower contributions (<10%). Next, we considered trunk mutations, which originate earliest in the tumor phylogenetic tree. When we analyzed precision for detection of trunk mutations, we found on average, 71% of clonal mutations in metastases were truncal, while only 55% of clonal mutations in primary tumors were truncal. Due to copy number deletions, not all trunk mutations were detected in metastases. Finally, on average, cfDNA had equal or better precision than 83% of primaries and 88% of metastases, suggesting cfDNA may provide more accurate trunk SSNV calls than tumor biopsies.

Conclusions: Through analyzing cfDNA and synchronous tumor biopsies from the same patient, we find trunk mutations are enriched in cfDNA as compared to the average single-site biopsy. We also predict that cfDNA represents multiple nodes in the inferred phylogeny. In cases where tumor biopsies are inaccessible, we demonstrate that cfDNA might be a promising alternative to detect trunk SSNVs. These results suggest that cfDNA may be complementary to tumor biopsies for disease monitoring and treatment selection in personalized medicine.

Citation Format: Samuel S. Freeman, Ziao Lin, Gavin Ha, Ignaty Leshchiner, Justin Rhoades, Dimitri Livitz, Daniel Rosebrock, Sarah C. Reed, Gregory Gydush, Christopher Lo, Denisse Rotem, Atish D. Choudhury, Daniel G. Stover, Heather A. Parsons, Jesse S. Boehm, J Christopher Love, Matthew Meyerson, Paul Grandgenett, Michael A. Hollingsworth, Viktor A. Adalsteinsson, Gad Getz. Liquid biopsies identify trunk mutations and reflect multiple tumors in a patient [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 LB-225.

NetSig: network-based discovery from cancer genomes

Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

Genetic interrogation of circulating multiple myeloma cells at single-cell resolution

Multiple myeloma (MM) remains an incurable disease, with a treatment-refractory state eventually developing in all patients. Constant clonal evolution and genetic heterogeneity of MM are a likely explanation for the emergence of drug-resistant disease. Monitoring of MM genomic evolution on therapy by serial bone marrow biopsy is unfortunately impractical because it involves an invasive and painful procedure. We describe how noninvasive and highly sensitive isolation and characterization of circulating tumor cells (CTCs) from peripheral blood at single-cell resolution recapitulate MM in the bone marrow. We demonstrate that CTCs provide the same genetic information as bone marrow MM cells and even reveal mutations with greater sensitivity than bone marrow biopsies in some cases. Single CTC RNA sequencing enables classification of MM and quantitative assessment of genes that are relevant for prognosis. We propose that the genomic characterization of CTCs should be included in clinical trials to follow the emergence of resistant subclones after MM therapy.

Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.

Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

The CRISPR-Cas9 system has revolutionized gene editing both on single genes and in multiplexed loss-of-function screens, enabling precise genome-scale identification of genes essential to proliferation and survival of cancer cells^1,2. However, previous studies reported that a gene-independent anti-proliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, leading to false positive results in copy number amplified regions^3,4. We developed CERES, a computational method to estimate gene dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy-number-specific effect. As part of our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this dataset. We found that CERES reduced false positive results and estimated sgRNA activity for both this dataset and previously published screens performed with different sgRNA libraries. Here, we demonstrate the utility of this collection of screens, upon CERES correction, in revealing cancer-type-specific vulnerabilities.

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Abstract B17: Identification of Druggable Targets through Functional Multi-Omics in Renal Medullary Carcinoma

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.

Emerging Opportunities for Target Discovery in Rare Cancers

Rare cancers pose unique challenges to research due to their low incidence. Barriers include a scarcity of tissue and experimental models to enable basic research and insufficient patient accrual for clinical studies. Consequently, an understanding of the genetic and cellular features of many rare cancer types and their associated vulnerabilities has been lacking. However, new opportunities are emerging to facilitate discovery of therapeutic targets in rare cancers. Online platforms are allowing patients with rare cancers to organize on an unprecedented scale, tumor genome sequencing is now routinely performed in research and clinical settings, and the efficiency of patient-derived model generation has improved. New CRISPR/Cas9 and small-molecule libraries permit cancer dependency discovery in a rapid and systematic fashion. In parallel, large-scale studies of common cancers now provide reference datasets to help interpret rare cancer profiling data. Together, these advances motivate consideration of new research frameworks to accelerate rare cancer target discovery.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway

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.

Abstract 5567: A scalable and integrated computational and experimental workflow to identify new driver genes in cancer genome data

High throughput sequencing has revolutionized the study of the cancer genome, enabling numerous discoveries in basic and clinical research. However, considerable sample sizes are required to find cancer driver genes with intermediate and low mutation frequencies, and for a large proportion of patients the molecular cause (e.g. driver gene(s)) of disease is unknown. Here, we describe an integrated computational and experimental workflow that combines cancer genome data, molecular network information, multiplexed in vivo tumorigenesis assays, and reanalysis of driver-gene-negative cancer patients to predict and validate new driver genes. We develop a statistic, network mutation burden, that combines molecular network information with data from 4,742 cancer genomes to accurately classify known driver genes across 21 tumor types and predict 62 driver gene candidates.Of these, 35 gene candidates were tested in multiplexed in vivo tumorigenesis cell assays using sensitized immortalized human embryonic kidney (HA1E-M) and immortalized human lung epithelial (SALE-Y) cell lines.Tumor formation in vivo was observed for 11 genes (2 in HA1E-M, 3 in SALE-Y, 6 in both). By reanalyzing 242 lung adenocarcinoma patients with an unknown molecular cause of disease we show that two of these candidates, TFDP2 and AKT2, are significantly amplified in multiple samples.Overall, we describe a scalable combined computational and experimental framework to predict and validate driver genes across many tumor types. Our proof-of-concept approach should become increasingly useful as the number of cancer genomes continues to grow.

Citation Format: Heiko Horn, Michael S. Lawrence, Candace R. Chouinard, Yashaswi Shrestha, Jessica Xin Hu, Elizabeth Worstell, Emily Shea, Nina Ilic, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Joshua D. Campbell, Jesse S. Boehm, Gad Getz, Kasper Lage. A scalable and integrated computational and experimental workflow to identify new driver genes in cancer genome data [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 5567. doi:10.1158/1538-7445.AM2017-5567

Recurrent and functional regulatory mutations in breast cancer

Genomic analysis of tumours has led to the identification of hundreds of cancer genes on the basis of the presence of mutations in protein-coding regions. By contrast, much less is known about cancer-causing mutations in non-coding regions. Here we perform deep sequencing in 360 primary breast cancers and develop computational methods to identify significantly mutated promoters. Clear signals are found in the promoters of three genes. FOXA1, a known driver of hormone-receptor positive breast cancer, harbours a mutational hotspot in its promoter leading to overexpression through increased E2F binding. RMRP and NEAT1, two non-coding RNA genes, carry mutations that affect protein binding to their promoters and alter expression levels. Our study shows that promoter regions harbour recurrent mutations in cancer with functional consequences and that the mutations occur at similar frequencies as in coding regions. Power analyses indicate that more such regions remain to be discovered through deep sequencing of adequately sized cohorts of patients.