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Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed computational models to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i)in silicaprioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii)in vivaCRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We also nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Taken together, we have described and validated an integrative computational and experimental framework that enables systematic identification of non-coding regulatory regions that drive human cancers.Statement of SignificanceWe developed an integrated computational and experimental platform to identify and characterize non-coding driver regulatory regions in metastatic prostate cancer patient data. One found enhancer region, GH22I030351, was shown to act on a bidirectional promoter that simultaneously regulates previously uncharacterized genes SF3A1 and CCDC157 in a tumor-promoting manner.

Supplementary Table S3 has primer sequences used to validate GBM subtype genes

Glioblastoma (GBM) responses to bevacizumab are invariably transient with acquired resistance. We profiled paired patient specimens and bevacizumab-resistant xenograft models pre- and post-resistance toward the primary goal of identifying regulators whose targeting could prolong the therapeutic window, and the secondary goal of identifying biomarkers of therapeutic window closure. Bevacizumab-resistant patient specimens and xenografts exhibited decreased vessel density and increased hypoxia versus pre-resistance, suggesting that resistance occurs despite effective therapeutic devascularization. Microarray analysis revealed upregulated mesenchymal genes in resistant tumors correlating with bevacizumab treatment duration and causing three changes enabling resistant tumor growth in hypoxia. First, perivascular invasiveness along remaining blood vessels, which co-opts vessels in a VEGF-independent and neoangiogenesis-independent manner, was upregulated in novel biomimetic 3D bioengineered platforms modeling the bevacizumab-resistant microenvironment. Second, tumor-initiating stem cells housed in the perivascular niche close to remaining blood vessels were enriched. Third, metabolic reprogramming assessed through real-time bioenergetic measurement and metabolomics upregulated glycolysis and suppressed oxidative phosphorylation. Single-cell sequencing of bevacizumab-resistant patient GBMs confirmed upregulated mesenchymal genes, particularly glycoprotein YKL-40 and transcription factor ZEB1, in later clones, implicating these changes as treatment-induced. Serum YKL-40 was elevated in bevacizumab-resistant versus bevacizumab-naïve patients. CRISPR and pharmacologic targeting of ZEB1 with honokiol reversed the mesenchymal gene expression and associated stem cell, invasion, and metabolic changes defining resistance. Honokiol caused greater cell death in bevacizumab-resistant than bevacizumab-responsive tumor cells, with surviving cells losing mesenchymal morphology. Employing YKL-40 as a resistance biomarker and ZEB1 as a target to prevent resistance could fulfill the promise of antiangiogenic therapy.Significance:Bevacizumab resistance in GBM is associated with mesenchymal/glycolytic shifts involving YKL-40 and ZEB1. Targeting ZEB1 reduces bevacizumab-resistant GBM phenotypes.

Supplementary Figures S1-S23 show some extra data including immunohistochemical stains and bar graphs that could not fit in the main text.

Supplementary Methods describes details about methods including animal work, microarray analysis, neurosphere assays, and hyaluronic acid assays that could not fit in the main methods.

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The Bcl-2 family encompasses a diverse set of apoptotic regulators that are dynamically activated in response to various cell-intrinsic and -extrinsic stimuli. An extensive variety of cell culture experiments have identified effects of growth factors, cytokines, and drugs on Bcl-2 family functions, but in vivo studies have tended to focus on the role of one or two particular members in development and organ homeostasis. Thus, the ability of physiologically relevant contexts to modulate canonical dependencies that are likely to be more complex has yet to be investigated systematically. In this study, we report findings derived from a pool-based shRNA assay that systematically and comprehensively interrogated the functional dependence of leukemia and lymphoma cells upon various Bcl-2 family members across many diverse in vitro and in vivo settings. This approach permitted us to report the first in vivo loss of function screen for modifiers of the response to a front-line chemotherapeutic agent. Notably, our results reveal an unexpected role for the extrinsic death pathway as a tissue-specific modifier of therapeutic response. In particular, our findings show that particular tissue sites of tumor dissemination play critical roles in demarcating the nature and extent of cancer cell vulnerabilities and mechanisms of chemoresistance. Cancer Res; 71(17); 5850–8. ©2011 AACR.

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Despite the importance of physical education (PE) lessons for physical activity in adolescents, the acute cognitive responses to PE lessons have not been explored; a gap in the literature that this study addresses. Following familiarisation, 76 (39 female) adolescents (12.2 ± 0.4 y) completed two trials (60 min games-based PE lesson and 60 min academic lesson) separated by 7-d in a counterbalanced, crossover design. Attention, executive function, working memory, and perception were assessed 30 min before, immediately post, and 45 min post-lesson in both trials. Participants were split into high-and low-fit groups based on a gender-specific median split of distance run on the multi-stage fitness test. Furthermore, participants were split into high and low MVPA groups based on a gender-specific median split of MVPA time (time spent >64% HR max) during the PE lesson. Overall, a 60 min games-based PE lesson had no effect on perception, working memory, attention, or executive function in adolescents (all p > 0.05) unless MVPA time is high. The physical activity-cognition relationship was moderated by MVPA, as working memory improved post-PE lesson in adolescents who completed more MVPA during their PE lesson (time*trial*MVPA interaction, p 2 = 0.119). Furthermore, high-fit adolescents displayed superior cognitive function than their low-fit counterparts, across all domains of cognitive function (main effect of fitness, all p 2 0.014–0.121). This study provides novel evidence that MVPA time moderates the cognitive response to a games-based PE lesson; and emphasises that higher levels of fitness are beneficial for cognitive function in adolescents.

RNA structural switches are key regulators of gene expression in bacteria, yet their characterization in Metazoa remains limited. Here we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3’UTR of the RORC transcript.In vivoDMS-MaPseq, coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identifytransfactors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated mRNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally-driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.

The search for new approaches in cancer therapy requires a mechanistic understanding of cancer vulnerabilities and anti-cancer drug mechanisms of action. Problematically, some effective therapeutics target cancer vulnerabilities that we do not understand and have poorly defined mechanisms of anti-cancer activity. One such drug is decitabine, which is a frontline therapeutic approved for the treatment of high-risk acute myeloid leukemia (AML). Decitabine is thought to kill cancer cells selectively via inhibition of DNA methyltransferase enzymes, but the genes and mechanisms involved remain unclear. Here, we apply an integrated multiomics and CRISPR functional genomics approach to identify genes and processes associated with response to decitabine in AML cells. Our integrated multiomics approach reveals RNA dynamics are key regulators of DNA hypomethylation induced cell death. Specifically, regulation of RNA decapping, splicing and RNA methylation emerge as critical regulators of decitabine killing. Our results provide insights into the mechanisms of decitabine anti-cancer activity in treatment of AML and identify combination therapies which could potentiate decitabine anti-cancer activity.

The search for cell-permeable drugs has conventionally focused on low-molecular weight (MW), nonpolar, rigid chemical structures. However, emerging therapeutic strategies break traditional drug design rules by employing flexibly linked chemical entities composed of more than one ligand. Using complementary genome-scale chemical-genetic approaches we identified an endogenous chemical uptake pathway involving interferon-induced transmembrane proteins (IFITMs) that modulates the cell permeability of a prototypical biopic inhibitor of MTOR (RapaLink-1, MW: 1784 g/mol). We devised additional linked inhibitors targeting BCR-ABL1 (DasatiLink-1, MW: 1518 g/mol) and EIF4A1 (BisRoc-1, MW: 1466 g/mol), uptake of which was facilitated by IFITMs. We also found that IFITMs moderately assisted some proteolysis-targeting chimeras and examined the physicochemical requirements for involvement of this uptake pathway.

The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. Despite this, the function of many genes and gene regulatory elements remains poorly characterized, which limits our ability to apply these insights to human disease. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes. In this Review, Przybyla and Gilbert describe the latest approaches for CRISPR-based functional genomics screens, including the adoption of single-cell transcriptomic read-outs and applications in characterizing the non-coding genome and mapping genetic interactions at scale.

Analysis of DNA methylation is a valuable tool to understand disease progression and is increasingly being used to create diagnostic and prognostic clinical biomarkers. While conversion of cytosine to 5-methylcytosine (5mC) commonly results in transcriptional repression, further conversion to 5-hydroxymethylcytosine (5hmC) is associated with transcriptional activation. Here we perform the first study integrating whole-genome 5hmC with DNA, 5mC, and transcriptome sequencing in clinical samples of benign, localized, and advanced prostate cancer. 5hmC is shown to mark activation of cancer drivers and downstream targets. Furthermore, 5hmC sequencing revealed profoundly altered cell states throughout the disease course, characterized by increased proliferation, oncogenic signaling, dedifferentiation, and lineage plasticity to neuroendocrine and gastrointestinal lineages. Finally, 5hmC sequencing of cell-free DNA from patients with metastatic disease proved useful as a prognostic biomarker able to identify an aggressive subtype of prostate cancer using the genes TOP2A and EZH2, previously only detectable by transcriptomic analysis of solid tumor biopsies. Overall, these findings reveal that 5hmC marks epigenomic activation in prostate cancer and identify hallmarks of prostate cancer progression with potential as biomarkers of aggressive disease. Significance: In prostate cancer, 5-hydroxymethylcytosine delineates oncogene activation and stage-specific cell states and can be analyzed in liquid biopsies to detect cancer phenotypes. See related article by Wu and Attard, p. 3880

CRISPR interference (CRISPRi) enables programmable, reversible, and titratable repression of gene expression (knockdown) in mammalian cells. Initial CRISPRi-mediated genetic screens have showcased the potential to address basic questions in cell biology, genetics, and biotechnology, but wider deployment of CRISPRi screening has been constrained by the large size of single guide RNA (sgRNA) libraries and challenges in generating cell models with consistent CRISPRi-mediated knockdown. Here, we present next-generation CRISPRi sgRNA libraries and effector expression constructs that enable strong and consistent knockdown across mammalian cell models. First, we combine empirical sgRNA selection with a dual-sgRNA library design to generate an ultra-compact (1-3 elements per gene), highly active CRISPRi sgRNA library. Next, we rigorously compare CRISPRi effectors to show that the recently published Zim3-dCas9 provides the best balance between strong on-target knockdown and minimal nonspecific effects on cell growth or the transcriptome. Finally, we engineer a suite of cell lines with stable expression of Zim3-dCas9 and robust on-target knockdown. Our results and publicly available reagents establish best practices for CRISPRi genetic screening.

Glioblastoma (GBM) responses to bevacizumab are invariably transient with acquired resistance. We profiled paired patient specimens and bevacizumab-resistant xenograft models pre- and post-resistance toward the primary goal of identifying regulators whose targeting could prolong the therapeutic window, and the secondary goal of identifying biomarkers of therapeutic window closure. Bevacizumab-resistant patient specimens and xenografts exhibited decreased vessel density and increased hypoxia versus pre-resistance, suggesting that resistance occurs despite effective therapeutic devascularization. Microarray analysis revealed upregulated mesenchymal genes in resistant tumors correlating with bevacizumab treatment duration and causing three changes enabling resistant tumor growth in hypoxia. First, perivascular invasiveness along remaining blood vessels, which co-opts vessels in a VEGF-independent and neoangiogenesis-independent manner, was upregulated in novel biomimetic 3D bioengineered platforms modeling the bevacizumab-resistant microenvironment. Second, tumor-initiating stem cells housed in the perivascular niche close to remaining blood vessels were enriched. Third, metabolic reprogramming assessed through real-time bioenergetic measurement and metabolomics upregulated glycolysis and suppressed oxidative phosphorylation. Single-cell sequencing of bevacizumab-resistant patient GBMs confirmed upregulated mesenchymal genes, particularly glycoprotein YKL-40 and transcription factor ZEB1, in later clones, implicating these changes as treatment-induced. Serum YKL-40 was elevated in bevacizumab-resistant versus bevacizumab-naïve patients. CRISPR and pharmacologic targeting of ZEB1 with honokiol reversed the mesenchymal gene expression and associated stem cell, invasion, and metabolic changes defining resistance. Honokiol caused greater cell death in bevacizumab-resistant than bevacizumab-responsive tumor cells, with surviving cells losing mesenchymal morphology. Employing YKL-40 as a resistance biomarker and ZEB1 as a target to prevent resistance could fulfill the promise of antiangiogenic therapy. Significance: Bevacizumab resistance in GBM is associated with mesenchymal/glycolytic shifts involving YKL-40 and ZEB1. Targeting ZEB1 reduces bevacizumab-resistant GBM phenotypes.

BackgroundAdvances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large-scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data.ResultsWe have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Our approach, which we have named the cancer data integrator (CanDI), is straightforward to implement, is well documented, and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology. We show that CanDI empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer.ConclusionsCanDI provides a flexible approach for large-scale data integration in cancer research enabling rapid generation of hypotheses. The CanDI data integrator is available athttps://github.com/GilbertLabUCSF/CanDI.

The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. Despite this, the function of many genes and gene regulatory elements remains poorly characterized, which limits our ability to apply these insights to human disease. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.

Genomic sequencing of thousands of tumors has revealed many genes associated with specific types of cancer. Similarly, large scale CRISPR functional genomics efforts have mapped genes required for cancer cell proliferation or survival in hundreds of cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, there are likely a number of undiscovered tumor specific driver genes that may represent potential drug targets. To identify such genetic dependencies, we performed genome-scale CRISPRi screens in metastatic prostate cancer models. We then created a pipeline in which we integrated pan-cancer functional genomics data with our metastatic prostate cancer functional and clinical genomics data to identify genes that can drive aggressive prostate cancer phenotypes. Our integrative analysis of these data reveals known prostate cancer specific driver genes, such as AR and HOXB13, as well as a number of top hits that are poorly characterized. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two top hit genes, KIF4A and WDR62. We demonstrate that both KIF4A and WDR62 drive aggressive prostate cancer phenotypes in vitro and in vivo in multiple models, irrespective of AR-status, and are also associated with poor patient outcome., It is hypothesized that there are a number of tumor specific driver genes for metastatic prostate cancer. Here, the authors perform genome-wide CRISPRi screens and integrate these data with metastatic prostate cancer functional and clinical genomics data to show that KIF4A and WDR62 drive aggressive prostate cancer phenotypes.

SUMMARY The major cap-binding protein eukaryotic translation initiation factor 4E (eIF4E), an ancient protein required for translation of all eukaryotic genomes, is a surprising yet potent oncogenic driver. The genetic interactions that maintain the oncogenic activity of this key translation factor remain unknown. In this study, we carry out a genome-wide CRISPRi screen wherein we identify more than 600 genetic interactions that sustain eIF4E oncogenic activity. Our data show that eIF4E controls the translation of Tfeb, a key executer of the autophagy response. This autophagy survival response is triggered by mitochondrial proteotoxic stress, which allows cancer cell survival. Our screen also reveals a functional interaction between eIF4E and a single anti-apoptotic factor, Bcl-xL, in tumor growth. Furthermore, we show that eIF4E and the exon-junction complex (EJC), which is involved in many steps of RNA metabolism, interact to control the migratory properties of cancer cells. Overall, we uncover several cancer-specific vulnerabilities that provide further resolution of the cancer translatome., Graphical Abstract, In brief Kuzuoglu-Ozturk et al. identify more than 600 genetic interactions that sustain oncogenic activity of the major cap-binding protein eIF4E by a genome-wide CRIPSRi screen. Their data reveal interactions among distinct cellular processes and eIF4E, uncovering several cancer-specific vulnerabilities.

Purpose: Protein kinases are known to play a prominent role in oncogenic progression across multiple cancer subtypes, yet their role in prostate cancer progression remains underexplored. The purpose of this study was to identify kinases that drive prostate cancer progression. Experimental Design: To discover kinases that drive prostate cancer progression, we investigated the association between gene expression of all known kinases and long-term clinical outcomes in tumor samples from 545 patients with high-risk disease. We evaluated the impact of genetic and pharmacologic inhibition of the most significant kinase associated with metastatic progression in vitro and in vivo. Results: DNA-dependent protein kinase (DNAPK) was identified as the most significant kinase associated with metastatic progression in high-risk prostate cancer. Inhibition of DNAPK suppressed the growth of both AR-dependent and AR-independent prostate cancer cells. Gene set enrichment analysis nominated Wnt as the top pathway associated with DNAPK. We found that DNAPK interacts with the Wnt transcription factor LEF1 and is critical for LEF1-mediated transcription. Conclusions: Our data show that DNAPK drives prostate cancer progression through transcriptional regulation of Wnt signaling and is an attractive therapeutic target in aggressive prostate cancer.

Functional genomics holds great promise for the dissection of cancer biology. The elucidation of genetic cooperation and molecular details that govern oncogenesis, metastasis, and response to therapy is made possible by robust technologies for perturbing gene function coupled to quantitative analysis of cancer phenotypes resulting from genetic or epigenetic perturbations. Multiplexed genetic perturbations enable the dissection of cooperative genetic lesions as well as the identification of synthetic lethal gene pairs that hold particular promise for constructing innovative cancer therapies. Lastly, functional genomics strategies enable the highly multiplexed in vivo analysis of genes that govern tumorigenesis as well as of the complex multicellular biology of a tumor, such as immune response and metastasis phenotypes. In this review, we discuss both historical and emerging functional genomics approaches and their impact on the cancer research landscape.

There are thousands of ubiquitously expressed mammalian genes, yet a genetic knockout can be lethal to one cell, and harmless to another. This context specificity confounds our understanding of genetics and cell biology. 2 large collections of pooled CRISPR screens offer an exciting opportunity to explore cell specificity. One explanation, synthetic lethality, occurs when a single “private” mutation creates a unique genetic dependency. However, by fitting thousands of machine learning models across millions of omic and CRISPR features, we discovered a “public” genetic architecture that is common across cell lines and explains more context specificity than synthetic lethality. This common architecture is built on CRISPR loss-of-function phenotypes that are surprisingly predictive of other loss-of-function phenotypes. Using these insights and inspired by the in silico lossy compression of images, we use machine learning to identify small “lossy compression” sets of in vitro CRISPR constructs where reduced measurements produce genome-scale loss-of-function predictions.

A general approach for heritably altering gene expression has the potential to enable many discovery and therapeutic efforts. Here, we present CRISPRoff-a programmable epigenetic memory writer consisting of a single dead Cas9 fusion protein that establishes DNA methylation and repressive histone modifications. Transient CRISPRoff expression initiates highly specific DNA methylation and gene repression that is maintained through cell division and differentiation of stem cells to neurons. Pairing CRISPRoff with genome-wide screens and analysis of chromatin marks establishes rules for heritable gene silencing. We identify single guide RNAs (sgRNAs) capable of silencing the large majority of genes including those lacking canonical CpG islands (CGIs) and reveal a wide targeting window extending beyond annotated CGIs. The broad ability of CRISPRoff to initiate heritable gene silencing even outside of CGIs expands the canonical model of methylation-based silencing and enables diverse applications including genome-wide screens, multiplexed cell engineering, enhancer silencing, and mechanistic exploration of epigenetic inheritance.

CRISPR genetic interaction mapping reveals networks of genes that drive cancer phenotypes.

Although DNA methylation is a key regulator of gene expression, the comprehensive methylation landscape of metastatic cancer has never been defined. Through whole-genome bisulfite sequencing paired with deep whole-genome and transcriptome sequencing of 100 castration-resistant prostate metastases, we discovered alterations affecting driver genes that were detectable only with integrated whole-genome approaches. Notably, we observed that 22% of tumors exhibited a novel epigenomic subtype associated with hypermethylation and somatic mutations in TET2, DNMT3B, IDH1 and BRAF. We also identified intergenic regions where methylation is associated with RNA expression of the oncogenic driver genes AR, MYC and ERG. Finally, we showed that differential methylation during progression preferentially occurs at somatic mutational hotspots and putative regulatory regions. This study is a large integrated study of whole-genome, whole-methylome and whole-transcriptome sequencing in metastatic cancer that provides a comprehensive overview of the important regulatory role of methylation in metastatic castration-resistant prostate cancer.

The major cap binding protein eIF4E, an ancient protein required for translation of all eukaryotic genomes, is a surprising yet potent oncogenic driver. The genetic interactions that maintain the oncogenic activity of this key translation factor remain unknown. Here we carried out a genome-wide CRISPRi screen wherein we identified over 600 genetic interactions that sustain eIF4E oncogenic activity. Our data show that eIF4E controls the translation of Tfeb, a key executer of the autophagy response. This autophagy survival response is triggered by mitochondrial proteotoxic stress, which allows cancer cell survival. Our screen also reveals a functional interaction between eIF4E and a single anti-apoptotic factor, Bcl-xL, in tumor growth. Furthermore, we show that eIF4E and the exon-junction complex (EJC), involved in many steps of RNA metabolism, interact to control the migratory properties of cancer cells. Overall, we have uncovered several cancer-specific vulnerabilities that provide unprecedented resolution of the cancer translatome.

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated 9 activation (CRISPRa) systems have enabled genetic screens in cultured cell lines to discover and characterize drivers and inhibitors of cancer cell growth. We adapted this system for use in vivo to assess whether modulating endogenous gene expression levels can result in functional outcomes in the native environment of the liver. We engineered the catalytically dead CRISPR-associated 9 (dCas9)-positive mouse, cyclization recombination-inducible (Cre) CRISPRa system for cell type-specific gene activation in vivo. We tested the capacity for genetic screening in live animals by applying CRISPRa in a clinically relevant model of liver injury and repopulation. We targeted promoters of interest in regenerating hepatocytes using multiple single guide RNAs (gRNAs), and employed high-throughput sequencing to assess enrichment of gRNA sequences during liver repopulation and to link specific gRNAs to the initiation of carcinogenesis. All components of the CRISPRa system were expressed in a cell type-specific manner and activated endogenous gene expression in vivo. Multiple gRNA cassettes targeting a proto-oncogene were significantly enriched following liver repopulation, indicative of enhanced division of cells expressing the proto-oncogene. Furthermore, hepatocellular carcinomas developed containing gRNAs that activated this oncogene, indicative of cancer initiation events. Also, we employed our system for combinatorial cancer genetics in vivo as we found that while clonal hepatocellular carcinomas were dependent on the presence of the oncogene-inducing gRNAs, they were depleted for multiple gRNAs activating tumor suppressors.ConclusionThe in vivo CRISPRa platform developed here allows for parallel and combinatorial genetic screens in live animals; this approach enables screening for drivers and suppressors of cell replication and tumor initiation. (Hepatology 2017).

Background: Gilteritinib is a clinically active FLT3 tyrosine kinase inhibitor (TKI) approved for relapsed/refractory FLT3-mutant AML, but nearly all patients treated with gilteritinib and other FLT3 TKIs eventually develop clinical resistance. Activating RAS/MAPK pathway mutations are a predominant non-FLT3 dependent resistance mechanism in patients treated with gilteritinib. AML blasts can also develop FLT3 TKI resistance secondary to paracrine MAPK activation stimulated by FLT3 Ligand, FGF2, or other protective cytokines within the bone marrow microenvironment (BME). To identify potential targets that sensitize AML cells to gilteritinib-induced apoptosis in a model of the BME, we performed a genome-wide CRISPR/Cas9 death screen in MOLM-14 FLT3-ITD+ human AML cells cultured in bone marrow stromal cell conditioned media. We hypothesize that identified genes represent promising combinatorial therapeutic targets that can enhance clinical efficacy of FLT3 TKIs in AML. Methods: To model stroma-mediated TKI resistance, we used the HS5 human bone marrow stromal cell line that secretes multiple cytokines (G-CSF, GM-CSF, FGF2) and supports myeloid progenitor proliferation in co-culture. MOLM-14 CRISPRi cells transduced with CRISPRi-v2 genome-wide sgRNA library were cultured in HS5 conditioned media for 24 hours and then treated with gilteritinib 250 nM. Cells were stained with a fluorogenic caspase 3/7 reagent and then fixed after 24 hours of drug treatment. Caspase-3 positive cells were sorted from the entire drug-treated cell population by FACS and guide RNAs enriched or depleted in this sample as compared to an untreated T0 sample were determined by NGS. Results: We identified several gene-level hits that were enriched in the apoptotic population (FDR Conclusions: The results and validation of our CRISPRi screen suggest that combined CDK8 and FLT3 inhibition is a novel strategy for augmenting gilteritinib cytotoxicity. Assessment of the activity of the combination in additional primary AML samples and in vivo murine models of AML is planned. Additional candidate targets already described and other Mediator and RNA pol II subunits from our screen are also being further evaluated to precisely define the transcriptional programs that influence FLT3 inhibitor resistance. Disclosures Logan: Pharmacyclics, Astellas, Jazz, Kite, Kadmon, Autolus, Amphivena: Research Funding; Amgen, Pfizer, AbbVie: Consultancy. Gilbert: Denali Therapeutics: Ended employment in the past 24 months, Other: Spouse/Significant Other's Employment; GSK: Consultancy, Research Funding; AstraZeneca: Research Funding; Chroma Medicine: Consultancy, Other: Co-founder. Smith: Revolutions Medicine: Research Funding; AbbVie: Research Funding; Daiichi Sankyo: Consultancy; Amgen: Honoraria; FUJIFILM: Research Funding; Astellas Pharma: Consultancy, Research Funding.

Inhibitors targeting KRAS(G12C), a mutant form of the guanosine triphosphatase (GTPase) KRAS, are a promising new class of oncogene-specific therapeutics for the treatment of tumors driven by the mutant protein. These inhibitors react with the mutant cysteine residue by binding covalently to the switch-II pocket (S-IIP) that is present only in the inactive guanosine diphosphate (GDP)–bound form of KRAS(G12C), sparing the wild-type protein. We used a genome-scale CRISPR interference (CRISPRi) functional genomics platform to systematically identify genetic interactions with a KRAS(G12C) inhibitor in cellular models of KRAS(G12C) mutant lung and pancreatic cancer. Our data revealed genes that were selectively essential in this oncogenic driver–limited cell state, meaning that their loss enhanced cellular susceptibility to direct KRAS(G12C) inhibition. We termed such genes “collateral dependencies” (CDs) and identified two classes of combination therapies targeting these CDs that increased KRAS(G12C) target engagement or blocked residual survival pathways in cells and in vivo. From our findings, we propose a framework for assessing genetic dependencies induced by oncogene inhibition.

Synergistic interactions between gene functions drive cellular complexity. However, the combinatorial explosion of possible genetic interactions (GIs) has necessitated the use of scalar interaction readouts (e.g. growth) that conflate diverse outcomes. Here we present an analytical framework for interpreting manifolds constructed from high-dimensional interaction phenotypes. We applied this framework to rich phenotypes obtained by Perturb-seq (single-cell RNA-seq pooled CRISPR screens) profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying true suppressors), and mechanistic elucidation of synthetic lethal interactions, including an unexpected synergy betweenCBLandCNN1driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.One Sentence SummaryPrinciples and mechanisms of genetic interactions are revealed by rich phenotyping using single-cell RNA sequencing.

Manifold destiny Mapping of genetic interactions (GIs) is usually based on cell fitness as the phenotypic readout, which obscures the mechanistic origin of interactions. Norman et al. developed a framework for mapping and understanding GIs. This approach leverages high-dimensional single-cell RNA sequencing data gathered from CRISPR-mediated, pooled perturbation screens. Diverse transcriptomic phenotypes construct a “manifold” representing all possible states of the cell. Each perturbation and GI projects the cell state to a particular position on this manifold, enabling unbiased ordering of genes in pathways and systematic classifications of GIs. Science , this issue p. 786