Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening

Eukaryotic Translation Initiation Factor 4 Gamma 1 (eIF4G1) is upregulated during Prostate cancer progression and modulates cell growth and metastasis

eIF4G1, a critical component of the eIF4F complex, is required for cap-dependent mRNA translation, a process necessary for tumor growth and survival. However, the role of eIF4G1 has not been evaluated in Prostate Cancer (PCa). We observed an increased eIF4G1 protein levels in PCa tissues as compared to normal tissues. Analysis of the TCGA data revealed that eIF4G1 gene expression positively correlated with higher tumor grade and stage. Furthermore, eIF4G1 was over-expressed and or amplified, in 16% patients with metastatic PCa (SU2C/PCF Dream Team dataset) and in 59% of castration-resistant prostate cancer (CRPC) patients (Trento/Cornell/Broad dataset). We showed for the first time that eIF4G1 expression was increased in PCa and that increased eIF4G1 expression associated with tumor progression and metastasis. We also observed high protein levels of eIF4G1 in PCa cell lines and prostate tissues from the TRAMP model of PCa as compared to normal prostate cell line and prostate tissues from the wild type mice. Knockdown of eIF4G1 in PCa cells resulted in decreased Cyclin D1 and p-Rb protein level, cell cycle delay, reduced cell viability and proliferation, impaired clonogenic activity, reduced cell migration and decreased mRNA loading to polysomes. Treatment with eIF4G complex inhibitor also impaired prostasphere formation. eIF4G1 knockdown or treatment with eIF4G complex inhibitor sensitized CRPC cells to Enzalutamide and Bicalutamide. Our results showed that eIF4G1 plays an important role in PCa growth and therapeutic resistance. These data suggested that eIF4G1 functions as an oncoprotein and may serve as a novel target for intervention in PCa and CRPC.

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.

Deconvolution of seed and RNA-binding protein crosstalk in RNAi-based functional genomics

RNA interference (RNAi) is a major powerful platform for gene perturbations, but is restricted by off-target mechanisms. Communication between RNAs, small RNAs, and RNA-binding proteins (RBPs) is a pervasive feature of cellular RNA networks. We present a new crosstalk scenario, designated as “crosstalk with endogenous RBPs (ceRBP)”, where siRNAs or miRNAs with seed sequences that overlap RBP motifs have extended biological effects by perturbing endogenous RBP activity. Systematic analysis of siRNA off-target data and genome-wide RNAi cancer lethality screens using 501 human cancer cell lines, a cancer dependency map, revealed that seed-to-RBP crosstalk is widespread, contributes to off-target activity, and affects RNAi performance. Specifically, deconvolution of the interactions between gene knockdown and seed-mediated silencing effects in the cancer dependency map showed widespread contributions of seed-to-RBP crosstalk to growth-phenotype modulation. These findings suggest a novel aspect of miRNA biology and offer a basis for improvement of RNAi agents and RNAi-based functional genomics.

Profound Tissue Specificity in Proliferation Control Underlies Cancer Drivers and Aneuploidy Patterns

Genomics has provided a detailed structural description of the cancer genome. Identifying oncogenic drivers that work primarily through dosage changes is a current challenge. Unrestrained proliferation is a critical hallmark of cancer. We constructed modular, barcoded libraries of human open reading frames (ORFs) and performed screens for proliferation regulators in multiple cell types. Approximately 10% of genes regulate proliferation, with most performing in an unexpectedly highly tissue-specific manner. Proliferation drivers in a given cell type showed specific enrichment in somatic copy number changes (SCNAs) from cognate tumors and helped predict aneuploidy patterns in those tumors, implying that tissue-type-specific genetic network architectures underlie SCNA and driver selection in different cancers. In vivo screening confirmed these results. We report a substantial contribution to the catalog of SCNA-associated cancer drivers, identifying 147 amplified and 107 deleted genes as potential drivers, and derive insights about the genetic network architecture of aneuploidy in tumors.

Applications of RNA Indexes for Precision Oncology in Breast Cancer

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.

Functional genomics identifies specific vulnerabilities in PTEN-deficient breast cancer

Background

Phosphatase and tensin homolog (PTEN) is one of the most frequently inactivated tumor suppressors in breast cancer. While PTEN itself is not considered a druggable target, PTEN synthetic-sick or synthetic-lethal (PTEN-SSL) genes are potential drug targets in PTEN-deficient breast cancers. Therefore, with the aim of identifying potential targets for precision breast cancer therapy, we sought to discover PTEN-SSL genes present in a broad spectrum of breast cancers.

Methods

To discover broad-spectrum PTEN-SSL genes in breast cancer, we used a multi-step approach that started with (1) a genome-wide short interfering RNA (siRNA) screen of ~ 21,000 genes in a pair of isogenic human mammary epithelial cell lines, followed by (2) a short hairpin RNA (shRNA) screen of ~ 1200 genes focused on hits from the first screen in a panel of 11 breast cancer cell lines; we then determined reproducibility of hits by (3) identification of overlaps between our results and reanalyzed data from 3 independent gene-essentiality screens, and finally, for selected candidate PTEN-SSL genes we (4) confirmed PTEN-SSL activity using either drug sensitivity experiments in a panel of 19 cell lines or mutual exclusivity analysis of publicly available pan-cancer somatic mutation data.

Results

The screens (steps 1 and 2) and the reproducibility analysis (step 3) identified six candidate broad-spectrum PTEN-SSL genes (PIK3CB, ADAMTS20, AP1M2, HMMR, STK11, and NUAK1). PIK3CB was previously identified as PTEN-SSL, while the other five genes represent novel PTEN-SSL candidates. Confirmation studies (step 4) provided additional evidence that NUAK1 and STK11 have PTEN-SSL patterns of activity. Consistent with PTEN-SSL status, inhibition of the NUAK1 protein kinase by the small molecule drug HTH-01-015 selectively impaired viability in multiple PTEN-deficient breast cancer cell lines, while mutations affecting STK11 and PTEN were largely mutually exclusive across large pan-cancer data sets.

Conclusions

Six genes showed PTEN-SSL patterns of activity in a large proportion of PTEN-deficient breast cancer cell lines and are potential specific vulnerabilities in PTEN-deficient breast cancer. Furthermore, the NUAK1 PTEN-SSL vulnerability identified by RNA interference techniques can be recapitulated and exploited using the small molecule kinase inhibitor HTH-01-015. Thus, NUAK1 inhibition may be an effective strategy for precision treatment of PTEN-deficient breast tumors.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0949-3) contains supplementary material, which is available to authorized users.

Disruption of mammalian SWI/SNF and polycomb complexes in human sarcomas: mechanisms and therapeutic opportunities

Soft-tissue sarcomas are increasingly characterized and subclassified by genetic abnormalities that represent underlying drivers of their pathology. Hallmark tumor suppressor gene mutations and pathognomonic gene fusions collectively account for approximately one-third of all sarcomas. These genetic abnormalities most often result in global transcriptional misregulation via disruption of protein regulatory complexes which govern chromatin architecture. Specifically, alterations to mammalian SWI/SNF (mSWI/SNF or BAF) ATP-dependent chromatin remodeling complexes and polycomb repressive complexes cause disease-specific changes in chromatin architecture and gene expression across a number of sarcoma subtypes. Understanding the functions of chromatin regulatory complexes and the mechanisms underpinning their roles in oncogenesis will be required for the design and development of new therapeutic strategies in sarcomas. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Toward an integrated map of genetic interactions in cancer cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene-background relationships. In addition to known dependencies, we identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.

Co-regulation of transcription by BRG1 and BRM, two mutually exclusive SWI/SNF ATPase subunits

BACKGROUND

SWI/SNF is a large heterogeneous multi-subunit chromatin remodeling complex. It consists of multiple sets of mutually exclusive components. Understanding how loss of one sibling of a mutually exclusive pair affects the occupancy and function of the remaining complex is needed to understand how mutations in a particular subunit might affect tumor formation. Recently, we showed that the members of the ARID family of SWI/SNF subunits (ARID1A, ARID1B and ARID2) had complex transcriptional relationships including both antagonism and cooperativity. However, it remains unknown how loss of the catalytic subunit(s) affects the binding and genome-wide occupancy of the remainder complex and how changes in occupancy affect transcriptional output.

RESULTS

We addressed this gap by depleting BRG1 and BRM, the two ATPase subunits in SWI/SNF, and characterizing the changes to chromatin occupancy of the remaining subunit and related this to transcription changes induced by loss of the ATPase subunits. We show that depletion of one subunit frequently leads to loss of the remaining subunit. This could cause either positive or negative changes in gene expression. At a subset of sites, the sibling subunit is either retained or gained. Additionally, we show genome-wide that BRG1 and BRM have both cooperative and antagonistic interactions with respect to transcription. Importantly, at genes where BRG1 and BRM antagonize one another we observe a nearly complete rescue of gene expression changes in the combined BRG/BRM double knockdown.

CONCLUSION

This series of experiments demonstrate that mutually exclusive SWI/SNF complexes have heterogeneous functional relationships and highlight the importance of considering the role of the remaining SWI/SNF complexes following loss or depletion of a single subunit.

Am I ready for CRISPR? A user's guide to genetic screens

Exciting new technologies are often self-limiting in their rollout, as access to state-of-the-art instrumentation or the need for years of hands-on experience, for better or worse, ensures slow adoption by the community. CRISPR technology, however, presents the opposite dilemma, where the simplicity of the system enabled the parallel development of many applications, improvements and derivatives, and new users are now presented with an almost paralyzing abundance of choices. This Review intends to guide users through the process of applying CRISPR technology to their biological problems of interest, especially in the context of discovering gene function at scale.

Eukaryotic initiation factor 4F-sidestepping resistance mechanisms arising from expression heterogeneity

There is enormous diversity in the genetic makeup and gene expression profiles between and within tumors. This heterogeneity leads to phenotypic variation and is a major mechanism of resistance to molecular targeted therapies. Here we describe a conceptual framework for targeting eukaryotic initiation factor (eIF) 4F in cancer-an essential complex that drives and promotes multiple Cancer Hallmarks. The unique nature of eIF4F and its druggability bypasses several of the heterogeneity issues that plague molecular targeted drugs developed for cancer therapy.

A Large-Scale RNAi Screen Identifies SGK1 as a Key Survival Kinase for GBM Stem Cells

Glioblastoma multiforme (GBM) is the most common type of primary malignant brain cancer and has a very poor prognosis. A subpopulation of cells known as GBM stem-like cells (GBM-SC) have the capacity to initiate and sustain tumor growth and possess molecular characteristics similar to the parental tumor. GBM-SCs are known to be enriched in hypoxic niches and may contribute to therapeutic resistance. Therefore, to identify genetic determinants important for the proliferation and survival of GBM stem cells, an unbiased pooled shRNA screen of 10,000 genes was conducted under normoxic as well as hypoxic conditions. A number of essential genes were identified that are required for GBM-SC growth, under either or both oxygen conditions, in two different GBM-SC lines. Interestingly, only about a third of the essential genes were common to both cell lines. The oxygen environment significantly impacts the cellular genetic dependencies as 30% of the genes required under hypoxia were not required under normoxic conditions. In addition to identifying essential genes already implicated in GBM such as CDK4, KIF11, and RAN, the screen also identified new genes that have not been previously implicated in GBM stem cell biology. The importance of the serum and glucocorticoid-regulated kinase 1 (SGK1) for cellular survival was validated in multiple patient-derived GBM stem cell lines using shRNA, CRISPR, and pharmacologic inhibitors. However, SGK1 depletion and inhibition has little effect on traditional serum grown glioma lines and on differentiated GBM-SCs indicating its specific importance in GBM stem cell survival.Implications: This study identifies genes required for the growth and survival of GBM stem cells under both normoxic and hypoxic conditions and finds SGK1 as a novel potential drug target for GBM. Mol Cancer Res; 16(1); 103-14. ©2017 AACR.

Autophagy-Independent Lysosomal Targeting Regulated by ULK1/2-FIP200 and ATG9

Iron is vital for many homeostatic processes, and its liberation from ferritin nanocages occurs in the lysosome. Studies indicate that ferritin and its binding partner nuclear receptor coactivator-4 (NCOA4) are targeted to lysosomes by a form of selective autophagy. By using genome-scale functional screening, we identify an alternative lysosomal transport pathway for ferritin that requires FIP200, ATG9A, VPS34, and TAX1BP1 but lacks involvement of the ATG8 lipidation machinery that constitutes classical macroautophagy. TAX1BP1 binds directly to NCOA4 and is required for lysosomal trafficking of ferritin under basal and iron-depleted conditions. Under basal conditions ULK1/2-FIP200 controls ferritin turnover, but its deletion leads to TAX1BP1-dependent activation of TBK1 that regulates redistribution of ATG9A to the Golgi enabling continued trafficking of ferritin. Cells expressing an amyotrophic lateral sclerosis (ALS)-associated TBK1 allele are incapable of degrading ferritin suggesting a molecular mechanism that explains the presence of iron deposits in patient brain biopsies.