RNA helicase DDX3 regulates RAD51 localization and DNA damage repair in Ewing sarcoma.

We previously demonstrated that RNA helicase DDX3X (DDX3) can be a therapeutic target in Ewing sarcoma (EWS), but its role in EWS biology remains unclear. The present work demonstrates that DDX3 plays a unique role in DNA damage repair (DDR). We show that DDX3 interacts with several proteins involved in homologous recombination, including RAD51, RECQL1, RPA32, and XRCC2. In particular, DDX3 colocalizes with RAD51 and RNA:DNA hybrid structures in the cytoplasm of EWS cells. Inhibition of DDX3 RNA helicase activity increases cytoplasmic RNA:DNA hybrids, sequestering RAD51 in the cytoplasm, which impairs nuclear translocation of RAD51 to sites of double-stranded DNA breaks, thus increasing sensitivity of EWS to radiation treatment, both in vitro and in vivo. This discovery lays the foundation for exploring new therapeutic approaches directed at manipulating DDR protein localization in solid tumors.

RNA Helicase DDX3 Regulates RAD51 Localization and DNA Damage Repair in Ewing Sarcoma.

We previously demonstrated that RNA helicase DDX3X (DDX3) can be a therapeutic target in Ewing sarcoma (EWS), but its role in EWS biology remains unclear. The present work demonstrates that DDX3 plays a unique role in DNA damage repair (DDR). We show that DDX3 interacts with several proteins involved in homologous recombination, including RAD51, RECQL1, RPA32, and XRCC2. In particular, DDX3 colocalizes with RAD51 and RNA:DNA hybrid structures in the cytoplasm of EWS cells. Inhibition of DDX3 RNA helicase activity increases cytoplasmic RNA:DNA hybrids, sequestering RAD51 in the cytoplasm, which impairs nuclear translocation of RAD51 to sites of double-stranded DNA breaks thus increasing sensitivity of EWS to radiation treatment, both in vitro and in vivo. This discovery lays the foundation for exploring new therapeutic approaches directed at manipulating DDR protein localization in solid tumors.

The landscape of therapeutic vulnerabilities in EGFR inhibitor osimertinib drug tolerant persister cells.

Third-generation EGFR tyrosine kinase inhibitors (EGFR-TKIs), including osimertinib, an irreversible EGFR-TKI, are important treatments for non-small cell lung cancer with EGFR-TKI sensitizing or EGFR T790M resistance mutations. While patients treated with osimertinib show clinical benefit, disease progression and drug resistance are common. Emergence of de novo acquired resistance from a drug tolerant persister (DTP) cell population is one mechanism proposed to explain progression on osimertinib and other targeted cancer therapies. Here we profiled osimertinib DTPs using RNA-seq and ATAC-seq to characterize the features of these cells and performed drug screens to identify therapeutic vulnerabilities. We identified several vulnerabilities in osimertinib DTPs that were common across models, including sensitivity to MEK, AURKB, BRD4, and TEAD inhibition. We linked several of these vulnerabilities to gene regulatory changes, for example, TEAD vulnerability was consistent with evidence of Hippo pathway turning off in osimertinib DTPs. Last, we used genetic approaches using siRNA knockdown or CRISPR knockout to validate AURKB, BRD4, and TEAD as the direct targets responsible for the vulnerabilities observed in the drug screen.

Nucleolar RNA polymerase II drives ribosome biogenesis.

Proteins are manufactured by ribosomes-macromolecular complexes of protein and RNA molecules that are assembled within major nuclear compartments called nucleoli1,2. Existing models suggest that RNA polymerases I and III (Pol I and Pol III) are the only enzymes that directly mediate the expression of the ribosomal RNA (rRNA) components of ribosomes. Here we show, however, that RNA polymerase II (Pol II) inside human nucleoli operates near genes encoding rRNAs to drive their expression. Pol II, assisted by the neurodegeneration-associated enzyme senataxin, generates a shield comprising triplex nucleic acid structures known as R-loops at intergenic spacers flanking nucleolar rRNA genes. The shield prevents Pol I from producing sense intergenic noncoding RNAs (sincRNAs) that can disrupt nucleolar organization and rRNA expression. These disruptive sincRNAs can be unleashed by Pol II inhibition, senataxin loss, Ewing sarcoma or locus-associated R-loop repression through an experimental system involving the proteins RNaseH1, eGFP and dCas9 (which we refer to as 'red laser'). We reveal a nucleolar Pol-II-dependent mechanism that drives ribosome biogenesis, identify disease-associated disruption of nucleoli by noncoding RNAs, and establish locus-targeted R-loop modulation. Our findings revise theories of labour division between the major RNA polymerases, and identify nucleolar Pol II as a major factor in protein synthesis and nuclear organization, with potential implications for health and disease.

Reconstruction of Ewing Sarcoma Developmental Context from Mass-Scale Transcriptomics Reveals Characteristics of EWSR1-FLI1 Permissibility.

Ewing sarcoma is an aggressive pediatric cancer of enigmatic cellular origins typically resulting from a single translocation event t (11; 22) (q24; q12). The resulting fusion gene, EWSR1-FLI1, is toxic or unstable in most primary tissues. Consequently, attempts to model Ewing sarcomagenesis have proven unsuccessful thus far, highlighting the need to identify the cellular features which permit stable EWSR1-FLI1 expression. By re-analyzing publicly available RNA-Sequencing data with manifold learning techniques, we uncovered a group of Ewing-like tissues belonging to a developmental trajectory between pluripotent, neuroectodermal, and mesodermal cell states. Furthermore, we demonstrated that EWSR1-FLI1 expression levels control the activation of these developmental trajectories within Ewing sarcoma cells. Subsequent analysis and experimental validation demonstrated that the capability to resolve R-loops and mitigate replication stress are probable prerequisites for stable EWSR1-FLI1 expression in primary tissues. Taken together, our results demonstrate how EWSR1-FLI1 hijacks developmental gene programs and advances our understanding of Ewing sarcomagenesis.

Deep learning of pharmacogenomics resources: moving towards precision oncology.

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

The molecular landscape of ETMR at diagnosis and relapse.

Embryonal tumours with multilayered rosettes (ETMRs) are aggressive paediatric embryonal brain tumours with a universally poor prognosis1. Here we collected 193 primary ETMRs and 23 matched relapse samples to investigate the genomic landscape of this distinct tumour type. We found that patients with tumours in which the proposed driver C19MC2-4 was not amplified frequently had germline mutations in DICER1 or other microRNA-related aberrations such as somatic amplification of miR-17-92 (also known as MIR17HG). Whole-genome sequencing revealed that tumours had an overall low recurrence of single-nucleotide variants (SNVs), but showed prevalent genomic instability caused by widespread occurrence of R-loop structures. We show that R-loop-associated chromosomal instability can be induced by the loss of DICER1 function. Comparison of primary tumours and matched relapse samples showed a strong conservation of structural variants, but low conservation of SNVs. Moreover, many newly acquired SNVs are associated with a mutational signature related to cisplatin treatment. Finally, we show that targeting R-loops with topoisomerase and PARP inhibitors might be an effective treatment strategy for this deadly disease.

Correction to: Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

Following publication of the original article [1], the authors provided an updated funding statement to the article. The updated statement is as follows.

Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. RESULTS: We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. CONCLUSIONS: Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

Author Correction: EWS-FLI1 increases transcription to cause R-loops and block BRCA1 repair in Ewing sarcoma.

In this Letter, the sentence beginning "This work was funded…." in the Acknowledgements should have read "CPRIT (RP140105) to J.C.R." rather than "CPRIT (RP150445) to J.C.R." This error has been corrected online.

Sorafenib improves alkylating therapy by blocking induced inflammation, invasion and angiogenesis in breast cancer cells.

Molecular targeted compounds are emerging as a strategy to improve classical chemotherapy. Herein, we describe that using low dose of the multikinase inhibitor sorafenib improves cyclophosphamide antitumor activity by inhibiting angiogenesis, metastasis and promoting tumor healing in MDA-MB231 xenografts and the 4T1-12B syngeneic breast cancer metastasis model. Mechanistic studies in MDA-MB231 cells revealed that alkylation upregulates inflammatory genes/proteins such as COX-2, IL8, CXCL2 and MMP1 in a MEK1/2-ERK1/2-dependent manner. These proteins enrich the secretome of cancer cells, stimulating cell invasion and angiogenesis via autocrine and paracrine mechanisms. Sorafenib inhibits MEK1/2-ERK1/2 pathway thereby decreasing inflammatory genes and mitigating cell invasion and angiogenesis at basal and alkylation-induced conditions whereas NRF2 and ER stress pathways involved in alkylation survival are not affected. In non-invasive/non-angiogenic breast cancer cells (SKBR3 and MCF7), alkylation did not elicit inflammatory responses with the only sorafenib effect being ERK1/2-independent ROS-dependent cytotoxicity when using higher drug concentrations. In summary, our data show that alkylating agents may elicit inflammatory responses that seems to contribute to malignant progression in specific breast cancer cells. Identifying and targeting drivers of this phenotype may offer opportunities to optimize combined drug regimens between classical chemotherapeutics and targeted agents.

EWS-FLI1 increases transcription to cause R-loops and block BRCA1 repair in Ewing sarcoma.

Ewing sarcoma is an aggressive paediatric cancer of the bone and soft tissue. It results from a chromosomal translocation, predominantly t(11;22)(q24:q12), that fuses the N-terminal transactivation domain of the constitutively expressed EWSR1 protein with the C-terminal DNA binding domain of the rarely expressed FLI1 protein. Ewing sarcoma is highly sensitive to genotoxic agents such as etoposide, but the underlying molecular basis of this sensitivity is unclear. Here we show that Ewing sarcoma cells display alterations in regulation of damage-induced transcription, accumulation of R-loops and increased replication stress. In addition, homologous recombination is impaired in Ewing sarcoma owing to an enriched interaction between BRCA1 and the elongating transcription machinery. Finally, we uncover a role for EWSR1 in the transcriptional response to damage, suppressing R-loops and promoting homologous recombination. Our findings improve the current understanding of EWSR1 function, elucidate the mechanistic basis of the sensitivity of Ewing sarcoma to chemotherapy (including PARP1 inhibitors) and highlight a class of BRCA-deficient-like tumours.

Alkylating Agent-Induced NRF2 Blocks Endoplasmic Reticulum Stress-Mediated Apoptosis via Control of Glutathione Pools and Protein Thiol Homeostasis.

Alkylating agents are a commonly used cytotoxic class of anticancer drugs. Understanding the mechanisms whereby cells respond to these drugs is key to identify means to improve therapy while reducing toxicity. By integrating genome-wide gene expression profiling, protein analysis, and functional cell validation, we herein demonstrated a direct relationship between NRF2 and Endoplasmic Reticulum (ER) stress pathways in response to alkylating agents, which is coordinated by the availability of glutathione (GSH) pools. GSH is essential for both drug detoxification and protein thiol homeostasis within the ER, thus inhibiting ER stress induction and promoting survival, an effect independent of its antioxidant role. NRF2 accumulation induced by alkylating agents resulted in increased GSH synthesis via GCLC/GCLM enzyme, and interfering with this NRF2 response by either NRF2 knockdown or GCLC/GCLM inhibition with buthionine sulfoximine caused accumulation of damaged proteins within the ER, leading to PERK-dependent apoptosis. Conversely, upregulation of NRF2, through KEAP1 depletion or NRF2-myc overexpression, or increasing GSH levels with N-acetylcysteine or glutathione-ethyl-ester, decreased ER stress and abrogated alkylating agents-induced cell death. Based on these results, we identified a subset of lung and head-and-neck carcinomas with mutations in either KEAP1 or NRF2/NFE2L2 genes that correlate with NRF2 target overexpression and poor survival. In KEAP1-mutant cancer cells, NRF2 knockdown and GSH depletion increased cell sensitivity via ER stress induction in a mechanism specific to alkylating drugs. Overall, we show that the NRF2-GSH influence on ER homeostasis implicates defects in NRF2-GSH or ER stress machineries as affecting alkylating therapy toxicity. Mol Cancer Ther; 15(12); 3000-14. ©2016 AACR.

Musashi1 Impacts Radio-Resistance in Glioblastoma by Controlling DNA-Protein Kinase Catalytic Subunit.

The conserved RNA-binding protein Musashi1 (MSI1) has been characterized as a stem cell marker, controlling the balance between self-renewal and differentiation and as a key oncogenic factor in numerous solid tumors, including glioblastoma. To explore the potential use of MSI1 targeting in therapy, we studied MSI1 in the context of radiation sensitivity. Knockdown of MSI1 led to a decrease in cell survival and an increase in DNA damage compared to control in cells treated with ionizing radiation. We subsequently examined mechanisms of double-strand break repair and found that loss of MSI1 reduces the frequency of nonhomologous end-joining. This phenomenon could be attributed to the decreased expression of DNA-protein kinase catalytic subunit, which we have previously identified as a target of MSI1. Collectively, our results suggest a role for MSI1 in double-strand break repair and that its inhibition may enhance the effect of radiotherapy.

Inhibition of FoxM1-Mediated DNA Repair by Imipramine Blue Suppresses Breast Cancer Growth and Metastasis.

PURPOSE: The approaches aimed at inhibiting the ability of cancer cells to repair DNA strand breaks have emerged as promising targets for treating cancers. Here, we assessed the potential of imipramine blue (IB), a novel analogue of antidepressant imipramine, to suppress breast cancer growth and metastasis by inhibiting the ability of breast cancer cells to repair DNA strand breaks by homologous recombination (HR). EXPERIMENTAL DESIGN: The effect of IB on breast cancer growth and metastasis was assessed in vitro as well as in preclinical mouse models. Besides, the therapeutic efficacy and safety of IB was determined in ex vivo explants from breast cancer patients. The mechanism of action of IB was evaluated by performing gene-expression, drug-protein interaction, cell-cycle, and DNA repair studies. RESULTS: We show that the systemic delivery of IB using nanoparticle-based delivery approach suppressed breast cancer growth and metastasis without inducing toxicity in preclinical mouse models. Using ex vivo explants from breast cancer patients, we demonstrated that IB inhibited breast cancer growth without affecting normal mammary epithelial cells. Furthermore, our mechanistic studies revealed that IB may interact and inhibit the activity of proto-oncogene FoxM1 and associated signaling that play critical roles in HR-mediated DNA repair. CONCLUSIONS: These findings highlight the potential of IB to be applied as a safe regimen for treating breast cancer patients. Given that FoxM1 is an established therapeutic target for several cancers, the identification of a compound that inhibits FoxM1- and FoxM1-mediated DNA repair has immense translational potential for treating many aggressive cancers. Clin Cancer Res; 22(14); 3524-36. ©2016 AACR.