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Differential Regulation of the Melanoma Proteome by Eif4a1 and Eif4e Cailin E

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Differential Regulation of the Melanoma Proteome by Eif4a1 and Eif4e Cailin E Published OnlineFirst November 22, 2016; DOI: 10.1158/0008-5472.CAN-16-1298 Cancer Integrated Systems and Technologies Research Differential Regulation of the Melanoma Proteome by eIF4A1 and eIF4E Cailin E. Joyce1,2,3, Adrienne G. Yanez1,2,3, Akihiro Mori4,5,6, Akinori Yoda1,2,3, Johanna S. Carroll1,2,3, and Carl D. Novina1,2,3 Abstract Small molecules and antisense oligonucleotides that inhibit eIF4A1 and eIF4E with patient survival. Finally, comparative the translation initiation factors eIF4A1 and eIF4E have been proteomic and transcriptomic analyses reveal extensive mech- explored as broad-based therapeutic agents for cancer treat- anistic divergence in response to eIF4A1 or eIF4E silencing. ment, based on the frequent upregulation of these two sub- Current models indicate that eIF4A1 and eIF4E function units of the eIF4F cap-binding complex in many cancer cells. together through the 50UTR to increase translation of onco- Here, we provide support for these therapeutic approaches genes. In contrast, our data demonstrate that the common with mechanistic studies of eIF4F-driven tumor progression effects of eIF4A1 and eIF4E on translation are mediated by the in a preclinical model of melanoma. Silencing eIF4A1 or eIF4E coding region and 30UTR. Moreover, their divergent effects decreases melanoma proliferation and invasion. There were occur through the 50UTR. Overall, our work shows that it will common effects on the level of cell-cycle proteins that could be important to evaluate subunit-specific inhibitors of eIF4F in explain the antiproliferative effects in vitro.Usingclinical different disease contexts to fully understand their anticancer specimens, we correlate the common cell-cycle targets of actions. Cancer Res; 77(3); 1–10. Ó2016 AACR. Introduction Classic models dictate that eIF4E is the rate-limiting factor for translation initiation in most normal tissues (11). Under phys- Steady-state translation initiation is primarily regulated iologic conditions, mRNAs are thought to compete for limiting through the cytoplasmic mRNA cap–binding complex, eukary- amounts of eIF4E. In this model, mRNAs with longer and more otic initiation factor 4F (eIF4F), comprised of the cap-binding 0 structured 5 UTRs are weakly translated. In cancer cells, increased protein eIF4E, the RNA helicase eIF4A, and the scaffolding availability of eIF4E leads to selective translation of highly sen- protein eIF4G (reviewed in ref.1). Increased protein levels of sitive oncogenes, such as MYC (12) and VEGF (13). More recently, eIF4E (2), phospho-eIF4E (3), eIF4A1 (4), and eIF4G (5) are 0 this model was extended to incorporate 5 UTR sequence elements associated with poor clinical outcomes in multiple cancer that confer eIF4E sensitivity such as the terminal oligonucleotide contexts and are linked to aggressive phenotypes in cellular 0 tract (5 TOP), pyrimidine-rich translational element (PRTE), and and preclinical models (6–8). In melanoma, many of the cytosine-enriched regulator of translation (CERT; refs.14–16). known oncogenic signaling pathways converge on eIF4F (9). 0 However, 5 UTR features do not explain the full spectrum of Targeted therapies that deactivate these pathways are initially eIF4E targets (17), including the class of mRNAs for which eIF4E effective, but nearly universal acquired resistance leads to high negatively regulates translation. Improved models are necessary mortality rates (10). Thus, second-line inhibition of eIF4F to predict outcomes of therapeutic inhibition of eIF4F in cancer. could significantly extend the period of response to targeted Targets of eIF4A and eIF4E have been identified using poly- therapies for patients with melanoma. somal RNA association or ribosome profiling to estimate trans- lation efficiency (16, 18–21). Some mRNAs have different requirements for individual eIF4F subunits (19, 22), but global 1Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, effects of eIF4A1 and eIF4E depletion have not been assessed in Boston, Massachusetts. 2Department of Medicine, Harvard Medical School, parallel in the same cancer model. Here, we characterize the Boston, Massachusetts. 3Broad Institute of Harvard and MIT, Cambridge, Mas- phenotypic and global proteomic effects of eIF4A1 and eIF4E sachusetts. 4Program in Systems Biology and Program in Molecular Medicine, inhibition in melanoma. We show that eIF4A1 and eIF4E pro- 5 University of Massachusetts, Worcester, Massachusetts. Onami team, The mote melanoma proliferation through shared, network-level 6 Systems Biology Institute, Tokyo, Japan. Laboratory for Developmental regulation of the cell cycle, and moreover, that eIF4F-dependent Dynamics, RIKEN Quantitative Biology Center, Hyogo, Japan. cell-cycle proteins are significant combinatorial predictors of Note: Supplementary data for this article are available at Cancer Research patient survival. Finally, we demonstrate that intrinsic features Online (http://cancerres.aacrjournals.org/). of mRNA-coding sequence (CDS) and 30UTR, but not 50UTR, Corresponding Author: Carl D. Novina, Dana-Farber Cancer Institute, Dana predict shared targets of eIF4A1 and eIF4E. These data demon- 1420B, 450 Brookline Avenue, Boston, MA 02215. Phone: 617-582-7961; Fax: 617- strate that eIF4A1 and eIF4E have distinct molecular conse- 582-7962; E-mail: [email protected] quences in the same cancer model, which implies that targeting doi: 10.1158/0008-5472.CAN-16-1298 eIF4F with different subunit-specific inhibitors will influence on- Ó2016 American Association for Cancer Research. target and off-target therapeutic outcomes. www.aacrjournals.org OF1 Downloaded from cancerres.aacrjournals.org on September 27, 2021. © 2016 American Association for Cancer Research. Published OnlineFirst November 22, 2016; DOI: 10.1158/0008-5472.CAN-16-1298 Joyce et al. Materials and Methods Quantitative mass spectrometry Knockdowns were performed in triplicate in 10-cm dishes and Cell culture cells were collected 72 hours post-transfection. Samples were WM858, WM46, and A375 were cultured in high-glucose processed and analyzed for MS3 mass spectrometry as described DMEM (Cellgro) supplemented with 10% FBS (Life Technol- previously (23). Briefly, cells were lysed, proteins were digested ogies). WM46 was transduced with lentivirus containing empty using LysC and trypsin, and peptides were labeled with Tandem SparQ IRES RFP vector (QM531A-2, System Biosciences), or Mass Tag 10-plex reagents and fractionated. Data were collected vector containing eIF4A1 (4096621, Open Biosystems) or on an Orbitrap Fusion mass spectrometer operating in MS3 eIF4E cDNA (5295521, Open Biosystems). RFP-positive cells mode using synchronous precursor selection for MS2 to MS3 in the middle quantiles were selected by flow cytometry. The fragmentation, and forward and reverse sequences were searched A375 melanoma cell line was a gift from Dr. David Fisher against a Uniprot human database (February 2014) using the (Massachusetts General Hospital, Boston, MA), and WM46 and SEQUEST algorithm. Additional processing steps included pro- WM858 were a gift from Dr. Levi Garraway (Dana-Farber tein assembly and quantification from peptides, and calculation Cancer Institute, Boston, MA), all obtained prior to 2012. All of Benjamini–Hochberg false discovery rates (FDR). Protein levels cell lines were authenticated via short tandem repeat profiling were normalized to the percent signal in each sample relative to at the American Tissue Culture Repository on May 19, 2016. the total signal across all multiplexed samples. Differentially expressed proteins had a fold change |1.25| in all three biolog- Transient knockdown ical replicates and FDR < 0.1. A375 and WM858 cells were transfected with Allstars Negative Control siRNA (Qiagen) or gene-specific siRNA at 7.5 nmol/L using Pathway and network analysis RNAiMAX (Life Technologies). Gene-specific siRNA sequences Gene set enrichment analysis (GSEA) was performed using were 50-GGACCAGATCTATGACATATT-30 and 50-GCATGGAGA- GSEA software (http://software.broadinstitute.org/gsea/index. TATGGACCAATT-30 for eIF4A1 (custom design, Qiagen) and jspGene). Differentially expressed proteins for each treatment 50-AAGAGCGGCTCCACCACTAAA-30 and 50-CAAAGCTTTGCTA- were ranked on the basis of their average fold-change across CAAATTTA-30 for eIF4E (Hs_EIF4E_1 and Hs_EIF4E_8, Qiagen). triplicates and analyzed against Reactome pathways (http:// siRNA-like off-target effects were assessed by siRNA-check (http:// www.reactome.org/pathways) using the GSEA Preranked tool. projects.insilico.us/SpliceCenter/siRNACheck). miRNA-like off- We applied a classic enrichment statistic, filtered gene sets with target effects were assessed by scanning 30UTRs for perfect matches >500 or <15 hits, and applied an FDR threshold 0.1. To visualize to the 8-mer seed region of each oligonucleotide (underlined enrichment of common pathways, we ranked all genes based on above). their average fold-change across all knockdown treatments and performed GSEA Preranked as above. The normalized enrichment Proliferation assays score calculates the degree to which a gene set is over-represented A total of 2,500 cells per well were seeded into 96-well plates at the extremes of a ranked list, accounting for size of the gene set. and, where appropriate, transfected with siRNA. After 24 hours, To predict functional outcomes of differentially expressed genes, cell culture medium was replaced with 10 mL Wst-1 (Pierce) in we used the Diseases and Functions tool in Ingenuity Pathway 100 mL complete medium.
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