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In Vitro and Ex Vivo Gene Expression Profiling Reveals Differential Kinetic Mitra et al. Blood Cancer Journal (2020) 10:78 https://doi.org/10.1038/s41408-020-00344-9 Blood Cancer Journal ARTICLE Open Access In vitro and ex vivo gene expression profiling reveals differential kinetic response of HSPs and UPR genes is associated with PI resistance in multiple myeloma Amit Kumar Mitra 1,2,3, Harish Kumar1, Vijay Ramakrishnan4, Li Chen 5,LindaBaughn6,ShajiKumar 4, S. Vincent Rajkumar 4 and Brian G. Van Ness 2 Abstract Extensive inter-individual variation in response to chemotherapy (sensitive vs resistant tumors) is a serious cause of concern in the treatment of multiple myeloma (MM). In this study, we used human myeloma cell lines (HMCLs), and patient-derived CD138+ cells to compare kinetic changes in gene expression patterns between innate proteasome inhibitor (PI)-sensitive and PI-resistant HMCLs following test dosing with the second-generation PI Ixazomib. We found 1553 genes that changed significantly post treatment in PI-sensitive HMCLs compared with only seven in PI-resistant HMCLs (p < 0.05). Genes that were uniquely regulated in PI-resistant lines were RICTOR (activated), HNF4A, miR-16-5p (activated), MYCN (inhibited), and MYC (inhibited). Ingenuity pathway analysis (IPA) using top kinetic response genes identified the proteasome ubiquitination pathway (PUP), and nuclear factor erythroid 2-related factor 2 (NRF2)- mediated oxidative stress response as top canonical pathways in Ix-sensitive cell lines and patient-derived cells, 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; whereas EIF2 signaling and mTOR signaling pathways were unique to PI resistance. Further, 10 genes were common between our in vitro and ex vivo post-treatment kinetic PI response profiles and Shaughnessy’s GEP80-postBz gene expression signature, including the high-risk PUP gene PSMD4. Notably, we found that heat shock proteins and PUP pathway genes showed significant higher upregulation in Ix-sensitive lines compared with the fold-change in Ix- resistant myelomas. Introduction therefore be categorized into: (1) innate resistance already Multiple myeloma (MM) remains an incurable disease present in drug-naive patients who never respond to with 5-year survival rate of 48.5% (NCI-SEER Cancer treatment, or (2) emerging/acquired resistance where a statistics)1. Extensive inter-individual variation in response patient’s tumor ultimately undergoes relapse or “acquires” to chemotherapy is a serious cause of concern in the the ability to resist therapy in course of treatment despite treatment of myeloma2,3. Not all patients respond equally good response to initial treatment3,4. Proteasome inhibi- well to treatment and those who do often develop resis- tors (PIs) are standard-of-care chemotherapeutic agents tance over the course of treatment. Drug resistance may for myeloma that impede tumor metastasis and angio- genesis by accelerating unfolded protein response (UPR) and by interfering with the nuclear factor-κB-enabled 3,5,6 Correspondence: Brian G. Van Ness ([email protected]) regulation of cell adhesion-mediated drug resistance . 1Department of Drug Discovery and Development, Harrison School of Alterations in gene expression levels have been shown Pharmacy, Auburn University, Auburn, AL, USA 2 to be associated with response to cancer drugs, including Department of Genetics, Cell Biology & Development, University of 7,8 Minnesota, Minneapolis, MN, USA PIs in myeloma . In our previous study, we used a panel Full list of author information is available at the end of the article. © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linktotheCreativeCommons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Blood Cancer Journal Mitra et al. Blood Cancer Journal (2020) 10:78 Page 2 of 13 of 50 human myeloma cell lines (HMCLs) representing Mayo Clinic, Minnesota (Mayo-Ix; NCT01415882). These the broad spectrum of biological and genetic hetero- were relapsed myeloma patients had less than six cycles of geneity of myeloma, and generated in vitro drug response prior treatment with a PI-based regimen and were not profile for the four PIs: bortezomib (velcade/Btz), carfil- refractory to the PI drug bortezomib (Bz)9. zomib (kyprolis/Cfz), oprozomib (Opz), and ixazomib (ninlaro/Ix) as single agents. As PI sensitivity in these In vitro chemosensitivity assays HMCLs were highly correlated (given a common target), Cells (HMCLs and patient-derived primary cells) were this suggested that any of these four PIs could be used as counted using countess automated cell counter (Invitro- surrogates in models to understand common PI resis- gen, Carlsbad, CA, USA) and seeded in 96-well plates at a tance. Further, we used machine learning-based compu- concentration of 4 × 105 cells per ml. Twenty-four (24 h) tational approaches to derive a gene expression profiling hours later, Ix, representative PI drug, was added as a (GEP) signature predictive of baseline PI response in single agent in increasing concentrations. Forty-eight myeloma9. (48 h) hours post Ix treatment, CellTiter-Glo luminescent Here, we were interested in understanding how gene cell viability assay (Promega, Madison, WI, USA) was expression patterns may be differentially influenced in used to perform cell viability assays according to manu- sensitive vs resistant myelomas following exposure to PIs facturer’s instructions using Synergy 2 Microplate Reader (kinetic PI response). Therefore, to characterize differ- (BioTek, Winooski, VT, USA). Survival curves were gen- ences in kinetic PI response, we compared transcriptome erated, percent survival values were calculated at each profiles of HMCLs in vitro as well as patient primary drug concentration and normalized to untreated controls. tumor cells ex vivo representative of highly PI-sensitive Half-maximal inhibitory concentration (IC50) values were and PI-resistant myelomas between baseline and post computed by nonlinear regression using sigmoidal treatment following test dosing with the model PI drug dose–response equation (variable slope). Area under ixazomib (Ix)3. Our results provide significant insights survival curve (AUSC) was calculated using trapezoidal into the wide heterogeneity by identifying key differences rule9. in PI-specific kinetic responses. Cell plating for kinetic Ix treatment and RNA-sequencing Materials and methods HMCLs and patient-derived primary myeloma tumor Drugs cells were plated at a density of 4 × 105 cells per mL. In all, Ix and Bz was procured from Takeda (Takeda Phar- 15 nM (IC50 of the most sensitive HMCL) and 40 nM maceuticals Inc., Deerfield, IL, USA). Cfz and Opz were (median IC50 of patient cells) of Ix were added to HMCLs obtained from Amgen (Thousand Oaks, CA, USA). Drugs and PMCs, respectively, after 24 hours of incubation. Cells were dissolved in dimethyl sulfoxide and stored at −20 °C. were collected 24 hours post-treatment as baseline (0 nM/ untreated) and post-treatment (treated). High-quality Antibodies RNA was extracted from untreated and treated mye- HSP40 (DNAJB1), HSPA1b (HSP70), HSP90alpla anti- loma cells using QIAshredder and RNeasy kit (Qiagen). bodies were purchased from Enzo Life Sciences (Farm- RNA concentration and integrity were assessed using the ingdale, NY, USA). Monoclonal anti-β-actin-peroxidase Nanodrop-8000 and Agilent 2100 Bioanalyzer and stored antibody produced in mouse was from Sigma-Aldrich (St at −80 °C. RNA integrity number/RIN threshold of eight Louis, MO, USA). Goat anti-Mouse IgG (H + L) Sec- was used for RNA-seq analysis. RNA-seq libraries were ondary antibody (horseradish peroxidase (HRP) con- constructed using Illumina TruSeq RNA sample Pre- jugated) was obtained from Thermo Fisher Scientific paration kit v2. Libraries were then size selected to gen- (Waltham, MA, USA). erate inserts of ~200 bp. RNA sequencing was performed on llumina’s HiSeq 2000 next-generation high-through- Cell lines put sequencing system using 50 bp paired-end protocol Twelve (12) myeloma cell lines—established, char- with depth of >20million reads per sample. Average acterized, and authenticated by our collaborators at The quality scores were thoroughly above Q30 for all libraries Translational Genomics Research Institute (TGen) and in both R1 and R2. NIH representing variabilities in PI-response in myeloma patients were selected from our HMCL panel described RNA-Seq data processing previously10. RNA-seq data were normalized and fragments per kilobase million values were used in further analysis using Patient samples Partek Genomics Suite and Galaxy data analysis software, CD138-selected plasma cells were derived from mye- an open source, web-based platform that provides tools loma patients enrolled in a phase-2 Ix clinical trial at necessary to create and execute RNA-seq analysis. In Blood Cancer Journal Mitra et al. Blood Cancer Journal (2020) 10:78 Page 3 of 13 brief, RNA-seq data analysis pipeline was developed using resistance modeled on list of significantly differentially Galaxy software workflow. Quality control (QC) check on regulated genes. IPA is a web-based software application the RNA-seq raw reads was performed using FastQC tool that integrates and interprets the data derived from dif- followed by read trimming to remove base positions that ferential mRNA expression analysis to: (i) identify the have a low median (or bottom quartile) score. Tophat2 most significantly affected canonical pathways predicted tool mapped processed RNA-seq reads to the hg19 to be activated or inhibited; (ii) predict upstream regulator human genome build.
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