Multiple myeloma cells' capacity to decompose H2O2 determines lenalidomide sensitivity.

Lenalidomide is an immunomodulatory drug (IMiDs) with clinical efficacy in multiple myeloma (MM) and other late B-cell neoplasms. Although cereblon (CRBN) is an essential requirement for IMiD action, the complete molecular and biochemical mechanisms responsible for lenalidomide-mediated sensitivity or resistance remain unknown. Here, we report that IMiDs work primarily via inhibition of peroxidase-mediated intracellular H2O2 decomposition in MM cells. MM cells with lower H2O2-decomposition capacity were more vulnerable to lenalidomide-induced H2O2 accumulation and associated cytotoxicity. CRBN-dependent degradation of IKZF1 and IKZF3 was a consequence of H2O2-mediated oxidative stress. Lenalidomide increased intracellular H2O2 levels by inhibiting thioredoxin reductase (TrxR) in cells expressing CRBN, causing accumulation of immunoglobulin light-chain dimers, significantly increasing endoplasmic reticulum stress and inducing cytotoxicity by activation of BH3-only protein Bim in MM. Other direct inhibitors of TrxR and thioredoxin (Trx) caused similar cytotoxicity, but in a CRBN-independent fashion. Our findings could help identify patients most likely to benefit from IMiDs and suggest direct TrxR or Trx inhibitors for MM therapy.

Access to the nucleus and functional association with c-Myc is required for the full oncogenic potential of ΔEGFR/EGFRvIII.

ΔEGFR is a potent glioblastoma oncogene which has been studied primarily as a plasma membrane kinase. Using intracranial xenograft studies in mice, we show that blocking ΔEGFR access to the nucleus attenuates its tumorigenicity and, conversely, that promoting nuclear accumulation enhances this, providing the first in vivo evidence that the nuclear actions of ΔEGFR contribute strongly to its oncogenic function. Nuclear actions of ΔEGFR include regulation of gene expression by participation in chromatin-bound complexes, and genome-wide mapping of these sequences by chromatin immunoprecipitation and massively parallel sequencing identified 2294 peaks. Bioinformatic analysis showed enrichment of the E-box motif in the dataset, and c-Myc and ΔEGFR were corecruited to the promoters of and transcriptionally activated a subset of nuclear ΔEGFR chromatin targets. Knockdown of c-Myc decreased the expression of these targets and diminished ΔEGFR-stimulated anchorage-independent colony formation. We conclude that transcriptional regulation of target genes by association with gene regulatory chromatin in cooperation with c-Myc by nuclear ΔEGFR makes a unique contribution to its oncogenicity and propose that this venue provides new targets for therapeutic intervention.

Protein and phosphoprotein levels in glioma and adenocarcinoma cell lines grown in normoxia and hypoxia in monolayer and three-dimensional cultures.

BACKGROUND: Three dimensional (3D) growths of cancer cells in vitro are more reflective of in situ cancer cell growth than growth in monolayer (2D). The present study is designed to determine changes in protein and phosphoprotein that reflect adaptation of tumor cells to 3D as compared to 2D. Since relative hypoxia is a common feature of most solid tumors, the present study also aims to look at the impact of transition from normoxia to hypoxia in these two growth conditions. RESULTS: Using reverse-phase protein arrays, we compared levels of 121 different phosphorylated and non-phosphorylated proteins in 5 glioma and 6 adenocarcinoma lines under conditions of 3D and monolayer culture in normoxia and hypoxia. A three-way analysis of variance showed levels of 82 antibodies differed between media (2D vs. 3D) and 49 differed between treatments (hypoxia vs. normoxia). Comparing 2D to 3D growth, 7 proteins were commonly (i.e., > 50% of tumors) elevated in 3D: FAK, AKT, Src, GSK3αß, TSC2, p38, and NFκßp65. Conversely, 7 other proteins are commonly decreased: ATRIP, ATR, ß-catenin, BCL-X, cyclin B1, Egr-1, and HIF-1α. Comparing normoxia to hypoxia, only NCKIPSD was commonly elevated in hypoxia; 6 proteins were decreased: cyclin B1, 4EBP1(Ser65), c-Myc, SMAD3(Ser423), S6(Ser235), and S6(Ser240). Hypoxia affected glioma cell lines differently from adenocarcinoma cell lines: 8 proteins were increased in gliomas (BAX, caspase 7, HIF-1α, c-JUN, MEK1, PARP 1 cleaved, Src, and VEGFR2) and none in adenocarcinomas. CONCLUSIONS: We identified subsets of proteins with clearly concordant/discordant behavior between gliomas and adenocarcinomas. In general, monolayer to 3D culture differences are clearer than normoxia to hypoxia differences, with anti-apoptotic, cytoskeletal rearrangement and cell survival pathways emphasized in the former and mTOR pathway, transcription, cell-cycle arrest modulation, and increased cell motility in the latter.

Different changes in protein and phosphoprotein levels result from serum starvation of high-grade glioma and adenocarcinoma cell lines.

Tumor cells undergoing serum starvation in vitro partially mimic metabolically stressed cells trying to adjust to a changed environment in vivo by inducing signal transduction and gene expression so that the tumor continues to grow. Our hypothesis is that the changes in protein and phosphoprotein levels after serum starvation may reflect the adapted phenotype of the tumor, which could be targeted for therapy. We used reverse-phase protein microarrays to interrogate five high-grade glioma cell lines and seven adenocarcinoma cell lines for differences in the level of 81 proteins and 25 phosphoproteins. All cell lines were studied in the well-fed condition of growth with 10% FBS and the starved condition of 0.5% FBS. Protein expression levels were normalized to beta-actin and trichotomized as increased (+1, upper 75th quartile), decreased (-1, lowest 25th quartile), or unchanged (0, others) to focus on the patterns of the biggest (and hopefully most robust) changes in protein and phosphoprotein levels. We examined these trichotomized values to better understand Starved-Fed differences among the cell lines and thereby gain better/clearer insight into the effects of serum starvation on potential cellular responses. In general, the expression of proteins and phosphoproteins 24 h after FBS starvation increased more often in glioma lines than in adenocarcinoma lines, which appeared to have fewer increased protein scores and more decreased scores. Many of the proteins increased in gliomas were downstream targets of the PTEN-PI-3 kinase-AKT, EGFR-MAPK-Stat, and transcription activator-polyamine signaling pathways. In adenocarcinomas, the expression of proteins and phosphoproteins generally increased in apoptosis pathways, while there were minor fluctuations in the other pathways above. Contrawise, gliomas become resistant to apoptosis after 24 h of serum starvation and upregulate transcription activators and polyamines more so than adenocarciomas.

Melding a New 3-Dimensional Agarose Colony Assay with the E(max) Model to Determine the Effects of Drug Combinations on Cancer Cells.

The goal of our study was two-fold: (i) develop a robust 3D colony assay methodology to interrogate drug combinations using GelCount and (ii) to develop 2-drug combinations that might be useful in the clinic for the treatment of high-grade gliomas. We used three glioma cell lines (U251MG, SNB19, and LNZ308) and two adenocarcinoma cell lines (MiaPaCa and SW480) grown as colonies in a two-tiered agarose cultures. We evaluated two-drug combinations of difluoromethylornithine (DFMO), carboplatin, vorinostat (SAHA), and docetaxel. To analyze for antitumor efficacy we used GelCount to measure the area under the curve for tumor colony volumes (microm(2) x OD) in each plate. The non-linear dose-response E(max) model and the interaction index based on the Loewe additivity are applied to calculate two-drug synergy, additive, and antagonistic interactions. For glioblastoma cell lines, (i) carboplatin followed by DFMO was synergistic or additive in 2/3 cell lines, (ii) carboplatin before SAHA was synergistic in 1 cell line, (iii) carboplatin before docetaxel was synergistic in 2/3 cell lines and partially additive in the third, (iv) SAHA before docetaxel was synergistic in 1/3 cell lines, (v) docetaxel before DFMO was additive or partially active in 3/3 cell lines, and (vi) DFMO plus SAHA was inactive regardless of order. In the MiaPaCA cell line, synergy occurred when DFMO followed carboplatin and, at short exposure times, when SAHA was combined with carboplatin (regardless of order). In the SW480 cell line synergy occurred only in short exposures for carboplatin followed by docetaxel; additive and mixed partial effects were also seen with DFMO plus carboplatin or docetaxel (regardless of order), carboplatin before DFMO, carboplatin before SAHA, and docetaxel before carboplatin. In conclusion, by applying the Gelcount automated counting and sizing of colonies and the use of E(max) and Loewe models to define drug interactions, we can reliably define drug combination efficacy as a function of log dose and duration of drug exposure.

A new preclinical 3-dimensional agarose colony formation assay.

The evaluation of new drug treatments and combination treatments for gliomas and other cancers requires a robust means to interrogate wide dose ranges and varying times of drug exposure without stain-inactivation of the cells (colonies). To this end, we developed a 3-dimensional (3D) colony formation assay that makes use of GelCount technology, a new cell colony counter for gels and soft agars. We used U251MG, SNB19, and LNZ308 glioma cell lines and MiaPaCa pancreas adenocarcinoma and SW480 colon adenocarcinoma cell lines. Colonies were grown in a two-tiered agarose that had 0.7% agarose on the bottom and 0.3% agarose on top. We then studied the effects of DFMO, carboplatin, and SAHA over a 3-log dose range and over multiple days of drug exposure. Using GelCount we approximated the area under the curve (AUC) of colony volumes as the sum of colony volumes (microm2xOD) in each plate to calculate IC50 values. Adenocarcinoma colonies were recognized by GelCount scanning at 3-4 days, while it took 6-7 days to detect glioma colonies. The growth rate of MiaPaCa and SW480 cells was rapid, with 100 colonies counted in 5-6 days; glioma cells grew more slowly, with 100 colonies counted in 9-10 days. Reliable log dose versus AUC curves were observed for all drugs studied. In conclusion, the GelCount method that we describe is more quantitative than traditional colony assays and allows precise study of drug effects with respect to both dose and time of exposure using fewer culture plates.