Bone Marrow Stromal Cells Induce Bortezomib Resistance Partly through a Transcriptome Modulation in Multiple Myeloma Cells
Bibliographic record
Abstract
Abstract INTRODUCTION It has been suggested that environment-mediated drug resistance (EMDR) might account for the intrinsic (de novo) resistance to therapy in some myeloma patients developing in early stages of the disease and contribute to acquired drug resistance in the course of treatment. Understanding the mechanisms exploited by bone marroe stromal cells (BMSCs) to generate such resistance has long been sought. Furthermore, knowledge of the role that changes in expression of various genes could play is scanty. In this study we explored how BMSCs could induce resistance to bortezomib and whether it would change the expression of an array of genes in MM cells in the presence of bortezomib. METHODS Using patient-derived BMSCs and a normal immortalized human BMSC line (HS-5), we set up a model in which several human myeloma cell lines (HMCLs), RPMI-8226, U266, MM1.S, OPM2 and H929, were co-cultured with stromal cells in the presence or absence of bortezomib. Drug cytotoxicity in HS-5 context was performed using XTT assay. Transwell system, Paraformaldehyde (PFA) fixation of BMSCs and treatment of BMSCs with Brefeldin-A (BFA, an inhibitor of intracellular protein transport) were used as controls to delineate the direct or indirect roles of BMSCs. Percentage of apoptotic cells was determined with anti-CD138/APC, annexin-V/FITC and PI staining in FACS. For gene expression array, co-cultures were applied to magnetic cell separation (EasySep, Stem Cell Technologies), total RNA was isolated from MM cell pellets and synthesized cDNAs were applied to real time PCR gene expression arrays. After normalization to 4 different housekeeping genes, fold changes in gene expression were analyzed in co-cultures compared to MM cell monocultures in the presence of bortezomib using the 2-ΔΔCtalgorithm. RESULTS In the XTT cytotoxicity assay, all tested HMCLs displayed a higher level of viability in the presence of stroma than in stroma-free conditions (average bortezomib IC50s: stroma 22nM vs no stroma 8.53nM, p<0.02). Moreover, FACS analysis for all HMCLs showed that the percentage of bortezomib-induced apoptosis in stroma context was significantly lower than that in stroma-free conditions (p<0.05). Notably, in BFA and PFA treatment settings the level of bortezomib-induced apoptosis was lower (but not significant) than that in Transwell system suggesting that both soluble factors secreted by BMSCs and direct adhesion contribute to stroma-induced resistance to bortezomib. Gene expression analyses in real time PCR showed that the induced resistance was associated with downregulation of some genes involved in cell cycle and proliferation control (CDKN2A, CDC6, CDK2, EGR1, PTEN), downregulation of some genes involved in p53 signaling pathway (PUMA, P73, P53, MDM2, NOXA, BAX) and downregulation of some genes involved in apoptosis (CASP3, PARP). CONCLUSION Our findings suggest that BMSCs induce resistance against cytotoxic and apoptotic effects of bortezomib in MM cells. To this end, in our tested HMCLs, the gene expression patterns may imply involvement of multiple pathways in controlling stroma-induced resistance to bortezomib. Although gene expression results were verified real time PCR readouts, changes in genes may not always correlate with protein changes. Hence, our study has given us the rationale to proceed with more in depth exploration as to whether BMSCs may render MM cells resistant to bortezomib by negative modulation of p53 signaling pathway controlling apoptosis and cell cycle. Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".