Novel Targeting Of Phospho-Marcks Overcomes Drug Resistance and Induces Anti-Tumor Activity In Preclinical Models Of Multiple Myeloma
Bibliographic record
Abstract
Abstract Multiple myeloma (MM) remains incurable due to the development of a drug-resistant phenotype after prolonged therapy. Myristoylated alanine-rich C-kinase substrate (MARCKS) is a protein kinase C (PKC) substrate that plays an important role in cell adhesion, spreading and invasion. Our previous studies found that overexpression of phospho-MARCKS (pMARCKS) was detected in developed drug resistant MM cell lines (RPMI-8226 R5, MM.1R) relative to their parental drug sensitive cell lines (RPMI-8226S, MM.1S). We hypothesized that pMARCKS is involved in chemo- and novel drug resistance in MM. To further evaluate the drug resistance, we exposed both RPMI-8226 R5 and MM.1R cell lines to varying dosages of bortezomib, dexamethasone, doxorubicin, and lenalidomide. By MTT assay, both resistant cell lines were found to have significantly higher viability to all 4 drugs compared to their respective non-resistant lines. In addition, Western blot analysis showed increased pMARCKS expressions in all 3 bortezomib resistant cell lines 8226.BR, OPM2.BR, and ANBL-6.BR as compared to their respective bortezomib sensitive cell lines. We next acquired MM patient samples collected at diagnosis and at relapse after bortezomib treatment, and investigated their pMARCKS expression with immunoblotting analyses. The patient samples collected from relapse after bortezomib treatment had higher pMARCKS expression than those collected at diagnosis. Furthermore, we studied additional 3 primary MM patient samples with high pMARCKS expressions and 3 with low expressions for their vaibility after a 36 hour bortezomib treatment, and found that the samples with high pMARCKS expressions were more resistant to bortezomib than those with low pMARCKS expressions (mean IC50 of 7.1 nM and mean IC50 of 4.8 nM, respectively; p = 0.042). Importantly, combination of a low dosage of bortezomib (5.0 nM) with either 2.5 uM or 5.0 uM of enzastaurin (an inhibitor of phospho-PKC), displayed a synergistic cytotoxicity on myeloma cells with high pMARCKS expressions. To further elucidate the role of pMARCKS in drug resistance, we knocked down pMARCKS expression by transfecting siMARCKS into 8226 R5 and MM.1R cells. Following the knockdown, both cell lines had significantly lower viability after treatment with either bortezomib, dexamethasone, doxorubicin, or lenolidomide, in comparison to empty vector controls. FACS analysis and annexin V assay of the knockdown cells and the control cells from both cell lines showed significantly induction of G1/S cell cycle arrest and apoptosis in the knockdown cells. The immunoprecipitation (IP) and chromatin immunoprecipitation (ChIP) DNA-qPCR analysis further demonstrated that pMARCKS regulates SKP2 expression through binding with E2F1, mediating SKP2/p27Kip1 cell cycle pathway. Finally, we investigated the effect of inhibition of pMARCKS in a 8226 R5 xenograft model of SCID mice. Mice injected with shMARCKS-transfected 8226 R5 cells and received bortezomib showed significant retardation of tumor growth and prolonged survival compared to the control groups. Taken together, our data indicate that pMARCKS is constitutively activated in resistant and relapsed MM cells and contributes to drug resistance by regulating E2F1 mediated SKP2/p27Kip1 cell cycle pathway, thus providing a preclinical rationale for targeting pMARCKS as a promising approach in patients with refractory/relapsed MM. Disclosures: Reece: BMS: Research Funding; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Merck: Honoraria, Research Funding; Millennium: Research Funding; Novartis: Honoraria; Onyx: Honoraria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".