Skp2 Inhibition Diminishes Essential Cell Survival Markers c-Maf and c-Myc, Enhancing Therapeutic Efficacy Against Drug Resistant Multiple Myeloma
Bibliographic record
Abstract
Multiple Myeloma (MM) presents challenges as a hematologic malignancy, with drug resistance leading to limited curative options and disease relapse despite advancements in therapy. Understanding the molecular mechanisms driving MM's development and progression is vital for identifying new treatment approaches. Clinical and experimental evidence supports the role of S-phase kinase-associated protein 2 (Skp2) in driving the progression of several cancers, including MM. We interrogated a public microarray dataset derived from clinical specimens of MM patients and observed a significantly higher expression of Skp2 within the relapsed patient cohort (N=28) compared to the newly diagnosed group (N=63) (P=0.0041). Additionally, we observed elevated Skp2 levels and decreased P27 Kip1 levels in drug-resistant MM cell lines (MM.1R and RPMI 8226.R5) compared to their parental cell lines (MM.1S and RPMI 8226). Hence, a comprehensive understanding of Skp2's molecular functions in drug-resistant MM becomes paramount for facilitating the development of precision-targeted interventions. We investigated Skp2's molecular interactions in MM by performing a targeted knockdown with siRNA in drug-resistant and sensitive cell lines (OPM1, RPMI.8226, MM.1S, and MM.1R). Immunoblot analysis showed reduced STAT3 activation and decreased levels of cell survival markers c-Maf and c-Myc. Treatment with 10uM of the small molecule inhibitor SkpinC1 also inhibited STAT3 activation and led to significant reductions in c-Maf and c-Myc protein levels compared to the vehicle-treated groups. These findings hold immense significance as c-Maf and c-Myc are intricately associated with MM progression and poorer outcomes, although there is limited information on their regulation by the Skp2/P27 axis. Functionally, treatment of the MM cell lines with increasing dosages of SkpinC1 showed a progressive decrease in MM cell line survival, with IC50 values spanning 5-15uM, signifying the broad applicability of the inhibitor among the tested cell lines. Moreover, flow cytometry-based analysis of the surviving cell population demonstrated that SkpinC1 based inhibition of the Skp2/P27 axis considerably decreased the percentage of proliferating M-phase cells, while elevating the proportion of quiescent and apoptotic cells relative to the control group. At the molecular level, targeted inhibition of the Skp2/P27 axis by SkpinC1 caused the accumulation of cell cycle inhibitors P27 Kip1, P21 (Cip1/Waf1), and P57 Kip2, activation of the tumor suppressive TP53 protein with a simultaneous decrease in phospho-MDM2 levels in addition to an increase in the levels of apoptotic markers cleaved PARP and Caspase 3. From a clinical perspective, we also probed the applicability of Skp2 inhibition using SkpinC1 as a potential strategy for managing therapeutic resistance in MM. We treated multiple MM cell lines (OPM1, RPMI.8226, MM.1S and MM.1R) using a combination of Lenalidomide (an immune modulator) or Bortezomib (a proteasomal inhibitor) with SkpinC1 and compared it with single agent treatments. Both Lenalidomide/SkpinC1 as well as Bortezomib/SkpinC1 showed a strong dose dependent decrease in the percentage of surviving cells relative to the single agent treatments, thereby highlighting the remarkable potential of targeting the Skp2/P27 axis for clinical therapy in MM. To gain deeper insights into the molecular roles of Skp2/P27 axis inhibition by SkpinC1, we subjected drug-resistant MM.1R cells to 10uM SkpinC1 treatment for 24 hours, followed by single-cell transcriptome sequencing. This approach illuminated considerable heterogeneity between the drug-treated and control cells, accompanied by global changes in various signaling pathways associated with cell survival. In conclusion, the inhibition of Skp2 in MM cells exerts a significant impact on crucial cell survival pathways, notably c-Maf and c-Myc, thereby promoting apoptosis and offering a promising therapeutic avenue to combat drug resistance in MM. The precise targeting of Skp2 using small molecule inhibitors, such as SkpinC1, demonstrates substantial therapeutic potential, enhancing the efficacy of combinatorial treatment approaches for more effective interventions in MM.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".