Pharmacogenomics and chemical library screens reveal a novel SCFSKP2 inhibitor that overcomes Bortezomib resistance in multiple myeloma.

While clinical benefit of the proteasome inhibitor (PI) bortezomib (BTZ) for multiple myeloma (MM) patients remains unchallenged, dose-limiting toxicities and drug resistance limit the long-term utility. The E3 ubiquitin ligase Skp1-Cullin-1-Skp2 (SCFSkp2) promotes proteasomal degradation of the cell cycle inhibitor p27 to enhance tumor growth. Increased SKP2 expression and reduced p27 levels are frequent in human cancers and are associated with therapeutic resistance. SCFSkp2 activity is increased by the Cullin-1-binding protein Commd1 and the Skp2-binding protein Cks1B. Here we observed higher CUL1, COMMD1 and SKP2 mRNA levels in CD138+ cells isolated from BTZ-resistant MM patients. Higher CUL1, COMMD1, SKP2 and CKS1B mRNA levels in patient CD138+ cells correlated with decreased progression-free and overall survival. Genetic knockdown of CUL1, COMMD1 or SKP2 disrupted the SCFSkp2 complex, stabilized p27 and increased the number of annexin-V-positive cells after BTZ treatment. Chemical library screens identified a novel compound, designated DT204, that reduced Skp2 binding to Cullin-1 and Commd1, and synergistically enhanced BTZ-induced apoptosis. DT204 co-treatment with BTZ overcame drug resistance and reduced the in vivo growth of myeloma tumors in murine models with survival benefit. Taken together, the results provide proof of concept for rationally designed drug combinations that incorporate SCFSkp2 inhibitors to treat BTZ resistant disease.

Gene expression profile alone is inadequate in predicting complete response in multiple myeloma.

With advent of several treatment options in multiple myeloma (MM), a selection of effective regimen has become an important issue. Use of gene expression profile (GEP) is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated the ability of GEP to predict complete response (CR) in MM. GEP from pretreatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional data sets from three different studies (n=511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four data sets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56-78% in test data sets and no significant difference with regard to GEP platforms, treatment regimens or in newly diagnosed or relapsed patients. Importantly, permuted P-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.

A gene expression signature for high-risk multiple myeloma.

There is a strong need to better predict the survival of patients with newly diagnosed multiple myeloma (MM). As gene expression profiles (GEPs) reflect the biology of MM in individual patients, we built a prognostic signature based on GEPs. GEPs obtained from newly diagnosed MM patients included in the HOVON65/GMMG-HD4 trial (n=290) were used as training data. Using this set, a prognostic signature of 92 genes (EMC-92-gene signature) was generated by supervised principal component analysis combined with simulated annealing. Performance of the EMC-92-gene signature was confirmed in independent validation sets of newly diagnosed (total therapy (TT)2, n=351; TT3, n=142; MRC-IX, n=247) and relapsed patients (APEX, n=264). In all the sets, patients defined as high-risk by the EMC-92-gene signature show a clearly reduced overall survival (OS) with a hazard ratio (HR) of 3.40 (95% confidence interval (CI): 2.19-5.29) for the TT2 study, 5.23 (95% CI: 2.46-11.13) for the TT3 study, 2.38 (95% CI: 1.65-3.43) for the MRC-IX study and 3.01 (95% CI: 2.06-4.39) for the APEX study (P<0.0001 in all studies). In multivariate analyses this signature was proven to be independent of the currently used prognostic factors. The EMC-92-gene signature is better or comparable to previously published signatures. This signature contributes to risk assessment in clinical trials and could provide a tool for treatment choices in high-risk MM patients.

Human acute myeloid leukemia cells with internal tandem duplications in the Flt3 gene show reduced proliferative ability in stroma supported long-term cultures.

Recently, in-frame internal tandem duplications have been reported within the regions coding for the juxtamembrane through the first tyrosine kinase domain of the Flt3 gene. These duplications have been reported to lead to autophosphorylation of the receptor. In this study we investigated the effect of such mutations in the Flt3 gene on the in vitro proliferation of human acute myeloid leukemia cells. The mutations were detected in 10 out of 59 AML bone marrow samples analyzed and were not restricted to a specific FAB class or cytogenetic aberration. PCR analysis of those samples showed all mutations to be present in exon 11 of the gene. Whilst samples without a mutation of the Flt3 gene showed an increased cell production in response to either IL-3 and G-CSF or IL-6, SCF, TPO and Flt3L in long-term stroma supported cultures, mutant samples failed to do so. As we could not find a relationship between the absence of a response and either FAB class or cytogenetic aberrations, we interpret these results as an indication that the internal tandem duplications in the Flt3 gene are the prime cause of this unresponsiveness. Although our study does not explain the mechanism by which these mutations cause this unresponsiveness it does suggest that AML cells need a wild-type Flt3 for optimal in vitro proliferation.