SUMMARY Using sequential gene expression profiling (GEP) samples, we defined a major functional group related to drug resistance that contains chromosomal instability (CIN) genes. One CIN gene in particular, NEK2, was highly correlated with drug resistance, rapid relapse, and poor outcome in multiple cancers. Over-expressing NEK2 in cancer cells resulted in enhanced CIN, cell proliferation and drug resistance, while targeting NEK2 by NEK2 shRNA overcame cancer cell drug resistance and induced apoptosis in vitro and in a xenograft myeloma mouse model. High expression of NEK2 induced drug resistance mainly through activation of the efflux pumps. Thus, NEK2 represents a strong predictor for drug resistance and poor prognosis in cancer and could be an important target for cancer therapy.

Cerebral cavernous malformation (CCM) is a disease of vascular malformations known to be caused by mutations in one of three genes: CCM1, CCM2 or CCM3. Despite several studies, the mechanism of CCM lesion onset remains unclear. Using a Ccm1 knockout mouse model, we studied the morphogenesis of early lesion formation in the retina in order to provide insight into potential mechanisms. We demonstrate that lesions develop in a stereotypic location and pattern, preceded by endothelial hypersprouting as confirmed in a zebrafish model of disease. The vascular defects seen with loss of Ccm1 suggest a defect in endothelial flow response. Taken together, these results suggest new mechanisms of early CCM disease pathogenesis and provide a framework for further study.

Deletion of a phenylalanine at position 1617 (delF1617) in the extracellular linker between segments S3 and S4 in domain IV of the human heart Na(+) channel (hH1a) has been tentatively associated with long QT syndrome type 3 (LQT3). In a mammalian cell expression system, we compared whole cell, gating, and single-channel currents of delF1617 with those of wild-type hH1a. The half points of the peak activation-voltage curve for the two channels were similar, as were the deactivation time constants at hyperpolarized test potentials. However, delF1617 demonstrated a significant negative shift of -7 mV in the half point of the voltage-dependent Na(+) channel availability curve compared with wild type. In addition, both the time course of decay of Na(+) current (I(Na)) and two-pulse development of inactivation of delF1617 were faster at negative test potentials, whereas they tended to be slower at positive potentials compared with wild type. Mean channel open times for delF1617 were shorter at potentials <0 mV, whereas they were longer at potentials >0 mV compared with wild type. Using anthopleurin-A, a site-3 toxin that inhibits movement of segment S4 in domain IV (S4-DIV), we found that gating charge contributed by the S4-DIV in delF1617 was reduced 37% compared with wild type. We conclude that deletion of a single amino acid in the S3-S4 linker of domain IV alters the voltage dependence of fast inactivation via a reduction in the gating charge contributed by S4-DIV and can cause either a gain or loss of I(Na), depending on membrane potential.

Identifying the best gene expression pattern associated with low-risk disease in patients with newly diagnosed multiple myeloma (MM) is important to direct clinical treatments. The MM Survival Index14 (MMSI14) was developed from GEP data sets of 22 normal plasma cells (NPC), 5 MM cell lines (MMCL), 44 monoclonal gammopathy of undetermined significance (MGUS), and 351 newly diagnosed MM patients. R/bioconductor and siggenes package were used to obtain heatmap, boxplot and histogram whose results were then analyzed by Kaplan-Meier analysis. Fourteen genes associated with low-risk disease in MM were identified. We validated the disease prognostic power of MMSI14 with an independent data set of other 214 newly diagnosed MM patients and also compared our model with the 70-gene, the 8-subgroup, IFM15, and HMCLs7 models. Survival analysis showed that a low MMSI14 signature was associated with longer survival. Applying MMSI14 to independent data sets, we were able to classify 39% of patients as low-risk, with a survival probability of more than 90% at 60 months. Multiple clinical parameters confirmed significant correlation between low- and high-risk subgroups defined by MMSI14. Comparing previously published models to the same data sets the MMSI14 model retained the best prognostic value. We have developed a new gene model (MMSI14) for defining low-risk, newly diagnosed MM. The multivariate comparative analysis confirmed that MMSI14 is the best available model to predict clinical outcome in MM patients.