Synergistic molecular vulnerabilities enhancing hypomethylating agents in myeloid malignancies have remained elusive. RNA-interference drug modifier screens identified antiapoptotic BCL-2 family members as potent 5-Azacytidine-sensitizing targets. In further dissecting BCL-XL, BCL-2 and MCL-1 contribution to 5-Azacytidine activity, siRNA silencing of BCL-XL and MCL-1, but not BCL-2, exhibited variable synergy with 5-Azacytidine in vitro. The BCL-XL, BCL-2 and BCL-w inhibitor ABT-737 sensitized most cell lines more potently compared with the selective BCL-2 inhibitor ABT-199, which synergized with 5-Azacytidine mostly at higher doses. Ex vivo, ABT-737 enhanced 5-Azacytidine activity across primary AML, MDS and MPN specimens. Protein levels of BCL-XL, BCL-2 and MCL-1 in 577 AML patient samples showed overlapping expression across AML FAB subtypes and heterogeneous expression within subtypes, further supporting a concept of dual/multiple BCL-2 family member targeting consistent with RNAi and pharmacologic results. Consequently, silencing of MCL-1 and BCL-XL increased the activity of ABT-199. Functional interrogation of BCL-2 family proteins by BH3 profiling performed on patient samples significantly discriminated clinical response versus resistance to 5-Azacytidine-based therapies. On the basis of these results, we propose a clinical trial of navitoclax (clinical-grade ABT-737) combined with 5-Azacytidine in myeloid malignancies, as well as to prospectively validate BH3 profiling in predicting 5-Azacytidine response.

In Escherichia coli, YaeT, together with four lipoproteins, YfgL, YfiO, NlpB, and SmpA, forms a complex that is essential for ␤-barrel outer membrane protein biogenesis. Data suggest that YfgL and YfiO make direct but independent physical contacts with YaeT. Whereas the YaeT-YfiO interaction needs NlpB and SmpA for complex stabilization, the YaeT-YfgL interaction does not. Using bioinformatics, genetics, and biochemical approaches, we have identified three residues, L173, L175, and R176, in the mature YfgL protein that are critical for both function and interactions with YaeT. A single substitution at any of these sites produces no phenotypic defect, but two or three simultaneous alterations produce mild or yfgL-null phenotypes, respectively. Interestingly, biochemical data show that all YfgL variants, including those with single substitutions, have weakened in vivo YaeT-YfgL interaction. These defects are not due to mislocalization or low steady-state levels of YfgL. Cysteine-directed cross-linking data show that the region encompassing L173, L175, and R176 makes direct contact with YaeT. Using the same genetic and biochemical strategies, it was found that altering residues D227 and D229 in another region of YfgL from E221 to D229 resulted in defective YaeT bindings. In contrast, mutational analysis of conserved residues V319 to H328 of YfgL shows that they are important for YfgL biogenesis but not YfgL-YaeT interactions. The five YfgL mutants defective in YaeT associations and the yfgL background were used to show that SurA binds to YaeT (or another complex member) without going through YfgL.

No abstract

No abstract

2881 Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. Significant efforts have been made to identify genetic changes and signatures that can predict clinical outcome and include them in the routine clinical care. Gene expression profiling (GEP) studies have achieved a central role in the study of multiple myeloma (MM), as they become a critical component in the risk-based stratification of the disease. To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells (obtained from the immunobead selection of CD138+ cells) from 489 MM samples in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, UAMS class, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of 57 samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. A macrophage index was calculated and used as a surrogate measurement of non-plasma cell contamination. A total of 49 samples (10%) were excluded from subsequent analysis as the macrophage index indicated a significant contamination with no plasma cells, hence potentially compromising the results. The percent of high-risk disease patients identified from different signatures ranged from 26.4% by using high proliferation index to 28.8% with high centrosome signature and 31.3% with high 70-gene index. This percent of high-risk cases based on the 70-gene index is similar to what was found in Total therapy 2 (TT2) and TT3 cohorts. A third of patients (33.2%) were classified as D1 in the TC class, followed by 11q13 (19.3%), D2 (16.4%), 4p16 (13.8%), MAF (6.1%), None (4.7%), D1+D2 (4.5%) and 6p21 (1.8%). The NF-kB pathway was likely activated in 45.5% to 59.5% of cases, depending on the index used for its calculation. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02) and D2 (p=0.05) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations with unknown partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making. Disclosures: Stewart: Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding. Fonseca:Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.