ALBERT

Description: Abstract 2957 Myeloma is intimately associated with osteolytic bone disease, resulting from myeloma cells' interactions with osteoclasts and osteoblasts and their progenitors, and is dependent on the changes it induces in bone metabolism for progression. Myeloma cell dependence on the bone marrow microenvironment is also evident experimentally, where interaction of primary myeloma plasma cells (MMPC) with osteoclasts (OC) and with mesenchymal stem cells (MSC) support the survival of primary myeloma cells. To understand the molecular mechanisms associated with the survival of MMPC, we used Acuity 4 software to analyze Affymetrix U133 Plus2 chip data and identify changes in gene expression in induced MMPC freshly isolated from 8 patients by interaction with OC and from 8 additional patients with MSC. Expression by MMPC of 675 genes was changed following interaction with OC; 552 genes were upregulated and 123 down regulated. Expression of 296 genes was changed in MSC co cultures (161 upregulated, 135 down regulated). Comparison of the genes whose expression was similarly changed in both co culture systems identified 72 probesets, representing 58 genes, that were commonly changed; 33 were upregulated and 25 down regulated. Ingenuity Pathway Analysis assigned 54 of the 58 genes to 4 distinguished networks of interrelated genes with high probability scores. We next tested the hypothesis that the expression of genes whose expression was commonly changed in the co culture systems has clinical significance. To accomplish this, we used gene expression data available on 127 relapsed patients who had been uniformly treated on our Total Therapy 2 protocol, and for whom gene expression (GEP) data at first relapse (RL) were available. 71 of these patients also had pre treatment (BL) GEP data; for these 71 patients we calculated change in expression of the 72 probesets as the ratio of RL/BL expression signal. We identified 7 genes whose expression changes were significantly (p≤0.05) associated with survival after relapse: These genes were, in order of significance: CCNE2, PECAM1, KLHL21, ICAM1, PLAU, ANPEP, and DUSP1, with p-values ranging from 0.017 to 0.05. Up regulation of PECAM1, ANPEP, DUSP1, and down regulation of CCNE2, KLHL21, ICAM1, and PLAU were associated with longer survival. We further determined whether expression level of these 72 probesets at relapse, defined by signal intensity, correlated with post relapse survival of the 127 patients; 18 genes were significantly (p

Description: Tumor–bone marrow microenvironment interactions in multiple myeloma (MM) are documented to play crucial roles in plasma-cell growth/survival. In vitro coculture of MM cells with osteoclasts supported cell survival and significantly down-regulated JUN expression. JUN expression in myeloma cells from late-stage and high-risk MM was significantly lower than in plasma cells from healthy donors, monoclonal gammopathy of undetermined significance, smoldering MM, and low-risk MM; patients with low-JUN–expressing MM cells had earlier disease-related deaths. JUN overexpression in MM cells induced cell death and growth inhibition and up-regulated expression of early growth response protein 1 (EGR-1), whose low expression also carried unfavorable clinical implications. EGR-1 knockdown in MM cells abrogated JUN overexpression-induced MM cell death and growth inhibition, indicating that EGR-1 acts directly downstream of JUN. JUN modulates myeloma cell apoptosis through interacting with EGR-1, which down-regulates Survivin and triggers caspase signaling. Importantly, high JUN or EGR-1 expression was associated with improved outcome in Total Therapy 3, in which bortezomib is given throughout therapy, versus Total Therapy 2, in which bortezomib is given only at relapse. Consistently, JUN or EGR-1 knockdown in cultured MM cells enhanced their resistance to bortezomib, demonstrating the crucial role of low JUN/EGR-1 expression in MM resistance to bortezomib.

Description: Acute myeloid leukemia (AML) is characterized by a block in differentiation of myeloid cells accompanied by suppression of normal production of blood cells and dissemination of immature cells from their normal hematopoietic environment. In order to gain insight into the mechanisms governing these features of leukemia, we have examined mRNA expression patterns in transgenic mice that express PML-RARA (associated with human acute promyelocytic leukemia, APL) and BCL2. Such mice represent a model system in which these genetic changes combine to initially arrest differentiation in a pre-leukemic state, with acute leukemia arising only upon the acquisition of additional genetic changes. In the present study we identified the set of genes whose transcription is altered in pre-leukemic and leukemic bone marrow in comparison to bone marrow from normal mice. We generated and analyzed three sets of gene expression data from cDNA microarrays: (1) PRE-LEUKEMIA vs. NORMAL (6600 substances), (2) LEUKEMIA vs. NORMAL (6677 substances) and (3) PRE-LEUKEMIA vs. LEUKEMIA (3762 substances). Greater than 90% of substances showed similar expression in the pre-leukemic and leukemic samples. Sixty unique genes were differentially expressed, in a statistically significant manner, with two-fold or more difference in expression, and presenting in all three datasets. Comparison of these 60 revealed genes to a published human AML expression dataset (Bullinger et al., N Engl J Med, 350:1605, 2004) identified eight genes with the same pattern of expression in our mouse model and in human APL, and four common genes with opposite expression. For mouse pre-leukemia vs. leukemia as compared to all human AMLs vs. APL, the eight genes that showed a similar change were: COL1A2, COL5A2, EIF5A, LAMR1, LGALS1, NOTCH2, SNN, and ACP5. Of particular interest to our laboratory are two genes: LGALS1 and NOTCH2. Among the identified genes the first is most over-expressed, and the second is most under-expressed in human APL. Next, we identified genes in our dataset co-expressed with LGALS1 or NOTCH2 (correlation coefficient 〉0.85). Among discovered genes were revealed transcription factors (NKX6-1, GTF3A, ZFP95, SOX11, HOXA4 and more), translation factors (EIF3S6, EIF5A, EIF4A1 and more), differentiation factors (RQCD1, NEUROD2, CREBBP/EP300 inhibitory protein 1 and more), oncogenes (Ski, COUP-TF1 and more) and others. We are currently seeking to identify which of these changes may contribute to AML pathogenesis. Gene profiling using such a comparative approach for analysis will allow better understanding of leukemogenesis, and can also be applied for studies of tumor progression in other malignancies.

Description: Myeloma is intimately associated with osteolytic bone disease, in part by inhibiting mesenchymal stem cell (MSC) differentiation into osteoblasts via blocking Wnt signaling (Tian, N Engl J Med2003;349:2483). Stimulation of osteoblast activity by bortezomib is associated with anti myeloma effects in patients (Zangari, Br J Haematol2005;131:71) and by MSC infusion in a model system (Yaccoby, Haematologica2006;91:192). To further evaluate the potential therapeutic utility of MSCs, we investigated the effects of MSCs on myeloma cell survival and the genomic consequences of MSC-myeloma cell interactions. CD138-purified myeloma (MM) cells from 16 consented patients were cocultured with MSCs derived from the bone marrow of 3 healthy donors (MSC 1, 2, &3, provided by Dr. D. Prockop, Tulane University). In 7 co-culture experiments, MSCs supported MM cell survival for 6–8 days compared to MM cells cultured alone, with a median viable cell count increasing by 2.3 fold v controls (1.2–4.1, p=0.003); in 4, coculture suppressed MM survival (median=0.5, range 0.3–0.7, p=0.016); and in 5 there was no effect (median=0.9, range 0.9–1.0). In 3 experiments, the effects of the different MSCs on MM cell survival varied inconsistently among the 3 MSCs, from 0.5 to 2.5 fold surviving cells. In order to understand this heterogeneity, we investigated the genomic consequences of MM-MSC interactions. RNA was extracted immediately after mixing (t=0) and following 18 hours co-culture (t=18), and changes in gene expression analyzed using the Affymetrix microarray system. Since myeloma cells adhere tightly to MSCs, we analyzed expression changes in 1,708 genes expressed only in MM cells and 4862 expressed only in MSCs. At t=18, 250 MM cell genes were changed by 〉2 fold compared with t=0 (222 up and 28 down regulated), and 1,036 MSC genes were changed 〉2 fold (1018 up and 18 downregulated). Each of the 3 MSCs also had unique genes expressed (40, 85 and 162 for MSC 1, 2, &3, respectively), of which expression of 12, 30, and 39, respectively, was changed by 〉2 fold. Analysis of 776 MM- and 2398 MSC-related genes after a signal cutoff of 500 with Ingenuity Pathways Analysis software showed changes in the expression of several groups of interrelated genes following co-culture. For MM, these included ERKs, AKT, NFKB, E2F1, and FOS. The transcription regulators BTG2, ACTN2, CALR, E2F1, E2F5, and ATF7 were up regulated, while FOS and FOXM1 were down regulated. Groups changed in MSCs included IL1B, CCND1, MYC, CTBP1, EGFR, RUNX1, HRAS, and SMAD3. There were minor differences in changes of the expression patterns of some of the probesets between the three MSCs, likely related to alternative splicing or to variations in the 3′-UTR. These studies continue in order to discern possible differences in the interactions between MM cells and MSCs, and to identify whether MM cells or MSC determine the outcome of these differences.