scholarly journals Acute Myelogenous Leukemia (AML) Mesenchymal Stromal Cell (MSC) Have Distinct Protein Expression Patterns Compared to Normal MSC

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3813-3813 ◽  
Author(s):  
Steven M. Kornblau ◽  
Peter Ruvolo ◽  
Rui-Yu Wang ◽  
Vivian Ruvolo ◽  
Yihua Qiu ◽  
...  
Keyword(s):  
Protein Expression ◽  
Cell Survival ◽  
Differential Expression ◽  
Hierarchical Clustering ◽  
Research Funding ◽  
Adipogenic Differentiation ◽  
Expression Patterns ◽  
Principal Component ◽  
Myelogenous Leukemia ◽  
Cluster Membership

Abstract Background: AML remains highly fatal, therefore understanding the mechanisms that cause chemoresistance is critical for developing more effective therapies. The leukemia bone marrow microenvironment (BME) and component MSC supports leukemia development and cell survival, implying a key role for MSC in resistance. We hypothesize that differences in the physiology of MSC in the leukemia BME (AML-MSC), relative to normal MSC (NL-MSC), exist and could be therapeutic targets. Methods: To compare the function of AML-MSC from NL-MSC a custom reverse phase protein array (RPPA) was made using cultured MSC from AML (N = 106) and healthy donor MSC (NL; N = 71) and probed with 151 antibodies against 114 total, 36 phospho sites (on 29 proteins) and 1 cleavage site. Both biased clustering and unbiased hierarchical clustering along with principal component analysis were used to analyze data. To examine the influence of age on protein expression, age matched AML and NL MSC were compared. Results: Comparison of protein expression in NL-MSC and AML-MSC identified 5 Sample Clusters (SC1..SC5) based on the differential expression of 83 of 151 proteins, which formed 5 Protein Clusters (PC1..PC5) (P < 0.000001, FDR = 0.0000017)(FIG 1). Distribution of NL-MSC was significantly skewed to SC1 (7 of 8) and SC3 (38 of 52) while AML-MSC dominated SC2 (37 of 45) and SC4 (45 of 59), (Χ2 = 45.3, df=4, P <0.0001). NL-MSC were characterized by low expression of proteins in PC1 and 2 and higher expression within PC 3,4 & 5 with SC1 having more extreme levels than SC3. Protein levels in AML dominant SC4 was opposite of SC1 and SC3 for all 5 PC and was designated as "AML". SC5 was a more extreme version of SC4, for PC2, 4 and 5 but resembled SC3 for PC1 and 3. In contrast, the AML dominant SC2 resembled NL-MSC dominant SC1 and SC3 for PC2, 4 and 5, resembling SC4 only in PC1 and 3. This cluster was designated as "NL-like AML". Proteins with universally higher expression in NL-MSC included: SMADs 1 and 4, STMN1, SIRT1, p-Foxo1/3 (S32), HSP90 and EIF2S1. AML MSC had higher levels of 18 proteins across all groups including CCND1, BCL-XL, STAT5, and PPP2R2A. Salvage cases were more often in SC2 (17 of 36) and SC4 (26 of 45) than in SC3 (3 of 15) (Χ2 = 6.44, df=2, P <0.04). The observed changes were similar within three age groups (<30, 30-49, 50-59) in 22 of 25 universally differentially expressed proteins, demonstrating age independence. MSC cluster membership correlated with cytogenetics: Unfavorable cytogenetics (41 % overall) comprised 30% of NL-Like SC2, 42% of "Normal" SC3 but 52% of "AML" SC4 cases (p= 0.04), and both favorable cytogenetics cases were in SC3. MSC subpopulation type was not associated with overall survival, remission duration, or AML mutation status (FLT3, NPM1, RAS). We confirmed higher differential expression of mRNA (by qRT-PCR) for some (BCL2L1, CCND1) but not all (p21) in 10 AML-MSC and 10 NL-MSC, suggesting that both transcriptional and translational mechanisms are involved. In a separate ASH submission Battula shows that AML-MSC cannot differentiate into adipocytes like NL-MSC. Ingenuity pathway analysis (IPA) of this dataset finds that PC3 members, which are highly expressed in NL-MSC SC1, 3 & 5, but low in AML SC2 & 4, are associated with adipogenesis. Notably PI3K/AKT and JAK/STAT signaling is higher in AML dominant SC4. Hierarchical clustering revealed that 9 proteins showed differential expression between diagnosis and salvage status ( P=0.05) with p-β catenin, p-RPS6, and Galectin 3 higher in salvage samples, while SMAD6, TCF4, LYN, integrin β3, p-EIF4BP1, and p-ELK1 were higher at diagnosis. IPA reveals these proteins are highly associated with osteoblast differentiation, molecular mechanism of cancer and stem cell pluripotency, suggesting potential mechanisms for how alterations in MSC protein expression could affect chemosensitivity. Summary: This study demonstrates that AML-MSC have two dominant protein expression signatures that are distinct from those of NL-MSC, with SC4 being associated with unfavorable cytogenetics and the salvage setting. Up-regulated pathways in AML-MSC are known to impact MSC cell survival and differentiation. Down regulated pathways may explain skewing towards osteogenic and away from adipogenic differentiation by AML-MSC. Experiments targeting MSC and assessing effects on AML blast survival are underway to determine if targeting MSC can reverse chemoresistance. Figure 1. Figure 1. Disclosures Konopleva: Novartis: Research Funding; AbbVie: Research Funding; Stemline: Research Funding; Calithera: Research Funding; Threshold: Research Funding. Andreeff:Oncoceutics, Inc.: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Protein Expression Signatures Predict Response, Relapse, and Survival after Venetoclax Containing Therapy in Adults with Acute Myeloid Leukemia

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 227-227
Author(s):  
Brandon Brown ◽  
Yihua Qiu ◽  
Fieke W Hoff ◽  
Steven M. Kornblau
Keyword(s):  
Overall Survival ◽  
Protein Expression ◽  
Hierarchical Clustering ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
High Dose ◽  
Cluster Membership ◽  
Prognostic Features ◽  
Protein Cluster ◽  
Cart Analysis

Abstract Introduction When added to cytarabine (Ara-c) or hypomethylating agents (HMA), the BCL2 inhibitor, venetoclax (VTX), has been reported to improve response and overall survival (OS) rates. However, resistance and relapse still occur in the majority, and, although alterations in MCL1 and BCLXL are noted at relapse, identification of prognostic features remain unknown, notably not correlating with expression of the BCL2 target. Identification of prognostic markers could guide VTX use in patients and/or post-remission therapy. We searched for protein expression targets individually and collectively to predict VTX response and relapse in AML. Methods Reverse Phase Protein Array (RPPA) was performed on diagnostic leukemia samples of 818 adults with AML, of which 143 received VTX including 33 in combination with high dose Ara-C, 5 with standard dose Ara-C, 50 with HMA, and 13 with HMA and targeted therapy. Protein expression levels were evaluated using 390 validated antibodies were analyzed in the context of clinical data compiled by retrospective chart review. Pearson correlation was used to identify significant protein-protein correlations. Survival curves were generated by the Kaplan-Meier method and survival data was analyzed by multivariate cox regression model. Protein expression signatures were identified by hierarchical clustering and predictive models of classifiers were determined by classification and regression trees (CART) analysis. Results We queried the 390 proteins assayed in the 143 VTX treated patients to identify proteins individually prognostic (p&lt;0.01) for OS (n=27) or remission duration (RD, n=44). Notably, neither MCL1, BCLXL nor BCL2 expression at diagnosis were prognostic of OS or RD. From these, unbiased hierarchical clustering revealed two cohorts (N=102 & 41 patients) for OS and RD. The clusters were similar for clinical features with no significant differences noted for, age, gender, performance status, cytogenetics, or the presence of molecular mutation markers FLT3.ITD, IDH1/2, NPM1 or TP53. The groups did not differ by therapy combination. Remission rates were insignificantly less in cluster 1 (61% vs 77%). Clear differences were observed for OS with estimated 3-yr overall survival 27% vs 66% (p=0.009, Figure 1) and relapse risk (RR) at 1-yr 45% vs 18% (p=0.001, Figure 2) in cluster 1 vs 2, respectively. In multivariate analysis, protein cluster membership was in independent prognostic factor for OS (along with TP53 and NPM1 mutations) but unfavorable cytogenetics was not. Prognostication did not vary based on cytogenetics or therapy received. For RD, protein cluster membership and unfavorable cytogenetics were the only independent predictors. Of the 44 proteins in the protein signature, CART modeling identified 3 - SPI1, NOTCH1.cle, and PTPN12 - that could predict clustering with a computed accuracy of 94.3%. Similarly, when these three proteins were used as training variables for random forest classification, the error rate was 3.7%. Several previously unrecognized potential therapeutic targets for preventing VTX resistance were also identified. Discussion Protein expression patterns, individually and in combination, were very highly predictive of outcome to VTX containing combination chemotherapy. A group with lower response rates, higher relapse rates, shorter RD and inferior OS was defined. A kit to prospectively determine cluster membership is in development. If validated this could be used to triage high-risk patients to alternate therapies, such as transplant, in CR1. Many new targets for combination therapy to prevent VTX resistance were identified and need to be tested in the laboratory for clinical relevance. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Cytokine Profiling of Acute Meylogenous Leukemia and Myelodysplasia.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2355-2355
Author(s):  
Steven M. Kornblau ◽  
David McCue ◽  
Kang L. Lu ◽  
Wenjing Chen ◽  
Kevin R. Coombes
Keyword(s):  
Protein Expression ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
Myelogenous Leukemia ◽  
High Expression ◽  
Leukemic Cells ◽  
Proteomic Profiling ◽  
Serum Samples ◽  
Average Linkage

Abstract Protein expression and activation determines the pathophysiology of leukemic cells in Myelodysplasia (MDS) and Acute Myelogenous Leukemia (AML) and is dependent on endogenous changes (e.g mutation, methylation) and exogenous signals from stromal interactions, cytokines (CTKN) and chemokines. We have previously performed proteomics on primary AML sample (using reverse phase protein arrays) and wanted to assess how cytokines affect protein expression and phosphorylation. Prior studies of CTKN expression in AML and MDS have generally measured individual CTKNs, but not provided an overall assessment of CTKN expression. We measured the level of 26 CTKN (IL-1RA, 1B, 2, 4 5, 6, 7 , 8 , 9, 10,12, 13, 15, 17, Eotaxin, FGFB, G-CSF, GM-CSF, IFNγ, IP10, MCP1, MIP1α, MIP1β, PDGF, TNFα and VEGF) using multiplex cytometry (Bioplex™, Biorad) in serum samples from 176 AML (138 untreated (New), 38 relapsed (REL)) and 114 MDS patients (97 New, 10 post biological therapy, 7 REL) and 19 normal (NL) subjects. Individual CTKN expression was not correlated with clinical features (e.g. age, gender, cytogenetics, FAB, HB, WBC, platelet etc). The levels of IL -1β, 4, 5, 6, 7,10,12, 13, 17, IFNγ, FGFB and MIP1α were significantly lower and IL-8 and 15 higher in AML/MDS compared to NL. The expression profiles of AML and MDS were statistically indistinguishable whether analyzed individually or by unsupervised hierarchical clustering, except for IL-8 and 13 (higher in AML) and VEGF (higher in MDS). When CTKN were evaluated individually in new AML cases higher levels of IL4, 5 and 10 correlated significantly with remission attainment, and higher levels of IL8, Il1Ra, IP-10, Mip1β, VEGF and ILR, correlated significantly with shorter survival. No CTKN predicted remission attainment or survival in MDS. Unsupervised hierarchical bootstrap clustering using Pearson correlation and average linkage of CTKN expression relative to other CTKN expression, where high levels of one CTKN correlated with high expression of the other, revealed 6 highly reproducible expression patterns: IL-1β 4, 7, 10, 12, 13, G-CSF, IFNγ, MIP1α and PDGF IL 1ra, 6, 8 Eotaxin, IP-10, MIP1β and VEGF, IL2, 9, 15 and GMCSF, IL5 IL-7, FGF-Basic, TNFα and MCP1. Similar unsupervised clustering of the samples based on CTKN expression using average linkage also revealed 5 disease clusters and a NL sample cluster (containing all 19 NL samples). Average expression levels of each CTKN in these 5 clusters show diminished expression of most CTKN that had high expression in the NL samples, with each group showing increase in expression in a distinct subset of CTKN relative to NL. Remission attainment was strongly associated with cytokine signature (P=0.005). Additional CTKN are being studied (SCF, TGFβ, IL3). Comparison of CTKN expression patterns with proteomic profiling of expression and phosphorylation status is pending. In summary, this is the largest sample set studied for multiple CTKN expression in AML and MDS and the first assessment of many of these CTKN in these diseases. Most CTKNs showed different expression in AML and MDS compared to NL. Interestingly, CTKN expression in AML and MDS were similar. Many CTKN are predictive of outcome individually. CTKN signatures distinguish groups of patients and are predictive of outcome. Correlation with proteomic profiling may suggest CTKN to target in combination with other targeted therapies to maximally affect activated pathways.

Survivin and Smac/DIABLO Protein Expression in Patients with Chronic Myelogenous Leukemia.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 4792-4792
Author(s):  
Flaviana R.S. Reis ◽  
Karina L. Silva ◽  
Debora L. Pereira ◽  
Claudete E. Klumb ◽  
Raquel C. Maia
Keyword(s):  
Protein Expression ◽  
Cell Survival ◽  
Chronic Myelogenous Leukemia ◽  
Mononuclear Cells ◽  
Survivin Expression ◽  
Chronic Phase ◽  
Previous Treatment ◽  
Myelogenous Leukemia ◽  
Apoptosis Protein ◽  
Sokal Score

Abstract Defects in apoptosis contribute to prolonged cell survival, growth factor-independent cell survival, resistance to cytotoxicity and alteration in cell cycle check points. The studies of these mechanisms can be used as a therapeutic tool to help the elimination of cancer cells. Survivin is a member of the inhibitor apoptosis protein (IAP) family that is involved in both control of the cell division and cell death regulation. One of the most significant features of survivin is represented by its different expression in cancer versus normal tissues, where survivin is strongly expressed in cancer and undetectable in differentiated normal cells. In contrast, Smac/DIABLO, a pro-apoptotic protein, which is normally expressed in health cells, inhibits the Survivin binding to caspases contributing to apoptosis. Considering the poor information according to the role of Survivin in Chronic Myelogenous Leukemia (CML) pathogenesis, in the present work we analyzed the Survivin expression in peripheral blood cells of CML patients. In addition, we also compared the Survivin expression with the Smac/DIABLO expression. Nineteen patients (13 males and 6 females) with CML were included. Within these cases, 10 were diagnosed as chronic phase (CP), 3 as accelerated phase (AP) and 6 as blastic phase (BP). Low Sokal score (<0.8) was observed in 11 patients, intermediate Sokal score (0.8–1,1) in 7, and high score (>1.1) in 3 patients. Ten patients had been treated previously (hydroxyurea, α-Interferon and/or imatinib) and 9 had not received any treatment. The protein expression was analyzed by Western blotting. The K-562 cellular linage was used as positive control for Survivin expression. Mononuclear cells from healthy donors were used as negative and positive controls of Survivin and Smac proteins, respectively. The comparison between Survivin and Smac expression showed higher Smac expression in patients with low Sokal score (7/9, 77.8%). Among the individuals with intermediate and high Sokal score approximately 30% (2/7) expressed more Smac than Survivin. In a group of CP patients 7 out of 10, or 70%, exibited higher levels of Smac than Survivin. AP patients showed higher level of Smac protein (2/3). Five out 6 (83.3%) BP patients demonstrated higher Survivin expression than Smac. Related to previous treatment, patients who had not received treatment showed higher Smac expression (6/10, 60%) as compared with Survivin expression. Our preliminary results suggest a relation between survivin expression and advanced stage of the disease. Moreover, the Smac expression in patients with CML could be a prognostic marker as demonstrated in patients with lower Sokal score. On the other hand, the high levels of survivin expression in BP patients and high Sokal score could suggest that Survivin can confer a more resistant phenotype to chemotherapy. Our data suggest that therapeutical regimen can reduce the Smac expression in some patients. Further investigation is needed to clarify these issues.

Effect of FLT3 and RAS Mutations on Protein Expression Patterns In Cases of Acute Myelogenous Leukemia (AML) with NPM1 Mutations

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2704-2704
Author(s):  
Steven M. Kornblau ◽  
Yujia Huo ◽  
Mark Brandt ◽  
Nianxiang Zhang ◽  
Yi Hua Qiu ◽  
...  
Keyword(s):  
Protein Expression ◽  
Expression Patterns ◽  
Myelogenous Leukemia ◽  
Cyclin D3 ◽  
Prognostic Impact ◽  
Newly Diagnosed ◽  
Npm1 Mutation ◽  
Ras Mutation ◽  
Ras Mutations ◽  
Prognostic Implications

Abstract Abstract 2704 Background. Mutations of NPM1, FLT3 and RAS are common in AML occurring in ∼ 30%, 25% and 10% of cases. The incidence and prognostic implications of the combinations of wildtype (WT) and mutant (MU) NPM1 and FLT3 are well described and have different prognostic implications. How these mutations effect the cellular biology of leukemic blasts at the protein level is unknown as is the effect of a RAS mutation on these combinations. Methods. To understand the biology underlying these changes we utilized a Reverse Phase Protein Array (RPPA) to measure the protein expression of 195 different antibodies in cases of newly diagnosed AML with different combinations of NPM1, FLT3 and RAS mutations. We also examined the MDACC experience with these events in newly diagnosed AML (non APL) patients that were treated at MDACC. Results. NPM1-MU was present in 20% (4/461) including 36% of diploid (67/185) cases and for all or diploid cases was associated with slightly better overall survival (OS, p= 0.07 and 0.15) and significantly better Event Free Survival (EFS, P=0.002 and 0.001) and longer remission duration (RemDur, p=0.0009, 0.001). FLT3-ITD or D835 mutations were present in 22% (99/450) of cases and 35% (65/183) of diploid cases, in which it was associated with inferior OS (p= 0.05 for all and diploid). RAS-MU were found in 16% (64/403) of cases and was not predictive of OS (p=.49) or RemDur (p=0.62). Mutation in both NPM1 and FLT occurred in 9.3% of cases, more commonly than expected by chance (O/E =2.14, Χ2= 39, P <0.00001). As observed by others, WT FLT3&NPM1 had superior OS (Median 212 weeks, vs. 50–54 weeks for the other combinations, p=0.03), EFS (Median 74 vs. 22–24 weeks, P = 0.002), but RemDur was similar for NPM1-MU cases regardless of FLT3 status (157 &d 132 weeks ) and was superior to NPM1-WT cases (42 & 48 weeks, P=0.002). Similar prognostic implications were found in diploid cases. Combinations of NPM-MU & RAS-MU occurred at expected frequencies (p=.63). Although only 11 patients had both NPM1 and RAS mutations (Median age 47 yrs, 2 favorable, 6 diploid and 3 unfavorable cytogenetics) these patients had markedly superior outcomes (median OS, EFS and RemDur, not reached) with 9 of 11 alive. All other combinations of NPM1 and RAS had similar outcomes. The observed frequency of combinations of all 3 (NPM/FLT3/RAS) were less than expected for MU-MU-MU, MU-WT-WT and WT-MU-WT (O/E.4, 0.61 and 0.66 respectively) and more than expected for MU-MU-WT (O/E 2.59). Only one patient had all three mutations. Patients with MU-WT-MU had superior OS, RemDur and EFS. For OS, NPM1-MU and FLT3 and RAS MU cases fared next best. While those with WT-WT-WT and MU-MU-WT formed a third group, finally those with only FLT3MU or only RAS MU fared worst. The RemDur of those with only NPM1-MU or NPM1 and FLT3 MU lasted longer than those with only RAS-MU or FLT3-MU while those with both FLT-MU and RAS-MU fared worst. In NPM1-MU of the 195 proteins assessed by RPPA, 14 (AKTp473, BAK, CyclinD3, CyclinE, ELKp383,FLI1, HSP90, JAB1, JNK2, MTOR, Nurr77, RAFB, SHIP2, TSC2) were significantly higher (P < bonferonni corrected p <0.00026) and 4 were significantly lower ( FAK, FOXO1.3A, MDM2, YAPp). Cases with both NPM1&FLT3 MU typically showed an accentuated version of this protein expression pattern, typically showing higher expression than either NPM1-MU or FLT3-MU individually. In addition, higher levels of Caspase 8, GAB2p, TNK1 ZNF342, and lower levels of FOXO3A and SHIP were observed in NPM1-FLT3 dual mutants. In contrast, the addition of a RAS mutation blunted many NPM1-MU associated changes ( BAX, Cyclin D3 and E, ELKp383, FAK, Fibronectin, IntegrinB3, NFKBP65, PDK1p, PKCdeltap507 and p664 PTENp, RAFB, VASP), amplified others (DJ1, FOXO3A, P38p180, SSBP, ZNF342) and led to new increases (EIF4E, EGR123, JAB1 SHP2 SMAD1) or decreases (ARC, GATA3, P70S6K, S6RPp235NPM1). Conclusion. We confirmed the prognostic impact of NPM1 and FLT3-ITD combinations as reported by others groups. We identified a new highly favorable prognostic group of cases (2.5%) with simultaneous NPM1 and RAS mutations and WT FLT3. The proteomic data identified that NPM1 mutation is strongly associated with activation of the RAF-AKT-MTOR axis suggesting that combinations of sorafenib and serolimus might be employed to interrupt this axis and increase chemosensitivity in patients with NPM1 mutations. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Functional proteomic profiling of AML predicts response and survival

Blood ◽  
2009 ◽  
Vol 113 (1) ◽  
pp. 154-164 ◽  
Author(s):  
Steven M. Kornblau ◽  
Raoul Tibes ◽  
Yi Hua Qiu ◽  
Wenjing Chen ◽  
Hagop M. Kantarjian ◽  
...  
Keyword(s):  
Protein Expression ◽  
Protein Function ◽  
Tandem Duplication ◽  
Expression Patterns ◽  
Principal Component ◽  
Proteomic Profiling ◽  
Prognostic Information ◽  
Internal Tandem Duplication ◽  
French American British

Abstract Because protein function regulates the phenotypic characteristics of cancer, a functional proteomic classification system could provide important information for pathogenesis and prognosis. With the goal of ultimately developing a proteomic-based classification of acute myeloid leukemia (AML), we assayed leukemia-enriched cells from 256 newly diagnosed AML patients, for 51 total and phosphoproteins from apoptosis, cell-cycle, and signal-transduction pathways, using reverse-phase protein arrays. Expression in matched blood and marrow samples were similar for 44 proteins; another 7 had small fold changes (8%-55%), suggesting that functional proteomics of leukemia-enriched cells in the marrow and periphery are similar. Protein expression patterns were independent of clinical characteristics. However, 24 proteins were significantly different between French-American-British subtypes, defining distinct signatures for each. Expression signatures for AML with cytogenetic abnormalities involving −5 or −7 were similar suggesting mechanistic commonalities. Distinct expression patterns for FMS-like tyrosine kinase 3–internal tandem duplication were also identified. Principal component analysis defined 7 protein signature groups, with prognostic information distinct from cytogenetics that correlated with remission attainment, relapse, and overall survival. In conclusion, protein expression profiling patterns in AML correlate with known morphologic features, cytogenetics, and outcome. Confirmation in independent studies may also provide pathophysiologic insights facilitating triage of patients to emerging targeted therapies.

scholarly journals Single Cell Multi-Omic Profiling of Multiple Myeloma with t(4;14) Finds an Immune Microenvironment Gene Signature That Correlates with Clinical Outcomes

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2653-2653
Author(s):  
Sanjay De Mel ◽  
Jonathan Adam Scolnick ◽  
Chern Han Yong ◽  
Xiaojing Huo ◽  
Stacy Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Protein Expression ◽  
Single Cell ◽  
Research Funding ◽  
Plasma Cells ◽  
Expression Patterns ◽  
Gene Signature ◽  
Response To Therapy ◽  
Diagnostic Samples

Abstract Background Multiple Myeloma (MM) is an incurable plasma cell (PC) malignancy and high risk MM remains an unmet clinical need. Translocation 4;14 occurs in 15% of MM and is associated with an adverse prognosis. A deeper understanding of the biology and immune micro-environment of t(4;14) MM is necessary for the development of effective targeted therapies. Single Cell multi-omics provides a new tool for phenotypic characterization of MM. Here we used Proteona's ESCAPE™ single cell multi-omics platform to study a cohort of patients with t(4;14) MM. Methods Diagnostic bone marrow (BM) samples from 13 patients with t(4;14) MM (one of whom had samples at diagnosis and relapse) were analysed using the ESCAPE™ platform from Proteona which simultaneously measures gene and cell surface protein expression of 65 proteins in single cells. Cryopreserved BM samples were stained with antibodies and subsequently sorted on CD138 expression. The CD138 positive and negative fractions were recombined at a 1:1 ratio for analysis using the 10x Genomics 3' RNAseq kit. Resulting data were analyzed with Proteona's MapSuite™ single cell analytics platform. In particular, Mapcell was used to annotate the cells and MapBatch was used for batch normalization in order to preserve rare cell populations. Results Patients had a median age of 63 years and received novel agent-based induction. Median progression free and overall survival (PFS and OS) were 22 and 34 months respectively. We first analyzed serial BM samples from an individual patient that were taken at diagnosis and relapse following bortezomib based treatment. The PCs in this patient showed variations in gene expression between diagnosis and relapse (Fig 1A), including the reduction of HIST1H2BG expression, which has previously been correlated with resistance to bortezomib. Subsequent analysis of the immune cells identified a shift in the ratio of T cells to CD14 monocytes from 5.7 at diagnosis to 0.6 at relapse suggesting a major change in the BM immune micro-environment in response to therapy. Next, we analyzed the malignant PCs of the diagnostic samples. As expected, MMSET (NSD2) was overexpressed in all PCs compared to normal PCs, while FGFR3 expression could be categorized into no expression of FGFR3, low expression (&lt;10% of cells expressing FGFR3) or high expression (&gt;80% of cells expressing FGFR3) (Fig 1B). No gene or protein expression patterns within the PCs were identified that correlated with PFS or OS in this cohort. Finally, we analyzed the immune micro-environment in the diagnostic samples (Fig 1C). While there was no overall discernable pattern of cell types present, one cluster of cells, annotated as 'unknown' cell type, suggested a small population of cells that had not been previously annotated in published single cell RNA-seq data. The cells were CD45+ and CD138 - both at the protein and RNA level, suggesting they are not plasma cells. We tested if the number of the 'unknown' cells in each sample correlated with PFS, but there was no significant correlation. We then used these cells to derive a gene signature profile which was expressed in most of the cells in the 'unknown' cluster as well as a minor fraction of cells in other clusters including some PCs. The number of cells expressing the gene signature negatively correlated with PFS, with samples containing more cells expressing the signature having a lower PFS than samples with fewer signature positive cells (Fig 2). The correlation remained significant whether we included PCs in the analysis or not, but was not significant amongst only the PC population, suggesting that the cells responsible for the correlation are from the immune micro-environment. Conclusions We present the first application of single cell multi-omic immune profiling in high-risk MM and demonstrate that t(4;14) is a phenotypically heterogenous disease. While no consistent gene or protein expression patterns were identified within the malignant cell population, we did identify gene expression changes in a relapsed patient sample that may reflect key alterations in the PCs responsible for therapy resistance. In addition, we identified a gene signature expressed in a rare population of non-plasma cells that significantly correlated with PFS in this patient cohort. These data highlight the potential of single cell multi-omic analysis to identify immune micro-environmental signatures that correlate with response to therapy in t(4;14) MM. Figure 1 Figure 1. Disclosures Scolnick: Proteona Pte Ltd: Current holder of individual stocks in a privately-held company. Huo: Proteona Pte Ltd: Ended employment in the past 24 months. Xu: Proteona Pte Ltd: Current Employment. Chng: Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria; Abbvie: Honoraria.

scholarly journals Deregulated Expression of Circular RNAs in Acute Myeloid Leukemia

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3894-3894
Author(s):  
Susanne Lux ◽  
Tamara J. Blätte ◽  
Sibylle Cocciardi ◽  
Klaus Schwarz ◽  
Hartmut Döhner ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Research Funding ◽  
Principal Component ◽  
Leukemic Cells ◽  
Circular Rnas ◽  
Expression Data ◽  
Leukemic Transformation ◽  
Advisory Committees ◽  
Binding Factor ◽  
The Impact

Abstract Background: Circular RNAs (circRNAs) constitute a class of abundant and highly conserved RNAs that show tissue- and developmental stage-specific expression. Recent studies have pointed to an important role of circRNAs in the pathology of solid tumors and differences in circRNA expression between healthy and cancerous tissue. Recently, we have shown that in the hematopoietic system, circRNA expression changes during myeloid differentiation and is also altered upon leukemic transformation. Aims: We aimed to comprehensively characterize the circular transcriptome in healthy and leukemic hematopoietic cells and determine changes in the circRNA repertoire throughout leukemic transformation. Moreover, we studied the impact of a leukemia-specific circRNA (circBCL11B) on proliferation, differentiation and viability of leukemic cells. Methods: We performed genome-wide circular transcriptome profiling of 16 healthy bone marrow (BM) samples and 63 AML patient samples using ribosomal RNA-depleted RNA-Seq and an in-house analysis script for the identification and quantification of circRNAs. AML patient samples included n=20 patients with a mutation of NPM1 (NPM1mut), 18 patients with splicing factor mutations (n=6 U2AF1mut, n=6 SRSF2mut, n=6 SF3B1mut) and 25 core binding factor (CBF) leukemias with t(8;21) (n=14) or inv(16) (n=11). Genes differentially expressing circRNAs were subjected to pathway analysis. Via shRNA-mediated knockdown (KD) in the OCI-AML 5 cell line, we evaluated the effect of circBCL11B on the proliferation, differentiation and viability of leukemic cells. Results: Healthy BM samples could be distinguished from AML samples based on their circRNA expression profile and different AML subtypes could be discriminated (Figure 1 A-D). While for many genes differential expression distinguishing between healthy and leukemic samples was seen on both the circular and linear mRNA level, we could also identify genes with leukemia-specific circRNA deregulation irrespective of differential expression of the respective parental gene. Candidate circRNAs, like circBCL11B which was exclusively detected in AML samples, were selected for further functional evaluation. While there was no increase in apoptosis or differentiation of OCI-AML5 cells upon knockdown of circBCL11B, we observed a slight decrease in proliferation (doubling time 31h in scrambled shRNA sample vs 39h in circBCL11B KD). Conclusions: Our work demonstrates that the circRNA repertoire and expression levels change with hematopoietic differentiation and are altered by leukemic transformation. In this "proof-of-principle" study we could identify AML-specific circRNAs and transcripts that might be involved in leukemic transformation and leukemia maintenance. The impact of spliceosome mutations on the expression of circRNAs is currently evaluated and warrants further studies. We anticipate our work to be a starting point for more comprehensive analyses of circular transcriptomes that will improve our understanding of the impact of deregulated circRNA expression on leukemogenesis. Figure 1: Altered circular RNA expression in AML patients compared to healthy control samples. A) Principal component analysis (PCA) visualizing circRNA expression data of 16 healthy CD34+ bone marrow (BM) samples (red) and 20 AML patients with mutated NPM1 (NPM1mut) (blue). B) Unsupervised hierarchical clustering of 16 healthy BM samples (red) and 20 NPM1mut patients (blue). C) Principal component analysis (PCA) visualizing circRNA expression data of 16 healthy BM samples (blue) and 35 core binding factor (CBF) leukemias, of which 14 samples derived from patients with t(8;21) (green) and 11 samples with inv(16) (red). D) Unsupervised hierarchical clustering of 16 healthy BM samples (pink), 14 CBF leukemias with t(8;21) (light red) and 11 CBF leukemias with inv(16) (green). Expression data was generated via ribosomal RNA-depleted RNA-Seq and reads derived from circRNAs were aligned and quantified using STAR and normalized and transformed using DESeq2. PCA was performed based on 500 genes with the highest variance of circRNA expression across all samples and the unsupervised hierarchical clustering is based on 5000 circRNA-expressing genes. Disclosures Döhner: Amgen: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Seattle Genetics: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Pfizer: Research Funding; Bristol Myers Squibb: Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; AROG Pharmaceuticals: Research Funding; Pfizer: Research Funding; AbbVie: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Agios: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Bullinger:Bayer Oncology: Research Funding; Janssen: Speakers Bureau; Sanofi: Research Funding, Speakers Bureau; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Pfizer: Speakers Bureau; Bristol-Myers Squibb: Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

Proteomic Profiling of AML Bone Marrow-Derived Mesenchymal Stem Cells (MSC) Reveals Marked Differences From Normal MSC.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2355-2355
Author(s):  
Rui-yu Wang ◽  
Yi Hua Qiu ◽  
Suk Young Yoo ◽  
Teresa McQueen ◽  
Ye Chen ◽  
...  
Keyword(s):  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Protein Expression ◽  
Differential Expression ◽  
Ingenuity Pathway Analysis ◽  
Pathway Analysis ◽  
Expression Patterns ◽  
P Value ◽  
Proteomic Profiling

Abstract Abstract 2355 The interaction of bone marrow (BM) mesenchymal stem cells (MSC) and acute myeloid leukemia (AML) cells creates a microenvironment (McrEnv) that supports and regulates the survival and proliferation of leukemic cells. These same BM McrEnv interactions can also create a sanctuary that protects subpopulations of AML blasts from chemotherapy. The mechanisms by which the BM-MSC McrEnv effects these changes remain unclear and the heterogeneity of these effects across different subtypes of AML (FAB, WHO or cytogenetics) is unknown. Furthermore, the functional differences between normal BM-MSC and AML BM-MSC biology are undefined. To address this we set out to perform proteomic profiling comparing normal and AML BM derived-MSC and to ascertain how AML BM-MSC protein expression patterns correlated with AML blast protein expression. We cultured BM samples for 4 to 8 weeks in MEM-alpha media with 20% fetal calf serum to isolate AML-MSC (n=106), and NL-MSC (n=70). Cells defined as MSC were positive for CD90 and CD105 and negative for CD45 by flow cytometry. Whole cell protein lysates were prepared. Matched AML protein samples prepared from mononuclear cell fractions from the same BM collection were available for most cases (n=96). We generated a custom Reverse Phase Protein Array (RPPA) from these samples and probed the array with 151 validated antibodies. Statistical analysis was performed by two-way ANOVA using Tukey's test to identify differential expression of individual proteins between the samples and Ingenuity pathway analysis was performed to elucidate differential pathway utilization. Comparison of AML-BM-MSC to NL-BM-MSC demonstrated similar levels of expression for 66 proteins (43.7%) but 85, 67 and 28 were different at the p-value of < 0.05, < 0.01 and <0.0001 with a false discovery rate (FDR) of 0.05, 0.01, 0.0001 respectively. By function, the 28 proteins with greatest differential expression (Italic = lower in AML-MSC) included cell cycle regulators (P21, Cyclin D1, CDK4), adhesion/integrins (CD49b, CD31, and galectin 3, signaling pathway members and their targets (Smad1, Smad4, Stat1, Stat5, pPDK1, GSK3), apoptosis regulators (Bak, BCLXL, Smac,) growth and proliferation regulators (pCREB, EIF2α, FOXO1α, Sirt1 Strathmin) and pIRS, cleaved Notch1, HSP90, TP53 CK2 and PP2A. Using mixed linear effectors protein expression levels in AML-MSC did not show correlation with patient's age (< 50 >), gender, blast count in BM or peripheral blood, or the percent of CD34 positivity. Protein expression in AML-BM-MSC from cases with favorable cytogenetics had significantly lower levels of GAB2, P27 P70S6K, SMAC and 14.3.3e and cases with unfavorable cytogenetics had significantly lower levels of antiapoptosis proteins Bax, Bad and BCL-XL and higher levels of Smac as well as lower levels of phosphor-FOXO3a and pELK. Levels of ARC were higher in cases with intermediate cytogenetics. Ingenuity pathway analysis also demonstrated differential utilization of several families of proteins regulating signal transduction, apoptosis and transcription and connected to surface growth factor receptors and adhesion molecules. As anticipated for cells of different origin, the expression patterns were completely different between AML BM-MSC and AML blasts for 131 of 151 proteins (86.1%) (Tukey's p-value <0.0001 and FDR 0.0001). These results suggest that protein expression in AML MSC is markedly different from that of NL-MSC. Differential expression was observed in multiple functional groups suggesting that AML-MSC are functionally distinct from NL-MSC. Since MSC influence adjacent and nearby AML blasts it is likely that these variances impact AML blast biology. Additional analysis is underway to determine if recurrent patterns of protein expression exist in AML-BM-MSC, how these differ from protein expression patterns in NL-MSC, and whether AML-BM-MSC protein expression patterns correlate with AML-blast protein expression patterns. Correlation between MSC patterns and AML-blast patterns would provide therapeutically targetable sites in MSC that could be exploited to influence AML blast biology. Disclosures: No relevant conflicts of interest to declare.

scholarly journals The Overexpression of NALP3 Inflammasome in Knee Osteoarthritis Is Associated with Synovial Membrane Prolidase and NADPH Oxidase 2

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Denise Clavijo-Cornejo ◽  
Karina Martínez-Flores ◽  
Karina Silva-Luna ◽  
Gabriela Angélica Martínez-Nava ◽  
Javier Fernández-Torres ◽  
...  
Keyword(s):  
Protein Expression ◽  
Knee Osteoarthritis ◽  
Protein Content ◽  
Synovial Membrane ◽  
Expression Patterns ◽  
Principal Component ◽  
Study Groups ◽  
Control Group ◽  
Nalp3 Inflammasome ◽  
Inflammatory State

Osteoarthritis is characterized by the presence of proinflammatory cytokines and reactive oxygen species. We aimed to clarify the role of prooxidant enzyme content at the synovial membrane level and how it correlates with the inflammatory process in patients with knee osteoarthritis (KOA). In synovial membranes from KOA patients and control group, we analyzed the protein content of prooxidant enzymes such as Nox2, xanthine oxidase (XO), and prolidase as well as the proinflammatory NALP3. Results show that protein content of prolidase and Nox2 increased 4.8- and 8.4-fold, respectively, and XO showed an increasing trend, while the NALP3 inflammasome increased 5.4-fold with respect to control group. Levels of prolidase and XO had a positive correlation between the levels of NALP3 and Nox2. By principal component analysis the protein expression pattern by study groups was evaluated. Three clusters were identified; protein expression patterns were higher for clusters two (prolidase) and three (XO and Nox2) between KOA patients and controls. Data suggest that prooxidant enzymes increase in synovial membrane of KOA patients and may contribute to the inflammatory state and degradation of the articular cartilage.

Mechanism of Drug Resistance to Dasatinib (BMS-354825) and Imatinib in Chronic Myelogenous Leukemia Cells.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1386-1386
Author(s):  
Seiichi Okabe ◽  
Tetsuzo Tauchi ◽  
Hal E. Broxmeyer ◽  
Kazuma Ohyashiki
Keyword(s):  
Drug Resistance ◽  
Protein Expression ◽  
Cell Lines ◽  
Chronic Myelogenous Leukemia ◽  
Expression Patterns ◽  
Resistant Cell ◽  
Myelogenous Leukemia ◽  
Antibody Microarray ◽  
Dual Inhibitor ◽  
Imatinib Resistance

Abstract The selective tyrosine kinase inhibitor, imatinib has shown remarkable clinical activity in patients with chronic myelogenous leukemia (CML), however, imatinib does not completely eradicate BCR/ABL-expressing cells, and cells resistant to imatinib develop. It has been reported that mutations in the kinase domain (KD) of BCR/ABL impairs binding of imatinib and causes resistance to it. So, additional therapy is necessary to overcome imatinib resistance in patients with an acute form of CML. The novel BCR/ABL inhibitor, dasatinib (BMS-354825), which A dual inhibitor of both Src and Abl kinases, suppresses the activity of these kinases at subnanomolar concentrations in imatinib resistance cells. Dasatinib is presently being evaluated in a clinical trial in CML patients with imatinib resistance, but drug resistance to dasatinib and imatinib are not fully evaluated. In this study, we used TF-1 BCR/ABL cell lines, which were transfected with the BCR/ABL gene, as well as parental TF-1 cells and K562 cell lines and established dasatinib resistant TF-1 BCR-ABL BMS-R and K562 BMS-R cells and imatinib resistant TF-1 BCR-ABL IM-R and K562 IM-R cells. We show here that dasatinib potently induced apoptosis of TF-1 BCR-ABL and K562 cells in 72 hours treatment. IC50 of dasatinib was 0.75nM (TF-1 BCR/ABL) and 1nM (K562) and IC50 of imatinib was 500nM (TF-1 BCR/ABL) and 750nM (K562). In the resistant cell lines, IC50 of dasatinib was 15μM (TF-1 BCR/ABL BMS-R) and 25μM (K562 BMS-R). TF-1 BCR/ABL BMS-R and K562 BMS-R cells were also resistant to imatinib (IC50: more than 10μM). TF-1 BCR-ABL IM-R and K562 IM-R cells were also resistant to dasatinib (IC50: 7.5 nM (TF-1 BCR/ABL IM-R) and the value was more than 10nM for K562 IM-R). There was no mutation in Abl kinase in these resistant cell lines suggesting that BCR/ABL mutation-independent mechanism is involved in resistance to imatinib and dasatinib. Because the correlation of parameters with defined protein expression patterns and protein signatures would predict disease progression and drug resistant, we investigated the protein expression pattern by using antibody microarray system (Antibody Microarray: Lab Vision Corporation). We found that dasatinib resistant cells had reduced protein levels of BCR/ABL. In TF-1 BCR/ABL BMS-R cells, Zap-70 protein was increased and activated. In contrast, the src kinase, lck, is increased and activated in K562 BMS-R cells. Moreover, mitogen-activated protein kinase (MAPK) and Akt were also activated in K562 BMS-R cells. We also found that src kinases, especially Lyn, as well as MAPK and Akt were activated in K562 IM-R cells. The phosphatase SHP-2 was decreased in dasatinib resistant cell lines. Signal-transducing activators of transcription 5 (STAT5) phosphorylation was reduced in K562 BMS-R cells. 24 hour removal of dasatinib from culture medium of K562 BMS-R led to apoptosis of the cells and activated caspase 3 and poly (ADP-ribose) polymerase (PARP). We also found that the phosphatase SHP-1 was increased after removal of dasatinib. The expression and protein activation signatures identified in this study provide insight into the mechanism of resistance to dasatinib and imatinib. Our study demonstrates development of dasatinib resistance and suggests that this information may be of therapeutic relevance.

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