Proteomic Profiling of Multiple Myeloma: Correlation of Protein and Gene Expression Data.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1705-1705
Author(s):  
Ricky D Edmondson ◽  
Sheeno P Thyparambil ◽  
Veronica MacLeod ◽  
Bart Barlogie ◽  
John D. Shaughnessy
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Expression Profiles ◽  
Single Agent ◽  
Clinical Samples ◽  
Proteomics Data

Abstract Although melphalan-based autologous stem cell transplantation has improved prognosis for patients diagnosed with Multiple Myeloma, survival varies from a few months to more than 15 years with an individual’s risk not accurately predicted with standard prognostic variables. Correlating genome-wide mRNA expression profiles in purified myeloma cells with outcome, we recently showed that that the differential expression of 70 genes could identify patients at high risk for early disease related death [1]. The utility of a high throughput proteomics platform in the analysis of clinical samples has great potential but as of yet none have been firmly established. Herein, we describe the use of such a platform and its utility in stratifying patients with Multiple Myeloma in terms of high and low risk disease. Preliminary analysis indicates that the proteomics data can separate the patients into risk groups, although the proteins responsible for the assignment are not identical to the 70 genes identified in the gene expression profiling experiments. In addition to the proteomic analysis of plasma cells enriched using anti-CD138 immunomagnetic beads from mononuclear cell fractions of bone marrow aspirates from newly diagnosed myeloma patients; we have performed (in triplicate) LCMS profiling on plasma cells from 30 patients isolated prior to and 48 hours after a single test-dose application of bortezomib at 1.0mg/m2. An aliquot of 100,000 plasma cells was enzymatically digested with trypsin and a fraction (~5,000 cells) analyzed using our proteomics platform (an Eksigent nanoHPLC coupled to a ThermoElectron LTQ-Orbitrap with data analyzed using the Elucidator software package from Rosetta Biosoftware). The correlation of the proteomic profiles to gene expression profiles and clinical parameters will be presented. The analysis of proteins that were observed to change (p<0.01) in abundance after the single agent dose of the proteasome inhibitor bortezomib yielded an unanticipated finding; the abundance of 30 proteins associated with the proteasome were observed to increase in a subset of patients. The majority of the patients with the increased levels of proteasome related proteins are predicted by GEP to have high risk disease. The proteomic data will be discussed in terms of its utility in the identification of activated pathways as well as in the development of a prognostic indicator as was achieved using gene expression profiling.

MyPRSR Molecular Subtypes of Multiple Myeloma Represent All High-Risk FISH Translocations Included in the mSMART 2.0 and R-ISS Guidelines

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3264-3264 ◽  
Author(s):  
Ryan K Van Laar ◽  
Ivan Borrelo ◽  
David Jabalayan ◽  
Ruben Niesvizky ◽  
Aga Zielinski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Research Funding ◽  
Plasma Cells ◽  
Molecular Subtypes ◽  
Risk Status ◽  
Significant Difference

Abstract Background: There is a global consensus that multiple myeloma patients with high-risk disease require additional monitoring and therapy compared to low/standard risk patients in order to maximize their chances of survival. Current diagnostic guidelines recommend FISH-based assessment of chromosomal aberrations to determine risk status (i.e. t(14;20), t(14;16), t(4;14) and/or Del17p), however, studies show FISH for MM may have a 20-30% QNS rate and is up to 15% discordant between laboratories, even when starting from isolated plasma cells. In this study we demonstrate that MyPRS gene expression profiling reproduces the key high risk translocations for MM risk stratification, in addition to having other significant advantages. Methods: Reproducibility studies show that MyPRS results are less than 1% discordant starting from isolated plasma cells and return successful results in up to 95% of cases. 270 MM patients from Johns Hopkins University (MD) and Weill Cornell Medicine (NY) had both FISH and MyPRS gene expression profiling performed between 2012 and 2016 using standard and previously published methodology, respectively. Results: Retrospective review of the matched FISH and MyPRS results showed: 25/28 (89%) patients wish FISH-identified t(4;14) were classified as MMSET (MS) subtype. 10/10 (100%) patients with t(14;16) or t(14;20) were classified as MAF-like (MF) subtype 62/67 (93%) patients with t(11;14) were assigned to the Cyclin D (1 or 2) subtype. Patients with FISH hyperdiploidy status were classified as the Hyperdiploid (HY) subtype or had multiple gains detected by the separate MyPRS Virtual Karyotype (VK) algorithm, included in MyPRS. TP53del was seen in patients with multiple molecular subtypes, predominantly Proliferation (PR) and MMSET (MS). Assessment of TP53 function by gene expression is a more clinically relevant prognostic marker than TP53del, as dysregulation of the tumor suppressor is affected by mutations as well as deletions. Analysis of the TP53 expression in the 39 patients with delTP53 showed a statistically significant difference, compared to patients without this deletion (P<0.0001). Conclusion: Gene expression profiling is a superior and more reliable method for determining an individual patients' prognostic risk status. The molecular subtypes of MM, as reported by Signal Genetics MyPRS assay, are driven by large-scale changes in gene expression caused by or closely associated with chromosomal changes, including translocations. Physicians who are managing myeloma patients and wishing to base their assessment of risk on R-ISS or mSMART Guidelines may obtain the required data points from either FISH or MyPRS, with the latter offering lower QNS rates, higher reproducibility, assessment of a larger number of cells and a substantially lower price point ($5,480 vs. $1,912; 2016 CMS data). A larger cohort study is now underway to further validate these observations. Figure GEP-based TP53 expression in patients with and without Del17p. P<0.0001 Figure. GEP-based TP53 expression in patients with and without Del17p. P<0.0001 Disclosures Van Laar: Signal Genetics, Inc.: Employment. Borrelo:Sidney Kimmel Cancer Institute: Employment. Jabalayan:Weill Cornell Medical Center: Employment. Niesvizky:Celgene: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy, Research Funding, Speakers Bureau; Onyx: Consultancy, Research Funding, Speakers Bureau. Zielinski:Signal Genetics, Inc.: Employment. Leigh:Signal Genetics, Inc.: Employment. Brown:Signal Genetics, Inc.: Employment. Bender:Signal Genetics, Inc.: Employment.

Intra-Tumoral Heterogeneity In Multiple Myeloma Gleaned From Gene Expression Profiling Analysis Of Plasma Cells and Bone Marrow Biopsies: Comparison Of Random Bone Marrow Samples and CT-Guided Fine Needle Aspirations Of MRI-Defined Focal Lesions

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3123-3123
Author(s):  
Bart Barlogie ◽  
Emily Hansen ◽  
Sarah Waheed ◽  
Jameel Muzaffar ◽  
Monica Grazziutti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
P Value ◽  
Focal Lesions ◽  
Molecular Subgroup

Abstract Intra-tumoral heterogeneity (ITH) is increasingly viewed as the Achilles heel of treatment failure in malignant disease including multiple myeloma (MM). Most MM patients harbor focal lesions (FL) that are recognized on MRI long before bone destruction is detectable by conventional X-ray examination. Serial MRI examinations show that eventually 60% of patients will achieve resolution of FL (MRI-CR). However, this will lag behind the onset of a clinical CR by 18 to 24 months, thus attesting to the biological differences between FL and diffuse MM growth patterns. Consequently, we performed concurrent gene expression profiling (GEP) analyses of plasma cells (PC) from both random bone marrow (RBM) via iliac crest and FL. Our primary aims were to first compare the molecular profiles of FL vs. RBM, second to determine if ITH existed (as defined molecular subgroup and risk), and finally to investigate if the bone marrow micro-environment (ME) contained a biologically interesting signature. A total of 176 patients were available for this study with a breakdown of: TT3 (n=23), TT4 for low-risk (n=131) and TT5 for high-risk MM (n=22). Regarding the molecular analyses of PCs, GEP-based risk (GEP-70, GEP-5) and molecular subgroup correspondence were examined for commonalties and differences between RBM and FL. A “filtering” approach for ME genes was also developed and bone marrow biopsy (BMBx) GEP data derived from this method is under analysis. PC risk correspondence between FL and RBM was 86% for GEP70 and 88% for the GEP5 model. Additionally, 82% had a molecular subgroup concordance, however, they did differ among subgroups (p=0.020) by Fisher's Exact Test. A lower concordance was noted in the CD2, LB, and PR subgroups (67%, 69%, 73%, respectively). GEP70 and GEP5 risk concordance between RBM and FL samples by molecular subgroup was also examined. The overall correlation coefficients were 0.619 (GEP70) and 0.597 (GEP5). The best correspondence was noted for CD1, MF and PR subgroups especially for the GEP5 model. HY, LB and MS showed intermediate correlations, while CD2 fared worst with values of only 0.322 for GEP70 and 0.267 for GEP5 model. Figure 1 portrays these data in more detail for the GEP70 and GEP5 models. Good correlations were noted between RBM and FL based risk scores in case of molecular subgroup concordance (left panels) in both GEP5 and GEP70 risk models, whereas considerable scatter existed in case of subgroup discordance (right panels). The clinical implications in TT4 regarding RBM and FL derived risk and molecular subgroup information, viewed in the context of standard prognostic baseline variables are portrayed in Table 1. High B2M levels at both cut-points imparted inferior OS and PFS as did low hemoglobin. Although present in 42% of patients, cytogenetic abnormalities (CA) did not affect outcomes. FL-based GEP5-defined high-risk designation conferred poor OS and PFS. B2M>5.5mg/L and FL-derived GEP5 high-risk MM, pertaining to 29% and 11% of patients, survived the multivariate model for both OS and PFS. Next, in examining PC-GEP differences among RBM and FL sites, 199 gene probes were identified with a false discovery rate (FDR) of 1x10-6. Additionally, 55 of the 199 belong to four molecular networks of inter related genes associated with: lipid metabolism, cellular movement, growth and proliferation, and cell-to-cell interactions. Multivariate analysis identified the GEP5 high risk designation of focal lesion PCs to be significantly prognostic with a HR=3.73 (p=0.023).Table 1Cox regression analysis of variables linked to overall and progression-free survival in TT4.Overall SurvivalProgression-Free SurvivalVariablen/N (%)HR (95% CI)P-valueHR (95% CI)P-valueMultivariateB2M > 5.5 mg/L38/130 (29%)3.71 (1.49, 9.22)0.0053.84 (1.58, 9.31)0.003FL GEP5 High Risk14/130 (11%)3.68 (1.19, 11.41)0.0243.73 (1.20, 11.62)0.023HR- Hazard Ratio, 95% CI- 95% Confidence Interval, P-value from Wald Chi-Square Test in Cox RegressionNS2- Multivariate results not statistically significant at 0.05 level. All univariate p-values reported regardless of significance.Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level.A multivariate p-value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection. Disclosures: No relevant conflicts of interest to declare.

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2543-2553 ◽  
Author(s):  
Annemiek Broyl ◽  
Dirk Hose ◽  
Henk Lokhorst ◽  
Yvonne de Knegt ◽  
Justine Peeters ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Medical Science ◽  
Protein Tyrosine Phosphatases ◽  
Molecular Classification ◽  
Newly Diagnosed ◽  
Expression Of Genes

Abstract To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.

scholarly journals Erratum: Dose-dense and less dose-intense total therapy 5 for gene expression profiling-defined high-risk multiple myeloma

2016 ◽  
Vol 6 (9) ◽  
pp. e471-e471 ◽  
Author(s):  
Y Jethava ◽  
A Mitchell ◽  
M Zangari ◽  
S Waheed ◽  
C Schinke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Total Therapy ◽  
Dose Dense

Gene Expression Profiling of Myeloma Cells To Predict Response to Primary Therapy with Thalidomide-Dexamethasone for Newly Diagnosed Multilple Myeloma.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 1494-1494
Author(s):  
Abderrahman Abdelkefi ◽  
John de Vos ◽  
Said Assou ◽  
Tarek Ben Othman ◽  
Jean-Francois Rossi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Treatment Strategy ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Gene List ◽  
Microarray Platform ◽  
Primary Therapy ◽  
Newly Diagnosed

Abstract Background: Thalidomide which represents an effective treatment strategy for relapsed/refractory multiple myeloma, actually represents a standard of care also for newly diagnosed multiple myeloma patients. Methods: In the present study, we adopted a gene expression profiling (GEP) strategy in an attempt to predict response (&gt; 50% reduction in serum M protein) to primary therapy with thalidomide-dexamethasone for newly diagnosed multiple myeloma. Plasma cells (CD138+) were purified from bone marrow aspirates from 17 patients at diagnosis, before initiation of treatment with thalidomide-dexamethasone. GEP was performed using the Affymetrix U133 Plus_2 microarray platform. The Affymetrix output (CEL files) was imported into Genespring 7.3 (Agilent technologies) microarray analysis software, where data files were normalized across chips using GCRMA and to the 50th percentile, followed by per gene normalization to median. Criteria of response were those established by Bladè et al. Results: After sufficient follow-up, responders (n=9) and nonresponders (n=8) were identified, and gene expression differences in baselines samples were examined. Of the 11000 genes surveyed, Wilcoxon rank sum test identified 149 genes that distinguished response from non response. A multivariate step-wise discriminant analysis (MSDA) revealed that 14 of the 149 genes could be used in a response predictor model (see table). Of interest, the gene list encompasses WXSC1, known to be involved in the chromosomal translocation t(4;14) (p16.3;q32.3) in multiple myeloma. Conclusion: These results could be the first step to adopt microfluidic cards, in an attempt to select at diagnosis patients who will respond favourably to a particular treatment strategy. List of 14 genes able to predict response to primary therapy with thalidomide-dexamethasone for newly diagnosed multiple myeloma. Gene ID Gene Name Chromosomal location 212771_at C10orf38 10p13 229874_x_at LOC400741 1p36.13 219690_at U2AF1L4 19q13.12 202207_at ARL7 2q37.1 243819_at GNG2 14q21 203753_at TCF4 18q21.1 235400_at FCRLM1 1q23.3 211474_s_at SERPINB6 6p25 226785_at ATP11C Xq27.1 215440_s_at BEXL1 Xq22.1–q22.3 209054_s_at WXSC1 4p16.3 227168_at FLJ25967 22p12.1 213355_at ST3GAL6 3q12.1 223218_s_at NFKBIZ 3p12–q12

scholarly journals Prediction of cytogenetic abnormalities with gene expression profiles

Blood ◽  
2012 ◽  
Vol 119 (21) ◽  
pp. e148-e150 ◽  
Author(s):  
Yiming Zhou ◽  
Qing Zhang ◽  
Owen Stephens ◽  
Christoph J. Heuck ◽  
Erming Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Parameters ◽  
Proof Of Concept ◽  
Accuracy Rate ◽  
Cytogenetic Abnormalities

Abstract Cytogenetic abnormalities are important clinical parameters in various types of cancer, including multiple myeloma. We developed a model to predict cytogenetic abnormalities in patients with multiple myeloma using gene expression profiling and validated it by different cytogenetic techniques. The model has an accuracy rate up to 0.89. These results provide proof of concept for the hypothesis that gene expression profiling is a superior genomic method for clinical molecular diagnosis and/or prognosis.

Identification of Novel Cluster Groups in High-Risk Pediatric B-Precursor Acute Lymphoblastic Leukemia (HR-ALL) by Gene Expression Profiling: Correlation with Clinical and Outcome Variables. a Children’s Oncology Group (COG) Study.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2256-2256
Author(s):  
Richard C. Harvey ◽  
I-Ming Chen ◽  
Kerem Ar ◽  
Stephen P. Hunger ◽  
Mignon Loh ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Risk Criteria ◽  
Cluster Group ◽  
Genetic Abnormalities ◽  
New Therapeutic Targets

Abstract Children with HR-ALL are traditionally defined by NCI Risk Criteria (age and white blood cell count) and comprise a highly heterogeneous group of ALL cases that have not been well characterized. In an effort to shed light on the genetic diversity of HR-ALL and identify new therapeutic targets in this resistant form of disease, we previously analyzed 207 patients enrolled on COG HR-ALL Trial P9906. These analyses revealed discrete gene expression profiles (Affymetrix U133 Plus2) that distinguished 8 distinct cluster groups. Two of these groups clustered patients with known underlying genetic abnormalities (E2A-PBX1/t(1;19) or MLL rearrangements) while the remaining 6 clusters were novel and the underlying genetic lesions remain to be identified. Two of the novel clusters were associated with extremely good (94.7% 4 year RFS) or very poor outcomes (20.9% 4 year RFS). In order to validate these findings and increase the size of the study cohort to enhance statistical power, we performed gene expression profiling in an additional 283 children with HR-ALL enrolled on COG Trial AALL0232. Patients enrolled on AALL0232 met NCI high risk criteria similar to those enrolled on P9906 but additionally included some HR-ALL patients with BCR-ABL or TEL-AML1 translocations, hypodiploidy, and favorable trisomies of chromosomes 4 and 10. As shown in the table below, the cluster designation and relative composition were very similar between the two cohorts. Two notable exceptions were the decreased number of Group 1 (MLL rearranged) members and the significantly elevated number of TEL-AML1 positive patients on AALL0232. In addition to identifying again the 6 novel clusters initially identified in the P9906 cohort and the TEL-AML1+ patients, AALL0232 contained another novel cluster group with a distinct gene signature. Expression profiles from P9906 and AALL0232 were merged to yield a cohort of 490 HR-ALL patients. When the same clustering methods were applied to the merged cohort, a similar set of clusters were identified with virtually identical group membership despite being independent experimental cohorts and clinical trials. The merged cohort permitted a more rigorous definition of gene signatures distinguishing each cluster, identifying the top 50 rank order genes associated with each cluster, and a more rigorous statistical analysis of the association of cluster group with outcome and clinical variables. Although the outcome data for the AALL0232 samples is not yet mature, the high degree of correlation of this cohort with certain P9906 clusters permits us to make predictions about outcome that will allow for validation with longer follow up. Furthermore, this larger cohort allows us to continue to identify potential new therapeutic targets and underlying genetic abnormalities in HR-ALL. Group P9906(207) AALL0232(283) 1 21 (10.1%) 10 (3.5%) 2 23 (11.1%) 23 (8.1%) 2A 11 (5.3%) 13 (4.6%) 4 13 (6.3%) 14 (4.9%) 5 11 (5.3%) 16 (5.7%) 6 21 (10.1%) 18 (6.4%) 8 24 (11.6%) 26 (9.2%) TEL-AML1 3 (1.4%) 55 (19.4%)

Infant Acute Lymphoblastic Leukemias Are Pan-Sensitive to Obatoclax Across molecular/Cytogenetic Subtypes, Especially MLL-ENL, and gene Expression Profiles Determine Obatoclax IC50: A Report on the Children's Oncology Group (COG) P9407 Trial

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2757-2757 ◽  
Author(s):  
Karen A. Urtishak ◽  
Li-San Wang ◽  
Richard Harvey ◽  
Susan R Atlas ◽  
I-Ming L. Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Single Agent ◽  
Gene Expression Profiles ◽  
Poor Survival ◽  
Molecular Cytogenetic ◽  
Ic50 Values ◽  
Mtt Assays

Abstract Abstract 2757 Introduction: The outcome of infants with acute lymphoblastic leukemia (ALL) remains poor because of the association of frequently occurring MLL translocations with drug resistance and vulnerability of the very young to treatment complications. The two most common MLL partner genes in infant ALL, AF4 (AFF1) and ENL (MLLT1), are associated with particularly poor survival. Better therapies are urgent. One candidate is obatoclax (GeminX Biotechnologies, Inc.), which targets interactions of pan-anti-apoptotic BCL-2 family proteins with BH3 proteins and is now in a Phase I trial for relapsed/refractory pediatric cancers (COG ADVL0816). Previously we showed potent single agent in vitro activity of obatoclax against MLL-rearranged infant ALL (Zhang ASH 2008). Here we evaluate correlations of obatoclax activity with MLL translocation status and gene expression profiles in a large number of cases of infant ALL to define molecular determinants of sensitivity. Methods: Bone marrow, peripheral blood or apheresis samples from the time of diagnosis in 54 infants (age 1–365 d, median 168 d; WBC count 15–1230×103/μL, median 445×103/μL) with ALL (n=52) or bilineal acute leukemia (n=2) were examined, 48 of which were from the COG P9407 trial. By molecular/cytogenetic classification, the cases were MLL-AF4+ (n= 28), MLL-ENL+ (n= 11), other MLL rearrangement positive (other MLL+) (n= 8) or MLL germline (MLL-) (n= 7). Single agent IC50 values from MTT assays after 72 h obatoclax exposures were determined in all cases (including 13 previously tested; Zhang ASH 2008) by plotting the surviving fractions. IC50s in the MLL-AF4+ group were compared to those in each of the other 3 molecular/cytogenetic groups by Wilcoxon's test. Gene expression profiling was performed on Affymetrix HG_U133 Plus2.0 arrays in 47 of the 48 COG P9407 cases. Spearman test was used to identify correlation between log2 expression levels for each probeset and IC50 values across subjects. A heatmap of significant probesets (p≤0.001) was generated by transforming expression levels to z-scores and ordering rows and columns by complete linkage hierarchical clustering. Ingenuity pathway analysis was applied to all probesets with p≤0.01 to identify pathways significantly correlated with IC50. Additional MTT assays were initiated to test sensitivity to agents targeting these pathways. Results: Even though most cases in all 4 groups were sensitive to obatoclax as indicated by IC50s within a clinically achievable range, MLL translocation status still had a significant effect on IC50. MLL-AF4+ cases were least sensitive and MLL-ENL+ cases were most sensitive to obatoclax. Respective IC50 ranges across all 54 cases were: MLL-AF4+, 26–918 nM; MLL-ENL+, 13–294 nM; other MLL+ 10–356 nM; MLL−, 31–488. Compared to MLL-AF4+, the IC50s in MLL-ENL+ cases were significantly lower (p=0.047), IC50s in other MLL+ cases were lower but the difference did not achieve significance (p=0.10), and IC50s in MLL- cases were not significantly different (p=0.64). In the 47 COG P9407 cases studied by MTT assay and gene expression profiling, 450 probesets defined a cluster of 16 cases with higher IC50s, which were predominantly MLL-AF4+ (68.7%). Ingenuity analysis identified significant correlations of the following canonical pathways with the IC50 in the same 47 cases: glycolysis/gluconeogenesis, mTOR signaling, regulation of eIF4 and p70S6K signaling, EIF2 signaling, and fructose and mannose metabolism. In preliminary analyses, cell lines with t(4;11) exhibited time and dose-dependant sensitivity to the eIF4e inhibitor ribavirin. Conclusions: In infant ALL, obatoclax has broad-spectrum activity and there is pan-sensitivity across MLL translocation subtypes and MLL− cases. Still specific MLL partner genes have a strong effect on obatoclax IC50 and there is exquisite sensitivity in MLL-ENL+ cases. This result is important because MLL-ENL is associated with particularly poor survival when conventional therapies are used. The association of differentially expressed genes in canonical cell signaling and metabolism pathways with differences in obatoclax sensitivity forms the basis to combine obatoclax with targeted agents directed at restoring these pathways to enhance responsiveness even further. Disclosures: Felix: None: Patent not licensed.

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