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.

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&lt;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.

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

scholarly journals High Levels of APOBEC3B Gene Expression Contribute to Poor Prognosis in Multiple Myeloma Patients

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3897-3897
Author(s):  
Valeriy V Lyzogubov ◽  
Pingping Qu ◽  
Cody Ashby ◽  
Adam Rosenthal ◽  
Antje Hoering ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Board Of Directors ◽  
Research Funding ◽  
Poor Prognosis ◽  
Target Genes ◽  
Plasma Cells ◽  
Nuclear Fraction ◽  
Advisory Committees ◽  
Significant Difference

Abstract Introduction: Poor prognosis and drug resistance in multiple myeloma (MM) is associated with increased mutational load. APOBEC3B is a major contributor to mutagenesis, especially in myeloma patients with t(14;16) MAF subgroup. It was shown recently that presence of the APOBEC signature at diagnosis is an independent prognostic factor for progression free survival (PFS) and overall survival (OS). We hypothesized that high levels of APOBEC3B gene expression at diagnosis may also have a prognostic impact in myeloma. To consider APOBEC3B as a potential target for therapy more studies are necessary to understand how APOBEC3B expression is regulated and how APOBEC3B generates mutations. Methods: Gene expression profiling (GEP, U133 Plus 2.0) of MM patients was performed. APOBEC3B gene expression levels were investigated in plasma cells of healthy donors (HD; n=34), MGUS (n=154), smoldering myeloma (SMM; n=219), MM low risk (LR; n=739), MM high risk (HR; n=129), relapsed MM (RMM; n=74), and primary plasma cell leukemia (pPCL; n=19) samples. The samples from relapse were taken on or after the progression/relapse date but within 30 days after progression/relapse from Total Therapy trials 3, 4, 5 & 6. GEP70 score was used to separate samples into LR and HR groups. We also investigated APOBEC3B expression in different MM molecular subgroups and used logrank statistics with covariate frequency distribution to determine an optimal cut off APOBEC3B expression value. Gene expression was compared in cases with low expression of APOBEC3B (log2<7.5) and high expression of APOBEC3B (log2>10), and an optimal cut-point in APOBEC3B expression was identified with respect to PFS. To explore the role of MAF and the non-canonical NF-ĸB pathway we performed functional studies using a cellular model of MAF downregulation. TRIPZ lentiviral shRNA MAF knockdown in the RPMI8226 cell lines was used to explore MAF-dependent genes. NF-ĸB proteins, p52 and RelB, were investigated in the nuclear fraction by immunoblot analysis. Results: Expression of APOBEC3B in HD control samples (log2=10.9) was surprisingly higher than in MGUS (log2=9.51), SMM (log2=9.09), and LR (log2=9.40) and was comparable to HR (log2=10.4) and RMM (log2=10.6) groups. Expression levels of APOBEC3B were gradually increased as disease progressed from SMM to pPCL. The high expression of APOBEC3B in HD places plasma cells at risk of APOBEC induced mutagenesis where the regulation of APOBEC3B function is compromised. The correlation between APOBEC3B expression and GEP70 score in MM was 0.37, and there was a significant difference in APOBEC3B expression between GEP70 high and low risk groups (p=0.0003). An optimal cut-point in APOBEC3B expression of log2=10.2 resulted in a significant difference in PFS (median 5.7 yr vs.7.4 yr; p=0.0086) and OS (median 9.1 yr vs. not reached; p<0.0001), between high and low expression. The highest APOBEC3B expression was detected in cases with a t(14;16). We analyzed t(14;16) cases with the APOBEC mutational signature and compared them to t(14;16) cases without the APOBEC signature and found elevated MAF (2-fold) and APOBEC3B (2.7-fold) gene expression in samples with the APOBEC signature. No APOBEC signature was detected in SMM cases, including those with a t(14;16). High APOBEC3B levels in myeloma patients was associated with overexpression of genes related to response to DNA damage and cell cycle control. Significant (p<0.05) increases of NF-κB target genes was seen in high APOBEC3B cases: TNFAIP3 (4.4-fold), NFKB2 (1.7-fold), NFKBIE (1.9-fold), RELB (1.4-fold), NFKBIA (2.0-fold), PLEK (2.5-fold), MALT1 (2.5-fold), WNT10A (2.4-fold). However, in t(14;16) cases there was no significant increase of NF-κB target genes except BIRC3 (2.5-fold) and MALT1 (2.0-fold). MAF downregulation in RPMI8226 cells did not lead to changes in NF-κB target gene expression but MAF-dependent genes were identified, including ETS1, SPP1, RUNX2, HGF, IGFBP2 and IGFBP3. Analysis of nuclear fraction of NF-ĸB proteins did not show significant changes in expression of p52 and RelB in RPMI8226 cells after MAF downregulation. Conclusions: Increased expression of APOBEC3B is a negative prognostic factor in multiple myeloma. MAF is a major factor regulating expression of APOBEC3B in the t(14;16) subgroup. NF-ĸB pathway activation is most likely involved in upregulation of APOBEC3B in non-t(14;16) subgroups. Disclosures Davies: TRM Oncology: Honoraria; MMRF: Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding; Takeda: Consultancy, Honoraria.

scholarly journals High-Risk Subtype of Ph-like Acute Lymphoblastic Leukemia (ALL) in Adults: Dismal Outcomes of CRLF2+ ALL Patients Treated with Intensive Chemotherapy

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1082-1082 ◽  
Author(s):  
Nitin Jain ◽  
Kathryn G. Roberts ◽  
Elias J. Jabbour ◽  
Keyur Patel ◽  
Karina Eterovic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Research Funding ◽  
Lymphoblastic Leukemia ◽  
Adult Patients ◽  
Hispanic Ethnicity ◽  
Oncology Research

Abstract Background:Ph-like acute lymphoblastic leukemia (ALL) is a high-risk subtype of ALL in children. There are limited and conflicted data on the incidence and prognosis of Ph-like ALL in adults. Methods:Patients with newly-diagnosed B-ALL who received frontline chemotherapy at MD Anderson Cancer Center underwent gene expression profiling of leukemic cells to identify Ph-like ALL. Gene expression profiling was performed on 148 RNA samples using either U133 Plus 2.0 microarrays, or a customized Taqman low density array (LDA) card to identify patients with the Ph-like ALL gene signature (Roberts et al. NEJM 2014). An additional 7 previously untreated patients were found to have CRLF2 overexpression by multicolor flow-cytometry (MFC), and received induction chemotherapy at MDACC were included in the outcome analysis (but not for subtype frequency calculation). We performed targeted sequencing of 303 recurrently mutated genes (L300 panel, MDACC) in 40 patients with CRFL2 rearrangements (15 with matched germline control). Minimal residual disease (MRD) was assessed by MFC, with a sensitivity of 0.01%. Results:Of 148 patients, 49 (33.1%) were Ph-like, 46 patients (31.1%) were Ph+, and 53 patients (35.8%) were of other B-ALL subtypes (B-other). The median age of Ph-like cohort was 33.5 years (range, 15-71), Ph+ cohort was 49 years (range, 22-84), and B-other was 38 years (range, 15-79). Within the Ph-like ALL cohort, 61% had overexpression of CRLF2. Patients received hyper-CVAD (80%) or an augmented-BFM regimen (20%). The rate of CR/CRp was similar in the 3 disease subgroups (Ph-like ALL 89%, Ph+ ALL 93%, B-other 94%, p = 0.57). However, patients with Ph-like ALL were significantly less likely to achieve MRD-negative remission (30% vs. 56% for Ph+ ALL vs. 87% for B-other, p <0.001). Patients with Ph-like ALL had significantly worse overall survival (OS) and event-free survival (EFS) compared to B-other with a 5-year survival of 23% (vs. 59% for B-other, p=0.006) (Figure 1A). The poor outcomes of Ph-like ALL were also observed when only hyper-CVAD treated patients were considered. Interestingly, 68% of the patients with Ph-like ALL (78% among the CRLF2+ cohort) were of Hispanic ethnicity. This was significantly higher compared to Ph+ ALL (35%) and B-other (30%), p <0.001. Patients with CRLF2 overexpression had significantly inferior OS, EFS, and remission duration when compared to other genomic subgroups, including the Ph-like non-CRLF2 group (Figure 1B). Notably, 5-year survival in the CRLF2+ group was <20%. The following were independently associated with inferior OS on multivariable analysis: age (hazard ratio [HR] 2.474, p<0.001); WBC count (HR 1.183, p=0.007); platelet count (HR 4.283, p<0.001) and Ph-like ALL (HR 1.579, p=0.04) (Table 1). The most common mutations by L300 sequencing of 40 patients with CRLF2 were JAK2 (n=19, 47.5%), KRAS (n=10, 25%), CRLF2 (n=7, 17.5%), NRAS (n=5, 12.5%), PAX5 (n=5, 12.5%), JAK1 (n=4, 10%) (Figure 2). The CRLF2 F232C mutation, noted in 7 (17.5%) patients in this study, appears more frequent than in pediatric patients (3/134, 2.2%, Chen et al. Blood 2012), and in range with a smaller adult series (3/14, 21.4%, Yoda et al. PNAS 2010). CRLF2 F232C mutations were mutually exclusive with JAK2/JAK1 mutations (except in one patient). Conclusions:Our findings show a high frequency of Ph-like ALL in adults; an increased frequency of Ph-like ALL in adults with Hispanic ethnicity; significantly inferior outcomes of adult patients with Ph-like ALL; and significantly worse outcomes in Ph-like ALL patients with CRLF2 overexpression. The frequency of CRLF2 F232C mutation appears to be higher in adult patients with B-ALL than in the children. Ph-like ALL represents a high-risk disease subtype of adult B-ALL. Novel strategies are needed to improve the outcome of these patients. Disclosures Jain: Pharmacyclics: Consultancy, Honoraria, Research Funding; Genentech: Research Funding; Incyte: Research Funding; BMS: Research Funding; Abbvie: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Celgene: Research Funding; ADC Therapeutics: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Servier: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Infinity: Research Funding; Novimmune: Consultancy, Honoraria. Jabbour:ARIAD: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy. Cortes:ARIAD: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Teva: Research Funding. O'Brien:Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria. Mullighan:Incyte: Membership on an entity's Board of Directors or advisory committees; Amgen: Speakers Bureau; Loxo Oncology: Research Funding. Konopleva:Reata Pharmaceuticals: Equity Ownership; Abbvie: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Stemline: Consultancy, Research Funding; Eli Lilly: Research Funding; Cellectis: Research Funding; Calithera: Research Funding.

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 Crowdsourced High-Risk Classifiers for Multiple Myeloma Patients Commonly Identify PHF19 As a Robust Progression Biomarker

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4370-4370
Author(s):  
Michael J Mason ◽  
Carolina D. Schinke ◽  
Christine Eng ◽  
Fadi Towfic ◽  
Fred Gruber ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Disease Progression ◽  
Board Of Directors ◽  
Research Funding ◽  
Plasma Cells ◽  
Employment Equity ◽  
Advisory Committees ◽  
Equity Ownership

Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

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