B034 A Gene Expression—Based Proliferation Index as Independent Prognostic Factor in Multiple Myeloma

2009 ◽  
Vol 9 ◽  
pp. S98
Author(s):  
D Hose ◽  
T Rème ◽  
T Hielscher ◽  
J Moreaux ◽  
T Meißner ◽  
...  
Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1667-1667
Author(s):  
Dirk Hose ◽  
Thierry Rème ◽  
Thomas Hielscher ◽  
Jérôme Moreaux ◽  
Tobias Meißner ◽  
...  

Abstract BACKGROUND. The proliferation-rate of primary myeloma cells is a strong adverse prognostic factor in various trials, but not routinely assessed, partially due to effort in obtaining it. AIM. As gene-expression profiling is increasingly considered a standard diagnostics in myeloma, we investigated the possibility to develop a prognostically relevant gene-expression based proliferation index (GPI). PATIENTS AND METHODS. Gene expression was determined by Affymetrix DNA-microarrays in 784 samples including two independent sets of 233 and 345 CD138-purified myeloma cells from previously untreated patients. The GPI was derived by selecting genes associated with proliferation (in terms of gene ontology) differentially expressed in proliferating malignant (human myeloma cell lines) and benign (plasmablastic) cells compared to non-proliferating, non-malignant cells (normal plasma cells and memory B-cells). The GPI comprises the sum of the expression values of 50 genes (ASPM, AURKA, AURKB, BIRC5, BRCA1, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CDC2, CDC20, CDC25C, CDC6, CDCA8, CDKN3, CEP55, CHEK1, CKS1B, CKS2, DLG7, ESPL1, GINS1, GTSE1, KIAA1794, KIF11, KIF15, KIF20A, KIF2C, KNTC2, MAD2L1, MCM10, MCM6, MKI67, NCAPD3, NCAPG, NCAPG2, NEK2, NPM1, PAK3, PCNA, PGAM1, PLK4, PTTG1, RACGAP1, SMC2, SPAG5. STIL, TPX2, ZWINT). Proliferation of primary myeloma cells was assessed by propidium iodinestaining (n=67). Chromosomal aberrations were assessed by comprehensive iFISH using a set of probes for the chromosomal regions 1q21, 6q21, 8p21, 9q34, 11q23, 11q13, 13q14.3, 14q32, 15q22, 17p13, 19q13, 22q11, as well as the translocations t(4;14)(p16.3;q32.3) and t(11;14)(q13;q32.3). RESULTS. In the two groups, 39 and 32 percent of primary myeloma cells show a GPI above the median plus three standard deviations of normal bone marrow plasma cells, respectively. The GPI is significantly higher in advanced- compared to early-stage myeloma (P=.001) and in patients harboring a gain of 1q21 (n=95, P<0.001). It correlates significantly with proliferation as determined by propidium iodine in primary myeloma cells (rs=.52, P<.001, n=67). The GPI as continuous variable is significantly predictive for event-free survival (EFS, n=120, P<.001, n=345, P<.001, respectively) and overall survival (OAS, n=345, P<.001) in patients treated with high-dose chemotherapy, independent of serum-β2-microglobulin (B2M) or ISS-stage. A GPI above the median (GPIhigh) delineated significantly inferior EFS (n=168, 41.6 vs. 26 months, P=.04, HR 1.57, CI [1.02,2.42]; n=345, 68.6 vs. 45.2 months, HR 1.55, CI [1.16,2.09], P=.003) and OAS (n=345, P<.001) in two independent cohorts of patients undergoing high-dose chemotherapy. By using B2M above 3.5 mg/l and GPI as staging variables, four groups with difference in median EFS (n=345, B2M <3.5mg/l, GPIhigh/low 76.1 months; B2M < 3.5mg/l, GPIhigh 62.4 months, B2M ≥3.5mg/l, GPIlow 41.8 months, B2M ≥3.5mg/l, GPI 36.1 months, P<.001) and OAS can be delineated. CONCLUSION. The GPI represents a validated tool for the assessment of proliferation in multiple myeloma patients, allows a risk stratification in terms of proliferation either alone or in combination with B2M or ISS, respectively, and has the potential to be used within a risk adapted targeting of anti-proliferative treatment.


2021 ◽  
Author(s):  
Rui Liu ◽  
Ying Shen ◽  
Xiaman Wang ◽  
Dong Wu ◽  
Meng Zhai ◽  
...  

Abstract Background: N6-methyladenosine is the most abundant RNA modification, which plays a prominent role in multiple biology processes, including tumorigenesis. Multiple myeloma (MM) is the second most frequent hematological cancer. However, the expression status and value of m6A-related genes in multiple myeloma remain elusive.Methods: m6A-related gene expression data and clinical information of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Based on differentially expressed analysis, protein-protein interaction (PPI) analysis, Pearson correlation analysis, Kaplan-Meier survival analysis and Cox regression analysis, the prognostic m6A-associated genes were identified. The receiver operation characteristic (ROC) curves were used to verify the prognostic and diagnostic efficiency. The molecular mechanisms were investigated by differentially expressed mRNA and microRNA analyses with the help of online database ENCORI and POSTAR.Results: Among 22 m6A-related genes, HNRNPC, RBM15B, RBM15, YTHDF3, YTHDF2, HNRNPA2B1 and IGF2BP2 were significantly upregulated, while ZC3H13, FTO, IGF2BP3, ALKBH5 and YTHDC1 were significantly downregulated in MM patients. The high expression of HNRNPC, HNRNPA2B1, YTHDF2 and the low expression of ZC3H13 were associated with adverse survival. Furthermore, the expression level of YTHDF2 was the independent prognostic factor of MM. ROC curves suggested the great prediction performance for MM patients. Differentially expressed mRNA and microRNA analyses indicated the probable involvement of miR-205/YTHDF2/EGR1 axis.Conclusions: Our study first systematically analyzed the expression status of m6A-related genes in multiple myeloma, identified HNRNPC, HNRNPA2B1, YTHDF2 and ZC3H13 that could be the potential prognostic biomarkers, especially YTHDF2, which may be implicated in the miR-205/YTHDF2/EGR1 axis.


2020 ◽  
Vol 20 (10) ◽  
pp. 704-711
Author(s):  
Stergios Intzes ◽  
Marianthi Symeonidou ◽  
Konstantinos Zagoridis ◽  
Zoe Bezirgiannidou ◽  
Aikaterini Pentidou ◽  
...  

2011 ◽  
Vol 130 (3) ◽  
pp. 735-742 ◽  
Author(s):  
Evangelos Terpos ◽  
Konstantinos Anargyrou ◽  
Eirini Katodritou ◽  
Efstathios Kastritis ◽  
Athanasios Papatheodorou ◽  
...  

Author(s):  
Xu Zhang ◽  
Hua Ma ◽  
Quan Zou ◽  
Jin Wu

ObjectiveThe aim of this study was to investigate the expression of cyclin-dependent kinase 1 (CDK1) in gastric cancer (GC), evaluate its relationship with the clinicopathological features and prognosis of GC, and analyze the advantage of CDK1 as a potential independent prognostic factor for GC.MethodsThe Cancer Genome Atlas (TCGA) data and corresponding clinical features of GC were collected. First, the aim gene was selected by combining five topological analysis methods, where the gene expression in paracancerous and GC tissues was analyzed by Limma package and Wilcox test. Second, the correlation between gene expression and clinical features was analyzed by logistic regression. Finally, the survival analysis was carried out by using the Kaplan–Meier. The gene prognostic value was evaluated by univariate and multivariate Cox analyses, and the gene potential biological function was explored by gene set enrichment analysis (GSEA).ResultsCDK1 was selected as one of the most important genes associated with GC. The expression level of CDK1 in GC tissues was significantly higher than that in paracancerous tissues, which was significantly correlated with pathological stage and grade. The survival rate of the CDK1 high expression group was significantly lower than that of the low expression group. CDK1 expression was significantly correlated with overall survival (OS). CDK1 expression was mainly involved in prostate cancer, small cell lung cancer, and GC and was enriched in the WNT signaling pathway and T cell receptor signaling pathway.ConclusionCDK1 may serve as an independent prognostic factor for GC. It is also expected to be a new target for molecular targeted therapy of GC.


Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1164-1164
Author(s):  
David Dingli ◽  
Grzegorz S. Nowakowski ◽  
Angela Dispenzieri ◽  
Martha Q. Lacy ◽  
Suzanne R. Hayman ◽  
...  

Abstract Background: The presence of circulating myeloma cells (CMC) detected by flow cytometry at the time of diagnosis of multiple myeloma is associated with a shortened response to therapy and reduced overall survival (OS). We hypothesized that the presence of CMC at the time of stem cell collection prior to high dose therapy (HDT) and autologous stem cell transplantation (ASCT) would identifies a cohort of patients with a high risk of rapid progression. Methods: The Mayo Clinic myeloma transplant database was queried for patients who were mobilized using cyclophosphamide and hematopoietic growth factors. CMC was determined using flow cytometry by gating on a population of CD38 bright and CD45 negative cells. The impact of CMC on OS and time to progression (TTP) and its role in the context of established prognostic parameters was evaluated. Results: Of 246 patients with MM undergoing ASCT, 95 had CMC. Patients with CMC had significantly higher plasma cell labeling index, adverse cytogenetics, B2-M and resistant disease. Complete response (CR) rates post transplant were 32% and 36% for patients with and without CMC (p=0.5034). OS was 33.2 and 58.6 months (p=0.0052) while TTP was 14.1 and 22 months respectively (p=0.0005). Figure Figure On multivariate analysis, CMC remained an independent prognostic factor in a model that included cytogenetics and disease status at time of transplant (p=0.0314). A prognostic system based on the presence or absence of CMC and karyotype abnormalities was developed. Patients with neither, one or both parameters had a median, OS of 55, 48 and 21.5 months respectively (p<0.0001) while TTP was 22, 15.4 and 6.5 months for the same groups (p<0.0001). Conclusion: The presence of CMC at the time of HDT and ASCT is an independent prognostic factor. The combination of CMC and cytogenetics provides a simple yet powerful scoring system that stratifies patients and guides their management.


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1829-1829
Author(s):  
Tobias Meiβner ◽  
Anja Seckinger ◽  
Thomas Hielscher ◽  
Thierry Rème ◽  
Jerome Moreaux ◽  
...  

Abstract Abstract 1829 Poster Board I-855 Introduction In addition to current clinical and cytogenetic risk factors, several highly predictive gene expression based risk stratifications have been proposed in multiple myeloma. At the same time, putative drugable targets have been identified which are only expressed in a subpopulation of myeloma patients (e.g. AURKA). Whereas assessment of both works well within a clinical trial or an experimental setting, they can currently not readily be applied to clinical routine. Methods As reference a group of 300 Affymetrix U133 Plus 2.0 DNA microarrays from patients with multiple myeloma is preprocessed using GC-RMA. Quality control of the DNA microarrays is implemented according to the MACQ-Project. Gene expression based prediction of sex, immunoglobulin- and light chain type is used as sample identity-test within a multicenter-setting. Gene expression based risk stratification (IFM-score, 70-gene high risk score, gene expression based proliferation index) and molecular classifications are assessed as published, as are individual target genes e.g. AURKA. To classify a patient within a prospective clinical routine setting, the documentation by value strategy (Kostka & Spang, 2008) was adapted for GC-RMA preprocessing and is used for documenting the quantitative preprocessing information of the reference group. The gene expression based report is developed in the open source language R, containing a GUI based on Gtk2, and the final report is created as a PDF-file. Results We present here our publicly available (http://code.google.com/p/gep-r) open source software-framework (GEP-R) that allows creating a gene expression based report from Affymetrix raw-data. The risk stratification of an individual patient is assessed and based on saved preprocessing information of a reference cohort by treating the individual patient's expression data as being part of this group, assuring comparable risk stratification. Results can be interpreted and commented within the report and a PDF based document be created. The generation of the report can be performed within short time on a standard computer. Conclusion Gene expression reporting allows validated assessment of risk and of individual therapeutic targets in myeloma patients within a clinical routine setting. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2869-2869
Author(s):  
Scott Van Wier ◽  
Esteban Braggio ◽  
Rafael Fonseca

Abstract Abstract 2869 Background: Chromosome abnormalities are universal in multiple myeloma (MM) and will ultimately categorize patients into hyperdiploid and non-hyperdiploid MM. Among non-hyperdiploid patients those that exhibit hypodiploidy have the most aggressive clinical phenotype. What genetic features are unique to hypodiploidy are not fully described. Therefore, we performed a comprehensive high-resolution analysis to differentiate and characterize hypodiploid MM. Materials and methods: MM patients were analyzed using a combination of array-based comparative genomic hybridization (aCGH) (n=275) and gene expression profiling (GEP) (n=239). Agilent 244K and Affymetrix U133A Plus 2.0 arrays were used in the aCGH and GEP experiments, respectively. Hypodiploid MM was differentiated using pseudokaryotyping based on aCGH findings. Samples estimated to have less than or equal to 44 chromosomes were designated hypodiploid, 45–47 chromosomes were nonhyperdiploid and greater than or equal to 48 and less than 74 chromosomes were considered hyperdiploid. Using GEP the main gene indices and signatures associated with outcome were determined including the translocation and cyclin D (TC) classification, UAMS 70-gene index, proliferation index, centrosome signature and NF-kB index. Differentially expressed genes were also investigated. Results: A total of 53 (19%) MM patients were classified into the hypodiploid group, mainly characterized by monosomies of chromosomes 13 (83%), 14 (42%), 22 (23%) and × (50%) (females) with p and/or q-arm aberrations including gains of 1q (51%) and 8q (25%) and losses of 1p (49%), 4p (21%), 4q (23%), 6q (38%), 8p (34%), 12p (25%), 12q (26%), 14q (32%), 16p (25%), 16q (51%) and 17p (25%). Patients with loss of 1p were associated with 4p- (p <0.029), 4q- (p<0.0001), 12p- (p<0.007), 12q- (p<0.0002), any 14 (p<0.022), 16p- (p<0.005), 16q- (p<0.001), and monosomy 22 (p<0.024). Patients with loss of 17p were associated with 12p- (p<0.025), 12q- (p<0.039) and 16q- (p<0.031). The main gene indices and signatures in MM showed that nearly one half of the hypodiploid patients having high-risk disease, ranging from 45% with a high 70-gene index, 47% with high centrosome signature and 51% with a high proliferation index. In addition, hypodiploid patients also displayed a translocation type signature in the TC classification defined by 11q13 (24%), 4p16 (24%) and maf (12%). Overall, 253 genes have >2 fold expression change comparing hypodiploid vs. hyperdiploid including a five fold decrease in the heparin-degrading endosulfatase gene SULF2, a decrease of genes in the TGF-b signaling pathway (MYC, ID3, SMAD1, LTBP1) and those involved in Wnt signaling (DKK1, FRZB). Up regulated genes included those from the p53 signaling pathway and cell cycle (CCND2, CDKN1C, RPRM), cell adhesion molecules (ITGB8, CD28) and tight junction pathway (RRAS2, RRAS, CSDA). Conclusion: This represents the most comprehensive genomic characterization of hypodiploid MM to date. These cases exhibit a high propensity for high-risk gene expression profiles and have a high prevalence of −13, −14, 1q gain and 1p loss as predicted. Given our findings it is likely that hypodiploid is not a separate category but rather the genetic “phenotype” of a more advanced clone. Today, using these two platforms together in a routine setting would provide the most comprehensive genetic analysis, important for individualized therapeutics. Disclosures: Fonseca: Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.


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