Risk Stratification Using a New Prognostic Model for Patients with Secondary Acute Myeloid Leukemia - Results of the DSIL-AML96 Trial.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 2652-2652
Author(s):  
Friedrich Stölzel ◽  
Walter E. Aulitzky ◽  
Heinrich Bodenstein ◽  
Martin Bornhäuser ◽  
Michael Kramer ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Myeloid Leukemia ◽  
Prognostic Model ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Allogeneic Hsct ◽  
Secondary Acute Myeloid Leukemia ◽  
Acute Myeloid

Abstract Abstract 2652 Poster Board II-628 Background: Secondary acute myeloid leukemia (sAML) following a myelodysplastic syndrome (mdsAML) or deriving as therapy-related AML (tAML) is regarded as an entity with a poor prognosis and patients are normally treated as high risk AML. However due to progress in elucidating the impact of molecular and cytogenetic markers and therefore combining biological and clinical data for prognosis and treatment outcome the aim of this analysis was to provide a prognostic scoring system for this entity by including clinical and laboratory data from patients being treated in the prospective AML96 trial of the DSIL study group. Patients and methods: A total of 318 patients with sAML (mdsAML = 239 and tAML = 79) were treated within the AML96 trial with a median follow-up for patients alive of 5.66 years (95% CI 4.426 – 6.895). All patients received double induction chemotherapy. Consolidation therapy contained high-dose cytosine arabinoside and for patients ' 60 years of age the option of autologous or allogeneic hematopoietic stem cell transplantation (HSCT) according to donor availability. Prognostic factors for survival were analyzed in the whole group of sAML patients in a multivariate Cox regression model for overall survival (OS) stratified by treatment groups (chemo-consolidation vs. allogeneic HSCT). Model selection was performed by backward selection applying the Likelihood-Ratio-Test. Results: Complete remission (CR) rate for all patients was 30.8% (n = 96). CR rate was lower in patients with mdsAML compared to patients with tAML (25.9% vs. 44.3%, p=.003). Patients with mdsAML were older and had a higher percentage of CD34+ blasts at diagnosis but to a lower extend aberrant karyotypes than patients with tAML. OS and disease free survival (DFS) at three years for all patients was 15.8% and 20.6%, respectively. While disease status (mdsAML vs. tAML) had no independent influence on survival, the dichotomized prognostic factors platelet count in the peripheral blood at diagnosis [HR = 0.535 (95% CI .415 – .689), p=<.000] as well as the NPM1 mutational status in the bone marrow at diagnosis [HR = 0.572 (95% CI .351 – .933), p=.025] were detected as independent predictors for overall survival. By combining these two variables, a prognostic model for OS with two risk groups for patients with sAML could be established with the low risk group being NPM1 positive or having platelets of >50 Gpt/l at diagnosis and the high risk group being NPM1 negative and having platelets of '50 Gpt/l at diagnosis. Three year OS for patients who received chemo-consolidation in the low risk group was 19.9% [95% CI = .128 - .270] and for patients in the high risk group 5.1% [95% CI = .014 - .088], p<.001. For patients who underwent allogeneic HSCT in first CR belonging to the low risk group the three year OS was 53.8% [95% CI = .346 - .730] and for patients in the high risk group 15.4% [95% CI = .000 - .35], p<.001. Conclusions: For patients with sAML we provide a new prognostic model for risk stratification: 1) NPM1+ or Platelets >50 Gpt/l defining a low risk group and 2) NPM1- and Platelets ' 50 Gpt/l defining a high risk group. Disclosures: No relevant conflicts of interest to declare.

Pretreatment Cytogenetic Abnormalities Are Predictive of Induction Success, Cumulative Incidence of Relapse and Overall Survival in Patients >60 Years of Age with Newly Diagnosed Acute Myeloid Leukemia.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3293-3293
Author(s):  
Richard F. Schlenk ◽  
Sabine Kayser ◽  
Martina Morhardt ◽  
Konstanze Döhner ◽  
Hartmut Döhner ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Myeloid Leukemia ◽  
Risk Group ◽  
Normal Karyotype ◽  
Risk Groups ◽  
High Risk Group ◽  
Newly Diagnosed ◽  
Balanced Translocations ◽  
Acute Myeloid

Abstract Purpose: Karyotype at diagnosis provides the most important prognostic information in younger adults with acute myeloid leukemia (AML). However, there are few data available looking in particular at patients (pts.) above 60 years of age. We prospectively analyzed 361 elderly pts. with newly diagnosed AML. All pts. were treated within the AMLHD98B treatment trial and received intensive induction and consolidation therapy. Pts. exhibiting a t(15;17) received an age-adjusted AIDA-regimen. Median follow-up time was 48 months. The median age was 67 years (range 60–85 years). Results: 160 pts. had a normal karyotype (44%); 48 pts. (13%) exhibited the balanced translocations t(8;21) (n=12), inv(16) (n=14), t(15;17) (n=11), or t(11q23) (n=11); in the absence of these balanced translocations, 73 pts. exhibited a single aberration, 179 pts. two aberrations, and 61 pts. a complex karyotype (≥3 aberrations; including 44 pts. with 5 or more aberrations). Analyses were normalized to the complete remission (CR) rate (52%), cumulative incidence of relapse (CIR) (77%) and overall survival (OS) (13%) after 4 years of pts. with normal karyotype. Pts. exhibiting a t(15;17) showed a significantly better CIR (29%) and OS (55%), whereas pts. with the other balanced translocations [t(8;21), inv(16)/t(16;16) and t(11q23)] did not differ from pts. with normal karyotype. The limited backward selected Cox-model for OS [t(15;17) excluded] revealed two risk groups: standard-risk [normal karyotype, t(8;21), inv(16), t(11q23), +8 and +11 in absence of a complex karyoytpe] and high-risk [all other aberrations]. The CR rates were 56% and 18%, and the OS-rates after 4 years for the standard- (n=223) and the high-risk group (n=127) were 15% and 0%, respectively. The MRC risk classification system for patients &gt;55 years applied to our patients revealed CR- and OS-rates after 4 years of 73% and 19%, 47% and 12%, as well as 7% and 0% for the low (n=26), intermediate (n=282) and high risk groups (n=44), respectively [t(15;17) excluded]. In conclusion, our risk classification system identified a large high-risk group (36%) of elderly patients with AML who did not benefit from intensive chemotherapy.

Treatment of Acute Myeloid Leukemia with the NOPHO-AML 2004 Study in Chinese Children: A Single Center Study

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 5270-5270
Author(s):  
Xiaoqin Feng ◽  
Chunfu Li
Keyword(s):  
Acute Myeloid Leukemia ◽  
High Risk ◽  
Myeloid Leukemia ◽  
Conflicts Of Interest ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Chinese Children ◽  
Hematopoietic Stem ◽  
Acute Myeloid

Abstract Objectives: The objective of the present study was to investigate the therapeutic efficacy and feasibility of NOPHO-AML 2004 study in the treatment of acute myeloid leukemia (AML; excluding acute promyelocytic leukemia) in Chinese children. Methods: Thirty-one children with novo AML treated with the NOPHO-AML 2004 study were recruited from Jan. 2010 to Dec. 2013, and the clinical data were retrospectively analyzed. Among 31 AML children, their age were from 2-14 years old (median age 8 years old). There were 12,15 and 4 children classified in low risk group, intermediate risk group and high risk group by cytogenetic risk classification respectively. Eight children received concomitant hematopoietic stem cell transplantation. Kaplan Meier method with Log-Rank testing was employed for survival analysis. Results: Follow-up was for a median 24 months (range: 5–50 months). The complete remission rate was 83.8%. The predicted 3-year leukemia free survival (LFS) rate was 53.8%. The LFS rate of low, intermediate and high risk group were 55.6%, 52.5% and 50.0% respectively. There was no significance in risk groups. The LFS rate of chemotherapy and chemotherapy concomitant HSCT were 42.7% and 87.5%, P<0.05. There were 2 cases of treatment related mortality including one case of sepsis and one case of ARDS. Conclusions: NOPHO-AML 2004 study is clinically efficacious for the treatment of AML in Chinese children. HSCT treatment had better outcome than only chemotherapy in childhood with non low risk AML in CR1 phase. Disclosures No relevant conflicts of interest to declare.

scholarly journals Weighted Gene Co-Expression Network Analysis Reveals a New Survival Model for Prognostic Prediction in Ewing Sarcoma

2021 ◽  
Author(s):  
Debao Li ◽  
Lei Wang ◽  
Guanghui Wang ◽  
Yaowen Yang ◽  
Weiyu Yang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Network Analysis ◽  
Ewing Sarcoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Survival Status

Abstract Background: Ewing sarcoma (ES) is a malignant bone or soft-tissue cancer that mainly arises in children and young adults. However, the prognosis of Ewing sarcoma remains very poor, and there is no effective prediction method. The aim of our study was to identify a prognostic model for ES patients based on prognosis-associated mRNA expression profiles. Methods: The GSE17679 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) between ES and normal control were identified using R package “limma”. A weighted gene co-expression network analysis (WGCNA) was used to screen gene modules associated with recurrence/metastasis and survival status based on DEGs. Results: The prognostic model was constructed based on genes in MEbrown module, which was most associated with recurrence/metastasis and survival status, using Kaplan-Meier survival and lasso regression analysis. Sixteen genes were screened to construct the prognostic model. ES patients were grouped into high- and low-risk groups based on the median of risk score calculated for each of them. ES patients in high-risk group have worse survival than patients in low-risk group. The AUCs (Area under the ROC curve) for 1-year, 3-year, and 6-year overall survival were 0.903, 0.995, 0.953. Conclusions: Taken together, our research constructed a prognostic model which has excellent prediction performance for overall survival of ES patients.

scholarly journals A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11911
Author(s):  
Lei Liu ◽  
Huayu He ◽  
Yue Peng ◽  
Zhenlin Yang ◽  
Shugeng Gao
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Gene Signature ◽  
Tnm Staging ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Risk Patients

Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.

scholarly journals A three-gene signature might predict prognosis in patients with acute myeloid leukemia

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Xin Zhu ◽  
Qian Zhao ◽  
Xiaoyu Su ◽  
Jinming Ke ◽  
Yunyun Yi ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Transcription Factor ◽  
High Risk ◽  
Myeloid Leukemia ◽  
Cox Regression ◽  
Risk Group ◽  
White Blood Cells ◽  
Gene Signature ◽  
Low Risk ◽  
Acute Myeloid

Abstract The identification of effective signatures is crucial to predict the prognosis of acute myeloid leukemia (AML). The investigation aimed to identify a new signature for AML prognostic prediction by using the three-gene expression (octamer-binding transcription factor 4 (OCT4), POU domain type 5 transcription factor 1B (POU5F1B) and B-cell-specific Moloney murine leukemia virus integration site-1 pseudogene 1 (BMI1P1). The expressions of genes were obtained from our previous study. Only the specimens in which three genes were all expressed were included in this research. A three-gene signature was constructed by the multivariate Cox regression analyses to divide patients into high-risk and low-risk groups. Receiver operating characteristic (ROC) analysis of the three-gene signature (area under ROC curve (AUC) = 0.901, 95% CI: 0.821–0.981, P&lt;0.001) indicated that it was a more valuable signature for distinguishing between patients and controls than any of the three genes. Moreover, white blood cells (WBCs, P=0.004), platelets (PLTs, P=0.017), percentage of blasts in bone marrow (BM) (P=0.011) and complete remission (CR, P=0.027) had significant differences between two groups. Furthermore, high-risk group had shorter leukemia-free survival (LFS) and overall survival (OS) than low-risk group (P=0.026; P=0.006), and the three-gene signature was a prognostic factor. Our three-gene signature for prognosis prediction in AML may serve as a prognostic biomarker.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

scholarly journals Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

scholarly journals An autophagy-related long non-coding RNA signature for patients with colorectal cancer

2021 ◽  
Author(s):  
Dongyan Zhao ◽  
Xizhen Sun ◽  
Sidan Long ◽  
Shukun Yao
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

Reduced TET2 expression in Patients with Myelodysplastic Syndromes and Acute Myeloid Leukemia.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2822-2822
Author(s):  
Renata Scopim-Ribeiro ◽  
Joao Machado-Neto ◽  
Paula de Melo Campos ◽  
Patricia Favaro ◽  
Adriana S. S. Duarte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
High Risk ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Erythroid Differentiation ◽  
Low Risk ◽  
High Risk Disease ◽  
Cd34 Cells ◽  
Acute Myeloid

Abstract Abstract 2822 Introduction: Acquired mutations in TET2 and DNMT3A have been found in myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML), and may predict a worse survival in these diseases. TET2 mutations are considered to be a loss-of-function mutation and results in decreased 5-hydroxymethylcitosine (5-hmc) levels. In normal CD34+ cells, TET2 silencing skews progenitor differentiation towards the granulomonocytic lineage at the expense of lymphoid and erythroid lineages. Dnmt3a participates in the epigenetic silencing of hematopoietic stem cell regulatory genes, enabling efficient differentiation. Here, we attempted to evaluate the expression of TET2 and DNMT3A in total bone marrow cells from normal donors, patients with MDS and AML, and in CD34+ cells from MDS and normal controls during erythroid differentiation. Materials and Methods: The study included normal donors (n = 21), patients with MDS (n = 43) and AML (n = 42) at diagnosis. All normal donors and patients provided informed written consent and the study was approved by the ethics committee of the Institution. MDS patients were stratified into low and high-risk according to WHO classification (RCUD/RCMD/RARS=31 and RAEB1/RAEB2=12). TET2 and DNMT3A mRNA expression was assessed by quantitative PCR. CD34+ cells from normal donors (n = 9) and low-risk MDS patients (n = 7) were submitted to erythroid differentiation. Cells were collected and submitted to immunophenotyping for GPA and CD71 (days 6 and 12) and q-PCR for TET2 and DNMT3A expression (days 6, 8 and 12). Results of gene expression in normal donors and patients are presented as median, minimum-maximum, and were compared using Mann-Whitney test. Student t test was used for comparison of gene expression during CD34+ erythroid diferentiation. Overall survival was defined from the time of sampling to the date of death or last seen. Univariate analysis for overall survival was conducted with the Cox proportional hazards model. Results: TET2 expression was significantly reduced in both AML (0.62; 0.01–32.69) and MDS (1.46; 0.17–21.30) compared to normal donors (2.72; 0.43–31.49); P<0.0001 and P=0.01, respectively. TET2 expression was also significantly reduced in AML compared to MDS (P=0.0007). MDS patients were stratified into low and high-risk disease, and we still observed a significant reduction in TET2 expression in high-risk (0.73, 0.17–7.25) when compared to low-risk (1.58; 0.48–21.30; P=0.02) patients, but no difference was noted between normal donors vs. low-risk MDS, and high-risk MDS vs. AML. In MDS cohort, the median overall survival was 14 months (range 1–83), increased TET2 expression was associated with a longer survival (HR, 0.44; 95% CI, 0.21–0.91, P=0.03), and, as expected, WHO high-risk disease was associated with a shorter survival (HR, 10.16; 95% CI, 3.06–33.72, P<0.001), even though the confidence interval (CI) was large. TET2 expression did not impact survival in our cohort of AML patients. The erythroid differentiation was effective in cells from normal donors and MDS patients, as demonstrated by the flow cytometry analyses of GPA and CD71. TET2 expression was significantly increased on day 12 of erythroid differentiation, P<0.05. On the other hand, DNMT3A expression was similar between normal donors (0.74; 0.22–1.53), MDS (0.78; 0.26–3.46) and AML (0.95, 0.15–6.46), and during erythroid differentiation, with no impact on survival. Conclusion: These data suggest that decreased TET2 expression may participate in leukemogenesis, and supports the participation of TET2 in the erythroid differentiation of MDS. DNMT3A was not differentially expressed in AML and MDS, indicating that the presence of mutations in this gene may be the predominant mechanism of changes in protein function. We thus suggest that decreased TET2 expression may explain the reduced levels of 5-hmc found in TET2 wild type patients, and may become a predictive marker for outcomes in MDS and other myeloid diseases. Further studies would be necessary to better elucidate the clinical relevance and biologic significance of our findings, and whether the decreased TET2 expression results in hypermethylation in these diseases. Disclosures: Maciejewski: NIH: Research Funding; Aplastic Anemia&MDS International Foundation: Research Funding.

The Combination of SKY92 and ISS Provides a Powerful Tool to Identify Both High Risk and Low Risk Multiple Myeloma Cases, Validation in Two Independent Cohorts

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2970-2970 ◽  
Author(s):  
Martin van Vliet ◽  
Joske Ubels ◽  
Leonie de Best ◽  
Erik van Beers ◽  
Pieter Sonneveld
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Research Funding ◽  
Prognostic Markers ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Standard Risk

Abstract Introduction Multiple Myeloma (MM) is a heterogeneous disease with a strong need for robust markers for prognosis. Frequently occurring chromosomal abnormalities, such as t(4;14), gain(1q), and del(17p) etc. have some prognostic power, but lack robustness across different cohorts. Alternatively, gene expression profiling (GEP) studies have developed specific high risk signatures such as the SKY92 (EMC92, Kuiper et al. Leukemia 2012), which has shown to be a robust prognostic factor across five different clinical datasets. Moreover, studies comparing prognostic markers have indicated that the SKY92 signature outperforms all other markers for identifying high risk patients, both in single and multivariate analyses. Similarly, when assessing the prognostic value of combinations of various prognostic markers, the SKY92 combined with ISS was the top performer, and also enables detection of a low risk group (Kuiper et al. ASH 2014). Here, we present a further validation of the low and high risk groups identified by the SKY92 signature in combination with ISS on two additional cohorts of patients with diverse treatment backgrounds, containing newly diagnosed, previously treated, and relapsed/refractory MM patients. Materials and Methods The SKY92 signature was applied to two independent datasets. Firstly, the dataset from the Total Therapy 6 (TT6) trial, which is a phase 2 trial for symptomatic MM patients who have received 1 or more prior lines of treatment. The TT6 treatment regime consists of VTD-PACE induction, double transplant with Melphalan + VRD-PACE, followed by alternating VRD/VMD maintenance. Affymetrix HG-U133 Plus 2.0 chips were performed at baseline for n=55 patients, and OS was made available previously (Gene Expression Omnibus identifier: GSE57317). However, ISS was not available for this dataset. Secondly, a dataset of patients enrolled at two hospitals in the Czech Republic, and one in Slovakia (Kryukov et al. Leuk&Lymph 2013). Patients of all ages, and from first line up to seventh line of treatment were included (treatments incl Bort, Len, Dex). For n=73 patients Affymetrix Human Gene ST 1.0 array, OS (n=66), and ISS (n=58) was made available previously (ArrayExpress accession number: E-MTAB-1038). Both datasets were processed from .CEL files by MAS5 (TT6), and RMA (Czech), followed by mean variance normalization per probeset across the patients. The SKY92 was applied as previously described (Kuiper et al. Leukemia 2012), and identifies a High Risk and Standard Risk group. In conjunction with ISS, the SKY92 Standard Risk group is then further stratified into low and intermediate risk groups (Kuiper et al. ASH 2014). Kaplan-Meier plots were created, and the Cox proportional hazards model was used to calculate Hazard Ratios (HR), and associated 1-sided p-values that assess whether the SKY92 High Risk group has worse survival than SKY92 Standard Risk group (i.e. HR>1). Results Figure 1 shows the Kaplan Meier plots of the SKY92 High Risk and Standard Risk groups on the TT6 and Czech cohorts. On the TT6 dataset, the SKY92 signature identifies 11 out of 55 patients (20%) as High Risk. In both datasets, the SKY92 High Risk group has significantly worse overall survival, HR=10.3, p=7.4 * 10-6 (TT6), and HR=2.6, p=2.2 * 10-2 (Czech). In addition, the combination of SKY92 with ISS on the Czech dataset identifies a low risk group of 14 out of 61 patients (23%), with a five year overall survival estimate of 100% versus 28.7% in the SKY92 High Risk group (HR=inf). Robustness of the SKY92 signature is further demonstrated by the fact that it validates on both datasets, despite different microarray platforms being used. Conclusions The SKY92 high risk signature has been successfully validated on two independent datasets generated using different microarray platforms. In addition, on the Czech data, the low risk group (SKY92 Standard Risk combined with ISS 1) has been successfully validated. Together, this signifies the robust nature of the SKY92 signature for high and low risk prediction, across treatments, and with applicability in newly diagnosed, treated, and relapsed/refractory MM patients. Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Figure 1. Kaplan-Meier plots showing a significantly poorer overall survival in patients identified as SKY92 High Risk (red curves), relative to SKY92 Standard Risk, on both the TT6 (left), and Czech (middle) datasets, as well as a low risk group by SKY92 & ISS1 on the Czech dataset (green curve, right). Disclosures van Vliet: SkylineDx: Employment. Ubels:SkylineDx: Employment. de Best:SkylineDx: Employment. van Beers:SkylineDx: Employment. Sonneveld:Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Karyopharm: Research Funding; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Research Funding.

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