scholarly journals Identification of prognostic signature based on the copy number variation (CNV) and expression in acute myeloid leukemia

2021 ◽  
Author(s):  
Changchun Niu ◽  
Di Wu ◽  
Alexander J. Li ◽  
Kevin H. Qin ◽  
Daniel A. Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Survival Analysis ◽  
Copy Number Variation ◽  
Copy Number ◽  
Myeloid Leukemia ◽  
Risk Groups ◽  
Number Variation ◽  
Acute Myeloid ◽  
Cancer Panel

Abstract Background Acute myeloid leukemia (AML) is caused by multiple genetic alterations in the hematopoietic progenitors, and molecular genetic analysis has provided useful information for AML diagnosis and prognosis. However, an integrative understanding of the prognostic value of specific copy number variation (CNV) and CNV-modulated gene expression has been limited. Methods We conducted an integrative analysis of CNV profiling and gene expression using data from the TARGET and TCGA AML cohorts. The CNV data from TCGA were analyzed using the GISTIC and all CNV data by genes on every patient were obtained. CNV survival analysis and mRNA survival analysis were conducted with the Multivariate Cox proportional hazards regression model using R software with “survminer” and “survival” packages. KEGG cancer panel genes were extracted from the cancer-related pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG). The R package “circlize” was used for mapping the CNV genes to chromosomes. Results From this investigation, we observed distinct CNV patterns in the AML risk groups as well as the expression of 251 genes significantly modulated by CNV in both cohorts. There were 102 CNV genes (located at 7q31-34, 16q24) associated with clinical outcomes in AML, which were identified in the TARGET cohort and validated in the TCGA cohort, three of which being miRNA genes (MIR29A, MIR183, MIR335) that overlapped with a KEGG cancer panel. Five genes were identified whose expression was modulated by CNV and significantly associated with clinical outcomes, and among them, the deletion of SEMA4D and CBFB were found to potentially have protective effects against AML. The result was also validated with patient marrow samples. Moreover, the distribution of CNV in these five CNV-modulated genes was independent of the risk groups, which suggests that they are independent prognosis factors. Conclusion Overall, this study identified 102 CNV genes and five CNV-modulated gene expression that is crucial for developing new modes of prognosis evaluation and target therapy for AML.

scholarly journals Identification of Prognostic Signature Based on the Copy Number Variation (CNV) and Expression in Acute Myeloid Leukemia

2020 ◽  
Author(s):  
Changchun Niu ◽  
Di Wu ◽  
Alexander J. Li ◽  
Kevin H. Qin ◽  
Daniel A. Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Survival Analysis ◽  
Copy Number Variation ◽  
Copy Number ◽  
Myeloid Leukemia ◽  
Risk Groups ◽  
Number Variation ◽  
Acute Myeloid ◽  
Cancer Panel

Abstract Purpose Acute myeloid leukemia (AML) is caused by multiple genetic alterations in the hematopoietic progenitors, and molecular genetic analysis has provided useful information for AML diagnosis and prognosis. However, an integrative understanding about the prognosis value of specific copy number variation (CNV) and CNV-modulated gene expression has been limited. Methods We conducted an integrative analysis of CNV profiling and gene expression using data from the TARGET and TCGA AML cohorts. The CNV data from TCGA were analyzed using the GISTIC. CNV survival analysis and mRNA survival analysis were conducted with the Multivariate Cox proportional hazards regression model using R software with “survminer” and “survival” packages. KEGG cancer panel genes were extracted from the cancer related pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG). The R package “circlize” was used for mapping the CNV genes to chromosomes. Results From this investigation, we observed distinct CNV patterns in the AML risk groups as well as the expression of 251 genes significantly modulated by CNV in both cohorts. There were 102 CNV genes (located at 7q31-34, 16q24) associated with clinical outcomes in AML, which were identified in the TARGET cohort and validated in the TCGA cohort, three of which being miRNA genes (MIR29A, MIR183, MIR335) that overlapped with a KEGG cancer panel. Five genes were identified whose expressions were modulated by CNV and significantly associated with clinical outcomes, and among them, the deletion of SEMA4D and CBFB were found to potentially have protective effects against AML. Moreover, the distribution of CNV in these five CNV-modulated genes was independent of the risk groups, which suggests that they are independent prognosis factors. Conclusion Overall, this study identified 102 CNV genes and five CNV-modulated gene expressions that are crucial for developing new modes of prognosis evaluation and target therapy for AML.

Identification ofRUNX1gene breakage and copy number variation in acute myeloid leukemia

2015 ◽  
Vol 38 (2) ◽  
pp. e23-e26
Author(s):  
L. Li ◽  
X.L. Qi ◽  
X.H. Chen ◽  
F.G. Ren ◽  
Z.F. Xu ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Copy Number Variation ◽  
Copy Number ◽  
Myeloid Leukemia ◽  
Number Variation ◽  
Acute Myeloid

scholarly journals Prospective Identification of Acute Myeloid Leukemia Patients Who Benefit from Gene-Expression Based Risk Stratification

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1397-1397
Author(s):  
Diego Chacon ◽  
Ali Braytee ◽  
Yizhou Huang ◽  
Julie Thoms ◽  
Shruthi Subramanian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Risk Stratification ◽  
Genetic Risk ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Risk Groups ◽  
High Fidelity ◽  
Improve Outcome ◽  
Acute Myeloid

Background: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy and risk stratification based on genetic and clinical variables is standard practice. However, current models incorporating these factors accurately predict clinical outcomes for only 64-80% of patients and fail to provide clear treatment guidelines for patients with intermediate genetic risk. A plethora of prognostic gene expression signatures (PGES) have been proposed to improve outcome predictions but none of these have entered routine clinical practice and their role remains uncertain. Methods: To clarify clinical utility, we performed a systematic evaluation of eight highly-cited PGES i.e. Marcucci-7, Ng-17, Li-24, Herold-29, Eppert-LSCR-48, Metzeler-86, Eppert-HSCR-105, and Bullinger-133. We investigated their constituent genes, methodological frameworks and prognostic performance in four cohorts of non-FAB M3 AML patients (n= 1175). All patients received intensive anthracycline and cytarabine based chemotherapy and were part of studies conducted in the United States of America (TCGA), the Netherlands (HOVON) and Germany (AMLCG). Results: There was a minimal overlap of individual genes and component pathways between different PGES and their performance was inconsistent when applied across different patient cohorts. Concerningly, different PGES often assigned the same patient into opposing adverse- or favorable- risk groups (Figure 1A: Rand index analysis; RI=1 if all patients were assigned to equal risk groups and RI =0 if all patients were assigned to different risk groups). Differences in the underlying methodological framework of different PGES and the molecular heterogeneity between AMLs contributed to these low-fidelity risk assignments. However, all PGES consistently assigned a significant subset of patients into the same adverse- or favorable-risk groups (40%-70%; Figure 1B: Principal component analysis of the gene components from the eight tested PGES). These patients shared intrinsic and measurable transcriptome characteristics (Figure 1C: Hierarchical cluster analysis of the differentially expressed genes) and could be prospectively identified using a high-fidelity prediction algorithm (FPA). In the training set (i.e. from the HOVON), the FPA achieved an accuracy of ~80% (10-fold cross-validation) and an AUC of 0.79 (receiver-operating characteristics). High-fidelity patients were dichotomized into adverse- or favorable- risk groups with significant differences in overall survival (OS) by all eight PGES (Figure 1D) and low-fidelity patients by two of the eight PGES (Figure 1E). In the three independent test sets (i.e. form the TCGA and AMLCG), patients with predicted high-fidelity were consistently dichotomized into the same adverse- or favorable- risk groups with significant differences in OS by all eight PGES. However, in-line with our previous analysis, patients with predicted low-fidelity were dichotomized into opposing adverse- or favorable- risk groups by the eight tested PGES. Conclusion: With appropriate patient selection, existing PGES improve outcome predictions and could guide treatment recommendations for patients without accurate genetic risk predictions (~18-25%) and for those with intermediate genetic risk (~32-35%). Figure 1 Disclosures Hiddemann: Celgene: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Research Funding; Bayer: Research Funding; Vector Therapeutics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Metzeler:Celgene: Honoraria, Research Funding; Otsuka: Honoraria; Daiichi Sankyo: Honoraria. Pimanda:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Beck:Gilead: Research Funding.

scholarly journals The Solute Carrier Family 2 Genes Are Potential Prognostic Biomarkers in Acute Myeloid Leukemia

2020 ◽  
Vol 19 ◽  
pp. 153303381989430 ◽  
Author(s):  
Binbin Lai ◽  
Yanli Lai ◽  
Yanli Zhang ◽  
Miao Zhou ◽  
Lixia Sheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Survival Analysis ◽  
Myeloid Leukemia ◽  
Solute Carrier Family ◽  
Multivariate Survival ◽  
Kaplan Meier ◽  
Family Gene ◽  
Solute Carrier ◽  
Acute Myeloid

Aims: The solute carrier family 2 (SLC2) genes are comprised of 14 members which are essential for the maintenance of glucose uptake and survival of tumour cells. This study was performed to investigate the associations of SLC2 family gene expression with mortality in acute myeloid leukemia (AML). Methods: Clinical features and SLC2 family gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus database. The associations between SLC2 family gene expression and clinicopathologic features were analyzed using linear regression model. Kaplan-Meier survival, univariate, multivariate survival analyses and validation analysis were performed to analyze the associations between SLC2 family gene expression and patients’ overall survival. Results: Patient mortality was positively associated with age and cytogenetic risk in AML patients. Kaplan-Meier survival analysis suggested that patients with high SLC2A5 and SLC2A10 expression showed poorer survival than those with low SLC2A5 and SLC2A10 expression. In contrast, patients with high SLC2A13 expression exhibited better prognosis than those with low SLC2A13 expression ( P < 0.05 for all cases, log rank test). Multivariate survival analysis and validation analysis confirmed that high expression of SLC2A5 and SLC2A10 and low expression of SLC2A13 were associated with increased mortality ( P = 0.00, Odd ratio [OR]:4.05, 95% Confidence Interval [CI]: 1.73-10.22; P = 0.00, OR: 3.66, 95% CI: 1.54-9.25; and P = 0.01, OR: 0.26, 95% CI: 0.09-0.68, respectively). Conclusion: SLC family gene expression, such as SLC2A5, SLC2A10 and SLC2A13, was significantly associated with prognosis of AML patients, their expression levels might become useful prognostic biomarkers in AML.

scholarly journals Integrated Stem Cell Signature and Cytomolecular Risk Determination in Pediatric Acute Myeloid Leukemia

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 28-29
Author(s):  
Benjamin J. Huang ◽  
Jenny L. Smith ◽  
Rhonda E. Ries ◽  
Amanda R. Leonti ◽  
Erin Lynn Crowgey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Young Adults ◽  
Stem Cell ◽  
Myeloid Leukemia ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Adolescents And Young Adults ◽  
Pediatric Aml ◽  
Acute Myeloid

Acute myeloid leukemia (AML) remains a therapeutic challenge with high mortality rates despite intensive and myeloablative therapies. Structural and sequence alterations have been linked to outcomes in pediatric AML and have been used for risk-based therapy allocation with modest success. Given the vast heterogeneity of AML, conventional cytogenetic and mutational (cytomolecular) biomarkers have not yielded a robust prognostic model: nearly one-third of pediatric patients deemed "low risk" relapse and, inversely, approximately one-third of those in "high risk" categories have favorable outcomes. AML studies in adults previously identified a leukemia stem cell score (LSC17) that was highly prognostic across five independent cohorts comprised of adult patients with diverse AML subtypes (n = 908). We reasoned that incorporating a similar scoring system in pediatric AML would lead to improved prognostic risk models. To assess for the effects of LSC17 on pediatric AML, we leveraged transcriptome sequencing data from bone marrow aspirates and peripheral blood collected from 1,503 children, adolescents, and young adults with AML at the time of diagnosis. Patients were enrolled on one of three upfront phase III Children's Oncology Group trials spanning the past three decades: CCG-2961, AAML0531, and AAML1031. In aggregate, patients with a high LSC17 score had an event free survival (EFS) of 36.9 ± 3.5% at 5 years from diagnosis compared to 55.3 ± 3.7% for those with low LSC17 scores (p &lt; 0.0001). (Figure 1A) LSC17 scores were also associated with adverse overall survival (OS): 51.9 ± 3.9% versus 73.8 ± 3.5% (p &lt; 0.0001) (data not shown). Intriguingly, we found that LSC17 scores significantly cluster within fusion groups and that median LSC17 scores closely correlate with survival based on fusion status (Figure 1B). Thus, when the impact of LSC17 scores was evaluated in the context of established cytomolecular risk groups, LSC17 scores were no longer predictive of outcome (Figure 1C). We then asked whether LSC gene expression data could be utilized to generate a more robust risk classification schema in the context of disease defining structural variants. Importantly, AMLs diagnosed in children, adolescents, and young adults are associated with frequent driver gene fusion alterations that also play an important role in risk stratification and transcriptional landscape (Figure 1D). We went on to confirm that AML fusion groups occupy distinct transcriptional stages of hematopoietic stem cell and myeloid progenitor maturation based on gene set enrichment analysis (GSEA) using normal hematopoiesis transcriptome experiments as their reference (data not shown). To develop more predictive biomarkers related to stemness, we used the 54 original LSC genes identified by Ng S, et al. and performed linear regression based on a least absolute shrinkage and selection operator (LASSO) algorithm to fit a Cox regression model for patients within each fusion group. The study population was divided into discovery (n = 752) and validation (n = 752) cohorts using stratified randomization based on fusion status (RUNX1-RUNX1T1, CBFB-MYH11, KMT2A, NUP98, CBFA2T3-GLIS2, and Other/None). In the discovery cohort, we identified distinct LSC signatures that best distinguished outcome cohorts in patients with conventional high/standard risk disease (KMT2A, NUP98, and Other/None fusions) (Figure 1E). For patients deemed favorable risk (RUNX1-RUNX1T1 and CBFB-MYH11 or core binding factor/CBF), LSC signatures were not reliably predictive based on "leave one out" cross validation. Therefore, we performed multivariable analysis incorporating clinical, mutational, and transcriptional signatures to determine the factors that best discriminated outcomes with CBF AML, and found GLIS2-like transcriptional signatures were most predictive. These cytomolecular and LSC (CM-LSC) biomarkers were then combined to build a robust risk determination model that was then validated in an independent cohort (Figure 1F). This study demonstrates that a 54 LSC gene expression panel can enhance the predictive power of conventional cytomolecular markers and can more effectively partition patients into risk groups. Figure 1 Disclosures Cooper: Celgene: Other: Spouse was an employee of Celgene (through August 2019).

Genomic Architecture and Treatment Response In Pediatric Acute Myeloid Leukemia: A Report From The Children’s Oncology Group

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 610-610
Author(s):  
Marijana Vujkovic ◽  
Edward F. Attiyeh ◽  
Rhonda E. Ries ◽  
Michelle Horn ◽  
Elizabeth K. Goodman ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Copy Number ◽  
Myeloid Leukemia ◽  
Risk Groups ◽  
Oncology Group ◽  
Risk Patients ◽  
Pediatric Aml ◽  
Standard Risk ◽  
Tumor Remission ◽  
Acute Myeloid

Abstract Background Childhood acute myeloid leukemia (AML) is characterized by chromosomal instability and requires intensive therapy for cure. Here we present a large-scale study from 3 Children's Oncology Group (COG) trials to define the genomic architectural profiles of pediatric AML and to describe whole-arm gains and losses and focal rearrangements using SNP microarrays, accounting for mutation status, non-neoplastic cell infiltration and aneuploidy. We also investigate the association between somatic copy number aberrations (CNAs) and event free survival (EFS). Patients and Methods A total of 505 matched tumor-remission samples from 459 children with de novo AML were obtained from the COG studies AAML-0531, AAML-03P1, and CCG-2961. 254 paired tumor-remission samples were genotyped on the Affymetrix SNP 6.0 chip at the University of Washington, Seattle, WA, and 251 on the Illumina 2.5M OmniQuad at the Children's Hospital of Philadelphia, PA. All genotyping output was converted to log R ratio and B-allele frequency values and annotations updated to hg19, GRCh37. Illumina intensities were additionally corrected for patterns of genomic wave. A matched allele-specific copy number analysis of tumors (ASCAT) was performed using ASCAT 2.2. A total of 246 (98%) Illumina and 247 (97%) Affymetrix samples passed quality control criteria. ASCAT profiles of patients genotyped on both platforms were manually reviewed and those with highest false CNA calls excluded. Samples with a low signal-to-noise ratio were either eliminated or visually annotated. All CNA segments were manually inspected and false positives removed. The resulting 452 ASCAT profiles were stratified into the risk categories 1) favorable, e.g. inv(16), t(16;16), t(8;21), NPM1, and CEBPα, 2) standard, e.g. normal karyotype, +8, +21, +22, del(7q), del(9q), abnormal 11q23, or other structural changes, and 3) poor, e.g. -5, -7, del(5q), or FLT3/ITD+. GISTIC 2.0 was used to identify genomic areas with significant recurrent aberrations, e.g. amplifications, deletions, and copy-neutral loss-of-heterozygosity (CN-LOH) events. Finally, we investigated whether profiles of genomic instability in the AML genome are predictive of 3 year EFS by using the total number of CNAs as a measure for allelic imbalance. Results The inter-platform concordance for samples genotyped on the two platforms was very high. On average leukemic samples acquire 1.14 somatic CNAs, with a mean of 1.1, 1.3, and 0.8 in the favorable, standard, and poor risk groups respectively (Fig 1). CN-LOH events are observed in 14% (n = 64) of the patients, with 28% involving chromosome 13, and others involving the arms of 11p (23%), 1p (11%), 9p (8%), 7q (6%), 19q (6%), and 3q (5%). Known mutations in AML were enriched in recurrent focal CNA regions, as shown by amplifications on 17q24 (*TK1), 1q32, 3q28, 11q23 (*MLL), 6q27 (*DLL1), 2q32.1, and 4q35.2 (Fig 2). Focal CN-LOH regions were confined to 11p15.5 (*NUP98, *PICALM, *WT1), 1p36.3 (*RUNX3, *NRAS), 9p24.3 (*MLLT3), 3q25.3, 6p23 and 7q35 (*MLL3). Deletions include 7q36.1 (*MLL3, *EZH2), 16p13.11 (*MYH11), 9q21.32, 11p13 (*WT1), 2q37.1 (*IDH1, *DNMT3A), 10p12.31 (*MLLT10), 11q23.3 (*MLL), 16q22.1 (*CBFB), and 1p36.3 (*RUNX3). The association between CNA status and 3 year EFS approached statistical significance for all patients (Table 1), and EFS was significantly lower in standard risk patients with CNAs (51% no CNA vs 34% with CNAs, P 0.03). CNA status did not alter the event risk in the other risk groups. Conclusions The number of CNAs occurring in this cohort is lower than previously reported. However, the presence of somatic CNAs in standard risk patients is significantly associated with a worse treatment outcome that is similar to the poor risk group. This study confirms the established regions of CNA enrichment in pediatric AML and identifies novel regions that may involve driving mediators of tumor fitness and/or acquired resistance to targeted therapies. Disclosures: No relevant conflicts of interest to declare.

Aggressive Aneuploid Acute Myeloid Leukemia Is Dependent on Alterations of P53, Gain of APC and PLK1 and Loss of RAD50

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1702-1702
Author(s):  
Giorgia Simonetti ◽  
Antonella Padella ◽  
Marco Manfrini ◽  
Italo Faria do Valle ◽  
Cristina Papayannidis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Dna Repair ◽  
Acute Myeloid Leukemia ◽  
Copy Number ◽  
Myeloid Leukemia ◽  
Molecular Mechanisms ◽  
Copy Number Loss ◽  
Mutation Load ◽  
Acute Myeloid

Abstract Chromosome number alterations, aneuploidy, is a hallmark of cancer. It occurs in about 15% of acute myeloid leukemia (AML) cases, is generally preserved throughout disease progression (Bochtler et al. Leukemia 2015) and correlates with adverse prognosis (Breems et al. JCO 2008, Papaemmanuil et al. NEJM 2016). This evidence highlights the need of understanding the molecular mechanisms that promote and sustain aneuploidy in AML, in order to define novel potential therapeutic targets. In the NGS-PTL project we profiled the genomic landscape of 536 hematological samples by whole exome sequencing (WES, Illumina). Among them, we analyzed 88 and 68 samples from aneuploid (A-) FLT3-wildtype AML (isolated trisomy and monosomy, complex and monosomal karyotype) and euploid (E-) AML (normal and complex karyotype, <3 abnormalities), respectively (100 bp, paired-end). Variants were called by MuTect and Varscan 2.0. WES output was integrated with genotype data (CytoScan HD Array, Affymetrix and Nexus Copy Number analysis) and gene expression profiling (HTA 2.0 and TAC 3.0, Affymetrix). A-AML showed an increased genomic instability, as confirmed by a higher mutation load compared with E-AML (median number of variants: 22 (range: 2-95) and 11 (range: 3-45), respectively, p<.001), which was associated with increased patients' age (median age of 62 for A-AML and 55 for E-AML, p<.05). The increased age and mutation load correlated with a mutational signature with prominence of C>A substitutions, compared with the C>T transition-related signature, which is prevalent in AML. A-AML was associated with mutations and/or heterozygous deletion of TP53 (p<.001), which co-occurred with copy number loss of both the tumor suppressor APC and the DNA repair gene RAD50 in 93% of cases (p<.001). Moreover, A-AML was enriched for a gene expression signature of p53-deficiency, independently of TP53 structural defects (p<.05, GSEA). Mutations and deregulated expression of genes involved in cell cycle contributed to the A-AML phenotype, with 68% A-AML vs. 32% E-AML patients (p<.01) carrying at least one genomic lesion affecting the process. The alterations targeted the following pathways: DNA repair (i.e. reduced RAD50 expression, p<.001), cell cycle checkpoints (i.e. mutated CHK2), regulation of PLK1 activity at G2/M transition (i.e. mutation and 2-fold upregulation of PLK1, p<.01), mitotic metaphase and anaphase (i.e. increased CDC20 level, p<.001) and separation of sister chromatids (i.e. mutated BUB1B, ESPL1, CENPO). Of note, a 3-gene signature composed of PLK1, CDC20 and RAD50, was able to discriminate 73% of patients between the A- and E-AML cohorts. This signature was confirmed at protein level. In parallel, E-AML showed a preferential dependency on epigenetic mechanisms, with recurrent genomic lesions of ASXL1/2, BCOR/L1, EZH2 and MLL, enrichment of FLT3 alterations and mutations activating RAS signal transduction (p<.05). Of note, a HOX-related signature characterized by overexpression of the HOX family members HOXA7, HOXB3 and MEIS1 identified E-AML. We show here for the first time the molecular mechanisms promoting and maintaining aneuploidy in AML. Our results indicate that p53 deficiency, either caused by somatic mutations, copy number loss, impaired DNA damage response and enhanced PLK1 signaling synergize with APCgain, RAD50 structural or functional loss and forced progression through mitosis, to override cell cycle and mitotic checkpoints and allow the formation of daughter cells with an aberrant chromosome number. These mechanisms cooperate with recurrent mutations of genes involved in protein ubiquitination and proteasome-mediated protein catabolic process in A-AML, indicative of the attempt of aneuploid cells to override the proteotoxic stress due to the unbalanced protein load generated by the aneuploid condition. This evidence provides the rationale for exploiting proteasome inhibition (Velcade), p53 reactivation (MDM2/4 inhibitor) and targeting of the cell cycle (CHK1/2 inhibitor) downstream to p53 (WEE1 inhibitor) as strategies for novel combination therapies against aggressive aneuploid AML, which are under clinical investigation in our Institution and may serve as a model for aneuploid tumors. GS and AP: equal contribution. Supported by: FP7 NGS-PTL project, ELN, AIL, AIRC, PRIN, progetto Regione-Università 2010-12 (L. Bolondi). Disclosures Haferlach: MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Martinelli:Ariad: Consultancy, Speakers Bureau; MSD: Consultancy; Celgene: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau; Genentech: Consultancy; Novartis: Speakers Bureau; BMS: Speakers Bureau; Pfizer: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau.

Risk stratification of intermediate-risk acute myeloid leukemia: integrative analysis of a multitude of gene mutation and gene expression markers

Blood ◽  
2011 ◽  
Vol 118 (4) ◽  
pp. 1069-1076 ◽  
Author(s):  
Veronika Rockova ◽  
Saman Abbas ◽  
Bas J. Wouters ◽  
Claudia A. J. Erpelinck ◽  
H. Berna Beverloo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Survival Analysis ◽  
Myeloid Leukemia ◽  
Intermediate Risk ◽  
Free Survival ◽  
Intermediate Group ◽  
Prognostic Importance ◽  
Survival Tree ◽  
Acute Myeloid

Abstract Numerous molecular markers have been recently discovered as potential prognostic factors in acute myeloid leukemia (AML). It has become of critical importance to thoroughly evaluate their interrelationships and relative prognostic importance. Gene expression profiling was conducted in a well-characterized cohort of 439 AML patients (age < 60 years) to determine expression levels of EVI1, WT1, BCL2, ABCB1, BAALC, FLT3, CD34, INDO, ERG and MN1. A variety of AML-specific mutations were evaluated, that is, FLT3, NPM1, N-RAS, K-RAS, IDH1, IDH2, and CEBPADM/SM (double/single). Univariable survival analysis shows that (1) patients with FLT3ITD mutations have inferior overall survival (OS) and event-free survival (EFS), whereas CEBPADM and NPM1 mutations indicate favorable OS and EFS in intermediate-risk AML, and (2) high transcript levels of BAALC, CD34, MN1, EVl1, and ERG predict inferior OS and EFS. In multivariable survival analysis, CD34, ERG, and CEBPADM remain significant. Using survival tree and regression methodologies, we show that CEBPADM, CD34, and IDH2 mutations are capable of separating the intermediate group into 2 AML subgroups with highly distinctive survival characteristics (OS at 60 months: 51.9% vs 14.9%). The integrated statistical approach demonstrates that from the multitude of biomarkers a greatly condensed subset can be selected for improved stratification of intermediate-risk AML.

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