Gene Expression Profiling and FLT3 Status Correlate with Outcome in De Novo Acute Myeloid Leukemia (AML) with Normal Karyotype: Results of Children’s Oncology Group (COG) Study POG #9421.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2372-2372
Author(s):  
Norman Lacayo ◽  
Soheil Meshinchi ◽  
Susana Raimondi ◽  
Chitra Saraiya ◽  
Maureen O’Brien ◽  
...  

Abstract The event-free survival (EFS) estimate for patients with normal karyotype (NK) on COG study POG #9421 (n=144) was 36%. We previously reported a subgroup of patients (n=68) with AML and NK that could be divided into 2 groups whose clinical outcomes correlated with abnormalities of FLT3 [internal tandem duplications (ITD) or activating loop mutations]. EFS estimates were 13% for patients with mutant FLT3 and 61% for children with wild-type FLT3 (P=0.01). We hypothesized that gene expression profiling would identify signatures that are linked to clinical outcome and can be used for risk determination. Cytogenetic testing was carried out in clinical laboratories at the institutions in which AML was diagnosed and then centrally reviewed. We analyzed bone marrow from 45 patients with NK on 43,760-element spotted arrays containing 41,751 unique genes and expressed sequence tags; arrays were obtained from the Stanford University Microarray Core Facility. FLT3 status (mutant or wild type) was determined by RT-PCR analysis of RNA from these 45 samples (exon 11 for ITDs, exon 17 for point mutations): 18 expressed mutant FLT3, 27 expressed wild-type FLT3. Using prediction analysis for microarrays (PAM) to find the minimum number of genes that identified samples associated with and unassociated with events (relapse or death), we identified a 128 gene cluster that differentiated patients with NK on the basis of clinical outcome (classification error rates were 15% for samples associated with events and 12.5% for event-unassociated samples). Significance analysis of microarrays (SAM) identified, with a false-discovery rate of 1.25%, 82 genes in the cluster whose expression differed significantly between the event-associated samples and the event-unassociated samples. Hierarchical clustering based on these 82 genes yielded 2 signatures: one event-associated and one event-unassociated. FLT3 Status Event-Associated Signature Event-Unassociated Signature Wild-type EFS=44% (n=15) EFS=90% (n=12) Mutant EFS=7% (n=13) EFS=60% (n=5) The median WBC counts at the time of diagnosis were 68 x 109/L in the event-associated group and 61 x 109/L in the event-unassociated group (P=0.27). The gene list and d-scores from SAM analysis were analyzed using Ingenuity Pathway Analysis software (Ingenuity™ Systems, Mountain View, CA). Canonical pathways associated with poor outcome included apoptosis signaling (↑BCL2A1, ↓BAK1), ERK/MAP signaling (↑RAC2), cell cycle (↓ABL1), SAPK/JNK signaling (↑RAC2, ↓CDC42), integrin signaling (↑RAC2, ↓BCAR3, ↓ABL1, ↓CDC42), and IL6 signaling (↑IL6R). We conclude that risk assignment for patients with NK can be more precisely determined by combining FLT3 analysis and gene expression profiling. Such an approach identified 4 distinct groups with different outcomes. We will validate these findings by analyzing additional diagnostic samples with normal karyotype. Prospective validation of this strategy in clinical trials may be warranted.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1915-1915
Author(s):  
Norman J. Lacayo ◽  
Maureen O’Brien ◽  
Shweta Jain ◽  
Soheil Meshinchi ◽  
Ron Yu ◽  
...  

Abstract We previously reported a 36% event-free survival (EFS) estimate for patients with normal karyotype (NK) on the COG study POG #9421 (n=144). In addition, we hypothesized that gene expression profiling would identify signatures linked to clinical outcome and useful for retrospective risk determination. Bone marrows in a subset of patients with NK (n=58) were analyzed using a 43,760-element spotted arrays containing 41,751 unique genes and expressed sequence tags; arrays were obtained from the Stanford University Microarray Core Facility. Prediction analysis for microarrays (PAM) was used to find genes that identified samples associated-with and unassociated-with events (relapse or death); after analyzing 28,711 genes with PAM we chose a 727-gene cluster that differentiated patients with NK on the basis of clinical outcome (cumulative classification error rate 19%). The analysis was biased for a larger number of genes in order to obtain a more biologically informative gene pathways analysis. Significance analysis of microarrays (SAM) on the PAM output identified 633 genes (false-discovery rate of 0%) that differed significantly between the event-associated and event-unassociated samples. Spearman based hierarchical clustering on these genes yielded 2 clusters with statistically significant different event-free survivals: 65% (n=24) for the event-unassociated curve and 23% (n=34) for the event-associated curve with P=0.01. The patients in these clusters did not differ at diagnosis for WBC (70K vs. 100K/microL with P=0.19) and age (10.1 vs. 9.9 yrs with P=0.83) by unpaired t-test; or for sex (P=0.11) and FLT3-ITD status (P=0.76) by Fisher’s exact test. The gene list (GenBank #) and fold-change in gene expression from SAM output were analyzed using Ingenuity Pathway Analysis software (Ingenuity™ Systems, Mountain View, CA). Canonical pathways identified 33 networks associated with event-unassociated outcome using 302 eligible genes that included: underexpressed CDC73, RAD50, SPARC, PTPN12, MXD1, TNF, ABCA1, STAT4, CCNA1, TNF, BCL2A1, JUN, BCL6 and AREG; and overexpressed RUNX3, FKBP9, FKBP8, MAP2K2, CHES1, HOXA11, HRK, CDK6, MGA, MAPK3, ABL1, HDAC7A, SMARCC2, SYK, MXD4, CDC42. Several of these genes have been previously reported to be associated with improved outcome in AML. However, two of these genes (MXD4 and MXD1) previously not identified as related to outcome in AML, but identified in our analysis in two highly interacting networks related to the MYC gene, result in a difference in EFS of 51% vs. 24% (P=0.04), suggesting that a smaller number of genes may be predictive of outcome. Conclusion: Risk assignment for patients with NK may be feasible by analyzing a limited number of genes. We will validate these findings by correlating gene expression results with quantitative real-time PCR. Prospective validation of this strategy in clinical trials may be warranted.


Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2354-2354
Author(s):  
Eleanor L. Woodward ◽  
Amanda F. Gilkes ◽  
Val Walsh ◽  
Steve J. Austin ◽  
Sarah B. Daly ◽  
...  

Abstract In AML, the majority of patients <60 years of age will enter remission but at least 50% will subsequently relapse, therefore the monitoring of minimal residual disease (MRD) during treatment has become an important issue. Current molecular markers for MRD are mainly limited to the RT-PCR detection of the fusion genes resulting from recurrent translocations which paradoxically are mostly limited to favourable risk groups who are least likely to relapse leaving the majority of the patients, including those with a normal karyotype, without a molecular marker suitable for monitoring. Two hundred and twenty patients have been assessed by gene expression profiling using Affymetrix U133A chips and the data analysed with the aim of identifying novel MRD markers for patients who do not currently have a suitable marker. As an initial “proof of principle”, we have identified possible MRD markers for patients with either t(15;17), t(8;21) or inv(16) and correlated with changes in expression of these markers with clinical changes as measured by established molecular MRD markers (PML-RARα or WT1). Of the expression profile from 22,283 probe sets in 29 cases of t(15;17), 20 genes were identified which had at least a two fold over expression which was unique to the t(15;17) subgroup. Of these several of the probe sets were related to the same gene, but from the reduced gene list 2 (HGF and ILGF binding protein) were selected for quantitation by quantitative PCR. Similarly the expression profile identified 20 genes which were unique to the 15 cases of t(8;21), and 20 genes which were unique to the 19 cases of inv(16). These included ETO and MYH11 representing the respective 3′ end of the respective fusion transcript. Three other genes (PRAME, POU4F1, and IL5RA) were selected for the t(8;21) cases and ST18, CLIP-170 and MNI for the inv(16) cases. When the relative quantitative expression of each of these “unique” genes was correlated with the expression of the established markers of minimal residual disease (PML-RARα or WTI) there was good correlation. These data suggest that gene expression profiling can identify ‘unique’ genes which can be used to develop specific markers for minimal residual disease monitoring for a larger proportion of cases of AML than is currently available.


2006 ◽  
Vol 39 (1) ◽  
Author(s):  
ÁNGELA D ARMENDÁRIZ ◽  
FELIPE OLIVARES ◽  
RODRIGO PULGAR ◽  
ALEX LOGUINOV ◽  
VERÓNICA CAMBIAZO ◽  
...  

Oncogene ◽  
2004 ◽  
Vol 23 (58) ◽  
pp. 9381-9391 ◽  
Author(s):  
Norbert Vey ◽  
Marie-Joëlle Mozziconacci ◽  
Agnès Groulet-Martinec ◽  
Stéphane Debono ◽  
Pascal Finetti ◽  
...  

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 8502-8502
Author(s):  
T. John ◽  
M. A. Black ◽  
T. Toro ◽  
C. A. Gedye ◽  
I. D. Davis ◽  
...  

8502 Background: Melanoma patients with clinically involved regional lymph nodes (Stage IIIB&C) represent a prognostically heterogeneous population. Current prognostic factors cannot distinguish the 30% of patients who will achieve long term survival from those who will relapse early. We hypothesized that gene expression profiling could identify these different prognostic groups and provide a greater understanding of the genetic mechanisms involved. Methods: Lymph node sections from 29 patients with Stage IIIB & IIIC melanoma and divergent clinical outcome as defined by time to tumor progression (TTP), including 16 poor (TTP<6 months) and 13 good (TTP>28 months) prognosis patients, were subjected to molecular profiling using spotted oligonucleotide arrays containing 30,888 probes as an initial test set. The differentially expressed genes were determined using a Wilcoxon-Mann-Whitney t-test with the false discovery rate controlling method of Benjamini-Hochberg and validated using quantitative real-time RT-PCR. Using logistic regression, a predictive score algorithm was developed based on the 15 genes for which the correlation between the two platforms was the strongest. The score was then applied to two independent validation sets of 10 and 14 patient samples. Results: Supervised analysis using differentially expressed genes was able to distinguish the two prognostic groups in the test set. The score correlated directly with clinical outcome, with higher scores associated with improved TTP. When the score was then applied to two independent sets of Stage III melanoma patient samples, it predicted clinical outcome accurately in 90% of samples. Conclusions: Stage IIIB and IIIC melanoma can be prognostically sub-classified according to the expression of 15 genes. To our knowledge this is the first study focused on Stage III disease using ex vivo patient samples. These results are encouraging and this genetic signature is currently being validated on a larger cohort. This method will allow appropriate stratification of stage III melanoma patients in adjuvant clinical trials, ameliorating the inherent biological heterogeneity that can confound these studies. [Table: see text]


2006 ◽  
Vol 340 (1) ◽  
pp. 105-110 ◽  
Author(s):  
Tohru Fujiwara ◽  
Hideo Harigae ◽  
Shinichiro Takahashi ◽  
Kazumichi Furuyama ◽  
Osamu Nakajima ◽  
...  

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