To define the biology driving the aggressive nature of acute myeloid leukemia (AML) in elderly patients.

Clinically annotated microarray data from 425 patients with newly diagnosed de novo AML from two publicly available data sets were analyzed after age-specific cohorts (young ≤ 45 years, n = 175; elderly ≥ 55 years; n = 144) were prospectively identified. Gene expression analysis was conducted utilizing gene set enrichment analysis, and by applying previously defined and tested signature profiles reflecting dysregulation of oncogenic signaling pathways and altered tumor environment.

Elderly AML patients as expected had worse overall survival and event-free survival compared with younger patients. Analysis of oncogenic pathways revealed that older patients had higher probability of RAS, Src, and tumor necrosis factor (TNF) pathway activation (all P < .0001). Hierarchical clustering revealed that younger patients with AML in cluster 2 had clinically worse survival, with high RAS, Src, and TNF pathway activation compared with patients in cluster 1. However, among elderly patients with AML, those in cluster 1 also demonstrated high RAS, Src, and TNF pathway activation but this did not translate into differences in survival.

AML in the elderly represents a distinct biologic entity characterized by unique patterns of deregulated signaling pathway variations that contributes to poor survival. These insights should enable development and adjustments of clinically meaningful treatment strategies in the older patient population.

The incidence of acute myeloid leukemia (AML) will likely increase over time, given that it is a disease of elderly persons, and it is expected that by 2030, persons ≥ 65 years in the United States will represent 20% of the population.1 Current therapy of AML includes remission induction with cytarabine (for 7 days) and an anthracycline (for 3 days) which in elderly patients leads to complete response (CR) rates of 40% to 50%.2 Factors involved in poor response rates and survival include: high incidence of poor-prognosis karyotypes (5q−, 7q−),2 high frequency of preceding myelodysplastic syndromes (MDS),3 impaired apoptosis,4 and an increased expression of proteins (eg, MDR1) involved in intrinsic resistance to chemotherapeutic agents.5 These biologic causes are compounded by host-related factors, like poor performance status, comorbidities, and organ function impairment that are a part of the normal aging process. Despite all the advances made in understanding the poor prognosis of AML in the elderly, the underlying biology at a molecular signaling pathway level driving the aggressive nature of AML arising in the elderly has yet to be defined. In this analysis we have evaluated the clinical significance of patterns of oncogenic pathway dysregulation in 425 patients with AML to illustrate the unique biologic phenotype of elderly AML patients.

Data Set and Patient Selection

Two publicly available data sets from Valk et al (GSE1159; n = 262)6 and Metzeler et al (GSE12417; n = 163)7 were utilized to perform our analysis. The selected data sets provided us with clinically-annotated gene expression values from Affymetrix Human Genome U133A or U95 gene chip array (Affymetrix, Santa Clara, CA) in 425 newly diagnosed de novo AML patients. Before applying signatures of pathway dysregulation, all data were Robust Multichip Average normalized (Affymetrix). All samples were analyzed and reported according to Minimum Information About Microarray Experiment guidelines. The definition of elderly for AML patients continues to be variable with some centers choosing 55 years and other using 65 years as the cutoff. We prospectively predefined patient cohorts as young (ie, ≤ 45 years; n = 175), and elderly (≥ 55 years; n = 144). The remaining 106 patients between the ages of 45 and 55 years were not included in this analysis, as our goal was to compare the biology of AML arising at the extremes of age.

Cross-Platform Affymetrix Gene Chip Comparison

When combining data sets from different platforms and different experiments, nonbiologic experimental variation (ie, batch effects) are most commonly faced by researchers; data set adjustment is necessary to counteract this confounder. To reduce the likelihood of batch effect, a normalizing algorithm, ComBat8 ( was applied before performing any analysis. The ComBat method applies either parametric or nonparametric empirical Bayes framework for adjusting data for batch effects that is robust to outliers in a given data set.

Oncogenic Pathway Analyses

Previously described signatures of oncogenic pathway dysregulation (eg, RAS, PI3K, Src, Beta-catenin, Myc, and E2F), cancer biology, and tumor microenvironment (eg, wound healing [WH] as a measure of angiogenesis, epigenetic stem-cell signature [EPI], and TNF) were applied to clinically annotated microarray data using MatLab Software, version 7.0.4 (MathWorks, EI Segundo, CA).912 Using Bayesian binary regression methodologies previously described,14 predictors for each of the aforementioned tumor microenvironment signatures as well as previously described oncogenic pathways were developed.

For our analysis hierarchical clustering of tumor samples was performed by using R/Bioconductor statistical packages.15,16 The predictive probability values of the patient tumor samples and their associated oncogenic pathway dysregulation and tumor microenvironment status were clustered together by using complete linkage clustering with the Euclidean distance metric, which will bring together objects whose absolute expressions are similar.17 Heatmaps were regenerated by using HeatmapViewer module of GenePattern version 2.0 (Broad Institute, Cambridge, MA).18 Standard Kaplan-Meier survival curves and their significance levels were generated for clusters with similar patterns of oncogenic pathway dysregulation and tumor microenvironment status using GraphPad version 4.03 (GraphPad Software, San Diego, CA; Statistical significance of the prognostic clusters is determined from pair-wise comparisons by using Kaplan-Meier survival plots. A prognostically significant result is defined by log-rank P ≤ .05.

Individual differences in the probability of oncogenic pathway dysregulation between young versus elderly patients with AML were analyzed via the nonparametric Mann-Whitney U test using Graph Pad Prism Software, version 4.03 and P ≤ .05 considered statistically significant. Gene set enrichment analysis (GSEA) methodology ( was also applied to the data sets to corroborate the results. GSEA is an analytic tool that identifies collections of genes that share a common function, chromosomal location, or regulation that are over-represented in a list of genes.19 When applied to a ranked list of genes, such as in a gene expression signature, this analysis selects gene sets that can differentiate between the two phenotypes classified by the signature. GSEA surpasses individual gene analysis of a signature, which may miss important associations of molecular pathways, by generating unifying biologic themes. GSEA generates an enrichment score that reflects the degree to which a gene set is over-represented at the extremes (top or bottom) of a ranked list of genes, and a nominal P value is produced that is an estimate of the statistical significance of the enrichment score for a single gene set.


Overall survival (OS) was defined as the time from diagnosis of AML to death. Event-free survival (EFS) was defined as the time from diagnosis to recurrence of AML or death, whichever occurred first, and was censored at time of last follow-up for those who were alive and in ongoing complete remission.

Clinical and Demographic Characteristics in Patients With AML

All patients in this analysis were identified from two data sets (GSE1159 and GSE12417) of patients diagnosed with de novo AML and there were no cases of MDS or secondary AML. Clinicopathologic, demographic, and molecular data including age at diagnosis, French-American-British subtype, baseline cytogenetics, initial white cell count and percentage blasts, platelet count, FLT3-ITD, NPM1, and CEBPA mutation status are included in Table 1. Of note, all 163 patients included in GSE12417 had a normal karyotype. Patients in GSE1159 were treated according to protocols of the Dutch–Belgian Hematology–Oncology Cooperative group and included 111 patients who ultimately underwent stem-cell transplantation (n = 69 allogeneic and n = 42 autologous).2022 Patients in GSE12417 were treated per the AMLCG-1999 protocol.23 Notably, all the patients included in our analysis received at least an anthracycline in combination with other cytotoxic agents for induction therapy.


Table 1. Clinical Characteristics of Patients With Acute Myeloid Leukemia Stratified by Age

Table 1. Clinical Characteristics of Patients With Acute Myeloid Leukemia Stratified by Age

Parameter Age (years)
≤ 45
≥ 55
No. % No. %
No. of patients 175 41.6 144 33.8
Median age, years 34.7 62.3
    Range 15.2-45 55-83
Median WBC ×10−3/mm3 31.3 33.7
    Range 0-289 0-287
Median blasts, % 72.5 80
    Range 0-98 0-100
Median platelets ×10−3/mm3 47 53
    Range 0-931 0-471
Numbers by FAB
    Mx 4 3
    M0 4 4
    M1 41 41
    M2 44 39
    M3 12 1
    M4 34 32
    M5 35 20
    M6 2 4
    Good 39 22 8 5.5
    Intermediate 91 51.4 115 79.8
        Normal/trisomy 8 84/7 112/3
    Poor 32 18 10 6.9
    Other/unknown 14 11
FLT3-ITD mutation
    Present 56 31.6 51 35.4
    Absent 121 68.4 93 64.5
NPM1 mutation
    Present 57 32.2 64 44.4
    Absent 120 67.8 80 55.6
CEBPA mutation
    Present 14 8 8 5.5
    Absent 158 89 130 90.2

Abbreviations: WBC, white blood cell; FAB, French-American-British; FLT3-ITD, Fms-like tyrosine kinase gene; ITD, internal tandem duplication; NPM1, nucleophosmin gene; CEBPA, CCAAT/enhancer binding protein alpha gene.

Dissecting Age-Specific Biology in AML

We have described an alternative approach that makes use of expression signatures of oncogenic signaling pathways and tumor microenvironment that can be used to profile the status of oncogenic pathways in a collection of biologic samples, including human tumors.9 After prospectively predefining the two age cohorts (≤ 45 and ≥ 55 years), survival was used as an end point to analyze if there were indeed any clinically relevant differences by age. This demonstrated (Fig 1A) a clinically and statistically significant difference in OS (median OS, 24.1 months in younger patients v 8.8 months in elderly patients; P = .001), and EFS (median EFS, 15.3 months in younger patients v 7.1 months in elderly patients; P < .0001). We then compared the probability of oncogenic pathway dysregulation between young and elderly patients with AML (Fig 1B). This analysis revealed statistically significant differences in the expression of E2F (P = .01), TNF, epigenetic stemness (EPI), angiogenesis (WH), RAS, PI3-Kinase (all P < .0001), and Src (P = .0005) between young and older patients with AML. Older patients with AML had a lower probability of E2F and PI3-kinase pathway activation but a higher probability of RAS, TNF, Src, and EPI pathway activation.

Relationship Between Oncogenic Pathway Dysregulation and Other Prognostic Factors in Young and Elderly Patients With AML

Several studies have confirmed that karyotypic abnormalities in patients with AML predict response to induction therapy and thus OS.24,25 We analyzed the impact of cytogenetics on OS in elderly and young patients with AML, and as expected in both groups, OS is significantly better in patients with good prognosis karyotypes compared with patients with normal karyotype (CNAML) and poor prognosis karyotypes (Appendix Fig A1A, online only). We then analyzed oncogenic pathway dysregulation in patients with CNAML, and interestingly, elderly patients with CNAML also demonstrate a lower probability of E2F (P = .003) and PI3-kinase (P < .0001) pathway activation but a higher probability of WH, EPI, Ras, TNF (P < .0001), and Src (P < .002) pathway activation (Appendix Fig A1B). Recent data have also suggested that elderly patients with AML negative for the FLT3-ITD mutation or positive for an NPM1 mutation tend to have better response rates and survival.26 In our analysis, elderly and young patients with AML who are FLT3-ITD positive have poor OS irrespective of NPM1 status. Elderly patients with AML who are FLT3-ITD negative but NPM1 positive, as expected, have very good OS at 29.7 months, like their younger counterparts. However, in the subgroup of patients who are FLT3-ITD negative and NPM1 negative, elderly patients have a much worse OS at 11.9 months, but younger patients in this group do better with OS of 30.4 months (Fig 1C). Also, all elderly patients irrespective of their FLT3-ITD and NPM1 mutation status again have dysregulated oncogenic pathways compared with younger patients, and this is true especially in the subgroup of elderly patients who are FLT3-ITD negative and also NPM1 negative who have increased RAS, TNF, and Src pathway activation (P < .0001; Appendix Fig A2, online only), which may in part explain the worse OS in this group of elderly patients.

Signatures of Oncogenic Pathway Dysregulation and Molecular Phenotypes in Younger (≤ 45 years) Patients With AML

A genomic analysis of expression data from AML in patients age ≤ 45 years was performed to gain additional insights into the biology of the disease while also allowing the results generated among older patients to be placed into context. To this end, specific patterns of oncogenic pathway dysregulation in 175 AML samples from patients age ≤ 45 years were evaluated. Hierarchical clustering revealed distinct patterns of oncogenic pathway dysregulation defining two main clusters in this large cohort of patients (Fig 2). Also, in younger patients with AML, there was a statistically significant difference between the two clusters in the activation of beta-catenin, E2F, Myc, PI3-kinase, Src, and TNF (all P < .0001) pathways (Fig 2). There was also a statistically significant difference in the activation of RAS (P = .0002) and angiogenesis (P = .002) pathways. Using survival as a clinically relevant phenotype, we further demonstrate that younger patients with AML identified by these clusters revealed clinically meaningful distinctions (Fig 3). Patients in cluster 1 (high E2F, Myc, and PI3-kinase pathway activity) had an OS of 34.9 months and EFS of 28.1 months compared with an OS of 17.1 months and EFS of 13.1 months for patients in cluster 2 (high RAS, Src, TNF, and beta-catenin pathway activity).

Signatures of Oncogenic Pathway Dysregulation and Molecular Phenotypes in Elderly (≥ 55 years) Patients With AML

In parallel to the analysis in younger patients, patterns of pathway dysregulation in 144 elderly patients with AML were also evaluated. Once again, hierarchical clustering revealed clear patterns of oncogenic pathway dysregulation defining two main clusters in this cohort of patients (Fig 4). Elderly patients in cluster 1 had a much higher probability of activation of beta-catenin, RAS, and Src (P < .0001) pathways, along with EPI (P = .02), and TNF (P = .0001) pathways, but had a lower probability of activation of WH pathways (P = .01) when compared with cluster 2. However, analysis of survival of in elderly AML patients identified by these clusters revealed no clinically or statistically significant differences in OS or EFS (Fig 5).

These results reveal significant disease heterogeneity among younger and elderly patients with AML that are prognostic and independent of currently available clinicopathologic variables. This might suggest that the differential expression of these various predictors of tumor biology and oncogenic pathways may be at least one explanation for the differential biology of AML in younger patients.

In addition, as a further measure of biologically relevant differences between younger and older patients with AML, GSEA was employed to identify differences in gene expression profiles from tumors arising in young and elderly AML patients. In younger AML patients three gene sets (Appendix Table A1, online only) were found to be significantly represented (enrichment score P < .01; Appendix Fig A3, online only), and these included genes involved in AML (ie, CEBPA, RUNX1T1, H-RAS) and genes involving the PI3-kinase/AKT pathway, thus corroborating our findings observed from the analysis of oncogenic pathways; genes in the mitochondrial pathway that are involved in proapoptotic signaling (ie, BAX, BIK, and the caspases); and genes involved in the ADP ribosylation factor pathway that leads to p53-dependent apoptosis.

The underlying biology and subsequent management of elderly patients with AML continues to be a challenge to the treating oncologist. While major strides have been made in the treatment of elderly patients with AML, the standard induction therapy across the globe for four decades continues to be the 7+3 regimen.27 These issues emphasize the importance of identifying molecular characteristics that might be exploited for new therapeutic strategies either alone or in combination with standard induction therapy.

In this study, we have employed a genomic approach to facilitate the genome-wide exploration of the biologic processes driving age-specific differences unique to AML. Our analysis looking at individual signaling pathways as a function of age reveals novel insights into the biology of AML in the elderly and highlights the importance of further evaluating specific patterns of oncogenic pathway dysregulation in AML to gain a deeper understanding of the biologic processes working in concert in younger and older AML patients. Our results confirm previous findings (ie, elderly patients with AML have worse OS and EFS compared with younger patients). Elderly patients with AML have an increased probability of RAS, Src, and TNF pathway activation when compared with their younger counterparts. These differences in oncogenic pathway dysregulation persist even when patients are analyzed by FLT3-ITD or NPM1 mutation status and also in patients with CNAML. In addition, our analysis of individual clusters within young and elderly patients also demonstrated similar oncogenic pathway dysregulation; but while this is clinically meaningful, there were no statistically significant differences in survival.

Although unique clusters of pathway dysregulation are representative of distinct phenotypes of leukemia survival, the purpose of our analysis was not to generate yet another prognostic strategy. Instead, our goal was to describe an approach that could potentially explain the age-specific biologic differences seen in elderly patients with AML, while also highlighting the potential for using targeted agents in a more rational manner—guided by the knowledge of oncogenic pathway dysregulation.

More recently, it has been demonstrated that Src family kinases promote AML cell survival through activation of signal transducers and activators of transcription (STAT), and inhibition of Src leads to apoptosis of leukemic cells.28,29 In our analysis, elderly patients with AML (when compared with their younger counterparts) and younger patients with AML with worse survival in cluster 2 both had increased expression of RAS, TNF, and Src. Thus, it does lead us to hypothesize that these subsets of patients might actually have better survival if treated with an Src inhibitor in combination with standard induction chemotherapy or a drug that targets the RAS-related pathway.

Our past work has demonstrated an association between predicting pathway dysregulation and sensitivity to therapeutics that target a component of the deregulated pathway.9 Thus, of perhaps most importance is the potential for the current data to reveal new therapeutic opportunities for patients at highest risk for AML recurrence, given that 75% to 80% of elderly AML patients relapse within 12 months of their remission.30 Traditional induction chemotherapy is inadequate to overcome these multiple genetic abnormalities and personalized therapy that incorporates specific targeted agents alone or in combination with traditional cytotoxic agents as induction or consolidation therapy may lead to better OS and EFS.

Finally, although host-related factors and performance status also play an important role in the prognosis of AML, we hope that this study has been able to dissect the biology of AML as a function of age with regard to the underlying molecular events.

© 2009 by American Society of Clinical Oncology

Supported in part by National Genome Research Network Plus Grant No. 01GS0876 from the German Ministry of Education and Research (S.K.B., C.B.).

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

The author(s) indicated no potential conflicts of interest.

Conception and design: Arati V. Rao, Peter J.M. Valk, David A. Rizzieri, Anil Potti, Bob Löwenberg

Provision of study materials or patients: Peter J.M. Valk, Klaus H. Metzeler, Ruud Delwel, Christian Buske, Stefan K. Bohlander, Bob Löwenberg

Collection and assembly of data: Arati V. Rao, Chaitanya Acharya

Data analysis and interpretation: Arati V. Rao

Manuscript writing: Arati V. Rao, Klaus H. Metzeler, Anil Potti

Final approval of manuscript: Arati V. Rao, Peter J.M. Valk, Klaus H. Metzeler, Chaitanya Acharya, Sascha A. Tuchman, Marvaretta M. Stevenson, David A. Rizzieri, Ruud Delwel, Christian Buske, Stefan K. Bohlander, Anil Potti, Bob Löwenberg

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Table A1. Gene List for Acute Myeloid Leukemia Pathway

Table A1. Gene List for Acute Myeloid Leukemia Pathway

Gene Symbol Gene Title Running ES Core Enrichment
LEF1 Lymphoid enhancer-binding factor 1 0.125692 Yes
MYC v-myc myelocytomatosis viral oncogene homolog (avian) 0.202936 Yes
JUP Junction plakoglobin 0.263615 Yes
CEBPA CCAAT/enhancer binding protein (C/EBP), alpha 0.320675 Yes
RUNX1T1 Runt-related transcription factor 1; translocated to, 1 (cyclin D-related) 0.329178 Yes
PIK3CG Phosphoinositide-3-kinase, catalytic, gamma polypeptide 0.354078 Yes
MAPK3 Mitogen-activated protein kinase 3 0.374759 Yes
PIK3CD Phosphoinositide-3-kinase, catalytic, delta polypeptide 0.398312 Yes
TCF7 Transcription factor 7 (T-cell specific, HMG-box) 0.392954 Yes
PML Promyelocytic leukemia 0.417405 Yes
CCNA1 Cyclin A1 0.430151 Yes
ZBTB16 Xinc finger and BTB domain containing 16 0.447117 Yes
PIK3CB Phosphoinositide-3-kinase, catalytic, beta polypeptide 0.465137 Yes
IKBKB Inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta 0.48225 Yes
MAP2K2 Mitogen-activated protein kinase kinase 2 0.50032 Yes
STAT5A Signal transducer and activator of transcription 5A 0.49849 Yes
HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 0.514698 Yes
RPS6KB1 Ribosomal protein S6 kinase, 70kDa, polypeptide 1 0.499054 Yes
BAD BCL2-antagonist of cell death 0.508495 Yes
RPS6KB2 Ribosomal protein S6 kinase, 70kDa, polypeptide 2 0.523194 Yes
KIT v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog 0.523877 Yes
ARAF v-raf murine sarcoma 3611 viral oncogene homolog 0.529957 Yes
CCND1 Cyclin D1 0.539145 Yes
SPI1 Spleen focus forming virus (SFFV) proviral integration oncogene spi1 0.531682 Yes
AKT1 v-akt murine thymoma viral oncogene homolog 1 0.542722 Yes
CHUK Conserved helix-loop-helix ubiquitous kinase 0.549972 Yes
EIF4EBP1 Eukaryotic translation initiation factor 4E binding protein 1 0.557298 Yes
STAT3 Signal transducer and activator of transcription 3 (acute-phase response factor) 0.521859 No
AKT2 v-akt murine thymoma viral oncogene homolog 2 0.514341 No
RARA Retinoic acid receptor, alpha 0.51628 No
PPARD Peroxisome proliferative activated receptor, delta 0.500362 No
FRAP1 FK506 binding protein 12-rapamycin associated protein 1 0.499001 No
NRAS Neuroblastoma RAS viral (v-ras) oncogene homolog 0.497925 No
PIK3CA Phosphoinositide-3-kinase, catalytic, alpha polypeptide 0.478015 No
RAF1 v-raf-1 murine leukemia viral oncogene homolog 1 0.456569 No
NFKB2 Nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100) 0.457162 No
RELA v-rel reticuloendotheliosis viral oncogene homolog A, nuclear factor of kappa light polypeptide gene enhancer in B-cells 3, p65 (avian) 0.433594 No
KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 0.421375 No
MAP2K1 Mitogen-activated protein kinase kinase 1 0.418652 No
MAPK1 Mitogen-activated protein kinase 1 0.41569 No
GRB2 Growth factor receptor-bound protein 2 0.407918 No
NFKB1 Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105) 0.398021 No
FLT3 fms-related tyrosine kinase 3 0.372989 No
SOS1 Son of sevenless homolog 1 (Drosophila) 0.370178 No
IKBKG Inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma 0.367159 No
SOS2 Son of sevenless homolog 2 (Drosophila) 0.342654 No
RUNX1 Runt-related transcription factor 1 (acute myeloid leukemia 1; aml1 oncogene) 0.33237 No
BRAF v-raf murine sarcoma viral oncogene homolog B1 0.310033 No
TCF7L1 Transcription factor 7-like 1 (T-cell specific, HMG-box) 0.317578 No
TCF7L2 Transcription factor 7-like 2 (T-cell specific, HMG-box) 0.299449 No
PIM2 pim-2 oncogene 0.308815 No
PIM1 pim-1 oncogene 0.158342 No
AKT3 v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma) 0.107854 No
Gene list for mitochondrial pathway
    BIK BCL2-interacting killer (apoptosis-inducing) 0.309591 Yes
    DFFB DNA fragmentation factor, 40kDa, beta polypeptide (caspase-activated DNase) 0.431033 Yes
    CASP6 Caspase 6, apoptosis-related cysteine peptidase 0.512852 Yes
    BCL2 B-cell CLL/lymphoma 2 0.610934 Yes
    BAX BCL2-associated X protein 0.62913 Yes
    ENDOG Endonuclease G 0.615082 Yes
    CASP3 Caspase 3, apoptosis-related cysteine peptidase 0.65795 Yes
    CASP7 Caspase 7, apoptosis-related cysteine peptidase 0.631126 Yes
    BIRC4 Baculoviral IAP repeat-containing 4 0.650552 Yes
    DFFA DNA fragmentation factor, 45kDa, alpha polypeptide 0.663659 Yes
    CYCS Cytochrome c, somatic 0.625265 No
    BIRC3 Baculoviral IAP repeat-containing 3 0.616066 No
    BIRC2 Baculoviral IAP repeat-containing 2 0.605349 No
    CASP9 Caspase 9, apoptosis-related cysteine peptidase 0.614755 No
    DIABLO Diablo homolog (Drosophila) 0.549594 No
    BID BH3 interacting domain death agonist 0.548267 No
    APAF1 Apoptotic peptidase activating factor 0.534064 No
    BCL2L1 BCL2-like 1 0.490797 No
    PDCD8 Programmed cell death 8 (apoptosis-inducing factor) 0.494257 No
    CASP8 Caspase 8, apoptosis-related cysteine peptidase 0.456582 No
Gene list for ARF pathway
    MYC v-myc myelocytomatosis viral oncogene homolog (avian) 0.260283 Yes
    TWIST1 Twist homolog 1 (acrocephalosyndactyly 3; Saethre-Chotzen syndrome) (Drosophila) 0.498255 Yes
    RB1 Retinoblastoma 1 (including osteosarcoma) 0.629833 Yes
    TP53 Tumor protein p53 (Li-Fraumeni syndrome) 0.672801 Yes
    ABL1 v-abl Abelson murine leukemia viral oncogene homolog 1 0.529016 No
    RAC1 ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1) 0.534247 No
    CDKN2A Cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) 0.499621 No
    PIK3CA Phosphoinositide-3-kinase, catalytic, alpha polypeptide 0.479612 No
    E2F1 E2F transcription factor 1 0.380493 No
    POLR1D Polymerase (RNA) I polypeptide D, 16kDa 0.381165 No
    MDM2 Mdm2, transformed 3T3 cell double minute 2, p53 binding protein (mouse) 0.377226 No
    POLR1B Polymerase (RNA) I polypeptide B, 128kDa 0.38045 No
    POLR1C Polymerase (RNA) I polypeptide C, 30kDa 0.391356 No
    TBX2 T-box 2 0.39137 No
    PIK3R1 Phosphoinositide-3-kinase, regulatory subunit 1 (p85 alpha) 0.300992 No



DOI: 10.1200/JCO.2009.22.2547 Journal of Clinical Oncology 27, no. 33 (November 20, 2009) 5580-5586.

Published online October 26, 2009.

PMID: 19858393

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