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Age-Specific Differences in Oncogenic Pathway Dysregulation in Patients With Acute Myeloid Leukemia
Abstract
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.
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.
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 (http://statistics.byu.edu/johnson/ComBat/) 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.
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).9–12 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; http://www.graphpad.com). 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 (http://www.broad.mit.edu/gsea/) 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.
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).20–22 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.
|
| 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 | ||
| Cytogenetics | ||||
| 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.
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.
Fig 1. (A) Kaplan-Meier survival curves demonstrating differences between young and elderly patients with AML. Kaplan-Meier survival curves were created using GraphPad Prism Software, version 4.03 and P ≤ .05 defined by log-rank test was considered statistically significant. (B) Differences in oncogenic pathway dysregulation between younger and elderly patients with AML. 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 was considered statistically significant. (C) Overall survival in young and elderly patients with AML based on FLT3-ITD and NPM1 mutation status. For this analysis elderly and young patients were divided into four groups: FLT3 negative NPM1 positive, FLT3 negative NPM1 negative, FLT3 positive NPM1 positive, and FLT3 positive NPM1 negative. Left graph demonstrates overall survival in elderly AML patients based on FLT3-ITD and NPM1 mutation status. Right graph demonstrates overall survival in younger AML patients based on FLT3-ITD and NPM1 mutation status. Kaplan-Meier survival curves were created using GraphPad Prism Software version 4.03, and P ≤ .05 defined by log-rank test was considered statistically significant.
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.
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).
Fig 2. Differences in oncogenic pathway dysregulation among younger patients with AML. The blue bars represent patients in cluster 1 and gold bars represent patients in cluster 2. Individual differences in the probability of oncogenic pathway dysregulation between clusters 1 and 2 of younger patients with AML were analyzed via the nonparametric Mann-Whitney U test using Graph Pad Prism Software, version 4.03, and P ≤ .05 was considered statistically significant.
Fig 3. Kaplan-Meier survival curves demonstrating differences between clusters of younger patients with AML. Kaplan-Meier survival curves were created using GraphPad Prism Software version 4.03, and P ≤ .05 defined by log-rank test was considered statistically significant.
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).
Fig 4. Differences in oncogenic pathway dysregulation among elderly patients with AML. The blue bars represent patients in cluster 1, and gold bars represent patients in cluster 2. Individual differences in the probability of oncogenic pathway dysregulation between clusters 1 and 2 of elderly patients with AML were analyzed via the nonparametric Mann-Whitney U test using Graph Pad Prism Software, version 4.03, and P ≤ .05 was considered statistically significant.
Fig 5. Kaplan-Meier survival curves demonstrating differences between clusters of elderly patients with AML. Kaplan-Meier survival curves were created using GraphPad Prism Software version 4.03, and P ≤ .05 defined by log-rank test was considered statistically significant.
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.
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
| 1. | R Yancik: Population aging and cancer: A cross-national concern Cancer Journal 11: 437– 441,2005 Crossref, Medline, Google Scholar |
| 2. | FR Appelbaum, H Gundacker, DR Head , etal: Age and acute myeloid leukemia Blood 107: 3481– 3485,2006 Crossref, Medline, Google Scholar |
| 3. | G Rossi, AM Pelizzari, D Bellotti , etal: Cytogenetic analogy between myelodysplastic syndrome and acute myeloid leukemia of elderly patients Leukemia 14: 636– 641,2000 Crossref, Medline, Google Scholar |
| 4. | SM Garrido, JJ Cooper, FR Appelbaum , etal: Blasts from elderly acute myeloid leukemia patients are characterized by low levels of culture- and drug-induced apoptosis Leukemia Res 25: 23– 32,2001 Crossref, Medline, Google Scholar |
| 5. | O Legrand, G Simonin, A Beauchamp-Nicoud , etal: Simultaneous activity of MRP1 and Pgp is correlated with in vitro resistance to daunorubicin and with in vivo resistance in adult acute myeloid leukemia Blood 94: 1046– 1056,1999 Crossref, Medline, Google Scholar |
| 6. | PJM Valk, RGW Verhaak, MA Beijen , etal: Prognostically useful gene-expression profiles in acute myeloid leukemia N Engl J Med 350: 1617– 1628,2004 Crossref, Medline, Google Scholar |
| 7. | KH Metzeler, M Hummel, CD Bloomfield , etal: An 86 probe set gene expression signature predicts survival in cytogenetically normal acute myeloid leukemia Blood 112: 4193– 4201,2008 Crossref, Medline, Google Scholar |
| 8. | WE Johnson, C Li, A Rabinovic: Adjusting batch effects in microarray expression data using empirical Bayes methods Biostatistics 8: 118– 127,2007 Crossref, Medline, Google Scholar |
| 9. | AH Bild, G Yao, JT Chang , etal: Oncogenic pathway signatures in human cancers as a guide to targeted therapies Nature 439: 353– 357,2006 Crossref, Medline, Google Scholar |
| 10. | J Pittman, E Huang, H Dressman , etal: Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes Proc Natl Acad Sci U S A 101: 8431– 8436,2004 Crossref, Medline, Google Scholar |
| 11. | J Pittman, E Huang, J Nevins , etal: Bayesian analysis of binary prediction tree models for retrospectively sampled outcomes Biostatistics 5: 587– 601,2004 Crossref, Medline, Google Scholar |
| 12. | Reference deleted Google Scholar |
| 13. | Reference deleted Google Scholar |
| 14. | M West, C Blanchette, H Dressman , etal: Predicting the clinical status of human breast cancer by using gene expression profiles Proc Natl Acad Sci U S A 98: 11462– 11467,2001 Crossref, Medline, Google Scholar |
| 15. | RC Gentleman, VJ Carey, DM Bates , etal: Bioconductor: Open software development for computational biology and bioinformatics Genome Biol 5: R80,2004 Crossref, Medline, Google Scholar |
| 16. | I Ross, RC Gentleman: R: A language for data analysis and graphics J Comput Graph Statist 5,1996 Google Scholar |
| 17. | MB Eisen, PT Spellman, PO Brown , etal: Cluster analysis and display of genome-wide expression patterns Proc Natl Acad Sci U S A 95: 14863– 14868,1998 Crossref, Medline, Google Scholar |
| 18. | M Reich, T Liefeld, J Gould , etal: GenePattern 2.0 Nat Genet 38: 500– 501,2006 Crossref, Medline, Google Scholar |
| 19. | A Subramanian, P Tamayo, VK Mootha , etal: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles Proc Natl Acad Sci U S A 102: 15545– 15550,2005 Crossref, Medline, Google Scholar |
| 20. | B Lowenberg, MA Boogaerts, SM Daenen , etal: Value of different modalities of granulocyte-macrophage colony-stimulating factor applied during or after induction therapy of acute myeloid leukemia J Clin Oncol 15: 3496– 3506,1997 Link, Google Scholar |
| 21. | B Lowenberg, W van Putten, M Theobald , etal: Effect of priming with granulocyte colony-stimulating factor on the outcome of chemotherapy for acute myeloid leukemia N Engl J Med 349: 743– 752,2003 Crossref, Medline, Google Scholar |
| 22. | GJ Ossenkoppele, WJ Graveland, P Sonneveld , etal: The value of fludarabine in addition to ARA-C and G-CSF in the treatment of patients with high-risk myelodysplastic syndromes and AML in elderly patients Blood 103: 2908– 2913,2004 Crossref, Medline, Google Scholar |
| 23. | T Buchner, WE Berdel, C Schoch , etal: Double induction containing either two courses or one course of high-dose cytarabine plus mitoxantrone and postremission therapy by either autologous stem-cell transplantation or by prolonged maintenance for acute myeloid leukemia J Clin Oncol 24: 2480– 2489,2006 Link, Google Scholar |
| 24. | D Grimwade, A Moorman, R Hills , etal: Impact of karyotype on treatment outcome in acute myeloid leukemia Ann Hematol 83 Suppl 1: S45– S48,2004 Google Scholar |
| 25. | D Grimwade, H Walker, G Harrison , etal: The predictive value of hierarchical cytogenetic classification in older adults with acute myeloid leukemia (AML): Analysis of 1065 patients entered into the United Kingdom Medical Research Council AML11 trial Blood 98: 1312– 1320,2001 Crossref, Medline, Google Scholar |
| 26. | S Scholl, C Theuer, V Scheble , etal: Clinical impact of nucleophosmin mutations and Flt3 internal tandem duplications in patients older than 60 yr with acute myeloid leukaemia Eur J Haematol 80: 208– 215,2008 Crossref, Medline, Google Scholar |
| 27. | PH Wiernik, PL Banks, DC Case Jr , etal: Cytarabine plus idarubicin or daunorubicin as induction and consolidation therapy for previously untreated adult patients with acute myeloid leukemia Blood 79: 313– 319,1992 Crossref, Medline, Google Scholar |
| 28. | Y Ozawa, AH Williams, ML Estes , etal: Src family kinases promote AML cell survival through activation of signal transducers and activators of transcription (STAT) Leukemia Res 32: 893– 903,2008 Crossref, Medline, Google Scholar |
| 29. | LJ Robinson, J Xue, SJ Corey: Src family tyrosine kinases are activated by Flt3 and are involved in the proliferative effects of leukemia-associated Flt3 mutations Exp Hematol 33: 469– 479,2005 Crossref, Medline, Google Scholar |
| 30. | B Lowenberg, R Zittoun, H Kerkhofs , etal: On the value of intensive remission-induction chemotherapy in elderly patients of 65+ years with acute myeloid leukemia: A randomized phase III study of the European Organisation for Research and Treatment of Cancer Leukemia Group J Clin Oncol 7: 1268– 1274,1989 Link, Google Scholar |
Fig A1. (A) Overall survival in young and elderly patients with AML based on cytogenetics. Left graph demonstrates overall survival curves for elderly AML patients, and right graph demonstrates overall survival curves for younger AML patients. Good prognosis karyotypes include patients with Inv 16, t(15;17), and t(8;21). Poor prognosis karyotypes include patients with −7, 7q−, −5, 5q−, abnormalities of 11q and 17p, +8 and several complex or multiple abnormalities involving two, three or more chromosomes. Kaplan-Meier survival curves were created using GraphPad Prism Software version 4.03, and P ≤ .05 defined by log-rank test was considered statistically significant. (B) Overall survival and oncogenic pathway dysregulation in patients with cytogenetically normal AML (CNAML). Left graph demonstrates differences in overall survival between young and elderly patients with CNAML. Kaplan-Meier survival curves were created using GraphPad Prism Software version 4.03 and P ≤ .05 defined by log-rank test was considered statistically significant. Right bar graph demonstrates differences in oncogenic pathway dysregulation between young and elderly patients with CNAML. The blue bars represent young AML patients and the gold bars represent elderly patients with AML. Individual differences in the probability of oncogenic pathway dysregulation between young and elderly patients with CNAML were analyzed via the nonparametric Mann-Whitney U test using Graph Pad Prism Software, version 4.03 and P ≤ .05 was considered statistically significant.
Fig A2. Oncogenic pathway dysregulation in young and elderly patients with AML based on FLT3-ITD and NPM1 status: (A) FLT3 negative/NPM1 positive, (B) FLT3 negative/NPM1 negative, (C) FLT3 positive/NPM1 positive, and (D) FLT3 positive/NPM1 negative. In all four graphs, the blue bars represent young AML patients and the gold bars represent elderly AML patients.
Fig A3. Gene set enrichment analysis for genes involved in (A) AML, (B) the mitochondrial pathway, and (C) the ARF pathway. Gene lists for the three pathways are available in Appendix Table A1 (online only).
|
| 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 |
