Originally published as JCO Early Release 10.1200/JCO.2006.09.3534 on February 20 2007

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Differential Gene Expression Patterns and Interaction Networks in BCR-ABL–Positive and –Negative Adult Acute Lymphoblastic Leukemias

Dejan Juric, Norman J. Lacayo, Meghan C. Ramsey, Janis Racevskis, Peter H. Wiernik, Jacob M. Rowe, Anthony H. Goldstone, Peter J. O'Dwyer, Elisabeth Paietta, Branimir I. Sikic

From the Divisions of Medical Oncology and Pediatric Hematology/Oncology, Stanford University School of Medicine, Stanford, CA; Our Lady of Mercy Cancer Center, New York Medical College, Bronx, NY; Hematology Department, Rambam Medical Center, Haifa, Israel; University College London Hospitals, London, United Kingdom; Division of Hematology-Oncology and Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA; and the Eastern Cooperative Oncology Group, Boston, MA

Address reprint requests to Branimir I. Sikic, MD, Oncology Division, Department of Medicine, Stanford University School of Medicine, CCSR 1105, 269 Campus Dr, Stanford, CA 94305-5151; e-mail: brandy{at}stanford.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Purpose: To identify gene expression patterns and interaction networks related to BCR-ABL status and clinical outcome in adults with acute lymphoblastic leukemia (ALL).

Patients and Methods: DNA microarrays were used to profile a set of 54 adult ALL specimens from the Medical Research Council UKALL XII/Eastern Cooperative Oncology Group E2993 trial (21 p185^BCR-ABL–positive, 16 p210^BCR-ABL–positive and 17 BCR-ABL–negative specimens).

Results: Using supervised and unsupervised analysis tools, we detected significant transcriptomic changes in BCR-ABL–positive versus –negative specimens, and assessed their validity in an independent cohort of 128 adult ALL specimens. This set of 271 differentially expressed genes (including GAB1, CIITA, XBP1, CD83, SERPINB9, PTP4A3, NOV, LOX, CTNND1, BAALC, and RAB21) is enriched for genes involved in cell death, cellular growth and proliferation, and hematologic system development and function. Network analysis demonstrated complex interaction patterns of these genes, and identified FYN and IL15 as the hubs of the top-scoring network. Within the BCR-ABL–positive subgroups, we identified genes overexpressed (PILRB, STS-1, SPRY1) or underexpressed (TSPAN16, ADAMTSL4) in p185^BCR-ABL–positive ALL relative to p210^BCR-ABL–positive ALL. Finally, we constructed a gene expression- and interaction-based outcome predictor consisting of 27 genes (including GRB2, GAB1, GLI1, IRS1, RUNX2, and SPP1), which correlated with overall survival in BCR-ABL–positive adult ALL (P = .0001), independent of age (P = .25) and WBC count at presentation (P = .003).

Conclusion: We identified prominent molecular features of BCR-ABL–positive adult ALL, which may be useful for developing novel therapeutic targets and prognostic markers in this disease.

	INTRODUCTION

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A better understanding of the biology of acute lymphoblastic leukemia (ALL) and advances in ALL therapy have led to long-term survival rates of approximately 80% in children. However, only 30% to 40% of adults with this disease achieve long-term disease-free survival.¹ The relatively poor outcome in adult ALL has been explained by an increased frequency of high-risk molecular subtypes with more aggressive clinical behavior and greater drug resistance, poorer tolerance and compliance with treatment, and less effective treatment regimens compared with childhood ALL.² A particularly poor prognosis is associated with the t(9;22) translocation and the presence of the BCR-ABL fusion transcript, which increases in frequency with age, from 2% to 5% in children to 20% to 40% in adults.^3-5 This molecular alteration exists in several forms and encodes multiple oncogenic products. Although the majority of chronic myelogenous leukemia (CML) patients express p210^BCR-ABL, both p185^BCR-ABL and p210^BCR-ABL can be found in ALL patients. These two isoforms arise from distinct breakpoints in BCR on chromosome 22 and ABL1 on chromosome 9, resulting in the fusion of exon 1 of BCR and exon 2 of ABL1, and fusion of exons 13/14 of BCR and exon 2 of ABL1, respectively.⁶

Several in vitro studies have demonstrated higher tyrosine kinase activity, increased transforming potential, and distinct downstream targets of p185^BCR-ABL compared with p210^BCR-ABL, suggesting that these two isoforms have different biologic and possibly clinical properties.⁷ The clinical implications of these findings are controversial, and although a majority of clinical studies failed to demonstrate significant differences of the two isoforms with regard to clinical outcome in adults with ALL,^4,8,9 some recent reports indicate that these two isoforms could be associated with different clinical phenotypes of adult ALL.¹⁰

In this report, we performed a comprehensive analysis of the gene expression profiles in a set of 37 BCR-ABL–positive (21 and 16 of which expressed p185^BCR-ABL and p210^BCR-ABL, respectively) and 17 BCR-ABL–negative adult ALL specimens from the Medical Research Council (MRC) UKALL XII/Eastern Cooperative Oncology Group (ECOG) E2993 trial.¹¹ First, we aimed to determine gene expression signatures and molecular alterations potentially involved in the complex mechanisms of BCR-ABL-mediated neoplastic transformation, and which could explain aggressive behavior of BCR-ABL–positive adult ALL. Second, we evaluated differential gene expression in the two major isoforms of BCR-ABL, to determine whether these two isoforms are associated with distinct transcriptional changes.

	PATIENTS AND METHODS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Patient Samples
Total RNA samples were isolated from cryopreserved pretreatment leukemia cells from 54 adult ALL patients entered onto the MRC UKALL XII/ECOG E2993 trial, as described previously.¹² Written informed consent was obtained from all patients, and the study was approved by the Institutional Review Board of Stanford University (Stanford, CA). All patients were immunophenotypically classified as early pre-B ALL. BCR-ABL–positive patients were selected based on the absence of additional cytogenetic abnormalities. BCR-ABL–negative patients had a normal diploid karyotype (at least 20 normal metaphases) and were negative for the most common ALL transcripts, MLL/AF4, E2A/PBX1, and TEL/AML1, as well as FLT3-gene internal tandem duplications.

Microarray Procedures
Gene expression profiling was performed using Stanford human cDNA arrays containing 41,421 cDNA elements, corresponding to 24,472 different UniGene cluster IDs. Hybridized array signals were processed using the 3DNA Array 900 Expression Array Detection Kit according to the manufacturer's instructions (Genisphere, Hatfield, PA). This indirect, two-step labeling procedure uses dendrimer-based signal amplification¹³ and avoids biases associated with commonly used sample amplification protocols.¹⁴ Details of these procedures are presented in the Appendix (online only).

Data Filtering and Transformation
Primary data were uploaded to the National Center for Biotechnology Information Gene Expression Omnibus (Series record: GSE5314), and to the Stanford Microarray Database,¹⁵ where all subsequent low-level analysis procedures were performed. Data were first log-transformed and normalized using local, intensity-dependent normalization.¹⁶ Nonflagged elements with signal-to-background ratios of at least 2.0 in either channel were selected based on their presence in at least 80% of samples. Finally, we selected only the clones with at least three-fold differential expression, in at least three samples, compared with their mean expression level across all the samples, resulting in 10,485 clones used for subsequent comparisons.

Unsupervised and Supervised Data Analysis
Unsupervised hierarchical clustering was performed in Cluster and visualized in Treeview.¹⁷ Supervised data analysis was performed in R (build 2.2.1; http://www.r-project.org) using a nonparametric t test as described previously.¹⁸ A two-sample Welch t statistic was computed for each gene in each comparison, and the statistical significance of the differential expression was estimated by 1,000 permutations of the class labels. The false discovery rate was estimated using q-value computation.¹⁹ Nearest centroid classification was performed using the freeware pamr package.²⁰

Survival Analysis
Kaplan-Meier survival analysis, log-rank tests, and Cox proportional hazards analysis were performed in R. Overall survival (OS) was measured from the on-study date until date of death regardless of cause, with censoring of patients alive at last follow-up. Comparisons of baseline clinical variables were made using a Fisher's exact test (categoric variables) or Wilcoxon test (continuous variables).

Interaction Networks and Functional Analysis
Gene ontology and gene interaction analyses were executed using Ingenuity Pathways Analysis tools 3.0 (http://www.ingenuity.com). The gene lists containing Entrez GeneIDs as clone identifiers, as well as fold-change or Wald score values from corresponding supervised analyses, were mapped to their corresponding gene object in the Ingenuity Pathways Knowledge Base (IPKB). These so-called focus genes were then used in the network generation algorithm, based on the curated list of molecular interactions in IPKB. Significance for the enrichment of the genes in a network with particular biologic functions was determined by the right-tailed Fisher's exact test, using a list of all the genes on the array as a reference set. Details of these procedures are presented in the Appendix.

Cross-Platform Validation
Affymetrix gene expression data for 12,625 probe-sets in 128 adult ALL samples were obtained from http://www.bioconductor.org/docs/papers/2003/Chiaretti/chiaretti2/index.html. Primary data were annotated and normalized as described previously.²¹ The log2-transformed and mean-centered file was used in all subsequent cross-platform analyses.

Quantitative Reverse Transcription Polymerase Chain Reaction
Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed with the ABI Prism 7900HT Sequence Detection System using SYBR GREEN PCR Master Mix (Applied Biosystems, Foster City, CA), according to the manufacturer's instructions. Details of the qRT-PCR procedures are presented in the Appendix.

	RESULTS

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BCR-ABL–Associated Gene Expression Signatures and Interaction Networks
We performed microarray-based gene expression analysis of a set of 37 BCR-ABL–positive and 17 BCR-ABL–negative ALL specimens. In supervised analysis, we identified 363 clones as differentially expressed in BCR-ABL–positive ALL compared with BCR-ABL–negative ALL (241 overexpressed and 122 underexpressed). Unsupervised two-way clustering of these clones distinguished the two subclasses of ALL with an overall accuracy of 93% (Fig 1A). They correspond to 271 unique Entrez GeneIDs, and are enriched for three highly relevant functions: cellular growth and proliferation (57 genes; P = .004 to 0.044), cell death (49 genes; P = .0007 to 0.049), and hematologic system development and function (40 genes; P = .00004 to 0.049). Selected genes from this set and their corresponding log fold-change values are displayed in Figure 1B.

View larger version (90K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. (A) Unsupervised hierarchical clustering of the 363 clones with significant differential expression in BCR-ABL–positive versus BCR-ABL–negative acute lymphoblastic leukemia; (B) names and log fold-change values of selected genes; (*) cross-platform validated genes.

View larger version (32K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 2. The top-scoring network of interactions among the differentially expressed genes in BCR-ABL–positive versus BCR-ABL–negative acute lymphoblastic leukemia. Node colors and shapes correspond to fold-change values and functional classes of the gene products, respectively. The table lists high-level functions with statistically significant enrichment.

View larger version (21K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 3. (A) Quantitative reverse transcription polymerase chain reaction analysis of selected genes in 54 samples (n = 54; mean and SEM of HPRT-normalized and median-centered levels; (*) P < .05. (B) Unsupervised hierarchical clustering of an independent set of 128 adult ALL specimens using 168 genes from our BCR-ABL gene expression signature represented in the validation set.

View larger version (36K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 4. (A) Unsupervised hierarchical clustering of the 14 clones with differential expression in p185BCR-ABL–positive and p210BCR-ABL–positive acute lymphoblastic leukemia; (B) decision margins from 10-fold cross-validation of the nearest-centroid classifier based on these 14 clones; (C) Kaplan-Meier analysis of overall survival (OS) in the p185BCR-ABL–positive versus p210BCR-ABL–positive ALL. EST, expressed sequence tag.

Survival Analysis in BCR-ABL–Positive ALL
Kaplan-Meier analysis of the p185^BCR-ABL–positive versus p210^BCR-ABL–positive ALL patients did not reveal a statistically significant difference in OS (Fig 4C). They also were not different in their age, WBC count at diagnosis (WBC), complete remission rate, or expression of myeloid markers CD13/CD33 (P > .05).

We then constructed a gene expression profiling–based predictor that does correlate with survival in our set of BCR-ABL–positive specimens. As a first step, we performed Cox proportional hazards analysis for each gene in the data set and used Wald scores from this analysis as a measure of their correlation with OS. In the second step, we performed molecular interaction analysis of the 524 clones with the top 5% of Wald scores using Ingenuity Pathways Analysis. The top scoring network from this analysis included 27 interacting genes (Fig 5A). Among these are GAB1, CD34, GNAQ, RASGRP1, NRG3, and SELL, all of which were correlated positively with survival, as well as GRB2, RAPGEF1, MRAS, GRAP2, IRS1, and RUNX2, which are correlated negatively with survival. The most over-represented function in this network is cellular differentiation (14 genes; P = 3.17^–6). Finally, the first principal component of the gene expression profile of these 27 genes was computed and used as a risk score for each sample. Importantly, our gene selection was not based solely on the maximal correlation with survival in the training set, but also relied on the known molecular interaction patterns of the candidate genes.

View larger version (31K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 5. The top-scoring survival-related network depicting a gene expression–and interaction-based predictor of overall survival (OS) in BCR-ABL–positive acute lymphoblastic leukemia (ALL), (A) with node colors and shapes corresponding to the Wald-score and functional class of the gene product, respectively; and (B) Kaplan-Meier analysis of OS in the BCR-ABL–positive ALL stratified by their first principal component (FPC) prediction score.

	DISCUSSION

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Expression profiles associated with various subtypes of ALL have been studied extensively by microarrays, primarily focused on childhood ALL.^21,23-30 Although genes suitable for classification of childhood ALL are capable of distinguishing the respective adult ALL subentities, gene expression profiles have demonstrated significant differences between childhood and adult BCR-ABL–positive ALL.^27,29

Our study identified 271 differentially expressed genes in BCR-ABL–positive versus BCR-ABL–negative adult ALL. Their functional analysis suggests that a substantial portion of these genes could be contributing factors in BCR-ABL–driven leukemogenesis. Their potential biologic relevance is supported further by the qRT-PCR validation, and especially by the differential patterns found in an independent set of 128 adult ALL samples. A relatively large overlap of 40% among assessable differentially expressed genes in the two unrelated studies deserves particular attention. Moreover, an evaluation of our BCR-ABL–positive ALL gene expression signature in a recently published CML study³¹ revealed considerable overlap in the gene expression patterns of BCR-ABL–positive ALL and CML blast crisis. Using the subset of genes found on both microarray platforms, our signature was able to distinguish completely CML blast crisis from CML chronic phase by hierarchical clustering (data not shown).

One of the validated genes, GAB1, a GRB2-associated binding protein 1, is a member of the Gab family of docking proteins. Although formation of the GRB2-SOS-GAB2 complex is one of the critical events in BCR-ABL signaling in CML, impaired function of GAB2 results in only partially diminished lymphoid leukemogenesis.^32,33 Overexpression of GAB1 in ALL cells might provide an alternative to GAB2.

Another consistent feature of BCR-ABL–positive adult ALL samples is overexpression of the class II major histocompatibility complex (MHC) genes, potentially associated with upregulation of several genes involved in transcriptional activation and cell surface trafficking of MHC molecules, such as CIITA, a master transactivator of the MHC class II genes.³⁴ A possibly related finding is an increased expression of CD83. Soluble CD83 is elevated in a number of hematologic malignancies, and can suppress both in vitro and in vivo antitumor responses, potentially contributing to the aggressive nature of BCR-ABL–positive ALL.³⁵ An additional mechanism of immune escape of BCR-ABL–positive ALL could be an increased expression of SERPINB9, which inactivates the cytotoxic protease granzyme B and prevents cytotoxic T lymphocyte–mediated apoptosis.³⁶

The aggressive phenotype of BCR-ABL–positive ALL could be associated with highly overexpressed genes involved in cell adhesion, invasion, and angiogenesis (including ITGA5, TJP2, ENG, MUC4, PTP4A3, NOV, MTSS1, CTNND1, and LOX). Lysyl oxidase (LOX), in particular, is responsible for the invasive properties of hypoxic human cancer cells and is required for focal adhesion kinase activity.³⁷ Focal adhesion kinase is associated with enhanced blast migration, increased cellularity, and poor prognosis in acute myeloid leukemia,³⁸ and could have a similar role in BCR-ABL–positive ALL.

Our study adds to previous reports by reconstructing interaction networks of the identified candidate genes. The most prominent network was constructed around FYN and IL15. These highly connected hubs might be excellent candidates for targeted disruption of the critical interaction networks, providing novel strategies for treatment of neoplasms associated with the numerous and often redundant molecular alterations. In this context, the dual tyrosine kinase inhibitor dasatinib, which targets imatinib-resistant BCR-ABL as well as FYN and other members of the Src-family of nonreceptor tyrosine kinases,³⁹ might have activity in the treatment of BCR-ABL–positive adult ALL, beyond its role as a BCR-ABL inhibitor. All of the above genes are expressed in both p185^BCR-ABL–positive and p210^BCR-ABL–positive ALL. A small number of genes were differentially expressed across the two isoforms. Although two cell adhesion modulators, TSPAN16 and ADAMTSL4, are overexpressed in p210^BCR-ABL–positive ALL, p185^BCR-ABL–positive ALL samples show a high expression of several known cell signaling regulators, such as PILRB, STS-1, and SPR1.^40-42

Contrary to prior in vitro studies,⁷ no differences were found in the expression of the Rho family of GTPases or their interaction partners. We found overexpression of some of these genes, such as CDC42EP3 and PLEKHG1, in both BCR-ABL–positive groups. It appears that these downstream targets can be activated independent of the Dbl homology domain found in p210^BCR-ABL. Indeed, it has been shown that both p185^BCR-ABL and p210^BCR-ABL form heterotetramers with BCR, allowing for Dbl homology domain–mediated effects to be present even in p185^BCR-ABL–positive leukemia cells.²²

Finally, our survival analyses provide additional insights into this aggressive disease. No difference was seen in the overall survival of patients with p185^BCR-ABL–positive versus p210^BCR-ABL–positive ALL in our set of samples. A multigene survival predictor that did correlate with survival had multiple elements participating in BCR-ABL-associated signaling pathways. GRB2, GAB1, GRAP2, RAPGEF1, MRAS, ETS2, IGFBP2, and RASGRP1 are involved in RAS signaling, but the nature of that association in our samples is not clear, especially because of the seemingly opposite effects of GRB2 and GAB1 on OS of the patients in the study. GLI1 and IRS1 have strong antiapoptotic effects through upregulation of Bcl-2 and downregulation of FasL.^43,44 RUNX2 overexpression interferes with T-cell differentiation; it causes enhanced cell migration, invasion, and survival of adherent cell types,⁴⁵ and it upregulates PI3K subunits and AKT, possibly amplifying PI3K signaling⁴⁶—another critical component of BCR-ABL downstream effects. PI3K is also responsible for BCR-ABL–induced expression of SPP1, which is an adverse prognostic factor in our study. Of note, it has been demonstrated that overexpression of SPP1 correlates with drug resistance in CML cells.⁴⁷ Additional experiments are necessary to validate our survival predictor and to elucidate potential mechanisms linking its gene elements with leukemic cell behavior.

In conclusion, we have defined and validated a set of candidate genes likely to play a role in BCR-ABL–driven leukemogenesis. We have also assessed differential expression associated with the two BCR-ABL isoforms. Finally, we constructed a gene expression–based survival predictor, which could shed light on determinants of outcomes of BCR-ABL–positive adult ALL. These findings may be useful for developing novel therapeutic targets and prognostic markers in this disease.

	AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

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The authors indicated no potential conflicts of interest.

	AUTHOR CONTRIBUTIONS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Conception and design: Dejan Juric, Norman J. Lacayo, Peter H. Wiernik, Jacob M. Rowe, Anthony H. Goldstone, Peter J. O'Dwyer, Elisabeth Paietta, Branimir I. Sikic

Financial support: Branimir I. Sikic

Administrative support: Branimir I. Sikic

Provision of study materials or patients: Peter H. Wiernik, Jacob M. Rowe, Anthony H. Goldstone, Peter J. O'Dwyer, Elisabeth Paietta

Collection and assembly of data: Dejan Juric, Norman J. Lacayo, Meghan C. Ramsey, Janis Racevskis, Elisabeth Paietta

Data analysis and interpretation: Dejan Juric, Norman J. Lacayo, Meghan C. Ramsey, Branimir I. Sikic

Manuscript writing: Dejan Juric, Branimir I. Sikic

Final approval of manuscript: Dejan Juric, Norman J. Lacayo, Meghan C. Ramsey, Janis Racevskis, Peter H. Wiernik, Jacob M. Rowe, Anthony H. Goldstone, Peter J. O'Dwyer, Elisabeth Paietta, Branimir I. Sikic

	Appendix

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Microarray procedures.
Gene expression profiling was performed using the 3DNA Array 900 Expression Array Detection Kit according to the manufacturer's instructions (Genisphere, Hatfield, PA). Briefly, 3 µg of both Universal Human Reference RNA (Stratagene, La Jolla, CA) and leukemia total RNA were separately reverse transcribed using the Cy3- and Cy5-specific primers, respectively, and hybridized together overnight at 65°C to a Stanford human cDNA microarray containing 41,421 cDNA elements, corresponding to 24,472 different UniGene cluster IDs. This was followed by a 4-hour secondary hybridization of the 3DNA Capture Reagent, which contains approximately 850 fluorescent dyes per 3DNA molecule. Microarrays were then coated with DyeSaver2 (Genisphere), immediately after the last wash, and scanned on a GenePix 4000B scanner (Axon Instruments, Union City, CA) using GenePix Pro 5.1 software (Axon Instruments).

Interaction networks and functional analysis.
Gene ontology and gene interaction analyses were executed using Ingenuity Pathways Analysis tools 3.0 (Ingenuity Systems, Mountain View, CA), a Web-delivered application that enables the discovery, visualization, and exploration of molecular interaction networks in gene expression data. The gene lists containing Entrez GeneIDs as clone identifiers, as well as fold-change or Wald score values determined by corresponding supervised analyses, were uploaded into the Ingenuity Pathway Analysis. Each clone identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. These so-called focus genes were then used as a starting point for generating biologic networks based on the curated list of molecular interactions in the Ingenuity Pathways Knowledge Base.

The focus gene with the highest triangle connectivity (ie, the highest number of pairs of other connected focus genes to which it is connected) was selected as a seed element for network generation. New genes were added to the network according to the specific connectivity (ie, the overlap between the current network and the new gene's direct neighborhood; the new gene and the set of genes exactly one link away). The focus genes with the highest specific connectivity were added sequentially to the growing network until the network reached an arbitrary number of 35 nodes. If the number of genes in the growing network did not reach this maximum number of nodes, smaller networks were merged through so-called linker genes—genes with the most links to both networks. Alternatively, nonfocus genes (genes that were not present in the user-defined input gene list) were also added to the network starting with the genes with the maximum number of links to the growing network. The ranking score was then computed for each network as the negative logarithm of the P value determined by a right-tailed Fisher's exact test, corresponding to the probability of finding as many or more focus genes in a set of genes randomly selected from the Ingenuity Pathway Knowledge Base and of the same size as the network in question). Similarly, significances for the enrichment of the genes in a network with particular biologic functions were determined by the right-tailed Fisher's exact test, using a list of all the genes on the array as a reference set.

Quantitative reverse transcription polymerase chain reaction.
Quantitative reverse transcription polymerase chain reaction analysis was performed with the ABI Prism 7900HT Sequence Detection System using SYBR GREEN PCR Master Mix (Applied Biosystems, Foster City, CA). Primers targeting the transcripts of IL2RA, MTSS1, BAALC, PTP4A3, GAB1, and HPRT1 were designed with the Primer3 program (http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi) and synthesized at the Stanford PAN Facility (Stanford, CA). Their sequences are as follows:

IL2RA Forward, ACTGCTCACGTTCATCATGG; IL2RA Reverse, ATGTGGCGTGTGGGATCT;

MTSS1 Forward, CTTCTTGGACGCCTTTCAGA; MTSS1 Reverse, CACATCCTGGTGAGAGCAGA;

BAALC Forward, ATGGCCTTCAGACCACAGAG; BAALC Reverse, TCTGTCCATCTGTTGGATGC;

PTP4A3 Forward, GGATGGCA TCACCGTTGT; PTP4A3 Reverse, GCCAGTCTTCCACTACCTTGC;

GAB1 Forward, TGGGTTCGTTGTATTTGTGA; GAB1 Reverse, GGTAAATCAGCTGGTGCTTG;

HPRT1 Forward, TGACACTGGCAAAACAATGCA; and HPRT1 Reverse, GGTCCTTTTCACCAGCAAGCT.

Total RNA was reverse-transcribed using SuperScript II (Invitrogen, Carlsbad, CA). Thermocycling for each PCR reaction was carried out in a final volume of 20 µL containing 0.5 ng of cDNA, forward and reverse primers at 300 nmol/L final concentration, and 1 x SYBR GREEN PCR Master Mix. After 10 minutes of initial denaturation at 95°C, the cycling conditions of 40 cycles consisted of denaturation at 95°C for 15 seconds, annealing at 55°C for 30 seconds, and elongation at 72°C for 30 seconds. All reactions were performed in triplicate. Dissociation curve analysis was performed after every run to confirm the primer specificity. Gene quantities were determined using standard curves, constructed by five serial dilutions of reverse transcription product of universal human reference RNA (Stratagene, La Jolla, CA), and gene expression levels were reported as log-transformed and median-centered ratios of quantities of the target transcript and HPRT1 as the reference transcript.

	NOTES

 
published online ahead of print at www.jco.org on February 20, 2007.

Supported by United States Public Health Service Grant No. CA 21115 to the Eastern Cooperative Oncology Group, and by the Sikic Laboratory Research Fund.

E.P. and B.I.S. contributed equally to this work.

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

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Submitted September 26, 2006; accepted January 8, 2007.

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