Genomic approaches to hematologic malignancies

Clinical quantitation of diagnostic and predictive gene expression levels in follicular and diffuse large B-cell lymphoma by RT-PCR gene expression profiling

Recent microarray gene expression profiling studies have identified gene signatures predictive of outcome, so-called "indicator" genes, for diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). However, measurement of these genes in routine practice remains difficult. We applied real-time polymerase chain reaction (PCR) to polyA cDNAs prepared from 106 archived human frozen lymph nodes (63 of FL, 25 of DLBCL, 10 reactive lymph nodes, and cases with paired samples of FL [4] and subsequent DLBCL [4]). Reverse transcription and polyA reverse transcriptase (RT)-PCR was performed, and resultant cDNA was probed by real-time PCR for 36 candidate indicator genes, selected from microarray studies. Nine genes showed statistically significant different expression between FL and DLBCL, including cyclin B, COL3A1, NPM3, H731, PRKCB1, OVGL, ZFPC150, HLA-DQ-a, and XPB. Of these, cyclin B, NPM3, and COL3A1 were higher in DLBCL. Six genes showed statistically significant higher expression in the neoplastic nodes compared with reactive nodes, namely PRKCB1, BCL-6, EAR2, ZFX, cyclin B, YY1. High levels of YY.1 were associated with a shorter survival interval in both FL and DLBCL. The method is simple, sensitive, and robust, facilitating routine use and may be used as a platform for clinical measurement of prognostic gene signatures.

Prediction of graft-versus-host disease in humans by donor gene-expression profiling

BACKGROUND

Graft-versus-host disease (GVHD) results from recognition of host antigens by donor T cells following allogeneic hematopoietic cell transplantation (AHCT). Notably, histoincompatibility between donor and recipient is necessary but not sufficient to elicit GVHD. Therefore, we tested the hypothesis that some donors may be "stronger alloresponders" than others, and consequently more likely to elicit GVHD.

METHODS AND FINDINGS

To this end, we measured the gene-expression profiles of CD4(+) and CD8(+) T cells from 50 AHCT donors with microarrays. We report that pre-AHCT gene-expression profiling segregates donors whose recipient suffered from GVHD or not. Using quantitative PCR, established statistical tests, and analysis of multiple independent training-test datasets, we found that for chronic GVHD the "dangerous donor" trait (occurrence of GVHD in the recipient) is under polygenic control and is shaped by the activity of genes that regulate transforming growth factor-beta signaling and cell proliferation.

CONCLUSIONS

These findings strongly suggest that the donor gene-expression profile has a dominant influence on the occurrence of GVHD in the recipient. The ability to discriminate strong and weak alloresponders using gene-expression profiling could pave the way to personalized transplantation medicine.

Expression of tumor-associated antigens in acute myeloid leukemia: implications for specific immunotherapeutic approaches

The expression of tumor-associated antigens (TAAs) might play a critical role in the control of minimal residual disease (MRD) in acute myeloid leukemia (AML), and therefore might be associated with clinical outcome in AML. In a DNA microarray analysis of 116 AML samples, we found a significant correlation between high mRNA levels of G250/CA9 and longer overall survival (P = .022), a similar trend with high mRNA levels of PRAME (P = .103), and a hint for RHAMM/HMMR. In contrast, for other TAAs like WT1, TERT, PRTN3, BCL2, and LAMR1, we found no correlation with clinical outcome. High expression of at least 1 of the 3 TAAs, RHAMM/HMMR, PRAME, or G250/CA9, provided the strongest favorable prognostic effect (P = .005). Specific T-cell responses were detected in 8 (47%) of 17 patients with AML in complete remission for RHAMM/HMMR-R3 peptide, in 7 (70%) of 10 for PRAME-P3 peptide, and in 6 (60%) of 10 for newly characterized G250/CA9-G2 peptide, a significant increased immune response compared with patients with AML patients who had refractory disease (P < .001). Furthermore, we could demonstrate specific lysis of T2 cells presenting these epitope peptides. In conclusion, expression of the TAAs RHAMM/HMMR, PRAME, and G250/CA9 can induce strong antileukemic immune responses, possibly enabling MRD control. Thus, these TAAs represent interesting targets for polyvalent immunotherapeutic approaches in AML.

Technology Insight: tuning into the genetic orchestra using microarrays--limitations of DNA microarrays in clinical practice

Scientific advances in the field of genetics and gene-expression profiling have revolutionized the concept of patient-tailored treatment. Analysis of differential gene-expression patterns across thousands of biological samples in a single experiment (as opposed to hundreds to thousands of experiments measuring the expression of one gene at a time), and extrapolation of these data to answer clinically pertinent questions such as those relating to tumor metastatic potential, can help define the best therapeutic regimens for particular patient subgroups. The use of microarrays provides a powerful technology, allowing in-depth analysis of gene-expression profiles. Currently, microarray technology is in a transition phase whereby scientific information is beginning to guide clinical practice decisions. Before microarrays qualify as a useful clinical tool, however, they must demonstrate reliability and reproducibility. The high-throughput nature of microarray experiments imposes numerous limitations, which apply to simple issues such as sample acquisition and data mining, to more controversial issues that relate to the methods of biostatistical analysis required to analyze the enormous quantities of data obtained. Methods for validating proposed gene-expression profiles and those for improving trial designs represent some of the recommendations that have been suggested. This Review focuses on the limitations of microarray analysis that are continuously being recognized, and discusses how these limitations are being addressed.

Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target

Glioblastoma is the most common primary malignant brain tumor of adults and one of the most lethal of all cancers. Patients with this disease have a median survival of 15 months from the time of diagnosis despite surgery, radiation, and chemotherapy. New treatment approaches are needed. Recent works suggest that glioblastoma patients may benefit from molecularly targeted therapies. Here, we address the compelling need for identification of new molecular targets. Leveraging global gene expression data from two independent sets of clinical tumor samples (n = 55 and n = 65), we identify a gene coexpression module in glioblastoma that is also present in breast cancer and significantly overlaps with the "metasignature" for undifferentiated cancer. Studies in an isogenic model system demonstrate that this module is downstream of the mutant epidermal growth factor receptor, EGFRvIII, and that it can be inhibited by the epidermal growth factor receptor tyrosine kinase inhibitor Erlotinib. We identify ASPM (abnormal spindle-like microcephaly associated) as a key gene within this module and demonstrate its overexpression in glioblastoma relative to normal brain (or body tissues). Finally, we show that ASPM inhibition by siRNA-mediated knockdown inhibits tumor cell proliferation and neural stem cell proliferation, supporting ASPM as a potential molecular target in glioblastoma. Our weighted gene coexpression network analysis provides a blueprint for leveraging genomic data to identify key control networks and molecular targets for glioblastoma, and the principle eluted from our work can be applied to other cancers.

Translating microarray data for diagnostic testing in childhood leukaemia

Background

Recent findings from microarray studies have raised the prospect of a standardized diagnostic gene expression platform to enhance accurate diagnosis and risk stratification in paediatric acute lymphoblastic leukaemia (ALL). However, the robustness as well as the format for such a diagnostic test remains to be determined. As a step towards clinical application of these findings, we have systematically analyzed a published ALL microarray data set using Robust Multi-array Analysis (RMA) and Random Forest (RF).

Methods

We examined published microarray data from 104 ALL patients specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent patient cohort.

Results

We achieved very high overall ALL subgroup prediction accuracies of about 98%, and were able to verify the robustness of these genes in an independent panel of 68 specimens obtained from a different institution and processed in a different laboratory. Our study established that the selection of discriminating genes is strongly dependent on the analysis method. This may have profound implications for clinical use, particularly when the classifier is reduced to a small set of genes. We have demonstrated that as few as 26 genes yield accurate class prediction and importantly, almost 70% of these genes have not been previously identified as essential for class distinction of the six ALL subgroups.

Conclusion

Our finding supports the feasibility of qRT-PCR technology for standardized diagnostic testing in paediatric ALL and should, in conjunction with conventional cytogenetics lead to a more accurate classification of the disease. In addition, we have demonstrated that microarray findings from one study can be confirmed in an independent study, using an entirely independent patient cohort and with microarray experiments being performed by a different research team.

Expression of Livin, an antiapoptotic protein, is an independent favorable prognostic factor in childhood acute lymphoblastic leukemia

Livin, a member of the inhibitor of apoptosis proteins, has been considered to be a poor prognostic marker in malignancies. However, little is known about the clinical relevance of Livin expression in childhood acute lymphoblastic leukemia (ALL). In this study, the expression of Livin was analyzed in 222 patients with childhood ALL using quantitative reverse transcriptase–polymerase chain reaction (RT-PCR) to investigate a possible association with the clinical features at diagnosis and treatment outcomes. Both Livin expression rates and expression levels were higher in patients with favorable prognostic factors. The expression rate was also higher in patients with a favorable day 7 bone marrow response to induction chemotherapy (P < .001). The Livin expression was related to the absence of relapse (P < .001). Similarly, the relapse-free survival rate (± 95% CI) was higher in patients with Livin expression than in patients without Livin expression (97.9% ± 4.0% versus 64.9% ± 11.8%, P < .001). Multivariate analysis for relapse-free survival demonstrated that Livin expression was an independent favorable prognostic factor in childhood ALL (P = .049). This study suggests that Livin expression is a novel prognostic marker in childhood ALL and thus needs to be incorporated into the patient stratification and treatment protocols.

Molecular Profiling of CD34+Cells in Idiopathic Myelofibrosis Identifies a Set of Disease-Associated Genes and Reveals the Clinical Significance of Wilms' Tumor Gene 1 (WT1)

This study was aimed at the characterization of a gene expression signature of the pluripotent hematopoietic CD34(+) stem cell in idiopathic myelofibrosis (IM), which would eventually provide novel pathogenetic insights and/or diagnostic/prognostic information. Aberrantly regulated genes were revealed by transcriptome comparative microarray analysis of normal and IM CD34(+) cells; selected genes were also assayed in granulocytes. One-hundred seventy four differentially expressed genes were identified and in part validated by quantitative polymerase chain reaction. Altered gene expression was corroborated by the detection of abnormally high CD9 or CD164, and low CXCR4, membrane protein expression in IM CD34(+) cells. According to class prediction analysis, a set of eight genes (CD9, GAS2, DLK1, CDH1, WT1, NFE2, HMGA2, and CXCR4) properly recognized IM from normal CD34(+) cells. These genes were aberrantly regulated also in IM granulocytes that could be reliably differentiated from control polycythemia vera and essential thrombocythemia granulocytes in 100% and 81% of cases, respectively. Abnormal expression of HMGA2 and CXCR4 in IM granulocytes was dependent on the presence and the mutational status of JAK2(V617F) mutation. The expression levels of both CD9 and DLK1 were associated with the platelet count, whereas higher WT1 expression levels identified IM patients with more active disease, as revealed by elevated CD34(+) cell count and higher severity score. In conclusion, molecular profiling of IM CD34(+) cells uncovered a limited number of genes with altered expression that, beyond their putative role in disease pathogenesis, are associated with patients' clinical characteristics and may have potential prognostic application.

Genomic profiling in clinical oncology. The predictive value of genomic information in cancer management

Genomic profiling is a significant addition to the clinician's armamentarium, enabling more specific diagnosis and targeted treatment of a wide range of diseases, including cancer. Although not yet widely used in a clinical setting, the new technology is providing detailed information about the particular genes expressed in malignant tissues. The predictive value of this information has been demonstrated in small clinical trials. The results of these and other groundbreaking studies are briefly reviewed.

Biologic pathways associated with relapse in childhood acute lymphoblastic leukemia: a Children's Oncology Group study

Outcome for children with childhood acute lymphoblastic leukemia (ALL) who relapse is poor. To gain insight into the mechanisms of relapse, we analyzed gene-expression profiles in 35 matched diagnosis/relapse pairs as well as 60 uniformly treated children at relapse using the Affymetrix platform. Matched-pair analyses revealed significant differences in the expression of genes involved in cell-cycle regulation, DNA repair, and apoptosis between diagnostic and early-relapse samples. Many of these pathways have been implicated in tumorigenesis previously and are attractive targets for intervention strategies. In contrast, no common pattern of changes was observed among late-relapse pairs. Early-relapse samples were more likely to be similar to their respective diagnostic sample while we noted greater divergence in gene-expression patterns among late-relapse pairs. Comparison of expression profiles of early- versus late-relapse samples indicated that early-relapse clones were characterized by overexpression of biologic pathways associated with cell-cycle regulation. These results suggest that early-relapse results from the emergence of a related clone, characterized by the up-regulation of genes mediating cell proliferation. In contrast, late relapse appears to be mediated by diverse pathways.

Imaging of multiple mRNA targets using quantum dot based in situ hybridization and spectral deconvolution in clinical biopsies

Gene expression mapping using microarray analysis has identified useful gene signatures for predicting outcome. However, little of this has been translated into clinically effective diagnostic tools as microarrays require high quality fresh-frozen tissue samples. We describe a methodology of multiplexed in situ hybridization (ISH) using a novel combination of quantum dot (QD)-labeled oligonucleotide probes and spectral imaging analysis in routinely processed, formalin-fixed paraffin embedded human biopsies. The conditions for QD-ISH were optimized using a poly d(T) oligonucleotide in decalcified bone marrow samples. Single and multiplex QD-ISH was performed in samples with acute leukemia and follicular lymphoma using oligonucleotide probes for myeloperoxidase, bcl-2, survivin, and XIAP. Spectral imaging was used for post hybridization tissue analysis, enabling separation of spatially colocalized signals. The method allows quantitative characterization of multiple gene expression using non-bleaching fluorochromes. This is expected to facilitate multiplex in situ transcript detection in routinely processed human clinical tissue.

Cancers as wounds that do not heal: differences and similarities between renal regeneration/repair and renal cell carcinoma

Cancers have been described as wounds that do not heal, suggesting that the two share common features. By comparing microarray data from a model of renal regeneration and repair (RRR) with reported gene expression in renal cell carcinoma (RCC), we asked whether those two processes do, in fact, share molecular features and regulatory mechanisms. The majority (77%) of the genes expressed in RRR and RCC were concordantly regulated, whereas only 23% were discordant (i.e., changed in opposite directions). The orchestrated processes of regeneration, involving cell proliferation and immune response, were reflected in the concordant genes. The discordant gene signature revealed processes (e.g., morphogenesis and glycolysis) and pathways (e.g., hypoxia-inducible factor and insulin-like growth factor-I) that reflect the intrinsic pathologic nature of RCC. This is the first study that compares gene expression patterns in RCC and RRR. It does so, in particular, with relation to the hypothesis that RCC resembles the wound healing processes seen in RRR. However, careful attention to the genes that are regulated in the discordant direction provides new insights into the critical differences between renal carcinogenesis and wound healing. The observations reported here provide a conceptual framework for further efforts to understand the biology and to develop more effective diagnostic biomarkers and therapeutic strategies for renal tumors and renal ischemia.

Genomic analysis of circulating cells: a window into atherosclerosis

Translational studies using genomic techniques in cardiovascular diseases are still in their infancy. Access to disease-associated cardiovascular tissues from patients has been a major impediment to progress in contrast to the diagnostic advances made by oncologists using gene expression on readily available tumor samples. Nonetheless, progress is being made for atherosclerosis by carefully designed experiments utilizing diseased tissue or surrogate specimens. This review details the rationale and findings of a study utilizing freshly isolated blood mononuclear cells from patients undergoing carotid endarterectomy due to atherosclerotic stenosis and from matched healthy subjects. By querying this cardiovascular tissue surrogate, the messenger RNA levels of the Finkel-Biskis-Jenkins osteosarcoma gene in circulating monocytes were found to correlate with atherosclerosis severity in patients and with 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor (statin) therapy in healthy subjects. The major finding of this investigation is discussed in relation to observations from other human atherosclerosis gene expression studies. These distinct studies converge to demonstrate the unequivocal importance of inflammation in atherosclerosis. Although the clinical utility of the specific findings remains open, the identification of similar genes by different investigations serves to validate our report. They also provide us with insights into pathogenesis that may impact future translational applications.

Childhood acute lymphoblastic leukemia in the age of genomics

The recent sequencing of the human genome and technical breakthroughs now make it possible to simultaneously determine mRNA expression levels of almost all of the identified genes in the human genome. DNA "chip" or microarray technology holds great promise for the development of more refined, biologically-based classification systems for childhood ALL, as well as the identification of new targets for novel therapy. To date gene expression profiles have been described that correlate with subtypes of ALL defined by morphology, immunophenotype, cytogenetic alterations, and response to therapy. Mechanistic insights into treatment failure have come from the definition of mRNA signatures that predict in vitro chemoresistance, as well as differences between blasts at relapse and new diagnosis. New bioinformatics tools optimize data mining, but validation of findings is essential since "over-fitting" the data is a common danger. In the future, genomic analysis will be complemented by evaluation of the cancer proteome.

Comparison of gene-expression profiles in parallel bone marrow and peripheral blood samples in acute myeloid leukaemia by real-time polymerase chain reaction

BACKGROUND

Gene signatures (Indicator genes) in bone marrow that provide more precise prognostication in haematological malignancy have been identified by microarray expression studies. It would be beneficial to measure these diagnostic signatures in peripheral blood.

AIMS

To determine the degree of correspondence of gene expression for a set of Indicator genes between bone marrow and peripheral blood in acute myeloid leukaemia (AML).

METHODS

Parallel bone marrow aspirate and peripheral blood samples were obtained from 19 patients diagnosed with AML and mononuclear cells isolated from both sample types. mRNA was globally amplified by polyadenylated real-time polymerase chain reaction (polyA RT-PCR); the expression of 15 AML Indicator genes, identified from previous microarray studies, was measured by RT-PCR. All values were normalised to the mean expression of three housekeeping genes (IF2-beta, GAP and RbS9) and were statistically compared using SPSS software.

RESULTS

No significant difference in expression between bone marrow and peripheral blood was observed for 10 of the genes (leptin receptor, CD33, adipsin, proteoglycan 1, MB-1, cyclin D3, hSNF2b, proteasome iota, HkrT-1 and E2A), indicating its possible use in monitoring disease activity in peripheral blood samples, whereas c-myb, HOXA9, LYN, cystatin c and LTC4s showed significantly different expression between bone marrow and peripheral blood samples.

CONCLUSION

These results indicate a possible use for the method in monitoring AML in peripheral blood by RT-PCR measurement of Indicator genes. In addition, the initial use of polyA PCR facilitates translation to very small clinical samples, including fractionated cell populations, of particular importance for monitoring haematological malignancy.

Aven overexpression: association with poor prognosis in childhood acute lymphoblastic leukemia

Aven expression has recently been identified as an anti-apoptotic protein. In this study, Aven expression in 91 children with acute lymphoblastic leukemia (ALL) was investigated for possible correlation with clinical features at diagnosis and treatment outcome. Aven expression was found to be higher in patients >or=10 years old or <1 year (P=0.003), and in patients with unfavorable cytogenetic abnormalities (P<0.001). Aven expression was also significantly higher in relapsed patients in the standard-risk group. Aven overexpression was an independent poor prognostic factor. These findings demonstrate that Aven expression can predict prognosis in childhood ALL.

Biomarkers of potential prognostic significance in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous disease for which the current approach to treatment is only successful for 50% of patients. The prognostic value of various clinical and biological factors in predicting treatment outcome is discussed in this paper. A review of the English literature was performed including original articles and relevant reviews from MEDLINE that addressed the topics of DLBCL biology and potential prognostic factors. The International Prognostic Index is, to date, the most successful clinical model for predicting outcome. In addition, a rapidly expanding list of molecules has been identified by conventional and newer diagnostic methods that may be of significant prognostic value. Gene expression profiling has led to the discovery of new biological subtypes of DLBCL based on patterns of gene expression, and a host of new genes that may play important roles in this disease. Various derangements in apoptosis, cell-cycle regulation, differentiation, and signal transduction have been noted, while the host environment and immune response also appear to modify clinical outcome. Although to our knowledge, the fundamental abnormalities underlying DLBCL remain elusive, progress is being continuously made to further the understanding of the biological heterogeneity of this disease and the use of various clinical and biological variables to predict treatment outcome. The goal is to be able to identify subgroups of patients at high risk of treatment failure and develop more effective treatment based on specific biological defects that may represent new rational therapeutic targets.

Comparison of mRNA abundance quantified by gene expression profiling and percentage of positive cells using immunophenotyping for diagnostic antigens in acute and chronic leukemias

BACKGROUND

Microarray analysis is considered a future diagnostic tool in leukemias. Whereas data accumulate on specific gene expression patterns in biologically defined leukemia entities, data on the correlation between flow cytometrically determined protein expression, which are essential in the diagnostic setting today, and microarray results are limited.

METHODS

The results obtained by microarray analysis were compared using the Affymetrix GeneChip HG-U133 system in parallel with flow cytometric findings of 36 relevant targets in 814 patients with newly diagnosed acute and chronic leukemias as well as in normal bone marrow samples.

RESULTS

In a total of 21,581 individual comparisons between signal intensities obtained by microarray analysis and percentages of positive cell as determined by flow cytometry, coefficients of correlation in the range of 0.171 to 0.807 were obtained. In particular, the degree of correlation was high in the following genes critical in the diagnostic setting: CD4, CD8, CD13 (ANPEP), CD33, CD23 (FCER2), CD64 (FCGR1A), CD117 (KIT), CD34, MPO, CD20 (MS4A1), CD7 (range of r, 0.589-0.807).

CONCLUSIONS

The present data prove the high degree of correlation between findings obtained by microarray analysis and flow cytometry. They are in favor of a future application of the microarray technology as a robust diagnostic tool in leukemias.

Pathogenesis of myelofibrosis with myeloid metaplasia

The primary disease process in myelofibrosis with myeloid metaplasia (MMM) is clonal myeloproliferation with varying degrees of phenotypic differentiation. This is characteristically accompanied by secondary intramedullary collagen fibrosis, osteosclerosis, angiogenesis, and extramedullary hematopoiesis. Modern clonality studies have confirmed the multipotent stem-cell origin of the neoplastic process in MMM. The nature of the specific oncogenic mutation(s) is currently being unraveled with the recent discovery of an association between a somatic point mutation of JAK2 tyrosine kinase (V617F) and bcr/abl-negative myeloproliferative disorders, including MMM. The pathogenetic mechanisms that underlie the secondary bone marrow stromal changes in MMM are also incompletely understood. Mouse models of this latter disease aspect have been constructed by either in vivo overexpression of thrombopoietin (TPOhigh mice) or megakaryocyte lineage restricted underexpression of the transcription factor GATA-1 (GATA-1low mice). Gene knockout experiments using such animal models have suggested the essential role of hematopoietic cell-derived transforming growth factor beta1 in inducing bone marrow fibrosis and stromal cell-derived osteoprotegerin in promoting osteosclerosis. However, experimental myelofibrosis in mice does not recapitulate clonal myeloproliferation that is fundamental to human MMM. Other cytokines that are implicated in mediating myelofibrosis and angiogenesis in MMM include basic fibroblast, platelet-derived, and vascular endothelial growth factors. It is currently assumed that such cytokines are abnormally released from clonal megakaryocytes as a result of a pathologic interaction with neutrophils (eg, emperipolesis). This latter phenomenon, through neutrophil-derived elastase, could also underlie the abnormal peripheral-blood egress of myeloid progenitors in MMM.

Gene expression profiling identifies BAX-delta as a novel tumor antigen in acute lymphoblastic leukemia

The identification of new tumor-associated antigens (TAA) is critical for the development of effective immunotherapeutic strategies, particularly in diseases like B-cell acute lymphoblastic leukemia (B-ALL), where few target epitopes are known. To accelerate the identification of novel TAA in B-ALL, we used a combination of expression profiling and reverse immunology. We compared gene expression profiles of primary B-ALL cells with their normal counterparts, B-cell precursors. Genes differentially expressed by B-ALL cells included many previously identified as TAA in other malignancies. Within this set of overexpressed genes, we focused on those that may be functionally important to the cancer cell. The apoptosis-related molecule, BAX, was highly correlated with the ALL class distinction. Therefore, we evaluated BAX and its isoforms as potential TAA. Peptides from the isoform BAX-delta bound with high affinity to HLA-A*0201 and HLA-DR1. CD8+ CTLs specific for BAX-delta epitopes or their heteroclitic peptides could be expanded from normal donors. BAX-delta-specific T cells lysed peptide-pulsed targets and BAX-delta-expressing leukemia cells in a MHC-restricted fashion. Moreover, primary B-ALL cells were recognized by BAX-delta-specific CTL, indicating that this antigen is naturally processed and presented by tumor cells. This study suggests that (a) BAX-delta may serve as a widely expressed TAA in B-ALL and (b) gene expression profiling can be a generalizable tool to identify immunologic targets for cancer immunotherapy.

Discovering causes and cures for cancer from gene expression analysis

Tumorigenesis is governed by a series of complex genetic and epigenetic changes. Both mechanisms can result in either the silencing or aberrant expression of messages in a cell. Gene expression profiling techniques such as the serial analysis of gene expression (SAGE) or microarray analysis can provide global overviews of these changes, as well identify key genes and pathways involved in this process. This review outlines the current roles of these techniques in cancer research, and how they may contribute to finding not only mechanisms of this disease, but potential targets for therapy.

Gene expression profiling in acute myeloid leukemia

Over the last decades, significant advances have been made in the knowledge and treatment of acute myeloid leukemia (AML). The WHO has recognized this new information by incorporating into its classification morphologic, immunophenotypic, genetic, and clinical features in an attempt to define biologically and clinically relevant entities. Nevertheless, well-defined cytogenetic subgroups exhibit considerable heterogeneity, and in many AML subtypes the pathogenic event is still not known. A classification system based on the underlying molecular pathogenetic abnormalities would be ideal, but such detailed knowledge is not yet available. Novel approaches in genomics, such as surveying the expression levels of thousands of genes in parallel using DNA microarray technology, open possibilities to further refine the studies on AML. Today, gene expression profiling in AML is becoming well established and has already been proven to be valuable in diagnosing different cytogenetic subtypes, discovering novel AML subclasses, and predicting clinical outcome. Recently, gene expression profiling studies in AML showed a remarkable level of concordance in findings, which may ultimately lead to an increasingly refined molecular taxonomy. While many challenges remain to be overcome, a combination of gene expression profiling with other microarray-based applications, high-throughput mutational analyses and proteomic approaches will not only significantly contribute to the classification and therapeutic decision making of AML, but also give important insights into the true pathobiologic nature of this type of leukemia.

Routine expression profiling of microarray gene signatures in acute leukaemia by real-time PCR of human bone marrow

Cancer subtype diagnosis using microarray signatures has the potential to transform pathological diagnosis but the routine measurement of genes signatures remains difficult. Reverse transcription polymerase chain reaction (RT-PCR) measurement of Indicator genes for acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL) was used to determine gene signatures. Bone marrow (BM) mononuclear cells were sorted into total, CD34(+) and CD34(-) fractions, and mRNAs globally amplified from each fraction using polyA PCR. The expression profile of the 17 top-ranked genes distinguishing AML and ALL were measured by RT-PCR in five ALL, 26 AML, 12 AML remission, four chronic myeloid leukaemia (CML) and nine morphologically normal BM samples. All but two of the genes measured showed similar expression in AML and ALL to that reported previously. Specifically, c-MYB (P </= 0.04) was significantly increased in ALL in the total fraction, whilst HOXA9 (P </= 0.19) and cystatin c (P </= 0.01) were increased in AML in the CD34(+) and CD34(-) fractions, respectively. c-MYB, hSNF2, RBAP48, HKRT-1, LYN, CD33, Adipsin and HOXA9 were increased in AML compared with remission AML, indicating an ability to determine disease activity. The method used is simple, sensitive and robust, enabling routine clinical use, and it can also be extended to other tumours types with gene signatures.

G1/S transcriptional networks modulated by the HOX11/TLX1 oncogene of T-cell acute lymphoblastic leukemia

The HOX11/TLX1 homeobox gene is aberrantly expressed in a subset of T-cell acute lymphoblastic leukemia (T-ALL). Here, we employed oligonucleotide microarrays to compare the expression profiles of the K3P and Sil leukemic cell lines originating from patients with HOX11+ T-ALL to that of Jurkat cells, which originated from a distinct subtype of T-ALL (TAL1+). To distinguish potential HOX11 target genes from those characteristic of the stage of HOX11 leukemic arrest, we also performed gene expression analysis on Jurkat cells, genetically engineered to express exogenous HOX11. The resulting HOX11 gene expression signature, which was validated for representative signaling pathways by transient transfection of reporter constructs, was characterized by elevated expression of transcriptional programs involved in cell proliferation, including those regulated by E2F, c-Myc and cAMP response element-binding protein. We subsequently showed that ectopic HOX11 expression resulted in hyperphosphorylation of the retinoblastoma protein (Rb), which correlated with inhibition of the major Rb serine/threonine phosphatase PP1. HOX11 also inhibited PP2A serine/threonine phosphatase activity concomitant with stimulation of the AKT/PKB signaling cascade. These results suggest that transcriptional deregulation of G1/S growth-control genes, mediated in large part through blockade of PP1/PP2A phosphatase activity, plays an important role in HOX11 pathobiology.

New insights into MLL gene rearranged acute leukemias using gene expression profiling: shared pathways, lineage commitment, and partner genes

Rearrangements of the MLL gene occur in both acute lymphoblastic and acute myeloid leukemias (ALL, AML). This study addressed the global gene expression pattern of these two leukemia subtypes with respect to common deregulated pathways and lineage-associated differences. We analyzed 73 t(11q23)/MLL leukemias in comparison to 290 other acute leukemias and demonstrate that 11q23 leukemias combined are characterized by a common specific gene expression signature. Additionally, in unsupervised and supervised data analysis algorithms, ALL and AML cases with t(11q23) segregate according to the lineage they are derived from, that is, myeloid or lymphoid, respectively. This segregation can be explained by a highly differing transcriptional program. Through the use of novel biological network analyses, essential regulators of early B cell development, PAX5 and EBF, were shown to be associated with a clear B-lineage commitment in lymphoblastic t(11q23)/MLL leukemias. Also, the influence of the different MLL translocation partners on the transcriptional program was directly assessed. Interestingly, gene expression profiling did not reveal a clear distinct pattern associated with one of the analyzed partner genes. Taken together, the identified molecular expression pattern of MLL fusion gene samples and biological networks revealed new insights into the aberrant transcriptional program in 11q23/MLL leukemias.

Clinical features of molecular pathology of solid tumours in childhood

The outlook for children with cancer has improved substantially over the past 20 years, with over three-quarters of children now surviving in the long term. Better use of existing cytotoxic drugs and supportive care have made large contributions, but some of the improvement in survival is due to a greater knowledge of childhood cancer at the cellular and molecular levels. As in leukaemias, several childhood solid tumours carry balanced chromosomal translocations, resulting in fusion genes that encode chimeric proteins with new oncogenic properties. Many of these fusion genes, and other genetic aberrations are tumour specific and are related to outcome. Tumour biology now plays an important part in identifying appropriate treatment through more accurate diagnoses and new risk stratifications based on molecular markers.

PAGE: parametric analysis of gene set enrichment

BACKGROUND

Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.

RESULTS

We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level.

CONCLUSION

PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.

Gene expression profiling using RNA extracted from whole blood: technologies and clinical applications

Gene expression profiling promises to provide an insight into normal biologic and pathologic processes with the hope of predicting disease outcome or indicating individualized courses of therapy. The entire process for gene expression profiling from clinical samples consists of sample collection, transport and storage of clinical sample, isolation of nucleic acids, enzymatic modification of nucleic acids, detection and data analysis. All steps exert an influence on the quality, accuracy and reliability of the final result. A standardization of the entire process from sample collection to nucleic acid analysis is therefore required in order to achieve reliable gene expression results. After providing a general overview of technologies for gene expression profiling and respective caveats, this review will focus on clinical applications of blood sample profiling.

HOXA genes are included in genetic and biologic networks defining human acute T-cell leukemia (T-ALL)

Using a combination of molecular cytogenetic and large-scale expression analysis in human T-cell acute lymphoblastic leukemias (T-ALLs), we identified and characterized a new recurrent chromosomal translocation, targeting the major homeobox gene cluster HOXA and the TCRB locus. Real-time quantitative polymerase chain reaction (RQ-PCR) analysis showed that the expression of the whole HOXA gene cluster was dramatically dysregulated in the HOXA-rearranged cases, and also in MLL and CALM-AF10-related T-ALL cases, strongly suggesting that HOXA genes are oncogenic in these leukemias. Inclusion of HOXA-translocated cases in a general molecular portrait of 92 T-ALLs based on large-scale expression analysis shows that this rearrangement defines a new homogeneous subgroup, which shares common biologic networks with the TLX1- and TLX3-related cases. Because T-ALLs derive from T-cell progenitors, expression profiles of the distinct T-ALL subgroups were analyzed with respect to those of normal human thymic subpopulations. Inappropriate use or perturbation of specific molecular networks involved in thymic differentiation was detected. Moreover, we found a significant association between T-ALL oncogenic subgroups and ectopic expression of a limited set of genes, including several developmental genes, namely HOXA, TLX1, TLX3, NKX3-1, SIX6, and TFAP2C. These data strongly support the view that the abnormal expression of developmental genes, including the prototypical homeobox genes HOXA, is critical in T-ALL oncogenesis.

Establishment of T cell-specific and natural killer cell-specific unigene sets: towards high-throughput genomics of leukaemia

We report the establishment of highly non-redundant unigene sets consisting of cDNA clones derived from T lymphocytes and natural killer cells. Each set consists of 10 506 and 13 409 clones, respectively, arrayed on nylon membranes in duplicate. The sets provide an excellent tool for genome-wide gene expression analysis studies in immunology research.