Recent advances in genome technologies and computational biology have facilitated genome-wide views of hematologic malignancy. In particular, comparative gene expression methods using DNA microarrays have allowed for the analysis of gene expression patterns in both primary patient material and model systems of hematopoietic development. This review provides an overview of the basic technologies underlying these approaches and provides a summary of recent progress in the genome-wide molecular classification of human acute leukemias and lymphomas and of initial attempts to define oncogene-mediated transcriptional programs using DNA microarrays.

CCAAT/enhancer binding protein alpha (C/EBPalpha) is an integral factor in the granulocytic developmental pathway, as myeloblasts from C/EBPalpha-null mice exhibit an early block in differentiation. Since mice deficient for known C/EBPalpha target genes do not exhibit the same block in granulocyte maturation, we sought to identify additional C/EBPalpha target genes essential for myeloid cell development. To identify such genes, we used both representational difference analysis and oligonucleotide array analysis with RNA derived from a C/EBPalpha-inducible myeloid cell line. From each of these independent screens, we identified c-Myc as a C/EBPalpha negatively regulated gene. We mapped an E2F binding site in the c-Myc promoter as the cis-acting element critical for C/EBPalpha negative regulation. The identification of c-Myc as a C/EBPalpha target gene is intriguing, as it has been previously shown that down-regulation of c-Myc can induce myeloid differentiation. Here we show that stable expression of c-Myc from an exogenous promoter not responsive to C/EBPalpha-mediated down-regulation forces myeloblasts to remain in an undifferentiated state. Therefore, C/EBPalpha negative regulation of c-Myc is critical for allowing early myeloid precursors to enter a differentiation pathway. This is the first report to demonstrate that C/EBPalpha directly affects the level of c-Myc expression and, thus, the decision of myeloid blasts to enter into the granulocytic differentiation pathway.

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.

In an effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.

The most damaging change during cancer progression is the switch from a locally growing tumour to a metastatic killer. This switch is believed to involve numerous alterations that allow tumour cells to complete the complex series of events needed for metastasis. Relatively few genes have been implicated in these events. Here we use an in vivo selection scheme to select highly metastatic melanoma cells. By analysing these cells on DNA arrays, we define a pattern of gene expression that correlates with progression to a metastatic phenotype. In particular, we show enhanced expression of several genes involved in extracellular matrix assembly and of a second set of genes that regulate, either directly or indirectly, the actin-based cytoskeleton. One of these, the small GTPase RhoC, enhances metastasis when overexpressed, whereas a dominant-negative Rho inhibits metastasis. Analysis of the phenotype of cells expressing dominant-negative Rho or RhoC indicates that RhoC is important in tumour cell invasion. The genomic approach allows us to identify families of genes involved in a process, not just single genes, and can indicate which molecular and cellular events might be important in complex biological processes such as metastasis.

MYC affects normal and neoplastic cell proliferation by altering gene expression, but the precise pathways remain unclear. We used oligonucleotide microarray analysis of 6,416 genes and expressed sequence tags to determine changes in gene expression caused by activation of c-MYC in primary human fibroblasts. In these experiments, 27 genes were consistently induced, and 9 genes were repressed. The identity of the genes revealed that MYC may affect many aspects of cell physiology altered in transformed cells: cell growth, cell cycle, adhesion, and cytoskeletal organization. Identified targets possibly linked to MYC's effects on cell growth include the nucleolar proteins nucleolin and fibrillarin, as well as the eukaryotic initiation factor 5A. Among the cell cycle genes identified as targets, the G1 cyclin D2 and the cyclin-dependent kinase binding protein CksHs2 were induced whereas the cyclin-dependent kinase inhibitor p21(Cip1) was repressed. A role for MYC in regulating cell adhesion and structure is suggested by repression of genes encoding the extracellular matrix proteins fibronectin and collagen, and the cytoskeletal protein tropomyosin. A possible mechanism for MYC-mediated apoptosis was revealed by identification of the tumor necrosis factor receptor associated protein TRAP1 as a MYC target. Finally, two immunophilins, peptidyl-prolyl cis-trans isomerase F and FKBP52, the latter of which plays a role in cell division in Arabidopsis, were up-regulated by MYC. We also explored pattern-matching methods as an alternative approach for identifying MYC target genes. The genes that displayed an expression profile most similar to endogenous Myc in microarray-based expression profiling of myeloid differentiation models were highly enriched for MYC target genes.

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation-for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia.

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

The TEL (translocation-Ets-leukemia or ETV6) locus, which encodes an Ets family transcription factor, is frequently rearranged in human leukemias of myeloid or lymphoid origins. By gene targeting in mice, we previously showed that TEL-/- mice are embryonic lethal because of a yolk sac angiogenic defect. TEL also appears essential for the survival of selected neural and mesenchymal populations within the embryo proper. Here, we have generated mouse chimeras with TEL-/- ES cells to examine a possible requirement in adult hematopoiesis. Although not required for the intrinsic proliferation and/or differentiation of adult-type hematopoietic lineages in the yolk sac and fetal liver, TEL function is essential for the establishment of hematopoiesis of all lineages in the bone marrow. This defect is manifest within the first week of postnatal life. Our data pinpoint a critical role for TEL in the normal transition of hematopoietic activity from fetal liver to bone marrow. This might reflect an inability of TEL-/- hematopoietic stem cells or progenitors to migrate or home to the bone marrow or, more likely, the failure of these cells to respond appropriately and/or survive within the bone marrow microenvironment. These data establish TEL as the first transcription factor required specifically for hematopoiesis within the bone marrow, as opposed to other sites of hematopoietic activity during development.

Genome-wide expression analysis was used to identify genes whose expression depends on the functions of key components of the transcription initiation machinery in yeast. Components of the RNA polymerase II holoenzyme, the general transcription factor TFIID, and the SAGA chromatin modification complex were found to have roles in expression of distinct sets of genes. The results reveal an unanticipated level of regulation which is superimposed on that due to gene-specific transcription factors, a novel mechanism for coordinate regulation of specific sets of genes when cells encounter limiting nutrients, and evidence that the ultimate targets of signal transduction pathways can be identified within the initiation apparatus.