Genomic Tools for Dissecting Oncogenic Transcriptional Networks in Human Leukemia

Genomic tools for dissecting oncogenic transcriptional networks in human leukemia

T Palomero1,2 and AA Ferrando1,2,3

1Institute for Cancer Genetics, Columbia University, New York, NY, USA; 2Department of Pathology, Columbia University Medical Center, New York, NY, USA and 3Department of Pediatrics, Columbia University Medical Center, New York, NY, USA

Chromatin immunoprecipitation (ChIP)-chip and ChIP-seq tion factor oncogenes and integrating this information with technologies are rapidly expanding our capacity to interrogate clinical and biological data will ultimately provide a compre- the location of transcription factor-binding sites in the human genome and to map the pattern of chromatin modiﬁcations hensive readout of the transcriptional network driving cell associated with the regulation of gene expression. The growth, proliferation and survival in the leukemic clone. application of these techniques to the study of hematologic malignancies will complement gene expression proﬁling

SPOTLIGHT studies to elucidate the structure and function of oncogenic Gene expression-based methods for the identification of transcriptional networks involved in the pathogenesis of transcription factor target genes leukemias and lymphomas. Leukemia (2009) 23, 1236–1242; doi:10.1038/leu.2008.394; The identification of direct target genes controlled by transcrip- published online 22 January 2009 Keywords: ChIP-Chip; ChIP-seq; ChIP; transcriptional network tion factors based on the analysis of gene expression profiles is a daunting task. However, gene expression signatures tightly associated with the expression or activation of a transcription factor of interest can be obtained in experiments that typically Introduction involve blocking protein synthesis with cycloheximide and the analysis of a time course series. These experiments typically One of the greatest challenges of the functional annotation of require a tight control of transcription factor function, which can the leukemia genome is the in-depth characterization of the be readily obtained by ligand stimulation or withdrawal in the transcription factor circuitry responsible for the regulation of case of ligand-activated transcription factors, such as the glucocorticoid and vitamin D receptors,11,12 and the PML- cellular function in leukemic cells. Mutations and chromosomal 13 rearrangements leading to structural abnormalities and/or RARA acute promyelocytic leukemia fusion oncoprotein. This aberrant expression of oncogenic transcription factor genes are approach has been extended to the analysis of additional some of the most prominent genetic alterations found in human transcription factors by generating fusions turning them into 1,2 estrogen-activated proteins by fusion with the hormone-binding leukemias. However, despite their importance, the specific 14–17 function and downstream targets of leukemia transcription domain of the estrogen receptor. Alternatively, a loss-of- factor oncogenes are largely unknown. Gene expression function variation of this approach can be achieved by profiling analysis of tumors driven by transcription factor inactivation of the transcription factor of interest by expression of a dominant negative form of the protein or by RNA oncogenes has played a key role in the classification and 18,19 molecular characterization of these diseases.3–5 However, these interference knockdown. Despite the success of these approaches do not allow distinctions to be made between approaches in the identification of relevant target genes for primary events directly controlled by oncogenic transcription oncogenic transcription factors, their use has been limited by factors and secondary transcriptional changes resulting from several technical caveats, including the need to express changes in cell proliferation or differentiation. Investigating the exogenous and structurally modified versions of the protein of direct transcriptional targets and the structure of the transcrip- interest and the broad biological effects of cycloheximide tional networks controlled by major leukemia oncogenes is treatment. Moreover, candidate target genes identified with essential to identify relevant effector genes and pathways that these techniques still need to be validated by methods that could be exploited for the development of targeted therapies. directly interrogate the binding and transcriptional effect of the In addition to transcription factor dysregulation, epigenetic transcription factor of interest. abnormalities could result in activation of oncogenic transcriptional programs and may play a major role in the pathogenesis Chromatin immunoprecipitation of human leukemias and lymphomas.6 The importance of understanding the role of epigenetic changes in hematologic A number of techniques to identify the direct target genes tumors is highlighted by the potential therapeutic use of agents controlled by transcription factors have been developed, all of targeting aberrant DNA methylation and histone deacetyla- which are based on the analysis of DNA isolated by chromatin tion.7–10 Mapping the leukemia epigenome, merging these data immunoprecipitation (ChIP). The ChIP method involves treating with the transcriptional program driven by leukemia transcrip- living cells with a crosslinking agentFusually formalde- hydeFthat fixes DNA-binding proteins to their interacting Correspondence: Dr AA Ferrando, Institute for Cancer Genetics, DNA sequences in the nucleus. Chromatin-bound genomic Columbia University, Irving Cancer Research Center 505A, 1130 St DNA is then extracted and fragmented by physical shearing or Nicholas Ave. New York, NY 10032, USA. E-mail: [email protected] enzymatic digestion. Next, specific DNA sequences associated Received 11 November 2008; revised 1 December 2008; accepted 5 with a particular protein are enriched by immunoaffinity December 2008; published online 22 January 2009 purification using a specific antibody against the DNA-binding Leukemia ChIP-Chip and ChIP-seq T Palomero and A Ferrando 1237 protein/transcription factor of interest. Immunoprecipitated interrogate the pattern of binding of a DNA-binding protein of DNA fragments are then assayed by PCR to determine the interest through all nonrepetitive DNA sequences in the association of the protein under study with a limited number of genome. known or suspected target sites. Although initial microarray platforms used for ChIP-chip analysis consisted of spotted arrays with PCR-amplified genomic regions spanning promoter sequences or CpG islands,21,23,24 ChIP-chip currently available commercial platforms have expanded the capacity and flexibility of this approach to include, in addition Over the past years, the analysis of protein–DNA interactions to promoter arrays, arrays analyzing the ENCODE regions of the has evolved from single-locus ChIP analysis to the analysis of genome, whole genome arrays and custom oligonucleotide DNA-binding sites at a genomic scale by combining ChIP assays arrays tiling specific genomic areas of interest. with microarray analysis.20 Because this approach combines Although ChIP-chip has rapidly become a well-established ChIP and DNA microarrays (also referred to as chips), it has technology, there are still some challenges and technical conventionally been referred to as ChIP-on-chip or ChIP-chip. limitations for this approach. Thus, most ChIP-chip protocols ChIP-chip combines a classic ChIP protocol with DNA require a large amount of cells as starting material, which microarray analysis to allow the detection of protein–DNA frequently limits its application to the analysis of cell lines. Some interactions in a non-biased way and with genome-wide protocols have been developed to analyze small amounts of 21,22 cells such as those derived from developing mouse embryos by coverage. Specifically, the ChIP-purified DNA is amplified, 25 fluorescently labeled and hybridized to DNA microarrays ChIP-chip. However, the requirement of large amounts of containing genomic DNA sequences along or in parallel with immunoprecipitated DNA demand using PCR-based amplifica- a control DNA sample corresponding to the total (non- tion protocols or whole genome amplification techniques to immunoprecipitated) genomic DNA. Array probes that corre- amplify the starting material, methods which have an inherent spond to the genomic-binding sites for the protein of interest are risk of introducing some bias in the representation and relative identified as those that show a significantly stronger fluorescent abundance of ChIP DNA fragments. Additionally, the ChIP-chip signal in the ChIP DNA than in the non-immunoprecipitated technique is not suitable for interrogating repetitive sequences in DNA control sample (Figure 1). Using ChIP-chip, it is possible to the genome, and sophisticated bioinformatic and statistical tools are required for data analysis.26–30 Finally, the requirement to perform biological replicas to address the reproducibility of the results and their independent validation in sequence-specific ChIP experiments of relevant targets of interest make ChIP-chip analysis a time consuming technique best suited to the analysis of a limited number of cell types and experimental conditions.

ChIP-seq

Early approaches aiming to sequence DNA fragments pulled down by ChIP using a variety of related protocols (ChIP-cloning, ChIP-SAGE and ChIP-PET)31–33 have been hampered by the high cost associated with DNA sequencing. However, the recent development of massive parallel sequencing technologies and their application to the analysis of ChIP DNA sequences is rapidly expanding our capacity for the analysis of protein–DNA interactions (for a comprehensive recent review see Chi34). Currently, there are three commercial platforms available, Roche (454) FLX Sequencer, Illumina Genome Analyzer and the Applied Biosystems SOLiD sequencer, with two more planning to hit the market in the next few yearsFHelicos SPOTLIGHT HeliScope Single Molecule and Pacific Biosciences SMRT DNA Sequencers. Each of these platforms faces the technical challenge of high throughput parallel DNA sequencing using different approaches that reflect on the output of the data.35 The most relevant differences among the three commercial platforms currently available are the lengths of the sequences and the amount of data produced by each of them (Table 1). The current Roche (454) instrument, the GS-FLX, provides relatively long reads which average 250 bp and has a maximum throughput of 100 Mb of sequence data per run. The introduction of the new Figure 1 Chromatin immunoprecipitation (ChIP)-based approaches Titanium FLX technology in the fall of 2008 aims to increase the for the identification of protein–DNA interactions. Cells are treated average length of the FLX platform to 400 bp and the total output with formaldehyde to crosslink proteins and DNA. After isolation, the of the system to 600 Mb per run. On the other side of the genomic DNA is sheared by sonication. Next, specific antibodies are spectrum, the Illumina and ABI SOLiD platforms generate used to immunoprecipitate the DNA bound to the proteins of interest, the crosslink is reversed and the DNA is used directly for sequencing shorter reads (32–40 bp for the Illumina, 35 bp for SOLiD) but (ChIP-seq) or fluorescently labeled to be hybridized in a genomic with a much higher number of reads, which ultimately generates microarray (ChIP-chip). 1–3 Gb of sequence data per run. Constant improvements in the

Leukemia Leukemia ChIP-Chip and ChIP-seq T Palomero and A Ferrando 1238 Table 1 Characteristics of next-generation DNA sequencers

Platform Roche (454) Illumina genome analyzer ABI SOLiD

Introduced 2004 2006 2007 Sequencing approach Pyrosequencing Polymerase-based sequencing by synthesis Sequence by ligation Output 600 Mb 1 Gb 3 Gb Read length 400 bp 25–40 bp 35 bp Time/run 7 h 4 days 5 days

number of reads per run and in sequence length are expected to consensus sequences, which in most cases had been identified result in expanded throughput capacity, and an upgraded through in vitro approaches, such as casting experiments. In version of the SOLiD platform that anticipates outputs of up to addition, even in the context of well characterized matrices, 20 Gb per run has been announced. Both the Roche (454) and pattern matching methods do not take into account the possible the Illumina Genome Analyzer have been used successfully for effect of other proteins in the transcriptional complex, which the analysis of chromatin modifications and transcription factor- may redirect or facilitate the binding of the transcription factor binding to DNA in human genomes.36–40 However, the shorter under study to DNA sequences not represented in the consensus reads and higher throughput provided by either Illumina or ABI matrix.42–44 In this regard, a recent study on the E2F-binding SPOTLIGHT SOLiD are considered to be an advantage, as they provide specificity confirmed the fact that only a small percentage of the higher coverage and deeper resolution for the identification of regions bound in vivo by E2F factors actually contain the E2F binding locations across the whole genome in a single run. Still, consensus motif.45 the issue of mapping putative binding events from short Pattern detection methods search for recurring and over- sequence reads is still a challenge. Improved methods with represented DNA motifs in specific subsets of sequences, such increased throughput and mate pair library protocols, which as genomic regions identified by ChIP-chip or ChIP-seq, as couples the information of the two ends of the DNA molecule to bound by a given transcription factor.42,46 Pattern detection enhance the fraction of molecules effectively mapped to the techniques can be used to identify putative DNA-binding motifs genome, partially address this issue. for transcription factors without previous knowledge of their A side-by-side comparison of ChIP-chip and ChIP-seq in the DNA-binding properties and for the discovery of neighbor analysis of STAT1 has shown a high level of concordance cooperating motifs specifically enriched in coregulated sets of between ChIP-chip and ChIP-seq data, although ChIP-seq genes. identified a larger number of STAT1 DNA-binding sites.37 In Comparative analysis of gene expression profiling and ChIP- addition, a detailed comparison of the accuracy and perfor- chip or ChIP-seq data has shown that for multiple transcription mance of ChIP-chip and ChIP-seq approaches has been recently factors, most DNA-binding sites do not seem to have a major addressed in a report that highlight the advantage of using transcriptional regulatory effect.47–50 Thus, the correct inter- ChIP-chip as stepping stone toward comprehensive ChIP-seq pretation of ChIP-chip and ChIP-seq data requires that these analysis.41 Overall, ChIP-seq approaches require less DNA than studies are combined with expression profiling analysis in the ChIP-on-chip and offer improved statistical robustness com- context of experiments modulating the expression or activity of pared with ChIP-chip analysis. In addition, ChIP-seq has the the transcription factor under study. A plausible explanation for potential to analyze binding throught the whole genome, the discrepancy between transcription factor location (DNA- including repetitive sequences. However, the high cost of binding data) and transcriptional regulation is that DNA binding ChIP-seq experiments and the intrinsic difficulties of analyzing by a transcription factor may not represent actual regulation in the enormous data output generated still limit its application. the cells analyzed, but the potential for a given locus to be regulated by a DNA-binding protein, which is only realized upon expression of temporally and/or tissue-restricted transcrip- Identification of transcription factor-binding sites tional coregulators. Alternatively, many transcription factors sites may have a redundant function with other transcriptional Initial studies aiming to identify the sequence motifs recognized regulators in that promoter and contribute only to a small by transcription factor proteins relied mostly on the analysis of fraction of the transcriptional activity of that gene. Finally, DNA- short DNA sequences isolated from oligonucleotide libraries in binding events not associated with transcriptional regulation cell-free assays by capture/affinity purification using the may reflect non-transcriptional functions of these proteins. transcription factor of interest as bait. The data generated by ChIP-chip and ChIP-seq experiments identifies a broad range of transcription factor DNA-binding events in the cell nucleus and Applications of ChIP-chip to the study of human leukemia facilitates the study of regulatory motifs recognized by and lymphoma transcription factors in the cell. In silico computational approaches for the prediction of The development of high throughput technologies for the study transcription factor-binding sites can be divided into pattern of DNA interactions has led to the mapping of protein-binding matching and pattern detection strategies. Pattern matching sites for some of the most prominent oncogenic factors in human methods use previous knowledge on the DNA sequences leukemias and lymphomas and to the identification of transcrip- recognized by a transcription factor, which is represented by tional networks that control important biological functions consensus sequences or more accurately as weight matrices, to associated with malignant transformation. Some relevant identify the regulatory motifs recognized by this particular DNA- examples include the elucidation of transcriptional regulatory binding protein in a given sequence. A caveat of these methods networks downstream of T-bet,51,52 FOXP353 and RUNX1,54 as is that they rely on the accuracy of previously described well as the characterization of the extended transcriptional

Leukemia Leukemia ChIP-Chip and ChIP-seq T Palomero and A Ferrando 1239 network downstream of nine transcription factors essential for PI3K–AKT pathway.65 Moreover, inhibition of oncogenic the pluripotency of embrionic stem cells.55 NOTCH1 was followed by transcriptional upregulation of PTEN, a critical tumor suppressor gene responsible for the termination of PI3K–AKT signaling in the cell. ChIP-chip Functional characterization of T-ALL transcription factor analysis of HES1, a transcriptional repressor directly controlled oncogenes by NOTCH166 and MYC, showed binding of these transcriptional regulators to the PTEN promoter in T-ALL cells. Thus, Aberrant expression of transcription factor oncogenes is a PTEN expression is controlled downstream of NOTCH1 by a hallmark of T-cell acute lymphoblastic leukemia (T-ALL). dual input circuit consisting of the repressor input provided by T-ALL transcription factor oncogenes include members of the HES1 and activation input delivered by c-MYC, which bHLH family, such as TAL1, TAL2, LYL1, BHLHB1 and MYC; attenuates the effect of the former.64,67,68 The importance of the LIM-only domain (LMO) genes LMO1 and LMO2; the this circuitry is highlighted by the identification of mutational orphan homeobox genes TLX1/HOX11 and TLX2/HOX11L2, loss of PTEN in T-ALL cells lines resistant to NOTCH1 members of the HOXA gene paralog group, including HOXA9 inhibition. In these tumors, the NOTCH-HES1/MYC-PTEN and HOXA10; MYB, and most prominently, the frequently transcriptional regulatory axis is disrupted, bypassing the mutated NOTCH1 gene.2,56,57 requirement of NOTCH1 signaling to maintain leukemic cell The TAL1 gene in chromosome band 1p32 plays an essential growth.64,67,68 role in the generation of early hematopoietic progenitors and in the development of erythroid and megakaryocytic cell lineages.58 Aberrant expression of TAL1 is present in over Genomic analysis of transcription factor oncogene targets in 60% of T-ALL cases2,59 and its oncogenic potential is illustrated B-cell lymphoma and myeloma by the induction of T-cell lymphoblastic tumors in transgenic mice.60,61 In T-ALL cells, TAL1 forms primarily inactive Several studies have addressed the analysis of the direct targets transcriptional complexes that contain the class I basic helix- and transcriptional networks controlled by c-MYC and BCL6 loop-helix (bHLH) proteins, E2A and HEB, suggesting that the transcription factor oncogenes in B-cell lymphoma. Pioneer oncogenic activity of TAL1 results from decreased expression of ChIP-on-chip work by the group of Bin Ren on the function of E2A/HEB target genes.62 The regulatory circuitry controlled by c-MYC in Burkitt lymphoma cells showed that MYC binds to a TAL1 in T-ALL has been analyzed in a study that integrated surprisingly large number of gene promoters.69 A result that has information from ChIP-chip analysis, gene expression profiling been subsequently confirmed by Zeller et al.32 in a study that of primary T-ALL samples and microarray expression analysis of analyzed c-MYC targets by a ChIP-PET sequencing approach. T-ALL cells in which TAL1 was inactivated by RNA inter- These results identify c-MYC as a global transcriptional regulator ference.18 This analysis uncovered a dual role for TAL1 as suggesting that c-MYC may induce transformation of hemato- repressor or activator of direct target genes. Importantly, a poietic progenitors by regulating the activity of a large transcriptional activation function for TAL1 was present in genes proportion of loci throughout the genome. whose promoters were also bound by E2A and HEB, thus BCL6 is a zinc-finger transcription factor with a key role in the challenging the existing hypothesis that TAL1 works primarily as development of germinal center B cells and in the pathogenesis a repressor of E2A/HEB transcriptional complexes. Notably, a of diffuse large B-cell lymphoma.70 ChIP-chip analysis of BCL6 number of TAL1 target genes identified in this work were in diffuse large B-cell lymphoma cells was used to define a specifically associated with the expression of this transcription signature of target genes distinguishing diffuse large B-cell factor in human primary leukemias.18 lymphoma tumors characteristically sensitive to BCL6 inhibi- Activating mutations in the NOTCH1 gene are the most tors.71 A more focused approach has been implemented for the prominent genetic abnormality in T-ALL, and inhibition of analysis of BCL6 as a transcriptional regulator of ATR, a gene NOTCH1 activation with g-secretase inhibitors has been whose product mediates the activation of a DNA damage proposed as a targeted therapy in this disease. Gene expression response.72 In this case, a locus-specific tiling array was used to analysis of T-ALL cell lines treated with a small molecule g- identify regulatory sequences in the ATR gene occupied by secretase inhibitor and ChIP-on-chip analysis of NOTCH1 in T- BCL6.72 This study, together with previous work linking the ALL cells showed that NOTCH1 binds to and regulates the function of BCL6 and the regulation of P53 function,73,74 expression of multiple genes in biosynthetic pathways, includ- identifies the attenuation of DNA damage response as an SPOTLIGHT ing genes involved in nucleotide metabolism, amino-acid important developmental function of BCL6, which enables cells biosynthesis, ribosome biogenesis and protein translation.63 to tolerate the physiologic occurrence of double-strand DNA Moreover, this study showed that NOTCH1 directly controls c- breaks during the rearrangement of immunoglobulin genes in MYC expression, so that NOTCH1 and c-MYC are integrated in germinal center B cells. an oncogenic feed-forward loop transcriptional network pro- In multiple myeloma, a transcriptional IRF4 regulatory moting leukemic cell growth.63 In agreement with this model, network involving a IRF4 and c-MYC autoregulatory circuit reverse engineering of NOTCH1 and c-MYC regulatory net- identified using a combination of ChIP-chip and expression works using gene expression profiles from primary T-ALL profiling following inactivation of IRF4 by short hairpin RNA has samples showed that NOTCH1 and c-MYC govern two highly proven to be essential for the survival of myeloma cells.75 interconnected transcriptional programs containing numerous common target genes.63 The analysis of the transcriptional regulatory network controlled by NOTCH1 in T-ALL has been Mapping histone modifications and dissecting the epigenetic subsequently extended by the characterization of a secondary code of the leukemia genome circuitry linking NOTCH1 signaling and the activity of the PI3K–AKT pathway.64 Functional analysis of NOTCH1 signaling One of the most revealing applications of ChIP-chip and in thymocyte development showed that NOTCH1 activation ChIPseq is the characterization of chromatin modifications is required to sustain cell metabolism and the activity of the associated with transcriptional regulation. Numerous studies

Leukemia Leukemia ChIP-Chip and ChIP-seq T Palomero and A Ferrando 1240 have shown that epigenetic changes may play an important role In a side-by-side comparison, ChIP-seq requires lower in the pathogenesis of human cancer. Moreover, molecular amounts of immunoprecipitated DNA and has a higher therapies targeting histone deacetylases and aberrant DNA sensitivity resulting in more effective detection of low affinity methylation are under development for the treatment of different DNA-binding sites. In addition, as microarray design and hematologic malignancies.7 A recent study of acute myeloid fabrication are not required, ChIP-seq can be directly applied and lymphoblastic leukemias analyzed the pattern of cytosine to the analysis of any organism. methylation and histone H3 lysine 9 acetylation by ChIP-chip, Overall, ChIP-chip and ChIP-seq studies provide a snapshot of and combined these results with gene expression data. This the transcriptional machinery in a particular cell type at a given analysis identified relevant biological pathways disregulated in time. As ChIP-chip and ChIP-seq methods evolve, it will be these tumors which were not readily identified using gene possible to address studies aiming to analyze dynamic changes expression profiling data alone.76 In a related study, Kuang in promoter occupancy, binding of cofactors and chromatin et al.77 analyzed CpG island regions in leukemia cell lines and modifications in response to different stimuli. However, the primary ALL samples and identified genes silenced by methyla- development of specific computational tools required for the tion in these tumors. Notably, methylation of multiple CpG integration and analysis of the vast amounts of data obtained islands was associated with a worse survival,77 as had been from these high throughput techniques is probably the most previously suggested in studies performed on methylation on relevant challenge in our way to understand the structure of selected groups of cancer-relevant genes in ALL.78,79 global transcriptional networks in normal and malignant Analysis of chromatin modifications in the context of tumors hematopoietic cells.82 harboring MLL rearrangements is particularly relevant given the SPOTLIGHT role of MLL in methylation of histone H3 at lysine 4 (H3K4); the effect of MLL fusion oncogenes in activating the expression of Acknowledgements multiple HOX genes and the characteristic poor prognosis associated with MLL rearrangements.8 ChIP-chip analysis of This study was supported by the National Institutes of Health MLL has shown that this histone methylase plays a dual role in (R01CA120196 and R01CA129382 to AF); the WOLF Foundation the control of gene expression functioning as both a global (AF), the Leukemia and Lymphoma Society (Grants 1287-08, transcriptional regulator and as a locus-specific activator. MLL 7189-08 and 6237-08 to AF), the Cancer Research Institute (AF), was found to bind to the proximal promoters most of actively the Swim Across America Foundation (AF). Adolfo Ferrando is a transcribed genes, but also to very specific regions in the Leukemia and Lymphoma Society Scholar. Teresa Palomero is a genome, primarily within the HOXA cluster, associated with recipient of a Young Investigator Award from the Alex’s Lemonade activation of gene expression.80 Notably, ChIP-chip analysis Stand Foundation. with an antibody against H3K43Me showed that in both instances, the regions occupied by MLL overlapped with the Conflict of interest histone H3 trymethylation mark. The authors declare no conflict of interest. Discussion, future challenges and perspectives References The development of ChIP-chip and ChIP-seq methods has overcome some of the major technical bottlenecks for mapping 1 Teitell M, Pandolfi P. Molecular genetics of acute lymphoblastic transcription factor-binding sites and for the genome-wide leukemia. Annu Rev Pathol 2008; 4. identification of chromatin modifications. However, some of 2 Ferrando A, Look A. Clinical implications of recurring chromoso- the technical limitations inherent to the ChIP technique still mal and associated molecular abnormalities in acute lymphoblas- pose a challenge for both ChIP-chip and ChIP-seq. First, ChIP tic leukemia. Semin Hematol 2000; 37: 381–395. protocols typically require large numbers of cells as starting 3 Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J et al. Molecular classification of cancer: class discovery and class material, which hinders the application of ChIP-chip and prediction by gene expression monitoring. Science 1999; 286: ChIP-seq in hematopoietic progenitors and clinical specimens. 531–537. However, successful analysis of histone modification after ChIP 4 Ferrando A, Neuberg D, Staunton J, Loh M, Huard C, Raimondi S with as few as 105 cells has been reported.81 Moreover, the et al. Gene expression signatures define novel oncogenic lower DNA requirements of ChIPseq approaches compared with pathways in T cell acute lymphoblastic leukemia. Cancer Cell ChIP-chip, may facilitate the implementation of protocols 2002; 1: 75–87. 5 Yeoh E, Ross M, Shurtleff S, Williams W, Patel D, Mahfouz R et al. adequate for the analysis of small populations of cells. Classification, subtype discovery, and prediction of outcome in Despite the increasing robustness of ChIP-chip and ChIP-seq pediatric acute lymphoblastic leukemia by gene expression protocols none of these methods is exempt of false negatives, the profiling. Cancer Cell 2002; 1: 133–143. rate of which may be affected by the relative abundance of the 6 Barski A, Cuddapah S, Cui K, Roh T, Schones D, Wang Z et al. transcription factor analyzed, the affinity of the different binding High-resolution profiling of histone methylations in the human sites, the effectiveness of the antibody in recognizing the genome. Cell 2007; 129: 823–837. 7 Jain N, Rossi A, Garcia-Manero G. Epigenetic therapy of leukemia: formaldehyde-fixed protein and the possible masking effects of An update. Int J Biochem Cell Biol 2009; 41: 72–80. different cofactors recruited to the transcriptional complex. 8 Krivtsov A, Armstrong S. MLL translocations, histone modifications Ideally, ChIP-chip and ChIP-seq experiments should be and leukaemia stem-cell development. Nat Rev Cancer 2007; 7: performed in multiple replicas performed with several 823–833. antibodies against the transcription factor of interest. Thus, a 9 Griffiths E, Gore S. DNA methyltransferase and histone deacetylase necessary step toward the comprehensive annotation of inhibitors in the treatment of myelodysplastic syndromes. Semin Hematol 2008; 45: 23–30. transcription factor-binding sites is the availability of 10 Atallah E, Garcia-Manero G. Use of hypomethylating agents in ChIP-validated antibodies for all transcriptional regulators myelodysplastic syndromes. Clin Adv Hematol Oncol 2007; 5: encoded in the mouse and human genomes. 544–552.

Leukemia Leukemia ChIP-Chip and ChIP-seq T Palomero and A Ferrando 1241 11 Wang T, Tavera-Mendoza L, Laperriere D, Libby E, MacLeod N, 33 Liu Y, Li F, Handler J, Huang C, Xiang Y, Neretti N et al. Global Nagai Y et al. Large-scale in silico and microarray-based regulation of nucleotide biosynthetic genes by c-Myc. PLoS ONE identification of direct 1,25-dihydroxyvitamin D3 target genes. 2008; 3: e2722. Mol Endocrinol 2005; 19: 2685–2695. 34 Chi KR. The year of sequencing. Nat Methods 2008; 5: 11–14. 12 U M, Shen L, Oshida T, Miyauchi J, Yamada M, Miyashita T. 35 Mardis E. Next-generation DNA sequencing methods. Annu Rev Identification of novel direct transcriptional targets of glucocorti- Genomics Hum Genet 2008; 9: 387–402. coid receptor. Leukemia 2004; 18: 1850–1856. 36 Boyle A, Davis S, Shulha H, Meltzer P, Margulies E, Weng Z et al. 13 Park D, Vuong P, de Vos S, Douer D, Koeffler H. Comparative High-resolution mapping and characterization of open chromatin analysis of genes regulated by PML/RAR alpha and PLZF/RAR across the genome. Cell 2008; 132: 311–322. alpha in response to retinoic acid using oligonucleotide arrays. 37 Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T Blood 2003; 102: 3727–3736. et al. Genome-wide profiles of STAT1 DNA association using 14 Wang D, Sevcikova S, Wen H, Roberts S, Lipsick J. v-Myb chromatin immunoprecipitation and massively parallel sequen- represses the transcription of Ets-2. Oncogene 2007; 26: cing. Nat Methods 2007; 4: 651–657. 1238–1244. 38 Johnson D, Mortazavi A, Myers R, Wold B. Genome-wide 15 Zeisig B, Milne T, Garcıá-Cue´llar M, Schreiner S, Martin M, Fuchs mapping of in vivo protein–DNA interactions. Science 2007; U et al. Hoxa9 and Meis1 are key targets for MLL-ENL-mediated 316: 1497–1502. 39 Jothi R, Cuddapah S, Barski A, Cui K, Zhao K. Genome-wide cellular immortalization. Mol Cell Biol 2004; 24: 617–628. identification of in vivo protein-DNA binding sites from ChIP-Seq 16 Grandori C, Gomez-Roman N, Felton-Edkins Z, Ngouenet C, data. Nucleic Acids Res 2008; 36: 5221–5231. Galloway D, Eisenman R et al. c-Myc binds to human ribosomal 40 Valouev A, Johnson D, Sundquist A, Medina C, Anton E, Batzoglou DNA and stimulates transcription of rRNA genes by RNA S et al. Genome-wide analysis of transcription factor binding sites polymerase I. Nat Cell Biol 2005; 7: 311–318. based on ChIP-Seq data. Nat Methods 2008; 5: 829–834. 17 Tamura T, Thotakura P, Tanaka T, Ko M, Ozato K. Identification of 41 Schmidt D, Stark R, Wilson MD, Brown GD, Odom DT. Genome- target genes and a unique cis element regulated by IRF-8 in scale validation of deep-sequencing libraries. PLoS ONE 2008; 3: developing macrophages. Blood 2005; 106: 1938–1947. e3713. 18 Palomero T, Odom D, O’Neil J, Ferrando A, Margolin A, Neuberg 42 Gangwal K, Sankar S, Hollenhorst P, Kinsey M, Haroldsen S, Shah D et al. Transcriptional regulatory networks downstream of TAL1/ A et al. Microsatellites as EWS/FLI response elements in Ewing’s SCL in T-cell acute lymphoblastic leukemia. Blood 2006; 108: sarcoma. Proc Natl Acad Sci USA 2008; 105: 10149–10154. 986–992. 43 Truscott M, Harada R, Vadnais C, Robert F, Nepveu A. p110 CUX1 19 Smith R, Owen L, Trem D, Wong J, Whangbo J, Golub T et al. cooperates with E2F transcription factors in the transcriptional Expression profiling of EWS/FLI identifies NKX2.2 as a critical activation of cell cycle-regulated genes. Mol Cell Biol 2008; 28: target gene in Ewing’s sarcoma. Cancer Cell 2006; 9: 405–416. 3127–3138. 20 Hudson M, Snyder M. High-throughput methods of regulatory 44 Leung J, Ehmann G, Giangrande P, Nevins J. A role for Myc in element discovery. Biotechniques 2006; 41: 673, 675, 677 passim. facilitating transcription activation by E2F1. Oncogene 2008; 27: 21 Ren B, Cam H, Takahashi Y, Volkert T, Terragni J, Young R et al. 4172–4179. E2F integrates cell cycle progression with DNA repair, replication, 45 Rabinovich A, Jin V, Rabinovich R, Xu X, Farnham P. E2F in vivo and G(2)/M checkpoints. Genes Dev 2002; 16: 245–256. binding specificity: comparison of consensus versus nonconsensus 22 Weinmann A, Yan P, Oberley M, Huang T, Farnham P. Isolating binding sites. Genome Res 2008; 18: 1763–1777. human transcription factor targets by coupling chromatin immu- 46 Elnitski L, Jin V, Farnham P, Jones S. Locating mammalian noprecipitation and CpG island microarray analysis. Genes Dev transcription factor binding sites: a survey of computational and 2002; 16: 235–244. experimental techniques. Genome Res 2006; 16: 1455–1464. 23 Weinmann A, Farnham P. Identification of unknown target genes 47 Hayakawa J, Mittal S, Wang Y, Korkmaz KS, Adamson E, English C of human transcription factors using chromatin immunoprecipita- et al. Identification of promoters bound by c-Jun/ATF2 during rapid tion. Methods 2002; 26: 37–47. large-scale gene activation following genotoxic stress. Mol Cell 24 Martone R, Euskirchen G, Bertone P, Hartman S, Royce T, 2004; 16: 521–535. Luscombe N et al. Distribution of NF-kappaB-binding sites across 48 Impey S, McCorkle S, Cha-Molstad H, Dwyer J, Yochum G, human chromosome 22. Proc Natl Acad Sci USA 2003; 100: Boss J et al. Defining the CREB regulon: a genome-wide analysis 12247–12252. of transcription factor regulatory regions. Cell 2004; 119: 25 O’Neill LP, VerMilyea MD, Turner BM. Epigenetic characteriza- 1041–1054. tion of the early embryo with a chromatin immunoprecipitation 49 Zhang X, Odom D, Koo S, Conkright M, Canettieri G, Best J et al. protocol applicable to small cell populations. Nat Genet 2006; 38: Genome-wide analysis of cAMP-response element binding protein 835–841. occupancy, phosphorylation, and target gene activation in human 26 Kim TH, Barrera LO, Zheng M, Qu C, Singer MA, Richmond TA tissues. Proc Natl Acad Sci USA 2005; 102: 4459–4464. 50 Phuc Le P, Friedman JR, Schug J, Brestelli JE, Parker JB, Bochkis IM et al. A high-resolution map of active promoters in the human et al. Glucocorticoid receptor-dependent gene regulatory genome. Nature 2005; 436: 876–880. networks. PLoS Genet 2005; 1: e16. 27 Li W, Meyer CA, Liu XS. A hidden Markov model for analyzing 51 Townsend M, Weinmann A, Matsuda J, Salomon R, Farnham P, ChIP-chip experiments on genome tiling arrays and its application Biron C et al. T-bet regulates the terminal maturation and to p53 binding sequences. Bioinformatics 2005; 21 (Suppl 1): homeostasis of NK and Valpha14i NKT cells. Immunity 2004; i274–i282. 20: 477–494. 28 Gibbons FD, Proft M, Struhl K, Roth FP. Chipper: discovering 52 Beima K, Miazgowicz M, Lewis M, Yan P, Huang T, Weinmann A. transcription-factor targets from chromatin immunoprecipitation T-bet binding to newly identified target gene promoters is cell microarrays using variance stabilization. Genome Biol 2005; 6:R96. type-independent but results in variable context-dependent func- 29 Qi Y, Rolfe A, MacIsaac KD, Gerber GK, Pokholok D, Zeitlinger J tional effects. J Biol Chem 2006; 281: 11992–12000. et al. High-resolution computational models of genome binding 53 Marson A, Kretschmer K, Frampton G, Jacobsen E, Polansky J, SPOTLIGHT events. Nat Biotechnol 2006; 24: 963–970. MacIsaac K et al. Foxp3 occupancy and regulation of key target 30 Zheng M, Barrera L, Ren B, Wu Y. ChIP-chip: data, model, and genes during T-cell stimulation. Nature 2007; 445: 931–935. analysis. Biometrics 2007; 63: 787–796. 54 Hug B, Ahmed N, Robbins J, Lazar M. A chromatin immuno- 31 Yochum G, Cleland R, Goodman R. A genome-wide screen for precipitation screen reveals protein kinase Cbeta as a direct {beta}-catenin binding sites identifies a downstream enhancer RUNX1 target gene. J Biol Chem 2004; 279: 825–830. element that controls c-Myc gene expression. Mol Cell Biol 2008; 55 Kim J, Chu J, Shen X, Wang J, Orkin S. An extended transcriptional 28: 7368–7379. network for pluripotency of embryonic stem cells. Cell 2008; 132: 32 Zeller K, Zhao X, Lee C, Chiu K, Yao F, Yustein J et al. Global 1049–1061. mapping of c-Myc binding sites and target gene networks in 56 O’Neil J, Look A. Mechanisms of transcription factor deregulation human B cells. Proc Natl Acad Sci USA 2006; 103: 17834–17839. in lymphoid cell transformation. Oncogene 2007; 26: 6838–6849.

Leukemia Leukemia ChIP-Chip and ChIP-seq T Palomero and A Ferrando 1242 57 Aifantis I, Raetz E, Buonamici S. Molecular pathogenesis of T-cell 70 Parekh S, Prive G, Melnick A. Therapeutic targeting of the BCL6 leukaemia and lymphoma. Nat Rev Immunol 2008; 8: 380–390. oncogene for diffuse large B-cell lymphomas. Leuk Lymphoma 58 Elefanty A, Begley C, Hartley L, Papaevangeliou B, Robb L. SCL 2008; 49: 874–882. expression in the mouse embryo detected with a targeted lacZ 71 Polo J, Juszczynski P, Monti S, Cerchietti L, Ye K, Greally J et al. reporter gene demonstrates its localization to hematopoietic, Transcriptional signature with differential expression of BCL6 vascular, and neural tissues. Blood 1999; 94: 3754–3763. target genes accurately identifies BCL6-dependent diffuse large B 59 Bash R, Hall S, Timmons C, Crist W, Amylon M, Smith R et al. cell lymphomas. Proc Natl Acad Sci USA 2007; 104: 3207–3212. Does activation of the TAL1 gene occur in a majority of patients 72 Ranuncolo S, Polo J, Dierov J, Singer M, Kuo T, Greally J et al. Bcl- with T-cell acute lymphoblastic leukemia? A pediatric oncology 6 mediates the germinal center B cell phenotype and lymphoma- group study. Blood 1995; 86: 666–676. genesis through transcriptional repression of the DNA-damage 60 Kelliher M, Seldin D, Leder P. Tal-1 induces T cell acute sensor ATR. Nat Immunol 2007; 8: 705–714. lymphoblastic leukemia accelerated by casein kinase IIalpha. 73 Phan R, Dalla-Favera R. The BCL6 proto-oncogene suppresses p53 EMBO J 1996; 15: 5160–5166. expression in germinal-centre B cells. Nature 2004; 432: 635–639. 61 Condorelli G, Facchiano F, Valtieri M, Proietti E, Vitelli L, 74 Phan R, Saito M, Basso K, Niu H, Dalla-Favera R. BCL6 interacts Lulli V et al. T-cell-directed TAL-1 expression induces with the transcription factor Miz-1 to suppress the cyclin- T-cell malignancies in transgenic mice. Cancer Res 1996; 56: dependent kinase inhibitor p21 and cell cycle arrest in germinal 5113–5119. center B cells. Nat Immunol 2005; 6: 1054–1060. 62 O’Neil J, Shank J, Cusson N, Murre C, Kelliher M. TAL1/SCL 75 Shaffer AL, Emre NC, Lamy L, Ngo VN, Wright G, Xiao W et al. induces leukemia by inhibiting the transcriptional activity of E47/ IRF4 addiction in multiple myeloma. Nature 2008; 454: 226–231. HEB. Cancer Cell 2004; 5: 587–596. 76 Figueroa M, Reimers M, Thompson R, Ye K, Li Y, Selzer R et al. An 63 Palomero T, Lim W, Odom D, Sulis M, Real P, Margolin A et al. integrative genomic and epigenomic approach for the study of NOTCH1 directly regulates c-MYC and activates a feed-forward- transcriptional regulation. PLoS ONE 2008; 3: e1882. SPOTLIGHT loop transcriptional network promoting leukemic cell growth. Proc 77 Kuang S, Tong W, Yang H, Lin W, Lee M, Fang Z et al. Genome- Natl Acad Sci USA 2006; 103: 18261–18266. wide identification of aberrantly methylated promoter associated 64 Palomero T, Sulis M, Cortina M, Real P, Barnes K, Ciofani M et al. CpG islands in acute lymphocytic leukemia. Leukemia 2008; 22: Mutational loss of PTEN induces resistance to NOTCH1 inhibition 1529–1538. in T-cell leukemia. Nat Med 2007; 13: 1203–1210. 78 Roman-Gomez J, Jimenez-Velasco A, Castillejo JA, Agirre X, 65 Ciofani M, Zuń˜iga-Pflu¨cker J. Notch promotes survival of pre-T Barrios M, Navarro G et al. Promoter hypermethylation of cancer- cells at the beta-selection checkpoint by regulating cellular related genes: a strong independent prognostic factor in acute metabolism. Nat Immunol 2005; 6: 881–888. lymphoblastic leukemia. Blood 2004; 104: 2492–2498. 66 Jarriault S, Brou C, Logeat F, Schroeter E, Kopan R, Israel A. 79 Roman-Gomez J, Cordeu L, Agirre X, Jimenez-Velasco A, San Jose- Signalling downstream of activated mammalian Notch. Nature Eneriz E, Garate L et al. Epigenetic regulation of Wnt-signaling 1995; 377: 355–358. pathway in acute lymphoblastic leukemia. Blood 2007; 109: 67 Palomero T, Dominguez M, Ferrando A. The role of the PTEN/AKT 3462–3469. Pathway in NOTCH1-induced leukemia. Cell Cycle 2008; 7: 80 Guenther M, Jenner R, Chevalier B, Nakamura T, Croce C, 965–970. Canaani E et al. Global and Hox-specific roles for the 68 Palomero T, Ferrando A. Oncogenic NOTCH1 control of MYC and MLL1 methyltransferase. Proc Natl Acad Sci USA 2005; 102: PI3K: challenges and opportunities for anti-NOTCH1 therapy in 8603–8608. T-cell acute lymphoblastic leukemias and lymphomas. Clin 81 Acevedo L, Iniguez A, Holster H, Zhang X, Green R, Farnham P. Cancer Res 2008; 14: 5314–5317. Genome-scale ChIP-chip analysis using 10 000 human cells. 69 Li Z, Van Calcar S, Qu C, Cavenee W, Zhang M, Ren B. A global Biotechniques 2007; 43: 791–797. transcriptional regulatory role for c-Myc in Burkitt’s lymphoma 82 Pop M, Salzberg S. Bioinformatics challenges of new sequencing cells. Proc Natl Acad Sci USA 2003; 100: 8164–8169. technology. Trends Genet 2008; 24: 142–149.

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