Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Genomics of Acute Myeloid Leukemia: The Next Generation

Genomics of Acute Myeloid Leukemia: The Next Generation REVIEW ARTICLE published: 01 May 2012 doi: 10.3389/fonc.2012.00040 1†‡ 2‡ 1 Laura Riva , Lucilla Luzi and Pier Giuseppe Pelicci * Department of Experimental Oncology, European Institute of Oncology, Milan, Italy IFOM, The FIRC Institute of Molecular Oncology Foundation, Milan, Italy Edited by: Acute myeloid leukemia (AML) is, as other types of cancer, a genetic disorder of somatic Napoleone Ferrara, Genentech, USA cells. The detection of somatic molecular abnormalities that may cause and maintain AML Reviewed by: is crucial for patient stratification. The development of mutation-specific therapeutic inter- Keisuke Ito, Beth Israel Deaconess ventions will hopefully increase cure rates and improve patients’ quality of life. This review Medical Center, USA illustrates how next generation sequencing technologies are changing the study of cancer Shridar Ganesan, University of Medicine and Dentistry of New genomics of adult AML patients. Jersey, USA Keywords: acute myeloid leukemia, next generation sequencing, somatic mutations, recurrent mutations *Correspondence: Pier Giuseppe Pelicci, Department of Experimental Oncology, European Institute of Oncology, Via Adamello, 16, 20139 Milan, Italy. e-mail: piergiuseppe.pelicci@ ifom-ieo-campus.it Present address: Laura Riva, Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia at the IFOM-IEO Campus, Milan, Italy. Laura Riva and Lucilla Luzi have contributed equally to this work. Acute myeloid leukemia (AML) is the most frequent hematolog- More recently, other mutations associated to AML have been ical malignancy in adults, with an estimated worldwide annual identified (FLT3, CEBP, NPM1, IDH1/2) and their prognostic incidence of three to four cases per 100,000 people. Despite inten- power investigated particularly in the intermediate risk category. FLT3–ITD and CEBPA mutations seem to associate with a bad sive research for new therapies and prognostic markers, it is still a disease with a highly variable prognosis among patients and a prognosis, while NPM1 and IDH1/2 are controversial. However, high mortality rate. Indeed, less than 50% of adult AML patients several challenges still lie ahead and markers are needed to predict have a 5-year overall survival rate (OS), and, in the elderly, only prognosis and sensibility to treatment. 20% survive 2 years (Gregory et al., 2009). Understanding the genetic lesions associated to AML is also In general, both prognosis and treatment choice for AML important in order to adjust for specific therapies. For example, patients are based on the presence or absence of specific genetic Acute Promyelocytic Leukemia (APL, one of the AML subtypes) is alterations, which determine AML classification in three risk treated with a combination of the differentiation-inducing agent based-categories: favorable, intermediate, and unfavorable. This ATRA (all-trans retinoic acid) and chemotherapy, which induces classification is usually based on cytogenetic information. AML long-term remissions or cure in 75–85% of patients. Some of the with a favorable prognosis includes patients with inv(16) (that newly described genetic lesions (e.g., FLT3) may be targeted by specific inhibitors which have shown anti-leukemic efficacy in pre- generates the CBFB–MYH11 fusion protein), t(15;17) (that gener- ates the PML–RARA fusion protein), or t(8;21) (that generates the liminary studies, and are now currently being evaluated in phase AML1–ETO fusion protein). The 5-year OS rate of patients in this III clinical trials. category is 55%. The unfavorable subgroup includes patients with The advent of second- (or next) generation sequencing tech- monosomy 5, monosomy 7, 11q23 (that generates MLL-highly nologies has dramatically accelerated biological and biomedical variable breakpoints on the partner fusion protein), or complex discoveries by enabling comprehensive analysis of genomes, tran- cytogenetics, and the 5-year OS rate is reduced to 11%. Favor- scriptomes, and DNA–protein interactions. These technologies able prognosis AML patients are usually treated with primary allow the identification of cancer-associated mutations at a single- chemotherapy, while high-risk patients are considered for allo- base resolution in an unbiased manner, and will likely revolution- genic stem cell transplantation in first remission if a suitable donor ize our understanding of cancer. A comprehensive description of is found. The intermediate subgroup includes normal karyotype somatic mutations in cancer is essential as it can (i) shed light on tumor initiation and progression mechanisms, (ii) assist patient (NK) AML patients. Patients belonging to this group have a 5-year OS rate ranging between 24 and 42%, depending on the study, but stratification for prognosis and treatment choice, and (iii) allow it is still largely unclear what might be the best therapeutic strategy the identification of new genes that can be specifically targeted by for them (Gregory et al., 2009; Tefferi et al., 2009). therapy. www.frontiersin.org May 2012 | Volume 2 | Article 40 | 1 Riva et al. AML mutational analysis and NGS Massive parallel sequencing is now discovering a growing num- The two parameters to take into considerations to understand ber of submicroscopic somatic mutations with prognostic signif- data analysis and interpretation are the “coverage” and the “read icance. These, together with the primary somatic genetic abnor- lengths.” Coverage is the number of tags aligned to each base of malities already identified, are enabling the drawing of patient the reference genome. A high coverage is desired because it can mutation profiles and will hopefully have a major impact on overcome errors in base calling and assembly, and it can reduce the clinical management of AML, not only as independent prog- false positives. Longer read lengths are more easily mapped to the nostic factors, but also as the foundation of genome-informed reference genome, increasing the proportion of the genome that personalized cancer treatments. is mappable. Moreover, longer read lengths are essential for the In this review, we will examine the somatic mutations recently detection of small indels. identified using next generation sequencing (NGS). First, we will Each of these techniques has pros and cons (see Table 3). describe which types of mutations can be detected by sequenc- Whole-genome sequencing allows identification of all the possible ing and comment on the pros and cons of different technological variants at once, and it is the best method to study chromo- approaches (synthesized in Table 3). Then, we will describe all somal rearrangements; however, it is expensive ($5000–$15,000 the identified mutations and the subsets of recurring mutations per sample, depending on the sequencing services and cover- according to sequencing technology and mutation type (cataloged age) and requires a high amount of starting material (usually in Tables 1 and 2). Finally, we will discuss future perspectives in 1 μg of genomic DNA). Exome-sequencing reduces costs ($1000– the use of NGS technologies in the clinical setting and existing $2000 per sample), but not the amount of starting material open challenges. (usually around 3 μg of genomic DNA), and allows high cov- erage in coding regions. Exome-sequencing relies on a capture step that may not have uniform efficiency, and the identification MASSIVE PARALLEL SEQUENCING APPROACHES FOR MUTATIONAL ANALYSIS IN AML of chromosomal rearrangements is restricted to exonic regions. To identify AML somatic mutations by NGS, sequencing is usually RNA-sequencing is capable of detecting variants present in the performed on DNA or RNA obtained from bone marrow sam- transcriptome and fusion genes of expressed genes (Maher et al., ples (with high level of tumor cellularity) and normal tissues (skin 2009). RNA-sequencing, which necessitates 0.1–4 μgof RNA as biopsies or peripheral blood) from the same AML patient when starting material, further reduces costs ($300–$500 per sample); in clinical remission. This approach aims to define somatic vari- importantly, while allowing identification of tumor-specific fusion ants, including single nucleotide variants (SNVs), short deletions transcripts or mRNA-splice variants, it also offers information on and insertions (indels), structural variants (SVs) such as translo- gene expression levels. There are three main disadvantages, how- cations, long insertions or deletions, and copy number variations ever, in using RNA-sequencing to detect somatic variants. First, the (CNVs), which are present in the tumor sample and absent in identification of the corresponding normal sample is challenging the matched control sample. Usually, the sequences from tumor and, even if one could successfully identify it, gene expression in and normal samples are mapped to the reference genome and the cancer cells is altered from that of normal cells. Second, SNVs and sequence changes (variants) that differ from the reference genome indels within genes that are transcribed at very low levels or in are identified. Variants present in both tumor and control samples those for which mutations may induce mRNA degradation may (generally referred to as germline variants) and variants matching be missed. Finally, the chance of errors due to reverse transcriptase known single nucleotide polymorphisms (SNPs) are discarded. and the phenomenon of RNA editing (Li et al., 2011) can make All the identified variants are then validated by using an these data difficult to interpret (Meyerson et al., 2010). independent sequencing technology, for example DNA Sanger sequencing. Finally, the validated variants are usually tested on WHOLE-GENOME SEQUENCING a large number of clinical samples, in order to determine their The first demonstration of the possibility to identify somatic actual frequency and to identify recurrent mutations. mutations in cancer genomes using sequencing technologies was Currently, there are three experimental approaches, which obtained in a patient with AML (NK, M1 subtype; Ley et al., are most frequently utilized to identify somatic mutations 2008). The authors, using single-end whole-genome sequencing, by NGS: whole-genome sequencing, exome-sequencing, and identified mutations in the entire genome but decided to vali- transcriptome-sequencing (also known as RNA-sequencing). date only those which (i) had occurred in coding sequences, (ii) Whole-genome sequencing allows the identification of the entire were non-synonymous, or (iii) were predicted to alter splicing sites DNA sequence of a given sample, at single-base resolution level. (all the 181 identified variants and 28 manually selected indels). Exome-sequencing, instead, is preceded by an exome capture step In this first study, the percentage of computationally identified that selects the coding regions of the genome (representing ∼1% false positive variants was quite high, since only 5% of the iden- of the genome). RNA-sequencing measures the transcriptome. tified mutations could be validated. The authors discovered 10 Sequencing is performed using either single-end or paired-end non-synonymous somatic mutations: eight novel SNVs and two tags (PET). In PET, short and paired reads are obtained from previously described indels (i.e., in NPM1 and FLT3; Table 1). the ends of DNA fragments for sequencing. The use of PET in They sequenced the 8 novel SNVs in 187 additional AML cases genome re-sequencing has advantages over the use of single tags, but could not find any of these variants. as it allows higher mapping specificity and the identification of In the following year, the same group sequenced another patient small and large insertions, deletions, and translocations, which is with cytogenetically normal AML-M1 (Mardis et al., 2009), using not possible using single-end tags. paired-end whole-genome sequencing. In this second attempt, Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 2 Riva et al. AML mutational analysis and NGS www.frontiersin.org May 2012 | Volume 2 | Article 40 | 3 Table 1 | Numerical summary of identified and validated variants found in adult AMLs by NGS technologies. Study Sequencing Leukemia Number of Non- SNV in SNV in Indels in Translocation Inversion Insertion Deletion CNV Targets platform type tumor synonymous non-coding splicing coding genes samples SNV regions sites regions Ley et al. (2008) Whole-genome H-NK 1 8 2 10 a b Mardis et al. (2009) Whole-genome H-NK 1 7 52 12 10 Ley et al. (2010) Whole-genome H-NK 1 1 1 d c Ramsingh et al. (2010) MicroRNAome , H-NK 1 1in3 -UTR 1 whole-genome Greif et al. (2011a) Exome capture H-M3 3 12 1 13 Greif et al. (2011b) RNA-seq H-NK 1 5 5 Link et al. (2011) Whole-genome H-(t-AML) 1 16 228 12 26 Wartman et al. (2011) Whole-genome M-M3-like 1 3 14 Welch et al. (2011) Whole-genome H-M3 1 12 2 1 1 3 15 e f Yan et al. (2011) Exome capture H-NK 9 58 8166 Grossmann et al. (2011) Exome capture H-NK 1 12 1 11 g h Ding et al. (2012) Whole-genome H-NK and 8 141 H-M3 TOT 26 human, 130 53 3 13 11 1 1 4 12 281 1 mouse SNV, single nucleotide variant; CNV, copy number variation; H, human, M, mouse; NK, normal karyotype; t-AML, therapy-related AML; the authors also report other synonymous SNVs that are not considered in b c d this table; in conserved or regulatory portions of the genome; in this work the authors describe an independent sequencing of the relapsed tumor derived from the same patient studied in Ley et al. (2008); no miRNA were found mutated from the sequencing of the microRNAome; re-analysis of the whole-genome experiment of Ley et al. (2010) led to the identification of the 3 -UTR SNV; 58 SNVs in 56 distinct genes; f g h 8 indels in 7 distinct genes; for this study we do not specify the number of the identified variants since they were classified by a different method; the nine genes whose mutations were relapse-specific and absent in the additional 200 primary AMLs considered for testing recurrently mutated genes are not reported Ding et al. (2012). Riva et al. AML mutational analysis and NGS Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 4 Table 2 | Catalog of genes targeted by recurrent molecular genetic abnormalities in adult AMLs as detected by NGS technologies. Gene Gene name Identified by Leukemia Frequency (%) Detailed leukemia Mutation type/s Status Reference symbol type subtypes (%) b c DNMT3A DNA (cytosine-5-)- Whole-genome H-M1/NK 62/281 (22.0) M0 (10), M1 (25.4), M2 Non-synonymous SNV Novel Ley et al. (2010) methyltransferase-3-alpha (16.7), M3 (0), M4 (32.8), M5 (57.1), M6 (0), M7 (33.3) Whole-genome H-M1/NK 11/38 (28.9) Not shown Non-synonymous SNV Novel Ley et al. (2010) Exome capture H-NK 32/355 (9.0) M1 (0), M2 (0), M3 (0), M4 Non-synonymous SNV Novel Yan et al. (2011) (13.6), M5 (20.5) Exome capture H-NK(*) 34/195 (17.4) – Non-synonymous SNV Novel Grossmann et al. (2011) Whole-genome H-NK 49/200 (24.5) Not shown Non-synonymous SNV, Novel Ding et al. (2012) frame-shift indel FLT3 Fms-related tyrosine kinase 3 Exome capture H-NK 21/112 (18.8) M5 Non-synonymous SNV, ITD Known Yan et al. (2011) Whole-genome H-M1/NK 51/185 (27.6) Not shown ITD Known Ley et al. (2008) Whole-genome H-NK 52/200 (26.0) Not shown Non-synonymous SNV, ITD Known Ding et al. (2012) IDH1 Isocitrate dehydrogenase 1 Whole-genome H-M1/NK 16/188 (8.5) Not shown Non-synonymous SNV Novel Mardis et al. (2009) Whole-genome H-NK 20/200 (10.0) Not shown Non-synonymous SNV Novel Ding et al. (2012) (NADP+), soluble IDH2 Isocitrate dehydrogenase 2 Whole-genome H-NK 19/200 (9.5) Not shown Non-synonymous SNV Novel Ding et al. (2012) (NADP+), mitochondrial MLL Myeloid/lymphoid or Exome capture H-NK 22/112 (19.6) M5 Translocation or partial Known Yan et al. (2011) mixed-lineage leukemia tandem duplication NPM1 Nucleophosmin (nucleolar Whole-genome H-M1/NK 43/180 (23.9) – Frame-shift indel Known Ley et al. (2008) Whole-genome H-M1/NK Not validated – Frame-shift indel Known Mardis et al. (2009) phosphoprotein B23, numatrin) Whole-genome H-NK 53/200 (26.5) Not shown Frame-shift indel Known Ding et al. (2012) NRAS Neuroblastoma RAS viral (v-ras) Whole-genome H-M1/NK 20/188 (10.6) Not shown Non-synonymous SNV Known Mardis et al. (2009) Exome capture H-NK 12/112 (10.7) M5 SNV Known Yan et al. (2011) oncogene homolog RUNX1 Runt-related transcription RNA-seq H-M1/NK 9/95 (9.5) Not shown Non-synonymous SNV Known Greif et al. (2011b) factor 1 Exome capture H-NK Not validated M5 Frame-shift indel Known Yan et al. (2011) Whole-genome H-NK 17/200 (8.5) Not shown Non-synonymous SNV, Known Ding et al. (2012) frame-shift indel TTN Titin Whole-genome H-NK 13/200 (6.5) Not shown In-frame indel, Novel Ding et al. (2012) Non-synonymous SNV WT1 Wilms tumor 1 Exome capture H-NK 3/112 (2.7) M5 Frame-shift indel, in-frame Known Yan et al. (2011) indel Whole-genome H-NK 13/200 (6.5) Not shown Frame-shift indel Known Ding et al. (2012) The 10 genes listed in this table were found in at least 5% of the tumor samples. SNV, single nucleotide variant; H, human, M, mouse; NK, normal karyotype; somatic mutations checked only in the same leukemia b c subtypes where originally identified; validation performed by Sanger re-sequencing of DNMT3A exons in 281 samples; identified through array-based genomic re-sequencing also by Yamashita few months earlier Yamashita et al. (2010); validation performed in 38 samples by whole-genome sequencing; NK(*) are normal karyotype AML patients not showing NPM1, CEBPA, FLT3–ITD, or MLL–PTD mutations. Riva et al. AML mutational analysis and NGS Table 3 | Comparison of pros and cons of whole-genome sequencing, exome-sequencing, and RNA-sequencing. Characteristics Whole-genome sequencing Exome-sequencing Transcriptome-sequencing Cost $5000–$15,000 per sample $1000–$2000 per sample The cheapest method: $300–$500 per sample Starting material 1 μg of genomic DNA 3 μg of genomic DNA 0.1–4 μgofRNA Detectable variants All possible variants Restricted to exonic regions, hard to identify Detection of variants present in the transcrip- structural variants, and copy number variations tome and fusion genes Pros Detection of all the possible Lower cost, greater depth of coverage, and Identification of tumor-specific fusion tran- variants present in a corresponding improvement in data quality scripts, mRNA-splice variants, and informa- genome essential to detect mutations at lower frequency tion on gene expression levels Cons Very expensive, so typically Hard to identify structural variants and copy Hard to identify the corresponding normal designed with low coverage number alterations samples. Hard to identify SNVs and indels in transcripts at low expression or for which mutations may induce mRNA degradation. Errors due to reverse transcriptase and the phenomenon of RNA editing can make these data difficult to interpret it was decided to validate not only SNVs and indels present in From this study, a mutually exclusive relationship was found coding regions and in consensus-splice site regions, but also those between DNMT3A mutations and the three classical AML translo- present in non-coding genes, in conserved regions, or in regions cations [t(15;17), t(8;21), and inv(16)], which correlate with low having regulatory potentials. Ultimately, they identified 7 non- cytogenetic risk. The same had been already observed for muta- synonymous SNVs, 1 splice site SNV, 2 indels in coding regions, tions of NPM1, IDH1, and IDH2 that usually do not appear in AML and 52 somatic point mutations in conserved or regulatory por- cells when one of the above-mentioned chromosomal rearrange- tions of the genome (Table 1). They tested these mutations in ments is present. However, an association between the DNMT3A additional 188 AML samples and found that the mutations on the mutation and mutations of these genes, and also FLT3, was shown IDH1 gene were also present in other AML samples at a frequency very clearly. Co-occurrence of DNMT3A mutations with MLL of ∼10% (Table 2). Furthermore, one of the 52 mutations found genomic variants, present in 11 of the 281 patients examined, in conserved or regulatory portions of the genome was detected was also never observed. Variations in the DNMT3A genomic in one additional AML tumor. Previously identified mutations, sequence were frequently found enriched in NK samples (44/119 such as NPM1 and NRAS, were also found amongst the mutations NK samples, 37%). Indeed, the presence of DNMT3A mutations, within coding regions. concomitantly with variations in FLT3, NPM1, IDH1, and IDH2, One year later, the researchers re-sequenced the genome contributed to identify a group of patients that strictly associated from the relapsed AML and control samples of the original with an intermediate cytogenetic risk, and to specifically exclude patient reported in 2008 (Ley et al., 2008), using paired-end patients with an adverse prognosis. Finally, DNMT3A mutations sequencing in order to obtain a higher depth of coverage (Ley were found associated with poor event-free and overall survival, et al., 2010). They found, among several other non-synonymous regardless of NPM1 status, age, and cytogenetic risk; patients also new mutations (not described) a 1-base pair (bp) deletion in carrying FLT3 tandem duplication had a significantly worse out- the DNA methyltransferase-3-alpha (DNMT3A) gene (identi- come. So far, the DNMT3A mutation is the most frequent novel fied through array-based genomic re-sequencing just few months genomic variation in AMLs identified and characterized thanks before; Yamashita et al., 2010; Table 1). To assess DNMT3A muta- to the application of massive parallel sequencing technologies tion frequency, the authors amplified and sequenced by Sanger (Table 2). technique the 24 exons of DNMT3A in 188 additional de novo Welch et al. (2011) have recently described a successful clinical AML samples (and their matched normal counterparts) and in application of whole genomic sequencing, presenting the case of a other 93 AML samples (without corresponding normal controls). patient with a difficult diagnosis of AML: the patient appeared to They ascertained that DNMT3A variants were present in 62 of have a hyper-granular APL-like leukemia, but it was impossible to the total 281 AML DNA samples examined (22%), definitely detect the PML–RARA oncogene by routine cytogenetic profiling proving that DNMT3A is recurrently mutated in AML. All the or FISH, and PCR was not done. The correct identification of an variations identified in the 188 matched-sample validation set APL is a critical requirement since APLs are the only AMLs that were confirmed to derive from somatic mutational events, since can be cured without allogeneic stem cell transplantation. Given DNMT3A mutations were not found in the normal sample set. the complexity of this case, the authors decided to apply whole- Two distinctive categories of DNMT3A mutations were found: genome sequencing to the patient’s leukemia cells (Table 1). This highly frequent SNVs, producing variations in the R882 amino led to the identification of the insertion of a segment of chromo- acid residue, and ∼20 other different widely distributed missense some 15 (containing the LOXL1 and PML genes) into the second mutations. intron of RARA on chromosome 17, generating the PML–RARA www.frontiersin.org May 2012 | Volume 2 | Article 40 | 5 Riva et al. AML mutational analysis and NGS fusion gene and two other fusion genes: LOXL1–PML and RARA– can induce the selection of rare tumor sub-populations harboring LOXL1. In the end, the patient was correctly diagnosed with APL specific gene mutations (clonal selection). As clonal selection was and got into remission after being treated with ATRA. Thus, whole- not shown in three of the eight analyzed cases but some relapse- genome sequencing can detect translocations that may be missed specific mutations were still found, alternative mechanisms of by cytogenetic profiling. Indeed, by analyzing 11 other cases of chemoresistance might have been present in these patients (the AML with APL-resembling features, the authors also found that, mutation could have been acquired during treatment). On the in two of these, the PML–RARA fusion gene had derived from other hand, they might have been already present in the primary an insertional translocation instead of a translocation. In addi- tumor, but had escaped identification due to the limited sensitiv- tion, Welch and colleagues identified, in the same tumor sample, ity of the detection assay (∼5%). Regrettably, the authors did not the presence of 12 non-synonymous SNVs, 1 inversion, 2 addi- investigate whether the identified relapse-specific mutations were tional translocations and 4 deletions. The frequencies of the 12 indeed responsible of the chemoresistance (i.e., whether they were SNVs were consistent with the presence of two different leukemic chemoresistance-specific mutations). This study identified a total clones. of 141 mutated genes present in primary AML, of which 129 were Finally, Link et al. (2011) identified a novel cancer susceptibility novel mutations in AML. Using 200 AML cases whose exomes gene by sequencing leukemic bone marrow and normal skin sam- were sequenced as part of the Cancer Genome Atlas AML project, ples from a patient with therapy-related AML and multiple early Ding et al. identified 126 of the 129 novel mutations in other AML onset primary tumors. They detected a germline deletion variant samples. that had caused the elimination of exons 7–9 of the TP53 gene. Furthermore, the authors discovered 16 non-synonymous SNVs, EXOME-SEQUENCING 2 variants in splice sites, 2 indels in coding regions, 8 SVs, and 12 Most whole-genome sequencing analyses only focused on variants somatic copy number alterations (Table 1). present in coding regions, as mutations in the coded portion of the Whole-genome sequencing has been also used to find somatic genome are easier to interpret because of their putative impact on mutations in mouse models of APL (Wartman et al., 2011). Wart- protein functions. This approach, although restrictive, has been man et al., in fact, identified three somatic non-synonymous SNVs nevertheless successful allowing the identification of many novel in leukemia samples from a PML–RAR knock-in mouse (Table 1). mutations. Since the publication of the first exome-sequencing One of the three mutations affected the Jak1 gene and recurred study in 2009 (Ng et al., 2009), many groups have been report- in 6 of the 89 additionally screened mice. An identical mutation ing the use of exome-sequencing to identify mutations present in in the human JAK1 gene had been already described in human cancer or in other pathological conditions (Meyerson et al., 2010; APLs. Furthermore, the authors found a 150-kb somatic deletion Singleton, 2011 for reviews). Novel mutations identified by exome- on chromosome X affecting the Kdm6a gene. A similar mutation sequencing in AML (Grossmann et al., 2011; Yan et al., 2011) and was also found in one of the 150 AML patients regarded as the APL (Greif et al., 2011a) patients have been also recently published. human leukemia population of comparison. Yan et al. (2011) published exome-sequencing data from bone Development of drug resistance has been linked to hundreds of marrow and control tissues derived from nine patients with AML- gene mutations in experimental models, using in vitro cell lines or M5. They validated 58 SNVs and 8 indels with Sanger sequencing, transgenic mice (e.g., MDR-1). There is no confirmation, however, identifying 66 somatic mutations in 63 genes (Table 1). These of any of them having a specific role in acquired clinical resistance somatic mutations included known variants (e.g., in NRAS and in following anticancer therapy, or that they can be used as prognos- FLT3)aswellasthe MLL–MLLT4 fusion gene. Other five AML-M5 tic factors to predict treatment outcome. Thus, the molecular basis cases without matched normal samples were sequenced and the of chemoresistance in human tumors, including AMLs, remains authors focused on additional mutations occurring in the 63 iden- largely unknown. tified genes. Furthermore, the authors checked all the sequence Recently, Ding et al. (2012) have reported the whole-genome changes detected in the 63 genes in other 98 AML-M5 leukemia analysis of primary/relapse tumor-pairs from 8 AML patients, samples (94 newly diagnosed and 4 relapsed); these variants were using NGS technologies. This is the first report of an exten- not present in the control set, consisting of 509 normal samples sive search of tumor mutations in relapsing tumors. Initially, the from healthy donors, or in the matched control samples. In total authors analyzed each tumor pair using a sequence protocol that 112 samples were tested and amongst these 14 genes were mutated, allows identification of high frequency mutations. They used a each in at least 2 of the 112 cases. Yan and colleagues selected 5 of sequence coverage of ∼30×, corresponding to low cell detection these 14 genes (DNMT3A, ATP2A, C10orf2, CCND3, GATA2)plus sensitivity. With this approach, Ding and colleagues documented a gene mutated only in one case (NSD1) and sequenced their entire the existence of relapse-specific mutations in all the analyzed cases. coding regions in the 98 AML-M5 leukemia samples, discovering The authors then looked for the presence of these relapse-specific three different DNMT3A variants in ∼20% of the samples. Inter- mutations in the primary tumors of origin, using a sequence pro- estingly, they observed that individuals with DNMT3A mutations tocol that allows identification of low-frequency mutations (in had a worse prognosis than those without and that these mutations this second phase the sequence coverage was∼500×, which corre- were common in elderly patients. sponds to a cell detection sensitivity of around 5%). Interestingly, To find cooperative mutations in APL, Greif et al. (2011a) exam- under these experimental conditions, a few relapse-specific muta- ined the exome-sequencing data of three APL patients who did not tions could be also detected in the respective primary tumors. have mutations in FLT3. After the exclusion of annotated poly- These data represent a direct demonstration that chemotherapy morphisms, the authors confirmed a total of 12 non-synonymous Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 6 Riva et al. AML mutational analysis and NGS SNVs and 1 indel in coding regions (Table 1). The identified nine missense mutations (9.5%) for RUNX1. Notably, RUNX1, mutations (including known mutations such as WT1 and NRAS) TLE4, and SHKBP1 mutations were mutually exclusive; moreover, did not overlap in the three APL patients, suggesting that the spec- TLE4 was found in samples carrying NPM1 and CEBPA variants, trum of mutations that can cooperate with PML–RARA might be whereas SHKBP1 was found in combination with NMP1 and FLT3 large and diverse. mutations. To date, this is the only high-throughput experiment NPM1 and CEBPA mutations are found in 60% of NK AML that has studied AML by RNA-seq. cases, but the remaining 40% are not well characterized. To bet- Small non-coding RNAs play a key role in regulating a large ter characterize this second group of AMLs, Grossmann et al. variety of biological processes, including tumorigenesis. Thus, it is (2011) sequenced a NK AML case with no mutations of the expected that they will be affected by mutations, like their cognate NPM1, CEBPA, FLT3–ITD, or MLL gene and identified 12 non- “coding genes.” In a recently published genome wide analysis of synonymous SNVs and 1 frame-shift deletion, corresponding to 11 microRNAs (miRNAs; Ramsingh et al., 2010), the authors applied distinct genes (Table 1). All these mutations were found to be het- NGS technologies to the characterization of the microRNAome in erozygous. The authors selected 4 of these 11 genes (BCOR, YY2, a sample from the same AML patient previously studied in 2008 SSRP1, and DNMT3A) and performed deep-sequencing analysis (Ley et al., 2008). They looked for miRNA mutations, aberrant of all their exons in other AML patients who had a karyotype sim- expression, and miRNA binding-site mutations, detecting several ilar to their original AML case (i.e., a NK in the absence of NPM1, new miRNAs (some of them expressed differently in the tumor CEBPA, FLT3–ITD mutations, and MLL partial tandem dupli- and control samples), no somatic mutations of miRNA genes, cation, PTD). They found that one case (1/16; 6.25%) carried a and one somatic mutation in the 3 -UTR of the TNFAINP2 gene, mutation in the SSRP1 gene, 4 (4/30; 13.3%) in DNMT3A and 5 which may result in the acquisition of a novel miRNA binding- (5/30; 16.6%) in BCOR. BCOR frequency was confirmed in a total site (Table 1). However, this gene was not mutated in 187 de novo of 82 NK cases with the above genetic features (14/82; 17%). In AMLs, suggesting that this mutation is rare in primary AMLs. a second phase of the study, to assess the real frequency of BCOR Likewise, no somatic mutations of miRNA genes were identified mutations in unselected patients with NK AMLs, Grossmann et al. in this leukemic genome. analyzed 262 unselected NK AML patients from an independent Italian cohort characterized for mutations in NPM1, FLT3–ITD, GENOMICS OF MYELODYSPLASTIC SYNDROMES BY NGS and DNMT3A. They found BCOR mutations in 10/262 (3.8%) Together with AMLs, myelodysplastic syndromes (MDSs), and cases; all these patients had a karyotype similar to their initial index myeloproliferative neoplasms (MPNs) include the majority of patient. Thus BCOR mutations appear to be mostly enriched in the myeloid malignancies. Thus, it is worth mentioning some muta- least characterized subgroup of NK AML, the subgroup with wild tions recently identified with NGS technologies in these patholo- type NPM1, FLT3–ITD, IDH1, and MLL genes. The authors also gies in relation to AML mutations. studied the frequency of BCOR mutations in 131 AML patients Myelodysplastic syndromes represent a heterogeneous group with cytogenetic abnormalities but no mutation was found. Inter- of clonal hemopathies, characterized by bone marrow dysplasia, estingly, BCOR mutations were usually associated with DMNT3A aberrant differentiation, peripheral cytopenia, increased incidence and only rarely with NPM1; finally, for NK leukemias, mutation in old age and risk of progression to AML. At the end of 2011, of the BCOR gene appeared associated with a worse outcome. four significant papers described specific mutations identified in MDSs by exome and whole-genome sequencing (Papaemmanuil TRANSCRIPTOME-SEQUENCING et al., 2011; Visconte et al., 2011; Yoshida et al., 2011; Graubert Greif et al. (2011b) had shown that transcriptome-sequencing by et al., 2012). These recent publications, as well as corollary papers RNA-seq could also be used to identify recurrent or rare muta- published soon after (Malcovati et al., 2011; Makishima et al., tions in leukemia. A bone marrow sample (≥90% cellularity) from 2012) clearly indicate that, besides karyotypic abnormalities (i.e., an NK AML patient and a normal sample from the peripheral 5q−, −7/7q−, trisomy 8, 20q−, and −Y) and “prototypic” gene blood of the same patient were compared by RNA-seq. Five tumor- mutations (e.g., TET2, RUNX1, TP53, ASXL1, NRAS/KRAS, EZH2, specific SNVs (in RUNX1, TLE4, SHKBP1, XPO7, and RRP8 genes) JAK2, and MPL), which had been linked to MDS for years, compo- were identified and validated (Table 1). Except for the mutation nents of the splicing machinery are recurrent targets of mutations in the RUNX1 gene, a known recurrent mutation in AML, the in MDSs and in myelodysplasia (e.g., U2AF1/U2AF35, SRSF2, other four were novel mutations. Variants in TLE4 and SHKBP1 ZRSR2, SF3B1, SF3A1). In particular, surprisingly high mutation were considered potentially relevant for further characterizations. frequencies (20–85%) were reported in the SF3B1 gene (Papaem- TLE4, in fact, had been previously identified as a putative tumor manuil et al., 2011; Visconte et al., 2011; Yoshida et al., 2011; suppressor and a possible cooperative gene of AML1–ETO in AML Makishima et al., 2012); these were almost specific to the MDS sub- patients with chromosome 9q deletions (Dayyani et al., 2008). types refractory anemia with ring sideroblast (RARS) and RARS SHKBP1, on the other hand, is putatively linked to leukemia associated with marked Thrombocytosis (RARS-T), suggesting through the interaction with SETA which mediates its binding that they might be virtually pathognomonic to these MDS groups. to CBL, an ubiquitin ligase involved in the degradation of FLT3. Little overlap was observed between SF3B1 and all the other To evaluate the frequency of these mutations, the authors re- mutations identified in genes of the spliceosome complex and those found so far in AML (Table S1 in Supplementary Material), sequenced the coding sequence for both TLE4 and SHKBP1,as well as for RUNX1, in 95 additionally NK AML patients. The authors suggesting that these splicing pattern mutations have a distinctive found two missense mutations (2%) for TLE4 and SHKBP1 and association with the pathogenesis of MDSs. Notably, 3 out of the www.frontiersin.org May 2012 | Volume 2 | Article 40 | 7 Riva et al. AML mutational analysis and NGS 57 AML samples (5.3%) from a 2087 patient cohort screened for of rare mutations (with a frequency lower than 5%). Yet, this target re-sequencing were reported to contain SF3B1 mutations might turn out to be a critical step for the identification of novel (Papaemmanuil et al., 2011); however, this is the first report of prognostic or therapeutic targets in AMLs. SF3B1 mutations in primary AML (even from larger cohorts), and In AMLs, much evidence suggests that primary translocations it is possible that the AML in these three patients derives from the [inv(16); t(15;17); t(8;21); and 11q23 translocations] are suffi- evolution of a preexisting MDS. This is indeed the case for the two cient to initiate leukemogenesis (initiating mutations), yet other AML patients (2/38) carrying a somatic SF3B1 mutation in the genetic alterations are needed for the selection of the full leukemia- study of Malcovati et al. (2011). phenotype (cooperating mutations). In fact: (i) these primary Interestingly, Graubert et al. (2012) work examined directly translocations are frequently found as the only cytogenetic abnor- the genetics of MDS when it evolves into secondary AML (sAML), mality in AML blasts; (ii) the expression of the associated fusion studying, by whole-genome sequencing, a sAML patient sample proteins induces a pre-leukemic state in mice; (iii) the murine and then genotyping the identified mutations in the matched leukemias that eventually develop have morphological and clini- MDS sample. The authors identified, among others, a missense cal properties that are near-identical to those of the corresponding mutation in the U2AF1/U2AF35 gene, an auxiliary factor of the human leukemias. Thus, in AMLs with primary translocations, U2 splicing complex; in 150 additional MDS de novo samples, this NGS might allow identification of mutations that cooperate with mutation had a frequency of 8.7%. In contrast to SF3B1 mutations fusion proteins to determine the leukemia-phenotype. that were associated with a relatively benign prognosis, mutations Genomic analyses are available for six AML cases with primary of the U2AF1/U2AF35 gene were associated with shorter survival translocations (five human APLs and one mouse APL; Table 1). and with an increased risk of developing sAML. Notably, the frequency of recurrent mutations in these cases is also extremely low (in total, 42 novel mutations were identi- Further studies are needed; however, these results seem to sug- gest that even if AML and MDS mutation patterns overall share fied but none had a frequency higher than 5%), suggesting that only few common mutated genes (16/290 AML mutated targets, myeloid leukemogenesis may initiate from the alteration of a Table S1 in Supplementary Material), this number is not expected few genetic pathways to then proceed through the alterations to occur simply by chance (Fisher’s exact test P-value = 0.0045). of many. Even more interesting, 6 of those 16 mutated genes belong to A similar scenario might apply to AMLs with a NK (78% of a group of 10 recurrent mutated genes found in AML (Fisher’s all sequenced cases). Mutations of NPM1 are found in ∼25% NK exact test P-value = 1.3e−09), suggesting that a selected fraction AMLs, are frequently associated with mutations of other recur- of recurrent mutations are involved in both AML and MDS patho- rently mutated genes, such as FLT3, and never found together with genesis. Thus genome sequencing of larger collections of samples primary translocations. Notably, as for the AML-associated fusion may provide new insights into the molecular basis of MDS clinical proteins, expression of mutant NPM1 in mice induces either a pre- heterogeneity and lead to the identification of syndrome subtypes leukemic state (Cheng et al., 2010, our unpublished data) or the with similar outcomes, e.g., AML progression and/or responses to occurrence of a frank leukemia, after a long (if expressed alone) or therapy. short (if co-expressed with others cooperative mutations) latency (Vassiliou et al., 2011, our unpublished data). Similarly to AMLs RECURRING SOMATIC MUTATIONS IN AML: THE STATE OF with primary translocations, AMLs with mutated NPM1 were THE ART found associated with 34 novel non-recurrent mutated genes by The NGS studies described so far, led to the identification of 281 NGS. Thus, NGS might contribute to identify cooperating muta- mutated genes in AML. Among them, 164 have been found in at tions in AMLs. Functional analyses of these mutations might least 2 AML patients (Table S1 in Supplementary Material), and then lead to the identification of cellular pathways that are crit- only 10 are recurrent, i.e., they have a frequency higher than 5% ical for the selection of the leukemia-phenotype, providing a and are found in more than 100 patients (Table 2). Notably, only biological classification of leukemias, regardless of the initiating 16 (∼6%) of the mutated genes were previously known, demon- genetic event. strating how powerful NGS technologies can be for the discovery of AML-associated mutations. MOLECULAR AND FUNCTIONAL CONSEQUENCES OF Analysis of the prevalence of these mutations, however, reveals MUTATIONS IN RECURRENTLY TARGETED GENES IN AML that 153 of the 265 novel mutations (∼58%) are found in at least To derive information about the molecular and patho-functional two AML patients (Table S1 in Supplementary Material). Notably, impact of mutations directly from the type of mutation and most of them (149/153, 97%) have a frequency lower than 5% from their location is always a not-trivial mission. In general, it in AMLs. Thus, these data suggest the existence of two classes of might be true that when a genetic variant is found persistently mutated genes in AMLs: one comprising few (10/281, 3.6%) and located at a single amino acid position, the lesion may trigger a frequently mutated genes, and the other comprising a larger set gain-of-function deleterious mechanism, as already established for of genes with very low mutation frequencies. Although these are known oncogenic mutations (e.g., RAS, NPM1). Loss-of-function partial data, as these mutations need to be confirmed in a larger is instead suggested by the finding of widely distributed divergent number of samples, known recurrent mutations appear to be over- mutations along the structure of the gene, as often observed for represented in the data-set of AML-associated mutations (Fisher’s several classical tumor suppressor genes (e.g., BRCA1 and TP53). exact test P-value = 2.3e−06), suggesting that NGS major contri- Actually, often, “hot spot” and dispersed mutations can be both bution to AML cancer genomics will probably be the detection found in the same gene, making a prediction more difficult. This is Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 8 Riva et al. AML mutational analysis and NGS the case of DNMT3A, the DNA (cytosine-5-)-methyltransferase- Three main challenges remain to be addressed. First, while 3-alpha, one of the most interesting newly identified recurrent these technologies can detect many somatic mutations in each targets of mutations in AML. patient, only a subset of them is probably involved in cancer ini- DNMT3A is an epigenetic modifying-enzyme known to be tiation and progression. Thus, it is essential to develop methods essential, together with DNMT3B, for the proper de novo methyla- to distinguish between passenger and driver mutations (Stratton tion of DNA. It is one of the novel, most frequently mutated genes et al., 2009). Second, an increasing number of mutations have been found in AML patients (DNMT3A mutation frequency: ∼20%) identified in AML. What links these genetic alterations to cancer and it is one of those also discovered to be recurrently mutated in progression? What complex interactions underlie AML pathogen- MDS (about 8%; Walter et al., 2011). Its mutated form in AML esis? Third, the use of massive parallel sequencing has also found a (i) is associated with mutations of NPM1, FLT3, IDH1, and CBPA, rewarding application in the identification of chromatin features; (ii) never appears in AML characterized by translocation events, the next challenge will be to integrate AML genomic information (iii) is prevalent in AML with NK, and (iv) is associated with poor with AML epigenomic profiles. survival. Help will come from the genome sequencing of 500 de novo Nearly half of the mutations in the DNMT3A gene are con- AML cases by the TCGA (http://tcga-data.nci.nih.gov/tcga), an centrated in positions affecting arginine 882 (R882), a conserved NIH consortium which aims to contribute to the understand- residue of the methyltransferase (MT) domain. The remaining ing of the molecular basis of cancer through the gathering variations are more largely distributed along the length of the gene, and analysis of different high-throughput data, such as DNA- although preferentially targeting the MT domain, as well. This sequencing, methylation, gene expression and miRNA expression structural observation suggests a loss-of-function mechanism. In data. We foresee different ways of interpreting the huge amount of support of this hypothesis,in vitro experiments showed that muta- information generated by cancer re-sequencing projects in order tionsinthe DNMT3A MT domain decrease the methyltransferase to link mutations identified by NGS technologies to leukemia activity of DNMT3A. In contrast, overexpression of DNMT3A in progression. PML–RARA expressing mice recently demonstrated the potential To correlate cancer mutated genes to cancer behavior we will cooperative nature of DNMT3A to induce APL (Subramanyam need to discriminate, within all the variants found in the sequenc- et al., 2010). Indeed, transplantation into irradiated mice of PML– ing projects, between passenger and driver mutations. Currently, + + RARA /DNMT3A bone marrow cells induced leukemia with the definition of driver mutations is usually based on mutation shorter latency and higher penetrance than transplantation of frequency (Wood et al., 2007), and mutations are defined as dri- cells only expressing the initiating protein PML–RARA, thus sug- vers when found in a larger number of AML genomes. Since many gesting a gain-on-function mechanism, possibly combined with driver mutations may be infrequent and contributing to cancer a dominant negative effect on the wild type proteins. Interest- development only in few tumors, we will need to test a large ingly DNMT3A mutations, although not dramatically altering number of tumor samples in order to discriminate between rare global DNA methylation levels in AML genomes, tend to pro- driver mutations and passenger mutations. Anyway, there are other duce modified methylation patterns in the proximity of specific purely computational ways to identify driver mutations, indepen- DNA regions and genes (Ley et al., 2010). Further experiments are dent of the evaluation of mutation frequency. These methods required to completely clarify mechanisms and roles of DNMT3A can identify driver mutations among those that cause changing and its association with co-occurring recurrent and rare genomic in the amino acid sequence of the associated protein. Methods alterations. as SIFT (Kumar et al., 2009) and PolyPhen-2 (Adzhubei et al., 2010) can predict for each non-synonymous mutation the impact FUTURE PROSPECTIVE AND OPEN CHALLENGES of the amino acid substitution on protein structure and func- So far, tumor and control samples from 26 AML patients have tions, using different features such as sequence homology, amino been sequenced but larger numbers of samples are expected to be acid physicochemical properties and protein structure-based fea- sequenced in the near future. These data will be crucial to dis- tures. Recently, new methods that use network-based approaches sect the complexity of somatic mutations, which contribute to (Torkamani and Schork, 2009; Cerami et al., 2010; Vandin et al., AML pathogenesis. No doubt, NGS platforms will be also further 2011) or machine learning algorithms (Carter et al., 2009)have employed for the discovery of mutations in mouse models of AML. been developed to identify driver mutations. The last 3 years, characterized by increased NSG applications, However, since the vast majority of somatic mutations are have seen a dramatic reduction in the costs of data generation, shared only between few patients, it might be more important an increase in coverage, and improvements in computational to identify driver pathways rather than driver mutations. Indeed, data analysis. Indeed, the number of validated mutations of the driver pathways can be reconstructed using network models con- computationally predicted variants ranges from 5% (Ley et al., taining driver mutations and other genes that may link them 2008) to 98% (Yan et al., 2011), thus reducing validation costs (Vandin et al., 2012). The identification of driver pathways is and increasing the automation of the identification processes. important to rationalize targets for therapeutic intervention. Many Cost reduction, increase in automation, availability of NGS in recent works identify driver pathways by integrating different types medium and small centers, and the possibility to simultane- of high-throughput data, such as copy number variant data and mRNA expression data, to identify driver CNVs, to stratify patients ously detect all the genetic variants existing in a cancer genome, have opened new opportunities for the employment of NGS in and to obtain mechanistic and prognostic insights (Akavia et al., clinical settings. 2010; Vaske et al., 2010; Jörnsten et al., 2011). www.frontiersin.org May 2012 | Volume 2 | Article 40 | 9 Riva et al. AML mutational analysis and NGS Lastly, it is possible to correlate specific mutations found in very variant” is a mutation acquired during the life span of an indi- large collections of patient samples with clinical outcomes, using vidual in a specific area of the body (e.g., bone marrow); the cell unsupervised and supervised machine learning methods. With the where the somatic mutation occurs, may give rise to a clonal prolif- unsupervised methods it is possible to cluster the mutational pro- eration event. A somatic variant can be easily distinguished from files of different patients to identify common alterations. With the a germline one by comparing the region of the mutated DNA supervised methods we can identify the features (or biomarkers) sequence with a corresponding sequence obtained from another that can better classify specific subgroups of AML. tissue of the same individual: in the first case the sequences will In order to determine what most likely drives AML, in addition be different, in the second identical. Both germline and somatic to the development of computational methods that can priori- mutations can be neutral (i.e., do not produce an observable tize candidate mutations, we will need to functionally characterize pathological phenotype) or deleterious (i.e., are directly respon- these mutated genes, using in vitro and/or in vivo experiments. sible or contribute to establish a perturbed unhealthy condition). The possibility of recognizing a subset of genetic variations Neutrality and deleteriousness are not always obvious, but can be with predictive and prognostic value will pave the way to a predicted based on the features of the specific areas of genomic mutation-specific, “personalized,” therapy choice. The molecular DNA, such as coding and regulatory potential or involvement in classification of AML patients will improve clinical outcome and splicing mechanisms. be essential for disease monitoring. We expect that in a not so Recurrent mutation: it generally indicates that the same somatic distant future, testing the presence of mutated genes in biopsies mutation is found in different individuals, usually carrying a before treatment will become clinical routine practice. tumor of the same type. Herein, a recurrent mutation is defined So far studies have focused on the identification of mutations as found in “at least 5% of the tested samples.” Since the chance that are present in the majority of tumor cells. Indeed, these works of finding a recurrent event is very low, it likely reflects the impor- have identified mutations with a frequency usually higher than tance that a somatic mutation may have on a tumorigenic or 25%. If we increase the coverage, and we improve bioinformat- disease predisposing phenotype. ics pipelines, we could aim to identify small sub-clones (<1%). Such an achievement could help addressing many important open ACKNOWLEDGMENTS questions such as the clonal evolution of tumors and, of clinical This work was supported by grants from AIRC and Italian Min- interest, the prediction of resistance to anti-tumoral treatments. istry of Health. LR is supported by a Reintegration AIRC/Marie Indeed, Ding et al. (2012) observed changes in mutant allele fre- Curie International Fellowship in Cancer Research. We thank C. quencies between the primary tumor and the relapse tumor, as Ronchini and E. Colombo for helpful discussions; P. Dalton and well as clonal evolution in 5/8 patients, suggesting that a popula- R. Aina for scientific editing. tion with potentially chemo-resistant mutations might pre-exist and expand after treatment. SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at “MUTATIONS” GLOSSARY BOX http://www.frontiersin.org/Molecular_and_Cellular_Oncology/ Genomic mutations, genetic variants, genomic alterations, or sim- abstract/21275 ply mutations or variants: they are all synonyms indicating varia- Table S1 | Catalog of all the genes with molecular genetic abnormalities in tions found in the DNA sequence derived from an individual with adult AML as detected by NGS technologies. The 290 genes listed in this respect to the “Reference genome sequence.” table were found in one or more tumor samples. This table includes also the Mutations can be germline or somatic. A “germline mutation” nine genes whose mutations were relapse-specific and absent in the additional gives rise to a mutation in the offspring; it is present in every 200 primary AMLs considered for testing recurrently mutated genes (Ding cell. SNPs belong to this class. A “somatic mutation” or “somatic et al., 2012). REFERENCES mutations: computational predic- and TLE4 from the del(9q) com- Wilson, R. K., and DiPersio, J. F. Adzhubei, I. A., Schmidt, S., Peshkin, tion of driver missense mutations. monly deleted region in AML coop- (2012). Clonal evolution in relapsed L., Ramensky, V. E., Gerasimova, A., Cancer Res. 69, 6660–6667. erates with AML1-ETO to affect acute myeloid leukaemia revealed by Bork, P., Kondrashov, A. S., and Sun- Cerami, E., Demir, E., Schultz, N., myeloid cell proliferation and sur- whole-genome sequencing. Nature yaev, S. R. (2010). A method and Taylor, B. S., and Sander, C. vival. Blood 111, 4338–4347. 481, 506–510. server for predicting damaging mis- (2010). Automated network analy- Ding, L., Ley, T. J., Larson, D. E., Graubert, T. A., Shen, D., Ding, L., sense mutations. Nat. Methods 7, sis identifies core pathways in Miller, C. A., Koboldt, D. C., Welch, Okeyo-Owuor, T., Lunn, C. L., Shao, 248–249. glioblastoma. PLoS ONE 5, e8918. J. S., Ritchey, J. K., Young, M. A., J., Krysiak, K., Harris, C. C., Koboldt, Akavia, U. D., Litvin, O., Kim, J., doi:10.1371/journal.pone.0008918 Lamprecht, T., McLellan, M. D., D. C., Larson, D. E., McLellan, M. D., Sanchez-Garcia, F., Kotliar, D., Caus- Cheng, K., Sportoletti, P., Ito, K., McMichael, J. F., Wallis, J. W., Lu, Dooling, D. J., Abbott, R. M., Fulton, ton, H. C., Pochanard, P., Mozes, Clohessy, J. G., Teruya-Feldstein, C., Shen, D., Harris, C. C., Dool- R. S., Schmidt, H., Kalicki-Veizer, J., E., Garraway, L. A., and Pe’er, D. J., Kutok, J. L., and Pandolfi, P. ing, D. J., Fulton, R. S., Fulton, L. O’Laughlin, M., Grillot, M., Baty, J., (2010). An integrated approach to P. (2010). The cytoplasmic NPM L., Chen, K., Schmidt, H., Kalicki- Heath, S., Frater, J. L., Nasim, T., uncover drivers of cancer. Cell 143, mutant induces myeloproliferation Veizer, J., Magrini, V. J., Cook, L., Link, D. C., Tomasson, M. H., West- 1005–1017. in a transgenic mouse model. Blood McGrath, S. D., Vickery, T. L., Wendl, ervelt, P., DiPersio, J. F., Mardis, E. Carter, H., Chen, S., Isik, L., Tyekucheva, 115, 3341–3345. M. C., Heath, S., Watson, M. A., R., Ley, T. J., Wilson, R. K., and Wal- S., Velculescu, V. E., Kinzler, K. Dayyani, F., Wang, J., Yeh, J.-R. J., Ahn, Link, D. C., Tomasson, M. H., Shan- ter, M. J. (2012). Recurrent muta- W., Vogelstein, B., and Karchin, E.-Y., Tobey, E., Zhang, D.-E., Bern- non, W. D., Payton, J. E., Kulka- tions in the U2AF1 splicing factor R. (2009). Cancer-specific high- stein, I. D., Peterson, R. T., and rni, S., Westervelt, P., Walter, M. in myelodysplastic syndromes. Nat. throughput annotation of somatic Sweetser, D. A. (2008). Loss of TLE1 J., Graubert, T. A., Mardis, E. R., Genet. 44, 53–57. Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 10 Riva et al. AML mutational analysis and NGS Gregory, T. K., Wald, D., Chen, Y., Ver- Vickery, T. L., Hundal, J., Cook, B., Bupathi, M., Guinta, K., Afa- twelve human exomes. Nature 461, maat, J. M., Xiong, Y., and Tse, W. L. L., Conyers, J. J., Swift, G. W., ble, M. G., Sekeres, M. A., Pad- 272–276. (2009). Molecular prognostic mark- Reed, J. P., Alldredge, P. A., Wylie, gett, R. A., Tiu, R. V., and Papaemmanuil, E., Cazzola, M., Boult- ers for adult acute myeloid leukemia T., Walker, J., Kalicki, J., Watson, Maciejewski, J. P. (2012). Muta- wood, J., Malcovati, L., Vyas, P., with normal cytogenetics. J. Hema- M. A., Heath, S., Shannon, W. D., tions in the spliceosome machin- Bowen, D., Pellagatti, A., Wain- tol. Oncol. 2, 23. Varghese, N., Nagarajan, R., West- ery, a novel and ubiquitous path- scoat, J. S., Hellstrom-Lindberg, E., Greif, P. A., Yaghmaie, M., Konstandin, ervelt, P., Tomasson, M. H., Link, way in leukemogenesis. Blood 119, Gambacorti-Passerini, C., Godfrey, N. P., Ksienzyk, B., Alimoghad- D. C., Graubert, T. A., DiPersio, J. 3203–3210. A. L., Rapado, I., Cvejic, A., Rance, dam, K., Ghavamzadeh, A., Hauser, F., Mardis, E. R., and Wilson, R. Malcovati, L., Papaemmanuil, E., R., McGee, C., Ellis, P., Mudie, L. J., A., Graf, A., Krebs, S., Blum, K. (2010). DNMT3A Mutations in Bowen, D. T., Boultwood, J., Stephens, P. J., McLaren, S., Massie, H., and Bohlander, S. K. (2011a). acute myeloid leukemia. N. Engl. J. C. E., Tarpey, P. S., Varela, I., Nik- Della Porta, M. G., Pascutto, C., Somatic mutations in acute promye- Med. 363, 2424–2433. Travaglino, E., Groves, M. J., God- Zainal, S., Davies, H. R., Shlien, A., locytic leukemia (APL) identified Ley, T. J., Mardis, E. R., Ding, L., Ful- frey, A. L., Ambaglio, I., Gallí, A., Jones, D., Raine, K., Hinton, J., But- by exome sequencing. Leukemia 25, ton, B., McLellan, M. D., Chen, Da Vià, M. C., Conte, S., Tauro, S., ler, A. P., Teague, J. W., Baxter, E. J., 1519–1522. K., Dooling, D., Dunford-Shore, B. Keenan, N., Hyslop, A., Hinton, J., Score, J., Galli, A., Della Porta, M. G., Greif, P. A., Eck, S. H., Konstandin, H., McGrath, S., Hickenbotham, M., Mudie, L. J., Wainscoat, J. S., Futreal, Travaglino, E., Groves, M., Tauro, S., N. P., Benet-Pagès, A., Ksienzyk, B., Cook, L., Abbott, R., Larson, D. P. A., Stratton, M. R., Campbell, P. Munshi, N. C., Anderson, K. C., El- Dufour, A., Vetter, A. T., Popp, H. D., E., Koboldt, D. C., Pohl, C., Smith, J., Hellström-Lindberg, E., Cazzola, Naggar, A., Fischer, A., Mustonen, V., Lorenz-Depiereux, B., Meitinger, T., S., Hawkins, A., Abbott, S., Locke, M., and Chronic Myeloid Disorders Warren, A. J., Cross, N. C. P., Green, Bohlander, S. K., and Strom, T. M. D., Hillier, L. W., Miner, T., Ful- Working Group of the International A. R., Futreal, P. A., Stratton, M. R., (2011b). Identification of recurring ton, L., Magrini, V., Wylie, T., Glass- Cancer Genome Consortium and and Campbell, P. J. (2011). Somatic tumor-specific somatic mutations in cock, J., Conyers, J., Sander, N., Shi, of the Associazione Italiana per SF3B1 mutation in myelodysplasia acute myeloid leukemia by tran- X., Osborne, J. R., Minx, P., Gor- la Ricerca sul Cancro Gruppo with ring sideroblasts. N. Engl. J. scriptome sequencing. Leukemia 25, don, D., Chinwalla, A., Zhao, Y., Ries, Italiano Malattie Mieloproliferative. Med. 365, 1384–1395. 821–827. R. E., Payton, J. E., Westervelt, P., (2011). Clinical significance of Ramsingh, G., Koboldt, D. C., Trissal, Grossmann, V., Tiacci, E., Holmes, A. Tomasson, M. H., Watson, M., Baty, SF3B1 mutations in myelodysplastic M., Chiappinelli, K. B., Wylie, T., B., Kohlmann, A., Martelli, M. P., J., Ivanovich, J., Heath, S., Shan- syndromes and myelodysplas- Koul, S., Chang, L.-W., Nagarajan, Kern, W., Spanhol-Rosseto, A., Klein, non, W. D., Nagarajan, R., Walter, tic/myeloproliferative neoplasms. R., Fehniger, T. A., Goodfellow, P., H.-U., Dugas, M., Schindela, S., M. J., Link, D. C., Graubert, T. A., Blood 118, 6239–6246. Magrini, V., Wilson, R. K., Ding, L., Trifonov, V., Schnittger, S., Hafer- DiPersio, J. F., and Wilson, R. K. Mardis, E. R., Ding, L., Dooling, D. Ley, T. J., Mardis, E. R., and Link, lach, C., Bassan, R., Wells, V. A., (2008). DNA sequencing of a cyto- J., Larson, D. E., McLellan, M. D., D. C. (2010). Complete character- Spinelli, O., Chan, J., Rossi, R., Bal- genetically normal acute myeloid Chen, K., Koboldt, D. C., Fulton, ization of the microRNAome in a doni, S., De Carolis, L., Goetze, K., leukaemia genome. Nature 4567218, R. S., Delehaunty, K. D., McGrath, patient with acute myeloid leukemia. Serve, H., Peceny, R., Kreuzer, K. 66–72. S. D., Fulton, L. A., Locke, D. P., Blood 116, 5316–5326. A., Oruzio, D., Specchia, G., Di Rai- Li, M., Wang, I. X., Li, Y., Bruzel, A., Magrini, V. J., Abbott, R. M., Vick- Singleton, A. B. (2011). Exome sequenc- mondo, F., Fabbiano, F., Sborgia, M., Richards, A. L., Toung, J. M., and ery, T. L., Reed, J. S., Robinson, J. S., ing: a transformative technology. Liso, A., Farinelli, L., Rambaldi, A., Cheung, V. G. (2011). Widespread Wylie, T., Smith, S. M., Carmichael, Lancet Neurol. 10, 942–946. Pasqualucci, L., Rabadan, R., Hafer- RNA and DNA sequence differences L., Eldred, J. M., Harris, C. C., Stratton, M. R., Campbell, P. J., and lach, T., and Falini, B. (2011). Whole- Futreal, P. A. (2009). The cancer in the human transcriptome. Science Walker, J., Peck, J. B., Du, F., Dukes, exome sequencing identifies somatic 333, 53–58. A. F., Sanderson, G. E., Brummett, genome. Nature 458, 719. mutations of BCOR in acute myeloid Link, D. C., Schuettpelz, L. G., Shen, D., A. M., Clark, E., McMichael, J. F., Subramanyam, D., Belair, C. D., Barry- leukemia with normal karyotype. Wang, J., Walter, M. J., Kulkarni, S., Meyer, R. J., Schindler, J. K., Pohl, Holson, K. Q., Lin, H., Kogan, S. Blood 118, 6153–6163. Payton, J. E., Ivanovich, J., Goodfel- C. S., Wallis, J. W., Shi, X., Lin, L., C., Passegué, E., and Blelloch, R. Jörnsten, R., Abenius, T., Kling, T., low, P. J., Le Beau, M., Koboldt, D. C., Schmidt, H., Tang, Y., Haipek, C., (2010). PML-RARα and Dnmt3a1 Schmidt, L., Johansson, E., Nordling, Dooling, D. J., Fulton, R. S., Bender, Wiechert, M. E., Ivy, J. V., Kalicki, cooperateinvivotopromoteacute T. E. M., Nordlander, B., Sander, C., R. H., Fulton, L. L., Delehaunty, K. J., Elliott, G., Ries, R. E., Payton, promyelocytic leukemia. Cancer Res. Gennemark, P., Funa, K., Nilsson, B., D., Fronick, C. C., Appelbaum, E. L., J. E., Westervelt, P., Tomasson, M. 70, 8792–8801. Lindahl, L., and Nelander, S. (2011). Schmidt, H., Abbott, R., O’Laughlin, H., Watson, M. A., Baty, J., Heath, Tefferi, A., Thiele, J., and Vardiman, J. Network modeling of the transcrip- M., Chen, K., McLellan, M. D., S., Shannon, W. D., Nagarajan, R., W. (2009). The 2008 World Health tional effects of copy number aberra- Varghese, N., Nagarajan, R., Heath, Link, D. C., Walter, M. J., Graubert, Organization classification system tions in glioblastoma. Mol. Syst. Biol. S., Graubert, T. A., Ding, L., Ley, T. T. A., DiPersio, J. F., Wilson, R. for myeloproliferative neoplasms. 7, 486. J., Zambetti, G. P., Wilson, R. K., and K., and Ley, T. J. (2009). Recur- Cancer 115, 3842–3847. Kumar, P., Henikoff, S., and Ng, P. Mardis, E. R. (2011). The identifi- ring mutations found by sequenc- Torkamani, A., and Schork, N. J. (2009). C. (2009). Predicting the effects cation of a novel TP53 cancer sus- ing an acute myeloid leukemia Identification of rare cancer driver of coding non-synonymous vari- ceptibility mutation through whole genome. N. Engl.J.Med. 361, mutations by network reconstruc- ants on protein function using genome sequencing of a patient with 1058–1066. tion. Genome Res. 19, 1570–1578. the SIFT algorithm. Nat. Protoc. 4, therapy-related AML. JAMA 305, Meyerson, M., Gabriel, S., and Getz, Vandin, F., Upfal, E., and Raphael, B. 1073–1081. 1568–1576. G. (2010). Advances in understand- J. (2011). Algorithms for detecting Ley, T. J., Ding, L., Walter, M. J., McLel- Maher, C. A., Kumar-Sinha, C., Cao, X., ing cancer genomes through second- significantly mutated pathways in lan, M. D., Lamprecht, T., Larson, D. Kalyana-Sundaram, S., Han, B., Jing, generation sequencing. Nat. Rev. cancer. J. Comput. Biol. 18, 507–522. E., Kandoth, C., Payton, J. E., Baty, J., X., Sam, L., Barrette, T., Palanisamy, Genet. 11, 685–696. Vandin, F., Upfal, E., and Raphael, B. Welch, J., Harris, C. C., Lichti, C. F., N., and Chinnaiyan, A. M. (2009). Ng, S. B., Turner, E. H., Robertson, P. J. (2012). De novo discovery of Townsend, R. R., Fulton, R. S., Dool- Transcriptome sequencing to detect D., Flygare, S. D., Bigham, A. W., mutated driver pathways in cancer. ing, D. J., Koboldt, D. C., Schmidt, gene fusions in cancer. Nature 458, Lee, C., Shaffer, T., Wong, M., Bhat- Genome Res. 22, 375–385. H., Zhang, Q., Osborne, J. R., Lin, 97–101. tacharjee, A., Eichler, E. E., Bamshad, Vaske, C. J., Benz, S. C., Sanborn, J. Z., L., O’Laughlin, M., McMichael, J. Makishima, H., Visconte, V., Sak- M., Nickerson, D. A., and Shen- Earl, D., Szeto, C., Zhu, J., Haussler, D., and Stuart, J. M. (2010). Infer- F., Delehaunty, K. D., McGrath, S. aguchi, H., Jankowska, A. M., Abu dure, J. (2009). Targeted capture D., Fulton, L. A., Magrini, V. J., Kar, S., Jerez, A., Przychodzen, and massively parallel sequencing of ence of patient-specific pathway www.frontiersin.org May 2012 | Volume 2 | Article 40 | 11 Riva et al. AML mutational analysis and NGS activities from multi-dimensional C., McLellan, M. D., Schmidt, H., D. R., Suh, E., Papadopoulos, N., W. K., Miyawaki, S., Sugano, S., cancer genomics data using PARA- Fulton, R. S., Abbott, R. M., Cook, Buckhaults, P., Markowitz, S. D., Haferlach, C., Koeffler, H. P., Shih, L. DIGM. Bioinformatics 26, i237–i245. L., McGrath, S. D., Fan, X., Dukes, Parmigiani, G., Kinzler, K. W., Vel- Y., Haferlach, T., Chiba, S., Nakauchi, Vassiliou, G. S., Cooper, J. L., Rad, R., A. F., Vickery, T., Kalicki, J., Lam- culescu, V. E., and Vogelstein, B. H., Miyano, S., and Ogawa, S. Li, J., Rice, S., Uren, A., Rad, L., Ellis, precht, T. L., Graubert, T. A., Tomas- (2007). The genomic landscapes of (2011). Frequent pathway mutations P., Andrews, R., Banerjee, R., Grove, son, M. H., Mardis, E. R., Wilson, R. human breast and colorectal cancers. of splicing machinery in myelodys- C., Wang, W., Liu, P., Wright, P., K., and Ley, T. J. (2011). Sequenc- Science 318, 1108–1113. plasia. Nature 478, 64–69. Arends, M., and Bradley, A. (2011). ing a mouse acute promyelocytic Yamashita, Y., Yuan, J., Suetake, I., Mutant nucleophosmin and cooper- leukemia genome reveals genetic Suzuki, H., Ishikawa, Y., Choi, Y. Conflict of Interest Statement: The ating pathways drive leukemia initi- events relevant for disease progres- L., Ueno, T., Soda, M., Hamada, T., authors declare that the research was ation and progression in mice. Nat. sion. J. Clin. Invest. 121, 1445–1455. Haruta, H., Akada, S., Miyazaki, Y., conducted in the absence of any com- Genet. 43, 470–475. Welch, J. S., Westervelt, P., Ding, L., Kiyoi, H., Ito, E., Naoe, T., Tomon- mercial or financial relationships that Visconte, V., Makishima, H., Jankowska, Larson, D. E., Klco, J. M., Kulka- aga, M., Toyota, M., Tajima, S., could be construed as a potential con- A., Szpurka, H., Traina, F., Jerez, A., rni, S., Wallis, J., Chen, K., Payton, Iwama, A., and Mano, H. (2010). flict of interest. O’Keefe, C., Rogers, H. J., Sekeres, J. E., Fulton, R. S., Veizer, J., Schmidt, Array-based genomic resequencing M. A., Maciejewski, J. P., and Tiu, H., Vickery, T. L., Heath, S., Wat- of human leukemia. Oncogene 29, Received: 22 December 2011; paper pend- R. V. (2011). SF3B1, a splicing fac- son, M. A., Tomasson, M. H., Link, 3723–3731. ing published: 24 January 2012; accepted: tor is frequently mutated in refrac- D. C., Graubert, T. A., DiPersio, J. Yan, X.-J., Xu, J., Gu, Z.-H., Pan, C.-M., 27 February 2012; published online: 01 tory anemia with ring sideroblasts. F., Mardis, E. R., Ley, T. J., and Wil- Lu, G., Shen, Y., Shi, J.-Y., Zhu, Y.- May 2012. Leukemia 26, 542–545. son, R. K. (2011). Use of whole M., Tang, L., Zhang, X.-W., Liang, Citation: Riva L, Luzi L and Pelicci Walter, M. J., Ding, L., Shen, D., Shao, genome sequencing to diagnose a W. X., Mi, J. Q., Song, H. D., Li, PG (2012) Genomics of acute J., Grillot, M., McLellan, M., Ful- cryptic fusion oncogene. JAMA 305, K. Q., Chen, Z., and Chen, S. J. myeloid leukemia: the next gen- ton, R., Schmidt, H., Kalicki-Veizer, 1577–1584. (2011). Exome sequencing identifies eration. Front. Oncol. 2:40. doi: J., O’Laughlin, M., Kandoth, C., Baty, Wood, L. D., Parsons, D. W., Jones, S., somatic mutations of DNA methyl- 10.3389/fonc.2012.00040 J., Westervelt, P., DiPersio, J. F., Lin, J., Sjöblom, T., Leary, R. J., Shen, transferase gene DNMT3A in acute This article was submitted to Frontiers Mardis, E. R., Wilson, R. K., Ley, D., Boca, S. M., Barber, T., Ptak, J., monocytic leukemia. Nat. Genet. 43, in Molecular and Cellular Oncology, a T. J., and Graubert, T. A. (2011). Silliman, N., Szabo, S., Dezso, Z., 309–315. specialty of Frontiers in Oncology. Recurrent DNMT3A mutations in Ustyanksky,V., Nikolskaya, T., Nikol- Yoshida, K., Sanada, M., Shiraishi, Y., Copyright © 2012 Riva, Luzi and Pelicci. patients with myelodysplastic syn- sky, Y., Karchin, R., Wilson, P. A., Nowak, D., Nagata, Y., Yamamoto, This is an open-access article distributed dromes. Leukemia 25, 1153–1158. Kaminker, J. S., Zhang, Z., Croshaw, R., Sato, Y., Sato-Otsubo, A., Kon, A., under the terms of the Creative Commons Wartman, L. D., Larson, D. E., Xiang, Z., R., Willis, J., Dawson, D., Ship- Nagasaki, M., Chalkidis, G., Suzuki, Attribution Non Commercial License, Ding, L., Chen, K., Lin, L., Cahan, itsin, M., Willson, J. K., Sukumar, S., Y., Shiosaka, M., Kawahata, R., Yam- which permits non-commercial use, dis- P., Klco, J. M., Welch, J. S., Li, C., Polyak, K., Park, B. H., Pethiyagoda, aguchi, T., Otsu, M., Obara, N., tribution, and reproduction in other Payton, J. E., Uy, G. L., Varghese, N., C. L., Pant, P. V., Ballinger, D. G., Sakata-Yanagimoto, M., Ishiyama, forums, provided the original authors and Ries, R. E., Hoock, M., Koboldt, D. Sparks, A. B., Hartigan, J., Smith, K., Mori, H., Nolte, F., Hofmann, source are credited. Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 12 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Frontiers in Oncology Pubmed Central

Genomics of Acute Myeloid Leukemia: The Next Generation

Frontiers in Oncology , Volume 2 – May 1, 2012

Loading next page...
 
/lp/pubmed-central/genomics-of-acute-myeloid-leukemia-the-next-generation-pPtpIm1sgo

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Pubmed Central
Copyright
Copyright © 2012 Riva, Luzi and Pelicci.
ISSN
2234-943X
eISSN
2234-943X
DOI
10.3389/fonc.2012.00040
Publisher site
See Article on Publisher Site

Abstract

REVIEW ARTICLE published: 01 May 2012 doi: 10.3389/fonc.2012.00040 1†‡ 2‡ 1 Laura Riva , Lucilla Luzi and Pier Giuseppe Pelicci * Department of Experimental Oncology, European Institute of Oncology, Milan, Italy IFOM, The FIRC Institute of Molecular Oncology Foundation, Milan, Italy Edited by: Acute myeloid leukemia (AML) is, as other types of cancer, a genetic disorder of somatic Napoleone Ferrara, Genentech, USA cells. The detection of somatic molecular abnormalities that may cause and maintain AML Reviewed by: is crucial for patient stratification. The development of mutation-specific therapeutic inter- Keisuke Ito, Beth Israel Deaconess ventions will hopefully increase cure rates and improve patients’ quality of life. This review Medical Center, USA illustrates how next generation sequencing technologies are changing the study of cancer Shridar Ganesan, University of Medicine and Dentistry of New genomics of adult AML patients. Jersey, USA Keywords: acute myeloid leukemia, next generation sequencing, somatic mutations, recurrent mutations *Correspondence: Pier Giuseppe Pelicci, Department of Experimental Oncology, European Institute of Oncology, Via Adamello, 16, 20139 Milan, Italy. e-mail: piergiuseppe.pelicci@ ifom-ieo-campus.it Present address: Laura Riva, Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia at the IFOM-IEO Campus, Milan, Italy. Laura Riva and Lucilla Luzi have contributed equally to this work. Acute myeloid leukemia (AML) is the most frequent hematolog- More recently, other mutations associated to AML have been ical malignancy in adults, with an estimated worldwide annual identified (FLT3, CEBP, NPM1, IDH1/2) and their prognostic incidence of three to four cases per 100,000 people. Despite inten- power investigated particularly in the intermediate risk category. FLT3–ITD and CEBPA mutations seem to associate with a bad sive research for new therapies and prognostic markers, it is still a disease with a highly variable prognosis among patients and a prognosis, while NPM1 and IDH1/2 are controversial. However, high mortality rate. Indeed, less than 50% of adult AML patients several challenges still lie ahead and markers are needed to predict have a 5-year overall survival rate (OS), and, in the elderly, only prognosis and sensibility to treatment. 20% survive 2 years (Gregory et al., 2009). Understanding the genetic lesions associated to AML is also In general, both prognosis and treatment choice for AML important in order to adjust for specific therapies. For example, patients are based on the presence or absence of specific genetic Acute Promyelocytic Leukemia (APL, one of the AML subtypes) is alterations, which determine AML classification in three risk treated with a combination of the differentiation-inducing agent based-categories: favorable, intermediate, and unfavorable. This ATRA (all-trans retinoic acid) and chemotherapy, which induces classification is usually based on cytogenetic information. AML long-term remissions or cure in 75–85% of patients. Some of the with a favorable prognosis includes patients with inv(16) (that newly described genetic lesions (e.g., FLT3) may be targeted by specific inhibitors which have shown anti-leukemic efficacy in pre- generates the CBFB–MYH11 fusion protein), t(15;17) (that gener- ates the PML–RARA fusion protein), or t(8;21) (that generates the liminary studies, and are now currently being evaluated in phase AML1–ETO fusion protein). The 5-year OS rate of patients in this III clinical trials. category is 55%. The unfavorable subgroup includes patients with The advent of second- (or next) generation sequencing tech- monosomy 5, monosomy 7, 11q23 (that generates MLL-highly nologies has dramatically accelerated biological and biomedical variable breakpoints on the partner fusion protein), or complex discoveries by enabling comprehensive analysis of genomes, tran- cytogenetics, and the 5-year OS rate is reduced to 11%. Favor- scriptomes, and DNA–protein interactions. These technologies able prognosis AML patients are usually treated with primary allow the identification of cancer-associated mutations at a single- chemotherapy, while high-risk patients are considered for allo- base resolution in an unbiased manner, and will likely revolution- genic stem cell transplantation in first remission if a suitable donor ize our understanding of cancer. A comprehensive description of is found. The intermediate subgroup includes normal karyotype somatic mutations in cancer is essential as it can (i) shed light on tumor initiation and progression mechanisms, (ii) assist patient (NK) AML patients. Patients belonging to this group have a 5-year OS rate ranging between 24 and 42%, depending on the study, but stratification for prognosis and treatment choice, and (iii) allow it is still largely unclear what might be the best therapeutic strategy the identification of new genes that can be specifically targeted by for them (Gregory et al., 2009; Tefferi et al., 2009). therapy. www.frontiersin.org May 2012 | Volume 2 | Article 40 | 1 Riva et al. AML mutational analysis and NGS Massive parallel sequencing is now discovering a growing num- The two parameters to take into considerations to understand ber of submicroscopic somatic mutations with prognostic signif- data analysis and interpretation are the “coverage” and the “read icance. These, together with the primary somatic genetic abnor- lengths.” Coverage is the number of tags aligned to each base of malities already identified, are enabling the drawing of patient the reference genome. A high coverage is desired because it can mutation profiles and will hopefully have a major impact on overcome errors in base calling and assembly, and it can reduce the clinical management of AML, not only as independent prog- false positives. Longer read lengths are more easily mapped to the nostic factors, but also as the foundation of genome-informed reference genome, increasing the proportion of the genome that personalized cancer treatments. is mappable. Moreover, longer read lengths are essential for the In this review, we will examine the somatic mutations recently detection of small indels. identified using next generation sequencing (NGS). First, we will Each of these techniques has pros and cons (see Table 3). describe which types of mutations can be detected by sequenc- Whole-genome sequencing allows identification of all the possible ing and comment on the pros and cons of different technological variants at once, and it is the best method to study chromo- approaches (synthesized in Table 3). Then, we will describe all somal rearrangements; however, it is expensive ($5000–$15,000 the identified mutations and the subsets of recurring mutations per sample, depending on the sequencing services and cover- according to sequencing technology and mutation type (cataloged age) and requires a high amount of starting material (usually in Tables 1 and 2). Finally, we will discuss future perspectives in 1 μg of genomic DNA). Exome-sequencing reduces costs ($1000– the use of NGS technologies in the clinical setting and existing $2000 per sample), but not the amount of starting material open challenges. (usually around 3 μg of genomic DNA), and allows high cov- erage in coding regions. Exome-sequencing relies on a capture step that may not have uniform efficiency, and the identification MASSIVE PARALLEL SEQUENCING APPROACHES FOR MUTATIONAL ANALYSIS IN AML of chromosomal rearrangements is restricted to exonic regions. To identify AML somatic mutations by NGS, sequencing is usually RNA-sequencing is capable of detecting variants present in the performed on DNA or RNA obtained from bone marrow sam- transcriptome and fusion genes of expressed genes (Maher et al., ples (with high level of tumor cellularity) and normal tissues (skin 2009). RNA-sequencing, which necessitates 0.1–4 μgof RNA as biopsies or peripheral blood) from the same AML patient when starting material, further reduces costs ($300–$500 per sample); in clinical remission. This approach aims to define somatic vari- importantly, while allowing identification of tumor-specific fusion ants, including single nucleotide variants (SNVs), short deletions transcripts or mRNA-splice variants, it also offers information on and insertions (indels), structural variants (SVs) such as translo- gene expression levels. There are three main disadvantages, how- cations, long insertions or deletions, and copy number variations ever, in using RNA-sequencing to detect somatic variants. First, the (CNVs), which are present in the tumor sample and absent in identification of the corresponding normal sample is challenging the matched control sample. Usually, the sequences from tumor and, even if one could successfully identify it, gene expression in and normal samples are mapped to the reference genome and the cancer cells is altered from that of normal cells. Second, SNVs and sequence changes (variants) that differ from the reference genome indels within genes that are transcribed at very low levels or in are identified. Variants present in both tumor and control samples those for which mutations may induce mRNA degradation may (generally referred to as germline variants) and variants matching be missed. Finally, the chance of errors due to reverse transcriptase known single nucleotide polymorphisms (SNPs) are discarded. and the phenomenon of RNA editing (Li et al., 2011) can make All the identified variants are then validated by using an these data difficult to interpret (Meyerson et al., 2010). independent sequencing technology, for example DNA Sanger sequencing. Finally, the validated variants are usually tested on WHOLE-GENOME SEQUENCING a large number of clinical samples, in order to determine their The first demonstration of the possibility to identify somatic actual frequency and to identify recurrent mutations. mutations in cancer genomes using sequencing technologies was Currently, there are three experimental approaches, which obtained in a patient with AML (NK, M1 subtype; Ley et al., are most frequently utilized to identify somatic mutations 2008). The authors, using single-end whole-genome sequencing, by NGS: whole-genome sequencing, exome-sequencing, and identified mutations in the entire genome but decided to vali- transcriptome-sequencing (also known as RNA-sequencing). date only those which (i) had occurred in coding sequences, (ii) Whole-genome sequencing allows the identification of the entire were non-synonymous, or (iii) were predicted to alter splicing sites DNA sequence of a given sample, at single-base resolution level. (all the 181 identified variants and 28 manually selected indels). Exome-sequencing, instead, is preceded by an exome capture step In this first study, the percentage of computationally identified that selects the coding regions of the genome (representing ∼1% false positive variants was quite high, since only 5% of the iden- of the genome). RNA-sequencing measures the transcriptome. tified mutations could be validated. The authors discovered 10 Sequencing is performed using either single-end or paired-end non-synonymous somatic mutations: eight novel SNVs and two tags (PET). In PET, short and paired reads are obtained from previously described indels (i.e., in NPM1 and FLT3; Table 1). the ends of DNA fragments for sequencing. The use of PET in They sequenced the 8 novel SNVs in 187 additional AML cases genome re-sequencing has advantages over the use of single tags, but could not find any of these variants. as it allows higher mapping specificity and the identification of In the following year, the same group sequenced another patient small and large insertions, deletions, and translocations, which is with cytogenetically normal AML-M1 (Mardis et al., 2009), using not possible using single-end tags. paired-end whole-genome sequencing. In this second attempt, Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 2 Riva et al. AML mutational analysis and NGS www.frontiersin.org May 2012 | Volume 2 | Article 40 | 3 Table 1 | Numerical summary of identified and validated variants found in adult AMLs by NGS technologies. Study Sequencing Leukemia Number of Non- SNV in SNV in Indels in Translocation Inversion Insertion Deletion CNV Targets platform type tumor synonymous non-coding splicing coding genes samples SNV regions sites regions Ley et al. (2008) Whole-genome H-NK 1 8 2 10 a b Mardis et al. (2009) Whole-genome H-NK 1 7 52 12 10 Ley et al. (2010) Whole-genome H-NK 1 1 1 d c Ramsingh et al. (2010) MicroRNAome , H-NK 1 1in3 -UTR 1 whole-genome Greif et al. (2011a) Exome capture H-M3 3 12 1 13 Greif et al. (2011b) RNA-seq H-NK 1 5 5 Link et al. (2011) Whole-genome H-(t-AML) 1 16 228 12 26 Wartman et al. (2011) Whole-genome M-M3-like 1 3 14 Welch et al. (2011) Whole-genome H-M3 1 12 2 1 1 3 15 e f Yan et al. (2011) Exome capture H-NK 9 58 8166 Grossmann et al. (2011) Exome capture H-NK 1 12 1 11 g h Ding et al. (2012) Whole-genome H-NK and 8 141 H-M3 TOT 26 human, 130 53 3 13 11 1 1 4 12 281 1 mouse SNV, single nucleotide variant; CNV, copy number variation; H, human, M, mouse; NK, normal karyotype; t-AML, therapy-related AML; the authors also report other synonymous SNVs that are not considered in b c d this table; in conserved or regulatory portions of the genome; in this work the authors describe an independent sequencing of the relapsed tumor derived from the same patient studied in Ley et al. (2008); no miRNA were found mutated from the sequencing of the microRNAome; re-analysis of the whole-genome experiment of Ley et al. (2010) led to the identification of the 3 -UTR SNV; 58 SNVs in 56 distinct genes; f g h 8 indels in 7 distinct genes; for this study we do not specify the number of the identified variants since they were classified by a different method; the nine genes whose mutations were relapse-specific and absent in the additional 200 primary AMLs considered for testing recurrently mutated genes are not reported Ding et al. (2012). Riva et al. AML mutational analysis and NGS Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 4 Table 2 | Catalog of genes targeted by recurrent molecular genetic abnormalities in adult AMLs as detected by NGS technologies. Gene Gene name Identified by Leukemia Frequency (%) Detailed leukemia Mutation type/s Status Reference symbol type subtypes (%) b c DNMT3A DNA (cytosine-5-)- Whole-genome H-M1/NK 62/281 (22.0) M0 (10), M1 (25.4), M2 Non-synonymous SNV Novel Ley et al. (2010) methyltransferase-3-alpha (16.7), M3 (0), M4 (32.8), M5 (57.1), M6 (0), M7 (33.3) Whole-genome H-M1/NK 11/38 (28.9) Not shown Non-synonymous SNV Novel Ley et al. (2010) Exome capture H-NK 32/355 (9.0) M1 (0), M2 (0), M3 (0), M4 Non-synonymous SNV Novel Yan et al. (2011) (13.6), M5 (20.5) Exome capture H-NK(*) 34/195 (17.4) – Non-synonymous SNV Novel Grossmann et al. (2011) Whole-genome H-NK 49/200 (24.5) Not shown Non-synonymous SNV, Novel Ding et al. (2012) frame-shift indel FLT3 Fms-related tyrosine kinase 3 Exome capture H-NK 21/112 (18.8) M5 Non-synonymous SNV, ITD Known Yan et al. (2011) Whole-genome H-M1/NK 51/185 (27.6) Not shown ITD Known Ley et al. (2008) Whole-genome H-NK 52/200 (26.0) Not shown Non-synonymous SNV, ITD Known Ding et al. (2012) IDH1 Isocitrate dehydrogenase 1 Whole-genome H-M1/NK 16/188 (8.5) Not shown Non-synonymous SNV Novel Mardis et al. (2009) Whole-genome H-NK 20/200 (10.0) Not shown Non-synonymous SNV Novel Ding et al. (2012) (NADP+), soluble IDH2 Isocitrate dehydrogenase 2 Whole-genome H-NK 19/200 (9.5) Not shown Non-synonymous SNV Novel Ding et al. (2012) (NADP+), mitochondrial MLL Myeloid/lymphoid or Exome capture H-NK 22/112 (19.6) M5 Translocation or partial Known Yan et al. (2011) mixed-lineage leukemia tandem duplication NPM1 Nucleophosmin (nucleolar Whole-genome H-M1/NK 43/180 (23.9) – Frame-shift indel Known Ley et al. (2008) Whole-genome H-M1/NK Not validated – Frame-shift indel Known Mardis et al. (2009) phosphoprotein B23, numatrin) Whole-genome H-NK 53/200 (26.5) Not shown Frame-shift indel Known Ding et al. (2012) NRAS Neuroblastoma RAS viral (v-ras) Whole-genome H-M1/NK 20/188 (10.6) Not shown Non-synonymous SNV Known Mardis et al. (2009) Exome capture H-NK 12/112 (10.7) M5 SNV Known Yan et al. (2011) oncogene homolog RUNX1 Runt-related transcription RNA-seq H-M1/NK 9/95 (9.5) Not shown Non-synonymous SNV Known Greif et al. (2011b) factor 1 Exome capture H-NK Not validated M5 Frame-shift indel Known Yan et al. (2011) Whole-genome H-NK 17/200 (8.5) Not shown Non-synonymous SNV, Known Ding et al. (2012) frame-shift indel TTN Titin Whole-genome H-NK 13/200 (6.5) Not shown In-frame indel, Novel Ding et al. (2012) Non-synonymous SNV WT1 Wilms tumor 1 Exome capture H-NK 3/112 (2.7) M5 Frame-shift indel, in-frame Known Yan et al. (2011) indel Whole-genome H-NK 13/200 (6.5) Not shown Frame-shift indel Known Ding et al. (2012) The 10 genes listed in this table were found in at least 5% of the tumor samples. SNV, single nucleotide variant; H, human, M, mouse; NK, normal karyotype; somatic mutations checked only in the same leukemia b c subtypes where originally identified; validation performed by Sanger re-sequencing of DNMT3A exons in 281 samples; identified through array-based genomic re-sequencing also by Yamashita few months earlier Yamashita et al. (2010); validation performed in 38 samples by whole-genome sequencing; NK(*) are normal karyotype AML patients not showing NPM1, CEBPA, FLT3–ITD, or MLL–PTD mutations. Riva et al. AML mutational analysis and NGS Table 3 | Comparison of pros and cons of whole-genome sequencing, exome-sequencing, and RNA-sequencing. Characteristics Whole-genome sequencing Exome-sequencing Transcriptome-sequencing Cost $5000–$15,000 per sample $1000–$2000 per sample The cheapest method: $300–$500 per sample Starting material 1 μg of genomic DNA 3 μg of genomic DNA 0.1–4 μgofRNA Detectable variants All possible variants Restricted to exonic regions, hard to identify Detection of variants present in the transcrip- structural variants, and copy number variations tome and fusion genes Pros Detection of all the possible Lower cost, greater depth of coverage, and Identification of tumor-specific fusion tran- variants present in a corresponding improvement in data quality scripts, mRNA-splice variants, and informa- genome essential to detect mutations at lower frequency tion on gene expression levels Cons Very expensive, so typically Hard to identify structural variants and copy Hard to identify the corresponding normal designed with low coverage number alterations samples. Hard to identify SNVs and indels in transcripts at low expression or for which mutations may induce mRNA degradation. Errors due to reverse transcriptase and the phenomenon of RNA editing can make these data difficult to interpret it was decided to validate not only SNVs and indels present in From this study, a mutually exclusive relationship was found coding regions and in consensus-splice site regions, but also those between DNMT3A mutations and the three classical AML translo- present in non-coding genes, in conserved regions, or in regions cations [t(15;17), t(8;21), and inv(16)], which correlate with low having regulatory potentials. Ultimately, they identified 7 non- cytogenetic risk. The same had been already observed for muta- synonymous SNVs, 1 splice site SNV, 2 indels in coding regions, tions of NPM1, IDH1, and IDH2 that usually do not appear in AML and 52 somatic point mutations in conserved or regulatory por- cells when one of the above-mentioned chromosomal rearrange- tions of the genome (Table 1). They tested these mutations in ments is present. However, an association between the DNMT3A additional 188 AML samples and found that the mutations on the mutation and mutations of these genes, and also FLT3, was shown IDH1 gene were also present in other AML samples at a frequency very clearly. Co-occurrence of DNMT3A mutations with MLL of ∼10% (Table 2). Furthermore, one of the 52 mutations found genomic variants, present in 11 of the 281 patients examined, in conserved or regulatory portions of the genome was detected was also never observed. Variations in the DNMT3A genomic in one additional AML tumor. Previously identified mutations, sequence were frequently found enriched in NK samples (44/119 such as NPM1 and NRAS, were also found amongst the mutations NK samples, 37%). Indeed, the presence of DNMT3A mutations, within coding regions. concomitantly with variations in FLT3, NPM1, IDH1, and IDH2, One year later, the researchers re-sequenced the genome contributed to identify a group of patients that strictly associated from the relapsed AML and control samples of the original with an intermediate cytogenetic risk, and to specifically exclude patient reported in 2008 (Ley et al., 2008), using paired-end patients with an adverse prognosis. Finally, DNMT3A mutations sequencing in order to obtain a higher depth of coverage (Ley were found associated with poor event-free and overall survival, et al., 2010). They found, among several other non-synonymous regardless of NPM1 status, age, and cytogenetic risk; patients also new mutations (not described) a 1-base pair (bp) deletion in carrying FLT3 tandem duplication had a significantly worse out- the DNA methyltransferase-3-alpha (DNMT3A) gene (identi- come. So far, the DNMT3A mutation is the most frequent novel fied through array-based genomic re-sequencing just few months genomic variation in AMLs identified and characterized thanks before; Yamashita et al., 2010; Table 1). To assess DNMT3A muta- to the application of massive parallel sequencing technologies tion frequency, the authors amplified and sequenced by Sanger (Table 2). technique the 24 exons of DNMT3A in 188 additional de novo Welch et al. (2011) have recently described a successful clinical AML samples (and their matched normal counterparts) and in application of whole genomic sequencing, presenting the case of a other 93 AML samples (without corresponding normal controls). patient with a difficult diagnosis of AML: the patient appeared to They ascertained that DNMT3A variants were present in 62 of have a hyper-granular APL-like leukemia, but it was impossible to the total 281 AML DNA samples examined (22%), definitely detect the PML–RARA oncogene by routine cytogenetic profiling proving that DNMT3A is recurrently mutated in AML. All the or FISH, and PCR was not done. The correct identification of an variations identified in the 188 matched-sample validation set APL is a critical requirement since APLs are the only AMLs that were confirmed to derive from somatic mutational events, since can be cured without allogeneic stem cell transplantation. Given DNMT3A mutations were not found in the normal sample set. the complexity of this case, the authors decided to apply whole- Two distinctive categories of DNMT3A mutations were found: genome sequencing to the patient’s leukemia cells (Table 1). This highly frequent SNVs, producing variations in the R882 amino led to the identification of the insertion of a segment of chromo- acid residue, and ∼20 other different widely distributed missense some 15 (containing the LOXL1 and PML genes) into the second mutations. intron of RARA on chromosome 17, generating the PML–RARA www.frontiersin.org May 2012 | Volume 2 | Article 40 | 5 Riva et al. AML mutational analysis and NGS fusion gene and two other fusion genes: LOXL1–PML and RARA– can induce the selection of rare tumor sub-populations harboring LOXL1. In the end, the patient was correctly diagnosed with APL specific gene mutations (clonal selection). As clonal selection was and got into remission after being treated with ATRA. Thus, whole- not shown in three of the eight analyzed cases but some relapse- genome sequencing can detect translocations that may be missed specific mutations were still found, alternative mechanisms of by cytogenetic profiling. Indeed, by analyzing 11 other cases of chemoresistance might have been present in these patients (the AML with APL-resembling features, the authors also found that, mutation could have been acquired during treatment). On the in two of these, the PML–RARA fusion gene had derived from other hand, they might have been already present in the primary an insertional translocation instead of a translocation. In addi- tumor, but had escaped identification due to the limited sensitiv- tion, Welch and colleagues identified, in the same tumor sample, ity of the detection assay (∼5%). Regrettably, the authors did not the presence of 12 non-synonymous SNVs, 1 inversion, 2 addi- investigate whether the identified relapse-specific mutations were tional translocations and 4 deletions. The frequencies of the 12 indeed responsible of the chemoresistance (i.e., whether they were SNVs were consistent with the presence of two different leukemic chemoresistance-specific mutations). This study identified a total clones. of 141 mutated genes present in primary AML, of which 129 were Finally, Link et al. (2011) identified a novel cancer susceptibility novel mutations in AML. Using 200 AML cases whose exomes gene by sequencing leukemic bone marrow and normal skin sam- were sequenced as part of the Cancer Genome Atlas AML project, ples from a patient with therapy-related AML and multiple early Ding et al. identified 126 of the 129 novel mutations in other AML onset primary tumors. They detected a germline deletion variant samples. that had caused the elimination of exons 7–9 of the TP53 gene. Furthermore, the authors discovered 16 non-synonymous SNVs, EXOME-SEQUENCING 2 variants in splice sites, 2 indels in coding regions, 8 SVs, and 12 Most whole-genome sequencing analyses only focused on variants somatic copy number alterations (Table 1). present in coding regions, as mutations in the coded portion of the Whole-genome sequencing has been also used to find somatic genome are easier to interpret because of their putative impact on mutations in mouse models of APL (Wartman et al., 2011). Wart- protein functions. This approach, although restrictive, has been man et al., in fact, identified three somatic non-synonymous SNVs nevertheless successful allowing the identification of many novel in leukemia samples from a PML–RAR knock-in mouse (Table 1). mutations. Since the publication of the first exome-sequencing One of the three mutations affected the Jak1 gene and recurred study in 2009 (Ng et al., 2009), many groups have been report- in 6 of the 89 additionally screened mice. An identical mutation ing the use of exome-sequencing to identify mutations present in in the human JAK1 gene had been already described in human cancer or in other pathological conditions (Meyerson et al., 2010; APLs. Furthermore, the authors found a 150-kb somatic deletion Singleton, 2011 for reviews). Novel mutations identified by exome- on chromosome X affecting the Kdm6a gene. A similar mutation sequencing in AML (Grossmann et al., 2011; Yan et al., 2011) and was also found in one of the 150 AML patients regarded as the APL (Greif et al., 2011a) patients have been also recently published. human leukemia population of comparison. Yan et al. (2011) published exome-sequencing data from bone Development of drug resistance has been linked to hundreds of marrow and control tissues derived from nine patients with AML- gene mutations in experimental models, using in vitro cell lines or M5. They validated 58 SNVs and 8 indels with Sanger sequencing, transgenic mice (e.g., MDR-1). There is no confirmation, however, identifying 66 somatic mutations in 63 genes (Table 1). These of any of them having a specific role in acquired clinical resistance somatic mutations included known variants (e.g., in NRAS and in following anticancer therapy, or that they can be used as prognos- FLT3)aswellasthe MLL–MLLT4 fusion gene. Other five AML-M5 tic factors to predict treatment outcome. Thus, the molecular basis cases without matched normal samples were sequenced and the of chemoresistance in human tumors, including AMLs, remains authors focused on additional mutations occurring in the 63 iden- largely unknown. tified genes. Furthermore, the authors checked all the sequence Recently, Ding et al. (2012) have reported the whole-genome changes detected in the 63 genes in other 98 AML-M5 leukemia analysis of primary/relapse tumor-pairs from 8 AML patients, samples (94 newly diagnosed and 4 relapsed); these variants were using NGS technologies. This is the first report of an exten- not present in the control set, consisting of 509 normal samples sive search of tumor mutations in relapsing tumors. Initially, the from healthy donors, or in the matched control samples. In total authors analyzed each tumor pair using a sequence protocol that 112 samples were tested and amongst these 14 genes were mutated, allows identification of high frequency mutations. They used a each in at least 2 of the 112 cases. Yan and colleagues selected 5 of sequence coverage of ∼30×, corresponding to low cell detection these 14 genes (DNMT3A, ATP2A, C10orf2, CCND3, GATA2)plus sensitivity. With this approach, Ding and colleagues documented a gene mutated only in one case (NSD1) and sequenced their entire the existence of relapse-specific mutations in all the analyzed cases. coding regions in the 98 AML-M5 leukemia samples, discovering The authors then looked for the presence of these relapse-specific three different DNMT3A variants in ∼20% of the samples. Inter- mutations in the primary tumors of origin, using a sequence pro- estingly, they observed that individuals with DNMT3A mutations tocol that allows identification of low-frequency mutations (in had a worse prognosis than those without and that these mutations this second phase the sequence coverage was∼500×, which corre- were common in elderly patients. sponds to a cell detection sensitivity of around 5%). Interestingly, To find cooperative mutations in APL, Greif et al. (2011a) exam- under these experimental conditions, a few relapse-specific muta- ined the exome-sequencing data of three APL patients who did not tions could be also detected in the respective primary tumors. have mutations in FLT3. After the exclusion of annotated poly- These data represent a direct demonstration that chemotherapy morphisms, the authors confirmed a total of 12 non-synonymous Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 6 Riva et al. AML mutational analysis and NGS SNVs and 1 indel in coding regions (Table 1). The identified nine missense mutations (9.5%) for RUNX1. Notably, RUNX1, mutations (including known mutations such as WT1 and NRAS) TLE4, and SHKBP1 mutations were mutually exclusive; moreover, did not overlap in the three APL patients, suggesting that the spec- TLE4 was found in samples carrying NPM1 and CEBPA variants, trum of mutations that can cooperate with PML–RARA might be whereas SHKBP1 was found in combination with NMP1 and FLT3 large and diverse. mutations. To date, this is the only high-throughput experiment NPM1 and CEBPA mutations are found in 60% of NK AML that has studied AML by RNA-seq. cases, but the remaining 40% are not well characterized. To bet- Small non-coding RNAs play a key role in regulating a large ter characterize this second group of AMLs, Grossmann et al. variety of biological processes, including tumorigenesis. Thus, it is (2011) sequenced a NK AML case with no mutations of the expected that they will be affected by mutations, like their cognate NPM1, CEBPA, FLT3–ITD, or MLL gene and identified 12 non- “coding genes.” In a recently published genome wide analysis of synonymous SNVs and 1 frame-shift deletion, corresponding to 11 microRNAs (miRNAs; Ramsingh et al., 2010), the authors applied distinct genes (Table 1). All these mutations were found to be het- NGS technologies to the characterization of the microRNAome in erozygous. The authors selected 4 of these 11 genes (BCOR, YY2, a sample from the same AML patient previously studied in 2008 SSRP1, and DNMT3A) and performed deep-sequencing analysis (Ley et al., 2008). They looked for miRNA mutations, aberrant of all their exons in other AML patients who had a karyotype sim- expression, and miRNA binding-site mutations, detecting several ilar to their original AML case (i.e., a NK in the absence of NPM1, new miRNAs (some of them expressed differently in the tumor CEBPA, FLT3–ITD mutations, and MLL partial tandem dupli- and control samples), no somatic mutations of miRNA genes, cation, PTD). They found that one case (1/16; 6.25%) carried a and one somatic mutation in the 3 -UTR of the TNFAINP2 gene, mutation in the SSRP1 gene, 4 (4/30; 13.3%) in DNMT3A and 5 which may result in the acquisition of a novel miRNA binding- (5/30; 16.6%) in BCOR. BCOR frequency was confirmed in a total site (Table 1). However, this gene was not mutated in 187 de novo of 82 NK cases with the above genetic features (14/82; 17%). In AMLs, suggesting that this mutation is rare in primary AMLs. a second phase of the study, to assess the real frequency of BCOR Likewise, no somatic mutations of miRNA genes were identified mutations in unselected patients with NK AMLs, Grossmann et al. in this leukemic genome. analyzed 262 unselected NK AML patients from an independent Italian cohort characterized for mutations in NPM1, FLT3–ITD, GENOMICS OF MYELODYSPLASTIC SYNDROMES BY NGS and DNMT3A. They found BCOR mutations in 10/262 (3.8%) Together with AMLs, myelodysplastic syndromes (MDSs), and cases; all these patients had a karyotype similar to their initial index myeloproliferative neoplasms (MPNs) include the majority of patient. Thus BCOR mutations appear to be mostly enriched in the myeloid malignancies. Thus, it is worth mentioning some muta- least characterized subgroup of NK AML, the subgroup with wild tions recently identified with NGS technologies in these patholo- type NPM1, FLT3–ITD, IDH1, and MLL genes. The authors also gies in relation to AML mutations. studied the frequency of BCOR mutations in 131 AML patients Myelodysplastic syndromes represent a heterogeneous group with cytogenetic abnormalities but no mutation was found. Inter- of clonal hemopathies, characterized by bone marrow dysplasia, estingly, BCOR mutations were usually associated with DMNT3A aberrant differentiation, peripheral cytopenia, increased incidence and only rarely with NPM1; finally, for NK leukemias, mutation in old age and risk of progression to AML. At the end of 2011, of the BCOR gene appeared associated with a worse outcome. four significant papers described specific mutations identified in MDSs by exome and whole-genome sequencing (Papaemmanuil TRANSCRIPTOME-SEQUENCING et al., 2011; Visconte et al., 2011; Yoshida et al., 2011; Graubert Greif et al. (2011b) had shown that transcriptome-sequencing by et al., 2012). These recent publications, as well as corollary papers RNA-seq could also be used to identify recurrent or rare muta- published soon after (Malcovati et al., 2011; Makishima et al., tions in leukemia. A bone marrow sample (≥90% cellularity) from 2012) clearly indicate that, besides karyotypic abnormalities (i.e., an NK AML patient and a normal sample from the peripheral 5q−, −7/7q−, trisomy 8, 20q−, and −Y) and “prototypic” gene blood of the same patient were compared by RNA-seq. Five tumor- mutations (e.g., TET2, RUNX1, TP53, ASXL1, NRAS/KRAS, EZH2, specific SNVs (in RUNX1, TLE4, SHKBP1, XPO7, and RRP8 genes) JAK2, and MPL), which had been linked to MDS for years, compo- were identified and validated (Table 1). Except for the mutation nents of the splicing machinery are recurrent targets of mutations in the RUNX1 gene, a known recurrent mutation in AML, the in MDSs and in myelodysplasia (e.g., U2AF1/U2AF35, SRSF2, other four were novel mutations. Variants in TLE4 and SHKBP1 ZRSR2, SF3B1, SF3A1). In particular, surprisingly high mutation were considered potentially relevant for further characterizations. frequencies (20–85%) were reported in the SF3B1 gene (Papaem- TLE4, in fact, had been previously identified as a putative tumor manuil et al., 2011; Visconte et al., 2011; Yoshida et al., 2011; suppressor and a possible cooperative gene of AML1–ETO in AML Makishima et al., 2012); these were almost specific to the MDS sub- patients with chromosome 9q deletions (Dayyani et al., 2008). types refractory anemia with ring sideroblast (RARS) and RARS SHKBP1, on the other hand, is putatively linked to leukemia associated with marked Thrombocytosis (RARS-T), suggesting through the interaction with SETA which mediates its binding that they might be virtually pathognomonic to these MDS groups. to CBL, an ubiquitin ligase involved in the degradation of FLT3. Little overlap was observed between SF3B1 and all the other To evaluate the frequency of these mutations, the authors re- mutations identified in genes of the spliceosome complex and those found so far in AML (Table S1 in Supplementary Material), sequenced the coding sequence for both TLE4 and SHKBP1,as well as for RUNX1, in 95 additionally NK AML patients. The authors suggesting that these splicing pattern mutations have a distinctive found two missense mutations (2%) for TLE4 and SHKBP1 and association with the pathogenesis of MDSs. Notably, 3 out of the www.frontiersin.org May 2012 | Volume 2 | Article 40 | 7 Riva et al. AML mutational analysis and NGS 57 AML samples (5.3%) from a 2087 patient cohort screened for of rare mutations (with a frequency lower than 5%). Yet, this target re-sequencing were reported to contain SF3B1 mutations might turn out to be a critical step for the identification of novel (Papaemmanuil et al., 2011); however, this is the first report of prognostic or therapeutic targets in AMLs. SF3B1 mutations in primary AML (even from larger cohorts), and In AMLs, much evidence suggests that primary translocations it is possible that the AML in these three patients derives from the [inv(16); t(15;17); t(8;21); and 11q23 translocations] are suffi- evolution of a preexisting MDS. This is indeed the case for the two cient to initiate leukemogenesis (initiating mutations), yet other AML patients (2/38) carrying a somatic SF3B1 mutation in the genetic alterations are needed for the selection of the full leukemia- study of Malcovati et al. (2011). phenotype (cooperating mutations). In fact: (i) these primary Interestingly, Graubert et al. (2012) work examined directly translocations are frequently found as the only cytogenetic abnor- the genetics of MDS when it evolves into secondary AML (sAML), mality in AML blasts; (ii) the expression of the associated fusion studying, by whole-genome sequencing, a sAML patient sample proteins induces a pre-leukemic state in mice; (iii) the murine and then genotyping the identified mutations in the matched leukemias that eventually develop have morphological and clini- MDS sample. The authors identified, among others, a missense cal properties that are near-identical to those of the corresponding mutation in the U2AF1/U2AF35 gene, an auxiliary factor of the human leukemias. Thus, in AMLs with primary translocations, U2 splicing complex; in 150 additional MDS de novo samples, this NGS might allow identification of mutations that cooperate with mutation had a frequency of 8.7%. In contrast to SF3B1 mutations fusion proteins to determine the leukemia-phenotype. that were associated with a relatively benign prognosis, mutations Genomic analyses are available for six AML cases with primary of the U2AF1/U2AF35 gene were associated with shorter survival translocations (five human APLs and one mouse APL; Table 1). and with an increased risk of developing sAML. Notably, the frequency of recurrent mutations in these cases is also extremely low (in total, 42 novel mutations were identi- Further studies are needed; however, these results seem to sug- gest that even if AML and MDS mutation patterns overall share fied but none had a frequency higher than 5%), suggesting that only few common mutated genes (16/290 AML mutated targets, myeloid leukemogenesis may initiate from the alteration of a Table S1 in Supplementary Material), this number is not expected few genetic pathways to then proceed through the alterations to occur simply by chance (Fisher’s exact test P-value = 0.0045). of many. Even more interesting, 6 of those 16 mutated genes belong to A similar scenario might apply to AMLs with a NK (78% of a group of 10 recurrent mutated genes found in AML (Fisher’s all sequenced cases). Mutations of NPM1 are found in ∼25% NK exact test P-value = 1.3e−09), suggesting that a selected fraction AMLs, are frequently associated with mutations of other recur- of recurrent mutations are involved in both AML and MDS patho- rently mutated genes, such as FLT3, and never found together with genesis. Thus genome sequencing of larger collections of samples primary translocations. Notably, as for the AML-associated fusion may provide new insights into the molecular basis of MDS clinical proteins, expression of mutant NPM1 in mice induces either a pre- heterogeneity and lead to the identification of syndrome subtypes leukemic state (Cheng et al., 2010, our unpublished data) or the with similar outcomes, e.g., AML progression and/or responses to occurrence of a frank leukemia, after a long (if expressed alone) or therapy. short (if co-expressed with others cooperative mutations) latency (Vassiliou et al., 2011, our unpublished data). Similarly to AMLs RECURRING SOMATIC MUTATIONS IN AML: THE STATE OF with primary translocations, AMLs with mutated NPM1 were THE ART found associated with 34 novel non-recurrent mutated genes by The NGS studies described so far, led to the identification of 281 NGS. Thus, NGS might contribute to identify cooperating muta- mutated genes in AML. Among them, 164 have been found in at tions in AMLs. Functional analyses of these mutations might least 2 AML patients (Table S1 in Supplementary Material), and then lead to the identification of cellular pathways that are crit- only 10 are recurrent, i.e., they have a frequency higher than 5% ical for the selection of the leukemia-phenotype, providing a and are found in more than 100 patients (Table 2). Notably, only biological classification of leukemias, regardless of the initiating 16 (∼6%) of the mutated genes were previously known, demon- genetic event. strating how powerful NGS technologies can be for the discovery of AML-associated mutations. MOLECULAR AND FUNCTIONAL CONSEQUENCES OF Analysis of the prevalence of these mutations, however, reveals MUTATIONS IN RECURRENTLY TARGETED GENES IN AML that 153 of the 265 novel mutations (∼58%) are found in at least To derive information about the molecular and patho-functional two AML patients (Table S1 in Supplementary Material). Notably, impact of mutations directly from the type of mutation and most of them (149/153, 97%) have a frequency lower than 5% from their location is always a not-trivial mission. In general, it in AMLs. Thus, these data suggest the existence of two classes of might be true that when a genetic variant is found persistently mutated genes in AMLs: one comprising few (10/281, 3.6%) and located at a single amino acid position, the lesion may trigger a frequently mutated genes, and the other comprising a larger set gain-of-function deleterious mechanism, as already established for of genes with very low mutation frequencies. Although these are known oncogenic mutations (e.g., RAS, NPM1). Loss-of-function partial data, as these mutations need to be confirmed in a larger is instead suggested by the finding of widely distributed divergent number of samples, known recurrent mutations appear to be over- mutations along the structure of the gene, as often observed for represented in the data-set of AML-associated mutations (Fisher’s several classical tumor suppressor genes (e.g., BRCA1 and TP53). exact test P-value = 2.3e−06), suggesting that NGS major contri- Actually, often, “hot spot” and dispersed mutations can be both bution to AML cancer genomics will probably be the detection found in the same gene, making a prediction more difficult. This is Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 8 Riva et al. AML mutational analysis and NGS the case of DNMT3A, the DNA (cytosine-5-)-methyltransferase- Three main challenges remain to be addressed. First, while 3-alpha, one of the most interesting newly identified recurrent these technologies can detect many somatic mutations in each targets of mutations in AML. patient, only a subset of them is probably involved in cancer ini- DNMT3A is an epigenetic modifying-enzyme known to be tiation and progression. Thus, it is essential to develop methods essential, together with DNMT3B, for the proper de novo methyla- to distinguish between passenger and driver mutations (Stratton tion of DNA. It is one of the novel, most frequently mutated genes et al., 2009). Second, an increasing number of mutations have been found in AML patients (DNMT3A mutation frequency: ∼20%) identified in AML. What links these genetic alterations to cancer and it is one of those also discovered to be recurrently mutated in progression? What complex interactions underlie AML pathogen- MDS (about 8%; Walter et al., 2011). Its mutated form in AML esis? Third, the use of massive parallel sequencing has also found a (i) is associated with mutations of NPM1, FLT3, IDH1, and CBPA, rewarding application in the identification of chromatin features; (ii) never appears in AML characterized by translocation events, the next challenge will be to integrate AML genomic information (iii) is prevalent in AML with NK, and (iv) is associated with poor with AML epigenomic profiles. survival. Help will come from the genome sequencing of 500 de novo Nearly half of the mutations in the DNMT3A gene are con- AML cases by the TCGA (http://tcga-data.nci.nih.gov/tcga), an centrated in positions affecting arginine 882 (R882), a conserved NIH consortium which aims to contribute to the understand- residue of the methyltransferase (MT) domain. The remaining ing of the molecular basis of cancer through the gathering variations are more largely distributed along the length of the gene, and analysis of different high-throughput data, such as DNA- although preferentially targeting the MT domain, as well. This sequencing, methylation, gene expression and miRNA expression structural observation suggests a loss-of-function mechanism. In data. We foresee different ways of interpreting the huge amount of support of this hypothesis,in vitro experiments showed that muta- information generated by cancer re-sequencing projects in order tionsinthe DNMT3A MT domain decrease the methyltransferase to link mutations identified by NGS technologies to leukemia activity of DNMT3A. In contrast, overexpression of DNMT3A in progression. PML–RARA expressing mice recently demonstrated the potential To correlate cancer mutated genes to cancer behavior we will cooperative nature of DNMT3A to induce APL (Subramanyam need to discriminate, within all the variants found in the sequenc- et al., 2010). Indeed, transplantation into irradiated mice of PML– ing projects, between passenger and driver mutations. Currently, + + RARA /DNMT3A bone marrow cells induced leukemia with the definition of driver mutations is usually based on mutation shorter latency and higher penetrance than transplantation of frequency (Wood et al., 2007), and mutations are defined as dri- cells only expressing the initiating protein PML–RARA, thus sug- vers when found in a larger number of AML genomes. Since many gesting a gain-on-function mechanism, possibly combined with driver mutations may be infrequent and contributing to cancer a dominant negative effect on the wild type proteins. Interest- development only in few tumors, we will need to test a large ingly DNMT3A mutations, although not dramatically altering number of tumor samples in order to discriminate between rare global DNA methylation levels in AML genomes, tend to pro- driver mutations and passenger mutations. Anyway, there are other duce modified methylation patterns in the proximity of specific purely computational ways to identify driver mutations, indepen- DNA regions and genes (Ley et al., 2010). Further experiments are dent of the evaluation of mutation frequency. These methods required to completely clarify mechanisms and roles of DNMT3A can identify driver mutations among those that cause changing and its association with co-occurring recurrent and rare genomic in the amino acid sequence of the associated protein. Methods alterations. as SIFT (Kumar et al., 2009) and PolyPhen-2 (Adzhubei et al., 2010) can predict for each non-synonymous mutation the impact FUTURE PROSPECTIVE AND OPEN CHALLENGES of the amino acid substitution on protein structure and func- So far, tumor and control samples from 26 AML patients have tions, using different features such as sequence homology, amino been sequenced but larger numbers of samples are expected to be acid physicochemical properties and protein structure-based fea- sequenced in the near future. These data will be crucial to dis- tures. Recently, new methods that use network-based approaches sect the complexity of somatic mutations, which contribute to (Torkamani and Schork, 2009; Cerami et al., 2010; Vandin et al., AML pathogenesis. No doubt, NGS platforms will be also further 2011) or machine learning algorithms (Carter et al., 2009)have employed for the discovery of mutations in mouse models of AML. been developed to identify driver mutations. The last 3 years, characterized by increased NSG applications, However, since the vast majority of somatic mutations are have seen a dramatic reduction in the costs of data generation, shared only between few patients, it might be more important an increase in coverage, and improvements in computational to identify driver pathways rather than driver mutations. Indeed, data analysis. Indeed, the number of validated mutations of the driver pathways can be reconstructed using network models con- computationally predicted variants ranges from 5% (Ley et al., taining driver mutations and other genes that may link them 2008) to 98% (Yan et al., 2011), thus reducing validation costs (Vandin et al., 2012). The identification of driver pathways is and increasing the automation of the identification processes. important to rationalize targets for therapeutic intervention. Many Cost reduction, increase in automation, availability of NGS in recent works identify driver pathways by integrating different types medium and small centers, and the possibility to simultane- of high-throughput data, such as copy number variant data and mRNA expression data, to identify driver CNVs, to stratify patients ously detect all the genetic variants existing in a cancer genome, have opened new opportunities for the employment of NGS in and to obtain mechanistic and prognostic insights (Akavia et al., clinical settings. 2010; Vaske et al., 2010; Jörnsten et al., 2011). www.frontiersin.org May 2012 | Volume 2 | Article 40 | 9 Riva et al. AML mutational analysis and NGS Lastly, it is possible to correlate specific mutations found in very variant” is a mutation acquired during the life span of an indi- large collections of patient samples with clinical outcomes, using vidual in a specific area of the body (e.g., bone marrow); the cell unsupervised and supervised machine learning methods. With the where the somatic mutation occurs, may give rise to a clonal prolif- unsupervised methods it is possible to cluster the mutational pro- eration event. A somatic variant can be easily distinguished from files of different patients to identify common alterations. With the a germline one by comparing the region of the mutated DNA supervised methods we can identify the features (or biomarkers) sequence with a corresponding sequence obtained from another that can better classify specific subgroups of AML. tissue of the same individual: in the first case the sequences will In order to determine what most likely drives AML, in addition be different, in the second identical. Both germline and somatic to the development of computational methods that can priori- mutations can be neutral (i.e., do not produce an observable tize candidate mutations, we will need to functionally characterize pathological phenotype) or deleterious (i.e., are directly respon- these mutated genes, using in vitro and/or in vivo experiments. sible or contribute to establish a perturbed unhealthy condition). The possibility of recognizing a subset of genetic variations Neutrality and deleteriousness are not always obvious, but can be with predictive and prognostic value will pave the way to a predicted based on the features of the specific areas of genomic mutation-specific, “personalized,” therapy choice. The molecular DNA, such as coding and regulatory potential or involvement in classification of AML patients will improve clinical outcome and splicing mechanisms. be essential for disease monitoring. We expect that in a not so Recurrent mutation: it generally indicates that the same somatic distant future, testing the presence of mutated genes in biopsies mutation is found in different individuals, usually carrying a before treatment will become clinical routine practice. tumor of the same type. Herein, a recurrent mutation is defined So far studies have focused on the identification of mutations as found in “at least 5% of the tested samples.” Since the chance that are present in the majority of tumor cells. Indeed, these works of finding a recurrent event is very low, it likely reflects the impor- have identified mutations with a frequency usually higher than tance that a somatic mutation may have on a tumorigenic or 25%. If we increase the coverage, and we improve bioinformat- disease predisposing phenotype. ics pipelines, we could aim to identify small sub-clones (<1%). Such an achievement could help addressing many important open ACKNOWLEDGMENTS questions such as the clonal evolution of tumors and, of clinical This work was supported by grants from AIRC and Italian Min- interest, the prediction of resistance to anti-tumoral treatments. istry of Health. LR is supported by a Reintegration AIRC/Marie Indeed, Ding et al. (2012) observed changes in mutant allele fre- Curie International Fellowship in Cancer Research. We thank C. quencies between the primary tumor and the relapse tumor, as Ronchini and E. Colombo for helpful discussions; P. Dalton and well as clonal evolution in 5/8 patients, suggesting that a popula- R. Aina for scientific editing. tion with potentially chemo-resistant mutations might pre-exist and expand after treatment. SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at “MUTATIONS” GLOSSARY BOX http://www.frontiersin.org/Molecular_and_Cellular_Oncology/ Genomic mutations, genetic variants, genomic alterations, or sim- abstract/21275 ply mutations or variants: they are all synonyms indicating varia- Table S1 | Catalog of all the genes with molecular genetic abnormalities in tions found in the DNA sequence derived from an individual with adult AML as detected by NGS technologies. The 290 genes listed in this respect to the “Reference genome sequence.” table were found in one or more tumor samples. This table includes also the Mutations can be germline or somatic. A “germline mutation” nine genes whose mutations were relapse-specific and absent in the additional gives rise to a mutation in the offspring; it is present in every 200 primary AMLs considered for testing recurrently mutated genes (Ding cell. SNPs belong to this class. A “somatic mutation” or “somatic et al., 2012). REFERENCES mutations: computational predic- and TLE4 from the del(9q) com- Wilson, R. K., and DiPersio, J. F. Adzhubei, I. A., Schmidt, S., Peshkin, tion of driver missense mutations. monly deleted region in AML coop- (2012). Clonal evolution in relapsed L., Ramensky, V. E., Gerasimova, A., Cancer Res. 69, 6660–6667. erates with AML1-ETO to affect acute myeloid leukaemia revealed by Bork, P., Kondrashov, A. S., and Sun- Cerami, E., Demir, E., Schultz, N., myeloid cell proliferation and sur- whole-genome sequencing. Nature yaev, S. R. (2010). A method and Taylor, B. S., and Sander, C. vival. Blood 111, 4338–4347. 481, 506–510. server for predicting damaging mis- (2010). Automated network analy- Ding, L., Ley, T. J., Larson, D. E., Graubert, T. A., Shen, D., Ding, L., sense mutations. Nat. Methods 7, sis identifies core pathways in Miller, C. A., Koboldt, D. C., Welch, Okeyo-Owuor, T., Lunn, C. L., Shao, 248–249. glioblastoma. PLoS ONE 5, e8918. J. S., Ritchey, J. K., Young, M. A., J., Krysiak, K., Harris, C. C., Koboldt, Akavia, U. D., Litvin, O., Kim, J., doi:10.1371/journal.pone.0008918 Lamprecht, T., McLellan, M. D., D. C., Larson, D. E., McLellan, M. D., Sanchez-Garcia, F., Kotliar, D., Caus- Cheng, K., Sportoletti, P., Ito, K., McMichael, J. F., Wallis, J. W., Lu, Dooling, D. J., Abbott, R. M., Fulton, ton, H. C., Pochanard, P., Mozes, Clohessy, J. G., Teruya-Feldstein, C., Shen, D., Harris, C. C., Dool- R. S., Schmidt, H., Kalicki-Veizer, J., E., Garraway, L. A., and Pe’er, D. J., Kutok, J. L., and Pandolfi, P. ing, D. J., Fulton, R. S., Fulton, L. O’Laughlin, M., Grillot, M., Baty, J., (2010). An integrated approach to P. (2010). The cytoplasmic NPM L., Chen, K., Schmidt, H., Kalicki- Heath, S., Frater, J. L., Nasim, T., uncover drivers of cancer. Cell 143, mutant induces myeloproliferation Veizer, J., Magrini, V. J., Cook, L., Link, D. C., Tomasson, M. H., West- 1005–1017. in a transgenic mouse model. Blood McGrath, S. D., Vickery, T. L., Wendl, ervelt, P., DiPersio, J. F., Mardis, E. Carter, H., Chen, S., Isik, L., Tyekucheva, 115, 3341–3345. M. C., Heath, S., Watson, M. A., R., Ley, T. J., Wilson, R. K., and Wal- S., Velculescu, V. E., Kinzler, K. Dayyani, F., Wang, J., Yeh, J.-R. J., Ahn, Link, D. C., Tomasson, M. H., Shan- ter, M. J. (2012). Recurrent muta- W., Vogelstein, B., and Karchin, E.-Y., Tobey, E., Zhang, D.-E., Bern- non, W. D., Payton, J. E., Kulka- tions in the U2AF1 splicing factor R. (2009). Cancer-specific high- stein, I. D., Peterson, R. T., and rni, S., Westervelt, P., Walter, M. in myelodysplastic syndromes. Nat. throughput annotation of somatic Sweetser, D. A. (2008). Loss of TLE1 J., Graubert, T. A., Mardis, E. R., Genet. 44, 53–57. Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 10 Riva et al. AML mutational analysis and NGS Gregory, T. K., Wald, D., Chen, Y., Ver- Vickery, T. L., Hundal, J., Cook, B., Bupathi, M., Guinta, K., Afa- twelve human exomes. Nature 461, maat, J. M., Xiong, Y., and Tse, W. L. L., Conyers, J. J., Swift, G. W., ble, M. G., Sekeres, M. A., Pad- 272–276. (2009). Molecular prognostic mark- Reed, J. P., Alldredge, P. A., Wylie, gett, R. A., Tiu, R. V., and Papaemmanuil, E., Cazzola, M., Boult- ers for adult acute myeloid leukemia T., Walker, J., Kalicki, J., Watson, Maciejewski, J. P. (2012). Muta- wood, J., Malcovati, L., Vyas, P., with normal cytogenetics. J. Hema- M. A., Heath, S., Shannon, W. D., tions in the spliceosome machin- Bowen, D., Pellagatti, A., Wain- tol. Oncol. 2, 23. Varghese, N., Nagarajan, R., West- ery, a novel and ubiquitous path- scoat, J. S., Hellstrom-Lindberg, E., Greif, P. A., Yaghmaie, M., Konstandin, ervelt, P., Tomasson, M. H., Link, way in leukemogenesis. Blood 119, Gambacorti-Passerini, C., Godfrey, N. P., Ksienzyk, B., Alimoghad- D. C., Graubert, T. A., DiPersio, J. 3203–3210. A. L., Rapado, I., Cvejic, A., Rance, dam, K., Ghavamzadeh, A., Hauser, F., Mardis, E. R., and Wilson, R. Malcovati, L., Papaemmanuil, E., R., McGee, C., Ellis, P., Mudie, L. J., A., Graf, A., Krebs, S., Blum, K. (2010). DNMT3A Mutations in Bowen, D. T., Boultwood, J., Stephens, P. J., McLaren, S., Massie, H., and Bohlander, S. K. (2011a). acute myeloid leukemia. N. Engl. J. C. E., Tarpey, P. S., Varela, I., Nik- Della Porta, M. G., Pascutto, C., Somatic mutations in acute promye- Med. 363, 2424–2433. Travaglino, E., Groves, M. J., God- Zainal, S., Davies, H. R., Shlien, A., locytic leukemia (APL) identified Ley, T. J., Mardis, E. R., Ding, L., Ful- frey, A. L., Ambaglio, I., Gallí, A., Jones, D., Raine, K., Hinton, J., But- by exome sequencing. Leukemia 25, ton, B., McLellan, M. D., Chen, Da Vià, M. C., Conte, S., Tauro, S., ler, A. P., Teague, J. W., Baxter, E. J., 1519–1522. K., Dooling, D., Dunford-Shore, B. Keenan, N., Hyslop, A., Hinton, J., Score, J., Galli, A., Della Porta, M. G., Greif, P. A., Eck, S. H., Konstandin, H., McGrath, S., Hickenbotham, M., Mudie, L. J., Wainscoat, J. S., Futreal, Travaglino, E., Groves, M., Tauro, S., N. P., Benet-Pagès, A., Ksienzyk, B., Cook, L., Abbott, R., Larson, D. P. A., Stratton, M. R., Campbell, P. Munshi, N. C., Anderson, K. C., El- Dufour, A., Vetter, A. T., Popp, H. D., E., Koboldt, D. C., Pohl, C., Smith, J., Hellström-Lindberg, E., Cazzola, Naggar, A., Fischer, A., Mustonen, V., Lorenz-Depiereux, B., Meitinger, T., S., Hawkins, A., Abbott, S., Locke, M., and Chronic Myeloid Disorders Warren, A. J., Cross, N. C. P., Green, Bohlander, S. K., and Strom, T. M. D., Hillier, L. W., Miner, T., Ful- Working Group of the International A. R., Futreal, P. A., Stratton, M. R., (2011b). Identification of recurring ton, L., Magrini, V., Wylie, T., Glass- Cancer Genome Consortium and and Campbell, P. J. (2011). Somatic tumor-specific somatic mutations in cock, J., Conyers, J., Sander, N., Shi, of the Associazione Italiana per SF3B1 mutation in myelodysplasia acute myeloid leukemia by tran- X., Osborne, J. R., Minx, P., Gor- la Ricerca sul Cancro Gruppo with ring sideroblasts. N. Engl. J. scriptome sequencing. Leukemia 25, don, D., Chinwalla, A., Zhao, Y., Ries, Italiano Malattie Mieloproliferative. Med. 365, 1384–1395. 821–827. R. E., Payton, J. E., Westervelt, P., (2011). Clinical significance of Ramsingh, G., Koboldt, D. C., Trissal, Grossmann, V., Tiacci, E., Holmes, A. Tomasson, M. H., Watson, M., Baty, SF3B1 mutations in myelodysplastic M., Chiappinelli, K. B., Wylie, T., B., Kohlmann, A., Martelli, M. P., J., Ivanovich, J., Heath, S., Shan- syndromes and myelodysplas- Koul, S., Chang, L.-W., Nagarajan, Kern, W., Spanhol-Rosseto, A., Klein, non, W. D., Nagarajan, R., Walter, tic/myeloproliferative neoplasms. R., Fehniger, T. A., Goodfellow, P., H.-U., Dugas, M., Schindela, S., M. J., Link, D. C., Graubert, T. A., Blood 118, 6239–6246. Magrini, V., Wilson, R. K., Ding, L., Trifonov, V., Schnittger, S., Hafer- DiPersio, J. F., and Wilson, R. K. Mardis, E. R., Ding, L., Dooling, D. Ley, T. J., Mardis, E. R., and Link, lach, C., Bassan, R., Wells, V. A., (2008). DNA sequencing of a cyto- J., Larson, D. E., McLellan, M. D., D. C. (2010). Complete character- Spinelli, O., Chan, J., Rossi, R., Bal- genetically normal acute myeloid Chen, K., Koboldt, D. C., Fulton, ization of the microRNAome in a doni, S., De Carolis, L., Goetze, K., leukaemia genome. Nature 4567218, R. S., Delehaunty, K. D., McGrath, patient with acute myeloid leukemia. Serve, H., Peceny, R., Kreuzer, K. 66–72. S. D., Fulton, L. A., Locke, D. P., Blood 116, 5316–5326. A., Oruzio, D., Specchia, G., Di Rai- Li, M., Wang, I. X., Li, Y., Bruzel, A., Magrini, V. J., Abbott, R. M., Vick- Singleton, A. B. (2011). Exome sequenc- mondo, F., Fabbiano, F., Sborgia, M., Richards, A. L., Toung, J. M., and ery, T. L., Reed, J. S., Robinson, J. S., ing: a transformative technology. Liso, A., Farinelli, L., Rambaldi, A., Cheung, V. G. (2011). Widespread Wylie, T., Smith, S. M., Carmichael, Lancet Neurol. 10, 942–946. Pasqualucci, L., Rabadan, R., Hafer- RNA and DNA sequence differences L., Eldred, J. M., Harris, C. C., Stratton, M. R., Campbell, P. J., and lach, T., and Falini, B. (2011). Whole- Futreal, P. A. (2009). The cancer in the human transcriptome. Science Walker, J., Peck, J. B., Du, F., Dukes, exome sequencing identifies somatic 333, 53–58. A. F., Sanderson, G. E., Brummett, genome. Nature 458, 719. mutations of BCOR in acute myeloid Link, D. C., Schuettpelz, L. G., Shen, D., A. M., Clark, E., McMichael, J. F., Subramanyam, D., Belair, C. D., Barry- leukemia with normal karyotype. Wang, J., Walter, M. J., Kulkarni, S., Meyer, R. J., Schindler, J. K., Pohl, Holson, K. Q., Lin, H., Kogan, S. Blood 118, 6153–6163. Payton, J. E., Ivanovich, J., Goodfel- C. S., Wallis, J. W., Shi, X., Lin, L., C., Passegué, E., and Blelloch, R. Jörnsten, R., Abenius, T., Kling, T., low, P. J., Le Beau, M., Koboldt, D. C., Schmidt, H., Tang, Y., Haipek, C., (2010). PML-RARα and Dnmt3a1 Schmidt, L., Johansson, E., Nordling, Dooling, D. J., Fulton, R. S., Bender, Wiechert, M. E., Ivy, J. V., Kalicki, cooperateinvivotopromoteacute T. E. M., Nordlander, B., Sander, C., R. H., Fulton, L. L., Delehaunty, K. J., Elliott, G., Ries, R. E., Payton, promyelocytic leukemia. Cancer Res. Gennemark, P., Funa, K., Nilsson, B., D., Fronick, C. C., Appelbaum, E. L., J. E., Westervelt, P., Tomasson, M. 70, 8792–8801. Lindahl, L., and Nelander, S. (2011). Schmidt, H., Abbott, R., O’Laughlin, H., Watson, M. A., Baty, J., Heath, Tefferi, A., Thiele, J., and Vardiman, J. Network modeling of the transcrip- M., Chen, K., McLellan, M. D., S., Shannon, W. D., Nagarajan, R., W. (2009). The 2008 World Health tional effects of copy number aberra- Varghese, N., Nagarajan, R., Heath, Link, D. C., Walter, M. J., Graubert, Organization classification system tions in glioblastoma. Mol. Syst. Biol. S., Graubert, T. A., Ding, L., Ley, T. T. A., DiPersio, J. F., Wilson, R. for myeloproliferative neoplasms. 7, 486. J., Zambetti, G. P., Wilson, R. K., and K., and Ley, T. J. (2009). Recur- Cancer 115, 3842–3847. Kumar, P., Henikoff, S., and Ng, P. Mardis, E. R. (2011). The identifi- ring mutations found by sequenc- Torkamani, A., and Schork, N. J. (2009). C. (2009). Predicting the effects cation of a novel TP53 cancer sus- ing an acute myeloid leukemia Identification of rare cancer driver of coding non-synonymous vari- ceptibility mutation through whole genome. N. Engl.J.Med. 361, mutations by network reconstruc- ants on protein function using genome sequencing of a patient with 1058–1066. tion. Genome Res. 19, 1570–1578. the SIFT algorithm. Nat. Protoc. 4, therapy-related AML. JAMA 305, Meyerson, M., Gabriel, S., and Getz, Vandin, F., Upfal, E., and Raphael, B. 1073–1081. 1568–1576. G. (2010). Advances in understand- J. (2011). Algorithms for detecting Ley, T. J., Ding, L., Walter, M. J., McLel- Maher, C. A., Kumar-Sinha, C., Cao, X., ing cancer genomes through second- significantly mutated pathways in lan, M. D., Lamprecht, T., Larson, D. Kalyana-Sundaram, S., Han, B., Jing, generation sequencing. Nat. Rev. cancer. J. Comput. Biol. 18, 507–522. E., Kandoth, C., Payton, J. E., Baty, J., X., Sam, L., Barrette, T., Palanisamy, Genet. 11, 685–696. Vandin, F., Upfal, E., and Raphael, B. Welch, J., Harris, C. C., Lichti, C. F., N., and Chinnaiyan, A. M. (2009). Ng, S. B., Turner, E. H., Robertson, P. J. (2012). De novo discovery of Townsend, R. R., Fulton, R. S., Dool- Transcriptome sequencing to detect D., Flygare, S. D., Bigham, A. W., mutated driver pathways in cancer. ing, D. J., Koboldt, D. C., Schmidt, gene fusions in cancer. Nature 458, Lee, C., Shaffer, T., Wong, M., Bhat- Genome Res. 22, 375–385. H., Zhang, Q., Osborne, J. R., Lin, 97–101. tacharjee, A., Eichler, E. E., Bamshad, Vaske, C. J., Benz, S. C., Sanborn, J. Z., L., O’Laughlin, M., McMichael, J. Makishima, H., Visconte, V., Sak- M., Nickerson, D. A., and Shen- Earl, D., Szeto, C., Zhu, J., Haussler, D., and Stuart, J. M. (2010). Infer- F., Delehaunty, K. D., McGrath, S. aguchi, H., Jankowska, A. M., Abu dure, J. (2009). Targeted capture D., Fulton, L. A., Magrini, V. J., Kar, S., Jerez, A., Przychodzen, and massively parallel sequencing of ence of patient-specific pathway www.frontiersin.org May 2012 | Volume 2 | Article 40 | 11 Riva et al. AML mutational analysis and NGS activities from multi-dimensional C., McLellan, M. D., Schmidt, H., D. R., Suh, E., Papadopoulos, N., W. K., Miyawaki, S., Sugano, S., cancer genomics data using PARA- Fulton, R. S., Abbott, R. M., Cook, Buckhaults, P., Markowitz, S. D., Haferlach, C., Koeffler, H. P., Shih, L. DIGM. Bioinformatics 26, i237–i245. L., McGrath, S. D., Fan, X., Dukes, Parmigiani, G., Kinzler, K. W., Vel- Y., Haferlach, T., Chiba, S., Nakauchi, Vassiliou, G. S., Cooper, J. L., Rad, R., A. F., Vickery, T., Kalicki, J., Lam- culescu, V. E., and Vogelstein, B. H., Miyano, S., and Ogawa, S. Li, J., Rice, S., Uren, A., Rad, L., Ellis, precht, T. L., Graubert, T. A., Tomas- (2007). The genomic landscapes of (2011). Frequent pathway mutations P., Andrews, R., Banerjee, R., Grove, son, M. H., Mardis, E. R., Wilson, R. human breast and colorectal cancers. of splicing machinery in myelodys- C., Wang, W., Liu, P., Wright, P., K., and Ley, T. J. (2011). Sequenc- Science 318, 1108–1113. plasia. Nature 478, 64–69. Arends, M., and Bradley, A. (2011). ing a mouse acute promyelocytic Yamashita, Y., Yuan, J., Suetake, I., Mutant nucleophosmin and cooper- leukemia genome reveals genetic Suzuki, H., Ishikawa, Y., Choi, Y. Conflict of Interest Statement: The ating pathways drive leukemia initi- events relevant for disease progres- L., Ueno, T., Soda, M., Hamada, T., authors declare that the research was ation and progression in mice. Nat. sion. J. Clin. Invest. 121, 1445–1455. Haruta, H., Akada, S., Miyazaki, Y., conducted in the absence of any com- Genet. 43, 470–475. Welch, J. S., Westervelt, P., Ding, L., Kiyoi, H., Ito, E., Naoe, T., Tomon- mercial or financial relationships that Visconte, V., Makishima, H., Jankowska, Larson, D. E., Klco, J. M., Kulka- aga, M., Toyota, M., Tajima, S., could be construed as a potential con- A., Szpurka, H., Traina, F., Jerez, A., rni, S., Wallis, J., Chen, K., Payton, Iwama, A., and Mano, H. (2010). flict of interest. O’Keefe, C., Rogers, H. J., Sekeres, J. E., Fulton, R. S., Veizer, J., Schmidt, Array-based genomic resequencing M. A., Maciejewski, J. P., and Tiu, H., Vickery, T. L., Heath, S., Wat- of human leukemia. Oncogene 29, Received: 22 December 2011; paper pend- R. V. (2011). SF3B1, a splicing fac- son, M. A., Tomasson, M. H., Link, 3723–3731. ing published: 24 January 2012; accepted: tor is frequently mutated in refrac- D. C., Graubert, T. A., DiPersio, J. Yan, X.-J., Xu, J., Gu, Z.-H., Pan, C.-M., 27 February 2012; published online: 01 tory anemia with ring sideroblasts. F., Mardis, E. R., Ley, T. J., and Wil- Lu, G., Shen, Y., Shi, J.-Y., Zhu, Y.- May 2012. Leukemia 26, 542–545. son, R. K. (2011). Use of whole M., Tang, L., Zhang, X.-W., Liang, Citation: Riva L, Luzi L and Pelicci Walter, M. J., Ding, L., Shen, D., Shao, genome sequencing to diagnose a W. X., Mi, J. Q., Song, H. D., Li, PG (2012) Genomics of acute J., Grillot, M., McLellan, M., Ful- cryptic fusion oncogene. JAMA 305, K. Q., Chen, Z., and Chen, S. J. myeloid leukemia: the next gen- ton, R., Schmidt, H., Kalicki-Veizer, 1577–1584. (2011). Exome sequencing identifies eration. Front. Oncol. 2:40. doi: J., O’Laughlin, M., Kandoth, C., Baty, Wood, L. D., Parsons, D. W., Jones, S., somatic mutations of DNA methyl- 10.3389/fonc.2012.00040 J., Westervelt, P., DiPersio, J. F., Lin, J., Sjöblom, T., Leary, R. J., Shen, transferase gene DNMT3A in acute This article was submitted to Frontiers Mardis, E. R., Wilson, R. K., Ley, D., Boca, S. M., Barber, T., Ptak, J., monocytic leukemia. Nat. Genet. 43, in Molecular and Cellular Oncology, a T. J., and Graubert, T. A. (2011). Silliman, N., Szabo, S., Dezso, Z., 309–315. specialty of Frontiers in Oncology. Recurrent DNMT3A mutations in Ustyanksky,V., Nikolskaya, T., Nikol- Yoshida, K., Sanada, M., Shiraishi, Y., Copyright © 2012 Riva, Luzi and Pelicci. patients with myelodysplastic syn- sky, Y., Karchin, R., Wilson, P. A., Nowak, D., Nagata, Y., Yamamoto, This is an open-access article distributed dromes. Leukemia 25, 1153–1158. Kaminker, J. S., Zhang, Z., Croshaw, R., Sato, Y., Sato-Otsubo, A., Kon, A., under the terms of the Creative Commons Wartman, L. D., Larson, D. E., Xiang, Z., R., Willis, J., Dawson, D., Ship- Nagasaki, M., Chalkidis, G., Suzuki, Attribution Non Commercial License, Ding, L., Chen, K., Lin, L., Cahan, itsin, M., Willson, J. K., Sukumar, S., Y., Shiosaka, M., Kawahata, R., Yam- which permits non-commercial use, dis- P., Klco, J. M., Welch, J. S., Li, C., Polyak, K., Park, B. H., Pethiyagoda, aguchi, T., Otsu, M., Obara, N., tribution, and reproduction in other Payton, J. E., Uy, G. L., Varghese, N., C. L., Pant, P. V., Ballinger, D. G., Sakata-Yanagimoto, M., Ishiyama, forums, provided the original authors and Ries, R. E., Hoock, M., Koboldt, D. Sparks, A. B., Hartigan, J., Smith, K., Mori, H., Nolte, F., Hofmann, source are credited. Frontiers in Oncology | Molecular and Cellular Oncology May 2012 | Volume 2 | Article 40 | 12

Journal

Frontiers in OncologyPubmed Central

Published: May 1, 2012

References