Acute promyelocytic leukemia (APL) is initiated by the PML-RARA fusion oncogene and has a characteristic expression profile that includes high levels of the Notch ligand JAG1. In this study, we used a series of bioinformatic, in vitro, and in vivo assays to assess the role of Notch signaling in human APL samples, and in a PML-RARA knockin mouse model of APL (Ctsg-PML-RARA). We identified a Notch expression signature in both human primary APL cells and in Kit+Lin−Sca1+ (KLS) cells from pre-leukemic Ctsg-PML-RARA mice. Both genetic and pharmacologic inhibition of Notch signaling abrogated the enhanced self-renewal seen in hematopoietic stem/progenitor cells (HSPCs) from pre-leukemic Ctsg-PML-RARA mice, but had no influence on cells from age-matched wildtype mice. In addition, 6 of 9 murine APL tumors tested displayed diminished growth in vitro when Notch signaling was inhibited pharmacologically. Finally, we found that genetic inhibition of Notch signaling with a dominant negative MAML protein reduced APL growth in vivo in a subset of tumors. These findings expand the role of Notch signaling in hematopoietic diseases, and further define the mechanistic events important for PML-RARA-mediated leukemogenesis.
Notch; Acute Promyelocytic Leukemia; Self-renewal
Most mutations in cancer genomes are thought to be acquired after the initiating event, which may cause genomic instability, driving clonal evolution. However, for acute myeloid leukemia (AML), normal karyotypes are common, and genomic instability is unusual. To better understand clonal evolution in AML, we sequenced the genomes of AML samples with a known initiating event (PML-RARA) vs. normal karyotype AML samples, and the exomes of hematopoietic stem/progenitor cells (HSPCs) from healthy people. Collectively, the data suggest that most of the mutations found in AML genomes are actually random events that occurred in HSPCs before they acquired the initiating mutation; the mutational history of that cell is “captured” as the clone expands. In many cases, only one or two additional, cooperating mutations are needed to generate the malignant founding clone. Cells from the founding clone can acquire additional cooperating mutations, yielding subclones that can contribute to disease progression and/or relapse.
To assess the genetic consequences of induced Pluripotent Stem Cell (iPSC) reprogramming, we sequenced the genomes of ten murine iPSC clones derived from three independent reprogramming experiments, and compared them to their parental cell genomes. We detected hundreds of single nucleotide variants (SNVs) in every clone, with an average of 11 in coding regions. In two experiments, all SNVs were unique for each clone and did not cluster in pathways, but in the third, all four iPSC clones contained 157 shared genetic variants, which could also be detected in rare cells (<1 in 500) within the parental MEF pool. This data suggests that most of the genetic variation in iPSC clones is not caused by reprogramming per se, but is rather a consequence of cloning individual cells, which “captures” their mutational history. These findings have implications for the development and therapeutic use of cells that are reprogrammed by any method.
Most patients with acute myeloid leukemia (AML) die from progressive disease after relapse, which is associated with clonal evolution at the cytogenetic level1,2. To determine the mutational spectrum associated with relapse, we sequenced the primary tumor and relapse genomes from 8 AML patients, and validated hundreds of somatic mutations using deep sequencing; this allowed us to precisely define clonality and clonal evolution patterns at relapse. Besides discovering novel, recurrently mutated genes (e.g. WAC, SMC3, DIS3, DDX41, and DAXX) in AML, we found two major clonal evolution patterns during AML relapse: 1) the founding clone in the primary tumor gained mutations and evolved into the relapse clone, or 2) a subclone of the founding clone survived initial therapy, gained additional mutations, and expanded at relapse. In all cases, chemotherapy failed to eradicate the founding clone. The comparison of relapse-specific vs. primary tumor mutations in all 8 cases revealed an increase in transversions, probably due to DNA damage caused by cytotoxic chemotherapy. These data demonstrate that AML relapse is associated with the addition of new mutations and clonal evolution, which is shaped in part by the chemotherapy that the patients receive to establish and maintain remissions.
To reveal the clonal architecture of melanoma and associated driver mutations, whole genome sequencing (WGS) and targeted extension sequencing were used to characterize 124 melanoma cases. Significantly mutated gene analysis using 13 WGS cases and 15 additional paired extension cases identified known melanoma genes such as BRAF, NRAS, and CDKN2A, as well as a novel gene EPHA3, previously implicated in other cancer types. Extension studies using tumors from another 96 patients discovered a large number of truncation mutations in tumor suppressors (TP53 and RB1), protein phosphatases (e.g., PTEN, PTPRB, PTPRD, and PTPRT), as well as chromatin remodeling genes (e.g., ASXL3, MLL2, and ARID2). Deep sequencing of mutations revealed subclones in the majority of metastatic tumors from 13 WGS cases. Validated mutations from 12 out of 13 WGS patients exhibited a predominant UV signature characterized by a high frequency of C->T transitions occurring at the 3′ base of dipyrimidine sequences while one patient (MEL9) with a hypermutator phenotype lacked this signature. Strikingly, a subclonal mutation signature analysis revealed that the founding clone in MEL9 exhibited UV signature but the secondary clone did not, suggesting different mutational mechanisms for two clonal populations from the same tumor. Further analysis of four metastases from different geographic locations in 2 melanoma cases revealed phylogenetic relationships and highlighted the genetic alterations responsible for differential drug resistance among metastatic tumors. Our study suggests that clonal evaluation is crucial for understanding tumor etiology and drug resistance in melanoma.
Acute promyelocytic leukemia (APL) is characterized by the t(15;17) translocation
that generates the fusion protein promyelocytic leukemia–retinoic acid
receptor α (PML-RARA) in nearly all cases. Multiple prior mouse models of
APL constitutively express PML-RARA from a variety of
non-Pml loci. Typically, all animals develop a myeloproliferative
disease, followed by leukemia in a subset of animals after a long latent period. In
contrast, human APL is not associated with an antecedent stage of myeloproliferation.
To address this discrepancy, we have generated a system whereby
PML-RARA expression is somatically acquired from the mouse
Pml locus in the context of Pml
haploinsufficiency. We found that physiologic PML-RARA expression
was sufficient to direct a hematopoietic progenitor self-renewal program in vitro and
in vivo. However, this expansion was not associated with evidence of
myeloproliferation, more accurately reflecting the clinical presentation of human
APL. Thus, at physiologic doses, PML-RARA primarily acts to increase
hematopoietic progenitor self-renewal, expanding a population of cells that are
susceptible to acquiring secondary mutations that cause progression to leukemia. This
mouse model provides a platform for more accurately dissecting the early events in
The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.
Sequencing the genomic DNA of cancers has revealed that tumors are not homogeneous. As a tumor grows, new mutations accumulate in individual cells, and as these cells replicate, the mutations are passed on to their offspring, which comprise only a portion of the tumor when it is sampled. We present a method for identifying the fraction of cells containing specific mutations, clustering them into subclonal populations, and tracking the changes in these subclones. This allows us to follow the clonal evolution of cancers as they respond to chemotherapy or develop therapy resistance, processes which may radically alter the subclonal composition of a tumor. It also gives us insight into the spatial organization of tumors, and we show that multiple biopsies from a single breast cancer may harbor different subclones that respond differently to treatment. Finally, we show that sequencing multiple samples from a patient's tumor is often critical, as it reveals cryptic subclones that cannot be discerned from only one sample. This is the first tool that can efficiently leverage multiple samples to identify these as distinct subpopulations of cells, thus contributing to understanding the biology of the tumor and influencing clinical decisions about therapy.
Cytotoxic lymphocytes use the granule exocytosis pathway to kill pathogen-infected cells and tumor cells. Although many genes in this pathway have been extensively characterized (e.g., perforin, granzymes A and B), the role of granzyme C is less clear. We therefore developed a granzyme C-specific mAb and used flow cytometry to examine the expression of granzyme B and C in the lymphocyte compartments of wild-type and mutant GzmB−/− cre mice, which have a small deletion in the granzyme B gene. We detected granzyme B and C expression in CD4+ and CD8+ T cells activated with CD3/CD28 beads or MLRs. Stimulation of NK cells in vitro with IL-15 also induced expression of both granzymes. Granzyme C up-regulation was delayed relative to granzyme B in wild-type lymphocytes, whereas GzmB−/− cre cells expressed granzyme C earlier and more abundantly on a per-cell basis, suggesting that the deleted 350-bp region in the granzyme B gene is important for the regulation of both granzymes B and C. Quantitative RT-PCR revealed that granzyme C protein levels were regulated by mRNA abundance. In vivo, a population of wild-type CD8αα+ intraepithelial lymphocytes constitutively expressed granzyme B and GzmB−/− cre intraepithelial lymphocytes likewise expressed granzyme C. Using a model of a persistent murine CMV infection, we detected delayed expression of granzyme C in NK cells from infected hosts. Taken together, these findings suggest that granzyme C is activated with persistent antigenic stimulation, providing nonredundant backup protection for the host when granzyme B fails.
Next-generation sequencing has been used to infer the clonality of heterogeneous tumor samples. These analyses yield specific predictions—the population frequency of individual clones, their genetic composition, and their evolutionary relationships—which we set out to test by sequencing individual cells from three subjects diagnosed with secondary acute myeloid leukemia, each of whom had been previously characterized by whole genome sequencing of unfractionated tumor samples. Single-cell mutation profiling strongly supported the clonal architecture implied by the analysis of bulk material. In addition, it resolved the clonal assignment of single nucleotide variants that had been initially ambiguous and identified areas of previously unappreciated complexity. Accordingly, we find that many of the key assumptions underlying the analysis of tumor clonality by deep sequencing of unfractionated material are valid. Furthermore, we illustrate a single-cell sequencing strategy for interrogating the clonal relationships among known variants that is cost-effective, scalable, and adaptable to the analysis of both hematopoietic and solid tumors, or any heterogeneous population of cells.
Human cancers are genetically diverse populations of cells that evolve over the course of their natural history or in response to the selective pressure of therapy. In theory, it is possible to infer how this variation is structured into related populations of cells based on the frequency of individual mutations in bulk samples, but the accuracy of these models has not been evaluated across a large number of variants in individual cells. Here, we report a strategy for analyzing hundreds of variants within a single cell, and we apply this method to assess models of tumor clonality derived from bulk samples in three cases of leukemia. The data largely support the predicted population structure, though they suggest specific refinements. This type of approach not only illustrates the biological complexity of human cancer, but it also has the potential to inform patient management. That is, precise knowledge of which variants are present in which populations of cells may allow physicians to more effectively target combinations of mutations and predict how patients will respond to therapy.
Acute promyelocytic leukemia (APL) is characterized by the t(15;17) chromosomal translocation, which results in fusion of the retinoic acid receptor α (RARA) gene to another gene, most commonly promyelocytic leukemia (PML). The resulting fusion protein, PML-RARA, initiates APL, which is a subtype (M3) of acute myeloid leukemia (AML). In this report, we identify a gene expression signature that is specific to M3 samples; it was not found in other AML subtypes and did not simply represent the normal gene expression pattern of primary promyelocytes. To validate this signature for a large number of genes, we tested a recently developed high throughput digital technology (NanoString nCounter). Nearly all of the genes tested demonstrated highly significant concordance with our microarray data (P < 0.05). The validated gene signature reliably identified M3 samples in 2 other AML datasets, and the validated genes were substantially enriched in our mouse model of APL, but not in a cell line that inducibly expressed PML-RARA. These results demonstrate that nCounter is a highly reproducible, customizable system for mRNA quantification using limited amounts of clinical material, which provides a valuable tool for biomarker measurement in low-abundance patient samples.
The Drug-Gene Interaction database (DGIdb) mines existing resources that generate hypotheses about how mutated genes might be targeted therapeutically or prioritized for drug development. It provides an interface for searching lists of genes against a compendium of drug-gene interactions and potentially druggable genes. DGIdb can be accessed at dgidb.org.
The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known(forexample, mitogen-activatedprotein kinase, phosphatidylinositol-3-OH kinase,Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the numberof driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.
To define the mutation spectrum in non-Down syndrome acute megkaryoblastic leukemia (non-DS-AMKL), we performed transcriptome sequencing on diagnostic blasts from 14 pediatric patients and validated our findings in a recurrency/validation cohort consisting of 34 pediatric and 28 adult AMKL leukemia samples. Our analysis identified a cryptic chromosome 16 inversion [inv(16)(p13.3q24.3)] in 27% of pediatric cases, which encodes a CBFA2T3-GLIS2 fusion protein. Expression of CBFA2T3-GLIS2 in Drosophila and murine hematopoietic cells induced bone morphogenic protein (BMP) signaling, and resulted in a marked increase in the self-renewal capacity of hematopoietic progenitors. These data suggest that expression of CBFA2T3-GLIS2 directly contributes to leukemogenesis.
Producing gene fusions through genomic structural rearrangements is a major mechanism for tumor evolution. Therefore, accurately detecting gene fusions and the originating rearrangements is of great importance for personalized cancer diagnosis and targeted therapy. We present a tool, BreakTrans, that systematically maps predicted gene fusions to structural rearrangements. Thus, BreakTrans not only validates both types of predictions, but also provides mechanistic interpretations. BreakTrans effectively validates known fusions and discovers novel events in a breast cancer cell line. Applying BreakTrans to 43 breast cancer samples in The Cancer Genome Atlas identifies 90 genomically validated gene fusions. BreakTrans is available at http://bioinformatics.mdanderson.org/main/BreakTrans
Summary: Despite recent progress, computational tools that identify gene fusions from next-generation whole transcriptome sequencing data are often limited in accuracy and scalability. Here, we present a software package, BreakFusion that combines the strength of reference alignment followed by read-pair analysis and de novo assembly to achieve a good balance in sensitivity, specificity and computational efficiency.
Supplementary data are available at Bioinformatics online
Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods under which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including indels, inversions, and translocations. We examined BreakDancer's performance in simulation, comparison with other methods, analysis of an acute myeloid leukemia sample, and the 1,000 Genomes trio individuals. We found that it substantially improved the detection of small and intermediate size indels from 10 bp to 1 Mbp that are difficult to detect via a single conventional approach.
The St. Jude Children’s Research Hospital–Washington University Pediatric Cancer Genome Project (PCGP) is participating in the international effort to identify somatic mutations that drive cancer. These cancer genome sequencing efforts will not only yield an unparalleled view of the altered signaling pathways in cancer but should also identify new targets against which novel therapeutics can be developed. Although these projects are still deep in the phase of generating primary DNA sequence data, important results are emerging and valuable community resources are being generated that should catalyze future cancer research. We describe here the rationale for conducting the PCGP, present some of the early results of this project and discuss the major lessons learned and how these will affect the application of genomic sequencing in the clinic.
Motivation: The sequencing of tumors and their matched normals is frequently used to study the genetic composition of cancer. Despite this fact, there remains a dearth of available software tools designed to compare sequences in pairs of samples and identify sites that are likely to be unique to one sample.
Results: In this article, we describe the mathematical basis of our SomaticSniper software for comparing tumor and normal pairs. We estimate its sensitivity and precision, and present several common sources of error resulting in miscalls.
Availability and implementation: Binaries are freely available for download at http://gmt.genome.wustl.edu/somatic-sniper/current/, implemented in C and supported on Linux and Mac OS X.
Contact: firstname.lastname@example.org; email@example.com
Supplementary information: Supplementary data are available at Bioinformatics online.
To correlate the variable clinical features of estrogen receptor positive (ER+) breast cancer with somatic alterations, we studied pre-treatment tumour biopsies accrued from patients in a study of neoadjuvant aromatase inhibitor (AI) therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to hematopoietic disorders. Mutant MAP3K1 was associated with Luminal A status, low grade histology and low proliferation rates whereas mutant TP53 associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon AI treatment. Pathway analysis demonstrated mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in ER+ breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumor biology but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.
Because PML-RARA-induced acute promyelocytic leukemia (APL) is a morphologically differentiated leukemia, many groups have speculated about whether its leukemic cell of origin is a committed myeloid precursor (e.g. a promyelocyte) versus an hematopoietic stem/progenitor cell (HSPC). We originally targeted PML-RARA expression with CTSG regulatory elements, based on the early observation that this gene was maximally expressed in cells with promyelocyte morphology. Here, we show that both Ctsg, and PML-RARA targeted to the Ctsg locus (in Ctsg-PML-RARA mice), are expressed in the purified KLS cells of these mice (KLS = Kit+Lin−Sca+, which are highly enriched for HSPCs), and this expression results in biological effects in multi-lineage competitive repopulation assays. Further, we demonstrate the transcriptional consequences of PML-RARA expression in Ctsg-PML-RARA mice in early myeloid development in other myeloid progenitor compartments [common myeloid progenitors (CMPs) and granulocyte/monocyte progenitors (GMPs)], which have a distinct gene expression signature compared to wild-type (WT) mice. Although PML-RARA is indeed expressed at high levels in the promyelocytes of Ctsg-PML-RARA mice and alters the transcriptional signature of these cells, it does not induce their self-renewal. In sum, these results demonstrate that in the Ctsg-PML-RARA mouse model of APL, PML-RARA is expressed in and affects the function of multipotent progenitor cells. Finally, since PML/Pml is normally expressed in the HSPCs of both humans and mice, and since some human APL samples contain TCR rearrangements and express T lineage genes, we suggest that the very early hematopoietic expression of PML-RARA in this mouse model may closely mimic the physiologic expression pattern of PML-RARA in human APL patients.
The myelodysplastic syndromes are a group of hematologic disorders that often evolve into secondary acute myeloid leukemia (AML). The genetic changes that underlie progression from the myelodysplastic syndromes to secondary AML are not well understood.
We performed whole-genome sequencing of seven paired samples of skin and bone marrow in seven subjects with secondary AML to identify somatic mutations specific to secondary AML. We then genotyped a bone marrow sample obtained during the antecedent myelodysplastic-syndrome stage from each subject to determine the presence or absence of the specific somatic mutations. We identified recurrent mutations in coding genes and defined the clonal architecture of each pair of samples from the myelodysplastic-syndrome stage and the secondary-AML stage, using the allele burden of hundreds of mutations.
Approximately 85% of bone marrow cells were clonal in the myelodysplastic-syndrome and secondary-AML samples, regardless of the myeloblast count. The secondary-AML samples contained mutations in 11 recurrently mutated genes, including 4 genes that have not been previously implicated in the myelodysplastic syndromes or AML. In every case, progression to acute leukemia was defined by the persistence of an antecedent founding clone containing 182 to 660 somatic mutations and the outgrowth or emergence of at least one subclone, harboring dozens to hundreds of new mutations. All founding clones and subclones contained at least one mutation in a coding gene.
Nearly all the bone marrow cells in patients with myelodysplastic syndromes and secondary AML are clonally derived. Genetic evolution of secondary AML is a dynamic process shaped by multiple cycles of mutation acquisition and clonal selection. Recurrent gene mutations are found in both founding clones and daughter subclones. (Funded by the National Institutes of Health and others.)
Early T-cell precursor acute lymphoblastic leukaemia (ETP ALL) is an aggressive malignancy of unknown genetic basis. We performed whole-genome sequencing of 12 ETP ALL cases and assessed the frequency of the identified somatic mutations in 94 T-cell acute lymphoblastic leukaemia cases. ETP ALL was characterized by activating mutations in genes regulating cytokine receptor and RAS signalling (67% of cases; NRAS, KRAS, FLT3, IL7R, JAK3, JAK1, SH2B3 and BRAF), inactivating lesions disrupting haematopoietic development (58%; GATA3, ETV6, RUNX1, IKZF1 and EP300) and histone-modifying genes (48%; EZH2, EED, SUZ12, SETD2 and EP300). We also identified new targets of recurrent mutation including DNM2, ECT2L and RELN. The mutational spectrum is similar to myeloid tumours, and moreover, the global transcriptional profile of ETP ALL was similar to that of normal and myeloid leukaemia haematopoietic stem cells. These findings suggest that addition of myeloid-directed therapies might improve the poor outcome of ETP ALL.
Myelodysplastic syndromes (MDS) are hematopoietic stem cell disorders that often progress to chemotherapy-resistant secondary acute myeloid leukemia (sAML). We used whole genome sequencing to perform an unbiased comprehensive screen to discover all the somatic mutations in a sAML sample and genotyped these loci in the matched MDS sample. Here we show that a missense mutation affecting the serine at codon 34 (S34) in U2AF1 was recurrently mutated in 13/150 (8.7%) de novo MDS patients, with suggestive evidence of an associated increased risk of progression to sAML. U2AF1 is a U2 auxiliary factor protein that recognizes the AG splice acceptor dinucleotide at the 3′ end of introns and mutations are located in highly conserved zinc fingers in U2AF11,2. Mutant U2AF1 promotes enhanced splicing and exon skipping in reporter assays in vitro. This novel, recurrent mutation in U2AF1 implicates altered pre-mRNA splicing as a potential mechanism for MDS pathogenesis.
Alterations in DNA methylation have been implicated in the pathogenesis of myelodysplastic syndromes (MDS), although the underlying mechanism remains largely unknown. Methylation of CpG dinucleotides is mediated by DNA methyltransferases, including DNMT1, DNMT3A, and DNMT3B. DNMT3A mutations have recently been reported in patients with de novo acute myeloid leukemia (AML), providing a rationale for examining the status of DNMT3A in MDS samples. Here, we report the frequency of DNMT3A mutations in patients with de novo MDS, and their association with secondary AML. We sequenced all coding exons of DNMT3A using DNA from bone marrow and paired normal cells from 150 patients with MDS and identified 13 heterozygous mutations with predicted translational consequences in 12/150 patients (8.0%). Amino acid R882, located in the methyltransferase domain of DNMT3A, was the most common mutation site, accounting for 4/13 mutations. DNMT3A mutations were expressed in the majority of cells in all tested mutant samples regardless of blast counts, suggesting that DNMT3A mutations occur early in the course of MDS. Patients with DNMT3A mutations had worse overall survival compared to patients without DNMT3A mutations (p=0.005) and more rapid progression to AML (p=0.007), suggesting that DNMT3A mutation status may have prognostic value in de novo MDS.
myelodysplastic syndrome; DNMT3A; mutation
The full complement of DNA mutations that are responsible for the pathogenesis of acute myeloid leukemia (AML) is not yet known.
We used massively parallel DNA sequencing to obtain a very high level of coverage (approximately 98%) of a primary, cytogenetically normal, de novo genome for AML with minimal maturation (AML-M1) and a matched normal skin genome.
We identified 12 acquired (somatic) mutations within the coding sequences of genes and 52 somatic point mutations in conserved or regulatory portions of the genome. All mutations appeared to be heterozygous and present in nearly all cells in the tumor sample. Four of the 64 mutations occurred in at least 1 additional AML sample in 188 samples that were tested. Mutations in NRAS and NPM1 had been identified previously in patients with AML, but two other mutations had not been identified. One of these mutations, in the IDH1 gene, was present in 15 of 187 additional AML genomes tested and was strongly associated with normal cytogenetic status; it was present in 13 of 80 cytogenetically normal samples (16%). The other was a nongenic mutation in a genomic region with regulatory potential and conservation in higher mammals; we detected it in one additional AML tumor. The AML genome that we sequenced contains approximately 750 point mutations, of which only a small fraction are likely to be relevant to pathogenesis.
By comparing the sequences of tumor and skin genomes of a patient with AML-M1, we have identified recurring mutations that may be relevant for pathogenesis.