Expression profiling reveals fundamental biological differences in acute myeloid leukemia with isolated trisomy 8 and normal cytogenetics

Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most frequent leukemia in adults with a high mortality rate. Current diagnostic criteria and selections of therapeutic strategies are generally based on gene mutations and cytogenetic abnormalities. Chemotherapy, targeted therapies, and hematopoietic stem cell transplantation (HSCT) are the major therapeutic strategies for AML. Two dilemmas in the clinical management of AML are related to its poor prognosis. One is the inaccurate risk stratification at diagnosis, leading to incorrect treatment selections. The other is the frequent resistance to chemotherapy and/or targeted therapies. Genomic features have been the focus of AML studies. However, the DNA-level aberrations do not always predict the expression levels of genes and proteins and the latter is more closely linked to disease phenotypes. With the development of high-throughput sequencing and mass spectrometry technologies, studying downstream effectors including RNA, proteins, and metabolites becomes possible. Transcriptomics can reveal gene expression and regulatory networks, proteomics can discover protein expression and signaling pathways intimately associated with the disease, and metabolomics can reflect precise changes in metabolites during disease progression. Moreover, omics profiling at the single-cell level enables studying cellular components and hierarchies of the AML microenvironment. The abundance of data from different omics layers enables the better risk stratification of AML by identifying prognosis-related biomarkers, and has the prospective application in identifying drug targets, therefore potentially discovering solutions to the two dilemmas. In this review, we summarize the existing AML studies using omics methods, both separately and combined, covering research fields of disease diagnosis, risk stratification, prognosis prediction, chemotherapy, as well as targeted therapy. Finally, we discuss the directions and challenges in the application of multi-omics in precision medicine of AML. Our review may inspire both omics researchers and clinical physicians to study AML from a different angle.

Gene dosage compensation: Origins, criteria to identify compensated genes, and mechanisms including sensor loops as an emerging systems-level property in cancer

The gene dosage compensation hypothesis presents a mechanism through which the expression of certain genes is modulated to compensate for differences in the dose of genes when additional chromosomes are present. It is one of the means through which cancer cells actively cope with the potential damaging effects of aneuploidy, a hallmark of most cancers. Dosage compensation arises through several processes, including downregulation or overexpression of specific genes and the relocation of dosage-sensitive genes. In cancer, a majority of compensated genes are generally thought to be regulated at the translational or post-translational level, and include the basic components of a compensation loop, including sensors of gene dosage and modulators of gene expression. Post-translational regulation is mostly undertaken by a general degradation or aggregation of remaining protein subunits of macromolecular complexes. An increasingly important role has also been observed for transcriptional level regulation. This article reviews the process of targeted gene dosage compensation in cancer and other biological conditions, along with the mechanisms by which cells regulate specific genes to restore cellular homeostasis. These mechanisms represent potential targets for the inhibition of dosage compensation of specific genes in aneuploid cancers. This article critically examines the process of targeted gene dosage compensation in cancer and other biological contexts, alongside the criteria for identifying genes subject to dosage compensation and the intricate mechanisms by which cells orchestrate the regulation of specific genes to reinstate cellular homeostasis. Ultimately, our aim is to gain a comprehensive understanding of the intricate nature of a systems-level property. This property hinges upon the kinetic parameters of regulatory motifs, which we have termed "gene dosage sensor loops." These loops have the potential to operate at both the transcriptional and translational levels, thus emerging as promising candidates for the inhibition of dosage compensation in specific genes. Additionally, they represent novel and highly specific therapeutic targets in the context of aneuploid cancer.

Clinically Significant CUX1 Mutations Are Frequently Subclonal and Common in Myeloid Disorders With a High Number of Co-mutated Genes and Dysplastic Features

OBJECTIVES

CUX1 mutations have been reported in myeloid neoplasms. We aimed to characterize the mutational landscape, clonal architecture, and clinical characteristics of myeloid disorders with CUX1 variants.

METHODS

We reviewed data from a targeted 62-gene panel with CUX1 variants. Variants were classified as of strong or potential clinical significance (tier I/tier II) or of unknown significance (VUS).

RESULTS

CUX1 variants were identified in 169 cases. The 49 tier I/tier II variants were found in older patients (mean age, 71 vs 60 years old) and predominantly inactivating alterations, while the 120 VUS cases were missense mutations. Monosomy 7/deletion 7q was more common in tier I/tier II cases. Co-mutations were detected in 96% of tier I/tier II cases (average, 3.7/case) but in only 61% of VUS cases (average, 1.5/case). Tier I/tier II CUX1 variants tend to be subclonal to co-mutations (ASXL1, SF3B1, SRSF2, TET2). Among myeloid disorders, tier I/tier II cases were more frequently diagnosed with myelodysplastic syndromes and had a higher number of bone marrow dysplastic lineages.

CONCLUSIONS

CUX1 mutations are seen with adverse prognostic features and could be a late clonal evolutional event of myeloid disorders. The differences between CUX1 tier I/tier II and VUS underscore the importance of accurate variant classification in reporting of multigene panels.

MYC dosage compensation is mediated by miRNA-transcription factor interactions in aneuploid cancer

We hypothesize that dosage compensation of critical genes arises from systems-level properties for cancer cells to withstand the negative effects of aneuploidy. We identified several candidate genes in cancer multiomics data and developed a biocomputational platform to construct a mathematical model of their interaction network with micro-RNAs and transcription factors, where the property of dosage compensation emerged for MYC and was dependent on the kinetic parameters of its feedback interactions with three micro-RNAs. These circuits were experimentally validated using a genetic tug-of-war technique to overexpress an exogenous MYC, leading to overexpression of the three microRNAs involved and downregulation of endogenous MYC. In addition, MYC overexpression or inhibition of its compensating miRNAs led to dosage-dependent cytotoxicity in MYC-amplified colon cancer cells. Finally, we identified negative correlation of MYC dosage compensation with patient survival in TCGA breast cancer patients, highlighting the potential of this mechanism to prevent aneuploid cancer progression.

Development of siRNA-Loaded Lipid Nanoparticles Targeting Long Non-Coding RNA LINC01257 as a Novel and Safe Therapeutic Approach for t(8;21) Pediatric Acute Myeloid Leukemia

Standard of care therapies for children with acute myeloid leukemia (AML) cause potent off-target toxicity to healthy cells, highlighting the need to develop new therapeutic approaches that are safe and specific for leukemia cells. Long non-coding RNAs (lncRNAs) are an emerging and highly attractive therapeutic target in the treatment of cancer due to their oncogenic functions and selective expression in cancer cells. However, lncRNAs have historically been considered ‘undruggable’ targets because they do not encode for a protein product. Here, we describe the development of a new siRNA-loaded lipid nanoparticle for the therapeutic silencing of the novel oncogenic lncRNA LINC01257. Transcriptomic analysis of children with AML identified LINC01257 as specifically expressed in t(8;21) AML and absent in healthy patients. Using NxGen microfluidic technology, we efficiently and reproducibly packaged anti-LINC01257 siRNA (LNP-si-LINC01257) into lipid nanoparticles based on the FDA-approved Patisiran (Onpattro^®) formulation. LNP-si-LINC01257 size and ζ-potential were determined by dynamic light scattering using a Malvern Zetasizer Ultra. LNP-si-LINC01257 internalization and siRNA delivery were verified by fluorescence microscopy and flow cytometry analysis. lncRNA knockdown was determined by RT-qPCR and cell viability was characterized by flow cytometry-based apoptosis assay. LNP-siRNA production yielded a mean LNP size of ~65 nm with PDI ≤ 0.22 along with a >85% siRNA encapsulation rate. LNP-siRNAs were efficiently taken up by Kasumi-1 cells (>95% of cells) and LNP-si-LINC01257 treatment was able to successfully ablate LINC01257 expression which was accompanied by a significant 55% reduction in total cell count following 48 h of treatment. In contrast, healthy peripheral blood mononuclear cells (PBMCs), which do not express LINC01257, were unaffected by LNP-si-LINC01257 treatment despite comparable levels of LNP-siRNA uptake. This is the first report demonstrating the use of LNP-assisted RNA interference modalities for the silencing of cancer-driving lncRNAs as a therapeutically viable and non-toxic approach in the management of AML.

Gene-set distance analysis (GSDA): a powerful tool for gene-set association analysis

Background

Identifying sets of related genes (gene sets) that are empirically associated with a treatment or phenotype often yields valuable biological insights. Several methods effectively identify gene sets in which individual genes have simple monotonic relationships with categorical, quantitative, or censored event-time variables. Some distance-based methods, such as distance correlations, may detect complex non-monotone associations of a gene-set with a quantitative variable that elude other methods. However, the distance correlations have yet to be generalized to associate gene-sets with categorical and censored event-time endpoints. Also, there is a need to determine which genes empirically drive the significance of an association of a gene set with an endpoint.

Results

We develop gene-set distance analysis (GSDA) by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables. We also develop a backward elimination procedure to identify a subset of genes that empirically drive significant associations. In simulation studies, GSDA more effectively identified complex non-monotone gene-set associations than did six other published methods. In the analysis of a pediatric acute myeloid leukemia (AML) data set, GSDA was the only method to discover that event-free survival (EFS) was associated with the 56-gene AML pathway gene-set, narrow that result down to 5 genes, and confirm the association of those 5 genes with EFS in a separate validation cohort. These results indicate that GSDA effectively identifies and characterizes complex non-monotonic gene-set associations that are missed by other methods.

Conclusion

GSDA is a powerful and flexible method to detect gene-set association with categorical, quantitative, or censored event-time variables, especially to detect complex non-monotonic gene-set associations. Available at https://CRAN.R-project.org/package=GSDA.

Supplementary information

The online version contains supplementary material available at 10.1186/s12859-021-04110-x.

Consequences of mitotic failure - The penalties and the rewards

Eukaryotic cells are usually diploid, meaning they contain two copies of each chromosome. However, aberrant chromosome numbers due to both, chromosome gains and losses, are often observed in nature. They can occur as a planned developmental step, but are more often an uninvited result of mitotic failure. Recent discoveries have improved our understanding of the cellular effects of aneuploidy - uneven chromosome numbers, and polyploidy - multiplication of entire sets of chromosomes - in eukaryotic cells. The results show that mitotic errors lead to rapid and extensive modifications of many cellular processes and affect proliferation, proteome balance, genome stability and more. The findings picture the cellular response to aneuploidy and polyploidy as a complex, tissue and context dependent network of events. Here I review the latest discoveries, with an emphasis on pathological aspects of aneuploidy and polyploidy in human cells.

Better leukemia-free survival with allogeneic than with autologous HCT in AML patients with isolated trisomy 8: a study from the ALWP of the EBMT

The indication for performing an allogeneic hematopoietic stem cell transplantation (allo-HCT) in patients with isolated trisomy 8 AML in first complete remission (CR) is still debated. Here, we compared outcomes of such patients given either allo-HCT or autologous (auto)-HCT. Inclusion criteria consisted of adult patients with de novo AML, isolated trisomy 8, first HCT between 2000 and 2018, CR1 at transplantation, and either auto-HCT or allo-HCT with a HLA-identical sibling donor (MSD) or a 10/10 HLA-matched unrelated donor (UD 10/10). A total of 401 patients met the inclusion criteria. They underwent an auto-HCT (n = 81), allo-HCT with a MSD (n = 186) or allo-HCT with a 10/10 UD (n = 134). At 3 years, relapse incidence, nonrelapse mortality and leukemia-free survival (LFS) were 59%, 5%, and 37%, respectively, in auto-HCT recipients; 31% (P < 0.001), 14% (P = 0.04), and 55% (P = 0.033), respectively, in MSD recipients and 29% (P < 0.001), 13% (P = 0.15), and 59% (P = 0.03), respectively, in UD 10/10 recipients. In multivariate analysis, in comparison to auto-HCT, MSD and UD 10/10 were associated with a lower risk of relapse (HR = 0.47, P < 0.001 and HR = 0.40, P < 0.001, respectively) translating to better LFS (HR = 0.69, P = 0.04 and HR = 0.60, P = 0.03, respectively). There was also a similar trend for overall survival (HR = 0.73, P = 0.12 and HR = 0.65, P = 0.08).

Prognostic significance of serum progranulin level in de novo adult acute lymphoblastic leukemia patients

Increased expression of progranulin (PGRN) has been reported in some hematological cancers, but limited information regarding its significance in acute lymphoblastic leukemia (ALL) is available. This study involved 60 subjects (40 de novo adult ALL patients and 20 controls). Serum PGRN level was measured by enzyme-linked immunosorbent assay and was correlated with patient outcome. Serum PGRN level was significantly higher in patients than controls. Serum PGRN level did not correlate with age, total leukocytic count, hemoglobin, platelets, absolute blast count in peripheral blood, lactate dehydrogenase, percent of blasts in bone marrow, gender, comorbidities, the presence of central nervous system infiltration, ALL phenotype, cytogenetics and risk of the disease. High serum PGRN level was not associated with inferior overall survival (OS) on univariate analysis. Regarding cumulative incidence of relapse (CIR) and disease-free survival (DFS), high PGRN level was associated with poor results on univariate analysis. Moreover, it tended to be independent risk factor on multivariate analysis for CIR but was not an independent predictor of inferior DFS. Serum PGRN level is significantly elevated in de novo adult ALL patients and may be used as a predictor of increased relapse risk.

Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics

Acute myeloid leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. We were interested in whether transcriptomic-based machine learning could predict AML status without requiring expert input. Using 12,029 samples from 105 different studies, we present a large-scale study of machine learning-based prediction of AML in which we address key questions relating to the combination of machine learning and transcriptomics and their practical use. We find data-driven, high-dimensional approaches—in which multivariate signatures are learned directly from genome-wide data with no prior knowledge—to be accurate and robust. Importantly, these approaches are highly scalable with low marginal cost, essentially matching human expert annotation in a near-automated workflow. Our results support the notion that transcriptomics combined with machine learning could be used as part of an integrated -omics approach wherein risk prediction, differential diagnosis, and subclassification of AML are achieved by genomics while diagnosis could be assisted by transcriptomic-based machine learning.

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Study presents one of the largest transcriptomics datasets to date for AML prediction

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Effective classifiers can be obtained by high-dimensional machine learning

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Accuracy increases with dataset size

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Includes challenging scenarios such as cross-study and cross-technology

Aneuploidy and gene expression: is there dosage compensation?

Aneuploidy (i.e., abnormal chromosome number) is the leading cause of miscarriage and congenital defects in humans. Moreover, aneuploidy is ubiquitous in cancer. The deleterious phenotypes associated with aneuploidy are likely a result of the imbalance in the levels of gene products derived from the additional chromosome(s). Here, we summarize the current knowledge on how the presence of extra chromosomes impacts gene expression. We describe studies that have found a strict correlation between gene dosage and transcript levels as wells as studies that have found a less stringent correlation, hinting at the possible existence of dosage compensation mechanisms. We conclude by peering into the epigenetic changes found in aneuploid cells and outlining current knowledge gaps and potential areas of future investigation.

Trisomy 8 in acute myeloid leukemia

Introduction: Trisomy 8 is one of the most common cytogenetic alterations in acute myeloid leukemia (AML), with a frequency between 10% and 15%.Areas covered: The authors summarize the latest research regarding biological, translational and clinical aspects of trisomy 8 in AML.Expert opinion: Trisomy 8 can be found together with other karyotypes, although it also occurs as a sole aberration. The last decade's research has brought attention to molecular genetic alterations as strong contributors of leukemogenesis. AML with trisomy 8 seems to be associated with mutations in DNA methylation genes, spliceosome complex genes, and myeloid transcription factor genes, and these alterations probably have stronger implication for leukemic pathogenesis, treatment and hence prognosis, than the existence of trisomy 8 itself. Especially mutations in the RUNX1 and ASXL1 genes occur in high frequencies, and search for such mutations should be mandatory part of the diagnostic workup. AML with trisomy 8 is classified as intermediate-risk AML after recent European Leukemia Net (ELN) classification, and hence allogenic hematopoietic stem cell transplantation (Allo-HSCT) should be consider as consolidation therapy for this patient group.Trisomy 8 is frequently occurring in AML, although future molecular genetic workup should be performed, to optimize the diagnosis and treatment of these patients.

Learning a Structural and Functional Representation for Gene Expressions: To Systematically Dissect Complex Cancer Phenotypes

Cancer is a heterogeneous disease, thus one of the central problems is how to dissect the resulting complex phenotypes in terms of their biological building blocks. Computationally, this is to represent and interpret high dimensional observations through a structural and conceptual abstraction into the most influential determinants underlying the problem. The working hypothesis of this report is to consider gene interaction to be largely responsible for the manifestation of complex cancer phenotypes, thus where the representation is to be conceptualized. Here, we report a representation learning strategy combined with regularizations, in which gene expressions are described in terms of a regularized product of meta-genes and their expression levels. The meta-genes are constrained by gene interactions thus representing their original topological contexts. The expression levels are supervised by their conditional dependencies among the observations thus providing a cluster-specific constraint. We obtain both of these structural constraints using a node-based graphical model. Our representation allows the selection of more influential modules, thus implicating their possible roles in neoplastic transformations. We validate our representation strategy by its robust recognitions of various cancer phenotypes comparing with various classical methods. The modules discovered are either shared or specify for different types or stages of human cancers, all of which are consistent with literature and biology.

Not Only Mutations Matter: Molecular Picture of Acute Myeloid Leukemia Emerging from Transcriptome Studies

The last two decades of genome-scale research revealed a complex molecular picture of acute myeloid leukemia (AML). On the one hand, a number of mutations were discovered and associated with AML diagnosis and prognosis; some of them were introduced into diagnostic tests. On the other hand, transcriptome studies, which preceded AML exome and genome sequencing, remained poorly translated into clinics. Nevertheless, gene expression studies significantly contributed to the elucidation of AML pathogenesis and indicated potential therapeutic directions. The power of transcriptomic approach lies in its comprehensiveness; we can observe how genome manifests its function in a particular type of cells and follow many genes in one test. Moreover, gene expression measurement can be combined with mutation detection, as high-impact mutations are often present in transcripts. This review sums up 20 years of transcriptome research devoted to AML. Gene expression profiling (GEP) revealed signatures distinctive for selected AML subtypes and uncovered the additional within-subtype heterogeneity. The results were particularly valuable in the case of AML with normal karyotype which concerns up to 50% of AML cases. With the use of GEP, new classes of the disease were identified and prognostic predictors were proposed. A plenty of genes were detected as overexpressed in AML when compared to healthy control, including KIT, BAALC, ERG, MN1, CDX2, WT1, PRAME, and HOX genes. High expression of these genes constitutes usually an unfavorable prognostic factor. Upregulation of FLT3 and NPM1 genes, independent on their mutation status, was also reported in AML and correlated with poor outcome. However, transcriptome is not limited to the protein-coding genes; other types of RNA molecules exist in a cell and regulate genome function. It was shown that microRNA (miRNA) profiles differentiated AML groups and predicted outcome not worse than protein-coding gene profiles. For example, upregulation of miR-10a, miR-10b, and miR-196b and downregulation of miR-192 were found as typical of AML with NPM1 mutation whereas overexpression of miR-155 was associated with FLT3-internal tandem duplication (FLT3-ITD). Development of high-throughput technologies and microarray replacement by next generation sequencing (RNA-seq) enabled uncovering a real variety of leukemic cell transcriptomes, reflected by gene fusions, chimeric RNAs, alternatively spliced transcripts, miRNAs, piRNAs, long noncoding RNAs (lncRNAs), and their special type, circular RNAs. Many of them can be considered as AML biomarkers and potential therapeutic targets. The relations between particular RNA puzzles and other components of leukemic cells and their microenvironment, such as exosomes, are now under investigation. Hopefully, the results of this research will shed the light on these aspects of AML pathogenesis which are still not completely understood.

Aneuploid acute myeloid leukemia exhibits a signature of genomic alterations in the cell cycle and protein degradation machinery

Background

Aneuploidy occurs in more than 20% of acute myeloid leukemia (AML) cases and correlates with an adverse prognosis.

Methods

To understand the molecular bases of aneuploid acute myeloid leukemia (A‐AML), this study examined the genomic profile in 42 A‐AML cases and 35 euploid acute myeloid leukemia (E‐AML) cases.

Results

A‐AML was characterized by increased genomic complexity based on exonic variants (an average of 26 somatic mutations per sample vs 15 for E‐AML). The integration of exome, copy number, and gene expression data revealed alterations in genes involved in DNA repair (eg, SLX4IP, RINT1, HINT1, and ATR) and the cell cycle (eg, MCM2, MCM4, MCM5, MCM7, MCM8, MCM10, UBE2C, USP37, CK2, CK3, CK4, BUB1B, NUSAP1, and E2F) in A‐AML, which was associated with a 3‐gene signature defined by PLK1 and CDC20 upregulation and RAD50 downregulation and with structural or functional silencing of the p53 transcriptional program. Moreover, A‐AML was enriched for alterations in the protein ubiquitination and degradation pathway (eg, increased levels of UHRF1 and UBE2C and decreased UBA3 expression), response to reactive oxygen species, energy metabolism, and biosynthetic processes, which may help in facing the unbalanced protein load. E‐AML was associated with BCOR/BCORL1 mutations and HOX gene overexpression.

Conclusions

These findings indicate that aneuploidy‐related and leukemia‐specific alterations cooperate to tolerate an abnormal chromosome number in AML, and they point to the mitotic and protein degradation machineries as potential therapeutic targets.

Aneuploid acute myeloid leukemia (A‐AML) is associated with genomic and transcriptional alterations in the cell cycle and protein degradation pathways. The upregulation of PLK1 and CDC20 and the downregulation of RAD50 and of a p53‐related signature are hallmarks of A‐AML.

"Super Gene Set" Causal Relationship Discovery from Functional Genomics Data

In this article, we present a computational framework to identify "causal relationships" among super gene sets. For "causal relationships," we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to "pathways, annotated lists, and gene signatures," or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on PAG-to-PAG Co-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal-either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall being 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.

Integrated bioinformatic analysis of microarray data reveals shared gene signature between MDS and AML

Myeloid disorders, especially myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), cause significant mobility and high mortality worldwide. Despite numerous attempts, the common molecular events underlying the development of MDS and AML remain to be established. In the present study, 18 microarray datasets were selected, and a meta-analysis was conducted to identify shared gene signatures and biological processes between MDS and AML. Using NetworkAnalyst, 191 upregulated and 139 downregulated genes were identified in MDS and AML, among which, PTH2R, TEC, and GPX1 were the most upregulated genes, while MME, RAG1, and CD79B were mostly downregulated. Comprehensive functional enrichment analyses revealed oncogenic signaling related pathway, fibroblast growth factor receptor (FGFR) and immune response related events, 'interleukine-6/interferon signaling pathway, and B cell receptor signaling pathway', were the most upregulated and downregulated biological processes, respectively. Network based meta-analysis ascertained that HSP90AA1 and CUL1 were the most important hub genes. Interestingly, our study has largely clarified the link between MDS and AML in terms of potential pathways, and genetic markers, which shed light on the molecular mechanisms underlying the development and transition of MDS and AML, and facilitate the understanding of novel diagnostic, therapeutic and prognostic biomarkers.

SETD7 Regulates the Differentiation of Human Embryonic Stem Cells

The successful use of specialized cells in regenerative medicine requires an optimization in the differentiation protocols that are currently used. Understanding the molecular events that take place during the differentiation of human pluripotent cells is essential for the improvement of these protocols and the generation of high quality differentiated cells. In an effort to understand the molecular mechanisms that govern differentiation we identify the methyltransferase SETD7 as highly induced during the differentiation of human embryonic stem cells and differentially expressed between induced pluripotent cells and somatic cells. Knock-down of SETD7 causes differentiation defects in human embryonic stem cell including delay in both the silencing of pluripotency-related genes and the induction of differentiation genes. We show that SETD7 methylates linker histone H1 in vitro causing conformational changes in H1. These effects correlate with a decrease in the recruitment of H1 to the pluripotency genes OCT4 and NANOG during differentiation in the SETD7 knock down that might affect the proper silencing of these genes during differentiation.

Genome-wide analysis of microRNA and mRNA expression signatures in cancer

Cancer is an extremely diverse and complex disease that results from various genetic and epigenetic changes such as DNA copy-number variations, mutations, and aberrant mRNA and/or protein expression caused by abnormal transcriptional regulation. The expression profiles of certain microRNAs (miRNAs) and messenger RNAs (mRNAs) are closely related to cancer progression stages. In the past few decades, DNA microarray and next-generation sequencing techniques have been widely applied to identify miRNA and mRNA signatures for cancers on a genome-wide scale and have provided meaningful insights into cancer diagnosis, prognosis and personalized medicine. In this review, we summarize the progress in genome-wide analysis of miRNAs and mRNAs as cancer biomarkers, highlighting their diagnostic and prognostic roles.

Rare Cytogenetic Abnormalities and Alteration of microRNAs in Acute Myeloid Leukemia and Response to Therapy

Acute myeloid leukemia (AML) is the most common acute leukemia in adults, which is heterogeneous in terms of morphological, cytogenetic and clinical features. Cytogenetic abnormalities, including karyotype aberrations, gene mutations and gene expression abnormalities are the most important diagnostic tools in diagnosis, classification and prognosis in acute myeloid leukemias. Based on World Health Organization (WHO) classification, acute myeloid leukemias can be divided to four groups. Due to the heterogeneous nature of AML and since most therapeutic protocols in AML are based on genetic alterations, gathering further information in the field of rare disorders as well as common cytogenetic abnormalities would be helpful in determining the prognosis and treatment in this group of diseases. Recently, the role of microRNAs (miRNAs) in both normal hematopoiesis and myeloid leukemic cell differentiation in myeloid lineage has been specified. miRNAs can be used instead of genes for AML diagnosis and classification in the future, and can also play a decisive role in the evaluation of relapse as well as response to treatment in the patients. Therefore, their use in clinical trials can affect treatment protocols and play a role in therapeutic strategies for these patients. In this review, we have examined rare cytogenetic abnormalities in different groups of acute myeloid leukemias according to WHO classification, and the role of miRNA expression in classification, diagnosis and response to treatment of these disorders has also been dealt with.

Trisomy 8 Acute Myeloid Leukemia Analysis Reveals New Insights of DNA Methylome with Identification of HHEX as Potential Diagnostic Marker

Trisomy 8 acute myeloid leukemia (AML) is the commonest numerical aberration in AML. Here we present a global analysis of trisomy 8 AML using methylated DNA immunoprecipitation-sequencing (MeDIP-seq). The study is based on three diagnostic trisomy 8 AML and their parallel relapse status in addition to nine non-trisomic AML and four normal bone marrows (NBMs). In contrast to non-trisomic DNA samples, trisomy 8 AML showed a characteristic DNA methylation distribution pattern because an increase in the frequency of the hypermethylation signals in chromosome 8 was associated with an increase in the hypomethylation signals in the rest of the chromosomes. Chromosome 8 hypermethylation signals were found mainly in the CpG island (CGI) shores and interspersed repeats. Validating the most significant differentially methylated CGI (P = 7.88 × 10(-11)) identified in trisomy 8 AML demonstrated a specific core region within the gene body of HHEX, which was significantly correlated with HHEX expression in both diagnostic and relapse trisomy 8 AMLs. Overall, the existence of extra chromosome 8 was associated with a global impact on the DNA methylation distribution with identification of HHEX gene methylation as a potential diagnostic marker for trisomy 8 AML.

The epigenetic landscape of aneuploidy: constitutional mosaicism leading the way?

The role of structural genetic changes in human disease has received substantial attention in recent decades, but surprisingly little is known about numerical chromosomal abnormalities, even though they have been recognized since the days of Boveri as partaking in different cellular pathophysiological processes such as cancer and genomic disorders. The current knowledge of the genetic and epigenetic consequences of aneuploidy is reviewed herein, with a special focus on using mosaic genetic syndromes to study the DNA methylation footprints and expressional effects associated with whole-chromosomal gains. Recent progress in understanding the debated role of aneuploidy as a driver or passenger in malignant transformation, as well as how the cell responds to and regulates excess genetic material in experimental settings, is also discussed in detail.

SFGD: a comprehensive platform for mining functional information from soybean transcriptome data and its use in identifying acyl-lipid metabolism pathways

BACKGROUND

Soybean (Glycine max L.) is one of the world's most important leguminous crops producing high-quality protein and oil. Increasing the relative oil concentration in soybean seeds is many researchers' goal, but a complete analysis platform of functional annotation for the genes involved in the soybean acyl-lipid pathway is still lacking. Following the success of soybean whole-genome sequencing, functional annotation has become a major challenge for the scientific community. Whole-genome transcriptome analysis is a powerful way to predict genes with biological functions. It is essential to build a comprehensive analysis platform for integrating soybean whole-genome sequencing data, the available transcriptome data and protein information. This platform could also be used to identify acyl-lipid metabolism pathways.

DESCRIPTION

In this study, we describe our construction of the Soybean Functional Genomics Database (SFGD) using Generic Genome Browser (Gbrowse) as the core platform. We integrated microarray expression profiling with 255 samples from 14 groups' experiments and mRNA-seq data with 30 samples from four groups' experiments, including spatial and temporal transcriptome data for different soybean development stages and environmental stresses. The SFGD includes a gene co-expression regulatory network containing 23,267 genes and 1873 miRNA-target pairs, and a group of acyl-lipid pathways containing 221 enzymes and more than 1550 genes. The SFGD also provides some key analysis tools, i.e. BLAST search, expression pattern search and cis-element significance analysis, as well as gene ontology information search and single nucleotide polymorphism display.

CONCLUSION

The SFGD is a comprehensive database integrating genome and transcriptome data, and also for soybean acyl-lipid metabolism pathways. It provides useful toolboxes for biologists to improve the accuracy and robustness of soybean functional genomics analysis, further improving understanding of gene regulatory networks for effective crop improvement. The SFGD is publically accessible at http://bioinformatics.cau.edu.cn/SFGD/, with all data available for downloading.

Transcriptome analysis identifies Bacillus anthracis genes that respond to CO2 through an AtxA-dependent mechanism

Background

Upon infection of a mammalian host, Bacillus anthracis responds to host cues, and particularly to elevated temperature (37°C) and bicarbonate/CO₂ concentrations, with increased expression of virulence factors that include the anthrax toxins and extracellular capsular layer. This response requires the presence of the pXO1 virulence plasmid-encoded pleiotropic regulator AtxA. To better understand the genetic basis of this response, we utilized a controlled in vitro system and Next Generation sequencing to determine and compare RNA expression profiles of the parental strain and an isogenic AtxA-deficient strain in a 2 × 2 factorial design with growth environments containing or lacking carbon dioxide.

Results

We found 15 pXO1-encoded genes and 3 chromosomal genes that were strongly regulated by the separate or synergistic actions of AtxA and carbon dioxide. The majority of the regulated genes responded to both AtxA and carbon dioxide rather than to just one of these factors. Interestingly, we identified two previously unrecognized small RNAs that are highly expressed under physiological carbon dioxide concentrations in an AtxA-dependent manner. Expression levels of the two small RNAs were found to be higher than that of any other gene differentially expressed in response to these conditions. Secondary structure and small RNA-mRNA binding predictions for the two small RNAs suggest that they may perform important functions in regulating B. anthracis virulence.

Conclusions

A majority of genes on the virulence plasmid pXO1 that are regulated by the presence of either CO₂ or AtxA separately are also regulated synergistically in the presence of both. These results also elucidate novel pXO1-encoded small RNAs that are associated with virulence conditions.

Use of gene expression microarrays for the study of acute leukemia

Genetic lesions found in acute leukemia drive the pathology of the disease in addition to forming reliable classifications of prognosis. However, there is still a reasonable heterogeneity of response among cases with the same genetic lesion. Moreover, many leukemia cases have no detectable genetic marker and these cases have marked heterogeneity of response. How can we learn more about the genes and pathways involved with leukemogenesis and response in the midst of such complexity? Gene expression microarrays are experimental platforms that allow for the simultaneous evaluation of the thousands of mRNA transcripts (the 'transcriptome'). This technology has revolutionized the study of leukemia, giving insight into genes and pathways involved in disease response and the biology involved in specific translocations.

Individual karyotypes at the origins of cervical carcinomas

Background

In 1952 Papanicolaou et al. first diagnosed and graded cervical carcinomas based on individual “abnormal DNA contents” and cellular phenotypes. Surprisingly current papilloma virus and mutation theories of carcinomas do not mention these individualities. The viral theory holds that randomly integrated, defective genomes of papilloma viruses, which are often untranscribed, cause cervical carcinomas with unknown cofactors 20–50 years after infection. Virus-free carcinomas are attributed to mutations of a few tumor-suppressor genes, especially the p53 gene. But the paradox of how a few mutations or latent defective viral DNAs would generate carcinomas with endless individual DNA contents, degrees of malignancies and cellular phenotypes is unsolved. Since speciation predicts individuality, we test here the theory that cancers are autonomous species with individual clonal karyotypes and phenotypes. This theory postulates that carcinogens induce aneuploidy. By unbalancing mitosis genes aneuploidy catalyzes chain reactions of karyotypic evolutions. Most such evolutions end with non-viable karyotypes but a few become new cancer karyotypes. Despite congenitally unbalanced mitosis genes cancer karyotypes are stabilized by clonal selections for cancer-specific autonomy.

Results

To test the prediction of the speciation theory that individual carcinomas have individual clonal karyotypes and phenotypes, we have analyzed here the phenotypes and karyotypes of nine cervical carcinomas. Seven of these contained papilloma virus sequences and two did not. We determined phenotypic individuality and clonality based on the morphology and sociology of carcinoma cells in vitro. Karyotypic individuality and clonality were determined by comparing all chromosomes of 20 karyotypes of carcinomas in three-dimensional arrays. Such arrays list chromosome numbers on the x-axis, chromosome copy numbers on the y-axis and the number of karyotypes arrayed on the z-axis. We found (1) individual clonal karyotypes and phenotypes in all nine carcinomas, but no virus-specific markers, (2) 1-to-1 variations between carcinoma-specific karyotypes and phenotypes, e.g. drug-resistance and cell morphology, (3) proportionality between the copy numbers of chromosomes and the copy numbers of hundreds of over- and under-expressed mRNAs, (4) evidence that tobacco-carcinogens induce cervical carcinomas via aneuploidy, consistent with the speciation theory.

Conclusions

Since the individual clonal karyotypes of nine carcinomas correlated and co-varied 1-to-1 with complex individual transcriptomes and phenotypes, we have classical genetic and functional transcriptomic evidence to conclude that these karyotypes encode carcinomas - much like the clonal karyotypes that encode conventional species. These individual karyotypes explain the individual “DNA contents”, the endless grades of malignancies and the complex individual transcriptomes and phenotypes of carcinomas.

Gene expression analysis of induced pluripotent stem cells from aneuploid chromosomal syndromes

Background

Human aneuploidy is the leading cause of early pregnancy loss, mental retardation, and multiple congenital anomalies. Due to the high mortality associated with aneuploidy, the pathophysiological mechanisms of aneuploidy syndrome remain largely unknown. Previous studies focused mostly on whether dosage compensation occurs, and the next generation transcriptomics sequencing technology RNA-seq is expected to eventually uncover the mechanisms of gene expression regulation and the related pathological phenotypes in human aneuploidy.

Results

Using next generation transcriptomics sequencing technology RNA-seq, we profiled the transcriptomes of four human aneuploid induced pluripotent stem cell (iPSC) lines generated from monosomy × (Turner syndrome), trisomy 8 (Warkany syndrome 2), trisomy 13 (Patau syndrome), and partial trisomy 11:22 (Emanuel syndrome) as well as two umbilical cord matrix iPSC lines as euploid controls to examine how phenotypic abnormalities develop with aberrant karyotype. A total of 466 M (50-bp) reads were obtained from the six iPSC lines, and over 13,000 mRNAs were identified by gene annotation. Global analysis of gene expression profiles and functional analysis of differentially expressed (DE) genes were implemented. Over 5000 DE genes are determined between aneuploidy and euploid iPSCs respectively while 9 KEGG pathways are overlapped enriched in four aneuploidy samples.

Conclusions

Our results demonstrate that the extra or missing chromosome has extensive effects on the whole transcriptome. Functional analysis of differentially expressed genes reveals that the genes most affected in aneuploid individuals are related to central nervous system development and tumorigenesis.

Constitutional trisomy 8 mosaicism as a model for epigenetic studies of aneuploidy

Background

To investigate epigenetic patterns associated with aneuploidy we used constitutional trisomy 8 mosaicism (CT8M) as a model, enabling analyses of single cell clones, harboring either trisomy or disomy 8, from the same patient; this circumvents any bias introduced by using cells from unrelated, healthy individuals as controls. We profiled gene and miRNA expression as well as genome-wide and promoter specific DNA methylation and hydroxymethylation patterns in trisomic and disomic fibroblasts, using microarrays and methylated DNA immunoprecipitation.

Results

Trisomy 8-positive fibroblasts displayed a characteristic expression and methylation phenotype distinct from disomic fibroblasts, with the majority (65%) of chromosome 8 genes in the trisomic cells being overexpressed. However, 69% of all deregulated genes and non-coding RNAs were not located on this chromosome. Pathway analysis of the deregulated genes revealed that cancer, genetic disorder, and hematopoiesis were top ranked. The trisomy 8-positive cells displayed depletion of 5-hydroxymethylcytosine and global hypomethylation of gene-poor regions on chromosome 8, thus partly mimicking the inactivated X chromosome in females.

Conclusions

Trisomy 8 affects genes situated also on other chromosomes which, in cooperation with the observed chromosome 8 gene dosage effect, has an impact on the clinical features of CT8M, as demonstrated by the pathway analysis revealing key features that might explain the increased incidence of hematologic malignancies in CT8M patients. Furthermore, we hypothesize that the general depletion of hydroxymethylation and global hypomethylation of chromosome 8 may be unrelated to gene expression regulation, instead being associated with a general mechanism of chromatin processing and compartmentalization of additional chromosomes.

Empirical pathway analysis, without permutation

Resampling-based expression pathway analysis techniques have been shown to preserve type I error rates, in contrast to simple gene-list approaches that implicitly assume the independence of genes in ranked lists. However, resampling is intensive in computation time and memory requirements. We describe accurate analytic approximations to permutations of score statistics, including novel approaches for Pearson's correlation, and summed score statistics, that have good performance for even relatively small sample sizes. Our approach preserves the essence of permutation pathway analysis, but with greatly reduced computation. Extensions for inclusion of covariates and censored data are described, and we test the performance of our procedures using simulations based on real datasets. These approaches have been implemented in the new R package safeExpress.

Single nucleotide polymorphism array-based karyotyping in acute myeloid leukemia or myelodysplastic syndrome with trisomy 8 as the sole chromosomal abnormality

The clinical heterogeneity of patients with acute myeloid leukemia (AML) or myelodysplastic syndrome (MDS) with trisomy 8 as the sole abnormality may result from cytogenetically undetectable genetic changes. The purpose of this study was to identify hidden genomic aberrations not detected by metaphase cytogenetics (MC) using high-resolution single nucleotide polymorphism array (SNP-A)-based karyotyping in AML/MDS patients with a sole trisomy 8. The study group included 8 patients (3 AML and 5 MDS) and array-based karyotyping was done using whole-genome SNP-A (SNP 6.0 and SNP 2.7M). By SNP-A, additional genomic aberrations not detected by MC were identified in 2 patients: 1 AML patient exhibited a copy-neutral loss of heterozygosity (CN-LOH) of 3q21.1-q29 and 11q13.1-q25 and the other patient with MDS (refractory cytopenia with unilineage dysplasia) had CN-LOH of 2p25.3-p15. In particular, the latter patient progressed to AML 18 months after the diagnosis. In 3 patients, aberrations in addition to trisomy 8 were not identified by SNP-A. In the remaining 3 patients, SNP-A could not detect trisomy 8, while trisomy 8 was found in 25-67% of metaphase cells by MC. This study suggests that additional genomic aberrations may in fact be present even in cases of trisomy 8 as sole abnormality by MC, and SNP-A could be a useful karyotyping tool to identify hidden aberrations such as CN-LOH.

Phase II pilot study of oral dasatinib in patients with higher-risk myelodysplastic syndrome (MDS) who failed conventional therapy

Given evidence for the role of Src family kinases, especially Lyn kinase, in myeloblast proliferation and the in vitro inhibitory activity of dasatinib on Src and Lyn, we conducted a phase II study to assess overall response to 100mg/day dasatinib in patients with higher-risk myelodysplastic syndrome (MDS), chronic myelomonocytic leukemia, or acute myeloid leukemia arising from MDS and who had failed prior treatment with azanucleoside analogs. Among 18 patients treated, 3 responded, 4 had stable disease, and 10 experienced disease progression. Toxicities were limited and consistent with previous reports. Dasatinib appears to be safe but with limited efficacy.

Genome wide analysis of acute myeloid leukemia reveal leukemia specific methylome and subtype specific hypomethylation of repeats

Methylated DNA immunoprecipitation followed by high-throughput sequencing (MeDIP-seq) has the potential to identify changes in DNA methylation important in cancer development. In order to understand the role of epigenetic modulation in the development of acute myeloid leukemia (AML) we have applied MeDIP-seq to the DNA of 12 AML patients and 4 normal bone marrows. This analysis revealed leukemia-associated differentially methylated regions that included gene promoters, gene bodies, CpG islands and CpG island shores. Two genes (SPHKAP and DPP6) with significantly methylated promoters were of interest and further analysis of their expression showed them to be repressed in AML. We also demonstrated considerable cytogenetic subtype specificity in the methylomes affecting different genomic features. Significantly distinct patterns of hypomethylation of certain interspersed repeat elements were associated with cytogenetic subtypes. The methylation patterns of members of the SINE family tightly clustered all leukemic patients with an enrichment of Alu repeats with a high CpG density (P<0.0001). We were able to demonstrate significant inverse correlation between intragenic interspersed repeat sequence methylation and gene expression with SINEs showing the strongest inverse correlation (R² = 0.7). We conclude that the alterations in DNA methylation that accompany the development of AML affect not only the promoters, but also the non-promoter genomic features, with significant demethylation of certain interspersed repeat DNA elements being associated with AML cytogenetic subtypes. MeDIP-seq data were validated using bisulfite pyrosequencing and the Infinium array.

Mosaiktrisomie 8p11.21q11.21 als Prädisposition für myeloische Leukämien

Bei der juvenilen myelomonozytären Leukämie (JMML) handelt es sich um eine myeloproliferative Erkrankung der frühen Kindheit. Bei vielen Patienten lassen sich zugrunde liegende somatische, aber auch konstitutionelle Mutationen in NRAS, KRAS, PTPN11, NF1 und CBL nachweisen. Zur Identifizierung submikroskopischer Veränderungen, die für die leukämische Transformation von Bedeutung sein können, wurden 20 JMML-Proben mittels hochauflösender Oligo-Microarray-basierter komparativer genomischer Hybridisierung (aCGH) untersucht. Bei 2 von 10 Patienten mit submikroskopischen Aberrationen konnte ein nahezu identischer Zugewinn von Chromosom 8 gezeigt werden, der sich in weiteren Untersuchungen als konstitutionelles Mosaik darstellte. Eine Übersicht von 27 Patienten mit einem konstitutionellen Trisomie-8-Mosaik (cT8M) und maligner Neoplasie zeigte, dass es sich meist um myeloische Neoplasien, auch JMML, handelt. Durch unsere Untersuchungen konnte die kritische Region auf Chromosom 8, deren Loci mutmaßlich an der Leukämieentstehung und/oder Progression beteiligt sein können, dramatisch reduziert werden: 8p11.21q11.21. Es bleibt zu klären in welcher Form das partielle Trisomie-8-Mosaik an der Leukämieentstehung beteiligt ist und in welcher Weise dies für verschiedenen Mutationssubtypen der JMML eine Rolle spielt.

The consequences of chromosomal aneuploidy on the transcriptome of cancer cells

Chromosomal aneuploidies are a defining feature of carcinomas, i.e., tumors of epithelial origin. Such aneuploidies result in tumor specific genomic copy number alterations. The patterns of genomic imbalances are tumor specific, and to a certain extent specific for defined stages of tumor development. Genomic imbalances occur already in premalignant precursor lesions, i.e., before the transition to invasive disease, and their distribution is maintained in metastases, and in cell lines derived from primary tumors. These observations are consistent with the interpretation that tumor specific genomic imbalances are drivers of malignant transformation. Naturally, this precipitates the question of how such imbalances influence the expression of resident genes. A number of laboratories have systematically integrated copy number alterations with gene expression changes in primary tumors and metastases, cell lines, and experimental models of aneuploidy to address the question as to whether genomic imbalances deregulate the expression of one or few key genes, or rather affect the cancer transcriptome more globally. The majority of these studies showed that gene expression levels follow genomic copy number. Therefore, gross genomic copy number changes, including aneuploidies of entire chromosome arms and chromosomes, result in a massive deregulation of the transcriptome of cancer cells. This article is part of a Special Issue entitled: Chromatin in time and space.

Integrated morphologic analysis for the identification and characterization of disease subtypes

BACKGROUND AND OBJECTIVE

Morphologic variations of disease are often linked to underlying molecular events and patient outcome, suggesting that quantitative morphometric analysis may provide further insight into disease mechanisms. In this paper a methodology for the subclassification of disease is developed using image analysis techniques. Morphologic signatures that represent patient-specific tumor morphology are derived from the analysis of hundreds of millions of cells in digitized whole slide images. Clustering these signatures aggregates tumors into groups with cohesive morphologic characteristics. This methodology is demonstrated with an analysis of glioblastoma, using data from The Cancer Genome Atlas to identify a prognostically significant morphology-driven subclassification, in which clusters are correlated with transcriptional, genetic, and epigenetic events.

MATERIALS AND METHODS

Methodology was applied to 162 glioblastomas from The Cancer Genome Atlas to identify morphology-driven clusters and their clinical and molecular correlates. Signatures of patient-specific tumor morphology were generated from analysis of 200 million cells in 462 whole slide images. Morphology-driven clusters were interrogated for associations with patient outcome, response to therapy, molecular classifications, and genetic alterations. An additional layer of deep, genome-wide analysis identified characteristic transcriptional, epigenetic, and copy number variation events.

RESULTS AND DISCUSSION

Analysis of glioblastoma identified three prognostically significant patient clusters (median survival 15.3, 10.7, and 13.0 months, log rank p=1.4e-3). Clustering results were validated in a separate dataset. Clusters were characterized by molecular events in nuclear compartment signaling including developmental and cell cycle checkpoint pathways. This analysis demonstrates the potential of high-throughput morphometrics for the subclassification of disease, establishing an approach that complements genomics.

A pathway analysis method for genome-wide association studies

For genome-wide association studies, we propose a new method for identifying significant biological pathways. In this approach, we aggregate data across single-nucleotide polymorphisms to obtain summary measures at the gene level. We then use a hierarchical Bayesian model, which takes the gene-level summary measures as data, in order to evaluate the relevance of each pathway to an outcome of interest (e.g., disease status). Although shifting the focus of analysis from individual genes to pathways has proven to improve the statistical power and provide more robust results, such methods tend to eliminate a large number of genes whose pathways are unknown. For these genes, we propose to use a Bayesian multinomial logit model to predict the associated pathways by using the genes with known pathways as the training data. Our hierarchical Bayesian model takes the uncertainty regarding the pathway predictions into account while assessing the significance of pathways. We apply our method to two independent studies on type 2 diabetes and show that the overlap between the results from the two studies is statistically significant. We also evaluate our approach on the basis of simulated data.

How mitotic errors contribute to karyotypic diversity in cancer

Aneuploidy is a common feature of cancer cells, and is believed to play a critical role in tumorigenesis and cancer progression. Most cancer cells also exhibit high rates of mitotic chromosome mis-segregation, a phenomenon known as chromosomal instability, which leads to high variability of the karyotype. Here, we describe the nature, nuances, and implications of cancer karyotypic diversity. Moreover, we summarize recent studies aimed at identifying the mitotic defects that may be responsible for inducing chromosome mis-segregation in cancer cells. These include kinetochore attachment errors, spindle assembly checkpoint dysfunction, mitotic spindle defects, and other cell division inaccuracies. Finally, we discuss how such mitotic errors generate karyotypic diversity in cancer cells.

Amplification of c-MYC and MLL Genes as a Marker of Clonal Cell Progression in Patients with Myeloid Malignancy and Trisomy of Chromosomes 8 or 11

Gene amplification (amp) is one of the basic mechanisms connected with overexpression of oncogenes. The c-MYC (located in 8q24) and MLL (located in 11q23) are the most often over represented genes that lead to a rapid proliferation of the affected cell clone in patients with myeloid neoplasms. Assessment of the level of amp c-MYC or amp MLL in the cases with trisomy 8 (+8) or trisomy 11 (+11) and myeloid malignances is necessary for a more precise estimation of the disease progression.

A total of 26 patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) were included in the study: 18 with +8, six with +11 and two with complex karyotypes suspected of the partial trisomy. Routine cytogenetic analysis and fluorescent in situ hybridization (FISH) were applied to indicate the chromosome alterations and genes amp in the bone marrow cells.

Amp c-MYC was observed in 12 from 18 (66.7%) patients with +8. All the patients with +11 demonstrated a different level of amp MLL. In most of the cases with MDS (9/10), the coincidence of the +8 or +11 with amp c-MYC or amp MLL, respectively, leads to transformation to AML and/or short overall survival. Our data suggest that amp c-MYC and amp MLL develop in conformity with +8 and +11, especially in cases with progressive deviations in the karyotype as an aggressive expansion of an aberrant cell clone and appearance of additional chromosome anomalies.

INTERRELATED TWO-WAY CLUSTERING AND ITS APPLICATION ON GENE EXPRESSION DATA

Microarray technologies are capable of simultaneously measuring the signals for thousands of messenger RNAs and large numbers of proteins from single samples. Arrays are now widely used in basic biomedical research for mRNA expression profiling and are increasingly being used to explore patterns of gene expression in clinical research. Most research has focused on the interpretation of the meaning of the microarray data which are transformed into gene expression matrices where usually the rows represent genes, the columns represent various samples. Clustering samples can be done by analyzing and eliminating of irrelevant genes. However, majority methods are supervised (or assisted by domain knowledge), less attention has been paid on unsupervised approaches which are important when little domain knowledge is available. In this paper, we present a new framework for unsupervised analysis of gene expression data, which applies an interrelated two-way clustering approach on the gene expression matrices. The goal of clustering is to identify important genes and perform cluster discovery on samples. The advantage of this approach is that we can dynamically manipulate the relationship between the gene clusters and sample groups while conducting an iterative clustering through both of them. The performance of the proposed method with various gene expression data sets is also illustrated.

Update on cytogenetic and molecular changes in myelodysplastic syndromes

Myelodysplastic syndromes (MDS) are characterized by ineffective hematopoiesis and a high propensity to transform to acute myeloid leukemia (AML). In the pathogenesis of the disease, both gene mutations and cytogenetic changes play an important role. The latter have been integrated into prognostic scoring systems including the IPSS (International Prognostic Scoring System) and WPSS (World Health Organization [WHO] classification-based Prognostic Scoring System). In these systems and in multivariate analyses comparing clinical and genetic data, complex karyotypes are associated with a particularly poor prognosis. del(5q) plays a distinct role by classifying the only genetically defined WHO subtype. Also, due to advancement in technology such as whole genome sequencing, the number of known mutations occurring in MDS is steadily increasing. Important recent discoveries include mutations in EZH2, DNMT3A, ASXL1 and IDH1/2. Like TET2, the most commonly mutated gene in MDS, all are involved in epigenetic regulation. Mutations such as ASXL1, RUNX1, EZH2, ETV6/TEL and TP53 have an adverse impact on patient overall survival. Early evidence suggests that some mutations might influence treatment response, necessitating reassessment of the prognostic effect of genetic alterations in the light of every new treatment. This review discusses clinical and biological effects of the most common cytogenetic and molecular aberrations in patients with MDS.

Dysregulation of gene expression in the artificial human trisomy cells of chromosome 8 associated with transformed cell phenotypes

A change in chromosome number, known as aneuploidy, is a common characteristic of cancer. Aneuploidy disrupts gene expression in human cancer cells and immortalized human epithelial cells, but not in normal human cells. However, the relationship between aneuploidy and cancer remains unclear. To study the effects of aneuploidy in normal human cells, we generated artificial cells of human primary fibroblast having three chromosome 8 (trisomy 8 cells) by using microcell-mediated chromosome transfer technique. In addition to decreased proliferation, the trisomy 8 cells lost contact inhibition and reproliferated after exhibiting senescence-like characteristics that are typical of transformed cells. Furthermore, the trisomy 8 cells exhibited chromosome instability, and the overall gene expression profile based on microarray analyses was significantly different from that of diploid human primary fibroblasts. Our data suggest that aneuploidy, even a single chromosome gain, can be introduced into normal human cells and causes, in some cases, a partial cancer phenotype due to a disruption in overall gene expression.

Unravelling the complexity of metastasis - molecular understanding and targeted therapies

Despite recognizing the devastating consequences of metastasis, we are not yet able to effectively treat cancer that has spread to vital organs. The inherent complexity of genomic alterations in late-stage cancers, coupled with numerous heterotypic interactions that occur between tumour and stromal cells, represent fundamental challenges in our quest to understand and control metastatic disease. The incorporation of genomic and other systems level approaches, as well as technological breakthroughs in imaging and animal modelling, have galvanized the effort to overcome gaps in our understanding of metastasis. Future research carries with it the potential to translate the wealth of new knowledge and conceptual advances into effective targeted therapies.

Constitutional trisomy 8p11.21-q11.21 mosaicism: a germline alteration predisposing to myeloid leukaemia

Juvenile myelomonocytic leukaemia (JMML) is a unique myeloproliferative disorder of early childhood. Frequently, mutations in NRAS, KRAS, PTPN11, NF1 or CBL are found in these patients. Monosomy 7 is the most common cytogenetic aberration. To identify submicroscopic genomic copy number alterations, 20 JMML samples were analysed by comparative genomic hybridization. Ten out of 20 samples displayed additional submicroscopic alterations. In two patients, an almost identical gain of chromosome 8 was identified. In both patients, fluorescence in situ hybridization confirmed a constitutional partial trisomy 8 mosaic (cT8M). A survey on 27 cT8M patients with neoplasms showed that 21 had myeloid malignancies, and five of these had a JMML. Notably, the region gained in our cases is the smallest gain of chromosome 8 reported in cT8M cases with malignancies so far. Our results dramatically reduce the critical region to 8p11.21q11.21 harbouring 31 protein coding genes and two non-coding RNAs, e.g. MYST3, IKBKB, UBE2V2, GOLGA7, FNTA and MIR486--a finding with potential implications for the role of somatic trisomy 8 in myeloid malignancies. Further investigations are required to more comprehensively determine how constitutional partial trisomy 8 mosaicisms may contribute to leukaemogenesis in different mutational subtypes of JMML and other myeloid malignancies.

Bayesian gene set analysis for identifying significant biological pathways

We propose a hierarchical Bayesian model for analyzing gene expression data to identify pathways differentiating between two biological states (e.g., cancer vs. non-cancer and mutant vs. normal). Finding significant pathways can improve our understanding of biological processes. When the biological process of interest is related to a specific disease, eliciting a better understanding of the underlying pathways can lead to designing a more effective treatment. We apply our method to data obtained by interrogating the mutational status of p53 in 50 cancer cell lines (33 mutated and 17 normal). We identify several significant pathways with strong biological connections. We show that our approach provides a natural framework for incorporating prior biological information, and it has the best overall performance in terms of correctly identifying significant pathways compared to several alternative methods.

Virtual CGH: an integrative approach to predict genetic abnormalities from gene expression microarray data applied in lymphoma

Background

Comparative Genomic Hybridization (CGH) is a molecular approach for detecting DNA Copy Number Alterations (CNAs) in tumor, which are among the key causes of tumorigenesis. However in the post-genomic era, most studies in cancer biology have been focusing on Gene Expression Profiling (GEP) but not CGH, and as a result, an enormous amount of GEP data had been accumulated in public databases for a wide variety of tumor types. We exploited this resource of GEP data to define possible recurrent CNAs in tumor. In addition, the CNAs identified by GEP would be more functionally relevant CNAs in the disease pathogenesis since the functional effects of CNAs can be reflected by altered gene expression.

Methods

We proposed a novel computational approach, coined virtual CGH (vCGH), which employs hidden Markov models (HMMs) to predict DNA CNAs from their corresponding GEP data. vCGH was first trained on the paired GEP and CGH data generated from a sufficient number of tumor samples, and then applied to the GEP data of a new tumor sample to predict its CNAs.

Results

Using cross-validation on 190 Diffuse Large B-Cell Lymphomas (DLBCL), vCGH achieved 80% sensitivity, 90% specificity and 90% accuracy for CNA prediction. The majority of the recurrent regions defined by vCGH are concordant with the experimental CGH, including gains of 1q, 2p16-p14, 3q27-q29, 6p25-p21, 7, 11q, 12 and 18q21, and losses of 6q, 8p23-p21, 9p24-p21 and 17p13 in DLBCL. In addition, vCGH predicted some recurrent functional abnormalities which were not observed in CGH, including gains of 1p, 2q and 6q and losses of 1q, 6p and 8q. Among those novel loci, 1q, 6q and 8q were significantly associated with the clinical outcomes in the DLBCL patients (p < 0.05).

Conclusions

We developed a novel computational approach, vCGH, to predict genome-wide genetic abnormalities from GEP data in lymphomas. vCGH can be generally applied to other types of tumors and may significantly enhance the detection of functionally important genetic abnormalities in cancer research.

Comparative expression profile of orphan receptor tyrosine kinase ROR1 in Iranian patients with lymphoid and myeloid leukemias

It has recently been shown that ROR1, a member of the receptor tyrosine kinase family, is overexpressed in leukemic B cells of Chronic Lymphocytic Leukemia (CLL) and a subset of Acute Lymphoblastic Leukemia (ALL). In this comparative study the expression profile of ROR1 mRNA was investigated in Iranian patients with CLL and Acute Myelogenous Leukemia (AML) and the results were compared with those previously reported in our Iranian ALL patients. RT-PCR was performed on bone marrow and/or peripheral blood samples of 84 CLL and 12 AML patients. CLL samples were classified into immunoglobulin heavy chain variable region (IGHV) gene mutated (n = 55) and unmutated (n = 29) and also indolent (n = 42) and progressive (n = 39) subtypes. ROR1 expression was identified in 94% of our CLL patients, but none of the AML patients expressed ROR1. No significant differences were observed between different CLL subtypes for ROR1 expression. Taken together the present data and our previous results on ROR1 expression in ALL, our findings propose ROR1 as a tumor-associated antigen overexpressed in a large proportion of lymphoid (CLL and ALL), but not myeloid (AML) leukemias. Expression of ROR1 seems to be associated to lineage and differentiation stages of leukemic cells with a potential implication for immunotherapy.

Gain of MYC underlies recurrent trisomy of the MYC chromosome in acute promyelocytic leukemia

The leukemogenic effects of Myc drive recurrent trisomy in a mouse model of acute myeloid leukemia.

Gain of chromosome 8 is the most common chromosomal gain in human acute myeloid leukemia (AML). It has been hypothesized that gain of the MYC protooncogene is of central importance in trisomy 8, but the experimental data to support this are limited and controversial. In a mouse model of promyelocytic leukemia in which the MRP8 promoter drives expression of the PML-RARA fusion gene in myeloid cells, a Myc allele is gained in approximately two-thirds of cases as a result of trisomy for mouse chromosome 15. We used this model to test the idea that MYC underlies acquisition of trisomy in AML. We used a retroviral vector to drive expression of wild-type, hypermorphic, or hypomorphic MYC in bone marrow that expressed the PML-RARA transgene. MYC retroviruses cooperated in myeloid leukemogenesis and suppressed gain of chromosome 15. When the PML-RARA transgene was expressed in a Myc haploinsufficient background, we observed selection for increased copies of the wild-type Myc allele concomitant with leukemic transformation. In addition, we found that human myeloid leukemias with trisomy 8 have increased MYC. These data show that gain of MYC can contribute to the pathogenic effect of the most common trisomy of human AML.