Epigenetic therapy restores normal hematopoiesis in a zebrafish model of NUP98-HOXA9-induced myeloid disease.

Acute myeloid leukemia (AML) occurs when multiple genetic aberrations alter white blood cell development, leading to hyperproliferation and arrest of cell differentiation. Pertinent animal models link in vitro studies with the use of new agents in clinical trials. We generated a transgenic zebrafish expressing human NUP98-HOXA9 (NHA9), a fusion oncogene found in high-risk AML. Embryos developed a preleukemic state with anemia and myeloid cell expansion, and adult fish developed a myeloproliferative neoplasm (MPN). We leveraged this model to show that NHA9 increases the number of hematopoietic stem cells, and that oncogenic function of NHA9 depends on downstream activation of meis1, the PTGS/COX pathway and genome hypermethylation through the DNA methyltransferase, dnmt1. We restored normal hematopoiesis in NHA9 embryos with knockdown of meis1 or dnmt1, as well as pharmacologic treatment with DNA (cytosine-5)-methyltransferase (DNMT) inhibitors or cyclo-oxygenase (COX) inhibitors. DNMT inhibitors reduced genome methylation to near normal levels. Strikingly, we discovered synergy when we combined sub-monotherapeutic doses of a histone deacetylase inhibitor plus either a DNMT inhibitor or COX inhibitor to block the effects of NHA9 on zebrafish blood development. Our work proposes novel drug targets in NHA9-induced myeloid disease, and suggests rational therapies by combining minimal doses of known bioactive compounds.

PCA and clustering reveal alternate mtDNA phylogeny of N and M clades.

Phylogenetic trees based on mtDNA polymorphisms are often used to infer the history of recent human migrations. However, there is no consensus on which method to use. Most methods make strong assumptions which may bias the choice of polymorphisms and result in computational complexity which limits the analysis to a few samples/polymorphisms. For example, parsimony minimizes the number of mutations, which biases the results to minimizing homoplasy events. Such biases may miss the global structure of the polymorphisms altogether, with the risk of identifying a "common" polymorphism as ancient without an internal check on whether it either is homoplasic or is identified as ancient because of sampling bias (from oversampling the population with the polymorphism). A signature of this problem is that different methods applied to the same data or the same method applied to different datasets results in different tree topologies. When the results of such analyses are combined, the consensus trees have a low internal branch consensus. We determine human mtDNA phylogeny from 1737 complete sequences using a new, direct method based on principal component analysis (PCA) and unsupervised consensus ensemble clustering. PCA identifies polymorphisms representing robust variations in the data and consensus ensemble clustering creates stable haplogroup clusters. The tree is obtained from the bifurcating network obtained when the data are split into k = 2,3,4,...,kmax clusters, with equal sampling from each haplogroup. Our method assumes only that the data can be clustered into groups based on mutations, is fast, is stable to sample perturbation, uses all significant polymorphisms in the data, works for arbitrary sample sizes, and avoids sample choice and haplogroup size bias. The internal branches of our tree have a 90% consensus accuracy. In conclusion, our tree recreates the standard phylogeny of the N, M, L0/L1, L2, and L3 clades, confirming the African origin of modern humans and showing that the M and N clades arose in almost coincident migrations. However, the N clade haplogroups split along an East-West geographic divide, with a "European R clade" containing the haplogroups H, V, H/V, J, T, and U and a "Eurasian N subclade" including haplogroups B, R5, F, A, N9, I, W, and X. The haplogroup pairs (N9a, N9b) and (M7a, M7b) within N and M are placed in nonnearest locations in agreement with their expected large TMRCA from studies of their migrations into Japan. For comparison, we also construct consensus maximum likelihood, parsimony, neighbor joining, and UPGMA-based trees using the same polymorphisms and show that these methods give consistent results only for the clade tree. For recent branches, the consensus accuracy for these methods is in the range of 1-20%. From a comparison of our haplogroups to two chimp and one bonobo sequences, and assuming a chimp-human coalescent time of 5 million years before present, we find a human mtDNA TMRCA of 206,000 +/- 14,000 years before present.

Analysis of breast cancer progression using principal component analysis and clustering.

We develop a new technique to analyse microarray data which uses a combination of principal components analysis and consensus ensemble k-clustering to find robust clusters and gene markers in the data. We apply our method to a public microarray breast cancer dataset which has expression levels of genes in normal samples as well as in three pathological stages of disease; namely, atypical ductal hyperplasia or ADH, ductal carcinoma in situ or DCIS and invasive ductal carcinoma or IDC. Our method averages over clustering techniques and data perturbation to find stable, robust clusters and gene markers. We identify the clusters and their pathways with distinct subtypes of breast cancer (Luminal,Basal and Her2+). We confirm that the cancer phenotype develops early (in early hyperplasia or ADH stage) and find from our analysis that each subtype progresses from ADH to DCIS to IDC along its own specific pathway, as if each was a distinct disease.

Data perturbation independent diagnosis and validation of breast cancer subtypes using clustering and patterns.

Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a "core cluster" of samples for each category, and from these we determine "patterns" of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not.

Logical analysis of diffuse large B-cell lymphomas.

OBJECTIVE: The goal of this study is to re-examine the oligonucleotide microarray dataset of Shipp et al., which contains the intensity levels of 6817 genes of 58 patients with diffuse large B-cell lymphoma (DLBCL) and 19 with follicular lymphoma (FL), by means of the combinatorics, optimisation, and logic-based methodology of logical analysis of data (LAD). The motivations for this new analysis included the previously demonstrated capabilities of LAD and its expected potential (1) to identify different informative genes than those discovered by conventional statistical methods, (2) to identify combinations of gene expression levels capable of characterizing different types of lymphoma, and (3) to assemble collections of such combinations that if considered jointly are capable of accurately distinguishing different types of lymphoma. METHODS AND MATERIALS: The central concept of LAD is a pattern or combinatorial biomarker, a concept that resembles a rule as used in decision tree methods. LAD is able to exhaustively generate the collection of all those patterns which satisfy certain quality constraints, through a systematic combinatorial process guided by clear optimization criteria. Then, based on a set covering approach, LAD aggregates the collection of patterns into classification models. In addition, LAD is able to use the information provided by large collections of patterns in order to extract subsets of variables, which collectively are able to distinguish between different types of disease. RESULTS: For the differential diagnosis of DLBCL versus FL, a model based on eight significant genes is constructed and shown to have a sensitivity of 94.7% and a specificity of 100% on the test set. For the prognosis of good versus poor outcome among the DLBCL patients, a model is constructed on another set consisting also of eight significant genes, and shown to have a sensitivity of 87.5% and a specificity of 90% on the test set. The genes selected by LAD also work well as a basis for other kinds of statistical analysis, indicating their robustness. CONCLUSION: These two models exhibit accuracies that compare favorably to those in the original study. In addition, the current study also provides a ranking by importance of the genes in the selected significant subsets as well as a library of dozens of combinatorial biomarkers (i.e. pairs or triplets of genes) that can serve as a source of mathematically generated, statistically significant research hypotheses in need of biological explanation.