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GENOME WIDE DNA METHYLATION PROFILING IS PREDICTIVE OF OUTCOME IN JUVENILE MYELOMONOCYTIC LEUKEMIA
Temporal patterns of gene expression via nonmetric multidimensional scaling analysis
Motivation: Microarray experiments result in large scale data sets that
require extensive mining and refining to extract useful information. We have
been developing an efficient novel algorithm for nonmetric multidimensional
scaling (nMDS) analysis for very large data sets as a maximally unsupervised
data mining device. We wish to demonstrate its usefulness in the context of
bioinformatics. In our motivation is also an aim to demonstrate that
intrinsically nonlinear methods are generally advantageous in data mining.
Results: The Pearson correlation distance measure is used to indicate the
dissimilarity of the gene activities in transcriptional response of cell
cycle-synchronized human fibroblasts to serum [Iyer et al., Science vol. 283,
p83 (1999)]. These dissimilarity data have been analyzed with our nMDS
algorithm to produce an almost circular arrangement of the genes. The temporal
expression patterns of the genes rotate along this circular arrangement. If an
appropriate preparation procedure may be applied to the original data set,
linear methods such as the principal component analysis (PCA) could achieve
reasonable results, but without data preprocessing linear methods such as PCA
cannot achieve a useful picture. Furthermore, even with an appropriate data
preprocessing, the outcomes of linear procedures are not as clearcut as those
by nMDS without preprocessing.Comment: 11 pages, 6 figures + online only 2 color figures, submitted to
Bioinformatic
Partial mixture model for tight clustering of gene expression time-course
Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to
this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored.
Results: In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate
information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a
simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms.
Conclusion: For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the ombination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset
under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion
Transcriptional landscape of neuronal and cancer stem cells
Tumor mass is composed by heterogeneous cell population including a subset of “cancer stem cells” (CSC).
Oncogenic signals foster CSC by transforming tissue stem cells or by reprogramming progenitor/differentiated
cells towards stemness. Thus, CSC share features with cancer and stem cells (e.g. self-renewal, hierarchical
developmental program leading to differentiated cells, epithelial/mesenchimal transition) and these latter are
maintained by the constitutive activation of stemness-promoting signals. CSC could trigger tumor formation,
drive to resistance to conventional therapeutics and underlie patients’ relapse. Indeed, stem cell signatures
have been associated with poor prognosis in various.
This background makes the identification of CSC molecular features mandatory to highlight the survival inner
working and to design novel CSC specific therapeutic strategies.
Medulloblastoma (MB) is the most common childhood malignant brain tumor and a leading cause of cancerrelated
morbidity and mortality. Current multimodal therapies are effective in about 50% of patients but often
cause long-term side effects, i.e. developmental, neurological, neuroendocrine and psychosocial deficits
(Northcott PA Nature Rev cancer 2012). For many years, MB treated as a single tumor entity despite the
divergent tumor histology, patients’ outcome and drug sensitivity, and also by the diversity of the stem cell of
origin. Very recently the scenario of human MB has dramatically changed since its heterogeneous biology has
been addressed by high-throughput gene expression analysis (oligonucleotide microarrays) or by the powerful
genomic next-generation sequencing. These led to the identification of four tumor subgroups (WNT, SHH,
Group 3 and Group 4) uncovering the existence of a highly diverse mutational spectra and gene expression.
However a quantitative approach has not yet been applied to the transcriptional landscape of Medulloblastoma
stem cells (MbSC) through RNA Next Generation Sequencing (RNA-Seq) technology. This is a relevant issue,
since RNA-Seq is able to interrogate the genome wide global transcriptome including new transcripts,
alternative spliced isoforms and non-coding RNAs.
Lower rhombic lip progenitors of the dorsal brainstem are considered the trigger cells in WNT tumors; in SHH
subgroup initiation cells are Prominin1+ CD15+ stem cells from the subventricular zone requiring the
commitment to Math1+ granule cell progenitors [GCP] of the external granule cell layer [EGL]; while Math1+ or
Math1- EGL-GCP or Prominin1+/lineage-negative stem cells sustain the MYC driven Group 3.
MbSC derived from SHH tumors and postnatal normal cerebellar stem cells (NcSC) have been reported to
share several features. A key signal for both of them is Hedgehog. Furthermore, both NcSC and MbSC display
up-regulation of stemness genes (e.g Sox2, Nestin, Nanog, Prom1). Finally, constitutive activation of the Shh
pathway by conditional deletion of Ptch1 inhibitory receptor in NcSC, promote medulloblastoma in vivo,
producing a mouse model of the human SHH tumor. Acquisition of stemness features may therefore represent
the first step of oncogenic conversion. Cooperation with additional oncogenic signals is however needed to
enhance MbSC tumorigenicity.
In order to understand the MbSCs transcriptional programs, we analyze by RNA-Seq, MbSC derived from
Ptch1+/- tumors (Ptch1+/- MbSC). This choice, of a genetically determined model of MB, has allowed us to
work with Ptch1+/- MbSC together with appropriate NcSC counterpart, and to analyze biological replicates
doing statistical analysis.
We identify a number of transcripts, annotated ones, novel isoforms, and long non-coding RNAs,
characterizing MbSC and/or NcSC. Some of these genes control stemness or are cancer related and
conserved in human medulloblastomas. Interestingly a subset of them, belonging to cell stress response, are
of prognostic relevance being significantly related to clinical outcome. Correlation of genes expression
characterizing MbSC with survival information from our human medulloblastomas database further
demonstrates the significance of these findings. Our data suggest that the modulation of normal and cancer
stem cell functions observed in vitro is effective in dissecting the transcriptional programs underlying the in
vivo behavior of human medulloblastomas
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