Gene expression profiles of AML derived stem cells; similarity to hematopoietic stem cells.

Tumors contain a fraction of cancer stem cells that maintain the propagation of the disease. The CD34(+)CD38(-) cells, isolated from acute myeloid leukemia (AML), were shown to be enriched leukemic stem cells (LSC). We isolated the CD34(+)CD38(-) cell fraction from AML and compared their gene expression profiles to the CD34(+)CD38(+) cell fraction, using microarrays. We found 409 genes that were at least twofold over- or underexpressed between the two cell populations. These include underexpression of DNA repair, signal transduction and cell cycle genes, consistent with the relative quiescence of stem cells, and chromosomal aberrations and mutations of leukemic cells. Comparison of the LSC expression data to that of normal hematopoietic stem cells (HSC) revealed that 34% of the modulated genes are shared by both LSC and HSC, supporting the suggestion that the LSC originated within the HSC progenitors. We focused on the Notch pathway since Jagged-2, a Notch ligand was found to be overexpressed in the LSC samples. We show that DAPT, an inhibitor of gamma-secretase, a protease that is involved in Jagged and Notch signaling, inhibits LSC growth in colony formation assays. Identification of additional genes that regulate LSC self-renewal may provide new targets for therapy.

Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices.

SUMMARY: We introduce a novel unsupervised approach for the organization and visualization of multidimensional data. At the heart of the method is a presentation of the full pairwise distance matrix of the data points, viewed in pseudocolor. The ordering of points is iteratively permuted in search of a linear ordering, which can be used to study embedded shapes. Several examples indicate how the shapes of certain structures in the data (elongated, circular and compact) manifest themselves visually in our permuted distance matrix. It is important to identify the elongated objects since they are often associated with a set of hidden variables, underlying continuous variation in the data. The problem of determining an optimal linear ordering is shown to be NP-Complete, and therefore an iterative search algorithm with O(n3) step-complexity is suggested. By using sorting points into neighborhoods, i.e. SPIN to analyze colon cancer expression data we were able to address the serious problem of sample heterogeneity, which hinders identification of metastasis related genes in our data. Our methodology brings to light the continuous variation of heterogeneity--starting with homogeneous tumor samples and gradually increasing the amount of another tissue. Ordering the samples according to their degree of contamination by unrelated tissue allows the separation of genes associated with irrelevant contamination from those related to cancer progression. AVAILABILITY: Software package will be available for academic users upon request.

Generation of unpredictable time series by a neural network.

A perceptron that "learns" the opposite of its own output is used to generate a time series. We analyze properties of the weight vector and the generated sequence, such as the cycle length and the probability distribution of generated sequences. A remarkable suppression of the autocorrelation function is explained, and connections to the Bernasconi model are discussed. If a continuous transfer function is used, the system displays chaotic and intermittent behavior, with the product of the learning rate and amplification as a control parameter.

Multichoice minority game.

The generalization of the problem of adaptive competition, known as the minority game, to the case of K possible choices for each player, is addressed, and applied to a system of interacting perceptrons with input and output units of a type of K-state Potts spins. An optimal solution of this minority game, as well as the dynamic evolution of the adaptive strategies of the players, are solved analytically for a general K and compared with numerical simulations.

Confidence in prediction by neural networks.

The idea that a trained network can assign a confidence number to its prediction, indicating the level of its reliability, is addressed and exemplified by an analytical examination of a perceptron with discrete and continuous output units. Results are derived for both Gibbs and Bayes scenarios. The information gain by the confidence number is estimated by various entropy measurements.