http://www.weizmann.ac.il/physics/complex/compphys/downloads/liate/
Predicting at the time of discovery the prognosis and metastatic potential of cancer is a major challenge in current clinical research. Numerous recent studies searched for gene expression signatures that outperform traditionally used clinical parameters in outcome prediction. Finding such a signature will free many patients of the suffering and toxicity associated with adjuvant chemotherapy given to them under current protocols, even though they do not need such treatment. A reliable set of predictive genes also will contribute to a better understanding of the biological mechanism of metastasis. Several groups have published lists of predictive genes and reported good predictive performance based on them. However, the gene lists obtained for the same clinical types of patients by different groups differed widely and had only very few genes in common. This lack of agreement raised doubts about the reliability and robustness of the reported predictive gene lists, and the main source of the problem was shown to be the small number of samples that were used to generate the gene lists. Here, we introduce a previously undescribed mathematical method, probably approximately correct (PAC) sorting, for evaluating the robustness of such lists. We calculate for several published data sets the number of samples that are needed to achieve any desired level of reproducibility. For example, to achieve a typical overlap of 50% between two predictive lists of genes, breast cancer studies would need the expression profiles of several thousand early discovery patients.DNA microarray gene expression data ͉ outcome prediction in cancer ͉ probably approximately correct sorting ͉ predictive gene list ͉ robustness O ne of the central challenges of clinical cancer research is prediction of outcome, i.e., of the potential for relapse and for metastasis. Identification of aggressive tumors at the time of diagnosis has direct bearing on the choice of optimal therapy for each individual. The need for sensitive and reliable predictors of outcome is most acute for early discovery breast cancer patients. Adjuvant chemotherapy is recognized to be useless for Ϸ75% of this group (1); it is believed that after surgery a large majority of these patients would remain disease free without any treatment. Nevertheless, they are often submitted to the same therapeutic regimen as the small fraction of those who really need chemotherapy and benefit from it.Considerable effort has been devoted recently to outcome prediction for several kinds of cancer on the basis of gene expression profiling (2-8), with special emphasis on breast carcinoma (9-13). Several of these studies reported considerable predictive success. These successes were, however, somewhat thwarted by two problems: (i) when one group's predictor was tested (G. Fuks, L.E.-D., and E.D., unpublished data) on another group's data (for the same type of cancer patients), the success rate decreased significantly; and (ii) comparison of the predictive gene lists (PGLs) discovered by different groups...
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Tumors contain a fraction of cancer stem cells that maintain the propagation of the disease. The CD34 þ CD38À cells, isolated from acute myeloid leukemia (AML), were shown to be enriched leukemic stem cells (LSC). We isolated the CD34 þ CD38À cell fraction from AML and compared their gene expression profiles to the CD34 þ CD38 þ 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.
Genomes undergo changes in organization as a result of gene duplications, chromosomal rearrangements and local mutations, among other mechanisms. In contrast to prokaryotes, in which genes of a common function are often organized in operons and reside contiguously along the genome, most eukaryotes show much weaker clustering of genes by function, except for few concrete functional groups. We set out to check systematically if there is a relation between gene function and gene organization in the human genome. We test this question for three types of functional groups: pairs of interacting proteins, complexes and pathways. We find a significant concentration of functional groups both in terms of their distance within the same chromosome and in terms of their dispersal over several chromosomes. Moreover, using Hi-C contact map of the tendency of chromosomal segments to appear close in the 3D space of the nucleus, we show that members of the same functional group that reside on distinct chromosomes tend to co-localize in space. The result holds for all three types of functional groups that we tested. Hence, the human genome shows substantial concentration of functional groups within chromosomes and across chromosomes in space.
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