We have assembled data from Caenorhabditis elegans DNA microarray experiments involving many growth conditions, developmental stages, and varieties of mutants. Co-regulated genes were grouped together and visualized in a three-dimensional expression map that displays correlations of gene expression profiles as distances in two dimensions and gene density in the third dimension. The gene expression map can be used as a gene discovery tool to identify genes that are co-regulated with known sets of genes (such as heat shock, growth control genes, germ line genes, and so forth) or to uncover previously unknown genetic functions (such as genomic instability in males and sperm caused by specific transposons).

To resolve the genetic heterogeneity within pediatric high-risk B-precursor acute lymphoblastic leukemia (ALL), a clinically defined poor-risk group with few known recurring cytogenetic abnormalities, we performed gene expression profiling in a cohort of 207 uniformly treated children with high-risk ALL. Expression profiles were correlated with genome-wide DNA copy number abnormalities and clinical and outcome features. Unsupervised clustering of gene expression profiling data revealed 8 unique cluster groups within these highrisk ALL patients, 2 of which were associated with known chromosomal translocations (t(1;19)(TCF3-PBX1) or MLL), and 6 of which lacked any previously known cytogenetic lesion. One unique cluster was characterized by high expression of distinct outlier genes AGAP1, CCNJ, CHST2/7, CLEC12A/B, and PTPRM; ERG DNA deletions; and 4-year relapse-free survival of 94.7% ؎ 5.1%, compared with 63.5% ؎ 3.7% for the cohort (P ‫؍‬ .01). A second cluster, characterized by high expression of BMPR1B, CRLF2, GPR110, and MUC4; frequent deletion of EBF1, IKZF1, RAG1-2, and IL3RA-CSF2RA; JAK mutations and CRLF2 rearrangements (P < .0001); and Hispanic ethnicity (P < .001) had a very poor 4-year relapsefree survival (21.0% ؎ 9.5%; P < .001). These studies reveal striking clinical and genetic heterogeneity in high-risk ALL and point to novel genes that may serve as new targets for diagnosis, risk classification, and therapy. (Blood. 2010; 116(23):4874-4884)

Cells in glucose-limited Saccharomyces cerevisiae cultures differentiate into quiescent (Q) and nonquiescent (NQ) fractions before entering stationary phase. To understand this differentiation, Q and NQ cells from 101 deletion-mutant strains were tested for viability and reproductive capacity. Eleven mutants that affected one or both phenotypes in Q or NQ fractions were identified. NQ fractions exhibit a high level of petite colonies, and nine mutants affecting this phenotype were identified. Microarray analysis revealed >1300 mRNAs distinguished Q from NQ fractions. Q cell-specific mRNAs encode proteins involved in membrane maintenance, oxidative stress response, and signal transduction. NQ-cell mRNAs, consistent with apoptosis in these cells, encode proteins involved in Ty-element transposition and DNA recombination. More than 2000 protease-released mRNAs were identified only in Q cells, consistent with these cells being physiologically poised to respond to environmental changes. Our results indicate that Q and NQ cells differentiate significantly, with Q cells providing genomic stability and NQ cells providing nutrients to Q cells and a regular source of genetic diversity through mutation and transposition. These studies are relevant to chronological aging, cell cycle, and genome evolution, and they provide insight into complex responses that even simple organisms have to starvation.

To determine whether gene expression profiling could improve risk classification and outcome prediction in older acute myeloid leukemia (AML) patients, expression profiles were obtained in pretreatment leukemic samples from 170 patients whose median age was 65 years. Unsupervised clustering methods were used to classify patients into 6 cluster groups (designated A to F) that varied significantly in rates of resistant disease (RD; P < .001), complete response (CR; P ‫؍‬ .023), and disease-free survival (DFS; P ‫؍‬ .023). Cluster A (n ‫؍‬ 24), dominated by NPM1 mutations (78%), normal karyotypes (75%), and genes associated with signaling and apoptosis, had the best DFS (27%) and overall survival (OS; 25% at 5 years). Patients in clusters B (n ‫؍‬ 22) and C (n ‫؍‬ 31) had the worst OS (5% and 6%, respectively); cluster B was distinguished by the highest rate of RD (77%) and multidrug resistant gene expression (ABCG2, MDR1). Cluster D was characterized by a "proliferative" gene signature with the highest proportion of detectable cytogenetic abnormalities (76%; including 83% of all favorable and 34% of unfavorable karyotypes). Cluster F (n ‫؍‬ 33) was dominated by monocytic leukemias (97% of cases), also showing increased NPM1 mutations (61%

Outcome for children with childhood acute lymphoblastic leukemia (ALL) who relapse is poor. To gain insight into the mechanisms of relapse, we analyzed gene-expression profiles in 35 matched diagnosis/relapse pairs as well as 60 uniformly treated children at relapse using the Affymetrix platform. Matched-pair analyses revealed significant differences in the expression of genes involved in cell-cycle regulation, DNA repair, and apoptosis between diagnostic and earlyrelapse samples. Many of these pathways have been implicated in tumorigenesis previously and are attractive targets for intervention strategies. In contrast, no common pattern of changes was observed among late-relapse pairs. Earlyrelapse samples were more likely to be similar to their respective diagnostic sample while we noted greater divergence in gene

Starved yeast cultures differentiate into quiescent (Q) and nonquiescent (NQ) cell fractions. The yeast GFP-fusion library (4159 strains) and high-throughput flow cytometry were used to study this process. This showed significant metabolic and physiologic differences between Q/NQ cells and provided new tools for studying their differentiation.

We present the application of our knowledge visualization tool, VxInsightா, to enable domain analysis for science and technology management within the enterprise. Data mining from sources of bibliographic information is used to define subsets of information relevant to a technology domain. Relationships between the individual objects (e.g., articles) are identified using citations, descriptive terms, or textual similarities. Objects are then clustered using a force-directed placement algorithm to produce a terrain view of the many thousands of objects. A variety of features that allow exploration and manipulation of the landscapes and that give detail on demand, enable quick and powerful analysis of the resulting landscapes. Examples of domain analyses used in S&T management at Sandia are given.

In this chapter, we argue that with careful attention to cell types in stationary-phase cultures of the yeast, S. cerevisiae provide an excellent model system for aging studies and hold much promise in pinpointing the set of causal genes and mechanisms driving aging. Importantly, a more detailed understanding of aging in this single celled organism will also shed light on aging in tissue-complex model organisms such as C. elegans and D. melanogaster. We feel strongly that the relationship between aging in yeast and tissue-complex organisms has been obscured by failure to notice the heterogeneity of stationary-phase cultures and the processes by which distinct cell types arise in these cultures. Although several studies have used yeast stationary-phase cultures for chronological aging, the majority of these studies have assumed that cultures in stationary phase are homogeneously composed of a single cell type. However, genome-scale analyses of yeast stationary-phase cultures have identified two major cell fractions: quiescent and non-quiescent, which we discuss in detail in this chapter. We review evidence that cell populations isolated from these cultures exhibit population-specific phenotypes spanning a range of metabolic and physiological processes including reproductive capacity, apoptosis, differences in metabolic activities, genetic hyper-mutability, and stress responses. The identification, in S. cerevisiae, of multiple sub-populations having differentiated physiological attributes relevant to aging offers an unprecedented opportunity. This opportunity to deeply understand yeast cellular (and population) aging programs will, also, give insight into genomic and metabolic processes in tissue-complex organism, as well as stem cell biology and the origins of differentiation.