RAFT1/FRAP/mTOR is a key regulator of cell growth and division and the mammalian target of rapamycin, an immunosuppressive and anticancer drug. Rapamycin deprivation and nutrient deprivation have similar effects on the activity of S6 kinase 1 (S6K1) and 4E-BP1, two downstream effectors of RAFT1, but the relationship between nutrient- and rapamycin-sensitive pathways is unknown. Using transcriptional profiling, we show that, in human BJAB B-lymphoma cells and murine CTLL-2 T lymphocytes, rapamycin treatment affects the expression of many genes involved in nutrient and protein metabolism. The rapamycin-induced transcriptional profile is distinct from those induced by glucose, glutamine, or leucine deprivation but is most similar to that induced by amino acid deprivation. In particular, rapamycin treatment and amino acid deprivation up-regulate genes involved in nutrient catabolism and energy production and down-regulate genes participating in lipid and nucleotide synthesis and in protein synthesis, turnover, and folding. Surprisingly, however, rapamycin had effects opposite from those of amino acid starvation on the expression of a large group of genes involved in the synthesis, transport, and use of amino acids. Supported by measurements of nutrient use, the data suggest that RAFT1 is an energy and nutrient sensor and that rapamycin mimics a signal generated by the starvation of amino acids but that the signal is unlikely to be the absence of amino acids themselves. These observations underscore the importance of metabolism in controlling lymphocyte proliferation and offer a novel explanation for immunosuppression by rapamycin.

Embryonal tumours of the central nervous system (CNS) represent a heterogeneous group of tumours about which little is known biologically, and whose diagnosis, on the basis of morphologic appearance alone, is controversial. Medulloblastomas, for example, are the most common malignant brain tumour of childhood, but their pathogenesis is unknown, their relationship to other embryonal CNS tumours is debated, and patients' response to therapy is difficult to predict. We approached these problems by developing a classification system based on DNA microarray gene expression data derived from 99 patient samples. Here we demonstrate that medulloblastomas are molecularly distinct from other brain tumours including primitive neuroectodermal tumours (PNETs), atypical teratoid/rhabdoid tumours (AT/RTs) and malignant gliomas. Previously unrecognized evidence supporting the derivation of medulloblastomas from cerebellar granule cells through activation of the Sonic Hedgehog (SHH) pathway was also revealed. We show further that the clinical outcome of children with medulloblastomas is highly predictable on the basis of the gene expression profiles of their tumours at diagnosis.

Aberrant gene expression is critical for tumor initiation and progression. However, we lack a comprehensive understanding of all genes that are aberrantly expressed in human cancer. Recently, DNA microarrays have been used to obtain global views of human cancer gene expression and to identify genetic markers that might be important for diagnosis and therapy. We review clinical applications of these novel tools, discuss some important recent studies, identify promising avenues of research in this emerging field of study, and discuss the likely impact that expression profiling will have on clinical oncology.

Tumor necrosis factor-alpha (TNF-alpha) is a contributing cause of the insulin resistance seen in obesity and obesity-linked type 2 diabetes, but the mechanism(s) by which TNF-alpha induces insulin resistance is not understood. By using 3T3-L1 adipocytes and oligonucleotide microarrays, we identified 142 known genes reproducibly upregulated by at least threefold after 4 h and/or 24 h of TNF-alpha treatment, and 78 known genes downregulated by at least twofold after 24 h of TNF-alpha incubation. TNF-alpha-induced genes include transcription factors implicated in preadipocyte gene expression or NF-kappaB activation, cytokines and cytokine-induced proteins, growth factors, enzymes, and signaling molecules. Importantly, a number of adipocyte-abundant genes, including GLUT4, hormone sensitive lipase, long-chain fatty acyl-CoA synthase, adipocyte complement-related protein of 30 kDa, and transcription factors CCAAT/enhancer binding protein-alpha, receptor retinoid X receptor-alpha, and peroxisome profilerator-activated receptor gamma were significantly downregulated by TNF-alpha treatment. Correspondingly, 24-h exposure of 3T3-L1 adipocytes to TNF-alpha resulted in reduced protein levels of GLUT4 and several insulin signaling proteins, including the insulin receptor, insulin receptor substrate 1 (IRS-1), and protein kinase B (AKT). Nuclear factor-kappaB (NF-kappaB) was activated within 15 min of TNF-alpha addition. 3T3-L1 adipocytes expressing IkappaBalpha-DN, a nondegradable NF-kappaB inhibitor, exhibited normal morphology, global gene expression, and insulin responses. However, absence of NF-kappaB activation abolished suppression of >98% of the genes normally suppressed by TNF-alpha and induction of 60-70% of the genes normally induced by TNF-alpha. Moreover, extensive cell death occurred in IkappaBalpha-DN-expressing adipocytes after 2 h of TNF-alpha treatment. Thus the changes in adipocyte gene expression induced by TNF-alpha could lead to insulin resistance. Further, NF-kappaB is an obligatory mediator of most of these TNF-alpha responses.

Despite extensive studies implicating tumor necrosis factor (TNF)-alpha as a contributing cause of insulin resistance, the mechanism(s) by which TNF-alpha alters energy metabolism in vivo and the tissue specificity of TNF-alpha action are unclear. Here, we investigated the effects of TNF-alpha infusion on gene expression and energy metabolism in adult rats. A 1-day TNF-alpha treatment decreased overall insulin sensitivity and caused a 70% increase (P = 0.005) in plasma levels of free fatty acids (FFAs) and a 46% decrease (P = 0.01) in ACRP30. A 4-day TNF-alpha infusion caused insulin resistance and significant elevation of plasma levels of FFAs and triglycerides and reduction of ACRP30. Plasma glucose concentration was not altered following TNF-alpha infusion for up to 4 days. As revealed by oligonucleotide microarrays, TNF-alpha evoked major and rapid changes in adipocyte gene expression, favoring FFA release and cytokine production, and fewer changes in liver gene expression, but favoring FFA and cholesterol synthesis and VLDL production. There was only a moderate repressive effect on skeletal muscle gene expression. We demonstrate that TNF-alpha antagonizes the actions of insulin, at least in part, through regulation of adipocyte gene expression including reduction in ACRP30 mRNA and induction of lipolysis resulting in increased plasma FFAs. TNF-alpha later alters systemic energy homeostasis that closely resembles the insulin resistance phenotype. Our data suggest that blockade of TNF-alpha action in adipose tissue may prevent TNF-alpha-induced insulin resistance in vivo.

Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention.

Prostate tumors are among the most heterogeneous of cancers, both histologically and clinically. Microarray expression analysis was used to determine whether global biological differences underlie common pathological features of prostate cancer and to identify genes that might anticipate the clinical behavior of this disease. While no expression correlates of age, serum prostate specific antigen (PSA), and measures of local invasion were found, a set of genes was identified that strongly correlated with the state of tumor differentiation as measured by Gleason score. Moreover, a model using gene expression data alone accurately predicted patient outcome following prostatectomy. These results support the notion that the clinical behavior of prostate cancer is linked to underlying gene expression differences that are detectable at the time of diagnosis.

In an effort to develop a genomics-based approach to the prediction of drug response, we have developed an algorithm for classification of cell line chemosensitivity based on gene expression profiles alone. Using oligonucleotide microarrays, the expression levels of 6,817 genes were measured in a panel of 60 human cancer cell lines (the NCI-60) for which the chemosensitivity profiles of thousands of chemical compounds have been determined. We sought to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-based classifiers of sensitivity or resistance for 232 compounds were generated and then evaluated on independent sets of data. The classifiers were designed to be independent of the cells' tissue of origin. The accuracy of chemosensitivity prediction was considerably better than would be expected by chance. Eighty-eight of 232 expression-based classifiers performed accurately (with P 0.05) on an independent test set, whereas only 12 of the 232 would be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction are feasible.

We have generated a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, we analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct subclasses of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.