GenePattern 2.0.
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Gene Expression Profiling↗
Biomedical subjects
Pablo Tamayo
Publications and source records attributed to Pablo Tamayo.
Inactivation of the Snf5 tumor suppressor stimulates cell cycle progression and cooperates with p53 loss in oncogenic transformation.
Snf5 (Ini1/Baf47/Smarcb1), a core member of the Swi/Snf chromatin remodeling complex, is a potent tumor suppressor whose mechanism of action is largely unknown. Biallelic loss of Snf5 leads to the onset of aggressive cancers in both humans and mice. We have developed an innovative and widely applicable analytical technique for cross-species validation of cancer models and show that the gene expression profiles of our Snf5 murine models closely resemble those of human Snf5-deficient rhabdoid tumors. We exploit this system to produce what we believe to be the first report documenting the effects on gene expression of inactivating a Swi/Snf subunit in normal mammalian cells and to identify the transcriptional pathways regulated by Snf5. We demonstrate that the tumor suppressor activity of Snf5 depends on its regulation of cell cycle progression; Snf5 inactivation leads to aberrant up-regulation of E2F targets and increased levels of p53 that are accompanied by apoptosis, polyploidy, and growth arrest. Further, conditional mouse models demonstrate that inactivation of p16Ink4a or Rb (retinoblastoma) does not accelerate tumor formation in Snf5 conditional mice, whereas mutation of p53 leads to a dramatic acceleration of tumor formation.
Animals↗
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.
Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
Cell Line, Tumor↗
GeneCruiser: a web service for the annotation of microarray data.
SUMMARY: GeneCruiser is a web service allowing users to annotate their genomic data by mapping microarray feature identifiers to gene identifiers from databases, such as UniGene, while providing links to web resources, such as the UCSC Genome Browser. It relies on a regularly updated database that retrieves and indexes the mappings between microarray probes and genomic databases. Genes are identified using the Life Sciences Identifier standard. AVAILABILITY: GeneCruiser is freely available in the following forms: Web service and Web application, http://www.genecruiser.org; GenePattern, GeneCruiser access has been integrated into our microarray analysis platform, GenePattern. http://www.genepattern.org.
Animals↗
Metagenes and molecular pattern discovery using matrix factorization.
We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of genes or initial conditions and able to detect alternative or context-dependent patterns of gene expression in complex biological systems. This ability, similar to semantic polysemy in text, provides a general method for robust molecular pattern discovery.
Algorithms↗
Combining gene expression profiles and clinical parameters for risk stratification in medulloblastomas.
PURPOSE: Stratification of risk in patients with medulloblastoma remains a challenge. As clinical parameters have been proven insufficient for accurately defining disease risk, molecular markers have become the focus of interest. Outcome predictions on the basis of microarray gene expression profiles have been the most accurate to date. We ask in a multivariate model whether clinical parameters enhance survival predictions of gene expression profiles. PATIENTS AND METHODS: In a cohort of 55 young patients (whose medulloblastoma samples have been analyzed previously for gene expression profile), associations between clinical and gene expression variables and survival were assessed using Cox proportional hazards models. Available clinical variables included age, stage (ie, the presence of disseminated disease at diagnosis), sex, histologic subtype, treatment, and status. RESULTS: Univariate analysis demonstrated expression profiles to be the only significant clinical prognostic factor (P=.03). In multivariate analysis, gene expression profiles predicted outcome independent of other criteria. Clinical criteria did not significantly contribute additional information for outcome predictions, although an exploratory analysis noted a trend for decreased survival of patients with metastases at diagnosis but favorable gene expression profile. CONCLUSION: Gene expression profiling predicts medulloblastoma outcome independent of clinical variables. These results need to be validated in a larger prospective study.
Adolescent↗
Gene expression-based classification of malignant gliomas correlates better with survival than histological classification.
In modern clinical neuro-oncology, histopathological diagnosis affects therapeutic decisions and prognostic estimation more than any other variable. Among high-grade gliomas, histologically classic glioblastomas and anaplastic oligodendrogliomas follow markedly different clinical courses. Unfortunately, many malignant gliomas are diagnostically challenging; these nonclassic lesions are difficult to classify by histological features, generating considerable interobserver variability and limited diagnostic reproducibility. The resulting tentative pathological diagnoses create significant clinical confusion. We investigated whether gene expression profiling, coupled with class prediction methodology, could be used to classify high-grade gliomas in a manner more objective, explicit, and consistent than standard pathology. Microarray analysis was used to determine the expression of approximately 12000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology.
Brain Neoplasms↗
PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.
DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1alpha and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.
Animals↗
Estimating dataset size requirements for classifying DNA microarray data.
A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.
Algorithms↗
A strategy for oligonucleotide microarray probe reduction.
BACKGROUND: One of the factors limiting the number of genes that can be analyzed on high-density oligonucleotide arrays is that each transcript is probed by multiple oligonucleotide probes. To reduce the number of probes required for each gene, a systematic approach to choosing the most representative probes is needed. A method is presented for reducing the number of probes per gene while maximizing the fidelity to the original array design. RESULTS: The methodology has been tested on a dataset comprising 317 Affymetrix HuGeneFL GeneChips. The performance of the original and reduced probe sets was compared in four cancer-classification problems. The results of these comparisons show that reduction of the probe set by 95% does not dramatically affect performance, and thus illustrate the feasibility of substantially reducing probe numbers without significantly compromising sensitivity and specificity of detection. CONCLUSIONS: The strategy described here is potentially useful for designing small, limited-probe genome-wide arrays for screening applications.
Acute Disease↗
Prediction of central nervous system embryonal tumour outcome based on gene expression.
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.
Adolescent↗
Gene expression correlates of clinical prostate cancer behavior.
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.
Adult↗
Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning.
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.
Antineoplastic Combined Chemotherapy Protocols↗