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Biomedical subjects

P Tamayo

Publications and source records attributed to P Tamayo.

18 recordsLinked to original sources
PubMed2001

Multiclass cancer diagnosis using tumor gene expression signatures.

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.

Biomarkers, Tumor↗
PubMed2001

Chemosensitivity prediction by transcriptional profiling.

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.

Drug Resistance, Neoplasm↗
PubMed2001

Cascades of transcriptional induction during human lymphocyte activation.

Lymphocyte activation is known to be associated with the induction of genes implicated in cytokine signaling and cellular proliferation. High-density microarrays offer the means to monitor global cellular expression profiles, temporal relationships between classes of transcripts, and alterations associated with human disease or immunosuppression. We sought to determine whether microarray analysis would accurately reflect the normal pattern of gene expression following human T cell activation, and whether the complex expression patterns identified could be analyzed to produce a functional profile of lymphocyte activation. We examined a time course of sequential expression profiles for 6,800 cellular transcripts in human lymphocytes activated with concanavalin A. Expression patterns were grouped using clustering analysis and validated using Northern blotting. Genes known to be induced following T cell activation were accurately identified, and the qualitative patterns of gene expression were well correlated between Northern and microarray analyses. Quantitative differences in gene expression levels were less well correlated between these two techniques. Expression profile analysis revealed the sequential induction of groups of functionally similar genes, whose temporal coregulation underscores known cellular events during T cell activation. This functional "fingerprint" of lymphocyte activation may prove useful for comparisons of lymphocyte responses under experimental conditions and in disease states.

Cluster Analysis↗
PubMed2001

Molecular classification of multiple tumor types.

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.

Algorithms↗
PubMed2000

Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks.

In an effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.

Antineoplastic Agents↗
PubMed2000

Expression analysis with oligonucleotide microarrays reveals that MYC regulates genes involved in growth, cell cycle, signaling, and adhesion.

MYC affects normal and neoplastic cell proliferation by altering gene expression, but the precise pathways remain unclear. We used oligonucleotide microarray analysis of 6,416 genes and expressed sequence tags to determine changes in gene expression caused by activation of c-MYC in primary human fibroblasts. In these experiments, 27 genes were consistently induced, and 9 genes were repressed. The identity of the genes revealed that MYC may affect many aspects of cell physiology altered in transformed cells: cell growth, cell cycle, adhesion, and cytoskeletal organization. Identified targets possibly linked to MYC's effects on cell growth include the nucleolar proteins nucleolin and fibrillarin, as well as the eukaryotic initiation factor 5A. Among the cell cycle genes identified as targets, the G1 cyclin D2 and the cyclin-dependent kinase binding protein CksHs2 were induced whereas the cyclin-dependent kinase inhibitor p21(Cip1) was repressed. A role for MYC in regulating cell adhesion and structure is suggested by repression of genes encoding the extracellular matrix proteins fibronectin and collagen, and the cytoskeletal protein tropomyosin. A possible mechanism for MYC-mediated apoptosis was revealed by identification of the tumor necrosis factor receptor associated protein TRAP1 as a MYC target. Finally, two immunophilins, peptidyl-prolyl cis-trans isomerase F and FKBP52, the latter of which plays a role in cell division in Arabidopsis, were up-regulated by MYC. We also explored pattern-matching methods as an alternative approach for identifying MYC target genes. The genes that displayed an expression profile most similar to endogenous Myc in microarray-based expression profiling of myeloid differentiation models were highly enriched for MYC target genes.

Blotting, Northern↗
PubMed1999

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

Acute Disease↗
PubMed1999

Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation-for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia.

Animals↗
PubMed1995

Effects of perinatal administration of haloperidol on GH pituitary contents and serum levels during the first postnatal month.

Perinatal dopaminergic blockade with haloperidol caused PRL increases in rat pituitary gland and serum which persisted during the first postnatal month. However the effects of dopamine on the synthesis and secretion of GH at these early ages are unknown. With the aim of investigating the effects of this blockade on postnatal GH secretion, haloperidol (1 mg/kg i.p.) was injected daily to pregnant rats from gestational day 16 until delivery and to pups from untreated mothers between postnatal days 2-6. GH pituitary contents and serum levels were measured weekly by RIA during the first postnatal month. The results showed that haloperidol induced a long-term increase in GH pituitary contents as well as a transient increase in serum levels. The results in serum are similar to those from human neonates indicating that dopamine plays a more important role as controller of the GH secretion in newborns than in adults.

Age Factors↗
PubMed1990

Alterations in prolactin secretion during the 1st postnatal month following perinatal dopaminergic blockade with haloperidol.

This research was intended to study the effects of perinatal haloperidol administration on the postnatal secretion of prolactin (PRL) with the aim of investigating the existence of a 'critical period' during which the lack of dopamine influence could cause long-term alterations in the secretion of this hormone. A first group of animals, composed of pregnant rats, was injected daily with haloperidol (1 mg/kg) from day 16 of gestation to delivery. A second group of newborn rats received the same dose from days 2 to 6 after birth. Pituitary and serum PRL were measured weekly by radioimmunoassay during the 1st postnatal month in pups from the injected mothers, in postnatally injected rats, and in controls. The results showed a significant increase in the pituitary amounts of PRL that was more intense after the prenatal treatment, especially in the females. In serum, the prenatal treatment induced PRL levels higher than in the controls, whereas the postnatally injected group exhibited a V-shaped response which has been described as characteristic of neuroleptic withdrawal. These data confirm the existence of a 'critical period' during which perinatal administration of haloperidol alters the postnatal PRL production and secretion patterns. The persistence of high PRL contents in pituitary may reflect an alteration in the hormone synthesis and/or an increase in the rate of somatomammotrophes that differentiate into lactotrophes after suppression of dopamine influence. The high PRL levels in serum indicate a failure in the control of PRL release, perhaps after damaging the tuberoinfundibular neurons as a consequence of the high prolactinemia induced by the treatment.

Animals↗
PubMed1970

A field on Che.

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Argentina↗