Biomedical subjects

Gregory Bloom

Publications and source records attributed to Gregory Bloom.

4 recordsLinked to original sources

PubMed2005

Prediction of radiation sensitivity using a gene expression classifier.

The development of a successful radiation sensitivity predictive assay has been a major goal of radiation biology for several decades. We have developed a radiation classifier that predicts the inherent radiosensitivity of tumor cell lines as measured by survival fraction at 2 Gy (SF2), based on gene expression profiles obtained from the literature. Our classifier correctly predicts the SF2 value in 22 of 35 cell lines from the National Cancer Institute panel of 60, a result significantly different from chance (P = 0.0002). In our approach, we treat radiation sensitivity as a continuous variable, significance analysis of microarrays is used for gene selection, and a multivariate linear regression model is used for radiosensitivity prediction. The gene selection step identified three novel genes (RbAp48, RGS19, and R5PIA) of which expression values are correlated with radiation sensitivity. Gene expression was confirmed by quantitative real-time PCR. To biologically validate our classifier, we transfected RbAp48 into three cancer cell lines (HS-578T, MALME-3M, and MDA-MB-231). RbAp48 overexpression induced radiosensitization (1.5- to 2-fold) when compared with mock-transfected cell lines. Furthermore, we show that HS-578T-RbAp48 overexpressors have a higher proportion of cells in G2-M (27% versus 5%), the radiosensitive phase of the cell cycle. Finally, RbAp48 overexpression is correlated with dephosphorylation of Akt, suggesting that RbAp48 may be exerting its effect by antagonizing the Ras pathway. The implications of our findings are significant. We establish that radiation sensitivity can be predicted based on gene expression profiles and we introduce a genomic approach to the identification of novel molecular markers of radiation sensitivity.

Carrier Proteins↗

PubMed2005

Classification of human tumors using gene expression profiles obtained after microarray analysis of fine-needle aspiration biopsy samples.

BACKGROUND: Gene expression profiling using gene-discovery, high-density microarray technologies is a powerful tool. One potential application is the development of tumor classifiers that predict the site of origin. For this technology to be relevant, however, it must be applicable to tumor biopsy samples, which most often are fine-needle aspiration biopsy (FNAB) samples. METHODS: Surgically resected tumors were sampled by FNAB using different gauge needles. A portion of the excised tumor was also collected. RNA samples were extracted using standard techniques and the quality and quantity of the RNA samples were measured for each sample. Thirteen representative FNAB samples and two representative tissue samples were submitted for microarray analysis and then subjected to a tumor classifier. RESULTS: Fourteen of 18 samples analyzed for quantity and quality of RNA yielded an adequate amount of RNA (> 1 microg total RNA). Tumor type contributed to the RNA yield because one of the four inadequate samples was retrieved from a patient with lobular carcinoma of the breast and the other three samples were retrieved from patients with retroperitoneal sarcomas. Of the 13 samples submitted for microarray analysis, 9 were classified correctly as to tumor type using a tissue-based tumor classifier. CONCLUSIONS: The authors demonstrated that FNABs reproducibly obtained an adequate amount of RNA for microarray analysis when a standardized collection procedure was used. Furthermore, the samples generated interpretable gene expression profiles that could be matched accurately with a tumor classifier established on tissue specimens. The current study showed that FNAB produced adequate material for microarray analysis when utilizing a standardized collection procedure.

Biopsy, Fine-Needle↗

PubMed2002

Naturally occurring amino acid polymorphisms in human immunodeficiency virus type 1 (HIV-1) Gag p7(NC) and the C-cleavage site impact Gag-Pol processing by HIV-1 protease.

Human immunodeficiency virus type 1 (HIV-1) protease activity is targeted at nine cleavage sites comprising different amino acid sequences in the viral Gag-Pol polyprotein. Amino acid polymorphisms in protease and in regions of Gag, particularly p7(NC) and the C-cleavage site between p2 and p7(NC), occur in natural variants of HIV-1 within infected patients. Studies were designed to examine the role of natural polymorphisms in protease and to identify determinants in Gag that modulate protease processing activity. Closely related Gag-Pol regions from an HIV-1-infected mother and two children were evaluated for processing in an inducible expression system, for protease activity on cleavage-site analogues, and for impact on replication by recombinant viruses. Gag-Pol regions displayed one of three processing phenotypes based on the appearance of Gag intermediates and accumulation of mature p24(CA). Gag-Pol regions that were processed rapidly to produce p24(CA) resulted in high-level replication by recombinant viruses, while slow-processing Gag-Pol variants resulted in recombinant viruses that replicated with reduced kinetics in both T cell lines and peripheral blood mononuclear cells. Direct impact by Gag sequences on processing by protease was assessed by construction of chimeric Gag-Pol regions and by site-directed mutagenesis. Optimal protease activity occurred when Gag and Pol regions were derived from the same gag-pol allele. Heterologous Gag regions generally diminished rates and extent of protease processing. Natural polymorphisms in novel positions in p7(NC) and the C-cleavage site have a dominant effect on protease processing activity. Accumulation of Gag products after processing at the C site appears to delay subsequent cleavage and production of mature p24(CA).

Alleles↗

PubMed2002

A simple method to improve probe set estimates from oligonucleotide arrays.

A popular commercially available oligonucleotide microarray technology employs sets of 25 base pair oligonucleotide probes for measurement of gene expression levels. A mathematical algorithm is required to compute an estimate of gene expression from the multiple probes. Previously proposed methods for summarizing gene expression data have either been substantially ad hoc or have relied on model assumptions that may be easily violated. Here we present a new algorithm for calculating gene expression from probe sets. Our approach is functionally related to leave-one-out cross-validation, a non-parametric statistical technique that is often applied in limited data situations. We illustrate this approach using data from our study seeking a molecular fingerprint of STAT3 regulated genes for early detection of human cancer.

Algorithms↗