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Jeffrey Trent

Publications and source records attributed to Jeffrey Trent.

13 recordsLinked to original sources
PubMed2025

Optimizing participant and community engagement in cancer genomic sequencing research.

PURPOSE: We describe strategies implemented across research centers of the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network to optimize engagement of participants and communities in cancer genomics research. We also present consensus definitions of engagement and engagement optimization, informed by our shared experiences in the Network. METHODS: Key informant interviews and a document review identified engagement and optimization strategies across PE-CGS research centers. Findings were synthesized using qualitative content analysis. Consensus on definitions of engagement and optimization were developed through iterative review by PE-CGS members. RESULTS: PE-CGS research centers adopted tailored strategies based on community needs and scientific gaps. Engagement strategies included community-based efforts (eg, advisory boards and newsletters) and participant-focused approaches (eg, enhanced informed consent and decision support tools). Optimization strategies leveraged scientific methods (eg, randomized controlled trials and surveys) to evaluate engagement. Engagement was described as the sustained and meaningful interactions between researchers, participants, and communities. Optimization was described as the application of scientific methods to refine and improve engagement and research processes and outcomes. CONCLUSION: Engagement and optimization strategies have informed research planning, conduct, and dissemination across PE-CGS. These approaches and definitions provide a foundation for developing evidence-based practices to strengthen participant and community involvement in cancer genomics research.

Humans↗
PubMed2004

Hereditary prostate cancer in Finland: fine-mapping validates 3p26 as a major predisposition locus.

In a recent genome-wide linkage (GWL) analysis of Finnish families at high risk for prostate cancer, we found two novel putative susceptibility loci at 3p25-p26 and 11q14. Here, we report the fine-mapping of these two critical regions at high resolution with 39 microsatellite markers in 16 families, including multiplex families that were not used in the GWL scan. The maximum multipoint HLOD was 3.39 at 3p26 and 1.42 at 11q14. The highest LOD scores were seen around markers D3S1270 and D3S4559 (alpha=0.89), covering approximately two megabases. The two known genes in this region CHL1 (cell adhesion molecule with homology to L1CAM) and CNTN6 (contactin 6) were screened for exonic mutations in the families showing the strongest linkage, but no disease-segregating sequence variants were observed. The recombination map pointed to a region proximal to the area of best linkage, suggesting that more genes may need to be investigated as candidates. These results provide strong evidence for the existence of a prostate cancer susceptibility gene at 3p26 in Finnish prostate cancer families. This locus has not been strongly linked with hereditary prostate cancer in other populations. However, the mildly positive 3p LOD scores in a recent GWL analysis of patients from the United States suggest that the locus may also be important in other populations.

Chromosome Mapping↗
PubMed2004

Genome-wide scanning for linkage in Finnish breast cancer families.

Only a proportion of breast cancer families has germline mutations in the BRCA1 or BRCA2 genes, suggesting the presence of additional susceptibility genes. Finding such genes by linkage analysis has turned out to be difficult due to the genetic heterogeneity of the disease, phenocopies and incomplete penetrance of the mutations. Isolated populations may be helpful in reducing the level of genetic heterogeneity and in providing useful starting points for further genetic analyses. Here, we report results from a genome-wide linkage analysis of 14 high-risk breast cancer families from Finland. These families tested negative for BRCA1 and BRCA2 germline mutations and showed no linkage to the 13q21 region, recently proposed as an additional susceptibility locus. Suggestive linkage was seen at marker D2S364 (2q32) with a parametric two-point LOD score of 1.61 (theta=0), and an LOD score of 2.49 in nonparametric analyses. Additional genotyping of a 40 cM chromosomal region surrounding the region of interest yielded a maximum parametric two-point LOD score of 1.80 (theta=0) at D2S2262 and a nonparametric LOD score of 3.11 at an adjacent novel marker 11291M1 in BAC RP11-67G7. A nonparametric multipoint LOD score of 3.20 was seen at 11291M1 under the assumption of dominant inheritance. While not providing proof of linkage considering the small number of families and large number of laboratory and statistical analyses performed, these results warrant further studies of the 2q32 chromosomal region as a candidate breast cancer susceptibility locus. Both linkage and association studies are likely to be useful, particularly in other isolated populations.

Base Sequence↗
PubMed2003

Identification and characterization of mouse Rab32 by mRNA and protein expression analysis.

Rab proteins, a subfamily of the ras superfamily, are low molecular weight GTPases involved in the regulation of intracellular vesicular transport. Cloning of human RAB32 was recently described. Presently, we report the cloning and characterization of the mouse homologue of Rab32. We show that murine Rab32 exhibits a ubiquitous expression pattern, with tissue-specific variation in expression level. Three cell types with highly specialized organelles, melanocytes, platelets and mast cells, exhibit relatively high level of Rab32. We show that in murine amelanotic in vitro transformed melanocytes as well as in human amelanotic metastatic melanoma cell lines, the expression of Rab32 is markedly reduced or absent, in parallel with the loss of expression of two key enzymes for the production of melanin, tyrosinase and Tyrp1. Therefore, in both mouse and human systems, the expression of Rab32 correlates with the expression of genes involved in pigment production. However, in melanoma samples, amelanotic due to a mutation in the tyrosinase gene, the expression of Rab32 remains at levels comparable to those observed in pigmented melanoma samples. Finally, we observed co-localization of Rab32 and the melanosomal proteins, Tyrp1 and Dct, indicating an association of Rab32 with melanosomes. Based on these data, we propose the inclusion of Rab32 to the so-called melanocyte/platelet family of Rab proteins.

Animals↗
PubMed2003

Localization of a novel melanoma susceptibility locus to 1p22.

Over the past 20 years, the incidence of cutaneous malignant melanoma (CMM) has increased dramatically worldwide. A positive family history of the disease is among the most established risk factors for CMM; it is estimated that 10% of CMM cases result from an inherited predisposition. Although mutations in two genes, CDKN2A and CDK4, have been shown to confer an increased risk of CMM, they account for only 20%-25% of families with multiple cases of CMM. Therefore, to localize additional loci involved in melanoma susceptibility, we have performed a genomewide scan for linkage in 49 Australian pedigrees containing at least three CMM cases, in which CDKN2A and CDK4 involvement has been excluded. The highest two-point parametric LOD score (1.82; recombination fraction [theta] 0.2) was obtained at D1S2726, which maps to the short arm of chromosome 1 (1p22). A parametric LOD score of 4.65 (theta=0) and a nonparametric LOD score of 4.19 were found at D1S2779 in nine families selected for early age at onset. Additional typing yielded seven adjacent markers with LOD scores >3 in this subset, with the highest parametric LOD score, 4.95 (theta=0) (nonparametric LOD score 5.37), at D1S2776. Analysis of 33 additional multiplex families with CMM from several continents provided further evidence for linkage to the 1p22 region, again strongest in families with the earliest mean age at diagnosis. A nonparametric ordered sequential analysis was used, based on the average age at diagnosis in each family. The highest LOD score, 6.43, was obtained at D1S2779 and occurred when the 15 families with the earliest ages at onset were included. These data provide significant evidence of a novel susceptibility gene for CMM located within chromosome band 1p22.

Adolescent↗
PubMed2003

Molecular classification of familial non-BRCA1/BRCA2 breast cancer.

In the decade since their discovery, the two major breast cancer susceptibility genes BRCA1 and BRCA2, have been shown conclusively to be involved in a significant fraction of families segregating breast and ovarian cancer. However, it has become equally clear that a large proportion of families segregating breast cancer alone are not caused by mutations in BRCA1 or BRCA2. Unfortunately, despite intensive effort, the identification of additional breast cancer predisposition genes has so far been unsuccessful, presumably because of genetic heterogeneity, low penetrance, or recessive/polygenic mechanisms. These non-BRCA1/2 breast cancer families (termed BRCAx families) comprise a histopathologically heterogeneous group, further supporting their origin from multiple genetic events. Accordingly, the identification of a method to successfully subdivide BRCAx families into recognizable groups could be of considerable value to further genetic analysis. We have previously shown that global gene expression analysis can identify unique and distinct expression profiles in breast tumors from BRCA1 and BRCA2 mutation carriers. Here we show that gene expression profiling can discover novel classes among BRCAx tumors, and differentiate them from BRCA1 and BRCA2 tumors. Moreover, microarray-based comparative genomic hybridization (CGH) to cDNA arrays revealed specific somatic genetic alterations within the BRCAx subgroups. These findings illustrate that, when gene expression-based classifications are used, BRCAx families can be grouped into homogeneous subsets, thereby potentially increasing the power of conventional genetic analysis.

Adult↗
PubMed2003

Integration of genomic technologies for accelerated cancer drug development.

New technologies have greatly increased the scientist's ability to investigate complex molecular interactions that occur in cancer development and to identify genetic alterations and drug targets. However, these new capabilities have not accelerated drug development efforts; rather, they may be contributing to increased research and development costs because the large number of new drug targets discovered through genomics need to be investigated in great detail to characterize their putative functional involvement in the disease process. One solution to this bottleneck in functional analysis is the use of high-throughput technologies to produce efficient processes that can rapidly handle the large flood of information at every stage of disease. This review examines the use of new and emerging DNA, tissue, and live-cell transfection microarray technologies that can be used to discover, validate, and translate information resulting from the completion of the Human Genome Project.

Biotechnology↗
PubMed2002

Gene expression signature of benign prostatic hyperplasia revealed by cDNA microarray analysis.

BACKGROUND: Despite the high prevalence of benign prostatic hyperplasia (BPH) in the aging male, little is known regarding the etiology of this disease. A better understanding of the molecular etiology of BPH would be facilitated by a comprehensive analysis of gene expression patterns that are characteristic of benign growth in the prostate gland. Since genes differentially expressed between BPH and normal prostate tissues are likely to reflect underlying pathogenic mechanisms involved in the development of BPH, we performed comparative gene expression analysis using cDNA microarray technology to identify candidate genes associated with BPH. METHODS: Total RNA was extracted from a set of 9 BPH specimens from men with extensive hyperplasia and a set of 12 histologically normal prostate tissues excised from radical prostatectomy specimens. Each of these 21 RNA samples was labeled with Cy3 in a reverse transcription reaction and cohybridized with a Cy5 labeled common reference sample to a cDNA microarray containing 6,500 human genes. Normalized fluorescent intensity ratios from each hybridization experiment were extracted to represent the relative mRNA abundance for each gene in each sample. Weighted gene and random permutation analyses were performed to generate a subset of genes with statistically significant differences in expression between BPH and normal prostate tissues. Semi-quantitative PCR analysis was performed to validate differential expression. RESULTS: A subset of 76 genes involved in a wide range of cellular functions was identified to be differentially expressed between BPH and normal prostate tissues. Semi-quantitative PCR was performed on 10 genes and 8 were validated. Genes consistently upregulated in BPH when compared to normal prostate tissues included: a restricted set of growth factors and their binding proteins (e.g. IGF-1 and -2, TGF-beta3, BMP5, latent TGF-beta binding protein 1 and -2); hydrolases, proteases, and protease inhibitors (e.g. neuropathy target esterase, MMP2, alpha-2-macroglobulin); stress response enzymes (e.g. COX2, GSTM5); and extracellular matrix molecules (e.g. laminin alpha 4 and beta 1, chondroitin sulfate proteoglycan 2, lumican). Genes consistently expressing less mRNA in BPH than in normal prostate tissues were less commonly observed and included the transcription factor KLF4, thrombospondin 4, nitric oxide synthase 2A, transglutaminase 3, and gastrin releasing peptide. CONCLUSIONS: We identified a diverse set of genes that are potentially related to benign prostatic hyperplasia, including genes both previously implicated in BPH pathogenesis as well as others not previously linked to this disease. Further targeted validation and investigations of these genes at the DNA, mRNA, and protein levels are warranted to determine the clinical relevance and possible therapeutic utility of these genes.

Adult↗
PubMed2002

Germline alterations of the RNASEL gene, a candidate HPC1 gene at 1q25, in patients and families with prostate cancer.

The RNASEL gene (2',5'-oligoisoadenylate-synthetase dependent) encodes a ribonuclease that mediates the antiviral and apoptotic activities of interferons. The RNASEL gene maps to the hereditary-prostate-cancer (HPC)-predisposition locus at 1q24-q25 (HPC1) and was recently shown to harbor truncating mutations in two families with linkage to HPC1. Here, we screened for RNASEL germline mutations in 66 Finnish patients with HPC, and we determined the frequency of the changes in the index patients from 116 families with HPC, in 492 patients with unselected prostate cancer (PRCA), in 223 patients with benign prostatic hyperplasia (BPH), and in 566 controls. A truncating mutation, E265X, was found in 5 (4.3%) of the 116 patients from families with HPC. This was significantly higher (odds ratio [OR] =4.56; P=.04) than the frequency of E265X in controls (1.8%). The highest mutation frequency (9.5%) was found in patients from families with four or more affected members. Possible segregation was detected only in a single family. However, the median age at disease onset for E265X carriers was 11 years less than that for noncarriers in the same families. In addition, of the four missense variants found, R462Q showed an association with HPC (OR=1.96; P=.07). None of the variants showed any differences between controls and either patients with BPH or patients with PRCA. We conclude that, although RNASEL mutations do not explain disease segregation in Finnish families with HPC, the variants are enriched in families with HPC that include more than two affected members and may also be associated with the age at disease onset. This suggests a possible modifying role in cancer predisposition. The impact that the RNASEL sequence variants have on PRCA burden at the population level seems small but deserves further study.

Age of Onset↗
PubMed2002

Stress-gene induction by low-dose gamma irradiation.

Using a human myeloid tumor cell line (ML-1), we detected induction of mRNA expression of several stress-responsive genes by doses of gamma rays as low as 2 cGy. For instance, the dose response for induction of CIP1/WAF1 and GADD45 appears to be linear over the range of 2 to 50 cGy and shows no evidence of a threshold for induction. Although 2 and 5 cGy exposures did not result in any detectable reduction in cloning efficiency nor in increased apoptosis in ML-1 cells, these exposures did produce a brief cell-cycle delay. We also used fluorescent cDNA microarray hybridization to investigate transcriptional stress responses following low doses of gamma rays and to identify additional radiation-responsive genes for inclusion in a stress-specific microarray we are developing. These studies provide insight into the molecular responses to physiologically relevant doses, which cannot necessarily be extrapolated from high-dose studies. The use of high throughput arrays will allow the identification of multiple stress-responsive genes that are radiation inducible in a variety of cell types and tissues. The expectation is that transcriptional stress responses will provide a molecular approach to monitoring for radiation exposure and detecting interindividual differences.

Gamma Rays↗
PubMedDate not supplied

Effects of HIV-1 Nef on cellular gene expression profiles.

The early human immunodeficiency virus (HIV) accessory protein Nef makes an important contribution to virulence, but the mechanisms by which Nef influences pathogenesis remain unclear. Many well-studied effects of Nef, like CD4 and class I MHC downregulation, occur posttranslationally. However, Nef has the potential to affect gene expression by interfering with cell signaling pathways and by virtue of structural features such as the Pro-X-X-Pro motif, which may interact with src homology region-3 domains of src-like kinases. We used a cDNA microarray screening strategy to identify cellular genes whose steady state transcriptional levels may be affected by Nef. We generated HeLa cell lines expressing wild-type or mutant HIV-1 nef protein sequences. Using cDNA microarray technology, we compared the patterns of cellular gene expression in the various cell lines to the pattern in non-Nef-expressing HeLa cells. By matching the patterns of cellular gene expression in HeLa cell lines expressing various Nefs with that of parental HeLa cells, we identified several cellular genes whose expression was modulated differentially by Nef and its mutants. We confirmed the differential expression of selected genes by RNA filter blotting. Genes expressed at higher levels included proteases, transcription factors, protein kinases, nuclear import/export proteins, adaptor molecules and cyclins, some of which have previously been implicated as being important for HIV replication and pathogenesis. The results indicate that Nef expression can alter the expression of cellular genes and suggest that this alteration in cellular gene expression may serve to optimize the cell to support the subsequent stages of viral replication.

Gene Expression↗
PubMedDate not supplied

Noise factor analysis for cDNA microarrays.

A microarray-image model is used that takes into account many factors, including spot morphology, signal strength, background fluorescent noise, and shape and surface degradation. The model yields synthetic images whose appearance and quality reflect that of real microarray images. The model is used to link noise factors to the fidelity of signal extraction with respect to a standard image-extraction algorithm. Of particular interest is the identification of the noise factors and their interactions that significantly degrade the ability to accurately detect the true gene-expression signal. This study uses statistical criteria in conjunction with the simulation of various noise conditions to better understand the noise influence on signal extraction for cDNA microarray images. It proposes a paradigm that is implemented in software. It specifically considers certain kinds of noise in the noise model and sets these at certain levels; however, one can choose other types of noise or use different noise levels. In sum, it develops a statistical package that can work in conjunction with the existing image simulation toolbox.

Algorithms↗