| 1. |
- Demissie, Meaza, et al.
(författare)
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Unequal group variances in microarray data analyses
- 2008
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 24:9, s. 1168-1174
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: In searching for differentially expressed (DE) genes in microarray data, we often observe a fraction of the genes to have unequal variability between groups. This is not an issue in large samples, where a valid test exists that uses individual variances separately. The problem arises in the small-sample setting, where the approximately valid Welch test lacks sensitivity, while the more sensitive moderated t-test assumes equal variance. Methods: We introduce a moderated Welch test (MWT) that allows unequal variance between groups. It is based on (i) weighting of pooled and unpooled standard errors and (ii) improved estimation of the gene-level variance that exploits the information from across the genes. Results: When a non-trivial proportion of genes has unequal variability, false discovery rate (FDR) estimates based on the standard t and moderated t-tests are often too optimistic, while the standard Welch test has low sensitivity. The MWT is shown to (i) perform better than the standard t, the standard Welch and the moderated t-tests when the variances are unequal between groups and (ii) perform similarly to the moderated t, and better than the standard t and Welch tests when the group variances are equal. These results mean that MWT is more reliable than other existing tests over wider range of data conditions. Availability: R package to perform MWT is available at http://www.meb.ki.se/similar to yudpaw Contact: yudi.pawitan@ki.se Supplementary information: Supplementary data are available at Bioinformatics online.
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| 2. |
- Setlur, Sunita R., et al.
(författare)
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Estrogen-dependent signaling in a molecularly distinct subclass of aggressive prostate cancer
- 2008
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Ingår i: Journal of the National Cancer Institute. - Oxford : Oxford University Press. - 0027-8874 .- 1460-2105. ; 100:11, s. 815-825
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The majority of prostate cancers harbor gene fusions of the 5'-untranslated region of the androgen-regulated transmembrane protease serine 2 (TMPRSS2) promoter with erythroblast transformation-specific transcription factor family members. The common fusion between TMPRESS2 and v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) is associated with a more aggressive clinical phenotype, implying the existence of a distinct subclass of prostate cancer defined by this fusion. METHODS: We used complementary DNA-mediated annealing, selection, ligation, and extension to determine the expression profiles of 6144 transcriptionally informative genes in archived biopsy samples from 455 prostate cancer patients in the Swedish Watchful Waiting cohort (1987-1999) and the United States-based Physicians(') Health Study cohort (1983-2003). A gene expression signature for prostate cancers with the TMPRSS2-ERG fusion was determined using partitioning and classification models and used in computational functional analysis. Cell proliferation and TMPRSS2-ERG expression in androgen receptor-negative (NCI-H660) prostate cancer cells after treatment with vehicle or estrogenic compounds were assessed by viability assays and quantitative polymerase chain reaction, respectively. All statistical tests were two-sided. RESULTS: We identified an 87-gene expression signature that distinguishes TMPRSS2-ERG fusion prostate cancer as a discrete molecular entity (area under the curve = 0.80, 95% confidence interval [CI] = 0.792 to 0.81; P < .001). Computational analysis suggested that this fusion signature was associated with estrogen receptor (ER) signaling. Viability of NCI-H660 cells decreased after treatment with estrogen (viability normalized to day 0, estrogen vs vehicle at day 8, mean = 2.04 vs 3.40, difference = 1.36, 95% CI = 1.12 to 1.62) or ERbeta agonist (ERbeta agonist vs vehicle at day 8, mean = 1.86 vs 3.40, difference = 1.54, 95% CI = 1.39 to 1.69) but increased after ERalpha agonist treatment (ERalpha agonist vs vehicle at day 8, mean = 4.36 vs 3.40, difference = 0.96, 95% CI = 0.68 to 1.23). Similarly, expression of TMPRSS2-ERG decreased after ERbeta agonist treatment (fold change over internal control, ERbeta agonist vs vehicle at 24 hours, NCI-H660, mean = 0.57- vs 1.0-fold, difference = 0.43-fold, 95% CI = 0.29- to 0.57-fold) and increased after ERalpha agonist treatment (ERalpha agonist vs vehicle at 24 hours, mean = 5.63- vs 1.0-fold, difference = 4.63-fold, 95% CI = 4.34- to 4.92-fold). CONCLUSIONS: TMPRSS2-ERG fusion prostate cancer is a distinct molecular subclass. TMPRSS2-ERG expression is regulated by a novel ER-dependent mechanism.
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| 3. |
- Wennmalm, Kristian, et al.
(författare)
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Gene expression in 16q is associated with survival and differs between Sørlie breast cancer subtypes
- 2007
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Ingår i: Genes, Chromosomes and Cancer. - : Wiley. - 1045-2257 .- 1098-2264. ; 46:1, s. 87-97
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Tidskriftsartikel (refereegranskat)abstract
- We have investigated the relationship between gene expression and chromosomal positions in 402 breast cancer patients. Using an overrepresentation approach based on Fisher's exact test, we identified disproportionate contributions of specific chromosomal positions to genes associated with survival. Our major finding is that the gene expression in the long arm of chromosome 16 stands out in its relationship to survival. This arm contributes 36 (18%) and 55 (11%) genes to lists negatively associated with recurrence-free survival (set to sizes 200 and 500). This is a highly disproportionate contribution from the 313 (2%) genes in this arm represented on the used Affymetrix U133A and B microarray platforms (Bonferroni corrected Fisher test: P < 2.2 x 10(-16)). We also demonstrate differential expression in 16q across tumor subtypes, which suggests that the ERBB2, basal, and luminal B tumors progress along a high grade-poor prognosis path, while luminal A and normal-like tumors progress along a low grade-good prognosis path, in accordance with a previously proposed model of tumor progression. We conclude that important biological information can be extracted from gene expression data in breast cancer by studying non-random connections between chromosomal positions and gene expression. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.
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