Markus Riester

Highly Specific Gene Silencing by Artificial MicroRNAs in Arabidopsis

Plant microRNAs (miRNAs) affect only a small number of targets with high sequence complementarity, while animal miRNAs usually have hundreds of targets with limited complementarity. We used artificial miRNAs (amiRNAs) to determine whether the narrow action spectrum of natural plant miRNAs reflects only intrinsic properties of the plant miRNA machinery or whether it is also due to past selection against natural miRNAs with broader specificity. amiRNAs were designed to target individual genes or groups of endogenous genes. Like natural miRNAs, they had varying numbers of target mismatches. Previously determined parameters of target selection for natural miRNAs could accurately predict direct targets of amiRNAs. The specificity of amiRNAs, as deduced from genome-wide expression profiling, was as high as that of natural plant miRNAs, supporting the notion that extensive base pairing with targets is required for plant miRNA function. amiRNAs make an effective tool for specific gene silencing in plants, especially when several related, but not identical, target genes need to be downregulated. We demonstrate that amiRNAs are also active when expressed under tissue-specific or inducible promoters, with limited nonautonomous effects. The design principles for amiRNAs have been generalized and integrated into a Web-based tool (http://wmd.weigelworld.org).

A Differentiation-Based Phylogeny of Cancer Subtypes

Histopathological classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. In this paper, we introduce a novel computational algorithm to rank tumor subtypes according to the dissimilarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia, breast cancer and liposarcoma subtypes and then apply it to a broader group of sarcomas. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors.

Reconstruction of pedigrees in clonal plant populations.

We present a Bayesian method for the reconstruction of pedigrees in clonal populations using co-dominant genomic markers such as microsatellites and single nucleotide polymorphisms (SNPs). The accuracy of the algorithm is demonstrated for simulated data. We show that the joint estimation of parameters of interest such as the rate of self-fertilization is possible with high accuracy even with marker panels of moderate power. Classical methods can only assign a very limited number of statistically significant parentages in this case and would therefore fail. Statistical confidence is estimated by Markov Chain Monte Carlo (MCMC) sampling. The method is implemented in a fast and easy to use open source software that scales to large datasets with many thousand individuals.

Distance in cancer gene expression from stem cells predicts patient survival

The degree of histologic cellular differentiation of a cancer has been associated with prognosis but is subjectively assessed. We hypothesized that information about tumor differentiation of individual cancers could be derived objectively from cancer gene expression data, and would allow creation of a cancer phylogenetic framework that would correlate with clinical, histologic and molecular characteristics of the cancers, as well as predict prognosis. Here we utilized mRNA expression data from 4,413 patient samples with 7 diverse cancer histologies to explore the utility of ordering samples by their distance in gene expression from that of stem cells. A differentiation baseline was obtained by including expression data of human embryonic stem cells (hESC) and human mesenchymal stem cells (hMSC) for solid tumors, and of hESC and CD34+ cells for liquid tumors. We found that the correlation distance (the degree of similarity) between the gene expression profile of a tumor sample and that of stem cells orients cancers in a clinically coherent fashion. For all histologies analyzed (including carcinomas, sarcomas, and hematologic malignancies), patients with cancers with gene expression patterns most similar to that of stem cells had poorer overall survival. We also found that the genes in all undifferentiated cancers of diverse histologies that were most differentially expressed were associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes. Thus, a stem cell-oriented phylogeny of cancers allows for the derivation of a novel cancer gene expression signature found in all undifferentiated forms of diverse cancer histologies, that is competitive in predicting overall survival in cancer patients compared to previously published prediction models, and is coherent in that gene expression was associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes associated with regulation of the multicellular state.

Peroxisome proliferator-activated receptor gamma (PPARG) gene amplifications in urothelial carcinoma (UC).

279 Background: PPARy is a steroid hormone receptor encoded by the PPARG gene (Chr 3p25). Data are conflicting regarding its role in UC. Some studies suggest activation has antiproliferative effects, and preclinical data indicate a potential role for PPARy agonists in cancer treatment. However, recent findings show that use of the PPARy agonist thiazolidinones (TZD) in type 2 diabetes is associated with increased risk of developing UC. To define the spectrum of PPARy aberrations in UC, DNA was evaluated to detect copy number gains/losses (CNG/CNL) of PPARG in three clinically annotated UC cohorts. METHODS Cohort A contained 97 patients with high-grade UC. Cohort B contained 94 patients with primary bladder tumors who developed metastatic UC. Cohort C had 33 patients with metastatic UC, including both primary and paired metastatic tissue for 14 patients. DNA from all tumors was subjected to copy number analysis (array comparative genomic hybridization for cohorts A and B and molecular inversion probe array for cohort C) to identify focal areas of CNG/CNL. Statistical analysis using RAE (cohort A) or GISTIC 2.0 (with CNG defined as log2 copy number ratio >0.8) in cohorts B/C was used to identify significant alterations. RESULTS The region containing PPARG showed CNG in 7%, 10%, and 15% of cohorts A, B and C, respectively. In cohort A, RAE p value was 5.36E-05 at the amplicon containing PPARG. In cohorts B and C GISTIC 2.0 q values were 9.75E-12 and 6.04E-10. PPARG CNG had no impact on survival in the cohorts with available survival data (A and B). Paired biopsies from primary and metastatic sites showed discordance in PPARG CNG in 2 cases. In 1 case CNG was present in the metastasis but not in the primary and in 1 case the opposite occurred. CONCLUSIONS PPARG shows significant CNGs in 7-15% of UC. Notably the RAF1 locus (a known oncogene) lies close to PPARG and could also represent the driver alteration within this amplicon. Investigation of the functional relevance of these CNGs is clearly warranted as it may be a driver of bladder cancer growth and progression. Determination of PPARG CNG status in UCs in a TZD treated population may provide mechanistic insights into the increased incidence of UC in patients treated with these agents.

Genomic characterization of metastatic urothelial carcinoma.

247 Background: The genetic profile of primary urothelial carcinoma (UC) has been well documented but no reports analyze specific chromosomal alterations in metastatic disease. We performed molecular inversion probe array (MIP) analysis to compare chromosomal gains or losses in metastatic and primary UC samples. METHODS 33 samples of metastatic UC and 30 primary samples were analyzed from 48 patients (pts), of which paired primary and metastatic tissue was available for 14 pts. DNA from all samples was subjected to molecular inversion probe array analysis to identify focal areas of copy number gain (CNG) or loss (CNL). We focused this analysis on 21 genes from signaling pathways known to be of interest in UC (Table). CNG or CNL was defined as a log2 copy number ratio ≥ 0.8 or ≤ -0.8. GISTIC 2.0 was used to identify significantly altered regions. RESULTS In the loci analyzed, there were significantly more alterations in metastases than primary samples (8.4% vs 4.3% p=0.002). In particular, there was a significantly higher frequency of E2F3CNG in metastases (27% vs 7% p=0.046). There was frequent discordance in alterations when comparing primary and metastatic tissue from the same patients: 7 of 14 pts harbored potentially oncogenic CNG/CNL in their metastases that were not present in the primary. CONCLUSIONS More alterations in UC-relevant genes were identified in metastases compared with primary tumors, in keeping with the multistep model of cumulative genetic change in cancer progression. More frequent CNG of the E2F3 gene was noted and may represent a mechanism of UC progression. Frequent discordance in alterations between primaries and metastases may be of significant clinical relevance in the future when selecting patients for appropriate molecularly targeted therapy. [Table: see text].