Converging or Crossing Curves: Untie the Gordian Knot or Cut it? Appropriate Statistics for Non-Proportional Hazards in Decitabine DACO-016 Study (AML)

Introduction: Among patients with acute myeloid leukemia (AML), the DACO-016 randomized study showed reduction in mortality for decitabine [Dacogen® (DAC), Eisai Inc., Woodcliff Lake, NJ, USA] compared with treatment choice (TC): at primary analysis the hazard ratio (HR) was 0.85 (95% confidence interval 0.69-1.04; stratified log-rank P=0.108). With two interim analyses, two-sided alpha was adjusted to 0.0462. With 1-year additional follow-up the HR reached 0.82 (nominal P=0.0373). These data resulted in approval of DAC in the European Union, though not in the United States. Though pre-specified, the log-rank test could be considered not optimal to assess the observed survival difference because of the non-proportional hazard nature of the survival curves. Methods: We applied the Wilcoxon test as a sensitivity analysis. Patients were randomized to DAC (N=242) or TC (N=243). One-hundred and eight (44.4%) patients in the TC arm and 91 (37.6%) patients in the DAC arm selectively crossed over to subsequent disease modifying therapies at progression, which might impact the survival beyond the median with resultant converging curves (and disproportional hazards). Results: The stratified Wilcoxon test showed a significant improvement in median (CI 95%) overall survival with DAC [7.7 (6.2; 9.2) months] versus TC [5.0 (4.3; 6.3) months; P=0.0458]. Conclusion: Wilcoxon test indicated significant increase in survival for DAC versus TC compared to log-rank test. Funding: Janssen-Cilag GmbH

Modeling the asymmetric evolution of a mouse and rat-specific microRNA gene cluster intron 10 of the Sfmbt2 gene

<p>Abstract</p> <p>Background</p> <p>The total number of miRNA genes in a genome, expression of which is responsible for the miRNA repertoire of an organism, is not precisely known. Moreover, the question of how new miRNA genes arise during evolution is incompletely understood. Recent data in humans and opossum indicate that retrotranspons of the class of short interspersed nuclear elements have contributed to the growth of microRNA gene clusters.</p> <p>Method</p> <p>We studied a large miRNA gene cluster in intron 10 of the mouse Sfmbt2 gene using bioinformatic tools.</p> <p>Results</p> <p>Mice and rats are unique to harbor a 55-65 Kb DNA sequence in intron 10 of the Sfmbt2 gene. This intronic region is rich in regularly repeated B1 retrotransposons together with inverted self-complementary CA/TG microsatellites. The smallest repeats unit, called MSHORT1 in the mouse, was duplicated 9 times in a tandem head-to-tail array to form 2.5 Kb MLONG1 units. The center of the mouse miRNA gene cluster consists of 13 copies of MLONG1. BLAST analysis of MSHORT1 in the mouse shows that the repeat unit is unique for intron 10 of the Sfmbt2 gene and suggest a dual phase model for growth of the miRNA gene cluster: arrangment of 10 MSHORT1 units into MLONG1 and further duplication of 13 head-to-tail MLONG1 units in the center of the miRNA gene cluster. Rats have a similar arrangment of repeat units in intron 10 of the Sfmbt2 gene. The discrepancy between 65 miRNA genes in the mouse cluster as compared to only 1 miRNA gene in the corresponding rat repeat cluster is ascribed to sequence differences between MSHORT1 and RSHORT1 that result in lateral-shifted, less-stable miRNA precursor hairpins for RSHORT1.</p> <p>Conclusion</p> <p>Our data provides new evidence for the emerging concept that lineage-specific retroposons have played an important role in the birth of new miRNA genes during evolution. The large difference in the number of miRNA genes in two closely related species (65 versus 1, mice versus rats) indicates that this species-specific evolution can be a rapid process.</p

Statistical Methods for Analyzing DNA and PamChip Microarray Data

In this research, we have addressed several questions on bioinformatics from statistical perspective. More specifically, we have developed several statistical methodologies to analyze spikes-in cDNA, peptide microarray data (based on PamGene technology) and human microRNA data (Alu-Repeats).For the preprocessing of spotted microarrays, different methods have been described. Overviews are given, e.g., by Leung and Cavalieri (2003), Quackenbush (2002), and Bilban (2002). In general, the preprocessing of spotted microarrays largely depends on the calculation of the log-ratios of the measured intensities. However, for some analyses, having access to absolute expression levels seems more suitable (Kerr et al., 2000). ANOVA models for absolute expression levels have been proposed, e.g., byWolfinger et al. (2001). We propose the use of external reference RNAs (also known as spike-in controls or spikes) to preprocess cDNA microarray data. We model the measured intensities as a function of the concentration in a statistical manner. This prompts us to use a preprocessing model proposed by Engelen et al. (2006) in the context of nonlinear mixed-effects models. In this way,we should be able to obtain the asymptotic prediction intervals for the estimated RNA sample for each gene. The novelty of this approach is that the uncertainty of the parameters of the preprocessing model can directly be ascertained. Furthermore, it also accounts for the various sources of variability associated with microarray experiments (Thilakarathne et al., 2009). Microarray experiments are subject to certain limitations, namely, slide-cost and time. The outcome is that the biological sample size and the technical replication are often very small that the application of conventional statistical techniques needs to be refined. To this end, we specifically target microarray experiments with a single intensity measurement per biological condition. Such datasets may arise in singlereplicated experiments (e.g. prokaryotes) or time course ones (Peeters et al., 2004) but they can also result from preprocessing methods (Thilakarathne et al., 2009). The model we proposed to handle this type of situation is a general one and one that can be developed to also handle more complex experimental designs (Thilakarathne et al., 2011a).MicroRNAs are 19 to 22 nucleotide long non-coding RNAs that influence gene expression by repressing translation or causing mRNA degradation (Bartel, 2004; Valencia-Sanchez et al., 2006). A recent study shows that the microRNA cluster on the human chromosome 19 (C19MC) is linked to Alu repeats which facilitated the expansion of C19MC. In this study, we try to show that the repeat elements in the microRNA cluster in human chromosome 19 are different from the repeat elements on the same chromosome. To test this, we compared the mean, median and standard deviation of the length of the repeat elements in C19MC to the distribution of the same characteristics in 1250 randomly selected windows of size 100Kb (Lehnert et al., 2009). Protein kinase plays an important role in oncology research. They are the enzymes that modify other proteins by adding a phosphate group (phosphorylation). Phosphorylation activates or deactivates a lot of protein enzymes, causing or preventing the mechanisms of diseases such as cancer and diabetes. A deregulated kinase activity can frequently cause diseases such as cancer. Therefore, inhibiting the kinase activity is important for controlling the activity of cancer cells. For instance, protein tyrosine kinases constitute a sizable class of drug targets in oncology (Versele et al., 2009). Identifying the responsive cell lines to a particular kinase inhibitor is therefore an important task in this genre of studies. In a protein kinase assay, the kinetic behavior of kinase can be monitored in real time using the PamGene technology (PamStation) for a set of cell lines. To analyze the PamChip microarray data, we propose a flexible semi-parametric mixed model. This approach would allow for the estimation of rate of phosphorylation (velocity) as a function of time, together with pointwise confidence intervals. Our model makes it possible to test whether the velocity of kinase inhibition differs from responding to non-responding cell lines. This can be tested at any time point and for entire time series profiles (Thilakarathne et al., 2011b).status: publishe

A nonlinear mixed-effects model for estimating calibration intervals for unknown concentrations in two-color microarray data with spike-ins

In this study, we propose a calibration method for preprocessing spiked-in microarray experiments based on nonlinear mixed-effects models. This method uses a spike-in calibration curve to estimate normalized absolute expression values. Moreover, using the asymptotic properties of the calibration estimate, 100(1-alpha)% confidence intervals for the estimated expression values can be constructed. Simulations are used to show that the approximations on which the construction of the confidence intervals are based are sufficiently accurate to reach the desired coverage probabilities. We illustrate applicability of our method, by estimating the normalized absolute expression values together with the corresponding confidence intervals for two publicly available cDNA microarray experiments (Hilson et al., 2004; Smets et al., 2008). This method can easily be adapted to preprocess one-color oligonucleotide microarray data with a slight adjustment to the mixed model

Evidence for co-evolution between human microRNAs and Alu-repeats

This paper connects Alu repeats, the most abundant repetitive elements in the human genome and microRNAs, small RNAs that alter gene expression at the post-transcriptional level. Base-pair complementarity could be demonstrated between the seed sequence of a subset of human microRNAs and Alu repeats that are integrated parallel (sense) in mRNAs. The most common target site coincides with the evolutionary most conserved part of Alu. A primate-specific gene cluster on chromosome 19 encodes the majority of miRNAs that target the most conserved sense Alu site. The individual miRNA genes within this cluster are flanked by an Alu-LINE signature, which has been duplicated with the clustered miRNA genes. Gene duplication events in this locus are supported by comparing repeat length variations of the LINE elements within the cluster with those in the rest of the chromosome. Thus, a dual relationship exists between an evolutionary young miRNA cluster and their Alu targets that may have evolved in the same time window. One hypothesis for this dual relationship is that these miRNAs could protect against too high rates of duplicative transposition, which would destroy the genome.status: publishe

Evidence for Co-Evolution between Human MicroRNAs and Alu-Repeats

This paper connects Alu repeats, the most abundant repetitive elements in the human genome and microRNAs, small RNAs that alter gene expression at the post-transcriptional level. Base-pair complementarity could be demonstrated between the seed sequence of a subset of human microRNAs and Alu repeats that are integrated parallel (sense) in mRNAs. The most common target site coincides with the evolutionary most conserved part of Alu. A primate-specific gene cluster on chromosome 19 encodes the majority of miRNAs that target the most conserved sense Alu site. The individual miRNA genes within this cluster are flanked by an Alu-LINE signature, which has been duplicated with the clustered miRNA genes. Gene duplication events in this locus are supported by comparing repeat length variations of the LINE elements within the cluster with those in the rest of the chromosome. Thus, a dual relationship exists between an evolutionary young miRNA cluster and their Alu targets that may have evolved in the same time window. One hypothesis for this dual relationship is that thes

The use of semiparametric mixed models to analyze PamChip® peptide array data: an application to an oncology experiment

Motivation: Phosphorylation by protein kinases is a central theme in biological systems. Aberrant protein kinase activity has been implicated in a variety of human diseases (e.g. cancer). Therefore, modulation of kinase activity represents an attractive therapeutic approach for the treatment of human illnesses. Thus, identification of signature peptides is crucial for protein kinase targeting and can be achieved by using PamChip (R) microarray technology. We propose a flexible semiparametric mixed model for analyzing PamChip (R) data. This approach enables the estimation of the phosphorylation rate (Velocity) as a function of time together with pointwise confidence intervals. Results: Using a publicly available dataset, we show that our model is capable of adequately fitting the kinase activity profiles and provides velocity estimates over time. Moreover, it allows to test for differences in the velocity of kinase inhibition between responding and non-responding cell lines. This can be done at individual time point as well as for the entire velocity profile