Conserved IKAROS-regulated genes associated with B-progenitor acute lymphoblastic leukemia outcome

Genetic alterations disrupting the transcription factor IKZF1 (encoding IKAROS) are associated with poor outcome in B lineage acute lymphoblastic leukemia (B-ALL) and occur in >70% of the high-risk BCR-ABL1+ (Ph+) and Ph-like disease subtypes. To examine IKAROS function in this context, we have developed novel mouse models allowing reversible RNAi-based control of Ikaros expression in established B-ALL in vivo. Notably, leukemias driven by combined BCR-ABL1 expression and Ikaros suppression rapidly regress when endogenous Ikaros is restored, causing sustained disease remission or ablation. Comparison of transcriptional profiles accompanying dynamic Ikaros perturbation in murine B-ALL in vivo with two independent human B-ALL cohorts identified nine evolutionarily conserved IKAROS-repressed genes. Notably, high expression of six of these genes is associated with inferior event-free survival in both patient cohorts. Among them are EMP1, which was recently implicated in B-ALL proliferation and prednisolone resistance, and the novel target CTNND1, encoding P120-catenin. We demonstrate that elevated Ctnnd1 expression contributes to maintenance of murine B-ALL cells with compromised Ikaros function. These results suggest that IKZF1 alterations in B-ALL leads to induction of multiple genes associated with proliferation and treatment resistance, identifying potential new therapeutic targets for high-risk disease

Series evaluation of Tweedie exponential dispersion model densities

Exponential dispersion models, which are linear exponential families with a dispersion parameter, are the prototype response distributions for generalized linear models. The Tweedie family comprises those exponential dispersion models with power mean-variance relationships. The normal, Poisson, gamma and inverse Gaussian distributions belong to the Tweedie family. Apart from these special cases, Tweedie distributions do not have density functions which can be written in closed form. Instead, the densities can be represented as infinite summations derived from series expansions. This article describes how the series expansions can be summed in an numerically efficient fashion. The usefulness of the approach is demonstrated, but full machine accuracy is shown not to be obtainable using the series expansion method for all parameter values. Derivatives of the density with respect to the dispersion parameter are also derived to facilitate maximum likelihood estimation. The methods are demonstrated on two data examples and compared with with Box-Cox transformations and extended quasi-likelihoood

Combining single and paired end RNAseq data for differential expression analyses

Combining RNA-seq data from different platforms should increase the power to detect differentially expressed genes, but may not be straightforward. Here we show how RUVs, a recently published method for removing unwanted variation and normalizing RNA-seq data, can combine the counts of single and paired end read libraries from formalin fixed, paraffin embedded tumor samples to permit differential expression analysis. Seven other intra- or inter-platform normalization methods are also described and the results are compared with those from RUVs