Walter and Eliza Hall Institute of Medical Research

About: Walter and Eliza Hall Institute of Medical Research is a nonprofit organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Antigen & Immune system. The organization has 5012 authors who have published 10620 publications receiving 873561 citations.

AMG 176, a Selective MCL1 Inhibitor, Is Effective in Hematologic Cancer Models Alone and in Combination with Established Therapies.

Abstract: The prosurvival BCL2 family member MCL1 is frequently dysregulated in cancer. To overcome the significant challenges associated with inhibition of MCL1 protein-protein interactions, we rigorously applied small-molecule conformational restriction, which culminated in the discovery of AMG 176, the first selective MCL1 inhibitor to be studied in humans. We demonstrate that MCL1 inhibition induces a rapid and committed step toward apoptosis in subsets of hematologic cancer cell lines, tumor xenograft models, and primary patient samples. With the use of a human MCL1 knock-in mouse, we demonstrate that MCL1 inhibition at active doses of AMG 176 is tolerated and correlates with clear pharmacodynamic effects, demonstrated by reductions in B cells, monocytes, and neutrophils. Furthermore, the combination of AMG 176 and venetoclax is synergistic in acute myeloid leukemia (AML) tumor models and in primary patient samples at tolerated doses. These results highlight the therapeutic promise of AMG 176 and the potential for combinations with other BH3 mimetics. SIGNIFICANCE: AMG 176 is a potent, selective, and orally bioavailable MCL1 inhibitor that induces a rapid commitment to apoptosis in models of hematologic malignancies. The synergistic combination of AMG 176 and venetoclax demonstrates robust activity in models of AML at tolerated doses, highlighting the promise of BH3-mimetic combinations in hematologic cancers.See related commentary by Leber et al., p. 1511.This article is highlighted in the In This Issue feature, p. 1494.

The tumor suppressor PTEN is exported in exosomes and has phosphatase activity in recipient cells.

Abstract: Exosomes are microvesicles of endosomal origin that are secreted, and their contents (proteins, lipids, DNA, or microRNAs) can alter the physiological states of recipient cells. We demonstrated that phosphatase and tensin homolog deleted on chromosome 10 (PTEN), a tumor suppressor protein normally localized in the cytoplasm and nucleus, was secreted in exosomes. Secreted PTEN was internalized by recipient cells with resultant functional activity, which resulted in reduced phosphorylation of the serine and threonine kinase Akt and reduced cellular proliferation. PTEN secretion in exosomes required Ndfip1, an adaptor protein for members of the Nedd4 family of E3 ubiquitin ligases. Without Ndfip1, neither Nedd4-1 nor Nedd4-2 promoted the recruitment of PTEN into exosomes. In addition, lysine 13 within PTEN, which is required for its ubiquitination by Nedd4-1, was required for exosomal transport of PTEN. These results implicate Ndfip1 as a molecular regulator of the exosomal export of PTEN, with consequences for non-cell-autonomous PTEN activity. Thus, we suggest that the ability of PTEN to exert phosphatase activity beyond the cell in which it is produced has implications for PTEN function during development, health, and disease.

Series evaluation of Tweedie exponential dispersion model densities

Abstract: 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 theTweedie 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.