The PI3K/mTOR inhibitor PF-04691502 induces apoptosis and inhibits microenvironmental signaling in CLL and the Eµ-TCL1 mouse model.

Current treatment strategies for chronic lymphocytic leukemia (CLL) involve a combination of conventional chemotherapeutics, monoclonal antibodies, and targeted signaling inhibitors. However, CLL remains largely incurable, with drug resistance and treatment relapse a common occurrence, leading to the search for novel treatments. Mechanistic target of rapamycin (mTOR)-specific inhibitors have been previously assessed but their efficacy is limited due to a positive feedback loop via mTOR complex 2 (mTORC2), resulting in activation of prosurvival signaling. In this study, we show that the dual phosphatidylinositol 3-kinase (PI3K)/mTOR inhibitor PF-04691502 does not induce an mTORC2 positive feedback loop similar to other PI3K inhibitors but does induce substantial antitumor effects. PF-04691502 significantly reduced survival coincident with the induction of Noxa and Puma, independently of immunoglobulin heavy chain variable region mutational status, CD38, and ZAP-70 expression. PF-04691502 inhibited both anti-immunoglobulin M-induced signaling and overcame stroma-induced survival signals and migratory stimuli from CXCL12. Equivalent in vitro activity was seen in the Eµ-TCL1 murine model of CLL. In vivo, PF-04691502 treatment of tumor-bearing animals resulted in a transient lymphocytosis, followed by a clear reduction in tumor in the blood, bone marrow, spleen, and lymph nodes. These data indicate that PF-04691502 or other dual PI3K/mTOR inhibitors in development may prove efficacious for the treatment of CLL, increasing our armamentarium to successfully manage this disease.

Surface IgM stimulation induces MEK1/2-dependent MYC expression in chronic lymphocytic leukemia cells.

Although long considered as a disease of failed apoptosis, it is now clear that chronic lymphocytic leukemia (CLL) cells undergo extensive cell division in vivo, especially in progressive disease. Signaling via the B-cell receptor is thought to activate proliferation and survival pathways in CLL cells and also has been linked to poor outcome. Here, we have analyzed the expression of the proto-oncoprotein MYC, an essential positive regulator of the cell cycle, after stimulation of surface IgM (sIgM). MYC expression was rapidly increased after sIgM stimulation in a subset of CLL samples. The ability of sIgM stimulation to increase MYC expression was correlated with sIgM-induced intracellular calcium fluxes. MYC induction was partially dependent on the MEK/ERK signaling pathway, and MYC and phosphorylated ERK1/2 were both expressed within proliferation centers in vivo. Although stimulation of sIgD also resulted in ERK1/2 phosphorylation, responses were relatively short lived compared with sIgM and were associated with significantly reduced MYC induction, suggesting that the kinetics of ERK1/2 activation is a critical determinant of MYC induction. Our results suggest that ERK1/2-dependent induction of MYC is likely to play an important role in antigen-induced CLL cell proliferation.

Sumo-regulatory SENP2 controls the homeostatic squamous mitosis-differentiation checkpoint.

Squamous or epidermoid cancer arises in stratified epithelia but also is frequent in the non-epidermoid epithelium of the lung by unclear mechanisms. A poorly studied mitotic checkpoint drives epithelial cells bearing irreparable genetic damage into epidermoid differentiation. We performed an RNA-sequencing gene search to target unknown regulators of this response and selected the SUMO regulatory protein SENP2. Alterations of SENP2 expression have been associated with some types of cancer. We found the protein to be strongly localised to mitotic spindles of freshly isolated human epidermal cells. Primary cells rapidly differentiated after silencing SENP2 with specific shRNAs. Loss of SENP2 produced in synchronised epithelial cells delays in mitotic entry and exit and defects in chromosomal alignment. The results altogether strongly argue for an essential role of SENP2 in the mitotic spindle and hence in controlling differentiation. In addition, the expression of SENP2 displayed an inverse correlation with the immuno-checkpoint biomarker PD-L1 in a pilot collection of aggressive lung carcinomas. Consistently, metastatic head and neck cancer cells that do not respond to the mitosis-differentiation checkpoint were resistant to depletion of SENP2. Our results identify SENP2 as a novel regulator of the epithelial mitosis-differentiation checkpoint and a potential biomarker in epithelial cancer.

Biological network topology features predict gene dependencies in cancer cell-lines.

Motivation: Protein-protein interaction (PPI) networks have been shown to successfully predict essential proteins. However, such networks are derived generically from experiments on many thousands of different cells. Consequently, conventional PPI networks cannot capture the variation of genetic dependencies that exists across different cell types, let alone those that emerge as a result of the massive cell restructuring that occurs during carcinogenesis. Predicting cell-specific dependencies is of considerable therapeutic benefit, facilitating the use of drugs to inhibit those proteins on which the cancer cells have become specifically dependent. In order to go beyond the limitations of the generic PPI, we have attempted to personalise PPI networks to reflect cell-specific patterns of gene expression and mutation. By using 12 topological features of the resulting PPIs, together with matched gene dependency data from DepMap, we trained random-forest classifiers (DependANT) to predict novel gene dependencies. Results: We found that DependANT improves the power of the baseline generic PPI models in predicting common gene dependencies, by up to 10.8% and is more sensitive than the baseline generic model when predicting genes on which only a small number of cell types are dependent. Availability and implementation: Software available at https://bitbucket.org/bioinformatics_lab_sussex/dependant2. Supplementary information: Supplementary data are available at Bioinformatics Advances online.