Lenalidomide inhibits the proliferation of CLL cells via a cereblon/p21(WAF1/Cip1)-dependent mechanism independent of functional p53.

Lenalidomide has demonstrated clinical activity in patients with chronic lymphocytic leukemia (CLL), even though it is not cytotoxic for primary CLL cells in vitro. We examined the direct effect of lenalidomide on CLL-cell proliferation induced by CD154-expressing accessory cells in media containing interleukin-4 and -10. Treatment with lenalidomide significantly inhibited CLL-cell proliferation, an effect that was associated with the p53-independent upregulation of the cyclin-dependent kinase inhibitor, p21(WAF1/Cip1) (p21). Silencing p21 with small interfering RNA impaired the capacity of lenalidomide to inhibit CLL-cell proliferation. Silencing cereblon, a known molecular target of lenalidomide, impaired the capacity of lenalidomide to induce expression of p21, inhibit CD154-induced CLL-cell proliferation, or enhance the degradation of Ikaros family zinc finger proteins 1 and 3. We isolated CLL cells from the blood of patients before and after short-term treatment with low-dose lenalidomide (5 mg per day) and found the leukemia cells were also induced to express p21 in vivo. These results indicate that lenalidomide can directly inhibit proliferation of CLL cells in a cereblon/p21-dependent but p53-independent manner, at concentrations achievable in vivo, potentially contributing to the capacity of this drug to inhibit disease-progression in patients with CLL.

In silico modeling predicts drug sensitivity of patient-derived cancer cells.

BACKGROUND: Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. METHODS: Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. RESULTS: Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. CONCLUSIONS: These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

Novel anti-glioblastoma agents and therapeutic combinations identified from a collection of FDA approved drugs.

BACKGROUND: Glioblastoma (GBM) is a therapeutic challenge, associated with high mortality. More effective GBM therapeutic options are urgently needed. Hence, we screened a large multi-class drug panel comprising the NIH clinical collection (NCC) that includes 446 FDA-approved drugs, with the goal of identifying new GBM therapeutics for rapid entry into clinical trials for GBM. METHODS: Screens using human GBM cell lines revealed 22 drugs with potent anti-GBM activity, including serotonergic blockers, cholesterol-lowering agents (statins), antineoplastics, anti-infective, anti-inflammatories, and hormonal modulators. We tested the 8 most potent drugs using patient-derived GBM cancer stem cell-like lines. Notably, the statins were active in vitro; they inhibited GBM cell proliferation and induced cellular autophagy. Moreover, the statins enhanced, by 40-70 fold, the pro-apoptotic activity of irinotecan, a topoisomerase 1 inhibitor currently used to treat a variety of cancers including GBM. Our data suggest that the mechanism of action of statins was prevention of multi-drug resistance protein MDR-1 glycosylation. This drug combination was synergistic in inhibiting tumor growth in vivo. Compared to animals treated with high dose irinotecan, the drug combination showed significantly less toxicity. RESULTS: Our data identifies a novel combination from among FDA-approved drugs. In addition, this combination is safer and well tolerated compared to single agent irinotecan. CONCLUSIONS: Our study newly identifies several FDA-approved compounds that may potentially be useful in GBM treatment. Our findings provide the basis for the rational combination of statins and topoisomerase inhibitors in GBM.

High-efficiency liposomal encapsulation of a tyrosine kinase inhibitor leads to improved in vivo toxicity and tumor response profile.

Staurosporine (STS) is a potent pan-kinase inhibitor with marked activity against several chemotherapy-resistant tumor types in vitro. The translational progress of this compound has been hindered by poor pharmacokinetics and toxicity. We sought to determine whether liposomal encapsulation of STS would enhance antitumor efficacy and reduce toxicity, thereby supporting the feasibility of further preclinical development. We developed a novel reverse pH gradient liposomal loading method for STS, with an optimal buffer type and drug-to-lipid ratio. Our approach produced 70% loading efficiency with good retention, and we provide, for the first time, an assessment of the in vivo antitumor activity of STS. A low intravenous dose (0.8 mg/kg) inhibited U87 tumors in a murine flank model. Biodistribution showed preferential tumor accumulation, and body weight data, a sensitive index of STS toxicity, was unaffected by liposomal STS, but did decline with the free compound. In vitro experiments revealed that liposomal STS blocked Akt phosphorylation, induced poly(ADP-ribose) polymerase cleavage, and produced cell death via apoptosis. This study provides a basis to explore further the feasibility of liposomally encapsulated STS, and potentially related compounds for the management of resistant solid tumors.