A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities.

Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value

Explaining Blood-Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms

The blood-brain barrier (BBB) represents a major obstacle to delivering drugs to the central nervous system (CNS), resulting in the lack of effective treatment for many CNS diseases including brain cancer. To accelerate CNS drug development, computational prediction models could save the time and effort needed for experimental evaluation. Here, we studied BBB permeability focusing on active transport (influx and efflux) as well as passive diffusion using previously published and self-curated data sets. We created prediction models based on physicochemical properties, molecular substructures, or their combination to understand which mechanisms contribute to BBB permeability. Our results show that features that predicted passive diffusion over membranes overlap with features that explain endothelial permeation of approved CNS-active drugs. We also identified physical properties and molecular substructures that positively or negatively predicted BBB transport. These findings provide guidance toward identifying BBB-permeable compounds by optimally matching physicochemical and molecular properties to BBB transport mechanisms

The TICking clock of EGFR therapy resistance in glioblastoma: Target Independence or target Compensation

Targeted therapy against driver mutations responsible for cancer progression has been shown to be effective in many tumor types. For glioblastoma (GBM), the epidermal growth factor receptor (EGFR) gene is the most frequently mutated oncogenic driver and has therefore been considered an attractive target for therapy. However, so far responses to EGFR-pathway inhibitors have been disappointing. We performed an exhaustive analysis of the mechanisms that might account for therapy resistance against EGFR inhibition. We define two major mechanisms of resistance and propose modalities to overcome them. The first resistance mechanism concerns target independence. In this case, cells have lost expression of the EGFR protein and experience no negative impact of EGFR targeting. Loss of extrachromosomally encoded EGFR as present in double minute DNA is a frequent mechanism for this type of drug resistance. The second mechanism concerns target compensation. In this case, cells will counteract EGFR inhibition by activation of compensatory pathways that render them independent of EGFR signaling. Compensatory pathway candidates are platelet-derived growth factor β (PDGFβ), Insulin-like growth factor 1 (IGFR1) and cMET and their downstream targets, all not commonly mutated at the time of diagnosis alongside EGFR mutation. Given that both mechanisms make cells independent of EGFR expression, other means have to be found to eradicate drug resistant cells. To this end we suggest rational strategies which include the use of multi-target therapies that hit truncation mutations (mechanism 1) or multi-target therapies to co-inhibit compensatory proteins (mechanism 2)

Identification of MEK162 as a Radiosensitizer for the Treatment of Glioblastoma

Glioblastoma (GBM) is a highly aggressive and lethal brain cancer type. PI3K and MAPK inhibitors have been studied pre-clinically in GBM as monotherapy, but not in combination with radiotherapy, which is a key component of the current standard treatment of GBM. In our study, GBM cell lines and patient representative primary cultures were grown as multicellular spheroids. Spheroids were treated with a panel of small-molecule drugs including MK2206, RAD001, BEZ235, MLN0128, and MEK162, alone and in combination with irradiation. Following treatment, spheroid growth parameters (growth rate, volume reduction, and time to regrow), cell-cycle distribution and expression of key target proteins were evaluated. In vivo, the effect of irradiation (3 x 2 Gy) without or with MEK162 (50 mg/kg) was studied in orthotopic GBM8 brain tumor xenografts with endpoints tumor growth and animal survival. The MAPK-targeting agent MEK162 was found to enhance the effect of irradiation as demonstrated by growth inhibition of spheroids. MEK162 downregulated and dephosphorylated the cell-cycle checkpoint proteins CDK1/CDK2/WEE1 and DNA damage response proteins p-ATM/p-CHK2. When combined with radiation, this led to a prolonged DNA damage signal. In vivo data on tumor-bearing animals demonstrated a significantly reduced growth rate, increased growth delay, and prolonged survival time. In addition, RNA expression of responsive cell cultures correlated to mesenchymal stratification of patient expression data. In conclusion, the MAPK inhibitor MEK162 was identified as a radiosensitizer in GBM spheroids in vitro and in orthotopic GBM xenografts in vivo. The data are supportive for implementation of this targeted agent in an early-phase clinical study in GBM patients. (C) 2017 AAC

A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities

Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value