Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interac tions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer ). Here, we perform a genome‐wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients’ response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

BackgroundThe insulin-like growth factor 1 (IGF1) signaling axis plays a major role in tumorigenesis. In a previous experiment, we chronically treated mice with several agonists of the IGF1 receptor (IGF1R). We found that chronic treatment with insulin analogues with high affinity towards the IGF1R (IGF1 and X10) decreased the mammary gland tumor latency time in a p53R270H/+WAPCre mouse model. Frequent injections with insulin analogues that only mildly activated the IGF1R in vivo (glargine and insulin) did not significantly decrease the tumor latency time in this mouse model.MethodsHere, we performed next-generation RNA sequencing (40 million, 100 bp reads) on 50 mammary gland tumors to unravel the underlying mechanisms of IGF1R-promoted tumorigenesis. Mutational profiling of the individual tumors was performed to screen for treatment-specific mutations. The transcriptomic data were used to construct a support vector machine (SVM) classifier so that the phenotypic characteristics of tumors exposed to the different insulin analogue treatments could be predicted. For translational purposes, we ran the same classifiers on transcriptomic (micro-array) data of insulin analogue-exposed human breast cancer cell lines. Genome-scale metabolic modeling was performed with iMAT.ResultsWe found that chronic X10 and IGF1 treatment resulted in tumors with an increased and sustained proliferative and invasive transcriptomic profile. Furthermore, a Warburg-like effect with increased glycolysis was observed in tumors of the X10/IGF1 groups and, to a lesser extent, also in glargine-induced tumors. A metabolic flux analysis revealed that this enhanced glycolysis programming in X10/IGF1 tumors was associated with increased biomass production programs. Although none of the treatments induced genetic instability or enhanced mutagenesis, mutations in Ezh2 and Hras were enriched in X10/IGF1 treatment tumors.ConclusionsOverall, these data suggest that the decreased mammary gland tumor latency time caused by chronic IGF1R activation is related to modulation of tumor progression rather than increased tumor initiation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0802-0) contains supplementary material, which is available to authorized users.

Effective carbon dioxide (CO2) capture requires solid, porous sorbents with chemically and thermally stable frameworks. Herein, we report two new carbon–carbon bonded porous networks that were synthesized through metal-free Knoevenagel nitrile–aldol condensation, namely the covalent organic polymer, COP-156 and 157. COP-156, due to high specific surface area (650 m2/g) and easily interchangeable nitrile groups, was modified post-synthetically into free amine- or amidoxime-containing networks. The modified COP-156-amine showed fast and increased CO2 uptake under simulated moist flue gas conditions compared to the starting network and usual industrial CO2 solvents, reaching up to 7.8 wt % uptake at 40 °C.