Chemical combination effects predict connectivity in biological systems.

Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.

Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages.

Death Receptor 5 (DR5) agonists demonstrate anti-tumor activity in preclinical models but have yet to demonstrate robust clinical responses. A key limitation may be the lack of patient selection strategies to identify those most likely to respond to treatment. To overcome this limitation, we screened a DR5 agonist Nanobody across >600 cell lines representing 21 tumor lineages and assessed molecular features associated with response. High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges. To address the translational challenge of establishing thresholds of gene expression, we developed a classifier based on ratios of genes that predicted response across lineages. The ratio classifier outperformed the DR5+Casp8 classifier, as well as standard approaches for feature selection and classification using genes, instead of ratios. This classifier was independently validated using 11 primary patient-derived pancreatic xenograft models showing perfect predictions as well as a striking linearity between prediction probability and anti-tumor response. A network analysis of the genes in the ratio classifier captured important biological relationships mediating drug response, specifically identifying key positive and negative regulators of DR5 mediated apoptosis, including DR5, CASP8, BID, cFLIP, XIAP and PEA15. Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo). Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response.

Inhibiting Tankyrases sensitizes KRAS-mutant cancer cells to MEK inhibitors via FGFR2 feedback signaling.

Tankyrases (TNKS) play roles in Wnt signaling, telomere homeostasis, and mitosis, offering attractive targets for anticancer treatment. Using unbiased combination screening in a large panel of cancer cell lines, we have identified a strong synergy between TNKS and MEK inhibitors (MEKi) in KRAS-mutant cancer cells. Our study uncovers a novel function of TNKS in the relief of a feedback loop induced by MEK inhibition on FGFR2 signaling pathway. Moreover, dual inhibition of TNKS and MEK leads to more robust apoptosis and antitumor activity both in vitro and in vivo than effects observed by previously reported MEKi combinations. Altogether, our results show how a novel combination of TNKS and MEK inhibitors can be highly effective in targeting KRAS-mutant cancers by suppressing a newly discovered resistance mechanism.

MDM2 antagonists synergize broadly and robustly with compounds targeting fundamental oncogenic signaling pathways.

While MDM2 inhibitors hold great promise as cancer therapeutics, drug resistance will likely limit their efficacy as single agents. To identify drug combinations that might circumvent resistance, we screened for agents that could synergize with MDM2 inhibition in the suppression of cell viability. We observed broad and robust synergy when combining MDM2 antagonists with either MEK or PI3K inhibitors. Synergy was not limited to cell lines harboring MAPK or PI3K pathway mutations, nor did it depend on which node of the PI3K axis was targeted. MDM2 inhibitors also synergized strongly with BH3 mimetics, BCR-ABL antagonists, and HDAC inhibitors. MDM2 inhibitor-mediated synergy with agents targeting these mechanisms was much more prevalent than previously appreciated, implying that clinical translation of these combinations could have far-reaching implications for public health. These findings highlight the importance of combinatorial drug targeting and provide a framework for the rational design of MDM2 inhibitor clinical trials.