Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets.

BACKGROUND: Drug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools. RESULTS: We describe a statistical and visualization framework for the analysis of large-scale drug combination screens. We developed an approach suitable for datasets with large number of drugs pairs even if small number of data points are available per drug pair. We demonstrate our approach using a systematic screen of all possible pairs among 108 cancer drugs applied to melanoma cell lines. In this dataset only two dose-response data points per drug pair and two data points per single drug test were available. We used a Bliss-based linear model, effectively borrowing data from the drug pairs to obtain robust estimations of the singlet viabilities, consequently yielding better estimates of drug synergy. Our method improves data consistency across dosing thus likely reducing the number of false positives. The approach allows to compute p values accounting for standard errors of the modeled singlets and combination viabilities. We further develop a synergy specificity score that distinguishes specific synergies from those arising with promiscuous drugs. Finally, we developed a summarized interactive visualization in a web application, providing efficient access to any of the 439,000 data points in the combination matrix ( http://www.cmtlab.org:3000/combo_app.html ). The code of the analysis and the web application is available at https://github.com/arnaudmgh/synergy-screen . CONCLUSIONS: We show that statistical modeling of single drug response from drug combination data can help determine significance of synergy and antagonism in drug combination screens with few data point per drug pair. We provide a web application for the rapid exploration of large combinatorial drug screen. All codes are available to the community, as a resource for further analysis of published data and for analysis of other drug screens.

Differential Effector Engagement by Oncogenic KRAS.

KRAS can bind numerous effector proteins, which activate different downstream signaling events. The best known are RAF, phosphatidylinositide (PI)-3' kinase, and RalGDS families, but many additional direct and indirect effectors have been reported. We have assessed how these effectors contribute to several major phenotypes in a quantitative way, using an arrayed combinatorial siRNA screen in which we knocked down 41 KRAS effectors nodes in 92 cell lines. We show that every cell line has a unique combination of effector dependencies, but in spite of this heterogeneity, we were able to identify two major subtypes of KRAS mutant cancers of the lung, pancreas, and large intestine, which reflect different KRAS effector engagement and opportunities for therapeutic intervention.

Preclinical evaluation of the imipridone family, analogs of clinical stage anti-cancer small molecule ONC201, reveals potent anti-cancer effects of ONC212.

Anti-cancer small molecule ONC201 upregulates the integrated stress response (ISR) and acts as a dual inactivator of Akt/ERK, leading to TRAIL gene activation. ONC201 is under investigation in multiple clinical trials to treat patients with cancer. Given the unique imipridone core chemical structure of ONC201, we synthesized a series of analogs to identify additional compounds with distinct therapeutic properties. Several imipridones with a broad range of in vitro potencies were identified in an exploration of chemical derivatives. Based on in vitro potency in human cancer cell lines and lack of toxicity to normal human fibroblasts, imipridones ONC206 and ONC212 were prioritized for further study. Both analogs inhibited colony formation, and induced apoptosis and downstream signaling that involves the integrated stress response and Akt/ERK, similar to ONC201. Compared to ONC201, ONC206 demonstrated improved inhibition of cell migration while ONC212 exhibited rapid kinetics of activity. ONC212 was further tested in >1000 human cancer cell lines in vitro and evaluated for safety and anti-tumor efficacy in vivo. ONC212 exhibited broad-spectrum efficacy at nanomolar concentrations across solid tumors and hematological malignancies. Skin cancer emerged as a tumor type with improved efficacy relative to ONC201. Orally administered ONC212 displayed potent anti-tumor effects in vivo, a broad therapeutic window and a favorable PK profile. ONC212 was efficacious in vivo in BRAF V600E melanoma models that are less sensitive to ONC201. Based on these findings, ONC212 warrants further development as a drug candidate. It is clear that therapeutic utility extends beyond ONC201 to include additional imipridones.

Overcoming sorafenib evasion in hepatocellular carcinoma using CXCR4-targeted nanoparticles to co-deliver MEK-inhibitors.

Sorafenib is a RAF inhibitor approved for several cancers, including hepatocellular carcinoma (HCC). Inhibition of RAF kinases can induce a dose-dependent "paradoxical" upregulation of the downstream mitogen-activated protein kinase (MAPK) pathway in cancer cells. It is unknown whether "paradoxical" ERK activation occurs after sorafenib therapy in HCC, and if so, if it impacts the therapeutic efficacy. Here, we demonstrate that RAF inhibition by sorafenib rapidly leads to RAF dimerization and ERK activation in HCCs, which contributes to treatment evasion. The transactivation of RAF dimers and ERK signaling promotes HCC cell survival, prevents apoptosis via downregulation of BIM and achieves immunosuppression by MAPK/NF-kB-dependent activation of PD-L1 gene expression. To overcome treatment evasion and reduce systemic effects, we developed CXCR4-targeted nanoparticles to co-deliver sorafenib with the MEK inhibitor AZD6244 in HCC. Using this approach, we preferentially and efficiently inactivated RAF/ERK, upregulated BIM and down-regulated PD-L1 expression in HCC, and facilitated intra-tumoral infiltration of cytotoxic CD8+ T cells. These effects resulted in a profound delay in tumor growth. Thus, this nano-delivery strategy to selectively target tumors and prevent the paradoxical ERK activation could increase the feasibility of dual RAF/MEK inhibition to overcome sorafenib treatment escape in HCC.