This file contains 16 Supplemental tables and 14 Supplemental Figures, as well as numerous NMR traces, synthetic chemistry schemes, mass spectra, tables, multiple graphics related to gene expression profiles, dose response curves, x-ray structures, westerns, etc.

Quantitative evaluation of how drugs combine to elicit a biological response is crucial for drug development. Evaluations of drug combinations are often performed using index-based methods, which are known to be biased and unstable. We examine how these methods can produce misleadingly structured patterns of bias, leading to erroneous judgments of synergy or antagonism. By contrast, response surface models are less prone to these defects and can be applied to a wide range of data that have appeared in recent literature, including the measurement of combination therapeutic windows and the analysis of discrete experimental measures, three-way drug combinations, and atypical response behaviors.

Quantitative evaluation of how drugs combine to elicit a biological response is crucial for drug development. Evaluations of drug combinations are often performed using index-based methods, which are known to be biased and unstable. We examine how these methods can produce misleadingly structured patterns of bias, leading to erroneous judgments of synergy or antagonism. By contrast, response surface models are less prone to these defects and can be applied to a wide range of data that have appeared in recent literature, including the measurement of combination therapeutic windows and the analysis of discrete experimental measures, three-way drug combinations, and atypical response behaviors.

This data set contains the data used in Twarog et al. (2021) to examine the robustness and utility of response surface models in drug combination analysis. It includes simulated experimental data for the evaluation of traditional index methods, as well as a processed library of interaction metrics evaluated on the Merck OncoPolyPharmacology Screen (O'Neil et al., 2016), the scripts used to implement those metrics on all tested combinations in that screen, and scripts to evaluate the performance of those metrics in comparison with real-world mechanistic classifications. Finally, the data set includes data from several published and unpublished drug combination experiments, and scripts which allow the analyses of those experiments to be replicated and applied to new data.

AbstractInhibition of members of the bromodomain and extraterminal (BET) family of proteins has proven a valid strategy for cancer chemotherapy. All BET identified to date contain two bromodomains (BD; BD1 and BD2) that are necessary for recognition of acetylated lysine residues in the N-terminal regions of histones. Chemical matter that targets BET (BETi) also interact via these domains. Molecular and cellular data indicate that BD1 and BD2 have different biological roles depending upon their cellular context, with BD2 particularly associated with cancer. We have therefore pursued the development of BD2-selective molecules both as chemical probes and as potential leads for drug development. Here we report the structure-based generation of a novel series of tetrahydroquinoline analogs that exhibit >50-fold selectivity for BD2 versus BD1. This selective targeting resulted in engagement with BD-containing proteins in cells, resulting in modulation of MYC proteins and downstream targets. These compounds were potent cytotoxins toward numerous pediatric cancer cell lines and were minimally toxic to nontumorigenic cells. In addition, unlike the pan BETi (+)-JQ1, these BD2-selective inhibitors demonstrated no rebound expression effects. Finally, we report a pharmacokinetic-optimized, metabolically stable derivative that induced growth delay in a neuroblastoma xenograft model with minimal toxicity. We conclude that BD2-selective agents are valid candidates for antitumor drug design for pediatric malignancies driven by the MYC oncogene.Significance:This study presents bromodomain-selective BET inhibitors that act as antitumor agents and demonstrates that these molecules have in vivo activity towards neuroblastoma, with essentially no toxicity.

Pediatric sarcomas represent a heterogeneous group of malignancies that exhibit variable response to DNA damaging chemotherapy. Schlafen family member 11 protein (SLFN11) increases sensitivity to replicative stress, and SLFN11 gene silencing has been implicated as a common mechanism of drug resistance in tumors in adults. We found SLFN11 to be widely expressed in our cohort of pediatric sarcomas. In sarcoma cell lines, protein expression strongly correlated with response to the PARP inhibitor talazoparib (TAL) and the topoisomerase I inhibitor irinotecan (IRN), with SLFN11 knockout resulting in significant loss of sensitivity in vitro and in vivo. However, SLFN11 expression was not associated with favorable outcomes in a retrospective analysis of our patient cohort; instead, the protein was retained and promoted tumor growth and evasion. Furthermore, we show that pediatric sarcomas develop resistance to TAL and IRN through impaired intrinsic apoptosis, and that resistance can be reversed by selective inhibition of BCL-XL. Statement of Significance The role of SLFN11 in pediatric sarcomas has not been thoroughly explored. In contrast to its activity in adult tumors, SLFN11 did not predict favorable outcomes in pediatric patients, was not silenced, and promoted tumor growth. Resistance to replicative stress in SLFN11-expressing sarcomas was reversed by selective inhibition of BCL-XL.