Orthotopic patient-derived xenografts of paediatric solid tumours.

Paediatric solid tumours arise from endodermal, ectodermal, or mesodermal lineages. Although the overall survival of children with solid tumours is 75%, that of children with recurrent disease is below 30%. To capture the complexity and diversity of paediatric solid tumours and establish new models of recurrent disease, here we develop a protocol to produce orthotopic patient-derived xenografts at diagnosis, recurrence, and autopsy. Tumour specimens were received from 168 patients, and 67 orthotopic patient-derived xenografts were established for 12 types of cancer. The origins of the patient-derived xenograft tumours were reflected in their gene-expression profiles and epigenomes. Genomic profiling of the tumours, including detailed clonal analysis, was performed to determine whether the clonal population in the xenograft recapitulated the patient's tumour. We identified several drug vulnerabilities and showed that the combination of a WEE1 inhibitor (AZD1775), irinotecan, and vincristine can lead to complete response in multiple rhabdomyosarcoma orthotopic patient-derived xenografts tumours in vivo.

A high-throughput quality control method for assessing the serial dilution performance of dose-response plates with acoustic ejection mass spectrometry.

This study aimed to develop a streamlined method for evaluating the dilution ratio of drug dose-response plates created by automated liquid handlers in the early stages of drug discovery. The quantitative techniques commonly used for this purpose have restrictions due to their limited linear dynamic range and inaccuracies in assessing serial dilution performance. To address this challenge, we describe a method based on acoustic ejection mass spectrometry (AEMS). The method involves using standard compounds and an internal standard to evaluate each dilution point in quality control (QC) plates. The samples are transferred to a chromatography-free tandem mass spectrometry system through an acoustic source, enabling the analysis of one sample per three seconds from a microtiter plate. This approach provides precise, accurate, label-free, and rapid data acquisition to support high-throughput screening efforts.

Using response surface models to analyze drug combinations.

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.

Data vignettes for the application of response surface models in drug combination analysis.

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.

A critical evaluation of methods to interpret drug combinations.

Combination therapy is increasingly central to modern medicine. Yet reliable analysis of combination studies remains an open challenge. Previous work suggests that common methods of combination analysis are too susceptible to noise to support robust scientific conclusions. In this paper, we use simulated and real-world combination datasets to demonstrate that traditional index methods are unstable and biased by pharmacological and experimental conditions, whereas response-surface approaches such as the BRAID method are more consistent and unbiased. Using a publicly-available data set, we show that BRAID more accurately captures variations in compound mechanism of action, and is therefore better able to discriminate between synergistic, antagonistic, and additive interactions. Finally, we applied BRAID analysis to identify a clear pattern of consistently enhanced AKT sensitivity in a subset of cancer cell lines, and a far richer array of PARP inhibitor combination therapies for BRCA1-deficient cancers than would be identified by traditional synergy analysis.

Erratum: BRAID: A Unifying Paradigm for the Analysis of Combined Drug Action.

This corrects the article DOI: 10.1038/srep25523.

BRAID: A Unifying Paradigm for the Analysis of Combined Drug Action.

With combination therapies becoming increasingly vital to understanding and combatting disease, a reliable method for analyzing combined dose response is essential. The importance of combination studies both in basic and translational research necessitates a method that can be applied to a wide range of experimental and analytical conditions. However, despite increasing demand, no such unified method has materialized. Here we introduce the Bivariate Response to Additive Interacting Doses (BRAID) model, a response surface model that combines the simplicity and intuitiveness needed for basic interaction classifications with the versatility and depth needed to analyze a combined response in the context of pharmacological and toxicological constraints. We evaluate the model in a series of simulated combination experiments, a public combination dataset, and several experiments on Ewing's Sarcoma. The resulting interaction classifications are more consistent than those produced by traditional index methods, and show a strong relationship between compound mechanisms and nature of interaction. Furthermore, analysis of fitted response surfaces in the context of pharmacological constraints yields a more concrete prediction of combination efficacy that better agrees with in vivo evaluations.

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis.

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2'-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used.

Targeting the DNA repair pathway in Ewing sarcoma.

Ewing sarcoma (EWS) is a tumor of the bone and soft tissue that primarily affects adolescents and young adults. With current therapies, 70% of patients with localized disease survive, but patients with metastatic or recurrent disease have a poor outcome. We found that EWS cell lines are defective in DNA break repair and are sensitive to PARP inhibitors (PARPis). PARPi-induced cytotoxicity in EWS cells was 10- to 1,000-fold higher after administration of the DNA-damaging agents irinotecan or temozolomide. We developed an orthotopic EWS mouse model and performed pharmacokinetic and pharmacodynamic studies using three different PARPis that are in clinical development for pediatric cancer. Irinotecan administered on a low-dose, protracted schedule previously optimized for pediatric patients was an effective DNA-damaging agent when combined with PARPis; it was also better tolerated than combinations with temozolomide. Combining PARPis with irinotecan and temozolomide gave complete and durable responses in more than 80% of the mice.