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A protocol producing orthotopic patient-derived xenografts at diagnosis, recurrence, and autopsy demonstrates proof of principle for using these tumours for basic and translational research on paediatric solid tumours. Xenograft archive Preclinical models of paediatric solid tumours that could help identify predictive biomarkers of a patient's sensitivity to therapy have been lacking. Over five years, the authors have developed an open access collection of orthotopic xenografts of 12 types of paediatric tumour. Genomic and epigenetic characterization reveals that xenografts retain characteristics of the tumour of origin. A high-throughput drug screen provides a resource for the community to identify potentially efficacious drug combinations. Paediatric solid tumours arise from endodermal, ectodermal, or mesodermal lineages 1 . Although the overall survival of children with solid tumours is 75%, that of children with recurrent disease is below 30% 2 . 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 .

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.

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.

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.

Although primary tumor control rates after surgery and/or radiation therapy (RT) are generally high in patients with Ewing sarcoma (EWS), those with unresectable tumors have failure rates approaching 30% and experience poorer outcomes. Additionally, although metastatic site irradiation is associated with improved survival, dose, and volume effects influence the long‐term toxicity risk. Consequently, it is important to identify novel systemic agents to enhance the therapeutic ratio of RT. Given the reported DNA damage response deficits in EWS, we hypothesized that PARP inhibitors (PARPis) would preferentially potentiate radiation relative to standard‐of‐care (SOC) chemotherapeutics. We investigated primary and recurrent SOC drugs and PARPis with varied trapping potential in combination with radiation in EWS cell lines. At physiologically relevant concentrations, the strong PARP trapper talazoparib (TAL) potentiated radiation to a greater extent than did SOC or other PARPis, although the magnitude of the effect was modest. The radiosensitizing effect of TAL was mediated through the induction of DNA double‐strand breaks, rather than through the catalytic inhibition of PARP1. Drug + RT combinations were further tested in vivo by using orthotopic xenograft models of EWS treated with image‐guided fractionated radiation. The addition of RT to the combination of TAL plus irinotecan (IRN), a recently evaluated clinical regimen for relapsed pediatric solid tumors, significantly prolonged survival and reduced tumor burden in all EWS‐treated mice. This triplet therapy (TAL + IRN + RT) was feasible and yielded responses in several patients with EWS and may represent a useful salvage strategy in recurrent or progressive disease. The addition of RT to the combination of TAL plus irinotecan (IRN), a recently evaluated clinical regimen for relapsed pediatric solid tumors, significantly prolonged survival and reduced tumor burden in EWS mouse models. This triplet therapy (TAL + IRN + RT) was feasible and yielded responses in several patients with EWS and may represent a useful salvage strategy in recurrent or progressive disease.

Scientific Reports 6: Article number: 25523; published online: 10 May 2016; updated: 17 May 2018 The original version of this Article contained an error in the indexing of the author Anang A. Shelat. This error has now been corrected.

The automated synthesis of small organic molecules from modular building blocks has the potential to transform our capacity to create medicines and materials 1 , 2 – 3 . Disruptive acceleration of this molecule-building strategy broadly unlocks its functional potential and requires the integration of many new assembly chemistries. Although recent advances in high-throughput chemistry 4 , 5 – 6 can speed up the development of appropriate synthetic methods, for example, in selecting appropriate chemical reaction conditions from the vast range of potential options, equivalent high-throughput analytical methods are needed. Here we report a streamlined approach for the rapid, quantitative analysis of chemical reactions by mass spectrometry. The intrinsic fragmentation features of chemical building blocks generalize the analyses of chemical reactions, allowing sub-second readouts of reaction outcomes. Central to this advance was identifying that starting material fragmentation patterns function as universal barcodes for downstream product analysis by mass spectrometry. Combining these features with acoustic droplet ejection mass spectrometry 7 , 8 we could eliminate slow chromatographic steps and continuously evaluate chemical reactions in multiplexed formats. This enabled the assignment of reaction conditions to molecules derived from ultrahigh-throughput chemical synthesis experiments. More generally, these results indicate that fragmentation features inherent to chemical synthesis can empower rapid data-rich experimentation. Mass spectrometry fragmentation patterns define analytical barcodes for the rapid, quantitative analysis of high-throughput chemical synthesis experiments.

Pediatric high-grade glioma (pHGG) is a major contributor to cancer-related death in children. In vitro and in vivo disease models reflecting the intimate connection between developmental context and pathogenesis of pHGG are essential to advance understanding and identify therapeutic vulnerabilities. Here we report establishment of 21 patient-derived pHGG orthotopic xenograft (PDOX) models and eight matched cell lines from diverse groups of pHGG. These models recapitulate histopathology, DNA methylation signatures, mutations and gene expression patterns of the patient tumors from which they were derived, and include rare subgroups not well-represented by existing models. We deploy 16 new and existing cell lines for high-throughput screening (HTS). In vitro HTS results predict variable in vivo response to PI3K/mTOR and MEK pathway inhibitors. These unique new models and an online interactive data portal for exploration of associated detailed molecular characterization and HTS chemical sensitivity data provide a rich resource for pediatric brain tumor research. Patient-derived xenografts provide a resource for basic and translational cancer research. Here, the authors generate multiple pediatric high-grade glioma xenografts, use omics technologies to show that they are representative of primary tumours and use them to assess therapeutic response.

Through screening a comprehensive collection of drugs approved for human use, we identified over 20 that oppose the antifungal activity of the echinocandins upon the infectious yeast, . More detailed evaluation of five such drugs, including the atypical antipsychotic aripiprazole and the tyrosine kinase inhibitor ponatinib, indicated they promote survival following exposure to the echinocandin antifungals. The activity of the five selected antagonists was dependent upon the Mkc1p MAPK pathway, however, ponatinib was paradoxically shown to suppress phosphorylation and therefore activation of Mkc1p itself. Components of several other signaling pathways are also required, including those of calcineurin and casein kinase-2, suggesting the observed antagonism required much of the cell wall stress responses previously described for . Transcriptome analysis revealed that the antagonists stimulated the expression of genes involved in xenobiotic and antifungal resistance, and suppressed the expression of genes associated with hyphal growth. Thus, the echinocandin antagonistic drugs modulate physiology in ways that could impact its pathogenicity and/or response to therapeutic intervention. Finally, a mutant lacking the Efg1p transcription factor, which has a central role in the activation of hyphal growth was found to have intrinsically high levels of echinocandin tolerance, suggesting a link between modulation of morphogenesis related signaling and echinocandin tolerance.

Rearrangments in Histone-lysine-N-methyltransferase 2A (KMT2Ar) are associated with pediatric, adult and therapy-induced acute leukemias. Infants with KMT2Ar acute lymphoblastic leukemia (ALL) have a poor prognosis with an event-free-survival of 38%. Herein we evaluate 1116 FDA approved compounds in primary KMT2Ar infant ALL specimens and identify a sensitivity to proteasome inhibition. Upon exposure to this class of agents, cells demonstrate a depletion of histone H2B monoubiquitination (H2Bub1) and histone H3 lysine 79 dimethylation (H3K79me2) at KMT2A target genes in addition to a downregulation of the KMT2A gene expression signature, providing evidence that it targets the KMT2A transcriptional complex and alters the epigenome. A cohort of relapsed/refractory KMT2Ar patients treated with this approach on a compassionate basis had an overall response rate of 90%. In conclusion, we report on a high throughput drug screen in primary pediatric leukemia specimens whose results translate into clinically meaningful responses. This innovative treatment approach is now being evaluated in a multi-institutional upfront trial for infants with newly diagnosed ALL. KMT2A rearranged infant acute lymphoblastic leukemia patients have a poor prognosis. Here, the authors use high throughput drug screening on primary infant specimens to identify a clinically active chemotherapy combination.