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Background CAR T cell therapy is a promising approach to improve outcomes and decrease toxicities for patients with cancer. While extraordinary success has been achieved using CAR T cells to treat patients with CD19-positive malignancies, multiple obstacles have so far limited the benefit of CAR T cell therapy for patients with solid tumors. Novel manufacturing and engineering approaches show great promise to enhance CAR T cell function against solid tumors. However, similar to single agent chemotherapy approaches, CAR T cell monotherapy may be unable to achieve high cure rates for patients with difficult to treat solid tumors. Thus, combinatorial drug plus CAR T cell approaches are likely required to achieve widespread clinical success. Methods We developed a novel, confocal microscopy based, high-content screen to evaluate 1114 FDA approved drugs for the potential to increase expression of the solid tumor antigen B7-H3 on the surface of osteosarcoma cells. Western blot, RT-qPCR, siRNA knockdown and flow cytometry assays were used to validate screening results and identify mechanisms of drug-induced B7-H3 upregulation. Cytokine and cytotoxicity assays were used to determine if drug pre-treatment enhanced B7-H3-CAR T cell effector function. Results Fifty-five drugs were identified to increase B7-H3 expression on the surface of LM7 osteosarcoma cells using a novel high-content, high-throughput screen. One drug, ingenol-3-angelate (I3A), increased B7-H3 expression by up to 100%, and was evaluated in downstream experiments. Validation assays confirmed I3A increased B7-H3 expression in a biphasic dose response and cell dependent fashion. Mechanistic studies demonstrated that I3A increased B7-H3 (CD276) mRNA, total protein, and cell surface expression via protein kinase C alpha activation. Functionally, I3A induced B7-H3 expression enhanced B7-H3-CAR T cell function in cytokine production and cytotoxicity assays. Conclusions This study demonstrates a novel high-content and high-throughput screen can identify drugs to enhance CAR T cell activity. This and other high-content technologies will pave the way to develop clinical trials implementing rational drug plus CAR T cell combinatorial therapies. Importantly, the technique could also be repurposed for an array of basic and translational research applications where drugs are needed to modulate cell surface protein expression.

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.

Background Medulloblastoma is the most frequently occurring malignant brain tumor in children. Current treatment strategies for medulloblastoma include aggressive surgery, cranio-spinal irradiation and adjuvant chemotherapy. Because current treatments can cause severe long-term side effects and are not curative, successful treatment remains a challenge. Methods In this study, we employed a high-throughput cell viability assay to screen 12,800 compounds and to identify drug candidates with anti-proliferative properties for medulloblastoma cells. We also tested these compounds for attenuating medulloblastoma tumor development using mouse xenografts. Results We identified two histone deacetylase inhibitors (dacinostat and quisinostat) with anti-proliferative properties for medulloblastoma cells. We showed that both compounds induce cytotoxicity, trigger cell apoptosis, and block cell cycle progression at the G2/M phase. In addition, dacinostat and quisinostat attenuated xenograft medulloblastoma growth in mice. Conclusions Our findings suggest that histone deacetylase inhibitors are potent therapeutic agents against medulloblastoma.

Background CAR T cell therapy is a promising approach to improve outcomes and decrease toxicities for patients with cancer. While extraordinary success has been achieved using CAR T cells to treat patients with CD19-positive malignancies, multiple obstacles have so far limited the benefit of CAR T cell therapy for patients with solid tumors. Novel manufacturing and engineering approaches show great promise to enhance CAR T cell function against solid tumors. However, similar to single agent chemotherapy approaches, CAR T cell monotherapy may be unable to achieve high cure rates for patients with difficult to treat solid tumors. Thus, combinatorial drug plus CAR T cell approaches are likely required to achieve widespread clinical success. Methods We developed a novel, confocal microscopy based, high-content screen to evaluate 1114 FDA approved drugs for the potential to increase expression of the solid tumor antigen B7-H3 on the surface of osteosarcoma cells. Western blot, RT-qPCR, siRNA knockdown and flow cytometry assays were used to validate screening results and identify mechanisms of drug-induced B7-H3 upregulation. Cytokine and cytotoxicity assays were used to determine if drug pre-treatment enhanced B7-H3-CAR T cell effector function. Results Fifty-five drugs were identified to increase B7-H3 expression on the surface of LM7 osteosarcoma cells using a novel high-content, high-throughput screen. One drug, ingenol-3-angelate (I3A), increased B7-H3 expression by up to 100%, and was evaluated in downstream experiments. Validation assays confirmed I3A increased B7-H3 expression in a biphasic dose response and cell dependent fashion. Mechanistic studies demonstrated that I3A increased B7-H3 (CD276) mRNA, total protein, and cell surface expression via protein kinase C alpha activation. Functionally, I3A induced B7-H3 expression enhanced B7-H3-CAR T cell function in cytokine production and cytotoxicity assays. Conclusions This study demonstrates a novel high-content and high-throughput screen can identify drugs to enhance CAR T cell activity. This and other high-content technologies will pave the way to develop clinical trials implementing rational drug plus CAR T cell combinatorial therapies. Importantly, the technique could also be repurposed for an array of basic and translational research applications where drugs are needed to modulate cell surface protein expression. Keywords: Ingenol-3-angelate, PKC, B7-H3, CAR, T cell, Osteosarcoma, Solid tumor, High-content screen, High-throughput screen

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.

Background Meiotic crossovers are the major mechanism by which haplotypes are shuffled to generate genetic diversity. Previously available methods for the genome-wide, high-resolution identification of meiotic crossover sites are limited by the laborious nature of the assay (as in sperm typing). Methods Several methods have been introduced to identify crossovers using high density single nucleotide polymorphism (SNP) array technologies, although programs are not widely available to implement such analyses. Results Here we present a two-generation \"reverse pedigree analysis\" method (analyzing the genotypes of two children relative to each parent) and a web-accessible tool to determine and visualize inheritance differences among siblings and crossover locations on each parental gamete. This approach is complementary to existing methods and uses informative markers which provide high resolution for locating meiotic crossover sites. We introduce a segmentation algorithm to identify crossover sites, and used a synthetic data set to determine that the segmentation algorithm specificity was 92% and sensitivity was 89%. The use of reverse pedigrees allows the inference of crossover locations on the X chromosome in a maternal gamete through analysis of two sons and their father. We further analyzed genotypes from eight multiplex autism families, observing a 1.462 maternal to paternal recombination ratio and no significant differences between affected and unaffected children. Meiotic recombination results from pediSNP can also be used to identify haplotypes that are shared by probands within a pedigree, as we demonstrated with a multiplex autism family. Conclusion Using \"reverse pedigrees\" and defining unique sets of genotype markers within pedigree data, we introduce a method that identifies inherited allelic differences and meiotic crossovers. We implemented the method in the pediSNP software program, and we applied it to several data sets. This approach uses data from two generations to identify crossover sites, facilitating studies of recombination in disease. pediSNP is available online at http://pevsnerlab.kennedykrieger.org/pediSNP .

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.

Abstract 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. We established 21 patient-derived pHGG orthotopic xenograft (PDOX) models and eight matched cell lines from diverse groups of pHGG. These models recapitulated histopathology, DNA methylation signatures, mutations and gene expression patterns of the patient tumors from which they were derived, and included rare subgroups not well-represented by existing models. We deployed 16 new and existing cell lines for high-throughput screening (HTS). In vitro HTS results predicted variable in vivo response to inhibitors of PI3K/mTOR and MEK signaling pathways. These unique new models and an online interactive data portal to enable exploration of associated detailed molecular characterization and HTS chemical sensitivity data provide a rich resource for pediatric brain tumor research. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://pbtp.stjude.cloud

CAR T cell therapy is a promising approach to improve outcomes and decrease toxicities for patients with cancer. While extraordinary success has been achieved using CAR T cells to treat patients with CD19-positive malignancies, multiple obstacles have so far limited the benefit of CAR T cell therapy for patients with solid tumors. Novel manufacturing and engineering approaches show great promise to enhance CAR T cell function against solid tumors. However, similar to single agent chemotherapy approaches, CAR T cell monotherapy may be unable to achieve high cure rates for patients with difficult to treat solid tumors. Thus, combinatorial drug plus CAR T cell approaches may ultimately be required to achieve widespread clinical success. In this regard, we developed a novel high-content and high-throughput screen to evaluate 1114 FDA approved drugs to increase expression of the solid tumor antigen B7-H3 in metastatic osteosarcoma cells. In this proof-of-principle screen, we demonstrate that ingenol-3-angelate increased B7-H3 (CD276) mRNA, total protein, and cell surface expression. Mechanistically, ingenol-3-angelate increased B7-H3 expression via protein kinase C alpha activation. Functionally, ingenol-3-angelate induced B7-H3 expression enhanced B7-H3-CAR T cell function, highlighting utility of the approach, and paving the way for expanding this high-throughput and high-content technique to study other tumor and CAR T cell combinations.