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Neurofibromatosis type 1 (NF1) is a genetic disorder caused by mutation of the NF1 gene that is associated with various symptoms, including the formation of benign tumors, called neurofibromas, within nerves. Drug treatments are currently limited. The mitogen‐activated protein kinase kinase (MEK) inhibitor selumetinib is used for a subset of plexiform neurofibromas (PNs) but is not always effective and can cause side effects. Therefore, there is a clear need to discover new drugs to target NF1‐deficient tumor cells. Using a Drosophila cell model of NF1, we performed synthetic lethal screens to identify novel drug targets. We identified 54 gene candidates, which were validated with variable dose analysis as a secondary screen. Pathways associated with five candidates could be targeted using existing drugs. Among these, chloroquine (CQ) and bafilomycin A1, known to target the autophagy pathway, showed the greatest potential for selectively killing NF1‐deficient Drosophila cells. When further investigating autophagy‐related genes, we found that 14 out of 30 genes tested had a synthetic lethal interaction with NF1. These 14 genes are involved in multiple aspects of the autophagy pathway and can be targeted with additional drugs that mediate the autophagy pathway, although CQ was the most effective. The lethal effect of autophagy inhibitors was conserved in a panel of human NF1‐deficient Schwann cell lines, highlighting their translational potential. The effect of CQ was also conserved in a Drosophila NF1 in vivo model and in a xenografted NF1‐deficient tumor cell line grown in mice, with CQ treatment resulting in a more significant reduction in tumor growth than selumetinib treatment. Furthermore, combined treatment with CQ and selumetinib resulted in a further reduction in NF1‐deficient cell viability. In conclusion, NF1‐deficient cells are vulnerable to disruption of the autophagy pathway. This pathway represents a promising target for the treatment of NF1‐associated tumors, and we identified CQ as a candidate drug for the treatment of NF1 tumors. We used synthetic lethal screens to find new approaches to treat neurofibromatosis type 1 (NF1) tumors. Inhibition of autophagy was identified as a robust method to selectively kill NF1‐deficient cells with minimal effects on healthy cells. Following assessment of a range of autophagy inhibitors, we determined that chloroquine has strong potential for repurposing to treat NF1‐associated tumors.

Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 ( NF1 ) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. Results We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 – 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 – 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier’s NF1 score was associated with NF1 protein concentration in these samples. Conclusions We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.

One of the hallmarks of cancer cells is their failure to respond to the cellular mechanism of apoptosis. The B-cell lymphoma 2 (BCL-2) family of proteins regulate apoptosis. Their ability to do so can be measured using several methods that in turn anticipate the fate of the cancer cell in response to apoptosis-inducing treatment. These assays ultimately identify the readiness of the cancer cell to undergo apoptosis, which is referred to as the mitochondrial priming state. These metrics, however, have been challenging to implement in the clinic. Methods: Here, we describe a unique method that relies on a panel of novel conformation-specific antibodies (termed PRIMAB) that can directly measure the mitochondrial priming state. These reagents are highly specific for complexes of their corresponding pro-survival protein interactions with the pro-apoptotic protein BIM. These BIM-containing heterodimeric complexes have long been established as hallmarks of primed cancer cells. Results: Using clinically amenable assay formats, PRIMABs were shown to detect the presence of these anti-apoptotic–pro-apoptotic complexes and their disruption by BH3-mimetic drugs. Moreover, PRIMABs were able to detect a shift in priming status following BH3-mimetic treatment, a factor associated with resistance to these drugs. In a panel of AML patient samples, we report a wide range of priming levels for each PRIMAB complex, demonstrating the potential for heterogeneity in responses. We also show that PRIMABs could be predictive of outcomes for AML patients following cytarabine-based treatment. Conclusions: PRIMABs provide novel and useful tools for cancer research and for clinical implementation as reagents providing predictive tests for treatment response.

Neurofibromatosis type 1 (NF1) is a multi-system, autosomal dominant genetic disorder driven by the systemic loss of the NF1 protein neurofibromin. Loss of neurofibromin in Schwann cells is particularly detrimental, as the acquisition of a 'second-hit' (e.g., complete loss of ) can lead to the development of plexiform neurofibromas (pNF). pNFs are painful, disfiguring tumors with an approximately 1 in 5 chance of sarcoma transition. Selumetinib and mirdametinib are currently the only medicines approved by the U.S. Food and Drug Administration (FDA) for the treatment of pNFs. This motivates the need to develop new therapies, either derived to treat haploinsufficiency or complete loss of function. To identify new therapies, we need to understand the impact neurofibromin has on Schwann cells. Here, we aimed to characterize differences in high-content microscopy in neurofibromin-deficient Schwann cells. We applied a fluorescence microscopy assay (called Cell Painting) to an isogenic pair of Schwann cell lines (derived from ipn02.3 2λ), one of wildtype genotype ( ) and one of null genotype ( ). We modified the canonical Cell Painting assay to mark four organelles/subcellular compartments: nuclei, endoplasmic reticulum, mitochondria, and F-actin. We utilized CellProfiler to perform quality control, illumination correction, segmentation, and cell morphology feature extraction. We segmented 20,680 wildtype and null cells, measured 894 significant cell morphology features representing various organelle shapes and intensity patterns, and trained a logistic regression machine learning model to predict the genotype of single Schwann cells. The machine learning model had high performance, with training and testing data yielding a balanced accuracy of 0.85 and 0.80, respectively. However, when applied to a new pair of Schwann cells, the model's balanced accuracy dropped to 0.5, which is no better than random chance. This performance decline appears to result from morphology differences introduced by non-biological factors (cloning procedures, origin of parental cell line, and CRISPR procedures) of the second cell line pair. We plan to improve upon this preliminary model by refining the morphology signature using a broader panel of Schwann cell lines. Our goal is to apply this enhanced signature in large-scale drug screens of -deficient cells to identify candidate therapeutic agents that specifically reverse the disease-associated morphology. Ultimately, we aim to identify agents that restore NF1 patient-derived Schwann cells to a phenotype resembling the wild-type and healthier state.

We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 - 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 - 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier's NF1 score was associated with NF1 protein concentration in these samples. We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.

Background We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. Results We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 - 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 - 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier's NF1 score was associated with NF1 protein concentration in these samples. Conclusions We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.

Neurofibromatosis type 1 (NF1) is a genetic multi-system disorder. Symptoms include near universal benign neurofibromas, as well as malignant tumours, including generally fatal malignant peripheral nerve sheath tumours. There are limited therapies for any NF1-associated tumours; therefore, there is a clear clinical need to discover new drugs that specifically target NF1-deficient tumour cells. Using a Drosophila NF1-KO cell model, we used synthetic lethal screening to identify candidate drug targets for NF1-deficient tumours and performed statistical enrichment analysis to identify further targets. We then assessed the top 72 candidate synthetic lethal partner genes to NF1 using Variable Dose Analysis, resulting in 15 candidate genes that decreased NF1-KO viability by >10% and were novel druggable targets for NF1. Autophagy inhibitors Chloroquine (CQ) and Bafilomycin A1 resulted in a significant reduction in NF1-KO cell viability, which was conserved in a panel of human NF1 mutant cell lines. AZT and Enzalutamide also selectively reduced NF1 mutant cell viability in human cell lines. Furthermore, the effect of CQ was conserved in a Drosophila NF1-mutant in vivo model. This study highlights two key points: 1) The use of Drosophila cells as a model to screen for drugs specifically targeting NF1 mutant cells was highly successful as candidate interactions were conserved across a panel of human NF1 mutant cells and an in vivo fly NF1 mutant model, and 2) NF1-deficient cells have vulnerability to disruption of the autophagy pathway, telomerase activity, and AR activity. These pathways/drugs represent promising targets for the potential treatment of NF1-associated tumours. Competing Interest Statement The authors have declared no competing interest.

Toxoplasma gondii is a protozoan parasite with a predation-mediated transmission cycle between rodents and felines. Intermediate hosts acquire Toxoplasma by eating parasite cysts which invade the small intestine, disseminate systemically and finally establish host life-long chronic infection in brain and muscles. Here we show that Toxoplasma infection can trigger a severe form of sustained cachexia: a disease of progressive lean weight loss that is a causal predictor of mortality in cancer, chronic disease and many infections. Toxoplasma cachexia is characterized by acute anorexia, systemic inflammation and loss of 20% body mass. Although mice recover from symptoms of peak sickness, they fail to regain muscle mass or visceral adipose depots. We asked whether the damage to the intestinal microenvironment observed at acute time points was sustained in chronic infection and could thereby play a role in sustaining cachexia. We found that parasites replicate in the same region of the distal jejunum/proximal ileum throughout acute infection, inducing the development of secondary lymphoid structures and severe, regional inflammation. Small intestine pathology was resolved by 5 weeks post-infection. However, changes in the commensal populations, notably an outgrowth of Clostridia spp., were sustained in chronic infection. Importantly, uninfected animals co-housed with infected mice display similar changes in commensal microflora but never display symptoms of cachexia, indicating that altered commensals are not sufficient to explain the cachexia phenotype alone. These studies indicate that Toxoplasma infection is a novel and robust model to study the immune-metabolic interactions that contribute to chronic cachexia development, pathology and potential reversal.