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One of the most common malignancies affecting adults with Neurofibromatosis type 1 (NF1) is the malignant peripheral nerve sheath tumor (MPNST), an aggressive and often fatal sarcoma that commonly arises from benign plexiform neurofibromas. Despite advances in our understanding of MPNST pathobiology, there are few effective therapeutic options, and no investigational agents have proven successful in clinical trials. To further understand the genomic heterogeneity of MPNST, and to generate a preclinical platform that encompasses this heterogeneity, we developed a collection of NF1-MPNST patient-derived xenografts (PDX). These PDX were compared with the primary tumors from which they were derived using copy number analysis, whole exome sequencing, and RNA sequencing. We identified chromosome 8 gain as a recurrent genomic event in MPNST and validated its occurrence by FISH in the PDX and parental tumors, in a validation cohort, and by single-cell sequencing in the PDX. Finally, we show that chromosome 8 gain is associated with inferior overall survival in soft-tissue sarcomas. These data suggest that chromosome 8 gain is a critical event in MPNST pathogenesis and may account for the aggressive nature and poor outcomes in this sarcoma subtype.

Low-grade gliomas (LGGs) are the most common childhood brain tumor in the general population and in individuals with the Neurofibromatosis type 1 (NF1) cancer predisposition syndrome. Surgical biopsy is rarely performed prior to treatment in the setting of NF1, resulting in a paucity of tumor genomic information. To define the molecular landscape of NF1-associated LGGs (NF1-LGG), we integrated clinical data, histological diagnoses, and multi-level genetic/genomic analyses on 70 individuals from 25 centers worldwide. Whereas, most tumors harbored bi-allelic NF1 inactivation as the only genetic abnormality, 11% had additional mutations. Moreover, tumors classified as non-pilocytic astrocytoma based on DNA methylation analysis were significantly more likely to harbor these additional mutations. The most common secondary alteration was FGFR1 mutation, which conferred an additional growth advantage in multiple complementary experimental murine Nf1 models. Taken together, this comprehensive characterization has important implications for the management of children with NF1-LGG, distinct from their sporadic counterparts.

Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

Neurofibromatosis type 1 (NF1) is a cancer predisposition disorder that results from inactivation of the tumor suppressor neurofibromin, a negative regulator of RAS signaling. Patients with NF1 present with a wide range of clinical manifestations, and the tumor with highest prevalence is cutaneous neurofibroma (cNF). Most patients harboring cNF suffer greatly from the burden of those tumors, which have no effective medical treatment. Ironically, none of the numerous NF1 mouse models developed so far recapitulate cNF. Here, we discovered that HOXB7 serves as a lineage marker to trace the developmental origin of cNF neoplastic cells. Ablating Nf1 in the HOXB7 lineage faithfully recapitulates both human cutaneous and plexiform neurofibroma. In addition, we discovered that modulation of the Hippo pathway acts as a “modifier” for neurofibroma tumorigenesis. This mouse model opens the doors for deciphering the evolution of cNF to identify effective therapies, where none exist today. Significance: This study provides insights into the developmental origin of cNF, the most common tumor in NF1, and generates the first mouse model that faithfully recapitulates both human cutaneous and plexiform neurofibroma. The study also demonstrates that the Hippo pathway can modify neurofibromagenesis, suggesting that dampening the Hippo pathway could be an attractive therapeutic target. This article is highlighted in the In This Issue feature, p. 1

Cutaneous neurofibromas (cNF) are a nearly ubiquitous symptom of neurofibromatosis type 1 (NF1), a disorder with a broad phenotypic spectrum caused by germline mutation of the neurofibromatosis type 1 tumour suppressor gene (NF1). Symptoms of NF1 can include learning disabilities, bone abnormalities and predisposition to tumours such as cNFs, plexiform neurofibromas, malignant peripheral nerve sheath tumours and optic nerve tumours. There are no therapies currently approved for cNFs aside from elective surgery, and the molecular aetiology of cNF remains relatively uncharacterised. Furthermore, whereas the biallelic inactivation of NF1 in neoplastic Schwann cells is critical for cNF formation, it is still unclear which additional genetic, transcriptional, epigenetic, microenvironmental or endocrine changes are important. Significant inroads have been made into cNF understanding, including NF1 genotype–phenotype correlations in NF1 microdeletion patients, the identification of recurring somatic mutations, studies of cNF-invading mast cells and macrophages, and clinical trials of putative therapeutic targets such as mTOR, MEK and c-KIT. Despite these advances, several gaps remain in our knowledge of the associated pathogenesis, which is further hampered by a lack of translationally relevant animal models. Some of these questions may be addressed in part by the adoption of genomic analysis techniques. Understanding the aetiology of cNF at the genomic level may assist in the development of new therapies for cNF, and may also contribute to a greater understanding of NF1/RAS signalling in cancers beyond those associated with NF1. Here, we summarise the present understanding of cNF biology, including the pathogenesis, mutational landscape, contribution of the tumour microenvironment and endocrine signalling, and the historical and current state of clinical trials for cNF. We also highlight open access data resources and potential avenues for future research that leverage recently developed genomics-based methods in cancer research.

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40&ndash, 60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions.

Nerve sheath tumors occur as a heterogeneous group of neoplasms in patients with neurofibromatosis type 1 (NF1). The malignant form represents the most common cause of death in people with NF1, and even when benign, these tumors can result in significant disfigurement, neurologic dysfunction, and a range of profound symptoms. Lack of human tissue across the peripheral nerve tumors common in NF1 has been a major limitation in the development of new therapies. To address this unmet need, we have created an annotated collection of patient tumor samples, patient-derived cell lines, and patient-derived xenografts, and carried out high-throughput genomic and transcriptomic characterization to serve as a resource for further biologic and preclinical therapeutic studies. In this work, we release genomic and transcriptomic datasets comprised of 55 tumor samples derived from 23 individuals, complete with clinical annotation. All data are publicly available through the NF Data Portal and at http://synapse.org/jhubiobank., Measurement(s)gene expression • gene_variantTechnology Type(s)RNA sequencing • exome sequencing • DNA sequencingFactor Type(s)tumor typeSample Characteristic - OrganismHomo sapiens • Homo sapiens/Mus musculus xenograft Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12037599

In addition to large plexiform neurofibromas (pNF), NF1 patients are frequently disfigured by cutaneous neurofibromas (cNF) and are often afflicted with chronic pain and itch even from seemingly normal skin areas. Both pNFs and cNF consist primarily of benign hyperproliferating nonmyelinating Schwann cells (nSC). While pNF clearly arise within deep nerves and plexuses, the role of cutaneous innervation in the origin of cNF and in chronic itch and pain is unknown. First, we conducted a comprehensive, multi-molecular, immunofluorescence (IF) analyses on 3mm punch biopsies from three separate locations in normal appearing, cNF-free skin in 19 NF1 patients and skin of 16 normal subjects. At least one biopsy in 17 NF1 patients had previously undescribed micro-lesions consisting of a small, dense cluster of nonpeptidergic C-fiber endings and the affiliated nSC consistently adjoining adnexal structures—dermal papillae, hair follicles, sweat glands, sweat ducts, and arterioles—where C-fiber endings normally terminate. Similar micro-lesions were detected in hind paw skin of mice with conditionally-induced SC Nf1-/- mutations. Hypothesizing that these microlesions were pre-cNF origins of cNF, we subsequently analyzed numerous overt, small cNF (s-cNF, 3–6 mm) and discovered that each had an adnexal structure at the epicenter of vastly increased nonpeptidergic C-fiber terminals, accompanied by excessive nSC. The IF and functional genomics assays indicated that neurturin (NTRN) and artemin (ARTN) signaling through cRET kinase and GFRα2 and GFRα3 co-receptors on the aberrant C-fiber endings and nSC may mutually promote the onset of pre-cNF and their evolution to s-cNF. Moreover, TrpA1 and TrpV1 receptors may, respectively, mediate symptoms of chronic itch and pain. These newly discovered molecular characteristics might be targeted to suppress the development of cNF and to treat chronic itch and pain symptoms in NF1 patients.

Growing evidence supports a role for the unfolded protein response (UPR) in carcinogenesis; however, the precise molecular mechanisms underlying this phenomenon remain elusive. Herein, we identified the circadian clock PER1 mRNA as a novel substrate of the endoribonuclease activity of the UPR sensor IRE1α. Analysis of the mechanism shows that IRE1α endoribonuclease activity decreased PER1 mRNA in tumor cells without affecting PER1 gene transcription. Inhibition of IRE1α signaling using either siRNA-mediated silencing or a dominant-negative strategy prevented PER1 mRNA decay, reduced tumorigenesis, and increased survival, features that were reversed upon PER1 silencing. Clinically, patients showing reduced survival have lower levels of PER1 mRNA expression and increased splicing of XBP1, a known IRE-α substrate, thereby pointing toward an increased IRE1α activity in these patients. Hence, we describe a novel mechanism connecting the UPR and circadian clock components in tumor cells, thereby highlighting the importance of this interplay in tumor development. Cancer Res; 73(15); 4732–43. ©2013 AACR.

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets., National Science Foundation (U.S.) (DB1-0821391), National Institutes of Health (U.S.) (Grant U54-CA112967), National Institutes of Health (U.S.) (Grant R01-GM089903), National Institutes of Health (U.S.) (P30-ES002109)

DNA microarrays were rapidly scaled up from 256 to 6.5 million targets, and although antibody microarrays were proposed earlier, sensitive multiplex sandwich assays have only been scaled up to a few tens of targets. Cross-reactivity, arising because detection antibodies are mixed, is a known weakness of multiplex sandwich assays that is mitigated by lengthy optimization. Here, we introduce (1) vulnerability as a metric for assays. The vulnerability of multiplex sandwich assays to cross-reactivity increases quadratically with the number of targets, and together with experimental results, substantiates that scaling up of multiplex sandwich assays is unfeasible. We propose (2) a novel concept for multiplexing without mixing named antibody colocalization microarray (ACM). In ACMs, both capture and detection antibodies are physically colocalized by spotting to the same two-dimensional coordinate. Following spotting of the capture antibodies, the chip is removed from the arrayer, incubated with the sample, placed back onto the arrayer and then spotted with the detection antibodies. ACMs with up to 50 targets were produced, along with a binding curve for each protein. The ACM was validated by comparing it to ELISA and to a small-scale, conventional multiplex sandwich assay (MSA). Using ACMs, proteins in the serum of breast cancer patients and healthy controls were quantified, and six candidate biomarkers identified. Our results indicate that ACMs are sensitive, robust, and scalable.