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Background Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1). To address the critical need for novel therapeutics in MPNST, we aimed to establish an ex vivo 3D platform that accurately captured the genomic diversity of MPNST and could be utilized in a medium-throughput manner for drug screening studies to be validated in vivo using patient-derived xenografts (PDX). Methods Genomic analysis was performed on all PDX-tumor pairs. Select PDX were harvested for assembly into 3D microtissues. Based on prior work in our labs, we evaluated drugs (trabectedin, olaparib, and mirdametinib) ex vivo and in vivo. For 3D microtissue studies, cell viability was the endpoint as assessed by Zeiss Axio Observer. For PDX drug studies, tumor volume was measured twice weekly. Bulk RNA sequencing was performed to identify pathways enriched in cells. Results We developed 13 NF1-associated MPNST-PDX and identified mutations or structural abnormalities in NF1 (100%), SUZ12 (85%), EED (15%), TP53 (15%), CDKN2A (85%) and chromosome 8 gain (77%). We successfully assembled PDX into 3D microtissues, categorized as robust (>90% viability at 48h), good (>50%), or unusable ( Conclusions These data support the successful establishment of a novel 3D platform for drug discovery and MPNST biology exploration in a system representative of the human condition.

Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual’s leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.

A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR).

A significant challenge facing rare disease communities is finding a sufficient quantity and variety of data to develop and test disease-specific hypotheses. Here we describe an approach to data sharing in which stakeholders from the neurofibromatosis (NF) research community collaborated to develop a disease-focused data portal with the goal of supporting scientists within and outside the community as well as clinicians and patient advocates.

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

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.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science., Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Our study details the stepwise evolution of gilteritinib resistance in FLT3-mutated acute myeloid leukemia (AML). Early resistance is mediated by the bone marrow microenvironment, which protects residual leukemia cells. Removing these supportive extrinsic ligands drives evolution of late, intrinsic resistance. Whole exome sequencing, CRISPR/Cas, metabolomics, proteomics, and pharmacologic approaches were used to mechanistically define both early and late resistance. Early resistant cells undergo metabolic reprogramming, grow more slowly, and are dependent upon Aurora kinase B (AURKB). Late resistant cells are characterized byexpansion of pre-existing NRAS mutant subclones and continued metabolic reprogramming. Our model closely mirrors the timing and mutations of AML patients treated with gilteritinib.Pharmacological inhibition of AURKB resensitized both early resistant cell cultures and primary leukemia cells from gilteritinib-treated AML patients. These findings support a combinatorial strategy to target early resistant AML cells with AURKB inhibitors and gilteritinib before the expansion of pre-existing resistance mutations occurs.

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.

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 which 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 success 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 to the primary tumors from which they were derived using copy number analysis, whole exome 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. Taken together, 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.

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.

Single-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be 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 ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) 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 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.

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.

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.

In acute myeloid leukemia (AML), activating mutations in FLT3 are the most common genetic abnormality. Multiple FLT3 inhibitors have been developed, including the FDA-approved inhibitor gilteritinib. However, AML patients only respond to gilteritinib for approximately 6 months due to the emergence of drug resistance. While gilteritinib eliminates blasts in peripheral circulation, residual blasts in the bone marrow microenvironment are protected by cytokines and growth factors. Persistence of these residual cells represents early resistance to treatment. How these cells adapt to survive in the marrow microenvironment remains unclear, but over time resistant subclones resume growth and lead to relapsed disease. At relapse, many patients have intrinsic resistance mutations, what we term late resistance. In this study, we used a stepwise model that charts the temporal evolution of early to late gilteritinib resistance. To recapitulate early resistance, we cultured the FLT3-ITD+ AML cell lines, MOLM-14 and MV4;11, with exogenous microenvironmental ligands that allow cells to become resistant to gilteritinib in a ligand-dependent manner. After 7 weeks, all cultures with ligand resumed growth (early resistance), whereas cells without ligand never resumed growth. Following ligand withdrawal, the cells become transiently sensitive to gilteritinib but resumed growth after 2 months (late resistance). We comprehensively analyzed early and late resistance by integrating whole exome sequencing, CRISPR/Cas9 screening, proteomics, metabolomics, and small-molecule inhibitor screening. Early resistance is characterized by slowly dividing cells and metabolic reprogramming, particularly with respect to lipid metabolism. Early resistant cultures also became uniquely dependent on Aurora kinase B (AURKB) for survival. We then validated these findings in primary AML cells from patients (N=11) treated with gilteritinib and found that early resistant cells demonstrated reduced cell cycle and alterations in lipid metabolism. Gene expression analysis of sequential stromal cell samples from AML patients (N=13) pre- and post gilteritinib treatment showed an increase in lipid metabolism following gilteritinib treatment, indicating that the microenvironment is also dynamic and in crosstalk with neighboring AML cells. Primary early resistant AML cells also became dependent on AURKB signaling, and were exquisitely sensitive to the combination of AURKB inhibitors and gilteritinib. In contrast, late resistance is driven by an expansion of pre-existing NRAS mutant subclones, consistent with the resistance profile of AML patients on gilteritinib. Metabolic reprogramming continued to evolve in late resistance with further dependence upon lipid metabolism. Our study provides mechanistic understanding of how the marrow microenvironment contributes to extrinsic early resistance, which then leads to late intrinsic resistance. We also define a unique vulnerability to AURKB inhibitors in early resistance that may thwart the expansion of late resistant NRAS subclones. Citation Format: Sunil K. Joshi, Tamilla Nechiporuk, Daniel Bottomly, Paul Piehowski, Julie A. Reisz, Janét Pittsenbarger, Andy Kaempf, Sara J.C. Gosline, Yi-Ting Wang, Tao Liu, Cristina E. Tognon, Angelo D’Alessandro, Jeffrey W. Tyner, Shannon K. McWeeney, Karin D. Rodland, Brian J. Druker, Elie Traer. The AML microenvironment catalyzes a step-wise evolution to gilteritinib resistance [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT022.

The neurocutaneous tumor predisposition disorder neurofibromatosis Type I (NF1) results from deregulation of RAS signaling in the MAPK pathway that leads to a wide spectrum of clinical presentations. Dermal or cutaneous neurofibromas (cNF), a Schwann cell tumor in the skin, affect more than 95% of individuals with NF1 and are a major source of emotional, physical, and social distress as NF1 patients can have thousands of these tumors covering most of their skin. Thus, patients with NF1 often identify these tumors as their greatest burden. To date, there is no available medical treatment for cNF and no known way to prevent them from developing. The major barriers that impede progress in this field are the lack of accurate models of these common cNF tumors for drug evaluation and a limited understanding of their pathogenesis as well as the identity of specific cell of origin that directly gives rise to cNF and signaling pathways essential for tumor development. We take advantage of genetic labeling for cell lineage tracing to identify mouse neural crest Cre lines that are expressed in the subpopulation of Schwann cell lineage that give rise to cNF when NF1 is deleted. We discovered that the Homeobox B7 transcription factor serves as the lineage marker to trace the developmental origin of cNF neoplastic cells that completely recapitulates human neurofibromatosis, generating a novel mouse model that spontaneously develops both cutaneous and plexiform neurofibroma. In addition, we discovered that modulation of the Hippo pathway acts as a modifier that enhances the MAPK pathway activation by NF1 loss to promote neurofibromagenesis, suggesting that dampening the Hippo pathway may serve as part of the comprehensive treatment approach for neurofibroma. This novel mouse model has begun to yield vital clues to neurofibroma pathogenesis and now opens the doors for deciphering the evolution of cNF to identify effective therapies, where none exist today. Citation Format: Zhiguo Chen, Juan Mo, Jean-Philippe Brosseau, Tracey Shipman, Yong Wang, Chung-Ping Liao, Jonathan M. Cooper, Robert J. Allaway, Sara J.C. Gosline, Justin Guinney, Thomas J. Carroll, Lu Q. Le. A novel model of neurofibroma that deciphers its developmental origin and susceptibility to modification by the Hippo pathway [abstract]. In: Proceedings of the AACR Special Conference on the Hippo Pathway: Signaling, Cancer, and Beyond; 2019 May 8-11; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(8_Suppl):Abstract nr B23.

Modern phenotypic high-throughput screens (HTS) present several challenges including identifying the target(s) that mediate the effect seen in the screen, characterizing ‘hits’ with a polypharmacologic target profile, and contextualizing screen data within the large space of drugs and screening models. To address these challenges, we developed the Drug–Target Explorer. This tool allows users to query molecules within a database of experimentally-derived and curated compound-target interactions to identify structurally similar molecules and their targets. It enables network-based visualizations of the compound-target interaction space, and incorporates comparisons to publicly-available in vitro HTS datasets. Furthermore, users can identify molecules using a query target or set of targets. The Drug Target Explorer is a multifunctional platform for exploring chemical space as it relates to biological targets, and may be useful at several steps along the drug development pipeline including target discovery, structure–activity relationship, and lead compound identification studies. Electronic supplementary material The online version of this article (10.1186/s13321-018-0297-4) contains supplementary material, which is available to authorized users.

Neurofibromatosis type I (NF1) is an autosomal dominant genetic condition characterized by peripheral nervous system tumors (PNSTs), including plexiform neurofibromas (pNFs) that cause nerve dysfunction, deformity, pain damage to adjacent structures, and can undergo malignant transformation. There are no effective therapies to prevent or treat pNFs. Drug discovery efforts are slowed by the ‘benign’ nature of the Schwann cells that are the progenitor cells of pNF. In this work we characterize a set of pNF-derived cell lines at the genomic level (via SNP Arrays, RNAseq, and Whole Exome- Sequencing), and carry out dose response-based quantitative high-throughput screening (qHTS) with a collection of 1,912 oncology-focused compounds in a 1536-well microplate cell proliferation assays. Through the characterization and screening of NF1−/−, NF1+/+ and NF1+/− Schwann cell lines, this resource introduces novel therapeutic avenues for the development for NF1 associated pNF as well as all solid tumors with NF1 somatic mutations. The integrated data sets are openly available for further analysis at http://www.synapse.org/pnfCellCulture.

Neurofibromatosis type 1 (NF1) is a cancer predisposition disorder that results from inactivation of the tumor-suppressor Neurofibromin, a negative regulator of RAS signaling. NF1 patients 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.

Neurofibromatosis type 1 (NF1) is a genetic disorder with a range of clinical manifestations such as widespread growth of benign tumours called neurofibromas, pain, learning disorders, bone deformities, vascular abnormalities and even malignant tumours. With the establishment of the Children’s Tumour Foundation biobank, neurofibroma samples can now be collected directly from patients to be analysed by the larger scientific community. This work describes a pilot study to characterize one class of neurofibroma, cutaneous neurofibromas, by molecularly profiling of ~40 cutaneous neurofibromas collected from 11 individual patients. Data collected from each tumour includes (1) SNP Arrays, (2) Whole genome sequencing (WGS) and (3) RNA-Sequencing. These data are now freely available for further analysis at http://www.synapse.org/cutaneousNF.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Small molecule screens are widely used to prioritize pharmaceutical development. However, determining the pathways targeted by these molecules is challenging, since the compounds are often promiscuous. We present a network strategy that takes into account the polypharmacology of small molecules in order to generate hypotheses for their broader mode of action. We report a screen for kinase inhibitors that increase the efficacy of gemcitabine, the first-line chemotherapy for pancreatic cancer. Eight kinase inhibitors emerge that are known to affect 201 kinases, of which only three kinases have been previously identified as modifiers of gemcitabine toxicity. In this work, we use the SAMNet algorithm to identify pathways linking these kinases and genetic modifiers of gemcitabine toxicity with transcriptional and epigenetic changes induced by gemcitabine that we measure using DNaseI-seq and RNA-seq. SAMNet uses a constrained optimization algorithm to connect genes from these complementary datasets through a small set of protein-protein and protein-DNA interactions. The resulting network recapitulates known pathways including DNA repair, cell proliferation and the epithelial-to-mesenchymal transition. We use the network to predict genes with important roles in the gemcitabine response, including six that have already been shown to modify gemcitabine efficacy in pancreatic cancer and ten novel candidates. Our work reveals the important role of polypharmacology in the activity of these chemosensitiz-ing agents., National Institutes of Health (U.S.) (Grant R01-GM089903), National Institutes of Health (U.S.) (Grant U01-CA184898)

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic. Cancer Res; 76(23); 6774–7. ©2016 AACR.

We construct a pathway de novo for microenvironment-specific genetic regulators of acute lymphoblastic leukemia using RNAi screening, and mRNA data., Data integration stands to improve interpretation of RNAi screens which, as a result of off-target effects, typically yield numerous gene hits of which only a few validate. These off-target effects can result from seed matches to unintended gene targets (reagent-based) or cellular pathways, which can compensate for gene perturbations (biology-based). We focus on the biology-based effects and use network modeling tools to discover pathways de novo around RNAi hits. By looking at hits in a functional context, we can uncover novel biology not identified from any individual ‘omics measurement. We leverage multiple ‘omic measurements using the Simultaneous Analysis of Multiple Networks (SAMNet) computational framework to model a genome scale shRNA screen investigating Acute Lymphoblastic Leukemia (ALL) progression in vivo. Our network model is enriched for cellular processes associated with hematopoietic differentiation and homeostasis even though none of the individual ‘omic sets showed this enrichment. The model identifies genes associated with the TGF-beta pathway and predicts a role in ALL progression for many genes without this functional annotation. We further experimentally validate the hidden genes – Wwp1, a ubiquitin ligase, and Hgs, a multi-vesicular body associated protein – for their role in ALL progression. Our ALL pathway model includes genes with roles in multiple types of leukemia and roles in hematological development. We identify a tumor suppressor role for Wwp1 in ALL progression. This work demonstrates that network integration approaches can compensate for off-target effects, and that these methods can uncover novel biology retroactively on existing screening data. We anticipate that this framework will be valuable to multiple functional genomic technologies – siRNA, shRNA, and CRISPR – generally, and will improve the utility of functional genomic studies.

High-throughput, ‘omic’ methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of ‘omic’ data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator., National Institutes of Health (U.S.) (grant U54CA112967), National Institutes of Health (U.S.) (grant U01CA184898), National Institutes of Health (U.S.) (grant U54NS091046), National Institutes of Health (U.S.) (grant R01GM089903)

Motivation: High-throughput datasets such as genetic screens, mRNA expression assays and global phospho-proteomic experiments are often difficult to interpret due to inherent noise in each experimental system. Computational tools have improved interpretation of these datasets by enabling the identification of biological processes and pathways that are most likely to explain the measured results. These tools are primarily designed to analyse data from a single experiment (e.g. drug treatment versus control), creating a need for computational algorithms that can handle heterogeneous datasets across multiple experimental conditions at once. Summary: We introduce SAMNetWeb, a web-based tool that enables functional enrichment analysis and visualization of high-throughput datasets. SAMNetWeb can analyse two distinct data types (e.g. mRNA expression and global proteomics) simultaneously across multiple experimental systems to identify pathways activated in these experiments and then visualize the pathways in a single interaction network. Through the use of a multi-commodity flow based algorithm that requires each experiment ‘share’ underlying protein interactions, SAMNetWeb can identify distinct and common pathways across experiments. Availability and implementation: SAMNetWeb is freely available at http://fraenkel.mit.edu/samnetweb., United States. National Institutes of Health (U54CA112967), United States. National Institutes of Health (R01GM089903), National Science Foundation (U.S.) (DB1-0821391)

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.

Insult to the endoplasmic reticulum (ER) activates the Unfolded Protein Response (UPR), a set of signaling pathways that protect the cell from the potential damage caused by improperly folded proteins. Accumulation of misfolded proteins in the ER lumen initiates a series of signal transduction events via activation of three transmembrane ER proteins: Ire1, Atf6 and PERK. Activation of these proteins results in the transcriptional up-regulation of the components of the folding, trafficking and degradation machinery in the ER. PERK further reduces the load on the ER via the phosphorylation of eIF2α, attenuating general protein translation. It is believed that the UPR evolved as a transcriptional response that up-regulates protein folding machinery in the ER and later gained the ability to decrease ER load by attenuating general protein translation in metazoa. However, our in silico analyses of protozoan parasites revealed an absence of proteins involved in the transcriptionally mediated UPR and the presence of both PERK and its target eIF2α. Consistent with these observations, stimulation of the UPR in Leishmania donovani identified an absence of up-regulation of the ER chaperone BiP, the canonical ER chaperone modulated by the UPR in higher eukaryotes, while exhibiting increased phosphorylation of eIF2α which has been shown to attenuate protein translation. We further observed that L. donovani is more sensitive to UPR inducing agents than host macrophages, suggesting that the less evolved stress response could provide a new avenue for therapeutic treatment of parasitic infections.

SummaryWe report more than 1400 proteins of the secretory-pathway proteome and provide spatial information on the relative presence of each protein in the rough and smooth ER Golgi cisternae and Golgi-derived COPI vesicles. The data support a role for COPI vesicles in recycling and cisternal maturation, showing that Golgi-resident proteins are present at a higher concentration than secretory cargo. Of the 1400 proteins, 345 were identified as previously uncharacterized. Of these, 230 had their subcellular location deduced by proteomics. This study provides a comprehensive catalog of the ER and Golgi proteomes with insight into their identity and function.

The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multiple assays (e.g. mRNA expression and phospho-proteomics) to measure a single condition. However, as experiments expand to accommodate various cellular conditions, proper analysis and interpretation of these data have become more challenging. Here we introduce a novel approach called SAMNet, for Simultaneous Analysis of Multiple Networks, that is able to interpret diverse assays over multiple perturbations. The algorithm uses a constrained optimization approach to integrate mRNA expression data with upstream genes, selecting edges in the protein–protein interaction network that best explain the changes across all perturbations. The result is a putative set of protein interactions that succinctly summarizes the results from all experiments, highlighting the network elements unique to each perturbation. We evaluated SAMNet in both yeast and human datasets. The yeast dataset measured the cellular response to seven different transition metals, and the human dataset measured cellular changes in four different lung cancer models of Epithelial-Mesenchymal Transition (EMT), a crucial process in tumor metastasis. SAMNet was able to identify canonical yeast metal-processing genes unique to each commodity in the yeast dataset, as well as human genes such as β-catenin and TCF7L2/TCF4 that are required for EMT signaling but escaped detection in the mRNA and phospho-proteomic data. Moreover, SAMNet also highlighted drugs likely to modulate EMT, identifying a series of less canonical genes known to be affected by the BCR-ABL inhibitor imatinib (Gleevec), suggesting a possible influence of this drug on EMT., National Institutes of Health (U.S.) (Grant U54CA112967), National Institutes of Health (U.S.) (Grant R01GN089903), National Science Foundation (U.S.) (Award DB1-0821391), Massachusetts Institute of Technology. Undergraduate Research Opportunities Program