Biology subjects

Allaway, R. J.

Publications and source records attributed to Allaway, R. J..

8 recordsLinked to original sources

bioRxiv2026

mafsmith: a Rust reimplementation of vcf2maf

The Mutation Annotation Format (MAF) is a standard interchange format for somatic variant data in tumor genomics. Converting variant call format (VCF) files to MAF requires functional annotation (through tools such as the Ensembl Variant Effect Predictor) and complex allele normalisation and field-mapping logic. The gold-standard implementation, vcf2maf, is written in Perl and could be made more computationally efficient by translating it to a newer language and adding support for parallel processing. Here we describe mafsmith, an implementation of vcf2maf in Rust. The mafsmith implementation of vcf2maf reimplements the allele-normalisation and field-mapping logic of vcf2maf and uses fastVEP for annotation, achieving field-for-field identical output across fifteen validated caller types and formats spanning germline, somatic, structural variant, and annotation-database VCFs. When both tools are run with the same Ensembl VEP annotation cache, mafsmith vcf2maf produces 0 conversion differences versus vcf2maf across 23 diverse datasets aligned to GRCh38 or GRCh37. The companion maf2vcf, vcf2vcf, and maf2maf subcommands were similarly validated against their reference Perl counterparts across six datasets. Benchmarked on multiple reference samples totalling 27.5 million variants, mafsmith achieves approximately 80-fold faster conversion of pre-annotated VCFs (range 74.3-84.1x), enabling faster and cheaper conversion of vcfs to mafs. mafsmith is open source under the same license as vcf2maf and available at https://github.com/nf-osi/mafsmith.

genomics↗

bioRxiv2025

Immortalization and Characterization of Schwann Cell Lines Derived from NF1 Associated Cutaneous Neurofibromas

Neurofibromatosis type 1 (NF1) is an autosomal dominant condition in which patients are heterozygous for a disruptive pathogenic variant in the NF1 gene. The most characteristic feature of the condition NF1 is the neurofibroma, a benign, multi-cellular tumor which initiates when a cell of the Schwann cell lineage gains a somatic pathogenic variant of the other NF1 allele. Neurofibromas developing at nerve termini in the skin are termed "cutaneous" neurofibromas (cNFs), while those developing within larger nerves are termed "plexiform." Most patients develop cNFs beginning in late childhood or early adulthood, continuing throughout life at variable rates. Some patients may develop only a few cNFs, while others suffer from thousands. There are no reliably effective physical or pharmaceutical therapies besides surgical removal. Although these are not life-threatening, they are disfiguring and can interfere with normal life functions. To provide a resource for research, we developed short-term cNF Schwann cell cultures from NF1 patients, from which we subsequently established the first semi-immortalized cNF cell lines through transduction with wild-type human telomerase reverse transcriptase (hTERT) and murine cyclin-dependent kinase 4 (mCdk4) genes. Here we present molecular, cellular, and functional characterization of these cell lines, which will be of utility for investigating and developing NF1 cNF therapies.

genomics↗

bioRxiv2024

Genomic features of NF1-associated peripheral nerve sheath tumors: a cohort analysis from the Johns Hopkins NF1 biospecimen repository

Neurofibromatosis type 1 (NF1) is an inherited neurocutaneous condition that predisposes to the development of peripheral nerve sheath tumors (PNST) including cutaneous neurofibromas (CNF), plexiform neurofibromas (PNF), atypical neurofibromatous neoplasms with unknown biological potential (ANNUBP), and malignant peripheral nerve sheath tumors (MPNST). The successful advancement of therapeutic development for NF1-associated PNST necessitates the systematic acquisition and analysis of human tumor specimens and their corresponding model systems. RNA sequencing (RNAseq) and whole exome sequencing (WES) data were generated from 73 and 114 primary human tumor samples, respectively. These pre-processed data, standardized for immediate computational analysis, are accessible through the NF Data Portal, allowing immediate interrogation. This analysis combines new and previously released samples, offering a comprehensive view of the entire cohort sequenced. As a dedicated effort to systematically bank tumor samples from people with NF1, in collaboration with molecular geneticists and computational biologists to advance understanding of NF1 biology, the Johns Hopkins NF1 biospecimen repository offers access to samples and genomic data to promote advancement of NF1-related therapies.

bioinformatics↗

bioRxiv2021

Crowdsourced Identification of Multi-Target Kinase Inhibitors for RET- and TAU- Based Disease: the Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ( polypharmacology). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology. Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, dirty; they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop cleaner therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.

pharmacology and toxicology↗

bioRxiv2020

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

cancer biology↗

bioRxiv2020

Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

Neurofibromatosis type 1 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, while 40-60% 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.

systems biology↗

bioRxiv2020

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.

biochemistry↗

bioRxiv2019

A clinically and genomically annotated nerve sheath tumor biospecimen repository.

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.

cancer biology↗