A Scalable Quality Assurance Process for Curating Oncology Electronic Health Records: The Project GENIE Biopharma Collaborative Approach.

PURPOSE: The American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative is a multi-institution effort to build a pan-cancer repository of genomic and clinical data curated from the electronic health record. For the research community to be confident that data extracted from electronic health record text are reliable, transparency of the approach used to ensure data quality is essential. MATERIALS AND METHODS: Four institutions participating in AACR's Project GENIE created an observational cohort of patients with cancer for whom tumor molecular profiling data, therapeutic exposures, and treatment outcomes are available and will be shared publicly with the research community. A comprehensive approach to quality assurance included assessments of (1) feasibility of the curation model through pressure test cases; (2) accuracy through programmatic queries and comparison with source data; and (3) reproducibility via double curation and code review. RESULTS: Assessments of feasibility resulted in critical modifications to the curation directives. Queries and comparison with source data identified errors that were rectified via data correction and curator retraining. Assessment of intercurator reliability indicated a reliable curation model. CONCLUSION: The transparent quality assurance processes for the GENIE BPC data ensure that the data can be used for analyses that support clinical decision making and advances in precision oncology.

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.

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.

Pharmacological and genomic profiling of neurofibromatosis type 1 plexiform neurofibroma-derived schwann cells.

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.

A high-throughput molecular data resource for cutaneous neurofibromas.

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.