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PurposeClinical analysis and reporting of somatically acquired copy number abnormalities (CNAs) detected through next-generation sequencing (NGS) is time consuming and requires significant expertise. Interpretation is complicated by other classes of variants such as coding mutations and gene fusions. Recent guidelines for the clinical assessment of tumor CNAs harmonize and simplify the reporting criteria but did not directly address NGS-specific concerns or the need for a standardized and scalable protocol for CNA analysis.MethodsWe developed a scalable NGS-derived CNA analysis protocol paired with a novel interactive web application, CNA Explorer and anaLyzer (CNAEL), to facilitate the rapid, scalable, and reproducible analysis and reporting of complex tumor-derived CNA profileshttps://CNAEL.sema4.com.ResultsNovel features of CNAEL include on-the-fly data rescaling to account for tumor ploidy, purity, and modal chromosomal copy number; integration of gene expression, coding, and fusion variants into review and automated genome-wide summarization to enable rapid reporting. We found that case curation times were significantly reduced when using CNAEL [median:7 mins, IQR = 4, 10.25] compared with our previous laboratory standard operating procedure [median: 61 mins, IQR = 23.75, 176,25] with p=4.631e-05.ConclusionCNAEL enables efficient and accurate clinical review and reporting of complex NGS-derived tumor copy number profiles.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in clinical presentation with an urgent need for better stratification tools for clinical development and care. In this study we used a crowdsourcing approach to address the problem of ALS patient stratification. The DREAM Prize4Life ALS Stratification Challenge was a crowdsourcing initiative using data from >10,000 patients from completed ALS clinical trials and 1479 patients from community-based patient registers. Challenge participants used machine learning and clustering techniques to predict ALS progression and survival. By developing new approaches, the best performing teams were able to predict disease outcomes better than currently available methods. At the same time, the integration of clustering components across methods led to the emergence of distinct consensus clusters, separating patients into four consistent groups, each with its unique predictors for classification. This analysis reveals for the first time the potential of a crowdsourcing approach to uncover covert patient sub-populations, and to accelerate disease understanding and therapeutic development.

e13155 Background: Next generation sequencing (NGS) technology is transforming the diagnosis and treatment of cancer. However, the massive scale of data has overwhelmed pathologists who need streamlined tools to process this data, automate report generation and minimize human errors. Methods: We developed the Variant interpretation station for ONCology, VONC, as an end-to-end solution for moving from NGS whole exome and transcriptome data to actionable clinical reports that support cancer diagnosis, prognosis, and personalized treatment strategies for solid and hematologic malignancies. Results: VONC integrates all steps for moving from raw NGS data, variant calling and LIMS, to comprehensive annotation of variants. The main functional feature of VONC is a transparent process that effectively combines automated and expert curation to identify clinically relevant and actionable driver variants. VONC also enables efficient management of multi-group, -role, -system and -site curation processes. In contrast to current tools, VONC handles all somatic and constitutional genomic alterations including SNV, indel, CNV, fusion, splicing, and gene expression. Key data sources include 1) 350,000 variants for 50 tumor types across 57,000 sequenced cancer patients; 2) variant frequencies estimated from 1.5M cancer patients; 3) expert curated literature evidence from 16,818 papers covering 26,496 alterations spanning 2,448 cancer driver genes; and 4) curated database of FDA-approved drugs and recruiting clinical trials. VONC presents a prioritized list of variants in oncogenes and tumor suppressors through functional (literature-based) and structure-based (hotspots) algorithms. This is coupled to all supporting information necessary for clinical decision making. Curators can quickly screen variant type, QC metrics, and frequency in sequencing cohorts of cancer patients as well as healthy subjects. Within minutes, variants can be triaged and annotated with FDA approved, NCCN guidelines reported, or literature supported therapeutics, including resistance and contraindicated. Conclusions: VONC is a clinically-ready tool with an intuitive end-user interface tailored for the rapid assessment of variants in cancer patients, to facilitate personalized cancer medicine in a high-throughput laboratory.

Advancing research and clinical care, and conducting successful and cost-effective clinical trials requires characterizing a given patient population. To gather a sufficiently large cohort of patients in rare diseases such as amyotrophic lateral sclerosis (ALS), we developed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) platform. The PRO-ACT database currently consists of >8600 ALS patient records from 17 completed clinical trials, and more trials are being incorporated. The database was launched in an open-access mode in December 2012; since then, >400 researchers from >40 countries have requested the data. This review gives an overview on the research enabled by this resource, through several examples of research already carried out with the goal of improving patient care and understanding the disease. These examples include predicting ALS progression, the simulation of future ALS clinical trials, the verification of previously proposed predictive features, the discovery of novel predictors of ALS progression and survival, the newly identified stratification of patients based on their disease progression profiles, and the development of tools for better clinical trial recruitment and monitoring. Results from these approaches clearly demonstrate the value of large datasets for developing a better understanding of ALS natural history, prognostic factors, patient stratification, and more. The increasing use by the community suggests that further analyses of the PRO-ACT database will continue to reveal more information about this disease that has for so long defied our understanding.

cDNA microarray technology is a powerful tool that allows the expression profiling of thousands of mRNA transcripts simultaneously. Despite technical and analytical challenges, the application of gene expression profiling in degenerative arthritis research will provide a better understanding of the disease at the molecular level and lead to new diagnostic markers and therapeutic targets. Profiling the gene expression of articular cartilage will lead to the identification of genes involved in cartilage matrix homeostasis and in disease initiation, progression and outcome. Analysis of gene expression patterns of synovium in rheumatoid arthritis and osteoarthritis (OA) may help identify targets for future disease management. Recently, gene profiling strategies have been applied to peripheral blood from subjects with OA, a novel development in OA diagnosis.