A Functional Precision Medicine Pipeline Combines Comparative Transcriptomics and Tumor Organoid Modeling to Identify Bespoke Treatment Strategies for Glioblastoma.

Li Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in TP53. TP53 is the most common mutated gene in human cancer, occurring in 30-50% of glioblastomas (GBM). Here, we highlight a precision medicine platform to identify potential targets for a GBM patient with LFS. We used a comparative transcriptomics approach to identify genes that are uniquely overexpressed in the LFS GBM patient relative to a cancer compendium of 12,747 tumor RNA sequencing data sets, including 200 GBMs. STAT1 and STAT2 were identified as being significantly overexpressed in the LFS patient, indicating ruxolitinib, a Janus kinase 1 and 2 inhibitors, as a potential therapy. The LFS patient had the highest level of STAT1 and STAT2 expression in an institutional high-grade glioma cohort of 45 patients, further supporting the cancer compendium results. To empirically validate the comparative transcriptomics pipeline, we used a combination of adherent and organoid cell culture techniques, including ex vivo patient-derived organoids (PDOs) from four patient-derived cell lines, including the LFS patient. STAT1 and STAT2 expression levels in the four patient-derived cells correlated with levels identified in the respective parent tumors. In both adherent and organoid cultures, cells from the LFS patient were among the most sensitive to ruxolitinib compared to patient-derived cells with lower STAT1 and STAT2 expression levels. A spheroid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. Two targeted therapies were selected for the patient of interest and resulted in radiographic disease stability. This manuscript supports the use of comparative transcriptomics to identify personalized therapeutic targets in a functional precision medicine platform for malignant brain tumors.

The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets.

BACKGROUND: The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the sequencing depth. While unmapped or non-exonic reads do not contribute to gene expression quantification, duplicate reads contribute to the quantification but are not informative for reproducibility. We show that mapped, exonic, non-duplicate (MEND) reads are a useful measure of reproducibility of RNA-Seq datasets used for gene expression analysis. FINDINGS: In bulk RNA-Seq datasets from 2,179 tumors in 48 cohorts, the fraction of reads that contribute to the reproducibility of gene expression analysis varies greatly. Unmapped reads constitute 1-77% of all reads (median [IQR], 3% [3-6%]); duplicate reads constitute 3-100% of mapped reads (median [IQR], 27% [13-43%]); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (median [IQR], 25% [16-37%]). MEND reads constitute 0-79% of total reads (median [IQR], 50% [30-61%]). CONCLUSIONS: Because not all reads in an RNA-Seq dataset are informative for reproducibility of gene expression measurements and the fraction of reads that are informative varies, we propose reporting a dataset's sequencing depth in MEND reads, which definitively inform the reproducibility of gene expression, rather than total, mapped, or exonic reads. We provide a Docker image containing (i) the existing required tools (RSeQC, sambamba, and samblaster) and (ii) a custom script to calculate MEND reads from RNA-Seq data files. We recommend that all RNA-Seq gene expression experiments, sensitivity studies, and depth recommendations use MEND units for sequencing depth.

Comparative Tumor RNA Sequencing Analysis for Difficult-to-Treat Pediatric and Young Adult Patients With Cancer.

Importance: Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes. Objective: To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer. Design, Setting, and Participants: This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children's Hospital (n = 31), Lucile Packard Children's Hospital at Stanford University (n = 80), CHOC Children's Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017. Exposures: Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant's tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient's tumor. Results of the UCSC analysis were presented to clinical partners. Main Outcomes and Measures: Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information. Results: Among 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq-derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%). Conclusions and Relevance: This study's findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.

Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE.

BACKGROUND: Funded by the National Institutes of Health (NIH), the aim of the Model Organism ENCyclopedia of DNA Elements (modENCODE) project is to provide the biological research community with a comprehensive encyclopedia of functional genomic elements for both model organisms C. elegans (worm) and D. melanogaster (fly). With a total size of just under 10 terabytes of data collected and released to the public, one of the challenges faced by researchers is to extract biologically meaningful knowledge from this large data set. While the basic quality control, pre-processing, and analysis of the data has already been performed by members of the modENCODE consortium, many researchers will wish to reinterpret the data set using modifications and enhancements of the original protocols, or combine modENCODE data with other data sets. Unfortunately this can be a time consuming and logistically challenging proposition. RESULTS: In recognition of this challenge, the modENCODE DCC has released uniform computing resources for analyzing modENCODE data on Galaxy (https://github.com/modENCODE-DCC/Galaxy), on the public Amazon Cloud (http://aws.amazon.com), and on the private Bionimbus Cloud for genomic research (http://www.bionimbus.org). In particular, we have released Galaxy workflows for interpreting ChIP-seq data which use the same quality control (QC) and peak calling standards adopted by the modENCODE and ENCODE communities. For convenience of use, we have created Amazon and Bionimbus Cloud machine images containing Galaxy along with all the modENCODE data, software and other dependencies. CONCLUSIONS: Using these resources provides a framework for running consistent and reproducible analyses on modENCODE data, ultimately allowing researchers to use more of their time using modENCODE data, and less time moving it around.

modMine: flexible access to modENCODE data.

In an effort to comprehensively characterize the functional elements within the genomes of the important model organisms Drosophila melanogaster and Caenorhabditis elegans, the NHGRI model organism Encyclopaedia of DNA Elements (modENCODE) consortium has generated an enormous library of genomic data along with detailed, structured information on all aspects of the experiments. The modMine database (http://intermine.modencode.org) described here has been built by the modENCODE Data Coordination Center to allow the broader research community to (i) search for and download data sets of interest among the thousands generated by modENCODE; (ii) access the data in an integrated form together with non-modENCODE data sets; and (iii) facilitate fine-grained analysis of the above data. The sophisticated search features are possible because of the collection of extensive experimental metadata by the consortium. Interfaces are provided to allow both biologists and bioinformaticians to exploit these rich modENCODE data sets now available via modMine.