Mobilization of innate and adaptive antitumor immune responses by the RNP-targeting antibody ATRC-101.

Immunotherapy approaches focusing on T cells have provided breakthroughs in treating solid tumors. However, there remains an opportunity to drive anticancer immune responses via other cell types, particularly myeloid cells. ATRC-101 was identified via a target-agnostic process evaluating antibodies produced by the plasmablast population of B cells in a patient with non-small cell lung cancer experiencing an antitumor immune response during treatment with checkpoint inhibitor therapy. Here, we describe the target, antitumor activity in preclinical models, and data supporting a mechanism of action of ATRC-101. Immunohistochemistry studies demonstrated tumor-selective binding of ATRC-101 to multiple nonautologous tumor tissues. In biochemical analyses, ATRC-101 appears to target an extracellular, tumor-specific ribonucleoprotein (RNP) complex. In syngeneic murine models, ATRC-101 demonstrated robust antitumor activity and evidence of immune memory following rechallenge of cured mice with fresh tumor cells. ATRC-101 increased the relative abundance of conventional dendritic cell (cDC) type 1 cells in the blood within 24 h of dosing, increased CD8+ T cells and natural killer cells in blood and tumor over time, decreased cDC type 2 cells in the blood, and decreased monocytic myeloid-derived suppressor cells in the tumor. Cellular stress, including that induced by chemotherapy, increased the amount of ATRC-101 target in tumor cells, and ATRC-101 combined with doxorubicin enhanced efficacy compared with either agent alone. Taken together, these data demonstrate that ATRC-101 drives tumor destruction in preclinical models by targeting a tumor-specific RNP complex leading to activation of innate and adaptive immune responses.

The UCSC Genome Browser database: 2017 update.

Since its 2001 debut, the University of California, Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu/) team has provided continuous support to the international genomics and biomedical communities through a web-based, open source platform designed for the fast, scalable display of sequence alignments and annotations landscaped against a vast collection of quality reference genome assemblies. The browser's publicly accessible databases are the backbone of a rich, integrated bioinformatics tool suite that includes a graphical interface for data queries and downloads, alignment programs, command-line utilities and more. This year's highlights include newly designed home and gateway pages; a new 'multi-region' track display configuration for exon-only, gene-only and custom regions visualization; new genome browsers for three species (brown kiwi, crab-eating macaque and Malayan flying lemur); eight updated genome assemblies; extended support for new data types such as CRAM, RNA-seq expression data and long-range chromatin interaction pairs; and the unveiling of a new supported mirror site in Japan.

Error rates for nanopore discrimination among cytosine, methylcytosine, and hydroxymethylcytosine along individual DNA strands.

Cytosine, 5-methylcytosine, and 5-hydroxymethylcytosine were identified during translocation of single DNA template strands through a modified Mycobacterium smegmatis porin A (M2MspA) nanopore under control of phi29 DNA polymerase. This identification was based on three consecutive ionic current states that correspond to passage of modified or unmodified CG dinucleotides and their immediate neighbors through the nanopore limiting aperture. To establish quality scores for these calls, we examined ~3,300 translocation events for 48 distinct DNA constructs. Each experiment analyzed a mixture of cytosine-, 5-methylcytosine-, and 5-hydroxymethylcytosine-bearing DNA strands that contained a marker that independently established the correct cytosine methylation status at the target CG of each molecule tested. To calculate error rates for these calls, we established decision boundaries using a variety of machine-learning methods. These error rates depended upon the identity of the bases immediately 5' and 3' of the targeted CG dinucleotide, and ranged from 1.7% to 12.2% for a single-pass read. We estimate that Q40 values (0.01% error rates) for methylation status calls could be achieved by reading single molecules 5-19 times depending upon sequence context.

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.

Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples.

PURPOSE: Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample. METHODS: We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression. RESULTS: We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns. CONCLUSION: This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.

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.

Assessing risk of recidivism among juvenile offenders: the development and validation of the recidivism risk instrument.

A recidivism risk instrument was developed and validated on a sample of juvenile offenders (N = 1,987) based on the need to classify juveniles by their likelihood of re-offense. Female recidivism (R(2) = 27%) was predicted by younger age at first expulsion from school, history of parent incarceration, gang involvement, felony class offense, and firearm use. Male recidivism (R(2) = 12%) was predicted by younger age at first adjudication, referrals, school suspensions, history of maternal incarceration, firearm use, running away, gang involvement, and destroying property/stealing. Cross-validation analyses indicated that high-risk offenders recidivated at more than five times the rate of low-risk offenders.