1,721,046 research outputs found
Abstract 393: Predicting DNA accessibility in the pan-cancer tumor genome using RNA-Seq, WGS, and deep learning
No full textAbstract
DNA accessibility, chromatin regulation, and genome methylation are key drivers of cancer transcription. However, there is much left to be understood about the functional implications of sequence-level data to the regulation of gene expression, especially when it comes to the noncoding genome. Recently [Kelley, D., Snoek, J., and Rinn, J., Genome Res. 2016] trained neural networks to effectively predict DNA accessibility in multiple cell types. These models make it possible to explore the impact of mutations on the predicted accessibility and thus directly link one aspect of the gene regulation puzzle all the way down to the sequence level. We present a model with improved performance on the original dataset of 164 ENCODE and Roadmap Epigenomics Consortium sample types, and then extend the method to provide predictions on any sample with RNA-Seq data without need of DNase-seq for the sample.
We first demonstrate that with several model and algorithmic changes we improve performance across 164 cell types from a mean AUC of 0.895 to a mean AUC of 0.910.
Unfortunately current accessibility models require DNase-seq for each new cell type. Models for detecting transcription factor binding sites, which rely on ChIP-seq for training data, also share this issue. In order to generalize sequence-based predictive models to apply to unseen cell types without requiring re-training we investigate using RNA-Seq as a proxy signature of cell type. The model aims to capture the interdependence of gene expression levels that characterize a cell with the regulatory logic in which sequence-level signatures are combined to determine accessibility without restriction to cell type. We explore the model’s performance when applied to held-out cell types in the ENCODE and Roadmap Epigenomics Consortium data as well as data from the TCGA Pan-Cancer initiative. We look for the impact of non-coding changes in whole-genome sequencing data in TCGA samples, and report on predicted differences in DNA accessibility across cancer subtypes.
Citation Format: Kamil Wnuk, Jeremi Sudol, Shahrooz Rabizadeh, Patrick Soon-Shiong, Christopher Szeto, Charles Vaske. Predicting DNA accessibility in the pan-cancer tumor genome using RNA-Seq, WGS, and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 393. doi:10.1158/1538-7445.AM2017-393</jats:p
Abstract 479: Modeling miRNA induced silencing in breast cancer with PARADIGM
No full textAbstract
Introduction: MicroRNAs play an important role in regulation of gene expression and are known biomarkers for breast cancer as well as other malignancies. PARADIGM is a pathway based algorithm that allows for integration of multiple genomic data types with a curated pathway database to make pathway activity predictions. We added a model of gene silencing due to miRNA to the PARADIGM algorithm in order to study miRNA expression in a pathway context.
Results: We curated a set of 7751 miRNA-mRNA interactions from the intersection of 3 target prediction algorithms. These interactions involved 66 miRNA and 2814 mRNA transcripts. We ran this model on global DNA copy number, RNAseq and miRNAseq data from 697 patients in the TCGA breast cancer cohort, and studied changes in the interactions between miRNAs and their targets between different tumor subtypes. The median activity of the RNA-induced silencing complex (RISC) predicted by our model is significantly higher in Basal tumors than other subtypes. In addition, RISC activity is significantly associated with overall survival of patients with Luminal A tumors. The miRNA-target pairs with the largest correlation changes between Basal and Luminal A subtypes were enriched for putative oncogenes and oncomirs. The mRNA targets are involved in a number of important signaling pathways including PI3K-AKT, JAK-STAT, and Ras. Many of these highly differential links involved the miR-16 family of miRNAs which are known tumor suppressors. miR-16 shows significantly lower activity in basal tumors than other subtypes.
Conclusions: By looking at changes in miRNA-target links between tumor subtypes, our algorithm was able to identify both miRNAs and target genes involved in pathways relevant to breast cancer. Our predictions of overall RNA-induced silencing activity show prognostic value in both determining subtype and predicting overall survival within subtypes.
Citation Format: Andrew J. Sedgewick, Panayiotis V. Benos, Shahrooz Rabizadeh, Patrick Soon-Shiong, Charles J. Vaske. Modeling miRNA induced silencing in breast cancer with PARADIGM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 479. doi:10.1158/1538-7445.AM2017-479</jats:p
Treatment of type I diabetes using encapsulated islets1Abbreviations: Tx, transplantation; IEQ, islet equivalent; BUN, blood urea nitrogen.1
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Encapsulated islet cell therapy for the treatment of diabetes: Intraperitoneal injection of islets
No full textAbstract 640: Subsets of HLA alleles are capable of binding neoantigens derived from mutations within cancer driving genes such as KRAS and EGFR
No full textAbstract
Background: Immunoncology has shown great promise as a low toxicity tool to combat several cancers. Use of checkpoint inhibitors against PD1 or CTLA4 unlocks the immune system’s ability to recognize tumor antigens and, more specifically, neoantigens caused by random mutations within cancers. The vast majority of neoantigens consist of private mutations unique to a patient’s tumor genome, but several cancers harbor recurrent mutations. Mutations in the KRAS gene, such as p.G12V, occur in roughly 25% of colorectal cancers. Mutations in EGFR occur in 10% and 35% of patients with non-small cell lung cancer in the US and East Asia, respectively. Even more prevalent are mutations within the TP53 tumor suppressor gene, with roughly 23000 unique protein variants reported to date. If these mutations in cancer driving genes are so prevalent in cancers, why are neoantigens against these targets not more readily available?
Results: We collected recurrent mutations across a variety of cancer driving genes such as KRAS, EGFR, TP53 and MYC and performed binding analysis using netMHC 3.4 to see which HLA alleles are capable of binding specific cancer mutations such as KRAS p.G12V. Using this method, we report all possible HLA alleles capable of binding these recurrent mutations within cancer genes. We further performed 3-dimensional modeling to determine whether complexes created by the HLA alleles and cancer neoepitopes are stable.
Conclusions: Several HLA alleles are capable of binding recurrent cancer mutations. These include both MHC Class 1 and Class 2 alleles. The variation in alleles capable of binding commonly mutated genes such as EGFR may explain the difference in prevalence of these mutations between geographic populations. Determining whether a certain HLA allele confers resistance to common cancer gene mutations may lead to identification of immune cells within these populations that can recognize neoantigens from commonly mutated cancer genes.
Citation Format: Andrew Nguyen, J Zachary Sanborn, Charles J. Vaske, Shahrooz Rabizadeh, Kayvan Niazi, Patrick Soon-Shiong, Steve Benz. Subsets of HLA alleles are capable of binding neoantigens derived from mutations within cancer driving genes such as KRAS and EGFR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 640. doi:10.1158/1538-7445.AM2017-640</jats:p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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