Journal of bioinformatics and systems biology : Open access最新文献

{"title":"Abstract 252: Navigating networks of oncology biomarkers mined from the scientific literature: A new open research tool","authors":"Kim Wager, Dheepa Chari, S. Ho, Tomas J Rees, R. J. Schijvenaars","doi":"10.1158/1538-7445.AM2021-252","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-252","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84238585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 262: Statistical Bliss: A novel framework for statistical assessment of drug synergy","authors":"Richard E. Grewelle, Kalin L. Wilson, D. Brantley-Sieders","doi":"10.1158/1538-7445.AM2021-262","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-262","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88457008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 243: Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond","authors":"L. Piccotti, L. Mirandola, M. Chiriva-Internati","doi":"10.1158/1538-7445.AM2021-243","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-243","url":null,"abstract":"Adoptive cell therapy has been proven a powerful approach for the cure of cancer and other diseases. In particular, the selection of appropriate immunogenic targets has been key to positive outcomes in clinical settings. The availability of RNA-Seq analysis, the accessibility to large data repositories such as TCGA and GTEx, and the creation of new bioinformatic tools have accelerated the process of neoantigen discovery. However, most of the current algorithms are encumbered by the intrinsic complexity of predicting antigen immunogenicity. Diamond™ is a novel artificial intelligence and cognitive machine and deep learning platform to predict peptide processing, HLA binding, and T cell activation. To validate the predictive value of DIAMOND algorithms, the meta-analyses of expression data of cancer-testis antigen New York Esophageal Squamous Cell Carcinoma 1 (NY-ESO-1) and predictions for the immunogenic peptides were compared to experimental data in the literature. In agreement with published clinical observations, DIAMOND metanalysis showed NY-ESO-1 genic overexpression in skin cutaneous melanoma, lung adenocarcinoma, and sarcoma. Moreover, DIAMOND predicted an MHC binding affinity of 0.289 with Supertype A2 for a new NY-ESO-1 peptide, which has been successfully targeted in clinical trials for patients with HLA-A*02:01, as well as it mirrored published data in its prediction of peptide affinity binding in NY-ESO-1–specific MHC II–restricted T cell receptors. Taken together these data support DIAMOND as a reliable platform for the discovery of new immunogenic targets for cancer therapy. Citation Format: Lucia Piccotti, Leonardo Mirandola, Maurizio Chiriva-Internati. Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 243.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"174 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88031642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 165: Enhanced processing of genomic sequencing data for pediatric cancers: GPUs and machine learning techniques for variant detection","authors":"E. Crowgey, Pankaj Vats, Karl R. Franke, G. Burnett, Ankit Sethia, T. Harkins, T. Druley","doi":"10.1158/1538-7445.AM2021-165","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-165","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90481225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 240: Gene fusion calling from RNA panel sequencing data: An ensemble learning approach","authors":"Kenneth B. Thomas, Y. Mou, C. Magnan, T. Gyuris, E. Shinbrot, Fernando Díaz, Steven Lau-Rivera, Segun Jung, V. Funari, L. Weiss","doi":"10.1158/1538-7445.AM2021-240","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-240","url":null,"abstract":"Introduction: Our goal is to improve gene fusion detection via RNA sequencing by combining multiple fusion callers through machine learning techniques. Background: Gene Fusion events are important drivers of malignancy. RNA sequencing (RNAseq) methods for detection of fusions have the advantage that multiple markers can be targeted at one time. Unlike DNA methods, in which it is challenging to capture fusion breakpoints, in RNA methods fusions are readily identified through chimeric transcripts. While many fusion calling algorithms exist for use on RNAseq data, sensitive fusion callers, needed for samples of low tumor content, often present high false positive rates - a result of aligning chimeric transcripts. Further, there currently is no single feature in NGS data that can be used to filter out false positive fusion calls. In order to achieve higher accuracy in fusion calls than can be achieved using individual fusion callers, we have weighted and combined the results of multiple fusion callers by systematic and objective means: an ensemble learning approach based on random forest models. Our method selects from data generated by three independent fusion callers supplemented by metrics obtained from in-house methods. It presents a metric that can be immediately interpreted as the probability that a candidate fusion call is a true fusion call. Methods: Random forest models were generated by use of the randomForest package in R, with tuning by the R caret package. Training data sets consisted of a balanced set of 394 fusion calls from clinical samples of solid tumors. For training, fusion calls with at least 10 supporting reads were deemed true or false based on manual review via IGV, and orthogonal methods including PCR with Sanger sequencing and the commercial Archer™ fusion CTL and Sarcoma panels. We present the results of training on data from the three well-known fusion callers Arriba, STAR-Fusion, and FusionCatcher, together with additional data from an in-house developed junction counting method, and fusion membership in a list of known fusions (a “white list”). Models were validated by 10-fold cross-validation. Results: In performance evaluations, false positive and false negative calls were presumed false based on orthogonal determinations. On that basis, our current best model has an accuracy of 94.9% (sensitivity 93.4%, specificity 96.7%). Currently, High Confidence fusion calls (calls with probability score greater than 70%) are the most common positive calls. These have been confirmed with 100% success. Conclusion: We have successfully integrated multiple fusion callers by means of random forest models. Our current model is validated for use on our solid tumor fusion calling pipeline. Citation Format: Kenneth B. Thomas, Yanglong Mou, Christophe Magnan, Tibor Gyuris, Eve Shinbrot, Fernando Lopez Diaz, Steven Lau-Rivera, Segun Jung, Vincent Funari, Lawrence M. Weiss. Gene fusion calling from RNA panel sequencing data: An ensemble lear","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78213570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 183: End-to-end training of convolutional network for breast cancer detection in two-view mammography","authors":"D. Petrini, C. Shimizu, G. Valente, Guilherme Folgueira, Guilherme Apolinario Silva Novaes, M. H. Katayama, P. Serio, R. A. Roela, T. Tucunduva, M. A. K. Folgueira, Hae Yong Kim","doi":"10.1158/1538-7445.AM2021-183","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-183","url":null,"abstract":"Background:Early computer-aided detection systems for mammography have failed to improve the performance of radiologists. With the remarkable success of deep learning, some recent studies have described computer systems with similar or even superior performance to that of human experts. Among them, Shen et al. (Nature Sci. Rep., 2019) present a promising “end-to-end” training approach. Instead of training a convolutional net with whole mammograms, they first train a “patch classifier” that recognizes lesions in small subimages. Then, they generalize the patch classifier to “whole image classifier” using the property of fully convolutional networks and the end-to-end approach. Using this strategy, the authors have obtained a per-image AUC of 0.87 [0.84, 0.90] in the CBIS-DDSM dataset. Standard mammography consists of two views for each breast: bilateral craniocaudal (CC) and mediolateral oblique (MLO). The algorithm proposed by Shen et al. processes only single-view mammography. We extend their work, presenting the end-to-end training of convolutional net for two-view mammography. Methods:First, we reproduced Shen et al.9s work, using the CBIS-DDSM dataset. We trained a ResNet50-based net for classifying patches with 224x224 pixels using segmented lesions. Then, the weights of the patch classifier were transferred to the whole image single-view classifier, obtained by removing the dense layers from the patch classifier and stacking one ResNet block at the top. This single-view classifier was trained using full images from the same dataset. Trying to replicate Shen et al.9s work, we obtained an AUC of 0.8524±0.0560, less than 0.87 reported in the original paper. We attribute this worsening to the fact that we are using only 2260 images with two views, instead of 2478 images from the original work. Finally, we built the two-view classifier that receives CC and MLO views as input. This classifier has inside two copies of the patch classifier, loaded with the weights from the single-view classifier. The features extracted by the two patch classifiers are concatenated and submitted to the ResNet block. The two-view classifier is end-to-end trained using full images, refining all its weights, including those inside the two patch classifiers. Results:The two-view classifier yielded an AUC of 0.9199±0.0623 in 5-fold cross-validation to classify mammographies into malignant/non-malignant, using single-model and without test-time data augmentation. This is better than the Shen et al.9s AUC (0.87), our single-view AUC (0.85). Zhang et al. (Plos One, 2020) present another two-view algorithm (without end-to-end training) with AUC of 0.95. However, this work cannot directly be compared with ours, as it was tested on a different set of images. Conclusions:We presented end-to-end training of convolutional net for two-view mammography. Our system9s AUC was 0.92, better than the 0.87 obtained by the previous single-view system. Citation Format: Daniel G. Petrini, C","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76684620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 153: Development of a workflow to handle the quality control and analysis of Olink protein biomarker data in early phase oncology clinical trials","authors":"Claire J. Guo, Mary Saltarelli, S. Lambert, H. Fang, Chun Zhang","doi":"10.1158/1538-7445.AM2021-153","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-153","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73607351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 210: Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration","authors":"Arpad M. Danos, Wan-Hsin Lin, J. Saliba, Angshumoy Roy, A. Church, Shruti Rao, D. Ritter, Kilannin Krysiak, A. Wagner, Erica K. Barnell, Lana M. Sheta, Adam C. Coffman, S. Kiwala, Joshua F. McMichael, L. Corson, Kevin E. Fisher, H. Williams, Matthew C. Hiemenz, K. Janeway, J. Ji, Kesserwan A. Chimene, L. Fuqua, L. Dyer, Huiling Xu, Jeffrey Jean, L. Satgunaseelan, Liying Zhang, T. Laetsch, D. Parsons, Ryan J. Schmidt, L. Schriml, K. Sund, S. Kulkarni, Subha Madhavan, Xinjie Xu, R. Kanagal-Shamana, M. Harris, Y. Akkari, Nurit Paz Yacov, P. Terraf, M. Griffith, O. Griffith, G. Raca","doi":"10.1158/1538-7445.AM2021-210","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-210","url":null,"abstract":"Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer. Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kess","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84487140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 225: Computational analysis of 5-fluorouracil antitumor activity in colon cancer using a mechanistic pharmacokinetic/pharmacodynamic model","authors":"Chenhui Ma, A. Almasan, Evren Gurkan-Cavusoglu","doi":"10.1158/1538-7445.AM2021-225","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-225","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72859961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 220: Identifying novel oncology targets and positioning existing targets through the prediction of cancer dependencies","authors":"M. Parikh, O. Elemento, Neel S. Madhukar, Coryandar Gilvary","doi":"10.1158/1538-7445.AM2021-220","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-220","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83278925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}