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

{"title":"Abstract 226: Tumor-infiltrating lymphocyte diversity and clear cell renal cell carcinoma","authors":"M. Ferrall-Fairbanks, N. Chakiryan, B. Chobrutskiy, Young-chul Kim, J. Teer, A. Berglund, J. Mulé, M. Fournier, E. Siegel, E. Katende, G. Blanck, B. Manley, P. Altrock","doi":"10.1158/1538-7445.AM2021-226","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-226","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75325906","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 245: A comprehensive characterization of hyper-morph, hypo-morph, and neo-morph mutations in cancer","authors":"Somnath Tagore, A. Califano","doi":"10.1158/1538-7445.AM2021-245","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-245","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72723647","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 264: Development of companion tests based on continuous markers: Illustration with blood-based tumor mutational burden in NSCLC cancer patients treated with atezolizumab","authors":"J. Gilhodes, F. Penault-Llorca, J. Mazières, M. Pérol, C. Chouaid, E. Leconte, J. Delord, T. Filleron","doi":"10.1158/1538-7445.AM2021-264","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-264","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76341685","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 198: Quantifying miRNA activity in single cell clusters","authors":"Gulden Olgun, Vishaka Gopalan, S. Hannenhalli","doi":"10.1158/1538-7445.AM2021-198","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-198","url":null,"abstract":"Background: MicroRNAs are small noncoding RNAs that mediate gene regulation at the post-transcriptional level via multiple mechanisms such as mRNA degradation, translational inhibition, and mRNA stabilization. They are involved in several cellular processes from development to homeostasis, and their deregulation is implicated in several diseases, including cancer. Since miRNA lacks the polyA tail, the standard single cell RNAseq protocols do not capture miRNAs, thus severely limiting our understanding of miRNA functions at cellular resolution. To overcome this limitation, we develop a novel machine learning method to infer the miRNA activity in a sample given its RNAseq profile. Methods: We develop a model using XGBoost, to predict miRNA profile in a sample from its global mRNA profile. We train and test the model using cross validation in the CCLE collection, as well as a number of healthy and cancer human tissue data obtained from GTEx and TCGA. We quantify the method9s performance as the correlation between actual and predicted miRNA expression values across the test samples. We validate our model in multiple single cell datasets where miRNA and mRNA profiles are available for the same cell types by assessing the model9s ability to identify cell type specific miRNAs based on a model trained on independent bulk datasets. Results: First, we show in CCLE collection, and multiple TCGA tissues, that a model based on all genes was far more accurate than the model based only on known targets. Our mean cross validation model accuracy across 10 tissues having greater than 100 paired miRNA and mRNA samples (in terms of Spearman correlation between predicted and actual expression of a miRNA) is 0.45 (min 0.39 in Pancreas to a max of 0.51 in Brain). In comparison to the normal tissues in GTEx, in the malignant counterpart in TCGA, due to greater heterogeneity, therefore greater variability in gene expression, our model performs significantly better (average cross validation accuracy improvement of 0.19). We have validated our model in independent single cell data. Using a model trained in bulk tissue data, we predict microRNA expression levels in a single cell based on the single cell RNA and compare our predicted fold difference by a miRNA9s expression between two cell types with the actual fold difference. We quantify the prediction accuracy as the correlation between the predicted and actual fold differences across all miRNAs. In a total of 4 cell type pair comparisons (different sets of kidney, brain, breast, and skin), our model achieves an average accuracy of 0.81 (ranging from 0.73 to 0.89), thus strongly validating our model. Our next step is to apply our model to study miRNA activities during T cell development, Pancreatic Ductal Adenocarcinoma, and Glioblastoma, in collaboration with experimentalists. Conclusions: Our method addresses a major bottleneck in studying miRNA activities at a cellular resolution and can be applied to any scRNA data to","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76103484","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 241: An efficient probe design algorithm for direct fusion targeting from RNA","authors":"C. Magnan, S. Rivera, F. Lopez-Diaz, Chenhui Ou, Kenneth B. Thomas, Hyunjun Nam, L. Weiss, Segun Jung, V. Funari","doi":"10.1158/1538-7445.AM2021-241","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-241","url":null,"abstract":"Background: The use of sequencing technologies to detect gene fusions (GFs) from RNA shows promising results for the future of cancer diagnosis and treatment. Major obstacles for this approach include target design and lack of well-curated databases of RNA breakpoints. Currently, off-the-shelf designs include full transcript targeting that results in massive and costly amounts of data. Directly targeting the known GFs from RNA by designing probes targeting the fusion junction sequence is studied here as an alternative to whole-exome sequencing (WES). We present notably a novel algorithm capable of designing the probes to accurately target the desired fusions from RNA. Methods: For a given GF detected either from DNA or from RNA, the algorithm is as follows: (1) Collect gene and isoform information for both partners from seven public databases; (2) For each candidate pair of isoforms, locate where the breakpoints will be observed and assign a score based on various criteria such as sequence completion, coding information, transcript support level, % identity with and % visible on hg38; (3) Select the top scoring pair of transcripts and extract the chimeric probe sequence. Two sets of probes extracted with this protocol targeting 524 and 1632 known GFs were synthetized and tested on several samples (Table 1). The Agilent SureSelect Human All Exon V6 capture kit was used to compare targeting efficiency against WES. Results: Targeted enrichment of a SeraSeq control showed a 5 to 20 fold increase in supporting evidence over WES. On 10 clinical samples, we observed 10-30x increase in supporting reads. A higher sensitivity is observed in both cases. Conclusion: We developed a novel algorithm capable of accurately identifying the most likely location of an RNA fusion junction and generating the probe sequences for oligo synthesis. This method not only enriches for more supporting data but also reduces the associated costs. Citation Format: Christophe N. Magnan, Steven P. Rivera, Fernando J. Lopez-Diaz, Chen-Yin Ou, Kenneth B. Thomas, Hyunjun Nam, Lawrence M. Weiss, Segun C. Jung, Vincent A. Funari. An efficient probe design algorithm for direct fusion targeting from RNA [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 241.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85179162","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 238: Exhaustive tumor specific antigen detection with RNAseq","authors":"Michael F. Sharpnack, Travis S. Johnson, R. Chalkley, Zhi Han, D. Carbone, Kun Huang, K. He","doi":"10.1158/1538-7445.AM2021-238","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-238","url":null,"abstract":"Background: Tumor specific antigen (TSA) identification in human cancer predicts response to immunotherapy and provides vaccine targets for precision medicine. In addition to neoantigens from somatic coding mutations, numerous non-mutated TSAs can elicit T-cell responses but are often overlooked by current methods. We present a method that accurately and comprehensively predict TSAs from RNAseq data regardless of mutation status. Methods: HLA-I genotypes were predicted with seq2HLA. RNAseq fastq files were translated into all possible peptides of length 8-11, and peptides with high expression in the tumor and comparatively low expression in normal were tested for their MHC-I binding potential with netMHCpan-4.0. We defined our predicted TSA by i) high expression in tumor samples, ii) low expression in normal samples, and iii) high predicted patient-specific MHC-I binding affinity. Results: We developed a novel pipeline for TSA prediction from RNAseq that is not limited to mutation-derived TSAs. This pipeline was used to predict all possible unique peptides size 8-11 on previously published murine and human lung and lymphoma tumors then validated on matched tumor and control lung adenocarcinoma (LUAD) samples. This pipeline is able to predict TSAs in MHC-I ligand-purified proteomics data with favorable performance to existing methods. Furthermore, neoantigens predicted by exomeSeq are typically poorly expressed at the RNA level, (28% of predicted neoantigens with >0 expression, mean of 15.6 reads/sample) and a fraction of them (47/6,928, 0.68%) are expressed in matched normal samples. Finally, a set of 6 TSAs are expressed in 22/39 (56%) of LUAD tumors and represent attractive vaccine targets. Conclusion: Direct quantification of RNAseq evidence of the potential peptidome in matched tumor and control RNAseq samples, via our novel pipeline, allows for exhaustive detection of TSAs. Citation Format: Michael Sharpnack, Travis Johnson, Robert Chalkley, Zhi Han, David Carbone, Kun Huang, Kai He. Exhaustive tumor specific antigen detection with RNAseq [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 238.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86912116","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 2: Addressing treatment resistance in models of lethal prostate cancer","authors":"A. Vasciaveo, Francisca Nunes de Almeida, Min Zou, Matteo Di Bernardo, A. Califano, C. Abate-Shen","doi":"10.1158/1538-7445.AM2021-2","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-2","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89557443","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 185: Monitoring of stem cell differentiation to mature hepatocytes with a machine learning-based AI model","authors":"Wei-Lei Yang, Zijun Huo, Shih‐Chen Chen, Dandan Zhu, Tien-Jen Liu, Dung-Fang Lee","doi":"10.1158/1538-7445.AM2021-185","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-185","url":null,"abstract":"Human embryonic stem cells (hESCs) and pluripotent stem cells (iPSCs)-based disease modelings are potential platforms for cancer research and development of new cancer therapeutics. Differentiation of stem cells is an essential step for those disease models. For tissue-specific differentiation, hESCs or iPSCs are cultured in specific receipts of induction and differentiation media for developing different types of tissue cells such as muscle, skin, and liver providing further study or clinical applications. During lineage differentiation, researcher needs to closely monitor stem cell differentiation to be on track via checking cell morphological changes under microscope since this procedure has high probability of failed differentiation results (e.g., no differentiation or differentiating unwanted tissue types). However, monitoring via microscopy is labor-intensive and time-consuming, and also has high inter-observer variation issues. Therefore, it significantly impedes the progression of stem cell research and clinical applications nowadays. In recent years, machine learning has shown promising results in many applications of artificial intelligence (AI) in different fields, especially computer vision and image analysis. AI-based computational tool will bring benefits like high-throughput, high accuracy, and reproductivity in many medical applications. In stem cell culture and differentiation, we believe that applying this new technology will help researcher detect abnormal stem cell differentiation at the early stage via microscopy to save time, labor, and cost for the study and aggregate reproducible data along the process. To this end, we developed a machine learning-based AI model to assist in monitoring morphological changes of hESCs culture in bright-field microscopy images obtained from different differentiation stages to mature hepatocytes. We conducted a pilot study to train an AI model estimating efficiency of stem cell differentiation at Hepatic Progenitor Cell (HPC) stage, which is a critical checkpoint for hepatocyte differentiation. To prepare datasets for training, experienced researchers annotated the morphology of HPC in hundreds of microscope images and determined a differentiation result (success/fail) for every image. During the model training, the initial model was first trained by a training dataset consisting of 341 success and 366 fail HPC results. Subsequently, a smaller separate dataset comprising of 86 success and 51 fail HPC results was then used for cross-validation. Finally, the test set containing 64 success and 29 fail HPC results was used to evaluate the AI model performance. In result, the AI model presented an excellent performance (accuracy= 0.978 and F1 score= 0.975). Our study suggests a potential application of AI-assisted monitoring model for stem cell culture and differentiation in the future. Citation Format: Wei-Lei Yang, Zijun Huo, ShihYu Chen, Dandan Zhu, Tien-Jen Liu, Dung-Fang Lee. Monitoring of ste","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79584796","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 246: Method to analyze mutational and phenotypic profiles from single cell for clonal evolution","authors":"Saurabh Parikh, M. Manivannan, Jacqueline Marin, K. Thompson, Daniel Mendoza, Benjamin Schroeder, Saurabh Gulati, Shu Wang, A. Ooi","doi":"10.1158/1538-7445.AM2021-246","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-246","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86147895","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 205: Astraea: A first-in-class biomarker database integrating genomic, transcriptomic, and tumor microenvironment properties for precision oncology","authors":"A. Gafurov, I. Mamichev, E. Vasileva, G. Sagaradze, Maria S Shitova, G. Nos, N. Kotlov, Jessica H. Brown, A. Bagaev, N. Fowler","doi":"10.1158/1538-7445.AM2021-205","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-205","url":null,"abstract":"Along with advances in precision oncology, checkpoint inhibitors and targeted therapies have substantially improved outcomes for cancer patients. However, many patients still demonstrate a limited response to these therapies due to many biological factors, including genetic heterogeneity, unique molecular profiles, and the complex features of the tumor microenvironment (TME). Therefore, the selection of personalized effective treatment requires a comprehensive source of therapy response biomarkers, enabling precision medicine strategies for therapy selection. Here, we present a first-in-class automated biomarker analysis database, Astraea, that comprehensively describes genomic, transcriptomic, and TME biomarkers across a wide array of cancers. Automated daily literature reviews of the therapeutic efficacy of biomarkers provided the foundation of Astraea. To date, the database contains a total of 4,116 published biomarkers associated with genomic events, the TME, and targeted proteomic, transcriptomic, and gene signatures. To ensure accuracy of the final inclusion of biomarkers in the database, a multi-step quality control process was implemented that includes an automatic validation step and manual review. After selection, each biomarker is organized into a unique profile in the database which includes assay specifics, the biomarker-associated cancer type, therapy, primary study design, and statistical analysis. Data available from The Cancer Genome Atlas (TCGA) was then used to aggregate interrelated biomarkers into 25 biologically meaningful clusters, with the most prominent clusters identified as components of the TME (i.e., cytotoxic T cells, B cells, fibroblasts) and proliferation rate signatures. The aggregation enabled an easier interpretation and understanding of potentially actionable molecular findings as well as insight into unique neoplastic drivers. To apply Astraea in a clinical setting, we then developed a platform to match therapies to patients based on 1) identified biomarkers prioritized according to level of evidence, including both number of associated publications, statistical strength of individual studies, and cohort size and 2) therapies scored according to supporting biomarkers and associated relevance (resistance/response). By providing comprehensive, up-to-date biomarker identification and matching through utilization of a large automated multi-platform database, this technique aids in the identification and application of biomarkers unique to each patient. Taken together, our results show that Astraea, accompanied by a multi-step personalized cancer therapy-matching platform, could improve precision medicine strategies and help optimize therapeutic decisions. Citation Format: Azamat Gafurov, Ivan Mamichev, Elena V. Vasileva, Georgy D. Sagaradze, Maria S. Shitova, Grigorii Nos, Nikita Kotlov, Jessica H. Brown, Alexander Bagaev, Nathan Fowler. Astraea: A first-in-class biomarker database integrating genomic, transcripto","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72942551","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}