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

{"title":"Abstract 178: Desmosome mutations in melanoma promote cellular proliferation and disease progression","authors":"Maayan Baron, T. Ideker","doi":"10.1158/1538-7445.AM2021-178","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-178","url":null,"abstract":"Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in the epithelia and other tissues under mechanical stress. Aberrant desmosome expression is often associated with developmental diseases leading to impaired tissue integrity. Recently, similar findings have been reported in cancer; Mutations in desmosomes genes have been observed in various cancer types including skin cancer, head and neck and lung cancer, however mostly epigenetic alterations have been used to associate desmosomes as suppressors of tumor metastasis. Here, we report that desmosomes are frequently mutated in seven cancer types. In melanoma, we find that over 70% of tumors have non-synonymous mutations in desmosomes, and that the desmosome mutational burden is associated with a strong decrease in mRNA expression levels in primary tumor samples (R = -0.23). Differential gene expression analysis and functional characterizations between mutant and wild-type tumors implicates the mutated cells in promoting cell proliferation at early stages of tumorigenesis. These results emerge uniquely from a systems-level analysis integrating multiple proteins in complexes and multiple cell types in heterogeneous tumors. Citation Format: Maayan Baron, Trey Ideker. Desmosome mutations in melanoma promote cellular proliferation and disease progression [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 178.","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":"86373588","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 203: SCLC-CellMiner: An extensive cell line genomic and pharmacology resource identifies a subgroup of small cell lung cancers sensitive to targeted therapies and immunotherapies","authors":"C. Tlemsani, L. Pongor, Fathi Elloumi, L. Girard, K. Huffman, N. Roper, S. Varma, Augustin Luna, V. Rajapakse, P. Boudou-Rouquette, R. Sebastian, K. Kohn, J. Krushkal, M. Aladjem, B. Teicher, P. Meltzer, W. Reinhold, J. Minna, Anish Thomas, Y. Pommier","doi":"10.1158/1538-7445.AM2021-203","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-203","url":null,"abstract":"The typical low life expectancy and limited therapeutic options for patients with small cell lung cancer (SCLC) caused the National Cancer Institute (NCI) to categorize SCLC as “recalcitrant” cancer. SCLC-CellMiner (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates drug sensitivity and genomic data from 118 patient-derived SCLC cell lines, providing a unique genomic and pharmacological resource. Transcriptomic profiling validates the SCLC consensus nomenclature based on expression of 4 master transcription factors NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and demonstrate differential transcriptional networks driven by these lineage specific transcription factors. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs MYCL and MYCN and inactivation of the NOTCH pathway in the neuroendocrine SCLC (N, A & P subgroups). By contrast, YAP1-driven SCLC (SCLC-Y) express the NOTCH pathway and co-express both YAP/TAZ and its negative regulator genes driving the Hippo pathway. SCLC-Y cell lines show the greatest resistance to the standard of care drugs (etoposide, cisplatin and topotecan) while PI3K-AKT-mTOR inhibitors show a higher activity in this subgroup. To explore the immune pathways and the potential value of the transciption factors based classification for selecting SCLC patients likely to respond to immune checkpoint inhibitors, we explored a transcriptome signature based on 18 established native immune response and antigen-presenting genes (APM score). The SCLC-Y cell lines are the only subset expressing innate immune response genes. SCLC-CellMiner is a powerfull tool demonstrating the value of cancer cell line genomic and pharmacological databases. Our analyses suggest the potential genomic molecular classifications to select patients for targeted therapies and immunotherapy, such as patients in the SCLC-Y subgroup who may be most responsive to immune checkpoints modulators. Citation Format: Camille Tlemsani, Lorinc Pongor, Fathi Elloumi, Luc Girard, Kenneth Huffman, Nitin Roper, Sudhir Varma, Augustin Luna, Vinodh Rajapakse, Pascaline Boudou-Rouquette, Robin Sebastian, Kurt Kohn, Julia Krushkal, Mirit Aladjem, Beverly Teicher, Paul Meltzer, William Reinhold, John Minna, Anish Thomas, Yves Pommier. SCLC-CellMiner: An extensive cell line genomic and pharmacology resource identifies a subgroup of small cell lung cancers sensitive to targeted therapies and immunotherapies [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 203.","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":"89101699","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 192: MutAnt: Mutation annotation machine learning algorithm for pathogenicity evaluation of single nonsynonymous nucleotide substitutions in cancer cells","authors":"Aleksandr Sarachakov, V. Svekolkin, Zoia Antysheva, Jessica H. Brown, A. Bagaev, N. Fowler","doi":"10.1158/1538-7445.AM2021-192","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-192","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":"80083665","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 173: Efficient representations of tumor diversity with paired DNA-RNA aberrations","authors":"Qian Ke, Wikum Dinalankara, L. Younes, D. Geman, L. Marchionni","doi":"10.1158/1538-7445.AM2021-173","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-173","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":"79764673","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 4: Temporal and spatial topography of cell proliferation in cancer","authors":"Giorgio Gaglia, S. Kabraji, Danae Argyropoulou, Yang Dai, J. Bergholz, S. Coy, Jia-Ren Lin, E. Winer, D. Dillon, Jean J. Zhao, P. Sorger, S. Santagata","doi":"10.1158/1538-7445.AM2021-4","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-4","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":"86266835","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 234: Risk of sepsis among patients with prostate cancer: A network-based modeling approach","authors":"A. Jazayeri, Niusha Jafari, Christopher C. Yang, N. Nikita, G. Yao","doi":"10.1158/1538-7445.AM2021-234","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-234","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":"77319839","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 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":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":"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":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":"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":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":"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":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":"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}