Navegando por Autor "Fonseca, André L."
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Artigo Bioinformatics analysis of the human surfaceome reveals new targets for a variety of tumor types(Hindawi, 2016-10-18) Fonseca, André L.; Silva, Vandeclécio Lira da; Fonseca, Marbella M.; Meira, Isabella Tanus Job e; Silva, Thayná Emília Oliveira; Kroll, José Eduardo; Ribeiro-dos-Santos, André M.; Freitas, Cléber R.; Furtado, Raimundo; Souza, Jorge Estefano Santana de; Ferreira, Beatriz Stransky; Souza, Sandro José deIt is estimated that 10 to 20% of all genes in the human genome encode cell surface proteins and due to their subcellular localization these proteins represent excellent targets for cancer diagnosis and therapeutics. Therefore, a precise characterization of the surfaceome set in different types of tumor is needed. Using TCGA data from 15 different tumor types and a new method to identify cancer genes, the -score, we identified several potential therapeutic targets within the surfaceome set. This allowed us to expand a previous analysis from us and provided a clear characterization of the human surfaceome in the tumor landscape. Moreover, we present evidence that a three-gene set—WNT5A, CNGA2, and IGSF9B—can be used as a signature associated with shorter survival in breast cancer patients. The data made available here will help the community to develop more efficient diagnostic and therapeutic tools for a variety of tumor typesArtigo Bioinformatics Analysis of the Human Surfaceome Reveals New Targets for a Variety of Tumor Types(2016-10-18) Fonseca, André L.; Silva, Vandeclécio L. da; Fonseca, Marbella M.; Meira, Isabella T. J.; Silva, Thayná E. da; Kroll, José Eduardo; Ribeiro-dos-Santos, André M.; Freitas, Cléber R.; Furtado, Raimundo; Souza, Sandro José de; Ferreira, Beatriz Stransky; Souza, Sandro José deIt is estimated that 10 to 20% of all genes in the human genome encode cell surface proteins and due to their subcellular localization these proteins represent excellent targets for cancer diagnosis and therapeutics. Therefore, a precise characterization of the surfaceome set in different types of tumor is needed. Using TCGA data from 15 different tumor types and a new method to identify cancer genes, the -score, we identified several potential therapeutic targets within the surfaceome set. This allowed us to expand a previous analysis from us and provided a clear characterization of the human surfaceome in the tumor landscape. Moreover, we present evidence that a three-gene set—WNT5A, CNGA2, and IGSF9B—can be used as a signature associated with shorter survival in breast cancer patients. The data made available here will help the community to develop more efficient diagnostic and therapeutic tools for a variety of tumor types.Artigo neoANT-HILL: an integrated tool for identification of potential neoantigens(2020-02-22) Coelho, Ana Carolina M. F.; Fonseca, André L.; Martins, Danilo L.; Lins, Paulo B. R.; Cunha, Lucas M. da; Souza, Sandro José deBackground: Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives. Results: We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity. Conclusion: neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available through github at https://github.com/neoanthill/neoANT-HILL.