| Autor: |
Kanishka Manna, Prashanthi Dharanipragada, Duah Alkam, Nathan L. Avaritt, Charity L. Washam, Tung-chin Chiang, Michael S Robeson, Ricky D. Edmondson, Samuel G. Mackintosh, Zhentao Yang, Yan Wang, Shirley H. Lomeli, Gatien Moriceau, Roger S. Lo, Alan J. Tackett, Stephanie D. Byrum |
| Rok vydání: |
2022 |
| Popis: |
Therapeutic approaches to treat melanoma include small molecule drugs that target activating protein mutations in pro-growth signaling pathways like the MAPK pathway. While beneficial to the approximately 50% of patients with activatingBRAFV600mutation, mono- and combination therapy with MAPK inhibitors is ultimately associated with acquired resistance. To better characterize the mechanisms of MAPK inhibitor resistance in melanoma, we utilize patient-derived xenografts and apply proteogenomic approaches leveraging genomic, transcriptomic, and proteomic technologies that permit the identification of resistance-specific alterations and therapeutic vulnerabilities. A specific challenge for proteogenomic applications comes at the level of data curation to enable multi-omics data integration. Here, we present a proteogenomic approach that uses custom curated databases to identify unique resistance-specific alternations in melanoma PDX models of acquired MAPK inhibitor resistance. We demonstrate this approach with aNRASQ61Lmelanoma PDX model from which resistant tumors were developed following treatment with a MEK inhibitor. Our multi-omics strategy addresses current challenges in bioinformatics by leveraging development of custom curated proteogenomics databases derived from individual resistant melanoma that evolves following MEK inhibitor treatment and is scalable to comprehensively characterize acquired MAPK inhibitor resistance across patient-specific models and genomic subtypes of melanoma. The computational workflow for curation of a proteogenomics database is described here. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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