Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers
Abstract Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S. cancer patients with detailed somatic mutation profi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55251-5 |
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| author | Ruishan Liu Shemra Rizzo Lisa Wang Nayan Chaudhary Sophia Maund Marius Rene Garmhausen Sarah McGough Ryan Copping James Zou |
| author_facet | Ruishan Liu Shemra Rizzo Lisa Wang Nayan Chaudhary Sophia Maund Marius Rene Garmhausen Sarah McGough Ryan Copping James Zou |
| author_sort | Ruishan Liu |
| collection | DOAJ |
| description | Abstract Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S. cancer patients with detailed somatic mutation profiling integrated with treatment and outcomes data extracted from electronic health records. We systematically identified 776 genomic alterations associated with survival outcomes across 20 distinct cancer types treated with specific immunotherapies, chemotherapies, or targeted therapies. Additionally, we demonstrate how mutations in particular pathways correlate with treatment response. Leveraging the large number of identified predictive mutations, we developed a machine learning model to generate a risk score for response to immunotherapy in patients with advanced non-small cell lung cancer (aNSCLC). Through rigorous computational analysis of large-scale clinico-genomic real-world data, this research provides insights and lays the groundwork for further advancements in precision oncology. |
| format | Article |
| id | doaj-art-5519b261b7e1489191aebe74c5049464 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-5519b261b7e1489191aebe74c50494642025-01-05T12:35:00ZengNature PortfolioNature Communications2041-17232024-12-0115111010.1038/s41467-024-55251-5Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancersRuishan Liu0Shemra Rizzo1Lisa Wang2Nayan Chaudhary3Sophia Maund4Marius Rene Garmhausen5Sarah McGough6Ryan Copping7James Zou8Department of Electrical Engineering, Stanford UniversityGenentechGenentechGenentechGenentechF. Hoffmann-La Roche Ltd.GenentechGenentechDepartment of Electrical Engineering, Stanford UniversityAbstract Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S. cancer patients with detailed somatic mutation profiling integrated with treatment and outcomes data extracted from electronic health records. We systematically identified 776 genomic alterations associated with survival outcomes across 20 distinct cancer types treated with specific immunotherapies, chemotherapies, or targeted therapies. Additionally, we demonstrate how mutations in particular pathways correlate with treatment response. Leveraging the large number of identified predictive mutations, we developed a machine learning model to generate a risk score for response to immunotherapy in patients with advanced non-small cell lung cancer (aNSCLC). Through rigorous computational analysis of large-scale clinico-genomic real-world data, this research provides insights and lays the groundwork for further advancements in precision oncology.https://doi.org/10.1038/s41467-024-55251-5 |
| spellingShingle | Ruishan Liu Shemra Rizzo Lisa Wang Nayan Chaudhary Sophia Maund Marius Rene Garmhausen Sarah McGough Ryan Copping James Zou Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers Nature Communications |
| title | Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers |
| title_full | Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers |
| title_fullStr | Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers |
| title_full_unstemmed | Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers |
| title_short | Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers |
| title_sort | characterizing mutation treatment effects using clinico genomics data of 78 287 patients with 20 types of cancers |
| url | https://doi.org/10.1038/s41467-024-55251-5 |
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