Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction
Abstract Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shar...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-76625-1 |
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| author | Corey Weistuch Kevin A. Murgas Jiening Zhu Larry Norton Ken A. Dill Allen R. Tannenbaum Joseph O. Deasy |
| author_facet | Corey Weistuch Kevin A. Murgas Jiening Zhu Larry Norton Ken A. Dill Allen R. Tannenbaum Joseph O. Deasy |
| author_sort | Corey Weistuch |
| collection | DOAJ |
| description | Abstract Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients. |
| format | Article |
| id | doaj-art-e1680c5d61d74dc0b12a00afe279b9bc |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e1680c5d61d74dc0b12a00afe279b9bc2025-08-20T02:13:27ZengNature PortfolioScientific Reports2045-23222024-11-0114111310.1038/s41598-024-76625-1Normal tissue transcriptional signatures for tumor-type-agnostic phenotype predictionCorey Weistuch0Kevin A. Murgas1Jiening Zhu2Larry Norton3Ken A. Dill4Allen R. Tannenbaum5Joseph O. Deasy6Department of Medical Physics, Memorial Sloan Kettering Cancer Center Department of Biomedical Informatics, Stony Brook UniversityDepartment of Applied Mathematics and Statistics, Stony Brook UniversityDepartment of Medicine, Memorial Sloan Kettering Cancer CenterLaufer Center for Physical and Quantitative Biology, Stony Brook UniversityDepartment of Applied Mathematics and Statistics, Stony Brook UniversityDepartment of Medical Physics, Memorial Sloan Kettering Cancer CenterAbstract Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.https://doi.org/10.1038/s41598-024-76625-1Molecular profilingMetastatic breast cancerDrug sensitivity predictionCancer ecology and evolutionPrognosis |
| spellingShingle | Corey Weistuch Kevin A. Murgas Jiening Zhu Larry Norton Ken A. Dill Allen R. Tannenbaum Joseph O. Deasy Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction Scientific Reports Molecular profiling Metastatic breast cancer Drug sensitivity prediction Cancer ecology and evolution Prognosis |
| title | Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction |
| title_full | Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction |
| title_fullStr | Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction |
| title_full_unstemmed | Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction |
| title_short | Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction |
| title_sort | normal tissue transcriptional signatures for tumor type agnostic phenotype prediction |
| topic | Molecular profiling Metastatic breast cancer Drug sensitivity prediction Cancer ecology and evolution Prognosis |
| url | https://doi.org/10.1038/s41598-024-76625-1 |
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