Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy
Abstract Background Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treat...
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Nature Portfolio
2025-01-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00728-z |
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| author | Akram Yazdani Heinz-Josef Lenz Gianluigi Pillonetto Raul Mendez-Giraldez Azam Yazdani Hanna Sanoff Reza Hadi Esmat Samiei Alan P. Venook Mark J. Ratain Naim Rashid Benjamin G. Vincent Xueping Qu Yujia Wen Michael Kosorok William F. Symmans John Paul Y. C. Shen Michael S. Lee Scott Kopetz Andrew B. Nixon Monica M. Bertagnolli Charles M. Perou Federico Innocenti |
| author_facet | Akram Yazdani Heinz-Josef Lenz Gianluigi Pillonetto Raul Mendez-Giraldez Azam Yazdani Hanna Sanoff Reza Hadi Esmat Samiei Alan P. Venook Mark J. Ratain Naim Rashid Benjamin G. Vincent Xueping Qu Yujia Wen Michael Kosorok William F. Symmans John Paul Y. C. Shen Michael S. Lee Scott Kopetz Andrew B. Nixon Monica M. Bertagnolli Charles M. Perou Federico Innocenti |
| author_sort | Akram Yazdani |
| collection | DOAJ |
| description | Abstract Background Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions. Methods We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes. This integrative approach involved germline genotype and tumor RNA-seq data from 1165 metastatic CRC patients. The patients were enrolled in a randomized clinical trial receiving either cetuximab or bevacizumab in combination with chemotherapy. An external cohort of paired CRC normal and tumor samples, along with protein-protein interaction databases, was used for replication. Results We identify promising predictive and prognostic gene signatures from pre-treatment gene expression profiles. Our study discerns sets of genes, each forming a signature that collectively contribute to define patient subgroups with different prognosis and response to the therapies. Using an external cohort, we show that the genes influencing OS within the signatures, such as FANCI and PRC1, are upregulated in CRC tumor vs. normal tissue. These signatures are highly associated with immune features, including macrophages, cytotoxicity, and wound healing. Furthermore, the corresponding proteins encoded by the genes within the signatures interact with each other and are functionally related. Conclusions This study underscores the utility of gene signatures derived from transcriptomic-causal networks in patient stratification for effective therapies. The interpretability of the findings, supported by replication, highlights the potential of these signatures to identify patients likely to benefit from cetuximab or bevacizumab. |
| format | Article |
| id | doaj-art-b4dc7a5a888e482abb16223df1bcaf2d |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-b4dc7a5a888e482abb16223df1bcaf2d2025-01-12T12:37:21ZengNature PortfolioCommunications Medicine2730-664X2025-01-015111010.1038/s43856-024-00728-zGene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapyAkram Yazdani0Heinz-Josef Lenz1Gianluigi Pillonetto2Raul Mendez-Giraldez3Azam Yazdani4Hanna Sanoff5Reza Hadi6Esmat Samiei7Alan P. Venook8Mark J. Ratain9Naim Rashid10Benjamin G. Vincent11Xueping Qu12Yujia Wen13Michael Kosorok14William F. Symmans15John Paul Y. C. Shen16Michael S. Lee17Scott Kopetz18Andrew B. Nixon19Monica M. Bertagnolli20Charles M. Perou21Federico Innocenti22Eshelman School of Pharmacy, University of North Carolina at Chapel HillUSC Norris Comprehensive Cancer CenterDepartment of Information Engineering, University of PadovaBiostatistics and Computational Biology Branch, National Institute of Environmental Health SciencesCenter of Perioperative Genetics and Genomics, Perioperative and Pain Medicine, Brigham & Women’s Hospital, Harvard Medical SchoolDivision of Oncology, University of North Carolina at Chapel HillSchool of Mathematics, University of Science and Technology of IranGamelectronicUniversity of California at San FranciscoDivision of the Biological Sciences, University of ChicagoDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel HillDepartment of Microbiology and Immunology, University of North Carolina at Chapel HillGenentech, South San FranciscoAlliance for Clinical Trials in OncologyDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel HillDepartment of Pathology, University of Texas MD Anderson Cancer CenterDepartments of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer CenterDepartments of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer CenterDepartments of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer CenterDuke Center for Cancer Immunotherapy, Duke UniversityDana-Farber/ Partners Cancer Care, Harvard Medical SchoolLineberger Comprehensive Cancer Center, University of North Carolina at Chapel HillEshelman School of Pharmacy, University of North Carolina at Chapel HillAbstract Background Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions. Methods We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes. This integrative approach involved germline genotype and tumor RNA-seq data from 1165 metastatic CRC patients. The patients were enrolled in a randomized clinical trial receiving either cetuximab or bevacizumab in combination with chemotherapy. An external cohort of paired CRC normal and tumor samples, along with protein-protein interaction databases, was used for replication. Results We identify promising predictive and prognostic gene signatures from pre-treatment gene expression profiles. Our study discerns sets of genes, each forming a signature that collectively contribute to define patient subgroups with different prognosis and response to the therapies. Using an external cohort, we show that the genes influencing OS within the signatures, such as FANCI and PRC1, are upregulated in CRC tumor vs. normal tissue. These signatures are highly associated with immune features, including macrophages, cytotoxicity, and wound healing. Furthermore, the corresponding proteins encoded by the genes within the signatures interact with each other and are functionally related. Conclusions This study underscores the utility of gene signatures derived from transcriptomic-causal networks in patient stratification for effective therapies. The interpretability of the findings, supported by replication, highlights the potential of these signatures to identify patients likely to benefit from cetuximab or bevacizumab.https://doi.org/10.1038/s43856-024-00728-z |
| spellingShingle | Akram Yazdani Heinz-Josef Lenz Gianluigi Pillonetto Raul Mendez-Giraldez Azam Yazdani Hanna Sanoff Reza Hadi Esmat Samiei Alan P. Venook Mark J. Ratain Naim Rashid Benjamin G. Vincent Xueping Qu Yujia Wen Michael Kosorok William F. Symmans John Paul Y. C. Shen Michael S. Lee Scott Kopetz Andrew B. Nixon Monica M. Bertagnolli Charles M. Perou Federico Innocenti Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy Communications Medicine |
| title | Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy |
| title_full | Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy |
| title_fullStr | Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy |
| title_full_unstemmed | Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy |
| title_short | Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy |
| title_sort | gene signatures derived from transcriptomic causal networks stratify colorectal cancer patients for effective targeted therapy |
| url | https://doi.org/10.1038/s43856-024-00728-z |
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