Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature
Abstract Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-023-00375-y |
| _version_ | 1797429401701318656 |
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| author | Jessica A. Scarborough Steven A. Eschrich Javier Torres-Roca Andrew Dhawan Jacob G. Scott |
| author_facet | Jessica A. Scarborough Steven A. Eschrich Javier Torres-Roca Andrew Dhawan Jacob G. Scott |
| author_sort | Jessica A. Scarborough |
| collection | DOAJ |
| description | Abstract Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer. |
| first_indexed | 2024-03-09T09:12:38Z |
| format | Article |
| id | doaj.art-1ca297187c7d4b68a14cd53a0ddf872d |
| institution | Directory Open Access Journal |
| issn | 2397-768X |
| language | English |
| last_indexed | 2024-03-09T09:12:38Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj.art-1ca297187c7d4b68a14cd53a0ddf872d2023-12-02T08:02:31ZengNature Portfolionpj Precision Oncology2397-768X2023-04-017111410.1038/s41698-023-00375-yExploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signatureJessica A. Scarborough0Steven A. Eschrich1Javier Torres-Roca2Andrew Dhawan3Jacob G. Scott4Systems Biology and Bioinformatics Department, School of Medicine, Case Western Reserve UniversityBiostatistics and Bioinformatics Program, Moffitt Cancer CenterDepartment of Radiation Oncology, Moffitt Cancer CenterNeurological Institute, Cleveland ClinicSystems Biology and Bioinformatics Department, School of Medicine, Case Western Reserve UniversityAbstract Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.https://doi.org/10.1038/s41698-023-00375-y |
| spellingShingle | Jessica A. Scarborough Steven A. Eschrich Javier Torres-Roca Andrew Dhawan Jacob G. Scott Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature npj Precision Oncology |
| title | Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature |
| title_full | Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature |
| title_fullStr | Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature |
| title_full_unstemmed | Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature |
| title_short | Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature |
| title_sort | exploiting convergent phenotypes to derive a pan cancer cisplatin response gene expression signature |
| url | https://doi.org/10.1038/s41698-023-00375-y |
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