Evaluation of single-sample network inference methods for precision oncology
Abstract A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many sa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-024-00340-w |
| _version_ | 1797274200510038016 |
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| author | Joke Deschildre Boris Vandemoortele Jens Uwe Loers Katleen De Preter Vanessa Vermeirssen |
| author_facet | Joke Deschildre Boris Vandemoortele Jens Uwe Loers Katleen De Preter Vanessa Vermeirssen |
| author_sort | Joke Deschildre |
| collection | DOAJ |
| description | Abstract A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on ‘normal tissue’ samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when ‘normal tissue’ samples are absent and we point out peculiarities of each method. |
| first_indexed | 2024-03-07T14:55:56Z |
| format | Article |
| id | doaj.art-e7e24e09cf2c412a8aa77f52b1b4ce12 |
| institution | Directory Open Access Journal |
| issn | 2056-7189 |
| language | English |
| last_indexed | 2024-03-07T14:55:56Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj.art-e7e24e09cf2c412a8aa77f52b1b4ce122024-03-05T19:28:52ZengNature Portfolionpj Systems Biology and Applications2056-71892024-02-0110111610.1038/s41540-024-00340-wEvaluation of single-sample network inference methods for precision oncologyJoke Deschildre0Boris Vandemoortele1Jens Uwe Loers2Katleen De Preter3Vanessa Vermeirssen4Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG)Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG)Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG)Department of Biomolecular Medicine, Ghent UniversityLab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG)Abstract A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on ‘normal tissue’ samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when ‘normal tissue’ samples are absent and we point out peculiarities of each method.https://doi.org/10.1038/s41540-024-00340-w |
| spellingShingle | Joke Deschildre Boris Vandemoortele Jens Uwe Loers Katleen De Preter Vanessa Vermeirssen Evaluation of single-sample network inference methods for precision oncology npj Systems Biology and Applications |
| title | Evaluation of single-sample network inference methods for precision oncology |
| title_full | Evaluation of single-sample network inference methods for precision oncology |
| title_fullStr | Evaluation of single-sample network inference methods for precision oncology |
| title_full_unstemmed | Evaluation of single-sample network inference methods for precision oncology |
| title_short | Evaluation of single-sample network inference methods for precision oncology |
| title_sort | evaluation of single sample network inference methods for precision oncology |
| url | https://doi.org/10.1038/s41540-024-00340-w |
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