Widespread redundancy in -omics profiles of cancer mutation states
Abstract Background In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant si...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-022-02705-y |
| _version_ | 1818235678109990912 |
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| author | Jake Crawford Brock C. Christensen Maria Chikina Casey S. Greene |
| author_facet | Jake Crawford Brock C. Christensen Maria Chikina Casey S. Greene |
| author_sort | Jake Crawford |
| collection | DOAJ |
| description | Abstract Background In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear. Results We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type. Conclusions Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option. |
| first_indexed | 2024-12-12T11:57:47Z |
| format | Article |
| id | doaj.art-b037729a8d594e50b35164bc440f5699 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-12-12T11:57:47Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-b037729a8d594e50b35164bc440f56992022-12-22T00:25:10ZengBMCGenome Biology1474-760X2022-06-0123112410.1186/s13059-022-02705-yWidespread redundancy in -omics profiles of cancer mutation statesJake Crawford0Brock C. Christensen1Maria Chikina2Casey S. Greene3Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of PennsylvaniaDepartment of Epidemiology, Geisel School of Medicine, Dartmouth CollegeDepartment of Computational and Systems Biology, School of Medicine, University of PittsburghDepartment of Biochemistry and Molecular Genetics, University of Colorado School of MedicineAbstract Background In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear. Results We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type. Conclusions Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.https://doi.org/10.1186/s13059-022-02705-y |
| spellingShingle | Jake Crawford Brock C. Christensen Maria Chikina Casey S. Greene Widespread redundancy in -omics profiles of cancer mutation states Genome Biology |
| title | Widespread redundancy in -omics profiles of cancer mutation states |
| title_full | Widespread redundancy in -omics profiles of cancer mutation states |
| title_fullStr | Widespread redundancy in -omics profiles of cancer mutation states |
| title_full_unstemmed | Widespread redundancy in -omics profiles of cancer mutation states |
| title_short | Widespread redundancy in -omics profiles of cancer mutation states |
| title_sort | widespread redundancy in omics profiles of cancer mutation states |
| url | https://doi.org/10.1186/s13059-022-02705-y |
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