Prediction of breast cancer treatment–induced fatigue by machine learning using genome-wide association data Journal Article
| Authors: | Lee, S.; Deasy, J. O.; Oh, J. H.; Di Meglio, A.; Dumas, A.; Menvielle, G.; Charles, C.; Boyault, S.; Rousseau, M.; Besse, C.; Thomas, E.; Boland, A.; Cottu, P.; Tredan, O.; Levy, C.; Martin, A. L.; Everhard, S.; Ganz, P. A.; Partridge, A. H.; Michiels, S.; Deleuze, J. F.; Andre, F.; Vaz-Luis, I. |
| Article Title: | Prediction of breast cancer treatment–induced fatigue by machine learning using genome-wide association data |
| Abstract: | Background: We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data. Methods: We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided. Results: Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve 1⁄4 0.59, P 1⁄4 .01) and marginally improved with clinical variables (area under the curve 1⁄4 0.60, P 1⁄4 .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P 1⁄4 .03), cognitive disorders (P 1⁄4 1.51 × 10-12), and synaptic transmission (P 1⁄4 6.28 × 10-8). Conclusions: Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. |
| Journal Title: | JNCI Cancer Spectrum |
| Volume: | 4 |
| Issue: | 5 |
| ISSN: | 2515-5091 |
| Publisher: | Oxford University Press |
| Date Published: | 2020-10-01 |
| Start Page: | pkaa039 |
| Language: | English |
| DOI: | 10.1093/jncics/pkaa039 |
| PROVIDER: | scopus |
| PMCID: | PMC7583150 |
| PUBMED: | 33490863 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 April 2021 -- Source: Scopus |
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