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Int J Radiat Oncol Biol Phys. 2018 May 1;101(1):128-135. doi: 10.1016/j.ijrobp.2018.01.054. Epub 2018 Jan 31.

Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.

Author information

1
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
2
Department of Radiation Oncology, University of Rochester Medical Center, New York, New York.
3
Department of Pathology, Albert Einstein College of Medicine, New York, New York; Department of Pediatrics, Albert Einstein College of Medicine, New York, New York.
4
Department of Radiation Oncology and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
5
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York. Electronic address: ohj@mskcc.org.

Abstract

PURPOSE:

Late genitourinary (GU) toxicity after radiation therapy limits the quality of life of prostate cancer survivors; however, efforts to explain GU toxicity using patient and dose information have remained unsuccessful. We identified patients with a greater congenital GU toxicity risk by identifying and integrating patterns in genome-wide single nucleotide polymorphisms (SNPs).

METHODS AND MATERIALS:

We applied a preconditioned random forest regression method for predicting risk from the genome-wide data to combine the effects of multiple SNPs and overcome the statistical power limitations of single-SNP analysis. We studied a cohort of 324 prostate cancer patients who were self-assessed for 4 urinary symptoms at 2 years after radiation therapy using the International Prostate Symptom Score.

RESULTS:

The predictive accuracy of the method varied across the symptoms. Only for the weak stream endpoint did it achieve a significant area under the curve of 0.70 (95% confidence interval 0.54-0.86; P = .01) on hold-out validation data that outperformed competing methods. Gene ontology analysis highlighted key biological processes, such as neurogenesis and ion transport, from the genes known to be important for urinary tract functions.

CONCLUSIONS:

We applied machine learning methods and bioinformatics tools to genome-wide data to predict and explain GU toxicity. Our approach enabled the design of a more powerful predictive model and the determination of plausible biomarkers and biological processes associated with GU toxicity.

PMID:
29502932
PMCID:
PMC5886789
DOI:
10.1016/j.ijrobp.2018.01.054
[Indexed for MEDLINE]
Free PMC Article

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