3 years ago

Machine Learning on a Genome-Wide Association Study to Predict Late Genitourinary Toxicity Following Prostate Radiotherapy

Late genitourinary (GU) toxicity after radiotherapy limits the quality of life of prostate cancer survivors, but efforts to explain GU toxicity using patient/dose information remain unsuccessful. We aimed to identify patients with higher congenital GU toxicity risk by identifying and integrating patterns in genome-wide single nucleotide polymorphisms (SNPs). Materials and Methods We applied a pre-conditioned random forest regression (PRFR) method for predicting risk from the genome-wide data to combine the effects of multiple SNPs and overcome statistical power limitations of single-SNP analysis. We studied a cohort of 324 prostate cancer patients who were self-assessed for four urinary symptoms at 2 years after radiotherapy using the International Prostate Symptom Score. Results The predictive accuracy of the methodology varied across symptoms. Only for the weak stream endpoint, it achieved a significant area under the curve of 0.70 (95% confidence interval: 0.54 – 0.86, p = 0.01) 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. Conclusion We applied machine learning methods and bioinformatics tools to genome-wide data to predict and explain GU toxicity. Our approach enabled designing a more powerful predictive model and finding plausible biomarkers and biological processes associated with GU toxicity.

Publisher URL: www.sciencedirect.com/science

DOI: S0360301618301263

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.