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Subtype classification and heterogeneous prognosis model construction in precision medicine.

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  • المؤلفون: You N;You N; He S; He S; Wang X; Wang X; Wang X; Wang X; Zhu J; Zhu J; Zhang H; Zhang H
  • المصدر:
    Biometrics [Biometrics] 2018 Sep; Vol. 74 (3), pp. 814-822. Date of Electronic Publication: 2018 Jan 22.
  • نوع النشر :
    Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: Biometric Society Country of Publication: United States NLM ID: 0370625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
    • بيانات النشر:
      Publication: Alexandria Va : Biometric Society
      Original Publication: Washington.
    • الموضوع:
      Models, Statistical* ; Prognosis* ; Proportional Hazards Models*; Ovarian Neoplasms/*classification ; Precision Medicine/*methods; Algorithms ; Computer Simulation ; DNA Repair/genetics ; Female ; Gene Expression/genetics ; Humans ; Ovarian Neoplasms/diagnosis ; Ovarian Neoplasms/genetics ; Risk Factors ; Survival Analysis
    • نبذة مختصرة :
      Common diseases including cancer are heterogeneous. It is important to discover disease subtypes and identify both shared and unique risk factors for different disease subtypes. The advent of high-throughput technologies enriches the data to achieve this goal, if necessary statistical methods are developed. Existing methods can accommodate both heterogeneity identification and variable selection under parametric models, but for survival analysis, the commonly used Cox model is semiparametric. Although finite-mixture Cox model has been proposed to address heterogeneity in survival analysis, variable selection has not been incorporated into such semiparametric models. Using regularization regression, we propose a variable selection method for the finite-mixture Cox model and select important, subtype-specific risk factors from high-dimensional predictors. Our estimators have oracle properties with proper choices of penalty parameters under the regularization regression. An expectation-maximization algorithm is developed for numerical calculation. Simulations demonstrate that our proposed method performs well in revealing the heterogeneity and selecting important risk factors for each subtype, and its performance is compared to alternatives with other regularizers. Finally, we apply our method to analyze a gene expression dataset for ovarian cancer DNA repair pathways. Based on our selected risk factors, the prognosis model accounting for heterogeneity consistently improves the prediction for the survival probability in both training and test datasets.
      (© 2018, The International Biometric Society.)
    • Grant Information:
      R01 DA016750 United States DA NIDA NIH HHS
    • Contributed Indexing:
      Keywords: EM algorithm; Finite-mixture Cox proportional hazards model; Heterogeneity; High-dimensional data; Subtype; Variable selection
    • الموضوع:
      Date Created: 20180124 Date Completed: 20190401 Latest Revision: 20220408
    • الموضوع:
      20221213
    • الرقم المعرف:
      10.1111/biom.12843
    • الرقم المعرف:
      29359319