Prediction of Early Breast Cancer Metastasis from DNA Microarray Data Using High-Dimensional Cox Regression Models

Conclusions: High-dimensional regression methods are attractive in prognostic studies because finding a small subset of genes may facilitate the transfer to the clinic, and also because they strengthen the robustness of the model by limiting the selection of false-positive predictive genes. With only six genes, the CoxBoost classifier predicted the 4-year status of metastatic disease with 93% sensitivity. Selecting a few genes related to ontologies other than cell proliferation might further improve the overall sensitivity performance.

Source: Cancer Informatics - May 5, 2015 Category: Cancer & Oncology Authors: Christophe ZemmourFrançois BertucciPascal FinettiBernard ChetritDaniel BirnbaumThomas FilleronJean-Marie Boher Source Type: research