Table of Contents
International Journal of Proteomics
Volume 2011, Article ID 896476, 16 pages
http://dx.doi.org/10.1155/2011/896476
Research Article

Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response

1Gonda/UCLA Breast Cancer Research Laboratory, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
2Department of Surgery, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
3Cardiovascular Proteomics Center, Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, MA 02118, USA
4Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
5Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
6Pasarow Mass Spectrometry Laboratory, Semel Institute and Department of Psychiatry and Biobehavioral Science, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
7Revlon/UCLA Breast Center, David Geffen School of Medicine at UCLA, 200 UCLA Medical Plaza, B265, Los Angeles, CA 90095, USA

Received 22 June 2011; Accepted 12 August 2011

Academic Editor: David E. Misek

Copyright © 2011 Jianbo He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

The error rates of tumor classification predicted by different sets of proteins in each model (SVM, KNN, DLDA, PAM, and SOM) were estimated by leaving-one-out test (GEPAS, version 4.0, http://www.gepas.org).

File A: SVM had the lowest error rate (10%, 4/39) in tumor classification using 20 proteins listed in Table 3.

File B and C: KNN had the lowest error rate (9%, 1/11) in predicting HER2-positive tumor response using 20 proteins listed in Table 4. By using KNN = 1 method, 100% (4/4) tumors in NR and 85.7% (6/7) tumors in pCR were correctly grouped.

File D and E: DLDA had the lowest error rate (18%, 2/11) in predicting TNBC tumor response using 30 proteins listed in Table 5. 85.7% (6/7) tumors in the R group and 75% (3/4) tumors in IR/NR group were correctly classified.

  1. Supplementary Material