Multiclass prediction with partial least square regression for gene expression data: applications in breast cancer intrinsic taxonomy.
Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS) regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n = 535). The agreement between PAM50 centroid-based single sample prediction (SSP) and PLS-regression was excellent (weighted Kappa: 0.988) within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression. If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed). Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes.
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan ; Cathay General Hospital SiJhih, New Taipei, Taiwan ; School of Medicine, Fu-Jen Catholic University, New Taipei, Taiwan ; School of Medicine, Taipei Medical University, Taipei, Taiwan.
Chi-Cheng
CC
Huang
School of Medicine, Taipei Medical University, Taipei, Taiwan ; Department of Surgery, Cathay General Hospital, Taipei, Taiwan.
Shih-Hsin
SH
Tu
School of Medicine, Taipei Medical University, Taipei, Taiwan ; Department of Surgery, Cathay General Hospital, Taipei, Taiwan.
Ching-Shui
CS
Huang
School of Medicine, Fu-Jen Catholic University, New Taipei, Taiwan ; Department of Surgery, Cathay General Hospital, Taipei, Taiwan.
Heng-Hui
HH
Lien
Graduate Institute of Physiology, National Taiwan University, Taipei City, Taiwan.
Liang-Chuan
LC
Lai
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
Eric Y
EY
Chuang
D014408:Biomarkers, Tumor / Q000096:biosynthesis*; D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics* / Q000473:pathology; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D006801:Humans; D016018:Least-Squares Analysis; D011379:Prognosis; D059467:Transcriptome
D014408:Biomarkers, Tumor