Ching-Shui Huang1,2, Tzu-Pin Lu3, Chih-Yi Liu4, Chi-Jung Huang5,6, Jen-Hwey Chiu7,8, Yen-Jen Chen7, Ling-Ming Tseng9,10, Chi-Cheng Huang11,12. 1. Division of General Surgery, Department of Surgery, Cathay General Hospital, Taipei, Taiwan. 2. School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan. 3. Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan. 4. Department of Pathology, Cathay General Hospital SiJhih, New Taipei, Taiwan. 5. Department of Medical Research, Cathay General Hospital, Taipei, Taiwan. 6. Department of Biochemistry, National Defense Medical Center, Taipei, Taiwan. 7. Comprehensive Breast Health Center, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou District, Taipei, 11217, Taiwan, ROC. 8. Institute of Traditional Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan. 9. Comprehensive Breast Health Center, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou District, Taipei, 11217, Taiwan, ROC. lmtseng@vghtpe.gov.tw. 10. School of Medicine, College of Medicine, National Yang-Ming University, Taipei, Taiwan. lmtseng@vghtpe.gov.tw. 11. Comprehensive Breast Health Center, Taipei Veterans General Hospital, No. 201, Sec. 2, Shipai Rd., Beitou District, Taipei, 11217, Taiwan, ROC. chishenh74@gmail.com. 12. School of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan. chishenh74@gmail.com.
Abstract
INTRODUCTION: The aim of the study was to perform digital RNA counting to validate a gene expression signature for operable breast cancers initially treated with curative intention, and the risk of recurrence, distant metastasis, and mortality was predicted. METHODS: Candidate genes were initially discovered from the coherent genomic and transcriptional alternations from microarrays, and the extended concurrent genes were used to build a risk stratification model from archived formalin-fixed paraffin-embedded (FFPE) tissues with the NanoString nCounter. RESULTS: The extended concurrent genes signature was prognostic in 144 Taiwanese breast cancers (5-year relapse-free survival: 89.8 and 69.4% for low- and high-risk group, log-rank test: P = 0.004). Cross-platform comparability was evidenced from significant and positive correlations for most genes as well as equal covariance matrix across 64 patients assayed for both microarray and digital RNA counting. DISCUSSION: Archived FFPE samples could be successfully assayed by the NanoString nCounter. The purposed signature was prognostic stratifying breast cancer patients into groups with distinct survival patterns, and clinical applicability of the residual risk model was proved.
INTRODUCTION: The aim of the study was to perform digital RNA counting to validate a gene expression signature for operable breast cancers initially treated with curative intention, and the risk of recurrence, distant metastasis, and mortality was predicted. METHODS: Candidate genes were initially discovered from the coherent genomic and transcriptional alternations from microarrays, and the extended concurrent genes were used to build a risk stratification model from archived formalin-fixed paraffin-embedded (FFPE) tissues with the NanoString nCounter. RESULTS: The extended concurrent genes signature was prognostic in 144 Taiwanese breast cancers (5-year relapse-free survival: 89.8 and 69.4% for low- and high-risk group, log-rank test: P = 0.004). Cross-platform comparability was evidenced from significant and positive correlations for most genes as well as equal covariance matrix across 64 patients assayed for both microarray and digital RNA counting. DISCUSSION: Archived FFPE samples could be successfully assayed by the NanoString nCounter. The purposed signature was prognostic stratifying breast cancer patients into groups with distinct survival patterns, and clinical applicability of the residual risk model was proved.
Entities:
Keywords:
Breast cancer; Digital RNA counting; Extended concurrent genes; Prognostic signature; Residual risk