title: Integrated gene expression profile predicts prognosis of breast cancer patients.
abstract: Gene expression data has in recent years demonstrated the superior capacity to predict the prognosis of breast cancer patients unreceiving adjuvant chemotherapy comparing to the information available from traditional clinical and pathological sources. Meanwhile, adjuvant chemotherapy can significantly improve survival of breast cancer. It would be inappropriate to ignore its effect on prognosis. We hypothesized that an integrated gene expression profile can predict the prognosis of breast cancer patients receiving chemotherapy. Therefore, we screened the specific gene markers and constructed an integrated 24-gene signature by low-density microarray including the "poor signature" and genes related to resistance to chemotherapy. The gene signature stratified correctly patients into good prognosis group and poor prognosis group. In addition, the Kaplan-Meier analyses for disease-free survival as a function of the 24-gene signature showed highly significant differences between the two groups (Log Rank test P < 0.0001 = Univariate and multivariate Cox's proportional-hazards regression analyses indicated that the signature represents the strongest independent prognostic factor for breast cancer patients. When compared with single signature, such as Oncotype DX and 70 poor signature, the integrated signature showed more predominant power of predication in breast cancer patients receiving chemotherapy. Such integrated signature will critically aid clinical decision making at the level of individualization for most breast cancer patients receiving chemotherapy.
authors.affiliation: Department of Oncology, Breast Cancer Institute, Cancer Hospital/Cancer Institute, Shanghai Medical College, Institutes of Biomedical Science, Fudan University, 270 Dong'an Road, Shanghai, 200032, P.R. China.
authors.forename: Lian-Fang
authors.initials: LF
authors.lastname: Li
authors.forename: Xiao-Jing
authors.initials: XJ
authors.lastname: Xu
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhao
authors.forename: Zhe-Bing
authors.initials: ZB
authors.lastname: Liu
authors.forename: Zhen-Zhou
authors.initials: ZZ
authors.lastname: Shen
authors.forename: Wei-Rong
authors.initials: WR
authors.lastname: Jin
authors.forename: Zhi-Ming
authors.initials: ZM
authors.lastname: Shao
mesh_terms: D000328:Adult; D000368:Aged; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000008:administration & dosage / Q000494:pharmacology / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000737:chemistry / Q000188:drug therapy / Q000235:genetics* / Q000401:mortality; D002277:Carcinoma / Q000737:chemistry / Q000188:drug therapy / Q000235:genetics / Q000401:mortality; D018270:Carcinoma, Ductal, Breast / Q000737:chemistry / Q000188:drug therapy / Q000235:genetics* / Q000401:mortality; D017024:Chemotherapy, Adjuvant; D003520:Cyclophosphamide / Q000008:administration & dosage / Q000494:pharmacology; D018572:Disease-Free Survival; D004317:Doxorubicin / Q000008:administration & dosage / Q000494:pharmacology; D015251:Epirubicin / Q000008:administration & dosage / Q000494:pharmacology; D005260:Female; D005472:Fluorouracil / Q000008:administration & dosage / Q000494:pharmacology; D005500:Follow-Up Studies; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects; D052138:Genes, Neoplasm; D006801:Humans; D053208:Kaplan-Meier Estimate; D008875:Middle Aged; D009363:Neoplasm Proteins / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011379:Prognosis; D016016:Proportional Hazards Models; D012333:RNA, Messenger / Q000032:analysis* / Q000096:biosynthesis; D012334:RNA, Neoplasm / Q000032:analysis* / Q000096:biosynthesis; D055815:Young Adult
chemical_list: D009363:Neoplasm Proteins; D012333:RNA, Messenger; D012334:RNA, Neoplasm; D015251:Epirubicin; D004317:Doxorubicin; D003520:Cyclophosphamide; D005472:Fluorouracil