Cited 92 times in
Predicting Degree of Benefit From Adjuvant Trastuzumab in NSABP Trial B-31
DC Field | Value | Language |
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dc.contributor.author | 백순명 | - |
dc.date.accessioned | 2014-12-18T09:52:05Z | - |
dc.date.available | 2014-12-18T09:52:05Z | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 0027-8874 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/88979 | - |
dc.description.abstract | BACKGROUND: National Surgical Adjuvant Breast and Bowel Project (NSABP) trial B-31 suggested the efficacy of adjuvant trastuzumab, even in HER2-negative breast cancer. This finding prompted us to develop a predictive model for degree of benefit from trastuzumab using archived tumor blocks from B-31. METHODS: Case subjects with tumor blocks were randomly divided into discovery (n = 588) and confirmation cohorts (n = 991). A predictive model was built from the discovery cohort through gene expression profiling of 462 genes with nCounter assay. A predefined cut point for the predictive model was tested in the confirmation cohort. Gene-by-treatment interaction was tested with Cox models, and correlations between variables were assessed with Spearman correlation. Principal component analysis was performed on the final set of selected genes. All statistical tests were two-sided. RESULTS: Eight predictive genes associated with HER2 (ERBB2, c17orf37, GRB7) or ER (ESR1, NAT1, GATA3, CA12, IGF1R) were selected for model building. Three-dimensional subset treatment effect pattern plot using two principal components of these genes was used to identify a subset with no benefit from trastuzumab, characterized by intermediate-level ERBB2 and high-level ESR1 mRNA expression. In the confirmation set, the predefined cut points for this model classified patients into three subsets with differential benefit from trastuzumab with hazard ratios of 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29; n = 100), 0.60 (95% CI = 0.41 to 0.89; P = .01; n = 449), and 0.28 (95% CI = 0.20 to 0.41; P < .001; n = 442; P(interaction) between the model and trastuzumab < .001). CONCLUSIONS: We developed a gene expression-based predictive model for degree of benefit from trastuzumab and demonstrated that HER2-negative tumors belong to the moderate benefit group, thus providing justification for testing trastuzumab in HER2-negative patients (NSABP B-47). | - |
dc.description.statementOfResponsibility | open | - |
dc.relation.isPartOf | JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Antibodies, Monoclonal, Humanized/therapeutic use* | - |
dc.subject.MESH | Antineoplastic Agents/therapeutic use* | - |
dc.subject.MESH | Breast Neoplasms/drug therapy* | - |
dc.subject.MESH | Breast Neoplasms/metabolism* | - |
dc.subject.MESH | Chemotherapy, Adjuvant | - |
dc.subject.MESH | Cohort Studies | - |
dc.subject.MESH | Estrogen Receptor alpha/genetics* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Gene Expression Profiling | - |
dc.subject.MESH | Gene Expression Regulation, Neoplastic* | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Odds Ratio | - |
dc.subject.MESH | Predictive Value of Tests | - |
dc.subject.MESH | Principal Component Analysis | - |
dc.subject.MESH | Proportional Hazards Models | - |
dc.subject.MESH | RNA, Messenger/metabolism | - |
dc.subject.MESH | Receptor, ErbB-2/genetics* | - |
dc.subject.MESH | Trastuzumab | - |
dc.subject.MESH | Treatment Outcome | - |
dc.title | Predicting Degree of Benefit From Adjuvant Trastuzumab in NSABP Trial B-31 | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Life Science (의생명과학부) | - |
dc.contributor.googleauthor | Katherine L. Pogue-Geile | - |
dc.contributor.googleauthor | Chungyeul Kim | - |
dc.contributor.googleauthor | Jong-Hyeon Jeong | - |
dc.contributor.googleauthor | Noriko Tanaka | - |
dc.contributor.googleauthor | Hanna Bandos | - |
dc.contributor.googleauthor | Patrick G. Gavin | - |
dc.contributor.googleauthor | Debora Fumagalli | - |
dc.contributor.googleauthor | Lynn C. Goldstein | - |
dc.contributor.googleauthor | Nour Sneige | - |
dc.contributor.googleauthor | Eike Burandt | - |
dc.contributor.googleauthor | Yusuke Taniyama | - |
dc.contributor.googleauthor | Olga L. Bohn | - |
dc.contributor.googleauthor | Ahwon Lee | - |
dc.contributor.googleauthor | Seung-Il Kim | - |
dc.contributor.googleauthor | Megan L. Reilly | - |
dc.contributor.googleauthor | Matthew Y. Remillard | - |
dc.contributor.googleauthor | Nicole L. Blackmon | - |
dc.contributor.googleauthor | Seong-Rim Kim | - |
dc.contributor.googleauthor | Zachary D. Horne | - |
dc.contributor.googleauthor | Priya Rastogi | - |
dc.contributor.googleauthor | Louis Fehrenbacher | - |
dc.contributor.googleauthor | Edward H. Romond | - |
dc.contributor.googleauthor | Sandra M. Swain | - |
dc.contributor.googleauthor | Eleftherios P. Mamounas | - |
dc.contributor.googleauthor | D. Lawrence Wickerham | - |
dc.contributor.googleauthor | Charles E. Geyer Jr | - |
dc.contributor.googleauthor | Joseph P. Costantino | - |
dc.contributor.googleauthor | Norman Wolmark | - |
dc.contributor.googleauthor | Soonmyung Paik | - |
dc.identifier.doi | 10.1093/jnci/djt321 | - |
dc.admin.author | false | - |
dc.admin.mapping | false | - |
dc.contributor.localId | A01823 | - |
dc.relation.journalcode | J01896 | - |
dc.identifier.eissn | 1460-2105 | - |
dc.identifier.pmid | 24262440 | - |
dc.identifier.url | http://jnci.oxfordjournals.org/content/105/23/1782.long | - |
dc.subject.keyword | Antibodies, Monoclonal, Humanized/therapeutic use* | - |
dc.subject.keyword | Antineoplastic Agents/therapeutic use* | - |
dc.subject.keyword | Breast Neoplasms/drug therapy* | - |
dc.subject.keyword | Breast Neoplasms/metabolism* | - |
dc.subject.keyword | Chemotherapy, Adjuvant | - |
dc.subject.keyword | Cohort Studies | - |
dc.subject.keyword | Estrogen Receptor alpha/genetics* | - |
dc.subject.keyword | Female | - |
dc.subject.keyword | Gene Expression Profiling | - |
dc.subject.keyword | Gene Expression Regulation, Neoplastic* | - |
dc.subject.keyword | Humans | - |
dc.subject.keyword | Odds Ratio | - |
dc.subject.keyword | Predictive Value of Tests | - |
dc.subject.keyword | Principal Component Analysis | - |
dc.subject.keyword | Proportional Hazards Models | - |
dc.subject.keyword | RNA, Messenger/metabolism | - |
dc.subject.keyword | Receptor, ErbB-2/genetics* | - |
dc.subject.keyword | Trastuzumab | - |
dc.subject.keyword | Treatment Outcome | - |
dc.contributor.alternativeName | Paik, Soon Myung | - |
dc.contributor.affiliatedAuthor | Paik, Soon Myung | - |
dc.rights.accessRights | not free | - |
dc.citation.volume | 105 | - |
dc.citation.number | 23 | - |
dc.citation.startPage | 1782 | - |
dc.citation.endPage | 1788 | - |
dc.identifier.bibliographicCitation | JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE, Vol.105(23) : 1782-1788, 2013 | - |
dc.identifier.rimsid | 33718 | - |
dc.type.rims | ART | - |
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