@article{RN6169,
   author = {Yang, J. Y. and Yoshihara, K. and Tanaka, K. and Hatae, M. and Masuzaki, H. and Itamochi, H. and Takano, M. and Ushijima, K. and Tanyi, J. L. and Coukos, G. and Lu, Y. and Mills, G. B. and Verhaak, R. G.},
   title = {Predicting time to ovarian carcinoma recurrence using protein markers},
   journal = {J Clin Invest},
   volume = {123},
   number = {9},
   pages = {3740-50},
   note = {1558-8238
Yang, Ji-Yeon
Yoshihara, Kosuke
Tanaka, Kenichi
Hatae, Masayuki
Masuzaki, Hideaki
Itamochi, Hiroaki
Cancer Genome Atlas (TCGA) Research Network
Takano, Masashi
Ushijima, Kimio
Tanyi, Janos L
Coukos, George
Lu, Yiling
Mills, Gordon B
Verhaak, Roel G W
P30 CA016672/CA/NCI NIH HHS/United States
CA143883/CA/NCI NIH HHS/United States
CA016672/CA/NCI NIH HHS/United States
P50 CA098258/CA/NCI NIH HHS/United States
5 P50 CA083639-12/CA/NCI NIH HHS/United States
P50 CA083639/CA/NCI NIH HHS/United States
U24 CA143883/CA/NCI NIH HHS/United States
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
United States
2013/08/16
J Clin Invest. 2013 Sep;123(9):3740-50. doi: 10.1172/JCI68509. Epub 2013 Aug 15.},
   abstract = {Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression-based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.},
   keywords = {Adult
Aged
Aged, 80 and over
Biomarkers, Tumor/*metabolism
Carcinoma, Ovarian Epithelial
Cluster Analysis
Disease-Free Survival
Female
Humans
Kaplan-Meier Estimate
Middle Aged
Multivariate Analysis
Neoplasm Proteins/*metabolism
Neoplasm Recurrence, Local/*metabolism/mortality
Neoplasms, Glandular and Epithelial/*metabolism/mortality
Ovarian Neoplasms/*metabolism/mortality
Prognosis
Proportional Hazards Models
Proteomics
Risk
Transcriptome},
   ISSN = {0021-9738 (Print)
0021-9738},
   DOI = {10.1172/jci68509},
   year = {2013},
   type = {Journal Article}
}

{"_id":"LYWcA75vGYYi9oRnJ","bibbaseid":"yang-yoshihara-tanaka-hatae-masuzaki-itamochi-takano-ushijima-etal-predictingtimetoovariancarcinomarecurrenceusingproteinmarkers-2013","author_short":["Yang, J. Y.","Yoshihara, K.","Tanaka, K.","Hatae, M.","Masuzaki, H.","Itamochi, H.","Takano, M.","Ushijima, K.","Tanyi, J. L.","Coukos, G.","Lu, Y.","Mills, G. B.","Verhaak, R. G."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Yang"],"firstnames":["J.","Y."],"suffixes":[]},{"propositions":[],"lastnames":["Yoshihara"],"firstnames":["K."],"suffixes":[]},{"propositions":[],"lastnames":["Tanaka"],"firstnames":["K."],"suffixes":[]},{"propositions":[],"lastnames":["Hatae"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Masuzaki"],"firstnames":["H."],"suffixes":[]},{"propositions":[],"lastnames":["Itamochi"],"firstnames":["H."],"suffixes":[]},{"propositions":[],"lastnames":["Takano"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Ushijima"],"firstnames":["K."],"suffixes":[]},{"propositions":[],"lastnames":["Tanyi"],"firstnames":["J.","L."],"suffixes":[]},{"propositions":[],"lastnames":["Coukos"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Lu"],"firstnames":["Y."],"suffixes":[]},{"propositions":[],"lastnames":["Mills"],"firstnames":["G.","B."],"suffixes":[]},{"propositions":[],"lastnames":["Verhaak"],"firstnames":["R.","G."],"suffixes":[]}],"title":"Predicting time to ovarian carcinoma recurrence using protein markers","journal":"J Clin Invest","volume":"123","number":"9","pages":"3740-50","note":"1558-8238 Yang, Ji-Yeon Yoshihara, Kosuke Tanaka, Kenichi Hatae, Masayuki Masuzaki, Hideaki Itamochi, Hiroaki Cancer Genome Atlas (TCGA) Research Network Takano, Masashi Ushijima, Kimio Tanyi, Janos L Coukos, George Lu, Yiling Mills, Gordon B Verhaak, Roel G W P30 CA016672/CA/NCI NIH HHS/United States CA143883/CA/NCI NIH HHS/United States CA016672/CA/NCI NIH HHS/United States P50 CA098258/CA/NCI NIH HHS/United States 5 P50 CA083639-12/CA/NCI NIH HHS/United States P50 CA083639/CA/NCI NIH HHS/United States U24 CA143883/CA/NCI NIH HHS/United States Comparative Study Journal Article Research Support, N.I.H., Extramural United States 2013/08/16 J Clin Invest. 2013 Sep;123(9):3740-50. doi: 10.1172/JCI68509. Epub 2013 Aug 15.","abstract":"Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression-based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.","keywords":"Adult Aged Aged, 80 and over Biomarkers, Tumor/*metabolism Carcinoma, Ovarian Epithelial Cluster Analysis Disease-Free Survival Female Humans Kaplan-Meier Estimate Middle Aged Multivariate Analysis Neoplasm Proteins/*metabolism Neoplasm Recurrence, Local/*metabolism/mortality Neoplasms, Glandular and Epithelial/*metabolism/mortality Ovarian Neoplasms/*metabolism/mortality Prognosis Proportional Hazards Models Proteomics Risk Transcriptome","issn":"0021-9738 (Print) 0021-9738","doi":"10.1172/jci68509","year":"2013","bibtex":"@article{RN6169,\n author = {Yang, J. Y. and Yoshihara, K. and Tanaka, K. and Hatae, M. and Masuzaki, H. and Itamochi, H. and Takano, M. and Ushijima, K. and Tanyi, J. L. and Coukos, G. and Lu, Y. and Mills, G. B. and Verhaak, R. G.},\n title = {Predicting time to ovarian carcinoma recurrence using protein markers},\n journal = {J Clin Invest},\n volume = {123},\n number = {9},\n pages = {3740-50},\n note = {1558-8238\nYang, Ji-Yeon\nYoshihara, Kosuke\nTanaka, Kenichi\nHatae, Masayuki\nMasuzaki, Hideaki\nItamochi, Hiroaki\nCancer Genome Atlas (TCGA) Research Network\nTakano, Masashi\nUshijima, Kimio\nTanyi, Janos L\nCoukos, George\nLu, Yiling\nMills, Gordon B\nVerhaak, Roel G W\nP30 CA016672/CA/NCI NIH HHS/United States\nCA143883/CA/NCI NIH HHS/United States\nCA016672/CA/NCI NIH HHS/United States\nP50 CA098258/CA/NCI NIH HHS/United States\n5 P50 CA083639-12/CA/NCI NIH HHS/United States\nP50 CA083639/CA/NCI NIH HHS/United States\nU24 CA143883/CA/NCI NIH HHS/United States\nComparative Study\nJournal Article\nResearch Support, N.I.H., Extramural\nUnited States\n2013/08/16\nJ Clin Invest. 2013 Sep;123(9):3740-50. doi: 10.1172/JCI68509. Epub 2013 Aug 15.},\n abstract = {Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression-based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.},\n keywords = {Adult\nAged\nAged, 80 and over\nBiomarkers, Tumor/*metabolism\nCarcinoma, Ovarian Epithelial\nCluster Analysis\nDisease-Free Survival\nFemale\nHumans\nKaplan-Meier Estimate\nMiddle Aged\nMultivariate Analysis\nNeoplasm Proteins/*metabolism\nNeoplasm Recurrence, Local/*metabolism/mortality\nNeoplasms, Glandular and Epithelial/*metabolism/mortality\nOvarian Neoplasms/*metabolism/mortality\nPrognosis\nProportional Hazards Models\nProteomics\nRisk\nTranscriptome},\n ISSN = {0021-9738 (Print)\n0021-9738},\n DOI = {10.1172/jci68509},\n year = {2013},\n type = {Journal Article}\n}\n\n","author_short":["Yang, J. Y.","Yoshihara, K.","Tanaka, K.","Hatae, M.","Masuzaki, H.","Itamochi, H.","Takano, M.","Ushijima, K.","Tanyi, J. L.","Coukos, G.","Lu, Y.","Mills, G. B.","Verhaak, R. 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