Biomarkers for predicting complete debulking in ovarian cancer: lessons to be learned

Carsten Lindberg Fagö-Olsen; Bent Ottesen; Ib Jarle Christensen; Estrid Høgdall; Lene Lundvall; Lotte Nedergaard; Svend-Aage Engelholm; Sofie Leisby Antonsen; Magnus Lydolph; Claus Høgdall

Biomarkers for predicting complete debulking in ovarian cancer: lessons to be learned

Carsten Lindberg Fagö-Olsen, Bent Ottesen, Ib Jarle Christensen, Estrid Høgdall, Lene Lundvall, Lotte Nedergaard, Svend-Aage Engelholm, Sofie Leisby Antonsen, Magnus Lydolph, Claus Høgdall

3 Citationer (Scopus)

Abstract

AIM: We aimed to construct and validate a model based on biomarkers to predict complete primary debulking surgery for ovarian cancer patients.

PATIENTS AND METHODS: The study consisted of three parts: Part I: Biomarker data obtained from mass spectrometry, baseline data and, surgical outcome were used to construct predictive indices for complete tumour resection; Part II: sera from randomly selected patients from part I were analyzed using enzyme-linked immunosorbent assay (ELISA) to investigate the correlation to mass spectrometry; Part III: the indices from part I were validated in a new cohort of patients.

RESULTS: Part I: The area under the receiver operating characteristic curve (AUC) was 0.82 for both indices. Part II: Linear regression analysis gave an R(2) value of 0.52 and 0.63 for transferrin and β2-microglobulin, respectively. Part III: The AUC of the two indices decreased to 0.64.

CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer.

Originalsprog	Engelsk
Tidsskrift	Anticancer Research
Vol/bind	34
Udgave nummer	2
Sider (fra-til)	679-682
Antal sider	4
ISSN	0250-7005
Status	Udgivet - 1 feb. 2014

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title = "Biomarkers for predicting complete debulking in ovarian cancer: lessons to be learned",

abstract = "AIM: We aimed to construct and validate a model based on biomarkers to predict complete primary debulking surgery for ovarian cancer patients.PATIENTS AND METHODS: The study consisted of three parts: Part I: Biomarker data obtained from mass spectrometry, baseline data and, surgical outcome were used to construct predictive indices for complete tumour resection; Part II: sera from randomly selected patients from part I were analyzed using enzyme-linked immunosorbent assay (ELISA) to investigate the correlation to mass spectrometry; Part III: the indices from part I were validated in a new cohort of patients.RESULTS: Part I: The area under the receiver operating characteristic curve (AUC) was 0.82 for both indices. Part II: Linear regression analysis gave an R(2) value of 0.52 and 0.63 for transferrin and β2-microglobulin, respectively. Part III: The AUC of the two indices decreased to 0.64.CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer.",

keywords = "Cohort Studies, Enzyme-Linked Immunosorbent Assay, Female, Humans, Linear Models, Logistic Models, Mass Spectrometry, Models, Statistical, Ovarian Neoplasms, Predictive Value of Tests, Tumor Markers, Biological",

author = "Fag{\"o}-Olsen, {Carsten Lindberg} and Bent Ottesen and Christensen, {Ib Jarle} and Estrid H{\o}gdall and Lene Lundvall and Lotte Nedergaard and Svend-Aage Engelholm and Antonsen, {Sofie Leisby} and Magnus Lydolph and Claus H{\o}gdall",

year = "2014",

month = feb,

day = "1",

language = "English",

volume = "34",

pages = "679--682",

journal = "Anticancer Research",

issn = "0250-7005",

publisher = "International Institute of Anticancer Research",

number = "2",

}

TY - JOUR

T1 - Biomarkers for predicting complete debulking in ovarian cancer

T2 - lessons to be learned

AU - Fagö-Olsen, Carsten Lindberg

AU - Ottesen, Bent

AU - Christensen, Ib Jarle

AU - Høgdall, Estrid

AU - Lundvall, Lene

AU - Nedergaard, Lotte

AU - Engelholm, Svend-Aage

AU - Antonsen, Sofie Leisby

AU - Lydolph, Magnus

AU - Høgdall, Claus

PY - 2014/2/1

Y1 - 2014/2/1

N2 - AIM: We aimed to construct and validate a model based on biomarkers to predict complete primary debulking surgery for ovarian cancer patients.PATIENTS AND METHODS: The study consisted of three parts: Part I: Biomarker data obtained from mass spectrometry, baseline data and, surgical outcome were used to construct predictive indices for complete tumour resection; Part II: sera from randomly selected patients from part I were analyzed using enzyme-linked immunosorbent assay (ELISA) to investigate the correlation to mass spectrometry; Part III: the indices from part I were validated in a new cohort of patients.RESULTS: Part I: The area under the receiver operating characteristic curve (AUC) was 0.82 for both indices. Part II: Linear regression analysis gave an R(2) value of 0.52 and 0.63 for transferrin and β2-microglobulin, respectively. Part III: The AUC of the two indices decreased to 0.64.CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer.

AB - AIM: We aimed to construct and validate a model based on biomarkers to predict complete primary debulking surgery for ovarian cancer patients.PATIENTS AND METHODS: The study consisted of three parts: Part I: Biomarker data obtained from mass spectrometry, baseline data and, surgical outcome were used to construct predictive indices for complete tumour resection; Part II: sera from randomly selected patients from part I were analyzed using enzyme-linked immunosorbent assay (ELISA) to investigate the correlation to mass spectrometry; Part III: the indices from part I were validated in a new cohort of patients.RESULTS: Part I: The area under the receiver operating characteristic curve (AUC) was 0.82 for both indices. Part II: Linear regression analysis gave an R(2) value of 0.52 and 0.63 for transferrin and β2-microglobulin, respectively. Part III: The AUC of the two indices decreased to 0.64.CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer.

KW - Cohort Studies

KW - Enzyme-Linked Immunosorbent Assay

KW - Female

KW - Humans

KW - Linear Models

KW - Logistic Models

KW - Mass Spectrometry

KW - Models, Statistical

KW - Ovarian Neoplasms

KW - Predictive Value of Tests

KW - Tumor Markers, Biological

M3 - Journal article

C2 - 24510999

SN - 0250-7005

VL - 34

SP - 679

EP - 682

JO - Anticancer Research

JF - Anticancer Research

IS - 2

ER -

Biomarkers for predicting complete debulking in ovarian cancer: lessons to be learned

Abstract

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