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Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies

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@article{f291c5e756014f2ebe487eaeff5f27db,
title = "Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies",
abstract = "Identification of the primary tumor site in patients with metastatic cancer is clinically important, but remains a challenge. Hence, efforts have been made towards establishing new diagnostic tools. Molecular profiling is a promising diagnostic approach, but tissue heterogeneity and inadequacy may negatively affect the accuracy and usability of molecular classifiers. We have developed and validated a microRNA-based classifier, which predicts the primary tumor site of liver biopsies, containing a limited number of tumor cells. Concurrently we explored the influence of surrounding normal tissue on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74.5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding normal tissue from the biopsy site may critically influence molecular classification. A significant improvement in classification accuracy was obtained when the influence of normal tissue was limited by application of a statistical contamination model.",
author = "Katharina Perell and Martin Vincent and Ben Vainer and Petersen, {Bodil Laub} and Birgitte Federspiel and M{\o}ller, {Anne Kirstine Hundahl} and Mette Madsen and Hansen, {Niels Richard} and Lennart Friis-Hansen and Nielsen, {Finn Cilius} and Gedske Daugaard",
note = "Copyright {\textcopyright} 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.",
year = "2015",
month = jan,
doi = "10.1016/j.molonc.2014.07.015",
language = "English",
volume = "9",
pages = "68--77",
journal = "Molecular Oncology",
issn = "1574-7891",
publisher = "Elsevier BV",
number = "1",

}

RIS

TY - JOUR

T1 - Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies

AU - Perell, Katharina

AU - Vincent, Martin

AU - Vainer, Ben

AU - Petersen, Bodil Laub

AU - Federspiel, Birgitte

AU - Møller, Anne Kirstine Hundahl

AU - Madsen, Mette

AU - Hansen, Niels Richard

AU - Friis-Hansen, Lennart

AU - Nielsen, Finn Cilius

AU - Daugaard, Gedske

N1 - Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

PY - 2015/1

Y1 - 2015/1

N2 - Identification of the primary tumor site in patients with metastatic cancer is clinically important, but remains a challenge. Hence, efforts have been made towards establishing new diagnostic tools. Molecular profiling is a promising diagnostic approach, but tissue heterogeneity and inadequacy may negatively affect the accuracy and usability of molecular classifiers. We have developed and validated a microRNA-based classifier, which predicts the primary tumor site of liver biopsies, containing a limited number of tumor cells. Concurrently we explored the influence of surrounding normal tissue on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74.5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding normal tissue from the biopsy site may critically influence molecular classification. A significant improvement in classification accuracy was obtained when the influence of normal tissue was limited by application of a statistical contamination model.

AB - Identification of the primary tumor site in patients with metastatic cancer is clinically important, but remains a challenge. Hence, efforts have been made towards establishing new diagnostic tools. Molecular profiling is a promising diagnostic approach, but tissue heterogeneity and inadequacy may negatively affect the accuracy and usability of molecular classifiers. We have developed and validated a microRNA-based classifier, which predicts the primary tumor site of liver biopsies, containing a limited number of tumor cells. Concurrently we explored the influence of surrounding normal tissue on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74.5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding normal tissue from the biopsy site may critically influence molecular classification. A significant improvement in classification accuracy was obtained when the influence of normal tissue was limited by application of a statistical contamination model.

U2 - 10.1016/j.molonc.2014.07.015

DO - 10.1016/j.molonc.2014.07.015

M3 - Journal article

C2 - 25131495

VL - 9

SP - 68

EP - 77

JO - Molecular Oncology

JF - Molecular Oncology

SN - 1574-7891

IS - 1

ER -

ID: 44962900