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

Katharina Perell; Martin Vincent; Ben Vainer; Bodil Laub Petersen; Birgitte Federspiel; Anne Kirstine Møller; Mette Madsen; Niels Richard Hansen; Lennart Friis-Hansen; Finn Cilius Nielsen; Gedske Daugaard

doi:10.1016/j.molonc.2014.07.015

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

Katharina Perell^*, Martin Vincent, Ben Vainer, Bodil Laub Petersen, Birgitte Federspiel, Anne Kirstine Møller, Mette Madsen, Niels Richard Hansen, Lennart Friis-Hansen, Finn Cilius Nielsen, Gedske Daugaard

^*Corresponding author for this work

7 Citations (Scopus)

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.

Original language	English
Journal	Molecular Oncology
Volume	9
Issue number	1
Pages (from-to)	68-77
ISSN	1574-7891
DOIs	https://doi.org/10.1016/j.molonc.2014.07.015
Publication status	Published - 1 Jan 2015

Keywords

Classification
Liver biopsy
Metastases
microRNA
Surrounding tissue
Tissue contamination

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Perell, K., Vincent, M., Vainer, B., Petersen, B. L., Federspiel, B., Møller, A. K., Madsen, M., Hansen, N. R., Friis-Hansen, L., Nielsen, F. C., & Daugaard, G. (2015). Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies. Molecular Oncology, 9(1), 68-77. https://doi.org/10.1016/j.molonc.2014.07.015

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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.",

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AU - Federspiel, Birgitte

AU - Møller, Anne Kirstine

AU - Madsen, Mette

AU - Hansen, Niels Richard

AU - Friis-Hansen, Lennart

AU - Nielsen, Finn Cilius

AU - Daugaard, Gedske

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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.

KW - Classification

KW - Liver biopsy

KW - Metastases

KW - microRNA

KW - Surrounding tissue

KW - Tissue contamination

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DO - 10.1016/j.molonc.2014.07.015

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EP - 77

JO - Molecular Oncology

JF - Molecular Oncology

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ER -

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

Abstract

Keywords

UN SDGs

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