Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies
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Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies. / Perell, Katharina; Vincent, Martin; Vainer, Ben; Petersen, Bodil Laub; Federspiel, Birgitte; Møller, Anne Kirstine; Madsen, Mette; Hansen, Niels Richard; Friis-Hansen, Lennart; Nielsen, Finn Cilius; Daugaard, Gedske.
I: Molecular Oncology, Bind 9, Nr. 1, 2015, s. 68-77.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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
AU - Madsen, Mette
AU - Hansen, Niels Richard
AU - Friis-Hansen, Lennart
AU - Nielsen, Finn Cilius
AU - Daugaard, Gedske
PY - 2015
Y1 - 2015
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
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: 123335160