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Clinical Outcome 3 Years After Autologous Chondrocyte Implantation Does Not Correlate With the Expression of a Predefined Gene Marker Set in Chondrocytes Prior to Implantation but Is Associated With Critical Signaling Pathways.

Stenberg J, de Windt TS, Synnergren J, Hynsjö L, van der Lee J, Saris DB, Brittberg M, Peterson L, Lindahl A - Orthop J Sports Med (2014)

Bottom Line: No significant difference in expression of the predefined marker set was observed between the success and failure groups.The subtle difference in gene expression regulation found between the 2 groups may strengthen the basis for further research, aiming at reliable biomarkers and quality control for tissue engineering in cartilage repair.This result is especially important as the chondrogenic potential of the chondrocytes is currently part of quality control measures according to European and American legislations regarding advanced therapies.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.

ABSTRACT

Background: There is a need for tools to predict the chondrogenic potency of autologous cells for cartilage repair.

Purpose: To evaluate previously proposed chondrogenic biomarkers and to identify new biomarkers in the chondrocyte transcriptome capable of predicting clinical success or failure after autologous chondrocyte implantation.

Study design: Controlled laboratory study and case-control study; Level of evidence, 3.

Methods: Five patients with clinical improvement after autologous chondrocyte implantation and 5 patients with graft failures 3 years after implantation were included. Surplus chondrocytes from the transplantation were frozen for each patient. Each chondrocyte sample was subsequently thawed at the same time point and cultured for 1 cell doubling, prior to RNA purification and global microarray analysis. The expression profiles of a set of predefined marker genes (ie, collagen type II α1 [COL2A1], bone morphogenic protein 2 [BMP2], fibroblast growth factor receptor 3 [FGFR3], aggrecan [ACAN], CD44, and activin receptor-like kinase receptor 1 [ACVRL1]) were also evaluated.

Results: No significant difference in expression of the predefined marker set was observed between the success and failure groups. Thirty-nine genes were found to be induced, and 38 genes were found to be repressed between the 2 groups prior to autologous chondrocyte implantation, which have implications for cell-regulating pathways (eg, apoptosis, interleukin signaling, and β-catenin regulation).

Conclusion: No expressional differences that predict clinical outcome could be found in the present study, which may have implications for quality control assessments of autologous chondrocyte implantation. The subtle difference in gene expression regulation found between the 2 groups may strengthen the basis for further research, aiming at reliable biomarkers and quality control for tissue engineering in cartilage repair.

Clinical relevance: The present study shows the possible limitations of using gene expression before transplantation to predict the chondrogenic and thus clinical potency of the cells. This result is especially important as the chondrogenic potential of the chondrocytes is currently part of quality control measures according to European and American legislations regarding advanced therapies.

No MeSH data available.


Related in: MedlinePlus

Multivariate data analysis of the centered and normalized array data. (A) Score plot of principal component analysis (PCA) of the centered and normalized data from the arrays including all 10 patients. (B) Loading plot of discriminant analysis using orthogonal partial least square analysis (OPLS-DA) of the data from the array including all 10 patients. Dots indicate the set of transcripts that participate most to the separation of the groups; black dots, the set of 1443 transcripts; gray dots, the cloud of excluded transcripts. (C) Coomans plot showing the prediction PCA models for the 5 success patients and the 5 failure patients where the 5% of the variables that participated the most to the OPLS-DA were used to perform the PCA. Dotted line, 5% confidence limit of the model. (D) Coomans plot showing the prediction for all 10 patients. In this analysis, 3 randomly selected patients from the success and failure groups were included. The 5% of the variables that participated the most in this OPLS-DA were used as data in the PCA models. The excluded patients failed to fit their respective model. Dotted line, 5% confidence limit of the model.
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fig2-2325967114550781: Multivariate data analysis of the centered and normalized array data. (A) Score plot of principal component analysis (PCA) of the centered and normalized data from the arrays including all 10 patients. (B) Loading plot of discriminant analysis using orthogonal partial least square analysis (OPLS-DA) of the data from the array including all 10 patients. Dots indicate the set of transcripts that participate most to the separation of the groups; black dots, the set of 1443 transcripts; gray dots, the cloud of excluded transcripts. (C) Coomans plot showing the prediction PCA models for the 5 success patients and the 5 failure patients where the 5% of the variables that participated the most to the OPLS-DA were used to perform the PCA. Dotted line, 5% confidence limit of the model. (D) Coomans plot showing the prediction for all 10 patients. In this analysis, 3 randomly selected patients from the success and failure groups were included. The 5% of the variables that participated the most in this OPLS-DA were used as data in the PCA models. The excluded patients failed to fit their respective model. Dotted line, 5% confidence limit of the model.

Mentions: The comparative microarray analysis did not reveal any global clustering between chondrocytes from the failure and success groups (Figure 1). The normalized data from the microarrays was also subjected to multivariate analysis. PCA of the total genome also failed to show a clear expressional disparity that can separate the success and failure groups (Figure 2A). Discriminant analysis using orthogonal partial least square analysis using the knowledge of success and failure classes as a discriminating variable did separate the global gene expression into 2 clear groups, representing success and failure patients. Selecting 5% of the transcripts that participated most to the separation of the success and failure groups resulted in a set of 1443 transcripts, as indicated in the PLS-DA loading plot (Figure 2B). Using those transcripts as variables in PCA models for the success and failure groups resulted in an acceptable classification (Figure 2C). However, a prediction analysis where a PCA model is made out of 3 randomly selected patients from each group and the selected 5% of the transcripts that separate these 6 patients in an additional OPLS-DA failed to predict the group classification of the 4 excluded patients (Figure 2D). Only 30% of the transcripts correlated between the OPLS-DA performed with all 10 patients and the OPLS-DA performed with 3 randomly selected patients from the success group and the failure group, respectively (data not shown). Additional data from the cell cultures (passage number and number of cell doublings) or the variables patient age, sex, and lesion size could not contribute to a separation of the 2 groups in the PCA. Neither the predefined set of gene markers nor the genes identified to be important for chondrogenic capacity by Dehne et al10 contributed to any separation of the 2 groups (data not shown). A Student t test was performed to identify genes with a low grade of differential expression between the failure and success groups. The t test identified 541 transcripts to be differentially expressed between groups. A total of 346 transcripts were induced in the success group and 195 transcripts in the failure group. The list of 541 transcripts was filtered to remove genes with small fold changes (<1.2). This approach resulted in 39 genes that were induced and 38 genes that were repressed in the success group (Tables 3 and 4). No intergroup differences were found for the predefined gene set FGFR3, BMP2, COL2A1, ACAN, CD44, and ACVRL1 and the gene list published by Dehne et al10 (Figure 3).


Clinical Outcome 3 Years After Autologous Chondrocyte Implantation Does Not Correlate With the Expression of a Predefined Gene Marker Set in Chondrocytes Prior to Implantation but Is Associated With Critical Signaling Pathways.

Stenberg J, de Windt TS, Synnergren J, Hynsjö L, van der Lee J, Saris DB, Brittberg M, Peterson L, Lindahl A - Orthop J Sports Med (2014)

Multivariate data analysis of the centered and normalized array data. (A) Score plot of principal component analysis (PCA) of the centered and normalized data from the arrays including all 10 patients. (B) Loading plot of discriminant analysis using orthogonal partial least square analysis (OPLS-DA) of the data from the array including all 10 patients. Dots indicate the set of transcripts that participate most to the separation of the groups; black dots, the set of 1443 transcripts; gray dots, the cloud of excluded transcripts. (C) Coomans plot showing the prediction PCA models for the 5 success patients and the 5 failure patients where the 5% of the variables that participated the most to the OPLS-DA were used to perform the PCA. Dotted line, 5% confidence limit of the model. (D) Coomans plot showing the prediction for all 10 patients. In this analysis, 3 randomly selected patients from the success and failure groups were included. The 5% of the variables that participated the most in this OPLS-DA were used as data in the PCA models. The excluded patients failed to fit their respective model. Dotted line, 5% confidence limit of the model.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4555627&req=5

fig2-2325967114550781: Multivariate data analysis of the centered and normalized array data. (A) Score plot of principal component analysis (PCA) of the centered and normalized data from the arrays including all 10 patients. (B) Loading plot of discriminant analysis using orthogonal partial least square analysis (OPLS-DA) of the data from the array including all 10 patients. Dots indicate the set of transcripts that participate most to the separation of the groups; black dots, the set of 1443 transcripts; gray dots, the cloud of excluded transcripts. (C) Coomans plot showing the prediction PCA models for the 5 success patients and the 5 failure patients where the 5% of the variables that participated the most to the OPLS-DA were used to perform the PCA. Dotted line, 5% confidence limit of the model. (D) Coomans plot showing the prediction for all 10 patients. In this analysis, 3 randomly selected patients from the success and failure groups were included. The 5% of the variables that participated the most in this OPLS-DA were used as data in the PCA models. The excluded patients failed to fit their respective model. Dotted line, 5% confidence limit of the model.
Mentions: The comparative microarray analysis did not reveal any global clustering between chondrocytes from the failure and success groups (Figure 1). The normalized data from the microarrays was also subjected to multivariate analysis. PCA of the total genome also failed to show a clear expressional disparity that can separate the success and failure groups (Figure 2A). Discriminant analysis using orthogonal partial least square analysis using the knowledge of success and failure classes as a discriminating variable did separate the global gene expression into 2 clear groups, representing success and failure patients. Selecting 5% of the transcripts that participated most to the separation of the success and failure groups resulted in a set of 1443 transcripts, as indicated in the PLS-DA loading plot (Figure 2B). Using those transcripts as variables in PCA models for the success and failure groups resulted in an acceptable classification (Figure 2C). However, a prediction analysis where a PCA model is made out of 3 randomly selected patients from each group and the selected 5% of the transcripts that separate these 6 patients in an additional OPLS-DA failed to predict the group classification of the 4 excluded patients (Figure 2D). Only 30% of the transcripts correlated between the OPLS-DA performed with all 10 patients and the OPLS-DA performed with 3 randomly selected patients from the success group and the failure group, respectively (data not shown). Additional data from the cell cultures (passage number and number of cell doublings) or the variables patient age, sex, and lesion size could not contribute to a separation of the 2 groups in the PCA. Neither the predefined set of gene markers nor the genes identified to be important for chondrogenic capacity by Dehne et al10 contributed to any separation of the 2 groups (data not shown). A Student t test was performed to identify genes with a low grade of differential expression between the failure and success groups. The t test identified 541 transcripts to be differentially expressed between groups. A total of 346 transcripts were induced in the success group and 195 transcripts in the failure group. The list of 541 transcripts was filtered to remove genes with small fold changes (<1.2). This approach resulted in 39 genes that were induced and 38 genes that were repressed in the success group (Tables 3 and 4). No intergroup differences were found for the predefined gene set FGFR3, BMP2, COL2A1, ACAN, CD44, and ACVRL1 and the gene list published by Dehne et al10 (Figure 3).

Bottom Line: No significant difference in expression of the predefined marker set was observed between the success and failure groups.The subtle difference in gene expression regulation found between the 2 groups may strengthen the basis for further research, aiming at reliable biomarkers and quality control for tissue engineering in cartilage repair.This result is especially important as the chondrogenic potential of the chondrocytes is currently part of quality control measures according to European and American legislations regarding advanced therapies.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.

ABSTRACT

Background: There is a need for tools to predict the chondrogenic potency of autologous cells for cartilage repair.

Purpose: To evaluate previously proposed chondrogenic biomarkers and to identify new biomarkers in the chondrocyte transcriptome capable of predicting clinical success or failure after autologous chondrocyte implantation.

Study design: Controlled laboratory study and case-control study; Level of evidence, 3.

Methods: Five patients with clinical improvement after autologous chondrocyte implantation and 5 patients with graft failures 3 years after implantation were included. Surplus chondrocytes from the transplantation were frozen for each patient. Each chondrocyte sample was subsequently thawed at the same time point and cultured for 1 cell doubling, prior to RNA purification and global microarray analysis. The expression profiles of a set of predefined marker genes (ie, collagen type II α1 [COL2A1], bone morphogenic protein 2 [BMP2], fibroblast growth factor receptor 3 [FGFR3], aggrecan [ACAN], CD44, and activin receptor-like kinase receptor 1 [ACVRL1]) were also evaluated.

Results: No significant difference in expression of the predefined marker set was observed between the success and failure groups. Thirty-nine genes were found to be induced, and 38 genes were found to be repressed between the 2 groups prior to autologous chondrocyte implantation, which have implications for cell-regulating pathways (eg, apoptosis, interleukin signaling, and β-catenin regulation).

Conclusion: No expressional differences that predict clinical outcome could be found in the present study, which may have implications for quality control assessments of autologous chondrocyte implantation. The subtle difference in gene expression regulation found between the 2 groups may strengthen the basis for further research, aiming at reliable biomarkers and quality control for tissue engineering in cartilage repair.

Clinical relevance: The present study shows the possible limitations of using gene expression before transplantation to predict the chondrogenic and thus clinical potency of the cells. This result is especially important as the chondrogenic potential of the chondrocytes is currently part of quality control measures according to European and American legislations regarding advanced therapies.

No MeSH data available.


Related in: MedlinePlus