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The use of Raman spectroscopy to differentiate between different prostatic adenocarcinoma cell lines.

Crow P, Barrass B, Kendall C, Hart-Prieto M, Wright M, Persad R, Stone N - Br. J. Cancer (2005)

Bottom Line: Principal component analysis was used to study the molecular differences that exist between cell lines and, in conjunction with linear discriminant analysis, was applied to 200 spectra to construct a diagnostic algorithm capable of differentiating between the different cell lines.The algorithm was able to identify the cell line of each individual cell with an overall sensitivity of 98% and a specificity of 99%.RS shows promise for application in the diagnosis and grading of CaP in clinical practise as well as providing molecular information on CaP samples in a research setting.

View Article: PubMed Central - PubMed

Affiliation: Biophotonics Research Group, Pullman Court, Gloucestershire Royal Hospital, Great Western Road, Gloucester GL1 3NN, UK. paul_crow@hotmail.com

ABSTRACT
Raman spectroscopy (RS) is an optical technique that provides an objective method of pathological diagnosis based on the molecular composition of tissue. Studies have shown that the technique can accurately identify and grade prostatic adenocarcinoma (CaP) in vitro. This study aimed to determine whether RS was able to differentiate between CaP cell lines of varying degrees of biological aggressiveness. Raman spectra were measured from two well-differentiated, androgen-sensitive cell lines (LNCaP and PCa 2b) and two poorly differentiated, androgen-insensitive cell lines (DU145 and PC 3). Principal component analysis was used to study the molecular differences that exist between cell lines and, in conjunction with linear discriminant analysis, was applied to 200 spectra to construct a diagnostic algorithm capable of differentiating between the different cell lines. The algorithm was able to identify the cell line of each individual cell with an overall sensitivity of 98% and a specificity of 99%. The results further demonstrate the ability of RS to differentiate between CaP samples of varying biological aggressiveness. RS shows promise for application in the diagnosis and grading of CaP in clinical practise as well as providing molecular information on CaP samples in a research setting.

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Related in: MedlinePlus

A bar chart demonstrating the prediction power of the diagnostic algorithm.
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fig7: A bar chart demonstrating the prediction power of the diagnostic algorithm.

Mentions: Table 1 gives the crossvalidated results achieved by the algorithm. The rows of the table show the numbers of spectra measured for each cell line. The columns of the table show the number of spectra that the algorithm has predicted as belonging to each cell line. By looking across each row of the table, the number of correctly predicted spectra for each cell line, shown in bold, can be seen. The remaining values within each row represent misclassifications by the algorithm. Figure 7 illustrates these data in bar chart form. The X-axis represents true cell line and the Z-axis represents Raman predicted cell line, with the Y-axis displaying the percentage of spectra predicted into each cell line. The diagonal row of large bars shows correct predictions by the algorithm, with all other bars representing misclassifications.


The use of Raman spectroscopy to differentiate between different prostatic adenocarcinoma cell lines.

Crow P, Barrass B, Kendall C, Hart-Prieto M, Wright M, Persad R, Stone N - Br. J. Cancer (2005)

A bar chart demonstrating the prediction power of the diagnostic algorithm.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2361812&req=5

fig7: A bar chart demonstrating the prediction power of the diagnostic algorithm.
Mentions: Table 1 gives the crossvalidated results achieved by the algorithm. The rows of the table show the numbers of spectra measured for each cell line. The columns of the table show the number of spectra that the algorithm has predicted as belonging to each cell line. By looking across each row of the table, the number of correctly predicted spectra for each cell line, shown in bold, can be seen. The remaining values within each row represent misclassifications by the algorithm. Figure 7 illustrates these data in bar chart form. The X-axis represents true cell line and the Z-axis represents Raman predicted cell line, with the Y-axis displaying the percentage of spectra predicted into each cell line. The diagonal row of large bars shows correct predictions by the algorithm, with all other bars representing misclassifications.

Bottom Line: Principal component analysis was used to study the molecular differences that exist between cell lines and, in conjunction with linear discriminant analysis, was applied to 200 spectra to construct a diagnostic algorithm capable of differentiating between the different cell lines.The algorithm was able to identify the cell line of each individual cell with an overall sensitivity of 98% and a specificity of 99%.RS shows promise for application in the diagnosis and grading of CaP in clinical practise as well as providing molecular information on CaP samples in a research setting.

View Article: PubMed Central - PubMed

Affiliation: Biophotonics Research Group, Pullman Court, Gloucestershire Royal Hospital, Great Western Road, Gloucester GL1 3NN, UK. paul_crow@hotmail.com

ABSTRACT
Raman spectroscopy (RS) is an optical technique that provides an objective method of pathological diagnosis based on the molecular composition of tissue. Studies have shown that the technique can accurately identify and grade prostatic adenocarcinoma (CaP) in vitro. This study aimed to determine whether RS was able to differentiate between CaP cell lines of varying degrees of biological aggressiveness. Raman spectra were measured from two well-differentiated, androgen-sensitive cell lines (LNCaP and PCa 2b) and two poorly differentiated, androgen-insensitive cell lines (DU145 and PC 3). Principal component analysis was used to study the molecular differences that exist between cell lines and, in conjunction with linear discriminant analysis, was applied to 200 spectra to construct a diagnostic algorithm capable of differentiating between the different cell lines. The algorithm was able to identify the cell line of each individual cell with an overall sensitivity of 98% and a specificity of 99%. The results further demonstrate the ability of RS to differentiate between CaP samples of varying biological aggressiveness. RS shows promise for application in the diagnosis and grading of CaP in clinical practise as well as providing molecular information on CaP samples in a research setting.

Show MeSH
Related in: MedlinePlus