Models of DNA structure achieve almost perfect discrimination between normal prostate, benign prostatic hyperplasia (BPH), and adenocarcinoma and have a high potential for predicting BPH and prostate cancer

Abstract

In our previous studies of DNA, wavenumber-absorbance relationships of infrared spectra analyzed by principal components analysis (PCA) were expressed as points in space. Each point represented a highly discriminating measure of structural modifications that altered vibrational and rotational motion, thus changing the spatial orientation of the points. PCA/Fourier transform-infrared technology has now provided a virtually perfect separation of clusters of points representing DNA from normal prostate tissue, BPH, and adenocarcinoma. The findings suggest that the progression of normal prostate tissue to BPH and to prostate cancer involves structural alterations in DNA that are distinctly different. The hydroxyl radical is likely a major contributor to these structural alterations, which is consistent with previous studies of breast cancer. Models based on logistic regression of infrared spectral data were used to calculate the probability of a tissue being BPH or adenocarcinoma. The models had a sensitivity and specificity of 100% for classifying normal vs. cancer and normal vs. BPH, and close to 100% for BPH vs. cancer. Thus, the PCA/Fourier transform-infrared technology was shown to be a powerful means for discriminating between normal prostate tissue, BPH and prostate cancer and has considerable promise for risk prediction and clinical application.

Figure 1

Two-dimensional PC plot derived by PCA/FT-IR spectral analysis showing distinct clustering of normal, BPH, and prostate cancer points. Notably, both of the groups of prostate lesions occur to the right of the points for the DNA of normal prostate. (See text for details.)

Figure 2

Comparison of the mean spectrum of cancer vs. normal tissue (A), BPH vs. normal tissue (B), and cancer vs. BPH (C). The lower plot of each panel shows the statistical significance of the difference in mean absorbance at each wavenumber, based on the unequal variance t test. P values are plotted on the log₁₀ scale.

Figure 3

Sigmoid curves depicting the probability of DNA being classified as normal tissue vs. cancer (A), normal tissue vs. BPH (B), and BPH vs. cancer (C). The curves are based on the logistic regression models depicted in Table 2. The predicted probabilities rise very rapidly over a narrow range, which reflects a high degree of discrimination among groups and a precipitous change in DNA structure associated with the normal to BPH and normal to cancer progressions. Each sample is plotted at its predicted probably.