Monitoring Radiotherapeutic Response in Prostate Cancer Patients Using High Throughput FTIR Spectroscopy of Liquid Biopsies

Dinesh K R Medipally, Thi Nguyet Que Nguyen, Jane Bryant, Valérie Untereiner, Ganesh D Sockalingum, Daniel Cullen, Emma Noone, Shirley Bradshaw, Marie Finn, Mary Dunne, Aoife M Shannon, John Armstrong, Fiona M Lyng, Aidan D Meade, Dinesh K R Medipally, Thi Nguyet Que Nguyen, Jane Bryant, Valérie Untereiner, Ganesh D Sockalingum, Daniel Cullen, Emma Noone, Shirley Bradshaw, Marie Finn, Mary Dunne, Aoife M Shannon, John Armstrong, Fiona M Lyng, Aidan D Meade

Abstract

Radiation therapy (RT) is used to treat approximately 50% of all cancer patients. However, RT causes a wide range of adverse late effects that can affect a patient's quality of life. There are currently no predictive assays in clinical use to identify patients at risk of normal tissue radiation toxicity. This study aimed to investigate the potential of Fourier transform infrared (FTIR) spectroscopy for monitoring radiotherapeutic response. Blood plasma was acquired from 53 prostate cancer patients at five different time points: prior to treatment, after hormone treatment, at the end of radiotherapy, two months post radiotherapy and eight months post radiotherapy. FTIR spectra were recorded from plasma samples at all time points and the data was analysed using MATLAB software. Discrimination was observed between spectra recorded at baseline versus follow up time points, as well as between spectra from patients showing minimal and severe acute and late toxicity using principal component analysis. A partial least squares discriminant analysis model achieved sensitivity and specificity rates ranging from 80% to 99%. This technology may have potential to monitor radiotherapeutic response in prostate cancer patients using non-invasive blood plasma samples and could lead to individualised patient radiotherapy.

Keywords: Fourier transform infrared spectroscopy; blood plasma; high throughput; prostate cancer; radiotherapy; toxicity.

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Mean FTIR spectra of plasma from patients at baseline, post hormone treatment, post radiotherapy, two and eight months after radiotherapy. Spectra were baseline corrected and vector normalized. Major bands are highlighted.
Figure 2
Figure 2
Difference spectra of plasma from patients post hormone treatment, post radiotherapy, at two months follow up and at eight months follow up. Difference spectra were computed by subtracting the mean spectra of plasma from the patients at different treatment time points from their mean spectra at baseline. The shaded regions depict the spectral regions which are significantly different between each sample set using a two-tailed t-test with p < 0.001.
Figure 3
Figure 3
Difference spectra of plasma from patients post radiotherapy, at two months follow up and at eight months follow up. Difference spectra were computed by subtracting the mean spectra of plasma from the patients at post radiotherapy time points from their mean spectra post hormone treatment. The shaded regions depict the spectral regions which are significantly different between each sample set using a two-tailed t-test with p < 0.001.
Figure 4
Figure 4
PCA of plasma spectra from patients from baseline through therapy and up to eight months follow up. (A) Score plots are shown for patients at baseline (blue), post hormone (red), post radiotherapy (yellow), two months follow up (purple) and eight months follow up (green) (B) PC-1 and PC-2 loading plots for regions 1000–1800 cm−1 and 2800–3100 cm−1. Covariance ellipses (95% confidence) are shown for each class.
Figure 4
Figure 4
PCA of plasma spectra from patients from baseline through therapy and up to eight months follow up. (A) Score plots are shown for patients at baseline (blue), post hormone (red), post radiotherapy (yellow), two months follow up (purple) and eight months follow up (green) (B) PC-1 and PC-2 loading plots for regions 1000–1800 cm−1 and 2800–3100 cm−1. Covariance ellipses (95% confidence) are shown for each class.
Figure 5
Figure 5
Typical cross-validated sensitivity and specificity for a PLS-DA model developed on FTIR spectra of plasma from patients at baseline and post radiotherapy with increase in the number of latent variables included in the model (LVs).
Figure 6
Figure 6
Difference spectra of plasma from patients at baseline and patients suffering from grade 0–1 and grade 2+ acute (immediately after completion of radiotherapy) and late (at eight months post radiotherapy) toxicity. The shaded regions depict the spectral regions which are significantly different between each sample set using a two-tailed t-test with p < 0.001.
Figure 7
Figure 7
PCA for plasma spectra from patients showing acute grade 0–1 and acute grade 2+ toxicity. (A) Score plots are shown for patients showing acute grade 0–1 (blue) and acute grade 2+ toxicity (red). (B) PC-1 and PC-2 loading plots for regions 1000–1800 cm−1 and 2800–3100 cm−1. Covariance ellipses (95% confidence) are shown for each class.
Figure 8
Figure 8
PCA for plasma spectra from patients showing late grade 0−1 and late grade 2+ toxicity. (A) Score plots are shown for patients showing late grade 0−1 (blue) and late grade 2+ (red) toxicity. (B) PC-1 and PC-2 loading plots for regions 1000–1800 cm−1 and 2800–3100 cm−1. Covariance ellipses (95% confidence) are shown for each class.
Figure 9
Figure 9
Experimental protocol of high throughput (HT)-FTIR analysis of blood plasma samples from sample preparation to data analysis (reproduced from [23] with permission from The Royal Society of Chemistry). 3-fold dilution: dilution by volume of one part plasma to two parts physiological water.

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