Vibrational spectroscopy of liquid biopsies for prostate cancer diagnosis

• Published in: Therapeutic advances in medical oncology Vol. 12; no. 18; p. 1758835920918499
• Main Authors: Medipally, Dinesh K. R., Cullen, Daniel, Untereiner, Valérie, Sockalingum, Ganesh D., Maguire, Adrian, Nguyen, Thi Nguyet Que, Bryant, Jane, Noone, Emma, Bradshaw, Shirley, Finn, Marie, Dunne, Mary, Shannon, Aoife M., Armstrong, John, Meade, Aidan D., Lyng, Fiona M
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 2020; Sage Publications Ltd; SAGE Journals; SAGE Publishing
• Subjects: Biochemistry, Molecular Biology; Biological Physics; Biophysics; Biopsy; Cancer; Cell Behavior; Cellular Biology; Diagnosis; Discriminant analysis; Engineering Sciences; Fourier analysis; Life Sciences; Lymphocytes; Malignancy; Optics; Original Research; Photonic; Physics; Plasma; Principal components analysis; Prostate cancer; Raman spectroscopy; Rectum; Signal and Image processing; Spectrum analysis; Subcellular Processes; prostate cancer; Raman spectroscopy; liquid biopsy; principal component analysis; partial least squares discriminant analysis and classical least squares fitting analysis; FTIR spectroscopy
• ISSN: 1758-8359; 1758-8340; 1758-8359
• DOI: 10.1177/1758835920918499

Background: Screening for prostate cancer with prostate specific antigen and digital rectal examination allows early diagnosis of prostate malignancy but has been associated with poor sensitivity and specificity. There is also a considerable risk of over-diagnosis and over-treatment, which highlights the need for better tools for diagnosis of prostate cancer. This study investigates the potential of high throughput Raman and Fourier Transform Infrared (FTIR) spectroscopy of liquid biopsies for rapid and accurate diagnosis of prostate cancer. Methods: Blood samples (plasma and lymphocytes) were obtained from healthy control subjects and prostate cancer patients. FTIR and Raman spectra were recorded from plasma samples, while Raman spectra were recorded from the lymphocytes. The acquired spectral data was analysed with various multivariate statistical methods, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and classical least squares (CLS) fitting analysis. Results: Discrimination was observed between the infrared and Raman spectra of plasma and lymphocytes from healthy donors and prostate cancer patients using PCA. In addition, plasma and lymphocytes displayed differentiating signatures in patients exhibiting different Gleason scores. A PLS-DA model was able to discriminate these groups with sensitivity and specificity rates ranging from 90% to 99%. CLS fitting analysis identified key analytes that are involved in the development and progression of prostate cancer. Conclusions: This technology may have potential as an alternative first stage diagnostic triage for prostate cancer. This technology can be easily adaptable to many other bodily fluids and could be useful for translation of liquid biopsy-based diagnostics into the clinic.