Prediction of therapeutic response using vibrational spectroscopy

1. A prognostic method of analyzing a biological biofluid sample from a cancer patient to predict the patient's response to a specified modality of cancer treatment, the prognostic method comprising:

obtaining diagnosis of a cancer patient, wherein the cancer is selected from esophageal cancer, colorectal cancer, prostate cancer, and breast cancer;

obtaining the biological biofluid sample from the cancer patient;

performing spectroscopy on the biological sample to obtain a spectrum; and

comparing the obtained spectrum with one or more spectra from a pre-classified library to calculate a probability of a response to the specified modality of cancer treatment by the cancer patient.

2. The method of claim 1 , wherein the modality of cancer treatment is selected from a group consisting of radiotherapy, chemotherapy, hormonal therapy, and radiotherapy combined with either chemotherapy or hormonal therapy.

3. The method of claim 1 , wherein the modality of cancer treatment comprises radiotherapy.

4. The method of claim 1 , wherein the biological biofluid sample is unirradiated.

5. The method of claim 1 , wherein the biological biofluid sample is a blood sample.

6. The method of claim 5 , comprising irradiating the in-vitro blood sample.

7. The method of claim 5 , wherein:

the blood sample is used whole and analyzed; or

plasma or lymphocytes are extracted from the blood sample and analyzed.

8. The method of claim 7 , wherein plasma is extracted and analyzed and the steps for analyzing the plasma are as follows:

extracting plasma from the blood sample;

depositing the plasma on a substrate;

acquiring Raman/FTIR spectra;

analyzing the obtained spectra by a statistical learning algorithm which compares the obtained spectra to spectra from a pre-classified library; and

predicting response to therapy based on the analysis of the immediately preceding step.

9. The method of claim 7 , wherein lymphocytes are extracted and analyzed and the steps for analyzing the lymphocytes are as follows:

extracting lymphocytes from the blood sample;

fixing the lymphocytes;

depositing the fixed lymphocytes on a substrate;

acquiring Raman/FTIR spectra;

analyzing the obtained spectra by a statistical learning algorithm which compares the obtained spectra to spectra from a pre-classified library; and

predicting response to therapy based on the analysis of the immediately preceding step.

10. The method of claim 1 , wherein the biological biofluid sample is a blood lymphocyte sample.

11. The method of claim 10 , comprising culturing blood lymphocyte cells as whole blood in-vitro.

12. The method of claim 1 , wherein the spectroscopy is vibrational spectroscopy.

13. The method of claim 12 , wherein the vibrational spectroscopy is performed using Raman spectroscopy or using FTIR spectroscopy.

14. The method of claim 1 , wherein the response to cancer treatment comprises tumor regression.

15. The method of claim 1 , wherein the response to cancer treatment comprises radiotherapeutic treatment toxicity or chemotherapeutic treatment toxicity.

16. The method of claim 1 , wherein the comparing comprises the use of spectral decomposition followed by analysis by a classifier.

17. The method of claim 16 , wherein the spectral decomposition comprises using principal component analysis (PCA) spectral decomposition.

18. The method according to claim 1 , wherein the cancer is prostate cancer.

19. The method according to claim 1 , wherein the method comprises monitoring the progress of treatment within the course of therapy using vibrational spectra of peripheral blood extracted from the patient.

20. A prognostic method of analyzing a biological sample from a cancer patient to predict the patient's response to a specified modality of cancer treatment, wherein the cancer comprises a solid tumor and a biopsy of the tumor has been taken, the prognostic method comprising:

obtaining a diagnosis of a cancer patient;

obtaining a sample from the tumor biopsy;

subjecting the sample to fixation and paraffin embedding;

performing microtoming on the sample;

performing spectroscopy on the sample to acquire FTIR/Raman spectra;

analyzing obtained images by a statistical learning algorithm which compares obtained spectra to spectra from a pre-classified library to calculate a probability of a response to the specified modality of cancer treatment by the cancer patient; and

predicting response to therapy based on the analysis of the immediately preceding step.