Predictive test for prognosis of myelodysplastic syndrome patients using mass spectrometry of blood-based sample
A method of predicting whether an MDS patient has a good or poor prognosis uses a general purpose computer configured as a classifier and mass-spectrometry data obtained from a blood-based sample. The classifier assigns a classification label of either Early or Late (or the equivalent) to the patient's sample. Patients classified as Early are predicted to have a poor prognosis or worse survival whereas those patients classified as Late are predicted to have a relatively better prognosis and longer survival time. The groupings demonstrated a large effect size between groups in Kaplan-Meier analysis of survival. Most importantly, while the classifications generated were correlated with other prognostic factors, such as IPSS score and genetic category, multivariate and subgroup analysis showed that they had significant independent prognostic power complementary to the existing prognostic factors.
1. A method of detecting a class label in a myelodysplastic syndrome (MDS) patient comprising the steps of:
(a) performing MALDI-TOF mass spectrometry on a blood-based sample obtained from the MDS patient by subjecting the sample to at least 100,000 laser shots and acquiring mass spectral data;
(b) obtaining integrated intensity values in the mass spectral data of a multitude of pre-determined mass-spectral features, wherein the mass-spectral features include a multitude of features listed in Appendix A, wherein the obtaining step (b) comprises obtaining integrated intensity values of at least 50 features listed in Appendix A; and
(c) operating on the integrated intensity values of the mass spectral data with a programmed computer implementing a classifier;
wherein in the operating step the classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of other MDS patients with the values obtained in step (b) with a classification algorithm and detects a class label for the sample, wherein when the class label for the sample indicates that the MDS patient has a relatively poor prognosis, administering hematopoietic stem cell transplants (HSCTs), high intensity chemotherapy, supportive care or combinations thereof to the MDS patient.
2. The method of claim 1 , wherein the classifier is configured as a combination of filtered mini-classifiers using a regularized combination method.
3. The method of claim 1 , wherein the obtaining step (b) comprises obtaining integrated intensity values of at least 100 features listed in Appendix A.
4. The method of claim 1 , wherein the obtaining step (b) comprises obtaining integrated intensity values of at least 300 features listed in Appendix A.
5. The method of claim 1 , wherein the high intensity chemotherapy is azacitidine or decitabine.
6. A method of detecting a class label in a myelodysplastic syndrome (MDS) patient comprising the steps of:
(a) performing MALDI-TOF mass spectrometry on a blood-based sample obtained from the MDS patient by subjecting the sample to at least 100,000 laser shots and acquiring mass spectral data;
(b) obtaining integrated intensity values in the mass spectral data of a multitude of pre-determined mass-spectral features, wherein the mass-spectral features include a multitude of features listed in Appendix A, wherein the obtaining step (b) comprises obtaining integrated intensity values of at least 50 features listed in Appendix A; and
(c) operating on the integrated intensity values of the mass spectral data with a programmed computer implementing a classifier;
wherein in the operating step the classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of other MDS patients with the values obtained in step (b) with a classification algorithm and detects a class label for the sample, wherein when the class label for the sample indicates that the MDS patient has a relatively good prognosis, administering lenalidomide, immunosuppressive therapy, azacytidine, or supportive therapy to the MDS patient.
7. The method of claim 6 , wherein the classifier is configured as a combination of filtered mini-classifiers using a regularized combination method.
8. The method of claim 6 , wherein the obtaining step (b) comprises obtaining integrated intensity values of at least 100 features listed in Appendix A.
9. The method of claim 6 , wherein the obtaining step (b) comprises obtaining integrated intensity values of at least 300 features listed in Appendix A.
10. A method of guiding treatment of a myelodysplastic syndrome (MDS) patient comprising the steps of:
(a) performing MALDI-TOF mass spectrometry on a blood-based sample obtained from the MDS patient by subjecting the sample to at least 100,000 laser shots and acquiring mass spectral data;
(b) performing a classification of the mass spectral data with the aid of a computer implementing a classifier, wherein the classifier is developed from a development set of samples from other MDS patients comprising iteratively training a classifier from the development set of samples and a multitude of mass spectral features listed in Appendix A to generate a class label; and
(c) supplying the mass spectrometry data of the sample obtained in step a) to the classifier implemented in a programmed computer trained to predict whether a melanoma patient is in a high risk group, wherein when the patient is classified into the high risk group the patient is predicted to be likely to benefit from hematopoietic stem cell transplants (HSCTs), high intensity chemotherapy, azacytidine, decitabine, supportive care, or combinations thereof.
11. The method of claim 10 , wherein where the patient is classified into the high risk group the patient administered hematopoietic stem cell transplants (HSCTs), high intensity chemotherapy, azacytidine, decitabine, supportive care, or combinations thereof.