METHOD FOR IDENTIFICATION OF CANCER PATIENTS WITH DURABLE BENEFIT FROM IMMUNOTEHRAPY IN OVERALL POOR PROGNOSIS SUBGROUPS
A blood-based sample from a cancer patient is subject to mass spectrometry and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.
1 . A method of detecting a class label in a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising:
(a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges from a multitude of mass-spectral features listed in Table 25; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and
(b) identifying the cancer patient as being in the class of patients determined to be a poor prognosis subgroup, and operating on the mass spectral data with a programmed computer implementing a second stage classification algorithm; wherein in the operating step the classifier compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples from a multitude of patients having the same type of cancer treated with an immunotherapy drug and detecting a class label for the sample.
2 . The method of claim 1 , wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-1 checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
3 . The method of claim 1 , wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
4 . A method of guiding treatment of lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of:
(a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in Table 25; using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup, and
(b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in the table and using the integrated intensity values in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to obtain a class label of Late or the equivalent which guides treatment of the patient to an immunotherapy drug.
5 . The method of claim 4 , wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-1 checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
6 . The method of claim 4 , wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.
7 . A method indicating the relative likelihood of success of an immunotherapy treatment for a lung cancer patient, a renal cell carcinoma patient, or a melanoma patient comprising the steps of:
(a) conducting a mass spectrometer test on a blood-based sample of the cancer patient to obtain a mass spectrum; obtaining integrated intensity values of selected features in the mass spectrum at one or more m/z ranges as shown in Table 25 using the integrated intensity values in a first stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients to identify the patient as being in a class of patients determined to be a poor prognosis subgroup,
(b) identifying the patient as being in the class of patients determined to be a poor prognosis subgroup, using the integrated intensity values as shown in the table in a second stage classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from the same type of cancer patients treated with an immunotherapy drug to a class label of Late or the equivalent which identifies the patient as likely to have durable benefit from an immunotherapy drug; and
(c) determining that the patient having a class label of Late or the equivalent is likely to respond to an immunotherapy drug.
8 . The method of claim 7 , wherein the immunotherapy drug comprises an antibody drug blocking ligand activation of the PD-1 checkpoint protein, anti-CTLA4 drugs, high dose interleukin-2, and combination therapies.
9 . The method of claim 7 , wherein the immunotherapy drug comprises a combination of two immunotherapy drugs.