Method for predicting efficacy of rar-alpha agonist
AssigneeTaiho Pharmaceutical Co., Ltd.
InventorKoji MURAKAMI
Life of this patent
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Abstract
A method for predicting preventive and/or therapeutic effect of an RAR-α agonist on a malignant tumor, which comprises the steps of measuring an expression level of a class of p160 family molecule and an expression level of a class of SP110 family molecule in a sample collected from the malignant tumor of a patient, and when the class of the p160 family molecule is dominant in a balance between the expression level of the class of the p160 family molecule and the expression level of the class of the SP110 family molecule, determining that an RAR-α agonist is effective for therapeutic treatment of the malignant tumor of the patient.
Claims13
A method for predicting therapeutic effect of an RAR-α agonist on a malignant tumor, which comprises the steps of measuring an expression level of a class of p160 family molecule and an expression level of a class of SP110 family molecule in the malignant tumor of a patient and observing a balance between said expression levels.
A method for therapeutic treatment of a malignant tumor, which comprises:
- (a) the step of collecting a sample of a malignant tumor from a patient with the malignant tumor,
- (b) measuring an expression level of a class of p160 family molecule and an expression level of a class of SP110 family molecule in the sample, and
- (c) observing a balance between the expression level of the class of the p160 family molecule and the expression level of the class of the SP110 family molecule, and administering an RAR-α agonist to the patient when the class of the p160 family molecules is dominant.
A medicament for therapeutic treatment of malignant tumor, which comprises an RAR-α agonist as an active ingredient and is administered when a class of p160 family molecule is dominant in a balance between an expression level of the class of the p160 family molecule and an expression level of a class of SP110 family molecule in a patient.
Dependencies are inferred from the claim text.