Systems, Compositions, And Methods For Discovery Of MSI And Neoepitopes That Predict Sensitivity To Checkpoint Inhibitors

Abstract

Systems and methods are presented that allow for predicting treatment response of a tumor to a checkpoint inhibitor. In one exemplary aspect, the treatment response is directly associated with a relatively high number of patient- and tumor-specific immunologically visible neoepitopes. Specific mutational patterns in the nucleic acid encoding the neoepitope may be further indicative of treatment response.

What is claimed is: 
     
         1 . A method of predicting positive treatment response of a tumor to a checkpoint inhibitor, comprising:
 obtaining from a patient omics data from a tumor tissue and a matched normal tissue, and using the omics data to determine a plurality of missense based patient- and tumor-specific neoepitopes;   filtering the neoepitopes to obtain HLA-matched neoepitopes, and quantifying the HLA-matched neoepitopes; and   determining, upon ascertaining that the quantity of HLA-matched neoepitopes has exceeded a predetermined threshold quantity, that the tumor is responsive to treatment with the checkpoint inhibitor.   
     
     
         2 . The method of  claim 1 , further comprising a step of filtering the HLA-matched neoepitopes by a mutation signature. 
     
     
         3 . The method of  claim 2 , wherein the mutation signature is a signature characteristic for UV-induced DNA damage or smoking-induced DNA damage. 
     
     
         4 . The method of  claim 1 , further comprising a step of using the omics data to detect at least one of microsatellite instability (MSI) and defective mismatch repair (MMR) in the diseased tissue. 
     
     
         5 . The method of  claim 1 , wherein the missense based patient- and tumor-specific neoepitopes have a length of between 7 and 20 amino acids. 
     
     
         6 . The method of  claim 1 , wherein the step of filtering comprises determination of affinity of the neoepitopes to at least one MHC Class I sub-type and to at least one MHC Class II sub-type of the patient. 
     
     
         7 . The method of  claim 1 , wherein the step of filtering further comprises a determination of expression level of the neoepitope. 
     
     
         8 . The method of  claim 1 , wherein the predetermined threshold quantity of HLA-matched neoepitopes is at least 100 HLA-matched neoepitopes. 
     
     
         9 . The method of  claim 8 , wherein the at least 100 HLA-matched neoepitopes have an affinity to at least one MEW Class I sub-type or to at least one MEW Class II sub-type of the patient of equal or less than 150 nM. 
     
     
         10 . The method of  claim 1 , wherein the checkpoint inhibitor is a CTLA-4 inhibitor or a PD-1 inhibitor. 
     
     
         11 . A method of predicting positive treatment response of a tumor to a checkpoint inhibitor, comprising
 obtaining from a patient omics data from a tumor tissue and a matched normal tissue, and using the omics data to determine a plurality of missense based patient- and tumor-specific neoepitopes;   filtering the neoepitopes to obtain HLA-matched neoepitopes, and quantifying the HLA-matched neoepitopes;   identifying a mutation signature for the quantified HLA-matched neoepitopes; and   using the quantity of neoepitopes and the mutation signature as determinants for positive treatment response of the tumor to the checkpoint inhibitor.   
     
     
         12 . The method of  claim 11 , wherein the mutation signature is characteristic for UV-induced DNA damage or smoking-induced DNA damage. 
     
     
         13 . The method of  claim 11 , further comprising a step of using the omics data to detect microsatellite instability (MSI). 
     
     
         14 . The method of  claim 11 , further comprising a step of using the omics data to detect defective mismatch repair (MMR) in the diseased tissue. 
     
     
         15 . The method of  claim 11 , wherein the missense based patient- and tumor-specific neoepitopes have a length of between 7 and 20 amino acids. 
     
     
         16 . The method of  claim 11 , wherein the step of filtering comprises determination of affinity of the neoepitopes to at least one MEW Class I sub-type and to at least one MHC Class II sub-type of the patient. 
     
     
         17 . The method of  claim 11 , wherein the step of filtering further comprises a determination of expression level of the neoepitope. 
     
     
         18 . The method of  claim 11 , wherein the predetermined threshold quantity of HLA-matched neoepitopes is at least 100 HLA-matched neoepitopes. 
     
     
         19 . The method of  claim 18 , wherein the at least 100 HLA-matched neoepitopes have an affinity to at least one MEW Class I sub-type or to at least one MEW Class II sub-type of the patient of equal or less than 150 nM. 
     
     
         20 . The method of  claim 11 , wherein the checkpoint inhibitor is a CTLA-4 inhibitor or a PD-1 inhibitor.