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