IP Library Granted Patent US 10,720,230
Granted Patent B2
US 10,720,230 · App. 16/006,129 · Granted Jul 21, 2020

Method for administering a checkpoint blockade therapy to a subject

Inventors: Feliks Frenkel (Moscow, RU); Nikita Kotlov (Moscow, RU); Alexander Bagaev (Moscow, RU); Maksym Artomov (Kirkwood, MO); Ravshan Ataullakhanov (Moscow, RU)
Assignee: BostonGene Corporation
G16B45/00C12Q1/6886G06F16/285G06F17/18G16B5/00G16B5/20G16B20/00G16B40/00G16B50/00G16H10/20G16H20/00G16H20/10G16H20/40G16H50/20G16H50/30G16H50/50G16H50/70G16H70/20C12Q2600/156C12Q2600/158
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Quick Facts
Patent No.
US 10,720,230
App. No.
16/006,129
Granted
Jul 21, 2020
Kind
B2
Abstract

Techniques for determining whether a subject is likely to respond to an immune checkpoint blockade therapy. The techniques include obtaining expression data for the subject, using the expression data to determine subject expression levels for at least three genes selected from the set of predictor genes consisting of BRAF, ACVR1B, MPRIP, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROBO4, RAB40C, HAUS8, SNAP23, SNX6, ACVR1B, MPRIP, COPS3, NLRX1, ELAC2, MON1B, ARF3, ARPIN, SPRYD3, FLI1, TIRAP, GSE1, POLR3K, PIGO, MFHAS1, NPIPA1, DPH6, ERLIN2, CES2, LHFP, NAIF1, ALCAM, SYNE1, SPINT1, SMTN, SLCA46A1, SAP25, WISP2, TSTD1, NLRX1, NPIPA1, HIST1H2AC, FUT8, FABP4, ERBB2, TUBA1A, XAGE1E, SERPINF1, RAI14, SIRPA, MT1X, NEK3, TGFB3, USP13, HLA-DRB4, IGF2, and MICAL1; and determining, using the determined expression levels and a statistical model trained using expression data indicating expression levels for a plurality of genes for a plurality of subjects, whether the subject is likely to respond to the immune checkpoint blockade therapy.

Claims (18)

1. A method for administering a checkpoint blockade therapy to a subject, the method comprising:

obtaining expression data for the subject;

using the expression data to determine expression levels, in the subject, for at least three genes selected from the set of predictor genes consisting of BRAF, ACVR1B, MPRIP, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROBO4, RAB40C, HAUS8, SNAP23, SNX6, ACVR1B, MPRIP, COPS3, NLRX1, ELAC2, MON1B, ARF3, ARPIN, SPRYD3, FLI1, TIRAP, GSE1, POLR3K, PIGO, MFHAS1, NPIPA1, DPH6, ERLIN2, CES2, LHFP, NAIF1, ALCAM, SYNE1, SPINT1, SMTN, SLCA46A1, SAP25, WISP2, TSTD1, NLRX1, NPIPA1, HIST1H2AC, FUT8, FABP4, ERBB2, TUBA1A, XAGE1E, SERPINF1, RAI14, SIRPA, MT1X, NEK3, TGFB3, USP13, HLA-DRB4, IGF2, and MICAL1;

determining, using the determined expression levels and a logistic regression model, that the subject is likely to respond to the checkpoint blockade therapy, wherein the checkpoint blockade therapy is a PD1 inhibitor and/or a CTLA4 inhibitor, wherein the logistic regression model comprises dependent variables each of which represents a respective ratio of a pair of genes, wherein the genes are selected from the set of predictor genes; and

administering the PD1 inhibitor and/or the CTLA4 inhibitor to the subject based on output of the logistic regression model.

2. The method of claim 1 , further comprising using the expression data to determine expression levels for at least six genes selected from the set of predictor genes.

3. The method of claim 1 , wherein the expression data is RNA expression data, DNA expression data, or protein expression data.

4. The method of claim 1 , wherein the dependent variables comprises at least two ratios selected from: MON1B:STX2, FAM234A:LIN37, DPH6:STX2, BRAF:RAI14, ADCK2:C14ORF80, POLR3K:HAUS8, URB1:TMEM181, GCLC:NEK3, RAB40C:HAUS8, NLRX1:ELAC2, CMIP:ROBO4, NXT2:FBXO5, EIF3H:NEK3, EHHADH:SNX6, DMTN:RASD1, SLC46A1:RBM8A, ACVR1B:GTF2H2, NPEPPS:HDAC2, CXCL16:BASP1, MFHAS1:DYRK3, ACVR1B:USP13, DPH6:C14ORF166, CES2:LHFP, ACVR1B:DCP1B, NAIF1:HDGFRP2, ABCC1:TRIO, GPR107:STX2, ZDHHC7:USP13, PRKAG1:TSEN2, PC:HAUS8, LRBA:CEP192, POM121C:CNPY4, KAT14:SETD5, SLC35A5:SNX6, ATP6V1A:GTDC1, TXNL4B:AKAP8L, SLC36A1:HAUS8, PSAP:SH3BP5, CMIP:OLFML2B, TATDN3:NEK3, TASP1:DDX5, SDC4:FUT8, TMEM254:COPS3, ARF3:MPRIP, SLC46A1:PMF1-BGLAP, ATP6V1A:FBXO30, MFHAS1:C2CD5, ERAP1:SYNE1, F11R:FYN, RCHY1:RNF146, ATP6V1A:PDCD5, ACVR1B:ELAC2, CLN3:HAUS8, NAIF1:HAUS8, PRKAG1:SOCS4, HNRNPH2:USP13, TPD52:MTRF1L, ACVR1B:C14ORF80, IST1:NPIPA1, DPH6:TCEAL1, CSNK2A1:MRGBP, CXCL16:FILIP1L, AGK:USP13, MYO18A:FYN, SIRPA:FLII, C16ORF58:FLII, TRIM11:AKAP8L, MFHAS1:DMPK, JMJD8:AKAP8L, DIAPH1:SYNE1, BCKDHA:HAUS8, TMEM254:PSMC5, ACVR1B:HTRA2, MON1B:SYNE1, DCAKD:PMF1-BGLAP, VWA5A:RASD1, TPD52:TRA2A, ZMIZ1:STX2, NUB1:C2CD5, GSE1:RAI14, AGFG1:STX2, NXT2:TRA2A, ACSS1:NPIPA5, FBXW8:USP13, CMIP:TRIO, AGPAT3:FYN, PSMF1:PTOV1, CREG1:TARS, SLC46A1:CEP131, SIRPA:SERPINF1, DNAJA2:HDAC2, ERLIN2:RAI14, FAM234A:ZNF428, CHMP1A:LIN37, FAM110A:TCF7, ACVR1B:COPS3, GSE1:DDX11, CREG1:ARFGAP3, BRPF3:USP13, MFHAS1:USP13, LAMP1:MAPK7, ACSS1:PMF1-BGLAP, SUFU:TRIO, ARF3:DAD1, NLRX1:TRA2A, NLRX1:SLC39A13, CMIP:SH3BP5, PPIF:HAUS8, ANKRD13A:SOCS4, F8:SYNE1, ATP6V1A:USP48, ACVR1B:MPRIP, TMEM141:HAUS8, TIRAP:MPRIP, ZDHHC12:HAUS8, SLC46A1:MED9, MFHAS1:NCAPD2, ERBB2:CENPL, JMJD7:PQBP1, PHKG2:AKAP8L, SLC36A1:SLC26A6, ATP6V1A:SPDL1, DCTN5:CEP89, IPPK:STX2, LAMB3:ADM, ARPIN:MPRIP, SLC46A1:FYN, ACVR1B:LTV1, GDE1:ZNF576, DMTN:GFPT2, LCMT2:RDH11, ACVR1B:CCDC66, ACVR1B:NEK3, SEC24B:MAP3K7, ZNF764:AKAP8L, CHMP1A:HAUS8, PIGO:USP13, ARF3:MAPK7, GSE1:CCDC66, ACVR1B:METTL17, C200RF196:HAUS8, ARF3:CBY1, BRPF3:MPRIP, SLC46A1:PSMC5, CMTR2:MAP3K7, TASP1:CCDC66, BRPF3:PIP5K1A, PIGO:FLII, MYO18A:ACIN1, PSMF1:AKAP8L, FBXW1:LTV1, CXCL16:ADAMTS2, SPRYD3:FLII, DPH6:FRA10AC1, PDXK:HAUS8, ACVR1B:WDR45B, MON1B:SMIM10L1, LAT:PRRX1, WDR24:AKAP8L, EHHADH:CCDC174, ACVR1B:CEP89, MFHAS1:ODF2, ALDH6A1:GOPC, GSE1:NBPF14, EHHADH:MEX3C, BRPF3:STX2, EHHADH:GTF2H2, PIGO:MPRIP, PRKAG1:STX2, EHHADH:SMIM10L1, SLC36A1:SH3PXD2A, ADCK2:STX2, SORD:PDCD5, ACVR1B:LLGL1, LAMTOR3:ZNF644, PIM1:MICAL2, CREG1:NPIPA1, GSE1:NPIPA1, PRR13:HAUS8, WDR55:RAD1, CMIP:NID2, DIAPH1:TARS, SNAP23:SNX6, GSE1:TSPYL2, C2ORF68:NPIPA1, MFHAS1:TRIO, DPH6:CGRRF1, KIF13B:STX2, PTK2B:TCF7, ATP2A2:STX2, ANKS1A:USP13, JRK:NEK3, LRBA:DDX5, IDH2:HAUS8, CCNF:HAUS8, CMIP:CHN1, STAU2:STX2, ACSS1:LHFP, GSR:STX2, IGF2R:FYN, CXCL16:ACVRL1.

5. The method of claim 1 , further comprising using the expression data to determine expression levels, in the subject, for at least eight genes from the group consisting of BRAF, ACVR1B, MPRIP, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROBO4, RAB40C, HAUS8, SNAP23, SNX6, ACVR1B, MPRIP, COPS3, NLRX1, ELAC2, MON1B, ARF3, ARPIN, SPRYD3, FLI1, TIRAP, GSE1, POLR3K, PIGO, MFHAS1, NPIPA1, DPH6, ERLIN2, CES2, LHFP, NAIF1, ALCAM, SYNE1, SPINT1, SMTN, SLCA46A1, SAP25, WISP2, TSTD1, NLRX1, NPIPA1, HIST1H2AC, FUT8, FABP4, ERBB2, TUBA1A, XAGE1E, SERPINF1, RAI14, SIRPA, MT1X, NEK3, TGFB3, USP13, HLA-DRB4, IGF2, and MICAL1.

6. The method of claim 1 , further comprising using the expression data to determine expression levels, in the subject, for at least eleven genes from the group consisting of BRAF, ACVR1B, MPRIP, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROBO4, RAB40C, HAUS8, SNAP23, SNX6, ACVR1B, MPRIP, COPS3, NLRX1, ELAC2, MON1B, ARF3, ARPIN, SPRYD3, FLI1, TIRAP, GSE1, POLR3K, PIGO, MFHAS1, NPIPA1, DPH6, ERLIN2, CES2, LHFP, NAIF1, ALCAM, SYNE1, SPINT1, SMTN, SLCA46A1, SAP25, WISP2, TSTD1, NLRX1, NPIPA1, HIST1H2AC, FUT8, FABP4, ERBB2, TUBA1A, XAGE1E, SERPINF1, RAI14, SIRPA, MT1X, NEK3, TGFB3, USP13, HLA-DRB4, IGF2, and MICAL1.

7. The method of claim 1 , wherein the logistic regression model comprises respective weights for the dependent variables, the method further comprising training the logistic regression model at least in part by:

estimating the weights using the expression data for a plurality of subjects and information indicating which of the plurality of subjects responded to the checkpoint blockade therapy and/or which of the plurality of subjects did not respond to the checkpoint blockade therapy.

8. The method of claim 7 , wherein training the logistic regression model comprises iteratively adding dependent variables for respective genes to the logistic regression model, at least in part by:

identifying a candidate gene;

augmenting a current logistic regression model with a dependent variable for the candidate gene to obtain an augmented logistic regression model;

evaluating performance of the augmented logistic regression model; and

determining to add the dependent variable for the candidate gene to the current logistic regression model based on results of evaluating the performance.

9. The method of claim 1 , wherein the subject has melanoma.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 8, 2019
From: ARTOMOV, MAKSYM
To: BOSTONGENE LLC
Reel/Frame 048275/0618 →
CHANGE OF NAME Recorded Oct 17, 2018
From: BOSTONGENE, LLC
To: BOSTONGENE CORPORATION
Reel/Frame 047244/0793 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 3, 2018
From: FRENKEL, FELIKS; KOTLOV, NIKITA; BAGAEV, ALEXANDER; ATAULLAKHANOV, RAVSHAN
To: BOSTONGENE, LLC
Reel/Frame 046261/0735 →
Continuity (4)
Provisional Application 62598440 · Dec 13, 2017
Provisional Application 62518787 · Jun 13, 2017
Related Publication 20180357362A1 · Dec 13, 2018
Related Publication 20190179998A9 · Jun 13, 2019
Cited By (1)
US 12,672,791