Systems and methods for identifying responders and non-responders to immune checkpoint blockade therapy

• US 10,706,954 B2
• Filed: 11/26/2019
• Issued: 07/07/2020
• Est. Priority Date: 06/13/2017
• Status: Active Grant

• Current Assignee: Bostongene Corporation
• Original Assignee: Bostongene Corporation
• Inventors: Frenkel, Feliks, Kotlov, Nikita, Bagaev, Alexander, Artomov, Maksym, Ataullakhanov, Ravshan
• Primary Examiner(s): Brusca, John S
• Application Number:
  US16/696,128
• Publication Number:
  US 20200098443A1
• Time in Patent Office: 224 Days
• Field of Search: None
• CPC Class Codes: C12Q 1/6886 for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/20 Probabilistic models
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 : relating to practices or guidelines

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

1. A method for determining whether or not a subject is likely to respond to a checkpoint blockade therapy, the method comprising:

obtaining RNA expression data for the subject having, suspected of having, or at risk of having cancer;

using the expression data to determine expression levels, in the subject, for at least five 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, that the subject is likely to respond to the checkpoint blockade therapy, wherein the statistical model comprises a logistic regression model,

wherein the checkpoint blockade therapy is a PD1 inhibitor and/or a CTLA4 inhibitor,

wherein the method further comprises administering to the subject a PD1 inhibitor, a CTLA4 inhibitor, or a PD1 inhibitor and a CTLA4 inhibitor.

2. The method of claim 1, wherein the statistical model comprises a first set of dependent variables, each representing a ratio of a pair of genes, wherein the genes are selected from:

BRAF, 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.

3. The method of claim 2, wherein the statistical model comprises a first set of dependent variables, each representing a ratio of a pair of genes, wherein the genes are selected from:

BRAF, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROBO4, RAB40C, HAUS8, SNAP23, SNX6, XAGE1E, SERPINF1, RAI14, SIRPA, MT1X, NEK3, TGFB3, USP13, HLA-DRB4, IGF2, and MICAL1.

4. The method of claim 2, wherein the statistical model comprises a first set of dependent variables, each representing a ratio of a pair of genes, wherein the genes comprise:

BRAF, RAI14, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROBO4, RAB40C, HAUS8, SNAP23, and SNX6.

5. The method of claim 2, wherein the statistical model comprises a first set of dependent variables, each representing a ratio of a pair of genes, wherein the genes consist of:

BRAF, RAI14, PRKAG1, STX2, AGPAT3, FYN, CMIP, ROBO4, RAB40C, HAUS8, SNAP23, and SNX6.

6. The method of claim 2, wherein the first set of dependent variables comprises at least two ratios selected from:

BRAF;

RAI14, ACVR1B;

MPRIP, ACVR1B;

COPS3, PRKAG1;

STX2, NLRX1;

ELAC2, MON1B;

STX2, ARF3;

MPRIP, ARPIN;

MPRIP, SPRYD3;

FLI1, TIRAP;

MPRIP, GSE1;

RAI14, POLR3K;

HAUS8, RAB40C;

HAUS8, PIGO;

MPRIP, MFHAS1;

USP13, GSE1;

NPIPA1, DPH6;

STX2, ERLIN2;

RAI14, CES2;

LHFP, and NAIF1;

HAUS8.

7. The method of claim 2, wherein the first set of 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, C160RF58;

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, FBXW11;

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.

8. The method of claim 2, wherein the first set of dependent variables consists of the ratios:

BRAF;

RAI14, PRKAG1;

STX2, AGPAT3;

FYN, CMIP;

ROBO4, RAB40C;

HAUS8, SNAP23;

SNX6.

9. The method of claim 2, wherein the first set of dependent variables comprises at least three ratios, at least four ratios, at least five ratios, or at least six ratios.

10. The method of claim 1, wherein 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.

11. The method of claim 1, wherein 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.

12. The method of claim 1, further comprising training the logistic regression model, the logistic regression model having a plurality of regression variables, each of the plurality of regression variables representing a ratio of a pair of genes for respective pairs of the predictor set of genes.

13. The method of claim 12, wherein the logistic regression model comprises a respective plurality of weights for the plurality of regression variables, wherein estimating the parameters of the statistical model comprises:

estimating the plurality of weights using the expression data for the 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.

14. The method of claim 12, wherein training the statistical model comprises iteratively adding regression variables for respective genes to the statistical model, at least in part by:

identifying a candidate gene in the subset of genes;

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

evaluating performance of the augmented statistical model; and

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

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

16. The method of claim 1, wherein the method further comprises providing, to a user, an indication of whether or not the subject is likely to respond to the checkpoint blockade therapy.