WO2021252891A2

WO2021252891A2 - In silico generated target lists

Info

Publication number: WO2021252891A2
Application number: PCT/US2021/037004
Authority: WO
Inventors: Ronald Herbst; Yang Lee
Original assignee: Pyxis Oncology, Inc.
Priority date: 2020-06-11
Filing date: 2021-06-11
Publication date: 2021-12-16
Also published as: WO2021252891A3

Abstract

The invention relates to methods for identifying gene targets by interrogating genes preferentially expressed with dysfunctional T cell markers, systems for generating an in silico target list preferentially expressed with dysfunctional T cell markers and with or without regulatory T cells (Treg) markers, methods of treating cancer by interrogating genes preferentially expressed with dysfunctional T cell markers and with or without Treg markers, methods of treating cancer by interrogating genes preferentially expressed by T effector cells and not by dysfunctional T cells or regulatory T cells (Tregs), and methods of treating autoimmune disease.

Description

IN SILICO GENERATED TARGET LISTS

RELATED APPLICATIONS

The present application claims priority to U.S. Provisional Patent Application Serial Number 63/037,687, filed June 11, 2020 and U.S. Provisional Patent Application Serial Number 63/040,600, filed June 18, 2020, which are incorporated by reference in their entirety.

BACKGROUND OF THE INVENTION

[0001] T cells play an important role in both oncology and autoimmune disease areas. Dysfunctional T cells and T regulatory cells (Tregs) contribute to the immunosuppressive environment of the tumor microenvironment (TME). Gene expression and cell-type specific biomarkers have been studied in each of these T cell subtypes. RNA sequencing and proteomic databases provide a means to validate surface expression of a protein prior to commitment of experimental validation after target nomination. Despite the advances in next generation sequencing and database availability for numerous tumor types, there still remains a need for a highly human-focused, and translationally-relevant system to identify targets which are amenable to monoclonal antibody therapy.

SUMMARY OF THE INVENTION

[0002] In some embodiments, a computer-implemented method for identifying candidate gene targets comprising interrogating genes preferentially expressed with dysfunctional T cell markers using a computer system configured for interrogating genes with a transcriptomic database and identifying said gene targets. In some embodiments, interrogating genes comprises performing a data mining algorithm using said computer system to generate an in silico target list. In some embodiments, said transcriptomic database comprises a single cell RNA sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof. [0003] In some embodiments, interrogating genes comprises receiving, bulk RNASeq data and calculating a plurality of gene correlations between expression of at least two genes present in the bulk RNASeq data. In some embodiments, calculating the plurality of gene correlations comprises determining a Spearman’s correlation, a Pearson’s correlation, or a combination thereof. In some embodiments, calculating the plurality of gene correlations further comprises calculating a correlation between 4- IBB and at least one other gene. In some embodiments, calculating the plurality of gene correlations comprises calculating a correlation between LAG3 and at least one other gene. In some embodiments, the at least two genes comprise 4- IBB and LAG3. In some embodiments, interrogating genes comprises multiplying said plurality of correlations and ranking the products of said multiplying according to correlation with 4- IBB gene expression, LAG3 gene expression, or a combination thereof.

[0004] In some embodiments, interrogating genes comprises receiving scRNASeq data and determining, as a function of scRNASeq data, a threshold value for identifying positive versus negative expression for a biomarker. In some embodiments, interrogating genes comprises performing dimension reduction and clustering of genes with Uniform Manifold Approximation and Projection (UMAP). In some embodiments, interrogating genes comprises performing dimension reduction and clustering of genes with t- distributed Stochastic Neighbor Embedding (t-SNE). In some embodiments, interrogating genes comprises binning cells into one or more expression groupings as a function of said threshold value. In some embodiments, interrogating genes comprises binning cells into one or more expression groupings selected from the group consisting of 4-1BB+/ LAG3+, 4-1BB+/ LAG3-, 4-1BB-/LAG3+, and 4-1BB-/ LAG3-.

[0005] In some embodiments, interrogating genes comprises screening genes as a function of co-expression of genes between dysfunctional T cells and at least a second cell type. In some embodiments, interrogating genes comprises screening genes as a function of co-expression of genes between dysfunctional T cells and regulatory T cells (Tregs). In some embodiments, interrogating genes comprises retrieving a plurality of proteomics data, determining, from said proteomics data, a cell surface protein status of said genes and filtering said genes as a function of the cell surface protein status. In some embodiments, interrogating genes comprises filtering said genes as a function of being present on the cell surface.

[0006] In some embodiments, interrogating genes comprises retrieving a plurality of therapeutics data, determining, from said therapeutics data, a therapeutic status of said genes and filtering said genes as a function of the therapeutic status. In some embodiments, interrogating genes comprises filtering said genes as a function of having an approved monoclonal antibody therapy.

[0007] In some embodiments, interrogating genes comprises performing a gene set enrichment analysis (GSEA) and binning said genes by biological process category as a function of the GSEA.

[0008] In some embodiments, interrogating genes comprises categorizing said genes according to i) cell surface protein status, ii) tumor indication, iii) therapeutic status, iv) biological process category, v) co-expression of genes with at least the second cell type, or a combination thereof. In some embodiments, interrogating genes comprises generating an in silico target list that includes genes i) expressed on the cell surface, ii) with tumor indication, iii) without approved monoclonal antibody therapy, iv) preferentially co-expressed with Treg markers, or any combination thereof.

[0009] In some embodiments, system for generating an in silico target list comprising a computer system configured for interrogating genes preferentially expressed on dysfunctional T cell markers with a transcriptomic database to generate said in silico target list.

[0010] In some embodiments, method of treating cancer comprising identifying a gene for modulation by interrogating genes preferentially expressed with dysfunctional T cell markers with a transcriptomic database and administering to a human an agent that modulates said gene. In some embodiments, said marker is CD3. In some embodiments, said marker is selected from the group consisting of CD8, LAG3, 4- IBB, or a combination thereof. In some embodiments, said gene is further preferentially expressed on regulatory T cells (Tregs) as marked by markers such as FOXP3. [0011] In some embodiments, said transcriptomic database is a single cell RNA- sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof. In some embodiments, said bulk transcriptomic database is The Cancer Genome Atlas (TCGA) database.

[0012] In some embodiments, said agent is an antibody. In some embodiments, said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CSRPl, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCK10, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRBl, HLADRB6, IFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRDl, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RAB11FEP1, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

[0013] In some embodiments, said agent is an antibody. In some embodiments, said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CRTAM, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRB1, HLADRB6, EFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRD1, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

[0014] In some embodiments, a method of treating cancer comprising identifying a gene for modulation by interrogating genes preferentially expressed by T effector cells and not by dysfunctional T cells or regulatory T cells (Tregs) with a transcriptomic database and administering to a human an agent that modulates said gene. In some embodiments, said genes preferentially expressed by T effector cells is selected from the group consisting of CD8, LAG3, 4- IBB, or a combination thereof. In some embodiments, said transcriptomic database is a single cell RNA-sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof. In some embodiments, said transcriptomic database is The Cancer Genome Atlas (TCGA) database.

[0015] In some embodiments, said agent is an antibody. In some embodiments, said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CSRPl, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRBl, HLADRB6, IFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRDl, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RAB11FEP1, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

[0016] In some embodiments, said agent is an antibody. In some embodiments, said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CRTAM, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRB1, HLADRB6, EFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRD1, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

[0017] In some embodiments, a method of treating autoimmune disease comprising identifying a gene for modulation by interrogating genes preferentially expressed with T cell markers with a transcriptomic database and administering to a human an agent that modulates said gene. In some embodiments, genes preferentially expressed by T cells comprises genes preferentially expressed with dysfunctional T cell markers with a transcriptomic database. In some embodiments, genes preferentially expressed by T cells comprises genes preferentially expressed by T effector cells and not by dysfunctional T cells or regulatory T cells (Tregs) with a transcriptomic database. In some embodiments, said marker is CD3. In some embodiments, said marker is selected from the group consisting of 4- IBB, LAG3, or a combination thereof.

[0018] In some embodiments, said transcriptomic database is a single cell RNA- sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof. In some embodiments, said bulk transcriptomic database is The Cancer Genome Atlas (TCGA) database.

[0019] In some embodiments, said agent is an antibody. In some embodiments, said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CSRPl, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCK10, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRBl, HLADRB6, IFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRB1, KLRC2, KLRC3, KLRC4, KLRD1, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAP1, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

[0020] In some embodiments, said agent is an antibody. In some embodiments, said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CRTAM, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRB1, HLADRB6, EFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRD1, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIG. 1 is a flow chart illustrating a data mining algorithm that begins with bulk or single cell RNA sequencing databases and resulting in binning of targets into different biological pathways.

[0022] FIGs. 2A-2C show a cluster diagram (FIG. 2A), scatter plot (FIG. 2B), and volcano plots (FIG. 2C) demonstrating the in silico identification of targets using differential gene expression analysis with a representative scRNASeq database.

[0023] FIGs. 3A-3D show flow cytometry confirmation of LAYN surface protein expression in human colorectal cancer tumor infiltrating T cells.

[0024] FIGs. 4A-4C show flow cytometry confirmation of CRTAM surface protein expression in human colorectal cancer tumor infiltrating T cells.

[0025] FIGs. 5A-5D show flow cytometry confirmation of CRTAM induction after stimulation of PBMC-isolated T cells post anti-CD3 and anti-CD28 induction compared to isotype staining control (FIGs. 5A and 5B), while unstimulated PBMC-isolated T cells showed little CRTAM induction (FIGs. 5C and 5D). Staining was performed with anti- CRTAM (FIGs. 5A and 5C) or isotype (FIGs. 5B and 5D) antibodies.

DETAILED DESCRIPTION

[0026] With the advent of next-generation sequencing and the accumulation of both bulk and single cell RNA sequencing (scRNASeq) databases available for multiple tumor types, traditional in vivo and in vitro methods used in the identification of dysfunctional T cell targets may be shifted towards an in silico approach, as described herein. Flow cytometric analysis may be used for identification of cell subtypes and the genes can be further used to perform differential expression analysis for identification of additional markers of these cell subtypes. Furthermore, availability of scRNASeq databases allow interrogation outside of typical forward mouse genetic screening. Those skilled in the art will understand that the disclosures described herein are non-limiting embodiments. The features illustrated or described in connection with one embodiment may be combined with the features of other embodiments. Such modifications and variations are intended to be included within the scope of the present disclosure.

Definitions

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this present disclosure pertains. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure and any invention(s) described or otherwise provided for herein.

[0028] All publications, patent applications, patents, patent publications, and other references cited herein are incorporated by reference in their entireties for the teachings relevant to the sentence and/or paragraph in which the reference is presented.

[0029] As used in the description of the present disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0030] As used herein, "and/or" refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative ("or").

[0031] The transitional phrase "consisting essentially of' means that the scope of a claim is to be interpreted to encompass the specified materials or steps recited in the claim, "and those that do not materially affect the basic and novel characteristic(s)" of the claimed invention.

[0032] By the terms "treat," "treating," or "treatment of," it is intended that the severity of the condition of the subject is reduced, or at least partially improved or modified, and that some alleviation, mitigation, or decrease in at least one clinical symptom is achieved.

[0033] The term "autoimmune disorders" as used herein refers to any disorder associated with an autoimmune reaction. Examples include, without limitation, multiple sclerosis, lupus, idiopathic pulmonary fibrosis, inflammatory bowel disease (e.g., Crohn’s disease, ulcerative colitis, collagen colitis), celiac disease, arthritis (e.g. rheumatoid arthritis, psoriatic arthritis), Guillain-Barre syndrome, vasculitis (e.g., Takayasu’s arteritis, giant cell arteritis, microscopic polyangiitis, rheumatoid vasculitis, granulomatosis with polyangiitis), systemic sclerosis, type I diabetes, Addison’s disease, alopecia areata, autoimmune skin disorders (e.g. psoriasis, atopic dermatitis, eczema, pemphigus vulgaris, bullous pemphigoid, seborrheic dermatitis), dermatomyositis, myositis, neuromyelitis optica, allergies, Behcet’s disease, Hashimoto thyroiditis, autoimmune hepatitis, primary sclerosing cholangitis, primary biliary cirrhosis, idiopathic inflammatory myopathy, polymyositis, myasthenia gravis, rheumatic fever, septic shock, Sjogren’s syndrome, interstitial lung disease, Systemic sclerosis, Inflammatory bowel disease (including ulcerative colitis and Crohn’s disease), Systemic lupus erythematosus, Type 1 diabetes, Multiple sclerosis. Other immune mediated process considered herein to be included in autoimmune diseases include: Organ transplant rejection and graft versus host disease (GVHD).

[0034] The term "cancer" as used herein refers to any malignant abnormal growth of cells. Examples include, without limitation, breast cancer, prostate cancer, lymphoma, skin cancer, pancreatic cancer, colon cancer, melanoma, malignant melanoma, ovarian cancer, brain cancer, primary brain carcinoma, head-neck cancer, glioma, glioblastoma, liver cancer, bladder cancer, non-small cell lung cancer, head or neck carcinoma, breast carcinoma, ovarian carcinoma, lung carcinoma, small-cell lung carcinoma, Wilms' tumor, cervical carcinoma, testicular carcinoma, bladder carcinoma, pancreatic carcinoma, stomach carcinoma, colon carcinoma, prostatic carcinoma, genitourinary carcinoma, thyroid carcinoma, esophageal carcinoma, myeloma, multiple myeloma, adrenal carcinoma, renal cell carcinoma, endometrial carcinoma, adrenal cortex carcinoma, malignant pancreatic insulinoma, malignant carcinoid carcinoma, choriocarcinoma, mycosis fungoides, malignant hypercalcemia, cervical hyperplasia, leukemia(e.g., acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myelogenous leukemia, chronic myelogenous leukemia, chronic granulocytic leukemia, acute granulocytic leukemia, hairy cell leukemia), neuroblastoma, rhabdomyosarcoma, Kaposi's sarcoma, polycythemia vera, essential thrombocytosis, Hodgkin's disease, non-Hodgkin's lymphoma, soft-tissue sarcoma, osteogenic sarcoma, primary macroglobulinemia, and retinoblastoma. In some embodiments, the cancer is selected from a group of tumorforming cancers.

[0035] The terms “bulk RNA sequencing (bulk RNASeq)” or “single-cell RNA sequencing (scRNASeq)” in reference to a database as used herein refers to a datastore of data generated from a sequencing technique that uses conventional next-generation sequencing (NGS) to reveal the identity of, and quantify the presence of, RNA in a biological sample. RNA sequencing data may be referred to as “transcriptomic data,” or as a “transcriptome.” Bulk RNASeq can refer to RNA sequencing of an entire sample, which may be homogenous or heterogenous, such as a tumor, a tissue, a cluster of cells, and the like. scRNASeq can refer to a genomic approach for the detection and quantitative analysis of RNA molecules down to individual cell resolution.

[0036] The term “dysfunctional T cells” as used herein refers to a T cell or population of T cells that have entered a dysfunctional or exhausted state. Dysfunctional T cells are typically characterized by sustained expression of inhibitory receptors and a transcriptional state distinct from that of functional effector or memory T cells.

[0037] The term “regulatory T cells (Tregs)” as used herein refers to a specialized subpopulation of T cells that act to suppress an immune response, thereby maintaining homeostasis and self-tolerance. Tregs are able to inhibit T cell proliferation and cytokine production and play a critical role in preventing autoimmunity. Different subsets with various functions of Treg cells exist. Tregs can be usually be identified by flow cytometry and in vitro functional assays.

[0038] The terms “effector T cell” or “T effectors” as used herein refers to a group of cells that includes several T cell types that actively respond to a stimulus, such as costimulation. Effector T cells include CD4+, CD8+, suppressor T cells, helper T cells (T_h cells), and the like.

[0039] The terms “biomarker,” “biological marker,” or “marker” as used herein, refer to a broad subcategory of biological, physiological, and/or clinical indications that can be measured accurately and reproducibly which relate to a biological, physiological, and/or clinical observation. A biomarker may refer to any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease. Biomarkers can include genes and gene products, such as RNAs, proteins and enzymes, macromolecules such as lipids and sugars, vitamins and vitamers, LDL/HDL cholesterol, peptides and metabolic products, indicia of bacteria, viral, and parasitic infection, and the like.

[0040] The term "antibody" as used herein refers to all types of immunoglobulins, including IgG, IgM, IgA, IgD, and IgE, and fragments thereof which have some therapeutic property. The antibody can be monoclonal or polyclonal and can be of any species of origin. The antibody can include chimeric antibodies, bi-specific antibodies, humanized antibodies, nanobodies, among other antibody types. Antibodies, as used herein, can refer to “monoclonal antibodies” or “therapeutic monoclonal antibodies”, which have monovalent affinity, intended to bind to a single epitope of a specific target for therapeutic purposes.

[0041] The term “target,” “target gene,” or “target list” as used herein refers to at least a biomarker, such as a gene, which represents a positive selection throughout the in silico target list generation methods described herein. As used herein, a target may be a gene and its cognate biological product.

[0042] The term “machine learning” as used herein refers to a broad category of algorithms, processes, and/or methods which are an application of artificial intelligence directed to producing decisions/outputs according to analysis and/or model building from observations in data, identifying patterns, and the like. Machine learning, as opposed to conventional software and applications where the commands/tasks are explicitly programmed beforehand, has a learning component where the algorithm iteratively improves upon outputting the decision/output based on observations in the data. Such a process may be referred to as “training” a machine learning algorithm or model.

[0043] The terms “Uniform Manifold Approximation and Projection (UMAP)” or “t- distributed Stochastic Neighbor Embedding (t-SNE)” as used herein refer to dimension reduction and clustering algorithms. Clustering can be considered an unsupervised machine learning task that aims to describe a hidden structure of objects. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology, which results in a practical scalable algorithm that applies to real world data. Similarly, t-SNE is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. Both are nonlinear dimensionality reduction techniques well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, the algorithms model each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability. Persons skilled in the art, with the benefit of this disclosure in its entirety, will be aware of the various machine learning algorithms for clustering and/or dimension reduction that may be used in place of UMAP and/or t- SNE, such as k-means, OPTICS, Principal Component Analysis (PCA), among others.

[0044] The term “data mining algorithm” as used herein refers to a set of heuristics and calculations that operate to capture and describe patterns identified in data. As used herein, the algorithm can analyze data inputs, such as immunohistochemistry (IHC), cell surface protein expression profiles, RNA sequencing reads, gene/protein expression by cell type, etc., to identify specific types of patterns or trends. The algorithm can use the results of this analysis over many iterations to identify optimal parameters for filtering and/or binning targets. These parameters are then applied across the data set to extract actionable patterns and detailed statistics. For example, the data mining algorithm can take various forms and be modeled to generate various outputs, such as a set of clusters that describe the relationship between genes in a dataset, a decision tree that filters targets and describes how different criteria affect that outcome, and a hierarchy of rules that describe how biomarkers are grouped together and the probabilities that biomarkers are related.

[0045] The term “Gene Set Enrichment Analysis (GSEA)” as used herein refers to a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA can be used with gene datasets from the Molecular Signatures Database (MsigDB). [0046] T cells play an important role in both oncology and autoimmune disease areas. Dysfunctional T cells and T regulatory cells (Tregs) have been demonstrated to contribute to the immunosuppressive environment of the tumor microenvironment (TME). Gene expression and cell-type specific biomarkers have been studied in each of these T cell subtypes. For example, lymphocyte-activating gene 3 (LAG3), originally designated CD223, and 4-1BB (also known as CD137; TNFRS9) are important biomarkers for dysfunctional T cells in both mice and humans. LAG3 is a cell surface molecule with diverse biologic effects on T cell function, such as an immune checkpoint receptor and is an attractive drug development target in developing new treatments for cancer and autoimmune disorders. 4- IBB is a member of the tumor necrosis factor (TNF) receptor family and acts as a co-stimulatory immune checkpoint molecule to regulate the immune response.

[0047] These biomarkers have been used in mice to isolate dysfunctional T cells from dissociated syngeneic tumors as well as in vitro induced systems, such as via flow cytometry. Subsequent differential RNA expression analysis in both in vivo and in vitro systems have identified additional genes in these Lag3+ 4-1BB+ T cells which may serve as a basis for therapy in reversing the immunosuppressive tumor microenvironment.

[0048] At a high level, the methods described herein exploit a proprietary combination of data mining, machine learning, and algorithmic filtering. This novel combination allows high-throughput analysis of observations (data mined from a collection of databases - immunohistochemistry (IHC), cell surface protein expression profiles, RNA sequencing reads, gene/protein expression by cell type, etc.) at levels that far exceed human capability, resulting in improved efficacy and accuracy as compared to currently available target identification methods.

[0049] In some aspects, the methods described herein comprise receiving an input of bulk RNASeq or scRNASeq data from a sequencing database. In the case of bulk RNA sequencing databases, gene correlations can be calculated. In analyzing RNA sequencing data, the methods comprise determining inter- and intragroup variability by calculating distance as represented by correlation between samples. Two measures of correlation that can be calculated are the Pearson’s coefficient and the Spearman’s rank correlation coefficient, which can describe the directionality and strength of the relationship between two variables. The correlations can be multiplied together, and their products ranked to determine which genes show the highest correlation.

[0050] In other aspects, the methods described herein comprise receiving an input from a scRNA-Seq database. In the case of scRNA-Seq databases, raw files from select single cell databases can be first processed into normalized and scaled data, followed by dimension reduction and clustering with machine learning algorithms, such as Uniform Manifold Approximation and Projection (UMAP) or t-distributed Stochastic Neighbor Embedding (t-SNE). Feature plot outputs can be used to confirm expected clustering and localization of biomarkers to determine the best cutoff for determining positive versus negative expression for any given biomarker. T cells can then be binned into different combinations of biomarker positive and negative categories, followed by differentiation expression analysis between the groups.

[0051] In another aspect, identified target genes can be filtered using a series of downstream filters including, for example,: i) a cell surface protein filter, ii) a cellular expression filter, iii) a regulatory T cell filter, iv) an approved drugs filter, or a combination thereof. These filters can be used sequentially, in any order, and/or in any combination.

[0052] In some aspects, methods used herein utilize proteomic databases including, for example, Human Protein Atlas, GtexPortal, and Cell Atlas which are used to determine gene and protein expression in i) normal state conditions, ii) tumor state conditions, iii) by tissue type, iv) cellular context, v) subcellular localization, or combinations thereof. A cell surface protein database was constructed using data combined from Cell Surface Protein Atlas (Bausch-Fluck 2015 and 2018) and Cell Atlas, a fluorescently determined subcellular localization database available through The Human Protein Atlas.

[0053] In other aspects, methods may include screening and binning gene targets. Target lists may be screened using therapeutics data (e.g., DrugCentral) for target novelty and unmet need, for example by selecting genes that are correlated with diseases, which do not have an existing FDA-approved monoclonal antibody therapy. Gene origination may be categorized using gene set enrichment analysis (GSEA) using data from the Molecular Signatures Database (MsigDB). Genes can then be binned into different biological categories and priorities may be given to top ranking genes within each biological process category. Biological process categories can include, for example, cellular function, cell signaling pathways, associated biological mechanisms, and the like.

[0054] In some aspects, the methods described herein comprise treating cancer by identifying one or more genes for modulation by interrogating genes preferentially expressed with dysfunctional T cell markers (e.g., using a transcriptomic database). In another aspect, the methods described herein comprise treating cancer by identifying one or more genes for modulation by interrogating genes preferentially expressed by T effector cells and not by dysfunctional T cells or regulatory T cells (Tregs) (e.g., using a transcriptomic database).

Computer-Implemented Method for Identifying Gene Targets

[0055] The present invention allows for isolation of candidate genes from human RNA expression data, which may render T cells more suppressive or dysfunctional, and thus amenable to direct modulation by monoclonal antibody therapy in treating cancer and/or autoimmune disease. In some embodiments, the methods disclosed herein include identifying gene targets by interrogating genes preferentially expressed with dysfunctional T cell markers using a computer system configured for interrogating genes with a transcriptomic database and identifying said gene targets.

[0056] A computing system may include one or more computers, computing devices, and/or computing components such as central processing units (CPUs) and/or graphical processing units (GPUs), associated software (operating system, etc.) and non-transitory storage media. Persons skilled in the art, with the full benefit of this disclosure in its entirety, will be aware of the various computing systems and capabilities of computing devices of the computing system necessary to support the methods and systems described herein.

[0057] Interrogating genes may include performing a data mining algorithm using a computer system to generate an in silico target list. Interrogating genes may include receiving a sequencing input from dysfunctional T cells (e.g., from a transcriptomic database). A transcriptomic database may be any database or knowledgebase that includes nucleic acid sequencing data and/or data regarding expression of gene products, for example, microarray databases such as the National Institutes of Health (NIH) public microarray database, gene expression resources such as SAGEmap, NanoString, Human Protein Atlas, GtexPortal, Cell Atlas, The Cancer Genome Atlas (TCGA), Cell Surface Protein Atlas, and the like. Two sources of RNA expression data may feed into the algorithm - bulk or single cell RNA sequencing data, as depicted in FIG. 1. The transcriptomic database may comprise a single cell RNA sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, and/or a combination thereof. In one aspect, both bulk and single cell RNA sequencing data are used to increase the robustness of the interrogation of genes and generation of targets. The sequencing input may comprise bulk RNASeq and/or scRNASeq data from a transcriptomic database, such as The Cancer Genome Atlas (TCGA).

[0058] Identifying a gene for modulation by interrogating genes preferentially expressed on dysfunctional T cell markers may comprise identifying a gene that is the etiological agent of a disease, such as cancer or autoimmune disease. In some aspects, the T cell marker can be CD3. CD3 (cluster of differentiation 3) is a protein complex and T cell coreceptor that is involved in activating both the cytotoxic T cell (CD8+ naive T cells) and T helper cells (CD4+ naive T cells). CD3 may be used as a marker to identify populations of T cells. These populations may then be pinpointed for retrieving transcriptomic data. In some aspects, the dysfunctional T cell marker may be CD8, LAG3, 4- IBB, and/or any combination thereof. These markers may be used as an initial filtering step to retrieve transcriptomic data that belongs to a cell type or population of interest.

[0059] 4- IBB and LAG3 have been identified as surface markers of dysfunctional T cells, and further validated in mice, thus it is possible to use these markers for identification of additional dysfunctional T cell markers with human RNA expression data from bulk or single cell sources. In bulk RNA sequencing databases, a plurality of gene correlations may be calculated between expression of at least two genes present in the sequencing input. For example, the gene correlations may be between (i) 4-1BB and at least one other gene, (ii) LAG3 and at least one other gene, and/or (iii) LAG3 and 4- 1BB. Analyzing bulk RNA sequencing data may include determining inter- and intragroup variability by calculating distance as represented by correlation between samples. Gene correlations may be calculated as either a Spearman’s correlation, a Pearson’s correlation, and/or a combination thereof. Both the Pearson’s correlation coefficient and the Spearman’s rank correlation coefficient can describe the directionality and strength of the relationship between two variables. The Pearson’s correlation may reflect the linear relationship between two variables accounting for differences in their mean and SD, whereas the Spearman’s rank correlation is a nonparametric measure using the rank values of the two variables. When bulk RNASeq data is used, a Spearman’s correlation and a Pearson’s correlation may be computed separately to identify genes with high degree of correlation with 4- IBB or LAG3. Such correlations may be examined separately for individual cancer indications, autoimmune indications, or combined indications.

[0060] The correlations may be ranked in gene interrogation. The Spearman’s and Pearson’s correlations are multiplied together, and the products ranked from highest to lowest to maximize for correlation with both 4- IBB and LAG3. For example, the ranking may be used to identify genes with the highest correlation with 4- IBB, with LAG3, and/or with both 4- IBB and LAG3. Ranking may include simply ranking the plurality of gene correlations as a function of, for example, correlation with 4- IBB gene expression, LAG3 gene expression, and/or a combination thereof. Multiplying may include, for example, multiplying the Pearson’s coefficient with the Spearman’s coefficient for each gene as a function of its correlation with LAG3, 4- IBB, and/or both LAG3 and 4- IBB. The bulk RNASeq gene correlations can then be ranked in any meaningful way, such as, for example, from highest level of gene correlation to lowest.

[0061] Although bulk RNA sequencing databases, such as TCGA, offer relatively deep sequencing for the primary cancers, they lack the ability to examine and isolate genes from individual cell types. Single cell RNA sequencing databases can be utilized separately, in parallel, and/or in addition to bulk RNA sequencing databases to enhance confidence of isolating cells relevant to the cell type(s) of interest. [0062] Raw files from selected scRNASeq database(s) may be first processed into normalized and scaled data, followed by dimension reduction and clustering with Uniform Manifold Approximation and Projection (UMAP) and/or t-distributed Stochastic Neighbor Embedding (t-SNE). Both are nonlinear dimensionality reduction techniques well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, the algorithms model each highdimensional object by a two- or three-dimensional point in a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.

[0063] UMAP and/or t-SNE outputs include feature plots which are used to confirm expected clustering and localization of biomarkers, such as CD8, LAG3 and 4-1BB. For example, in silico identification of targets using differential gene expression analysis with a representative scRNA-Seq database is demonstrated in the cluster diagram (FIG. 2 A), scatter plot (FIG. 2B), and volcano plots (FIG. 2C). Scatter plots may be used subsequently to visualize biomarker population spread of each biomarker, as shown in FIG. 2B, which allows for setting of threshold values for calling positive versus negative status for any given biomarker and/or population of biomarkers or cells. Violin and scatter plots may be used first to determine an accurate cutoff for calling positive vs. negative 4- IBB and LAG3. For example, such a threshold value can be used to determine LAG3+/- and 4-1BB+/-. Ranking correlations with both 4-1BB and LAG3, for example from highest correlation to lowest, may be used to determine cutoffs for genes preferentially expressed with LAG3+ and/or 4-1BB+ from bulk RNASeq data. Cutoffs may be used to determine overexpression, preferential co-expression, among other designations, for example relative to a reference point such as “normal expression level.” UMAP and/or t-SNE feature plots can be used to determine more accurate threshold values for designating positive vs. negative 4- IBB and LAG3 in scRNASeq data.

[0064] Interrogating genes may include binning cells into one or more expression groupings as a function of the threshold value. For example, individual CD8 T cells (from scRNASeq) can then be binned as either double positive or double negative for 4- IBB and/or LAG3, followed by differential gene expression analysis between the groups. Interrogating genes can include binning biomarkers into one or more expression groupings as a function of the threshold value. For example, individual biomarkers (e.g., genes) may be binned into categories such as ‘preferentially co-expressed category’ with double positive or double negative 4- IBB and/or LAG3 cells, followed by differential gene expression analysis between the groups.

[0065] In the case of dysfunctional T cells, differential gene expression analyses may be performed on CD8+ LAG3+/4-1BB+ dysfunctional T cells against CD8+ LAG3-/4-1BB- cytotoxic T cells for scRNA-seq databases from multiple tumor indications. Tumor indication may include designations related to the tissue type where the level of expression is anticipated, the cancer type(s) the expression coincides with, whether the expression level indicates a malignant expression level, and the like. Differential expression analysis may include using the normalized sequencing read count data to perform statistical analysis as a means to discover quantitative changes in expression levels between experimental groups, such as normal tissue versus malignant tissue, or dysfunctional T cell versus functional T cell. For example, the methods described herein may comprise determining a statistically significant difference in gene expression between CD8+ LAG3+/4-1BB+ dysfunctional T cells and CD8+ LAG3-/4-1BB- cytotoxic T cells. Such an analysis may provide information regarding which genes are preferentially expressed by dysfunctional T cells over normal, functional T cell subsets. Volcano plots may be used to visualize and determine genes which are both upregulated and statistically significant, determined by a fixed adjusted -log P value, for example as depicted in FIG. 2C.

[0066] Interrogating gene may include binning genes as a function of the differential gene expression analyses. Genes may be binned according to the analysis of bulk RNASeq data (correlations and ranking), according to the analysis of scRNASeq data (dimension reduction/clustering and differential gene expression analysis), or more preferably according to the analysis of both bulk RNASeq and scRNASeq analyses. In this way, combining the analyses from both bulk RNASeq and scRNASeq provides a more robust gene identification method. Binning genes may include categorizing genes as a function of a threshold value indicating a value of gene expression, above which for example may indicate preferential co-expression of gene(s) in LAG3+/- and/or 4-1BB+/- dysfunctional T cells. Binning genes may include categorizing genes as a function of clustering using UMAP/t-SNE, wherein clustering indicates groupings of genes that are preferentially coexpressed in LAG3+/- and/or 4-1BB+/- T cells. Genes identified in either or both of bulk RNASeq and scRNASeq approaches may be subjected to several downstream filters.

[0067] Interrogating genes may include a series of downstream filters such as: i) a cell surface protein filter, ii) a cellular expression filter, iii) a regulatory T cell filter, iv) an approved drugs filter, and combinations thereof. These filters can be used sequentially, in any order, and/or in any combination. Not all filters must be used to generate the in silico targets list. Persons skilled in the art will appreciate that with each additional filter, the identification of targets is increasingly more robust.

[0068] As antibody-based therapies can only directly target cell surface protein, it is critical that candidate gene products are verified to be at least partially, if not predominantly, expressed on the cell surface. The cell surface protein filter can be used for screening cell surface protein (and their associated genes) using proteomics data from multiple sources including empirical mass spectrometry data, machine-learning predicted data, and cell line surface staining data from Cell Atlas database. In addition, histologic images from Human Protein Atlas database also serve to help visually determine whether proteins are localized to the plasma membrane, where stained images appear reliable. Such data may be retrieved from the above sources and used to filter genes that encode for proteins that are not cell surface-accessible by determining a cell surface protein status. Cell surface protein status may include a variety of determinations, such as whether the target is exposed on the cell surface, comprises a cell surface extracellular domain, transmembrane or integral protein, is purely a cytosolic protein, is cleavable from the cell surface, present on the cell surface only at particular times, and the like. The cell surface protein status of genes can allow binning of the genes, such as placing into a “removed” category, filtered from the target list at this step if the genes result in products that are not cell-surface associated. Alternatively, or additionally, positive selection can be used where genes are filtered into a category that designates a cell surface protein status that indicates the gene product is cell-surface associated. [0069] The cellular expression filter may be used to filter genes that are appropriately expressed relative to normal tissues. Both normal RNA sequencing data and histology data can be used to select for genes with expected levels of expression in tumors, while such genes exhibit reduced level or reduced spectrum of expression in a variety of normal tissues. For example, potential candidate genes in the dysfunctional T cells targeted for treatment with monoclonal antibodies may be overexpressed in immunohistochemistry (IHC) of tumor sections, in the lymphocyte compartment, while not present in great quantity in IHC of normal tissue. Such IHC data may originate from the Human Protein Atlas database and RNA data from normal tissues such as from GTExPortal database. Genes, such as those that are found at expected, normal expression levels may be binned, such as placed into a “removed” category, filtered from the target list at this step. Alternatively, or additionally, positive selection can be used where genes are filtered into a category that designates that the gene product resembles tumor microenvironment expression.

[0070] Interrogating genes using a downstream filter may comprise screening genes as a function of co-expression of genes between dysfunctional T cells and at least a second cell type. The second cell type may include, for example, other specific populations of T cells such as effector T cells or regulatory T cells. A regulatory T cell filter may be based on observations made from multiple scRNASeq databases that several canonical genes for dysfunctional T cells have a high propensity for co-expression by regulatory T cells. Thus, co-expression of a gene by both dysfunctional CD8 T cells and CD4 regulatory T cells are flagged and ranked as high priority candidates. Genes, such as those that are not co-expressed between both dysfunctional T cells and Tregs may be binned, such as placed into a “removed” category, filtered from the target list at this step. Alternatively, or additionally, positive selection may be used where genes are filtered into a category that designates that the gene is co-expressed between dysfunctional T cells and Tregs.

[0071] The approved drugs filter includes mining a plurality of therapeutics data describing currently available therapeutics to identify those genes on the target list that are lacking in treatment modalities. For example, using data from the Drug Central database, the approved drugs filter can be generated to identify which of the candidate targets already have FDA-approved drugs. Candidate gene targets can be screened against the extensive list of existing FDA-approved drugs and assigned a therapeutic status. Therapeutic status may include the number of approved treatments, the types of approved treatments (e.g., monoclonal antibody, antibody-drug conjugate, small molecule inhibitor), and the like. Targets with cognate FDA-approved drugs can be prioritized lower after confirmation by literature and/or clinical data. Targets lacking in FDA- approved drugs may resemble novel targets, or targets with unmet need. Targets may be filtered, or binned, according to the presence of FDA-approved drugs. For example, targets with FDA-approved monoclonal antibody therapies could be filtered from the target list.

[0072] Interrogating genes may include binning targets to different biological pathways. In order to diversify target gene candidates to different biological processes, as well as understand the biological pathway and mechanism of action, the list of genes that are up- and down-regulated may be inputted into a Gene Set Enrichment Analysis (GSEA). Major collections of gene sets from MSigDB (Broad Institute) can be used for these analyses. Gene sets include: hallmark, positional, curated, regulatory target, computational, ontology, oncogenic signature, immunologic signature and cell type signature gene sets. Biological pathways of statistical significance may be attached to each target gene.

[0073] Interrogating genes may comprise categorizing genes according to i) cell surface protein status, ii) therapeutic status, iii) biological process category, iv) co-expression of genes with at least a second cell type, or a combination thereof. For example, generating an in silico target list that includes genes that are bona fide targets may include genes that are: 1) expressed on the cell surface, 2) with tumor indication, 3) without approved monoclonal antibody therapy, 4) preferentially co-expressed with dysfunctional T cell markers, or any combination thereof. The in silico target list may include a variety of categories related to biological process category. For example, targets may be categorized by disease, such as a cancer target list, an autoimmune disease target list, among other designations. In such an example, the cancer target list may include only genes that after interrogation are found to be: 1) expressed on the cell surface, 2) with tumor indication, 3) without approved monoclonal antibody therapy, 4) preferentially co-expressed with dysfunctional T cell markers, or any combination thereof.

Systems for Generating an In Silico Target List

[0074] In one aspect, a system for generating an in silico target list comprises a computer system configured for interrogating genes preferentially expressed on dysfunctional T cell markers with a transcriptomic database to generate said in silico target list. The computing system may include one or more computers and/or computing devices such as central processing units (CPUs) and/or graphical processing units (GPUs), associated software (operating system, etc.) and non-transitory storage media. Persons skilled in the art will be aware of the various computing systems and capabilities of computing devices of the system necessary to support the methods and systems described herein. Interrogating genes and generating the in silico target list can be performed as described above.

[0075] Systems for generating an in silico target list may include any electronic equipment controlled by a processor (CPU/GPU), containing non-transitory storage media or computer-readable media (CRM) which can store data, such as computer- executable code and software, for performing the methods disclosed herein. Systems for generating an in silico target list may include any computing system which has capability for communicating with a transcriptome database, such as via a wireless communication (intemet/Wi-Fi, WCDMA or TD-SCDMA air interfaces, LTE, LTE Advanced (LTE-A), HSPA, 3GPP2 CDMA2000 and other Radio Access Technology (RAT) (e.g., lxRTT, lxEV-DO, HRPD, eHRPD), IEEE 802.11 (WLAN or Wi-Fi), IEEE 802.16 (WiMAX), 3G, 4G, 5G generation wireless systems, enhanced mobile broadband (eMBB), International Mobile Telecommunications-Advanced (IMT- Advanced) Standards, Bluetooth, and the like). Systems for generating an in silico target list may include any computing system that can work as part of a network, such as a distributed network, sharing a set of common communication protocols over digital interconnections for the purpose of sharing resources located on or provided by the network nodes. [0076] Systems for generating an in silico target list may include any computing system which is capable of performing machine learning algorithms, process, and/or models, such as UMAP and t-SNE. Machine learning algorithms can be computationally intensive and require specific configurations, such as requiring multi-core CPUs, hyperthreading of CPUs, distributed computation between multiple processors and processor types, and the like. Persons skilled in the art will be aware of the various capabilities a computing system would require to perform the methods disclosed herein.

Methods of Treating Autoimmune Diseases or Cancer by Identifying Targets Preferentially Expressed with Dysfunctional T Cells Markers

[0077] In some aspects, a method for treating autoimmune diseases or cancer includes identifying a gene for modulation using the methods described above (e.g., by interrogating genes preferentially expressed with dysfunctional T cell markers with a transcriptomic database) and administering an agent that modulates the identified gene. In some embodiments, the marker is CD3. In some embodiments, the marker is selected from the group consisting of CD8, LAG3, 4-1BB, or a combination thereof. In some embodiments, the gene is further preferentially expressed on regulatory T cells (Tregs). In some embodiments, the transcriptomic database is a single cell RNA-sequencing (sc- RNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof. In some embodiments, the bulk transcriptomic database is a cancer genome atlas (TCGA) database. In some embodiments, the agent is an antibody.

[0078] The methods described herein may be used to treat any autoimmune disease, such as, for example, multiple sclerosis, lupus, idiopathic pulmonary fibrosis, inflammatory bowel disease (e.g., Crohn’s disease, ulcerative colitis, collagen colitis), celiac disease, arthritis (e.g. rheumatoid arthritis, psoriatic arthritis), Guillain-Barre syndrome, vasculitis (e.g., Takayasu’s arteritis, giant cell arteritis, microscopic polyangiitis, rheumatoid vasculitis, granulomatosis with polyangiitis), systemic sclerosis, type I diabetes, Addison’s disease, alopecia areata, autoimmune skin disorders (e.g. psoriasis, atopic dermatitis, eczema, pemphigus vulgaris, bullous pemphigoid, seborrheic dermatitis), dermatomyositis, myositis, neuromyelitis optica, allergies, Behcet’s disease, Hashimoto thyroiditis, autoimmune hepatitis, primary sclerosing cholangitis, primary biliary cirrhosis, idiopathic inflammatory myopathy, polymyositis, myasthenia gravis, rheumatic fever, septic shock, Sjogren’s syndrome, interstitial lung disease, Systemic sclerosis, Inflammatory bowel disease (including ulcerative colitis and Crohn’s disease), Systemic lupus erythematosus, Type 1 diabetes, Multiple sclerosis. Other immune mediated process considered herein to be included in autoimmune diseases include: Organ transplant rejection and graft versus host disease (GVHD).

[0079] The methods described herein may be used to treat any type of cancer, for example, breast cancer, prostate cancer, lymphoma, skin cancer, pancreatic cancer, colon cancer, melanoma, malignant melanoma, ovarian cancer, brain cancer, primary brain carcinoma, head-neck cancer, glioma, glioblastoma, liver cancer, bladder cancer, nonsmall cell lung cancer, head or neck carcinoma, breast carcinoma, ovarian carcinoma, lung carcinoma, small-cell lung carcinoma, Wilms' tumor, cervical carcinoma, testicular carcinoma, bladder carcinoma, pancreatic carcinoma, stomach carcinoma, colon carcinoma, prostatic carcinoma, genitourinary carcinoma, thyroid carcinoma, esophageal carcinoma, myeloma, multiple myeloma, adrenal carcinoma, renal cell carcinoma, endometrial carcinoma, adrenal cortex carcinoma, malignant pancreatic insulinoma, malignant carcinoid carcinoma, choriocarcinoma, mycosis fungoides, malignant hypercalcemia, cervical hyperplasia, leukemia(e.g., acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myelogenous leukemia, chronic myelogenous leukemia, chronic granulocytic leukemia, acute granulocytic leukemia, hairy cell leukemia), neuroblastoma, rhabdomyosarcoma, Kaposi's sarcoma, polycythemia vera, essential thrombocytosis, Hodgkin's disease, non-Hodgkin's lymphoma, soft-tissue sarcoma, osteogenic sarcoma, primary macroglobulinemia, and retinoblastoma.

[0080] In some embodiments, the identified gene for modulation is selected from the group consisting of 4-1BB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP 1 A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD100, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHN1, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CMKLR1, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR2B, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRB1, HLADRB6, EFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRD1, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRAT1, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831. In some embodiments, the identified gene for modulation is selected from the group consisting of LAYN, TTYH3, CADM1, SIRPG, ENPP5, CD109, CD300A, FCRL6, KIRDL2, TSPAN-6, CD72, BTNL8, BTN3A1, BTN2A2.

[0081] In some embodiments, the identified gene for modulation is selected from the group consisting of 4-1BB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP 1 A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADM1, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD100, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHN1, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CMKLR1, CRTAM, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR2B, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPA1, HLADPB1, HLADQA1, HLADQA2, HLADRA, HLADRBl, HLADRB6, IFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRDl, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADL1, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831. In some embodiments, the identified gene for modulation is selected from the group consisting of LAYN, TTUΉ3, CADMl, SIRPG, ENPP5, CD109, CD300A, FCRL6, KIRDL2, TSPAN-6, CD72, BTNL8, BTN3A1, BTN2A2.

[0082] In some aspects, the agent for treatment can be an anti-4- IBB antibody, anti-A2M antibody, anti-ABCA2 antibody, anti-ABCA7 antibody, anti-ABCD2 antibody, anti- ABB antibody, anti-ACLY antibody, anti-ACVR2A antibody, anti-ADAM19 antibody, anti- ADC Y3 antibody, anti-ADIPOR2 antibody, anti-ADORA2A antibody, anti-AIFl antibody, anti-AN06 antibody, anti-AOAH antibody, anti-AP2Ml antibody, anti- APOBEC3D antibody, anti-APOBEC3G antibody, anti-APOBEC3H antibody, anti- APOL3 antibody, anti-ARHGAP9 antibody, anti-ATPlA3 antibody, anti-ATPlB3 antibody, anti-ATP6APl antibody, anti-ATP6VlA antibody, anti-B3GNT2 antibody, anti-BATF antibody, anti-BIN2 antibody, anti-BST2 antibody, anti-BTN2A2 antibody, anti-BTN3Al antibody, anti-BTNL8 antibody, anti-C16orf54 antibody, anti-Clorfl62 antibody, anti-C3ARl antibody, anti-CACNA2D2 antibody, anti-CADMl antibody, anti- CALU antibody, anti-CANTl antibody, anti-CARD16 antibody, anti-CASDl antibody, anti-CCRl antibody, anti-CCR2 antibody, anti-CD 100 antibody, anti-CD 109 antibody, anti-CD151 antibody, anti-CD 160 antibody, anti-CD2 antibody, anti-CD200Rl antibody, anti-CD27 antibody, anti-CD300A antibody, anti-CD37 antibody, anti-CD38 antibody, anti-CD3D antibody, anti-CD3G antibody, anti-CD40LG antibody, anti-CD47 antibody, anti-CD5 antibody, anti-CD52 antibody, anti-CD55 antibody, anti-CD58 antibody, anti- CD59 antibody, anti-CD6 antibody, anti-CD72 antibody, anti-CD8 antibody, anti-CD80 antibody, anti-CD82 antibody, anti-CD84 antibody, anti-CD86 antibody, anti-CD8B antibody, anti-CD96 antibody, anti-CEACAM21 antibody, anti-CELSR3 antibody, anti- CHN1 antibody, anti-CHSTll antibody, anti-CIITA antibody, anti-CLDN7 antibody, anti-CLEC7A antibody, anti-CLECLl antibody, anti-CLICl antibody, anti-CLSTN3 antibody, anti-CMKLRl antibody, anti-CSRPl antibody, anti-CST7 antibody, anti-CTSA antibody, anti-CTSB antibody, anti-CTSC antibody, anti-CTSD antibody, anti-CTSW antibody, anti-CX3CRl antibody, anti-CXCL13 antibody, anti-CYSLTRl antibody, anti- DCBLD1 antibody, anti-DOCKlO antibody, anti-DPEP2 antibody, anti-DPP4 antibody, anti-DSE antibody, anti-EDEM3 antibody, anti-ENOl antibody, anti-ENPP5 antibody, anti-EPHAl antibody, anti-EPHA4 antibody, anti-EPOR antibody, anti-EPSTIl antibody, anti-ERMAP antibody, anti-ERVK131 antibody, anti-ETV7 antibody, anti-F2R antibody, anti-FAM174B antibody, anti-FASLG antibody, anti-FCGR2B antibody, anti-FCGR3A antibody, anti-FCRL3 antibody, anti-FCRL6 antibody, anti-FGR antibody, anti-GALNTl antibody, anti-GART antibody, anti-GBP 1 antibody, anti-GBP4 antibody, anti-GBP5 antibody, anti-GFIl antibody, anti-GEVLAPl antibody, anti-GEVLAP2 antibody, anti- GEMAP4 antibody, anti-GEMAP6 antibody, anti-GEMAP7 antibody, anti-GEMAP8 antibody, anti-GLIPRl antibody, anti-GOLMl antibody, anti-GPR137 antibody, anti- GPR183 antibody, anti-GPR19 antibody, anti-GPR22 antibody, anti-GPR25 antibody, anti-GPR68 antibody, anti-GPR85 antibody, anti-GPSM3 antibody, anti-GZMA antibody, anti-GZMH antibody, anti-GZMK antibody, anti-HAPLN3 antibody, anti- HCRTR1 antibody, anti-HCST antibody, anti-HEGl antibody, anti-HEXB antibody, anti- HLADMA antibody, anti-HLADMB antibody, anti-HLADOA antibody, anti-HLADPAl antibody, anti-HLADPBl antibody, anti-HLADQAl antibody, anti-HLADQA2 antibody, anti-HLADRA antibody, anti-HLADRBl antibody, anti-HLADRB6 antibody, anti-EFI30 antibody, anti-IFNAR2 antibody, anti-IFNGRl antibody, anti-IFNLRl antibody, anti- IGFLR1 antibody, anti-IKZFl antibody, anti-IKZF3 antibody, anti-ILlORB antibody, anti-ILURA antibody, anti-IL12RBl antibody, anti-IL16 antibody, anti-IL18BP antibody, anti-IL18RAP antibody, anti-IL21R antibody, anti-IL2RB antibody, anti- IL2RG antibody, anti-IL4R antibody, anti-ITGAl antibody, anti-ITGA2 antibody, anti- ITGA6 antibody, anti-ITGAX antibody, anti-ITGBl antibody, anti-ITPRIP antibody, anti-JAK2 antibody, anti-JAKMEPl antibody, anti-KCNK5 antibody, anti-KIR2DLl antibody, anti-KIR2DL3 antibody, anti-KIR2DL4 antibody, anti-KIR2DS4 antibody, anti-KER3DLl antibody, anti-KIR3DL2 antibody, anti-KIRDL2 antibody, anti-KLRBl antibody, anti-KLRC2 antibody, anti-KLRC3 antibody, anti-KLRC4 antibody, anti- KLRD1 antibody, anti-KLRGl antibody, anti-LAG3 antibody, anti-LAIRl antibody, anti-LAT2 antibody, anti-LAYN antibody, anti-LDLR antibody, anti-LDLRAD4 antibody, anti-LGR6 antibody, anti-LPXN antibody, anti-LRPAPl antibody, anti-LSPl antibody, anti-LSTl antibody, anti-LTA antibody, anti-LTB4R antibody, anti-LY6E antibody, anti-LY9 antibody, anti-LYSMD3 antibody, anti-MFGE8 antibody, anti- MFSD12 antibody, anti-MFSD8 antibody, anti-MGAT4B antibody, anti-MER155HG antibody, anti-MMP25 antibody, anti-MS4Al antibody, anti-MS4A6A antibody, anti- NAALADLl antibody, anti-NCFIB antibody, anti-NCF4 antibody, anti-NCRl antibody, anti-NCSTN antibody, anti-NKG7 antibody, anti-NLRC3 antibody, anti-NOP56 antibody, anti-NOTCHl antibody, anti-NRROS antibody, anti-NTRKl antibody, anti- OPRM1 antibody, anti-P2RX7 antibody, anti-P2RYll antibody, anti-PAM antibody, anti-PHEX antibody, anti-PLEKH02 antibody, anti-PLTP antibody, anti-PLXDCl antibody, anti-PLXNDl antibody, anti-POFUT2 antibody, anti-POLRIA antibody, anti- PON2 antibody, anti-PRSS23 antibody, anti-PSMB9 antibody, anti-PSTPIPl antibody, anti-PTCHl antibody, anti-PTGDR antibody, anti-PTGER4 antibody, anti-PTPN22 antibody, anti-PTPN6 antibody, anti-PTPN7 antibody, anti-PTPRCAP antibody, anti- PTPRJ antibody, anti-PTPRM antibody, anti-PTPRN2 antibody, anti-PYHINl antibody, anti-RABllFIPl antibody, anti-RASSF5 antibody, anti-RGL4 antibody, anti-RPL17 antibody, anti-RPL18 antibody, anti-RPL21 antibody, anti-RPL27A antibody, anti-RPL3 antibody, anti-RPL32 antibody, anti-RPL34 antibody, anti-RPL35 antibody, anti- RPL35A antibody, anti-RPL4 antibody, anti-RPL9 antibody, anti-RPLP2 antibody, anti- RPS23 antibody, anti-RPS8 antibody, anti-RUNX3 antibody, anti-SIPRI antibody, anti- S1PR4 antibody, anti-S!PR5 antibody, anti-SCPEPl antibody, anti-SELPLG antibody, anti-SEMA4A antibody, anti-SERINC5 antibody, anti-SH2DlA antibody, anti-SH2D2A antibody, anti-SIGIRR antibody, anti-SIRPG antibody, anti-SLA antibody, anti-SLA2 antibody, anti-SLClA5 antibody, anti-SLC29A3 antibody, anti-SLC36A4 antibody, anti- SLC39A14 antibody, anti-SLC39A4 antibody, anti-SLC39A6 antibody, anti-SLC39A8 antibody, anti-SLC3Al antibody, anti-SLC3A2 antibody, anti-SLC41Al antibody, anti- SLC41A3 antibody, anti-SLC43Al antibody, anti-SLC43A3 antibody, anti-SLC4A2 antibody, anti-SLC4A5 antibody, anti-SLC5A3 antibody, anti-SLC7A5 antibody, anti- SLC9A1 antibody, anti-SORLl antibody, anti-SPPL2A antibody, anti-SRGN antibody, anti-ST8SIAl antibody, anti-STT3B antibody, anti-SUCO antibody, anti-TAPl antibody, anti-TBX21 antibody, anti-TCTNl antibody, anti-TENMl antibody, anti-TGFBl antibody, anti-TGFBR2 antibody, anti-THEMIS antibody, anti-TIMD4 antibody, anti- TM9SF4 antibody, anti-TMC8 antibody, anti-TMEM104 antibody, anti-TMEM106B antibody, anti-TMEM116 antibody, anti-TMEM123 antibody, anti-TMEM140 antibody, anti-TMEM154 antibody, anti-TMEM179B antibody, anti-TMEM204 antibodies, anti- TMEM219 antibody, anti-TMIGD2 antibody, anti-TMPO antibody, anti-TNFAEP8L2 antibody, anti-TNFRSFlOB antibody, anti-TNFRSFlA antibody, anti-TNFRSFIB antibody, anti-TNFSF4 antibody, anti-TNFSF9 antibody, anti-TORlAIPl antibody, anti- TOR3A antibody, anti-TP53I13 antibody, anti-TRABD2A antibody, anti-TRAF3EP3 antibody, anti-TRATl antibody, anti-TRIM28 antibody, anti-TSPAN17 antibody, anti- TSPAN18 antibody, anti-TSPAN6 antibody, anti-TTC24 antibody, anti-TTN antibody, anti-TTYH3 antibody, anti-TXNDCll antibody, anti-TXNDC15 antibody, anti-TYMP antibody, anti-U2AFl antibody, anti-UBAC2 antibody, anti-XCL2 antibody, anti-YIPFl antibody, anti-ZBPl antibody, anti-ZC3H12D antibody, anti-ZDHHC5 antibody, anti- ZNF683 antibody, anti-ZNF80 antibody, and anti-ZNF831 antibody. In some embodiments, the agent is selected from the group consisting of anti-LA YN antibody, anti-TTYH3 antibody, anti-CADMl antibody, anti-SIRPG antibody, anti-ENPP5 antibody, anti-CD109 antibody, anti-CD300A antibody, anti-FCRL6 antibody, anti- KIRDL2 antibody, anti-TSPAN-6 antibody, anti-CD72 antibody, anti-BTNL8 antibody, anti-BTN3Al antibody, and anti-BTN2A2 antibody.

[0083] In some aspects, the agent for treatment can be an anti-4- IBB antibody, anti-A2M antibody, anti-ABCA2 antibody, anti-ABCA7 antibody, anti-ABCD2 antibody, anti- ABI3 antibody, anti-ACLY antibody, anti-ACVR2A antibody, anti-ADAM19 antibody, anti- ADC Y3 antibody, anti-ADEPOR2 antibody, anti-ADORA2A antibody, anti-AEFl antibody, anti-AN06 antibody, anti-AOAH antibody, anti-AP2Ml antibody, anti- APOBEC3D antibody, anti-APOBEC3G antibody, anti-APOBEC3H antibody, anti- APOL3 antibody, anti-ARHGAP9 antibody, anti-ATPlA3 antibody, anti-ATPlB3 antibody, anti-ATP6APl antibody, anti-ATP6VlA antibody, anti-B3GNT2 antibody, anti-BATF antibody, anti-BIN2 antibody, anti-BST2 antibody, anti-BTN2A2 antibody, anti-BTN3Al antibody, anti-BTNL8 antibody, anti-C16orf54 antibody, anti-Clorfl62 antibody, anti-C3ARl antibody, anti-CACNA2D2 antibody, anti-CADMl antibody, anti- CALU antibody, anti-CANTl antibody, anti-CARD16 antibody, anti-CASDl antibody, anti-CCRl antibody, anti-CCR2 antibody, anti-CD 100 antibody, anti-CD 109 antibody, anti-CD151 antibody, anti-CD 160 antibody, anti-CD2 antibody, anti-CD200Rl antibody, anti-CD27 antibody, anti-CD300A antibody, anti-CD37 antibody, anti-CD38 antibody, anti-CD3D antibody, anti-CD3G antibody, anti-CD40LG antibody, anti-CD47 antibody, anti-CD5 antibody, anti-CD52 antibody, anti-CD55 antibody, anti-CD58 antibody, anti- CD59 antibody, anti-CD6 antibody, anti-CD72 antibody, anti-CD8 antibody, anti-CD80 antibody, anti-CD82 antibody, anti-CD84 antibody, anti-CD86 antibody, anti-CD8B antibody, anti-CD96 antibody, anti-CEACAM21 antibody, anti-CELSR3 antibody, anti- CHN1 antibody, anti-CHSTll antibody, anti-CIITA antibody, anti-CLDN7 antibody, anti-CLEC7A antibody, anti-CLECLl antibody, anti-CLICl antibody, anti-CLSTN3 antibody, anti-CMKLRl antibody, anti-CRTAM antibody, anti-CSRPl antibody, anti- CST7 antibody, anti-CTSA antibody, anti-CTSB antibody, anti-CTSC antibody, anti- CTSD antibody, anti-CTSW antibody, anti-CX3CRl antibody, anti-CXCL13 antibody, anti-CYSLTRl antibody, anti-DCBLDl antibody, anti-DOCKlO antibody, anti-DPEP2 antibody, anti-DPP4 antibody, anti-DSE antibody, anti-EDEM3 antibody, anti-ENOl antibody, anti-ENPP5 antibody, anti-EPHAl antibody, anti-EPHA4 antibody, anti-EPOR antibody, anti-EPSTIl antibody, anti-ERMAP antibody, anti-ERVK131 antibody, anti- ETV7 antibody, anti-F2R antibody, anti-FAM174B antibody, anti-FASLG antibody, anti- FCGR2B antibody, anti-FCGR3A antibody, anti-FCRL3 antibody, anti-FCRL6 antibody, anti-FGR antibody, anti-GALNTl antibody, anti-GART antibody, anti-GBP 1 antibody, anti-GBP4 antibody, anti-GBP5 antibody, anti-GFIl antibody, anti-GIMAPl antibody, anti-GEMAP2 antibody, anti-GEMAP4 antibody, anti-GEMAP6 antibody, anti-GEMAP7 antibody, anti-GIMAP8 antibody, anti-GLIPRl antibody, anti-GOLMl antibody, anti- GPR137 antibody, anti-GPR183 antibody, anti-GPR19 antibody, anti-GPR22 antibody, anti-GPR25 antibody, anti-GPR68 antibody, anti-GPR85 antibody, anti-GPSM3 antibody, anti-GZMA antibody, anti-GZMH antibody, anti-GZMK antibody, anti- HAPLN3 antibody, anti-HCRTRl antibody, anti-HCST antibody, anti-HEGl antibody, anti-HEXB antibody, anti-HLADMA antibody, anti-HLADMB antibody, anti-HLADOA antibody, anti-HLADPAl antibody, anti-HLADPBl antibody, anti-HLADQAl antibody, anti-HLADQA2 antibody, anti-HLADRA antibody, anti-HLADRBl antibody, anti- HLADRB6 antibody, anti-IFI30 antibody, anti-IFNAR2 antibody, anti-IFNGRl antibody, anti-IFNLRl antibody, anti-IGFLRl antibody, anti-EKZFl antibody, anti-EKZF3 antibody, anti-ILlORB antibody, anti-ILllRA antibody, anti-IL12RBl antibody, anti- IL16 antibody, anti-IL18BP antibody, anti-IL18RAP antibody, anti-IL21R antibody, anti- IL2RB antibody, anti-IL2RG antibody, anti-IL4R antibody, anti-ITGAl antibody, anti- ITGA2 antibody, anti-ITGA6 antibody, anti-ITGAX antibody, anti-ITGBl antibody, anti- ITPRIP antibody, anti-JAK2 antibody, anti-JAKMIPl antibody, anti-KCNK5 antibody, anti-KER2DLl antibody, anti-KIR2DL3 antibody, anti-KIR2DL4 antibody, anti- KIR2DS4 antibody, anti-KIR3DLl antibody, anti-KIR3DL2 antibody, anti-KERDL2 antibody, anti-KLRBl antibody, anti-KLRC2 antibody, anti-KLRC3 antibody, anti- KLRC4 antibody, anti-KLRDl antibody, anti-KLRGl antibody, anti-LAG3 antibody, anti-LAERl antibody, anti-LAT2 antibody, anti-LAYN antibody, anti-LDLR antibody, anti-LDLRAD4 antibody, anti-LGR6 antibody, anti-LPXN antibody, anti-LRPAPl antibody, anti-LSPl antibody, anti-LSTl antibody, anti-LTA antibody, anti-LTB4R antibody, anti-LY6E antibody, anti-LY9 antibody, anti-LYSMD3 antibody, anti-MFGE8 antibody, anti-MFSD12 antibody, anti-MFSD8 antibody, anti-MGAT4B antibody, anti- MER155HG antibody, anti-MMP25 antibody, anti-MS4Al antibody, anti-MS4A6A antibody, anti-NAALADLl antibody, anti-NCFIB antibody, anti-NCF4 antibody, anti- NCR1 antibody, anti-NCSTN antibody, anti-NKG7 antibody, anti-NLRC3 antibody, anti- NOP56 antibody, anti-NOTCHl antibody, anti-NRROS antibody, anti-NTRKl antibody, anti-OPRMl antibody, anti-P2RX7 antibody, anti-P2RYll antibody, anti-PAM antibody, anti-PHEX antibody, anti-PLEKH02 antibody, anti-PLTP antibody, anti-PLXDCl antibody, anti-PLXNDl antibody, anti-POFUT2 antibody, anti-POLRIA antibody, anti- PON2 antibody, anti-PRSS23 antibody, anti-PSMB9 antibody, anti-PSTPIPl antibody, anti-PTCHl antibody, anti-PTGDR antibody, anti-PTGER4 antibody, anti-PTPN22 antibody, anti-PTPN6 antibody, anti-PTPN7 antibody, anti-PTPRCAP antibody, anti- PTPRJ antibody, anti-PTPRM antibody, anti-PTPRN2 antibody, anti-PYHINl antibody, anti-RABllFIPl antibody, anti-RASSF5 antibody, anti-RGL4 antibody, anti-RPL17 antibody, anti-RPL18 antibody, anti-RPL21 antibody, anti-RPL27A antibody, anti-RPL3 antibody, anti-RPL32 antibody, anti-RPL34 antibody, anti-RPL35 antibody, anti- RPL35A antibody, anti-RPL4 antibody, anti-RPL9 antibody, anti-RPLP2 antibody, anti- RPS23 antibody, anti-RPS8 antibody, anti-RUNX3 antibody, anti-SIPRI antibody, anti- S1PR4 antibody, anti-SlPR5 antibody, anti-SCPEPl antibody, anti-SELPLG antibody, anti-SEMA4A antibody, anti-SERINC5 antibody, anti-SH2DlA antibody, anti-SH2D2A antibody, anti-SIGIRR antibody, anti-SIRPG antibody, anti-SLA antibody, anti-SLA2 antibody, anti-SLClA5 antibody, anti-SLC29A3 antibody, anti-SLC36A4 antibody, anti- SLC39A14 antibody, anti-SLC39A4 antibody, anti-SLC39A6 antibody, anti-SLC39A8 antibody, anti-SLC3Al antibody, anti-SLC3A2 antibody, anti-SLC41Al antibody, anti- SLC41A3 antibody, anti-SLC43Al antibody, anti-SLC43A3 antibody, anti-SLC4A2 antibody, anti-SLC4A5 antibody, anti-SLC5A3 antibody, anti-SLC7A5 antibody, anti- SLC9A1 antibody, anti-SORLl antibody, anti-SPPL2A antibody, anti-SRGN antibody, anti-ST8SIAl antibody, anti-STT3B antibody, anti-SUCO antibody, anti-TAPl antibody, anti-TBX21 antibody, anti-TCTNl antibody, anti-TENMl antibody, anti-TGFBl antibody, anti-TGFBR2 antibody, anti-THEMIS antibody, anti-TIMD4 antibody, anti- TM9SF4 antibody, anti-TMC8 antibody, anti-TMEM104 antibody, anti-TMEM106B antibody, anti-TMEM116 antibody, anti-TMEM123 antibody, anti-TMEM140 antibody, anti-TMEM154 antibody, anti-TMEM179B antibody, anti-TMEM204 antibodies, anti- TMEM219 antibody, anti-TMIGD2 antibody, anti-TMPO antibody, anti-TNFAEP8L2 antibody, anti-TNFRSFlOB antibody, anti-TNFRSFlA antibody, anti-TNFRSFIB antibody, anti-TNFSF4 antibody, anti-TNFSF9 antibody, anti-TORlAIPl antibody, anti- TOR3A antibody, anti-TP53I13 antibody, anti-TRABD2A antibody, anti-TRAF3EP3 antibody, anti-TRATl antibody, anti-TRIM28 antibody, anti-TSPAN17 antibody, anti- TSPAN18 antibody, anti-TSPAN6 antibody, anti-TTC24 antibody, anti-TTN antibody, anti-TTYH3 antibody, anti-TXNDCll antibody, anti-TXNDC15 antibody, anti-TYMP antibody, anti-U2AFl antibody, anti-UBAC2 antibody, anti-XCL2 antibody, anti-YIPFl antibody, anti-ZBPl antibody, anti-ZC3H12D antibody, anti-ZDHHC5 antibody, anti- ZNF683 antibody, anti-ZNF80 antibody, and anti-ZNF831 antibody. In some embodiments, the agent is selected from the group consisting of anti-LA YN antibody, anti-TTYH3 antibody, anti-CADMl antibody, anti-SIRPG antibody, anti-ENPP5 antibody, anti-CD109 antibody, anti-CD300A antibody, anti-FCRL6 antibody, anti- KIRDL2 antibody, anti-TSPAN-6 antibody, anti-CD72 antibody, anti-BTNL8 antibody, anti-BTN3Al antibody, and anti-BTN2A2 antibody.

[0084] Alternatively, the agent for treatment may belong to any one of a variety of treatment paradigms such as cell therapy, gene therapy, small molecule, etc. Although in silico target lists may preferentially include interrogated genes that lack associated therapies, it is possible that methods described herein identify a target which is not yet approved for treatment with an approved agent. In such an instance, the target may represent a novel target for the agent, but the agent may still be used for treatment. Alternatively, or additionally, an approved agent may target a related biological process category of a target, where the agent may still be efficacious in treatment as the agent modulates an upstream and/or downstream gene product related to the target in biological pathway.

[0085] Methods of treating cancer by identifying a gene for modulation by interrogating genes preferentially expressed with dysfunctional T cells may include methods for interrogating genes and/or generating in silico target lists, as described above.

Methods of Treating Autoimmune Diseases or Cancer by Identifying Targets Preferentially Expressed by Effector T Cells and Not by Dysfunctional T Cells or Trees

[0086] In some aspects a method for treating autoimmune diseases or cancer includes identifying a gene for modulation by interrogating genes preferentially expressed by T effectors and not by dysfunctional T cells or regulatory T cells (Tregs) with a transcriptomic database, as described above, and administering to a human an agent that modulates said gene. In some embodiments, the genes preferentially expressed by T effector cells are selected from the group consisting of CD8, LAG3, 4-1BB, and a combination thereof. In some embodiments, the transcriptomic database is a single cell RNA-sequencing (sc-RNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof. In some embodiments, the transcriptomic database is a cancer genome atlas (TCGA) database. In some embodiments, the agent is an antibody.

[0087] The methods described herein may be used to treat any autoimmune disease, such as, for example, multiple sclerosis, lupus, idiopathic pulmonary fibrosis, inflammatory bowel disease (e.g., Crohn’s disease, ulcerative colitis, collagen colitis), celiac disease, arthritis (e.g. rheumatoid arthritis, psoriatic arthritis), Guillain-Barre syndrome, vasculitis (e.g., Takayasu’s arteritis, giant cell arteritis, microscopic polyangiitis, rheumatoid vasculitis, granulomatosis with polyangiitis), systemic sclerosis, type I diabetes, Addison’s disease, alopecia areata, autoimmune skin disorders (e.g. psoriasis, atopic dermatitis, eczema, pemphigus vulgaris, bullous pemphigoid, seborrheic dermatitis), dermatomyositis, myositis, neuromyelitis optica, allergies, Behcet’s disease, Hashimoto thyroiditis, autoimmune hepatitis, primary sclerosing cholangitis, primary biliary cirrhosis, idiopathic inflammatory myopathy, polymyositis, myasthenia gravis, rheumatic fever, septic shock, Sjogren’s syndrome, interstitial lung disease, Systemic sclerosis, Inflammatory bowel disease (including ulcerative colitis and Crohn’s disease), Systemic lupus erythematosus, Type 1 diabetes, Multiple sclerosis. Other immune mediated process considered herein to be included in autoimmune diseases include: Organ transplant rejection and graft versus host disease (GVHD).

[0088] The methods described herein may be used to treat any type of cancer, for example, breast cancer, prostate cancer, lymphoma, skin cancer, pancreatic cancer, colon cancer, melanoma, malignant melanoma, ovarian cancer, brain cancer, primary brain carcinoma, head-neck cancer, glioma, glioblastoma, liver cancer, bladder cancer, nonsmall cell lung cancer, head or neck carcinoma, breast carcinoma, ovarian carcinoma, lung carcinoma, small-cell lung carcinoma, Wilms' tumor, cervical carcinoma, testicular carcinoma, bladder carcinoma, pancreatic carcinoma, stomach carcinoma, colon carcinoma, prostatic carcinoma, genitourinary carcinoma, thyroid carcinoma, esophageal carcinoma, myeloma, multiple myeloma, adrenal carcinoma, renal cell carcinoma, endometrial carcinoma, adrenal cortex carcinoma, malignant pancreatic insulinoma, malignant carcinoid carcinoma, choriocarcinoma, mycosis fungoides, malignant hypercalcemia, cervical hyperplasia, leukemia(e.g., acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myelogenous leukemia, chronic myelogenous leukemia, chronic granulocytic leukemia, acute granulocytic leukemia, hairy cell leukemia), neuroblastoma, rhabdomyosarcoma, Kaposi's sarcoma, polycythemia vera, essential thrombocytosis, Hodgkin's disease, non-Hodgkin's lymphoma, soft-tissue sarcoma, osteogenic sarcoma, primary macroglobulinemia, and retinoblastoma.

[0089] In some embodiments, the identified gene for modulation is selected from the group consisting of 4-1BB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP 1 A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD100, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHN1, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CMKLR1, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR2B, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAP1, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRBl, HLADRB6, EFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRDl, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

[0090] In some embodiments, the identified gene for modulation is selected from the group consisting of 4-1BB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP 1 A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD100, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHN1, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CMKLR1, CRTAM, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR2B, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAPl, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRBl, HLADRB6, IFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRD1, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

[0091] In some aspects, the agent for treatment can be an anti-4- IBB antibody, anti-A2M antibody, anti-ABCA2 antibody, anti-ABCA7 antibody, anti-ABCD2 antibody, anti- ABB antibody, anti-ACLY antibody, anti-ACVR2A antibody, anti-ADAM19 antibody, anti- ADC Y3 antibody, anti-ADIPOR2 antibody, anti-ADORA2A antibody, anti-AIFl antibody, anti-ANOό antibody, anti-AOAH antibody, anti-AP2Ml antibody, anti- APOBEC3D antibody, anti-APOBEC3G antibody, anti-APOBEC3H antibody, anti- APOL3 antibody, anti-ARHGAP9 antibody, anti-ATPlA3 antibody, anti-ATPlB3 antibody, anti-ATP6APl antibody, anti-ATP6VlA antibody, anti-B3GNT2 antibody, anti-BATF antibody, anti-BIN2 antibody, anti-BST2 antibody, anti-BTN2A2 antibody, anti-BTN3Al antibody, anti-BTNL8 antibody, anti-C16orf54 antibody, anti-Clorfl62 antibody, anti-C3ARl antibody, anti-CACNA2D2 antibody, anti-CADMl antibody, anti- CALU antibody, anti-CANTl antibody, anti-CARD16 antibody, anti-CASDl antibody, anti-CCRl antibody, anti-CCR2 antibody, anti-CD 100 antibody, anti-CD 109 antibody, anti-CD151 antibody, anti-CD 160 antibody, anti-CD2 antibody, anti-CD200Rl antibody, anti-CD27 antibody, anti-CD300A antibody, anti-CD37 antibody, anti-CD38 antibody, anti-CD3D antibody, anti-CD3G antibody, anti-CD40LG antibody, anti-CD47 antibody, anti-CD5 antibody, anti-CD52 antibody, anti-CD55 antibody, anti-CD58 antibody, anti- CD59 antibody, anti-CD6 antibody, anti-CD72 antibody, anti-CD8 antibody, anti-CD80 antibody, anti-CD82 antibody, anti-CD84 antibody, anti-CD86 antibody, anti-CD8B antibody, anti-CD96 antibody, anti-CEACAM21 antibody, anti-CELSR3 antibody, anti- CHN1 antibody, anti-CHSTll antibody, anti-CIITA antibody, anti-CLDN7 antibody, anti-CLEC7A antibody, anti-CLECLl antibody, anti-CLICl antibody, anti-CLSTN3 antibody, anti-CMKLRl antibody, anti-CSRPl antibody, anti-CST7 antibody, anti-CTSA antibody, anti-CTSB antibody, anti-CTSC antibody, anti-CTSD antibody, anti-CTSW antibody, anti-CX3CRl antibody, anti-CXCL13 antibody, anti-CYSLTRl antibody, anti- DCBLD1 antibody, anti-DOCKlO antibody, anti-DPEP2 antibody, anti-DPP4 antibody, anti-DSE antibody, anti-EDEM3 antibody, anti-ENOl antibody, anti-ENPP5 antibody, anti-EPHAl antibody, anti-EPHA4 antibody, anti-EPOR antibody, anti-EPSTIl antibody, anti-ERMAP antibody, anti-ERVK131 antibody, anti-ETV7 antibody, anti-F2R antibody, anti-FAM174B antibody, anti-FASLG antibody, anti-FCGR2B antibody, anti-FCGR3A antibody, anti-FCRL3 antibody, anti-FCRL6 antibody, anti-FGR antibody, anti-GALNTl antibody, anti-GART antibody, anti-GBP 1 antibody, anti-GBP4 antibody, anti-GBP5 antibody, anti-GFIl antibody, anti-GEVLAPl antibody, anti-GIMAP2 antibody, anti- GEMAP4 antibody, anti-GEMAP6 antibody, anti-GEMAP7 antibody, anti-GEMAP8 antibody, anti-GLIPRl antibody, anti-GOLMl antibody, anti-GPR137 antibody, anti- GPR183 antibody, anti-GPR19 antibody, anti-GPR22 antibody, anti-GPR25 antibody, anti-GPR68 antibody, anti-GPR85 antibody, anti-GPSM3 antibody, anti-GZMA antibody, anti-GZMH antibody, anti-GZMK antibody, anti-HAPLN3 antibody, anti- HCRTR1 antibody, anti-HCST antibody, anti-HEGl antibody, anti-HEXB antibody, anti- HLADMA antibody, anti-HLADMB antibody, anti-HLADOA antibody, anti-HLADPAl antibody, anti-HLADPBl antibody, anti-HLADQAl antibody, anti-HLADQA2 antibody, anti-HLADRA antibody, anti-HLADRBl antibody, anti-HLADRB6 antibody, anti-EFI30 antibody, anti-IFNAR2 antibody, anti-IFNGRl antibody, anti-IFNLRl antibody, anti- IGFLR1 antibody, anti-IKZFl antibody, anti-IKZF3 antibody, anti-ILlORB antibody, anti-ILllRA antibody, anti-IL12RBl antibody, anti-IL16 antibody, anti-IL18BP antibody, anti-IL18RAP antibody, anti-IL21R antibody, anti-IL2RB antibody, anti- IL2RG antibody, anti-IL4R antibody, anti-ITGAl antibody, anti-ITGA2 antibody, anti- ITGA6 antibody, anti-ITGAX antibody, anti-ITGBl antibody, anti-ITPRIP antibody, anti-JAK2 antibody, anti-JAKMEPl antibody, anti-KCNK5 antibody, anti-KIR2DLl antibody, anti-KIR2DL3 antibody, anti-KIR2DL4 antibody, anti-KIR2DS4 antibody, anti-KER3DLl antibody, anti-KIR3DL2 antibody, anti-KIRDL2 antibody, anti-KLRBl antibody, anti-KLRC2 antibody, anti-KLRC3 antibody, anti-KLRC4 antibody, anti- KLRD1 antibody, anti-KLRGl antibody, anti-LAG3 antibody, anti-LAIRl antibody, anti-LAT2 antibody, anti-LAYN antibody, anti-LDLR antibody, anti-LDLRAD4 antibody, anti-LGR6 antibody, anti-LPXN antibody, anti-LRPAPl antibody, anti-LSPl antibody, anti-LSTl antibody, anti-LTA antibody, anti-LTB4R antibody, anti-LY6E antibody, anti-LY9 antibody, anti-LYSMD3 antibody, anti-MFGE8 antibody, anti- MFSD12 antibody, anti-MFSD8 antibody, anti-MGAT4B antibody, anti-MER155HG antibody, anti-MMP25 antibody, anti-MS4Al antibody, anti-MS4A6A antibody, anti- NAALADLl antibody, anti-NCFIB antibody, anti-NCF4 antibody, anti-NCRl antibody, anti-NCSTN antibody, anti-NKG7 antibody, anti-NLRC3 antibody, anti-NOP56 antibody, anti-NOTCHl antibody, anti-NRROS antibody, anti-NTRKl antibody, anti- OPRM1 antibody, anti-P2RX7 antibody, anti-P2RYll antibody, anti-PAM antibody, anti-PHEX antibody, anti-PLEKH02 antibody, anti-PLTP antibody, anti-PLXDCl antibody, anti-PLXNDl antibody, anti-POFUT2 antibody, anti-POLRIA antibody, anti- PON2 antibody, anti-PRSS23 antibody, anti-PSMB9 antibody, anti-PSTPIPl antibody, anti-PTCHl antibody, anti-PTGDR antibody, anti-PTGER4 antibody, anti-PTPN22 antibody, anti-PTPN6 antibody, anti-PTPN7 antibody, anti-PTPRCAP antibody, anti- PTPRJ antibody, anti-PTPRM antibody, anti-PTPRN2 antibody, anti-PYHINl antibody, anti-RABllFIPl antibody, anti-RASSF5 antibody, anti-RGL4 antibody, anti-RPL17 antibody, anti-RPL18 antibody, anti-RPL21 antibody, anti-RPL27A antibody, anti-RPL3 antibody, anti-RPL32 antibody, anti-RPL34 antibody, anti-RPL35 antibody, anti- RPL35A antibody, anti-RPL4 antibody, anti-RPL9 antibody, anti-RPLP2 antibody, anti- RPS23 antibody, anti-RPS8 antibody, anti-RUNX3 antibody, anti-SIPRI antibody, anti- S1PR4 antibody, anti-SlPR5 antibody, anti-SCPEPl antibody, anti-SELPLG antibody, anti-SEMA4A antibody, anti-SERINC5 antibody, anti-SH2DlA antibody, anti-SH2D2A antibody, anti-SIGIRR antibody, anti-SIRPG antibody, anti-SLA antibody, anti-SLA2 antibody, anti-SLClA5 antibody, anti-SLC29A3 antibody, anti-SLC36A4 antibody, anti- SLC39A14 antibody, anti-SLC39A4 antibody, anti-SLC39A6 antibody, anti-SLC39A8 antibody, anti-SLC3Al antibody, anti-SLC3A2 antibody, anti-SLC41Al antibody, anti- SLC41A3 antibody, anti-SLC43Al antibody, anti-SLC43A3 antibody, anti-SLC4A2 antibody, anti-SLC4A5 antibody, anti-SLC5A3 antibody, anti-SLC7A5 antibody, anti- SLC9A1 antibody, anti-SORLl antibody, anti-SPPL2A antibody, anti-SRGN antibody, anti-ST8SIAl antibody, anti-STT3B antibody, anti-SUCO antibody, anti-TAPl antibody, anti-TBX21 antibody, anti-TCTNl antibody, anti-TENMl antibody, anti-TGFBl antibody, anti-TGFBR2 antibody, anti-THEMIS antibody, anti-TIMD4 antibody, anti- TM9SF4 antibody, anti-TMC8 antibody, anti-TMEM104 antibody, anti-TMEM106B antibody, anti-TMEM116 antibody, anti-TMEM123 antibody, anti-TMEM140 antibody, anti-TMEM154 antibody, anti-TMEM179B antibody, anti-TMEM204 antibodies, anti- TMEM219 antibody, anti-TMIGD2 antibody, anti-TMPO antibody, anti-TNFAEP8L2 antibody, anti-TNFRSFlOB antibody, anti-TNFRSFlA antibody, anti-TNFRSFIB antibody, anti-TNFSF4 antibody, anti-TNFSF9 antibody, anti-TORlAIPl antibody, anti- TOR3A antibody, anti-TP53I13 antibody, anti-TRABD2A antibody, anti-TRAF3EP3 antibody, anti-TRATl antibody, anti-TRIM28 antibody, anti-TSPAN17 antibody, anti- TSPAN18 antibody, anti-TSPAN6 antibody, anti-TTC24 antibody, anti-TTN antibody, anti-TTYH3 antibody, anti-TXNDCll antibody, anti-TXNDC15 antibody, anti-TYMP antibody, anti-U2AFl antibody, anti-UBAC2 antibody, anti-XCL2 antibody, anti-YIPFl antibody, anti-ZBPl antibody, anti-ZC3H12D antibody, anti-ZDHHC5 antibody, anti- ZNF683 antibody, anti-ZNF80 antibody, and anti-ZNF831 antibody. Alternatively, the agent for treatment may belong to any one of a variety of treatment paradigms such as cell therapy, gene therapy, siRNA, small molecule, etc. Although in silico target lists may preferentially include interrogated genes that lack associated therapies, it is possible that methods described herein identify a target which is not yet approved for treatment with an approved agent. In such an instance, the target may represent a novel target for the agent, but the agent may still be used for treatment. Alternatively, or additionally, an approved agent may target a related biological process category of a target, where the agent may still be efficacious in treatment as the agent modulates an upstream and/or downstream gene product related to the target in biological pathway.

[0092] In some aspects, the agent for treatment can be an anti-4- IBB antibody, anti-A2M antibody, anti-ABCA2 antibody, anti-ABCA7 antibody, anti-ABCD2 antibody, anti- ABB antibody, anti-ACLY antibody, anti-ACVR2A antibody, anti-ADAM19 antibody, anti- ADC Y3 antibody, anti-ADIPOR2 antibody, anti-ADORA2A antibody, anti-AIFl antibody, anti-AN06 antibody, anti-AOAH antibody, anti-AP2Ml antibody, anti- APOBEC3D antibody, anti-APOBEC3G antibody, anti-APOBEC3H antibody, anti- APOL3 antibody, anti-ARHGAP9 antibody, anti-ATPlA3 antibody, anti-ATPlB3 antibody, anti-ATP6APl antibody, anti-ATP6VlA antibody, anti-B3GNT2 antibody, anti-BATF antibody, anti-BIN2 antibody, anti-BST2 antibody, anti-BTN2A2 antibody, anti-BTN3Al antibody, anti-BTNL8 antibody, anti-C16orf54 antibody, anti-Clorfl62 antibody, anti-C3ARl antibody, anti-CACNA2D2 antibody, anti-CADMl antibody, anti- CALU antibody, anti-CANTl antibody, anti-CARD16 antibody, anti-CASDl antibody, anti-CCRl antibody, anti-CCR2 antibody, anti-CD 100 antibody, anti-CD 109 antibody, anti-CD151 antibody, anti-CD 160 antibody, anti-CD2 antibody, anti-CD200Rl antibody, anti-CD27 antibody, anti-CD300A antibody, anti-CD37 antibody, anti-CD38 antibody, anti-CD3D antibody, anti-CD3G antibody, anti-CD40LG antibody, anti-CD47 antibody, anti-CD5 antibody, anti-CD52 antibody, anti-CD55 antibody, anti-CD58 antibody, anti- CD59 antibody, anti-CD6 antibody, anti-CD72 antibody, anti-CD8 antibody, anti-CD80 antibody, anti-CD82 antibody, anti-CD84 antibody, anti-CD86 antibody, anti-CD8B antibody, anti-CD96 antibody, anti-CEACAM21 antibody, anti-CELSR3 antibody, anti- CHN1 antibody, anti-CHSTll antibody, anti-CIITA antibody, anti-CLDN7 antibody, anti-CLEC7A antibody, anti-CLECLl antibody, anti-CLICl antibody, anti-CLSTN3 antibody, anti-CMKLRl antibody, anti-CRTAM antibody, anti-CSRPl antibody, anti- CST7 antibody, anti-CTSA antibody, anti-CTSB antibody, anti-CTSC antibody, anti- CTSD antibody, anti-CTSW antibody, anti-CX3CRl antibody, anti-CXCL13 antibody, anti-CYSLTRl antibody, anti-DCBLDl antibody, anti-DOCKlO antibody, anti-DPEP2 antibody, anti-DPP4 antibody, anti-DSE antibody, anti-EDEM3 antibody, anti-ENOl antibody, anti-ENPP5 antibody, anti-EPHAl antibody, anti-EPHA4 antibody, anti-EPOR antibody, anti-EPSTIl antibody, anti-ERMAP antibody, anti-ERVK131 antibody, anti- ETV7 antibody, anti-F2R antibody, anti-FAM174B antibody, anti-FASLG antibody, anti- FCGR2B antibody, anti-FCGR3A antibody, anti-FCRL3 antibody, anti-FCRL6 antibody, anti-FGR antibody, anti-GALNTl antibody, anti-GART antibody, anti-GBP 1 antibody, anti-GBP4 antibody, anti-GBP5 antibody, anti-GFIl antibody, anti-GIMAPl antibody, anti-GEMAP2 antibody, anti-GEMAP4 antibody, anti-GEMAP6 antibody, anti-GEMAP7 antibody, anti-GIMAP8 antibody, anti-GLIPRl antibody, anti-GOLMl antibody, anti- GPR137 antibody, anti-GPR183 antibody, anti-GPR19 antibody, anti-GPR22 antibody, anti-GPR25 antibody, anti-GPR68 antibody, anti-GPR85 antibody, anti-GPSM3 antibody, anti-GZMA antibody, anti-GZMH antibody, anti-GZMK antibody, anti- HAPLN3 antibody, anti-HCRTRl antibody, anti-HCST antibody, anti-HEGl antibody, anti-HEXB antibody, anti-HLADMA antibody, anti-HLADMB antibody, anti-HLADOA antibody, anti-HLADPAl antibody, anti-HLADPBl antibody, anti-HLADQAl antibody, anti-HLADQA2 antibody, anti-HLADRA antibody, anti-HLADRBl antibody, anti- HLADRB6 antibody, anti-IFI30 antibody, anti-IFNAR2 antibody, anti-IFNGRl antibody, anti-EFNLRl antibody, anti-IGFLRl antibody, anti-EKZFl antibody, anti-EKZF3 antibody, anti-ILlORB antibody, anti-ILllRA antibody, anti-IL12RBl antibody, anti- IL16 antibody, anti-IL18BP antibody, anti-IL18RAP antibody, anti-IL21R antibody, anti- IL2RB antibody, anti-IL2RG antibody, anti-IL4R antibody, anti-ITGAl antibody, anti- ITGA2 antibody, anti-ITGA6 antibody, anti-ITGAX antibody, anti-ITGBl antibody, anti- ITPRIP antibody, anti-JAK2 antibody, anti-JAKMIPl antibody, anti-KCNK5 antibody, anti-KER2DLl antibody, anti-KIR2DL3 antibody, anti-KIR2DL4 antibody, anti- KIR2DS4 antibody, anti-KIR3DLl antibody, anti-KIR3DL2 antibody, anti-KERDL2 antibody, anti-KLRBl antibody, anti-KLRC2 antibody, anti-KLRC3 antibody, anti- KLRC4 antibody, anti-KLRDl antibody, anti-KLRGl antibody, anti-LAG3 antibody, anti-LAERl antibody, anti-LAT2 antibody, anti-LAYN antibody, anti-LDLR antibody, anti-LDLRAD4 antibody, anti-LGR6 antibody, anti-LPXN antibody, anti-LRPAPl antibody, anti-LSPl antibody, anti-LSTl antibody, anti-LTA antibody, anti-LTB4R antibody, anti-LY6E antibody, anti-LY9 antibody, anti-LYSMD3 antibody, anti-MFGE8 antibody, anti-MFSD12 antibody, anti-MFSD8 antibody, anti-MGAT4B antibody, anti- MER155HG antibody, anti-MMP25 antibody, anti-MS4Al antibody, anti-MS4A6A antibody, anti-NAALADLl antibody, anti-NCFIB antibody, anti-NCF4 antibody, anti- NCR1 antibody, anti-NCSTN antibody, anti-NKG7 antibody, anti-NLRC3 antibody, anti- NOP56 antibody, anti-NOTCHl antibody, anti-NRROS antibody, anti-NTRKl antibody, anti-OPRMl antibody, anti-P2RX7 antibody, anti-P2RYll antibody, anti-PAM antibody, anti-PHEX antibody, anti-PLEKH02 antibody, anti-PLTP antibody, anti-PLXDCl antibody, anti-PLXNDl antibody, anti-POFUT2 antibody, anti-POLRIA antibody, anti- PON2 antibody, anti-PRSS23 antibody, anti-PSMB9 antibody, anti-PSTPIPl antibody, anti-PTCHl antibody, anti-PTGDR antibody, anti-PTGER4 antibody, anti-PTPN22 antibody, anti-PTPN6 antibody, anti-PTPN7 antibody, anti-PTPRCAP antibody, anti- PTPRJ antibody, anti-PTPRM antibody, anti-PTPRN2 antibody, anti-PYHINl antibody, anti-RABllFIPl antibody, anti-RASSF5 antibody, anti-RGL4 antibody, anti-RPL17 antibody, anti-RPL18 antibody, anti-RPL21 antibody, anti-RPL27A antibody, anti-RPL3 antibody, anti-RPL32 antibody, anti-RPL34 antibody, anti-RPL35 antibody, anti- RPL35A antibody, anti-RPL4 antibody, anti-RPL9 antibody, anti-RPLP2 antibody, anti- RPS23 antibody, anti-RPS8 antibody, anti-RUNX3 antibody, anti-SIPRI antibody, anti- S1PR4 antibody, anti-SlPR5 antibody, anti-SCPEPl antibody, anti-SELPLG antibody, anti-SEMA4A antibody, anti-SERINC5 antibody, anti-SH2DlA antibody, anti-SH2D2A antibody, anti-SIGIRR antibody, anti-SIRPG antibody, anti-SLA antibody, anti-SLA2 antibody, anti-SLClA5 antibody, anti-SLC29A3 antibody, anti-SLC36A4 antibody, anti- SLC39A14 antibody, anti-SLC39A4 antibody, anti-SLC39A6 antibody, anti-SLC39A8 antibody, anti-SLC3Al antibody, anti-SLC3A2 antibody, anti-SLC41Al antibody, anti- SLC41A3 antibody, anti-SLC43Al antibody, anti-SLC43A3 antibody, anti-SLC4A2 antibody, anti-SLC4A5 antibody, anti-SLC5A3 antibody, anti-SLC7A5 antibody, anti- SLC9A1 antibody, anti-SORLl antibody, anti-SPPL2A antibody, anti-SRGN antibody, anti-ST8SIAl antibody, anti-STT3B antibody, anti-SUCO antibody, anti-TAPl antibody, anti-TBX21 antibody, anti-TCTNl antibody, anti-TENMl antibody, anti-TGFBl antibody, anti-TGFBR2 antibody, anti-THEMIS antibody, anti-TIMD4 antibody, anti- TM9SF4 antibody, anti-TMC8 antibody, anti-TMEM104 antibody, anti-TMEM106B antibody, anti-TMEM116 antibody, anti-TMEM123 antibody, anti-TMEM140 antibody, anti-TMEM154 antibody, anti-TMEM179B antibody, anti-TMEM204 antibodies, anti- TMEM219 antibody, anti-TMIGD2 antibody, anti-TMPO antibody, anti-TNFAEP8L2 antibody, anti-TNFRSFlOB antibody, anti-TNFRSFlA antibody, anti-TNFRSFIB antibody, anti-TNFSF4 antibody, anti-TNFSF9 antibody, anti-TORlAIPl antibody, anti- TOR3A antibody, anti-TP53I13 antibody, anti-TRABD2A antibody, anti-TRAF3EP3 antibody, anti-TRATl antibody, anti-TRIM28 antibody, anti-TSPAN17 antibody, anti- TSPAN18 antibody, anti-TSPAN6 antibody, anti-TTC24 antibody, anti-TTN antibody, anti-TTYH3 antibody, anti-TXNDCll antibody, anti-TXNDC15 antibody, anti-TYMP antibody, anti-U2AFl antibody, anti-UBAC2 antibody, anti-XCL2 antibody, anti-YIPFl antibody, anti-ZBPl antibody, anti-ZC3H12D antibody, anti-ZDHHC5 antibody, anti- ZNF683 antibody, anti-ZNF80 antibody, and anti-ZNF831 antibody. Alternatively, the agent for treatment may belong to any one of a variety of treatment paradigms such as cell therapy, gene therapy, siRNA, small molecule, etc. Although in silico target lists may preferentially include interrogated genes that lack associated therapies, it is possible that methods described herein identify a target which is not yet approved for treatment with an approved agent. In such an instance, the target may represent a novel target for the agent, but the agent may still be used for treatment. Alternatively, or additionally, an approved agent may target a related biological process category of a target, where the agent may still be efficacious in treatment as the agent modulates an upstream and/or downstream gene product related to the target in biological pathway.

[0093] Methods of treating autoimmune diseases or cancer by identifying a gene for modulation by interrogating genes preferentially expressed with dysfunctional T cells may include methods for interrogating genes and/or generating in silico target lists, as described above.

[0094] The following Examples do not in any way limit the scope of the present disclosure, and any invention(s) provided for herein. One of ordinary skill in the art will recognize the numerous modifications and variations that may be performed without altering the spirit or scope of the present disclosure. Such modifications and variations are encompassed within the scope of the present disclosure. The entire contents of all references, patents, and published patent applications cited throughout this application are herein incorporated by reference.

Exemplification

Example 1: Generation of In Silico Target List and Validation of Target List

[0095] Multiple candidate target genes were identified, including Layilin (LAYN) and Cytotoxic And Regulatory T-Cell Molecule (CRT AM), by applying the methods disclosed herein to multiple RNA expression database of human origin. The presence of targets in dissociated cells from the tumor microenvironment demonstrated that the filters yielded highly relevant human targets, even at the in silico stage.

[0096] Primary human tissue samples were derived from Cooperative Human Tissue Network (CHTN), dissociated and stained with primary conjugated antibodies against human CD4, CD8, CD25, CD127 and LAYN purchased from commercial sources. Flow cytometry showed LAYN expression on both CD4 and CD8 T cells, and CD127- within the CD4+ populations consistent with Tregs (see FIGs. 3A-3D). Similarly, dissociated human tissues were stained with primary conjugated antibodies against human CD4, CD8 and CRTAM purchased from commercial sources. Flow cytometry showed CRTAM expression on both CD4+ and CD8+ T cells (see FIGs 4A-4C). Expression of CRTAM was found to be induced in PBMC-isolated T cells activated chronically with anti-CD3 and anti-CD28 cross-linking antibodies while not in non-simulated control, consistent with expression from activated T cells. These flow cytometry data confirmed the described in silico approach yields human T cell relevant target candidates that can be confirmed and verified experimentally in human primary tumor samples (see FIGs 5A- 5D).

Example 2: Generation of In Silico Target List for Dysfunctional T Cells using scRNASeq databases using CD8, LAG3, and 4-1BB

[0097] Publicly available transcriptomic databases were combined with the in silico algorithm to generate target lists as follows: Dysfunctional T cell: using single cell RNA- sequencing (scRNASeq) databases interrogating genes preferentially expressed with dysfunctional T cell markers such as CD8, LAG3 and 4-1BB (TNSRSF9).

[0098] Dysfunctional T cell gene list with scRNASeq databases using CD8, LAG3 and 4- 1BB: LAYN, CD86, PHEX, TNFSF4, KIR2DL4, ITGA2, FAM174B, CD38, KIR2DS4, PON2, MS4A6A, KCNK5, CD72, ITGAX, TNFSF9, SLC7A5, TIMD4, SEMA4A, TNFRSFIB, SIRPG, HLA-DRA, HLA-DOA, CD27, TTYH3, KIR2DL3, KIR3DL2, CD200R1, CD59, BTN2A2, TCTN1, IL2RB, PAM, CD82, CTSD, KIR2DL1, SLC29A3, PTPRJ, NOTCH1, B3GNT2, SLC4A2, IGFLR1, SCPEP1, KIR3DL1, GPR25, ADCY3, FASLG, P2RX7, IFNLR1, NTRK1, CHST11, BST2, LDLRAD4, TXNDCll, CLSTN3, KLRC3, IL21R, ADORA2A, KLRC2, TTN, YIPF1, GPR19, TOR3A, NAALADLl, SLC41A1, MFGE8, SLC4A5, POLR1A, TSPAN17, GALNT1, CD84, NCR1, SLC3A2, TMEM140, PTGER4, CTSA, TXNDC15, CADMl, SLC39A6, LDLR, TMPO, GOLM1, CALU, LY6E, ABCA2, TMEM154, EPOR, CELSR3, GPR68, PTPRN2, IFNAR2, PLTP, ATP6V1A, ITPRIP, TMEM179B, CTSC, SLC43A3, F2R, ATP1B3, ST8SIA1, SLC9A1, IL2RG, ENOl, SLC1A5, ITGA1, HEG1, GART, SLC39A4, CD3D, ATP1A3, CANT1, ATP6AP1, SLC39A14, CSRPl, U2AF1, SPPL2A, P2RY11, AP2M1, NCSTN, ACLY, CEACAM21, CASD1, CD8B, SLC5A3, CD2, CLIC1, HCRTR1, GZMA, GPR137, HEXB, CTSB, MGAT4B, GPR22, IL4R, ITGB1, ILIORB, ERVK13-1, CD3G, TMEM219, TOR1AEP1, ERMAP, SUCO, OPRMl, RAB11FIP1, LRPAPl, CD58, IL12RB1, POFUT2.

[0099] Example 3: Generation of In Silico Target List for Dysfunctional T Cells and Tress with scRNASeq databases using CD8, LAG3, 4-1BB, and FoxP3

[00100] Publicly available transcriptomic databases were combined with the in silico algorithm to generate potential target lists as follows: Dysfunctional T cell and Treg: using scRNASeq databases interrogating genes preferentially expressed by both dysfunctional T cell and Treg.

[00101] Dysfunctional T cell and Treg gene list with scRNASeq databases using CD8, LAG3, 4-1BB, and FoxP3: LAYN, FAM174B, TTYH3, TNFRSF1B, CADMl, CD27, SLC7A5, IL2RB, SIRPG, IL21R, ADCY3, SLC41A1, PTPRJ, CD59, NTRK1, CCR1, PAM, TTN, IGFLR1, CTSC, BTN2A2, P2RX7, FCRL3, CHST11, BST2, CTSA, PLTP, ADORA2A, SLC43A3, B3GNT2, SLC3A2, TSPAN17, CD82, IFNAR2, YIPF1, TMEM140, TXNDC11, CD151, NAALADLl, GALNT1, SLC1A5, SPPL2A, SLC43A1, SLC41A3, ADIPOR2, MFGE8, IL2RG, SLC5A3, EPOR, CSRPl, CLIC1, LRPAPl.

Example 4: Generation of In Silico Target List for Dysfunctional T Cells with bulk

RNASeq database using CD8, LAG3, and 4-1BB

[00102] Publicly available transcriptomic databases were combined with the in silico algorithm to generate potential target lists as follows: Dysfunctional T cell: using bulk the cancer genome atlas (TCGA) transcriptomic database interrogating genes with correlations with dysfunctional T cell markers such as CD8, LAG3, and 4- IBB.

[00103] Dysfunctional T cell gene list generated with TCGA database using CD8, LAG3, and 4-1BB: CRTAM, SLA2, FASLG, NKG7, SIRPG, IL12RB1, IL2RB, GBP5, GZMA, GZMK, IL21R, PYHIN1, GZMH, SH2D1A, CD86, CD80, CD96, TBX21, ARHGAP9, GBP1, GBP4, TRATl, CD6, GIMAP4, APOBEC3G, IL18BP, CST7, APOL3, TAPI, CD72, KLRDl, FCGR3A, CTSW, TRAF3IP3, HCST, CD27, IL2RG, LTA, HLA-DPA1, SLA, PSTPIP1, BIN2, TNFRSF1B, LAIR1, THEMIS, HLA-DRA, MS4A6A, HLA-DPB1, PTPN22, ABI3, CIITA, IKZF1, SELPLG, PSMB9, CD84, APOBEC3H, CCR1, SRGN, PTPRCAP, TNFAIP8L2, AIF1, XCL2, ZBP1, IFI30, PTPN7, C3AR1, NLRC3, CD38, TYMP, HLA-DQA1, AOAH, GIMAP1, MIR155HG, GIMAP7, ZNF831, IL18RAP, LY9, LST1, NCF1B, Clorfl62, HLA-DOA, CD5, FCRL3, CCR2, SH2D2A, EPSTI1, CD300A, HAPLN3, CLECL1, ZNF80, GIMAP6, HLA-DMA, CD37, APOBEC3D, ETV7, RGL4, JAKMIP1, TTC24, HLA-DRBl, ABCD2, CLEC7A, BTN3A1, GIMAP2, NCF4, GFI1, S1PR4, HLA-DMB, TMC8, GPSM3, CXCL13, FCRL6, BATF, IL16, KIR2DL4, HLA-DQA2, LPXN, CD8B, ZC3H12D, HLA-DRB6, FGR, PTPN6, ZNF683, PLEKH02, BTN2A2, CARD 16, LSP1, KLRC4, C16orf54, RUNX3, JAK2, LAT2, RASSF5, ITGAX, GIMAP8, IKZF3, NCR1, DOCK10, CD52.

Example 5: Generation of In Silico Target List for Effector T Cells with scRNASeq databases using CDS, LAG3, 4-1 BB, and FoxP3 [00104] Publicly available transcriptomic databases were combined with the in silico algorithm to generate potential target lists as follows: Effector T cell: scRNASeq databases interrogating genes preferentially expressed by T effectors and not by dysfunctional T cell or Treg.

[00105] Effector T cell: scRNASeq databases interrogating genes preferentially expressed on T effectors and not on dysfunctional T cells or Tregs: CD40LG, CD300A, PTGDR, PLXDC1, FCRL6, EPHA4, DPP4, PLXND1, PTCH1, A2M, S1PR5, S1PR1, EPHA1, LGR6, TRABD2A, KLRG1, ITGA6, TMIGD2, PTPRM, CD160, MS4A1, PRSS23, LTB4R, CCR2, CX3CR1, DPEP2, TMEM204, IFNGR1, KLRB1, SORL1, TSPAN18, CD55, IL18RAP, TMEM116, SLC36A4, SERINC5, LAIR1, RPS8, MFSD12, GPR183, TMEM123, CYSLTR1, RPL3, TGFBR2, ACVR2A, ADAM 19, RPLP2, TGFB1, RPS23, ABCA7, RPL27A, EDEM3, CD6, DSE, RPL32, RPL4, DCBLD1, RPL34, RPL18, STT3B, RPL35, IL11RA, RPL17, TMEM106B, RPL9, TMEM104, AN06, TENM1, TNFRSF1A, CLDN7, NOP56, GPR85, MMP25, CD47, GLEPR1, UBAC2, SLC39A8, RPL35A, CACNA2D2, SIGIRR, NRROS, TRIM28, MFSD8, RPL21, TNFRSF10B, SLC3A1, LYSMD3, ZDHHC5, TM9SF4, TP53I13.

[00106] The entire teachings of all documents cited herein are hereby incorporated herein by reference.

Claims

CLAIMS What is claimed is:

1. A computer-implemented method for identifying gene targets comprising: a. interrogating genes preferentially expressed with dysfunctional T cell markers using a computer system configured for interrogating genes with a transcriptomic database; and b. identifying said gene targets.

2. The method of claim 1, wherein interrogating genes comprises performing a data mining algorithm using said computer system to generate an in silico target list.

3. The method of any one of the preceding claims, wherein said transcriptomic database comprises a single cell RNA sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof.

4. The method of any one of the preceding claims, wherein interrogating genes comprises: a. receiving, bulk RNASeq data; and b. calculating a plurality of gene correlations between expression of at least two genes present in the bulk RNASeq data.

5. The method of any one of the preceding claims, wherein calculating the plurality of gene correlations comprises determining a Spearman’s correlation, a Pearson’s correlation, or a combination thereof.

6. The method of any one of the preceding claims, wherein calculating the plurality of gene correlations further comprises calculating a correlation between 4- IBB and at least one other gene.

7. The method of any one of the preceding claims, wherein calculating the plurality of gene correlations comprises calculating a correlation between LAG3 and at least one other gene.

8. The method of any one of the preceding claims, wherein the at least two genes comprise 4- IBB and LAG3.

9. The method of any one of the preceding claims, wherein interrogating genes comprises: a. multiplying said plurality of correlations; and b. ranking the products of said multiplying according to correlation with 4- IBB gene expression, LAG3 gene expression, or a combination thereof.

10. The method of any one of the preceding claims, wherein interrogating genes comprises: a. receiving, scRNASeq data; and b. determining, as a function of scRNASeq data, a threshold value for identifying positive versus negative expression for a biomarker.

11. The method of any one of the preceding claims, wherein interrogating genes comprises performing dimension reduction and clustering of genes with Uniform Manifold Approximation and Projection (UMAP).

12. The method of any one of the preceding claims, wherein interrogating genes comprises performing dimension reduction and clustering of genes with t- distributed Stochastic Neighbor Embedding (t-SNE).

13. The method of any one of the preceding claims, wherein interrogating genes comprises binning cells into one or more expression groupings as a function of said threshold value.

14. The method of any one of the preceding claims, wherein interrogating genes comprises binning cells into one or more expression groupings selected from the group consisting of 4-1BB+/ LAG3+, 4-1BB+/ LAG3-, 4-1BB-/LAG3+, and 4- 1BB-/ LAG3-.

15. The method of any one of the preceding claims, wherein interrogating genes comprises screening genes as a function of co-expression of genes between dysfunctional T cells and at least a second cell type.

16. The method of any one of the preceding claims, wherein interrogating genes comprises screening genes as a function of co-expression of genes between dysfunctional T cells and regulatory T cells (Tregs).

17. The method of any one of the preceding claims, wherein interrogating genes comprises: a. retrieving a plurality of proteomics data; b. determining, from said proteomics data, a cell surface protein status of said genes; and c. filtering said genes as a function of the cell surface protein status.

18. The method of any one of the preceding claims, wherein interrogating genes comprises filtering said genes as a function of being present on the cell surface.

19. The method of any one of the preceding claims, wherein interrogating genes comprises: a. retrieving a plurality of therapeutics data; b. determining, from said therapeutics data, a therapeutic status of said genes; and c. filtering said genes as a function of the therapeutic status.

20. The method of any one of the preceding claims, wherein interrogating genes comprises filtering said genes as a function of having an approved monoclonal antibody therapy.

21. The method of any one of the preceding claims, wherein interrogating genes comprises: a. performing a gene set enrichment analysis (GSEA); and b. binning said genes by biological process category as a function of the GSEA.

22. The method of any one of the preceding claims, wherein interrogating genes comprises categorizing said genes according to i) cell surface protein status, ii) tumor indication, iii) therapeutic status, iv) biological process category, v) co expression of genes with at least the second cell type, or a combination thereof.

23. The method of any one of the preceding claims, wherein interrogating genes comprises generating an in silico target list that includes genes i) expressed on the cell surface, ii) with tumor indication, iii) without approved monoclonal antibody therapy, iv) preferentially co-expressed with Treg markers, or any combination thereof.

24. A system for generating an in silico target list comprising: a computer system configured for interrogating genes preferentially expressed on dysfunctional T cell markers with a transcriptomic database to generate said in silico target list.

25. A method of treating cancer comprising: a. identifying a gene for modulation by interrogating genes preferentially expressed with dysfunctional T cell markers with a transcriptomic database; and b. administering to a human an agent that modulates said gene.

26. The method of claim 25, wherein said marker is CD3.

27. The method of claim 25 or 26, wherein said marker is selected from the group consisting of CD8, LAG3, 4- IBB, or a combination thereof.

28. The method of any one of claims 25-27, wherein said gene is further preferentially expressed on regulatory T cells (Tregs).

29. The method of any one of claims 25-28, wherein said transcriptomic database is a single cell RNA-sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof.

30. The method of any one of claims 25-29, wherein said bulk transcriptomic database is The Cancer Genome Atlas (TCGA) database.

31. The method of any one of claims 25-30, wherein said agent is an antibody.

32. The method of any one of claims 25-31, wherein said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, AD AMI 9, ADCY3, ADIPOR2,

ADORA2A, AEF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD 109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CUT A, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CRTAM, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAPl, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLEPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQA1, HLADQA2, HLADRA, HLADRBl, HLADRB6, IFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPREP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRDl, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABl 1FIP1, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

33. A method of treating autoimmune disease or cancer comprising: a. identifying a gene for modulation by interrogating genes preferentially expressed by T effector cells and not by dysfunctional T cells or regulatory T cells (Tregs) with a transcriptomic database; and b. administering to a human an agent that modulates said gene.

34. The method of claim 33, wherein said genes preferentially expressed by T effector cells is selected from the group consisting of CD8, LAG3, 4- IBB, or a combination thereof.

35. The method of claim 33 or 34, wherein said transcriptomic database is a single cell RNA-sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof.

36. The method of any one of claims 33-35, wherein said bulk transcriptomic database is The Cancer Genome Atlas (TCGA) database.

37. The method of any one of claims 33-36, wherein said agent is an antibody.

38. The method of any one of claims 33-37, wherein said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, AD AMI 9, ADCY3, ADIPOR2, ADORA2A, AEF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54,

Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD 109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37,

CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CUT A, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CRTAM, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GEMAPl, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLEPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQA1, HLADQA2, HLADRA, HLADRBl, HLADRB6, IFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, IL11RA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPREP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRD1, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1, RABl 1FIP1, RASSF5, RGL4,

RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.

39. A method of treating autoimmune disease comprising: a. identifying a gene for modulation by interrogating genes preferentially expressed with T cell markers with a transcriptomic database; and b. administering to a human an agent that modulates said gene.

40. The method of claim 39, wherein genes preferentially expressed by T cells comprises genes preferentially expressed with dysfunctional T cell markers with a transcriptomic database.

41. The method of claim 39 or 40, wherein genes preferentially expressed by T cells comprises genes preferentially expressed by T effector cells and not by dysfunctional T cells or regulatory T cells (Tregs) with a transcriptomic database.

42. The method of any one of claims 39-41, wherein said marker is CD3.

43. The method of any one of claims 39-42, wherein said marker is selected from the group consisting of 4- IBB, LAG3, or a combination thereof.

44. The method of any one of claims 39-43, wherein said transcriptomic database is a single cell RNA-sequencing (scRNASeq) database, a bulk RNA sequencing (bulk RNASeq) database, or a combination thereof.

45. The method of any one of claims 39-44, wherein said bulk transcriptomic database is The Cancer Genome Atlas (TCGA) database.

46. The method of any one of claims 39-45, wherein said agent is an antibody.

47. The method of any one of claims 39-45, wherein said identified gene for modulation is selected from the group consisting of 4- IBB, A2M, ABCA2, ABCA7, ABCD2, ABI3, ACLY, ACVR2A, ADAM 19, ADCY3, ADIPOR2, ADORA2A, AIF1, AN06, AOAH, AP2M1, APOBEC3D, APOBEC3G, APOBEC3H, APOL3, ARHGAP9, ATP1A3, ATP1B3, ATP6AP1, ATP6V1A, B3GNT2, BATF, BIN2, BST2, BTN2A2, BTN3A1, BTNL8, C16orf54, Clorfl62, C3AR1, CACNA2D2, CADMl, CALU, CANT1, CARD 16, CASD1, CCR1, CCR2, CD109, CD151, CD160, CD2, CD200R1, CD27, CD300A, CD37, CD38, CD3D, CD3G, CD40LG, CD47, CD5, CD52, CD55, CD58, CD59, CD6, CD72, CD8, CD80, CD82, CD84, CD86, CD8B, CD96, CEACAM21, CELSR3, CHST11, CIITA, CLDN7, CLEC7A, CLECL1, CLIC1, CLSTN3, CRTAM, CSRP1, CST7, CTSA, CTSB, CTSC, CTSD, CTSW, CX3CR1, CXCL13, CYSLTR1, DCBLD1, DOCKIO, DPEP2, DPP4, DSE, EDEM3, ENOl, ENPP5, EPHA1, EPHA4, EPOR, EPSTI1, ERMAP, ERVK131, ETV7, F2R, FAM174B, FASLG, FCGR3A, FCRL3, FCRL6, FGR, GALNT1, GART, GBP1, GBP4, GBP5, GFI1, GIMAPl, GIMAP2, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GLIPR1, GOLM1, GPR137, GPR183, GPR19, GPR22, GPR25, GPR68, GPR85, GPSM3, GZMA, GZMH, GZMK, HAPLN3, HCRTR1, HCST, HEG1, HEXB, HLADMA, HLADMB, HLADOA, HLADPAl, HLADPBl, HLADQAl, HLADQA2, HLADRA, HLADRBl, HLADRB6, EFI30, IFNAR2, IFNGR1, IFNLR1, IGFLR1, IKZF1, IKZF3, ILIORB, ILllRA, IL12RB1, IL16, IL18BP, IL18RAP, IL21R, IL2RB, IL2RG, IL4R, ITGA1, ITGA2, ITGA6, ITGAX, ITGB1, ITPRIP, JAK2, JAKMIP1, KCNK5, KIR2DL1, KIR2DL3, KIR2DL4, KIR2DS4, KIR3DL1, KIR3DL2, KIRDL2, KLRBl, KLRC2, KLRC3, KLRC4, KLRDl, KLRG1, LAG3, LAIR1, LAT2, LAYN, LDLR, LDLRAD4, LGR6, LPXN, LRPAPl, LSP1, LST1, LTA, LTB4R, LY6E, LY9, LYSMD3, MFGE8, MFSD12, MFSD8, MGAT4B, MIR155HG, MMP25, MS4A1, MS4A6A, NAALADLl, NCF1B, NCF4, NCR1, NCSTN, NKG7, NLRC3, NOP56, NOTCH1, NRROS, NTRK1, OPRM1, P2RX7, P2RY11, PAM, PHEX, PLEKH02, PLTP, PLXDC1, PLXND1, POFUT2, POLR1A, PON2, PRSS23, PSMB9, PSTPIP1, PTCH1, PTGDR, PTGER4, PTPN22, PTPN6, PTPN7, PTPRCAP, PTPRJ, PTPRM, PTPRN2, PYHIN1,

RABllFEPl, RASSF5, RGL4, RPL17, RPL18, RPL21, RPL27A, RPL3, RPL32, RPL34, RPL35, RPL35A, RPL4, RPL9, RPLP2, RPS23, RPS8, RUNX3, S1PR1, S1PR4, S1PR5, SCPEP1, SELPLG, SEMA4A, SERINC5, SH2D1A, SH2D2A, SIGIRR, SIRPG, SLA, SLA2, SLC1A5, SLC29A3, SLC36A4, SLC39A14, SLC39A4, SLC39A6, SLC39A8, SLC3A1, SLC3A2, SLC41A1, SLC41A3, SLC43A1, SLC43A3, SLC4A2, SLC4A5, SLC5A3, SLC7A5, SLC9A1, SORL1, SPPL2A, SRGN, ST8SIA1, STT3B, SUCO, TAPI, TBX21, TCTN1, TENM1, TGFB1, TGFBR2, THEMIS, TIMD4, TM9SF4, TMC8, TMEM104, TMEM106B, TMEM116, TMEM123, TMEM140, TMEM154, TMEM179B, TMEM204, TMEM219, TMIGD2, TMPO, TNFAIP8L2, TNFRSF10B, TNFRSF1A, TNFRSF1B, TNFSF4, TNFSF9, TOR1AIP1, TOR3A, TP53I13, TRABD2A, TRAF3IP3, TRATl, TRIM28, TSPAN17, TSPAN18, TSPAN6, TTC24, TTN, TTYH3, TXNDC11, TXNDC15, TYMP, U2AF1, UBAC2, XCL2, YIPF1, ZBP1, ZC3H12D, ZDHHC5, ZNF683, ZNF80, and ZNF831.