Methods for predictive modeling of alzheimer's disease and progression

Abstract

Provided herein is the identification of target proteins for determination of clinical aspects of Alzheimer's disease in a subject. Further provided are methods for diagnosing Alzheimer's disease, determining the progression or rate of memory decline of Alzheimer's disease, and predicting amyloid-tau status, brain amyloidosis status, and plasma p-tau217 status of a subject, using predictor value comparison to thresholds. Also provided are methods of selecting a subject for inclusion in a clinical trial, methods for treating the subject in need thereof, and kits providing the same.

Description

Docket 047563-870867METHODS FOR PREDICTIVE MODELING OF ALZHEIMER’S DISEASE AND PROGRESSION FIELD OF THE TECHNOLOGY

[0001] The present disclosure relates to the discovery of target proteins that are particularly effective for prediction of clinical status in connection with Alzheimer's disease (AD), and the use of these biomarkers to improve clinical diagnosis and monitoring of AD, and for guiding treatment decisions.BACKGROUND

[0002] Alzheimer disease (AD) is a complex neurodegenerative disorder, that is the most common cause of dementia. While AD primarily affects older individuals, with symptoms typically appearing after the age of 65 (late-onset AD), about 1-5% of the cases have symptoms before 65 (early -onset AD). This progressive neurodegenerative disorder is characterized by the accumulation of amyloid-beta (A[3) plaques, neurofibrillary tangles consisting of hyperphosphorylated and aggregated tau protein, and widespread neuronal loss. These pathological changes lead to cognitive decline, memory loss, behavioral changes, and ultimately, a loss of independence and functioning.

[0003] Despite extensive research efforts, the exact mechanisms underlying the development and progression of AD remain incomplete. In recent years, proteomic studies have been instrumental in shedding light on the molecular complexities involved in AD. These proteomic studies have focused on the brain or CSF, and a handful of studies were also performed in plasma. However, these studies have generally included a relatively small number of AD cases or general dementia. Therefore, there is a need for a large-scale plasma proteomic study using well -characterized AD patients in order to provide for more accurate biomarkers for predicting the clinical statuses associated with AD.BRIEF SUMMARY

[0004] In some aspects, provided herein is a method of diagnosing Alzheimer’s disease in a subject, the method comprising a) providing a blood sample obtained from the subject; b) determining i) an age of the subject at the obtainment of the blood sample in years; ii) a biological sex of the subject at tire obtainment of the blood sample: and iii) a normalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2,1108365568.1Docket 047563-870867FAM118 A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, or NRBP1; 3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; 4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1, SETD3. EEF1A1. PMM2, CDK16. ICAM5, ESMI, CPB1. DHRS9, CCNYL1. ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1; 6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; and / or 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1. AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1. ODC1. KIR2DS2, CPLX2. FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; c) calculating a predictor value (PV) for diagnosing Alzheimer’s disease based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) diagnosing the subject as having Alzheimer's disease, wherein i) tire subject is diagnosed as having Alzheimer’s disease when the PV is greater than a threshold level; or ii) the subject is diagnosed as not having Alzheimer’s disease when the PV is less than or equal to the threshold level.

[0005] In other aspects, provided herein is a method of predicting likelihood of progression from cognitively normal to symptomatic Alzheimer's disease in a subject, the method comprising a) providing a blood sample obtained from the subject; b) determining i) an age of the subject at the obtainment of the blood sample in years; ii) a biological sex of the subject at the obtainment of the blood sample; and iii) a normalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4. PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, or NRBP1; 3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1. TFF3, FABP3, FABP4, VEGFA, or CPLX1; 4) the fourth target protein is selected from VAT1. SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3.2108365568.1Docket 047563-870867EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SC02, or CES1; 5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1; 6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY 1; and / or 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3. NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; c) calculating a predictor value (PV) for likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5. TP6, and / or TP7; and d) predicting the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject, wherein i) the subject is predicted to be likely to progress from cognitively normal to symptomatic Alzheimer’s disease when the PV is greater than a threshold level; or ii) the subject is predicted to be not likely to progress from cognitively normal to symptomatic Alzheimer’s disease when the PV is less than or equal to tire threshold level.

[0006] In other aspects, provided herein is a method of predicting amyloid / tau (AT) status in a subject, the method comprising a) providing a blood sample obtained from the subject; b) determining i) an age of the subject at the obtainment of the blood sample in years; ii) a biological sex of the subject at tire obtainment of the blood sample; and iii) a nonnalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118 A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, or NRBP1; 3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2. PTN. IAPP, CDK16. RAB3C, DSTN, UBE2H, ODC1. MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; 4) the fourth target protein is selected from VAT1, SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1. NLGN1, MENT. GSTA1, SEMA4G, CUZD1, NEFL, GATM. TBCA. or LRRN1;3108365568.1Docket 047563-870867

[0007] 6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; and / or 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, ROR1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22. ARL1, REG3A, CTSF. IAPP, GXYLT1. OMG, or NPTXR; c) calculating a predictor value (PV) for amyloid / tau (AT) status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) predicting the amyloid / tau (AT) status in the subject, wherein i) the subject is predicted to have a positive amyloid / tau (AT) status when the PV is greater than a threshold level; or ii) the subject is predicted to have a negative amyloid / tau (AT) status when the PV is less than or equal to the threshold level.

[0008] In other aspects, provided herein is a method of predicting brain amyloidosis status in a subject, the method comprising a) providing a blood sample obtained from the subject; b) determining i) an age of the subject at the obtainment of the blood sample in years: ii) a biological sex of the subject at the obtainment of the blood sample: and iii) a nonnalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118 A, or REG4; 2) tire second target protein is selected from CTF1, CHL1, ADGRF 1. PCDHAC2, or NRBP 1 ; 3) the third target protein is selected from CPLX2. SETD3, GRB 10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2. PTN. IAPP, CDK16. RAB3C, DSTN, UBE2H, ODCI. MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; 4) the fourth target protein is selected from VAT1, SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9. CCNYL1, ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25. HSPD1, ELAVL2, B3GAT1. NLGN1. MENT. GSTA1, SEMA4G, CUZD1. NEFL, GATM. TBCA. or LRRN1: 6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; and / or 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, ROR1, GAS1, ODCI, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, SI00A12, NPTN, LIN7A. VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22. ARL1, REG3A, CTSF. IAPP, GXYLT1. OMG, or NPTXR; c) calculating a4108365568.1Docket 047563-870867predictor value (PV) for brain amyloidosis status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) predicting the brain amyloidosis status in the subject, wherein i) tire subject is predicted to have a positive brain amyloidosis status when the PV is greater than a threshold level; or ii) the subject is predicted to have a negative brain amyloidosis status when the PV is less than or equal to the threshold level.

[0009] In other aspects, provided herein is a method of predicting plasma p-Tau217 status in a subject, the method comprising a) providing a blood sample obtained from the subject; b) determining i) an age of the subject at the obtainment of the blood sample in years: ii) a biological sex of the subject at the obtainment of the blood sample: and iii) a nonnalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118 A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF 1. PCDHAC2, or NRBP 1 ; 3) the third target protein is selected from CPLX2. SETD3, GRB 10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2. PTN. 1APP, CDK16. RAB3C, DSTN, UBE2H, ODC1. MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; 4) the fourth target protein is selected from VAT1, SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25. HSPD1, ELAVL2, B3GAT1. NLGN1, MENT. GSTA1, SEMA4G, CUZD1. NEFL, GATM. TBCA. or LRRN1: 6) the sixth target protein is selected from FAHD2A, TATDN3. NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11 A2, LECT2, PSME1 , RPS6KB 1 , or THY1 ; and / or 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, ROR1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A. VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22. ARL1, REG3A, CTSF. IAPP, GXYLT1. OMG, or NPTXR; c) calculating a predictor value (PV) for plasma p-Tau217 status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) predicting the plasma p-Tau217 status in the subject, wherein i) the subject is predicted to have a positive plasma p-Tau217 status when the PV is greater than a threshold level; or ii) the subject is predicted to have a negative plasma p-Tau217 status when the PV is less than or equal to the threshold level.5108365568.1Docket 047563-870867

[0010] In other aspects, provided herein is a method of selecting a subject for inclusion in a clinical trial for Alzheimer’s disease, the method comprising a) providing a blood sample obtained from the subject; b) determining i) an age of the subject at the obtainment of tire blood sample in years; ii) a biological sex of the subject at the obtainment of the blood sample; and iii) a nonnalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118 A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF 1 , PCDHAC2, or NRBP 1 ; 3) the third target protein is selected from CPLX2, SETD3, GRB 10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2. PTN. IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1. MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3. NMB, GXYLT1. WFIKKN2, HIBCH. SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; 4) the fourth target protein is selected from VAT1, SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT. GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1; 6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; and / or 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, ROR1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; c) calculating a predictor value (PV) for selecting a subject for inclusion in a clinical trial based on the age and biological sex of the subject, and the nonnalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) selecting the subject for inclusion in the clinical trial, wherein i) the subject is selected for inclusion in the clinical trial when the PV is greater than a threshold level; or ii) the subject is not selected for inclusion in the clinical trial when the PV is less than or equal to the threshold level.

[0011] In some aspects, tire normalized protein level of the one or more target protein in (b)(iii) is the normalized protein level of TP1, TP2, TP3, TP4, TP5, TP6, and TP7, and the PV in (c) is calculated based on the normalized protein level of TP1, TP2, TP3, TP4, TP5, TP6, and TP7. In some aspects, the PV in (c) is calculated according to Fonnula I, wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female, and the threshold value is 0.298.6108365568.1Docket 047563-870867

[0012] In some aspects, a) the first target protein is ACHE; b) the second target protein is CTF 1 ; c) the third target protein is CPLX2; d) the fourth target protein is VAT1; e) the fifth target protein is SPC25; f) the sixth target protein is FAHD2A; and / or g) the seventh target protein is NPTXR.

[0013] In some aspects, tire normalized protein level of each target protein is a normal deviate score, standardized score, or Z-score, wherein the Z-score is a normalized distribution of the level of the target protein within a population of samples, wherein the normalization sets the mean of the target protein to a value of 0 and sets the standard deviation (SD) of the target protein in the population to a value of 1. In some aspects, the Z-score of each target protein is calculated according to Fonnula (IV), wherein x is the observed level of the target protein in a specific individual, m is the mean level of the target protein within a population: and s is the standard deviation of the target protein in the population.

[0014] In some aspects, the method further comprises determining the nonnalized protein level of plasma p-Tau217 in the blood sample.

[0015] In other aspects, provided herein is a method of treating Alzheimer’s disease in a subject, comprising administering to the subject a therapeutic agent based on a) the diagnosis of Alzheimer’s disease in the subject according to tire methods described herein; b) the prediction of likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject according to the methods described herein: c) the prediction of amyloid / tau (AT) status in the subject according to the methods described herein; d) the prediction of brain amyloidosis status in the subject according to the methods described herein; e) the prediction of plasma p-Tau217 status in the subject according to the methods described herein; or f) the selection of the subject for inclusion in the trial according to the methods described herein, hi some aspects, the therapeutic agent is an A[3-targeting therapeutic agent, or a tau-targeting therapeutic agent. In some aspects, the A[3-targeting therapeutic agent selected from Aducanumab, Donanemab, Lecanemab. Gantenerumab. Solanezumab, or Genezumab; or the tau-targeting therapeutic agent is selected from Gosuranemab, Zagotenemab, Tilavonemab Semorinemab, Buntanetap, E2814, Semorinemab, or Bepranemab.

[0016] In other aspects, provided herein is a kit, comprising a) a means for quantifying the protein level of one or more target protein selected from a first target protein, second target protein, third target protein, fourth target protein, fifth target protein, sixth target protein, and / or seventh target protein, wherein i) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4; ii) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl; iii) the third target7108365568.1Docket 047563-870867protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; iv) the fourth target protein is selected from VAT1, SLITRK3, GOT2, UGDEI, PLOD2, CTBP1, SETD3, EEF1A1, PMM2. CDK16, ICAM5, ESMI. CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1: v) the fifth target protein is selected from SPC25, HSPD1. ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1; vi) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY 1; and / or vii) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1. GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1. ATP1B2, MRE11, IFT22, ARL1, REG3A. CTSF, IAPP, GXYLT1, OMG, or NPTXR; and b) instructions for i) diagnosing Alzheimer’s disease in the subject according to the methods described herein; ii) predicting the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject according to the methods described herein; iii) predicting the amyloid / tau (AT) status in the subject according to the methods described herein; iv) predicting the brain amyloidosis status in the subject according to the methods described herein; v) predicting the plasma p-Tau217 status in the subject according to the methods described herein: or vi) selecting a subject for inclusion in a clinical trial for Alzheimer’s disease according to the methods described herein.

[0017] In other aspects, provided herein is a method of diagnosing Alzheimer’s disease in a subject, the method comprising a) providing a blood sample obtained from the subject; b) determining i) a normalized protein level of plasma p-Tau217 in the blood sample; ii) an age of the subject at the obtainment of the blood sample in years; iii) a biological sex of the subject at the obtainment of the blood sample; and iv) a normalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or a seventh target protein (TP7). wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, or NRBP1; 3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP. NPTXR, TMEFF1, THY1. SCG3, NMB, GXYLT1. WFIKKN2, HIBCH, SCN2B, GAS1. TFF3, FABP3. FABP4, VEGFA, or CPLX1: 4) the8108365568.1Docket 047563-870867fourth target protein is selected from VATL SLITRK3, G0T2, UGDH, PLOD2, CTBPL SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZDl, NEFL, GATM, TBCA, or LRRN1; 6) the sixth target protein is selected from FAHD2A. TATDN3, NPTN, HAVCR1. PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1. RPS6KB1, or THY 1; 7) the seventh target protein is selected from NPTXR. BTG4, FLRT2. TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; c) calculating a predictor value (PV) for diagnosing Alzheimer's disease based on the nonnalized expression level of plasma p-Tau217, age and biological sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3. TP4, TP5, TP6. and / or TP7; and d) diagnosing the subject as having Alzheimer’s disease, wherein i) the subject is diagnosed as having Alzheimer’s disease when the PV is greater than a threshold level; or ii) the subject is diagnosed as not having Alzheimer’s disease when the PV is less than or equal to the threshold level.[0018 J In other aspects, provided herein is a method of predicting likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in a subject, the method comprising a) providing a blood sample obtained from the subject; b) determining i) a normalized protein level of plasma p-Tau217 in the blood sample; ii) an age of the subject at the obtainment of the blood sample in years; iii) a biological sex of the subject at the obtainment of the blood sample; and iv) a normalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or a seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF1. PCDHAC2, or NRBP1; 3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B. SMOC2. PTN. IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THYE SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; 4) the fourth target protein is selected from VAT1, SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1; 6) the sixth target protein is selected from FAHD2A, TATDN3.9108365568.1Docket 047563-870867NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THYl; 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2. CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; c) calculating a predictor value (PV) for likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease based on the normalized expression level of plasma p-Tau217, age and biological sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) predicting the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject, wherein i) the subject is predicted to be likely to progress from cognitively normal to symptomatic Alzheimer’s disease when the PV is greater than a threshold level; or ii) the subject is predicted to be not likely to progress from cognitively normal to symptomatic Alzheimer’s disease when the PV is less than or equal to the threshold level.

[0019] In other aspects, provided herein is a method of predicting amyloid / tau (AT) status in a subject, the method comprising a) providing a blood sample obtained from the subject; b) determining i) a normalized protein level of plasma p-Tau217 in the blood sample; ii) an age of the subject at the obtainment of the blood sample in years: iii) a biological sex of the subject at the obtainment of the blood sample; and iv) a normalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6). and / or a seventh target protein (TP7). wherein 1) the first target protein is selected from ACHE, PF4. IFT22, ASB9, PLTP. LTBP4, BAG4, PDCD6IP. SPARCL1. OAF, MZF1. ODC1. TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, or NRBP1; 3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1: 4) the fourth target protein is selected from VAT1. SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1; 6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3. NECAP2, TMEM132D, APOM. SCN2B, RPS6KB1, AMY2B, DSTN,10108365568.1Docket 047563-870867ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; c) calculating a predictor value (PV) for amyloid / tau (AT) status based on the normalized expression level of plasma p-Tau217, age and biological sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) predicting the amyloid / tau (AT) status in the subject, wherein i) the subject is predicted to have a positive amyloid / tau (AT) status when the PV is greater than a threshold level; or ii) the subject is predicted to have a negative amyloid / tau (AT) status when the PV is less than or equal to the threshold level.100201 In other aspects, provided herein is a method of predicting brain amyloidosis status in a subject, the method comprising a) providing a blood sample obtained from the subject: b) determining i) a normalized protein level of plasma p-Tau217 in the blood sample; ii) an age of the subject at the obtainment of the blood sample in years; iii) a biological sex of the subject at the obtainment of the blood sample; and iv) a normalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP1). a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TPS), a sixth target protein (TP6). and / or a seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, TFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4; 2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, or NRBP1; 3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B. SMOC2. PTN. IAPP, CDK16, RAB3C, DSTN, UBE2H. ODC1. MATN2. AGRP. NPTXR, TMEFF1. THY1, SCG3, NMB. GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3. FABP3, FABP4, VEGFA, or CPLX1 ; 4) the fourth target protein is selected from VAT1, SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1; 5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL. GATM, TBCA, or LRRN1; 6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, ROR1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR: c) calculating a predictor value (PV) for brain amyloidosis status based on the normalized expression level of plasma p-Tau217, age and biological11108365568.1Docket 047563-870867sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) predicting the brain amyloidosis status in the subject, wherein i) the subject is predicted to have a positive brain amyloidosis status when the PV is greater than a threshold level; or ii) the subject is predicted to have a negative brain amyloidosis status when the PV is less than or equal to the threshold level.

[0021] In other aspects, provided herein is a method of selecting a subject for inclusion in a clinical trial for Alzheimer’s disease, tire method comprising a) providing a blood sample obtained from the subject; b) determining i) a normalized protein level of plasma p-Tau217 in the blood sample; ii) an age of the subject at the obtainment of the blood sample in years; iii) a biological sex of the subject at the obtainment of the blood sample: and iv) a normalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP 1 ), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or a seventh target protein (TP7), wherein 1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118 A, or REG4; 2) tire second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, or NRBP1; 3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2, PTN, TAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; 4) the fourth target protein is selected from VAT1, SLITRK3. GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9. CCNYL1, ACAT1, SCO2. or CES1: 5) the fifth target protein is selected from SPC25. HSPD1, ELAVL2. B3GAT1. NLGN1. MENT. GSTA1, SEMA4G, CUZD1. NEFL. GATM. TBCA. or LRRN1; 6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1 , PCDHGA 12, OLR1 , CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; 7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, ROR1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9. RNF25, S100A12, NPTN, LIN7A. VEGFA, ADAMTSL1. LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; c) calculating a predictor value (PV) for inclusion in a clinical trial for Alzheimer’s disease based on the normalized expression level of plasma p-Tau217, age and biological sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and d) selecting the subject for inclusion in a clinical trial for Alzheimer’s disease, wherein i) the subject is selected for inclusion in the clinical12108365568.1Docket 047563-870867trial when the PV is greater than a threshold level; or ii) the subject is not selected for inclusion in the clinical trial when the PV is less than or equal to the threshold level.

[0022] In some aspects, tire nonnalized protein level of the one or more target protein in (b)(iv) is the normalized protein level of TP1, TP2. TP3, TP4, TP5. TP6, and TP7, and the PV in (c) is calculated based on the normalized protein level of TP1, TP2, TP3, TP4, TP5, TP6, and TP7. In some aspects, the PV in (c) is calculated according to Formula II, wherein the value for biological sex is 0 when tire subject is a male or is 1 when the subject is a female, and the threshold value is 0.346.

[0023] In some aspects, a) the first target protein is ACHE; b) the second target protein is CTF1; c) the third target protein is CPLX2; d) the fourth target protein is VAT1; e) the fifth target protein is SPC25; f) the sixth target protein is FAHD2A; and g) the seventh target protein is NPTXR.

[0024] In some aspects, tire normalized protein level of each target protein is a normal deviate score, standardized score, or Z-score, wherein the Z-score is a normalized distribution of the level of the target protein within a population, wherein the normalization sets the mean of the target protein to a value of 0 and sets the standard deviation (SD) of the target protein in the population to a value of 1. In some aspects, the Z-score of each target protein is calculated according to Formula (IV), wherein x is the observed level of the target protein in a specific individual, m is the mean level of the target protein within a population: and s is the standard deviation of the target protein in the population.

[0025] In other aspects, provided herein is a method of treating Alzheimer’s disease in a subject, comprising administering to the subject a therapeutic agent based on a) tire diagnosis of Alzheimer’s disease in the subject according to the methods described herein; b) the prediction of likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject according to the methods described herein; c) the prediction of amyloid / tau (AT) status in the subject according to the methods described herein; d) the prediction of amyloid imaging status in the subject according to the methods described herein; or e) the prediction of plasma p-Tau217 status in the subject according to the methods described herein. In some aspects, the therapeutic agent is an A|3-targeting therapeutic agent, or a tau-targeting therapeutic agent. In some aspects, the A[3-targeting therapeutic agent selected from Aducanumab, Donanemab, Lecanemab, Gantenerumab, Solanezumab, or Genezumab; or the tau-targeting therapeutic agent is selected from Gosuranemab, Zagotenemab, Tilavonemab Semorinemab, Buntanetap, E2814, Semorinemab, or Bepranemab.

[0026] In other aspects, provided herein is a kit, comprising a) a means for quantifying the protein level of plasma p-Tau217 in the blood sample, and the protein level of one or more target protein in the blood13108365568.1Docket 047563-870867sample selected from a first target protein, second target protein, third target protein, fourth target protein, fifth target protein, sixth target protein, and / or seventh target protein, wherein i) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, 0DC1, TATDN3, SMOC1, FERT2, NRP2, FAM118A, or REG4; ii) the second target protein is selected from CTF1, CHE1, ADGRF1. PCDHAC2, or NRBP1; iii) the third target protein is selected from CPEX2. SETD3, GRB10, ARHGAP45. EEF1A1. D0K1. RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYET1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1; iv) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1. ACAT1, SCO2, or CES1; v) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1. NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1; vi) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THYl; and / or vii) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1. KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1. ATP1B2, MRE11, IFT22, ARL1, REG3A. CTSF, IAPP, GXYLT1, OMG, or NPTXR; and b) instructions for i) diagnosing Alzheimer’s disease in the subject according to the methods described herein; ii) predicting the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject according to the methods described herein; iii) predicting the amyloid / tau (AT) status in tire subject according to the methods described herein; or iv) predicting the brain amyloidosis status in the subject according to the methods described herein; or v) selecting a subject for inclusion in a clinical trial for Alzheimer’s disease according to the methods described herein.BRIEF DESCRIPTION OF THE DRAWINGS[0027j FIG- 1 shows a graphical depiction of the study overview of Example 1. Plasma samples from the Knight ADRC and Stanford ADRC cohorts were analyzed using the SomaLogic SomaScan assay, measuring a total of 6,905 analytes targeting 6,106 proteins. Differential abundance analyses were conducted to compare cognitively normal individuals with those diagnosed with Alzheimer's disease. A meta-analysis was then performed using results from both discovery and replication datasets. Identified proteins were further validated using Alzheimer’s disease biomarkers and findings from external studies. Subsequent analyses assessed the risk of progression to Alzheimer’s disease. Enrichment tests were conducted to explore associations with pathways, cell types, and organs. Finally, a predictive model was trained to evaluate the performance of the selected proteins14108365568.1Docket 047563-870867

[0028] FIGS. 2A-2H shows results for differential abundance analysis. FIG. 2A shows an overview of the process for identifying plasma biomarkers dysregulated in clinical Alzheimer’s disease. FIGS. 2B-2C shows volcano plots displaying proteins with significantly different abundances when comparing cognitively normal controls (CO) and Alzheimer's disease (AD) patients in Discovery (FIG. 2B) and Replication datasets (FIG. 2C). Red dots indicate proteins with a p-value < 0.05. FIG. 2D shows a scatter plot comparing effect sizes between the Discovery and Replication datasets, focusing on analytes that were significant in the Discovery dataset (1,646 analytes). The central dotted line shows the comparison pattern, with outer dotted lines marking the 95% confidence interval. FIG. 2E shows a volcano plot displaying proteins with significantly different abundances when comparing individuals who converted to AD. FIG. 2F shows a scatter plot comparing effect sizes from the meta-analysis and AD risk analysis. FIG. 2G shows a bar plot comparing effect sizes from the AD risk and clinical status analyses for 22 analytes significant in both the meta-analysis and AD risk analysis. FIG. 2H shows a heatmap displaying correlations between 22 aptamers and AD-related phenotypes.

[0029] FIGS. 3A-3D show replication in external datasets. FIGS. 3A-3C show scatter plots comparing effect sizes from the meta-analysis with those from ROSMAP (FIG. 3A), GNPC (FIG. 3B), and a combined meta-analysis of ROSMAP and GNPC (FIG. 3C), focusing on 456 proteins identified as significant in clinical Alzheimer's disease (AD). Colors indicate significance levels: red for proteins significant in the external dataset and gray for those significant only in the current study. Tire central dotted line shows the comparison pattern, with outer dotted lines marking the 95% confidence interval. FIG. 3D shows an upset plot diplaying the overlap of significant analytes from the current study, ROSMAP, GNPC, and the combined meta-analysis with ROSMAP and GNPC, highlighting the 456 significant proteins.

[0030] FIGS. 4A-4B show pathway and network analyses of AD associated proteins. FIG. 4A shows a heatmap displaying the association of specific genes with biological pathways and processes from GO, IPA. and Reactome. The color coding corresponds to the specific group each pathway belongs to.FIG. 4A shows networks of two key modules M4 and M7 identified through the MEGENA. Nodes represent individual proteins, with up-regulated proteins colored in red and down-regulated proteins in blue. Protein without significant changes in AD are shown in gray. Hub proteins are shown as larger nodes.

[0031] FIGS. 5A-5E show predictive performance of the model with 7 plasma proteins. FIG. 5 A shows a diagram depicting how the datasets were used fortraining and testing the predictive model. FIG. 5B shows receiver operating characteristic (ROC) curves evaluating the model's predictive power in15108365568.1Docket 047563-870867classifying clinical Alzheimer’s disease (AD) status using two independent datasets: the test set from Discovery dataset (black) and the Replication dataset (orange). FIG. 5C shows ROC curves for distinguishing AD biomarker status, including CSF AT status (orange), amyloid PET (pink), and p-tau217 (blue). FIG. 5D shows ROC curves for distinguishing dementia with Lewy bodies (DLB; orange), frontotemporal dementia (FTD; pink), and Parkinson's disease (PD; purple). FIG. 5E shows Kaplan-Meier survival curves for progression to AD after the initial blood draw. The biomarker-negative group (green line) and the biomarker-positive group (red line) are plotted, illustrating the proportion of participants remaining cognitively normal over years of follow-up. Tire p-value indicates the significant difference in progression to AD between individuals predicted to have AD versus controls, according to the prediction model.

[0032] FIG. 6 shows a heatmap displaying correlations between 456 significant aptamers and AD-related phenotypes.

[0033] FIGS. 7A-7H show effect size comparisons with other studies. FIGS. 7A-7B show effect size comparison with the Ali et al. study. FIG. 7A shows proteins identified in the current study. FIG. 7B shows proteins identified by Ali et al. that are associated with AT status based on cerebrospinal fluid (CSF). The central dotted line shows the comparison pattern, with outer dotted lines marking the 95% confidence interval. FIGS. 7C-7D show effect size comparison with the Walker et al. study. FIG. 7C shows proteins identified in the current study. FIG. 7D shows proteins identified by Walker et al. that are associated with incidents of dementia in plasma. The central dotted line shows the comparison patern, with outer doted lines marking the 95% confidence interval. FIGS. 7E-7F show effect size comparison with the Sung et al. study. FIG. 7E shows proteins identified in the current study. FIG. 7F shows proteins identified by Sung et al. that are associated with Alzheimer’s disease (AD) clinical status in plasma. The central doted line shows the comparison patern, with outer doted lines marking the 95% confidence interval. FIGS. 7G-7H show effect size comparison with the Satlecker et al. study. FIG. 7G shows proteins identified in the current study. FIG. 7H show proteins identified by Satlecker et al. that are associated with AD clinical status in plasma. The central doted line shows the comparison patern, with outer doted lines marking the 95% confidence interval.

[0034] FIGS. 8A-8C show analysis of AD biomarkers. FIG. 8A shows analysis of CSF-based AT status. FIG. 8A (left) shows a volcano plot displaying proteins with significantly different abundances between AT- and AT+ groups. FIG. 8A (right) shows a scater plot comparing with effect sizes from the metaanalysis (clinical diagnosis of AD). The central doted line in the scater plot shows the comparison patern, with outer lines marking the 95% confidence interval. FIG. 8B shows analysis of amyloid PET16108365568.1Docket 047563-870867imaging. FIG. 8B (left) shows a volcano plot displaying proteins with significantly different abundances between A- and A+ groups. FIG. 8B (right) shows a scatter plot comparing with effect sizes from the meta-analysis (clinical diagnosis of AD). Hie central dotted line in the scatter plot shows the comparison pattern, with outer lines marking the 95% confidence interval. FIG. 8C shows analysis of plasma p-tau217. FIG. 8C (left) shows a volcano plot displaying proteins with significantly different abundances between T- and T+ groups. FIG. 8C (right) shows a scatter plot comparing with effect sizes from the meta-analysis (clinical diagnosis of AD). The central dotted line in the scatter plot shows the comparison pattern, with outer lines marking the 95% confidence interval.

[0035] FIGS. 9A-9D show analysis of progression to symptomatic Alzheimer’s Disease with single aptamers. FIG. 9A shows a volcano plot displaying protein associations with progression to symptomatic Alzheimer’s disease (AD). Proteins in red were nominally significant (p < 0.05). FIG. 9B shows a Venn diagram showing the overlap between proteins significant in the clinical diagnosis of AD (orange) and those significant in progression to symptomatic AD (gray). FIG. 9C shows a scatter plot comparing effect sizes between the association with AD risk (x-axis) vs the analysis of progression to symptomatic AD (y-axis) for proteins significant in both analyses. Points in red are significant proteins in both analyses. FIG. 9D shows a forest plot displaying the hazard ratios of the top 20 aptamers from the Cox proportional hazards model.

[0036] FIGS. 10A-10C show three interaction networks identified using Ingenuity Pathway Analysis (IPA). The color-coded nodes represent various types of molecules and edges indicate predicted interactions between them.

[0037] FIGS. 11A-11J show evaluation of the model's predictive performance. FIG. 11A shows receiver operating characteristic (ROC) curves evaluating the predictive power in classifying clinical Alzheimer’s disease (AD). The following datasets were used: test set from the Discovery dataset with the proteomic model (solid black) and baseline model (dashed black), and the Replication dataset with the proteomic model (solid orange), baseline model (dashed orange), and baseline model including APOE s4 genotype (pink). FIG. 1 IB shows ROC curves evaluating tire model's predictive power in classifying AD biomarkers. Tire biomarkers included: CSF-based AT status with the proteomic model (solid orange) and baseline model (dashed orange), amyloid PET imaging with the proteomic model (solid pink) and baseline model (dashed pink), and plasma p-tau217 with the proteomic model (solid blue) and baseline model (dashed blue). FIG. 11C shows ROC curves evaluating the model's predictive power external datasets. ROSMAP (solid orange) and baseline model (dashed orange), GNPC (solid blue) and baseline model (dashed blue) are shown. FIG. 1 ID shows ROC curves evaluating the model's predictive power17108365568.1Docket 047563-870867for CSF-based AT status (AT- vs. AT+) by dataset. The following datasets were used: both datasets with the proteomic model (solid orange) and baseline model (dashed orange), discovery dataset with the proteomic model (solid pink) and baseline model (dashed pink), and replication dataset with the proteomic model (solid blue) and baseline model (dashed blue). FIG. 1 IE shows ROC curves evaluating the model's predictive power in classifying amyloid PET imaging status (A- vs. A+) with the proteomic model (solid pink) and baseline model (dashed pink). FIG. 1 IF shows ROC curves assessing the model's predictive power in classifying plasma p-tau217 status (T- vs. T+) with the proteomic model (solid blue) and baseline model (dashed blue). FIG. 11G shows ROC curves evaluating the model's predictive power in classifying CSF-based AT status (AT- vs. AT+) by cohort. The following cohorts were analyzed: both cohorts with the proteomic model (solid orange) and baseline model (dashed orange), Knight ADRC with the proteomic model (solid pink) and baseline model (dashed pink), and Stanford ADRC with the proteomic model (solid blue) and baseline model (dashed blue). FIG. 11H shows ROC curves evaluating the predictive power in classifying other neurodegenerative diseases. Tire following diseases were analyzed: DLB with the proteomic model (solid orange) and baseline model (dashed orange), FTD with the proteomic model (solid pink) and baseline model (dashed pink), and PD with the proteomic model (solid blue) and baseline model (dashed blue). FIG. Ill shows a forest plot illustrating the area under the curve (AUC) for 7 aptamers used in the predictive model, along with the AUCs for both the baseline and proteomic models. FIG. 11 J shows a plot illustrating the change in mean AUC from cross-validation as the lambda value for lasso regression changes. The numbers on the upper x-axis indicate the number of selected variables in lasso regression, with the selected lambda value marked by a dotted line.

[0038] FIGS. 12A-12G show analysis of progression in cognitive impairment and progression to symptomatic AD with predictive model. FIGS. 12A-12C show changes in Cognitive Dementia Rating-Sum of Boxes (CDR-SB) scores (y-axis) overtime (x-axis) in both groups (FIG. 12A), the Alzheimer's disease (AD) group (FIG. 12B), and the cognitively normal group (FIG. 12C), as categorized by the prediction model. FIGS. 12D-12F show boxplots displaying the rate of CDR-SB changes per year in both groups (FIG. 12D), the AD group (FIG. 12E), and the cognitively normal group (FIG. 12F), categorized by the prediction model. FIG. 12G shows ROC curves evaluating the model's predictive power in classifying progression to symptomatic AD, with three follow-up periods: 5 -year (orange), 10-year (pink), and 15-year (blue).

[0039] FIG. 13 shows prediction of the clinical status of Alzheimer’s disease using an improved predictive model comprising age, sex. the seven target proteins described herein, along with p-tau217 levels.18108365568.1Docket 047563-870867

[0040] FIG. 14 shows prediction of the clinical status of Alzheimer’s disease using an improved predictive model comprising age, sex, a single target proteins (e.g., TP5) described herein, both with an without inclusion of p-tau217 levels.

[0041] FIGS. 15A-15F show orthogonal validation of the SomaLogic-based model on the Alamar and Olink platforms, showing comparison of the predictive power of proteins between the platforms.Demographic information for the Knight ADRC and Stanford ADRC cohorts utilized, including details on age (with standard deviation), percentage of males, and proportion of APOE4+ individuals for each group are shown in TABLE 21. FIG. 15A shows ROC curves showing the predictive power of singleprotein models, adjusted for sex and age. FIG. 15B shows ROC curves for two-protein models, adjusted for sex and age. FIG. 15C depicts a whisker plot showing AUC values for two-protein models in predicting CSF AT status, amyloid PET status, and p-Tau217 positivity, with adjustments for sex and age. The whisker plot includes 95% confidence intervals. FIGS. 15D-15F show comparison of the predictive power of proteins between SomaLogic and Olink platforms. FIG. 15D shows ROC curves comparing single-protein models, adjusted for sex, age, and plate ID. FIG. 15E shows ROC curves for four-protein models, adjusted for sex, age, and plate ID. FIG. 15F depicts a whisker plot showing AUC values for single-protein models, adjusted for sex, age, and plate ID, with 95% confidence intervals. Statistical metrics from the replication analysis are shown in TABLE 22, including AUC, positive predictive value (PPV), negative predictive value (NPV), specificity, sensitivity, and accuracy, are provided for each model.

[0042] FIGS. 16A-16C show orthogonal validation of the SomaLogic-based model on the Olink platform in Stanford Cohort. Demographic information for the Olink platform data in the Stanford ADRC cohort, including age (with standard deviation), percentage of males, and proportion of APOE4+ individuals for each group is shown in TABLE 23. FIG. 16A shows ROC curves of individual aptamers in distinguishing AD from CO, with all models adjusted for sex, age, and Plate ID. FIG. 16B shows ROC curves of a four-protein model on the Olink platform, adjusted for sex, age, and Plate ID. FIG. 16C shows a whisker plot showing AUCs of individual-protein and four-protein models across platforms for predicting clinical status of AD with 95% confidence intervals. Statistical metrics from the replication analysis, including AUC, positive predictive value (PPV), negative predictive value (NPV), specificity, sensitivity, and accuracy, are provided for each model in TABLE 25.

[0043] FIGS. 17A-17B show orthogonal validation of the SomaLogic-based model on the Olink platform in BBRC Cohort. Demographic information for the Olink platform data in the BBRC cohort, including age (with standard deviation) and percentage of males is shown in TABLE 25. FIG. 17A shows19108365568.1Docket 047563-870867ROC curves of a four-protein model on both the Soma and Olink platform, adjusted for sex and age. FIG. 17B shows ROC curves of a seven-protein model on the Soma platform, adjusted for sex and age.DETAILED DESCRIPTION OF THE INVENTIONI. Terminology

[0044] So that the present invention may be more readily understood, certain terms are first defined. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of the invention pertain. Many methods and materials similar, modified, or equivalent to those described herein can be used in the practice of the embodiments of the present invention without undue experimentation, the preferred materials and methods are described herein. In describing and claiming the embodiments of the present invention, the following terminology will be used in accordance with the definitions set out below.

[0045] Tire term "a" or "an" entity may refer to one or more of that entity; for example, “a target protein” may be understood to represent one or more target proteins. As such, tire temrs "a" (or "an"), "one or more," and "at least one" can be used interchangeably herein.

[0046] Furthermore, "and / or" where used herein in a phrase listing alternative specified features or components may refer to a specific disclosure of each of the specified features or components without each other, or with each other in combination. The tenn “and / or” when used in a phrase listing components A, B, and C in the alternative, such as “A, B, and / or B” is intended to include “A” (alone), “B” (alone), “C” (alone), “A and B”, “A and C”, “B and C”, or “A, B, and C”

[0047] Tire term “about”, as used herein, may refer to variation of a numerical quantity that can occur, for example, through typical measuring techniques and equipment, with respect to any quantifiable variable, including, but not limited to, mass, volume, time, distance, and amount. Further, given solid and liquid handling procedures used in the real world, there is certain inadvertent error and variation that is likely through differences in the manufacture, source, or purity of the ingredients used to make the compositions or carry out the methods and the like. The term “about” also encompasses these variations, which can be up to ± 5%, but can also be ± 4%, 3%, 2%,1%, etc.

[0048] For recitation of numeric ranges herein, each intervening number at the same decimal level there between (e.g., the same degree of precision) is explicitly contemplated. For example, for the range of 6-9, the numbers 6, 7, 8 and 9 are explicitly contemplated, and for the range 6.0-7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6,9, and 7.0 are explicitly contemplated.20108365568.1Docket 047563-870867

[0049] The term “AP” refers to peptides derived from a region in the carboxy tenninus of a larger protein called amyloid precursor protein (APP). The gene encoding APP is located on chromosome 21. There are many forms of A that may have toxic effects: Ap peptides are typically 37-43 amino acid sequences long, though they can have truncations and modifications changing their overall size. They can be found in soluble and insoluble compartments, in monomeric, oligomeric and aggregated forms, intracellularly or extracellularly, and may be complexed with other proteins or molecules. The adverse or toxic effects of A may be attributable to any or all of the above noted forms, as well as to others not described specifically. For example, two such Ap isoforms include A 40 and Ap42; with the A 42 isoform being particularly fibrillogenic or insoluble and associated with disease states. The term “Ap” typically refers to a plurality of A species without discrimination among individual A species. Specific A species are identified by the size of the peptide, e.g., A 42, A 40, A 38 etc.

[0050] “A amyloidosis” may be defined as it is clinically understood (e.g., abnormal A deposition in the brain). A subject that is detennined to have Ap amyloidosis is referred to herein as “amyloid positive,” while a subject that is determined to not have A amyloidosis is referred to herein as “amyloid negative.” There are accepted indicators of Ap amyloidosis in the art. At the time of this disclosure, A amyloidosis can be directly measured by amyloid imaging (e.g., PiB PET, fluorbetapir, or other imaging methods known in the art) or indirectly measured by decreased cerebrospinal fluid (CSF) A 42 or a decreased CSF A 42 / 40 ratio. [11C]PIB-PET imaging with mean cortical binding potential (MCBP) score > 0.18 may be an indicator of Ap amyloidosis, as is cerebral spinal fluid (CSF) A 42 concentration of about 1 ng / ml measured by immunoprecipitation and mass spectrometry (IP / MS)). Alternatively, a cut-off ratio for CSF A 42 / 40 that maximizes the accuracy in predicting amyloid-positivity as detennined by PIB-PET can be used. Values such as these, or others known in the art and / or used in the examples, may be used alone or in combination to clinically confirm Ap amyloidosis. See, for example, Klunk W E et al. Ann Neurol 55(3) 2004, Fagan A M et al. Ann Neurol, 2006, 59(3), Patterson et. al, Annals of Neurology, 2015, 78(3): 439-453, or Johnson et al., J. Nuc. Med., 2013, 54(7): 1011-1013, each hereby incorporated by reference in its entirety. Subjects with Ap amyloidosis may or may not be symptomatic, and symptomatic subjects may or may not satisfy the clinical criteria for a disease associated with Ap amyloidosis. Non-limiting examples of symptoms associated with A amyloidosis may include impaired cognitive function, altered behavior, abnormal language function, emotional dysregulation, seizures, dementia, and impaired nervous system structure or function. Diseases associated with Ap amyloidosis include but are not limited to Alzheimer’s Disease (AD). Subjects with Ap amyloidosis are at an increased risk of developing a disease associated with Ap amyloidosis.21108365568.1Docket 047563-870867

[0051] A '‘clinical sign of Ap amyloidosis” may refer to a measure of Ap deposition known in the art. Clinical signs of A|3 amyloidosis may include, but are not limited to, A |3 deposition identified by amyloid imaging (e g. PiB PET, fluorbetapir, or other imaging methods known in the art) or by decreased cerebrospinal fluid (CSF) Ap42 or A[342 / 40 ratio. See, for example, Klunk WE et al. Ann Neurol 55(3) 2004, and Fagan AM et al. Ann Neurol 59(3) 2006, each hereby incorporated by reference in its entirety. Clinical signs of Ap amyloidosis may also include measurements of the metabolism of Ap, in particular measurements of A[342 metabolism alone or in comparison to measurements of the metabolism of other A|3 variants (e.g. A[337, A038, A|339, A04O, and / or total A|3), as described in U.S. Patent Serial Nos. 14 / 366,831, 14 / 523,148 and 14 / 747,453, each hereby incorporated by reference in its entirety. Additional methods are described in Albert et al. Alzheimer’s & Dementia 2007 Vol. 7, pp. 170-179; McKhann et al., Alzheimer’s & Dementia 2007 Vol. 7, pp. 263-269; and Sperling et al., Alzheimer’s & Dementia 2007 Vol. 7. pp. 280-292, each hereby incorporated by reference in its entirety. Importantly, a subject with clinical signs of A amyloidosis may or may not have symptoms associated with A[3 deposition. Yet subjects with clinical signs of Ap amyloidosis are at an increased risk of developing a disease associated with Ap amyloidosis.

[0052] A “candidate for amyloid imaging” refers to a subject that has been identified by a clinician as an individual for whom amyloid imaging may be clinically warranted. As a non-limiting example, a candidate for amyloid imaging may be a subject with one or more clinical signs of Ap amyloidosis, one or more Ap plaque associated symptoms, or combinations thereof. A clinician may recommend amyloid imaging for such a subject to direct his or her clinical care. As another non-limiting example, a candidate for amyloid imaging may be a potential participant in a clinical trial for a disease associated with Ap amyloidosis (either a control subject or a test subject).

[0053] An “Ap plaque associated symptom” may refer to any symptom caused by or associated with the formation of amyloid plaques, being composed of regularly ordered fibrillar aggregates called amyloid fibrils. Exemplary Ap plaque associated symptoms may include, but are not limited to, neuronal degeneration, impaired cognitive function, impaired memory, altered behavior, emotional dysregulation, seizures, impaired nervous system structure or function, and an increased risk of development or worsening of Alzheimer’s disease. Neuronal degeneration may include a change in structure of a neuron (including molecular changes such as intracellular accumulation of toxic proteins, protein aggregates, etc. and macro level changes such as change in shape or length of axons or dendrites, change in myelin sheath composition, loss of myelin sheath, etc.), a change in function of a neuron, a loss of function of a neuron, death of a neuron, or any combination thereof. Impaired cognitive function may include but is not limited to difficulties with memory, attention, concentration, language, abstract thought, creativity, executive 22108365568.1Docket 047563-870867function, planning, and organization. Altered behavior may include, but is not limited to, physical or verbal aggression, impulsivity, decreased inhibition, apathy, decreased initiation, changes in personality, abuse of alcohol, tobacco or drugs, and other addiction-related behaviors. Emotional dysregulation may include, but is not limited to, depression, anxiety, mania, irritability, and emotional incontinence.Seizures may include but are not limited to generalized tonic-clonic seizures, complex partial seizures, and non-epileptic. psychogenic seizures. Impaired nervous system structure or function may include, but is not limited to, hydrocephalus, Parkinsonism, sleep disorders, psychosis, impairment of balance and coordination. This may include motor impairments such as monoparesis, hemiparesis, tetraparesis, ataxia, ballismus and tremor. This also may include sensory' loss or dysfunction including olfactory, tactile, gustatory , visual and auditory sensation. Furthermore, this may include autonomic nervous system impairments such as bowel and bladder dysfunction, sexual dysfunction, blood pressure and temperature dysregulation. Finally, this may include hormonal impairments attributable to dysfunction of the hypothalamus and pituitary gland such as deficiencies and dysregulation of growth hormone, thyroid stimulating hormone, lutenizing hormone, follicle stimulating hormone, gonadotropin releasing hormone, prolactin, and numerous other hormones and modulators.[0054J The term “tau” may refer to a plurality of isoforms encoded by the gene MAPT (or homolog thereof), as well as species thereof that are C-terminally truncated in-vivo, N-terminally truncated in-vivo, post-translationally modified in-vivo, or any combination thereof. As used herein, the terms “tau” and “tau protein” and “tau species” may be used interchangeably. In many animals, including but not limited to humans, non-human primates, rodents, fish, cattle, frogs, goats, and chicken, tau is encoded by the gene MAPT. In animals where the gene is not identified as MAPT. a homolog may be identified by methods well known in the art.

[0055] In humans, there are six isofomis of tau that are generated by alternative splicing of exons 2, 3, and 10 of MAPT. These isoforms range in length from 352 to 441 amino acids. Exons 2 and 3 encode 29-amino acid inserts each in the N-terminus (called N). and full-length human tau isoforms may have both inserts (2N), one insert (IN), or no inserts (ON). All frill-length human tau isoforms also have three repeats of the microtubule binding domain (called R). Inclusion of exon 10 at the C-terminus leads to inclusion of a fourth microtubule binding domain encoded by exon 10. Hence, full-length human tau isoforms may be comprised of four repeats of the microtubule binding domain (exon 10 included: Rl, R2, R3, and R4) or three repeats of the microtubule binding domain (exon 10 excluded: Rl, R3, and R4). Human tau may or may not be post-translationally modified. For example, it is known in the art that tau may be phosphorylated, ubiquinated, glycosylated, and glycated. Human tau also may or may not be proteolytical ly processed in-vivo at the C-terminus, at the N-terminus, or at the C-terminus and the N- 23108365568.1Docket 047563-870867terminus. Accordingly, the term “human tau” encompasses the 2N3R, 2N4R, 1N3R, 1N4R, 0N3R, and 0N4R isoforms, as well as species thereof that are C-terminally truncated in-vivo, N-terminally truncated in-vivo, post-translationally modified in-vivo, or any combination thereof. Alternative splicing of the gene encoding tau similarly occurs in other animals.

[0056] An antibody, as used herein, refers to a complete antibody as understood in the art, i.e. consisting of two heavy chains and two light chains, and also to any antibody-like molecule that has an antigen binding region, including, but not limited to, antibody fragments such as Fab’, Fab, F(ab’)2, single domain antibodies, Fv, and single chain Fv. Tire tenn antibody also refers to a polyclonal antibody, a monoclonal antibody, a chimeric antibody and a humanized antibody. The techniques for preparing and using various antibody-based constructs and fragments are well known in the art. Means for preparing and characterizing antibodies are also well known in the art (See, e.g. Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, 1988; herein incorporated by reference in its entirety).

[0057] As used herein, the term “aptamer” may refer to a polynucleotide, generally an RNA or DNA that has a useful biological activity in terms of biochemical activity, molecular recognition or binding attributes. An aptamer may have molecular binding activity such as binging to a target molecule at a specific epitope. It is generally accepted that an aptamer, which is specific in its binding to a target protein as described (e.g., in Section IV), may be synthesized and / or identified by in-vitro evolution methods. The aptamer may comprise a phosphorothioate backbone (e.g., mono- or di-thioated). or comprise other chemical modifications (e.g. for enhanced binding or stability of the aptamer). Means for preparing and characterizing aptamers, including by in-vitro evolution methods, are well known in the art. See, for instance US 7,939,313, herein incorporated by reference in its entirety.

[0058] The term "binds to" may refer to a binding agent or binding molecule (e.g., an antibody or antigen-binding fragment thereof, or an aptamer) that binds to an epitope on a target molecule. The binding may be via a paratope (e.g., antigen binding domain) of the binding agent or binding molecule. The binding interaction may entail recognition between the antigen binding domain and the epitope. A binding molecule may “specifically bind” to an epitope when it binds to that epitope via its paratope (e.g., antigen-binding domain) more readily than the paratope would bind to an unrelated (e.g., random) epitope. The binding agent or binding molecule may not cross react to a significant extent with other proteins that are not a target protein (e.g., as described herein Section IV).II. Subjects24108365568.1Docket 047563-870867

[0059] The methods as described herein (e.g., in Section V) may be performed on a subject, or performed with a biological sample obtained from a subject. As used herein, the term “subject” may refer to a mammal. The mammals may include, but are not limited to, humans, primates, livestock, rodents, or a pet mammal. Tire subject may be a laboratory animal, optionally wherein the laboratory animal is genetically engineered.

[0060] The subject may be a healthy subject. The subject may have no clinical signs or symptoms of a neurodegenerative disease. Tire subject may be undiagnosed with a neurodegenerative disease. The subject may be amyloid negative. The subject may have no dementia. The subject may have a clinical dementia rating (CDR) score of 0.

[0061] A subject as described herein may be at risk of developing a neurodegenerative disease. The subject may have one or more clinical sign or symptom of a neurodegenerative disease. The subject may¬ be previously diagnosed ith a neurodegenerative disease. The subject may be amyloid positive. Tire subject may have dementia. The subject may have a clinical dementia rating (CDR) score of 0.5 to 1.0. The subject may have a CDR score of > 1.0 to 2.0 (e.g.., such as in moderate Alzheimer’s disease). The subject may have a CDR score of > 2.0.

[0062] The neurodegenerative disease as described herein may be a tauopathy, optionally wherein the tauopathy is selected from Alzheimer’s disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), or frontotemporal lobar degeneration (FTLD).

[0063] In each of the methods described herein, a subject may or may not be symptomatic. An “asymptomatic subject,” as used herein, may refer to a subject that does not show any signs or symptoms of Alzheimer’ss disease. Alternatively, a subject may exhibit signs or symptoms of a tauopathy (e.g.. memory loss, misplacing things, changes in mood or behavior, etc.,) but not show sufficient cognitive or functional impairment for a clinical diagnosis. A symptomatic or an asymptomatic subject may have brain amyloidosis; however, prior knowledge of amyloidosis is not a requisite for treatment. In still further embodiments, a subject may have AD. In any of the aforementioned embodiments, a subject may carry one of the gene mutations known to cause an inherited tauopathy (e.g., Alzheimer’s disease). In alternative embodiments, a subject may not carry a gene mutation known to cause an inherited tauopathy (e.g., Alzheimer’s disease).

[0064] A subject may be waiting for medical care or treatment, may be under medical care or treatment, or may have received medical care or treatment. The subject may be alive, or may be deceased (e.g., as in the case of performing a method as described herein on or with a biological sample).25108365568.1Docket 047563-870867

[0065] As used herein, the term “control”, “normal”, or “healthy” subject or population of subjects may refer to that subject or population not having any clinical signs or symptoms of a neurodegenerative disease. For instance, a control subject or population would not have a tauopathy, Alzheimer’s disease, Ab amyloidosis, or a clinical disease associated with Ab amyloidosis, which may optionally be confirmed by qualitative or quantitative test results.III. Biological samples

[0066] Suitable biological samples in a method as described herein (e.g., in Section V) include a blood sample obtained from a subject as described herein (e.g., in Section II). The blood sample may be derived from blood, preferably peripheral (or circulating) blood. The blood sample can be whole blood, plasma or serum, although plasma is typically preferred.

[0067] The blood sample should typically be large enough to allow the measurement of the level of target proteins, and optionally a marker of neurodegeneration (e.g., p-tau217). A blood sample may be from about 0.5 ml to about 10 ml. More than one sample may be pooled for a particular time point. The blood sample may be collected directly as part of the method. Alternatively, a previously-obtained blood sample may be used. Methods of collecting a blood sample are well known in the art. For example, venipuncture, with or without a catheter, may be used to collect a blood sample, hr another example, a finger stick, or the equivalent, may be used to collect a blood sample. Additives may or may not be added to the collected blood prior to plasma separation. Suitable additives include citrate, heparin, EDTA, Tween, and protease inhibitors.

[0068] Suitable biological samples in a method as described herein (e.g., in Section V) may also include a cerebrospinal fluid (CSF) sample obtained from a subject as described herein (e.g., in Section II).

[0069] Tire methods as described herein may be performed by using or analyzing a biological sample obtained from a subject at a timepoint that is prior to, or is concurrent with, the timepoint that the method is perfonned. Tire timepoint of the obtainment of the biological sample from the subject may be the same day that the method is performed; or 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 8 days, 9 days, 10 days, 1 1 days, 12 days, 13 days, 14 days, 15-30 days inclusive, 31-45 days inclusive, 46-60 days inclusive, 61-90 days inclusive, 90-365 days inclusive, or greater than one year prior to the timepoint that the method is performed.IV. Target proteins26108365568.1Docket 047563-870867

[0070] The methods as described herein (e.g., in Section V) involve determination of the level of or amount of one or more target proteins. The target proteins identified herein are especially well suited as biomarkers for Alzheimer’s disease, particularly when used in one of the disclosed methods. Tire one or more target protein of any method as described herein (e.g. Section V) may selected from any of the proteins listed in LIST 1, LIST 2, LIST 3, LIST 4. LIST 5, LIST 6, LIST 7, or as shown in TABLE 17. Amino acid sequences of each of the described target proteins can be obtained from a publicly available depository', optionally at the websites of the Universal Protein resource (UniProt) of the Swiss-Prot, TrEMBL and PIR-PSD databases, and the National Center for Biotechnology Information (NCBI) database of the National Institute of Health (NIH). The UniProt accession identification numbers of certain target proteins are provided herein, which may be searched at the publicly available depository to retrieve the amino acid sequence of the protein, or domains or fragments thereof.Included target proteins

[0071] In some embodiments of a method as described herein (e.g., in Section V), the one or more target protein is selected from any one of the proteins provided in LIST 1, wherein LIST 1 comprises the proteins of: ACHE (e.g., Acetylcholinesterase, UniProt ID:P22303); PF4 (e.g.. Platelet factor 4, UniProt ID:P02776); IFT22 (e.g., Intraflagellar transport protein 22 homolog, UniProt ID:Q9H7X7); ASB9 (e.g., Ankyrin repeat and SOCS box protein 9, UniProt ID:Q96DX5); PLTP (e.g., Phospholipid transfer protein, UniProt ID:P55058); LTBP4 (e.g., Latent-transforming growth factor beta-binding protein 4, UniProt ID:Q8N2S1): BAG4 (e.g., BAG family molecular chaperone regulator 4, UniProt ID:O95429): PDCD6IP (e.g.. Programmed cell death 6-interacting protein, UniProt ID:Q8WUM4); SPARCL1 (e.g., SPARC-like protein 1, UniProt ID:Q14515): OAF (e.g., Out at first protein homolog, UniProt ID:Q86UD1); MZF1 (e.g., Myeloid zinc finger 1, UniProt ID:P28698); ODC1 (e.g., Ornithine decarboxylase, UniProt ID:P11926); TATDN3 (e.g., Putative deoxyribonuclease TATDN3, UniProt ID:Q17R31); SMOC1 (e.g., SPARC -related modular calcium -binding protein 1, UniProt ID:Q9H4F8); FLRT2 (e.g., Leucine-rich repeat transmembrane protein FLRT2, UniProt ID:O43155); NRP2 (e.g.. Neuropilin-2, UniProt ID:O60462); FAM118A (e.g.. Protein FAM118A, UniProt ID:Q9NWS6): or REG4 (e.g., Regenerating islet-derived protein 4, UniProt ID:Q9BYZ8). In some embodiments of a method as described herein (e.g., in Section V), the first target protein (e.g., “TP1”) is a protein selected from LIST 1. In some embodiments, the target protein selected from LIST 1 is ACHE.

[0072] In some embodiments of a method as described herein (e.g., in Section V), the one or more target protein is selected from any one of the proteins provided in LIST 2, wherein LIST 2 comprises the proteins of: CTF1 (e.g., Cardiotrophin-1, UniProt ID:Q16619); CHL1 (e.g., Neural cell adhesion27108365568.1Docket 047563-870867molecule Ll-like protein, UniProt ID:000533); ADGRF1 (e.g, Adhesion G-protein coupled receptor Fl, UniProt ID:Q5T601); PCDHAC2 (e.g., Protocadhenn alpha-C2, UniProt ID:Q9Y5I4); or NRBP1 (e.g. Nuclear receptor-binding protein, UniProt ID:Q9UHY1). In some embodiments of a method as described herein (e.g, in Section V), the second target protein (e.g, "TP2") is a protein selected from UIST 2. In some embodiments, the target protein selected from UIST 2 is CTF1.

[0073] In some embodiments of a method as described herein (e.g, in Section V), the one or more target protein is selected from any one of the proteins provided in UIST 3, wherein UIST 3 comprises the proteins of: CPUX2 (e.g, Complexin-2, UniProt ID:Q6PUV4); SETD3 (e.g, Histone-lysine N-methyltransferase setd3. UniProt ID:Q86TU7); GRB10 (e.g. Growth factor receptor-bound protein 10, UniProt ID:Q13322); ARHGAP45 (e.g, Rho GTPase-activating protein 45, UniProt 1D:Q92619):EEF1A1 (e.g, Elongation factor 1-alpha 1, UniProt ID:P68104): DOK1 (e.g. Docking protein 1, UniProt ID:Q99704); RAB1B (e.g, Ras-related protein Rab-IB, UniProt ID:Q9H0U4); SMOC2 (e.g, SPARC-related modular calcium-binding protein 2, UniProt ID:Q9H3U7); PTN (e.g, Pleiotrophin, UniProt ID:P21246); IAPP (e.g. Islet amyloid polypeptide, UniProt ID:P10997); CDK16 (e g, Cyclin-dependent kinase 16, UniProt ID:Q00536): RAB3C (e.g, Ras-related protein Rab-3C, UniProt ID:Q96E17); DSTN (e.g. Destrin, UniProt 1D:P6O981); UBE2H (e.g, Ubiquitin-conjugating enzyme E2 H, UniProt ID:P62256); ODC1 (e.g, Ornithine decarboxylase, UniProt ID:P11926): MATN2 (e.g, Matrilin-2, UniProt ID:000339); AGRP (e.g. Agouti-related protein, UniProt ID:000253); NPTXR (e.g. Neuronal pentraxin receptor, UniProt ID:O95502); TMEFF1 (e.g, Tomoregulin-1, UniProt ID:Q8IYR6); THY1 (e.g, Thy-1 membrane glycoprotein, UniProt ID:P04216); SCG3 (e g, Secretogranin-3. UniProt ID:Q8WXD2); NMB (e.g, Neuromedin-B, UniProt ID:P08949); GXYLT1 (e.g. Glucoside xylosyltransferase 1, UniProt ID:Q4G148); WFIKKN2 (e.g, WAP, Kazal, immunoglobulin, Kunitz and NTR domain -containing protein 2, UniProt ID:Q8TEU8); HIBCH (e.g, 3 -hydroxyisobutyryl -Co A hydrolase, mitochondrial, UniProt ID:Q6NVY1); SCN2B (e.g. Sodium channel subunit beta-2, UniProt ID:O60939); GAS1 (e.g. Growth arrest-specific protein 1, UniProt ID:P54826); TFF3 (e.g. Trefoil factor 3, UniProt ID:Q07654); FABP3 (e.g. Fatty acid-binding protein, heart, UniProt ID:P05413); FABP4 (e.g. Fatty acid-binding protein, adipocyte, UniProt ID:P15090); VEGFA (e.g.. Isoform L-VEGF165, UniProt ID:P15692); or CPLX1 (e.g, Complexin-1, UniProt ID:O14810). In some embodiments of a method as described herein (e.g, in Section V), the third target protein (e.g, “TP3”) is a protein selected from LIST 3. In some embodiments, the target protein selected from LIST 3 is CPLX2.

[0074] In some embodiments of a method as described herein (e.g, in Section V), the one or more target protein is selected from any one of the proteins provided in LIST 4, wherein LIST 4 comprises the proteins of: ATI (e.g. Synaptic vesicle membrane protein VAT-1 homolog, UniProt ID:Q99536);28108365568.1Docket 047563-870867SLITRK3 (e.g., SLIT and NTRK-like protein 3, UniProt ID:O94933); G0T2 (e.g., Aspartate aminotransferase, mitochondrial, UniProt ID:P00505); UGDH (e.g., UDP-glucose 6-dehydrogenase, UniProt ID:060701); PLOD2 (e.g., Procollagen-lysine,2-oxoglutarate 5-dioxygenase 2, UniProt ID:000469); CTBP1 (e.g., C-terminal-binding protein 1, UniProt ID:Q13363); SETD3 (e.g., Histonelysine N-methyltransferase setd3, UniProt ID:Q86TU7); EEF1A1 (e.g., Elongation factor 1-alpha 1, UniProt ID:P68104); PMM2 (e.g.. Phosphomannomutase 2. UniProt ID:O15305): CDK16 (e.g., Cyclin-dependent kinase 16, UniProt ID:Q00536); ICAM5 (e.g.. Intercellular adhesion molecule 5, UniProt ID:Q9UMF0); ESMI (e.g., Endothelial cell-specific molecule 1, UniProt ID:Q9NQ30); CPB1 (e.g., Carboxypeptidase B, UniProt ID:P15086); DHRS9 (e.g., Dehydrogenase / reductase SDR family member 9, UniProt ID:Q9BPW9); CCNYL1 (e.g., Cyclin-Y-like protein 1, UniProt ID:Q8N7R7); ACAT1 (e.g., Acetyl-CoA acetyltransferase, mitochondrial, UniProt ID:P24752); SCO2 (e.g., Protein SCO2 homolog, mitochondrial, UniProt ID: 043819); or CES1 (e.g., Liver carboxylesterase 1, UniProt ID:P23141). In some embodiments of a method as described herein (e.g., in Section V), the fourth target protein (e.g., “TP4”) is a protein selected from LIST 4. In some embodiments, the target protein selected from LIST 4 is VAT 1.[0075 J In some embodiments of a method as described herein (e.g., in Section V), the one or more target protein is selected from any one of the proteins provided in LIST 5, wherein LIST 5 comprises the proteins of: SPC25 (e.g., Kinetochore protein Spc25, UniProt ID:Q9HBM1); HSPD1 (e.g., 60 kDaheat shock protein, mitochondrial, UniProt ID:P10809); ELAVL2 (e.g., ELAV-like protein 2, UniProt ID:Q12926); B3GAT1 (e.g., Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase 1, UniProt ID:Q9P2W7); NLGN1 (e g., Neuroligin-1, UniProt ID:Q8N2Q7); MENT (e.g.. Protein MENT, UniProt ID:Q9BUN1); GSTA1 (e.g., Glutathione S-transferase AL UniProt ID:P08263); SEMA4G (e.g., Semaphorin-4G, UniProt ID:Q9NTN9); CUZD1 (e.g., CUB and zona pellucida-like domain-containing protein 1, UniProt ID:Q86UP6); NEFL (e.g., Neurofilament light polypeptide, UniProt ID:P07196); GATM (e.g., Glycine amidinotransferase, mitochondrial, UniProt ID:P50440); TBCA (e.g., Tubulinspecific chaperone A, UniProt ID:O75347); or LRRN1 (e.g., Leucine-rich repeat neuronal protein 1, UniProt ID:Q6UXK5). In some embodiments of a method as described herein (e.g., in Section V), the fifth target protein (e.g., ‘TP5”) is a protein selected from LIST 5. In some embodiments, the target protein selected from LIST 5 is SPC25.

[0076] In some embodiments of a method as described herein (e.g., in Section V), the one or more target protein is selected from any one of the proteins provided in LIST 6, wherein LIST 6 comprises the proteins of: FAHD2A (e.g., Fumarylacetoacetate hydrolase domain-containing protein 2A, UniProt ID:Q96GK7); TATDN3 (e.g., Putative deoxyribonuclease TATDN3, UniProt ID:Q17R31); NPTN (e.g.,29108365568.1Docket 047563-870867Neuroplastin, UniProt ID:Q9Y639); HAVCR1 (e.g., Hepatitis A virus cellular receptor 1, UniProt ID:Q96D42); PCDHGA12 (e.g., Protocadherin gamma-A12, UniProt ID:060330); 0LR1 (e.g., Oxidized low-density lipoprotein receptor 1, UniProt ID:P78380); CXCU12 (e.g., Stromal cell-derived factor 1, UniProt ID:P48061); COU11A2 (e.g., Collagen alpha-2(XI) chain, UniProt ID:P13942); UECT2 (e.g., Ueukocyte cell -derived chemotaxin-2, UniProt ID:O14960): PSME1 (e.g., Proteasome activator complex subunit 1, UniProt ID:Q06323): RPS6KB1 (e.g., Ribosomal protein S6 kinase beta-1. UniProt ID:P23443); or THY1 (e.g., Thy-1 membrane glycoprotein, UniProt ID:P04216). In some embodiments of a method as described herein (e.g., in Section V), the sixth target protein (e.g., “TP6”) is a protein selected from LIST 6. In some embodiments, the target protein selected from LIST 6 is FAHD2A.[0077J In some embodiments of a method as described herein (e.g., in Section V), the one or more target protein is selected from any one of the proteins provided in LIST 7, wherein LIST 7 comprises the proteins of: NPTXR (e.g., Neuronal pentraxin receptor, UniProt ID:O95502); BTG4 (e.g., Protein BTG4, UniProt ID:Q9NY30); FLRT2 (e.g., Leucine-rich repeat transmembrane protein FLRT2, UniProt ID:O43155); TATDN3 (e.g., Putative deoxyribonuclease TATDN3, UniProt ID:Q17R31); NECAP2 (e.g., Adaptin ear-binding coat-associated protein 2. UniProt ID:Q9NVZ3); TMEM132D (e g., Transmembrane protein 132D, UniProt 1D:Q14C87); APOM (e.g., Apolipoprotein M, UniProt ID:O95445): SCN2B (e.g.. Sodium channel subunit beta-2, UniProt ID:O60939); RPS6KB1 (e.g., Ribosomal protein S6 kinase beta-1, UniProt ID:P23443); AMY2B (e.g., Alpha-amylase 2B, UniProt ID:P 19961); DSTN (e.g., Destrin, UniProt ID:P60981); ENTPD1 (e.g., Ectonucleoside triphosphate diphosphohydrolase 1, UniProt ID:P49961); FASN (e.g., Fatty acid synthase, UniProt ID:P49327); THY1 (e.g., Thy-1 membrane glycoprotein, UniProt ID:P04216); PTN (e.g., Pleiotrophin, UniProt ID:P21246); R0R1 (e.g.. Inactive tyrosine-protein kinase transmembrane receptor R0R1, UniProt ID:Q01973); GAS1 (e.g.. Growth arrest-specific protein 1 , UniProt ID:P54826); ODC 1 (e.g., Ornithine decarboxylase, UniProt ID:P11926); KIR2DS2 (e.g., Killer cell immunoglobulin-like receptor 2DS2, UniProt ID:P43631); CPLX2 (e.g., Complexin-2, UniProt ID:Q6PUV4); FABP4 (e.g., Fatty acid-binding protein, adipocyte, UniProt ID:P15090); DCXR (e.g., L-xylulose reductase, UniProt ID:Q7Z4W1); PSMB9 (e.g., Proteasome subunit beta type-9. UniProt ID:P28065); RNF25 (e.g., E3 ubiquitin-protein ligase RNF25, UniProt ID:Q96BH1); S100A12 (e.g.. Protein S100-A12, UniProt ID:P80511); NPTN (e.g., Neuroplastin, UniProt ID:Q9Y639); LIN7A (e.g., Protein lin-7 homolog A, UniProt ID:O14910); VEGFA (e.g., Isoform L-VEGF165, UniProt ID:P15692); ADAMTSL1 (e.g., ADAMTS-like protein 1, UniProt ID:Q8N6G6); LAS2 (e.g., Uncharacterized protein C18orf54, UniProt ID:Q8IYD9); CYB561D1 (e.g., Cytochrome b561 domain-containing protein 1, UniProt ID:Q8N8Q1); ATP1B2 (e.g.. Sodium / potassium-transporting ATPase subunit beta-2. UniProt ID:P14415); MRE11 (e.g.. Double-strand break repair30108365568.1Docket 047563-870867protein MRE11, UniProt ID:P49959); IFT22 (e.g., Intraflage liar transport protein 22 homolog, UniProt ID:Q9H7X7); ARL1 (e.g., ADP-ribosylation factor-like protein 1, UniProt ID:P40616); REG3A (e.g., Regenerating islet-derived protein 3-alpha, UniProt ID:Q06141); CTSF (e.g., Cathepsin F, UniProt ID:Q9UBX1); IAPP (e.g., Islet amyloid polypeptide, UniProt ID:P10997); GXYUT1 (e.g., Glucoside xylosyltransferase 1, UniProt ID:Q4G148); or OMG (e.g.. Oligodendrocyte-myelin glycoprotein, UniProt ID:P23515). In some embodiments of a method as described herein (e.g.. in Section V), the seventh target protein (e.g.,C'TP7”) is a protein selected from LIST 7. In some embodiments, the target protein selected from LIST 7 is NPTXR.Excluded proteins

[0078] In some embodiments of a method as described herein (e.g., in Section V), a target protein is not selected from (e.g., the method excludes) any one of the proteins provided in LIST 8, wherein LIST 8 comprises the proteins of: A1BG, A1CF, A2M, A2ML1, A4GALT, A4GNT, AACS, AADAT, AAGAB, AAMDC, AARS1, AARSD1, AASDHPPT. ABCC6, ABCD4, ABCF3. ABHD10, ABHD12, ABHD14A, ABHD14B, ABHD4, ABHD5, ABU, ABL1, ABL2, ABLIM3, ABO, ABR, ABRAXAS1, ABRAXAS2, ACAA1, ACAD8, ACADL, ACADM, ACADSB, ACADVL, ACAN, ACAP2, ACAT2, ACBD4, ACBD6, ACBD7, ACE, ACE2, ACLY, ACO1, ACOT12, ACOT13, ACOT8, ACOX1, ACPI, ACP2, ACP3, ACP5, ACP6, ACP7, ACRV1, ACSF2, ACSS2, ACTN1, ACTN2, ACTN4, ACVR1B, ACVR2A, ACVR2B. ACVRL1, ACY1, ACY3, ACYP1, ACYP2, ADA, ADA2, ADAM10, ADAMI 1, ADAM12, ADAM 15. AD AMU. ADAM19, ADAM22, ADAM23. ADAM28, ADAM29, ADAM30, ADAM32. ADAM7, ADAM8, ADAM9, ADAMDEC1, ADAMTSL ADAMTS13, ADAMTS15, ADAMTS3, ADAMTS4, ADAMTS5, ADAMTS6, ADAMTSL2, ADAT1, ADAT2, ADCYAP1, ADCYAP1R1, ADD2, ADGRB1, ADGRB2, ADGRB3, ADGRDL ADGRE2, ADGRE5, ADGRF2, ADGRF5, ADGRG1, ADGRG2, ADGRG5, ADGRL3, ADH1A, ADH1B, ADH1C, ADH4, ADH5, ADH6, ADH7, ADI1, ADIPOQ, ADK, ADM, ADM2, ADPGK, ADPRH, ADPRS, ADRM1, ADSL, ADSS1, ADSS2, AFAP1L1, AFAP1L2, AFM, AFP, AGA, AGAP2, AGAP3, AGER, AGFG2. AGGF1, AGO1, AGO3, AGR2, AGR3, AGRN, AGT, AGXT, AHCYL1, AHSA1, AHSG, AIDA, AIF1, AIF1L, AIFM1, AIM2, AIMP1, AIP, AIPL1, AK1, AK2, AK4, AK5, AKAP7, AKIRIN2, AKR1A1, AKR1B1, AKR1C1, AKR1C2, AKR1C3, AKR1C4, AKR1D1, AKR7A2, AKR7A3, AKT1, AKT1S1, AKT2, AKT3, ALAD, ALCAM, ALDH1A1, ALDH1A2, ALDH1A3, ALDH1B1, ALDH2, ALDH3A1, ALDH3B1, ALDH5A1, ALDH6A1, ALDOA, ALDOB, ALDOC, ALG2, ALK, ALKAL2, ALKBH2, ALKBH3, ALOX15B, ALOX5, ALPG. ALPI, ALPL, ALPP. AMBP, AMH, AMIGO 1, AMIG02, AMMECR1L, AMN.AMOT, AMPD2, AMPH, AMT, AMY1A, AMY2A, ANAPC10, ANAPC7, ANG, ANGPT1, ANGPT2, ANGPT4, ANGPTL1, ANGPTL3, ANGPTL4, ANGPTL7, ANGPTL8, ANK2, ANKMY2, ANKRA2,31108365568.1Docket 047563-870867ANKRD1, ANKRD16, ANKRD2, ANKRD27, ANKRD40, ANKRD45, ANKRD46, ANKRD54, ANKS3, AN0S1, ANP32A, ANP32B, ANP32C, ANPEP, ANR63, ANTKMT, ANTXR1, ANTXR2, ANXA1, ANXA10, ANXA11, ANXA13, ANXA2, ANXA3, ANXA4, ANXA5, ANXA6, ANXA8, ANXA9, AOC1, AOC2, AOC3, AP1AR, AP1B1, AP1G2, AP1S2, AP2A2, AP2B1, AP4M1, APAF1, APBB1, APBB1IP, APBB2, APBB3, APCDD1, APCS. APEX1, APEX2. APLN. APLP1, APLP2, APMAP, APOAL APOA2, APOA4, APOA5, APOB. APOBEC2, APOBEC3G, APOCI. APOC2, APOC3, APOD, APOE, APOF, APOH, APOL1, APOL2, APOL3, APP, APPL1, APRT, APTX, AQP4, ARAF, ARC, AREG, ARF1, ARF3, ARF4, ARF5, ARF6, ARFGAP1, ARFGAP2, ARFIP1, ARFIP2, ARG1, ARG2, ARHGAP1, ARHGAP22, ARHGAP24, ARHGAP25, ARHGAP26, ARHGAP30, ARHGAP36, ARHGAP5, ARHGAP6, ARHGDIA, ARHGDIB, ARHGEF1, ARHGEF10, ARHGEF16, ARHGEF2, ARHGEF25, ARHGEF7, ARID1A, ARID3A, ARID3C, ARL11, ARL15, ARL2BP, ARL3, ARL4D. ARL5A, ARL5B, ARL6, ARL6IP1, ARL6IP5, ARL8A. ARL8B, ARL9, ARMC10. ARMC3. ARMC5, ARNT, ARPC1B, ARPC2, ARPC3, ARPC5, ARPP19, ARPP21, ARRB1, ARRDC3, ARRDC5, ARSA, ARSB, ARSK, ART3, ART4, ART5, ARTN, AS3MT, ASAHI, ASAH2, ASAP2, ASAP3, ASB8, ASCC1, ASCC2, ASF1A, ASF1B, ASGR1, ASGR2, ASH1L, ASH2L, ASIC4, ASIP, ASL, ASMTL, ASPH, ASPN, ASPSCR1, ASRGL1, ASTL, ATAD1, ATAD2, ATE1, ATF1, ATF3, ATF5. ATF6, ATF6B, ATG3. ATG4A, ATG4B, ATG4C, ATG5. ATG7, ATIC, ATL3. ATOH1, ATOX1, ATP13A1, ATP1B1. ATP1B4, ATP23, ATP2A3, ATP4B, ATP5IF1, ATP5MF. ATP5PB, ATP5PF, ATP5PO, ATP6V1C1, ATP6V1C2, ATRN, ATXN10, ATXN3, AUH, AURKA, AURKB, AVEN, AXIN2, AZGP1, AZINI, AZU1, B2M, B3GALT1, B3GALT2, B3GALT5, B3GALT6, B3GAT3, B3GLCT, B3GNT2, B3GNT4, B3GNT6, B3GNT8, B4GALNT1, B4GALT1, B4GALT2, B4GALT3, B4GALT5, B4GALT6, B4GALT7, B9D2, BACH2. BAD, BAG1, BAG2, BAG3, BAG5, BAG6, BAGE2. BAGE3, BAIAP2, BAMBI, BAP1, BAP18, BARD1, BASP1, BATF, BATF3. BCAM, BCAN, BCAP29, BCAP31. BCAR3, BCAT2, BCCIP, BCD1N3D, BCHE, BCL10. BCL11A. BCL2. BCL2A1, BCL2L10, BCL2L11, BCL2L2, BCL6, BCL7A, BDH2, BDNF, BDP1, BECN1, BEGAIN, BEND6, BET1L, BEX2, BEX4, BEX5, BGLAP, BGN, BHMT2, BICD1, BICDL1, BID, BIK, BINI, BIN2, BIN3, BIRC2, BIRC3, BIRC5, BIRC7, BLK, BLMH, BLNK, BLOC1S1, BLOC1S2, BLOC1S3, BLOC1S5, BLOC1S6, BLVRA, BLVRB, BMERB1, BMF, BMI1, BMP10, BMP15, BMP2, BMP3, BMP4, BMP5, BMP6, BMP7. BMP8B, BMPER, BMPR1A, BMPR1B. BMPR2, BMT2, BMX, BNIP3, BNIP3L, BOC, BOLA1, BOLA2. BOLA3, BORCS5. BPGM, BPHL. BPI, BPIFA1, BPIFA2, BPIFBL BPNT1, BPNT2, BPTF, BRAF, BRAT1, BRD1, BRD2, BRD4, BRDT, BRF1, BRICD5, BRK1, BRMS1L, BROX, BRPF1, BRSK2, BSG, BST1, BST2, BTC, BTD, BTG1, BTG2, BTK, BTLA, BTN1A1, BTN2A1, BTN2A2, BTN3A2, BTN3A3, BTNL3, BTNL8, BTNL9, BUB1, BUD31, BZW2, ClOorflOd, Cllorf49, Cllorf68, Cllorf87, C12orf65, C12orf76, C14or©3, C15orf40, C16orf54,32108365568.1Docket 047563-870867C16orf71, C16orf72, C17orf67, C17orf78, C18orf32, C19orfl8, C19orf53, C19orf54, CID, C1GALT1|C1GALT1C1, C1GALT1C1, Clorfll5, Clorfl62, Clorfl85, Clorfl98, Clorf210, Clorf226, Clorf43, ClorfSO, C1QA|C1QB|C1QC, C1QBP, C1QC, C1QL1, C1QL2, C1QL3, C1QL4, C1QTNF1, C1QTNF3, C1QTNF4, C1QTNF5, C1QTNF9, C1R, C1RL, CIS, C2, C20orfl73, C20orf202, C22orfl5, C22orf23, C2CD2L, C2orf66, C2orf68, C2orf73, C3, C3orfl8, C4A|C4B. C4BPA, C4BPB. C4orf36, C5. C5|C6, C5orf38. C5orf46. C5orf63, C6. C6orf89, C7, C7orf69, C8G, C9, CAI, CA10, CAI 1. CA12, CAI3. CA2, CA3, CA4, CA5A, CA6, CA7, CA8, CA9, CAB39, CAB39L, CABLES2, CABP2, CABP5, CABP7, CACNA2D3, CACNB3, CACNB4, CACYBP, CADM1, CADM2, CADM3, CADPS2, CALB1, CALB2, CALCB, CALCOCO2, CALCR, CALD1, CALN1, CALR, CALY, CAMK1, CAMKID, CAMK2A, CAMK2B, CAMK2D, CAMK4, CAMKK1, CAMLG, CAMP, CAND1, CANT1, CANX, CAPG, CAPN1|CAPNS1, CAPN2, CAPN3, CAPN9, CAPNS1. CAPNS2, CAPS, CAPSL, CAPZA1. CAPZB. CARD17, CARD18. CARD19, CARD9. CARHSP1, CARNMT1. CARS1, CARTPT, CASK, CASP10, CASP2, CASP3, CASP4, CASP5, CASP7, CASP8, CASQ1, CASQ2, CASS4, CAST, CASTOR1, CAT, CATIP, CAV2, CAV3, CAVIN1, CBARP, CBFB, CBL, CBLC, CBLIF, CBLN1, CBLN2, CBLN4, CBR1, CBR3, CBR4, CBS, CBX1, CBX2, CBX3, CBX5, CBX7, CCDC103, CCDC107, CCDC115, CCDC126, CCDC134, CCDC140, CCDC149, CCDC167, CCDC24, CCDC25, CCDC43, CCDC50. CCDC51, CCDC59, CCDC69. CCDC80, CCDC89, CCDC9, CCDC90B, CCDC97, CCK, CCL1, CCL11, CCL13. CCL14, CCL15, CCL16. CCL17, CCL18, CCL19. CCL2, CCL20.CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL3, CCL3L1, CCL4L1, CCL5, CCL7, CCL8, CCN1, CCN2, CCN3, CCN4, CCN5, CCN6, CCNA1, CCNA2, CCNB1, CCNB1IP1, CCNB2, CCNE1, CCNH, CCPG1, CCT5, CD109, CD14, CD163, CD177, CD1A, CD1B, CD1D, CD2, CD200, CD200R1, CD200R1L, CD207, CD209, CD22. CD226, CD244, CD247, CD248, CD27, CD274, CD28. CD2AP, CD300A, CD300C. CD300E. CD300LB, CD300LF, CD300LG, CD320, CD33, CD34. CD36. CD37, CD38, CD3E, CD3G. CD4. CD40EG, CD44. CD46, CD47, CD48. CD5. CD55, CD58, CD59, CD5L, CD6, CD63, CD68, CD69, CD7, CD70, CD72, CD74, CD79A, CD79B, CD80, CD81, CD82, CD83, CD84, CD86, CD8A, CD8A|CD8B, CD9, CD93, CD96, CD99L2, CDA, CDC25A, CDC25B, CDC34, CDC37, CDC42, CDC42BPA, CDC42BPB, CDC42EP4, CDCP1, CDH1, CDH10, CDH11, CDH12, CDH13. CDH15, CDH17, CDH2. CDH20, CDH23, CDH3. CDH4, CDH5, CDH6, CDH7, CDHR1. CDHR3, CDHR5, CDK1|CCNB1, CDK15, CDK2, CDK2 CCNA2, CDK20, CDK2AP1, CDK2AP2, CDK5RAP3, CDK8|CCNC, CDKL2. CDKN1A, CDKN1B, CDKN2B. CDKN2C, CDKN2D, CDKN3, CDNF, CDON, CDSN, CDX1, CDY1, CDYL2, CEACAM16, CEACAM19, CEACAM20, CEACAM21, CEACAM3, CEACAM4, CEACAM7, CEACAM8, CEBPA, CEBPB, CEBPE, CEBPG, CEL, CELA1, CELA2A, CELA3B, CELF2, CEMIP2, CENPV, CENPW, CEP112, CEP20, CEP43, CER1, CERS5, CERT1, CES3, CETN1, CETN2, CETN3, CETP, CFAP298, CFAP300,33108365568.1Docket 047563-870867CFAP36, CFAP45, CFB, CFC1, CFD, CFDP1, CFH, CFHR1, CFHR2, CFHR3, CFHR4, CFHR5, CFL CFL1, CFP, CFTR, CG073, CGA, CGA|CGB3|CGB7, CGA|FSHB, CGA|LHB, CGA|TSHB, CGB2, CGB3, CGGBP1, CGREF1, CHAC1, CHAC2, CHAD, CHCHD10, CHCHD7, CHD1L, CHD7, CHEK1, CHEK2, CHFR, CHGA, CHGB, CHI3L1, CHI3L2, CHIA, CHID1, CHIT1, CHKB, CHMP1A, CHMP1B. CHMP2A, CHMP2B, CHMP3, CHMP4A. CHMP6, CHN1, CHP1, CHRD, CHRDL1, CHRDL2. CHRNA5, CHRNB3, CHST1, CHST1O, CHST11. CHST12. CHST14. CHST15, CHST2, CHST3, CHST4, CHST5, CHST6, CHST9, CHTF8, CIAO1, CIB1, CILP, CILP2, CINP, CIRBP, CISDI, CISD2, CK094, CKAP2, CKAP4, CKB, CKB|CKM, CKM, CKMT1A, CKMT2, CKS1B, CLC, CLCA1, CLCA2, CLDN1, CLEC10A, CLEC11A, CLEC12A, CLEC1A, CLEC1B, CLEC2A, CLEC2B, CLEC2D, CLEC2L, CLEC3B, CLEC4A, CLEC4C, CLEC4D, CLEC4G, CLEC6A, CLEC7A, CLEC9A, CLIC1, CLIC2, CLIC3, CLIC4, CLIC5. CLINT1. CLMP. CLN5, CLNK, CLPP, CLPS, CLPSL1, CLPSL2, CLSTN1, CLSTN2. CLSTN3, CLTA. CLTC. CLU. CLUAP1, CLUH, CLULI. CLYBL. CMA1, CMBL, CMC4, CMIP, CMPK1, CMTM4, CMTR1, CNDP1, CNEP1R1, CNGA2, CNGB1, CNMD, CNN1, CNN2, CNOT1, CNOT9, CNP, CNPY3, CNPY4, CNRIP1, CNTF, CNTFR, CNTN1, CNTN2, CNTN3, CNTN4, CNTN5, CNTN6, CNTNAP2, CNTNAP5, COA3, COA4, COA7, COASY, COCH, COG8, COL10A1, COL13A1, COL15A1, COL18A1, COL1A1, COL20A1, COL23A1, COL25A1, COL28A1, COL2A1, COL3A1, COL5A1, COL6A1, COL6A2, COL6A3, COL6A5, COL8A1, COL9AI. COL9A3, COLECIO, COLECI 1, COLEC12, COLGALT1, COLGALT2, COMMD1, COMMDIO, COMMD5, COMMD6, COMMD7, COMMD8, COMMD9, COMP, COMT, COMTD1, COPB2, COPE, COPS2, COPS5, COPS7B, COPS8, COQIOA, COQ5, COQ6, COQ7, COQ8B, COQ9, COTL1, COX19, COX5A, COX5B, COX6A2, COX6C, COX7A1, COX7A2L, CPA1, CPA2, CPA4, CPB2, CPD, CPE, CPLX3, CPM, CPN1, CPN2, CPNE1, CPNE6, CPNE7, CPOX, CPPED1, CPQ, CPT1B. CPTP, CPVL, CPXM1, CPZ, CR1, CR2, CRABP1, CRACR2A. CRADD. CRAT, CRCP, CREB1. CREB3L1. CREB3L2, CREB3L4, CREBBP. CREBL2, CREBZF. CREG1, CRELD1, CREM, CRH, CRHBP, CRIM1, CRIP2, CRIPT, CRISP2, CRISP3, CRISPLD2, CRK, CRKL, CRLF1, CRLF1 CLCF1, CRLF2, CRLF3, CRMP1, CRNN, CROT, CRP, CRPPA, CRTAC1, CRTAM, CRTC3, CRYAA, CRYBA2, CRYBB1, CRYBB2, CRYBB3, CRYGA, CRYGC, CRYGD, CRYGS, CRYL1. CRYZ, CRYZL1, CS, CS084, CSAG1, CSAG2, CSDC2, CSDE1, CSF1, CSF1R, CSF2, CSF2RA, CSF2RB, CSF3, CSF3R, CSGALNACT2, CSH1|CSH2, CSK, CSMD1, CSMD2, CSNK1D, CSNK1G1, CSNK1G2. CSNK2AL CSNK2A1|CSNK2B, CSNK2A2, CSNK2A2|CSNK2B, CSNK2B. CSPG4, CSRP3, CST1, CST2, CST3, CST4, CST5, CST6, CST7, CST8, CSTB, CT45A1, CT45A3, CT55, CT83, CTAG1A, CTCF, CTDSP1, CTDSPL, CTH, CTHRC1, CTLA4, CTNNA1, CTNNA2, CTNNA3, CTNNB1, CTNNBIP1, CTPS1, CTRB1, CTRB2, CTRC, CTSA, CTSB, CTSC, CTSE, CTSG, CTSH. CTSK, CTSO, CTSS, CTSV, CTSZ, CUBN. CUEDC1, CUL1, CUL3, CUL4B, CUL9,34108365568.1Docket 047563-870867CUT A, CUTC, CWF19L1, CXADR, CXCL1, CXCL10, CXCL11, CXCL13, CXCL14, CXCL16, CXCL17, CXCL2, CXCL3, CXCL5, CXCL6, CXCL8, CXCL9, CXorf38, CYB5A, CYB5D2, CYB5R1, CYB5R2, CYB5R3, CYB5R4, CYBC1, CYCS, CYGB, CYLD, CYP2C19, CYP3A4, CYREN, CYRIA, CYRIB, CYSRT1, CYTH1, CYTH2, CYTH3, CYTH4, CYTIP, DAB2. DAG1, DAO, DAP, DAPK1, DAPK2. DAPK3, DAPP1, DARS1, DARS2, DAXX, DAZAP1, DBI. DBNDD1, DBNDD2, DBNL, DBT, DCAF11. DCAF12. DCAF5, DCBLD1, DCC, DCK, DCLK1, DCLK3. DCN, DCP1A. DCPS, DCT, DCTD, DCTN2, DCTPP1, DCUN1D1, DCUN1D2, DCUN1D3, DCUN1D5, DDAH1, DDC, DDHD2, DDI1, DDE, DDIT3, DDIT4, DDOST, DDR1, DDR2, DDRGK1, DDT, DDX1, DDX19A, DDX19B, DDX23, DDX25, DDX39B, DDX46, DDX58, DDX6, DEAF1, DECR1, DECR2, DEF6, DEFA1, DEFA3, DEFA5, DEFBI, DEFB103A, DEFB104A, DEFB106A, DEFB107A, DEFB108B, DEFBI 10, DEFBI 12, DEFBI 13, DEFBI 15, DEFBI 16. DEFBI 18, DEFBI 19, DEFB121, DEFB125. DEFB127, DEFB128. DEFB129, DEFB135, DEFB136. DEFB4A, DENR, DEPPI. DERL1, DES, DESIE DFFA, DGCR2, DGCR6, DGKB, DHFR, DHH, DHODH, DHRS11, DHRS3, DHRS4, DHX38, DHX58, DHX8, DHX9, DIABLO, DIAPH1, DIDOI, DIMT1, DIPK1C, DIRAS1, DIRAS3, DIXDC1, DKK1, DKK2, DKK3, DKK4, DKKL1, DLAT, DLD, DLG2, DLG3, DLG4, DLGAP4, DLK1, DLK2, DLL1, DLL3, DLL4, DLST, DLX2, DLX3, DLX4, DMBT1, DMC1, DMKN, DMRTB1, DMRTC2, DMTN, DNAI1, DNAJA2, DNAJA4, DNAJB1, DNAJB11, DNAJB12, DNAJB13. DNAJB14.DNAJB2, DNAJB3. DNAJB4, DNAJB6, DNAJB8. DNAJB9, DNAJC1, DNAJC10, DNAJC11, DNAJC12, DNAJC15, DNAJC16, DNAJC17, DNAJC18, DNAJC19, DNAJC27, DNAJC30, DNAJC4, DNAJC5B, DNALI1, DNASE1L2, DNASE2B, DNER, DNM1, DNM2, DNMT3L, DNPEP, DNPH1, DOC2B, DOCK2, DOCK9, DOHH, DOK2, DOT1L, DPCD, DPEP1, DPEP2, DPH2, DPH5, DPP10, DPP4, DPP6, DPP7, DPP8, DPPA4, DPT, DPY30, DPYSL2, DPYSL3, DPYSL4, DPYSL5, DR1, DRAP1. DRAXIN, DRGX, DSC1, DSC2, DSC3, DSCAM. DSCAML1, DSCC1. DSG2, DSG3, DSG4, DTD1, DTD2, DTNA, DTNBP1, DTX1, DTX3L. DTYMK, DUPD1. DUS2. DUSP10. DUSP13.DUSP1 , DUSP16, DUSP18, DUSP19, DUSP21, DUSP23, DUSP26, DUSP28, DUSP3, DUSP4, DUSP6, DUT, DVL2, DYNC1LI1, DYNC1LI2, DYNLL1, DYNLL2, DYNLRB1, DYNLRB2, DYNLT1, DYNLT3, DYRK1A, DYRK2, DYRK3, DYTN, E2F5, EBAG9, EBI3, EBP, ECE1, ECEL1, ECHDC1, ECHS1, ECU, ECI2, ECM1, ECRG4, EDA, EDA2R, EDAR, EDC4, EDDM3A, EDDM3B, EDEM2, EDF1, EDIL3. EDN1, EDN2, EDN3, EEA1, EED, EEF1B2, EEF1D, EEF1E1, EEF1G.EEF2K. EEF2KMT, EFCAB1, EFCAB14, EFEMPL EFHD1, EFHD2. EFNA1, EFNA2. EFNA3, EFNA4, EFNA5, EFNB1, EFNB2, EFNB3, EFS, EGF, EGFL6, EGFLAM, EGFR, EGLN1, EGLN2, EGLN3, EHBP1, EHD1, EHD2, EHD3, EHD4, EHF, EHMT2, EID3, EIF1, EIF1AD, EIF1AX, EIF1AY, EIF1B, EIF2A, EIF2B1, EIF2S2, EIF3B, EIF3G, EIF3J, EIF3M, EIF4A1, EIF4A2, EIF4A3, EIF4B, EIF4E, EIF4E2, EIF4EBP1, EIF4EBP2. EIF4EBP3, EIF4G1, EIF4G2, EIF4G3, EIF4H, EIF5, EIF5A,35108365568.1Docket 047563-870867EIF5A2, ELAC1, ELANE, ELAP0R1, ELAPOR2, ELAVLL ELF5, ELFN2, ELK1, ELK3, ELK4, ELL, ELL2, ELL3, ELM01, ELMO2, ELP1, EMC1, EMC10, EMC2, EMC4, EMC8, EMG1, EMIDI, EMILIN3, EML2, ENAH, ENDOU, ENG, ENGASE, ENO1, ENO2, ENO3, ENOX2, ENPEP, ENPP2, ENPP5, ENPP6, ENPP7, ENSA, ENTPD2, ENTPD5, ENTPD6, EOLA1, EPB41, EPB41L1, EPDR1, EPHA1, EPHA10, EPHA2, EPHA3, EPHA4, EPHA5, EPHA6, EPHA7, EPHA8, EPHB1, EPHB2, EPHB3. EPHB4, EPHB6. EPHX2, EPNL EPO. EPOR. EPS15L1, EPS8L1, EPS8L2. EPS8L3. EQTN, ERAP1, ERAP2, ERBB2, ERBB3, ERBB4, ERC1, ERCC1, ERCC4, EREG, ERH, ERLEC1, ERLIN1, ERMAP, ERMN, ERN1, ERO1A, ERO1B, ERP27, ERP29. ERVV-1, ESI, ESAM, ESD, ESPN, ESRP1, ESRRA, ESS2, ETFA, ETHE1, ETNK1, ETNK2, ETS2, ETV2, ETV4, ETV7, EURL, EVA1B, EVA1C, EVL, EVPL, EWSR1, EXD1, EXOC7, EXOG, EXOSC 1, EXOSC3, EXOSC5, EXOSC8, EXTL2, EYA2, EYS, EZH2. EZR. F10, Fl 1, FUR, F13A1|F13B, F13B, F2, F229A, F3, F5, F7, F8. F9. FAAH2, FABPL FABP12. FABP2, FABP5, FABP6. FADD, FAF2, FAH. FAHD1, FAIM, FAM102B, FAM107A, FAM118B, FAM151A, FAM151B, FAM160B1, FAM162A, FAM162B, FAM163B, FAM171A1, FAM171A2, FAM171B, FAM172A, FAM174A, FAM177A1, FAM189A2, FAM204A, FAM209B, FAM20A, FAM20B, FAM20C, FAM210A, FAM219B, FAM221A, FAM221B, FAM234B, FAM241A, FAM241B, FAM24B, FAM3A, FAM3B, FAM3C, FAM3D, FAM50A, FAM86B1, FAM89B, FAM92A, FAM9B, FANCF, FANCL, FANK1, FAP, FARS2, FAS, FASLG, FATE1, FAXDC2. FBL. FBLIM1, FBLN1, FBLN5. FBLN7, FBPL FBP2, FBXL4. FBXL5, FBXO28, FBXO3, FBXO48, FCAMR, FCAR, FCER2, FCGR1A, FCGR2B, FCGR3A, FCGR3B, FCGRT, FCHSD1, FCMR, FCN1, FCN2, FCN3, FCRL1, FCRL2, FCRL3, FCRL4, FCRL5, FCRL6, FCRLA, FCRLB, FDCSP, FDPS, FDX2, FEN1, FER, FERMT3, FETUB, FEV, FGA|FGB|FGG, FGB, FGD2, FGF1, FGF10, FGF12, FGF16, FGF17, FGF18, FGF19, FGF2, FGF20, FGF22, FGF23, FGF3, FGF4, FGF5, FGF6. FGF7, FGF8, FGF9, FGFBP1, FGFBP3, FGFR1, FGFR2, FGFR3. FGFR4, FGFRL1, FGG, FGLL FGL2, FGR. FH. FHIT, FHLL FHODL FIBCD1, F1BP. FIG4, FIS 1, FJXL FKBP14. FKBP1A, FKBP1B, FKBP2, FKBP3, FKBP5, FKBP6, FKBP7, FLU, FLII, FLJ44635, FLNA, FLRT1, FLRT3, FLT1, FLT3LG, FLT4, FMO3, FMOD, FMRI, FN3K, FNBP1, FNDC4, FNDC8, FNTA, FOLH1, FOLR1, FOLR2, FOLR3, FOSL2, FOXC2, FOXJ2, FOXL2, FOXM1, FOXO1, FOXO3, FOXO4, FOXP1, FOXP3, FOXP4, FOXRED1, FREM1, FREM2, FRG1, FRRS1L, FRS2, FRZB, FSCN1, FSD1, FSHB, FST. FSTL1, FSTL3, FSTL4, FSTL5, FTH1, FTH1|FTL, FTHL17, FTL, FTMT, FTO, FUBP1, FUBP3. FUCA1, FUNDC1, FURIN, FUT10, FUT11. FUT2, FUT3, FUT5. FUT7, FUT8. FUT9. FXN, FXYD2, FXYD6, FYN, FZD1, FZD10, FZD2, FZD4, FZD5, FZD7, FZD8, FZD9, G0S2, G3BP1, G3BP2, G6PD, GAA, GABI, GABARAP, GABARAPL1, GABARAPL2, GABBR1, GABBR2, GABPB1, GAD1, GADD45GIP1, GAGE12F, GAGE2A, GAGE2B, GAGE2D, GALE, GALK1, GALK2, GALNS, GALNT1, GALNT10, GALNT11, GALNT13, GALNT14. GALNT16, GALNT2,36108365568.1Docket 047563-870867GALNT3, GALNT4, GALNT9, GALP, GAN, GAP43, GAPDHS, GARS1, GAS2, GAS6, GAS7, GATAD1, GBA, GBA3, GBP1, GBP2, GBP6, GC, GCA, GCDH, GCG, GCH1, GCHFR, GCK, GCKR, GCLM, GCNT1, GCNT4, GCSH, GDA, GDAP1L1, GDF10, GDF11, GDF11|MSTN, GDF15, GDF2, GDF3, GDF5, GDF7, GDF9, GDI1, GDI2, GDNF, GDPD1, GEM, GEMIN6, GEMIN7, GET3, GFAP, GFER, GFPT1, GFPT2, GFRA1, GFRA2, GFRA3, GFRAL, GGA1, GGA3, GGCT, GGH, GGPS1, GGT2, GGT5, GH1, GH2, GHDC, GHR, GHRH. GHRL. GID8, GIMAP4, GIMAP6, GIMAP7, GIMD1. GINS1, GIP, GIPC1, GIPC2, GJA1, GJA8, GJD2, GK5, GKN2, GLA, GLCE, GLDN, GLIPR1, GLIPR2, GLO1, GLP1R, GLRX, GLRX2, GLRX3, GLRX5, GLS, GLT8D1, GLT8D2, GLTP, GLTPD2, GLUD1, GLUD2, GLUL, GLYAT, GLYATL2, GM2A, GMDS, GMEB2, GMFB, GMFG, GMNN, GMPPA, GMPR, GMPR2, GNAI1, GNAI3, GNAQ, GNAS, GNAS|GNAS, GNE, GNG11, GNG12. GNGT2, GNLY, GNMT, GNPDA1, GNPTG, GNRH1, GNRH2, GNS, GNT2C, GOLGA7, GOLM1, GOLM2, GOLPH3. GOLPH3L. GORAB. GOSR1, GOT1, GP1BA. GP1BB, GP5, GP6, GPCE GPC2, GPC3, GPC4, GPC5, GPC6, GPCPD1, GPD1, GPHA2, GPHN, GPI, GPN1, GPNMB, GPR101, GPR107, GPR135, GPR142, GPR26, GPR37, GPR68, GPT, GPT2, GPX1, GPX2, GPX3, GPX5, GPX7, GRAMD1C, GRAMD2B, GRAP, GRAP2, GRB14, GRB2, GRB7, GREM1, GREM2, GRHPR, GRIA4, GRID1, GRID2, GRIK2, GRIP API, GRK2, GRK5, GRM4, GRN, GRP, GRPEL1, GSC2, GSK3A. GSK3B. GSKIP, GSN, GSPT2, GSR, GSS, GSTA2, GSTA3, GSTA4, GSTK1. GSTM1, GSTM3, GSTM4, GSTM5. GSTCH, GSTP1. GSTT1, GSTT2, GSTT2B. GSTZ1, GTF2A2, GTF2B. GTF2E1. GTF2E2, GTF2F2, GTF2I, GTPBP10, GTSF1, GUCA1A, GUCA1B, GUCA2B, GUCD1, GUK1, GUSB, GYG2, GZMA, GZMB, GZMH, GZMK, GZMM, Hl-10, Hl-2, H2AC1, H2AC11, H2AW, H2AZ1, H2BC12, H2BC21, H2BU1, H3C1, H6PD, HAAO, HABP2, HABP4, HACL1, HADH, HAGH, HAGHL, HAMP, HAO1, HAPLN1, HAPLN4, HARS1, HATE HAUS1, HAVCR2, HAX1, HBB, HBD, HBE1, HBEGF, HBG2, HBQ1, HBZ, HCAR2, HCCS, HCK, HCLS1, HCN1, HDAC2. HDAC4, HDAC6, HDAC8. HDGF. HDGFLR HDGFL2, HDGFL3, HDHD2, HDLBP. HEBPR HECW1, HECW2, HENMT1, HEPACAM, HEPACAM2, HEPH, HEPHL1, HERC1, HERC4, HERC5, HERPUD2, HES3, HES5, HES6, HEXA, HEXB, HEXIM1, HEXIM2, HEY1, HGD, HGF, HGFAC, HGS, HHIP, HHLA2, HHLA3, HIBADH, HID1, HIF1A, HIF1AN, HIKESHI, HINT1, HINT2, HIP1R, HIPK3, HIRIP3, HJV. HK1, HK2, HK3, HLA-C, HLA-DQA2, HLA-DRB3, HLA-E, HLA-G, HLF, HMBS, HMG20A, HMG20B. HMGA1, HMGA2, HMGB2, HMGB3, HMGCL, HMGCR, HMGCS1, HMGCS2, HMGN1, HMGN3, HMOX1, HMOX2, HMX2. HMX3. HNF1A, HNF4A, HNMT, HNRNPAO, HNRNPAE HNRNPA2B 1, HNRNPAB, HNRNPC, HNRNPD, HNRNPDL, HNRNPF, HNRNPH1, HNRNPK, HNRNPLL, HNRNPM, HNRNPR, HOGA1, HOMER1, HOMER2, HOMER3, HOPX, HORMAD2, HOXA11, HOXA5, HOXD4, HP, HPCAL1, HPD, HPGD, HPGDS, HPRT1, HPSE, HPX, HRAS, HRC, HRG, HRK, HS2ST1, HS3ST1, HS3ST3A1, HS3ST3B1, HS3ST4, HS3ST5,37108365568.1Docket 047563-870867HS6ST1, HS6ST2, HS6ST3, HSD11B1, HSD17BL HSD17B10, HSD17B11, HSD17B14, HSD17B4, HSD17B7, HSDL2, HSF1, HSF2BP, HSH2D, HSP90AA1, HSP90AB1, HSP90B1, HSPA12A, HSPA13, HSPA1A, HSPA1B, HSPA1L, HSPA2, HSPA6, HSPA8, HSPA9, HSPB1, HSPB3, HSPB6, HSPBP1, HSPE1, HSPG2, HSPH1, HTATIP2, HTN1, HTN3, HTR2A, HTR6, HTR7, HTRA1, HTRA2, HUS1, HYAL1, HYAL4, HYKK, HY0U1. HYPK, IARS1, IBSP, ICAM1. ICAM2, ICAM3, ICAM4, ICOSLG, IDE ID2, IDE, IDH3G, IDI1, IDI2, IDO1, IDS. IDUA, IER2. IER3, IFI16, IFIH1, IFIT2. IFIT3, IFNA1, IFNA10, IFNA14, IFNA16, IFNA2, IFNA21, IFNA4, IFNA5, IFNA6, IFNA7, IFNA8, IFNAR1, IFNB1, IFNE, IFNG, IFNGR1, IFNGR2, IFNL1, IFNL2, IFNL3, IFNL4, IFNLR1, IFNW1, IFRD1, IGBP1, IGDCC3, IGDCC4, IGF1, IGF1R, IGF2, IGF2R, IGFALS, IGFBP1, IGFBP2, IGFBP3, IGFBP4, IGFBP5, IGFBP6, IGFBP7, IGFBPL1, IGFLR1, IGHA1|IGHA2, IGHD, IGHE, IGHG1, IGHG2, IGHG4, IGHM, IGKV1-5, IGLL1, IGLON5, IGSF11, IGSF3, IGSF8, IGSF9B, IHH, IK, IKBKB, IKBKG. IL10, IL10RA. IL10RB, ILI1, ILI1RA, ILI2A|EBI3, IL12A|IL12B. IL12B. IL12B|IL23A, IL12RB1, IL12RB2, IL13, IL13RA1, IL15, IL15RA, IL16, IL17A, IL17A|IL17F, IL17B, IL17C, IL17D, IL17F, IL17RA, IL17RB, IL17RC, IL17RD, IL18, IL18BP, IL18R1, IL18RAP, IL19, ILIA, IL1F1O, IL1R1, IL1R2, IL1RAP, IL1RAPL1, IL1RAPL2, IL1RL1, IL1RL2, IL1RN, IL20, IL20RA, IL20RB, IL21, IL21R, IL22, IL22RA1, IL22RA2, IL23R, IL24, IL25, IL26, IL27|EBI3, IL27RA, IL2RA, IL2RB, IL2RG, IL3. IL31, IL31RA, IL32. IL36A. IL36B, IL36G, IL3RA. IL4, IL4I1. IL4R, IL5, IL5RA, IL6, IL6|IL6R, IL6R. IL6ST. IL7, IL7R, IL9, ILF2, ILF3, ILK, ILKAP, IMMP2L. IMPA1, IMPA2, IMPACT. IMPDH1, IMPDH2, INA, ING1, ING4, INHA, INHA|INHBA, INHBA, INHBA|INHBB, INHBA|INHBC, INHBB, INHBC, INIP, INO80E, INPP4A, INPP4B, INPP5A, INPP5B, INPP5D, INPP5E, INPP5F, INS, INSIGI, INSL3, INSL4, INSL5, INSL6, INSR, INSRR, INTS3, IP6K1, IP6K2, IQCD, IQCF1, IQCF3, IRAG1, IRAG2, IRAK4, IRF1, IRF2, IRF3, IRF4, IRF5, IRF6, IRF8, IRF9. ISCU, ISG15, ISLE ISLR2. ISM1, ISOCI, IST1, ITCH, ITGA1|ITGB1, ITGA11, ITGA11|ITGB1. ITGA2, ITGA2|ITGB1. ITGA2B|ITGB3, ITGA3|ITGB1. ITGA4|ITGB1. ITGA5. 1TGA5|1TGBI, ITGA6, ITGAL, ITGAL|ITGB2, ITGAM, ITGAV|ITGB1, ITGAV|ITGB3, ITGAV|ITGB5, ITGAV|ITGB6, ITGAV|ITGB8, ITGB1BP2, ITGB2, ITGB5, ITGB6, ITGB7, ITIH1, ITIH2, ITIH3, ITIH4, ITIH5, ITLN1, ITM2A, ITM2C, ITPA, ITPK1, ITPKA, ITPKC, ITPRIPL1, ITSN1, IVD, IZUMO1, IZUMO4, JAG1, JAG2, JAK2, JAKMIP3, JAM2, JAM3, JAML, JARID2, JCHAIN. JDP2, JMJD6, JOSD1. JPH1, JPH3, JPH4. JPT1, JPT2, JTB, JUN. JUND. JUP, KAAG1, KARS1, KAT2A. KAT2B. KAT5. KATNAL1, KAZALDL KBTBD11. KCNA10, KCNAB2. KCNAB3. KCNE3, KCNE5. KCNF1, KCNG4, KCNIP1, KCNIP3, KCNMB3, KCNN1, KCNRG, KCTD1, KCTD10, KCTD13, KCTD15, KCTD17, KCTD3, KCTD4, KCTD5, KCTD6, KCTD7, KDF1, KDM1A, KDM4C, KDR, KDSR, KEAP1, KEL, KERA, KHDC1L, KHDRBS2, KHK, KHSRP, KIAA0040, KIAA0319L, KIAA1143, KIAA1549L, KIAA2013. KIF16B, KIF1C, KIF22, KIF23, KIF3A, KIF3B. KIF3C, KIFBP,38108365568.1Docket 047563-870867KIN, KIR2DLL KIR2DL2, KIR2DL3, KIR2DL4, KIR2DL5A, KIR2DS4, KIR3DL1, KIR3DL3, KIR3DS1, KIRREL1, KIRREL2, KIRREL3, KISSI, KIT, KITLG, KL, KLB, KLC1, KLC3, KLF4, KLF9, KLHL12, KLHL13, KLHL14, KLHL2, KLHL3, KLHL40, KLHL41, KLHL7, KLK10, KLK11, KLK13, KLK14, KLK15, KLK3, KLK4, KLK5, KLK7, KLK8, KLK9, KLKB1, KLRB1, KLRC1, KLRC3. KLRC4, KLRF1, KLRG2, KLRK1, KMO. KMT2C, KMT2D, KMT5C, KNG1, KPNA2, KPNA4. KPNA5, KPNA6, KPNB1. KRAS. KREMEN1, KREMEN2, KRT1. KRT14, KRT16, KRT17, KRT18, KRT19, KRT20, KRT34, KRT5, KRT7, KRT71, KRT72, KRTAP2-4, KRTDAP, KTI12, KXD1, KYAT1, KYAT3, KYNU, L1CAM, L3HYPDH, L3MBTL2, LACC1, LACTB2, LAD1, LAG3, LAIR1, LAIR2, LAMA2|LAMB1|LAMC1, LAMA3, LAMA4, LAMC2, LAMP1, LAMTOR3, LANCL1, LANCL2, LAP3, LARGE1, LASPI, LAT, LAT2, LAYN, LCAT, LCE3B, LCE3C, LCK, LCMT1, LCN1, LCN8, LCNL1, LCORL. LCP1, LCT, LCTL, LDB1, LDB2, LDHA. LDHB. LDHC, LDLR, LDLRAD3, LDLRAD4. LDLRAPI, LDOC1. LEAP2, LEFTY2, LEMD1, LEP, LEPR.LETMD1, LGALS1, LGALS13, LGALS2, LGALS3, LGALS3BP, LGALS4, LGALS7, LGALS8, LGALS9, LGALSL, LGI3, LGMN, LGR4, LGR5, LGSN, LHB, LHCGR, LIF, LIFR, LILRA1, LILRA2, LILRA3, LILRA4, LILRA5, LILRA6, LILRB1, LILRB2, LILRB3, LILRB4, LILRB5, LIMA1, LIMD1, LIMD2, LIMK1, LIN28B, LIN7B, LINGO1, LINGO3, LIPG, LIPK, LIPN, LMAN1, LMAN2, LMAN2L, LMNB1. LMNB2, LMO3. LMO4. LMOD1. LNPK, LNX1, LONP1, LOXL2, LOXL3, LPCAT2, LPIN1, LPL, LPO, LRATD2. LRCH4, LRFNI, LRFN2, LRFN3, LRFN4, LRIG1. LRIG3, LRIT2, LRIT3, LRP1, LRP10, LRP11, LRP12, LRP1B, LRP2, LRP2BP, LRP4, LRP5, LRP6, LRP8, LRPAP1, LRRC15, LRRC20, LRRC24, LRRC25, LRRC3, LRRC32, LRRC37A2, LRRC3B, LRRC4, LRRC4B, LRRC4C, LRRC52, LRRC59, LRRC74A, LRRC75A, LRRFIP2, LRRK2, LRRTM1, LRRTM2, LRRTM3, LRRTM4, LRSAM1, LRTM1, LRTM2, LSAMP, LSM1, LSM12, LSM3, LSM4, LSMEM1, LSP1, LST1. LTA, LTA|LTB, LTA4H, LTB4R. LTF, LTK, LUM, LXN, LY6D, LY6G6C, LY6G6D. LY75, LY86. LY9. LYG1. LYG2, LYN. LYPD1, LYPD3. LYPD8, LYPLAL1, LYRM1, LYSMD3, LYSMD4, LYVE1, LYZL2, LZIC, LZTFL1, M1AP, MACO1, MACROD1, MACROD2, MACROH2A1, MAD IL 1, MAD2L1, MAD2L1BP, MAD2L2, MADCAM1, MAEA, MAFG, MAG, MAGEA10, MAGEA3, MAGEA4, MAGEA6, MAGEA8, MAGEBIO, MAGED1, MAGE, MAGIX, MAGOH, MAGOHB, MALT1, MAMDC2, MAN1A2, MAN1B1, MAN1C1. MAN2B2, MANBA, MANEA, MANF. MANSC1, MANSC4, MAP1LC3A, MAP1LC3B, MAP1LC3B2, MAP2K1, MAP2K2. MAP2K3, MAP2K4. MAP2K5, MAP2K6. MAP3K10, MAP3K11, MAP3K3.MAP3K7|TAB1, MAP4K1, MAP4K3, MAP4K5, MAP6D1, MAPK1, MAPK10, MAPK11, MAPK12, MAPK13, MAPK14, MAPK3, MAPK6, MAPK8, MAPK9, MAPKAPK2, MAPKAPK5, MAPRE1, MAPRE2, MAPRE3, MAPT, MARCKSL1, MARCO, MARK3, MARS1, MARVELD2, MASP1, MAST4, MAT1A, MAT2A, MAT2B, MATK, MATN3, MATN4, MAX, MAZ, MB, MB21D2, MBD1,39108365568.1Docket 047563-870867MBD4, MBL2, MBLAC2, MBNL1, MBNL2, MBTPS1, MCAM, MCCDL MCEE, MCF2L, MCL1, MCM6, MCTS1, MDGA1, MDGA2, MDH1, MDH2, MDK, MDM1, MDM2, MDM4, MDP1, MEI, ME2, MECP2, MED1, MEDIO, MED11, MED4, MEF2C, MEF2D, MEGF1O, MEIG1, MEIS2, MEMO1, MEN1, MEOX1, MEOX2, MEP1A, MEPE, MERTK, MESD, MET, METAP1, METAP1D, METAP2, METRNL, METTL1, METTL21C, METTL24, METTL26, METTL2B, METTL3. MF API, MFAP2, MFAP3. MFAP3L, MFAP4. MFAP5, MFGE8, MFN1, MFNG, MFRP, MGA, MGAT1, MGAT2, MGAT3, MGAT4B, MGAT4C, MGAT5, MGMT, MGP, MIA, MICA, MICAL1, MICALL1, MICALL2, MICB, MICOSIO, MICU2, MICU3, MID2, MIEF1, MIF, MILR1, MINAR1, MINPP1, MIOX, MITD1, MITF, MKNK1, MKX, MLANA, MLEC, MLF1, MLIP, MLN, MLX, MLYCD, MMAB, MMACHC, MMADHC, MMEL1, MMGT1, MMP1, MMP10, MMP12, MMP13, MMP14, MMP16, MMP17. MMP19, MMP2, MMP20, MMP3, MMP7, MMP8, MMP9, MMRN2, MNAT1, MNDA. MNXI. MOBIB. MOB3B. MOB4, MOCOS, MOG. MORF4LL MORF4L2, MOSPD1. MPC1, MPDZ, MPG, MPI, MPIG6B, MPL, MPO, MPP2, MPP5, MPP6, MPP7, MPPED2, MPST, MPZ, MPZL1, MRAP, MRAP2, MRAS, MRC1, MRC2, MREG, MRFAP1L1, MRI1, MRM1, MRM2, MRPL1, MRPL10, MRPL12, MRPL14, MRPL2, MRPL21, MRPL28, MRPL32, MRPL33, MRPL34, MRPL38, MRPL50. MRPL52, MRPL55, MRPS14, MRRF, MRTO4, MSANTD2, MSC, MSH2, MSI2, MSLN, MSMB. MSMP, MSN, MSR1, MSRA, MSRB1, MSRB2, MSRB3. MST1, MSTN, MSX2, MTAP, MTCPI, MTFR1. MTHFD1, MTHFD2, MTHFD2L, MTHFS, MTIF3, MTMR1. MTMR6. MTMR7, MTPAP, MTRF1L, MTSS2, MTUS2, MTX2, MUC1, MUC16, MUCL1, MUL1, MUSK, MUSTN1, MUTYH, MVB12B, MVD, MVK, MVP, MX1, MXI1, MXRA7, MXRA8, MYB, MYBPC1, MYBPC2, MYBPC3, MYBPH, MYC, MYCBP, MYCT1, MYDGF, MYG1, MYL12A, MYL12B, MYL3, MYL4, MYL5, MYL6, MYL6B, MYL7, MYL9, MYLPF, MYNN, MY06, MYOC, MY0M2, MY0M3, MYORG. MYRF, MYSM1, MYZAP, MZB1, MZT2A, N4BP2L2, N6AMT1, NAA10, NAA20. NAA50. NAA80, NAAA, NAALAD2. NAALADL1, NAB1, NAB2, NABP1. NACA. NADK, NADK2, NAE1 , NAE1 |UBA3, NAGK, NAGLU, NAGPA, NAGS, NAIF1 , NAMPT, NANOG, NANP, NANS, NAP1L1, NAP1L2, NAP1L4, NAPA, NAPB, NAPEPLD, NAPG, NARF, NARS1, NARS2, NAT1, NAT14, NATD1, NAXE, NBL1, NBR1, NCALD, NCAM1, NCAM2, NCAN, NCBP1, NCBP2, NCF1, NCF2, NCK1, NCK2, NCKIPSD, NCL, NCMAP, NCOA2, NCOA7, NCR1, NCR2, NCR3, NCR3LG1, NDC80, NDE1. NDEL1, NDNF, NDP, NDRG1, NDRG2, NDRG3, NDRG4. NDST1, NDUFA2. NDUFA5, NDUFAFL NDUFAF2, NDUFAF3, NDUFAF5. NDUFAF8. NDUFB10, NDUFB11, NDUFB6, NDUFB8, NDUFS4, NDUFV2, NECAB3, NECTIN1, NECTIN2, NECTIN3, NECTIN4, NEDD8, NEDD9, NEFH, NEGRI, NEIL1, NEIL2, NEK7, NELFA, NELFE, NELLI, NELL2, NENF, NEO1, NET1, NETO1, NETO2, NEU1, NEURL1, NEURL4, NEUROG1, NEUROG3, NFASC. NFATC1, NFATC4, NFE2L1, NFE2L2, NFIA, NFKBL NFKBIA, NFKBIB, NFKBID, NFU1,40108365568.1Docket 047563-870867NFYA, NGDN, NGF, NGFR, NGRN, NHEJ1, NHLRC2, NHLRC3, NHP2, NIDI, NID2, NIF3L1, NIP7, NIPAL4, NISCH, NIT2, NKD2, NKIRAS1, NKIRAS2, NLGN2, NLGN3, NLGN4X, NLGN4Y, NLRP1, NLRP10, NLRP4, NME1, NME2, NME4, NME7, NME8, NMES1, NMI, NMNAT1, NMRAL1, NMRK1, NMRK2, NMT1, NMT2, NMU, NNMT, NODAL, NOG, NOMO2, NONO, NOP16, NOS3, NOSIP, NOTCH1, NOTCH2, NOTCH3, NOTUM, NOVAI, NPDC1, NPEPL1, NPFF, NPIPB3, NPL, NPLOC4. NPM1, NPM2, NPNT, NPPA, NPPB, NPPC. NPS. NPTX1, NPTX2. NPW, NPY, NQO1, NQO2, NR1D1, NR1D2, NR1H2, NR1H4, NR2C2AP, NR3C1, NR3C2, NR4A1, NR5A2, NRAC, NRAS, NRBF2, NRCAM, NRDC, NREP, NRG1, NRG2, NRG3, NRG4, NRGN, NRIP3, NRL, NRN1L, NRP1, NRSN1, NRXN1, NRXN2, NRXN3, NSDHL, NSF, NSFL1C, NSMCE1, NSMCE2, NSUN6, NT5C, NT5C2, NT5C3A, NT5C3B, NT5DC1, NT5DC3, NT5E, NT5M, NTAN1, NTAQ1, NTF3, NTF4. NTM, NTMT1, NTN1, NTN4. NTNG1, NTRK1, NTRK2. NTRK3, NTS, NUB1, NUBP1, NUBP2. NUCB1, NUCB2, NUDC, NUDCD2, NUDCD3, NUDT1. NUDT10, NUDT11, NUDT12. NUDT14, NUDT15, NUDT16, NUDT16L1, NUDT2, NUDT21, NUDT3, NUDT7, NUDT9, NUMB, NUMBL, NUP210L, NUP98, NUTF2, NVL, NXF1, NXPH1, NXPH2, NXPH3, NXT1, NXT2, OAS1, OASL, OAT, OAZ1, OBP2A, OBP2B, OCIAD1, OCLN, OCRL, OGFR, OGGI, OGN, OGT, OIT3, OLA1, OLAH, OLFM1, OLFM2, OLFM3, OLFM4, OLFML1, OLFML3, OLIG1, OMD, OMP, OOSP2, OPALIN, OPCML, OPHN1, OPRPN. OPTC, ORC6, OSBP, OSBPL11, OSBPL1A, OSBPL9, OSCAR, OSM. OSMR, OSTF1, OSTML OSTN. OTC, OTOR, OTUB2. OTUD3, OTUD7B, OTULIN, OTX1, OTX2, OVCA2, OXCT1, OXLD1, OXSM, OXSRL OXT, P2RX6, P3H1, P4HA1, P4HA2, P4HB, PA2G4, PABPC3, PABPC4, PABPC5, PABPN1, PACSIN1, PACSIN2, PACSIN3, PADI2, PADI4, PAEP, PAFAH1B2, PAFAH1B3, PAFAH2, PAGE4, PAGR1, PAICS, PAIP1, PAK3, PAK4, PAK5, PAK6, PAL4D, PALM, PALMD, PAM, PANK1, PANK3, PAPLN, PAPOLG, PAPPA, PAPPA2, PAPSS1, PARK7. PARP1, PARP11, PARP16, PARVA, PATE1, PATE4, PAWR, PAX3, PAX4. PAX8, PAX1P1, PBK, PBRM1, PC, PCBDL PCBD2, PCBPL PCBP2. PCBP3, PCDHL PCDH10, PCDH12, PCDH17, PCDH8, PCDH9, PCDHA4, PCDHA7, PCDHAC1, PCDHB1, PCDHB10, PCDHB2, PCDHGA1, PCDHGA10, PCDHGA2, PCDHGB1, PCDHGC3, PCDHGC5, PCK2, PCLAF, PCMT1, PCNA, PCNP, PCOLCE, PCOLCE2, PCP4L1, PCSK1, PCSK1N, PCSK2, PCSK7, PCSK9, PCTP, PCYOX1, PCYOX1L, PCYT1A, PDAPL PDCD1, PDCD1LG2, PDCD4, PDCD5, PDCD6, PDCL, PDCL2, PDCL3, PDE11A, PDE1A. PDE1B, PDE3A. PDE4A, PDE4C, PDE4D, PDE5A, PDE6D, PDE6H. PDE7A, PDE9A. PDGFA. PDGFB, PDGFC, PDGFD. PDGFRA, PDGFRB. PDGFRL, PDHA2, PDHB, PDHX, PDIA3, PDIA4, PDIA5, PDIA6, PDILT, PDK2, PDLIM1, PDLIM3, PDLIM4, PDLIM5, PDP1, PDPK1, PDRG1, PDXK, PDXP, PDYN, PDZD11, PDZD7, PDZK1, PEA 15, PEAR1, PEBP1, PECAM1, PECR, PELI1, PELI2, PELO, PENK, PES1, PET 117, PEX14, PEX26, PEX5, PF4V1, PFDN1, PFDN2, PFDN4, PFDN5, PFKFB3, PFKM, PFN1, PFN2, PFN4, PGA4, PGAM4, PGD, PGF,41108365568.1Docket 047563-870867PGK1, PGK2, PGLS, PGLYRPL PGLYRP2, PGLYRP3, PGML PGM2, PGM5, PGP, PGPEP1, PGR, PGRMC1, PGRMC2, PHB2, PHETA2, PHEX, PHF11, PHF3, PHF6, PHGDH, PHKA1, PH0SPH01, PHOSPHO2, PHYH, PHYHD1, PHYHIP, PHYHIPL, PI15, PI3, PIANP, PIAS3, PIAS4, PICK1, PID1, PIEZO1, PIGR, PIH1D1, PIH1D2, PIK3AP1, PIK3C2A, PIK3C3, PIK3CA|PIK3R1, PIK3IP1, PIK3R1, PILRA, PIM1, PIM2, PIN1, PIN4, PIP, PIP4K2A, PIP4K2B, PIPOX, PIR, PITPNA, PITPNB, PITPNM3, PITX2, PIWIL1, PKD2. PKDCC, PKLR, PKM. PKN1, PKN2, PKP2. PLA1A, PLA2G10, PLA2G12A, PLA2G12B, PLA2G1B, PLA2G2A, PLA2G2C, PLA2G2D, PLA2G2E, PLA2G4A, PLA2G5, PLA2G7, PLA2R1, PLAAT2, PLAAT3, PLAAT4, PLAT, PLAU, PLAUR, PLBD1, PLBD2, PLCB1, PLCD1, PLCD3, PLCG1, PLCG2, PLCXD3, PLD3, PLD5, PEEK, PLEKHA1, PLEKHA3, PLEKHA4, PLEKHA7, PLEKHB1, PLEKHF2, PLEKHM2, PLG, PLIN3, PLK1, PLOD3, PLPBP, PLPP4, PLS1, PLS3, PLSCR3, PLVAP, PLXDC1, PLXDC2, PLXNA1, PLXNA4, PLXNB2, PLXNB3, PLXNC1. PLXND1, PMEL, PMEPA1, PMF1, PML, PMM1. PMP2, PMPCA, PMS2, PMVK, PNKP, PNLIP, PNLIPRP1, PNLIPRP2, PNMA2, PNMT, PNOC, PNP, PNPLA2, PNPO, PNPT1, PODXL2, POFUT1, POGLUT1, POGLUT3, POLB, POLD4, POLE2, POLE3, POLH, POLI, POLM, POLR2C, POLR2E, POLR2F, POLR2I, POLR2J, POLR2K, POLR2M, POLR3F, POLR3K, POMC, P0MGNT1, P0MGNT2, POMK, PON1, PON2, POP7, POR, POSTN, POTEG, POTEM, POU2AF1, POU2F1, POU6F1, PP13G, PPA1, PPA2, PPARA, PPARG, PPARGC1A, PPBP, PPCDC, PPCS. PPFIA1, PPIA. PPIB, PPIC. PPIE, PPIF, PPILI, PPIL3. PPM1A, PPM1B, PPMID. PPM1F, PPM1G, PPM1L, PPM1M, PPME1, PPP1CC, PPP1R10, PPP1R14A, PPP1R1A, PPP1R2, PPP1R27, PPP1R3B, PPP1R42, PPP1R9B, PPP2R1A, PPP2R3A, PPP2R5A, PPP3CA|PPP3R1, PPP3R1, PPP3R2, PPP4C, PPP4R3A, PPT1, PPTC7, PPY, PQBP1, PRAME, PRC1, PRCP, PRDM4, PRDX1, PRDX3, PRDX4, PRDX5, PRDX6, PREP, PRG2, PRG3, PRIM1, PRKAA1|PRKAB1|PRKAG1, PRKAA2|PRKAB2|PRKAG1, PRKACA, PRKAR1A, PRKAR1B, PRKAR2A, PRKAR2B, PRKCA, PRKCB. PRKCD, PRKCG, PRKCL PRKCQ, PRKCSH, PRKCZ. PRKG1, PRKN, PRKRA, PRL, PRLH. PRLR, PRMT1, PRMT3, PRNP, PROC, PROCR, PROK1, PROK2, PROMI, PROS!, PRPF18, PRPF19, PRPF6, PRPSL PRPS2, PRPSAP1, PRPSAP2, PRR15, PRR16, PRR27, PRRG1, PRRG4, PRRT2, PRRX1, PRSS1, PRSS2, PRSS22, PRSS27, PRSS3, PRSS35, PRSS37, PRSS57, PRSS8, PRTFDC1, PRTG, PRTN3, PRX, PRXL2A, PSAP, PSAPL1, PSAT1, PSG1, PSG11, PSG2, PSG3, PSG4, PSG5, PSG6, PSG7, PSG8, PSG9. PSIP1, PSMA1, PSMA2, PSMA3, PSMA4, PSMA6, PSMA7, PSMB1, PSMB10, PSMB2, PSMB3. PSMB4, PSMB5, PSMB6. PSMB7, PSMC3, PSMC3IP. PSMC5, PSMDIO. PSMD1L PSMD4. PSMD5, PSMD6, PSMD7, PSMD9, PSME2, PSME3, PSMG2, PSMG3, PSMG4, PSPC1, PSPH, PSPN, PSTPIP1, PTCHD3, PTEN, PTER, PTGDS, PTGES2, PTGFRN, PTGR1, PTGR2, PTGS2, PTH, PTH1R, PTH2, PTHLH, PTK2, PTK2B, PTK6, PTK7, PTP4A1, PTP4A2, PTP4A3, PTPA, PTPMT1, PTPN1, PTPN11, PTPN13, PTPN2, PTPN4, PTPN6, PTPN7, PTPN9, PTPRD, PTPRH, PTPRJ, PTPRN.42108365568.1Docket 047563-870867PTPRR, PTPRS, PTPRU, PTRH2, PTRHDL PTS, PTTG1, PTX3, PUDP, PUF60, PURA, PURB, PUS1, PVR, PVRIG, PWWP2B, PWWP3A, PXDN, PXDNL, PXYLP1, PYCARD, PYCR1, PYCR2, PYDC1, PYGB, PYGL, PYY, PZP, QDPR, QKI, QPCT, QPCTL, QPRT, QRFP, QS0X1, QSOX2, QTRT1, QTRT2, RAB11A, RABI IB, RAB11FIP3, RABIS, RAB14, RAB18, RAB1A, RAB21, RAB22A, RAB23, RAB24, RAB26, RAB27A, RAB27B, RAB2A, RAB2B. RAB31, RAB32, RAB33A, RAB35, RAB37. RAB38, RAB39B, RAB3B, RAB3D, RAB3IL1, RAB4A, RAB4B, RAB5A. RAB5B, RAB5C, RAB6A, RAB6B, RAB7A, RAB7B, RAB8B, RABEP1, RABEPK, RABGAP1L, RABIF, RABL3, RABL6, RAC1, RAC2, RAC3, RACGAP1, RACK1, RADI, RAD18, RAD23A, RAD23B, RAD51C, RAD51D, RAET1G, RAET1L, RAFI, RAG1, RALA, RAMP1, RAMP3, RAN, RANBP1, RANBP3, RANGAP1, RAP1GAP, RAP1GDS1, RAP2A, RAPGEF1, RAPGEF5, RARRES1, RARRES2, RASA1, RASSF2, RASSF5, RBI, RBBP5. RBBP6, RBBP9. RBF0X1, RBFOX2, RBKS, RBL1, RBL2, RBM17, RBM18, RBM19. RBM22, RBM23, RBM24. RBM3. RBM39, RBM4, RBM41, RBM46. RBMS1, RBP1, RBP2, RBP4, RBP5, RBP7, RBPMS2, RCAN1, RCAN2, RCAN3, RCN3, RCSD1, RCVRN, RDH10, RDH12, RDH13, RDH16, RDX, RECQL, REEP2, REGIA, REGIB, REG3G, RELB, RELL1, RELL2, RELN, RELT, REMI, REN, REPIN 1, REPS2, RER1, RERG, RET, RETN, RETNLB, RETREG1, REXO2, RFC4, RFESD, RFFL, RFK, RFNG. RFPL3, RFX5, RFXAP, RGMA, RGMB, RGN, RGS10. RGS14, RGS16, RGS18. RGS19, RGS21, RGS3, RGS4, RGS5, RGS7, RHEX, RHOA, RHOB, RHOC, RHOD, RHOG, RHOQ, RHOT1, RHPN2, RIC3. RIC8A, RIDA. RILP, RILPLL RILPL2, RIOX2, RIPK2, RIPPLYL RIPPLY3, RLBP1, RLN2, RLN3, RMDN3, RMI1, RNASE1, RNASE10, RNASE13, RNASE2, RNASE3, RNASE4, RNASE6, RNASEH1, RNASET2, RND1, RND3, RNF114, RNF122, RNF128, RNF13, RNF141, RNF146, RNF148, RNF149, RNF150, RNF215, RNF24, RNF31, RNF34, RNF41, RNF43, RNF8, RNGTT, RNPC3, RNPEP, ROBO1, ROBO2, ROBO3, ROBO4, ROCK2, ROGDI, ROPN1B. ROR2, RORB, RORC. ROS1, RP9, RPA2, RPAIN, RPE, RPIA. RPL11, RPL12. RPL26L1, RPL30, RPL38. RPL5, RPLP2, RPN1, RPN2, RPP25, RPP25L. RPP30, RPP40, RPRD1A, RPRD1B, RPS14, RPS19, RPS20, RPS25, RPS27A, RPS3, RPS3A, RPS4X, RPS5, RPS6KA1, RPS6KA3, RPS6KA6, RPS6KB2, RPS7, RPSA, RRAD, RRAGC, RRAS, RRAS2, RRBP1, RRM1, RRM2, RRM2B, RSI, RSPO1, RSPO2, RSPO3, RSPO4, RSRP1, RTCA, RTF1, RTKN, RTL6, RTN1, RTN4IP1, RTN4R, RTN4RL1, RTP4, RTRAF, RUFY1, RUNX3, RUVBL1, RWDD1, RWDD4, RXFP1, RXRA. RYBP, RYK, S100A10, S100A1L S100A13, S100A14, S100A16. S100A2, S100A4, S100A5, SI00A6. S100A7. S100A8, S100A8|S100A9, S100A9, SWOP, S100Z, SAAL SAA2. SAA4, SAE1|UBA2, SAG, SAMD12, SAMD4B, SAMHD1, SAMM50, SAMSN1, SAP18, SAP30, SAP30L, SAR1A, SAR1B, SARG, SARS1, SARS2, SAT1, SAT2, SATB1, SATB2, SAYSD1, SBDS, SBSN, SCAMP5, SCAND1, SCARA3, SCARA5, SCARB1, SCARB2, SCARF1, SCARF2, SCCPDH, SCFD1, SCGB1D1, SCGB1D2, SCGB2A1, SCGB3A1, SCGN, SCIMP, SON, SCLY, SCMH1, SCN3B, SCO1,43108365568.1Docket 047563-870867SCP2, SCP2DL SCPEP1, SCRN3, SCT, SCUBEL SCUBE3, SDC3, SDCBP, SDCBP2, SDF2, SDF2L1, SDHAF1, SDHAF2, SDHAF4, SDHB, SDSL, SEC13, SEC22A, SEC61B, SEC61G, SECTM1, SEL1L2, SELE, SELENBP1, SELENOF, SELENOH, SELENOM, SELENOS, SELENOW, SELL, SELP, SELPLG, SEMA3A, SEMA3B, SEMA3C, SEMA3E, SEMA3G, SEMA4A, SEMA4B, SEMA4C, SEMA4D. SEMA4F, SEMA5A, SEMA5B, SEMA6A, SEMA6B. SEMA6C, SEMA6D, SEMA7A, SEMG1, SENP1, SENP2. SENP7, SENP8, SEPHS1, SEPSECS, SEPTIN10, SEPTIN11, SEPTIN3, SEPTIN5, SEPTIN6, SERAC1, SERF1A, SERF2, SERPINA1, SERPINA10, SERPINA11, SERPINA12, SERPINA3, SERPINA4, SERPINA6, SERPINA7, SERPINA9, SERPINB1, SERPINBI3. SERPINB2, SERPINB3, SERPINB4, SERPINB5, SERPINB8, SERPINB9, SERPINC1, SERPIND1, SERPINE1, SERPINE2, SERPINF1, SERPINF2, SERPING1, SERPINH1, SERPINI2, SERTAD3, SET, SETBP1, SETD2, SETMAR, SEZ6L, SEZ6L2, SF1, SF3B4, SF3B6, SFN, SFRP1, SFRP2, SFRP4, SFRP5, SFTA2, SFTPB. SFTPD, SFXN5. SGF29, SGKL SGK3, SGSH, SGTA, SGTB, SH2B3, SH2D3C. SH3BGRL, SH3BGRL2, SH3BGRL3, SH3BP2, SH3BP5, SH3GL2, SH3GL3, SH3GLB1, SH3GLB2, SH3KBP1, SH3PXD2B, SHANK1, SHANK3, SHARPIN, SHBG, SHC1, SHC2, SHC4, SHCBP1L, SHD, SHH, SHISAL1, SHISAL2A, SHKBP1, SHMT1, SHMT2, SHOX, SIAE, SIAH1, SIGIRR, SIGLECL SIGLEC11, SIGLEC12, SIGLEC14, SIGLEC15, SIGLEC5, SIGLEC6, SIGLEC7, SIGLEC8, SIGLEC9. SIL1, SIRPA, SIRPB1, SIRPB2, SIRPG. SIRT2. SIRT3. SIRT5. SIRT6. SIT1, SIX6, SKA1, SKIL, SKP1, SKP2. SLAMF1, SLAMF6. SLAMF7, SLAMF8. SLAMF9, SLBP. SLC14A1, SLC14A2, SLC16A3, SLC22A16, SLC25A1, SLC25A18, SLC25A38, SLC26A11, SLC26A5, SLC26A7, SLC27A2, SLC30A5, SLC35G2, SLC3A1, SLC3A2, SLC41A2, SLC4A8, SLC5A5, SLC66A1L, SLC6A14, SLC6A16, SLC6A9, SLC9A3R1, SLCO5A1, SLFNL1, SLIT1, SLIT2, SLIT3, SLITRK1, SLITRK2. SLITRK4, SLITRK5, SLITRK6. SLPI, SLTM, SMAD1, SMAD2, SMAD3, SMAD4, SMAD5, SMARCC1, SMC3, SMCO1, SMCO2, SMIM10, SMIM13, SMIM24, SMIM3, SMIM9, SMN1, SMNDC1, SMPDL SMPDL3A, SMR3A. SMS, SMTN, SMURF 1. SMURF2, SMYD2, SNAI2, SNAP23, SNAP25, SNAP29, SNAPIN, SNCA, SNCAIP, SNCG, SND1, SNF8, SNIP1, SNPH, SNRNP25, SNRPA, SNRPA1, SNRPB2, SNRPC, SNRPD2, SNRPD3, SNRPE, SNRPF, SNRPG, SNTA1, SNTB1, SNTN, SNUB, SNUPN, SNX1, SNX11, SNX12, SNX15, SNX16, SNX17, SNX27, SNX3, SNX4, SNX5, SNX8, SOCS3, SOCS7, SOD1. SOD2, SOD3, SORBS3, SORCS1, SORCS2, SORCS3, SORD, SORT1. SOS1, SOST, SOSTDC1, SOXIO, SOX2, SOX6, SOX9, SP6, SPA17.SPACAL SPACA3. SPACA5, SPAG11A, SPAG11B, SPAG7. SPANXAL SPANXN3, SPANXN4, SPARC, SPART, SPAST, SPATA20, SPATA22, SPATA24, SPATA31D4, SPATA33, SPATA46, SPATA5, SPATA9, SPATC1L, SPCS1, SPDEF, SPDL1, SPECC1, SPEF1, SPHK1, SPHK2, SPI1, SPIN1, SPIN3, SPINK1, SPINK13, SPINK14, SPINK2, SPINK4, SPINK5, SPINK6, SPINK7, SPINK8, SPINK9, SPINEL SPINT2, SPINT3, SPN, SPOCK1, SPOCK2. SPOCK3, SPON1, SPON2, SPOP.44108365568.1Docket 047563-870867SPPL SPR, SPREDL SPRN, SPSBL SPTAN1, SPTLCL SPTLC2, SQSTM1, SRA1, SRC, SRFBP1, SRGAP2, SRGN, SRI, SRL, SRM, SRP14, SRP19, SRPK1, SRPK2, SRRT, SRSF6, SRSF7, SRXN1, SRY, SSB, SSBP1, SSMEM1, SSNA1, SSPN, SSR1, SSR2, SSRP1, SST, SSTR1, SSU72, SSX4, ST13, ST3GAL1, ST3GAL2, ST3GAL5, ST3GAL6, ST6GAL1, ST6GAL2, ST6GALNAC1, ST6GALNAC3, ST6GALNAC4. ST6GALNAC5, ST6GALNAC6, ST8SIA1, ST8SIA2, ST8SIA3, ST8SIA4, ST8SIA6. STAB1. STAC, STAMBP, STAMBPL1, STAP1. STAR, START) 10, STARD5. STARD7, STAT1.STAT3, STAT5A, STAT5B, STAT6, STATH, STAU1, STAU2, STC1, STC2, STIM1, STK10, STK16, STK17B, STK24, STK4, STMN1, STMN2, STMN3, STMN4, STOM, STOML1, STOML2, STUB1, STX10, STX12, STX17, STX18, STX1A, STX1B, STX2, STX3, STX4, STX6, STX7, STX8, STXBP4, STXBP6, STYX, SUB1, SUDS3, SUFU, SUGT1, SULF2, SULT1A1, SULT1A3, SULT1B1, SULT1C2, SULT1C4, SULT1E1. SULT2A1. SULT2B1, SULT4A1, SULT6B1, SUMF1, SUMF2. SUMO2.SUMO3, SUMO4, SUN3. SUN5, SUOX. SURF1, SUSD1, SUSD3, SV2A. SVBP, SVEP1. SVIP, SWAP70, SYAP1, SYCE1L, SYF2, SYK, SYN3, SYNE2, SYNGR3, SYNJ2BP, SYT1, SYT11, SYT12, SYT13, SYT17, SYT2, SYT4, SYT5, SYT6, SYT7, SYT8, SYT9, SYTL1, SYTL2, SYTL4, TAC1, TAC4, TACC3, TACO1, TACSTD2, TADA1, TADA2A, TAF12, TAF15, TAFA2, TAFA3, TAFA4, TAFA5, TAGLN, TAGLN2, TAGLN3, TALDO1, TANK, TAPBP, TAPBPL, TARBP2, TARDBP, TAT, TATDN1. TAX1BP1. TAX1BP3. TBC1D13, TBC1D20, TBC1D22B, TBC1D24, TBC1D25, TBC1D28, TBC1D5, TBCB, TBCC, TBCE, TBCEL. TBK1, TBL2, TBP, TBPL1, TBX22. TBX3, TBX5, TBXAS1, TCAP, TCEA1, TCEA2, TCEA3, TCEAL1, TCEAL2, TCEAL3, TCEAL4, TCEAL5, TCEAL7, TCEAL8, TCF21, TCF24, TCF4, TCL1A, TCL1B, TCN1, TCN2, TCP1, TCP10L3, TCP11, TCP11L1, TCP2L, TCTN2, TDGF1, TDO2, TDP1, TEAD3, TEAD4, TEC, TECTB, TEF, TEK, TEN1, TENM2, TENM3. TENM4, TEPSIN, TERB2, TERFI, TES, TESC, TEX29, TEX30, TF, TFAM, TFB1M, TFB2M, TFCP2L1, TFF1, TFF2, TFPI, TFPI2, TFR2. TFRC, TG. TGFB1, TGFB2. TGFB3, TGFBI, TGFBR1. TGFBR2, TGFBR3, TG1F2, TG1F2LX, TG1F2LY, TGM1, TGM2, TGM4, THAP11, THAP2, THAP4, THBS1, THBS2, THBS3, THBS4, THEM4, THG1L, THOC1, THOP1, THPO, THRA, THRB, THSD1, THSD7A, THTPA, THYN1, TIA1, TIAL1, TIAM1, TIAM2, TICAM2, TIE1, TIFA, TIGAR, TIGIT, TIMD4, TIMELESS, TIMM10, TIMM10B, TIMM13, TIMM21, TIMM23, TIMM50, TIMM8A, TIMP1, TIMP2, TIMP3, TIMP4. TINAGL1, TINF2, TIPIN, TIPRL, TIRAP, TJP1, TK1, TK2, TKFC, TKT, TLDC2, TLE5, TLK1. TLL1, TLN2, TLNRD1, TLR1, TLR10, TLR2, TLR3. TLR4, TLR4|LY96, TLR5. TM2D1, TMA16, TMCC3, TMCO5A. TMED10, TMED2, TMED4. TMED9.TMEM106A, TMEM106B, TMEM108, TMEM119, TMEM132A, TMEM132B, TMEM132C, TMEM154, TMEM167A, TMEM167B, TMEM185A, TMEM190, TMEM225B, TMEM230, TMEM234, TMEM237, TMEM25, TMEM38B, TMEM52, TMEM52B, TMEM59L, TMEM70, TMEM87B, TMEM8B, TMEM9, TMIGD2, TMOD1, TMOD2, TMOD3, TMOD4, TMPO, TMPRSS11A,45108365568.1Docket 047563-870867TMPRSS11B, TMPRSS11D, TMPRSS15, TMPRSS5, TMPRSS6, TMUB1, TMUB2, TMX1, TMX3, TNC, TNF, TNFAIP1, TNFAIP3, TNFAIP6, TNFAIP8, TNFRSF10A, TNFRSF1OB, TNFRSF1OD, TNFRSF11A, TNFRSF11B, TNFRSF12A, TNFRSF13B, TNFRSF13C, TNFRSF14, TNFRSF17, TNFRSF18, TNFRSF19, TNFRSF1A, TNFRSF1B, TNFRSF21, TNFRSF25, TNFRSF4, TNFRSF6B, TNFRSF8, TNFRSF9, TNFSF1O, TNFSF11, TNFSF12, TNFSF13B, TNFSF14, TNFSF15, TNFSF18, TNFSF4, TNFSF8, TNFSF9, TNIP1, TNKS, TNMD. TNNI2. TNNI3, TNNT2. TNR. TNRC6B, TNS2, TNS4, TNXB, TOLLIP, TOMI, TOM1L1, TOMM20, TOPI, TOPBP1, TOR1AIP1, TOR1AIP2, TOR4A, TOX3, TP53, TP53I13, TP53I3, TP63, TPD52L2, TPGS2, TPH1, TPI1, TPK1, TPM1, TPM3, TPM4, TPMT, TPO, TPP1, TPP2, TPPP, TPPP2, TPPP3, TPRKB, TPSAB1, TPSB2, TPSG1, TPST1, TPST2, TPT1, TRA2B, TRABD, TRABD2A, TRAF1, TRAF4, TRAPPCI 3, TRAPPC2, TRAPPC3, TRAPPC4, TRAPPC5, TRAPPC6A, TRAPPC6B, TRAT1, TRDMT1, TREM1, TREM2, TREML1, TREML2. TREX2, TRH, TRIAPI, TRIB2, TRIL. TRIM21. TRIM27, TRIM28, TRIM3, TRIM40, TRIM5, TRIM54, TRIM55, TRIM62, TRIM72, TRIM9, TRIML1, TRIO, TRIP10, TRMT112, TRPT1, TRUB1, TSC2, TSC22D3, TSEN15, TSEN34, TSG101, TSHR, TSLP, TSN, TSNARE1, TSPEAR, TSR3, TSSC4, TSSK1B, TSSK2, TST, TSTA3, TSTD1, TTC1, TTC17, TTC25, TTC27, TTC32, TTC33, TTC36, TTC9, TTC9B, TTL, TTLL12, TTN, TTP AL, TUFT1, TULP1, TWF1, TWF2, TWSG1, TXLNA, TXN, TXNDC11, TXNDC12. TXNDC5, TXNL1, TXNL4B, TXNRD1, TXNRD3NB, TYK2, TYMP, TYMS, TYMSOS, TYRO3. TYRP1, TYW3, TYW5. U2AF2, UAP1. UBA1. UBA3. UBA5, UBA6, UBAC1, UBASH3A, UBASH3B, UBB, UBC, UBD, UBE2A, UBE2B, UBE2C, UBE2D1, UBE2D1|UBB, UBE2D2, UBE2D3, UBE2D3|UBB, UBE2E1, UBE2E3, UBE2F, UBE2G1, UBE2G2, UBE2I, UBE2J1, UBE2K, UBE2L3, UBE2L3|UBB, UBE2L6, UBE2M, UBE2N, UBE2N|UBE2V1, UBE2N|UBE2V2, UBE2Q1, UBE2Q2, UBE2R2, UBE2S, UBE2T, UBE2V1, UBE2V2, UBE2W, UBE2Z. UBE3A, UBE4A, UBL3. UBL4A, UBL5, UBL7, UBLCP1, UBQLN2, UBQLN3, UBQLN4, UBTD2. UBXN2B. UBXN4, UCHL1, UCHL5, UCK1. UCK2, UCMA, UCN3. UFC1. UFD1, UFM1, UGP2, UGT1A1, UGT1A6, UGT1A8, UGT2A1, UGT2B15, ULBP1, ULBP2, ULBP3, ULK3, UMOD, UMODL1, UNC119B, UNC13A, UNC13D, UNC45A, UNC5A, UNC5B, UNC5D, UNC93B1, UNG, UPB1, UPP1, UPP2, UPRT, UQCRB, URM1, UROD, UROS, USB1, USF2, USH1C, USO1, USP1, USP1|WDR48, USP10, USP11, USP12, USP12|WDR48, USP14, USP15. USP19, USP2, USP21, USP22, USP25, USP28, USP3, USP30, USP4, USP46, USP46|WDR48. USP5, USP8. UST, UTP6, UTS2B, UTS2R. UXS1, VAC14, VAMP1, VAMP2. VAMP3. VAMP4. VAMP7, VAMP8, VANGL1, VAPA, VAPB, VASN, VASP, VAT1L, VAV1, VAV3, VBP1, VCAM1, VCAN, VCL, VCP, VCPIP1, VCX, VCX3A, VDR, VEGFB, VEGFC, VEGFD, VGLL4, VHL, VHLL, VIL1, VILL, VIM, VIP, VIT, VMAC, VNN2, VOPP1, VPS25, VPS26A, VPS26B, VPS29, VPS4A, VPS4B, VPS53, VPS9D1, VRK1, VSIG1, VSIG10, VSIG10L, VSIG2, VSIG4, VSIG8, VSIR. VSNL1, VSTM1, VSTM2L, VSTM4,46108365568.1Docket 047563-870867VSX1, VTAL VTI1A, VTI1B, VTN, VWAL VWA2, VWC2, VWC2L, WARSI, WARS2, WAS, WASL, WBP1L, WBP2, WDR18, WDR26, WDR48, WDR5, WEE2, WFDC1, WFDC10A, WFDC1OB, WFDC11, WFDC13, WFDC2, WFDC3, WFDC5, WFDC6, WFDC8, WFIKKN1, WIFI, WNK1, WNK3, WNT10B, WNT11, WNT16, WNT3A, WNT5B, WNT7A, WRNIP1, WSCD2, WTAP, WWC1, WWOX, WWP1, WWP2, XAGE2, XBP1, XCL1, XCL2, XDH, XG, XPNPEP1, XPNPEP3, XPO5, XRCC1. XRCC4, XRCC6, XXYLT1. XYLB. XYLT2, YAE1, YAP1, YARS1. YARS2, YBX1, YBX2, YEATS4, YES1, YIPF6, YJEFN3, YJU2, YME1L1, YTHDC1, YWHAB, YWHAG, YWHAH, YWHAQ, YWHAZ, YY1, ZADH2, ZAP70, ZBP1, ZBTB1O, ZBTB16, ZBTB33, ZBTB7A, ZC3H8, ZC4H2, ZCCHC17, ZCCHC18, ZDHHC14, ZDHHC4, ZFAND1, ZFAND2B, ZFAND3, ZFAND5, ZFP36, ZFP91, ZFYVE27, ZG16, ZHX1, ZHX2, ZHX3, ZKSCAN7, ZNF1O, ZNF134, ZNF174, ZNF175, ZNF18, ZNF18O, ZNF23, ZNF230, ZNF263, ZNF264, ZNF275, ZNF329, ZNF334, ZNF34, ZNF382, ZNF41, ZNF41O, ZNF415, ZNF483, ZNF526, ZNF56O. ZNF566. ZNF580. ZNF593, ZNF69. ZNF696, ZNF75D, ZNF774, ZNF843, ZNRF3, ZP4, ZPBP, ZPBP2, ZPLD1, ZRANB1, ZSWIM7, ZW10, ZWILCH, ZWINT, or ZYX.Determining protein level

[0079] Tire methods as described herein (e.g, in Section V) utilize determination of the level of or amount of one or more target protein from a biological sample. Any method generally recognized by a skilled artisan for protein quantification may be used. Convenient methods for determining the level of or amount of protein include UV absorbance assays (e.g., absorbance at 280 nm), Bradford assay (e.g., Coomassie dye), Lowry assay (e.g., Folin-Ciocalteu reagent). Biuret method (e.g., copper ions in an alkaline solution), bicinchoninic acid methods, immunoassay (e g., enzyme-linked immunosorbent assay (ELISA)), Kjeldahl methods, mass spectrometry (MS), and binding agent or binding molecule based methods for quantification, and are contemplated for use in any of the methods described herein (e.g., in Section V).

[0080] The biological sample may be purified prior to determination of target protein levels or amounts. For instance, the biological sample may be purified to enrich the sample for one or more target protein as described herein (e.g., LISTS 1-7 in Section IV). The biological sample may be purified to deplete the sample of any one of the target proteins provided in List 8. The purification may be performed by any method recognized in the art as being suitable for a biological sample, such as a blood sample. The purification may be performed using chemical means, such as protein precipitation. The purification may be performed using chromatography, such as liquid chromatography (LC). Tire purification may be performed using affinity chromatography, wherein the affinity is specific for one or more target proteins.47108365568.1Docket 047563-870867The purification may be performed using a pull-down assay, for example based on protein interactions. The purification may be performed using ultracentrifugation. The proteins in a biological sample may be processed prior to determination of the protein level or amount. For instance, a biological sample may be digested by a proteolytic enzyme.

[0081] Mass spectrometry is an advantageous technique for determining protein level because it provides high specificity and can identify multiple proteins simultaneously. For complex samples with many proteins, MS may be more suitable due to its ability to separate and identify multiple peptides. MS often utilizes specialized equipment and expertise. The proteolytic fragments for mass spectrometry may be separated by a liquid chromatography system interfaced with a high-resolution mass spectrometer (e.g., LC-MS system). Suitable LC-MS systems may comprise a <1.0 mm ID column and use a flow rate less than about 100 pl / min. In preferred embodiments, a nanoflow LC-MS system is used (e.g., about 50-100 pm ID column and a flow rate of < 1 pL / min, preferably about 100-800 nL / min, more preferably about 200-600 nL / min). In an exemplary' embodiment, an LC-MS system may' comprise a 0.05 mM ID column and use a flow rate of about 400 nL / min.

[0082] Tandem mass spectrometry may be used to improve resolution, as is known in the art, or technology may improve to achieve the resolution of tandem mass spectrometry' with a single mass analyzer. Suitable types of mass spectrometers are known in the art. These include, but are not limited to. quadrupole, time-of-flight, ion trap and Orbitrap, as well as hybrid mass spectrometers that combine different types of mass analyzers into one architecture (e.g., Orbitrap Fusion™ Tribrid™ Mass Spectrometer, Orbitrap Fusion™ Lumos™ Mass Spectrometer, Orbitrap Tribrid™ Eclipse™ Mass Spectrometer, Q Exactive Mass Spectrometer, each from ThermoFisher Scientific). In an exemplary embodiment, an LC-MS system may comprise a mass spectrometer selected from Orbitrap Fusion™ Tribrid™ Mass Spectrometer, Orbitrap Fusion™ Lumos™ Mass Spectrometer, Orbitrap Tribrid™ Eclipse™ Mass Spectrometer, or a mass spectrometer with similar or improved ion-focusing and iontransparency at the quadrupole. Suitable mass spectrometry protocols may be developed by optimizing the number of ions collected prior to analysis (e.g., AGC setting using an orbitrap) and / or injection time.

[0083] Methods for assessing an amount of protein expression using binding agent or binding molecule based methods are known in the art and all suitable methods for assessing an amount of protein known to one of skill in the art are contemplated within the scope of the present disclosure. In some embodiments, the method to assess an amount of a target protein is a binding agent- or binding molecule-based method. In general, this approach assesses an amount of protein expression by contacting a biological sample with binding agent or binding molecule that is specific for the target protein under conditions effective to allow48108365568.1Docket 047563-870867for formation of a complex between the binding agent or binding molecule and the target protein.Binding agent- and binding molecule-based method may occur in solution, or the binding agent or binding molecule may be immobilized on a solid surface. Non-limiting examples of suitable surfaces include microtiter plates, test tubes, beads, resins, and other polymers.

[0084] A binding agent or binding molecule may be attached to the substrate in a wide variety of ways, as will be appreciated by those in the art. Tire binding agent or binding molecule may either be synthesized first, with subsequent attachment to the substrate, or may be directly synthesized on the substrate. The substrate and the binding agent or binding molecule may be derivatized with chemical functional groups for subsequent attachment of the two. For example, the substrate may be derivatized with a chemical functional group including, but not limited to. amino groups, carboxyl groups, oxo groups or thiol groups. Using these functional groups, the binding agent or binding molecule may be attached directly using the functional groups or indirectly using linkers.

[0085] The binding agent or binding molecule may also be attached to the substrate non-covalently. For example, a biotinylated binding agent or binding molecule may be prepared, which may bind to surfaces covalently coated with streptavidin, resulting in attachment. Alternatively, binding agent or binding molecule may be synthesized on tire surface using techniques such as photopolymerization and photolithography. Additional methods of attaching binding agents or binding molecules to solid surfaces and methods of synthesizing biomolecules on substrates are well known in the art, i.e. VLSIPS technology from Affymetrix (e.g.. see U.S. Pat. No. 6.566,495, and Rockett and Dix, Xenobiotica 30(2): 155-177, each of which are hereby incorporated by reference in their entirety).

[0086] Contacting the sample with an binding agent or binding molecule under effective conditions for a period of time sufficient to allow formation of a complex generally involves adding the binding agent or binding molecule composition to the sample and incubating the mixture for a period of time long enough for the binding agent or binding molecule to bind to any antigen present. After this time, the complex will be washed and the complex may be detected by any method well known in the art. Methods of detecting the binding agent-or binding molecule-target protein complex are generally based on the detection of a label or marker. Tire tenn “label”, as used herein, refers to any substance attached to an epitope binding agent, or other substrate material, in which the substance is detectable by a detection method. Non-limiting examples of suitable labels include luminescent molecules, chemiluminescent molecules, fluorochromes, fluorescent quenching agents, colored molecules, radioisotopes, scintillants, biotin, avidin, stretpavidin, protein A, protein G, antibodies or fragments thereof, polyhistidine, Ni2+, Flag tags, myc tags, heavy metals, and enzymes (including alkaline phosphatase, peroxidase, and49108365568.1Docket 047563-870867luciferase). Methods of detecting a binding agent- or binding molecule-target protein complex based on the detection of a label or marker are well known in the art.

[0087] Immunoassays can be easier to perfonn and more readily available than MS systems.Immunoassays are advantageous because they utilize binding agents or binding molecules to specifically detect a single target protein with high sensitivity. Any binding agent or binding molecule as described herein (e.g., specifically binds to a target protein) may be used for determining the level of a target protein. Target proteins can be measured and quantified by immunoassay . Immunoassay s can be run in a number of different formats. Generally speaking, immunoassays can be divided into two categories: competitive immmunoassays and non-competitive immunoassays. In a competitive immunoassay, an unlabeled analyte in a sample competes with labeled analyte to bind an antibody. Unbound analyte is washed away and the bound analyte is measured. In a noncompetitive immunoassay, the antibody is labeled, not the analyte. Non-competitive immunoassays may use one antibody (e.g. the capture antibody is labeled) or more than one antibody (e.g. at least one capture antibody which is unlabeled and at least one ‘‘capping” or detection antibody which is labeled.). Suitable labels are described herein.

[0088] In an embodiment, the binding agent- or binding molecule-based method of determining of protein level is an immunoassay. In another embodiment, the binding agent or binding molecule method of detennining protein level is selected from: an enzyme linked immunoassay (ELISA), a fluorescence based assay, a dissociation enhanced lanthanide fluoroimmunoassay (DELFIA), a radiometric assay, a multiplex immunoassay, and a cytometric bead assay (CBA). In some embodiments, the binding agent-or binding molecule-based method of determining of protein level is a radioimmunoassay. In some embodiments, the binding agent- or binding molecule-based method of determining of protein level is an immunoblot or Western blot. In some embodiments, the binding agent- or binding molecule -based method of determining of protein level is an array. In some embodiment, the binding agent- or binding molecule -based method of detennining of protein level is flow cytometry. In some embodiments, the binding agent- or binding molecule-based method of determining of protein level is immunohistochemistry (IHC). IHC uses an antibody to detect and quantify antigens in intact tissue samples and methods of performing IHC are well known in the art.Normalizing protein levels

[0089] Determination of the level of or amount of one or more target protein may comprise normalization of the observed level or amount of the protein in the biological sample. The normalization may comprise statistical techniques that rescale observed protein levels in a dataset to a standard normal50108365568.1Docket 047563-870867distribution. The level of or amount of one or more target protein may be a normalized protein level or normalized amount. The normalized protein level or amount of protein may be a normal deviate score, standardized score, or Z-score. In some embodiments, the Z-score may be a normalized distribution of the level of the target protein within a population of samples. In some embodiments, the normalization sets the mean of the target protein to a value of 0 and sets the standard deviation (SD) of the target protein in the population to a value of 1.

[0090] In some embodiments, a Z-score of a target protein is calculated according to Formula (IV): Z=(x-m) / s, wherein x is the observed level of the target protein in a specific individual, m is the mean level of the target protein within a population; and s is the standard deviation of the target protein in the population.V. Methods

[0091] The present disclosure provides for methods of (a) diagnosing Alzheimer’s disease; (b) predicting or determining progression of Alzheimer’s disease; (c) predicting or determining amyloid / tau (AT) status; (d) predicting or determining brain amyloidosis status; or (e) predicting or determining plasma p-tau217 status in a subject or population of subjects. Another aspect of the present disclosure is a method of selecting a subject for inclusion in a clinical trial for a tauopathy (e.g.. Alzheimer's disease). The methods as described herein may comprise determining or detecting the protein level (e.g., normalized protein level) of one or more target proteins as described herein in a biological sample. Each of the target proteins as described herein may be indicative criteria for any of the methods of this disclosure.

[0092] Any of the methods described herein may be an ex-vivo or in-vitro method. Such ex-vivo or in-vitro methods may be performed by using or analyzing a biological sample (e.g., blood sample) obtained from a subject. For instance, the ex-vivo or in-vitro methods may be performed separately from the subject or not on the subject (e.g., the method is not performed on a body, such as a human body).

[0093] A method as described herein method may be performed by a medically-trained clinician (e.g., a doctor). The method may also be performed by someone that is not a medically-trained clinician, for example a technician, laboratory equipment operator, or data operator. The method may also be performed by the subject themselves.Diagnosing Alzheimer’s disease

[0094] Another aspect of the present disclosure is a method of diagnosing Alzheimer’s disease. The method may be for diagnosing Alzheimer's disease in a subject or a population of subjects. The method 51108365568.1Docket 047563-870867of diagnosing Alzheimer’s disease may be an ex-vivo or in-vitro method performed using a biological sample (e.g., a blood sample) obtained from a subject.

[0095] In some embodiments, the method of diagnosing Alzheimer’s disease may comprise (a) providing a biological sample obtained from the subject: (b) determining (i) an age of the subject at the obtainment of the biological sample in years; (ii) a biological sex of the subject at the obtainment of the biological sample; and (iii) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6). and / or seventh target protein (TP7): (c) calculating a predictor value (PV) for diagnosing Alzheimer’s disease based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) diagnosing the subject as having Alzheimer’s disease. In some embodiments, the biological sample is a blood sample.

[0096] In some embodiments, the method of diagnosing Alzheimer's disease may comprise (a) providing a biological sample obtained from the subject; (b) determining (i) a normalized protein level of plasma p-tau217 in the biological sample; (ii) an age of the subject at the obtainment of the biological sample in years; (iii) a biological sex of the subject at the obtainment of the biological sample; and (iv) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5). a sixth target protein (TP6), and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for diagnosing Alzheimer’s disease based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) diagnosing the subject as having Alzheimer’s disease. In some embodiments, the biological sample is a blood sample. In some embodiments of the method of diagnosing Alzheimer’s disease, when the normalized protein level of plasma p-tau217 in the biological sample is determined, the one or more target protein is a single target protein, optionally wherein the single target protein is SPC25 (e.g..Formula (III)).

[0097] A diagnosis of having Alzheimer's disease may be made when the PV is greater than a threshold level. A diagnosis of not having Alzheimer’s disease may be made when the PV is less than or equal to the threshold level. The predictor value for diagnosing Alzheimer’s disease may be generated as understood in the art, or according to an equation described herein (e g., Formula (I), Formula (II), or Formula (III)).52108365568.1Docket 047563-870867

[0098] In some embodiments, the method of diagnosing Alzheimer’s disease may comprise determining a normalized protein level in the biological sample of one or more target protein selected from any of the proteins provided in LIST 1, LIST 2, LIST 3, LIST 4, LIST 5, LIST 6, LIST 7, or TABLE 17. In some embodiments of tire method of diagnosing Alzheimer's disease, the target protein (e.g., first target protein (TP1)) is a protein selected from LIST 1, optionally wherein tire target protein is ACHE. In some embodiments of the method of diagnosing Alzheimer’s disease, the target protein (e.g., second target protein (TP2)) is a protein selected from LIST 2, optionally wherein the target protein is CTF1. In some embodiments of the method of diagnosing Alzheimer’s disease, the target protein (e.g., third target protein (TP3)) is a protein selected from LIST 3, optionally wherein the target protein is CPLX2. In some embodiments of tire method of diagnosing Alzheimer's disease, the target protein (e.g., fourth target protein (TP4)) is a protein selected from LIST 4, optionally wherein the target protein is VAT1. In some embodiments of the method of diagnosing Alzheimer’s disease, the target protein (e.g., fifth target protein (TP5)) is a protein selected from LIST 5, optionally wherein the target protein is SPC25. In some embodiments of the method of diagnosing Alzheimer’s disease, the target protein (e.g., sixth target protein (TP6)) is a protein selected from LIST 6, optionally wherein the target protein is FAHD2A. In some embodiments of the method of diagnosing Alzheimer's disease, the target protein (e.g., seventh target protein (TP7)) is a protein selected from LIST 7, optionally wherein the target protein is NPTXR. In some embodiments of the method of diagnosing Alzheimer’s disease, the protein level of a first target protein (TP1), second target protein (TP2), third target protein (TP3), fourth target protein (TP4), fifth target protein (TP5), sixth target protein (TP6), and seventh target protein (TP7) are determined. In some embodiments of tire method of diagnosing Alzheimer's disease, the TP1 is ACHE, the TP2 is CTF1, the TP3 is CPLX2, the TP4 is VAT1, the TP5 is SPC25, the TP6 is FAHD2A, and the TP7 is NPTXR. Progression of Alzheimer’s disease

[0099] Another aspect of the present disclosure are methods for predicting or detennining progression of Alzheimer's disease. The method may be for predicting or determining progression of Alzheimer's disease in a subject or a population of subjects. The method of predicting or determining progression of Alzheimer’s disease may be an ex-vivo or in-vitro method performed using a biological sample (e.g., a blood sample) obtained from a subject.

[0100] In some embodiments, the method of predicting or determining progression of Alzheimer's disease may comprise (a) providing a biological sample obtained from the subject: (b) determining (i) an age of the subject at the obtainment of the biological sample in years; (ii) a biological sex of tire subject at the obtaimnent of the biological sample; and (iii) a normalized protein level in the biological sample of53108365568.1Docket 047563-870867one or more target protein selected from a first target protein (TP 1 ), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining progression of Alzheimer's disease based on the age and biological sex of the subject, and the normalized protein level of tire one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining progression of Alzheimer’s disease. In some embodiments, the biological sample is a blood sample.

[0101] In some embodiments, the method of predicting or determining progression of Alzheimer’s disease may comprise (a) providing a biological sample obtained from the subject; (b) detennining (i) a normalized protein level of plasma p-tau217 in the biological sample: (ii) an age of the subject at the obtainment of the biological sample in years: (iii) a biological sex of the subject at the obtainment of the biological sample; and (iv) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6). and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining progression of Alzheimer’s disease based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining progression of Alzheimer’s disease. In some embodiments, the biological sample is a blood sample. In some embodiments of the method of predicting or detennining progression of Alzheimer’s disease, when the normalized protein level of plasma p-tau217 in the biological sample is determined, the one or more target protein is a single target protein, optionally wherein the single target protein is SPC25 (e.g., Formula (III)).

[0102] A prediction or determination of progression of Alzheimer’s disease may be made when the PV is greater than a threshold level. A prediction or detennination of not having progression of Alzheimer’s disease may be made when the PV is less than or equal to the threshold level. The progression of Alzheimer’s disease may be the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease. A prediction or determination of being likely to progress from cognitively normal to symptomatic Alzheimer’s disease may be made when the PV is greater than a threshold level. A prediction or determination of not being likely to progress from cognitively nonnal to symptomatic Alzheimer’s disease may be made when the PV is less than or equal to than the threshold level. Hie predictor value for the prediction or determination of progression of Alzheimer’s disease (e.g., likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease) may be generated as54108365568.1Docket 047563-870867understood in the art, or according to an equation described herein (e.g.. Formula (I), Formula (II), or Formula (III)).

[0103] In some embodiments, the method of predicting or determining progression of Alzheimer’s disease may comprise determining a normalized protein level in the biological sample of one or more target protein selected from any of the proteins provided in LIST 1, LIST 2, LIST 3, LIST 4, LIST 5, LIST 6, LIST 7, or TABLE 17. In some embodiments of the method of predicting or determining progression of Alzheimer’s disease, the target protein (e.g., first target protein (TP1)) is a protein selected from LIST 1, optionally wherein the target protein is ACHE. In some embodiments of the method of predicting or detennining progression of Alzheimer’s disease, the target protein (e.g., second target protein (TP2)) is a protein selected from LIST 2, optionally wherein the target protein is CTF1. In some embodiments of the method of predicting or determining progression of Alzheimer’s disease, the target protein (e.g., third target protein (TP3)) is a protein selected from LIST 3, optionally wherein the target protein is CPLX2. In some embodiments of the method of predicting or determining progression of Alzheimer’s disease, the target protein (e.g., fourth target protein (TP4)) is a protein selected from LIST 4, optionally wherein the target protein is VAT1. In some embodiments of the method of predicting or determining progression of Alzheimer’s disease, the target protein (e.g., fifth target protein (TP5)) is a protein selected from LIST 5, optionally wherein the target protein is SPC25. In some embodiments of the method of predicting or determining progression of Alzheimer’s disease, the target protein (e.g., sixth target protein (TP6)) is a protein selected from LIST 6, optionally wherein the target protein is FAHD2A. In some embodiments of the method of predicting or determining progression of Alzheimer’s disease, the target protein (e.g., seventh target protein (TP7)) is a protein selected from LIST 7, optionally wherein the target protein is NPTXR. In some embodiments of the method of predicting or determining progression of Alzheimer’s disease, the protein level of a first target protein (TP 1), second target protein (TP2), third target protein (TP3), fourth target protein (TP4), fifth target protein (TP5), sixth target protein (TP6), and seventh target protein (TP7) are determined. In some embodiments of tire method of predicting or determining progression of Alzheimer’s disease, the TP1 is ACHE, the TP2 is CTF1, the TP3 is CPLX2, the TP4 is VAT1, the TP5 is SPC25, the TP6 is FAHD2A, and the TP7 is NPTXR.Amyloid / tau (AT) status

[0104] Another aspect of the present disclosure are methods for predicting or detennining amyloid / tau (AT) status. The method may be for predicting or detennining amyloid / tau (AT) status in a subject or a population of subjects. The method of predicting or determining amyloid / tau (AT) status may be an ex-55108365568.1Docket 047563-870867vivo or in-vitro method performed using a biological sample (e.g., a blood sample) obtained from a subject.

[0105] In some embodiments, the method of predicting or determining amyloid / tau (AT) status may comprise (a) providing a biological sample obtained from the subject: (b) determining (i) an age of the subject at the obtainment of the biological sample in years: (ii) a biological sex of the subject at the obtainment of the biological sample; and (iii) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining amyloid / tau (AT) status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining amyloid / tau (AT) status. In some embodiments, the biological sample is a blood sample.

[0106] In some embodiments, the method of predicting or determining amyloid / tau (AT) status may comprise (a) providing a biological sample obtained from the subject: (b) determining (i) a normalized protein level of plasma p-tau217 in the biological sample; (ii) an age of the subject at the obtainment of the biological sample in years; (iii) a biological sex of the subject at the obtainment of the biological sample; and (iv) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TPS), a sixth target protein (TP6). and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining amyloid / tau (AT) status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining amyloid / tau (AT) status. In some embodiments, the biological sample is a blood sample. In some embodiments of the method of predicting or detennining amyloid / tau (AT) status, when the normalized protein level of plasma p-tau217 in the biological sample is determined, the one or more target protein is a single target protein, optionally wherein the single target protein is SPC25 (e.g., Formula (III)).

[0107] A prediction or determination of positive amyloid / tau (AT) status may be made when the PV is greater than a threshold level. A prediction or detennination of negative amyloid / tau (AT) status may be made when the PV is less than or equal to the threshold level. The predictor value for the prediction or determination of amyloid / tau (AT) status may be generated as understood in the art, or according to an equation described herein (e.g., Formula (I), Formula (II), or Formula (III)).56108365568.1Docket 047563-870867

[0108] In some embodiments, the method of predicting or determining amyloid / tau (AT) status may comprise determining a normalized protein level in the biological sample of one or more target protein selected from any of the proteins provided in LIST 1, LIST 2, LIST 3, LIST 4, LIST 5, LIST 6, LIST 7, or TABLE 17. In some embodiments of the method of predicting or determining amyloid / tau (AT) status, the target protein (e.g., first target protein (TP1)) is a protein selected from LIST 1. optionally wherein the target protein is ACHE. In some embodiments of the method of predicting or determining amyloid / tau (AT) status, the target protein (e.g., second target protein (TP2)) is a protein selected from LIST 2, optionally wherein the target protein is CTF1. In some embodiments of the method of predicting or determining amyloid / tau (AT) status, the target protein (e.g., third target protein (TP3)) is a protein selected from LIST 3, optionally wherein the target protein is CPLX2. In some embodiments of the method of predicting or determining amyloid / tau (AT) status, the target protein (e.g., fourth target protein (TP4)) is a protein selected from LIST 4, optionally wherein tire target protein is VAT 1. In some embodiments of the method of predicting or determining amyloid / tau (AT) status, the target protein (e.g., fifth target protein (TP5)) is a protein selected from LIST 5, optionally wherein the target protein is SPC25. In some embodiments of the method of predicting or detennining amyloid / tau (AT) status, the target protein (e.g., sixth target protein (TP6)) is a protein selected from LIST 6, optionally wherein the target protein is FAHD2A. In some embodiments of tire method of predicting or determining amyloid / tau (AT) status, the target protein (e.g., seventh target protein (TP7)) is a protein selected from LIST 7, optionally wherein the target protein is NPTXR. In some embodiments of the method of predicting or determining amyloid / tau (AT) status, the protein level of a first target protein (TP1), second target protein (TP2), third target protein (TP3), fourth target protein (TP4), fifth target protein (TP5), sixth target protein (TP6), and seventh target protein (TP7) are detennined. In some embodiments of the method of predicting or detennining amyloid / tau (AT) status, the TP1 is ACHE, the TP2 is CTF1, the TP3 is CPLX2. the TP4 is VAT1, tire TP5 is SPC25. the TP6 is FAHD2A. and the TP7 is NPTXR.Brain amyloidosis status

[0109] Another aspect of the present disclosure are methods for predicting or determining brain amyloidosis status. Hie method may be for predicting or determining brain amyloidosis status in a subject or a population of subjects. The method of predicting or determining brain amyloidosis status may be an ex-vivo or in-vitro method perfomied using a biological sample (e.g., a blood sample) obtained from a subject.

[0110] In some embodiments, the method of predicting or determining brain amyloidosis status may comprise (a) providing a biological sample obtained from the subject; (b) determining (i) an age of the57108365568.1Docket 047563-870867subject at the obtainment of the biological sample in years; (ii) a biological sex of the subject at the obtainment of the biological sample; and (iii) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining brain amyloidosis status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining brain amyloidosis status. In some embodiments, the biological sample is a blood sample.

[0111] In some embodiments, the method of predicting or determining brain amyloidosis status may comprise (a) providing a biological sample obtained from the subject; (b) determining (i) a normalized protein level of plasma p-tau217 in the biological sample; (ii) an age of the subject at the obtainment of the biological sample in years; (iii) a biological sex of the subject at the obtainment of the biological sample; and (iv) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6). and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining brain amyloidosis status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP 1 , TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining brain amyloidosis status. In some embodiments, the biological sample is a blood sample. In some embodiments of the method of predicting or detennining brain amyloidosis status, when the normalized protein level of plasma p-tau217 in the biological sample is determined, the one or more target protein is a single target protein, optionally wherein the single target protein is SPC25 (e.g., Formula (III)).

[0112] A prediction or determination of positive brain amyloidosis status may be made when the PV is greater than a threshold level. A prediction or determination of negative amyloidosis status may be made when the PV is less than or equal to the threshold level. The predictor value for the prediction or determination of amyloidosis status may be generated as understood in the art, or according to an equation described herein (e.g., Formula (I), Formula (II), or Formula (III)).

[0113] In some embodiments, the method of predicting or determining brain amyloidosis status may comprise determining a normalized protein level in the biological sample of one or more target protein selected from any of the proteins provided in LIST 1, LIST 2, LIST 3, LIST 4, LIST 5, LIST 6, LIST 7, or TABLE 17. In some embodiments of the method of predicting or determining brain amyloidosis status, tire target protein (e.g., first target protein (TP 1 )) is a protein selected from LIST 1, optionally58108365568.1Docket 047563-870867wherein the target protein is ACHE. In some embodiments of the method of predicting or determining brain amyloidosis status, the target protein (e.g., second target protein (TP2)) is a protein selected from LIST 2, optionally wherein the target protein is CTF1. In some embodiments of the method of predicting or detennining brain amyloidosis status, the target protein (e.g., third target protein (TP3)) is a protein selected from LIST 3, optionally wherein the target protein is CPLX2. In some embodiments of tire method of predicting or determining brain amyloidosis status, the target protein (e.g., fourth target protein (TP4)) is a protein selected from LIST 4, optionally wherein tire target protein is VAT1. In some embodiments of the method of predicting or determining brain amyloidosis status, the target protein (e.g., fifth target protein (TP5)) is a protein selected from LIST 5, optionally wherein the target protein is SPC25. In some embodiments of the method of predicting or detennining brain amyloidosis status, the target protein (e.g., sixth target protein (TP6)) is a protein selected from LIST 6. optionally wherein the target protein is FAHD2A. In some embodiments of the method of predicting or determining brain amyloidosis status, the target protein (e.g., seventh target protein (TP7)) is a protein selected from LIST 7, optionally wherein the target protein is NPTXR. In some embodiments of the method of predicting or determining brain amyloidosis status, the protein level of a first target protein (TP1), second target protein (TP2), third target protein (TP3), fourth target protein (TP4), fifth target protein (TP5), sixth target protein (TP6). and seventh target protein (TP7) are determined. In some embodiments of the method of predicting or determining brain amyloidosis status, the TP1 is ACHE, the TP2 is CTF1, the TP3 is CPLX2, the TP4 is VAT1, the TP5 is SPC25, the TP6 is FAHD2A, and the TP7 is NPTXR.Plasma p-tau217

[0114] Another aspect of the present disclosure are methods for predicting or determining plasma p-tau217 status. The method may be for predicting or detennining plasma p-tau217 status in a subject or a population of subjects. Tire method of predicting or determining plasma p-tau217 status may be an ex-vivo or in-vitro method perfonned using a biological sample (e.g.. a blood sample) obtained from a subject.

[0115] In some embodiments, the method of predicting or determining plasma p-tau217 status may comprise (a) providing a biological sample obtained from the subject; (b) determining (i) an age of the subject at the obtainment of the biological sample in years; (ii) a biological sex of the subject at the obtainment of the biological sample; and (iii) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining59108365568.1Docket 047563-870867plasma p-tau217 status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining plasma p-tau217 status. In some embodiments, the biological sample is a blood sample.

[0116] A prediction or determination of positive plasma p-tau217 status may be made when the PV is greater than a threshold level. A prediction or determination of negative plasma p-tau217 status may be made when the PV is less than or equal to the threshold level. The predictor value for the prediction or determination of plasma p-tau217 status may be generated as understood in the art, or according to an equation described herein (e.g., Formula (I), Fonnula (II), or Formula (III)).

[0117] In some embodiments, the method of predicting or determining plasma p-tau217 status may comprise determining a normalized protein level in the biological sample of one or more target protein selected from any of the proteins provided in LIST 1, LIST 2, LIST 3, LIST 4, LIST 5, LIST 6, LIST 7, or TABLE 17. In some embodiments of die method of predicting or detennining plasma p-tau217 status, the target protein (e.g., first target protein (TP1)) is a protein selected from LIST 1, optionally wherein the target protein is ACHE. In some embodiments of the method of predicting or determining plasma p-tau217 status, the target protein (e.g., second target protein (TP2)) is a protein selected from LIST 2, optionally wherein the target protein is CTF1. hi some embodiments of the method of predicting or determining plasma p-tau217 status, die target protein (e.g., third target protein (TP3)) is a protein selected from LIST 3, optionally wherein the target protein is CPLX2. In some embodiments of die method of predicting or determining plasma p-tau217 status, the target protein (e.g.. fourth target protein (TP4)) is a protein selected from LIST 4, optionally wherein die target protein is VAT 1. In some embodiments of the method of predicting or determining plasma p-tau217 status, the target protein (e.g., fifth target protein (TP5)) is a protein selected from LIST 5, optionally wherein the target protein is SPC25. In some embodiments of the method of predicting or detennining plasma p-tau217 status, the target protein (e.g., sixth target protein (TP6)) is a protein selected from LIST 6. optionally wherein the target protein is FAHD2A. In some embodiments of the method of predicting or determining plasma p-tau217 status, the target protein (e.g., seventh target protein (TP7)) is a protein selected from LIST 7, optionally wherein the target protein is NPTXR. In some embodiments of the method of predicting or determining plasma p-tau217 status, die protein level of a first target protein (TP1), second target protein (TP2), third target protein (TP3), fourth target protein (TP4), fifth target protein (TP5), sixth target protein (TP6). and seventh target protein (TP7) are determined. In some embodiments of the method of predicting or determining plasma p-tau217 status, the TP1 is ACHE, die TP2 is CTF1, the TP3 is CPLX2. the TP4 is VAT1, the TP5 is SPC25, the TP6 is FAHD2A, and the TP7 is NPTXR.60108365568.1Docket 047563-870867Selection for clinical trial

[0118] Another aspect of the present disclosure is a method of selecting a subject for inclusion in a clinical trial. The subject may meet the inclusion criteria for the clinical trial. The clinical trial may be for Alzheimer’s disease. The clinical trial may be for an amyloid-beta or tau therapy. The method of selecting a subject for inclusion in a clinical trial may be an ex-vivo or in-vitro method performed using a biological sample (e.g., a blood sample) obtained from a subject.

[0119] In some embodiments, the method of selecting a subject for inclusion in a clinical trial may comprise (a) providing a biological sample obtained from the subject; (b) determining (i) an age of the subject at the obtainment of the biological sample in years: (ii) a biological sex of the subject at the obtainment of the biological sample; and (iii) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for selecting the subject for inclusion in a clinical trial based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) selecting the subject for inclusion in a clinical trial. In some embodiments, the biological sample is a blood sample.

[0120] A selection of a subject for inclusion in a clinical trial may be made when the PV is greater than a threshold level. A decision for not selecting a subject for inclusion in a clinical trial may be made when the PV is less than or equal to the threshold level. The predictor value for the selection of the subject for inclusion in a clinical trial may be generated as understood in the art, or according to an equation described herein (e.g., Formula (I), Formula (II), or Formula (III)).

[0121] In some embodiments, the method of selecting a subject for inclusion in a clinical trial may comprise determining a normalized protein level in the biological sample of one or more target protein selected from any of the proteins provided in LIST 1, LIST 2, LIST 3, LIST 4, LIST 5, LIST 6, LIST 7, or TABLE 17. In some embodiments of the method of selecting a subject for inclusion in a clinical trial, the target protein (e.g., first target protein (TP1)) is a protein selected from LIST 1, optionally wherein the target protein is ACHE. In some embodiments of the method of selecting a subject for inclusion in a clinical trial, the target protein (e.g., second target protein (TP2)) is a protein selected from LIST 2, optionally wherein the target protein is CTF1. In some embodiments of the method of selecting a subject for inclusion in a clinical trial, the target protein (e.g., third target protein (TP3)) is a protein selected from LIST 3, optionally wherein the target protein is CPLX2. hi some embodiments of the method of selecting61108365568.1Docket 047563-870867a subject for inclusion in a clinical trial, the target protein (e.g., fourth target protein (TP4)) is a protein selected from LIST 4, optionally wherein the target protein is VAT1. In some embodiments of the method of selecting a subject for inclusion in a clinical trial, the target protein (e.g., fifth target protein (TP5)) is a protein selected from LIST 5, optionally wherein the target protein is SPC25. In some embodiments of the method of selecting a subject for inclusion in a clinical trial, the target protein (e.g.. sixth target protein (TP6)) is a protein selected from LIST 6, optionally wherein the target protein is FAHD2A. In some embodiments of the method of selecting a subject for inclusion in a clinical trial, the target protein (e.g., seventh target protein (TP7)) is a protein selected from LIST 7, optionally wherein the target protein is NPTXR. In some embodiments of the method of selecting a subject for inclusion in a clinical trial, tire protein level of a first target protein (TP1), second target protein (TP2), third target protein (TP3), fourth target protein (TP4), fifth target protein (TP5). sixth target protein (TP6), and seventh target protein (TP7) are determined. In some embodiments of the method of selecting a subject for inclusion in a clinical trial, the TP1 is ACHE, the TP2 is CTF1, the TP3 is CPLX2, the TP4 is VAT1, the TP5 is SPC25, the TP6 is FAHD2A, and the TP7 is NPTXR.

[0122] The design of clinical trials for AD and therapies can be greatly aided by the methods disclosed herein. Many clinical trials are designed to test the efficacy of imaging agents or therapeutic agents that target a specific pathophysiological change which occurs prior to the onset of AD symptoms. The efficacy of these various agents can be improved by administering the agents to subjects that have exceeded certain threshold values, as described herein. Similarly, clinical trials selecting subjects with symptoms of Alzheimer’s disease pathology would also benefit from being able to accurately discriminate an enrollee's pathology in order to determine if efficacy is associated with a particular disease state. Accordingly, measuring target protein levels as described herein prior to selecting a subject in a clinical trial, in particular into a treatment arm of a clinical trial, may result in smaller trials and / or improved outcomes. In some instances, methods described herein may be developed and used as a companion diagnostic for a therapeutic agent.Memory decline

[0123] Another aspect of the present disclosure is a method of predicting or determining the rate of memory decline. The method may be for predicting or determining the rate of memory decline in a subject or a population of subjects. The method of predicting or the rate of memory decline may be an ex-vivo or in-vitro method performed using a biological sample (e.g., a blood sample) obtained from a subject. As used herein this section, the term “rate of memory decline” may be interchanged with “Alzheimer’s disease progression”.62108365568.1Docket 047563-870867

[0124] The rate of memory decline may be a change in a Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score for the subject. CDR-SB scores are a measure of dementia severity (e.g., memory loss) and progression of Alzheimer’s disease. A subject’s CDR-SB score may range from 0 to 18, with higher scores indicating more severe dementia (e.g., memory loss) and / or progression of Alzheimer’s disease. In some embodiments, a CDR-SB score of 0.5 to 4 indicates the subject is questionable cognitive impairment to very mild dementia. In some embodiments, a CDR-SB score of 4.5 to 9 indicates the subject has mild dementia. In some embodiments, a CDR-SB score of 9.5 to 15.5 indicates the subject has moderate dementia, hr some embodiments, a CDR-SB score of 16 to 18 indicates the subject has severe dementia.

[0125] In some embodiments, the method of predicting or determining the rate of memory decline may comprise (a) providing a biological sample obtained from the subject; (b) determining (i) an age of the subject at the obtainment of the biological sample in years; (ii) a biological sex of the subject at the obtainment of the biological sample; and (iii) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6). and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining the rate of memory decline based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining the rate of memory' decline. In some embodiments, the biological sample is a blood sample.

[0126] In some embodiments, the method of predicting or determining the rate of memory decline may comprise (a) providing a biological sample obtained from the subject; (b) determining (i) a normalized protein level of plasma p-tau217 in the biological sample; (ii) an age of the subject at the obtainment of the biological sample in years; (iii) a biological sex of the subject at the obtainment of the biological sample; and (iv) a normalized protein level in the biological sample of one or more target protein selected from a first target protein (TP1). a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7); (c) calculating a predictor value (PV) for predicting or determining the rate of memory decline based on the age and biological sex of the subject, and tire normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and (d) predicting or determining the rate of memory decline. In some embodiments, the biological sample is a blood sample. In some embodiments of the method of predicting or determining the rate of memory decline, when the normalized protein level of plasma p-tau217 in the biological sample is determined, the one or more target protein is a single target protein, optionally wherein the single target protein is SPC25 (e.g., Formula (III)).63108365568.1Docket 047563-870867

[0127] A prediction or determination of having an increased rate of memory decline (e.g., compared to a negative control) may be made when the PV is greater than a threshold level. The rate of memory decline may be the change of CDR-SB score for the subject, over a period of time (e.g., per year). In some embodiments, the increased rate of memory decline is a rate of change of the CDR-SB score in the subject of about 0.46 to about 2.02 CDR-SB score per year, optionally about 0.46 CDR-SB score per year, about 1.4 CDR-SB score per year, or about 2.02 CDR-SB score per year. A prediction or determination of having a decreased rate of memory' decline (e.g., compared to a positive control) may be made when the PV is less than or equal to the threshold level. In some embodiments, the decreased rate of memory' decline is a rate of change of tire CDR-SB score in the subject of about 0.0962 to about 1.67 CDR-SB score per year, optionally about 0.0962 CDR-SB score per year, about 0.386 CDR-SB score per year, or about 1.67 CDR-SB score per year. The predictor value for the prediction or determination of the rate of memory decline may be generated as understood in the art. or according to an equation described herein (e.g., Formula (I), Formula (II), or Formula (III)).

[0128] In some embodiments, the method of predicting or determining the rate of memory decline may comprise determining a normalized protein level in the biological sample of one or more target protein selected from any of the proteins provided in LIST 1, LIST 2, LIST 3, LIST 4, LIST 5, LIST 6, LIST 7, or TABLE 17. In some embodiments of the method of predicting or determining the rate of memory decline, the target protein (e.g., first target protein (TP1)) is a protein selected from LIST 1, optionally wherein the target protein is ACHE. In some embodiments of tire method of predicting or determining the rate of memory decline, the target protein (e g., second target protein (TP2)) is a protein selected from LIST 2, optionally wherein the target protein is CTF 1. In some embodiments of the method predicting or determining the rate of memory decline, the target protein (e.g., third target protein (TP3)) is a protein selected from LIST 3, optionally wherein the target protein is CPLX2. In some embodiments of the method of predicting or determining the rate of memory' decline, the target protein (e.g., fourth target protein (TP4)) is a protein selected from LIST 4, optionally wherein the target protein is VAT1. In some embodiments of the method of predicting or determining the rate of memory decline, the target protein (e.g., fifth target protein (TP5)) is a protein selected from LIST 5, optionally wherein the target protein is SPC25. In some embodiments of the method of predicting or determining the rate of memory decline, the target protein (e.g., sixth target protein (TP6)) is a protein selected from LIST 6, optionally wherein the target protein is FAHD2A. In some embodiments of the method of predicting or determining the rate of memory decline, the target protein (e.g., seventh target protein (TP7)) is a protein selected from LIST 7, optionally wherein the target protein is NPTXR. In some embodiments of the method of predicting or determining the rate of memory decline, the protein level of a first target protein (TP1), second target64108365568.1Docket 047563-870867protein (TP2), third target protein (TP3), fourth target protein (TP4), fifth target protein (TP5), sixth target protein (TP6), and seventh target protein (TP7) are determined. In some embodiments of the method of predicting or determining the rate of memory decline, the TP1 is ACHE, the TP2 is CTF1, the TP3 is CPLX2, the TP4 is VAT1, the TP5 is SPC25, the TP6 is FAHD2A, and the TP7 is NPTXR.Calculation of prediction values

[0129] The methods as described herein may comprise calculation of a predictor value (PV), wherein the PV is used for (a) diagnosing Alzheimer's disease; (b) predicting progression of Alzheimer’s disease; (c) predicting amyloid / tau (AT) status; (d) predicting brain amyloidosis status; (e) predicting plasma p-tau217 status; (f) selecting a subject for inclusion in aclinical trial; or (g) predicting the rate of memory decline in the subject. A predictive model for generating predictor values may be developed in according with the methods described herein the Examples. Coefficients from the predictive models (c.g., TABLE 12) can be used for generating predictor value equations. One or more (e.g., up to all) of TP1, TP2, TP3. TP4, TP5, TP6. and / or TP7 may be incorporated into the predictive model. In some embodiments, all seven target proteins TP1-TP7 are incorporated in predictive model. In some embodiments, p-tau217 is incorporated into the predictive model (e.g., and into the PV equation).

[0130] The predictor value generated by the equation may be compared to a threshold value . The threshold value can be determined based on the number of target proteins included in the analysis, in accordance with the Examples.Predictor values according to Formula I

[0131] In some embodiments of amethod as described herein (e.g., when the protein level of TP1, TP2, TP3, TP4, TP5, TP6, and TP7 are determined), the predictor value may be calculated according to Fonnula I:PV = (0.618 X TP1) + (0.245 X TP2) + (0.723 X TP3) + (-0.224 X TP4) + (0.492 X TP5) + (—0.385 x TP6) 4- (—0.799 x TP7) + (0.05 x age) + (0.182 x biological sex) - 4.696Formula (I),wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female.65108365568.1Docket 047563-870867

[0132] The threshold value for a predictor value calculated by Formula I for a method as described herein may be 0.298, optionally wherein TP1 is a protein selected from LIST 1, TP2 is a protein selected from LIST 2, TP3 is a protein selected from LIST 3, TP4 is a protein selected from LIST 4, TP5 is protein selected from LIST 5, TP6 is protein selected from LIST 6, and TP7 is protein selected from LIST 7. In a specific embodiment, TP1 is ACHE, TP2 is CTF1, TP3 is CPLX2; TP4 is VAT1, TP5 is SPC25, TP6 is FAHD2A. and TP7 is NPTXR.

[0133] Tire threshold value for a predictor value calculated by Formula I for a method as described herein may be determined empirically, optionally wherein TP 1 is a protein selected from LIST 1, TP2 is a protein selected from LIST 2, TP3 is a protein selected from LIST 3, TP4 is a protein selected from LIST 4, TP5 is protein selected from LIST 5, TP6 is protein selected from LIST 6, and TP7 is protein selected from LIST 7. Tn a specific embodiment, TP1 is ACHE, TP2 is CTF1 , TP3 is CPLX2: TP4 is VAT1, TP5 is SPC25, TP6 is FAHD2A, and TP7 is NPTXR.Predictor values according to Formula II

[0134] In some embodiments of a method as described herein (e.g., when the protein level of pTau-217, TP1, TP2, TP3, TP4, TP5, TP6, and TP7 are determined), the predictor value may be calculated according to Formula II:PV = (1.10 x p - Tau217) + (0.363 x TP1) + (0.245 x TP2) + (0.723 x TP3)+ (-0.224 x TP4) + (0.492 x TP5) + (-0.385 x TP6) + (-0.799 x TP7) + (0.04 x age) + (0.222 x biological sex) — 4.45Formula (II),wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female.

[0135] Tire threshold value for a predictor value calculated by Formula II for a method as described herein may be 0.346, optionally wherein TP1 is a protein selected from LIST 1, TP2 is a protein selected from LIST 2, TP3 is a protein selected from LIST 3. TP4 is a protein selected from LIST 4, TP5 is protein selected from LIST 5, TP6 is protein selected from LIST 6, and TP7 is protein selected from LIST 7. In a specific embodiment, TP1 is ACHE, TP2 is CTF1, TP3 is CPLX2; TP4 is VATL TP5 is SPC25, TP6 is FAHD2A, and TP7 is NPTXR.

[0136] The threshold value for a predictor value calculated by Formula II for a method as described herein may be determined empirically, optionally wherein TP 1 is a protein selected from LIST 1, TP2 is a66108365568.1Docket 047563-870867protein selected from LIST 2, TP3 is a protein selected from LIST 3, TP4 is a protein selected from LIST 4, TP5 is protein selected from LIST 5, TP6 is protein selected from LIST 6, and TP7 is protein selected from LIST 7. In a specific embodiment, TP1 is ACHE, TP2 is CTF1, TP3 is CPLX2; TP4 is VAT1, TP5 is SPC25, TP6 is FAHD2A, and TP7 is NPTXR.Predictor values according to Formula III

[0137] In some embodiments of a method as described herein (e.g., when the protein level of pTau-217 and only TP5 are determined), the predictor value may be calculated according to Formula III:PV = (1.10 x p - Tau217) + (0.492 x TP5) + (0.04 x age) + (0.222 x biological sex) - 4.45Formula (III),wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female.

[0138] The threshold value for a predictor value calculated by Formula III for a method as described herein may be 0.346, optionally wherein TP5 is protein selected from LIST 5. In a specific embodiment, TP5 is SPC25.

[0139] The threshold value for a predictor value calculated by Formula III for a method as described herein may be determined empirically, optionally wherein TP5 is protein selected from LIST 5. In a specific embodiment, TP5 is SPC25.Treatment

[0140] Tire present disclosure provides for methods of treating Alzheimer’s disease in a subject in need thereof. The method comprises administering to the subject a therapeutic agent based on (a) a diagnosis of Alzheimer’s disease; (b) a prediction of progression of Alzheimer’s disease; (c) a prediction of amyloid / tau (AT) status: (d) a prediction of brain amyloidosis status; (e) a prediction of plasma p-Tau217 status; (f) a selection of the subject for inclusion in a clinical trial, as described herein; or (g) a prediction of the rate of memory decline. For example the subject may be determined to have a PV score of greater than a threshold value as described herein (e.g., 0.298 or 0.346). In some embodiments, the subject is administered a pharmaceutical composition comprising a therapeutic agent, as described herein.

[0141] The terms “treat,” “treating,” or “treatment” may refer to the provision of medical care by a trained and licensed professional to a subject in need thereof. The medical care may be a diagnostic test, a therapeutic treatment, and / or a prophylactic or preventative measure. Tire object of therapeutic and 67108365568.1Docket 047563-870867prophylactic treatments is to prevent or slow down (lessen) an undesired physiological change or disease / disorder (e.g., Alzheimer’s disease). Beneficial or desired clinical results of therapeutic or prophylactic treatments include, but are not limited to, alleviation of symptoms, diminishment of extent of disease, stabilized (e.g., not worsening) state of disease, a delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total), whether detectable or undetectable. Treatment may also encompass prolonging survival as compared to expected survival if not receiving treatment. Those in need of treatment include those already with the disease, condition, or disorder (e.g., Alzheimer’s disease) as well as those prone to have the disease, condition or disorder or those in which the disease, condition or disorder is to be prevented. Accordingly, a subject in need of treatment may or may not have any symptoms or clinical signs of disease.

[0142] In an aspect, the therapeutic agent administered to the subject is an Af>-targeting therapeutic agent. In some embodiments, the Ap-targeting therapeutic agent is a binding agent or binding molecule (e.g., an antibody or antigen-binding fragment thereof, or an aptamer) that binds to A . In some embodiments, the Ap-targeting therapeutic agent may be selected from Aducanumab, Donanemab, Lecanemab, Gantenerumab, Solanezumab, or Genezumab.

[0143] In an aspect, the therapeutic agent administered to the subject is a tau-targeting therapeutic agent. In some embodiments, the tau-targeting therapeutic agent is a binding agent or binding molecule (e.g., an antibody or antigen-binding fragment thereof, or an aptamer) that binds to tau. The tau-targeting therapeutic agent may alter tau phosphorylation patterns, antagonize tau aggregation, or increase clearance of pathological tau isoforms and / or aggregates. In some embodiments, the tau-targeting therapeutic agent may selected may be an anti-tau anti-sense oligonucleotide, anti-tau small interfering RNA, an tau production inhibitor, or a tau active vaccine. In some embodiments, the tau-targeting therapeutic agent may be selected from Gosuranemab, Zagotenemab, Tilavonemab Semorinemab, Buntanetap, E2814, Semorinemab, or Bepranemab.

[0144] In an aspect, the therapeutic agent is selected from Lecanemab, Donanemab, AADvacl, ACI-3024, ACI-35, APNmAb005, ASN51, AZP2006, BIIB076, BIIB080, BIIB113, Bepranemab, Dasatinib + Quercetin, E2814, Epothilone D, Gosuranemab, JNJ-63733657, LMTM, LY3372689, Lu AF87908, MK-2214, NIO752, GLX-07010, PNT001. PRX005. RG7345, RemberTM, Semorinemab, TPI 287.Tideglusib, Tilavonemab, or Zagotenemab.

[0145] In an aspect, the therapeutic agent may be selected from: a cholinesterase inhibitor; an N-methyl D-aspartate (NMDA) antagonist; an antidepressant (e.g., a selective serotonin reuptake inhibitor, an68108365568.1Docket 047563-870867atypical antidepressant, an aminoketone, a selective serotonin and norepinephrine reuptake inhibitor, a tricyclic antidepressant, etc.); a gamma-secretase inhibitor; a beta-secretase inhibitor; an anti-TREM2 binding agent or binding molecule (e.g., an antibody, including antigen-binding fragments, variants or derivatives thereof); a TREM2 agonist; a stem cell or population thereof; dietary supplement (e.g., lithium water, omega-3 fatty acids with lipoic acid, long chain triglycerides, genistein, resveratrol, curcumin, or grape seed extract); an antagonist of the serotonin receptor 6; a p38alpha MAPK inhibitor; a recombinant granulocyte macrophage colony-stimulating factor; a passive immunotherapy; an active vaccine (e.g. CAD 106, AF20513, etc.); atau protein aggregation inhibitor (e.g. TRxO237, methylthionimium chloride, etc.); a therapy to improve blood sugar control (e.g., insulin, exenatide, liraglutide pioglitazone, etc.), an anti-inflammatory agent; a phosphodiesterase 9A inhibitor; a sigma- 1 receptor agonist; a kinase inhibitor; a phosphatase activator; a phosphatase inhibitor; an angiotensin receptor blocker; a CB1 and / or CB2 endocannabinoid receptor partial agonist; a b-2 adrenergic receptor agonist: a nicotinic acetylcholine receptor agonist; a 5-HT2A inverse agonist; an alpha-2c adrenergic receptor antagonist; a 5-HT 1 A and 1 D receptor agonist; a Glutaminyl-peptide cyclotransferase inhibitor; a selective inhibitor of APP production; a monoamine oxidase B inhibitor; a glutamate receptor antagonist; a AMPA receptor agonist; a nerve growth factor stimulant; a HMG-CoA reductase inhibitor; a neurotrophic agent; a muscarinic Ml receptor agonist; a GABA receptor modulator; a PPAR-gamma agonist: a microtubule protein modulator; a calcium channel blocker; an antihypertensive agent: a statin: or any combination thereof.

[0146] In an exemplary embodiment, the therapeutic agent may comprise a kinase inhibitor. Suitable kinase inhibitors may inhibit a thousand-and-one amino acid kinase (TAOK), CDK, GSK-3p, MARK, CDK5, or Fyn. In another exemplary embodiment, the therapeutic agent may comprise a phosphatase activator. As a non-limiting example, a phosphatase activator may increase the activity of protein phosphatase 2A.

[0147] In an aspect, the therapeutic agent may be an imaging or diagnostic agent. Non-limiting examples of imaging agents or diagnostic agents include functional imaging agents (e.g. fluorodeoxyglucose, etc.) and molecular imaging agents (e.g., Pittsburgh compound B, florbetaben, florbetapir, flutemetamol, radionuclide-labeled antibodies, etc.).VI. Kits

[0148] Also provided in this disclosure are kits. Such kits can include a composition of matter described herein (e.g., a binding agent or binding molecule targeting a targeting protein) and, in certain embodiments, instructions for performing a method as described herein (e.g., in Section V). Such kits can69108365568.1Docket 047563-870867facilitate performance of the methods described herein. When supplied as a kit, the different components of the composition can be packaged in separate containers and admixed immediately before use.Components include, but are not limited to systems, assays, binding agent or binding molecule (e.g., targeting protein epitope biding agents), reagents, internal standards, or software. Such packaging of the components separately can, if desired, be presented in a pack or dispenser device. The pack may, for example, comprise metal or plastic foil such as a blister pack. Such packaging of the components separately can also, in certain instances, permit long-term storage without losing activity of the components.

[0149] Kits may also include reagents in separate containers such as, for example, sterile water or saline to be added to a lyophilized active component packaged separately. For example, sealed glass ampules may contain a lyophilized component and in a separate ampule, sterile water, sterile saline or sterile each of which has been packaged under a neutral non-reacting gas, such as nitrogen. Ampules may consist of any suitable material, such as glass, organic polymers, such as polycarbonate, polystyrene, ceramic, metal or any other material typically employed to hold reagents. Other examples of suitable containers include bottles that may be fabricated from similar substances as ampules, and envelopes that may consist of foil-lined interiors, such as aluminum or an alloy. Other containers include test tubes, vials, flasks, bottles, syringes, and the like. Containers may have a sterile access port, such as a bottle having a stopper that can be pierced by a hypodermic injection needle. Other containers may have two compartments that are separated by a readily removable membrane that upon removal permits the components to mix.Removable membranes may be glass, plastic, rubber, and the like.

[0150] In certain embodiments, kits can be supplied with instructional materials. Instructions may be printed on paper or other substrate, and / or may be supplied as an electronic-readable medium or video. Detailed instructions may not be physically associated with the kit; instead, a user may be directed to an Internet web site specified by the manufacturer or distributor of the kit. Said instructions may be derived from any of the methods as described herein (e.g.. in Section V).

[0151] In an aspect, a kit comprises a means for quantifying the protein level of one or more target protein selected from a first target protein, second target protein, third target protein, fourth target protein, fifth target protein, sixth target protein, and / or seventh target protein, and instructions for (a) diagnosing Alzheimer’s disease (b) predicting progression of Alzheimer’s disease; (c) predicting amyloid / tau (AT) status; (d) predicting brain amyloidosis status; (e) predicting plasma p-Tau217 status; (f) selecting a subject for inclusion in a clinical trial for Alzheimer’s disease; or (g) predicting the rate of memory decline, as described herein (e.g., in Section V). A means for quantifying one or more (e.g., all of) TP1,70108365568.1Docket 047563-870867TP2, TP3, TP4, TP5, TP6, and / or TP7 may provided in the kit. In some embodiments, the kit further comprises a means for quantifying the protein level of p-tau217.

[0152] A control sample or a reference sample as described herein can be a sample from a healthy subject or from a randomized group of subjects. A reference value can be used in place of a control or reference sample, which was previously obtained from a healthy subject or a group of healthy subject. A control sample or a reference sample can also be a sample with a known amount of a detectable compound or a spiked sample.

[0153] The methods and algorithms of the invention may be enclosed in a controller or processor.Furthermore, methods and algorithms of the present invention, can be embodied as a computer implemented method or methods for performing such computer-implemented method or methods, and can also be embodied in the fonn of a tangible or non-transitory computer readable storage medium containing a computer program or other machine -readable instructions (herein “computer program”), wherein when the computer program is loaded into a computer or other processor (herein “computer”) and / or is executed by the computer, the computer becomesan apparatus for practicing the method or methods. Storage media for containing such computer program include, for example, floppy disks and diskettes, compact disk (CD)-ROMs (whether or not writeable), DVD digital disks, RAM and ROM memories, computer hard drives and back-up drives, external hard drives, “thumb” drives, “cloud storage”, and any other storage medium readable by a computer. The method or methods can also be embodied in the form of a computer program, for example, whether stored in a storage medium or transmitted over a transmission medium such as electrical conductors, fiber optics or other light conductors, or by electromagnetic radiation, wherein when the computer program is loaded into a computer and / or is executed by the computer, the computer becomes an apparatus for practicing the method or methods. The method or methods may be implemented on a general purpose microprocessor or on a digital processor specifically configured to practice the process or processes. When a general-purpose microprocessor is employed, the computer program code configures the circuitry of the microprocessor to create specific logic circuit arrangements. Storage medium readable by a computer includes medium being readable by a computer per se or by another machine that reads the computer instructions for providing those instructions to a computer for controlling its operation. Such machines may include, for example, machines for reading the storage media mentioned above.EXAMPLES71108365568.1Docket 047563-870867

[0154] The following examples illustrate various iterations of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples that follow represent techniques discovered by the inventors to function well in the practice of the invention. Those of skill in the art should, however, in light of the present disclosure, appreciate that changes may be made in the specific embodiments that are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention. Therefore, all matter set forth or shown in the accompanying drawings is to be interpreted as illustrative and not in a limiting sense.Example 1: Large-scale Plasma Proteomic Profiling Unveils Novel Diagnostic Biomarkers and Pathways for Alzheimer's Disease

[0155] Alzheimer disease (AD) is a complex neurodegenerative disorder. Proteomic studies have been instrumental in identifying AD-rclatcd proteins present in the brain, cerebrospinal fluid, and plasma. Tire present study comprehensively examined 6,905 plasma proteins in more than 3,300 well-characterized individuals to identify new proteins, pathways, and predictive model for AD. A three-stage analysis (discovery, replication, and meta-analysis) identified 416 proteins (322 novel) associated with clinical AD status and the findings were further validated in two external datasets including more than 7,000 samples and four previous studies. Pathway analysis revealed that these proteins are involved on endothelial and blood hemostatic (ACHE, SMOC1, SMOC2, VEGFA, VEGFB, SPARC) capturing blood brain barrier (BBB) disruption due to disease. Other pathways are capturing known processed implicated on AD such as lipid dysregulation (APOE. BINI, CLU, SMPD1, PLA2G12A. CTSF) or immune response (C5, CFB, DEFA5, FBXL4), that includes proteins known to be part of the causal pathway indicating that some of the identified proteins and pathways are involved on disease pathogenesis. An enrichment of brain and neural pathways (axonal guidance signaling or myelination signaling) indicating that, in fact, blood proteomic captures brain- and disease-related changes, w hich can lead to the identification of novel biomarkers and predictive models. Machine learning was employed to identify a set of seven proteins that were highly predictive of both clinical AD (AUC > 0.72) and biomarker-defmed AD status (AUC > 0.88). These extensive findings underscore the potential of using plasma proteins as biomarkers for early detection and monitoring of AD, as well as potentially guiding treatment decisions.

[0156] Alzheimer disease (AD) is the most common cause of dementia, contributing to 60-80% of dementia cases (Alzheimers Dement 20, 3708-3821 (2024)). While AD primarily affects older individuals, with symptoms typically appearing after the age of 65 (late-onset AD), about 1-5% of the cases have symptoms before 65 (early -onset AD). This progressive neurodegenerative disorder is characterized by the accumulation of amyloid-beta (A[3) plaques, neurofibrillary tangles consisting of72108365568.1Docket 047563-870867hyperphosphorylated and aggregated tau protein, and widespread neuronal loss. These pathological changes lead to cognitive decline, memory loss, behavioral changes, and ultimately, a loss of independence and functioning. Tire levels of these proteins (A[340, A|342, phosphorylated tau (p-tau)181, p-tau217, and specific tau peptides (MTBR-243)) have been shown to be reliable cerebrospinal fluid (CSF) and plasma biomarkers (Hansson et al., Alzheimers Dement 14, 1470-1481 (2018): Hansson et al., Alzheimers Res Ther 11, 34 (2019): Janelidze et aL. Nat Med 26, 379-386 (2020); Karikari et al.. Lancet Neurol 19, 422-433 (2020); Barthelemy et aL, Nat Med 30, 1085-1095 (2024); Ashton et al.. Biomark Med 12, 799-812 (2018); Horie et al., Nat Med 29, 1954-1963 (2023)) that capture amyloid pathology in the brain (Mila-Aloma et al., Nat Med 28, 1797-1801 (2022); Cohen et al., Mol Cell Neurosci 97, 3-17 (2019)), and tau pathology as measured by Tau-PET scans (Horie et al., Nat Med 29, 1954-1963 (2023)).

[0157] AD is a complex and multifactorial condition, influenced by genetic, environmental, and lifestyle factors. Despite extensive research efforts, the exact mechanisms underlying the development and progression of AD remain incomplete. In recent years, proteomic studies have been instrumental in shedding light on the molecular complexities involved in AD. By analyzing proteins present in biological fluids and tissues, such as plasma, CSF, and brain tissue, proteomic studies (Johnson et al., Nat Med 26.769-780 (2020); de Geus et al., Sci Rep 13, 22406 (2023); Higginbotham et al.. Neurobiol Dis 186, 106286 (2023); Dammer et al., Alzheimers Res Ther 14, 174 (2022); Sung et al., Sci Transl Med 15, eabq5923 (2023); Ali et al., Res Sq (2024)) have identified several protein biomarkers associated with AD. These biomarkers hold promise for early detection, accurate diagnosis, and monitoring of the progression. Furthermore, proteomic studies have the potential to unravel the underlying mechanisms and pathways contributing to AD pathology.

[0158] Although most of the proteomic studies in AD have been focused on the brain or CSF (Johnson et al., Nat Med 26, 769-780 (2020); de Geus et aL, Sci Rep 13, 22406 (2023); Higginbotham et aL, Neurobiol Dis 186, 106286 (2023); Dammer et aL, Sci Transl Med 16, eadn3504 (2024); Ping et aL, Sci Data 7, 315 (2020)). a handful of studies were also performed in plasma. Previous plasma proteomic studies in AD using Mass Spectrometry, Olink, or SomaScan have analyzed around 150-300 AD cases and controls (Dammer et aL, Alzheimers Res Ther 14, 174 (2022); Roberts et al.,NPJ Aging 9, 18 (2023); Chouliaras et aL, J Neurol Neurosurg Psychiatry 93, 651-658 (2022); Shi et aL, Alzheimers Dement 19, 3350-3364 (2023); Frick et aL, medRxiv (2023)). Recent studies in larger cohorts by Walker, et al. (Nat Aging 1, 473-489 (2021)) and Sung, et al. (Sci Transl Med 15, eabq5923 (2023)) have identified tens of proteins in plasma associated with AD. Walker considered a population-based design with more than 4,000 individuals, but with only 400 dementia patients. With plasma proteomic data (SomaScan 5K), they identified 38 proteins associated with the risk of developing AD. These proteins were part of the 73108365568.1Docket 047563-870867lipid, immunity, metabolic signaling, and hemostasis pathways. These proteins showed low to moderate predictive power with AUCs of 0.66-0.75. In another study, Sung, et al. (Sci Transl Med 15, eabq5923 (2023)) generated plasma proteomic data (SomaScan 1.3K) in an AD cohort and identified 26 proteins that showed a predictive power comparable to CSF A[3 and tau (AUC=0.79). These proteins were enriched for programmed cell death and endolysosomal dysfunction. These and other studies are just a few examples of how plasma proteomic studies in AD have been instrumental in identifying potential biomarkers for AD as well as identifying new pathways implicated in the disease. However, these studies have generally included a relatively small number of AD cases or general dementia. Therefore, there is a need for a large-scale plasma proteomic study using well-characterized AD patients.

[0159] To address this need, 6,905 plasma proteins in 1,270 deeply phenotyped AD and 2,096 cognitively normal individuals were comprehensively examined, including a notably larger group of AD subjects compared to previous studies. A robust set of plasma proteins associated with clinical AD status were identified through rigorous stages and additional validations. We subsequently identified related pathways and novel biomarkers for predicting clinical AD and monitoring disease progression.Study Design

[0160] A three-stage analytic framework was identified to identify plasma proteomic signatures for AD and pathways implicated in AD pathogenesis and to develop a predictive model. In a discovery dataset of 2,131 samples (750 AD cases and 1,381 controls [CO]) from the Knight Alzheimer’s Disease Research Center (ADRC), proteins associated with clinical AD status were identified. These associations were replicated in an additional 1,235 samples (520 AD cases and 715 controls) from the Knight ADRC and Stanford ADRC cohorts (FIG. 1, TABLE 1).TABLE 1: Demographics information of participants for identification of biomarkers.74108365568.1Docket 047563-870867Discovery ReplicationClinical status CO AD CO ADSample size, n 1381 750 715 520Agemean± SD 72.4 ± 10.5 78.1 ± 8.7 72.5 ± 9.5 76.5 ± 9.4 Range (min, max) (27, 101) (33. 104) (33, 101) (42.98) SexFemale 54.31% 59.22% 42.86% 54.55% Male 45.69% 40.78% 57.14% 45.45% APOE genotypeAPOE e4± 33.1% 57.9% 25.5% 56.5%NA 6 5 54 14Amyloid positivityA± 632 110 143 88A- 271 264 48 93NA 478 376 524 339

[0161] Meta-analyses were performed using proteins that showed nominal associations and consistent directionality in the discovery and replication dataset. The selected proteins were further validated using two external datasets from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP; 150 AD and 322 CO) and Global Neurodegeneration Proteomics Consortium (GNPC: 1,733 AD and 4,833 CO). To determine the novelty of these protein associations, the findings were compared with those reported in plasma by Walker, et al. (Nat Aging 1, 473-489 (2021)), Sung, et al. (Sei Transl Med 15, eabq5923 (2023)), Sattlecker, et al. (Alzheimers Dement 10, 724-734 (2014)), and in CSF by Ali, et al. (Res Sq (2024)). As AD status was based on clinical diagnosis and cognitive tests, cases may include non-AD dementia, and controls may include preclinical AD. Therefore, additional sensitivity analyses were performed by comparing our results with those based on AD biomarker status (assessed through CSF A and tau, amyloid imaging, or plasma p-tau217; TABLE 2).TABLE 2: Demographic characteristics of samples with AD biomarkersCSF AT status Amyloid PET imaging plasma pTau-217 Group AT- AT+ A- A+ T- T+ Samplesize, n 315 127 265 122 1348 1404 Agemean±SD 66.3 ± 8.4 72.1 ± 8 67.1 ± 8.2 73.5 ± 7.9 71.6 ± 10.7 78.1 ± 8.7 Range (min, max) (37,91) (45,91) (31,90) (37,90) -27,104 -33,101 SexFemale 177 73 152 62 818 791 Male 138 54 113 60 530 613 APOE genotype75108365568.1Docket 047563-870867CSF AT status Amyloid PET imaging plasma pTau-217 Group AT- AT+ A- A+ T- T+ APOE E4+ 82 91 82 74 383 775 NA 0 NA 0 1 11 19

[0162] To identify relevant biological processes, functions, and networks, pathway and network analyses of the AD-associated plasma proteins were performed. A machine learning-based classifier was then developed to predict the clinical status of AD. Predictive power was assessed for predicting biomarker status, progression from presymptomatic to symptomatic AD. and rate of memory decline. Finally, specificity for AD was evaluated by examining the predictive model for samples in dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and Parkinson's disease (PD).ResultsPlasma proteins dysregulated with clinical AD6,905 plasma proteins (SomaScan 7K) were quantified and three-stage analysis was conducted to identify plasma proteins that exhibited significant differences between cognitively normal individuals and individuals with clinical AD (FIG. 2A). Demographics of the cohorts and proteomic quality control (QC) procedures are shown in Materials and Methods. Associations were identified using regression of clinical AD on normalized Z-score protein levels (Timsina et al., Neurobiol Dis 190, 106373 (2024)), including age and sex as covariates. In the discovery stage, 1,540 proteins (1,646 aptamers) were identified that were nominally associated with clinical AD status. Among these proteins, 416 proteins (456 aptamers) showed nominal association in the replication stage with the same direction as the discovery data. In the meta-analysis, all 416 proteins (456 aptamers) passed FDR correction, based on atotal of 6,905 aptamers. Differential abundance analysis results are shown in FIGS. 2B-2G.

[0163] Some of the identified proteins, such as neuronal pentraxin receptor (NPTXR), dehydrogenase / reductase SDR family member 9 (DHRS9), D-3 -phosphoglycerate dehydrogenase (PHGDH), and islet amyloid polypeptide (IAPP), have not been reported in any previous plasma or CSF studies on AD. Several proteins, including SPARC-related modular calcium-binding protein 1 (SMOC1), complexin-2 (CPLX2), spondin-1 (SPON1). neurofilament light polypeptide (NFL), or alpha-1-antichymotrypsin complex (SERPINA3), have been noted in earlier plasma proteomic studies or are known AD biomarkers (e.g., NFL). Lastly, proteins like kinetochore protein Spc25 (SPC25), cardiotrophin- 1 (CTF1), acetylcholinesterase (ACHE), and leucine-rich repeat neuronal protein 1 (LRRN1) had not been reported to be associated with AD or dementia in recent large-scale plasma76108365568.1Docket 047563-870867proteomics studies (Sung et al., Sei Transl Med 15, eabq5923 (2023); Walker et al., Nat Aging 1, 473-489 (2021)), although they were identified in a recent large-scale CSF proteomic study (Ali et al., Res Sq (2024)).Replication in external datasets

[0164] To further validate findings, plasma proteomics (SomaScan 7K) data from the ROSMAP study was analyzed (322 AD and 150 CO) (TABLE 3). Of tire aptamers identified in the study, 99% (453 aptamers) were present in the ROSMAP dataset, and 88% had a consistent direction of effect sizes (p=2.89x 10-67; FIG. 3A, TABLE 4). The correlation of effect sizes across these 453 aptamers was 0.793 (p=4.02* 10-99). In addition, 42% of the aptamers (192 aptamers) were nominally significant.TABLE 3: Demographic characteristics of samples in external datasetsTABLE 4: Overlap and effect size correlation of proteins with external proteomics datasets <77108365568.1Docket 047563-870867<

[0165] Additionally, data from the GNPC, a large collaborative study that includes plasma proteomics (SomaScan 7K) from 1,733 AD cases and 4,833 controls (TABLE 3), was leveraged for the analysis. All significant aptamers identified in this study (n=456) were present in the GNPC. 78% (354 aptamers) showed consistent direction (p=6.19x 10-34: FIG. 3B, TABLE 4) and showed a strong effect size correlation with Pearson correlation of 0.616 (p=2.13x10-40) when considered nominally significant aptamers. A total of 75% (346 aptamers) of the 456 identified aptamers were nominally significant in GNPC, and 72.5% (331 aptamers) passed multiple testing correction.101661 Subsequently, a meta-analysis was performed combining results from the ROSMAP and GNPC datasets. Of the 456 significant aptamers, 78% (353 aptamers) demonstrated consistent direction of effect sizes (p=1.90x 10-34; FIG. 3C, TABLE 4). In this meta-analysis, 77% (350 aptamers) were nominally significant, and 73.5% (333 aptamers) remained significant after multiple testing correction. Hie correlation of effect size for 453 aptamers that were present in the meta-analysis was 0.675 (p=1.33x 10-66). Taken all this together, of the 453 aptamers that were present on the ROSMAP or the GNPC, a total of 384 aptamers (84%) showed nominal association in one of the studies or the meta-analyses. These results represent a completely independent replication and fully support the validity of our findings.78108365568.1Docket 047563-870867Comparison with previous plasma studies

[0167] The results were systematically compared to the previous findings from three plasma studies: Walker, et al. (Nat Aging 1, 473-489 (2021)), Sung, et al. (Sci Transl Med 15. eabq5923 (2023)) and Sattlecker. et al. (Alzheimers Dement 10, 724-734 (2014)). By examining SomaScan 5K data in 4,110 samples (428 dementia cases), Walker, et al. (Nat Aging 1, 473-489 (2021)) identified 38 proteins significantly associated with incident dementia, after correcting for multiple testing. Among the 456 aptamers (416 proteins) identified in this study, 319 aptamers were present in the result by Walker, et al. (Nat Aging 1, 473-489 (2021)) (TABLE 5 and FIG. 7). Among those, three aptamers were reported to be significant after correction in the original study, 52 aptamers showed a nominal association, and 176 (55%) showed a consistent direction between the two studies. Overall, the 52 aptamers that were nominally significant in the study showed an effect size correlation with a Pearson coefficient of 0.872 (p=4.16* 10-17). In their study, all subjects were controls at the time of the plasma collection, among which several of them developed dementia (not just AD) between 5 to 25 years after plasma collection. Despite employing a survival analysis for incident dementia, there was a high correlation between the results from these two studies.

[0168] Sung, et al. (Sci Transl Med 15, eabq5923 (2023)) analyzed plasma proteomics (SomaScan 1.3K) data from 105 AD cases and 254 controls and identified 26 proteins associated with AD after Bonferroni correction. Out of the 456 aptamers identified in our study, 67 aptamers were present in their result. Among these, two aptamers passed multiple test correction, 34 aptamers were nominally significant, and 53 (78%) showed consistent direction (TABLE 5 and FIG. 7). Among 34 aptamers, aptamers that were nominally significant in the study (Sung et al., Sci Transl Med 15, eabq5923 (2023)) exhibited a strong correlation, with a Pearson coefficient of 0.805 (p=9.42* 10-09).

[0169] Sattlecker, et al. (Alzheimers Dement 10, 724-734 (2014)) analyzed plasma SomaScan data measuring 1,001 proteins from 331 cases and 211 controls, and identified 138 nominally significant proteins associated with AD, and four proteins after FDR correction. Out of the 456 aptamers identified in our study, 76 (84%) aptamers were present in their result. Among these, two aptamers were found to be significant after applying multiple testing correction, 22 aptamers were nominally significant, and 64 aptamers show ed consistent direction in their study (TABLE 5 and FIG. 7). The effect size correlation for aptamers that were nominally significant (n=22) exhibited a correlation of 0.811 (p=4.72x 10-09).TABLE 5: Overlap and effect size correlation of proteins with four previous proteomics studies79108365568.1Docket 047563-870867<Summary of Published workAll studies (CSF & plasma)No. of proteins included at least in one study: 454No. of proteins reported to significant after multiple correction at least in one study: 181No. of proteins nominally at least in one study: 248No. of proteins not reported to be significant in any study: 208No. of proteins not included in any of the previous studies: 280108365568.1Docket 047563-870867Plasma onlyNo. of proteins included at least in one study: 327No. of proteins reported to significant after multiple correction at least in one study: 12No. of proteins nominally at least in one study: 94No. of proteins not reported to be significant in any study: 362No. of proteins not included in any of the previous studies: 129

[0170] Taking all this together, out of the 456 aptamers identified in the current study, 82 aptamers have been reported to be nominally significant in at least one of the previous plasma studies, and 12 aptamers have been reported to be significant after multiple test correction. Notably, when considering the nominally significant aptamers in plasma studies, the correlation between the effect sizes was consistently above 0.8. These results demonstrate that the current findings are similar to those already examined previously and provide additional results due to increased statistical power. Furthermore, these comparisons also demonstrate that tire remaining 362 aptamers (322 proteins) out of 456 were reported here for the first time, providing a significance to this study.Cross-tissue comparison

[0171] A recent large-scale CSF proteomics study (N=2,286) identified 2,173 aptamers (2,029 proteins) associated with clinical and biomarker AD status (Ali et al., Res Sq (2024)). This study was conducted using proteomics data from Knight ADRC cohort and used the same platfonn and a similar analytical approach, which allowed us to determine the overlap of proteomic signatures between CSF and plasma. Among the 456 aptamers identified in this study, 445 were present in the CSF study. It was found that 174 aptamers were statically significant after multiple testing correction in CSF. The top three proteins in plasma (aSPC25, CTF1, and ACHE) also showed strong association in CSF (p<1.01><10-3), but these were not reported in the previous plasma studies. Hie effect size in CSF and plasma for these 445 aptamers showed a moderate correlation of 0.419 (p=2.07* 10-20). Of the top 100 plasma aptamers, 45 (45%) were significant in CSF, and of the top 200 plasma aptamers, 85 (42%) were significant in CSF.

[0172] Among the 2,173 aptamers (2,029 proteins) identified in the CSF study, 174 aptamers (164 proteins) were associated after multiple test correction in plasma (TABLE 5 and FIG. 7). SMOC1, which ranked among the top 10 significant proteins in plasma study, was previously identified as the most significant finding in CSF and was reported in previous studies by Sung, et al. (Sci Transl Med 15, eabq5923 (2023)), Walker, et al. (Nat Aging 1, 473-489 (2021)), and Shen, et al. (medRxiv (2024)). However, other top CSF proteins, such as YWHAG, TM0D2, YWHAB, PPP3R1, TM0D3, DLG2, YWHAZ, YWHAE, MAPRE3, ATP6V1F, H0MER1, were not significant in plasma. In fact, of the top81108365568.1Docket 047563-87086720 CSF proteins, only two were significant in plasma (4 in the top 50; and 8 in the top 100). Only about 8% of the CSF-associated proteins showed association in plasma. Despite some overlap across tissues, proteomic signatures in CSF and plasma appear to be different, leading to a different set of proteins (and subsequently different biomarkers) and implying unique underlying biology.

[0173] Integrating the analyses comparing the proteins identified in this study with those identified in previous plasma and CSF studies, of tire 456 aptamers, 208, including NTPXR and FAHD2A, were novel and have not reported in any previous plasma or CSF study. Additionally, 154 aptamers (SPC25, CFT1, ACHE), hereinafter as “plasma-novel”, were not found in previous plasma studies, but reported to be associated with AD in CSF. There were 82 aptamers (i.e MIA, CPLX1. and APOE), hereinafter as “plasma-nominal”, that have been reported to be associated with AD at nominal significance in previous studies. There were 12 (i.e: APOB, SERPINA3, or COL10A1, “plasma-significant”) that passed multiple test correction in previous studies.The identified proteins are also associated with AD biomarker-status

[0174] While this study focused on clinical status of AD, analysis of biomarker-based AD status was performed and compared with the clinical AD status to evaluate whether the identified proteins also capture underlying AD pathology. Biomarker status was defined using three different approaches: 1) amyloid / tau (AT) status based on Af>42 and p-taul81 values in CSF (105 A+T+ vs. 256 A-T-), 2) amyloid imaging (122 A+ vs. 265 A-), and 3) plasma p-tau217 (Ibanez et al., medRxiv (2024)) (1,404 T+ vs 1,348 T-; FIG. 8, TABLE 6).TABLE 6: Overlap and effect size correlation of proteins with AD biomarkers<& & &82108365568.1Docket 047563-870867

[0175] As the sample size was larger for the plasma p-tau217, we found larger overlap of significant proteins (281 of the 456 aptamers) for this AD biomarker than for CSF AT or amyloid status (TABLE 6). However, in this analyses, amyloid imaging status showed the largest number of proteins (82%) with consistent effect size, as well as the highest effect size correlation (p=0.764), followed by CSF AT status (74% of concordant effect size proteins and p=0.705). The top eight proteins in our main analyses (including SPC25. CTF1, ACHE, LRRN1, NFL, CPLX2, SMOC1, TBCA) were also significant across all three biomarker analyses. In summary, for each of these analyses, strong correlations were found in the effect size (Pearson correlation > 0.7) and direction of the association (FIG. 8, TABLE 6).Identifying proteins associated with progression to symptomatic AD

[0176] Among the cognitively normal participants at blood draw included in this study, 761 had subsequent follow-up clinical assessments (3.5 ± 2.4 years) after plasma collection (maximum=15 years; TABLE 7). Of these, 83 individuals progressed to symptomatic AD (from CDR=0 to CDR>0 and clinical diagnosis of AD). These data were leveraged to identify proteins associated with progression to symptomatic AD. In the survival analysis with a Cox proportional hazards model, including age at blood draw and sex as covariates, 625 proteins were found to be nominally associated with progression (FIG. 8). Of the 456 aptamers associated with clinical status, 22 aptamers were also associated with progression to symptomatic AD. Twenty (out of these 22) had consistent effect size directions with the analysis of clinical AD status. The two proteins in the opposite direction were MIA and COL10A1. MIA is primarily expressed in melanocytes and Schwann cells and is involved in extracellular matrix organization and cell-matrix adhesion. COL10A1 is expressed in oligodendrocytes and neurons and is known to contribute to cell proliferation and migration.TABLE 7: Demographic overview of participants in conversion analysisFinal clinical status AD COSample size, n 83 678Agemean ± SD 74.6 ± 7.5 81 ± 8.5 Range (min, max) (57 ,97) (58, 101)SexFemale 405 52Male 273 31Time to eventmean ± SD 3.5 ± 2.4 3.1 ± 2.5min, max (1.15) (1,13)APOE genotypeAPOE E4+, n (%) 219 (31.9%) 29 (34.9%) Amyloid positivityA+ 18 359A- 20 15283108365568.1Docket 047563-870867Final clinical status AD CONA 45 167Note: All participants were CO at tire time of blood sample collectionPathways involved in plasma proteins

[0177] Cell-type enrichment and pathway analyses was performed for the 416 proteins (456 aptamers) that exhibited association with clinical AD to better understand the biological processes these proteins were involved in. Brain cell type enrichment analysis revealed that these 416 proteins were primarily enriched in oligodendrocytes (p=3.67* 10-2), astrocytes (p=1.86x 10-2), and depleted in neuronal proteins (p=7.54* 10-3; TABLE 8), suggesting that they reflect pathways related to axonal and overall brainhealth.TABLE 8: Brain cell type enrichment analysis

[0178] Pathway analysis indicates that these plasma proteins arc primarily involved into five main areas:1) lipid metabolism; 2) immune and hemostasis 3) extracellular matrix; and 4) neuronal-related pathways (FIG. 9A; TABLES 9. 10. 11).TABLE 9: Pathway enrichment analysis using Gene Ontology database84108365568.1Docket 047563-87086785108365568.1Docket 047563-870867TABLE 10: Canonical pathway enrichment analysis using IPA86108365568.1Docket 047563-87086787108365568.1Docket 047563-87086788108365568.1Docket 047563-87086789108365568.1Docket 047563-87086790108365568.1Docket 047563-87086791108365568.1Docket 047563-87086792108365568.1Docket 047563-87086793108365568.1Docket 047563-87086794108365568.1Docket 047563-87086795108365568.1Docket 047563-87086796108365568.1Docket 047563-870867TABLE 11: Pathway enrichment analysis using Re actome database97108365568.1Docket 047563-87086798108365568.1Docket 047563-87086799108365568.1Docket 047563-870867100108365568.1Docket 047563-870867101108365568.1Docket 047563-870867102108365568.1Docket 047563-870867

[0179] The most significant lipid-related pathways included transport (FDR p=2.45 x 10-3), localization (FDRp=4.20x 10-3) and catabolism of lipids (FDR p=2.12x10-2; TABLE 9). These lipid processes include proteins that are already known to be part of the causal pathway of AD such as APOE, CLU and BIN 1. Although APOE protein levels have been reported to be nominally associated with AD risk in previous plasma studies, this is the first time its association after multiple test correction in a proteomic study was performed. CLU, a known protein involved in lipid metabolism, has not been reported to be associated with AD in either CSF or plasma in previous studies. SPP1, also known as osteopontin, is involved in lipid-associated macrophages mediated inflammation (Vegting et al., bioRxiv, 2024.2003.2013.584866 (2024)) and has been associated with microglia activation and memory decline103108365568.1Docket 047563-870867(Lopes et al., Alzheimers Dement 20, 525-537 (2024)). Although lipids metabolism is known to be part of AD pathogenesis, this study identified 40 novel proteins that have not been associated with AD previously and their involvement for this pathway, thereby extending previous findings. These proteins included SMPD1 (Choi et al., Exp Mol Med 56, 301-310 (2024)), PLA2G12A (Gattaz et al, Biol Psychiatry 37, 13-17 (1995)), CTSF (Gattaz et al.. Biol Psychiatry 37, 13-17 (1995)) or SPARC (Chen et al. Brain Res Bull 163, 178-189 (2020)) that have been reported to carry variants increasing risk for AD, neurodegeneration or affect blood-brain barrier.

[0180] These analyses additionally identified pathways related to immune and hemostasis-related processes (TABLE 10). Immune and inflammation pathways were driven by the novel proteins C5. CFB. DEFA5, FBXL4, NCAM1, P4HB, and PPBP, as well as “plasma-novel” proteins CTF1, CCL5, CRP, FOXO1, STX1A, and VAT1. They were part of the complement pathways (C5, C7, CFB) orthe GPVI-mediated activation cascade (all proteins part of this pathways were novel: CDC42, CLEC IB, RAC1, RAC2) which is involved in immune response and more importantly in vascular integrity. Other pathways related to general integrity of the blood functions and hemostasis included elevated platelet cytosolic Ca2+ (p=8.71 * 10-10; CLU, PF4, and PPBP), platelet activation, signaling and aggregation (p=7.13x10-5; CDC42, CLU), and neutrophil extracellular trap signaling pathways (p=1.78x 10-5;COL10A1, ROBO2, and VEGFA).

[0181] Extracellular several matrix-related pathways were also identified such as collagen-containing extracellular matrix (p=3.55x 10-5), extracellular matrix (p=8.39x 10-7), and extracellular matrix binding (p=4.10x 10-5) with ACHE, SMOC1, SMOC2, VEGFA, SPARC, which also point to overall vascular health as VEGFA, and SMOC proteins are related to vascular endothelial cells. Although SMOC1 and SMOC2 were among the most significant proteins associated with AD in CSF, additional novel endothelial and vascular related proteins were found to be associated with AD risk in this study such as NCAM1 or VEGFA.

[0182] In addition to blood or immune response pathways, a large number of proteins were related to the brain and the central nervous system including axonal guidance signaling (p=6.46x 10-5, EIF4E, ROBO2 and VEGFA) and myelination signaling (p= 3.98x 10-3, involving AKT1, AKT3, and BMP6). Also found to be related were 14-3-3-mediated signaling and FOXO-mediated transcription (p< 3.16x 10-5; FOXOL YWHAQ) along with several neuronal system pathways including synaptogenesis signaling (p=1.02x 10-4 involving FOXO1, YWHAQ, APOE, CPLX1, CPLX2 and STX1A), and GABA synthesis, release, reuptake, and degradation (p=3.80x 10-3; involving CPLX1, STX1A, and SYT1; TABLE 10). Othertop pathways included signal transduction pathway (p<4.77x10-2, involving AKT1, AKT3, and104108365568.1Docket 047563-870867RXRA) and those related to neurotransmitter release cycle (p=8.50x 10-4; involving SLITRK3, ARHGAP30, and ARHGAP45) such as RHOA GTPase cycle (p=2.32* 10-2) and RHO GTPase cycle (p=4.19x10-2), involving SHMT2 and RAC1.Plasma Protein Co-Expression Network in AD

[0183] To further elucidate the complex interactions across all the 6,905 plasma proteins, Multiscale Embedded Gene co-Expression Network Analysis (MEGENA) was utilized (Song et al., PLoS Comput Biol 11, el004574 (2015)). In total. 274 modules were detected in the protein co-expressed network, with an average of 81 proteins, ranging from 10 to 1,590.

[0184] Of the 274 modules, thirty-one modules were significantly enriched for the identified 456 aptamers (416 proteins) associated with the clinical status of AD. Modules M4 and M7 were the topranked modules based on aggregated Cauchy association test (ATAC) method (Liu et al., Am J Hum Genet 104, 410-421 (2019)). M4, consisting of 1,519 proteins included as hub proteins 18 ‘’novel” proteins (UBA1, ADH4, or GSTA2), as well as plasma-novel (MAPK10, MANF) and “plasma-nominal” (GSK3A). This module was primarily enriched for the down-regulated proteins (mean effect size -3.62; FIG. 4B). M4 was enriched in oligodendrocytes (p=8.52x 10-2) and involved in intracellular transport (p=5.17x10-17, PPP ICC and YWHAQ, both “plasma-novel” proteins), translation (p=8.20x 10-11, involving AKT1 (plasma-novel) and EIF4E (novel)) and macroautophagy (p=7.53x 10-10, involving AKT1 and RAB1B (novel).

[0185] In contrast, consisting of 930 proteins, M7 included three “novel” proteins (IGFBP2, ROR1, and REG3A) as well as SMOC1 (reported in CSF multiple times) as hub proteins. This module was enriched for up-regulated proteins (mean effect size 2.26: FIG. 4B). M7 was enriched in endothelial cells (p=7.53x 10-10, SMOC1, SMOC2, VEGFA COLEC12, and GPD1 among others) and neurons (p=2.35 x 10-3, NPTXR; ROBO2 and CPLX2) and was enriched in extracellular matrix organization (p=5.46x 10-13, SMOC1 and SMOC2), axon guidance (p=7.73x 10-13, involving ROBO2 and VEGFA), and axonogenesis (p=2.43 x 10-12, involving NRP2 and NRCAM), re-emphasizing the pivotal role of extracellular matrix and axonal processes in AD. In addition, this result supports several brain and neuronal pathways identified above for plasma proteins associated with AD.Seven plasma proteins with high AD-specific predictive power

[0186] Finally, it was examined whether the 456 aptamers (416 proteins) associated with AD could be leveraged to create improved predictive models for clinical diagnosis or biomarkers status. Tire least105108365568.1Docket 047563-870867absolute shrinkage and selection operator (Lasso) regression model was used to select proteins for predicting the clinical status including age and sex as covariates. The discovery dataset was split, with 70% used for training the model (discovery-train: 967 cognitively normal and 525 AD participants) and the remaining 30% for validating the model (discovery-test). In addition, the replication dataset and external datasets (ROSMAP and GNPC) were also considered to examine predictive power of the model.

[0187] The discovery -training data was used to identify the minimum set of proteins that maintain a strong predictive power and to determine the cutoff value for prediction and weights for these identified proteins (TABLE 12).TABLE 12: Coefficients of prediction model

[0188] These coefficients enabled the generation of an equation for calculating predictor values (PV) for use in the methods as described herein (e.g., in Section V):PV = (0.618 X TP1) + (0.245 X TP2) + (0.723 X TP3) + (-0.224 x TP4) + (0.492 x TP5) + (-0.385 x TP6) + (-0.799 x TP7)+ (0.05 x age) + (0.182 x biological sex) — 4.696Formula (I),wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female.A cutoff value of 0.298 was determined to be the optimal threshold value for PV comparison for the methods described herein.

[0189] This model (with pre-determined set of proteins, their weights, and cutoff value) was then applied to examine their predictive power for clinical and biomarker status, as well as progression to symptomatic106108365568.1Docket 047563-870867AD and rate of memory decline. The seven-protein (SPC25, CTF1, ACHE, CPLX2, VAT1, NPTXR and FAHD2A) predictive model resulted AUC of 0.796 in the discovery-test and 0.72 in the replication, showing significantly better performance (p=8.21><10T0) than tire baseline model (AUC of 0.661, with age and sex; TABLE 13 and TABLE 14). Similar predictive power was observed for GNPC cohort (AUC of 0.715) and ROSMAP cohort (AUC of 0.757) (FIGS. 5 and 10).

[0190] Even though these AUCs were higher than the baseline model, the model did not show a performance good enough to be clinically relevant, where AUCs > ~0.8 are expected. Therefore, this model was tested for predicting amyloid status (based on amyloid imaging), plasma p-tau217, or CSF A|3 and p-taul 81. In this analysis, the predictive power for this model was significantly higher than that for clinical status, showing an AUC of 0.88 for amyloid positivity, 0.89 for CSF amyloid / tau status, and 0.88 for plasma p-tau217 status (FIG. 5B, TABLES 13 and 14), suggesting that the lower AUC found when using clinical status could be due to the presence of presymptomatic cases among the controls.TABLE 13: Summary of prediction performance for predictive model107108365568.1Docket 047563-870867For neurodegenerative diseases other than AD (DLB, FTD. and PD), performance are represented as mean values from 100 iterations.TABLE 14: Summary of prediction performance for predictive model for other neurodegenerative diseasesFor neurodegenerative diseases other than AD (DLB. FTD, and PD). performance are represented as mean (min. max) values from 100 iterations.

[0191] To test this hypothesis and further examined whether this model could identify a subset of individuals that would progress from cognitively normal to symptomatic AD. The study considered 761 individuals who were cognitively normal at the time of plasma draw, of whom 83 eventually progressed to symptomatic AD. Using the Cox proportional hazard model, it was found that individuals who were predicted positive based on the seven-protein model (i.e., predicted as AD) had a 2.7 times higher risk of developing symptomatic AD compared to the predicted negative group (hazard ratio=2.71 ; p= 1.99 x 10-4;108108365568.1Docket 047563-870867FIG. 5E). When analyzing the risk of developing symptomatic AD in 5, 10, or 15 years, this sevenprotein model showed an AUC ranging from 0.71 to 0.73 (FIG. 12).

[0192] It was also investigated if this seven-protein model could also have the power to predict rate of memory decline. To assess this, the study considered 1.672 individuals who had longitudinal follow-up information with Clinical Dementia Rating sum of boxes (CDR-SB). This analysis revealed that those individuals who were positive for the seven-protein signature exhibited a significantly faster rate of memory decline (slope of CDR-SB / year) than those negative for the seven-protein signature (TABLE 15, FIG. 12). Specifically, approximately 1 point of CDR-SB per year, was observed between the two predicted groups (1.40 vs. 0.39: p=1.95x 10-64), when comparing all individuals regardless of the diagnosis at blood draw. The rate of change in CDR-SB was further analyzed in individuals who were cognitively normal (CO) or had AD at the time of the blood draw. Cognitively normal individuals who predicted positive for the seven-protein signature exhibited significantly higher CDR-SB changes (slope = 0.46) compared to those who tested negative for this model (slope = 0.09; p = 2.84x10-21). Among cases, those positive for the seven-protein signature also showed higher CDR-SB / slopes than those negative (2.02 vs 1.67 per year, respectively; p = 1.85x10-2).TABLE 15: Summary of analysis results for progression in CDR-SBClinical diagnosis All samples AD CO„ Samp .le si .ze, n Predicted-positive 862 521 341Predicted-negative 810 149 661R ,,at .e of CDR- cS vB> ch ..ange Predicted-prositive 1.4 2.02 0.46Predicted-negative 0.386 1.67 0.0962p-value 1.95X10-641.85xl0'22.84xl0'21TABLE 15 summarizes the number of samples used in the analysis and the corresponding results.The findings are presented based on clinical diagnosis of AD and categorized into two groups according to the trained model: Predicted-positive (predicted as AD) and Predicted-negative (predicted as cognitively normal, CO). Abbreviations: CDR-SB: clinical dementia rating scale sum of boxes.

[0193] Finally, it was evaluated whether this model is specific to AD or whether it possesses some ability to predict dementia caused by other neurodegenerative diseases (TABLE 16). Tire AUC for predicting PD and DLB was 0.546 and 0.661, which was similar to that of the baseline model indicating a low overlap in associated proteins of AD with PD, as well as with DLB (FIG. 5D, TABLE 13). However, the AUCs for predicting FTD was 0.73, which is similar to that of clinical AD (AUC=0.796).TABLE 16: Demographics information of participants with other neurodegenerative diseases109108365568.1Docket 047563-870867Clinical status FTD DLB PDSample size, n 32 78 703Agemean± SD 75.5 ± 12.1 72.3 ± 9 67 1 ± 9.9Range (min, max) (54,96) (55,95) (31,92)SexFemale 16 25 262Male 16 53 441TABLE 16 summarizes basic demographic information of study participants with other neurodegenerative diseases. Abbreviations: DLB: dementia with Lewy bodies; FTD: frontotemporal dementia; PD: Parkinson's disease; SD, standard deviation.Discussion

[0194] This large-scale study examined nearly 7,000 plasma proteomic data from 1,270 clinical AD and 2,096 cognitively normal individuals, identifying 416 proteins that were associated with clinical AD status, which were further replicated with two additional datasets. The study used biomarker status based on amyloid imaging, CSF AT, or plasma p-tau217 to confirm the results. The study also identified the seven-protein predictive model that provided good predictive power for clinical AD and biomarker-based status, indicating that these proteins are associated with AD dementia.

[0195] This study used a tw o-stage (discovery and replication) to identity those proteins associated with AD risk, finding 456 aptamers (416 proteins) significant in both stages, with the same direction and passing stringent multiple test correction in the meta-analyses. More importantly, to further validate the findings, they were replicated with two external datasets (ROSMAP and GNPC), supporting the validity of the findings. Among the 416 proteins (456 aptamers) identified in the study, 20% (76 proteins / 94 aptamers) have been reported to be associated nominally in previous plasma studies (Sung et al., Sci Transl Med 15, eabq5923 (2023); Walker et al., Nat Aging 1, 473-489 (2021); Sattlecker et al., Alzheimers Dement 10, 724-734 (2014)), indicating that 80% of the findings (340 proteins / 362 aptamers) were novel. Within reported findings, 87% (66 proteins / 82 aptamers) showed a nominal association in previous plasma studies, but they are reported here as significant after multiple test correction, for the first time, illustrating statistical power of this study. Furthermore, of the novel proteins, 154 aptamers also have supporting evidence for association with AD risk in CSF. In addition, the study also found a significant correlation between the effect size for the proteins identified in this study and those from previous plasma studies. Together, this supports and provides replication for these findings. However, as this study includes a very' large number of samples and proteins, a total of 208110108365568.1Docket 047563-870867aptamers (182 proteins) were reported to be associated with AD in plasma proteomics in this study for the first time. They included SPC25, VAT1, and PPBP, that were involved on signal transduction, and SPARC, NCAM1 and VEGFA implicated in endothelial pathways. Other known proteins identified in this study and part of these pathways included SM0C1, SM0C2, and ACHE, that have been reported to be associated with AD in CSF and brain in previous studies (Ali et al., Res Sq (2024); Shen et al., medRxiv (2024)).

[0196] In contrast to recent plasma proteomic studies, this study considered well -characterized AD patients. Two recent population-based studies, Atherosclerosis Risk in Communities (ARIC) study and the UK Biobank, had a limited number of dementia cases (428 and 1,417), identifying 38 and 5 proteins associated with memory decline, respectively (Walker et aL, Nat Aging 1, 473-489 (2021); Guo et al., Nat Aging 4, 247-260 (2024)). Both studies analyzed dementia in general, which included not only AD but also other types of dementias, leading to heterogeneity and loss of power. Some of the blood samples in those studies were collected 15 to 20 years before the clinical diagnosis. Although it is known that some key AD biomarkers change years before clinical onset, not all proteins start changing that early during the disease progression (Shen et al., medRxiv (2024)). Furthermore, some samples included as controls in those studies were from young individuals who may go on to develop dementia, which also impacts statistical power.

[0197] As several large-scale proteomic studies have been perfonned in CSF, it is important to compare the plasma results with those from CSF. To do this this study leveraged the largest CSF proteomic study perfonned to date, which included 2,286 CSF samples profiled using the same proteomic platform and cohort as in this study, facilitating direct comparison. It was found that of around 45% of the plasma-associated proteins were also reported to be associated in CSF. However, conversely, only 8% of the CSF-associated proteins showed association in plasma, indicating that CSF has more power to identify proteins associated with AD, probably due to its close contact with brain. Another potential explanation is platform limit of detection, assuming brain proteins have low concentrations in plasma. However, this is unlikely, as the SomaScan assay was originally optimized for plasma, and similar number of proteins passed QC in both tissues. All these together suggest that proteomic signatures in CSF and plasma appear to be different, leading to a different set of proteins associated with AD depending on the tissue analyzed.

[0198] There is an open question in the field about how well blood / plasma proteomics capture brain-related (such as AD) processes and whether it is a valid biofluid to identify novel biomarkers and learn about processes implicated in neurodegeneration. It appears that the enriched pathways may capture general processes (blood homeostasis, extracellular matrix pathways, signaling), a detail analyses of the111108365568.1Docket 047563-870867identified pathways and proteins clearly indicate that there is an enrichment of endothelial proteins (ACHE, SM0C1, SM0C2, VEGFA, VEGFB, SPARC, NRCAM, NCAM1). In addition, several blood homeostasis (including the plasma-novel proteins: ATP1B2, PF4, PPBP, SPARC) pathways were identified, which may reflect vascular health. These results suggest that the pathways identified in these analyses are capturing dysregulation of tire endothelia cells, which would lead BBB dysfunction and a subsequent leakage of brain proteins in the blood.

[0199] Tire brain-related pathways included pathways related to synaptic transmission and upregulation of the GABA (gamma-aminobutyric acid) synthesis, release, reuptake, and degradation pathway. GABA is the primary inhibitory neurotransmitter in the brain, crucial for regulating neuronal excitability. One study found increased GABA in reactive astrocytes in the dentate gyrus of a mouse model, which led to tonic inhibition, a persistent reduction in neuronal activity, and memory deficiencies (Wu et al., Nat Commun 5, 4159 (2014)). Another mouse study reported a bell-shaped relationship between GABA accumulation in hippocampal astrocytes and amy loidosis (Brawek et al., Neurobiol Aging 61, 187-197 (2018)). As another brain-relevant pathway, this study found the myelination signaling pathway, which is vital for maintaining the integrity of nerve fibers, with neurofibrillary tangle formation, abnonnal synaptic contraction, and activation of microglia and astrocyte release of inflammatory cytokines (Cai et al., Behav Brain Res 414, 113481 (2021 )). Taken together, this study highlights several important pathways related brain related processes that may be captured in blood due to dysfunction of the endothelia leading to BBB leakage. Some of these pathways are not a just mere reflection of the neurodegeneration but also part of the causal pathways. Several pathways (lipid metabolism, immune response< include proteins that are known part (APOE, CLU. BINI) of the causal pathways and therefore could become potential intervention targets.

[0200] In order to learn more about AD biology and identify those processes that are part of the disease, pathway analyses was performed to identify five major super-pathways: 1) lipid metabolism, brain, 2) inflammation. 3), and 5) neuronal-related pathways. These pathways included newly identified proteins, such as APOB, SCP2, and CRP, along with well-established AD-related proteins including CLU, APOE or BINI, that have been identified in genetics studies, supporting that some of these pathways and processes are part of the causal pathway and not only secondary to the disease. Other known proteins identified in this study and part of these pathways included SMOC1, SMOC2, and ACHE, that have been reported to be associated with AD in CSF and brain in previous studies (Ali et aL, Res Sq (2024); Shen et al., medRxiv (2024)). These findings corroborate previous research indicating that plasma proteomics is significantly related to lipid metabolism (Walker et al., Nat Aging 1, 473-489 (2021)) and the extracellular matrix (Dammer et al., Alzheimers Res Ther 14, 174 (2022)).112108365568.1Docket 047563-870867

[0201] As collection of blood is considered to be minimally invasive, there is substantial interest in developing blood-based biomarkers. Plasma p-tau217 shows very strong predictive power for amyloid positivity (Barthelemy et al., Nat Med 30, 1085-1095 (2024)), but this biomarker captures AD pathology and not overall dementia or disease status. In addition, with new anti-Ap therapies that may substantially remove fibrillar Ap deposits as detected by amyloid-PET scan in the brain, some recent studies indicate that p-tau217 decreases according to amyloid removal, even if neurodegeneration and disease progression may have not been stopped (Bateman et al., Amyloid Reduction and Dementia Progression in Dominantly Inherited Alzheimer's Disease after Long-Term Gantenerumab Treatment: Results from the Dian-Tu Trial). Therefore, additional non-tau and non-A biomarkers are needed. For this reason, this study sought to determine if the proteins identified could be leveraged to create novel predictive models.Through machine learning this study was able to identify a set of seven proteins that showed high AUC for clinical status and replicated in several studies (AUOO.72). Although this AUC may seem low, it was hypothesized that this could be because around 30% of the clinical controls may have already developed AD pathology. When examining the predictive power of this model for biomarker status based on amyloid imaging, CSF AT status or plasma p-tau217, the AUC was over 0.879. In addition, when testing if this model could identify those individuals that were cognitively normal at blood draw but later would progress to symptomatic AD. this study found a strong association (hazard ratio=2.71; p=1.99x 10-4). confirming that in fact this model can identify people with AD pathology and not only clinical status. Although we do not yet understand how Ap therapies might alter this predictive power, this high correlation of our model with the rate of progression in CDR-SB suggests that the proteins in the model may reflect overall brain health in AD cases, not just Ap pathology. However, additional studies are needed to confirm whether these seven proteins or combination of proteins highly correlated with these seven proteins (TABLE 17) can be used as biomarkers in a clinical setting.TABLE 17: Correlation of predictor target protein aptamers113108365568.1Docket 047563-870867114108365568.1Docket 047563-870867

[0202] Another strength of the study is that it also examined the predictive performance of this model with other non-AD dementia, which is rarely done for biomarker discovery. This study found that the model shows a very low overlap with PD (AUC=0.546), and DLB (AUC=0.661), but not with FTD (AUC=0.73). Some overlap across AD and FTD might be expected due to potential misdiagnosis as well as shared or mixed pathologies across these diseases. Hie number of samples for FTD and DLB (32 in FTD and 78 in DLB) may not be enough to fully distinguish AD from FTD or from DLB. Additional and large studies in these groups are needed to create disease-specific predictive models and determine how specific the model presented here is for AD.

[0203] In summary, this large-scale plasma proteomic study identified proteins associated with AD, developed a robust prediction model that accurately predicted AD status, examined differences between predicted groups in progression to symptomatic AD and dementia, and revealed pathways and biological115108365568.1Docket 047563-870867processes relevant to AD. These findings from extensive analysis provide the potential of plasma proteins as biomarkers for routine clinical uses for the early detection of AD and guiding AD treatment decisions.Materials and MethodsCohorts

[0204] Plasma samples used in this study were obtained from the Charles F. and Joanne Knight ADRC (Knight ADRC, n=2,948), Stanford ADRC (n=450), Movement Disorder Clinic at Washington University (MARS, n=985). Religious Orders Study and Memory and Aging Project (ROSMAP, n=472), and Global Neurodegeneration Proteomics Consortium (GNPC, n=6,566).Knight ADRC

[0205] Tire Knight ADRC at Washington University School of Medicine in St. Louis has conducted prospective studies on memory and aging since 1979. These studies involved the recruitment and longitudinal assessment of community-dwelling adults over the age of 45. Knight ADRC research was approved by the Washington University Human Research Protection Office. All study participants provided written informed consent.

[0206] Annual assessments of the participants were performed by experienced clinicians using a semistructured interview and detailed neurological examination. Interviews included the symptomatic individual and knowledgeable collateral sources in accordance with the Uniform Data Set protocol of the National Alzheimer’s Coordinating Center (Morris et al., Alzheimer Disease & Associated Disorders 20 (2006)). A clinical diagnosis of dementia is considered by study clinicians after each annual assessment, integrating results from the clinical assessment and bedside measures of cognitive function (Day ct al., Neurology 88, 1273-1281 (2017)). Dementia was diagnosed according to the National Institute of Neurological Disorders and Stroke (NINDS) criteria (McKhann et aL. Neurology 34, 939-939 (1984)) and National Institute on Aging-Alzheimer's Association Work Group criteria for participants assessed after 2011 (McKhann et al., Alzheimer's & Dementia 7, 263-269 (2011)). Diagnosis of AD dementia was made in accordance with criteria developed by working groups from the National Institute of Aging and the Alzheimer’s Association (McKhann et aL, Alzheimer's & Dementia 7, 263-269 (2011)). Knight ADRC participants were included in this study if they had no cognitive impairments and a global clinical dementia rating (CDR) score of 0 at the time of enrollment.

[0207] A total of 2,948 participants were included in the present study (mean age 75, 58% female, 42% with one or more APOE e4 risk alleles). Among these participants, 1,694 participants were classified as 116108365568.1Docket 047563-870867cognitively normal, 1,222 participants were diagnosed with AD, and 32 participants were diagnosed with FTD. Among 1,694 cognitively normal individuals, 83 individuals later progressed into symptomatic AD. SomaScan 7K proteomics data generated from these participants' samples were utilized for this study.

[0208] Plasma samples were collected in the morning without fasting. After collection, plasma samples were immediately centrifuged and stored at -80°C until they were sent for protein measurement. A detailed description of the Knight ADRC sample collection and multi -omics data available can be found elsewhere (Fernandez et al., Sci Data 11, 768 (2024)). Plasma samples were collected from 1995 to 2021. Between 1998 and 2006 blood draw protocol was different than others, therefore year of sample collection was used to split the dataset into discovery and replication datasets for differential abundance analysis and predictive modeling.

[0209] In the discovery dataset (Knight ADRC 1995-1997 and 2007-2021) 1,381 participants were classified as cognitively nonnal. 750 participants were diagnosed with AD, and 10 participants were diagnosed with FTD. In the replication dataset (Knight ADRC 1998-2006), 313 participants were classified as cognitively nonnal, 472 participants were diagnosed with AD, and 22 participants were diagnosed with FTD.Stanford ADRC

[0210] Tire Stanford ADRC cohort consisted of individuals enrolled in a longitudinal observational study, including clinical dementia subjects and age-sex-matched nondemented subjects. Tire acquisition of plasma samples w as conducted with the approval of the Institutional Review Board of Stanford University and written consent was obtained from all participants. Further details are described in Oh, et al. (Oh et al., Nature 624, 164-172 (2023)).

[0211] A total of 450 participants w ere included in the replication cohort of the present study (mean age 70, 59% female. 30% with one or more APOE e4 risk alleles). Among them, 402 participants were classified as cognitively nonnal and 48 participants were diagnosed with Alzheimer's disease. SomaScan 7K proteomics data generated from these participants' samples were utilized for this study.Movement Disorder Clinic at Washington University

[0212] Movement Disorder Clinic at Washington University (MARS) cohort focused on participants with PD and other movement disorders. The primary objective of the study was to identify potential biomarkers of early disease progression and memory decline in individuals with PD (Ibanez et al., Acta 117108365568.1Docket 047563-870867Neuropathologica Communications 8, 196 (2020); Benitez et al., Molecular Neurodegeneration 11, 29 (2016)). Samples, including DNA, blood RNA, and CSF, are collected from these participants for research purposes.

[0213] A total of 985 participants were included in the present study (mean age 67. 42% female, 21% with one or more APOE E4 risk alleles). Among them, 204 participants were classified as cognitively normal, 78 participants were diagnosed with DLB, and 703 participants were diagnosed with PD.SomaScan 7K proteomics data generated from these participants' samples were utilized for this study.ROSMAP

[0214] Tire Religious Orders Study and Rush Memory and Aging Project (ROSMAP) are both community-based longitudinal cohort studies run by the Rush Alzheimer's Disease Center, focusing on aging and Alzheimer's disease (AD). ROSMAP comprises the Religious Orders Study (ROS) and the Rush Memory and Aging Project (MAP), both of which involve older individuals who agreed to brain donation after death (Bennett et al., J Alzheimers Dis 64, S161-S189 (2018)). The ROS study, initiated in 1994, enrolled participants from over 40 religious communities across the United States, focusing on those without known dementia at enrollment. The MAP study, which began in 1997, was designed to complement ROS by enrolling a broader population from continuous care retirement communities and individual home visits in northeastern Illinois. Both studies involve annual evaluations of participants' physical and cognitive functions, with diagnostic assessments for conditions such as dementia, Alzheimer's disease, stroke, parkinsonism, and depression. Upon participants' deaths, comprehensive neuropathologic evaluations are conducted following standardized procedures, including assessments of AD pathology, cerebral infarcts, Lewy body disease, and other aging-related pathologies. Both studies were approved by an Institutional Review Board of Rush University Medical Center. All participants signed informed and repository consents and an Anatomic Gift Act.

[0215] A total of 472 participants were included in the present study (mean age 85, 74% female, 26% with one or more APOE e4 risk alleles). Among them, 322 participants were classified as cognitively normal and 150 participants were diagnosed with AD and confirmed with neuropathology. SomaScan 7K proteomics data generated from these participants' samples were utilized for this study.GNPC

[0216] The GNPC was an initiative aimed at discovering biomarkers by integrating and expanding proteomic data from thousands of patient samples across global dementia cohorts, resulting in a118108365568.1Docket 047563-870867comprehensive, harmonized proteomics dataset (Bringmann et aL, Alzheimer's Association International Conference. (ALZ)). Cross-sectional and longitudinal SomaScan (SomaLogic, Boulder, CO) proteomics data were incorporated into this initiative. The GNPC successfully brought together over 40,000 samples from more than 23 different international cohorts, created a harmonized dataset that covers multiple neurodegenerative disorders, and includes approximately 300 million protein measurements. GNPC neurodegenerative disorders include AD, PD, FTD, amyotrophic lateral sclerosis ALS.

[0217] A total of 6,566 participants were included in the present study (mean age 72, 41% female). Unlike other cohorts, this study considered CDR and clinical diagnosis for defining the case and control group for this cohort. Individuals with CDR=0 and CDR>0 were defined as control and case groups, respectively. Absent CDR data, this study relied on clinical AD diagnosis to assign case-control status. Among them, 4,833 participants were in the control group and 1,733 participants were in the case group. SomaScan 7K proteomics data generated from these participants' samples were utilized for this study. Proteomics data and quality control

[0218] To measure the protein expression levels, a multiplexed, single-stranded DNA aptamer assay (SomaScan) developed by SomaLogic (Boulder, CO) was employed, which allowed the measurement of approximately 7,000 proteins. Protein levels were reported as relative units of intensity (RFU).SomaLogic initially performed the data normalization, and subsequent, centralized quality control (QC) steps were applied to the normalized proteomics data.

[0219] Additional QC was performed. Analytes for which 85% of the total samples failed the limit of detection (LOD) filter were removed. Analytes were removed if their scale factor deviated by more than 0.5 points from the median scale factor in any of the plates or if their coefficient of variation (CV) exceeded 0.15. At the level of individual observations, this study identified outliers by expression levels (log 10 transformed) were greater than the lower limit of the third quartile plus 1.5 times the interquartile range (IQR) or less than the upper limit of the first quartile minus 1.5 times the IQR. Outliers were assigned missing values. Finally, this study measured the call-rate (the proportion of successful measurements) for each analyte and subject. During a first pass, any analyte or subject with a call-rate less than 65% was removed. After recalculating call-rates, a second pass was performed with a more stringent criterion that removed analytes or subjects with less than a 85% call-rate.

[0220] For Knight ADRC proteomic samples, 6,905 analytes and 2,948 samples remained for further analysis after following the QC process. Principal components analyses was performed to identify potential batch effects in the total Knight ADRC sample. When plotting the first two principal119108365568.1Docket 047563-870867components, we observed two clusters. After further analysis, it was recognized that samples with blood drawn from 1998-2006 clustered separately from other samples. To mitigate systematic bias caused by this batch effect, the Knight ADRC sample was divided by blood draw year so that one population could serve as the discovery cohort and the other as part of the replication cohort. Z-score nonnalization was conducted on each cluster; these values were used in subsequent analysis.Biomarker-based classification

[0221] Some level of misdiagnosis of AD can be expected by dementia clinical diagnosis, especially in early stages of disease, with some estimates as high as 30% (Hansson et al., Alzheimers Dement 18, 2669-2686 (2022)). Furthermore, the biological severity of AD does not always correspond directly to the clinical severity of the disease (Hansson et aL, Alzheimers Dement 18, 2669-2686 (2022); Jack et al., Alzheimers Dement (2024)). However, biomarkers offer a reliable means to identify AD pathology in individuals exhibiting cognitively impairment. Thus, sensitivity analyses of biomarker-based diagnoses was performed to further understand the pathology of AD.

[0222] Tire biomarker-based classification of AD in this research study was based on 1) CSF biomarkers, 2) amyloid PET, and 3) plasma p-tau217. For CSF biomarkers, AJ3-42 and p-taul81 were measured using the LumiPulse G platfonn by Fujirebio. Quantitative amyloid-PET data were acquired using AV45 and Pittsburgh compound B (PIB), and the data was normalized to reference cerebellar regions to obtain standardized uptake value ratios (SUVR) in a composite of cortical brain areas. Finally, p-tau217 levels in plasma were measured using the NULISAseq platform from Alamar Bioscience (Fremont, CA).Further details are available in Ibanez, et al. (Ibanez et al., medRxiv (2024)).

[0223] To identify biomarker cutoffs for CSF classification, amyloid PET imaging, and p-tau217, a Gaussian mixture model (GMM) approach was utilized, as previously reported (Timsina et al.. Neurobiol Dis 190, 106373 (2024)), using the "mclust" package (Version 6.0.0) in the R. Z-score values were calculated for each measurement to determine the cutoffs for dichotomization. Hrese cutoffs were then used to infer the corresponding raw value cutoffs, providing biologically meaningful biomarker levels. The same approach was applied to dichotomize all four measurements.

[0224] For Knight ADRC cohort, samples with CSF-based AT status (256 AT- vs. 105 AT+), amyloid PET imaging (265 A- vs. 122 A+), and plasma p-tau217 (1348 T- vs. 1404 T+) were used. Forthe Stanford ADRC cohort, we utilized the CSF-based AT status provided by the study, which was measured using CSF (59 AT- vs. 22 AT+).120108365568.1Docket 047563-870867Differential abundance analysis

[0225] A total of 6,905 analytes were retained for the differential abundance analysis of protein levels between the AD and control (CO) groups. First, a linear regression analysis was performed with the Discovery study population with protein level as the outcome, clinical diagnosis (AD vs. CO) as the primary exposure, and age at blood draw and sex as covariates. Second, proteins with a nominal association to AD (p<0.05) were tested in the replication study population. Linear regression was utilized as above but added cohort as a covariate. Lastly, a meta-analysis was conducted for those proteins that were nominally significant in both discovery’ and replication and demonstrated consistent directionality. Effect sizes and p-values obtained from the two datasets were used for the meta-analysis using tire metapro R package (Timsina et al., Neurobiol Dis 190, 106373 (2024)). To address multiple testing comparisons and determine statistical significance, a false discovery’ rate (FDR) p-value threshold of less than 0.05 was employed using the Benjamini-Hochberg (BH) procedure.

[0226] Additionally, other AD-related phenotypes available in the Knight ADRC were leveraged to investigate their associations with protein levels. These phenotypes included amyloid PET imaging, CDR, Mini-Mental State Examination (MMSE) score, CSF biomarkers such as A[342 / 40, total tan, and p-taul81, tau PET imaging, and white matter hyperintensity volume. The number of samples used for each phenotypes varied depending on data availability. To assess the correlations between protein abundances and these AD-related phenotypes, the Spearman correlation coefficient was used. To visualize the results, a heatmap plot was generated with proteins clustered using a hierarchical clustering algorithm.Sensitivity analysis

[0227] A sensitivity analysis was also performed for AD biomarker status using Amyloid PET and p-tau217 status. As above, linear regression was used with protein level as the outcome, biomarkergroup as the exposure, and age at blood draw and sex as covariates. Effect sizes from sensitivity analysis with AD biomarkers and summary statistics from these external studies were used to assess the correlation with meta-analysis effect sizes from clinical diagnosis of AD. Tire "cor test" function in R was utilized.

[0228] To contextualize the study’s findings, a comprehensive comparison between results obtained from the clinical status-based meta-analysis and other relevant external proteomics studies was conducted. Specifically, three plasma studies (Walker, et al., Nat Aging 1, 473-489 (2021); Sung, et al., Sci Transl Med 15, eabq5923 (2023), and Sattlecker, et al., Alzheimers Dement 10, 724-734 (2014) and one CSF study (Ali, et al.) were considered in the analysis. From the studies performed herein, summary statistics were gathered for comparison to previous studies.121108365568.1Docket 047563-870867

[0229] Binomial distribution was used to compute fold-enrichment of proteins with consistent direction of effect sizes. This enabled calculation of the fold-enrichment by comparing the observed number of proteins with a consistent direction of association to what would be expected by chance. Assuming the null hypothesis of no enrichment, the number of available proteins was considered for comparison and the probability of a protein being in a consistent direction was set as 0.5, assuming that there is an equal chance for proteins to show either a consistent or inconsistent direction of association. The expected number of proteins in a consistent direction was obtained by multiplying the number of available proteins by 0.5. The fold-enrichment was determined by calculating the ratio of observed consistency to expected consistency. A computed p-value was used to assess the statistical significance of tire observed enrichment using the binomial distribution. This p-value represents tire probability of observing the given number of proteins (or a larger number) with a consistent direction of association, assuming the null hypothesis of no enrichment.Progression to Symptomatic AD

[0230] A survival analysis using the Cox proportional hazard regression model was performed to investigate the potential association between each protein and progression to symptomatic AD. This analysis utilized the survival R package (version 3.5-5). The Cox proportional hazard model incorporated age at plasma draw and sex as covariates along with each analyte’s expression level. For the analysis, only cognitively nonnal individuals (CDR=0) at blood draw from Knight ADRC cohort were included. Subsequently, the clinical longitudinal data was leveraged to identify participants that converted to AD during the follow-up period (n=83). More detailed demographics are summarized in TABLE 6. The time-to-event variable was calculated by subtracting the age at the blood draw date from the age at the last CDR test or the age at the onset of AD for CO and AD groups, respectively. The “ggadjustedcurves” function from the survminer R package (version 0.4.9) was used to visualize the results in Kaplan-Meier plots.Pathway Analyses

[0231] To examine the potential biological pathways associated with identified biomarkers from the differential abundant analysis, a pathway enrichment analysis using Gene Ontology (GO) for all three ontologies: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) was conducted. Qiagen Ingenuity Pathway Analysis (IP A) was used to identify canonical pathways, molecular networks, and biologic functions prioritized by proteins associated with AD.122108365568.1Docket 047563-870867

[0232] The annotation file provided by SomaLogic was used to map the aptamers to Entrez Gene ID. The background genes for the pathway analysis were set to include all unique genes covered by the SomaScan 7k assay, which comprised 6,101 genes. The pathway enrichment analysis was performed using the "enrichGO" function from the R clusterProfiler (Wu et al., Innovation (Camb) 2, 100141 (2021)) package version 4.12.0, which is available in Bioconductor. To address multiple testing comparisons and determine statistical significance, a FDRp-value threshold of less than 0.05 was employed using the BH procedure.

[0233] Qiagen IPA is a bioinformatics tool that leverages the human-curated Ingenuity pathway¬ knowledge base to link study findings to pathways, diseases, functions, and networks. IPA analysis considers effect size, direction, and p-values to implicate biologic action. Canonical pathways are well-characterized, interactive metabolic and cell-signaling pathways, and applied an FDR threshold for statistical significance. Gene and molecular interaction networks feature edges (relationships) from curate findings in the biomedical literature. These findings are also used to relate genes and proteins to biologic functions and diseases.Cell-type specific analysis

[0234] in this study, cell-type enrichment analysis was perfonned using a reference database from a study conducted by Zhang, et al. (Neuron 89, 37-53 (2016)). This reference database comprised various cell types in different human tissues derived from brain RNA sequencing data. The analysis focused on examining potential associations between identified biomarkers from the differential abundance analysis and specific cell types, including oligodendrocytes, astrocytes, endothelial, microglia / macrophage, neurons. To achieve this, hypergeometric test was employed to assess enrichment.MEGENA

[0235] Protein co-expression networks were constructed, and modules were identified by using the Multiscale Embedded Gene co-Expression Network Analysis (MEGENA) approach, as previously described (Song et aL, PLoS Comput Biol 11, el004574 (2015); Wang et al., Alzheimers Dement (2023)). Significant pathways associated with each module were identified using the same methods as described in the previous sections. The specificity of cell types in the modules were evaluated by the hypergeometric test using the human cell type gene signatures (McKenzie et al., Sci Rep 8, 8868 (2018)) as reference following the method described in Wang et al., Genome Med 8, 104 (2016); Kajiwara et al., Acta Neuropathol Commun 6. 144 (2018). Additionally, hub proteins of modules were detected using internal function in the MEGENA package (Song et al., PLoS Comput Biol 11, el004574 (2015)).123108365568.1Docket 047563-870867

[0236] To mitigate potential impacts from covariates, protein expression data were adjusted for sex and age using a linear model, and the residuals from the regression were used to construct the co-expression network. To identify and prioritize co-expression modules that were significant in the analysis, the enrichment of the protein signature over the MEGENA modules was assessed. Proteins were divided into subsets based on positive or negative effect sizes to assess their enrichment in the MEGENA modules. The enrichment results were summarized into a ranking metric using an ensemble ranking approach (Liu et al.. Am J Hum Genet 104, 410-421 (2019); Wang et aL, Genome Med 8, 104 (2016)).Predictive model

[0237] With identified proteins from differential abundant analysis, a prediction model was trained for the clinical status of AD and CO groups. To train the prediction model, the Discovery dataset was divided into a training set and a testing set. Tire training set consisted of 70% of the Discovery dataset (967 CO and 525 AD). The remaining 30% of the Discovery dataset (414 CO, 225 AD) and the Replication dataset (715 CO, 520 AD) were used for testing the model.

[0238] A Lasso regression model was employed, utilizing normalized protein expression values as input with age at blood draw’ and sex included as covariates. Tire LI regularization parameter, X, was tuned through hyperparameter optimization using five-fold cross-validation with tire "cv.glmnet" function from the glmnet R package. To reduce model complexity and prevent overfitting, the highest Z value that retained 90% performance relative to the best model was selected. The variables selected from the Lasso model were then used to retrain a logistic regression model with age and sex as covariates.

[0239] The prediction model was tested using the same cutoff for prediction and weights on biomarkers statuses, such as amyloid imaging, CSF biomarkers, and plasma p-tau217. and other neurodegenerative diseases, including DLB, FTD, and PD. For other diseases, 100 iterations w ere performed to obtain robust results. During these iterations, the dataset was undersampled while maintaining a matched distribution of sex across sample groups. By undersampling, the dataset is balanced in terms of the number of cases and controls for each group to assess the model's perfonnance in an unbiased manner. In the case of the Knight ADRC dataset, CO samples used for training the model were excluded and undersampled for 100 iterations to match sex and sample collection year cluster distribution across sample groups.

[0240] To evaluate the perfonnance of the model, several metrics were calculated, including the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive124108365568.1Docket 047563-870867predictive value (PPV). Mean, minimum, and maximum values of these metrics were reported for other neurodegenerative diseases, providing a comprehensive assessment of the model's performance.Predictive modeling for progression to symptomatic AD

[0241] To investigate the impact of predictor proteins in the trained prediction model on the progression to symptomatic AD, survival analysis using the Cox proportional hazard regression model was conducted, similar to the previous analysis. However, in this analysis, samples were categorized into two groups: predicted-positive (predicted as AD) and predicted-negative (predicted as CO), based on the prediction cutoff defined during the model training phase. The predicted group was used as the independent variable of the model with other covariates. The same samples and calculation of time-to-event, as described earlier, were utilized. More detailed demographic information can be found in TABLE 6.

[0242] The analysis was perfonned using the same functions as described earlier. The Cox proportional hazard model incorporated age at plasma draw and sex as covariates, along with the binarized variable representing the predicted status. The aim was to determine whether there was a difference in the rate of progression to symptomatic AD between the two predicted groups.Predictive modeling for AD progression

[0243] Tire CDR Sum of Boxes (CDR-SB) was used to examine the relationship between the predictive model and the progression of dementia severity. Using the trained prediction model, the samples were categorized into two groups: predicted-positive (predicted as AD) and predicted-negative (predicted as CO). This analysis aimed to compare the rate of dementia progression in individuals and determine if there were any differences in the rate of changes in CDR-SB between the clinical statuses (AD and CO). The study utilized a total of 1,672 samples (1,002 CO and 670 AD) from the Knight ADRC, where longitudinal CDR-SB data were available.

[0244] For each sample, the CDR record closest to the date of the blood draw within a 180-day window was selected as the initial record, while the latest available CDR record was chosen as the last record. To calculate the change in CDR-SB per year for each sample, the initial and last CDR-SB records were compared. A Wilcoxon rank-sum test with a significance threshold of p<0.05 was perfonned to see whether there is a significant difference in terms of the rate of progression between the two groups.Additionally, the changes in CDR-SB were visualized with respect to the years from the initial CDR test date.Example 2: p-tau217 Improvement of Predictive Model125108365568.1Docket 047563-870867

[0245] It was next investigated whether inclusion of a clinical marker for Alzheimer’s disease (e.g., p-tau217) could improve the predictive model. The approach was similar to the scheme used in Example 1. Samples from the MAP (PIGEON) dataset were utilized, and plasma p-tau217 levels measured using the NULISAseq platform from Alamar Bioscience (Fremont, CA) were added (see Ibanez, et al. (medRxiv (2024)). The data was separated to discovery and replication datasets by sample draw year. 70% of discovery data was used for training, and 30% of discovery data was used for testing, as shown in TABLE 18.TABLE 18: Study Design

[0246] Tire predictive model (e.g., Formula I) was improved such that clinical status was determined by age, biological sex, the normalized level of target proteins (e.g., TP1-TP7), and the normalized p-Tau217 levels.

[0247] In the discovery test set, the inclusion of p-Tau217 in the predictive model on clinical status increased accuracy, producing an -0.05 AUG increase, from 0.796 to 0.847 (FIG. 13).

[0248] In the replication dataset, a slightly better improvement was observed, with an -0.06 AUC increase, from 0.721 to 0.788 (FIG. 13).

[0249] The modified model generated coefficients for an equation for calculating predictor values (PV) for use in the methods as described herein (e.g., in Section V):PV = (1.10 x p - Tau217) + (0.363 x TP1) + (0.245 x TP2)+ (0.723 x TP3) + (-0.224 x TP4) + (0.492 x TP5) + (-0.385 x TP6) + (-0.799 x TP7) + (0.04 x age) + (0.222 x biological sex) — 4.45Formula (II),126108365568.1Docket 047563-870867wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female. A cutoff value of 0.346 was determined to be the optimal threshold value for PV comparison for the methods utilizing p-tau217. Calculated coefficients of the prediction model are shown below in TABLE 19.TABLE 19: Coefficients of prediction modelExample 3: Reduction of Predictive Model Parameters

[0250] It was next investigated whether the number of parameters (e.g., target proteins) in the predictive model could be reduced, while still retaining diagnostic accuracy. The model was focused on a single target protein, SPC25 (e.g., aptamer X22782.80), which was shown to be highly significant to the initial seven-parameter predictive model (see FIG. 111).

[0251] Samples were processed using the same techniques as Example 2. Samples from the MAP (PIGEON) dataset were utilized, and plasma p-tau217 levels measured using the NULISAseq platfomi from Alamar Bioscience (Fremont, CA) were added (see Ibanez, et al. (medRxiv (2024)). The data was separated to discovery and replication datasets by sample draw year. 70% of discovery data was used for training, and 30% of discovery data was used fortesting, as shown in TABLE 18.

[0252] The predictive model (e.g., Formula I) was improved such that clinical status was determined by age, biological sex, the normalized level of the SPC25 (e.g., TP5) as the only target protein, both with and without the normalized p-Tau217 levels.127108365568.1Docket 047563-870867

[0253] In the discovery test set using SPC25 as the only target protein, the resulting AUC of the predictive model on clinical status was 0.719. The inclusion of p-Tau217 improved the predictive model on clinical status to 0.842 (-0.12 AUC improvement). Results are shown in FIG. 14.

[0254] In the replication dataset using SPC25 as the only target protein, the resulting AUC of the predictive model on clinical status was 0.7. The inclusion of p-Tau217 improved the predictive model on clinical status to 0.761 (-0.06 AUC improvement). Results are shown in FIG. 14.

[0255] The modified model generated coefficients for an equation for calculating predictor values (PV) for use in the methods as described herein (e.g., in Section V):PV = (1.10 x p - Tau217) + (0.492 x TP5) + (0.04 x age)+ (0.222 x biological sex) — 4.45Formula (III).wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female. A cutoff value of 0.346 was determined to be the optimal threshold value for PV comparison for the methods utilizing p-tau217. Calculated coefficients of the prediction model are shown below in TABUE 19.TABLE 20: Coefficients of prediction modelExample 4: Orthogonal validation of the predictive model

[0256] In order to further validate the robustness of the findings, additional platforms were utilized to cross-validate the performance of predictive model. Specifically, the Alamar and Olink platforms were utilized to independently assess the predictive power of the identified proteins. Comparative analyses were conducted between SomaScan and Alamar data from the Knight- ADRC cohort (1,166 cases and 1,595 controls; FIGS. 15A-C), as well as between SomaScan and Olink data from the Stanford- ADRC cohort (94 cases and 596 controls, FIGS. 15D-15F, 16A-C, TABLES 21-22), ALFA cohort at Barcelona Beta Brain Research Center (BBRC; cognitive normal 132 CSF A+, 260 CSF A-, FIGS. 17A-17B,128108365568.1Docket 047563-870867TABLE 25). Both single-analyte and multi-analyte performances were tested. Since none of the platforms include all seven proteins identified in Example 1 , a head-to-head comparison between SomaLogic vs Alamar or Olink using the samples sample, and only the proteins present in each platform. Tire differences in AUC values for both single-protein and multi-protein models were less than 0.07.TABLE 21: Demographic information for the Knight ADRC and Stanford ADRC cohorts Platform Group #Samples Age(SD) %male %APOE4+.nn CO 1595 72.94 (±10.73) 40.00 31.10Knight ADRCR6677.63 (±8.81) 43.48 57.03c. CO 596 71.93 (±7.35) 41.43 23.49Stanford ADRC9470.67 (±11.19) 44.68 63.83TABLE 22: Statistical metrics from the replication analysisCohort Platform Aptamer AUC PPV NPV Specificity Sensitivity Accuracy X10980.ll 0.687 0.527 0.736 0.588 0.685 0.627 SomaLogic X89974 0.650 0.511 0.720 0.473 0.749 0.590 Knight X10980.ll +X8997.4 0.704 0.561 0.728 0.670 0.627 0.653 ADRC ACHE 0.669 0.532 0.711 0.702 0.543 0.641 Alamar NPTXR 0.672 0.549 0.711 0.604 0.663 0.628 ACHE + NPTXR 0.728 0.581 0.754 0.719 0.625 0.683 ACHE 10980.11 0.888 0.431 0.968 0.825 0.830 0.826 CTF1.13732.79 0.857 0.371 0.968 0.772 0.840 0.782 SomaLogic VAT1.18175.65 0.812 0.317 0.948 0.744 0.745 0.744 NPTXR.8997.4 0.853 0.373 0.962 0.783 0.809 0.786 Stanford All Four Proteins 0.952 0.558 0.985 0.885 0.915 0.889 ADRC OID30585_ACHE 0.933 0.553 0.970 0.893 0.830 0.884 OID20061 CTF1 0.788 0.296 0.944 0.722 0.734 0.723 Olink OID21382_VAT1 0.799 0.374 0.937 0.827 0.649 0.802 OID20191_NPTXR 0.847 0.530 0.943 0.907 0.660 0.873 All Four Proteins 0.946 0.780 0.973 0.963 0.830 0.944

[0257] Validation of the ACHE+NPTXR combination was performed using the Alamar platform and observed an AUC of 0.704 based on SomaScan-based protein measure and 0.728 with Alamar (1,166 AD cases and 1,595 controls from the Knight-ADRC, FIGS. 15A-15C, TABLES 21-22). SomaLogic and Olink data generated from 94 cases and 596 controls from the Stanford-ADRC cohort was also analyzed. The predictive power of four proteins that are included on Olink (ACHE, NPTXR, CTF1, and VAT1) was 0.946 compared to 0.952 with SomaScan (FIGS. 15D-15F TABLES 21-22). Additional analyses were also performed including all the Stanford-ADRC Olink data, without the need to have matching SomaLogic data. This approach increased the number of samples that were analyzed (120 AD cases and 725 controls; FIGS. 16A-16C, TABLES 23-24). The predictive power for these four proteins in this extended dataset was 0.874, which is in line, with that of the original model. Similarly, the Somalogic 7-129108365568.1Docket 047563-870867protein prediction model could be replicated using data from the ALFA cohort at the BBRC (AUC=; 0.751; 132 CSF A+, 260 CSF A-; FIGS. 17A-17B, TABLE 25). Data from this study was used to orthogonally replicate this model, as Olink data was also available for this cohort. As in the case of the Stanford dataset only ACHE, NPTXR, CTF1, and VAT1 were present on the ALFA cohort. Therefore, the Somalogic vs tire Olink prediction model was compared including these four proteins. Comparable predictive power was observed using Olink (0.657) and SomaScan (0.695; FIGS. 17A-17B. TABLE 25) These independent validation efforts confirmed the consistency and robustness of the prediction model across platforms.TABLE 23: Demographic information for the Olink platform data in the Stanford ADRC cohort Platform Group #Samples Age(SD) %male %APOE4+CO 725 7210 (±750) 4067 2083ADRCOlinkOID21382 VAT1 0.618 0.265 0.896 0.790 0.454 0.742AL’RCOID20191 NPTXR 0.747 0.280 0.931 0.704 0.689 0.710All Four Proteins 0.874 0.506 0.953 0.879 0.739 0.859TABLE 25: Demographic information for the Olink platform data in the BBRC cohort Platform CSF A Status #Samples Age(SD) % male( lIlTl ,K A- 260 60.47 (±4.47) 38.1A± 132 62.13 (±4.98z) 40.2Materials and MethodsALFA at BarcelonaBeta Brain Research Center (BBRC)

[0258] The ALFA cohort is established as a research platform to understand the early pathophysiological alterations in preclinical AD and is composed of cognitive normal individuals (between 45 and 75 years at baseline), enriched for family history of AD and genetic risk factors for AD. CSF amyloid-P (A|3) status was defined by the CSF A|342 / 40 ratio, and participants were classified as CSF A0-positive (A+) if CSF130108365568.1Docket 047563-870867A042 / 4O < 0.071. A total of 392 participants of the ALFA (Alzheimer and Families) cohort were included in this analysis.Alamar / NULISA proteomics QC

[0259] Protein expression levels were measured using the NUcleic acid Linked Immuno-Sandwich Assay (NULISA) that allows for the simultaneous characterization of over 100 target proteins. Protein concentrations are reported in NULISA Protein Quantification (NPQ) units, which are calculated by normalizing sequence quantification counts for intraplate and intensity variability. These values are subs...

Claims

Docket 047563-870867CLAIMS1. A method of diagnosing Alzheimer's disease in a subject, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) an age of the subject at the obtainment of the blood sample in years:(ii) a biological sex of the subject at the obtainment of the blood sample; and (iii) a normalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein(1 ) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, 0DC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4;(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3. G0T2, UGDH, PLOD2. CTBP1. SETD3, EEF1A1, PMM2. CDK16, ICAM5. ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(6) the sixth target protein is selected from FAHD2A, TATDN3. NPTN.HAVCR1, PCDHGA12, 0LR1, CXCL12, C0L11A2, LECT2, PSME1, RPS6KB1, or THY1; and / or(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, 0DC1, KIR2DS2, CPLX2. FABP4, DCXR, PSMB9, RNF25, S100A12. NPTN,133108365568.1Docket 047563-870867LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, orNPTXR; (c) calculating a predictor value (PV) for diagnosing Alzheimer’s disease based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6. and / or TP7; and(d) diagnosing the subject as having Alzheimer’s disease, wherein(i) the subject is diagnosed as having Alzheimer’s disease when the PV is greater than a threshold level; or(ii) the subject is diagnosed as not having Alzheimer’s disease when the PV is less than or equal to the threshold level.

2. A method of predicting likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in a subject, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) an age of the subject at the obtainment of the blood sample in years;(ii) a biological sex of the subject at the obtainment of the blood sample; and (iii) a normalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein(1) the first target protein is selected from ACHE, PF4, IFT22. ASB9, PLTP, LTBP4, BAG4, PDCD6IP. SPARCL1. OAF. MZF1. 0DC1. TATDN3, SMOC 1 , FLRT2, NRP2, FAM 118 A, or REG4;(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45. EEF1A1. D0K1. RAB1B, SM0C2, PEN, IAPP, CDK16, RAB3C, DSTN, UBE2H, 0DC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1. SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9. CCNYL1, ACAT1, SCO2. or CES1;134108365568.1Docket 047563-870867(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(6) the sixth target protein is selected from FAEID2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY 1: and / or(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A. CTSF, IAPP. GXYLT1, OMG, or NPTXR; (c) calculating a predictor value (PV) for likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and(d) predicting the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject, wherein(i) the subject is predicted to be likely to progress from cognitively normal to symptomatic Alzheimer’s disease when the PV is greater than a threshold level; or(ii) the subject is predicted to be not likely to progress from cognitively normal to symptomatic Alzheimer’s disease when the PV is less than or equal to the threshold level.

3. A method of predicting amyloid / tau (AT) status in a subject, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) an age of the subject at the obtainment of the blood sample in years;(ii) a biological sex of the subject at the obtainment of the blood sample; and (iii) a normalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein135108365568.1Docket 047563-870867(1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4;(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1. SETD3, EEF1A1, PMM2, CDK16, ICAM5. ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(6) the sixth target protein is selected from FAHD2A, TATDN3. NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2. PSME1, RPS6KB1, or THY 1; and / or(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2. FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA. ADAMTSL1, LAS2. CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR: (c) calculating a predictor value (PV) for amyloid / tau (AT) status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and(d) predicting the amyloid / tau (AT) status in the subject, wherein(i) the subject is predicted to have a positive amyloid / tau (AT) status when the PV is greater than a threshold level; or(ii) the subject is predicted to have a negative amyloid / tau (AT) status when the PV is less than or equal to the threshold level.136108365568.1Docket 047563-8708674. A method of predicting brain amyloidosis status in a subject, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) an age of the subject at the obtainment of the blood sample in years;(ii) a biological sex of the subject at the obtainment of the blood sample; and (iii) a normalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein(1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF. MZF1, 0DC1, TATDN3, SMOC1, FLRT2. NRP2, FAM118A. or REG4;(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, 0DC1. MATN2. AGRP. NPTXR, TMEFF1, THY1, SCG3, NMB. GXYLT1, WFIKKN2, HIBCH, SCN2B. GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NEGN1. MENT, GSTA1, SEMA4G, CUZD1. NEFL. GATM, TBCA, or LRRN1;(6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, C0L11A2, LECT2, PSME1, RPS6KB1, or THY1; and / or(7) the seventh target protein is selected from NPTXR. BTG4, FLRT2.TATDN3, NECAP2. TMEM132D. APOM, SCN2B. RPS6KB1.AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, 0DC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A. CTSF, IAPP. GXYLT1, OMG, or NPTXR;137108365568.1Docket 047563-870867(c) calculating a predictor value (PV) for brain amyloidosis status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and(d) predicting the brain amyloidosis status in the subject, wherein(i) the subject is predicted to have a positive brain amyloidosis status when the PV is greater than a threshold level: or(ii) the subject is predicted to have a negative brain amyloidosis status when the PV is less than or equal to the threshold level.

5. A method of predicting plasma p-Tau217 status in a subject, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) an age of the subject at the obtainment of the blood sample in years;(ii) a biological sex of the subject at the obtaimnent of the blood sample; and (iii) a normalized protein level in tire blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2). a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or seventh target protein (TP7), wherein(1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, 0DC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4;(2) the second target protein is selected from CTF 1 , CHL 1 , ADGRF 1 , PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, 0DC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA. or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1. NEFL, GATM, TBCA. or LRRN1:138108365568.1Docket 047563-870867(6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; and / or(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D. APOM, SCN2B, RPS6KB1, AMY2B. DSTN. ENTPD1, FASN. THY1. PEN. R0R1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; (c) calculating a predictor value (PV) for plasma p-Tau217 status based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4. TP5, TP6, and / or TP7; and(d) predicting the plasma p-Tau217 status in the subject, wherein(i) the subject is predicted to have a positive plasma p-Tau217 status when the PV is greater than a threshold level; or(ii) the subject is predicted to have a negative plasma p-Tau217 status when the PV is less than or equal to the threshold level.

6. A method of selecting a subject for inclusion in a clinical trial for Alzheimer’s disease, the method comprising(a) providing a blood sample obtained from the subject:(b) determining(i) an age of the subject at the obtainment of the blood sample in years;(ii) a biological sex of the subject at the obtainment of the blood sample; and (iii) a normalized protein level in the blood sample of one or more target protein selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a si xth target protein (TP6), and / or seventh target protein (TP7), wherein(1) the first target protein is selected from ACFIE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF. MZF1, 0DC1, TATDN3, SMOC1, FLRT2. NRP2, FAM118A. or REG4:(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16,139108365568.1Docket 047563-870867RAB3C, DSTN, UBE2H, 0DC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1. SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9. CCNYL1, ACAT1, SCO2. or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THYE and / or(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA. ADAMTSL1, LAS2. CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A. CTSF, IAPP. GXYLT1, OMG, or NPTXR; (c) calculating a predictor value (PV) for selecting a subject for inclusion in a clinical trial based on the age and biological sex of the subject, and the normalized protein level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and(d) selecting the subject for inclusion in the clinical trial, wherein(i) the subject is selected for inclusion in the clinical trial when the PV is greater than a threshold level; or(ii) the subject is not selected for inclusion in the clinical trial when the PV is less than or equal to the threshold level.

7. The method of any one of claims 1-6, wherein the normalized protein level of the one or more target protein in (b)(iii) is the normalized protein level of TP1, TP2, TP3, TP4, TP5, TP6, and TP7, and the PV in (c) is calculated based on the normalized protein level of TP1, TP2, TP3, TP4, TP5, TP6, and TP7.

8. Tire method of claim 7, wherein the PV in (c) is calculated according to Formula I:140108365568.1Docket 047563-870867PV = (0.618 X TP1) + (0.245 X TP2) + (0.723 X TP3) + (-0.224 x TP4)+ (0.492 x TP5) + (—0.385 x TP6) + (-0.799 x TP7) + (0.05 x age) + (0.182 x biological sex) — 4.696Formula (I),wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female, andthe threshold value is 0.298.

9. The method of any one of claims 1-8, wherein(a) the first target protein is ACHE;(b) the second target protein is CTF 1 ;(c) the third target protein is CPLX2;(d) the fourth target protein is VAT1 ;(e) the fifth target protein is SPC25;(f) the sixth target protein is FAHD2A; and / or(g) the seventh target protein is NPTXR.

10. The method of any one of claims 1-9, wherein the normalized protein level of each target protein is a normal deviate score, standardized score, or Z-score, wherein the Z-score is a nonnalized distribution of the level of the target protein within a population of samples, wherein the normalization sets the mean of the target protein to a value of 0 and sets the standard deviation (SD) of the target protein in the population to a value of 1.

11. The method of claim 10, wherein the Z-score of each target protein is calculated according to Formula (IV)x — mZ = - sFormula (IV),wherein x is the observed level of the target protein in a specific individual, m is the mean level of the target protein within a population; and x is the standard deviation of the target protein in the population.

12. The method of any one of claims 1-11, wherein the method further comprises determining the normalized protein level of plasma p-Tau217 in the blood sample.141108365568.1Docket 047563-87086713. A method of treating Alzheimer’s disease in a subject, comprising administering to the subject a therapeutic agent based on(a) the diagnosis of Alzheimer’s disease in the subject according to claim 1;(b) the prediction of likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject according to claim 2;(c) the prediction of amyloid / tau (AT) status in the subject according to claim 3;(d) the prediction of brain amyloidosis status in the subject according to claim 4;(e) the prediction of plasma p-Tau217 status in the subject according to claim 5; or(f) the selection of the subject for inclusion in the trial according to claim 6.

14. The method of claim 13, wherein the therapeutic agent is an Ap-targeting therapeutic agent, or a tau-targeting therapeutic agent.

15. The method of claim 14, wherein(a) the Ap-targeting therapeutic agent selected from Aducamimab, Donanemab, Lecanemab, Gantenemmab, Solanezumab, or Genezumab; or(b) the tau-targeting therapeutic agent is selected from Gosuranemab, Zagotenemab, Tilavonemab Semorinemab, Buntanetap, E2814, Semorinemab, or Bepranemab.

16. A kit, comprising(a) a means for quantifying the protein level of one or more target protein selected from a first target protein, second target protein, third target protein, fourth target protein, fifth target protein, sixth target protein, and / or seventh target protein, wherein (i) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4. PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SM0C1. FLRT2, NRP2, FAM 118A. or REG4;(ii) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(iii) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, 0DC1, MATN2, AGRP, NPTXR. TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH. SCN2B, GAS1, TFF3. FABP3, FABP4, VEGFA, or CPLX1:142108365568.1Docket 047563-870867(iv) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(v) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(vi) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1; and / or(vii) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1. AMY2B. DSTN, ENTPD1. FASN. THY1, PEN, R0R1, GAS1, ODC1, KIR2DS2. CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; and(b) instructions for(i) diagnosing Alzheimer’s disease in the subject according to claim 1:(ii) predicting the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject according to claim 2:(iii) predicting the amyloid / tau (AT) status in the subject according to claim 3;(iv) predicting the brain amyloidosis status in the subject according to claim 4;(v) predicting the plasma p-Tau217 status in the subject according to claim 5; or (vi) selecting a subject for inclusion in a clinical trial for Alzheimer’s disease according to claim 6.

17. A method of diagnosing Alzheimer's disease in a subject, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) a normalized protein level of plasma p-Tau217 in the blood sample;(ii) an age of the subject at the obtainment of the blood sample in years:(iii) a biological sex of the subject at the obtaimnent of the blood sample; and (iv) a nonnalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP1), a second target protein (TP2). a third143108365568.1Docket 047563-870867target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or a seventh target protein (TP7), wherein (1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, 0DC1, TATDN3, SM0C1, FLRT2. NRP2, FAM118A, or REG4:(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA. or CPLX1:(4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1. NEFL, GATM, TBCA. or LRRN1:(6) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1;(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2.TATDN3, NECAP2, TMEM132D. APOM, SCN2B, RPS6KB1, AMY2B. DSTN. ENTPD1, FASN. THYE PTN. R0R1. GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; (c) calculating a predictor value (PV) for diagnosing Alzheimer’s disease based on the normalized expression level of plasma p-Tau217, age and biological sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and(d) diagnosing the subject as having Alzheimer’s disease, wherein(i) the subject is diagnosed as having Alzheimer’s disease when the PV is greater than a threshold level; or144108365568.1Docket 047563-870867(ii) the subject is diagnosed as not having Alzheimer’s disease when the PV is less than or equal to the threshold level.

18. A method of predicting likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in a subject, the method comprising(a) providing a blood sample obtained from the subject:(b) determining(i) a normalized protein level of plasma p-Tau217 in the blood sample;(ii) an age of the subject at the obtainment of the blood sample in years;(iii) a biological sex of the subject at the obtainment of the blood sample; and (iv) a normalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP 1 ), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or a seventh target protein (TP7), wherein (1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF. MZF1, 0DC1, TATDN3, SM0C1, FLRT2. NRP2. FAM118A. or REG4;(2) the second target protein is selected from CTF1 , CHL1 , ADGRF 1 , PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, 0DC1. MATN2. AGRP. NPTXR, TMEFF1, THYE SCG3, NMB. GXYLT1, WFIKKN2. HIBCH, SCN2B. GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(6) the si xth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, C0L11A2, LECT2, PSME1, RPS6KB1, or THY1:145108365568.1Docket 047563-870867(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, ROR1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A. CTSF, IAPP. GXYLT1, OMG, or NPTXR; (c) calculating a predictor value (PV) for likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease based on the normalized expression level of plasma p-Tau217, age and biological sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and(d) predicting the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject, wherein(i) the subject is predicted to be likely to progress from cognitively normal to symptomatic Alzheimer’s disease when the PV is greater than a threshold level; or(ii) the subject is predicted to be not likely to progress from cognitively nonnal to symptomatic Alzheimer’s disease when the PV is less than or equal to the threshold level.

19. A method of predicting amyloid / tau (AT) status in a subject, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) a normalized protein level of plasma p-Tau217 in the blood sample;(ii) an age of the subject at the obtainment of the blood sample in years:(iii) a biological sex of the subject at the obtainment of the blood sample; and (iv) a nonnalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP1), a second target protein (TP2). a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or a seventh target protein (TP7), wherein (1) the first target protein is selected from ACFIE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, 0DC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4;(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;146108365568.1Docket 047563-870867(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, DOK1, RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3. GOT2.UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM. TBCA, or LRRN1;(6) the sixth target protein is selected from FAHD2A, TATDN3. NPTN.HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY1;(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN. THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2. FABP4, DCXR, PSMB9, RNF25, S100A12. NPTN. LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; (c) calculating a predictor value (PV) for amyloid / tau (AT) status based on the normalized expression level of plasma p-Tau217, age and biological sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3, TP4. TP5, TP6, and / or TP7; and(d) predicting the amyloid / tau (AT) status in the subject, wherein(i) the subject is predicted to have a positive amyloid / tau (AT) status when the PV is greater than a threshold level; or(ii) the subject is predicted to have a negative amyloid / tau (AT) status when the PV is less than or equal to the threshold level.

20. A method of predicting brain amyloidosis status in a subject, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) a normalized protein level of plasma p-Tau217 in the blood sample;147108365568.1Docket 047563-870867(ii) an age of the subject at the obtainment of the blood sample in years;(iii) a biological sex of the subject at the obtainment of the blood sample; and (iv) a normalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or a seventh target protein (TP7), wherein (1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, 0DC1, TATDN3, SMOC1, FLRT2, NRP2, FAM118A, or REG4;(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, 0DC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3. G0T2, UGDH, PLOD2, CTBP1. SETD3, EEF1A1, PMM2, CDK16, ICAM5. ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(6) the sixth target protein is selected from FAHD2A, TATDN3. NPTN, HAVCR1, PCDHGA12, 0LR1, CXCE12, C0L11A2, LECT2. PSME1, RPS6KB 1 , or THY 1 ;(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, 0DC1, KIR2DS2, CPLX2. FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA. ADAMTSL1, LAS2. CYB561D1, ATP1B2. MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, or NPTXR; (c) calculating a predictor value (PV) for brain amyloidosis status based on the normalized expression level of plasma p-Tau217, age and biological sex of the subject, and the148108365568.1Docket 047563-870867normalized expression level of the one or more of TP1, TP2, TP3, TP4, TP5, TP6, and / or TP7; and(d) predicting the brain amyloidosis status in the subject, wherein(i) the subject is predicted to have a positive brain amyloidosis status when the PV is greater than a threshold level: or(ii) the subject is predicted to have a negative brain amyloidosis status when the PV is less than or equal to the threshold level.

21. A method of selecting a subject for inclusion in a clinical trial for Alzheimer’s disease, the method comprising(a) providing a blood sample obtained from the subject;(b) determining(i) a normalized protein level of plasma p-Tau217 in the blood sample;(ii) an age of the subject at the obtainment of the blood sample in years;(iii) a biological sex of tire subject at the obtainment of the blood sample; and (iv) a normalized protein level in the blood sample of one or more target proteins selected from a first target protein (TP1), a second target protein (TP2), a third target protein (TP3), a fourth target protein (TP4), a fifth target protein (TP5), a sixth target protein (TP6), and / or a seventh target protein (TP7), wherein (1) the first target protein is selected from ACHE, PF4, IFT22, ASB9, PLTP, LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, 0DC1, TATDN3, SMOC1, FLRT2. NRP2, FAM118A. or REG4;(2) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(3) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45, EEF1A1, D0K1, RAB1B, SM0C2, PTN, IAPP, CDK16, RAB3C, DSTN, UBE2H, 0DC1. MATN2. AGRP. NPTXR, TMEFF1, THY1, SCG3, NMB. GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(4) the fourth target protein is selected from VAT1, SLITRK3, G0T2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1, ACAT1, SCO2, or CES1;149108365568.1Docket 047563-870867(5) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(6) the sixth target protein is selected from FAEID2A, TATDN3, NPTN, HAVCR1, PCDHGA12, 0LR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB 1, or THY 1 ;(7) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN, ENTPD1, FASN, THY1, PTN, R0R1, GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSL1, LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A. CTSF, IAPP. GXYLT1, OMG, or NPTXR; (c) calculating a predictor value (PV) for inclusion in a clinical trial for Alzheimer’s disease based on the normalized expression level of plasma p-Tau217, age and biological sex of the subject, and the normalized expression level of the one or more of TP1, TP2, TP3, TP4, TP5. TP6, and / or TP7; and(d) selecting the subject for inclusion in a clinical trial for Alzheimer’s disease, wherein (i) the subject is selected for inclusion in the clinical trial when the PV is greater than a threshold level: or(ii) the subject is not selected for inclusion in the clinical trial when the PV is less than or equal to the threshold level.

22. The method of any one of claims 17-21, wherein the normalized protein level of the one or more target protein in (b)(iv) is the normalized protein level of TP1 , TP2, TP3, TP4, TP5, TP6, and TP7, and the PV in (c) is calculated based on the normalized protein level of TP1, TP2, TP3, TP4, TP5, TP6, and TP7.

23. Tire method of claim 22, wherein the PV in (c) is calculated according to Formula II:PV = (1.10 x p - Tau217) + (0.363 x TP1) + (0.245 x TP2)+ (0.723 X TP3) + (-0.224 X TP4) + (0.492 X TP5) + (-0.385 x TP6) + (-0.799 x TP7) + (0.04 x age) -I- (0.222 x biological sex) — 4.45Formula (II),wherein the value for biological sex is 0 when the subject is a male or is 1 when the subject is a female, and150108365568.1Docket 047563-870867the threshold value is 0.346.

24. Tire method of any one of claims 17-23, wherein(a) the first target protein is ACHE;(b) the second target protein is CTF 1 ;(c) the third target protein is CPLX2;(d) the fourth target protein is VAT1 ;(e) the fifth target protein is SPC25;(f) the sixth target protein is FAHD2A; and(g) the seventh target protein is NPTXR.

25. Tire method of any one of claims 17-24, wherein the normalized protein level of each target protein is a normal deviate score, standardized score, or Z-scorc, wherein the Z-scorc is a normalized distribution of the level of the target protein within a population, wherein the normalization sets the mean of tire target protein to a value of 0 and sets the standard deviation (SD) of the target protein in the population to a value of 1.

26. Tire method of claim 25, wherein the Z-score of each target protein is calculated according to Formula (IV)x — mZ = - sFormula (IV),wherein x is the observed level of the target protein in a specific individual, m is the mean level of the target protein within a population; and 5 is the standard deviation of the target protein in the population.

27. A method of treating Alzheimer’s disease in a subject, comprising administering to the subject a therapeutic agent based on(a) the diagnosis of Alzheimer’s disease in the subject according to claim 17;(b) the prediction of likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject according to claim 18;(c) the prediction of amyloid / tau (AT) status in the subject according to claim 19;(d) the prediction of amyloid imaging status in the subject according to claim 20; or (e) the prediction of plasma p-Tau217 status in the subject according to claim 21.151108365568.1Docket 047563-87086728. The method of claim 27, wherein the therapeutic agent is an AP-targeting therapeutic agent, or a tau-targeting therapeutic agent.

29. The method of claim 28, wherein(a) the Ap-targeting therapeutic agent selected from Aducamimab, Donanemab, Lecanemab.Gantenemmab, Solanezumab, or Genezumab; or(b) the tau-targeting therapeutic agent is selected from Gosuranemab, Zagotenemab, Tilavonemab Semorinemab, Buntanetap, E2814, Semorinemab, or Bepranemab.

30. A kit, comprising(a) a means for quantifying the protein level of plasma p-Tau217 in the blood sample, and the protein level of one or more target protein in the blood sample selected from a first target protein, second target protein, third target protein, fourth target protein, fifth target protein, sixth target protein, and / or seventh target protein, wherein(i) the first target protein is selected from ACHE, PF4. IFT22, ASB9, PLTP.LTBP4, BAG4, PDCD6IP, SPARCL1, OAF, MZF1, ODC1, TATDN3, SMOC1, FLRT2, NRP2, FAM 118A, or REG4;(ii) the second target protein is selected from CTF1, CHL1, ADGRF1, PCDHAC2, orNRBPl;(iii) the third target protein is selected from CPLX2, SETD3, GRB10, ARHGAP45.EEF1A1, DOK1, RAB1B, SMOC2, PTN, IAPP, CDK16, RAB3C. DSTN, UBE2H, ODC1, MATN2, AGRP, NPTXR, TMEFF1, THY1, SCG3, NMB, GXYLT1, WFIKKN2, HIBCH, SCN2B, GAS1, TFF3, FABP3, FABP4, VEGFA, or CPLX1;(iv) the fourth target protein is selected from VAT1, SLITRK3, GOT2, UGDH, PLOD2, CTBP1, SETD3, EEF1A1, PMM2, CDK16, ICAM5, ESMI, CPB1, DHRS9, CCNYL1. ACAT1, SCO2, or CES1;(v) the fifth target protein is selected from SPC25, HSPD1, ELAVL2, B3GAT1, NLGN1, MENT, GSTA1, SEMA4G, CUZD1, NEFL, GATM, TBCA, or LRRN1;(vi) the sixth target protein is selected from FAHD2A, TATDN3, NPTN, HAVCR1, PCDHGA12, OLR1, CXCL12, COL11A2, LECT2, PSME1, RPS6KB1, or THY 1 ; and / or(vii) the seventh target protein is selected from NPTXR, BTG4, FLRT2, TATDN3, NECAP2, TMEM132D, APOM, SCN2B, RPS6KB1, AMY2B, DSTN,152108365568.1Docket 047563-870867ENTPD1, FASN, THYE PTN, RORL GAS1, ODC1, KIR2DS2, CPLX2, FABP4, DCXR, PSMB9, RNF25, S100A12, NPTN, LIN7A, VEGFA, ADAMTSLL LAS2, CYB561D1, ATP1B2, MRE11, IFT22, ARL1, REG3A, CTSF, IAPP, GXYLT1, OMG, orNPTXR; and(b) instructions for(i) diagnosing Alzheimer’s disease in the subject according to claim 17;(ii) predicting the likelihood of progression from cognitively normal to symptomatic Alzheimer’s disease in the subject according to claim 18;(iii) predicting the amyloid / tau (AT) status in the subject according to claim 19; or (iv) predicting the brain amyloidosis status in the subject according to claim 20; or (v) selecting a subject for inclusion in a clinical trial for Alzheimer’s disease according to claim 21.

31. All that is disclosed herein.153108365568.1

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