Methods for prognosing and treating acute myeloid leukemia

Abstract

The present disclosure provides methods and compositions for prognosing and treating acute myeloid leukemia. The present disclosure further provides methods and compositions of identifying a prognostic risk comprising detecting the expression level of at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1.

Description

TITLE OF THE INVENTIONMETHODS FOR PROGNOSING AND TREATING ACUTE MYELOID LEUKEMIA CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority of U.S. Provisional Appl. Ser. No. 63 / 573,150, filed April 2, 2024, the entire disclosure of each of which is incorporated herein by reference.FIELD OF THE INVENTION

[0002] This present disclosure relates to the field of cancer therapeutics, and more specifically to methods and compositions for prognosing and treating acute myeloid leukemia.BACKGROUND OF THE INVENTION

[0003] Inflammation plays a critical role in the onset of myeloid malignancies and resistance to treatment. Chronic inflammation and immune dysregulation are associated with myeloid malignancy, including hematopoietic stem cell disfunction, loss of quiescence, myeloid differentiation bias, clonal expansion, and cell proliferation. There remains a continuing need in the art to identify key inflammatory markers for AML prognosis and to inform treatment decisions. The present disclosure describes novel methods and compositions for prognosing and treating AML, including methods and compositions for rapid prognostication of patients with newly diagnosed AML using a simple blood test. This provides a significant advance in the art as current prognostication of AML requires the detection of cytogenetic and molecular aberrations from bone marrow biopsies, which require several weeks to complete.SUMMARY OF THE INVENTION

[0004] In one aspect, the present disclosure provides a method of identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the method comprising: a) detecting the expression level of at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1 in a sample from said subject; b) calculating an Leukemia Inflammatory Risk Score for said sample based on the expression level of said at least two proteins; and c) identifying the prognostic risk of the subject based on said Leukemia Inflammatory Risk Score. In oneembodiment, the method comprises detecting the expression level of at least three, at least four, at least five, at least six, at least seven, or at least eight proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1 in the sample from the subject. In another embodiment, the method comprises detecting the expression level of FGF23, GFAP, IFNL1, MUC16, OSMR, PDGFA, and VSNL1 in the sample from the subject. In yet another embodiment, the method comprises detecting the expression level of FGF23, GFAP, 1L33, MUC16, OSMR, LCN2, PDGFA, and VSNL1 in the sample from the subject. In yet another embodiment, the sample comprises a body fluid. In yet another embodiment, the body fluid is serum or plasma. The Leukemia Inflammatory Risk Score, in one embodiment, is calculated as a weighted sum of the expression level of the at least two proteins. The Leukemia Inflammatory Risk Score, in another embodiment, is calculated using the formula 0.073 (FGF23) + 0.206 (GFAP) + 0.099 (IFNL1) + 0.178 (MUC16) + 0.396 (OSMR) - 0.091 (PDGFA) - 0.326 (VSNL1). The Leukemia Inflammatory Risk Score, in yet another embodiment, is calculated using the formula 0.053 (FGF23) + 0.250 (GFAP) + 0.182 (IL33) + 0. 128 (LCN2) + 0.114 (MUC16) + 0.396 (OSMR) - 0.169 (PDGFA) -0.410 (VSNL1). In yet another embodiment, the at least two proteins are OSMR and VSNL1. The at least three proteins, in still yet another embodiment, are OSMR, VSNL1, and GFAP. The at least four proteins, in one embodiment, are OSMR, VSNL1, GFAP, and MUC16. The at least four proteins, in another embodiment, are OSMR, VSNL1, GFAP, and PDGFA. The at least four proteins, in yet another embodiment, are OSMR, VSNL1, GFAP, and IL33. The at least four proteins, in still yet another embodiment, are OSMR, VSNL1, GFAP, and LCN2. In another embodiment, the method may further comprise identifying the calculated Leukemia Inflammatory Risk Score as a high calculated Leukemia Inflammatory Risk Score, a medium calculated Leukemia Inflammatory Risk Score, or as a low calculated Leukemia Inflammatory Risk Score and administering a first treatment regimen, a second treatment regimen, or a third treatment regimen to the subject. The calculated Leukemia Inflammatory Risk Score, in yet another embodiment, is a low calculated Leukemia Inflammatory Risk Score or a medium calculated Leukemia Inflammatory Risk Score, and the method may comprise administering the first treatment regimen or the second treatment regimen to said subject, wherein the first treatment regimen or the second treatment regimen comprises venetoclax. The calculated Leukemia Inflammatory Risk Score, in still yet another embodiment, is a high calculated Leukemia Inflammatory Risk Score or a medium calculated Leukemia Inflammatory Risk Score, and the method comprises administering the second treatment regimen or the third treatment regimen to said subject, wherein the second treatment regimen or the third treatmentregimen does not comprise venetoclax. In one embodiment, the administering comprises injection, microneedle administration, oral administration, buccal administration, vaginal administration, inhalation, intraosseous administration, trans nasal application, topical administration, transdermal application, or rectal administration. In another embodiment, the method may comprise administering a second therapy to said subject. In yet another embodiment, the second therapy is selected from the group consisting of chemotherapy, radiation therapy, immunotherapy, stem cell transplant, and surgery.

[0005] In another aspect, the present disclosure provides a method of identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the method comprising: a) detecting the expression level of OSMR in a sample from said subject; b) identifying the expression level of OSMR as a high OSMR expression level or as a low OSMR expression level; and c) identifying the prognostic risk of the subject based on said high OSMR expression level or said low OSMR expression level. In one embodiment, the sample comprises a body fluid. In another embodiment, the body fluid is serum or plasma. The identifying the high OSMR expression level, in yet another embodiment, comprises identifying an expression level of OSMR that is higher than about the 95thpercentile compared to an expression level of OSMR identified in a population of healthy individuals. In still yet another embodiment, identifying the high OSMR expression level comprises identifying an expression level of OSMR of at least about 285 ng / ml. The identifying the low OSMR expression level, in one embodiment, comprises identifying an expression level of OSMR of less than about 285 ng / ml. In another embodiment, identifying the low OSMR expression level comprises identifying an expression level of OSMR that is lower than about the 95thpercentile compared to an expression level of OSMR identified in a population of healthy individuals. In one embodiment, the method may further comprise administering a first treatment regimen or a second treatment regimen to said subject. In another embodiment, the expression level of OSMR is a low OSMR expression level, and the method comprises administering the first treatment regimen to said subject, wherein the first treatment regimen comprises venetoclax. In yet another embodiment, the expression level of OSMR is a high OSMR expression level, and the method comprises administering the second treatment regimen to said subject, wherein the second treatment regimen does not comprise venetoclax. In still yet another embodiment, the administering comprises injection, microneedle administration, oral administration, buccal administration, vaginal administration, inhalation, intraosseous administration, trans nasal application, topical administration, transdermal application, or rectal administration. Themethods of the present disclosure, in one embodiment, may further comprise administering a second therapy to said subject. The second therapy, in yet another embodiment, is selected from the group consisting of chemotherapy, radiation therapy, immunotherapy, stem cell transplant, and surgery.

[0006] In another aspect, the present disclosure provides a method of identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the method comprising: a) detecting the expression level of at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1 in a sample from said subject; b) identifying a ELN risk classification of said subject; c) calculating a Leukemia Inflammatory Response Syndrome Index Score for said sample based on the expression level of said at least two proteins and on said ELN risk classification; and d) identifying the prognostic risk of the subject based on said Leukemia Inflammatory Response Syndrome Index Score. The sample, in one embodiment, comprises a body fluid. In another embodiment, the body fluid is serum or plasma. The at least two proteins, in yet another embodiment, are OSMR and VSNL1. In still yet another embodiment, the Leukemia Inflammatory Response Syndrome Index Score is calculated using the formula: a) 0.48 (OSMR) - 0.28 (VSNL1), wherein the ELN risk classification of said subject is favorable; b) 0.778 + 0.48 (OSMR) - 0.28 (VSNL1 ), wherein the ELN risk classification of said subject is intermediate; or c) 1.060 + 0.48 (OSMR) - 0.28 (VSNL1), wherein the ELN risk classification of said subject is adverse. In one embodiment, the method may further comprise identifying the calculated Leukemia Inflammatory Response Syndrome Index Score as a high calculated Leukemia Inflammatory Response Syndrome Index Score or as a low calculated Leukemia Inflammatory Response Syndrome Index Score and administering a first treatment regimen or a second treatment regimen to said subject. The calculated Leukemia Inflammatory Response Syndrome Index Score, in one embodiment, is a low calculated Leukemia Inflammatory Response Syndrome Index Score, and the method comprises administering the first treatment regimen to said subject, wherein the first treatment regimen comprises venetoclax. The calculated Leukemia Inflammatory Response Syndrome Index Score, in another embodiment, is a high calculated Leukemia Inflammatory Response Syndrome Index Score, and the method comprises administering the second treatment regimen to said subject, wherein the second treatment regimen does not comprise venetoclax. In yet another embodiment, the administering comprises injection, microneedle administration, oral administration, buccal administration, vaginal administration, inhalation, intraosseous administration, trans nasalapplication, topical administration, transdermal application, or rectal administration. In still yet another embodiment, the method further comprises administering a second therapy to said subject. The second therapy, in one embodiment, is selected from the group consisting of chemotherapy, radiation therapy, immunotherapy, stem cell transplant, and surgery.

[0007] In yet another aspect, the present disclosure provides a kit for identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the kit comprising at least two antigen binding proteins specific for at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. In one embodiment, the kit comprises a) at least three antigen binding proteins specific for at least three proteins selected from the group consisting of FGF23 , GFAP, IFNL1 , IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; b) at least four antigen binding proteins specific for at least four proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; c) at least five antigen binding proteins specific for at least five proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; d) at least six antigen binding proteins specific for at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; or e) at least seven antigen binding proteins specific for at least seven proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. In another embodiment, the kit comprises at least seven antigen binding proteins specific for at least seven proteins selected from the group consisting of FGF23, GFAP, IFNL1, MUC16, OSMR, PDGFA, and VSNL1. In yet another embodiment, the kit comprises at least eight antigen binding proteins specific for at least eight proteins selected from the group consisting of FGF23, GFAP, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. The at least two proteins, in one embodiment, are OSMR and VSNL1. The at least three proteins, in another embodiment, are OSMR, VSNL1, and GFAP. The at least four proteins, in still yet another embodiment, are OSMR, VSNL1, GFAP, and MUC16. The at least four proteins, in one embodiment, are OSMR, VSNL1, GFAP, and PDGFA. The at least four proteins, in another embodiment, are OSMR, VSNL1, GFAP, and IL33. The at least four proteins, in yet another embodiment, are OSMR, VSNL1, GFAP, and LCN2.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

[0009] FIG. 1 shows an overview of the patient characteristics and data quality for the NULISA assay performed on 362 newly diagnosed AML patients for 251 inflammatory proteins. Panel A, Oncoprint of the cytogenetic groups and 81-gene next generation sequencing (NGS) mutational profiles of the 362 newly diagnosed AML patients in the inhouse discovery cohort. The top histogram displays the number of mutations detected by NGS in each individual patient, whereas the right histogram depicts the frequency of each mutation across the cohort. Panel B, Overall frequency of protein detectability across the 362 samples. Panel C, Individual protein detectability in 362 samples.

[0010] FIG. 2 Kaplan Meier overall survival curve for the seven proteins retained after univariate selection followed by LASSO regression. Protein expression stratified by median to separate high and low groups. Fibroblast growth factor 23 (FGF23). Glial fibrillary acidic protein (GFAP). Interferon lambda 1 / Interleukin-29 (IFNL1). Mucin 16 / CA-125 (MUC16). Oncostatin M Receptor (OSMR). Platelet derived growth factor alpha (PDGFA). Visinin Like 1 (VSNL1).

[0011] FIG. 3 Kaplan Meier overall survival curve for the eight proteins retained after univariate selection followed by LASSO regression. Protein expression stratified by median to separate high and low groups. Fibroblast growth factor 23 (FGF23). Glial fibrillary acidic protein (GFAP). Interleukin-33 (IL33). Mucin 16 / CA-125 (MUC16). Oncostatin M Receptor (OSMR). Lipocalin-2 (LCN2). Platelet derived growth factor alpha (PDGFA). Visinin Like 1 (VSNL1)

[0012] FIG. 4 shows results obtained using the 7 Marker Leukemia Inflammatory Risk Score (LIRS). Panel A, Kaplan Meier overall survival curve of LIRS split into high, medium, and low risk group by score tertile in the training cohort. Panel B, Model performance of LIRS compared with ELN and ELN combined with LIRS by concordance index (C-Index). Panel C, Model performance of LIRS compared with ELN and ELN combined with LIRS by dynamic area under the curve (AUC) over time. Panel D, Kaplan Meier overall survival curve of LIRS split into high, medium, and low risk group by score tertile in the validation cohort.Panel E, Forrest plot of the multivariable cox model incorporating known prognostic factors for AML overall survival and LIRS. Panel F, Kaplan Meier overall survival curve of LIRS when patients were split in into high and low groups based on a cutoff of the 95thpercentile of LIRS scores calculated from a population healthy individuals. LIRS high (> 7.496972 NPQ units); LIRS low (< 7.496972 NPQ units).

[0013] FIG. 5 shows results obtained using the 8 Marker Leukemia Inflammatory Risk Score (LIRS). Panel A, Workflow of machine learning pipeline identifying the most prognostic inflammatory proteins. Panel B, Kaplan Meier survival curves of patients with high, medium, and low LIRS risk separated by score tertile in the training cohort. Panel C, Cubic spline showing the log hazard ratio across the continuous LIRS score in the training cohort. Panel D, Kaplan Meier survival curves of patients in the validation cohort separated by LIRS high, medium, and low risk groups by score tertile. Panel E, Forest plot of the multivariable cox model in validation cohort incorporating significant prognostic factors for overall survival in AML with LIRS.

[0014] FIG. 6 demonstrates that the 8 Marker LIRS further refines ELN. Panel A, Model performance of LIRS, the ELN, and ELN combined with LIRS, by concordance index (C- Index). Panel B, Model performance of LIRS, the ELN, ELN combined with LIRS by timedependent area under the curve (AUC) of the receiver operating characteristic (ROC). Panel C, Kaplan Meier overall survival curves in patients classified as ELN favorable risk split by LIRS high, medium, and low risk groups by LIRS score tertile defined in the training cohort. Panel D, Kaplan Meier overall survival curves in patients of ELN intermediate risk by separated by LIRS score tertile defined in the training cohort. Panel E, Kaplan Meier overall survival curve in ELN adverse risk patients by LIRS score tertile defined in the training cohort. Panel F, Reclassification of LIRS groups for ELN risk group.

[0015] FIG. 7 demonstrates that OSMR is a strong prognostic feature in newly diagnosed AML. Panel A, Cox multivariable model for overall survival using known prognostic clinical variables and the eight LIRS serum proteins. Panel B, LIRS proteins ranked by coefficient and cumulative C-Index.. Panel C, Model performance of OSMR, the ELN, and ELN combined with OSMR by concordance index (C-Index) in training cohort. Panel D, Response rate (complete response (CR) or complete response with incomplete hematologic recovery (CRi)) to induction therapy by high or low OSMR level (split by median). Panel E, Four- and eight- week early mortality rate by high or low OSMR level (split by median). Panel F, Kaplan Meier overall survival curve split by high and low OSMR in the prospectively collected internalvalidation cohort. Panel G, Kaplan Meier overall survival curve split by high and low OSMR in the external validation cohort of intensively treated patients. All patients in this cohort were selected who had received the standard 7+3 induction and had survival of at least 1 year to test for the ability to prognosticate long term survival outcomes independent of early mortality.

[0016] FIG. 8 demonstrates that OSMR is secreted by stomal cells in AML. Panel A, Kaplan Meier overall survival curve of OSMR high and low group for patients with both transcriptomic and proteomic profiling (n=21 ). Panel, B, Enriched HALLMARK pathways based on ranked gene list. Panel C, Volcano plot showing differentially expressed proteins between OSMR high and low group in discovery cohort, prospective cohort and PMCC cohort, respectively. Panel D, Expression of OSM, LIFR6, OSMR, and IL6ST on subtypes of hematopoietic stem and progenitor cell (HSPC) and mesenchymal stromal cells (MSC) in healthy samples. Panel E, OSM-OSMR / IL6ST / LIFR interaction between subtypes of HSPC and MSC. Panel F, Percent of cells positive for surface OSMR expression by flow cytometry comparing AML blasts and AML MSC. Panel G, Multiplex immunofluorescence of AML BM tissue reveals OSMR expression in MSCs, with minimal expression in leukemic cells. This figure depicts an individual cores stained using Opal multiplexed immunohistochemistry showing a representative micrograph of a patient with de novo, treatment-naive CD34+ AML at diagnosis. OSMR expression is prominent in CXCL12+ MSCs but minimal in CD34+ leukemic cells. DAPI serves as a nuclear counterstain. Scale bar: 50 pm. Panel H, Soluble OSMR concentration from culture of AML blasts vs AML MSCs.

[0017] FIG. 9 shows Kaplan Meier curves for survival of patients in the validation cohort (n = 117) with high or low LIRS-IS scores, defined as greater or less than the 50th percentile, respectively.

[0018] FIG. 10 demonstrates that LIRS-IS provides additional prognostication in patients identified as ELN 2022 adverse risk. Top Panel, shows the subgrouping of patients by ELN 2022 risk. Bottom Panel, shows Kaplan Meier curves for survival by LIRS-IS score in patients who were deemed adverse risk by ELN (n = 82).

[0019] FIG. 11 shows Kaplan Meier curves for survival of patients in the validation cohort with high or low LIRS scores (Panel A) or LIRS-IS scores (Panel B), defined as greater or less than the 50th percentile, respectively, who did or did not receive venetoclax as part of their treatment regimen.DETAILED DESCRIPTION OF THE INVENTION

[0020] The present disclosure provides methods and compositions for prognosing and treating acute myeloid leukemia. Inflammation plays a critical role in the onset of myeloid malignancies and resistance to acute myeloid leukemia (AML) therapies. Chronic inflammation and immune dysregulation are associated with myeloid malignancy, including hematopoietic stem cell disfunction, loss of quiescence, myeloid differentiation bias, clonal expansion, and cell proliferation. There remains a continuing need in the art to identify key inflammatory markers for AML prognosis and to inform treatment decisions. The present disclosure describes novel methods and compositions for prognosing and treating AML, including methods and compositions for rapid prognostication of patients with newly diagnosed AML using a simple blood test. This provides a significant advance in the art as currently prognostication of AML requires the detection of cytogenetic and molecular aberrations from bone marrow biopsies, which typically require several weeks to complete.A. Methods and Compositions for Identifying a Prognostic Risk

[0021] In certain aspects the present disclosure provides a method of identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the method comprising: a) detecting the expression level of at least two proteins selected from the group consisting of fibroblast growth factor 23 (FGF23), glial fibrillary acidic protein (GFAP), interferon lambda 1 (IFNL1), interleukin- 33 (IL33), cancer antigen 125 (MUC16), oncostatin M receptor (OSMR), lipocalin-2 (LCN2), platelet-derived growth factor A (PDGFA), and visinin-like protein 1 (VSNL1 ) in a sample from the subject; calculating a Leukemia Inflammatory Risk Score for the sample based on the expression level of the at least two proteins; and identifying the prognostic risk of the subject based on the Leukemia Inflammatory Risk Score. In some embodiments, the methods of the present disclosure may comprise detecting the expression of at least three, four, five, six, seven, or eight proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. The methods of the present disclosure may comprise, in particular embodiments, detecting the expression level of OSMR and VSNL1, the expression level of OSMR, VSNL1, and GFAP, the expression level of OSMR, VSNL1, and GFAP, and MUC16, the expression level of OSMR, VSNL1, GFAP, and PDGFA, the expression level of OSMR, VSNL1, GFAP, and IL33, the expression level of OSMR, VSNL1, GFAP, and LCN2, the expression level of FGF23, GFAP, IFNL1, MUC16, OSMR, PDGFA, and VSNL1, or the expression level of FGF23, GFAP, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1.

[0022] The methods of the present disclosure may comprise, in certain embodiments, detecting the expression level of: 1) OSMR, VSNL1, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, LCN2, and PDGFA; 2) OSMR and GFAP, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of VSNL1, FGF23, 1FNL1, 1L33, MUC16, LCN2, and PDGFA; 3) VSNL1 and GFAP, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of OSMR, FGF23, IFNL1, IL33, MUC16, LCN2, and PDGFA; 4) OSMR, MUC16, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, VSNL1, LCN2, and PDGFA; 5) VSNL1 and MUC16, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or a least six proteins selected from the group consisting of OSMR, FGF23, IFNL1, IL33, OSMR, LCN2, and PDGFA; 6) OSMR and IL33, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, MUC16, LCN2, PDGFA, and VSNL1; 7) OSMR and LCN2, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, PDGFA, and VSNL1; 8) OSMR and PDGFA and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, LCN2, and VSNL1; 9) VSNL1 and IL33, and at least one protein at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, MUC16, OSMR, LCN2, and PDGFA; 10) VSNL1 and LCN2, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, and PDGFA; 11) VSNL1 and PDGFA, and at least one protein, at least two proteins, at least three proteins, at least four proteins, at least five proteins, or at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, and LCN2; or 12) any combination of at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight proteins selected from thegroup consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1.

[0023] OSMR is the beta subunit that heterodimerizes with either interleukin 6 signal transducer (gpl30) to form the type 11 oncostatin M (OSM) receptor or with IL-31 receptor A to form the IL-31 receptor alpha (IL-3 IRA). Both OSM and IL-31 are members of the IL-6 family of cytokines, where OSM binds to the heterodimeric receptor complex consisting of OSMR / gpl30 or leukemia inhibitory factor (LIF) receptor / gpl30 and IL-31 binds to the heterodimeric receptor complex consisting of OSMR / IL-31RA. Signaling through OSMR complexes drives JAK / STAT and MAPK signaling. OSM, in particular, has been shown to have growth inhibition effects on melanoma cells yet the soluble form of OSMR has been previously detected in multiple human cancers and has been described as a decoy receptor in lung adenocarcinomas. While OSM signaling has been described to play a role in normal hematopoiesis, the present disclosure demonstrates for the first time that soluble OSMR has a profound clinical impact in AML. In one embodiment, the present disclosure provides a method of identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the method comprising: a) detecting the expression level of OSMR in a sample from the subject; b) identifying the expression level of OSMR as a high OSMR expression level or as a low OSMR expression level; and c) identifying the prognostic risk of the subject based on the high OSMR expression level or the low OSMR expression level. In certain embodiments, a high OSMR expression level may be indicated by detecting a concentration of OSMR of at least about 250 ng / ml, about 255 ng / ml, about 260 ng / ml, about 265 ng / ml, about 270 ng / ml, about 275 ng / ml, about 280 ng / ml, about 285 ng / ml, about 290 ng / ml, about 295 ng / ml, or about 300 ng / ml, including all ranges and values derivable therebetween. In certain embodiments, a high OSMR expression level may be indicated when the expression level of OSMR is higher than about the 95thpercentile compared to an expression level of OSMR identified in a population of healthy individuals. In some embodiments, a low expression level of OSMR may be indicated by detecting a concentration of OSMR of less than about 250 ng / ml, about 255 ng / ml, about 260 ng / ml, about 265 ng / ml, about 270 ng / ml, about 275 ng / ml, about 280 ng / ml, about 285 ng / ml, about 290 ng / ml, about 295 ng / ml, or about 300 ng / ml, including all ranges and values derivable therebetween. In particular embodiments, a low OSMR expression level may be indicated when the expression level of OSMR is lower than about the 95thpercentile compared to an expression level of OSMR identified in a population of healthy individuals.

[0024] The methods of the present disclosure may be used to identify prognostic risk and / or inform treatment decisions in patients afflicted with or at risk of developing any hematological cancer. Non-limiting examples of hematological cancers include Hodgkin lymphoma, NonHodgkin lymphoma, myeloma, light chain myeloma, non-secretory myeloma, solitary plasmacytoma, extramedullary plasmacytoma, monoclonal gammopathy of undetermined significance, smoldering multiple myeloma, immunoglobulin D myeloma, immunoglobulin E myeloma, acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myeloid leukemia, chronic myeloid leukemia, chronic myelomonocytic leukemia, chronic eosinophilic leukemia, chronic neutrophilic leukemia, essential thrombocythemia, polycythemia vera, and chronic idiopathic myelofibrosis.

[0025] As used herein the term “prognostic risk” refers to the possible outcomes of a subject afflicted with or at risk of developing a cancer. In one embodiment, the subject may be afflicted with or at risk of developing AML. The possible outcomes measured to determine prognostic risk include, but are not limited to, responsiveness to certain treatments, duration or extent of remission, potential survival rate, probability of relapse, and overall survival. In certain embodiments, factors may affect prognostic risk include, but are not limited to, demographic characteristics (e.g., age, race, sex, etc.), disease-specific characteristics (e.g., cancer stage), genetic characteristics (e.g., risk gene), co-morbid conditions (e.g., other conditions accompanying the cancer), and inflammatory marker profile or signature. A favorable prognostic risk, in some embodiments, may indicate that the subject is more likely to be responsive to certain treatments, more likely to survive, or less likely to relapse compared to a patient with an unfavorable prognostic risk. An unfavorable prognostic risk, in particular embodiments, may indicate that the subject is less likely to be responsive to certain treatments, less likely to survive, or more likely to relapse compared to patient with a favorable prognostic risk.

[0026] As used herein the term “signature” refers to a group of RNA molecules, genes, and / or proteins. In some embodiments, the group of RNA molecules, genes, and / or proteins may be related by their association with certain cell types, biological functions, phenotypes, or cellular pathways. A signature, in certain embodiments, refers to a group of inflammatory markers associated with the initiation or progression of AML. A signature, in particular embodiments, may include additional information in addition to information regarding the group of genes or proteins. Such information may include, but is not limited to, patient demographics or disease characteristics. Types of signatures include, but are not limited to an inflammatory markersignature, a Leukemia Inflammatory Risk signature, and a LIRS-IS signature. A “signature score” as used herein refers to a value calculated based on the expression level of a signature. As used herein, the term “expression level,” refers to the detected, expressed, or accumulated amount of a biomarker. Expression levels can be represented, for example, as the amount or the rate of synthesis of a messenger RNA (mRNA) encoded by a gene, the amount or the rate of synthesis of a polypeptide or protein encoded by a gene, or the amount or the rate of synthesis of a biological molecule accumulated in a cell or biological fluid. In certain embodiments, an expression level may refer to an absolute amount of a molecule in a sample or to a relative amount of the molecule in a sample. Expression levels, in particular embodiments, may be determined under steady-state or non-steady-state conditions.

[0027] An “inflammatory marker signature” as used herein refers to a signature that comprises at least two RNA molecules, genes, or proteins associated with inflammation or immune regulation. In one embodiment, an inflammatory marker signature may refer to a signature that comprises at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1, or at least two genes encoding at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. Examples of an inflammatory marker signature include but are not limited to a 7 Marker Leukemia Inflammatory Risk signature, an 8 Marker Leukemia Inflammatory Risk signature, and a LIRS-IS signature. An “inflammatory signature score” as used herein refers to a value calculated based on the expression level of an inflammatory signature.

[0028] An “upregulated” RNA molecule, protein, or gene, as used herein, refers to an RNA molecule, protein, or gene that demonstrates an increased expression level in response to a given treatment or condition, or in certain subject groups. A “downregulated” RNA molecule, protein, or gene refers to an RNA molecule, protein, or gene that demonstrates a decreased expression level in response to a given treatment or condition, or in certain patient groups. In particular embodiments, the expression level of an RNA molecule, protein, or gene can remain unchanged in response to given treatment or condition. A RNA molecule, protein, or gene from a subject may be upregulated, for example, when the expression level of the RNA molecule, protein, or gene is increased at least about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 100%, about 200%, about 300%, about 500%, about 1,000%, or about 5,000%, including all ranges and values derivable therebetween. Similarly, an RNA molecule, protein, or gene maybe downregulated when the expression level of the RNA molecule, protein, or gene is decreased by at least about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 99%, including all ranges and values derivable therebetween.

[0029] The terms “detecting,” “determining,” “measuring,” “evaluating,” “assessing,” and “assaying” as used herein refer to any form of measurement and include detecting or determining whether an element is present or not. These terms include quantitative and / or qualitative determinations. Assays for detecting or determining expression levels are known in the art and any such method may be used according to the embodiments of the present disclosure. Non-limiting examples of such assays include RT-PCR, DNA microarray, RNA- Seq, ELISA, western blot, immunohistochemistry, and protein microarray.

[0030] As used herein the term “sample” refers to a material or mixture of materials containing at least one component of interest. As used herein the term “biological sample” refers to sample obtained from a biological subject. A biological sample of the present disclosure may include, but is not limited to, a body fluid sample, a blood sample, a urine sample, a feces sample, a semen sample, a serum sample, a plasma sample, a saliva sample, a cerebrospinal fluid sample, a cell sample, a tissue sample, and a tumor sample. As used herein the term “healthy” as it relates to a sample, subject, individual, or population refers to a subject, individual, or population that is not afflicted with the disorder or disease being studied. A healthy sample is collected from a subject, individual, or population that is not afflicted with the disease or disorder being studied. In certain embodiments, a healthy subject, individual, or population is not afflicted with a hematological cancer. In one embodiment, a healthy subject, individual, or population is not afflicted with acute myeloid leukemia.

[0031] As used herein, the term “calculating” refers to a determination made using mathematics. In some embodiments, calculating may refer to calculating a weighted sum. As used herein, the term “weighted sum” refers a value calculated by multiplying each value in the set by its weight, then adding the products. An Leukemia Inflammatory Risk Score, in some embodiments of the present disclosure, is calculated as a weighted sum of the expression level of at least two proteins, at least three proteins, at least four proteins, at least five proteins, at least six proteins, or at least seven proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. In some embodiments, a Leukemia Inflammatory Risk Score may be calculated using the formula W 1 (protein 1) ± W2 (protein 2), wherein W1 and W2 represent the weight variable for the expression level ofprotein 1 and protein 2, respectively. In further embodiments, an Leukemia Inflammatory Risk Score may be calculated using a formula selected from the group consisting of: W1 (protein 1) ± W2 (protein 2) ± W3 (protein 3), W1 (protein 1) ± W2 (protein 2) ± W3 (protein 3) ± W4 (protein 4), W1 (protein 1) ± W2 (protein 2) ± W3 (protein 3) ± W4 (protein 4) ± W5 (protein 5), W1 (protein 1) ± W2 (protein 2) ± W3 (protein 3) ± W4 (protein 4) ± W5 (protein 5) ± W6 (protein 6), and W1 (protein 1) ± W2 (protein 2) ± W3 (protein 3) ± W4 (protein 4) ± W5 (protein 5) ± W6 (protein 6) ± W7 (protein 7) ± W8 (protein 8), wherein Wl, W2, W3, W4, W5, W6, W7, and W8 represent the weight variable for the expression level of protein 1, protein 2, protein 3, protein 4, protein 5, protein 6, protein 7, and protein 8, respectively. The weight variable for FGF23, in some embodiments, is about 0.03 to about 0.1, about 0.04 to about 0.06, about 0.06 to about 0.08, about 0.07 to about 0.08, about 0.05 to about 0.06, about 0.05 to about 0.055, or about 0.07 to about 0.075, including all ranges and values derivable therebetween. The weight variable for GFAP, in particular embodiments, is about 0.18 to about 0.28, about 0.19 to about 0.27, about 0.20 to about 0.26, about 0.22 to about 0.26, about 0.24 to about 0.26, about 0.19 to about 0.21, or about 0.195 to about 0.21, including all ranges and values derivable therebetween. In certain embodiments, the weight variable for IFNL1 is about 0.07 to about 0.12, about 0.08 to about 0.11, about 0.09 to about 0.10, or about 0.095 to about 0.10, including all ranges and values derivable therebetween. The weight variable for IL33, in particular embodiments, is about 0. 15 to about 0.21, about 0.16 to about 0.20, or about 0.17 to about 0.19, including all ranges and values derivable therebetween. In some embodiments, the weight variable for LCN2 is about 0.10 to about 0.16, about 0.12 to about 0.14, or about 0.12 to about 0.13, including all ranges and values derivable therebetween. The weight variable for MUC16, in some embodiments, is about 0.090 to about 0.15, about 0.090 to about 0.14, about 0.090 to about 0.15, about 0.10 to about 0.12, about 0.15 to about 0.20, about 0.16 to about 0.20, about 0.17 to about 0.19, or about 0.17 to about 0.18, including all ranges and variable derivable therebetween. In particular embodiments, the weight variable for OSMR is about 0.35 to about 0.45, about 0.36 to about 0.44, about 0.37 to about 0.43, about 0.38 to about 0.42, about 0.39 to about 0.41, or about 0.395 to about 0.405, including all ranges and variable derivable therebetween. The weight variable for PDGFA, in certain embodiments, is about - 0.07 to about -0.20, about -0.070 to about -0.12, about -0.080 to about -0.11, about -0.080 to about -0.10, or about -0.085 to about -0.095, about -0.14 to about -0.20, about -0.15 to about - 0.19, about -0.16 to about -0.18, or about -0.16 to about -0.17, including all ranges and variables derivable therebetween. In some embodiments, the weight variable for VSNL1 is about -0.27 to about -0.50, about -0.30 to about -0.50, about -0.30 to about -0.45,about -0.35 to about -0.45, about -0.40 to about -0.42, about -0.27 to about -0.37, about -0.28 to about -0.36, about -0.29 to about -0.35, about -0.30 to about -0.34, or about -0.31 to about - 0.33, including all ranges and variables derivable therebetween. In one embodiment, a Leukemia Inflammatory Risk Score may be calculated using the formula 0.073 (FGF23) + 0.206 (GFAP) + 0.099 (IFNL1) + 0.178 (MUC16) + 0.396 (OSMR) - 0.091 (PDGFA) - 0.326 (VSNL1). In another embodiment, a Leukemia Inflammatory Risk Score may be calculated using the formula 0.053 (FGF23) + 0.250 (GFAP) + 0.182 (1L33) + 0.128 (LCN2) + 0.114 (MUC16) + 0.396 (OSMR) - 0.169 (PDGFA) - 0.410 (VSNL1). In some embodiments, the values used for each inflammatory protein may represent the concentration of the protein as calculated in NULISA Protein Quantification Units (NPQ).

[0032] In another aspect, the present disclosure provides a method of identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the method comprising: a) detecting the expression level of at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1 in a sample from the subject; b) identifying a ELN risk classification of the subject; c) calculating a Leukemia Inflammatory Response Syndrome Index Score for the sample based on the expression level of the at least two proteins and the ELN risk classification; and d) identifying the prognostic risk of the subject based and the Leukemia Inflammatory Response Syndrome Index Score. As used here the term “ELN risk classification” refers to the risk classification described in Dbhner el al. Blood, 140(12), 2022, the entire disclosure of which is incorporated herein by reference. Table 1 provides a summary of the genetics utilized at initial diagnosis to classify patients as favorable risk, intermediate risk, or adverse risk in Dohner et al. In particular embodiments, the methods of the present disclosure may comprise detecting the expression level of OSMR and VSNL1. In some embodiments, a Leukemia Inflammatory Response Syndrome Index Score may be calculated using the following formula E + W1 (protein 1) + W2 (protein 2), wherein E represents the ELN risk classification variable and W 1 and W2, represent the weight variable for the expression level of protein 1 and protein 2, respectively. In particular embodiments, the ELN risk classification variable is about 0 when the ELN classification of the subject is favorable. The ELN risk classification variable, in certain embodiments, is about 0.70 to about 0.90, about 0.70 to about 0.80, or about 0.75 to about 0.80, including all ranges and variable derivable therebetween, when the ELN classification of the subject is intermediate. When the ELN risk classification of the subject is adverse, in particular embodiments, the ELN risk classification variable is about 0.85 to about1.2, about 0.90 to about 1.10, 0.95 to about 1.10, or about 1.0 to about 1.10, including all ranges and values derivable therebetween. The weight variable for OSMR, in some embodiments, is about 0.40 to about 0.55, about 0.41 to about 0.54, about 0.42 to about 0.53, about 0.43 to about 0.52, about 0.44 to about 0.51, about 0.45 to about 0.50, about 0.46 to about 0.50, or about 0.47 to about 0.49, including all ranges and variables derivable therebetween. In certain embodiments, the weight variable for VSNL1 is about -0.20 to about -0.35, about -0.21 to about -0.34, about -0.22 to about -0.33, about -0.23 to about -0.32, about -0.24 to about -0.31, about -0.25 to about -0.30, about -0.26 to about -0.29, or about -0.27 to about -0.29, including all ranges and variables derivable therebetween. The Leukemia Inflammatory Response Syndrome Index Score, in one embodiment, is calculated using the formula: 0.48 (OSMR) - 0.28 (VSNL1), wherein the ELN risk classification of the subject is favorable. The Leukemia Inflammatory Response Syndrome Index Score, in another embodiment, is calculated using the formula 0.778 + 0.48 (OSMR) - 0.28 (VSNL1), wherein the ELN risk classification of the subject is intermediate. The Leukemia Inflammatory Response Syndrome Index Score, in yet another embodiment, is calculated using the formula 1.060 + 0.48 (OSMR) - 0.28 (VSNLl), wherein the ELN risk classification of the subject is adverse.Table 1. Summary of ELN Classification.

[0033] In yet another aspect, the present disclosure provides a kit for identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the kit comprising at least two antigen binding proteins specific for at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. In certain embodiments, the kit comprises: a) at least three antigen binding proteins specific for at least three proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; b) at least four antigen binding proteins specific for at least four proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; c) at least five antigen binding proteins specific for at least five proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; d) at least six antigen binding proteins specific for at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; or e) at least seven antigen binding proteins specific for at least seven proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. In one embodiment, the kit comprises at least seven antigen binding proteins specific for at least seven proteins selected from the group consisting of FGF23, GFAP, IFNL1, MUC16, OSMR, PDGFA, and VSNL1.In another embodiment, the kit comprises eight antigen binding proteins specific for FGF23, GFAP, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. In one embodiment, the kit comprises at least two antigen binding proteins, wherein the first antigen binding protein is specific for OSMR, and the second antigen binding protein is specific for VSNL1. In another embodiment, the kit comprises at least three antigen binding proteins, wherein the first, second, and third antigen binding proteins are specific for OSMR, VSNL1, and GFAP, respectively. In yet another embodiment, the kit comprises at least four antigen binding proteins, wherein the first, second, third, and fourth antigen binding proteins are specific for a) OSMR, VSNL1, and GFAP, and MUC16, respectively; b) OSMR, VSNL1, GFAP, and PDGFA, respectively; c) OSMR, VSNL1, GFAP, and IL33, respectively; or d) OSMR, VSNL1, GFAP, and LCN2, respectively. In still yet another embodiment, the kit may comprise at least seven antigen binding proteins, wherein the first, second, third, fourth, fifth, sixth, and seventh antigen binding proteins are specific for FGF23, GFAP, IFNL1, MUC16, OSMR, PDGFA, and VSNL1, respectively. In one embodiment, the kit may comprise at least eight antigen binding proteins, wherein the first, second, third, fourth, fifth, sixth, seventh, and eighth antigen binding proteins are specific for FGF23, GFAP, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1, respectively. A kit of the present disclosure may comprise, in certain embodiments, a least one, at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight antigen binding proteins wherein: 1) the first antigen binding protein is specific for OSMR, the second antigen binding protein is specific for VSNL1, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, LCN2, and PDGFA; 2) the first antigen binding protein is specific for OSMR, the second antigen binding protein is specific for GFAP, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of VSNL1, FGF23, IFNL1, IL33, MUC16, LCN2, and PDGFA; 3) the first antigen binding protein is specific for VSNL1, the second antigen binding protein is specific for GFAP, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of OSMR, FGF23, IFNL1, IL33, MUC16, LCN2, and PDGFA; 4) the first antigen binding protein is specific for OSMR, the second antigen binding protein is specific for MUC16, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, IL33, VSNL1, LCN2, and PDGFA; 5) the first antigen binding protein is specific for VSNL1, the second antigen binding proteins is specific for MUC16, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins areeach specific for a protein selected from the group consisting of OSMR, FGF23, IFNL1, IL33, OSMR, LCN2, and PDGFA; 6) the first antigen binding proteins is specific for OSMR, the second antigen binding protein is specific for IL33, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, MUC16, LCN2, PDGFA, and VSNL1; 7) the first antigen binding proteins is specific for OSMR, the second antigen binding proteins is specific for LCN2, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, PDGFA, and VSNL1; 8) the first antigen binding protein is specific for OSMR, the second antigen binding protein is specific for PDGFA, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, LCN2, and VSNL1; 9) the first antigen binding proteins is specific for VSNL1, the second antigen binding protein is specific for IL33, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, MUC16, OSMR, LCN2, and PDGFA; 10) the first antigen binding protein is specific for VSNL1, the second antigen binding protein is specific for LCN2, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, and PDGFA; or 11) the first antigen binding proteins is specific for VSNL1, the second antigen binding protein is specific for PDGFA, and the third, fourth, fifth, sixth, seventh, or eighth binding proteins are each specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, and LCN2 . In particular embodiments, the kit may comprise any combination of at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight antigen binding proteins, wherein each binding protein is specific for a protein selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. In some embodiments, each of the at least two, at least three, at least four, at least five, at least six, or at least seven antigen binding proteins is specific for a different protein selected from the group consisting of F FGF23 , GFAP, IFNL1 , IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1. In one embodiment, the kit comprises a first antigen binding protein specific for FGF23, a second antigen binding protein specific for GFAP, a third antigen binding protein specific for IFNL1, a fourth antigen binding protein specific for MUC16, a fifth antigen binding protein specific for OSMR, a sixth antigen binding protein specific for PDGFA, and a seventh antigen binding protein specific for VSNL1. In another embodiment, the kit comprises a first antigen binding protein specific for FGF23, asecond antigen binding proteins specific for GFAP, a third antigen binding protein specific for IL33, a fourth antigen binding proteins specific for MUC16, a fifth antigen binding proteins specific for OSMR, a sixth antigen binding protein specific for LCN2, a seventh antigen binding protein specific for PDGFA, and an eighth antigen binding protein specific for VSNLL

[0034] Antibodies and antigen binding fragments are both members of the broader genus that includes all antigen binding proteins. The term “antibody” as used herein refers to an intact immunoglobulin of any isotype or an antibody fragment that can compete with an intact antibody for specific binding to the target antigen. An “antigen binding fragment” as used herein refers to refers to a portion of a protein which is capable of binding specifically to an antigen. The term “antigen binding protein” as used herein refers to any protein that binds a specified target antigen. In some embodiments of the present disclosure the specified target antigen is a protein selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1, or fragments of any thereof. An antigen binding protein includes but is not limited to antibodies and antigen binding fragments. Antibodies of the present disclosure may include but are not limited to mouse, rabbit, goat, chicken, rat, chimeric, humanized, fully human, and bispecific antibodies. The antigen binding proteins, antibodies, and binding fragments of the present disclosure may be produced using any technique known in the art. Non-limiting examples of such techniques include production in hybridomas, production by recombinant DNA techniques, and production by enzymatic or chemical cleavage of intact antibodies. An antibody or antigen binding fragment may include, in many embodiments, two full-length heavy chains and two full-length light chains. In some embodiments, an antibody, antigen binding fragment, or an antigen binding protein may include an antibody derivative, an antibody variant, an antibody fragment, or an antibody mutant. Non-limiting examples of antibodies, antigen binding fragments, and antigen binding proteins include monoclonal antibodies, bispecific antibodies, minibodies, domain antibodies, synthetic antibodies, antibody mimetics, chimeric antibodies, humanized antibodies, human antibodies, antibody fusions, antibody conjugates, peptibodies, and fragments thereof.

[0035] The phrase “specifically (or selectively) binds” or “specifically (or selectively) immunoreactive with,” when referring to a protein or peptide, refers to a binding reaction that is determinative of the presence of the protein or complex, often in a heterogeneous population of proteins or complexes. For example, the antigen binding proteins of the present disclosure may specifically bind FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, or VSNL1. FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1 specificantigen binding proteins are known in the art and any such antigen binding protein may be used according to the methods of the present disclosure. Thus, under typical immunoassay conditions, a specified antigen binding protein may bind to a particular protein or complex at least two times the background. In specific embodiments, a specified antigen binding protein may bind a particular protein or complex at least 2, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, or at least 100 times background, including any range or value derivable therebetween. Specific binding to an antigen binding protein under such conditions requires an antigen binding protein that is selected by virtue of its specificity for a particular protein or complex. A variety of assay formats known in the art may be used to select antigen binding protein specifically immunoreactive with a particular protein or complex and any such assay may be used to select an antigen binding protein of the present disclosure. The antigen binding protein of the present disclosure may specifically bind, in particular embodiments, to FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, or VSNL1. In some embodiments, an antigen binding protein of the present disclosure may cross-react with a small number of highly similar antigens. The term “competes” as used herein in the context of antigen binding proteins that compete for the same epitope refers to the competition between antigen binding proteins as determined by an assay in which the antigen binding protein being tested prevents or reduces specific binding of a reference antigen binding protein to a common antigen. Numerous types of competitive binding assays can be used to determine if one antigen binding protein competes with another, for example: solid phase direct or indirect radioimmunoassay (RIA), solid phase direct or indirect enzyme immunoassay (EIA), sandwich competition assay (see, e.g., Stahli et al., 1983, Methods in Enzymology 9:242-253); solid phase direct biotin-avidin EIA (see, e.g., Kirkland et al., 1986, J. Immunol.l37:3614-3619) solid phase direct labeled assay, solid phase direct labeled sandwich assay (see, e.g., Harlow and Lane, 1988, Antibodies, A Laboratory Manual, Cold Spring Harbor Press); solid phase direct label RIA using 1-125 label (see, e.g., Morel et al., 1988, Molec. Immunol.25:7-15); solid phase direct biotin-avidin EIA (see, e.g., Cheung, et al., 1990, Virology 176:546-552); and direct labeled RIA (Moldenhauer et al., 1990, Scand. I. Immunol.32:77-82). In certain embodiments, antigen binding proteins identified by a competition assay (competing antigen binding proteins) include antigen binding proteins that bind to the same epitope as the reference antigen binding protein, and antigen binding proteins binding to an adjacent epitope sufficiently proximal to the epitope bound by the reference antigen binding protein for steric hindrance to occur. In particular embodiments, when acompeting antigen binding protein is present in excess, the competing antigen binding protein will reduce specific binding of a reference antigen binding protein to a common antigen by at least about 40% to about 45%, about 45% to about 50%, about 50% to about 55%, about 55% to about 60%, about 60% to about 65%, about 65% to about 70%, about 70% to about 75%, about 75% to about 85%, about 80% to about 85%, about 85% to about 90%, about 90% to about 95%, or about 95% to about 99%, including all ranges derivable therebetween.

[0036] “Binding affinity” as used herein refers to the strength of the sum total of non-covalent interactions between a single binding site of a molecule (e.g., an antibody) and its binding partner (e.g., an antigen). In some embodiments, the term binding affinity may refer to the intrinsic binding affinity reflecting a 1: 1 interaction between members of a binding pair (e.g., antibody and antigen). The affinity of a molecule X for its partner Y can generally be represented by the dissociation constant (Kd). Binding affinity can be measured by any number of common methods known in the art, and any such method may be used according to the embodiments of the present disclosure. Low-affinity antibodies generally bind antigen slowly and tend to dissociate readily, whereas high-affinity antibodies generally bind antigen faster and tend to remain bound longer.

[0037] The term “antigen” as used herein refers to a substance capable of inducing an adaptive immune response. Antigen binding proteins associated with an adaptive immune response specifically bind to their target antigen. In some embodiments, an antigen may be a molecule that binds to antigen- specific receptors but cannot induce an immune response alone. Nonlimiting examples of antigens include proteins, polysaccharides, and lipids. Antigens, in particular embodiments, may include but are not limited to parts of bacteria (coats, capsules, cell walls, flagella, fimbria, and toxins), viruses, and other microorganisms. In some embodiments, antigens also include tumor antigens that antigens generated by mutations in tumors. Antigens may also include immunogens and haptens.

[0038] The term “epitope” as used herein refers to the specific group of atoms or amino acids of an antigen to which an antigen binding protein specifically binds. In some embodiments, an epitope may be a linear epitope or a conformational epitope. A linear epitope, in particular embodiments, may be formed by a continuous sequence of amino acids of the antigen. A conformational epitope, in certain embodiments, may be comprised of discontinuous sections of the amino acid sequence of an antigen. A linear epitope many interact with an antigen binding protein, in particular embodiments, based on primary structure. A conformational epitope may interact with an antigen binding protein, in certain embodiments, based on the 3Dstructure of the antigen. An epitope, in some embodiments, may be about 3 to about 10, about 4 to about 9, about 4 to about 8, about 4 to about 7, or about 5 to about 6 amino acids in length, including all ranges derivable therebetween. In particular embodiments, two antigen binding proteins may bind the same epitope if they exhibit competitive binding for the antigen.

[0039] The term "isolated " as used herein in reference to, for example, an antibody, antigen binding fragment, an antigen binding protein, a polypeptide, or polynucleotide molecule, refers to a molecule that is not associated with naturally associated components that accompany the molecule in its native state. In some embodiments, an isolated molecule is substantially free of other molecules from the same species, is expressed by a cell from a different species, or is expressed by a different cell type that the cell type in which it is expressed in nature. A molecule that is chemically synthesized or that is expressed in a cellular system different from the cell from which it naturally originates, will be isolated from its naturally associated components. A molecule, in certain embodiments, may also be rendered substantially free of naturally associated components by isolation, using purification techniques well known in the art. Molecule purity or homogeneity may be assayed by a number of means well known in the art. For example, the purity of a polypeptide sample may be assayed using polyacrylamide gel electrophoresis and staining of the gel to visualize the polypeptide using techniques well known in the art. For certain purposes, higher resolution may be provided by using HPLC or other means well known in the art for purification.

[0040] A kit of the present disclosure may, in certain embodiments, include components for any suitable assay platform that may be used to determine the expression level of an RNA molecule or protein in a sample. A kit of the present disclosure may include, in certain embodiments, a dipstick, a membrane, a chip, a disk, a test strip, a filter, a microsphere, a slide, a multi-well plate, an optical fiber, or a solid support system for capturing antigen / antigen binding protein complexes. Examples of solid support systems include, but are not limited to, plastic, silicon, metal, resin, glass, membrane, gel, polymer, sheet, polysaccharide, capillary, film, plate, and slide solid support systems. In one embodiment, the kit may further comprise instructions for use.B. Methods of Treatment and Therapeutic Compositions

[0041] In certain aspects, the present disclosure provides methods, pharmaceutical compositions, and therapeutic compositions for the treatment of cancer. In one embodiment, the cancer is AML. In certain embodiments, the methods of the present disclosure maycomprise administering a therapy selected from the group consisting of chemotherapy, a small molecule inhibitor, an antibody drug conjugate, stem cell transplant, venetoclax, gilteritinib, enasidenib, ivosidenib, and gemtuzumab ozogamicin. In certain embodiments, the methods of present disclosure may comprise identifying a calculated Leukemia Inflammatory Risk Score or a calculated Leukemia Inflammatory Response Syndrome Index Score as a high or low, or as a high, medium, or low score, and administering a first treatment regimen, a second treatment regimen, or a third treatment regimen to the subject. In one embodiment, a high calculated Leukemia Inflammatory Risk Score is defined as greater than the 50thpercentile of the population tested or of a reference population. In another embodiment a low calculated Leukemia Inflammatory Risk Score is defined as less than the 50thpercentile of the population tested or of a reference population. In some embodiments, the methods of present disclosure may comprise identifying a calculated Leukemia Inflammatory Risk Score or a calculated Leukemia Inflammatory Response Syndrome Index Score as a low, medium, or high calculated score. In one embodiment, a low score may be defined as less than about the 33rdpercentile of the population tested or of a reference population. In another embodiment, a medium score may be defined as between about the 33rdpercentile and about the 66thpercentile of the population tested or of a reference population. In yet another embodiment, a high score may be defined as greater than about the 66thpercentile of the population tested or of a reference population. In one embodiment, the LIRS medium value may be about 8.19 NULIS A Protein Quantification (NPQ) units, wherein the range is 6.15 to 11.11 NPQ units. In another embodiment, the 95% confidence interval is about 7.01 to about 9.48 NPQ units. In yet another embodiment, for the three groups in tertile, the high group has a value greater than 8.71 NPQ units, the low group has a value less than or equal to 7.82 NPQ units, and the medium group has a value above 7.82 and below 8.71 NPQ units. In still yet another embodiment, the LIRS score for samples derived from healthy individuals may be determined and the 95% confidence interval calculated. A high LIRS score, in one embodiment, may be defined as a LIRS score that is higher than about the 95thpercentile of the LIRS scores calculated in a population of healthy individuals. A low LIRS score, in another embodiment, may be defined as a LIRS score that is lower than about the 95thpercentile of the LIRS scores calculated in a population of healthy individuals. In still yet another embodiment, the 95thpercentile of LIRS scores from healthy individuals may be about 7.5 NPQ units. The high LIRS score as defined by healthy individuals, in one embodiment, may be greater than or equal to about 7.5 NPQ units. The low LIRS scored as defined by healthy individuals, in another embodiment, may be less than or equal to about 7.5 NPQ units. In a specificembodiment, the high LIRS score is greater than 7.496972 NPQ units and the low LIRS score is less than or equal to about 7.496972 NPQ units.

[0042] In particular embodiments, the first treatment regimen is administered when the calculated Leukemia Inflammatory Risk Score or the calculated Leukemia Inflammatory Response Syndrome Index Score is medium or low. In some embodiments, the second treatment regimen is administered when the calculated Leukemia Inflammatory Risk Score or the calculated Leukemia Inflammatory Response Syndrome Index Score is medium or high. The third treatment regiment, in some embodiments, may be administered when the calculated Leukemia Inflammatory Risk Score or the calculated Leukemia Inflammatory Response Syndrome Index Score is high. The first treatment regimen, in one embodiment, comprises venetoclax. The first treatment regimen, in another embodiment, comprises chemotherapy, a small molecule inhibitor, an antibody drug conjugate, stem cell transplant, venetoclax, gilteritinib, enasidenib, ivosidenib, or gemtuzumab ozogamicin. The second treatment regimen, in another embodiment, comprises venetoclax. The second treatment regimen, in still yet another embodiment, may comprise chemotherapy, a small molecule inhibitor, an antibody drug conjugate, stem cell transplant, venetoclax, gilteritinib, enasidenib, ivosidenib, or gemtuzumab ozogamicin. In one embodiment, the second treatment regimen does not comprise venetoclax. The third treatment regimen, in another embodiment, comprises venetoclax. The third treatment regimen, in still yet another embodiment, may comprise chemotherapy, a small molecule inhibitor, an antibody drug conjugate, stem cell transplant, venetoclax, gilteritinib, enasidenib, ivosidenib, or gemtuzumab ozogamicin. In one embodiment, the third treatment regimen does not comprise venetoclax.

[0043] In some embodiments, a therapy of the present disclosure may be combined with a pharmaceutically acceptable carrier. As used herein, a “pharmaceutically acceptable carrier,” “pharmaceutically acceptable adjuvant,” or “adjuvant” refers to reagents, cells, compounds, materials, compositions, and / or dosage forms that are not only compatible with a therapeutic agent, or other agents to be administered therapeutically, but also are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other complication commensurate with a reasonable benefit / risk ratio. Also included may be an agent that modifies the effect of other agents and is useful in preparing a therapeutic compound or pharmaceutical compound or composition that is generally safe, non-toxic, and neither biologically nor otherwise undesirable. Such an agent may be added to a therapeutic composition or pharmaceuticalcomposition to modify for example the cellular target, cellular localization, or cellular uptake of a therapeutic agent as described herein. Such an agent may include any excipient, diluent, carrier, or adjuvant that is acceptable for pharmaceutical use. Such an agent may be non- naturally occurring, or may be naturally occurring, but not naturally found in combination with other agents in the therapeutic or pharmaceutical composition.

[0044] As used herein, a “therapeutic compound” or “therapeutic composition” refers to a composition comprising a therapeutic agent of the present disclosure. In one embodiment, the composition is capable of reducing, stabilizing, or eliminating tumor growth or tumor progression in a subject. In another embodiment, the composition is capable of reducing, stabilizing, or eliminating tumor size in a subject.

[0045] A compound or composition of the present disclosure is meant to encompass a composition suitable for administration to a subject, such as a mammal, particularly a human subject. In general, a therapeutic composition is sterile, and preferably free of contaminants that are capable of eliciting an undesirable response within the subject (e.g., the compound(s) in the composition are pharmaceutical grade). Therapeutic compositions may be designed for administration to subjects in need thereof via a number of different routes of administration including oral, intravenous, intraarticular, intraarterial, buccal, rectal, parenteral, intraperitoneal, intradermal, intratracheal, intramuscular, subcutaneous, inhalation, vaginal, intraosseous, trans nasal, injection, microneedle, topical, and transdermal. The appropriate dosage of a composition, as described herein, may be determined based on the type of disease to be treated, the severity and course of the disease, the clinical condition of the individual, clinical history, response to the treatment, and the discretion of the attending physician. In some embodiments, therapeutic compositions provided by the present disclosure may include various "unit doses." A unit dose is defined as containing a predetermined quantity of the therapeutic composition. The quantity to be administered, and the particular route and formulation, is within the skill of determination of those in the clinical arts. A unit dose need not be administered as a single injection but may comprise continuous infusion over a set period of time. In some respects, a unit dose comprises a single administrable dose.

[0046] Precise amounts of the therapeutic composition also depend on the judgment of the practitioner and are peculiar to each individual. Factors affecting dose include physical and clinical state of the patient, the route of administration, the intended goal of treatment (alleviation of symptoms versus cure) and the potency, stability and toxicity of the particular therapeutic substance or other therapies a subject may be undergoing.T1

[0047] As used herein, “subject” or “patient” refers to animals, including humans, who are treated with the inhibitors, therapeutic compounds, or compositions or in accordance with the methods described herein. For diagnostic or research applications, a wide variety of mammals may be suitable subjects, including rodents (e.g., mice, rats, hamsters), rabbits, primates, and swine, such as inbred pigs and the like. In particular embodiments, a subject in need of therapy may be any subject who comprises an AML cancer cell as described herein. In another embodiment, the subject may be afflicted with or at risk of developing a cancer as described herein. Non-limiting examples of such diseases or conditions include lung cancer, small cell lung cancer, pulmonary large cell neuroendocrine carcinoma, skin cancer, pancreatic cancer, non-small cell lung cancer (NSCLC), colorectal cancer, appendiceal cancer, hematopoietic cancer, breast cancer, head and neck cancer, prostate cancer, kidney cancer, bladder cancer, liver cancer, esophagus cancer, stomach cancer, thyroid cancer, small bowel adenocarcinoma, hepatobiliary cancer, gynecological cancer, and AML.

[0048] A composition, as described herein, may include, in particular embodiments, a combination of therapeutic agents. In some embodiments, a composition as described here may be administered as a single composition or as more than one composition. Different compositions as provided herein, in certain embodiments, may be administered by the same route of administration or by different routes of administration.

[0049] A pharmaceutical composition of the present disclosure may comprise, in some embodiments, a targeting molecule. In one embodiment, the targeting molecule may be cellspecific or tissue-specific. Numerous such targeting molecules are known in the art and any such targeting molecule may be used according to the present disclosure. In certain embodiments, a composition of the present disclosure may be modified with or conjugated to a peptide, a protein, a colloidal molecule, or a polymer to facilitate delivery or adsorption. The pharmaceutical composition of the present disclosure, in some embodiments, may be serum-free, endotoxin-free, or sterile.

[0050] In certain embodiments, the compositions and methods for treating an individual described herein may be combined with any other composition or method of treatment known in the art. The compositions and methods may be administered in any suitable manner known in the art. For example, a first and a second therapeutic agent or inhibitor may be administered sequentially (at different times) or concurrently (at the same time). In some aspects, a first and a therapeutic agent or inhibitor may be administered in separate compositions. In certainembodiments, a first and a second cancer treatment or inhibitor may be administered in the same composition.

[0051] Non-limiting examples of additional treatment modalities that may be included in combination with the compositions and methods provided herein include a therapeutic agent or surgery. In specific embodiments, the methods and compositions of the present disclosure may be combined with other therapies directed towards the treatment of AML as described herein.

[0052] The term "about" is used to indicate that a value includes the standard deviation of the mean for the device or method being employed to determine the value. The use of the term "or" in the claims is used to mean "and / or" unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive. When used in conjunction with the word "comprising" or other open language in the claims, the words "a" and "an" denote "one or more," unless specifically noted otherwise. The terms "comprise," "have," and "include" are open-ended linking verbs. Any forms or tenses of one or more of these verbs, such as "comprises," "comprising," "has," "having," "includes," and "including," are also open-ended. For example, any method that "comprises," "has," or "includes" one or more steps is not limited to possessing only those one or more steps and also covers other unlisted steps. Similarly, any system or method that "comprises," "has," or "includes" one or more components is not limited to possessing only those components and covers other unlisted components.

[0053] Other objects, features, and advantages of the present disclosure are apparent from detailed description provided herein. It should be understood, however, that the detailed description and any specific examples provided, while indicating specific embodiments of the disclosure, are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this detailed description. Any embodiment of the present disclosure may be used in combination with any other embodiment described herein.

[0054] All references herein are incorporated herein by reference in their entirety.EXAMPLES

[0055] The following examples are included to illustrate embodiments of the present disclosure. It should be appreciated by those of skill in the art that the techniques disclosedin the examples that follow represent techniques discovered by the inventor to function well in the practice of the invention. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

[0056] Inflammation plays a critical role in the onset of myeloid malignancies and resistance to acute myeloid leukemia (AML) therapies. Chronic inflammation and immune dysregulation are associated with myeloid malignancy, including hematopoietic stem cell disfunction, loss of quiescence, myeloid differentiation bias, clonal expansion, and cell proliferation. There remains a continuing need in the art to identify key inflammatory markers for AML prognosis and to inform treatment decisions.Example 1: Identification and Validation of Key Inflammatory Marker Signature for AML Prognosis.

[0057] Serum samples were collected from 362 adult patients with newly diagnosed AML prior to starting therapy. Serum samples were analyzed for 251 inflammatory markers using the NUcleic acid Linked Immuno-Sandwich Assay (NULISA™). An overview of baseline clinical and molecular characteristics of these patients are depicted in FIG. 1, Panel A. The patients were followed clinically for a median of 26 months. The demographics and disease characteristics of the patient population are provided in Table 2. The median age of the cohort was 67 (range: 19 -89) with most patients in this cohort having de novo AML (75.7%) and adverse risk disease by ELN 2022 (64.9%). The most common mutations were in TP53 (31.5%), K / NRAS (26.0%), DNMT3A (19.1%), NPM1 (18.8%), and TET2 (15.2%). 36.7% of patients received intensive induction regimen, while 63.3% received a non-intensive induction regimen. Overall, 58% of patients received an induction regimen containing venetoclax.

[0058] Using the NULISA assay, inflammatory proteins were readily detected in AML patient serum, with an average detectability of 97.3% across all 251 proteins (FIG. 1, Panel B). Only three proteins were detectable in less than 50% of samples, FLT3LG, IL-32, and MDK (FIG. 1, Panel C). To validate the accuracy and validity of the proteomic analysis, the correlationbetween the two overlapping proteins also measured in the clinical lab, EPO and CRP, was assessed at the time of diagnosis. There was a positive correlation between both EPO (R=0.9, p<0.001) and CRP (R=0.79, p<0.001) measured by the clinical lab and by NULISA. Additionally, other known clinical correlations were readily recapitulated including inverse relationships between EPO and hemoglobin (R=-0.33, p<0.001), IL-7 and absolute lymphocyte count (R=-0.43, p<0.001), and positive correlations between FGF23 and serum creatinine, and FGF23 with phosphorus (R=0.42, p<0.001and R=0. 18, p<0.001, respectively). These findings support the clinical validity of multiplexed serum protein measurements with the NULISA assay.

[0059] In addition to the initial AML cohort, serum from 26 adult healthy donors (HD) were also profiled by NULISA. Principal component analyses using all 251 inflammatory proteins revealed distinct clustering of HD and AML patients. Differential expression analysis revealed 73 proteins significantly upregulated, and 55 proteins downregulated in AML as compared to HD. To identify cluster of proteins associated with clinical outcomes, a weighted correlation network analysis (WGCNA) was performed on inflammatory proteins in AML patients. WGCNA revealed five functional inflammatory protein modules. Patients with higher scores in modules 1 and 4 experienced worse overall survival (OS), whereas higher scores in modules 3 and 5 conferred improved outcomes. Protein network analysis revealed multiple interactions between proteins of the interferon family in module 1 and IL-6, LIF, and OSMR in module 4. No survival difference was observed based on module 2 score. These data support differential inflammatory profiles in AML versus healthy individuals, and the prognostic value of inflammatory protein networks in AML.

[0060] The relationship between the 251 inflammatory proteins and known cytogenetic and molecular AML characteristics was also explored. Interestingly, unsupervised hierarchical clustering demonstrated no clear pattern of grouping between ELN 2022 or cytogenetic groups with protein expression patterns. Additionally, by PCA analysis there was no clear pattern within specific cytogenetic or mutational profiles that defined a unique group of inflammatory proteomes. This demonstrates that the inflammatory proteome profiles were largely independent of AML subtype or molecular characteristics, further highlighting the strength of this approach in uncovering AML subgroups beyond the genomic-centric approach.

[0061] In order to select the cytokines that were most predictive of survival, univariate regression for each cytokine was applied. For the 148 cytokines that were significant aftermultiple imputation correction, a LASSO regression narrowed the total to 9 inflammatory markers that were highly predictive of survival. These 9 inflammatory markers are fibroblast growth factor (FGF23) (HR 2.11 95% CI: 1.60 - 2.79 p=1.65e-07), glial fibrillary acidic protein (GFAP) (HR 1.91 95% CI: 1.44 - 2.52 p=5.29e-06), interleukin-33 (IL33) (HR 2.00 95% CI: 1.52 - 2.64, p<0.001) interferon lambda 1 (IFNL1) (HR 1.66 95% CI: 1.27 - 2.21 p=2.30e-04), cancer antigen 125 (MUC16) (HR 2.52 95% CI: 1.90 - 3.34 p=1.46e-10), oncostatin-M receptor (OSMR) (HR 2.15 95% Cl: 1.63 - 2.84 p=5.97e-08), lipocalin-2 (LCN2) (HR 1.96 95% CI: 1.48 - 2.58, p<0.00I), platelet-derived growth factor A (PDGFA) (HR 0.67 95% CI: 0.51 - 0.88 p=4.21e-03), and visinin-like protein 1 (VSNL1) (HR 0.58 95% CI: 0.44 - 0.76 p=9.08e-05) (FIG. 2) (FIG. 3). These associations remained significant in patients treated with either intensive or non-intensive induction regimens, with the exception for GFAP in those treated with non-intensive induction regimens. In one embodiment, 7 of these inflammatory markers (FGF23, GFAP, IFNL1, MUC16, OSMR, PDGFA and VSNL1) were then used (as the 7 Marker Leukemia Inflammatory Risk Score (LIRS)) in a Cox multivariate regression analysis in the training cohort to develop a weighted prediction model of overall survival (OS). In another embodiment, 8 of these inflammatory markers (FGF23, GFAP, IL33, MUC16, OSMR, LCN2, PDGFA and VSNL1 were then used (as the 8 Marker Leukemia Inflammatory Risk Score (LIRS)) in a Cox multivariate regression analysis in the training cohort to develop a weighted prediction model of overall survival (OS). In yet another embodiment, the 7 Marker LIRS is calculated using the formula 0.073 (FGF23) + 0.206 (GFAP) + 0.099 (IFNL1) + 0.178 (MUC16) + 0.396 (OSMR) - 0.091 (PDGFA) - 0.326 (VSNL1). In still yet another embodiment, the 8 Marker LIRS is calculated using the formula 0.053 (FGF23) + 0.250 (GFAP) + 0.182 (IL33) + 0.128 (LCN2) + 0.114 (MUC16) + 0.396 (OSMR) - 0.169 (PDGFA) - 0.410 (VSNL1). In some embodiments, the values used for each inflammatory protein may represent the concentration of the protein as calculated in NULISA Protein Quantification Units (NPQ). There were no significant differences in baseline characteristics between the training and validation cohorts. Overall survival was similar between the training and validation cohort.Table 2. Demographics and Disease Characteristics of Patient Population Utilized to Identify Key Inflammatory Markers for AML Prognosis.

[0062] The 7 Marker LIRS score split AML patients into three prognostically distinct groups based on high, medium, and low score (FIG. 4, Panel A). When used as a continuous variable, increasing 7 Marker LIRS score had a consistent increase in hazard of death in the training and validation cohorts. To test the performance of the 7 Marker LIRS as a predictive model concordance index (C-index) and dynamic AUC of the 7 Marker LIRS compared with ELN 2022 were calculated. The 7 Marker LIRS significantly outperformed ELN 2022 for overall survival and the addition of ELN with the 7 Marker LIRS had the best performance in the training cohort (FIG. 4, Panel B; FIG. 4, Panel C). The 7 Marker LIRS was validated in the validation cohort where it remained highly significant of overall survival (FIG. 4, Panel D). Using a multivariable cox model, the 7 Marker LIRS was independently significant of known clinical and molecular prognostic factors in AML (HR 2.31 95% CI: 1.85 - 2.89, p<0.001) (FIG. 4, Panel E). The 7 Marker LIRS scores were calculated using samples from healthy individuals and the 95% confidence interval calculated. The 7 Marker LIRS scores split AML patients into 2 prognostically significant groups based on low (defined as less than or equal to the 95thpercentile of the LIRS scores from healthy individuals) and high (defined as greater than the 95thpercentile of the LIRS scores from healthy individuals) scores (FIG. 4, Panel F). The 7 Marker LIRS scores were calculated using the following formula: 0.073 (FGF23) + 0.206 (GFAP) + 0.099 (IFNL1) + 0.178 (MUC16) + 0.396 (OSMR) - 0.091 (PDGFA) - 0.326 (VSNL1).

[0063] A Schematic of the workflow of the machine learning pipeline identifying the most prognostic inflammatory proteins is shown if FIG. 5, Panel A. The 8 Marker LIRS split AML patients into three prognostically distinct groups based on high, medium, and low scores (FIG. 5, Panel B). When used as a continuous variable, increasing 8 marker LIRS score was associated with a consistent increase in hazard of death (FIG. 5, Panel C). The 8 Marker LIRS was next validated in the internal validation cohort where cutoff points defined in the training cohort were applied and remained significant in prognostication of OS (FIG. 5, Panel D). Importantly, the 8 Marker LIRS remained prognostic when censoring for allogeneic SCT in first remission (CR1) in all and in the subset of patients treated with intensive induction regimens. These findings demonstrate that the 8 Marker LIRS provides valuable predictive insights into patient outcomes regardless of subsequent transplant status, further underscoring its potential as a robust biomarker for risk stratification in AML management. The 8 Marker LIRS was also prognostic for relapse-free survival (RFS), relapse hazard, and event-free survival (EFS). Furthermore, to determine the independent prognostic value of LIRS, amultivariable cox model was built by first conducting univariate analysis in training cohort on known clinical and molecular prognostic factors. Factors associated with outcome (p<0.05 in univariate analysis) were fitted into the multivariate model with the 8 Marker LIRS in the validation cohort. By multivariable Cox adjustment, the 8 Marker LIRS was independent of known clinical and molecular prognostic factors in AML including age, ELN 2022 risk, total bilirubin, platelets, and creatinine (HR 6.10 for LIRS high 95% CI: 2.26 - 16.47, p<0.001) (FIG. 5, Panel E).

[0064] To test the clinical utility and predictive accuracy of the 8 Marker LIRS, concordance index (C-index) and time-dependent area under the curve (AUC) analysis were compared between the 8 Marker LIRS and ELN 2022 as a benchmark prognostic model. The 8 Marker LIRS outperformed ELN 2022 for OS based on the C-index and AUC in training (FIG. 6, Panel A and Panel B) and validation cohorts, respectively. Furthermore, the addition of ELN with the 8 Marker LIRS mildly improved performance in the training cohort (FIG. 6, Panel A and Panel B) and minimally attenuated the performance in validation cohort, supporting the independent strength of the 8 Marker LIRS in predicting outcomes in AML. Even within each of the ELN 2022 risk groups, LIRS was able to further refine prognosis (FIG. 6, Panels C-F). Using the 8 Marker LIRS, 20% of patients with favorable risk ELN would be re-classified as LIRS high risk experiencing a median OS of less than 3 months. Alternatively, 20% of patients who would be ELN 2022 adverse risk would be re-classified as favorable by LIRS and experienced a median OS of 50 months. This remained true when examining ELN 2022 in only the subset of patients receiving intensive induction where LIRS outperformed ELN 2022. Additionally, when isolating the patients treated with non-intensive induction, LIRS remained significant and further stratified the ELN 2024 model for patients treated with less intensive regimens and LIRS outperformed ELN 2024. Therefore, 8 Marker LIRS demonstrates prognostic significance in patient cohorts regardless of treatment intensity and adds additional prognostic value to current ELN models.Example 2: Validation of OSMR as a Single Prognostic Variable for AML Prognosis.

[0065] To determine which of the 9 proteins in the 7 Marker or 8 Marker LIRS are independently prognostic, they were individually placed into a multivariable cox model. After multivariable correction OSMR (HR 1.58, 95% CI: 1.1.18 - 2.11, p=0.002), PDGFA (HR 0.91, 96% CI: 0.83 - 1.00, PMJ.049) and VSNL1 (HR 0.73, 95% CI: 0.60 - 0.90, p=0.003)remained significantly associated with survival along with age, ELN 2022 intermediate, and ELN 2022 adverse risk (FIG. 7, Panel A)).

[0066] To assess the best performing proteins in LIRS, the proteins were sorted based on their prognostic importance. In the 7 Marker and the 8 Marker LIRS when the proteins were sorted based on their prognostic importance using the C-index, OSMR and VSNL1 were the best performing factors with the highest C-index. OSMR was the best performing factor with additive contributions from the other proteins (FIG. 7, Panel B). Increasing OSMR concentration led to consistent increase in hazard of death. Furthermore, OSMR as a single prognostic variable had a higher C-index than ELN 2022 (FIG. 7, Panel C). OSMR remained significant when censoring for SCT in CR 1 in all and patients treated with intensive induction regimens. OSMR further stratified survival in ELN favorable and adverse risk groups, specifically and OSMR was prognostic for RFS, relapse hazard, and EFS. In the ELN intermediate risk group, OSMR did not appear to be as prognostic of survival, although limited by the smaller sample size. OSMR remained significant when examining ELN 2022 in only the subset of patients receiving intensive induction. Additionally, in the patients treated with non-intensive induction, OSMR remained significant and further stratified the ELN 2024 model for patients treated with less intensive regimens. OSMR also outperformed ELN 2024.

[0067] Given that OSMR is highly predicative of OS in newly diagnosed AML patients and is independent of commonly known prognostic factors, OSMR was investigated for its ability to predict additional relevant clinical endpoints including early mortality and response to induction therapy. Patients with high OSMR levels had lower complete remission (CR) / complete remission with incomplete hematologic recovery (CRi) rates after induction therapy in both intensive (87% vs 65%, p=0.0028) (FIG. 7, Panel D) and non-intensive induction regimens (73% vs 48%, p<0.001) (FIG. 7, Panel D). Additionally, patients with high OSMR had higher rates of early mortality at 4 weeks (12% vs 3%, p=0.0016) and 8 weeks (30% vs 10%, p<0.001) (FIG. 7, Panel E). Given the high sensitivity of target detection of NULIS A, OSMR and VSNL1 were tested in patient samples using commercial ELISA kits. A random subsample of 80 patients from the internal cohort were selected for further testing by commercial ELISA. VSNL1 had very low detectability by ELISA, detected in 10 of 80 samples (12.5%), consistent with its lower expression by the more sensitive NULISA. However, OSMR was robustly detectable in all samples by ELISA. There was a strongcorrelation between OSMR measured by the NULISA and ELISA (r=0.64, p<0.001) and OSMR was highly detectable in AML patients compared to healthy. To establish a reference range, OSMR was quantified in eight healthy serum samples by ELISA. Using a cutoff of above the 95thpercentile of the healthy samples (285 ng / ml), AML patients with high concentration of OSMR had significantly worse OS (p=0.0028).

[0068] To validate the prognostic impact of OSMR, sera was prospectively collected on 68 consecutive newly diagnosed AML patients seen between lanuary 2023 and October 2023 with a median follow up of 9 months. OSMR was further validated as prognostic in this cohort (FIG. 7, Panel F). Next, NULISA was applied to an external validation cohort of 113 newly diagnosed AML patients from PMCC with samples collected prior to receiving 7+3 induction chemotherapy (7 days of cytarabine (AraC; 100 mg / m2 per day by continuous infusion) and 3 days of daunorubicin (DNR; 45 mg / m2 per day, given on days 1, 2, and 3)). With a median follow up of 69 months, OSMR was the strongest predictor of survival in the 7+3 cohort, further validating its prognostic effect in AML across institutions (FIG. 7, Panel G).

[0069] To investigate further biological insights of patients with elevated levels of soluble OSMR bulk RNA-sequencing was performed on a subset of AML patients from the prospective MD Anderson cohort. 21 AML patients were identified from this cohort with peripheral blood samples available. These patients were stratified into “OSMR high” and “OSMR low” groups based on median soluble OSMR expression. The Kaplan Meier curve indicated OSMR high group had a worse outcome, confirming the approach (FIG. 8, Panel A). Differential gene analysis and gene set enrichment analysis showed significant enrichment of IL6-JAK-STAT (NES=1.82; P=0.0003) and inflammatory response (NES=1.44; P=0.0036) pathways in OSMR high group (FIG. 8, Panel B) demonstrating proinflammatory signaling and JAK-STAT pathways enrichment in OSMR high patients. Differential protein expression was then performed based on the OSMR expression in each cohort and upregulated pro-inflammatory cytokines were observed in OSMR high groups, including IL-6 and OSM, which are known to activate downstream JAK / STAT signaling pathways (FIG. 8, Panel C). Finally, to identify the cellular source of OSMR publicly available single cell data from bone marrow capturing hemopoietic and stromal elements was leveraged. Gene expression and cell-cell interaction analysis across different cell types showed that THY1- mesenchymal stromal cells (MSC) and Adipo-MSC had highestexpression of OSMR (FIG. 8, Panel D). Cell-cell communication analysis revealed that OSM- OSMR / IL6ST signaling stems from hematopoietic stem and progenitor cells towards the MSCs, demonstrating the potential source of OSMR from MSCs (FIG. 8, Panel E). To confirm this finding, flow cytometry was performed on primary AML blasts (n=7) and little to no surface expression of OSMR on AML cells was observed (avg OSMR+ 0.57% (FIG. 8, Panel F). However, MSCs cultured from bone marrow of AML patients (n=7) showed strong surface expression of OSMR (avg OSMR+ 84.87%) (FIG. 8, Panel F). This was additionally confirmed using multiplex fluorescent immunohistochemistry on AML bone marrow biopsy samples identifying OSMR expression limited to MSCs (FIG. 8, Panel G). Lastly, it was confirmed that AML MSCs (n=4) secrete OSMR which was detectable by ELISA from culture supernatant but not from AML blasts (n=3) (FIG. 8, Panel H). This data demonstrates that MSCs are the source of sOSMR and that an AML-MSC niche interaction through OSMR that leads to a pro-inflammatory state in AML patients which portends worse survival.Example 3: Validation of a Leukemia Inflammatory Response Syndrome Index Score for AML Prognosis.

[0070] The seven inflammatory markers identified as highly predictive of survival in the 7 Marker Assay of Example 1 were also used together with other clinical variables to develop a weighted prediction model of OS (as the Leukemia Inflammatory Response Syndrome Index Score (LIRS-IS)). Table 3 shows the clinical variable and cytokines evaluated to develop the LIRS-IS. Table 4 provides a summary of the inflammatory markers utilized to calculate the 7 Marker LIRS and LIRS-IS scores, respectively.Table 3. Hazard Ratio of Clinical Variables and 7 Cytokines Fitted in the Cox Multivariate Model.Table 4. Inflammatory Markers in LIRS and LIRS-IS.

[0071] Kaplan Meier curves were constructed for survival of patients in the validation cohort (n = 117) with high or low LIRS-IS scores, defined as greater or less than the 50* percentile, respectively. LIRS-IS were calculated using the following formulas: 0.48 (OSMR) - 0.28 (VSNL1) if favorable ELN; 0.778 + 0.48 (OSMR) - 0.28 (VSNL1) if intermediate ELN; and 1.060 + 0.48 (OSMR) - 0.28 (VSNL1) if adverse ELN. As shown in FIG. 9 a high LIRS-IS predicts significantly worse survival in patients with AML.

[0072] To determine whether the LIRS-IS provides additional prognostication compared to the prognostication provided by ELN 2022 classification, patients were grouped by ELN 2022 risk as well as LIRS-IS score. Kaplan Meier curves for survival by LIRS-IS score in patients who were deemed adverse risk by ELN (n = 82) were constructed. FIG. 10, Top Panel, shows the subgrouping of patients by ELN 2022 risk. As shown in FIG. 10, Bottom Panel, LIRS-IS surprisingly provides additional prognostication in patients identified as ELN 2022 adverse risk.

[0073] Next Kaplan Meier curves were constructed for survival of patients in the validation cohort with high or low LIRS-IS scores, defined as greater or less than the 50th percentile, respectively, who did or did not receive venetoclax as part of their treatment regimen (FIG. 11). As shown in FIG. 11, patients with low LIRS-IS scores respond significantly better to treatment regimens comprising venetoclax compared to patients with high LIRS-IS scores.Example 4: Materials and Methods.

[0074] Sample collection and preparation. In house serum samples were collected prospectively. Blood was collected in red top serum tubes and serum was aliquoted same day without additives into cryovials and stored at -80 °C. External validation plasma samples were collected prospectively. Blood was collected in EDTA tubes and then centrifuged at 3000 rpm (220 g) for 10 minutes at 4 °C. Plasma was aliquoted 1 mL into 1.5 mL tubes and immediately frozen and stored at -80 °C. Healthy serum samples were collected from healthy blood donors that donated blood to the MD Anderson Blood Bank. Serum was collected in red top serum tubes and were aliquoted same day without additives into cryovials and stored at -80 °C. For NULISA testing, samples were thawed and aliquoted, then shipped frozen on dry ice overnight to Alamar Biosciences for data collection. For ELISA testing, the samples were thawed, aliquoted, and ran on the same day.

[0075] Inflammatory proteome analysis and enzyme-linked immunosorbent assay. Methods used for the NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) were generally performed as described in Feng et al. Nature Communications 14(1):7238, 2023. The NULISAseq assay involves conjugating antibodies with partially double-stranded DNA, and forming an immunocomplex with target molecules, which are then captured by paramagnetic oligo-dT beads. The captured immunocomplexes are washed, released, recaptured by streptavidin-coated beads, and undergo DNA ligation to form a new DNA reporter molecule, the levels of which are quantified by next generation sequencing. For NGS,the library was prepared by amplifying the products by 16 cycles of PCR. The library was cleaned using Ampure XP reagent (Beckman Coulter, Indianapolis, IN), following the manufacturer’s protocol. The library was then quantified using the Qubit IX dsDNA HS assay kit (Thermo Fisher, Waltham, MA) before being loaded on a NextSeq 1000 / 2000 instrument (Illumina, San Diego, CA) using a P2 reagent kit (100 cycles).

[0076] For OSMR, the Human OSMR ELISA kit from abeam (catalog number: ab309187) was utilized. Manufacturer recommended protocol was followed for the ELISA protocol. Briefly, a standard curve was made with the standard solution to a high standard of 2,800 pg / mL. Samples were diluted 1:750 in assay buffer and 50 pL of diluted sample was manually pipetted into pre-coated plates. 50 pL of the antibody cocktail was added to each well. The plate was sealed and incubated for 1 hour at room temperature on a plate shaker set to 400 rpm. Each well was then washed with 3 x 350 pL IX Wash Buffer PT with wash buffer remaining in each well for 10 seconds. 100 pL of TMB Development Solution was added to each well and incubated for 10 minutes in the dark on a plate shaker set to 400 rpm. 100 pL of Stop Solution was then added to each well and the plate was placed on a plate shaker for 1 minute to mix. Finally, the OD at 450 nm was recorded on a Synergy Hl (BioTek) instrument.

[0077] For VSNL1, the Human VILIP I VSNL1 (Sandwich ELISA) ELISA kit from LSBio (catalog number: LS-F6528-1) was utilized. Manufacturer recommended protocol was followed for the ELISA protocol. Briefly, a standard curve was made with the standard solution to a high standard of 10 ng / mL. Samples were diluted 1:1 in assay buffer and 100 pL of diluted sample was manually pipetted into pre-coated plates, sealed, and incubated at 37 °C for 1 hour. Liquid was aspirated and 100 pl of Detection Reagent A solution was added to each well, covered and incubated for 1 hour at 37 °C. Each well was then washed with 3 x 350 pL I X Wash Buffer with wash buffer remaining in each well for 10 seconds. 100 pL of Detection Reagent A solution was added to each well, sealed and incubated at 37 °C for 30 minutes. Wells were aspirated and washed 5 times with wash buffer. 90pl of TMB Substrate solution was added to each well, covered, and incubated for 20 minutes at 37 °C. 50pl of Stop Solution was then added to each well. Finally, the OD at 450 nm was recorded on a Synergy Hl (BioTek) instrument.

[0078] NULISAseq data processing and normalization. NGS data were processed using the NULISAseq algorithm (Alamar Biosciences). The sample- and target-specific barcodes were quantified and normalization by dividing the target counts + 1 for each sample well by that well’s internal control (mCherry) counts + 1. Interplate control (IPC) was used for inter-platenormalization. IPC normalization was performed by dividing the counts by the medians of the three target- specific IPC wells on that plate, and then rescaled by the factor 104. IPC- normalized counts were log2-transformed forming the NULISA Protein Quantification (NPQ) units. NPQ units were used for downstream analyses.

[0079] Statistics. For overall survival model development, the 362 AML patients from the in-house cohort were evaluated. For feature selection, all 251 proteins were considered and used in their NPQ units. Each protein was first fitted to the univariate Kaplan- Meier (KM) analysis. Proteins significantly associated with overall survival (OS; p < 0.05) were retained to build the regularized Cox model with least absolute shrinkage and selection operator (LASSO) regression to define a core set of highly predictive features. Grid search and 10- fold cross validation were applied to select the regularization penalty for best accuracy. The proteins with non-zero coefficient further underwent a stepwise selection bi-directionally to obtain the final model for OS. For the within-cohort validation, the whole cohort was split into training set (70%) and validation set (30%). All samples in the training set were used to build the final Cox model and coefficients were extracted to form the formula for the risk scores. The validation set was used to evaluate the performance of the predictive model. The model was further validated using the external cohort of 113 samples from the external cohort.

[0080] Multivariable regularized Cox models were built to evaluate the independent prognostic effect of the LIRS model and individual proteins where known prognostic clinical and molecular variables were included. Model performance was evaluated and compared by concordance index (C-Index) and dynamic area under the curve (AUC). Protein importance was determined by ranked by absolute coefficient from the LIRS model and cumulative C- Index was calculated upon the inclusion of proteins one by one in each iteration. The LIRS model was split into three groups based on the top, middle, and lower third of patients ranked by score. For OSMR by NULISA, high and low OSMR levels were split by median NPQ values. For the OSMR ELISA, 8 healthy serum samples were collected in the same run, high OSMR was defined as an OSMR concentration above the upper 95thpercentile of the healthy data. Hazard ratio (HR) values, 95% confidence intervals (CI) and P values were reported. All statistical tests were two-sided. All statistical analyses were performed using R software (v.4.2.1).* *

[0081] All of the methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this disclosure have been described in terms of preferred embodiments or aspects, it will be apparent to those of skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit, and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

Claims

CLAIMS1. A method of identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the method comprising: a) detecting the expression level of at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUCf6, OSMR, LCN2, PDGFA, and VSNL1 in a sample from said subject; b) calculating a Leukemia Inflammatory Risk Score for said sample based on the expression level of said at least two proteins; and c) identifying the prognostic risk of the subject based on said Leukemia Inflammatory Risk Score.

2. The method of claim 1 , the method comprising detecting the expression level of at least three, at least four, at least five, at least six, at least seven, or at least eight proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNLlin said sample from said subject.

3. The method of claim 1 , the method comprising: a) detecting the expression level of FGF23, GFAP, IFNL1, MUC16, OSMR, PDGFA, and VSNL1 in said sample from said subject; or b) detecting the expression level of FGF23, GFAP, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1 in said sample from said subject.

4. The method of claim 1 , wherein said sample comprises a body fluid.

5. The method of claim 4, wherein the body fluid is serum or plasma.

6. The method of claim 1, wherein the Leukemia Inflammatory Risk Score is calculated as a weighted sum of the expression level of the at least two proteins.

7. The method of claim 1 , wherein the Leukemia Inflammatory Risk Score is calculated using the formula: a) 0.073 (FGF23) + 0.206 (GFAP) + 0.099 (IFNL1) + 0.178 (MUC16) + 0.396 (OSMR) - 0.091 (PDGFA) - 0.326 (VSNL1); orb) 0.053 (FGF23) + 0.250 (GFAP) + 0.182 (IL33) + 0.128 (LCN2) + 0.114 (MUC16) + 0.396 (OSMR) - 0.169 (PDGFA) - 0.410 (VSNL1).

8. The method of claim 1, wherein the at least two proteins are OSMR and VSNL1.

9. The method of claim 2, wherein the at least three proteins are OSMR, VSNL1, and GFAP.

10. The method of claim 2, wherein the at least four proteins are: a) OSMR, VSNL1, GFAP, and MUC16; b) OSMR, VSNL1, GFAP, and PDGFA; c) OSMR, VSNL1, GFAP, and IL33; or d) OSMR, VSNL1, GFAP, and LCN2.

11. The method of claim 1 , the method further comprising: a) identifying the calculated Leukemia Inflammatory Risk Score as a high calculated Leukemia Inflammatory Risk Score or as a low calculated Leukemia Inflammatory Risk Score and administering a first treatment regimen or a second treatment regimen to said subject; or b) identifying the calculated Leukemia Inflammatory Risk Score as a high calculated Leukemia Inflammatory Risk Score, a medium calculated Leukemia Inflammatory Risk Score, or as a low calculated Leukemia Inflammatory Risk Score and administering a first treatment regimen, a second treatment regimen, or a third treatment regiment to said subject.

12. The method of claim 11, wherein the calculated Leukemia Inflammatory Risk Score is a low calculated Leukemia Inflammatory Risk Score or a medium calculated Inflammatory Risk Score, and the method comprises administering the first treatment regimen or the second treatment regimen to said subject, wherein the first treatment regimen or the second treatment regimen comprises venetoclax.

13. The method of claim 11, wherein the calculated Leukemia Inflammatory Risk Score is a high calculated Leukemia Inflammatory Risk Score or a medium calculated Leukemia Risk Score, and the method comprises administering the second treatment regimen or the third treatment regimen to said subject, wherein the second treatment regimen or the third treatment regimen does not comprise venetoclax.

14. The method of claim 11, wherein said administering comprises injection, microneedle administration, oral administration, buccal administration, vaginal administration, inhalation, intraosseous administration, trans nasal application, topical administration, transdermal application, or rectal administration.

15. The method of claim 11, further comprising administering a second therapy to said subject.

16. The method of claim 15, wherein said second therapy is selected from the group consisting of chemotherapy, radiation therapy, immunotherapy, stem cell transplant, and surgery.

17. A method of identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the method comprising: a) detecting the expression level of OSMR in a sample from said subject; b) identifying the expression level of OSMR as a high OSMR expression level or as a low OSMR expression level; and c) identifying the prognostic risk of the subject based on said high OSMR expression level or said low OSMR expression level.

18. The method of claim 17, wherein said sample comprises a body fluid.

19. The method of claim 18, wherein the body fluid is serum or plasma.

20. The method of claim 17, wherein identifying the high OSMR expression level comprises identifying an expression level of OSMR that is higher than about the 95thpercentile compared to an expression level of OSMR identified in a population of healthy individuals.

21. The method of claim 17, wherein identifying the low OSMR expression level comprises identifying an expression level of OSMR that is lower than about the 95thpercentile compared to an expression level of OSMR identified in a population of healthy individuals.

22. The method of claim 17, the method further comprising administering a first treatment regimen or a second treatment regimen to said subject.

23. The method of claim 22, wherein the expression level of OSMR is a low OSMR expression level, and the method comprises administering the first treatment regimen to said subject, wherein the first treatment regimen comprises venetoclax.

24. The method of claim 22, wherein the expression level of OSMR is a high OSMR expression level, and the method comprises administering the second treatment regimen to said subject, wherein the second treatment regimen does not comprise venetoclax.

25. The method of claim 22, wherein said administering comprises injection, microneedle administration, oral administration, buccal administration, vaginal administration, inhalation, intraosseous administration, trans nasal application, topical administration, transdermal application, or rectal administration.

26. The method of claim 22, further comprising administering a second therapy to said subject.

27. The method of claim 26, wherein said second therapy is selected from the group consisting of chemotherapy, radiation therapy, immunotherapy, stem cell transplant, and surgery.

28. A kit for identifying a prognostic risk in a subject afflicted with or at risk of developing acute myeloid leukemia, the kit comprising at least two antigen binding proteins specific for at least two proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1 .

29. The kit of claim 28, wherein the kit comprises: a) at least three antigen binding proteins specific for at least three proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; b) at least four antigen binding proteins specific for at least four proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; c) at least five antigen binding proteins specific for at least five proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; d) at least six antigen binding proteins specific for at least six proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1; ore) at least seven antigen binding proteins specific for at least seven proteins selected from the group consisting of FGF23, GFAP, IFNL1, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1.

30. The kit of claim 28, wherein the kit comprises: a) at least seven antigen binding proteins specific for at least seven proteins selected from the group consisting of FGF23, GFAP, IFNL1, MUC16, OSMR, PDGFA, and VSNL1; or b) at least eight antigen binding proteins specific for at least eight proteins selected from the group consisting of FGF23, GFAP, IL33, MUC16, OSMR, LCN2, PDGFA, and VSNL1.

31. The kit of claim 28, wherein the at least two proteins are OSMR and VSNL1.

32. The kit of claim 29, wherein the at least three proteins are OSMR, VSNL1, and GFAP.

33. The kit of claim 29, wherein the at least four proteins are: a) OSMR, VSNL1, GFAP, and MUC 16; b) OSMR, VSNL1, GFAP, and PDGFA; c) OSMR, VSNL1, GFAP, and IL33; or d) OSMR, VSNL1, GFAP, and LCN2.

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