Attorney Docket No.243735.000296 METHODS FOR IMPROVED RISK STRATIFICATION OF ADULT AND PEDIATRIC ACUTE MYELOID LEUKEMIA PATIENTS USING INFLAMMATION GENE SIGNATURES CROSS REFERENCE TO RELATED APPLICATION [0001] This patent application claims priority to U.S. Provisional Application No.63/424,822, filed on November 11, 2022, the disclosure of which is incorporated by reference herein in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH [0001] This invention was made with government support under Grant Nos. R01 CA 173636, R01 CA 228135, R01 CA 242020, and R01 HL 159175 awarded by National Institutes of Health (NIH). The government has certain rights in the invention. FIELD [0002] The disclosed technology relates to methods for improved risk stratification of Acute Myeloid Leukemia (AML) patients, and more particularly, for improved risk stratification of adult and pediatric AML patients using inflammation gene signatures (iScore). BACKGROUND [0003] AML is the most common acute leukemia in adults, and accounts for approximately 15% of acute leukemias in children. Despite the approval of multiple targeted therapies, treatment options remain limited and survival is dismal [1]. The bone marrow (BM) microenvironment plays an important role in supporting myeloid cell transformation and the clonal outgrowth of AML. Disease progression is accompanied by changes in the BM mesenchymal niche [2], however the immune response to AML establishment and progression in the BM has not been thoroughly characterized. [0004] Inflammation is one of the hallmarks of cancer [3], and is associated with many types of solid malignancies [4]. In AML, inflammation has been linked to progression from myelodysplastic syndrome to AML [5]. Additionally, it was shown that inflammatory cytokines can regulate hematopoietic stem cells and promote disease progression in animal models [6]. Furthermore, several mutations in genes associated with myeloid malignancies have been shown to render hematopoietic stem cells more susceptible to inflammation [7]. As AML is a disease 1 165016996v1 Attorney Docket No.243735.000296 prevalent mainly in older individuals, age-induced inflammation may contribute to AML development in elderly patients. In solid malignancies, inflammation is often associated with a unique immune microenvironment, and can affect response to immunotherapy and patient prognosis [8]. However, the effects of inflammation on the composition of the BM immune microenvironment and clinical outcomes in AML have not been demonstrated. In addition, attempts to target immune cell function in AML have so far yielded only modest results [9, 10], highlighting the need for further understanding of the AML immune microenvironment and the role of inflammation in AML. [0005] Significant advances in single cell technologies have facilitated generation of high- resolution maps of healthy tissues and malignancies [11-14]. These cell atlases have led to important insights into disease development, as well as identification of novel therapeutic targets. In AML, single cell RNA-sequencing (scRNA-Seq) of adult AML BM samples has revealed distinct differentiation hierarchies and connected them to specific oncogenic drivers [15]. Moreover, single cell DNA sequencing, combined with single cell epitope quantification, has yielded insights into clonal evolution of AML, and identified surface markers associated with specific mutations [16]. SUMMARY [0006] As specified in the Background, there is a need for improved risk stratification and treatment of Acute Myeloid Leukemia (AML) patients. The present disclosure addresses these and other needs by providing methods and compositions as specified below. [0007] In one aspect, the present disclosure provides a method for determining a survival risk for an adult human subject diagnosed with AML, the method comprising: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR, and b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4. In some embodiments, the method further comprises c) 2 165016996v1 Attorney Docket No.243735.000296 comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort. In some embodiments, the method further comprises d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0008] In another aspect, the present disclosure provides a method of treating an adult human subject diagnosed with AML, the method comprising: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TY-ROBP, and VSIR; b) calculating an inflammation- associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort, and d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0009] In some embodiments of any of the above methods, the method comprises determining the expression level of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 genes. [0010] In some embodiments of any of the above methods, the adult human subject is at least 21 years old. [0011] In some embodiments of any of the above methods, the reference cohort is The Cancer Genome Atlas (TCGA) cohort, Beat AML cohort, or Alliance cohort. [0012] In another aspect, the present disclosure provides a method for determining a survival risk for a pediatric human subject diagnosed with AML, the method comprising: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are 3 165016996v1 Attorney Docket No.243735.000296 selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA- DPA1, COMMD3, and HSP90AA1, and b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 5. In some embodiments, the method further comprises c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort. In some embodiments, the method further comprises d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0013] In a further aspect, the present disclosure provides a method of treating a pediatric human subject diagnosed with AML, the method comprising: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 5; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort, and d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0014] In some embodiments of any of the above methods involving pediatric subjects, the method comprises determining the expression level of at least 3, 4, 5, 6, 7, 8, 9, 10, or 11 genes. [0015] In yet another aspect, the present disclosure provides a method for determining an event- free survival risk for a pediatric human subject diagnosed with AML, the method comprising: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1, and b) calculating an 4 165016996v1 Attorney Docket No.243735.000296 inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 6. In some embodiments, the method further comprises c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort. In some embodiments, the method further comprises d) classifying the subject into a high risk group for event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for event- free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0016] In a further aspect, the present disclosure provides a method of treating a pediatric human subject diagnosed with AML, the method comprising: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 6; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort, and d) classifying the subject into a high risk group for event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0017] In some embodiments of any of the above methods involving pediatric subjects, the method comprises determining the expression level of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 genes. [0018] In some embodiments of any of the above methods involving pediatric subjects, the pediatric subject is 0-21 years old. [0019] In some embodiments of any of the above methods involving pediatric subjects, the reference cohort is Therapeutically Applicable Research To Generate Effective Treatments (TARGET) cohort or the Netherlands microarray cohort. 5 165016996v1 Attorney Docket No.243735.000296 [0020] In some embodiments of any of the above methods, the reference cohort is an age-matched group of subjects known to have AML with known survival outcomes. [0021] In some embodiments of any of the above methods, the method further comprises administering a more aggressive treatment if the subject classifies into a high risk group or a less aggressive treatment if the subject classifies into a low risk group. In some embodiments, the more aggressive treatment comprises a stem cell transplantation at the first complete remission. In some embodiments, the more aggressive treatment comprises an intensified chemotherapy, targeted inhibitors, non-targeted inhibitors alone or in combination with hypomethylating agents, antibodies, or any combinations thereof. In some embodiments, the less aggressive treatment comprises a standard chemotherapy (e.g., administering cytarabine and/or anthracyclines). [0022] In some embodiments of any of the above methods, the method further comprises administering a stem cell transplantation at the first complete remission to the subject classified into a high risk group. In some embodiments, the stem cell transplant is selected from an allogenic transplant, an autologous transplant, and any combinations thereof. In some embodiments, the stem cell transplant is from a matched-related donor, matched-unrelated donor, or haploidentical donor. [0023] In some embodiments of any of the above methods, the gene expression level is determined by measuring mRNA level. In some embodiments, the mRNA level is determined using RNA sequencing, qPCR, panel sequencing, or an array. [0024] In some embodiments of any of the above methods, the subject sample is selected from bone marrow, peripheral blood, tissue biopsy, and cerebrospinal fluid (CSF). [0025] In some embodiments of any of the above methods, the subject is a newly diagnosed patient. In some embodiments of any of the above methods, a patient at relapse after a treatment. [0026] In another aspect, the present disclosure provides a method for determining the risk of an adult human subject developing a condition associated with clonal hematopoiesis, wherein the adult human subject is diagnosed with clonal hematopoiesis, the method comprising: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR, 6 165016996v1 Attorney Docket No.243735.000296 and b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4. In some embodiments, the method further comprises c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort. In some embodiments, the method further comprises d) classifying the subject into a high risk group for developing a condition associated with clonal hematopoiesis if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for developing a condition associated with clonal hematopoiesis if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0027] In another aspect, the present disclosure provides a method for treating or preventing a condition associated with clonal hematopoiesis in an adult human subject in need thereof, wherein the adult human subject is diagnosed with clonal hematopoiesis, the method comprising: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR, b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort, and d) classifying the subject into a high risk group for developing a condition associated with clonal hematopoiesis if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for developing a condition associated with clonal hematopoiesis if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0028] In some embodiments, the condition associated with clonal hematopoiesis is a cardiovascular disease, a cardiac event, chronic kidney disease, or chronic liver disease. In some embodiments, the cardiovascular disease is atherosclerosis, coronary artery disease, or venous thromboembolic disease. In some embodiments, the cardiac event is a myocardial infarction. 7 165016996v1 Attorney Docket No.243735.000296 [0029] In some embodiments, the method comprises determining the expression level of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 genes. [0030] In some embodiments, the adult human subject is at least 21 years old. [0031] In some embodiments, the reference cohort is The Cancer Genome Atlas (TCGA) cohort, Beat AML cohort, or Alliance cohort. [0032] In some embodiments, the method further comprises administering one or more anti- inflammatory therapies, if the subject classifies into a high risk group. [0033] In some embodiments, the gene expression level is determined by measuring mRNA level. In some embodiments, the mRNA level is determined using RNA sequencing, qPCR, panel sequencing, or an array. [0034] In some embodiments, the subject sample is selected from bone marrow and blood. [0035] In some embodiments, the subject is a newly diagnosed patient. [0036] In another aspect, the present disclosure provides a composition comprising a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR. [0037] In yet another aspect, the present disclosure provides a kit comprising (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR, (ii) optionally, one or more reagents for quantifying mRNA level, and (iii) optionally, instructions for use. [0038] In a further aspect, the present disclosure provides an array comprising probes complementary and/or hybridizable to at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, 8 165016996v1 Attorney Docket No.243735.000296 PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR. [0039] In another aspect, the present disclosure provides a composition comprising a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA- DPA1, COMMD3, and HSP90AA1. [0040] In a further aspect, the present disclosure provides a kit comprising (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1, (ii) optionally, one or more reagents for quantifying mRNA level, and (iii) optionally, instructions for use. [0041] In yet another aspect, the present disclosure provides an array comprising probes complementary and/or hybridizable to at least two genes selected from COTL1, GSN, HGF, HLA- DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1. [0042] In another aspect, the present disclosure provides a composition comprising a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1. [0043] In a further aspect, the present disclosure provides a kit comprising (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1, (ii) optionally, one or more reagents for quantifying mRNA level, and (iii) optionally, instructions for use. [0044] In another aspect, the present disclosure provides an array comprising probes complementary and/or hybridizable to at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1. [0045] In a separate aspect, the present disclosure provides a survival risk stratification and/or therapy guidance software tool for adult and/or pediatric human subjects diagnosed with AML 9 165016996v1 Attorney Docket No.243735.000296 comprising non-transitory computer readable medium with instructions thereon, that when executed by a processor, cause the processor to: a) provide a user interface into which a plurality of gene expression values can be entered, the gene expression values being for a plurality of genes, wherein the plurality of genes are at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR, or at least two genes selected from COTL1, GSN, HGF, HLA- DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1, or at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; b) calculate an inflammation-associated gene expression score (iScore) for the subject based on the weighted sum of the plurality of gene expression values, wherein the coefficient value for each gene is as provided in Tables 4-6; c) calculate a risk stratification score for the subject based at least in part on a comparison of the subject iScore to a median iScore of a reference cohort; and d) provide, at the user interface, a visual representation of the risk stratification score. In some embodiments, the subject is an adult subject or a pediatric subject. In some embodiments, the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the iScore of the subject. [0046] In another aspect, the present disclosure provides a condition development risk stratification and/or preventative measure guidance software tool for adult human subjects diagnosed with clonal hematopoiesis comprising non-transitory computer readable medium with instructions thereon, that when executed by a processor, cause the processor to: a) provide a user interface into which a plurality of gene expression values can be entered, the gene expression values being for a plurality of genes, wherein the plurality of genes are at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; b) calculate an inflammation-associated gene expression score (iScore) for the subject based on the weighted sum of the plurality of gene expression values, wherein the coefficient value for each gene is as 10 165016996v1 Attorney Docket No.243735.000296 provided in Table 4; c) calculate a risk stratification score for the subject based at least in part on a comparison of the subject iScore to a median iScore of a reference cohort; and d) provide, at the user interface, a visual representation of the risk stratification score. In some embodiments, the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the iScore of the subject. [0047] Further implementations, features, and aspects of the disclosed technology, and the advantages offered thereby, are described in greater detail hereinafter, and can be understood with reference to the following detailed description, accompanying drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS [0048] Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and which illustrate various implementations, aspects, and principles of the disclosed technology. [0049] Figures 1A-1H illustrate the single cell landscape of adult and pediatric AML, according to example implementations of the disclosed technology. (1A) UMAP projection of healthy donors (n=10), adult (n=20) and pediatric (n=22) AML BM cells. (1B) Split UMAP projection of healthy donors, adult and pediatric AML cells, annotated by cell type based on transcriptome and surface protein expression. (1C) UMAP representation of cells from healthy donors (control) or AML patients, highlighting patient-specific clusters in AML. (1D) InferCNV heatmap demonstrating copy gains in chromosomes 1, 5, 8 and 19 for sample AML 3050. (1E) UMAP projection of control, CNV ⁺ and CNV- cells from sample AML 3050. (1F) Quantification of malignant and microenvironment (ME) cells from sample AML 3050. (1G) UMAP projection of healthy donors, malignant and microenvironment cells from AML patients, following inferCNV and occupancy score analysis. (1H) Split UMAP projection of annotated cells from healthy donors, malignant and microenvironment populations in the BM. All UMAP projections are based on the same coordinates. [0050] Figure 2A-2E illustrate inflammatory pathways in malignant AML cells, according to example implementations of the disclosed technology. (2A) UMAP representation of healthy donor HSPC and myeloid cells and malignant cells from adult and pediatric AML patients. (2B) UMAP representation of healthy donor and malignant cells, annotated by cell type. (2C) UMAP representation of cells expressing inflammation-related features identified by NMF. (2D) Volcano 11 165016996v1 Attorney Docket No.243735.000296 plots depicting genes enriched (right) or depleted (left) in malignant HSPC from adult and pediatric AML patients. (2E) Volcano plots depicting genes enriched (right) or depleted (left) in malignant myeloid cells from adult and pediatric AML patients. [0051] Figures 3A-3G illustrate atypical B cells are associated with high inflammation in AML, according to example implementations of the disclosed technology. (3A) Split UMAP projection of B cells from healthy donors (n=10), adult (n=20) and pediatric (n=22) AML BM, annotated based on transcriptome and surface protein expression. (3B) Quantification of atypical B cells in healthy donors (n=10), and AML (n=30) BM. Wilcoxon test was used to evaluate statistical significance. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (3C) Correlation between the atypical B cell signature and the inflammation signature in the Alliance cohort (adult patients, n=872) and the TARGET-AML cohort (pediatric patients, n=157). (3D) Representative FACS plot showing gating strategy for atypical B cells in BM aspirates. (3E) Quantification of FACS analysis of atypical B cells in BM aspirates from low inflammation (n=4) and high inflammation (n=4) AML patients. Error bars represent standard deviation. T-test was used to evaluate statistical significance. (3F) Heatmap of genes upregulated or downregulated in atypical B cells from AML patients, compared to control. (3G) CD72 surface protein expression on atypical B cells from control (n=2) and AML patients (n=10). Wilcoxon test was used to evaluate statistical significance. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5 x IQR. All statistical tests shown in this figure are two-sided. [0052] Figures 4A-4K illustrate T cell responses in human AML, according to example implementations of the disclosed technology. (4A) Split UMAP projection of T and NK cells from healthy donors (n=10), adult (n=20) and pediatric (n=22) AML patients. (4B) Quantification of cytotoxic CD8 ⁺ T cells in healthy donors, pediatric and adult AML patients. HD_Y – healthy donors 19-26 years old (n=5), HD_O – healthy donors 39-55 years old (n=5), PED – pediatric AML (n=22), AD – adult AML (n=20). Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. Kruskal- Wallis test was used to evaluate statistical significance in multi-group comparison, whereas Wilcoxon test was used for two-group comparisons. (4C) Quantification of T _Reg in healthy donors, pediatric and adult AML patients. Box plots represent the median with the box bounding the 12 165016996v1 Attorney Docket No.243735.000296 interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. Kruskal- Wallis test was used to evaluate statistical significance in multi-group comparison, whereas Wilcoxon test was used for two-group comparisons. (4D) Quantification of T _Reg in low (n=12) or high inflammation (n=6) pediatric AML patients. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. Wilcoxon test was used to evaluate statistical significance. (4E) Quantification of GZMK ⁺ CD8 ⁺ T cells in low (n=12) or high inflammation (n=6) pediatric AML patients. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. Wilcoxon test was used to evaluate statistical significance. (4F) Heatmap of expression of the T _PEx gene signature in CD8+ T cells in the BM. (4G) Pie charts representing the fraction of small (0-1%), large (1-10%) or hyperexpanded (10- 100%) T cell clones in controls and AML patients (adult - n=7, pediatric – n=3). Younger HD – Healthy donors 19-22 years old (n=3), older HD – healthy donors 43-55 years old (n=2). (4H) UMAP projection of T cells from healthy donors (n=5), adult (n=7) and pediatric (n=3) AML patients, annotated based on transcriptome. (4I) UMAP projection of T cell clones from healthy donors (n=5), adult (n=7) and pediatric (n=3) AML patients. (4J) Quantification of CD8 ⁺ subsets from expanded clones for sample AML 0134. A full list of expanded clonotypes is shown in Table 10. (4K) Clonal diversity in low inflammation (n=75) and high inflammation (n=76) AML patients from the TCGA cohort. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. Wilcoxon test was used to evaluate statistical significance. All statistical tests shown in this figure are two-sided. [0053] Figures 5A-5L illustrate iScore associates with distinct subsets of human AML, according to example implementations of the disclosed technology. (5A) t-SNE representation of bulk RNA- seq data of adult AML patients in the Alliance cohort. (5B) t-SNE representation of bulk RNA- seq data of pediatric AML patients in a large bulk RNA-seq cohort. (5C) Adult iScore in bulk RNA-seq data of patients in the Alliance cohort. (5D) Pediatric iScore in bulk RNA-seq data of patients in a pediatric bulk RNA-seq cohort. (5E) Overall survival of high and low iScore adult AML patients in the Alliance cohort. Log rank test was used to evaluate significance. (5F) Overall survival of high and low iScore pediatric AML patients in the TARGET-AML cohort. Log rank test was used to evaluate significance. (5G) Overall survival of adult ELN Favorable high and low iScore patients in the Alliance cohort. Log rank test was used to evaluate significance. (5H) Overall 13 165016996v1 Attorney Docket No.243735.000296 survival of adult ELN Intermediate high and low iScore patients in the Alliance cohort. Log rank test was used to evaluate significance. (5I) Overall survival of adult ELN Adverse high and low iScore patients in the Alliance cohort. Log rank test was used to evaluate significance. (5J) 8-year predicted overall survival in low, intermediate and high risk patients in a pediatric cohort. (5K) Event free survival in high and low iScore patients in the Alliance AML cohort. Log rank test was used to evaluate significance. (5L) Event free survival in high and low iScore pediatric patients. Log rank test was used to evaluate significance. [0054] Figures 6A-6E illustrate cell populations in the human bone marrow, according to example implementations of the disclosed technology. (6A) Heatmap of average expression of top RNA cluster markers for different cell subsets in the BM (left), heatmap of average expression of surface protein markers for different cell subsets in the BM (right). HSC – hematopoietic stem cells, MPP – multipotent progenitors, GMP – granulocyte-monocyte progenitors, MEP – megakaryocyte progenitors, LymP – lymphoid progenitors, DC – dendritic cells, Ery – erythrocytes. (6B) Quantification of HSPC subsets in the BM. HD_Y – Healthy donors 19-26 years old (n=5), HD_O – healthy donors 39-55 years old (n=5), PED – pediatric AML patients (n=22), AD – adult AML patients (n=20). Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (6C) Quantification of myeloid subsets in the BM. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (6D) Quantification of B cell subsets in the BM. (6E) Quantification of conventional (CD4 ⁺ , CD8 ⁺ ), non-conventional (MAIT, JG) and NK cells in the BM. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. All statistical tests shown in this figure are two-sided. Pair-wise comparisons were evaluated using Wilcoxon test, multi-group comparisons were evaluated using Kruskal-Wallis test. [0055] Figures 7A-7C illustrate separation of malignant and microenvironment cells in AML samples, according to example implementations of the disclosed technology. (7A) InferCNV heatmaps for patients with clinically annotated chromosome gains or losses. (7B) InferCNV heatmaps for healthy donor BM samples. (7C) UMAP projection of Healthy donors, CNV+ and CNV- cells from patients with annotated chromosome gains or losses (left), quantification of malignant and microenvironment cells for each sample (right). 14 165016996v1 Attorney Docket No.243735.000296 [0056] Figures 8A-8C illustrate validation of occupancy score methods, according to example implementations of the disclosed technology. (8A) UMAP projection depicting cell clustering for calculation of occupancy scores. (8B) UMAP projection of occupancy score. (8C) UMAP projection of malignant and microenvironment cells based on occupancy scores (left) or single cell genotyping (right). [0057] Figures 9A-9B illustrate non-annotated karyotype aberrations detected by InferCNV, according to example implementations of the disclosed technology. (9A) InferCNV heatmaps for patient samples with non-annotated karyotype aberrations. (9B) Patient-by-patient quantification of broad cell types in malignant cells. [0058] Figure 10 illustrates pathogenic programs in AML, according to example implementations of the disclosed technology. UMAP projections of cells expressing different gene expression programs identified by NMF. [0059] Figures 11A-11G illustrate inflammatory signatures in AML, according to example implementations of the disclosed technology. (11A) Pathway analysis for genes in the adult (left) and pediatric (right) inflammation signatures. (11B) Overlap between genes in the adult and pediatric inflammation signatures. (11C) Pearson correlation between age and inflammation score in the adult AML cohort. (11D) Inflammation score in older controls (n=5) and adult AML patients (n=20) in the single cell cohort. Dashed line represents cutoff for high or low inflammation. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (11E) Inflammation score in younger controls (n=5) and pediatric AML patients (n=22). Dashed line represents cutoff for high or low inflammation. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (11F) Heatmap of average expression of the pediatric inflammation signature in malignant cells from pediatric patients. Max CT – maximum cell count. Infants – 0-3 years old (n=6), children – 3-12 years old (n=9), teens – 12-21 years old (n=7). (11G) Heatmap of average expression of the adult inflammation signature in malignant cells from adult patients. Max CT – maximum cell type. [0060] Figures 12A-12I illustrate inflammatory B cells in AML, according to example implementations of the disclosed technology. (12A) Heatmap of average expression of surface protein markers in different B cell subsets. CLP – common lymphoid progenitor. (12B) Heatmap of average expression of RNA markers in different B cell subsets. (12C) Quantification of Atypical 15 165016996v1 Attorney Docket No.243735.000296 B cells split by young healthy donor (n=5), older healthy donors (n=5) adult (n=14) and pediatric (n=19) AML patients. Note the reduction in patient numbers due to exclusion of patients with less than 50 B cells in the BM. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (12D) Pearson correlation between the atypical B cell gene signature and the adult inflammation signature in the TCGA cohort (n=152). (12E) UMAP representation of B cells from wild type (WT, n=7) and Tet2 mutant (n=11) mouse BM. (12F) UMAP representation of WT and Tet2 mutant cell distribution in B cell clusters. (12G) Heatmap showing expression of the mouse atypical B cell gene signature in B cell clusters in WT (n=7) and Tet2 (n=11) mutant mouse BM. (12H) Quantification of atypical B cells in aged WT (n=3) or Tet2 mutant mice (n=7). Mild – mild disease, severe – severe disease. Statistical tests in this panel are two-sided. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (12I) Inflammation scores of samples used for FACS validation of atypical B cell expansion in high inflammation AML BM. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. All pair-wise comparisons were evaluated using Wilcoxon test, multi-group comparisons were evaluated using Kruskal-Wallis test. [0061] Figures 13A-13C illustrate T cell responses in AML, according to example implementations of the disclosed technology. (13A) Heatmap of average expression of surface protein markers in different T cell subsets. T _CM – central memory T cells; T _Reg – regulatory T cells; T _RM – resident memory T cells. (13B) Heatmap of average expression of RNA markers in different T cell subsets. (13C) Quantification of T cell subsets in high and low inflammation AML patients. [0062] Figures 14A-14D illustrate clonal expansion of T cells in AML, according to example implementations of the disclosed technology. (14A) Gating strategy for sorting of T cells from AML or healthy donor BM aspirates. (14B) Pie charts representing the fraction of small (0-1%), large (1-10%) and hyperexpanded (10-100%) clones in individual samples. (14C) Quantification of CD8 ⁺ subsets from expanded clones in AML patients. (14D) Clonal diversity in infants (0-3 years old, n=37), children (3-12 years old, n=59) and teens (12-21 years old, n=49) from the TARGET-AML bulk RNA-Seq cohort. All statistical tests shown in this figure are two-sided. All box plots represent the median with the box bounding the interquartile range (IQR) and whiskers 16 165016996v1 Attorney Docket No.243735.000296 showing the most extreme points within 1.5× IQR. All pair-wise comparisons were evaluated using Wilcoxon test. [0063] Figures 15A-15I illustrate clinical implications of inflammation in AML, according to example implementations of the disclosed technology. (15A) Overall survival of high and low inflammation adult AML patients in the Alliance cohort. Log rank test was used to evaluate significance. (15B) Overall survival of high and low inflammation pediatric AML patients in the TARGET-AML cohort. Log rank test was used to evaluate significance. (15C) Distribution of the iScore in adult AML patients in the Alliance cohort, by risk (Adverse risk - n=274, Intermediate risk - n=176, Favorable - n=359). Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (15D) Distribution of the iScore in pediatric AML patients in the TARGET cohort, by risk (High risk – n=105, intermediate risk – n=95, low risk – n=136). Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. (15E) Overall survival association of iScore and LSC17 in adult AML patients assessed by global test. (15F) Overall survival association of iScore and other prognostic predictors in pediatric AML patients assessed by global test. (15G) Overall survival in high and low iScore patients in the TCGA AML cohort (<60 yrs). (15H) 8-year predicted overall survival (OS) in favorable, intermediate and adverse risk adult AML patients in the BeatAML cohort, based on iScore. (15I) 8-year predicted overall survival (OS) in low, intermediate and high risk pediatric patients in a pediatric microarray cohort, based on iScore. [0064] Figures 16A-16E illustrate effect of iScore on event free survival in AML, according to example implementations of the disclosed technology. (16A) Event free survival in high and low iScore Favorable risk patients in adult patients in the TCGA AML cohort (<60 yrs). Log rank test was used to evaluate significance. (16B) Event free survival in pediatric patients in a microarray cohort. Log rank test was used to evaluate significance. (16C) Event Free survival in high and low iScore favorable risk patients in the Alliance AML cohort. Log rank test was used to evaluate significance. (16D) Event free survival in high and low iScore intermediate risk patients in the Alliance AML cohort. Log rank test was used to evaluate significance. (16E) Event free survival in high and low iScore adverse risk patients in the Alliance AML cohort. Log rank test was used to evaluate significance. 17 165016996v1 Attorney Docket No.243735.000296 DETAILED DESCRIPTION [0065] As specified in the Background Section, there is great need for improving upon methods used for classifying AML patients to determine optimal methods of treatment. As such, embodiments of the present disclosure relate generally to methods for improved risk stratification of AML patients, and more particularly, for improved risk stratification of adult and pediatric AML patients using iScore. Definitions [0066] To facilitate an understanding of the principles and features of the various embodiments of the invention, various illustrative embodiments are explained below. Although exemplary embodiments of the invention are explained in detail, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the invention is limited in its scope to the details of construction and arrangement of components set forth in the following description or examples. The invention is capable of other embodiments and of being practiced or carried out in various ways. Also, in describing the exemplary embodiments, specific terminology will be resorted to for the sake of clarity. [0067] It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural references unless the context clearly dictates otherwise. For example, reference to a component is intended also to include composition of a plurality of components. References to a composition containing “a” constituent is intended to include other constituents in addition to the one named. In other words, the terms “a,” “an,” and “the” do not denote a limitation of quantity, but rather denote the presence of “at least one” of the referenced item. [0068] As used herein, the term “and/or” may mean “and,” it may mean “or,” it may mean “exclusive-or,” it may mean “one,” it may mean “some, but not all,” it may mean “neither,” and/or it may mean “both.” The term “or” is intended to mean an inclusive “or.” [0069] Also, in describing the exemplary embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents which operate in a similar manner to accomplish a similar purpose. It is to be understood that embodiments of the disclosed technology may be practiced without these specific details. In other instances, well-known methods, 18 165016996v1 Attorney Docket No.243735.000296 structures, and techniques have not been shown in detail in order not to obscure an understanding of this description. References to “one embodiment,” “an embodiment,” “example embodiment,” “some embodiments,” “certain embodiments,” “various embodiments,” etc., indicate that the embodiment(s) of the disclosed technology so described may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrase “in one embodiment” does not necessarily refer to the same embodiment, although it may. [0070] As used herein, the term "about" should be construed to refer to both of the numbers specified as the endpoint (s) of any range. Any reference to a range should be considered as providing support for any subset within that range. Ranges may be expressed herein as from “about” or “approximately” or “substantially” one particular value and/or to “about” or “approximately” or “substantially” another particular value. When such a range is expressed, other exemplary embodiments include from the one particular value and/or to the other particular value. Further, the term “about” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within an acceptable standard deviation, per the practice in the art. Alternatively, “about” can mean a range of up to ±20%, preferably up to ±10%, more preferably up to ±5%, and more preferably still up to ±1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 2-fold, of a value. Where particular values are described in the application and claims, unless otherwise stated, the term “about” is implicit and in this context means within an acceptable error range for the particular value. [0071] Throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., 19 165016996v1 Attorney Docket No.243735.000296 as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range. [0072] By “comprising” or “containing” or “including” is meant that at least the named compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, method steps, even if the other such compounds, material, particles, method steps have the same function as what is named. [0073] It is noted that terms like “specifically,” “preferably,” “typically,” “generally,” and “often” are not utilized herein to limit the scope of the claimed invention or to imply that certain features are critical, essential, or even important to the structure or function of the claimed invention. Rather, these terms are merely intended to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the present invention. [0074] The dimensions and values disclosed herein are not to be understood as being strictly limited to the exact numerical values recited. Instead, unless otherwise specified, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as “50 mm” is intended to mean “about 50 mm.” [0075] As used herein, the term “subject” or “patient” refers to mammals and includes, without limitation, humans and animals, e.g., horses, cats, and dogs. In a preferred embodiment, the subject is human, and most preferably a human that has been diagnosed with AML. [0076] As used herein, the term “reference cohort” refers to an age-matched group of subjects known to have AML with known survival outcomes. [0077] The terms “sample”, “subject sample” and “test sample” are used herein to refer to any fluid, cell, or tissue sample from a subject which can be assayed for determining gene expression level. In some embodiments, the sample may include bone marrow (BM), peripheral blood (PB), tissue biopsy, cerebrospinal fluid (CSF), or white blood cells (WBCs) obtained from peripheral blood (PB) or bone marrow (BM). [0078] As used herein, the term “weighted sum of expression” means multiplying the expression level of each gene by its respective Cox regression beta coefficient, and adding together each result. For example, the weighted sum of expression of Gene A and Gene B is: (coefficient of Gene A)x(expression level of Gene A) + (coefficient of Gene B)x(expression level of Gene B). 20 165016996v1 Attorney Docket No.243735.000296 [0079] The term “overall survival (OS)” as used herein in connection with AML refers to a risk of death from AML. [0080] The term “event-free survival” as used herein in connection with AML refers to the length of time after the end of the primary treatment for AML that the patient remains free of certain complications or events that the treatment was intended to prevent or delay. These events may include, for example, resistant leukemia, relapse, secondary malignancy, or death resulting from any cause. [0081] The term “nucleic acid” includes DNA and RNA, and can be double stranded or single stranded. [0082] The term “hybridize” or “hybridizable” refers to the sequence specific non-covalent binding interaction with a complementary nucleic acid. In some embodiments, the hybridization is under high stringency conditions. Appropriate stringency conditions which promote hybridization can be appreciated by those skilled in the art, or can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.16.3.6. For example, 6.0× sodium chloride/sodium citrate (SSC) at about 45° C., followed by a wash of 2.0×SSC at 50° C. may be employed. [0083] The term “probe” as used herein refers to a nucleic acid molecule that can hybridize to a target nucleic acid sequence. The length of probe may depend on the hybridization conditions and the sequences of the probe and the target nucleic acid sequence. In some embodiments, the probe may be at least 8, 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 400, 500 or more nucleotides in length. [0084] The term “primer” as used herein refers to a nucleic acid molecule which is capable of acting as a point of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid sequence, is induced (e.g., in the presence of nucleotides and a polymerase, and at a suitable temperature and pH). The primer can be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer may depend upon one or more factors, including temperature, sequences of the primer, and the methods used. A primer may contain 10-25 or more nucleotides, although it can contain less or more. Those of ordinary skill in the art will appreciate the factors involved in determining the appropriate length of a primer. [0085] The terms “treat” or “treatment” of a state, disorder or condition include: (1) preventing, delaying, or reducing the appearance of at least one clinical or sub-clinical symptom of the state, 21 165016996v1 Attorney Docket No.243735.000296 disorder or condition developing in a subject that may be afflicted with or predisposed to the state, disorder or condition but does not yet experience or display clinical or subclinical symptoms of the state, disorder or condition; or (2) inhibiting the state, disorder or condition, i.e., arresting, reducing or delaying the development of the disease or a relapse thereof (in case of maintenance treatment) or at least one clinical or sub-clinical symptom thereof; or (3) relieving the disease, i.e., causing regression of the state, disorder or condition or at least one of its clinical or sub-clinical symptoms. The benefit to a subject to be treated is either statistically significant or at least perceptible to the patient or to the physician. [0086] The term “therapeutic” as used herein means a treatment and/or prophylaxis. A therapeutic effect is obtained by suppression, diminution, remission, or eradication of a disease state. [0087] As used herein the term “therapeutically effective” applied to a dose or amount refers to that quantity of a compound or pharmaceutical composition that when administered to a subject for treating (e.g., preventing or ameliorating) a state, disorder or condition, is sufficient to effect such treatment. The “therapeutically effective amount” will vary depending on the compound administered as well as the disease and its severity and the age, weight, physical condition and responsiveness of the subject to be treated. [0088] As used herein, “therapy resistance” may mean any instance where cancer cells are resisting the effects of a therapy. For example, a therapy resistance may occur when cancers that have been responding to the therapy suddenly begin to grow. Therapy resistance may be a failure to achieve complete response (CR) after initial induction. [0089] In the context of the field of medicine, the term “prevent” encompasses any activity which reduces the burden of mortality or morbidity from a disease. Prevention can occur at primary, secondary and tertiary prevention levels. While primary prevention avoids the development of a disease, secondary and tertiary levels of prevention encompass activities aimed at preventing the progression of a disease and the emergence of symptoms as well as reducing the negative impact of an already established disease by restoring function and reducing disease-related complications. Methods of the Invention [0090] In an exemplary embodiment, the present disclosure provides a method for risk classification of an AML patient. The method may include determining an expression level of a 22 165016996v1 Attorney Docket No.243735.000296 plurality of genes, and calculating an iScore for the AML patient, the iScore based on a weighted sum of expression of these genes. [0091] In an exemplary embodiment, the plurality of genes may include at least two genes selected from the genes listed in Table 4, Table 5, or Table 6. In an exemplary embodiment, the plurality of genes may include at least two genes selected from the genes listed in Table 4. In an exemplary embodiment, the plurality of genes may include at least two genes selected from the genes listed in Table 5 or Table 6. [0092] In an exemplary embodiment, the plurality of genes may include any two or more genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR. [0093] In an exemplary embodiment, the AML patient may be an adult AML patient, and the iScore may be configured to predict an overall survival and/or event-free survival associated with the adult AML patient. [0094] In an exemplary embodiment, the plurality of genes including any two or more genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR may be used for determining a survival risk for and/or treating an adult AML patient. [0095] In an exemplary embodiment, the plurality of genes may include any two or more genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA- DPA1, COMMD3, and HSP90AA1. [0096] In an exemplary embodiment, the plurality of genes may include any two or more genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1. [0097] In an exemplary embodiment, the AML patient may be a pediatric AML patient, and the iScore may be configured to predict an overall survival and/or event-free survival associated with the pediatric AML patient. 23 165016996v1 Attorney Docket No.243735.000296 [0098] In an exemplary embodiment, the plurality of genes including any two or more genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA- DPA1, COMMD3, and HSP90AA1 may be used for determining a survival risk for and/or treating a pediatric AML patient. [0099] In an exemplary embodiment, the plurality of genes may include any two or more genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1 may be used for determining a survival risk for and/or treating a pediatric AML patient. [0100] In an exemplary embodiment, the AML patient may be a pediatric AML patient, the iScore may include a pediatric event free survival (EFS) iScore, and the pediatric EFS iScore may be configured to predict an event free survival associated with the pediatric AML patient. [0101] In an exemplary embodiment, identifying the plurality of genes may be conducted using a LASSO penalized proportional hazards model. [0102] In an exemplary embodiment, the method may further include isolating mononuclear cells from BM, performing sequencing of the mononuclear cells via single cell RNA-sequencing (scRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), annotating the mononuclear cells based on respective transcriptional profiles and cell surface protein expressions, separating malignant cells from the mononuclear cells, the malignant cells including one or more types of cells, and/or identifying the plurality of genes based on the one or more types of cells. [0103] In an exemplary embodiment, the one or more types of cells may include atypical B cells, cells having a dysfunctional B cell subtype, CD8 ⁺ GZMK ⁺ T cells, and/or regulatory T cells. [0104] In an exemplary embodiment, separating the malignant cells from the mononuclear cells may include identifying a subset of patients of the plurality of patients, the subset of patients expressing high levels of inflammatory genes in the malignant cells. [0105] In an exemplary embodiment, the method may further include determining whether the iScore exceeds a predetermined threshold (e.g., a reference cohort). The method may include classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample is higher or equal to the median iScore of a reference cohort, or 24 165016996v1 Attorney Docket No.243735.000296 classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample is below the median iScore of the reference cohort. [0106] In an exemplary embodiment, the method may include treating the AML patient with a more aggressive treatment if the patient classifies into a high risk group based on the iScore or a less aggressive treatment if the patient classifies into a low risk group based on the iScore. [0107] In an exemplary embodiment, the reference cohort is an age-matched group of subjects known to have AML with known survival outcomes. In an exemplary embodiment, the reference cohort for adult AML patients is The Cancer Genome Atlas (TCGA) cohort [63]. In an exemplary embodiment, the reference cohort for adult AML patients is Beat AML cohort [65]. In an exemplary embodiment, the reference cohort for adult AML patients is Alliance cohort [66]. In an exemplary embodiment, the reference cohort for pediatric AML patients is Therapeutically Applicable Research To Generate Effective Treatments (TARGET) cohort [64]. In an exemplary embodiment, the reference cohort for pediatric AML patients is the Netherlands microarray cohort. In an exemplary embodiment, the reference cohort of the present disclosure comprises gene expression level (e.g., RNA sequencing) data from age-matched patients that were diagnosed with AML, have AML, had AML, were treated for AML, and who had a relapse of AML. [0108] In an exemplary embodiment, the method of prognosis or determining a survival risk for an adult human subject with AML comprises: a) determining the expression level of at least two genes selected from any of the genes in Table 4, in a test sample obtained from the subject; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of each of the genes determined in step (a), wherein the coefficient value for each gene is as provided in Table 4; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group or a low risk group for overall survival and/or event-free survival based on a high iScore or a low iScore, respectively, as compared to a reference cohort of AML patients. [0109] In an exemplary embodiment, the method of prognosis or determining a survival risk for a pediatric human subject with AML comprises: a) determining the expression level of at least two genes selected from any of the genes in Table 5, in a test sample obtained from the subject; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of each of the genes determined in step (a), wherein the coefficient value for each gene is as provided in Table 5; c) comparing the iScore calculated in step (b) with 25 165016996v1 Attorney Docket No.243735.000296 a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group or a low risk group for overall survival and/or event-free survival based on a high iScore or a low iScore, respectively, as compared to a reference cohort of AML patients. [0110] In an exemplary embodiment, the method of prognosis or determining a survival risk for a pediatric human subject with AML comprises: a) determining the expression level of at least two genes selected from any of the genes in Table 6, in a test sample obtained from the subject; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of each of the genes determined in step (a), wherein the coefficient value for each gene is as provided in Table 6; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group or a low risk group for event-free survival based on a high iScore or a low iScore, respectively, as compared to a reference cohort of AML patients. [0111] In an exemplary embodiment, a method for determining a survival risk for an adult human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR, and b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4. [0112] In an exemplary embodiment, a method for determining a survival risk for an adult human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for 26 165016996v1 Attorney Docket No.243735.000296 each gene is as provided in Table 4; and c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort. [0113] In an exemplary embodiment, a method for determining a survival risk for an adult human diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0114] In some embodiments, the methods described herein can be used in combination with the 17-gene stemness score (LSC17 score) in determining a survival risk for an adult human subject diagnosed with Acute Myeloid Leukemia (AML). Details on the 17 genes and their coefficients, forming the LSC17 score, are described in Ng, S. W. K. et al. A 17-gene stemness score for rapid determination of risk in acute leukemia. Nature 540, 433–437 (2016) and U.S. Pat. No.11,111,542, both of which are incorporated by reference herein in their entireties. [0115] In an exemplary embodiment, the method further comprises: determining the gene expression level of the following 17 genes in a test sample from the subject: AKR1C3, ARHGAP22, CD34, CDK6, CPXM1, DMNT3B, DPYSL3, EMP1, GPR56, KIAA0125, LAPTM4B, MMRN1, NGFRAP1, NYNRIN, SMIM24, SOCS2, and ZBTB46; and calculating a leukemia stem cell score (LSC score) comprising the weighted sum expression of each of the 17 genes. In an exemplary embodiment, the method further comprises: classifying the subject into the high risk group based on a high LSC Score in reference to a control cohort of AML patients. 27 165016996v1 Attorney Docket No.243735.000296 [0116] In an exemplary embodiment, the weighted sum expression is calculated using the following coefficient values for each gene: AKR1C3 (-0.0402), ARHGAP22 (-0.0138), CD34 (0.0338), CDK6 (-0.0704), CPXM1 (-0.0258), DMNT3B (0.0874), DPYSL3 (0.0284), EMP1 (0.0146), GPR56 (0.0501), KIAA0125 (0.0196), LAPTM4B (0.00582), MMRN1 (0.0258), NGFRAP1 (0.0465), NYNRIN (0.00865), SMIM24 (-0.0226), SOCS2 (0.0271), and ZBTB46 (- 0.0347). [0117] In an exemplary embodiment, the method further comprises comparing the calculated iScore and the LSC17 score, respectively, with a corresponding iScore and LSC17 score, respectively, for a reference cohort. [0118] In an exemplary embodiment, the method further comprises: classifying the subject into a high risk group for overall survival and/or event-free survival if the calculated iScore and the LSC17 score for the subject sample , respectively, is higher or equal to the median iScore and LSC17 score, respectively, of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the calculated iScore and the LSC17 score for the subject sample, respectively, is below the median iScore and LSC17 score, respectively, of the reference cohort. [0119] In an exemplary embodiment, a method of treating an adult human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TY-ROBP, and VSIR; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. 28 165016996v1 Attorney Docket No.243735.000296 [0120] In an exemplary embodiment, the method further comprises: determining the gene expression level of the following 17 genes in a test sample from the subject: AKR1C3, ARHGAP22, CD34, CDK6, CPXM1, DMNT3B, DPYSL3, EMP1, GPR56, KIAA0125, LAPTM4B, MMRN1, NGFRAP1, NYNRIN, SMIM24, SOCS2, and ZBTB46; calculating a leukemia stem cell score (LSC score) comprising the weighted sum expression of each of the 17 genes; classifying the subject into the high risk group based on a high LSC Score in reference to a control cohort of AML patients; comparing the calculated iScore and the LSC17 score, respectively, with a corresponding iScore and LSC17 score, respectively, for a reference cohort; and classifying the subject into a high risk group for overall survival and/or event-free survival if the calculated iScore and the LSC17 score for the subject sample, respectively, is higher or equal to the median iScore and LSC17 score, respectively, of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the calculated iScore and the LSC17 score for the subject sample, respectively, is below the median iScore and LSC17 score, respectively, of the reference cohort. [0121] In an exemplary embodiment, the weighted sum expression is calculated using the following coefficient values for each gene: AKR1C3 (-0.0402), ARHGAP22 (-0.0138), CD34 (0.0338), CDK6 (-0.0704), CPXM1 (-0.0258), DMNT3B (0.0874), DPYSL3 (0.0284), EMP1 (0.0146), GPR56 (0.0501), KIAA0125 (0.0196), LAPTM4B (0.00582), MMRN1 (0.0258), NGFRAP1 (0.0465), NYNRIN (0.00865), SMIM24 (-0.0226), SOCS2 (0.0271), and ZBTB46 (- 0.0347). [0122] In an exemplary embodiment, a method for determining a survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA- DPA1, COMMD3, and HSP90AA1; and b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 5. [0123] In an exemplary embodiment, a method for determining a survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA- 29 165016996v1 Attorney Docket No.243735.000296 DPA1, COMMD3, and HSP90AA1; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 5; and c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort. [0124] In an exemplary embodiment, a method for determining a survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA- DPA1, COMMD3, and HSP90AA1; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 5; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0125] In some embodiments, the methods described herein can be used in combination with the 6-gene stemness score (LSC6 score) in determining a survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML). Details on the 6 genes and their coefficients, forming the LSC6 score, are described in Elsayed, A. H. et al. A 6-gene leukemic stem cell score identifies high risk pediatric acute myeloid leukemia. Leukemia 34, 735-745 (2020), which is incorporated by reference herein in its entirety. [0126] In an exemplary embodiment, the method further comprises: determining the gene expression level of the following 6 genes in a test sample from the subject: CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; and calculating a leukemia stem cell score (LSC6 score) comprising the weighted sum expression of each of the 6 genes. In an exemplary embodiment, the method further comprises: classifying the subject into the high risk group based on a high LSC6 score in reference to a control cohort of AML patients. 30 165016996v1 Attorney Docket No.243735.000296 [0127] In an exemplary embodiment, the weighted sum expression is calculated using the following coefficient values for each gene: CD34 (0.0171), DNMT3B (0.189), FAM30A (0.0516), GPR56 (0.054), SPINK2 (0.109), and SOCS2 (0.141). [0128] In an exemplary embodiment, the method further comprises: comparing the calculated iScore and the LSC6 score, respectively, with a corresponding iScore and LSC6 score, respectively, for a reference cohort. [0129] In an exemplary embodiment, the method further comprises: classifying the subject into a high risk group for overall survival and/or event-free survival if the calculated iScore and the LSC6 score for the subject sample, respectively, is higher or equal to the median iScore and LSC6 score, respectively, of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the calculated iScore and the LSC6 score for the subject sample, respectively, is below the median iScore and LSC6 score, respectively, of the reference cohort. [0130] In an exemplary embodiment, a method of treating a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 5; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0131] In an exemplary embodiment, the method further comprises: determining the gene expression level of the following 6 genes in a test sample from the subject: CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; calculating a leukemia stem cell score (LSC score) comprising the weighted sum expression of each of the 6 genes; classifying the subject into the high risk group based on a high LSC Score in reference to a control cohort of AML patients; comparing the calculated iScore and the LSC6 score, respectively, with a corresponding iScore 31 165016996v1 Attorney Docket No.243735.000296 and LSC6 score, respectively, for a reference cohort; and classifying the subject into a high risk group for overall survival and/or event-free survival if the calculated iScore and the LSC6 score for the subject sample, respectively, is higher or equal to the median iScore and LSC6 score, respectively, of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the calculated iScore and the LSC6 score for the subject sample, respectively, is below the median iScore and LSC6 score, respectively, of the reference cohort. [0132] In an exemplary embodiment, the weighted sum expression is calculated using the following coefficient values for each gene: CD34 (0.0171), DNMT3B (0.189), FAM30A (0.0516), GPR56 (0.054), SPINK2 (0.109), and SOCS2 (0.141). [0133] In an exemplary embodiment, a method for determining an event-free survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA- DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; and b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 6. [0134] In an exemplary embodiment, a method for determining an event-free survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA- DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 6; and c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort. [0135] In an exemplary embodiment, a method for determining an event-free survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, 32 165016996v1 Attorney Docket No.243735.000296 wherein the genes are selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA- DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 6; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group for event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0136] In some embodiments, the methods described herein can be used in combination with the 6-gene stemness score (LSC6 score) in determining a survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML). Details on the 6 genes and their coefficients, forming the LSC6 score, are described in Elsayed, A. H. et al. A 6-gene leukemic stem cell score identifies high risk pediatric acute myeloid leukemia. Leukemia 34, 735-745 (2020), which is incorporated by reference herein in its entirety. [0137] In an exemplary embodiment, the method further comprises: determining the gene expression level of the following 6 genes in a test sample from the subject: CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; and calculating a leukemia stem cell score (LSC score) comprising the weighted sum expression of each of the 6 genes. In an exemplary embodiment, the method further comprises: classifying the subject into the high risk group based on a high LSC Score in reference to a control cohort of AML patients. [0138] In an exemplary embodiment, the weighted sum expression is calculated using the following coefficient values for each gene: CD34 (0.0171), DNMT3B (0.189), FAM30A (0.0516), GPR56 (0.054), SPINK2 (0.109), and SOCS2 (0.141). [0139] In an exemplary embodiment, the method further comprises: comparing the calculated iScore and the LSC6 score, respectively, with a corresponding iScore and LSC6 score, respectively, for a reference cohort. [0140] In an exemplary embodiment, the method further comprises: classifying the subject into a high risk group for overall survival and/or event-free survival if the calculated iScore and the LSC6 score for the subject sample, respectively, is higher or equal to the median iScore and LSC6 score, 33 165016996v1 Attorney Docket No.243735.000296 respectively, of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the calculated iScore and the LSC6 score for the subject sample, respectively, is below the median iScore and LSC6 score, respectively, of the reference cohort. [0141] In an exemplary embodiment, a method of treating a pediatric human subject diagnosed with Acute Myeloid Leukemia (AML), comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 6; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0142] In an exemplary embodiment, the method further comprises: determining the gene expression level of the following 6 genes in a test sample from the subject: CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; calculating a leukemia stem cell score (LSC score) comprising the weighted sum expression of each of the 6 genes; classifying the subject into the high risk group based on a high LSC Score in reference to a control cohort of AML patients; comparing the calculated iScore and the LSC6 score, respectively, with a corresponding iScore and LSC6 score, respectively, for a reference cohort; and classifying the subject into a high risk group for overall survival and/or event-free survival if the calculated iScore and the LSC6 score for the subject sample, respectively, is higher or equal to the median iScore and LSC6 score, respectively, of the reference cohort, or classifying the subject into a low risk group for overall survival and/or event-free survival if the calculated iScore and the LSC6 score for the subject sample, respectively, is below the median iScore and LSC6 score, respectively, of the reference cohort. 34 165016996v1 Attorney Docket No.243735.000296 [0143] In an exemplary embodiment, the weighted sum expression is calculated using the following coefficient values for each gene: CD34 (0.0171), DNMT3B (0.189), FAM30A (0.0516), GPR56 (0.054), SPINK2 (0.109), and SOCS2 (0.141). [0144] In an exemplary embodiment, the present disclosure provides a method for determining the risk of an adult human subject developing a condition associated with clonal hematopoiesis, wherein the adult human subject is diagnosed with clonal hematopoiesis, comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR, and b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4. [0145] In an exemplary embodiment, a method for determining the risk of an adult human subject developing a condition associated with clonal hematopoiesis, wherein the adult human subject is diagnosed with clonal hematopoiesis, comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein the coefficient value for each gene is as provided in Table 4; and c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort. [0146] In an exemplary embodiment, a method for determining the risk of an adult human subject developing a condition associated with clonal hematopoiesis, wherein the adult human subject is diagnosed with clonal hematopoiesis, comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, 35 165016996v1 Attorney Docket No.243735.000296 CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein the coefficient value for each gene is as provided in Table 4; and c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort, c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort, and d) classifying the subject into a high risk group for developing a condition associated with clonal hematopoiesis if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for developing a condition associated with clonal hematopoiesis if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0147] In an exemplary embodiment, a method for treating or preventing a condition associated with clonal hematopoiesis in a subject in need thereof, wherein the adult human subject is diagnosed with clonal hematopoiesis, comprises: a) determining the expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; b) calculating an inflammation-associated gene expression score (iScore) for the subject sample as the weighted sum of expression of the genes determined in step (a), wherein coefficient value for each gene is as provided in Table 4; c) comparing the iScore calculated in step (b) with a corresponding iScore for a reference cohort; and d) classifying the subject into a high risk group for developing a condition associated with clonal hematopoiesis if the iScore for the subject sample calculated in step (b) is higher or equal to the median iScore of the reference cohort, or classifying the subject into a low risk group for developing a condition associated with clonal hematopoiesis if the iScore for the subject sample calculated in step (b) is below the median iScore of the reference cohort. [0148] In an exemplary embodiment, the condition associated with clonal hematopoiesis is a cardiovascular disease (Marnell et al, Journal of Molecular and Cellular Cardiology, 2021, which 36 165016996v1 Attorney Docket No.243735.000296 is incorporated by reference in its entirety), a cardiac event, chronic kidney disease, or chronic liver disease. [0149] In an exemplary embodiment, the cardiovascular disease is atherosclerosis, coronary artery disease, or venous thromboembolic disease. [0150] In an exemplary embodiment, the cardiac event is a myocardial infarction. [0151] In some embodiments, the method comprises determining the expression level of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 genes. [0152] In some embodiments, the adult human subject is at least 21 years old. [0153] In some embodiments, the reference cohort is The Cancer Genome Atlas (TCGA) cohort, Beat AML cohort, or Alliance cohort. [0154] In some embodiments, the method further comprises administering one or more anti- inflammatory therapies. In an exemplary embodiment, a patient with a high iScore is administered an anti-inflammatory therapy. [0155] Anti-inflammatory therapies as used herein may be used to prevent progression of atherosclerosis and associated cardiovascular events and/or myeloid disorders. Anti-inflammatory therapies are treatments that reduce swelling or inflammation. Anti-inflammatory therapies may be nonsteroidal anti-inflammatory drugs (NSAIDs). Anti-inflammatory therapies may be steroid injections. Anti-inflammatory agents may be anakinra, colchicine, canakinumab, darapladib, inclacumab, varespladib, pexelizumab, losmapimod, and methotrexate, or a combination thereof. [0156] In some embodiments, the subject sample is selected from bone marrow and blood. [0157] In some embodiments, the subject is a newly diagnosed patient. [0158] In an exemplary embodiment, a kit is provided. In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; (ii) optionally, one or more reagents for quantifying mRNA level, and (iii) optionally, instructions for use. 37 165016996v1 Attorney Docket No.243735.000296 [0159] In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; (ii) one or more reagents for quantifying mRNA level, and (iii) optionally, instructions for use. [0160] In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; (ii) one or more reagents for quantifying mRNA level, and (iii) instructions for use. [0161] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from AKR1C3, ARHGAP22, CD34, CDK6, CPXM1, DMNT3B, DPYSL3, EMP1, GPR56, KIAA0125, LAPTM4B, MMRN1, NGFRAP1, NYNRIN, SMIM24, SOCS2, and ZBTB46; (ii) optionally, one or more reagents for quantifying mRNA level, and (iii) optionally, instructions for use. [0162] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from AKR1C3, ARHGAP22, CD34, CDK6, CPXM1, DMNT3B, DPYSL3, EMP1, GPR56, KIAA0125, LAPTM4B, MMRN1, NGFRAP1, NYNRIN, SMIM24, SOCS2, and ZBTB46; (ii) one or more reagents for quantifying mRNA level, and (iii) optionally, instructions for use. [0163] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from AKR1C3, ARHGAP22, CD34, CDK6, CPXM1, DMNT3B, DPYSL3, EMP1, GPR56, KIAA0125, LAPTM4B, MMRN1, NGFRAP1, NYNRIN, SMIM24, SOCS2, and ZBTB46; (ii) one or more reagents for quantifying mRNA level, and (iii) instructions for use. 38 165016996v1 Attorney Docket No.243735.000296 [0164] In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1; (ii) optionally, one or more reagents for quantifying mRNA level; and (iii) optionally, instructions for use. [0165] In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1; (ii) one or more reagents for quantifying mRNA level; and (iii) optionally, instructions for use. [0166] In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1; (ii) one or more reagents for quantifying mRNA level; and (iii) instructions for use. [0167] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; (ii) optionally, one or more reagents for quantifying mRNA level; and (iii) optionally, instructions for use. [0168] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; (ii) one or more reagents for quantifying mRNA level; and (iii) optionally, instructions for use. [0169] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; (ii) one or more reagents for quantifying mRNA level; and (iii) instructions for use. [0170] In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; 39 165016996v1 Attorney Docket No.243735.000296 (ii) optionally, one or more reagents for quantifying mRNA level; and (iii) optionally, instructions for use. [0171] In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; (ii) one or more reagents for quantifying mRNA level; and (iii) optionally, instructions for use. [0172] In an exemplary embodiment, a kit comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; (ii) one or more reagents for quantifying mRNA level; and (iii) instructions for use. [0173] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; (ii) optionally, one or more reagents for quantifying mRNA level; and (iii) optionally, instructions for use. [0174] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; (ii) one or more reagents for quantifying mRNA level; and (iii) optionally, instructions for use. [0175] In an exemplary embodiment, a kit described herein further comprises: (i) a plurality of isolated probes and/or primers, wherein each probe or primer hybridizes to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; (ii) one or more reagents for quantifying mRNA level; and (iii) instructions for use. [0176] In an exemplary embodiment, an array is provided. In an exemplary embodiment, an array comprises probes complementary to at least two genes selected from any of the genes in Table 4, Table 5, or Table 6. [0177] In an exemplary embodiment, an array comprises probes complementary to at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, 40 165016996v1 Attorney Docket No.243735.000296 HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR. [0178] In an exemplary embodiment, an array described herein further comprises probes complementary to at least two genes selected from AKR1C3, ARHGAP22, CD34, CDK6, CPXM1, DMNT3B, DPYSL3, EMP1, GPR56, KIAA0125, LAPTM4B, MMRN1, NGFRAP1, NYNRIN, SMIM24, SOCS2, and ZBTB46. [0179] In an exemplary embodiment, an array comprises probes complementary to at least two genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA- DPA1, COMMD3, and HSP90AA1. [0180] In an exemplary embodiment, an array described herein further comprises probes complementary to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2. [0181] In an exemplary embodiment, an array comprises probes complementary to at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1. [0182] In an exemplary embodiment, an array described herein further comprises probes complementary to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2. [0183] In an exemplary embodiment, a composition comprises a plurality of isolated probes and/or primers, wherein each probe and/or primer hybridizes to at least two genes selected from any of the genes in Table 4, Table 5, or Table 6. [0184] In an exemplary embodiment, a composition comprises a plurality of isolated probes and/or primers, wherein each probe and/or primer hybridizes to at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR. [0185] In an exemplary embodiment, a composition described herein further comprises a plurality of isolated probes and/or primers, wherein each probe and/or primer hybridizes to at least two genes selected from AKR1C3, ARHGAP22, CD34, CDK6, CPXM1, DMNT3B, DPYSL3, EMP1, 41 165016996v1 Attorney Docket No.243735.000296 GPR56, KIAA0125, LAPTM4B, MMRN1, NGFRAP1, NYNRIN, SMIM24, SOCS2, and ZBTB46. [0186] In an exemplary embodiment, a composition comprises a plurality of isolated probes and/or primers, wherein each probe and/or primer hybridizes to at least two genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1. [0187] In an exemplary embodiment, a composition described herein further comprises a plurality of isolated probes and/or primers, wherein each probe and/or primer hybridizes to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2. [0188] In an exemplary embodiment, a composition comprises a plurality of isolated probes and/or primers, wherein each probe and/or primer hybridizes to at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1. [0189] In an exemplary embodiment, a composition described herein further comprises a plurality of isolated probes and/or primers, wherein each probe and/or primer hybridizes to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2. [0190] In an exemplary embodiment, the adult human subject is at least 21 years old. In an exemplary embodiment, the adult human subject is less than 60 years old. In an exemplary embodiment, the adult human subject is more than 60 years old. In an exemplary embodiment, the adult human subject is 21 to 60 years old. In an exemplary embodiment, the adult human subject is of African ancestry. [0191] In an exemplary embodiment, the pediatric human subject is less than 21 years old. In an exemplary embodiment, the pediatric human subject is about 12 to less than 21 years old. In an exemplary embodiment, the pediatric human subject is less than 12 years old. In an exemplary embodiment, the pediatric human subject is 2 to 12 years old. In an exemplary embodiment, the pediatric human patient is an adolescent pediatric patient. In an exemplary embodiment, the pediatric human subject is of African ancestry. [0192] In an exemplary embodiment, the at least two genes is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 genes from Table 4. In another exemplary embodiment, the at least two genes selected from 42 165016996v1 Attorney Docket No.243735.000296 any of the genes in Table 4 is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 genes. [0193] In an exemplary embodiment, the at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or 38 genes may include ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and/or VSIR. [0194] In an exemplary embodiment, the at least two genes is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 genes from Table 6. [0195] In an exemplary embodiment, the at least two genes selected from any of the genes in Table 6 is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 genes. [0196] In an exemplary embodiment, the at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 genes may include AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and/or XAF1. [0197] In an exemplary embodiment, the at least two genes is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 genes. In an exemplary embodiment, the at least two genes selected from any of the genes in Table 5 is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 genes. [0198] In an exemplary embodiment, the at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 genes may include COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and/or HSP90AA1. [0199] In an exemplary embodiment, determining the gene expression profile (GEP) further comprises building a subject GE profile from the determined expression of at least two genes. [0200] In an exemplary embodiment, determining the gene expression level further comprises obtaining a reference gene expression profile (GEP) associated with a prognosis, wherein the subject gene expression level and the gene reference expression profile each have values representing the expression level of at least two genes. [0201] In an exemplary embodiment, the gene expression level is determined using RNA sequencing (e.g., single-cell RNA sequencing (sc-RNA-seq)), single cell T cell receptor 43 165016996v1 Attorney Docket No.243735.000296 sequencing (sc-TCR-seq), cellular indexing of transcriptomes and epitopes by sequencing (CITE- seq), qPCR, panel sequencing, an array, or any combination thereof. [0202] In an exemplary embodiment, the method further comprises a therapy. In an exemplary embodiment, the therapy may be a first and second therapy. In an exemplary embodiment, the first and second therapies may be different. In an exemplary embodiment, the therapy may be a first therapy and any concurrent therapy. In an exemplary embodiment, the any concurrent therapy as a second therapy, a third therapy, and more. In an exemplary embodiment, the first therapy and the any concurrent therapy may be different. [0203] In an exemplary embodiment, the therapy is an aggressive therapy. In an exemplary embodiment, a patient with high iScore is administered an aggressive therapy. In an exemplary embodiment, the aggressive therapy is a stem cell transplant. In an exemplary embodiment, the aggressive therapy is an intensified chemotherapy. [0204] In an exemplary embodiment, a patient with low iScore is administered a standard therapy. [0205] In an exemplary embodiment, the standard therapy is selected from a chemotherapy, targeted drug therapy, non-chemo drug therapy, surgery, radiation therapy, or any combination thereof. In an exemplary embodiment, the standard therapy is a chemotherapy. In an exemplary embodiment, the standard therapy is a targeted drug therapy. In an exemplary embodiment, the standard therapy is a non-chemo drug therapy. In an exemplary embodiment, the standard therapy is a surgery. In an exemplary embodiment, the standard therapy is a radiation therapy. [0206] In an exemplary embodiment, the chemotherapy is selected from cytarabine, anthracycline drug, daunorubicin, idarubicin, clabridine, fludarabine, mitoxantrone, etoposide, 6-thioguanine, hydroxyurea, corticosteroid drug, prednisone, dexamethasone, methotrexate, 6-mercaptopurine, azacitidine, decitabine, and any combination thereof. [0207] In an exemplary embodiment, the targeted drug therapy is selected from FLT3 inhibitors, IDH inhibitor, BCL-2 inhibitors, Hedgehog pathway inhibitors, and any combination thereof. In an exemplary embodiment, the targeted drug therapy is selected from midostaurin, gilteritinib, ivosidenib, enasidenib, gemtuzumab ozogamicin, venetoclax, glasdegib, and any combination thereof. [0208] In an exemplary embodiment, the non-chemo drug therapy is selected from ATRA, arsenic trioxide, and a combination thereof. 44 165016996v1 Attorney Docket No.243735.000296 [0209] In an exemplary embodiment, the stem cell transplant is selected from an allogenic stem cell transplant, an autologous stem cell transplant, and any combination thereof. In an exemplary embodiment, the stem cell transplant is a bone marrow (BM) transplant. In an exemplary embodiment, the stem cell transplant is administered at the first complete remission. In an exemplary embodiment, the stem cell transplant is administered to a patient with a high iScore. In an exemplary embodiment, the stem cell transplant is administered to a patient with high iScore at the first complete remission. [0210] In another exemplary embodiment, the present disclosure provides a method for risk classification of an adult AML patient. The method may include determining an expression of a plurality of genes, wherein the plurality of genes includes ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR; calculating an iScore for the adult AML patient, the iScore based on a weighted sum of expression; determining whether the iScore exceeds a predetermined threshold; responsive to determining the iScore exceeds the predetermined threshold, treating the adult AML patient with one or more first therapies; and responsive to determining the iScore does not exceed the predetermined threshold, treating the adult AML patient with one or more second therapies. [0211] In an exemplary embodiment, the predetermined threshold may be approximately 50 percent. [0212] In another exemplary embodiment, the present disclosure provides a method for risk classification of a pediatric AML patient. The method may include determining an expression of a plurality of genes, wherein the plurality of genes includes COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1; calculating an iScore for the pediatric AML patient, the iScore based on a weighted sum of expression; determining whether the iScore exceeds a predetermined threshold; responsive to determining the iScore exceeds the predetermined threshold, treating the pediatric AML patient with one or more first therapies; and responsive to determining the iScore does not exceed the predetermined threshold, treating the pediatric AML patient with one or more second therapies. 45 165016996v1 Attorney Docket No.243735.000296 [0213] In another exemplary embodiment, the present disclosure provides a method for risk classification of a pediatric AML patient. The method may include determining an expression of a plurality of genes, wherein the plurality of genes includes AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; calculating an iScore for the pediatric AML patient, the iScore based on a weighted sum of expression; determining whether the iScore exceeds a predetermined threshold; responsive to determining the iScore exceeds the predetermined threshold, treating the pediatric AML patient with one or more first therapies; and responsive to determining the iScore does not exceed the predetermined threshold, treating the pediatric AML patient with one or more second therapies. [0214] In an exemplary embodiment, the predetermined threshold may be approximately 50 percent. [0215] In an exemplary embodiment, a survival risk stratification and/or therapy guidance software tool is provided. In an exemplary embodiment, a survival risk stratification and/or therapy guidance software tool for adult or pediatric human subjects diagnosed with Acute Myeloid Leukemia (AML) comprises non-transitory computer readable medium with instructions thereon, that when executed by a processor, cause the processor to: a) provide a user interface into which a plurality of gene expression values can be entered, the gene expression values being for a plurality of genes, wherein the plurality of genes are at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, TMEM176B, TNFRSF1B, TYROBP, and VSIR, or at least two genes selected from COTL1, GSN, HGF, HLA-DQA1, HLA-DQA2, IFI6, IFITM2, RHOG, HLA-DPA1, COMMD3, and HSP90AA1, or at least two genes selected from AIF1, CD36, CD97, COTL1, CYBA, GPI, GSN, HSP90AA1, HSPB1, ID2, IFI6, PLSCR1, PMAIP1, RAB37, RHOG, RNASET2, CD44, COMMD3, HLA-DQA1, CCL5, HGF, IGF2BP2, P2RX1, and XAF1; b) calculate an inflammation-associated gene expression score (iScore) for the subject based on the weighted sum of the plurality of gene expression values, wherein the coefficient value for each gene is as provided in Tables 4-6; c) calculate a risk stratification score for the subject based at least in part 46 165016996v1 Attorney Docket No.243735.000296 on a comparison of the subject iScore to a median iScore of a reference cohort; and d) provide, at the user interface, a visual representation of the risk stratification score. [0216] In an exemplary embodiment, the survival risk stratification and/or therapy guidance software tool for adult or pediatric human subjects diagnosed with Acute Myeloid Leukemia (AML) further comprises non-transitory computer readable medium with instructions thereon, that when executed by a processor, cause the processor to: a) provide a user interface into which a plurality of gene expression values can be entered, the gene expression values being for a plurality of genes, wherein the plurality of genes are at least two genes selected from AKR1C3, ARHGAP22, CD34, CDK6, CPXM1, DMNT3B, DPYSL3, EMP1, GPR56, KIAA0125, LAPTM4B, MMRN1, NGFRAP1, NYNRIN, SMIM24, SOCS2, and ZBTB46, or at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2; b) calculate a stemness score (LSC17 or LSC6) for the subject based on the weighted sum of the plurality of gene expression values, wherein the coefficient value for each gene is as provided herein; c) calculate a risk stratification score for the subject based at least in part on a comparison of the subject stemness score to a median stemness score of a reference cohort; and d) provide, at the user interface, a visual representation of the risk stratification score. [0217] In an exemplary embodiment, the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the iScore of the subject. [0218] In an exemplary embodiment, the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the LSC17 of the subject. [0219] In an exemplary embodiment, the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the LSC6 of the subject. [0220] In an exemplary embodiment, a condition development risk stratification and/or preventative measure guidance software tool for adult human subjects diagnosed with clonal hematopoiesis comprising non-transitory computer readable medium with instructions thereon, that when executed by a processor, cause the processor to: a) provide a user interface into which a plurality of gene expression values can be entered, the gene expression values being for a plurality of genes, wherein the plurality of genes are at least two genes selected from ALOX5, AP2S1, ATP2B1, ATP8B4, CAPZB, CCL7, CD38, CD79A, CHMP4B, CST3, CTSG, CXCL2, CYBA, EIF2AK2, FCN1, FGR, FYB1, FYN, GSTP1, HMOX1, HOMER3, IRAK3, KMT2E, MEF2C, MPO, PABPC1, PSMA6, PSME1, RETN, FGCC, SAMHD1, SLC11A1, SLC2A3, TFPI, 47 165016996v1 Attorney Docket No.243735.000296 TMEM176B, TNFRSF1B, TYROBP, and VSIR; b) calculate an inflammation-associated gene expression score (iScore) for the subject based on the weighted sum of the plurality of gene expression values, wherein the coefficient value for each gene is as provided in Table 4; c) calculate a risk stratification score for the subject based at least in part on a comparison of the subject iScore to a median iScore of a reference cohort; and d) provide, at the user interface, a visual representation of the risk stratification score. In some embodiments, the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the iScore of the subject. [0221] The following examples are illustrative, but not limiting, of the methods, kits, arrays, and compositions described herein. Other suitable modifications and adaptations known to those skilled in the art are within the scope of the following embodiments. EXAMPLES [0222] The present disclosure is also described and demonstrated by way of the following examples. However, the use of these and other examples anywhere in the specification is illustrative only and in no way limits the scope and meaning of the invention or of any exemplified term. Likewise, the invention is not limited to any particular preferred embodiments described here. Indeed, many modifications and variations of the invention may be apparent to those skilled in the art upon reading this specification, and such variations can be made without departing from the invention in spirit or in scope. The invention is therefore to be limited only by the terms of the appended claims along with the full scope of equivalents to which those claims are entitled. EXAMPLE 1. Examination of the Bone Marrow (BM) Immune Microenvironment in Adult and Pediatric AML. [0223] It has been proposed that providing a comprehensive census of the bone marrow (BM) immune microenvironment in adult and pediatric AML patients may help to distinguish malignant and non-malignant cells. Further, characterization of unique inflammation signatures highly expressed in the malignant cells of a subset of AML patients may provide insight into inferior treatment outcomes. This inflammatory microenvironment may be associated with alterations in lymphoid populations. The following example may help to identify atypical B cells, a dysfunctional B cell subtype enriched in high-inflammation AML patients, as well as an increase 48 165016996v1 Attorney Docket No.243735.000296 in CD8 ⁺ GZMK ⁺ T cells and regulatory T cells accompanied by a reduction in T cell clonal expansion. Further, this example may suggest that a subset of patients have inflammatory immune microenvironments that blunt the anti-tumor immune response, and may provide a method of determining an iScore that associates with survival outcomes in both adult and pediatric patients. The addition of the iScore may refine currently utilized risk stratifications for both adult and pediatric patients, and may enable identification of patients in need of more aggressive treatment approaches. Overall, the following example may provide a first framework for classifying AML patients based on their immune microenvironment, and may provide a rationale for consideration of the inflammatory state in the clinical setting. Materials and Methods Human Patient Samples [0224] Cryopreserved de-identified bone marrow (BM) aspirates from newly diagnosed AML patients were obtained from the OSU Leukemia Tissue Bank (adult AML samples, n=29, 48% female), St. Jude Children’s Research Hospital (pediatric AML samples, n=18) or the Children’s Oncology Group AML cell bank (pediatric AML samples, n=3). Overall in the pediatric cohort, 52% of patients were female. All subjects provided written consent for banking and research use of these specimens, according to the Declaration of Helsinki in accordance with the regulations of the institutional review boards of all participating institutes. Cryopreserved primary human bone marrow mononuclear cells (n=10) were obtained from STEMCELL Technologies (catalog #70001) or from StemExpress (catalog #BMMNC025C). Frozen Human BM Mononuclear Cells Preparation [0225] Frozen human BM samples were thawed and transferred into 50mL conical tubes containing PBS + 2% fetal bovine serum (FBS). Cell suspensions were centrifuged at 350 x g for 5 minutes at 4 ^q C, and the supernatant was discarded. Samples were then subjected to dead cell depletion, using a dead cell removal kit (Miltenyi Biotec, 130-090-101), or stained with DAPI (0.5Pg/mL) and sorted for live cells (DAPI ^neg ), using a FACSAria IIu SORP cell sorter (BD Biosciences). For cell sorting, all samples were gated based on forward and side scatter, followed by doublet exclusion, and then gated on DAPI ^neg for viable cells. Samples were sorted into 5mL 49 165016996v1 Attorney Docket No.243735.000296 poly-propylene tubes containing 300PL ice-cold PBS + 2% FBS. Following cell sorting, samples were centrifuged at 350 x g for 5 minutes at 4 ^q C. [0226] For CITE-seq [17], enriched live cells were first tagged with cell-hashing oligo-tagged antibodies (1:250, Biolegend) according to manufacturer’s instructions. Samples were counted, and a maximum of 10 ⁵ cells for each sample was pooled together and stained either with a custom CITE-Seq panel (1:100 for all antibodies) or with a CITE-Seq antibody cocktail (Biolegend) according to the manufacturer’s instructions. [0227] For scTCR-Seq, T cells were enriched using a pan T cell isolation kit (Miltenyi Biotec, 130-096-535) or sorted for live CD45 ⁺ CD3 ⁺ cells. For cell sorting, samples were stained with PerCP-conjugated anti-human CD45 (1:400, Biolegend, 304025), FITC-conjugated anti-human CD3 (1:100, Biolegend, 300452) and DAPI (0.5Pg/mL). Sorted samples were gated based on forward and side scatter, followed by doublet exclusion and then gated on DAPI ^neg for viable cells. CD45 ⁺ CD3 ⁺ cells were sorted into 5mL poly-propylene tubes containing 300PL ice-cold PBS + 2% FBS. Following cell sorting, samples were centrifuged at 350xg for 5 minutes at 4 ^q C. [0228] Libraries were prepared using the Chromium Single Cell 3’ Reagent Kits (v3 and v3.1, CITE-Seq) or the Chromium Single Cell Immune Profiling Kits (v1.1 and v2, scTCR-Seq, 10x Genomics). Hashtag and antibody-derived tag (ADT) libraries were prepared according to the New York Genome Center CITE-Seq and hashing protocol (citeseq.files.wordpress.com/2019/02/cite-seq_and_hashing_pr otocol_190213.pdf), incorporated herein by reference. Libraries were sequenced on an Illumina NovaSeq 6000. Single Cell RNA/CITE Sequencing Pre-Processing [0229] Raw sequencing reads were converted to FASTQ format using Illumina bcl2fastq software. Cell Ranger Single Cell Gene Expression Software (version 5.0, 10x Genomics) was used to demultiplex and align raw 3’ library reads to GRCh38 (version 2020-A). All following downstream analysis was performed using the Seurat R package (version 3.2.2) [54], and all visualizations were generated using ggplot2 (version 2_3.3.3). Cells with less than 400 or more than 6000 unique feature counts, as well as cells with more than 15% transcripts originating from mitochondrial genes to filter low-quality cells and droplets that may have captured multiple cells were excluded. 50 165016996v1 Attorney Docket No.243735.000296 [0230] To demultiplex hashed libraries, hashtag oligonucleotides (HTO) were normalized using a centered log ratio (CLR) transformation across cells and HTODemux function in Seurat was applied. The 0.99 quantile was used as a cut off (positive.quantile=0.99) to define a cell as hashtag positive. Cells positive for more than one hashtag were excluded as doublets. [0231] Independently, Souporcell [55] was used on each of the hashed libraries to identify cells in which wrong hashtags may have been assigned. Souporcell remaps raw reads to GRCh38 reference using minimap2, identifies candidate variants using freebayes and counts cell alleles supported for each cell with vartrix. Sparse mixture model clustering is then used on the cell allele counts to detect doublets and infer genotypes of each cluster. Cells assigned as doublets and cells in which genotype and HTODemux assignment did not match were excluded. [0232] To further exclude doublets deriving from two cells from the same patient and from non- hashed libraries, the data was filtered using the scDblFinder package (version 1.5.13, https://github.com/plger/scDblFinder). For each of the hashed libraries, the recoverDoublets function was used to identify cells similar to those identified as doublets by HTODemux. For the non-hashed libraries, scDblFinder function was ran, with trajectoryMode=TRUE, which generates cluster-based artificial doublets to identify doublets. [0233] To reduce ambient RNA contamination, the SoupX package was used [56]. SoupX uses empty droplets to identify ambient RNA expression profiles present in each library. To further estimate a global contamination rate, SoupX clusters the cells and identifies marker genes for each cluster to estimate the contamination in each cell. The most common contamination estimate is then used to remove contamination in each of the clusters. Contamination estimates in the inventors’ libraries varied between 1 and 9.2%. SoupX estimated counts were used in all downstream analysis. [0234] After filtering, RNA expression data was normalized by total expression, multiplied by a scaling factor of 10,000 and log-transformed. For antibody derived tags (ADT), counts were divided by the geometric mean of each corresponding feature across cells and then log-transformed (CLR transformation). On average 4123.4 cells per patient with a mean and median of 1620.772 and 1581.257 genes detected per cell were captured. Analysis of Single Cell RNA/CITE Sequencing Data Clustering and Visualization 51 165016996v1 Attorney Docket No.243735.000296 [0235] To visualize RNA expression similarities between cells in two-dimensional space, the scaled data matrix was used to perform principal component analysis (PCA) on the 2,000 most variable genes. Uniform manifold approximation and projection (UMAP) [57] on the first 30 principal components with 25 nearest neighbors defining the neighborhood size and a minimum distance of 0.3 was ran. A shared nearest neighbor (SNN) graph using 25 nearest neighbors and clustered the graph using a range of resolution from 0.1-10 to explore the clusters – resolution 2, which yielded 85 clusters, was used for subsequent broad cell type annotation and occupancy scoring analysis. Broad Cell Type Annotation [0236] Broad cell types, HSPC, myeloid, B, T/NK cells and erythrocytes, were annotated using known cell type markers as previously described [58]. Malignant and Microenvironment Division [0237] For patients with clinically annotated karyotype aberrations, the inferCNV was ran to identify the malignant cells (version 1.2.1) [22]. The inferCNV was ran on each patient individually annotating the broad cell type (HSPC, Myeloid, T, NK, B) within the control and patient cells. InferCNV was run with default settings except min_cell_per_gene=10, cutoff=0.1, denoise=TRUE, HMM=TRUE, analysis_mode=”subclusters”. [0238] Cell type specific expression patterns can introduce noise in the analysis. To validate CNV+ T, B, and NK cells, the inferCNV was ran on the T, B and NK cells of all patients, annotating the more granular cell types within each broad cell type compartments. Only CNV+ T, NK, and B cells that were detected in both analysis were kept. [0239] To make use of cluster information, an occupancy score for each of the 85 clusters was calculated. For each patient and cluster, the number of cells from the patient was divided by the sum of the patient and control cells. When the occupancy score exceeded a threshold of 0.7, the cluster was designated as patient-specific, and therefore malignant. By combining the CNV positive cells with patient occupancy scoring, it was possible to confidently split malignant and microenvironment cells in 39 out of 42 AML patients. 3 patients with incomplete split were excluded from further analysis. 52 165016996v1 Attorney Docket No.243735.000296 Granular Cell Type Annotation [0240] Microenvironment and control cells were split into broad cell types (HSPC, B, T, NK, myeloid). To account for biological and technical batch effects, Harmony integration (version 1.0) [59] was applied to each of the broad cell type objects using default parameters, and the individual patients as integration variable. The first 20 dimensions of the harmony embeddings were then used to generate UMAPs with 20 nearest neighbors defining the neighborhood size and a minimum distance of 0.3 for each of the broad cell types. SNN graphs were constructed using the first 20 dimensions of the harmony embeddings, and clustered the graph using a range of resolutions from 0.5-3. The following resolutions were used for manual cluster annotation based on cluster markers: HSPC – resolution 1, 21 clusters; Myeloid cells– resolution 1, 20 clusters; T/NK cells – resolution 3, 35 clusters; B cells – resolution 2, 22 clusters; Erythrocytes – resolution 1, 14 clusters. To identify cluster markers, differential expression analysis was performed between cells within each cluster against all other cells using the Wilcoxon rank sum test with Bonferroni multiple- comparison correction (detected in at least 10% of the cluster cells, log2 fold change > 0.25 or < - 0.25, adjusted p<0.05). Clusters expressing markers of other lineages were excluded as potential doublets from further analysis. To visualize the cluster markers for each identified cell type, cluster markers were re-calculated based on final cell type annotation and a cell-type average expression matrix was calculated. The top 20 marker genes for each cell type were shown in a row scaled heatmap using heatmap package (version 1.0.12) for each of the cell type subsets and the combined full annotation, as well as selected surface protein markers to further validate cell type annotations. Wilcoxon rank sum test was used to compare differences between two groups, whereas Kruskal- Wallis test was used for more than two groups. Annotation of Malignant Cells [0241] In order to assign a cell type identity to the AML malignant cells, the FindTransferAnchors function was used in Seurat [60] to identify pairwise correspondence between cells in the annotated microenvironment/control cells and malignant cells and projected the cell type labels using the TransferData function using the first 30 principal components. Non-Negative Matrix Factorization 53 165016996v1 Attorney Docket No.243735.000296 [0242] To identify common gene expression profiles in malignant cells of adult and pediatric AML patients, malignant and healthy counterpart HSPC/myeloid populations were grouped and performed non-negative matrix factorization (NMF) using cNMF (v1.1) [61] which uses the NMF implementation in scikit-learn version 20.0. All genes expressed in less than 50 cells were filtered and used only the top 2000 over-dispersed genes in the NMF analysis. To identify the most stable and accurate number of components (k) within the range of 20 to 35 over 25 iterations, silhouette score and Frobenius reconstruction error were used as implemented in cNMF. K = 22 emerged as the smallest most stable solution. The consensus solution was determined over 250 iterations using a density threshold of 0.04 to exclude outlier solutions. UMAP visualization of the malignant and healthy counterpart HSPC/myeloid was based on the same 2000 over-dispersed genes used in the NMF analysis. Cell type related gene expression profiles (GEPs) were excluded by evaluating GEPs usage in healthy control cells.12 cell type specific GEPs, 2 patient-specific and 8 commonly used GEPs across the malignant cells were identified. Marker genes for each GEP were identified using multiple least squares regression of normalized z-scored gene expression against the consensus GEP usage matrix as implemented in cNMF and positively associated genes were used for subsequent GO analysis as in Kotliar et al [61]. All analysis was performed in Python (v3.7.0) using scanpy (v1.6.0), pandas (v1.1.3), numpy (v1.19.2), matplotlib (v3.3.2) and seaborn for visualizations (v0.11.2). Differential Gene Expression [0243] To identify differentially expressed genes between the malignant cells and their corresponding healthy counterparts, malignant cells were broadly divided into HSPC-like and Myeloid-like, and separated adult and pediatric patients as well as younger and older healthy controls. To avoid strong patient-specific effects, the malignant cells were down-sampled to a maximum of 500 cells from each patient. Adult and pediatric malignant cells were compared to their corresponding age matched controls. MAST [62] was used to perform differential expression analysis as implemented in Seurat, which uses a generalized linear model framework to incorporate cellular detection rates as a covariant. All genes detected in at least 10% of the AML cells with a log2 fold change larger than 0.25 or smaller than -0.25 and a Bonferroni adjusted p < 0.05 were considered as differentially expressed. Due to an imbalance of male and female patients in the utilized cohort, all genes derived from X and Y chromosomes were removed. To determine 54 165016996v1 Attorney Docket No.243735.000296 differentially expressed genes between AML patients and healthy control atypical B cells, all patients’ atypical B cells (463 cells) were combined and compared to all atypical B cells identified in healthy controls (294 cells) as described above. Deriving the Inflammation Signatures [0244] Gene ontology (GO) analysis for Biological Pathway (BP) subontology was performed on the differentially expressed genes using enrichGO function from clusterProfiler package (version 3.14.3) with default parameters and expressed genes as a background. EnrichGO uses a one-sided Fisher exact test to determine overrepresentation of a specific pathway and perform Benjamini Hochberg correction for multiple comparison by default. Pathways were further filtered for duplicated terms using the simplify function in clusterProfiler with default parameters. All inflammation related terms within the top 30 GO terms of up- and down-regulated genes were used to establish inflammation signatures for adult and pediatric patients independently. The final inflammation signatures included 246 and 187 genes in adults and pediatrics, respectively. [0245] To generate inflammation score cut offs for adult and pediatric patients based on the utilized single cell cohorts, pseudo-bulk data was generated for each patient and the healthy controls and compared their inflammation scores. Inflammation scores were calculated using the same approach as for the broad cell type annotation described above, using patient averages instead of cluster averages in the calculation. High and low inflammation thresholds for adult and pediatric patients were assigned based on inflammation score distribution (Adults – 50% high inflammation, pediatric – 32% high inflammation). Those groupings were used in all single cell downstream analysis. FACS Analysis of Atypical B cells in High and Low Inflammation AML Patients [0246] Frozen human BM samples were thawed and transferred into 50mL conical tubes containing PBS + 2% fetal bovine serum (FBS). Cell suspensions were centrifuged at 350 x g for 5 minutes at 4 ^q C, and the supernatant was discarded. Cells were stained in an antibody cocktail containing BV605-conjugated anti-human CD45 (1:400, Biolegend, 304042), Biotin-conjugated anti-human CD19 (1:400, Biolegend, 302203) and APC-conjugated anti-human FcRL5 (1:100, eBioscience, 50-3078-42), PE-Cy7-conjugated Streptavidin (1:400, BD, 557598) and DAPI 55 165016996v1 Attorney Docket No.243735.000296 (0.5Pg/mL). Samples were analyzed on the BD Fortessa. High and low inflammation grouping was determined based on bulk RNA sequencing results, as described below. TCR/5’ RNA Sequencing Data Analysis [0247] 5’ RNA sequencing data was aligned to GRCh38 (version 2020-A) using Cell Ranger Single Cell Gene Expression Software (version 6.0.1, 10x Genomics) and subsequent analysis was performed in Seurat R package (version 4.0.2) [54]. Visualization and clustering of data was performed as described for the 3’ data. Broad cell types were called and non-T cell clusters were excluded from further analysis. T cells from the 15 patients were integrated using Harmony (version 1.0) [59] and the UMAP was generated using the first 20 harmony embeddings, the 20 nearest neighbors to define the neighborhood size and a minimum distance of 0.3. The scRepertoire package (version 1.1.4) was used to visualize and integrate the TCR data with Seurat. Cell type annotation was performed based on cluster markers as described above (resolution 1.5, 27 clusters). The JG T cells were annotated based on presence of TRGV9, TRDV1 or TRDV2, and MAIT cells based on TRAV1-2 expression. Due to better capture of T cell relevant genes in the 5’ data, the labels were transferred from the 5’ T cells to the 3’ T cells as described above. A list of expanded clonotypes from each patient can be found in Tables 1-3. Table 1: Inflammation Gene Weights for Adult Patients < 60 Years Gene coxph.beta 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta VIM -0.0069325 57 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta CTSG 0.05069363 58 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta ID2 0.0416712 59 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta HMGA1 -0.0052945 60 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta CXCL3 0.04807741 61 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta ISG15 0.00444223 62 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta BRK1 -0.2507009 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta HOXA9 -0.0332705 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta CD81 0.06151305 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta DBNL -0.0889225 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta DDX21 0.04658544 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta HLA-DQA1 0.02758698 a e : n ammaton ene eg ts or e atrc atents Gene coxph.beta 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta COTL1 -0.1911816 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta CST3 -0.0696216 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta FLT3 -0.0330859 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta HLA-A 0.02420478 165016996v1 Attorney Docket No.243735.000296 Gene coxph.beta IFITM2 0.25992706 [0248] TRUST imputed TCR data for the TCGA and TARGET cohort was derived from Zhang et al [44]. TCR CDR3s per kilo TCR reads (CPK) were used to define clonotype diversity according to the original publication and divided TCGA and TARGET patients into high and low inflammation groups based on average log2 transformed mean adult and pediatric inflammation scores respectively. Adult patients were split based on the median scores, whereas pediatric patients were split based on the top vs bottom two tertiles. AML Bulk RNA-Sequencing Cohorts [0249] The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (LAML) [63] and Therapeutically Applicable Research To Generate Effective Treatments (TARGET) [64] AML RNA sequencing data as well as clinical and survival annotations were downloaded from UCSC GDC Xena Hub (https://gdc.xenahubs.net). Beat AML data was derived from [65]. Analysis was limited to diagnostic/de in TCGA-LAML and Beat AML to match the Alliance cohort data. Alliance RNA sequencing data from de novo AML patients was derived from GSE137851, GSE63646 and newly generated as in Papaioannou et al [66]. To generate gene signatures, one to the expression values were added, and used the average of the log2 transformed values as the gene set score. Pearson’s product moment correlation coefficient was used to determine correlations between signatures. t-SNE Visualization of AML Bulk Cohorts [0250] t-SNE gene expression maps were constructed as in Fornerod et al., 2021 [46]. For the adult AML cohort (n=872), the 400 most variant genes based on median absolute deviation were used, excluding gender specific gens (n=21) and HBB and genes clustering with HBB (n=31), which were previously shown to correlate with sample purity. t-SNE was run twice (10,000 iterations) with perplexity value 10, and the run with lowest final error was selected. For visualization of oncogenic drivers, priority was MECOM-r, KMT2A-r, CEBPAdm, RUNX1- RUNX1T1, CBFB-MYH11, FLT3-ITD, NPM1, TP53, RUNX1, ASXL1/2, IDH1/2. For the 73 165016996v1 Attorney Docket No.243735.000296 pediatric cohort (n=435) t-SNE coordinates were taken from Fornerod et al., 2021 [46], and visualization of oncogenic drivers was identical except that all FLT3-ITD mutations were co- colored. Outcome analysis Adult patients [0251] The molecular characteristics and outcome associations of 872 adult patients with de novo AML who were enrolled on CALGB/Alliance study protocols based on intensive cytarabine/daunorubicin-based chemotherapy were investigated. For all studies, per protocol, patients did not receive an allogeneic hematopoietic stem cell transplantation in first complete remission (CR). All patients gave written informed consent for participation in the studies. All study protocols were in accordance with the Declaration of Helsinki and approved by Institutional Review Boards at each treatment center. All patients were enrolled on CALGB 8461 (cytogenetic studies), CALGB 9665 (leukemia tissue bank) and CALGB 20202 (molecular studies) companion protocols. Mutational profiling of all patients had previously been published [67] and performed centrally at The Ohio State University by targeted amplicon sequencing using the MiSeq platform (Illumina, San Diego, CA) and additional Sanger sequencing for CEBPA mutations, adding up to a total of 81 genes analyzed. All outcome analyses on Alliance patients were performed by the Alliance Statistics and Data Center using SAS 9.4 and TIBCO Spotfire S+ 8.2. Definition of Clinical Endpoints and Statistics for Adult AML Patients [0252] Clinical endpoints were defined according to generally accepted criteria. Patients not known to have relapsed or died at last follow-up were censored on the date they were last examined. Overall survival (OS) was measured from the date of diagnosis to the date of death from any cause; patients not known to have died at last follow-up are censored on the date they were last known to be alive. Patients alive and in CR at last follow-up were censored. Estimated probabilities of OS were calculated using the Kaplan-Meier method, and the log-rank test evaluated differences between survival distributions. Inflammatory risk score was calculated from gene expression values weighted by Cox Proportional Hazard beta value for OS and summed per 74 165016996v1 Attorney Docket No.243735.000296 sample. All statistical analyses on Alliance patients were performed by the Alliance Statistics and Data Center. Pediatric Patients [0253] For the pediatric cohort with OS data (n=409), inflammatory risk score was calculated from gene expression values [46] of 185/187 inflammatory genes matched (Tables 4-5) weighted by Cox Proportional Hazard beta value and summed per sample. For the validation microarray cohort (n=386, excluding cases which overlapped with the RNA-seq cohort), the same per-gene-weights were used. 163/187 inflammatory genes matched. In case multiple probe sets matched a gene symbol, the probe set with highest specificity was selected. If specificity was the same, highest selectivity was selected. Probe sets with specificity <=0.8 were removed (n=6). Patient characteristics and datasets of both pediatric cohorts have been previously published [46]. Table 4: Adult iScore Genes Gene name LASSO OS Frequency beta mean ALOX5 1000 0.07179486 75 165016996v1 Attorney Docket No.243735.000296 Gene name LASSO OS Frequency beta mean SAMHD1 1000 0.2073884 Gene name LASSO OS Frequency Beta mean COTL1 1000 -0.0960934 LASSO Penalized Proportional Hazard Model [0254] To improve clinical applicability of the inflammation score, 1,000 iterations of leave-out- 10% cross validation of a LASSO penalized proportional hazards model with the cv.glmnet function of the glmnet R package [68] was performed on the Alliance cohort for the adult inflammation score, and the extended TARGET-AML cohort for the pediatric inflammation score. Genes selected in at least 900 of the 1,000 iterations were retained in the final model with coefficients defined as the average of their estimates over the 1,000 iterations. The final score (iScore) for adult and pediatric patients contained 38 and 11 genes, respectively, and was trained based on overall survival data. For adult and pediatric iScores, the performance of the signature 76 165016996v1 Attorney Docket No.243735.000296 was validated on independent AML cohorts in which the iScore was computed as a linear combination of expression values of the winner genes and fixed value coefficients defined as described above (for adult: TCGA, Beat AML; for pediatric: microarray cohort). The distribution of the score by risk was visualized using boxplot. The association of the score with OS and event free survival (EFS) was first addressed with Kaplan-Meier estimation where the continuous score was dichotomized using recursive partitioning method in the rpart function of the rpart R package [69]. Cut points were determined based on overall survival (separately for <60 and >=60 year old patients in adults) and the same groups were used in the event-free survival analysis. Cox proportional hazard regression was also applied to examine the association between the continuous score and survival adjusting for risk using the cph function of rms R package [70]. Hazard ratio and their confidence intervals were computed, and the shape of the association was plotted. Global Test [0255] Global associations between inflammatory gene expression sets (see above), genomic variables, LSC17 [45], pLSC6 and OS were calculated using the global test package in R [71, 72], with interactions. Tet2 Mutant Mice [0256] The B cell compartment was separately integrated using Seurat anchor-based integration method [60]. To visualize the data in 2-dimensional space, the first 20 principal components of the scaled integrated matrix were used to run UMAP projection. Atypical B cells were identified using previously published gene signatures [73]. Statistics and Reproducibility [0257] No statistical method was used to predetermine sample size, but the sample sizes were similar to those reported in other publications [15,19]. For malignant and microenvironment analysis, 3 patients for whom malignant and microenvironment cells could not be confidently separated were excluded. For analysis of B and T cells, 9 patients where less than 50 B or T cells were captured were excluded. No other data was excluded from the analysis. The experiments were not randomized. The investigators were not blinded to sample allocation during data collection, analysis and outcome assessment. 77 165016996v1 Attorney Docket No.243735.000296 [0258] All statistical analysis was performed in R version 3.6.1. Only Alliance cohort outcome analysis was performed using SAS 9.4 and TIBCO Spotfire S+ 8.2. Comparisons of numerical variables according to disease state or inflammation state were carried out using the Wilcoxon rank-sum for two- and Kruskal-Wallis test for multiple-group comparisons. Whenever multiple tests were performed, p-values were corrected for multiple comparison using Bonferroni correction unless otherwise stated. Estimated probabilities of OS were calculated using the Kaplan- Meier method, and the log-rank test to evaluate differences between survival distributions. Data Availability [0259] Human single cell RNA-seq, CITE-seq and TCR-seq data was submitted to GEO repository and can be accessed under GEO accession series number GSE185381. The RNA expression data can be interactively explored and downloaded on the Single Cell Portal: singlecell.broadinstitute.org/single_cell/study/SCP1987. Newly generated RNA-sequencing data from the Alliance cohort can be accessed on GEO using accession series number GSE216738. Previously published AML cohorts and mouse scRNA-seq data that were re-analyzed here are available under GSE137851, GSE63646, GSE182615. The human AML bulk RNA-Seq data were derived from the TCGA Research Network: http://cancergenome.nih.gov/. Source data have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request Results Malignant and Immune Landscape of Adult and Pediatric AML [0260] To examine the BM immune microenvironment in adult and pediatric AML, scRNA-Seq was performed on 10 BM samples from healthy donors (young donors - median age 20, age range 19-26 years, and older donors – median age 47, age range 39-53, total 50,244 cells), 20 diagnostic BM aspirates from adult AML patients (median age 68.5, age range 32-84 years, 89,733 cells), and 22 diagnostic BM aspirates from pediatric AML patients (median age 7.4, age range 2 months to 21 years, 74,440 cells). Uniform manifold approximation and projection (UMAP) demonstrated remodeling of the BM in both adult and pediatric AML with some clusters overlapping in healthy and AML BM, and some clusters dominated by either adult or pediatric AML cells (Fig.1A). To start characterizing the changes in the BM immune microenvironment in AML, cells were 78 165016996v1 Attorney Docket No.243735.000296 annotated based on their transcriptional profile and cell surface protein expression (Figs.1B, 6A). AML patients had an increase in specific subsets of hematopoietic stem and progenitor cells (HSPC) – hematopoietic stem cells (HSC), multipotent progenitors (MPP) and granulocyte- monocyte progenitors (GMP, Fig. 6B). Myeloid populations were largely unchanged, but individual patients had expansion of specific myeloid cell types (Fig.6C). In the lymphoid lineage, pre- and pro-B cells were severely depleted in patients, and plasmablasts and plasma cells were diminished in pediatric AML patients (Fig. 6D). CD4 ⁺ and CD8 ⁺ T cells were also depleted in pediatric patients, while MAIT cells and NK cells were diminished in both adult and pediatric patients (Fig.6E). Overall, this analysis demonstrates that the BM immune microenvironment is strongly altered in AML patients, with potential implications for disease progression. [0261] To further characterize the remodeled BM immune microenvironment in AML, it was sought to separate malignant cells from their healthy counterparts. In scRNA-seq analysis of solid tumors, malignant cells often form distinct, patient-specific clusters after dimensionality reduction [18-21]. It was therefore hypothesized that patient-specific clusters (Fig. 1C) may represent malignant cells. InferCNV has been used in solid malignancies to identify tumor cells [22], but to date has not been used in leukemias, in part due to the relative paucity of chromosome gains or losses in hematologic malignancies. To examine whether patient-specific clusters were enriched in cells containing chromosomal copy number variations, inferCNV was applied. The utilized patient cohort includes several patients with documented chromosome gains or losses, which were effectively captured by InferCNV (Figs. 1D, 7A). Notably, any copy number variations (CNV) were not detected in healthy BM samples (Fig. 7B). In patients with annotated karyotype abnormalities most cells carrying copy number variations (CNV ⁺ ) occupied patient specific clusters, allowing us to separate malignant cells from immune microenvironment cells (Figs.1E, 1F, 7C). The high abundance of CNV ⁺ cells in patient-specific clusters supports the hypothesis that these represent malignant cells from individual patients. Therefore, all cells were clustered and an occupancy score was calculated for each cluster, representing the fraction of cells from an individual patient in each cluster (Figs.8A, 8B) [23]. Clusters with an occupancy score larger than 0.70, indicating more than 70% of cells in the cluster originated from one patient, were designated as patient-specific clusters. Patient-specific clusters were then annotated as malignant cells specific to a given AML patient. 79 165016996v1 Attorney Docket No.243735.000296 [0262] To validate this approach, a previously published AML scRNA-seq dataset was analyzed, where malignant cells were identified using single-cell genotyping for specific mutations [15]. Annotation of malignant cells by occupancy score overlapped with single-cell genotyping detection of malignant cells (Figs. 8C, 8D). In the utilized patient cohort, small chromosomal aberrations were detected in patients without annotated chromosome gains or losses (Fig. 9A), suggesting that scRNA-Seq can provide karyotypic information. Patient occupancy scoring and single cell karyotyping with inferCNV allowed us to effectively separate the majority of malignant and microenvironment cells in 39 out of 42 patients (Fig.1G). For the 3 remaining patients, overlap with healthy donor cells and lack of CNV+ cells prevented us from confidently identifying malignant cells, and they were therefore excluded from further analysis. Malignant cells consisted mostly of myeloid cells or HSPC, with small fractions of T and B cells (Fig.1H). In 11 out of 42 (26%) patients, small numbers of CNV+ cells in all hematopoietic lineages were identified, including B, T and NK cells, suggesting that in these patients, chromosomal gains/losses occurred at an early developmental stage, allowing for dissemination across all hematopoietic lineages. T and B cells carrying copy number variations were present in both pediatric and adult patients (Fig. 9B), in line with previous reports of identification of leukemic mutations across all hematopoietic lineages [16]. Inflammatory Gene Signatures in Adult and Pediatric AML [0263] To characterize pathogenic processes underlying AML progression, non-negative matrix factorization (NMF) and differential expression analysis was performed on healthy and malignant HSPC and myeloid cells. Common gene expression profiles identified in NMF included cell-type specific, as well as distinct cellular programs. Malignant cells showed expression of programs enriched for cell cycle, RNA splicing, unfolded protein response, metabolic processes and inflammation (Figs. 2A-C, 10A). Further differential expression analysis, comparing different subsets of malignant cells to their counterparts in healthy donors (i.e., malignant HSPC-like cells compared to healthy donor HSPC and malignant myeloid-like cells compared to healthy donor myeloid cells) revealed dysregulated expression of a number of genes associated with inflammatory processes, including class II antigen presentation (HLA-DRA, HLA-DMA, HLA- DPB1, HLA-DPA1), S100 alarmins (S100A6, S100A4, S100A12), chemokines (CXCL8), and interferon response genes (IRF2BP2, ISG15, IFI44L, IFI27) (Figs. 2D, 2E). To further examine 80 165016996v1 Attorney Docket No.243735.000296 the role of inflammation in AML, adult-specific and pediatric-specific inflammation signatures were generated, consisting of inflammation-related genes that were dysregulated in malignant cells from adult or pediatric patients, respectively. While both the pediatric and adult inflammation signatures consist of similar pathways (Fig. 11A), only 95 genes overlap between the signatures (Fig. 11B), in line with known differences in tumor genomics and immune system maturation between pediatric and adult AML patients. However, despite differences in most of the individual inflammation-associated genes, shared resulting pathway activation between pediatric and adult patients suggests an inflammatory immune response is an important factor in AML which is present across the entire age spectrum of leukemia patients. [0264] To examine expression of the inflammation signatures in AML patients, pseudo-bulk expression data was generated from the scRNA-seq cohorts, then classified the patients based on their inflammatory states, defined by the identified inflammation-associated gene expression program. Inflammation is known to increase with age; therefore the inflammatory state was examined separately in the pediatric and adult cohorts. In adult AML patients, there was no correlation between the age of the patients and their inflammatory state. Indeed, the youngest patient in the utilized adult cohort had the highest inflammation score (Fig. 11C). Expression of the adult inflammation signature in adult patients was compared to older healthy BM donors, and the expression of the pediatric inflammation signature in pediatric patients to younger healthy BM donors. In adult patients, approximately half of the patients (9 out of 20, 45%) had increased inflammation compared to healthy donors (Fig. 11D). In pediatric patients, the inflammation score clearly separated patients into two groups (Fig.11E), with 7/22 patients (32%) showing high inflammation scores. To optimize analysis of the inflammation program in AML, the top 50% of adult patients were designated as highly inflamed and the bottom 50% as low inflammation, and the top third of pediatric patients as high inflammation while the bottom two thirds were designated as low inflammation. Patient-by-patient examination revealed that mRNA expression of inflammation-related genes varied between AML patients in both the adult and pediatric single cell cohort (Figs.11F, 11G). Atypical B cells Are Associated with Inflammation in AML [0265] Separation of malignant and myeloid cells enabled us to examine the effect of inflammation on the AML immune microenvironment. To investigate changes in the AML immune 81 165016996v1 Attorney Docket No.243735.000296 microenvironment, lymphoid lineages in the BM were assessed. Initially, B cells and annotated different populations were clustered based on transcriptome and surface protein expression (Figs. 3A, 12A, 12B). Interestingly, a subset of B cells, atypical B cells (expressing ITGAX, FCRL3, FCRL5), were enriched in adult and pediatric AML patients combined (Figs.3B, 12C). As atypical B cells are often found in patients with chronic or recurrent infections [24-27], it was examined whether they were more abundant in high inflammation AML patients. Analysis of the Alliance, the TCGA and the TARGET-AML pediatric cohorts revealed that the atypical B cell gene signature is highly correlated with the inflammatory state in both adult and pediatric AML patients (Figs. 3C, 12D), indicating that such cells are specifically enriched in AML patients with high inflammation. [0266] TET2 is frequently mutated in AML patients [28, 29] and was previously found to be associated with inflammation [30, 31]. Therefore, a BM scRNA-seq dataset of mice carrying mutations in Tet2 was examined, developing myeloid malignancies, including AML [32]. B cells from the BM of wild type (WT) and mutant mouse BM (Fig. 12E) were clustered, identifying clusters enriched in WT or Tet2-mutant mice (Fig. 12F). Expression of the atypical B cell gene signature was examined across all B cell clusters, identifying 3 B cell clusters enriched in Tet2- mutant mice and expressing atypical B cell marker genes (clusters 2, 4, and 9, Figs.12F, 12G), in agreement with the human CITE-Seq studies. In Tet2-mutant mice, the percentage of atypical B cells in the BM additionally correlated with disease severity (Fig.12H). [0267] To further validate the enrichment of atypical B cells in high inflammation AML patients, FACS analysis of BM samples from an additional cohort of adult AML patients with high or low inflammation scores was performed (Fig.12I). High inflammation patients had a higher percentage of atypical B cells in the BM compared to patients with low inflammation (Figs.3D, 3E). Atypical B cells consist of both class-switched and non-class-switched B cells, and are thought to be a suppressive B cell population with impaired antibody-production capacity [25, 27]. To assess the function of atypical B cells in AML patients, the transcriptional profile of atypical B cells in healthy BM donors and in AML patients was compared. Atypical B cells from AML patients expressed high levels of genes involved in B cell activation, such as CD83 [33], JUND [34], FOSB [34] and NFKB2 [35], as well as NR4A3, NR4A2 and ITGB2, that have been previously reported to be upregulated in atypical B cells from patients with chronic infections [27]. Furthermore, IRF8, which is associated with B cell anergy [36], was also upregulated in AML patients. On the other 82 165016996v1 Attorney Docket No.243735.000296 hand, genes involved in the germinal center reaction, such as BANK1 [37], PRKCB [38], and TXNIP [39] were downregulated in AML patients (Fig.3F). CITE-seq analysis revealed that cell surface marker CD72, which inhibits B cell receptor signaling [40], was also upregulated in AML- associated atypical B cells (Fig.3G). Overall, these data suggest that atypical B cells in AML are a suppressive B cell population, which are prevalent in the high inflammation AML BM. Diverging T cell Responses in High- and Low-Inflammation AML [0268] It was next sought to characterize the T and NK cell compartment in AML. The T and NK cells were annotated based on their transcriptome and surface protein expression (Figs.4A, 13A, 13B), and quantified different sub-populations in the BM. It has previously been reported that cytotoxic CD8+ T cells are depleted and regulatory T cells (TReg) are expanded in AML patients [15]. Significant changes in either cytotoxic or TReg populations in either adult or pediatric patients in the single cell cohort were not observed, although cytotoxic T cells were slightly expanded in patients’ BM (Figs.4B, 4C). Inflammation is known to affect T cell populations in solid tumors, and inflamed tumors are considered more immunogenic in this setting [4, 41]. Therefore, it was sought to examine the effects of inflammation on the T cell compartment in AML. While in adult patients any significant differences between high and low inflammation patients were not observed (Fig.13C), in pediatric patients, TReg and GZMK+ CD8+ T cells were significantly expanded in inflamed patients (Figs.4D, 4E). GZMK+ CD8+ T cells have previously been shown to be progenitors of terminally exhausted CD8+ T cells (TPEx) that traffic to sites of inflammation, and were suggested to respond to immune checkpoint blockade therapy [42, 43]. GZMK+ CD8+ T cells in the test dataset expressed a TPEx gene signature, including several exhaustion markers (PDCD1, TIGIT, TOX, Fig. 4F). Overall, these data suggest the T cell response is suppressed in pediatric high inflammation AML patients. [0269] To further characterize the T cell response to AML, T cells were sorted from the BM of 5 healthy donors, 3 pediatric and 7 adult AML patients (Fig.14A) and performed single cell T cell receptor sequencing (scTCR-seq). Examination of clone distribution in the BM revealed that while in adult AML patients T cell clones are expanded, in 3 out of 4 (75%) of the pediatric clonal expansion of T cells was not observed (Figs.4G, 14B). These patients were characterized by very young age (<3.5 years), suggesting that the T cell response is abrogated in early childhood AML patients. Indeed, an older pediatric patient (4.7 years old) did demonstrate clonal expansion of T 83 165016996v1 Attorney Docket No.243735.000296 cells at similar levels to adult patients (Fig. 14B), suggesting that anti-tumor T cell response in AML could be defined by patient age. [0270] It was further sought to characterize expanded T cell clones in the AML BM. Projection of clonal information on the T cell UMAP revealed that the majority of expanded clones in adult and pediatric AML are activated CD8+ T cells (Figs, 4H, 4I). Further examination of expanded clones from AML patients revealed that cells from specific expanded clones can be found across all CD8 ⁺ activation states, demonstrating a continuum of activation (Figs.4J, 14C). Finally, it was examined how inflammation affects clonal expansion in AML patients. Analysis of deconvoluted bulk TCR data for the TCGA dataset [44] demonstrated that high inflammation AML patients have increased clonal diversity, indicating higher immunogenic potential and less clonal expansion in high inflammation AML patients (Fig. 4K). However, in pediatric patients from the TARGET-AML cohort, a significant difference in clonal diversity between high and low inflammation patients was not observed (Fig.14D), potentially as a result of overall lower clonal expansion in pediatric patients. In conclusion, inflammation affects the T cell response and repertoire in AML patients, leading to an abrogated T cell response. The disclosed data raises the possibility that low inflammation patients may be more likely to respond to T cell-stimulating therapies. Clinical Implications of Inflammation in AML [0271] To study the effects of inflammation on patient prognosis, the association between the inflammation signature genes and overall survival in adult and pediatric patients was examined. In adult patients, 78 and 116 genes (31.7% and 47.1%) were negatively associated with overall survival (for patients < 60 years old and patients >=60 years old, respectively), whereas in pediatric patients, 63 genes (33.8%) were negatively associated with overall survival, suggesting a subset of the inflammation gene signature has prognostic value in AML (Tables 1-3). To better examine the association of inflammation with survival, an inflammation risk score was derived, incorporating the Cox regression beta coefficient value for each gene. High inflammation risk score correlated with reduced overall survival (OS), in both adult and pediatric patients (Figs.15A, 15B). Next, the inflammation gene sets of both pediatric and adult patients was reduced to generate clinically applicable gene signatures, using sparse regression analysis on the inflammation gene signatures in bulk RNA-seq cohorts for adult (Alliance) and pediatric (TARGET-AML) patients. This resulted in 38 and 11 core inflammation genes representing the survival risk associated with 84 165016996v1 Attorney Docket No.243735.000296 inflammation genes (iScore) for adult and pediatric patients, respectively (Tables 4-5), with continuous distribution across both cohorts as well as across all risk stratifications (Figs. 15C, 15D). [0272] To further delineate molecular features associated with higher or lower iScores, the iScore levels across known AML-associated molecular subtypes were compared. Adult and pediatric bulk RNA-sequencing data were visualized using t-distributed stochastic neighbor embedding (t-SNE) based on correlations of the most variable genes in each cohort, resulting in clusters that reflected the transcriptional identity and mutation profile of the patients (Figs. 5A, 5B). In both pediatric and adult AML patients, strong associations of the iScore with specific molecular drivers and transcriptional identity profiles was observed (Figs. 5C, 5D). Specifically, there was a strong association of low iScore with favorable molecular features such as inv(16) AML (p<0.001 adult and pediatric cohorts) NPM1 mutations (p<0.001 adult cohort, p=0.08 pediatric cohort). In contrast, established molecular adverse prognosticators, such as complex karyotype (p<0.001 adult cohort), CBFA2T3-GLIS2 (p<0.001 pediatric cohort), FLT3-ITD (p<0.001 adult cohort), RUNX1 (p<0.001 adult cohort) and TP53 mutations (p<0.001 adult cohort) associated with high iScore. Notably, despite the strong association with known molecular drivers and established outcome predictors, the iScore had added prognostic value in both adult and pediatric patients. In adult patients, a high iScore added independent prognostic impact in the context of other clinical prognostic parameters, such as stemness (LSC17 score [45]), in both younger and older patients (Fig. 15E). In pediatric AML patients, consideration of the inflammatory state provided independent prognostic information in addition to established clinical parameters such as oncogenic drivers, transcriptional identifiers and LSC6 stemness score (Fig.15F) [46]. [0273] Next, to assess the iScore adds clinically relevant information to established clinical and molecular parameters associated with treatment response and survival, the performance of the iScore was tested by itself, as well as in the context of other risk stratifiers, including the European LeukemiaNet (ELN) 2017 genetic risk classification for adult patients. Remarkably, in both adult and pediatric [46] AML patients, those with higher iScores had inferior OS (Figs.5E, 5F). These results were validated in TCGA and BeatAML for adult patients, and in a pediatric AML cohort of 399 patients with microarray data (Figs. 15G-I, adult – ELN favorable, intermediate and adverse; pediatric – genomic stratification per NCT03164057). Notably, implementation of the iScore within the ELN risk categories was able to markedly refine the current risk groups. 85 165016996v1 Attorney Docket No.243735.000296 Specifically, the OS of ELN favorable risk patients with high iScore was close to that of low iScore adverse risk patients (Figs. 5H, 5I), suggesting that these patients may benefit from different treatment intensities and/or modalities than conventional chemotherapy alone. In fact, examination of the iScore in patients with different risk stratifications (adult - ELN favorable, intermediate and adverse) revealed that patients with high iScore have worse outcomes across all risk stratifications (Fig.5G-J). Furthermore, in both adult (<60) and pediatric patients, patients with high iScore had worse event free survival (EFS) (Figs. 5K, 5L, 16A-E). For pediatric patients, where OS is generally better than for adult patients, the iScore can further be defined based on EFS, to improve patient stratification (Table 6). Overall, these data suggest that clinical implementation of iScore could refine patient risk stratification, which is a major determinant in the decision to transplant a patient in first remission or treat them with chemotherapy alone. Table 6: EFS Pediatric Scores Gene name Beta.mean.weight AIF1 0.11646192 Discussion Attorney Docket No.243735.000296 [0274] AML is an aggressive hematological cancer with low survival rates, in both adult and pediatric patients. In the methods disclosed herein, a comprehensive census of the BM microenvironment in adult and pediatric AML was provided. Malignant and microenvironment cells in the BM were distinguished, inflammatory programs in adult and pediatric AML patients were characterized, a detailed analysis of the different components of the BM immune microenvironment in AML was provided and described clinically relevant inflammation risk scores (iScore) that improve patient risk stratification. [0275] Adult and pediatric AML have similar clinical manifestations. However, in adults, AML is thought to be a progressive disease, arising over years due to acquisition of sequential mutations, and often going through pre-malignant stages of clonal hematopoiesis and myelodysplasia. In pediatric patients, AML is often thought to arise due to acquisition of mutations during early development of the hematopoietic system. Therefore, it is remarkable that the BM microenvironment in adult and pediatric AML patients was largely similar, with only a few exceptions. One is the increase in plasma cells in adult patients, which could reflect immunity to different pathogens acquired over years. The other is the lack of T cell clonal expansion in infant patients, a finding supported by recent bulk RNA-Seq and in silico TCR clonality predictions [44]. The T cell compartment can be immature in newborns; however infants can acquire T cell immunity to bacterial or viral pathogens [47]. It is possible that in infant AML patients, mutations acquired in utero disseminate across all hematopoietic lineages, affecting development of the immune system. In addition, early acquisition of AML-associated mutations may induce tolerance to these mutations, which prevents their recognition by T and B cells. [0276] While the disclosed analysis demonstrated that inflammation can be present across all differentiation stages in AML, the analysis provides a strong association between a more myeloid- like phenotype and inflammation. Thus, while inflammation is a global pathogenic module in AML, it is possible that the inflammation signature is partially driven by a specific differentiation stage. Studies suggest inflammation may play a role in many aspects of AML including disease progression, chemoresistance, and myelosuppression [48]. Further, inflammation can lead to a prothrombotic events such as stroke and cardiovascular complications (PMID: 33958774) [49]. The associations that was identified with specific B and T cell populations in the BM microenvironment suggest that this may be a functional feature of AML malignant cells, with important clinical implications. 87 165016996v1 Attorney Docket No.243735.000296 [0277] Strikingly, the data showed that inflammation may be strongly associated with enrichment of atypical B cells, a B cell population which emerges during chronic infections and is thought to serve as a suppressive B cell population, limiting auto-immunity [24-27]. In AML, atypical B cells express genes associated with B cell anergy and inhibition of BCR signaling, indicating that they serve a suppressive role in the AML microenvironment. Thus, while the immune system is activated, the disclosed findings suggest that in a subset of patients, inflammation can trigger an ineffective response characterized by atypical B cells. Targeting of atypical B cells may therefore be beneficial for high inflammation AML patients. [0278] Inflammation further remodeled the T cell compartment in pediatric but not adult AML patients. This may be due to differences in the inflammatory program in malignant cells, in line with a study identifying innate immune response genes as the main source of differential niche interactions between adult and pediatric AML in a mouse model [50]. High inflammation pediatric AML patients had an expansion of GZMK ⁺ precursor CD8 ⁺ T cells. Precursor CD8 ⁺ T cells can express high levels of immune checkpoints, and are thought to drive the response to ICB [42, 43, 51]. In AML, it has recently been reported that GZMK ⁺ T cells are enriched in patients responding to PD-1 blockade [52]. Furthermore, T _Reg were enriched in high inflammation pediatric AML patients, potentially curbing the T cell response to AML. Therefore, it is possible that pediatric AML patients expressing high levels of the inflammation gene signature will benefit from ICB or therapies aimed to diminish T _Reg activity. [0279] AML is often considered to be a “cold” tumor due to its low tumor mutation burden (TMB) and the poor response to ICB by AML patients [9, 10]. However, here it was demonstrated that subsets of adult and pediatric AML patients express inflammatory gene signatures in malignant cells, suggestive of an immune response, but conversely this response is associated with a poor outcome. Notably, a subset of inflammation-related genes provides independent prognostic information in both adult and pediatric patients. Therefore, examining the patient’s iScore in a clinical setting may be an important factor to be considered for more accurate prognosis assessment. This is particularly relevant for low and intermediate risk AML patients that currently receive chemotherapy alone and may benefit from intensification with stem cell transplant in first remission. Interestingly, inflammation has also been associated with relapse and reduced event- free survival in AML [53], suggesting that monitoring, and possibly modifying, inflammation in AML patients may be important in determining treatment and prognosis. 88 165016996v1 Attorney Docket No.243735.000296 [0280] In summary, the disclosure provides a unique overview of the BM immune microenvironment in AML. The disclosure demonstrates that inflammation plays an important role in shaping the AML microenvironment, and identify immune populations that are uniquely expanded in high inflammation AML patients. The disclosure describes an iScore with independent prognostic impact in AML. The disclosure proposes that stratifying AML patients based on their iScore could refine risk stratification in AML. * * * [0281] The present invention is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description. Such modifications are intended to fall within the scope of the appended claims. [0282] All patents, applications, publications, test methods, literature, and other materials cited herein are hereby incorporated by reference in their entirety as if physically present in this specification. References 1. Shallis, R. M., Wang, R., Davidoff, A., Ma, X. & Zeidan, A. M. Epidemiology of acute myeloid leukemia: Recent progress and enduring challenges. Blood Reviews 36, 70–87 (2019). 2. Baryawno, N. et al. A Cellular Taxonomy of the Bone Marrow Stroma in Homeostasis and Leukemia. 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