WO2024102484A1 - Procédés pour une stratification des risques améliorée de patients atteints de leucémie myéloïde aiguë adulte et pédiatrique à l'aide de signatures géniques d'inflammation - Google Patents

Procédés pour une stratification des risques améliorée de patients atteints de leucémie myéloïde aiguë adulte et pédiatrique à l'aide de signatures géniques d'inflammation Download PDF

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WO2024102484A1
WO2024102484A1 PCT/US2023/037167 US2023037167W WO2024102484A1 WO 2024102484 A1 WO2024102484 A1 WO 2024102484A1 US 2023037167 W US2023037167 W US 2023037167W WO 2024102484 A1 WO2024102484 A1 WO 2024102484A1
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iscore
subject
genes
aml
cohort
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PCT/US2023/037167
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Iannis Aifantis
Bettina NADORP
Audrey Lasry SANDLER
Ann-Kathrin Eisfeld
Tanja GRUBER
Stanley POUNDS
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New York University
St. Jude Children's Reserach Hospital, Inc.
Ohio State Innovation Foundation
The Board Of Trustees Of The Leland Stanford Junior University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • AML Acute Myeloid Leukemia
  • iScore inflammation gene signatures
  • BM bone marrow
  • AML myeloid malignancies
  • 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.
  • inflammation is often associated with a unique immune microenvironment, and can affect response to immunotherapy and patient prognosis [8].
  • the effects of inflammation on the composition of the BM immune microenvironment and clinical outcomes in AML have not been demonstrated.
  • 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
  • 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.
  • 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.
  • 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),
  • 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.
  • the adult human subject is at least 21 years old.
  • the reference cohort is The Cancer Genome Atlas (TCGA) cohort, Beat AML cohort, or Alliance cohort.
  • 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.
  • iScore inflammation-associated gene expression score
  • 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.
  • 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 method comprises determining the expression level of at least 3, 4, 5, 6, 7, 8, 9, 10, or 11 genes.
  • 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
  • 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.
  • 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
  • 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.
  • the pediatric subject is 0-21 years old.
  • 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]
  • the reference cohort is an age-matched group of subjects known to have AML with known survival outcomes.
  • 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.
  • the more aggressive treatment comprises a stem cell transplantation at the first complete remission.
  • 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.
  • the less aggressive treatment comprises a standard chemotherapy (e.g., administering cytarabine and/or anthracyclines).
  • the method further comprises administering a stem cell transplantation at the first complete remission to the subject classified into a high risk group.
  • the stem cell transplant is selected from an allogenic transplant, an autologous transplant, and any combinations thereof.
  • the stem cell transplant is from a matched-related donor, matched-unrelated donor, or haploidentical donor.
  • 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.
  • the subject sample is selected from bone marrow, peripheral blood, tissue biopsy, and cerebrospinal fluid (CSF).
  • CSF cerebrospinal fluid
  • 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
  • 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.
  • 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
  • the condition associated with clonal hematopoiesis is a cardiovascular disease, a cardiac event, chronic kidney disease, or chronic liver disease.
  • the cardiovascular disease is atherosclerosis, coronary artery disease, or venous thromboembolic disease.
  • the cardiac event is a myocardial infarction. 7 165016996v1 Attorney Docket No.243735.000296 [0029]
  • 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.
  • the adult human subject is at least 21 years old.
  • the reference cohort is The Cancer Genome Atlas (TCGA) cohort, Beat AML cohort, or Alliance cohort.
  • the method further comprises administering one or more anti- inflammatory therapies, if the subject classifies into a high risk group.
  • the gene expression level is determined by measuring mRNA level.
  • the mRNA level is determined using RNA sequencing, qPCR, panel sequencing, or an array.
  • the subject sample is selected from bone marrow and blood.
  • the subject is a newly diagnosed patient.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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, S
  • the subject is an adult subject or a pediatric subject.
  • the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the iScore of the subject.
  • 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, HOMER
  • the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the iScore of the subject.
  • FIG. 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.
  • 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.
  • (2C) UMAP representation of cells expressing inflammation-related features identified by NMF.
  • FIG. 3A-3G illustrate atypical B cells are associated with high inflammation in AML, according to example implementations of the disclosed technology.
  • FIGS 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.
  • FIG. 6A-6E illustrate cell populations in the human bone marrow, according to example implementations of the disclosed technology.
  • FIG. 8A-8C illustrate validation of occupancy score methods, according to example implementations of the disclosed technology.
  • Figures 9A-9B illustrate non-annotated karyotype aberrations detected by InferCNV, according to example implementations of the disclosed technology.
  • (9B) Patient-by-patient quantification of broad cell types in malignant cells.
  • 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.
  • Figures 11A-11G illustrate inflammatory signatures in AML, according to example implementations of the disclosed technology.
  • FIG. 12A-12I illustrate inflammatory B cells in AML, according to example implementations of the disclosed technology.
  • FIGS. 13A-13C illustrate T cell responses in AML, according to example implementations of the disclosed technology.
  • Figures 14A-14D illustrate clonal expansion of T cells in AML, according to example implementations of the disclosed technology.
  • 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.
  • 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.
  • references to a composition containing “a” constituent is intended to include other constituents in addition to the one named.
  • 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.
  • 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.
  • 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.
  • “about” can mean within an acceptable standard deviation, per the practice in the art.
  • “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.
  • the term can mean within an order of magnitude, preferably within 2-fold, of a value.
  • 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.
  • the term “reference cohort” refers to an age-matched group of subjects known to have AML with known survival outcomes.
  • 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.
  • 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).
  • BM bone marrow
  • PB peripheral blood
  • CSF cerebrospinal fluid
  • WBCs white blood cells
  • 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.
  • 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).
  • the term “overall survival (OS)” as used herein in connection with AML refers to a risk of death from AML.
  • 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.
  • the term “nucleic acid” includes DNA and RNA, and can be double stranded or single stranded.
  • hybridize or “hybridizable” refers to the sequence specific non-covalent binding interaction with a complementary nucleic acid.
  • 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.
  • SSC sodium chloride/sodium citrate
  • probe 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.
  • primer 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.
  • 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.
  • 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.
  • 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.
  • therapy resistance may mean any instance where cancer cells are resisting the effects of a therapy.
  • 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.
  • CR complete response
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • the AML patient may be a pediatric AML patient
  • the iScore may include a pediatric event free survival (EFS) iScore
  • the pediatric EFS iScore may be configured to predict an event free survival associated with the pediatric AML patient.
  • identifying the plurality of genes may be conducted using a LASSO penalized proportional hazards model.
  • 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.
  • scRNA-seq single cell RNA-sequencing
  • CITE-Seq cellular indexing of transcriptomes and epitopes by sequencing
  • 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.
  • 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.
  • 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.
  • 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.
  • the reference cohort is an age-matched group of subjects known to have AML with known survival outcomes.
  • the reference cohort for adult AML patients is The Cancer Genome Atlas (TCGA) cohort [63].
  • the reference cohort for adult AML patients is Beat AML cohort [65].
  • the reference cohort for adult AML patients is Alliance cohort [66].
  • the reference cohort for pediatric AML patients is Therapeutically Applicable Research To Generate Effective Treatments (TARGET) cohort [64].
  • the reference cohort for pediatric AML patients is the Netherlands microarray cohort.
  • 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.
  • gene expression level e.g., RNA sequencing
  • 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.
  • iScore inflammation-associated gene expression score
  • 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.
  • iScore inflammation-associated gene expression score
  • 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.
  • iScore inflammation-associated gene expression score
  • a method for determining a survival risk for an adult human subject diagnosed with Acute Myeloid Leukemia 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
  • a method for determining a survival risk for an adult human subject diagnosed with Acute Myeloid Leukemia 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
  • iScore inflammation-associated gene expression score
  • a method for determining a survival risk for an adult human diagnosed with Acute Myeloid Leukemia 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
  • 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.
  • LSC17 score 17-gene stemness score
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • a method of treating an adult human subject diagnosed with Acute Myeloid Leukemia 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
  • 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 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).
  • a method for determining a survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia 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.
  • iScore inflammation-associated gene expression score
  • a method for determining a survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia 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.
  • iScore inflammation-associated gene expression score
  • a method for determining a survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia 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
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • a method of treating a pediatric human subject diagnosed with Acute Myeloid Leukemia 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
  • 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
  • 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).
  • a method for determining an event-free survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia 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.
  • iScore inflammation-associated gene expression score
  • a method for determining an event-free survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia 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.
  • iScore inflammation-associated gene expression score
  • a method for determining an event-free survival risk for a pediatric human subject diagnosed with Acute Myeloid Leukemia 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
  • 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.
  • 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.
  • LSC score leukemia stem cell score
  • 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.
  • 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).
  • 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.
  • 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.
  • a method of treating a pediatric human subject diagnosed with Acute Myeloid Leukemia 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
  • 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 i
  • LSC score leukemia stem
  • 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).
  • 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
  • a method for determining the risk of an adult human subject developing a condition associated 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 expression level of at least two genes in a sample obtained from the subject, wherein the genes are selected from ALOX5, AP2S
  • a method for determining the risk of an adult human subject developing a condition associated 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, TNFRSF1
  • 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
  • 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.
  • the cardiovascular disease is atherosclerosis, coronary artery disease, or venous thromboembolic disease.
  • the cardiac event is a myocardial infarction.
  • 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.
  • the adult human subject is at least 21 years old.
  • the reference cohort is The Cancer Genome Atlas (TCGA) cohort, Beat AML cohort, or Alliance cohort.
  • the method further comprises administering one or more anti- inflammatory therapies.
  • a patient with a high iScore is administered an anti-inflammatory therapy.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • an array is provided.
  • an array comprises probes complementary to at least two genes selected from any of the genes in Table 4, Table 5, or Table 6.
  • 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.
  • 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.
  • 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.
  • an array described herein further comprises probes complementary to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2.
  • 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.
  • an array described herein further comprises probes complementary to at least two genes selected from CD34, DMNT3B, FAM30A, GPR56, SPINK2, and SOCS2.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the at least two genes is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 genes.
  • 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.
  • 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.
  • determining the gene expression profile further comprises building a subject GE profile from the determined expression of at least two genes.
  • 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.
  • GEP reference gene expression profile
  • 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.
  • the method further comprises a therapy.
  • the therapy may be a first and second therapy.
  • the first and second therapies may be different.
  • the therapy may be a first therapy and any concurrent therapy.
  • the any concurrent therapy as a second therapy, a third therapy, and more.
  • the first therapy and the any concurrent therapy may be different.
  • the therapy is an aggressive therapy.
  • a patient with high iScore is administered an aggressive therapy.
  • the aggressive therapy is a stem cell transplant.
  • the aggressive therapy is an intensified chemotherapy.
  • a patient with low iScore is administered a standard therapy.
  • the standard therapy is selected from a chemotherapy, targeted drug therapy, non-chemo drug therapy, surgery, radiation therapy, or any combination thereof.
  • the standard therapy is a chemotherapy.
  • the standard therapy is a targeted drug therapy.
  • the standard therapy is a non-chemo drug therapy.
  • the standard therapy is a surgery.
  • the standard therapy is a radiation therapy.
  • 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.
  • the targeted drug therapy is selected from FLT3 inhibitors, IDH inhibitor, BCL-2 inhibitors, Hedgehog pathway inhibitors, and any combination thereof.
  • the targeted drug therapy is selected from midostaurin, gilteritinib, ivosidenib, enasidenib, gemtuzumab ozogamicin, venetoclax, glasdegib, and any combination thereof.
  • the non-chemo drug therapy is selected from ATRA, arsenic trioxide, and a combination thereof. 44 165016996v1 Attorney Docket No.243735.000296 [0209]
  • the stem cell transplant is selected from an allogenic stem cell transplant, an autologous stem cell transplant, and any combination thereof.
  • the stem cell transplant is a bone marrow (BM) transplant.
  • 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
  • the predetermined threshold may be approximately 50 percent.
  • 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.
  • 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,
  • 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, MEF2
  • 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
  • the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the iScore of the subject.
  • the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the LSC17 of the subject.
  • the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the LSC6 of the subject.
  • 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
  • the instructions are configured to cause the processor to provide, at the user interface, a visual representation of the iScore of the subject.
  • 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.
  • 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.
  • 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.
  • 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).
  • FBS fetal bovine serum
  • T cells were enriched using a pan T cell isolation kit (Miltenyi Biotec, 130-096-535) or sorted for live CD45 + CD3 + 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.
  • 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_protocol_190213.pdf), incorporated herein by reference. Libraries were sequenced on an Illumina NovaSeq 6000.
  • HTO hashtag oligonucleotides
  • CLR centered log ratio
  • Souporcell 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).
  • the recoverDoublets function was used to identify cells similar to those identified as doublets by HTODemux.
  • trajectoryMode TRUE
  • 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%.
  • RNA expression data was normalized by total expression, multiplied by a scaling factor of 10,000 and log-transformed.
  • ADT antibody derived tags
  • counts were divided by the geometric mean of each corresponding feature across cells and then log-transformed (CLR transformation).
  • CLR transformation log-transformed
  • Uniform manifold approximation and projection [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].
  • 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.
  • 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.
  • 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.
  • 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.
  • 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]
  • NMF non-negative matrix factorization
  • cNMF v1.1
  • All genes expressed in less than 50 cells were filtered and used only the top 2000 over-dispersed genes in the NMF analysis.
  • silhouette score and Frobenius reconstruction error were used as implemented in cNMF.
  • 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.
  • 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.
  • TCR CDR3s per kilo TCR reads 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].
  • OS Overall survival
  • the final score (iScore) for adult and pediatric patients contained 38 and 11 genes, respectively, and was trained based on overall survival data.
  • iScore 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.
  • 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.
  • UMAP Uniform manifold approximation and projection
  • HSC hematopoietic stem and progenitor cells
  • MPP multipotent progenitors
  • GMP granulocyte- monocyte progenitors
  • Fig. 6B Myeloid populations were largely unchanged, but individual patients had expansion of specific myeloid cell types (Fig.6C).
  • BM immune microenvironment is strongly altered in AML patients, with potential implications for disease progression.
  • 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.
  • 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).
  • CNV + copy number variations
  • FIG. 8C, 8D Annotation of malignant cells by occupancy score overlapped with single-cell genotyping detection of malignant cells.
  • Fig. 9A 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.
  • Fig.1H 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].
  • NMF non-negative matrix factorization
  • Atypical B cells are Associated with Inflammation in AML
  • Separation of malignant and myeloid cells enabled us to examine the effect of inflammation on the AML immune microenvironment.
  • lymphoid lineages in the BM were assessed.
  • B cells and annotated different populations were clustered based on transcriptome and surface protein expression (Figs. 3A, 12A, 12B).
  • a subset of B cells, atypical B cells (expressing ITGAX, FCRL3, FCRL5), were enriched in adult and pediatric AML patients combined (Figs.3B, 12C).
  • 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.
  • 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 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.
  • the percentage of atypical B cells in the BM additionally correlated with disease severity (Fig.12H).
  • 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).
  • 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.
  • 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].
  • TPEx terminally exhausted CD8+ T cells
  • PDCD1, TIGIT, TOX, Fig. 4F exhaustion markers
  • 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.
  • 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.
  • 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).
  • OS overall survival
  • Figs.15A, 15B 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.
  • 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).
  • t-SNE stochastic neighbor embedding
  • AML is an aggressive hematological cancer with low survival rates, in both adult and pediatric patients.
  • 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.
  • iScore inflammation risk scores
  • 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.
  • 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.
  • 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].
  • 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].
  • 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.
  • 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.
  • 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].
  • GZMK + T cells are enriched in patients responding to PD-1 blockade [52].
  • 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.
  • 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].
  • TMB tumor mutation burden
  • ICB ICB
  • 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.
  • 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.
  • 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.
  • Atypical B cells are part of an alternative lineage of B cells that participates in responses to vaccination and infection in humans.
  • Souporcell robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat Methods 17, 615–620 (2020). 56. Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet- based single-cell RNA sequencing data. GigaScience 9, giaa151 (2020). 57. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 37, 38–44 (2019). 58. Witkowski, M. T. et al. Extensive Remodeling of the Immune Microenvironment in B Cell Acute Lymphoblastic Leukemia. Cancer Cell 37, 867-882.e12 (2020).
  • RNA HOXB-AS3 regulates ribosomal RNA transcription in NPM1-mutated acute myeloid leukemia. Nat Commun 10, 5351 (2019). 67. The mutational oncoprint of recurrent cytogenetic abnormalities in adult patients with de novo acute myeloid leukemia

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Abstract

La technologie de l'invention concerne des procédés pour une stratification des risques améliorée de la leucémie myéloïde aiguë (LMA) de patients, et plus particulièrement, pour une stratification des risques améliorée de patients atteints de LMA adulte et pédiatrique à l'aide de signatures géniques d'inflammation (iScore).
PCT/US2023/037167 2022-11-11 2023-11-10 Procédés pour une stratification des risques améliorée de patients atteints de leucémie myéloïde aiguë adulte et pédiatrique à l'aide de signatures géniques d'inflammation WO2024102484A1 (fr)

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