Gene signature for prognosing melanoma outcome and response to immunotherapy

Abstract

The disclosure relates to methods of identifying a unique cell type in a tumor biopsy sample based upon gene expression, and uses thereof for prognosing patients with melanoma.

Description

[0001] GENE SIGNATURE FOR PROGNOSING MELANOMA OUTCOME AND RESPONSE TO IMMUNOTHERAPY

[0002] PRIORITY STATEMENT

[0003] This application claims priority to U.S. Provisional Application No. 63 / 573,317, filed April 2, 2024, the entire contents of which are incorporated herein by reference for all purposes.

[0004] FIELD

[0005] The disclosure relates to methods of identifying a unique cell type in a tumor biopsy sample based upon gene expression, and uses thereof for prognosing patients with melanoma.

[0006] BACKGROUND

[0007] Melanoma is a malignant tumor affecting over 300,000 people worldwide as a new diagnosis every year. Most cases of melanoma appear to be early stage or Stage I (>70%) with less Stage II-IV patients diagnosed annually. However, melanoma may recur or metastasize resulting in carryover of “cured” patients into the number of new diagnoses every year. Current methodology for classifying the stage or malignancy of melanoma rely on the depth of the tumor, a sentinel lymph node biopsy or lymph nodes with growth indicative of cancer, and known metastases of melanoma. Other techniques for further stratification rely on tumor sequencing for known mutations with treatment selection affecting less than 50% of afflicted patients. In addition, anti PD- 1 therapy has become standardized treatment for melanoma patients with advancing disease, but has a poor rate of response that does not correlate well with currently available biomarkers. As such, there is a need for improved methods of prognosing melanoma and predicting responsiveness to immune checkpoint therapy, which will guide personalized treatment for patients.

[0008] SUMMARY

[0009] Provided herein are methods of detecting infiltration of a myeloid cluster of hybrid immune cells in a tumor biopsy sample obtained from a subject, comprising measuring expression of a plurality of genes in the tumor biopsy sample. The plurality of genes comprise: one or more Group 1 genes selected from CLEC10A, FLT3, CD ID, RTN1, GPAT3, AC009093.2, PKIB, CCSER1, KCNK6, PDE4A, APAF1, and SNX20; one or more Group 2 genes selected from HLA-DQA1 , HLA-DQB 1 , HLA-DRA, HLA- DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA-DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, IFI30, AIF1, TYROBP, CD68, FCER1G, RAB31, CTSS, RNASET2, SAMHD1, TYMP, VAMP8, ITGB2, and HCLS1; and one or more Group 3 genes selected from LGALS2, SLAMF8, CALHM6, C15orf48, HLA-DQB2, HLA-DQA2, CD300C, CPVL, ALDH2, FCGR2B, FGL2, ANKRD22, CD86, P2RY6, IL4I1, GAPT, RNASE6, FPR3, CLEC4A, CD74, CD33, FBP1, IL18, MS4A6A, PLD4, SPINT2, SIGLEC7, CLEC12A, PLA2G7, SLC8A1, LY86, OSCAR, LILRB4, SPI1, CSTA, WDFY4, IGSF6, CFP, PTGS1, CARD9, C1QB, SERPINA1, MNDA, FCGR1A, MARCH1, CST3, C1QC, FGD2, C1QA, CXCL16, CLIC2, MPEG1, SIGLEC9, HCK, CD72, HLA-DOA, FCGR1B, LILRB2, TNFSF13, TFEC, PRAM1, ADORA3, SIGLEC10, CSF1R, LILRB1, VSIG4, SLC31A2, CIITA, LRRC25, CLEC7A, CSF2RA, RGS18, AC020656.1, C19orf38, TMEM176B, KMO, PTAFR, OLR1, CYBB, AREG, Clorf54, MS4A4A, PLBD1, TM6SF1, C3AR1, MRC1, GNA15, CXorf21, P2RY13, SMCO4, ADAP2, PILRA, OTULINL, ALOX5, PLXDC2, SLC7A7, TREM2, MS4A7, PID1, CD163, TLR2, TMEM176A, RASSF4, CD40, NLRP3, ZNF385A, DSE, PSTPIP2, ANPEP, TNFAIP8L2, SCIMP, SYK, LILRB3, FGR, KYNU, IRF8, FAM49A, TNFSF13B, CLEC4E, VASH1, ADA2, IL1B, JAML, NCF2, TMEM273, IRF5, RBM47, FCGR3A, MSR1, IL18BP, CCR1, DAPP1, ADAM28, BCAT1, PLEK, ITGAX, GPR34, LAIR1, LGALS9, CD300A, LILRA2, LRRK2, CD302, BASP1, FCN1, CD14, BTK, SLC43A2, LYN, NF AMI, FPR1, IL13RA1, ATP8B4, SLCO2B1, TBXAS1, SLC15A3, LAT2, FES, SIGLEC1, IL1R2, MAFB, RAB20, EAF2, NA A A, CD83, STX11, CSF3R, MIR181 A1HG, TLR4, ZNF710, SLC1A3, LIPA, PAK1, GPR183, IRAK3, EMILIN2, MILR1, OGFRL1, IFNGR1, ITGAM, KCTD12, TRPM2, B3GNT5, DOK3, CTSH, ADGRE2, PTPRE, FOLR2, PLAUR, C5AR1, RNF144B, SULF2, SLC40A1, GCA, FCGR2A, METTL7A, MAP3K8, TGFB, NAIP, HVCN1, TNFAIP2, THEMIS2, MFSD1, UNC93B1, ALDH3B1, JAK2, DOK1, PLEKHO1, CCDC200, TMEM106A, ABL3, AXL, MANBA, C9orf72, AC004687.1, MPP1, PIK3AP1, SH3TC1, GSAP, SLC37A2, HACD4, GLIPR1, RELT, ATF5, MERTK, TET2, KCNMA1, SH2B3, NRROS, FCGRT, UBE2E2, VSIR, NCF4, STAB1, LRRK1, TREM1, FUCA1, NAGA, PARVG, GRN, MAN2B1 , APOCI , NCF1 , CD4, SLC11 Al, EPB41L3, ETS2, RASGEF1B, ODF3B, ARRB2, SLC25A19, ATP2B1-AS1, WAS, LAP3, NAGK, CCL3L1, RNF130, PSAP, AOAH, CASS4, LPCAT2, ST8SIA4, FCHO2, SYNGR2, NFKBID, CHST15, HBEGF, PLAGL1, TCN2, CREG1, SMIM3, SDSL, GM2A, NCKAP1L, TNFSF10, RP2, RIN3, CTSC, DPYD, RPS6KA4, EPSTI1, GAA, RGL1, DENND3, GLIPR2, THBS1, ACSL1, CEBPD, PLAC8, C3, CD93, RGS2, PPT1, RHOG, SLC16A3, IGFLR1, DRAM1, RILPL2, GLRX, HM0X1, KLHL6, CAMK1, C2, SIPA1L1, RGS10, HNMT, MYD88, F13A1, POU2F2, ARHGAP18, RASSF2, BID, DHRS3, DMXL2, TBC1D8, PRKAG2, RIPK2, SLAMF7, ETV6, MAN1A1, SQOR, OSBPL11, DENND1B, MXD1, ACP2, NIPSNAP3A, NUDT16, NPC2, SRGN, LACTB, FUOM, ITGB2-AS1, SPTLC2, TPK1, DOCK5, LRP1, GLUL, CAMKID, COTL1, UBE2D1, ALCAM, ADCY7, HDAC9, LGMN, DOCK4, RGS19, SCPEP1, ZYX, GK, PLSCR1, NMI, DAPK1, and TEX14.

[0010] The myeloid cluster of hybrid immune cells are identified as having increased expression of at least one Group 1 gene, at least one Group 2 gene, and at least one Group 3 gene. In some embodiments, the subject has or is suspected of having melanoma.

[0011] In some embodiments, the Group 1 genes are selected from CLEC10A, FLT3, CD1D, RTN1, GPAT3, and AC009093.2. In some embodiments, the Group 2 genes are selected from HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA- DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, and IFI30. In some embodiments, the Group 3 genes are selected from ALDH2, ANKRD22, C15orf48, CIQA, C1QB, C1QC, CALHM6, CARD9, CD300C, CD33, CD74, CD86, CFP, CLEC12A, CLEC4A, CLIC2, CPVL, CST3, CSTA, CXCL16, FBP1, FCGR1A, FCGR2B, FGD2, FGL2, FPR3, GAPT, HCK, HLA-DQA2, HLA-DQB2, IGSF6, IL18, IL4I1, LGALS2, LILRB4, LY86, MARCH1, MNDA, MPEG1, MS4A6A, OSCAR, P2RY6, PLA2G7, PLD4, PTGS1, RNASE6, SERPINA1, SIGLEC7, SIGLEC9, SLAMF8, SLC8A1, SPI1, SPINT2, and WDFY4.

[0012] In some embodiments, expression of the plurality of genes in the tumor biopsy sample is measured by sequencing. For example, in some embodiments the sequencing comprises single cell RNA sequencing, targeted gene expression sequencing, or bulk RNA sequencing. In some embodiments, the method further comprises calculating a percent infiltration of the myeloid cluster of immune cells in the tumor biopsy sample relative to the total cell count of the sample.

[0013] In some embodiments, the method further comprises prognosing melanoma in the subject based upon the percent infiltration. For example, in some embodiments prognosing melanoma comprises identifying the subject as having increased risk of recurrence, metastasis, and / or disease progression when the percent infiltration is below a threshold value. In some embodiments, prognosing melanoma comprises identifying the subject as at risk for poor overall survival or not at risk of poor overall survival based on the percent infiltration. For example, in some embodiments the method comprises identifying the subject as at risk for poor overall survival when the percent infiltration is below a threshold value, or not at risk of poor overall survival when the percent infiltration is above a threshold value. For any of the methods herein, the threshold value may be 2.6%. In some embodiments, the threshold value is 1.9%.

[0014] In some embodiments, the method further comprises monitoring the subject with increased frequency and / or providing an aggressive anti-cancer treatment to the subject identified as having increased risk of recurrence, metastasis, and / or disease progression or identified as at risk for poor overall survival. In some embodiments, the aggressive anti-cancer treatment is selected from chemotherapy, radiation therapy, immunotherapy (e.g. immune checkpoint inhibitor therapy, adoptive cell transfer, cancer vaccine, cytokine therapy), targeted therapy (e.g. therapy targeting BRAF, MEK, etc.), and a combination thereof. In some embodiments, the melanoma is surgically removed and an aggressive anti-cancer treatment is provided to the subject. In some embodiments, the aggressive anti-cancer treatment is an elevated dose and / or increased dosing frequency of the anti-cancer treatment (e.g. the chemotherapy, immunotherapy, targeted therapy, radiation therapy, or combination thereof) compared to the dose or dosing frequency that would otherwise be provided to a subject not at increased risk of recurrence, metastasis, and / or disease progression or not at risk of poor overall survival.

[0015] In some embodiments, the method comprises determining responsiveness to a treatment for melanoma in the subject based upon the percent infiltration. For example, in some embodiments the subject has received at least one dose of the treatment for melanoma prior to the tumor biopsy sample being obtained from the subject, and determining responsiveness comprises identifying the subject as likely to have a positive response to the treatment when the percent infiltration is equal to or above a threshold value, or identifying the subject as unlikely to have a positive response to the treatment when the percent infiltration is below the threshold value. In some embodiments, the threshold value is 2.6%. In some embodiments, the threshold value is 1.9%. In some embodiments, the method comprises providing an alternative antimelanoma therapy to the subject identified as unlikely to have a positive response.

[0016] In some embodiments, the subject is a mammal. In some embodiments, the subject is a human.

[0017] BRIEF DESCRIPTION OF THE DRAWINGS

[0018] FIGS. 1A-1D show overall results of clustering on single-cell RNA sequencing of melanoma patients. FIG. 1 A shows overall clustering and sub-clustering results using scCESS depicted on two-coordinate feature map using UMAP. FIG. IB shows overall, frequency of cell type compared to all sequenced cells by tissue source analyzed for between group differences using GraphPad Prism using one way ANOVA with Tukey’s multiple comparisons testing; * p < 0.05, ** p < 0.01, *** p <0.001, **** p <0.0001. FIG. 1C shows gene expression by cluster with selected genes as log2FC value on heatmap. FIG. ID shows a heatmap of relative gene expression of selected genes on two coordinate feature UMAP.

[0019] FIG. 2 shows outgoing signal pattern and incoming signal pattern depicted by CellChat Analysis with depiction of signal motif by cell type cluster. T cells showed increased MHC-I outgoing signaling (inflammatory signaling) and myeloid cells upregulated LAIR1 and APRIL (inflammatory signaling).

[0020] FIGS. 3A-3E show myeloid Cell Type 1 (M_C1) infiltration improves overall survival (OS) in metastatic melanoma. FIG. 3A shows Kaplan-Meier survival curve depicting overall survival (OS) for brain metastases cohort when stratified by M_C1 cell frequency identified by recursive partitioning analysis (RPA). FIG. 3B shows RPA results of training brain metastases cohort depicting each “Node” identified with cut-off frequency of M_C1 cell determined by analysis. FIG. 3C shows a table depicting patient characteristics between RPA groups with univariate analysis conducted. FTG. 3D shows cox regression analysis of RPA groups on OS with calculated Hazard Ratio. FIG. 3E shows median OS months calculated for each RPA group.

[0021] FIGS. 4A-4E show M_C1 infiltration is affected by nivolumab treatment in pre vs on treatment biopsy and improved overall survival (OS). FIG. 4A shows Kaplan-Meier plot of Overall Survival (OS) Months for patients stratified by M_C1 infiltration nodes based on pretreatment biopsy samples with bulk RNA sequencing with cell type percentages calculated by deconvolution using CIBERSORT. FIG. 4B shows multivariate cox regression analysis of M_C1 infiltration nodes for pre-treatment biopsy sequencing. FIG. 4C shows OS by stratification of M CI infiltration nodes for on treatment biopsy samples. FIG. 4D shows multivariate cox regression analysis of M_C1 infiltration nodes for on treatment biopsy sequencing. FIG. 4E shows Stuart-Marxwell test of treatment effect of Nivolumab on pre-treatment to on treatment node assignment based on bulk RNA sequencing results analyzed by deconvolution using CIBERSORT.

[0022] FIGS. 5A-5F show overall survival (OS) in TCGA cohort by stage of melanoma and M_C1 infiltration. Kaplan-Meier analysis of Overall Survival (OS) months stratified by stage of melanoma at diagnosis (FIG. 5A) and M_C1 infiltration nodes by Stage of melanoma (FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E, and FIG. 5F).

[0023] DEFINITIONS

[0024] To facilitate an understanding of the present technology, a number of terms and phrases are defined below. Additional definitions are set forth throughout the detailed description.

[0025] Unless otherwise defined herein, scientific and technical terms used in connection with the present disclosure shall have the meanings that are commonly understood by those of ordinary skill in the art. The meaning and scope of the terms should be clear; in the event, however, of any latent ambiguity, definitions provided herein take precedent over any dictionary or extrinsic definition. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular.

[0026] The articles “a” and “an” are used herein to refer to one or to more than one (z.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element, e.g., a plurality of elements. As used herein, the modifier “about” used in connection with a quantity is inclusive of the stated value and has the meaning dictated by the context (for example, it includes at least the degree of error associated with the measurement of the particular quantity). The modifier “about” should also be considered as disclosing the range defined by the absolute values of the two endpoints. For example, the expression “from about 2 to about 4” also discloses the range “from 2 to 4.” The term “about” may refer to ±10% of the indicated number. For example, “about 10%” may indicate a range of 9% to 11%, and “about 1” may mean from 0.9 - 1.1. Other meanings of “about” may be apparent from the context, such as rounding off; for example, “about 1” may also mean from 0.5 to 1.4.

[0027] The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The singular forms “a,” “and” and “the” include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other embodiments “comprising,” “consisting of’ and “consisting essentially of,” the embodiments or elements presented herein, whether explicitly set forth or not.

[0028] For the recitation of numeric ranges herein, each intervening number therebetween with the same degree of precision is explicitly contemplated. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the number 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.

[0029] As used herein, the terms “treat”, “treatment”, and “treating” refer to administration of an agent or therapy to a subject for the purpose of achieving a desired clinical result. Beneficial or desired clinical results include, but are not limited to, alleviation of symptoms; diminishment of the extent of a condition, disorder, or disease stabilized (i.e., not worsening) state of condition, disorder, or disease; delay in onset or slowing of condition, disorder, or disease progression; amelioration of the condition, disorder, or disease state or remission (whether partial or total), whether detectable or undetectable; an amelioration of at least one measurable physical parameter, not necessarily discernible by the patient; or enhancement or improvement of condition, disorder, or disease. Treatment includes eliciting a clinically significant response without excessive levels of side effects. Treatment also includes prolonging survival as compared to expected survival if not receiving treatment. “Treating” cancer can refer to reducing the size of a tumor, eliminating a tumor, reducing the number of tumors, eliminating all tumors, reducing risk of metastasis, improving overall survival, and the like.

[0030] A “subject” or “patient” broadly refers to any living organism, and more specifically to an animal including human and non-human animals. In some embodiments, the subject is a mammal. Examples of mammals include, but arc not limited to, any member of the Mammalian class: humans, non-human primates such as chimpanzees, and other apes and monkey species; farm animals such as cattle, horses, sheep, goats, swine; domestic animals such as rabbits, dogs, and cats; laboratory animals including rodents, such as rats, mice and guinea pigs, and the like. Examples of non-mammals include, but are not limited to, birds, fish, and the like. In one embodiment, the mammal is a human. In some embodiments, the subject has or is suspected of having melanoma.

[0031] The term “myeloid cluster of hybrid immune cells” or “M CI” cell are used interchangeably herein. M_C1 cells are characterized by increased expression of one or more Group 1 genes, one or more Group 2 genes, and one or more Group 3 genes, as described in detail herein.

[0032] The term “tumor biopsy sample” refers to a tissue sample having or suspected of having tumor cells. The tumor biopsy sample may contain additional cell types in addition to the tumor cells. In some embodiments, the tumor biopsy sample is obtained from (e.g. removed from) a subject having or suspected of having melanoma. In some embodiments, the tumor biopsy sample is a biopsy from a primary melanoma. In some embodiments, the tumor biopsy is a biopsy from a metastatic melanoma. In some embodiments, the tumor biopsy sample is obtained from a subject after the subject has received at least one dose of a treatment for melanoma. In some embodiments, the tumor biopsy sample is obtained from the subject before the subject has received any treatment for melanoma.

[0033] DETAILED DESCRIPTION

[0034] The aspects and embodiments described herein are predicated at least in part on the discovery of a novel gene signature, referred to herein as the M_C1 gene signature. The M_C1 gene signature comprises one or more Group 1 genes, one or more Group 2 genes, and one or more Group 3 genes, as described in more detail below. In some embodiments, expression of genes in the M_C1 gene signature is indicative of melanoma prognosis (e.g. risk of poor overall survival) and / or indicative of responsiveness to an anti-melanoma therapy for the subject. In some embodiments, provided herein are methods and kits for measuring the M_C1 gene signature (e.g. measuring the expression of the genes that comprise the M_C1 gene signature). In some embodiments, the M_C1 gene signature is measured in a tumor tissue sample, such as a tissue obtained from a subject having or suspected of having melanoma.

[0035] In some embodiments, the M_C 1 gene signature is indicative of infiltration of a myeloid cluster of hybrid immune cells, referred to herein as M_C1 cells, into the tumor sample. In some embodiments, the amount of infiltration of M_C1 cells into the tumor tissue sample (e.g. relative to the total amount of cells in the tumor tissue sample) is indicative of melanoma prognosis (e.g. risk of poor overall survival) and / or is indicative of responsiveness to an anti-melanoma therapy. In some embodiments, the M_C1 signature is measured to evaluate infiltration of a myeloid cluster of hybrid immune cells in a tumor, such as a melanoma. In some embodiments, the M_C1 gene signature is indicative of one or more cell states in tumor tissue that are drivers of and / or indicators of cancer outcome. Regardless of the biological underpinning of the M_C1 gene signature, it is demonstrated herein the degree of M..C1 gene signature expression measured in bulk tumor samples is variable among subjects and serves as a prognostic biomarker of cancer outcome. For example, as demonstrated herein, M..C1 gene signature serves as a robust prognostic indicator in melanoma tissue from all stages of the disease, and serves as an indicator of responsiveness to anti -melanoma therapy. In some embodiments, the methods herein are used to assess, predict, prognose or otherwise estimate the likelihood of various cancer outcomes. Cancer outcomes include, for example: likelihood of progression-free survival; survival probability, such as three-year survival, five-year survival, ten-year survival, overall survival, discasc-frcc survival, or life expectancy; probability of progression to the next stage; probability of recurrence; assessing metastatic potential; and assessment of patient response to current and / or past treatment, etc.

[0036] In some aspects, provided herein arc methods of identifying a myeloid cluster of hybrid immune cells in a tumor biopsy sample obtained from a subject. The disclosure further provides uses thereof, such as to classify individuals affected with melanoma into distinct subtypes using tumor biopsy samples (e.g. samples of primary cutaneous tumor, sentinel or metastatic lymph nodes, and / or metastatic tumor), prognose melanoma outcomes, determine responsiveness to anti-cancer therapies, and the like. In some embodiments, the methods provided herein arc used to prognose melanoma in an individual afflicted with Stage I melanoma, Stage II melanoma, Stage III melanoma, or Stage IV, advanced or metastatic melanoma. In some embodiments, the methods herein are used to prognose an individual afflicted with melanoma and treated by an anti-melanoma therapy. In some embodiments, the anti-melanoma therapy is an immunotherapy including but not limited to therapeutics targeting PD-1 / PD-L1, CTLA-4, etc. In some embodiments, the anti-melanoma therapy is chemotherapy, radiation, or a targeted therapy including but not limited to therapeutics targeting BRAF, MEK, etc. The methods provided herein can be applied to both single cell or bulk RNA sequencing data from a primary skin tumor, sentinel lymph node, or metastases.

[0037] In some aspects, provided herein are methods of detecting infiltration of a myeloid cluster of hybrid immune cells in a tumor biopsy sample obtained from a subject. In some embodiments, the tumor biopsy sample is obtained from a subject having or suspected of having melanoma. In some embodiments, the subject has stage I melanoma. In some embodiments, the subject has stage II melanoma. In some embodiments, the subject has Stage III or Stage IV melanoma. In some embodiments, the subject has received at least one dose of a treatment for melanoma.

[0038] In some aspects, methods of measuring the M_C1 gene signature comprise measuring expression of a plurality of genes in a tumor biopsy sample. As described above, expression of the M_C1 gene signature may be indicative of infiltration of M_C1 cells in the tumor. In some aspects, methods of measuring the M_C1 gene signature or methods of detecting infiltration of a myeloid cluster of hybrid immune cells (referred to herein as “MC_1 cells”) in a tumor biopsy sample obtained from a subject comprise measuring expression of a plurality of genes in the tumor biopsy sample. In some embodiments, expression of the plurality of genes is directly or indirectly indicative of cell types in the tumor biopsy sample. In some embodiments, the plurality of genes comprise: one or more Group 1 genes selected from CLEC10A, FLT3, CD1D, RTN1, GPAT3, AC009093.2, PKIB, CCSER1, KCNK6, PDE4A, APAF1, and SNX20; one or more Group 2 genes selected from HLA-DQA1 , HLA-DQB 1 , HLA-DRA, HLA- DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA-DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, IFI30, AIF1, TYROBP, CD68, FCER1G, RAB31, CTSS, RNASET2, SAMHD1, TYMP, VAMP8, ITGB2, HCLS1; and one or more Group 3 genes selected from LGALS2, SLAMF8, CALHM6, C15orf48, HLA-DQB2, HLA-DQA2, CD300C, CPVL, ALDH2, FCGR2B, FGL2, ANKRD22, CD86, P2RY6, IL4I1, GAPT, RNASE6, FPR3, CLEC4A, CD74, CD33, FBP1, IL18, MS4A6A, PLD4, SPINT2, SIGLEC7, CLEC12A, PLA2G7, SLC8A1, LY86, OSCAR, LILRB4, SPI1, CSTA, WDFY4, IGSF6, CFP, PTGS1, CARD9, C1QB, SERPINA1, MNDA, FCGR1A, MARCH1, CST3, C1QC, FGD2, C1QA, CXCL16, CLIC2, MPEG1, SIGLEC9, HCK, CD72, HLA-DOA, FCGR1B, LILRB2, TNFSF13, TFEC, PRAM1, ADORA3, SIGLEC10, CSF1R, LILRB1, VSIG4, SLC31A2, CIITA, LRRC25, CLEC7A, CSF2RA, RGS18, AC020656.1, C19orf38, TMEM176B, KMO, PTAFR, OLR1, CYBB, AREG, Clorf54, MS4A4A, PLBD1, TM6SF1, C3AR1, MRC1, GNA15, CXorf21, P2RY13, SMCO4, ADAP2, PILRA, OTULINL, ALOX5, PLXDC2, SLC7A7, TREM2, MS4A7, PID1, CD163, TLR2, TMEM176A, RASSF4, CD40, NLRP3, ZNF385A, DSE, PSTPIP2, ANPEP, TNFAIP8L2, SCIMP, SYK, LILRB3, FGR, KYNU, IRF8, FAM49A, TNFSF13B, CLEC4E, VASH1, ADA2, IL1B, JAML, NCF2, TMEM273, IRF5, RBM47, FCGR3A, MSR1, IL18BP, CCR1, DAPP1, ADAM28, BCAT1, PLEK, ITGAX, GPR34, LAIR1, LGALS9, CD300A, LILRA2, LRRK2, CD302, BASP1, FCN1, CD14, BTK, SLC43A2, LYN, NF AMI, FPR1, IL13RA1, ATP8B4, SLCO2B1, TBXAS1, SLC15A3, LAT2, FES, SIGLEC1, IL1R2, MAFB, RAB20, EAF2, NA A A, CD83, STX11, CSF3R, MIR181 A1HG, TLR4, ZNF710, SLC1A3, LIPA, PAK1, GPR183, IRAK3, EMILIN2, MILR1, OGFRL1, IFNGR1, ITGAM, KCTD12, TRPM2, B3GNT5, DOK3, CTSH, ADGRE2, PTPRE, FOLR2, PLAUR, C5AR1, RNF144B, SULF2, SLC40A1, GCA, FCGR2A, METTL7A, MAP3K8, TGFB, NAIP, HVCN1, TNFAIP2, THEMIS2, MFSD1, UNC93B1, ALDH3B1, JAK2, DOK1, PLEKHO1, CCDC200, TMEM106A, ABL3, AXL, MANBA, C9orf72, AC004687.1, MPP1, PIK3AP1, SH3TC1, GSAP, SLC37A2, HACD4, GLIPR1, RELT, ATF5, MERTK, TET2, KCNMA1, SH2B3, NRROS, FCGRT, UBE2E2, VSIR, NCF4, STAB1, LRRK1, TREM1, FUCA1, NAGA, PARVG, GRN, MAN2B1 , APOCI , NCF1 , CD4, SLC11 Al, EPB41L3, ETS2, RASGEF1B, ODF3B, ARRB2, SLC25A19, ATP2B1-AS1, WAS, LAP3, NAGK, CCL3L1, RNF130, PSAP, AOAH, CASS4, LPCAT2, ST8SIA4, FCHO2, SYNGR2, NFKBID, CHST15, HBEGF, PLAGL1, TCN2, CREG1, SMIM3, SDSL, GM2A, NCKAP1L, TNFSF10, RP2, RIN3, CTSC, DPYD, RPS6KA4, EPSTI1, GAA, RGL1, DENND3, GLIPR2, THBS1, ACSL1, CEBPD, PLAC8, C3, CD93, RGS2, PPT1, RHOG, SLC16A3, IGFLR1, DRAM1, RILPL2, GLRX, HM0X1, KLHL6, CAMK1, C2, SIPA1L1, RGS10, HNMT, MYD88, F13A1, POU2F2, ARHGAP18, RASSF2, BID, DHRS3, DMXL2, TBC1D8, PRKAG2, RIPK2, SLAMF7, ETV6, MAN1A1, SQOR, OSBPL11, DENND1B, MXD1, ACP2, NIPSNAP3A, NUDT16, NPC2, SRGN, LACTB, FUOM, ITGB2-AS1, SPTLC2, TPK1, DOCK5, LRP1, GLUL, CAMKID, COTL1, UBE2D1, ALCAM, ADCY7, HDAC9, LGMN, DOCK4, RGS19, SCPEP1, ZYX, GK, PLSCR1, NMI, DAPK1, TEX14.

[0039] In some embodiments, the myeloid cluster of hybrid immune cells are identified by having increased expression of at least one Group 1 gene (e.g. at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, or each Group 1 gene) at least one Group 2 gene (e.g. at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, or each Group 2 gene), and at least one Group 3 gene (e.g. at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, or at least 50 Group 3 genes).

[0040] In some embodiments, expression of a given gene is evaluated relative to the expression of the same gene in all cell types present in a tumor tissue sample. In some embodiments, “increased expression” of a gene indicates that the expression of the gene in the myeloid cluster of hybrid immune cells is increased relative to the expression of the gene in all other cell types present in the tumor biopsy sample. In some embodiments, the myeloid cluster of hybrid immune cells are identified by having increased expression of the at least one Group 1 gene, the at least one Group 2 gene, and the at least one Group 3 gene relative to the expression of the same Group 1 gene, Group 2 gene, or Group 3 gene in all other cell types in the tumor biopsy sample. “Increased expression” may indicate any suitable increase in the expression of the gene, for example an increase by at least 1%, at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, or more.

[0041] In some embodiments, the plurality of genes comprises five or more Group 1 genes. In some embodiments, the plurality of genes comprises ten or more Group 1 genes. In some embodiments, the plurality of genes comprises each Group 1 gene. In some embodiments, the plurality of genes comprises five or more Group 2 genes. In some embodiments, the plurality of genes comprises ten or more Group 2 genes. In some embodiments, the plurality of genes comprises 20 or more Group 2 genes. In some embodiments, the plurality of genes comprises each Group 2 gene. In some embodiments, the plurality of genes comprises five or more Group 3 genes. In some embodiments, the plurality of genes comprises ten or more Group 3 genes. In some embodiments, the plurality of genes comprises 20 or more Group 3 genes. In some embodiments, the plurality of genes comprises 30 or more Group 3 genes. In some embodiments, the plurality of genes comprises 40 or more Group 3 genes. In some embodiments, the plurality of genes comprises 50 or more Group 3 genes.

[0042] In some embodiments, measuring the M_C1 gene signature or detecting infiltration of MC_1 cells comprises measuring the expression of one or more group 1 genes, one or more group 2 genes, and five or more group 3 genes in the tumor biopsy sample. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 gene, and at least one group 3 gene. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least five group 3 genes.

[0043] In some embodiments, measuring the M_C1 gene signature or detecting infiltration of MC_1 cells comprises measuring the expression of three or more group 1 genes, five or more group 2 genes, and five or more group 3 genes in the tumor biopsy sample. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 gene, and at least one group 3 gene. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells arc identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least five group 3 genes.

[0044] In some embodiments, measuring the M_C1 gene signature or detecting infiltration of MC_1 cells comprises measuring the expression of five or more group 1 genes, five or more group 2 genes, and 10 or more group 3 genes in the tumor biopsy sample. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 gene, and at least one group 3 gene. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 10 group 3 genes. In some embodiments, measuring the M_C1 gene signature or detecting infiltration of MC_1 cells comprises measuring the expression of five or more group 1 genes, 10 or more group 2 genes, and 20 or more group 3 genes in the tumor biopsy sample. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells arc identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group

[0045] 1 genes, at least five group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 15 group 3 genes.

[0046] In some embodiments, measuring the M_C 1 gene signature or detecting infiltration of MC_1 cells comprises measuring the expression of five or more group 1 genes, 10 or more group

[0047] 2 genes, and 40 or more group 3 genes in the tumor biopsy sample. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 gene, and at least five group 3 gene. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least three group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least three group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least 20 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 20 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 20 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least five group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 20 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 10 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 15 group 3 genes. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 20 group 3 genes.

[0048] In some embodiments, the Group 1 genes are selected from CLEC10A, FLT3, CD1D, RTN1 , GPAT3, and AC009093.2. In some embodiments, the myeloid cluster of hybrid immune cells arc identified as having increased expression of at least one of, at least 2 of, at least 3 of, at least 4 of, at least 5 of, or each of CLEC10A, FLT3, CD1D, RTN1, GPAT3, and AC009093.2, along with increased expression of at least one (e.g. at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,

[0049] 14, 15, 16, 17, 18, 19, 20, etc.) group 2 gene and at least one (e.g. at least 1, 2, 3, 4, 5, 6, 7, 8, 9,

[0050] 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,

[0051] 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, etc.) group 3 gene. In some embodiments, the Group 2 genes are selected from HLA-DQA1 , HLA-DQB1 , HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA-DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, and IFI30. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one of (e.g. at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or each of) HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA-DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, and IFI30 along with increased expression of at least one (e.g. at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) group 1 gene and at least one (e.g. at least 1, 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, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, etc.) group 3 gene.

[0052] In some embodiments, the Group 3 genes are selected from ALDH2, ANKRD22, C15orf48, C1QA, C1QB, C1QC, CALHM6, CARD9, CD300C, CD33, CD74, CD86, CFP, CLEC12A, CLEC4A, CLIC2, CPVL, CST3, CSTA, CXCL16, FBP1, FCGR1A, FCGR2B, FGD2, FGL2, FPR3, GAPT, HCK, HLA-DQA2, HLA-DQB2, IGSF6, IL18, IL4I1, LGALS2, LILRB4, LY86, MARCH1, MNDA, MPEG1, MS4A6A, OSCAR, P2RY6, PLA2G7, PLD4, PTGS1, RNASE6, SERPINA1, SIGLEC7, SIGLEC9, SLAMF8, SLC8A1, SPI1, SPINT2, and WDFY4. In some embodiments, the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one of (e.g. at least 1, 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, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, etc.) of ALDH2, ANKRD22, C15orf48, C1QA, C1QB, C1QC, CALHM6, CARD9, CD300C, CD33, CD74, CD86, CFP, CLEC12A, CLEC4A, CLIC2, CPVL, CST3, CSTA, CXCL16, FBP1, FCGR1A, FCGR2B, FGD2, FGL2, FPR3, GAPT, HCK, HLA-DQA2, HLA-DQB2, IGSF6, IL18, IL4I1 , LGALS2, LILRB4, LY86, MARCH1 , MNDA, MPEG1, MS4A6A, OSCAR, P2RY6, PLA2G7, PLD4, PTGS1, RNASE6, SERPINA1, SIGLEC7, SIGLEC9, SLAMF8, SLC8A1, SPH, SPINT2, and WDFY4 along with increased expression of at least one (e.g. at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) group 1 gene and at least one (e.g. at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, etc.) group 2 gene.

[0053] In some embodiments, the group 1 genes are selected from CLEC10A, FLT3, CD ID, RTN1, GPAT3, and AC009093.2, the group 2 genes are selected from HLA-DQA1, HLA- DQB1, HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA-DRB5, HLA-DMB, Clorfl62, LST1 , HLA-DMA, and IFI30, and the group 3 genes are selected from ALDH2, ANKRD22, C15orf48, C1QA, C1QB, C1QC, CALHM6, CARD9, CD300C, CD33, CD74, CD86, CFP, CLEC12A, CLEC4A, CLIC2, CPVL, CST3, CSTA, CXCL16, FBP1, FCGR1A, FCGR2B, FGD2, FGL2, FPR3, GAPT, HCK, HLA-DQA2, HLA-DQB2, IGSF6, IL18, IL4I1, LGALS2, LILRB4, LY86, MARCH1, MNDA, MPEG1, MS4A6A, OSCAR, P2RY6, PLA2G7, PLD4, PTGS1, RNASE6, SERPINA1, SIGLEC7, SIGLEC9, SLAMF8, SLC8A1, SPI1, SPINT2, and WDFY4. In some embodiments, the myeloid cluster of hybrid immune cells is identified as having increased expression of at least one (e.g. at least 1, 2, 3, 4, 5, or 6) of CLEC10A, FLT3, CD1D, RTN1, GPAT3, and AC009093.2, at least one (e.g. at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13) of HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DPB1, HLA- DPA1, HLA-DRB1, LYZ, HLA-DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, and IFI30, and at least one (e.g. at least 1, 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, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, etc.) of ALDH2, ANKRD22, C15orf48, C1QA, C1QB, C1QC, CALHM6, CARD9, CD300C, CD33, CD74, CD86, CFP, CLEC12A, CLEC4A, CLIC2, CPVL, CST3, CSTA, CXCL16, FBP1, FCGR1A, FCGR2B, FGD2, FGL2, FPR3, GAPT, HCK, HLA-DQA2, HLA- DQB2, IGSF6, IL18, IL4I1, LGALS2, LILRB4, LY86, MARCH1, MNDA, MPEG1, MS4A6A, OSCAR, P2RY6, PLA2G7, PLD4, PTGS1, RNASE6, SERPINA1, SIGLEC7, SIGLEC9, SLAMF8, SLC8A1, SPI1, SPINT2, and WDFY4.

[0054] In some embodiments, expression of the plurality of genes in the tumor biopsy sample is measured by sequencing (e.g. single cell RNA sequencing, targeted gene expression sequencing, bulk RNA sequencing). In some embodiments, expression of the plurality of genes is measured by sequencing to measure levels of RNA transcribed from a given gene. In some embodiments, expression of the plurality of genes in the tumor biopsy sample is measured by sequencing, such as single cell RNA sequencing (scRNA sequencing), which sequencing data is directly indicative of infiltration of MC_1 cells in the tumor biopsy sample. In some embodiments, a single-cell RNA analysis is performed on the sample to identify and quantify expression of the plurality of genes in the cells of the tumor tissue and thereby quantify the amount of M_C1 cells present in the tumor tissue. In some embodiments, expression of the plurality of genes in the tumor biopsy sample is measured by bulk RNA sequencing, and the infiltration of MC_1 cells in the tumor biopsy sample is calculated indirectly by deconvolution of the bulk RNA sequencing data to estimate the frequency of MC_1 cells based upon gene expression. In some embodiments, the methods herein involve (a) obtaining a tumor biopsy sample from the subject; (b) measuring expression of the plurality of genes in the tumor biopsy sample (e.g. the one or more Group 1 genes, the one or more Group 2 genes, and the one or more Group 3 genes that comprise the M_C1 gene signature, as described in detail above) by bulk RNA sequencing analysis; and (c) performing a deconvolution process to estimate the M_C1 cell abundance (e.g. infiltration) in the tumor biopsy sample. In some embodiments, the method further involves (d) prognosing melanoma outcome and / or predicting responsiveness to an anti-melanoma therapy based on the infiltration of M_C1 cells in the sample determined by the deconvolution process. The deconvolution analysis may be performed using any selected set of genes to establish the abundance of non-M_Cl cell types in the sample, for example reference gene expression profiles from cell types or subpopulations known to be present in a tumor sample containing numerous cell types, and a selected panel of M_C1 gene signature genes to assess the abundance of M_C1 cells in the sample. Deconvolution methods known in the art include, for example: CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts), quanTIseq, xCell, DeconRNASeq, MuSiC (Multi-subject Single-cell deconvolution), and MCP- counter (Microenvironment Cell Populations -counter).

[0055] In some embodiments, the method further comprises determining a percent infiltration of the myeloid cluster of immune cells in the tumor biopsy sample relative to the total cell count of the sample. The total cell count may be determined by any suitable technique. For example, the total cell count may be determined manually (e.g. manual cell counting, such as using a hemocytometer) or may be determined by automated methods such as flow cytometry, digital pathology image analysis, and the like. In some embodiments, the total cell count is determined by sequencing using one or more gene expression signatures indicative of other cell types in the sample (e.g. indicative of tumor cells, immune cells, etc.).

[0056] In some embodiments, the method comprises prognosing melanoma in the subject based upon the percent infiltration. In some embodiments, prognosing melanoma comprises identifying the subject as having increased risk of poor overall survival. In some embodiments, poor overall survival indicates a life expectancy of less than 4 years, less than 3 years, less than 2 year, or less than 1 year. In some embodiments, prognosing melanoma comprises identifying the subject as having increased risk of recurrence, metastasis, and / or disease progression when the percent infiltration is below a threshold value. In some embodiments, a subject at increased risk of disease progression is likely to have a comparatively low overall survival (e.g. shorter life expectancy) compared to a subject not at increased risk of disease progression. In some embodiments, the threshold value is 2.6%. In some embodiments, the threshold value is 1.9%. In some embodiments, the method further comprises monitoring the subject with increased frequency and / or providing an aggressive anti-cancer treatment to the subject when the subject is identified as having increased risk of recurrence, metastasis, and / or disease progression (e.g. shorter life expectancy) or identified as at risk of poor overall survival. In some embodiments, the aggressive anti-cancer treatment is selected from chemotherapy, radiation therapy, immunotherapy (e.g. immune checkpoint inhibitor therapy, adoptive cell transfer, cancer vaccine, cytokine therapy), targeted therapy (e.g. therapy targeting BRAF, MEK, etc.), and a combination thereof. In some embodiments, the melanoma is surgically removed and an aggressive anti-cancer treatment is provided to the subject. In some embodiments, the aggressive anti-cancer treatment is an elevated dose and / or increased dosing frequency of the anti-cancer treatment (e.g. the chemotherapy, immunotherapy, targeted therapy, radiation therapy, or combination thereof) compared to the dose or dosing frequency that would otherwise be provided to a subject not at increased risk of recurrence, metastasis, and / or disease progression or poor overall survival.

[0057] In some embodiments, the methods of detecting infiltration of MC_1 cells in a tumor biopsy sample are used to determine responsiveness to a treatment for melanoma in the subject based upon the percent infiltration of MC_1 cells in the tumor biopsy sample. In some embodiments, the treatment for melanoma is given to the subject as a neoadjuvant therapy before surgical removal of the tumor. As such, in some embodiments the subject has received at least one dose of a treatment for melanoma prior to surgical removal of the tumor, and a tumor biopsy sample is obtained from the subject after the at least one dose of the treatment for melanoma to determine responsiveness to the treatment. In some embodiments, the subject has received at least one dose of the treatment for melanoma and determining responsiveness comprises identifying the subject as likely to have a positive response to the treatment when the percent infiltration is equal to or above a threshold value, or identifying the subject as unlikely to have a positive response to the treatment when the percent infiltration is below the threshold value. A positive response indicates that the subject exhibits one or more desirable responses to the treatment (c.g. tumor size reduced, number of tumors reduced, likelihood of metastasis reduced, cancer progression inhibited, etc.). In some embodiments, the threshold value is 2.6%. In some embodiments, the threshold value is 1.9%. In some embodiments, the treatment for melanoma that the subject has received prior to the tumor biopsy sample being obtained is chemotherapy, radiation therapy, immunotherapy (e.g. immune checkpoint inhibitor therapy, adoptive cell transfer, cancer vaccine, cytokine therapy), targeted therapy (e.g. therapy targeting BRAF, MEK, etc.), or a combination thereof. For example, in some embodiments the subject has received at least one dose of immunotherapy, at least one dose of targeted therapy, at least one dose of chemotherapy, at least one dose of radiation therapy, and the method is used to determine responsiveness to the treatment for melanoma in the subject. In some embodiments, the method comprises providing an alternative anti-melanoma therapy to the subject identified as unlikely to have a positive response to the treatment for melanoma. An alternative anti-melanoma therapy indicates a treatment different from the treatment that the subject has already received. For example, if the subject is indicated to be unlikely to have a positive response to immunotherapy, the subject may be given an alternative therapy (e.g. chemotherapy, radiation therapy, targeted therapy, etc.)

[0058] In some embodiments, the methods of detecting infiltration of MC_1 cells in a tumor biopsy sample are used to treat a subject having melanoma. In some embodiments, treating a subject having melanoma comprises performing any of the methods of detecting infiltration of a myeloid cluster of hybrid immune cells in a tumor biopsy sample described herein to identify the subject as at risk of poor overall survival or not at risk of poor overall survival, and providing a tailored anti-melanoma treatment to the subject based upon the identification of risk. For example, in some embodiments a subject identified as not of risk of poor overall survival is given standard anti-cancer treatment, e.g. surgical removal of the melanoma. As another example, in some embodiments a subject identified as at risk of poor overall survival is given an aggressive anti-cancer treatment. In some embodiments, the aggressive anti-cancer treatment is selected from chemotherapy, radiation therapy, immunotherapy (e.g. immune checkpoint inhibitor therapy, adoptive cell transfer, cancer vaccine, cytokine therapy), targeted therapy (e.g. therapy targeting BRAF, MEK, etc.), and a combination thereof. In some embodiments, the melanoma is surgically removed and an aggressive anti-cancer treatment is provided to the subject. In some embodiments, the aggressive anti-cancer treatment is an elevated dose and / or increased dosing frequency of the anti-canccr treatment (c.g. the chemotherapy, immunotherapy, targeted therapy, radiation therapy, or combination thereof) compared to the dose or dosing frequency that would otherwise be provided to a subject not at risk of poor overall survival.

[0059] For any of the methods described herein, the subject may be a mammal. In some embodiments, the subject is a human. In some embodiments, the subject has or is suspected of having melanoma. In some embodiments, the subject has or is suspected of having Stage I, II, III, IV, or advanced stage melanoma.

[0060] In some embodiments, the subject has melanoma and the tumor biopsy sample is a melanoma biopsy sample. In various embodiments, the melanoma may be any of superficial spreading melanoma, lentigo maligna melanoma, nevoid melanoma, desmoplastic melanoma, acral lentiginous melanoma, amelanotic melanoma, nodular melanoma, or spitzoid melanoma. Advantageously, the methods of the invention may be applied to melanoma samples derived from tumor of any stage, for example Stage I, Stage II, Stage III, or Stage IV melanoma.

[0061] In some aspects, provided herein arc a set of reagents for performance of the methods described herein. In some aspects, provided herein are kits for the performance of the methods described herein. The set of reagents / kit may include any number of components necessary or useful for the performance of the methods disclosed herein or sub-steps thereof. In some embodiments, the set of reagents or the kit comprises reagents for measuring the expression of a plurality of genes (e.g. one or more Group 1 genes, one or more Group 2 genes, and one or more Group 3 genes comprising the M_C1 gene signature, as described in detail herein) in a sample. In some embodiments, the set of reagents or the kit comprises reagents for amplification of the plurality of genes. Reference to a “gene” herein includes reference to a segment of DNA and includes reference to an RNA sequence transcribed from the gene (e.g. transcribed from that segment of DNA). As such, the reagents in the set of reagents or in the kit herein (e.g. the primers, probes) may be used to measure expression of DNA or to measure expression of RNA. In either case, measuring expression of DNA or measuring expression of RNA is encompassed in the expression “measuring expression of a gene”, “measuring expression of a plurality of genes”, and the like. In some embodiments, the reagents comprise capture probes for the targeted amplification of the plurality of genes (e.g. a plurality of genes comprising the M_C1 gene signature). In some embodiments, probes comprise nucleic acid sequences complementary to, and capable of binding portions of the plurality of genes present in a sample. In some embodiments, the probes are complementary to RNA. In one embodiment, the kit comprises primers (e.g. primer pairs) designed to amplify the M_C1 signature genes present in the sample, the primer pairs comprising oligonucleotide sequences configured to selectively amplify selected M CI genes. In some embodiments, the primer pairs amplify RNA.

[0062] In some embodiments, the set of reagents or the kit comprises a plurality of primer pairs, wherein each primer pair amplifies a distinct gene. For example, in some embodiments the set of reagents or the kit comprises a plurality of primer pairs, wherein each primer pair comprises a portion complementary to a gene or a portion of a gene in the MC_1 gene signature. For example, in some embodiments the set of reagents or the kit comprises a plurality of primer pairs, wherein each primer pair comprises a portion complementary to a gene or a gene portion of one or more Group 1 genes (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 Group 1 genes), one or more Group 2 genes (e.g. one or more, three or more, five or more, 10 or more, 15 or more, 20 or more, or 20-25 Group 2 genes), and one or more Group 3 genes (e.g. one or more, five or more, 10 or more, 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more Group 3 genes) as described in detail above. Additional kit elements may include standards, reagents, buffers, collection tubes, and other elements useful in performing RNA-seq and quantification of M_C1 signature RNAs. Additional kit elements may comprise printed instructions or digital storage media on which machine-readable instructions are present to facilitate performance of the M_C1 analyses of the invention. In some aspects, provided herein are uses of reagents for amplification of a plurality of genes (e.g. a plurality of genes comprising the M_C1 gene signature) for the methods described herein, including methods of measuring expression of the M_C1 gene signature and / or methods of detecting infiltration of M_C1 cells in a sample. In some embodiments, the sample is a tumor biopsy sample obtained from a subject.

[0063] EXAMPLES

[0064] EXAMPLE 1 While immunotherapy has revolutionized the management of advanced melanoma, modem therapeutic selection has become increasingly complex with both the adoption of neoadjuvant approaches and the recent approvals of multiple systemic agents. Despite significant improvements for some patients, overall response remains unpredictable, with response rates of 10-15% for ipilimumab, 33-44% for pembrolizumab / nivolumab, 58% for ipilimumab + nivolumab, and 48% progression free survival for nivolumab + relatlimab. Inherent or acquired resistance can also be developed leading to further complex responses, such as the average resistance to dabrafenib within 1 year.

[0065] The myeloid compailment includes a variety of cell types including but not limited to neutrophils, eosinophils, basophils, monocytes, macrophages, and dendritic cells. The understanding of the roles of tumor associated macrophages in tumor ecosystems and immunotherapy response is unclear. Herein, the role of myeloid cells and tumor associated macrophages (TAMs) in suppressing immune responses that may contribute to anti-PD-1 and other immunotherapy failures was investigated. Specifically, analysis of a comprehensive scRNA sequencing data set using machine learning techniques was conducted with the objective to define myeloid signatures conserved across human melanoma to define subtype populations of critical relevance by biodistribution, tumor stage or recurrence, and impacting immunotherapeutic responses. Herein 6 myeloid cell clusters were identified, termed M_C1 to M_C6, with gene expression showing differences in structure and function. The M_C1 subtype was prognostic in brain metastatic melanoma outcome for overall survival (OS) based on the overall frequency. This finding was externally validated using publicly available bulk RNA sequencing data from a clinical trial (advanced melanoma treated with nivolumab) and the tumor cancer genome atlas (TCGA) for Stage I-IV. The M_C1 cell type appears to be a hybrid dendritic cell and macrophage that shows prognostic relevance across all stages of melanoma and response prediction with immunotherapy. The M_C1 subtype provided herein can be used as a biomarker of prognosis in melanoma and cell therapy.

[0066] RESULTS

[0067] Cell Types in early, advanced, and metastatic Melanoma & Interaction A total of 123 samples were included in the data set for scRNA sequencing with a total of 102,971 single cells. A total of 45 cell types including tumor cells, fibroblasts, endothelial cells, NK cells, CD3 T cells, CD4 T cells, CD8 T cells, B cells, and myeloid cells were found after clustering as depicted in Figure la. Gene expression of canonical lineage markers were evaluated to broadly classify cells into tumor, T cells, NK cells, B cells, and myeloid cells as shown in Figure 1c. When evaluating overall expression of cell types by tissue type there were significant differences in T cells, B cells, NK cells, extracellular matrix cells (ECM), and tumor cells as shown in Figure lb. However, myeloid cell expression was not significantly different by tissue types. Individual cell types within these clusters were evaluated for differences in expression between tissues types with significant differences between groups. Analysis of the M_C1 cluster is shown in Figure Id.

[0068] Tumor Cells

[0069] 13 clusters were identified with increased expression of tumor related markers including PMEL (Figure la and 1c). Cluster 3 cells were elevated in primary acral melanoma but no other groups with significant differences on ANOVA. When evaluating this cluster by a cut-off log2FC of 2.5 with 80% expression and a 30% change in expression compared to other clusters, these cells appeal- to be significantly elevated in BRAF for gene expression, average Eog2FC 2.98, p=0.00. Other tumor clusters with increased BRAF expression include 5, 4, and 2. Cluster 16 cells were significantly elevated in brain metastases when compared to cerebrospinal fluid metastases, non-brain metastases, and primary skin melanoma. These cells highly express NRN1, STXBP1, VAT1, MEANA, BIRC7, and S100A13 which supports brain specific melanoma tumor cells. Clusters increased in expression in primary vs metastatic melanoma include 14_2, with increased expression CTAG2, and 14_20, with increased expression CTNNA2.

[0070] Myeloid Cells

[0071] 6 clusters express ITGAM and are of myeloid origin (Figure la and 1c). Cluster M_C1 expresses ITGAX, CD14, FCGR3A, CD68, CD163, MRC1, CSF1R, PECAM1, TREM2 and uniquely FET3. FET3 suggests M_C1 may have features of a hybrid dendritic cell precursor and macrophage type with no significant differences by tissue type (FIG. ID). M_C2 has a similar profile, but does not express FET3 and does express MARCO, pointing to an inflammatory macrophage specific differentiation with no significant differences by tissue type. M_C3 does not express FLT3 or MARCO and has a similar profile and appears to be another inflammatory macrophage subtype with no significant differences by tissue type. M_C6 demonstrates similar profile to M_C3 without TREM2 expression, pointing to regulatory macrophage differentiation and was increased in brain vs non-brain metastases. M_C4 expresses SELL and does not express MRC, pointing to monocytic or inflammatory differentiation with increased expression in brain vs non-brain metastases. M_C5 expresses similar markers to other myeloid clusters, but uniquely expresses CX3CR1 and P2RY12, with no significant differences by tissue type despite association with microglial differentiation. Table 1. shows gene overexpressed in the M_C1 cluster with a log2FC cutoff of 3.0.

[0072] T1

[0073] CD8 Expressing Cells

[0074] 6 clusters express CD8A or CD8B with CD3 genes (Figure la and 1c). Clusters 12_1 , 12_2, and 12_3 uniquely overexpress NKG7, GZMB, and GZMH, possibly pointing to NKT cells more polarized to NK type cells. Cluster 12_1 has high expression of CCL5 (log2FC 3.56, p=0.00) and NKG7 (log2FC 3.53, p=0.00) pointing to an activated and cytotoxic phenotype. Cluster 12_2 has high expression of GNEY (log2FC 7.03, p=0.00) and NKG7 (log2FC 3.07, p=0.00) also showing activation markers. Cluster 12_3 highly expresses CCE4, NKG7, CCE5, CST7, and GZMA complementing activation and cytotoxicity markers although CST7 may be paradoxical and CCE4 may assist in recruitment of macrophages and dendritic cells. Cluster 14_4 highly expressed exhaustion signatures 1E7R, CD52, PTPRC, PRDM1 and S100A4. Cluster 14_7 expresses increased PDCD1, log2FC 1.64, p=2.37E-17, and Cluster 14_14 also expresses PDCD1 and S100A4. Clusters 14_4 and 14_14 are both more highly expressed in CSF metastases compared to other metastatic sites of disease pointing toward loss of T cell cytotoxicity in the most malignant patient sub-type.

[0075] CD4 Expressing Cells

[0076] 11 clusters express CD4 with CD3 genes, indicating a multitude of potential cell subsets (Figure la and 1c). Cluster 14_22 (FOXP3, IL2RA, ICOS, CTLA-4) and Cluster 14_5 (FOXP3, ICOS, CTLA-4, CCR6) appear to be two different regulatory CD4 (Treg) cells. There appear to be no significant differences in expression by total sample in 14_5 by tissue, however, 14_22 appears to be significantly elevated in CSF metastases compared to brain metastases and primary acral melanoma. Cluster 14_13 is upregulated for PDCD1 and is significantly elevated in metastatic melanoma compared to brain metastases and primary acral melanoma. Cluster 14_15 (IL4R high) was without differences between tissue types. Cluster 14_12 is singularly upregulated for CCR4 with increased expression in brain metastases compared to CSF metastases, non-brain metastases, primary acral, and primary skin melanoma. Clusters 14_1 and 14_ 11 are upregulated for GATA3 and appear to increased in non-brain metastases compared to primary acral melanoma (Cluster 14_1) and increased compared to brain metastases, CSF metastases, primary acral, and primary melanoma (14_11). Cluster 14_21 appears to be uniquely uprcgulatcd for BCL-6 signifying importance as a T follicular helper cell without differences between tissues. Clusters 14_17, 14_6, and 14_8 appears to have characteristics of early undifferentiated CD4 T cells.

[0077] CD3 Expressing Cells

[0078] Four clusters express CD3D, CD3E, and CD3G without CD8 or CD4 genes (Figure la and 1c). Clusters 14_10 and 14_18 are increased in metastatic acral compared to all other tissue types. Clusters 14_16 and 14_19 are increased in CSF metastases compared to all other tissue types.

[0079] Extracellular Matrix (ECM) Cells

[0080] Three clusters represent fibroblasts and endothelial cells (Figure la and 1c). Cluster 8 highly expressed endothelial cell markers CD34 and PECAM-1 and does not differ by tissue type. Clusters 11 and 13 are both elevated in expression of C0L1A1 a fibroblast marker. Cluster 11 is increased in acral metastases compared to brain metastases and non-brain metastases while Cluster 13 is not different by tissue type.

[0081] B Cells

[0082] 2 clusters highly express B cell associated lineage genes CD79A and CD79B (Figure la and 1c). Cluster 6_1 is increased in acral metastases compared to brain metastases, primary acral, and primary melanoma. In addition, 6_1 is increased in non-brain metastases compared to CSF metastases, primary acral, primary melanoma, and brain metastases.

[0083] Cell Chat Analysis

[0084] Myeloid clusters had outgoing upregulation of LAIR 1 and APRIL signaling which have paradoxical immune effects and incoming LAIR1, CD39, and CD6 (FIG. 2) This finding of a highly inflammatory state of myeloid cell clusters with paradoxical regulation signals underscores the complexities of myeloid functions that have rendered “Ml” and “M2” classification systems inadequate to comprehensively describe cellular functions in tumors. For tumor cells, outgoing signaling patterns varied by tumor cell, however, incoming signals appeared to be highly upregulated in pattern 2 by the VISTA pathway known for downregulating immune cell function. Outgoing signals for NKT and CD8 T cell related cell clusters included IFN-II and incoming signals CLEC and MHC-I. Other T cells overlapped with these outgoing and incoming signals. B cells had a diverse group of outgoing and incoming signals and may possibly downregulate outgoing ESAM pathways and incoming SELL pathways. Endothelial cluster and CAF clusters had increased outgoing ESAM and PERIOSTIN signaling (Figure 2).

[0085] Myeloid Immune Cell Associated with Survival in Metastatic Melanoma

[0086] Kaplan-Meier curves were generated, p=0.029, (Figure 3a) based on the results and cutoff values determined by percentage infiltration of total sample on recursive partitioning analysis (RPA) (Figure 3b). Node 2 represents patients wherein M_C1 cell abundance in the tumor (e.g. M_C1 infiltration) greater than or equal to 2.6% of total cells. This node is considered the “high infiltration” group. Node 4 represents patients wherein M_C1 cell abundance in the tumor (e.g. M CI infiltration) is less than 1.9% of the total cells. This node is considered the “low infiltration” group. Node 5 represents patients wherein M_C1 cell abundance in the tumor (e.g. M_C1 infiltration) is greater than or equal to 1.9% of the total cells and less than 2.6% of the total cells. This node is considered the “medium infiltration” group. As shown in Figure 3a, Node 2 had the longest overall survival since brain metastasis and Node 5 (the medium infiltration group) had the worst overall survival.

[0087] The only significant difference between groups with treatment factors was an increased rate of targeted therapy in the best and worse survival groups (31% vs 5.3% vs 43%, p=0.036) in Figure 3c. In addition, although 22 of 42 patients had response treatment with available data the best survival group still had 56% of patients with No Response (NR) in Figure 3c. On cox regression analysis Node 5 vs Node 2 was significantly different, HR 4.24, 95% CI 1.4-12.9, p=0.011 in Figure 3d. However, Node 4 vs Node 2 approached significance, HR 1.89, 95% CI 0.82-4.25, p=0.13 in Figure 3d. Median Overall Survival (OS) months differed between Node 2 at 52 vs Node 4 at 32 vs Node 5 as 13 in Figure 3e.

[0088] Interaction of Myeloid Immune Cell and Other Cell Types in Metastatic Melanoma

[0089] Across statistical tests the following cell types were significantly different between groups: M_C1, M_C3, M_C4, 9, and M_C6. Overall, it appears that myeloid cell types appear to be the most significant prognostic factor between each group. In addition, Cluster 9 appears to be highest in Node 5, with the worst OS outcomes and increased expression of ACO 15802.6, PRMT5, TROVE2, PYCR1, and MINOS 1 compared to other clusters. Other than tumor cells, the worst OS group (Node 5) has lower M_C1 and M_C3 clusters but similar monocytes and regulatory macrophage type cells.

[0090] External Validation of Myeloid Cell Type on Survival in Advanced Melanoma & Cell Interaction

[0091] After calculating Kullback-Leibler (KL) Divergence for each model against the distribution of cell types in the original scRNA sequencing dataset, the CIBER methodology produced results that most closely matched the distribution expected of cell types on the data from the nivolumab clinical trial data set. The nivolumab clinical trial data set included a cohort of advanced melanoma patients with pre-treatment biopsy and on-treatment biopsy bulk RNA sequencing while receiving nivolumab. Pre-treatment biopsy M_C1 infiltration scores did not correlate with OS (Figure 4a & Figure 4b) p=0.71 while on-treatment biopsy scores predicted OS (Figure 4c & Figure 4d) p=0.048. A shift in M_C1 infiltration scores was clinically but not statistically significant while undergoing treatment with nivolumab in Figure 4e, p=0.0695.

[0092] Differences in frequencies in individual cell types in each pre-treatment, on-treatment, and paired treatment sample were analyzed with statistical testing. For pre-treatment biopsies, M_C1 groups were associated with significant differences in clusters 14_19 and M_C 1 across statistical testing. Without accounting for response on nivolumab, high M_C1 infiltration scores were associated with low 14_19 (early CD3).

[0093] For on-treatment biopsies M_C1 groups were associated with significant differences in clusters 12_1, 14_2, 14_6, 2, 6_2, 9 & M_C1 across statistical testing. Tumor clusters 2 and 9 negatively correlated with OS and 14_2 positively correlated with OS. High 12_1 (NKT Activated with PD1), lower 14_6 (early CD3), and higher 6_2 (B Cell Type 2) were associated with improved M_C1 infiltration and OS. This points to both a cellular and humoral response in the immune system in predicting improved OS based on M_C1 infiltration.

[0094] Furthermore, paired patient samples for pre-treatment biopsies were analyzed by changes from M_C1 infiltration groups by three categories while undergoing treatment: Better, Stable, and Worse. When evaluating pre-treatment biopsies, patients have poor response to nivolumab and subsequent M_C1 infiltration when starting with high M_C1 and 12_3 (PD1+NKT) cells. However, when 14_9 (early CD3) was high, patients improved on nivolumab. Analysis of on- treatment biopsies was inconclusive.

[0095] External Validation of Myeloid Cell Type on Survival in TCGA Melanoma & Cell Interaction

[0096] After calculating Kullback-Leibler (KL) Divergence for each model against the distribution of cell types in our original scRNA sequencing dataset, the CIBER methodology produced results that most closely matched the distribution expected of cell types on the TCGA data set.

[0097] Kaplan-Meier plots were calculated by stage of melanoma at diagnosis (Figure 5a), by M_C1 infiltration for all patients (Figure 5b), M_C1 infiltration for Stage I (Figure 5c), M_C1 infiltration for Stage II (Figure 5d), M_C1 infiltration for Stage III (Figure 5e), and M_C1 infiltration for Stage IV (Figure 5f) all with significant p-values. Taken together, it appeal’s that M_C1 infiltration groups are prognostic of OS in the TCGA. There appears to be a paradoxical relationship of Node 5 patients with worse OS in Stage I and complex interactions with OS in Stage II / III / I V . However, the highest and lowest infiltration e.g. Node 2 and Node 4 appear to be prognostic on all Stages of melanoma (Figure 5).

[0098] Analyzing differences between infiltration groups across statistical testing found 21 cell types that had significance across the three statistical methods. Only cluster 9 tumor cells showed significant differences between all three infiltration groups. 12_1 (NKT) was significantly different between all three groups. Clusters 14_4 (CD8 T Cell Exhausted) and 14_7 (CD8 T Cell Exhausted with PD1) were both significantly increased in Node 4 vs the two other groups. Cluster 14_6 (Naive CD4 1) was highest in Node 4 and 14_17 (Naive CD43) highest in Node 2 with differences between all groups. Cluster 14_22 (CD4 Treg 2) was highest in Node 2 with no difference between Node 4 and 5.

[0099] Overall, this points to high M_C1 infiltration being associated with less malignant tumor (Cluster 9), increased cytotoxic NKT vs exhausted T cell (12_1 vs 14_4 / 14_7), increased T reg (14_22), and B Cell infiltration (6_l / 6_2). This supports an immune mediated hypothesis directed by increased antigen presentation by a DC-macrophage (M_C1) cell leading to both cellular and humoral immune response as shown by increased cytotoxic NKT Cells (12_1 ) and B Cells (6_l / 6_2).

[0100] Discussion

[0101] A total of 45 cell types including tumor cells (13), fibroblasts (2), endothelial cells (1), NK cells (3), CD3 T cells (4), CD4 T cells (11), CD8 T cells (3), B cells (2), and myeloid cells (6) were found after clustering with scCCESS. When analyzing differences between major cell groups by tissue type (e.g. primary melanoma, acral, brain metastases, etc.) only myeloid cells showed no differences in frequency by tissue type Figure 1. Overall, tumor cells showed heterogeneity within tissue types as well as some tissue specific elevations in tumor clusters which supports both tissue specificity in melanoma sub-types and consistency for melanoma. Immune cell subsets between tissue types varied; however, immune cell subsets between primary melanoma vs non-brain metastatic melanoma differed in the following clusters: 14_6 (early CD4), 14_8 (early CD4), 14_11 (CD4 + GATA3), 14_16 (early CD3), 14_19 (early CD3), and 6_1 (B Cell Type 1). Generally, this reflects more systemic immune activation in metastatic melanoma compared to primary melanoma. CellChat analysis appeared consistent with understanding of immune cluster function, although tumor cell clusters were heterogenous in signaling pathways.

[0102] When examining the myeloid cell clusters on OS in the brain metastases subset of patients, M_C1 was the only cell type with significant clinical association based on the infiltration cut-off scores found using RPA. Interestingly, high infiltration (Node 2) had the best survival; medium infiltration (Node 5) had the worst survival, and low infiltration (Node 4) appeared to be in the middle survival curve (Figure 3A). Other than M_C1 infiltration only M_C3 (Macrophage Inflammatory Type 2) appeared to be significantly different between all three groups. Other between group differences appear to be driven by tumor heterogeneity (Cluster 2, 9, and 10), M_C4 (monocyte), M_C6 (Macrophage Regulatory), and 14_16 (early CD3).

[0103] The model was externally validated in a clinical trial data set using bulk RNA sequencing with a deconvolution method with minimized KL Divergence using our scRNA labeling as a map for cell identification. When analyzing a subset of patients treated with nivolumab with matched biopsies prior to treatment and while on treatment, our model showed significant differences by M_C1 infiltration node when assessing the on-treatment biopsy in Figure 4. No other cell types other than M_C1 were significantly different between all three groups. However, between group differences with agreement in all three analyses included tumor heterogeneity (Cluster 2, 9, and 14_2), 12_1 (NKT Cell Type 1), 14_6 (early CD3), and 6_2 (B Cell Type 2). This shows consistency in tumor cell type when compared to the brain metastases data, but includes other novel mechanisms associated with NKT and B Cells. This correlation supports the importance of checkpoint activity and therapeutic targeting across myeloid populations, with a possible mechanism of M_C1 function as a DC-Macrophage to increase B cell response. When evaluating changes in response groups, the treatment effect of nivolumab was not significant in Figure 4, p=0.069. 4 of 7 patients (57%) with high M_C1 infiltration on pre-treatment biopsy had a shift to lower infiltration on nivolumab. 12 of 35 patients (34%) shifted from a lower infiltration group into high infiltration Node 2 while 23 patients did not change M_C1 infiltration (66%).

[0104] When externally validating the model using the TCGA, results showed the M_C1 was prognostic across all stages in the high (Node 2) and low (Node 4) groups in Figure 5. However, Node 5 appears to be prognostic as the worst OS only in Stage I melanoma and does not appear to be significantly different when compared to Node 2 in other stages of melanoma. Given the ability to predict OS in Stage I melanoma despite including this in the model, M_C1 infiltration provides a novel method of evaluating early stage melanoma.

[0105] The cell type identified herein, M_C1, appears to be a novel hybrid cell exhibiting markers of macrophages and dendritic cells. Biological development shows these cells are related with terminal differentiation in each sub-type. Overall, this finding underscores the restriction of under-classifying macrophage phenotypes and behaviors using single or binary nomenclatures. Limited, positive- selection based labeling strategies may have failed to identify hybrid-type cells of importance and have likely contributed to both the current lack of clarity in TAM behaviors and historically limited descriptions of significant TAM differences in checkpoint-refractory disease.

[0106] METHODS

[0107] Data extraction Five single-cell RNA sequencing (scRNA-seq) datasets and two bulk RNA sequencing datasets for humans from publicly available repositories were used, including Gene Expression Omnibus (GEO)( ncbi.nlm.nih.gov / geo), Single Cell Portal (singlecell.broadinstitute.org / single_cell) and The Cancer Genome Atlas (TCGA) (cancer.gov / ccg / research / genome-sequencing / tcga). For the 10X Genomics data, raw sequencing files were obtained using SRA-Toolkit (v3.0.2) via the fastq-dump utility. After obtaining the raw sequencing data, gene expression (GEX) matrices were generated through a comprehensive pipeline. First, the data were demultiplexed to separate individual cell barcodes from sequencing reads. Next, barcode processing was performed to filter out low-quality or ambiguous barcodes, ensuring accurate assignment of reads to specific cells. The processed reads were then aligned to the human reference genome Hg38 using a highly optimized alignment algorithm. Finally, gene quantification was carried out, whereby aligned reads were counted to generate a matrix of gene expression levels across all cells. All steps in this pipeline were performed using the 10X Genomics CellRanger software (v7.1.0). For datasets generated using the SMART-Seq2 protocol, GEX matrices were directly downloaded from the Gene Expression Omnibus (GEO) and Single Cell Portal repositories.

[0108] Single-cell RNA Data Filtering and Normalization

[0109] To enhance the accuracy of gene expression estimates, the 'remove-background' function in CellBender (version 0.3.0) was used for each sample (Fleming, S. J. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods 20, 1323-1335 (2023)) CellBender utilizes a deep learning-based approach to model and remove technical artifacts, such as background noise and ambient RNA, which can contaminate single-cell RNA sequencing data. By estimating the true biological signal from the raw gene-by-cell count data, CellBender generates ambient-corrected count matrices that provide a more accurate reflection of the cellular transcriptome.

[0110] These corrected matrices were then imported into R (version 4.2.2) and converted into Seurat objects using Seurat (version 4.9.9.9086). Stringent cell selection criteria were applied to ensure data quality: cells were retained if they exhibited less than 10% or up to 20% mitochondrial reads, expressed between 3,500 and 7,500 genes and had unique molecular identifier (UMI) counts ranging from 500 to 60,000. Next, doublet cells were identified using Scrublet (version 0.2.3) which computes doublet scores based on the expected doublet rate, preset at 10%. Cells flagged as doublets by Scrublet with the default parameters were excluded from further analysis.

[0111] For datasets generated using the SMART-Seq2 protocol, we incorporated pre-filtered datasets sourced from public repositories, including GEO and Single Cell Portal.

[0112] After data filtering, the gene-by-cell expression matrices were merged using the 'merge' function. Pre-processing using SCTransform (version 0.4.0) was then conducted, which incorporates a variance- stabilizing transformation (VST) method. SCTransform replaces conventional normalization and scaling procedures with a regularized negative binomial regression model, effectively controlling for technical noise. This method adjusts gene expression measurements by modeling unwanted variation and emphasizing biological variability.

[0113] Unsupervised dimensional reduction and clustering

[0114] For dimensionality reduction, Principal Component Analysis (PC A) was conducted. PC A reduces the dimensionality of the data by transforming the original variables into a new set of variables, which are linear combinations of the original variables. These new variables, called Principal Components (PCs), are ordered so that the first few retain most of the variation present in the original variables.

[0115] Upon completion of PCA, the datasets were prepared for integration using Harmony, applied through the 'RunHarmony' function with specified covariates: project, sequencing techniques, tissues, and donors. Harmony leverages a model-based approach to adjust the principal components across different datasets, mitigating the impact of batch effects and other technical discrepancies. This step ensures that the integrated data are aligned in a shared dimensional space that more accurately reflects the underlying biological heterogeneity.

[0116] Following the successful integration of datasets, Uniform Manifold Approximation and Projection (UMAP) was employed for further dimension reduction. The top 30 corrected principal components derived from Harmony were used. UMAP operates on a manifold learning technique that maps high-dimensional data into a more manageable two-dimensional space, facilitating the visualization and interpretation of complex data structures and relationships. Clustering and identification of marker genes

[0117] To determine the optimal number of clusters across the entire cell population, the 'estimate_k' function from scCCESS was utilized (version 0.3.3), configuring the criteria method to use NMI criterion and selecting the k-means algorithm. We set the ensemble size to 10 to ensure robustness by averaging results across multiple initializations. Once the optimal cluster number was identified, it was applied in the 'ensemble_cluster' function, again using the k-means algorithm, to partition the cells into distinct clusters based on their gene expression profiles.

[0118] Subsequently, the myeloid cell population was focused on. The clustering process was repeated specifically for these cells. The 'estimate k' function was first used, using the same parameters as those applied to the whole cell population. Then the 'cnscmblc clustcr' was used with the k-means algorithm and the newly determined optimal number of clusters to better capture the unique expression patterns of the myeloid cells. This independent analysis resulted in a different optimal number of clusters, reflecting the distinct biological characteristics of the myeloid cells. This procedure was repeated on three additional clusters representing B cells, NK cells, and T cells.

[0119] Next, significant marker genes were identified for each cell subset using the ' FindMarkers' function in Seurat. The MAST method was employed for statistical testing (Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015)), which is well-suited for single-cell RNA-seq data, particularly in handling sparsity and zeroinflation. To ensure robustness in the marker identification process, the minimum percentage (min.pct) of cell expressing a gene was set to 10%, meaning that a gene had to be expressed in at least 10% of cells in either of the groups being compared. Additionally, the logarithmic fold change threshold (logic. threshold) of 0.1 was applied, focusing on genes that exhibited at least a modest difference in expression levels. These criteria allowed us to identify marker genes that are both statistically significant and biologically relevant for each subset of cells.

[0120] CellChat Analysis CellChat version 2. 1 .2 was used for cell-cell communication computation to assess signaling changes using our defined cell types

[0121] Deconvolution of Bulk RNA Datasets for Cell Type Estimation

[0122] Integrated single cell dataset with cell type labels was used to deconvolute bulk RNA-seq data of TCGA cutaneous melanoma. Estimation of cell proportion were computed using CIBERSORTx version 1.0.1, BayesPrism version 2.2.2, and MuSiC version 1.0. Models were compared for fit based on Kullback-Leibler Divergence in R version 4.3.2 for all 3 deconvolution methods.

[0123] Recursive Partitioning Analysis

[0124] Recursive partitioning analysis (RPA) was conducted in R version 4.3.2 with package rpart. Myeloid cell types M_C 1 to M_C6 were evaluated with prognostic implications on overall survival (OS) on a cohort of n=42 samples with complete data with melanoma brain metastases. Duplicated samples (e.g. more than 1 biopsy at 1 timepoint for a patient) were averaged in percent expression for analysis.

[0125] Survival Analysis

[0126] Survival analysis was conducted in R version 4.3.2 using packages survival and survmincr. Values of M_C1 infiltration from RPA analysis were used as thresholds for risk groups termed ‘nodes’ for analyses on the following cohorts: Melanoma brain metastases, nivolumab clinical trial data set, tumor cancer genome atlas (TCGA) melanoma. Kaplan-Meier curves were generated and statistical testing using log-rank analysis and cox regression analysis was used to compared differences between groups.

[0127] Statistical Analysis Between Groups

[0128] Groups were tested for differences based on clinical factors using Fisher’s exact test. For treatment effect of nivolumab on risk ‘node’ the Stuart-Maxwell test was conducted. For differences in frequencies of cell types based on cell type clusters in the training and validation data cohorts the following statistical tests were conducted: ANOVA, Kruskal-Wallis with Benjamini-Hochberg correction, and Wilcoxon Rank Sum with Benjamini-Hochberg correction. All analyses were conducted in R version 4.3.2.

Claims

CLAIMS1. A method of detecting infiltration of a myeloid cluster of hybrid immune cells in a tumor biopsy sample obtained from a subject, the method comprising measuring expression of a plurality of genes in the tumor biopsy sample, wherein the plurality of genes comprise: one or more Group 1 genes selected from CLEC10A, FLT3, CD1D, RTN1, GPAT3, AC009093.2, PKIB, CCSER1, KCNK6, PDE4A, APAF1, and SNX20; one or more Group 2 genes selected from HLA-DQA1, HLA-DQB1, HLA-DRA, HLA- DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA-DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, IFI30, AIF1, TYROBP, CD68, FCER1G, RAB31, CTSS, RNASET2, SAMHD1, TYMP, VAMP8, ITGB2, and HCLS1; and one or more Group 3 genes selected from EGAES2, SEAMF8, CAEHM6, C15orf48, HLA-DQB2, HEA-DQA2, CD300C, CPVE, ALDH2, FCGR2B, FGE2, ANKRD22, CD86, P2RY6, IE4I1, GAPT, RNASE6, FPR3, CEEC4A, CD74, CD33, FBP1, IE18, MS4A6A, PED4, SPINT2, SIGEEC7, CEEC12A, PEA2G7, SEC8A1, EY86, OSCAR, LIERB4, SPI1, CSTA, WDFY4, IGSF6, CFP, PTGS1, CARD9, C1QB, SERPINA1, MNDA, FCGR1A, MARCH1, CST3, C1QC, FGD2, C1QA, CXCE16, CEIC2, MPEG1, SIGLEC9, HCK, CD72, HLA-DOA, FCGR1B, LILRB2, TNFSFE3, TFEC, PRAM1, ADORA3, SIGEEC10, CSF1R, EIERB1, VSIG4, SEC31A2, CIITA, ERRC25, CLEC7A, CSF2RA, RGS18, AC020656.1, C19orf38, TMEM176B, KMO, PTAFR, OLR1, CYBB, AREG, Clorf54, MS4A4A, PLBD1, TM6SF1, C3AR1, MRC1, GNA15, CXorf21, P2RY13, SMCO4, ADAP2, PIERA, OTULINL, ALOX5, PLXDC2, SLC7A7, TREM2, MS4A7, PID1, CD163, TLR2, TMEM176A, RASSF4, CD40, NLRP3, ZNF385A, DSE, PSTPIP2, ANPEP, TNFAIP8L2, SCIMP, SYK, LILRB3, FGR, KYNU, IRF8, FAM49A, TNFSF13B, CLEC4E, VASH1, ADA2, IL1B, JAML, NCF2, TMEM273, IRF5, RBM47, FCGR3A, MSR1, IL18BP, CCR1, DAPP1, ADAM28, BCAT1, PLEK, ITGAX, GPR34, LAIR1, LGALS9, CD300A, LILRA2, LRRK2, CD302, BASP1, FCN1, CD14, BTK, SLC43A2, LYN, NF AMI, FPR1, IL13RA1, ATP8B4, SLCO2B1, TBXAS1, SLC15A3, LAT2, FES, SIGLEC1, IL1R2, MAFB, RAB20, EAF2, NAAA, CD83, STX11, CSF3R, MIR181A1HG, TLR4, ZNF710,SLC1 A3, LIPA, PAK 1 , GPR183, IRAK3, EMILIN2, MILR1 , 0GFRL1 , IFNGR1 , ITGAM, KCTD12, TRPM2, B3GNT5, D0K3, CTSH, ADGRE2, PTPRE, FOLR2, PLAUR, C5AR1, RNF144B, SULF2, SLC40A1, GCA, FCGR2A, METTL7A, MAP3K8, TGFB, NAIP, HVCN1, TNFAIP2, THEMIS2, MFSD1, UNC93B1, ALDH3B1, JAK2, D0K1, PLEKHO1, CCDC200, TMEM106A, ABB, AXL, MANBA, C9orl72, AC004687.1, MPP1, PIK3AP1, SH3TC1, GSAP, SLC37A2, HACD4, GLIPR1, RELT, ATF5, MERTK, TET2, KCNMA1, SH2B3, NRROS, FCGRT, UBE2E2, VSIR, NCF4, STAB1, LRRK1, TREM1, FUCA1, NAGA, PARVG, GRN, MAN2B1, APOCI, NCF1, CD4, SLC11A1, EPB41L3, ETS2, RASGEF1B, ODF3B, ARRB2, SLC25A19, ATP2B1-AS1, WAS, LAP3, NAGK, CCL3L1, RNF130, PSAP, AOAH, CASS4, LPCAT2, ST8SIA4, FCHO2, SYNGR2, NFKBID, CHST15, HBEGF, PLAGL1, TCN2, CREG1, SMIM3, SDSL, GM2A, NCKAP1L, TNFSF10, RP2, RIN3, CTSC, DPYD, RPS6KA4, EPSTI1, GAA, RGL1, DENND3, GLIPR2, THBS1, ACSL1, CEBPD, PLAC8, C3, CD93, RGS2, PPT1, RHOG, SLC16A3, IGFLR1, DRAM1, RILPL2, GLRX, HM0X1, KLHL6, CAMK1, C2, SIPA1L1, RGS10, HNMT, MYD88, F13A1, POU2F2, ARHGAP18, RASSF2, BID, DHRS3, DMXL2, TBC1D8, PRKAG2, RIPK2, SLAMF7, ETV6, MAN1A1, SQOR, OSBPL11, DENND1B, MXD1, ACP2, NIPSNAP3A, NUDT16, NPC2, SRGN, LACTB, FUOM, ITGB2-AS1, SPTLC2, TPK1, DOCK5, LRP1, GLUL, CAMKID, COTL1, UBE2D1, ALCAM, ADCY7, HDAC9, LGMN, DOCK4, RGS19, SCPEP1, ZYX, GK, PLSCR1, NMI, DAPK1, and TEX14; wherein the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one Group 1 gene, at least one Group 2 gene, and at least one Group 3 gene, and wherein the subject has or is suspected of having melanoma.

2. The method of claim 1, comprising measuring the expression of one or more group 1 genes, one or more group 2 genes, and five or more group 3 genes, wherein: a) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 gene, and at least one group 3 gene; or b) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least fivegroup 3 genes.

3. The method of claim 1, comprising measuring the expression of three or more group 1 genes, five or more group 2 genes, and five or more group 3 genes, wherein: a) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 gene, and at least one group 3 gene; b) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least five group 3 genes; c) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least five group 3 genes; d) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least five group 3 genes; or e) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least five group 3 genes.

4. The method of claim 1, comprising measuring the expression of five or more group 1 genes, five or more group 2 genes, and 10 or more group 3 genes, wherein: a) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 gene, and at least one group 3 gene; b) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least five group 3 genes; c) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least 10 group 3 genes;d) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least five group 3 genes; or e) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least five group 3 genes; or f) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least five group 3 genes; g) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 10 group 3 genes; h) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least five group 3 genes; or i) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 10 group 3 genes.

5. The method of claim 1, comprising measuring the expression of five or more group 1 genes, 10 or more group 2 genes, and 20 or more group 3 genes, wherein: a) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least five group 3 genes; b) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least 10 group 3 genes; c) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least five group 3 genes;d) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least 10 group 3 genes; e) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least 15 group 3 genes; f) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least five group 3 genes; g) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 10 group 3 genes; h) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 15 group 3 genes; i) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least five group 3 genes; j) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 10 group 3 genes; k) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 15 group 3 genes; l) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least five group 3 genes; m) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 10 group 3 genes;n) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 15 group 3 genes; o) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 10 group 3 genes; or p) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 15 group 3 genes.

6. The method of claim 1, comprising measuring the expression of five or more group 1 genes, 10 or more group 2 genes, and 40 or more group 3 genes, wherein: a) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 gene, and at least five group 3 gene; b) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least one group 2 genes, and at least 10 group 3 genes; c) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least three group 2 genes, and at least 10 group 3 genes; d) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 gene, at least three group 2 genes, and at least 15 group 3 genes; e) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least one group 1 genes, at least three group 2 genes, and at least 20 group 3 genes; f) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least five group 3 genes;g) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 10 group 3 genes; h) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 15 group 3 genes; i) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least three group 2 genes, and at least 20 group 3 genes; j) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least five group 3 genes; k) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 10 group 3 genes; l) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 15 group 3 genes; m) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least three group 1 genes, at least five group 2 genes, and at least 20 group 3 genes; n) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least five group 3 genes; o) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 10 group 3 genes; p) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 15 group 3 genes;q) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least five group 2 genes, and at least 20 group 3 genes; r) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 10 group 3 genes; s) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 15 group 3 genes; or t) the myeloid cluster of hybrid immune cells are identified as having increased expression of at least five group 1 genes, at least 10 group 2 genes, and at least 20 group 3 genes.

7. The method of any one of claims 1-6, wherein the Group 1 genes are selected from CLEC10A, FLT3, CD1D, RTN1, GPAT3, and AC009093.2.

8. The method of any one of claims 1-7, wherein the Group 2 genes are selected from HLA- DQA1, HLA-DQB1, HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA- DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, and IFI30.

9. The method of any one of claims 1-8, wherein the Group 3 genes are selected from ALDH2, ANKRD22, C15orf48, C1QA, C1QB, C1QC, CALHM6, CARD9, CD300C, CD33, CD74, CD86, CFP, CEEC12A, CEEC4A, CEIC2, CPVE, CST3, CSTA, CXCE16, FBP1, FCGR1A, FCGR2B, FGD2, FGE2, FPR3, GAPT, HCK, HEA-DQA2, HEA-DQB2, IGSF6, IE18, IE4I1, EGAES2, EIERB4, EY86, MARCH1, MNDA, MPEG1, MS4A6A, OSCAR, P2RY6, PEA2G7, PED4, PTGS1, RNASE6, SERPINA1, SIGEEC7, SIGEEC9, SEAMF8, SEC8A1, SPI1, SPINT2, and WDFY4.

10. The method of any one of the preceding claims, wherein expression of the plurality of genes in the tumor biopsy sample is measured by sequencing.11 . The method of claim 10, wherein the sequencing comprises single cell RNA sequencing, targeted gene expression sequencing, or bulk RNA sequencing.

12. The method of any one of claims 1-11, further comprising calculating a percent infiltration of the myeloid cluster of immune cells in the tumor biopsy sample relative to the total cell count of the sample.

13. The method of claim 12, further comprising prognosing melanoma in the subject based upon the percent infiltration.

14. The method of claim 13, wherein prognosing melanoma comprises identifying the subject as at risk of poor overall survival when the percent infiltration is below a threshold value.

15. The method of claim 14, wherein the threshold value is 2.6%.

16. The method of claim 14, wherein the threshold value is 1.9%17. The method of any one of claims 14-16, further comprising monitoring the subject with increased frequency and / or providing an aggressive anti-cancer treatment to the subject identified as at risk of poor overall survival.

18. The method of claim 12, further comprising determining responsiveness to a treatment for melanoma in the subject based upon the percent infiltration.

19. The method of claim 18, wherein the subject has received at least one dose of the treatment for melanoma prior to the tumor biopsy sample being obtained from the subject, and wherein determining responsiveness comprises identifying the subject as likely to have a positive response to the treatment when the percent infiltration is equal to or above a threshold value, or identifying the subject as unlikely to have a positive response to the treatment when the percent infiltration is below the threshold value.

20. The method of claim 19, wherein the threshold value is 2.6%.

21. The method of claim 19, wherein the threshold value is 1.9%.

22. The method of any one of claims 19-21, further comprising providing an alternative antimelanoma therapy to the subject identified as unlikely to have a positive response.

23. The method of any one of the preceding claims, wherein the subject has Stage I or Stage II melanoma.

24. A method of treating a subject having melanoma, comprising: a) obtaining a tumor sample from the subject after the subject has received at least one treatment for melanoma; b) detecting infiltration of a myeloid cluster of hybrid immune cells in the tumor biopsy by the method of any one of claims 1-12 to identify the subject as responsive or not- responsive to the at least one treatment; and c) providing an alternative treatment for melanoma to the subject identified as not- responsive to the at least one treatment.

25. The method of claim 24, wherein a percent infiltration of the myeloid cluster of hybrid immune cells in the tumor biopsy sample less than a threshold value identifies the subject as not-responsive to the at least one treatment.

26. The method of claim 25, wherein threshold value is 2.6%.

27. The method of claim 25, wherein threshold value is 1.9%.

28. A method of treating a subject having melanoma, comprising: a) detecting infiltration of a myeloid cluster of hybrid immune cells in a tumor biopsy sample obtained from the subject to identify the subject as at risk of poor overallsurvival or not at risk of poor overall survival; wherein infiltration is detected by the method of any one of claims 1-12; and b) providing an aggressive anti-cancer treatment to the subject identified as at risk of poor overall survival; or c) providing standard anti-cancer treatment to the subject identified as not at risk of poor overall survival.

29. The method of claim 28, wherein the standard anti-cancer treatment comprises surgical removal of the tumor.

30. The method of claim 28 or claim 29, wherein the aggressive anti-cancer treatment is selected from chemotherapy, immunotherapy, targeted therapy, radiation therapy, and a combination thereof.

31. The method of any one of the preceding claims, wherein the subject is a human.

32. A kit comprising reagents for measuring expression of the following: a) one or more Group 1 genes selected from CLEC10A, FLT3, CD ID, RTN1, GPAT3, AC009093.2, PKIB, CCSER1, KCNK6, PDE4A, APAF1, and SNX20; b) one or more Group 2 genes selected from HLA-DQA1, HLA-DQB 1, HLA-DRA, HLA-DPB1, HLA-DPA1, HLA-DRB1, LYZ, HLA-DRB5, HLA-DMB, Clorfl62, LST1, HLA-DMA, IFL30, AIF1, TYROBP, CD68, FCER1G, RAB31, CTSS, RNASET2, SAMHD1, TYMP, VAMP8, ITGB2, and HCLS1; and c) one or more Group 3 genes selected from LGALS2, SLAMF8, CALHM6, C15orf48, HLA-DQB2, HLA-DQA2, CD300C, CPVL, ALDH2, FCGR2B, FGL2, ANKRD22, CD86, P2RY6, IL4I1, GAPT, RNASE6, FPR3, CLEC4A, CD74, CD33, FBP1, IL18, MS4A6A, PLD4, SPINT2, SIGLEC7, CLEC12A, PLA2G7, SLC8A1, LY86, OSCAR, LILRB4, SPI1, CSTA, WDFY4, IGSF6, CFP, PTGS1, CARD9, C1QB, SERPINA1, MNDA, FCGR1A, MARCH1, CST3, C1QC, FGD2, C1QA, CXCL16, CLIC2, MPEG1, SIGLEC9, HCK, CD72, HLA-DOA, FCGR1B, LILRB2, TNFSF13, TFEC, PRAM1, ADORA3, SIGLEC10, CSF1R, LILRB1, VSIG4,SLC31 A2, CIITA, LRRC25, CLEC7A, CSF2RA, RGS18, AC020656.1, C19orf38, TMEM176B, KMO, PTAFR, OLR1, CYBB, AREG, Clorf54, MS4A4A, PLBD1, TM6SF1, C3AR1, MRC1, GNA15, CXorf21, P2RYE3, SMCO4, ADAP2, PILRA, OTULINL, ALOX5, PLXDC2, SLC7A7, TREM2, MS4A7, PID1, CD163, TLR2, TMEM176A, RASSF4, CD40, NLRP3, ZNF385A, DSE, PSTPIP2, ANPEP, TNFAIP8L2, SCIMP, SYK, LILRB3, FGR, KYNU, IRF8, FAM49A, TNFSFE3B, CLEC4E, VASH1, ADA2, IL1B, JAML, NCF2, TMEM273, IRF5, RBM47, FCGR3A, MSR1, IL18BP, CCR1, DAPP1, ADAM28, BCAT1, PLEK, ITGAX, GPR34, LAIR1, LGALS9, CD300A, LILRA2, LRRK2, CD302, BASP1, FCN1, CD14, BTK, SLC43A2, LYN, NF AMI, FPR1, IL13RA1, ATP8B4, SLCO2B1, TBXAS1, SLC15A3, LAT2, FES, SIGLEC1, IL1R2, MAFB, RAB20, EAF2, NAAA, CD83, STX11, CSF3R, MIR181A1HG, TLR4, ZNF710, SLC1A3, LIPA, PAK1, GPR183, IRAK3, EMILIN2, MILR1, OGFRL1, IFNGR1, ITGAM, KCTD12, TRPM2, B3GNT5, DOK3, CTSH, ADGRE2, PTPRE, FOLR2, PLAUR, C5AR1, RNF144B, SULF2, SLC40A1, GCA, FCGR2A, METTL7A, MAP3K8, TGFB, NAIP, HVCN1, TNFAIP2, THEMIS2, MFSD1, UNC93B1, ALDH3B1, JAK2, DOK1, PLEKHO1, CCDC200, TMEM106A, ABB, AXL, MANBA, C9orf72, AC004687.1, MPP1, PIK3AP1, SH3TC1, GSAP, SLC37A2, HACD4, GLIPR1, RELT, ATF5, MERTK, TET2, KCNMA1, SH2B3, NRROS, FCGRT, UBE2E2, VSIR, NCF4, STAB1, LRRK1, TREM1, FUCA1, NAGA, PARVG, GRN, MAN2B1, APOCI, NCF1, CD4, SLC11A1, EPB41L3, ETS2, RASGEF1B, ODF3B, ARRB2, SLC25A19, ATP2B1-AS1, WAS, LAP3, NAGK, CCL3L1, RNF130, PSAP, AOAH, CASS4, LPCAT2, ST8SIA4, FCHO2, SYNGR2, NFKBID, CHST15, HBEGF, PLAGL1, TCN2, CREG1, SMIM3, SDSL, GM2A, NCKAP1L, TNFSF10, RP2, RIN3, CTSC, DPYD, RPS6KA4, EPSTI1, GAA, RGL1, DENND3, GLIPR2, THBS1, ACSL1, CEBPD, PLAC8, C3, CD93, RGS2, PPT1, RHOG, SLC16A3, IGFLR1, DRAM1, RILPL2, GLRX, HM0X1, KLHL6, CAMK1, C2, SIPA1L1, RGS10, HNMT, MYD88, F13A1, POU2F2, ARHGAP18, RASSF2, BID, DHRS3, DMXL2, TBC1D8, PRKAG2, RIPK2, SLAMF7, ETV6, MAN1A1, SQOR, OSBPL11, DENND1B, MXD1, ACP2, NIPSNAP3A, NUDT16, NPC2, SRGN, LACTB, FUOM, ITGB2-AS1, SPTLC2, TPK1, DOCK5, LRP1, GLUL, CAMKID,C0TL1 , UBE2D1 , ALCAM, ADCY7, HDAC9, LGMN, DOCK4, RGS19, SCPEP1 , ZYX, GK, PLSCR1, NMI, DAPK1, and TEX14.

33. The kit of claim 32, wherein the reagents comprise a plurality of primer pairs, wherein each primer pair amplifies a distinct gene.

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