WO2025208227A1

WO2025208227A1 - Prognostic method for acute myeloid leukemia (aml) and non-canonical aml regeneration

Info

Publication number: WO2025208227A1
Application number: PCT/CA2025/050492
Authority: WO
Inventors: Cameron HOLLANDS; Mick Bhatia; Amro ELRAFIE
Original assignee: McMaster University
Current assignee: McMaster University
Priority date: 2024-04-03
Filing date: 2025-04-03
Publication date: 2025-10-09
Anticipated expiration: 2026-10-03

Abstract

This disclosure relates to methods for identifying Acute Myeloid Leukemia (AML) regeneration enriched cells (RECs) in a patient sample, as well as methods for purifying RECs and for generating RECs, such as for use in an assay for screening therapeutic agents. Also described herein are methods of predicting the prognosis, risk of relapse and/or treatment response in patients with AML, as well as methods for selecting patients with AML for treatment, and methods for treating patients with AML. Further provided are kits for use in the methods described herein.

Description

PROGNOSTIC METHOD FOR ACUTE MYELOID LEUKEMIA (AML) AND NON- CANONICAL AML REGENERATION

RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application 63/573,574 filed on April 3, 2024, herein incorporated by reference.

FIELD

[0002] The present disclosure relates to identification of a new population of cells and to methods for determining prognosis, treatment response and/or risk of relapse in patients with Acute Myeloid Leukemia based on this newly identified population.

BACKGROUND

[0003] Similar to other stem cell driven cancers, acute myeloid leukemia (AML) cells are assembled in a hierarchy. AML represents an aggressive and heterogeneous hematological cancer characterized by a block in differentiation that affects myeloid lineages of the hematopoietic tissue (1,2). This results in an accumulation of poorly differentiated blast cells in the patient bone marrow (BM) (2). From a treatment perspective, the major barrier in achieving disease-free survival of AML patients is maintaining a state of clinical remission (CR) defined by less than 5% blasts (3,4) and thus preventing disease regrowth above this threshold. Subsets of leukemic cells remain and survive in the BM post chemotherapy that are capable of reinitiating disease and giving rise to relapsed AML responsible for high mortality rates (5). The nature, properties, and overall dynamics of the enduring cells that contribute to and are responsible for the AML regeneration processes are poorly understood. Altogether, this has limited consistent biomarker detection to better manage patients post chemotherapy or to develop novel targeted therapies for AML relapse.

[0004] Residual leukemic cells responsible for regeneration of disease post chemotherapy are believed to be enriched for leukemia stem cells (LSCs) and define the basis of “canonical AML regeneration” contributing to relapsed disease. LSCs are considered to be at the cellular apex of this hierarchically arranged malignant tissue, and are operationally defined by the ability to engraft and initiate patient-specific leukemia upon transplant into immune deficient mice (6,7). Features associated with LSCs are quiescence and self-renewal, which supports the notion that LSCs can evade the anti-proliferative chemotherapy by maintaining a dormant state (8), and subsequently drive canonical regeneration. As in vivo engraftment tests are laborious and retrospective, molecular surrogate definitions of LSCs using complex gene profiling have been devised (9,10). The LSC-R and the weighted gene profile of LSC17 (10) were defined by their ability to correlate to engraftment activity and overall survival (OS) of AML patients, respectively (9,10). However, findings of recent studies using in vivo xenograft models have suggested the involvement of elements other than LSCs in disease regrowth, thereby transcending the canonical concept of AML regeneration. These studies mimicked the clinical treatment of AML in xenograft models and all observed reductions in LSC numbers and quiescence following chemotherapy (11-13) providing collective evidence that properties of self-renewing cells, quiescent cells, and immunophenotypically primitive cells are in fact depleted following chemotherapy treatment in patients, patient derived xenograft (PDX) models, and in vitro models of AML (11-13). These observations suggest non-canonical alternative mechanisms contribute to the complexity of AML regeneration (12,14) and underscore the need for further characterization into the complex nature of AML regeneration post chemotherapy treatment vs. naive LSC states.

[0005] To date, the cellular and molecular basis of AML regeneration post chemotherapy has yet to be sufficiently resolved to combat disease relapse. Given the dynamic nature of AML regeneration, there is a need to evaluate multiple timepoints of regeneration using detailed molecular analyses.

[0006] The background herein is included solely to explain the context of the disclosure.

This is not to be taken as an admission that any of the material referred to was published, known, or part of the common general knowledge as of the priority date.

SUMMARY

[0007] The inventors have identified a new population of cells, AML Regeneration Enriched Cells (RECs), through temporal single cell trans criptomic characterization of AML hierarchical regeneration in response to chemotherapy. RECs are defined by a cell surface marker profile of CD74⁺ CD68⁺ CD45^bnght CD³⁴', and have high side scatter in flow cytometry applications (SSC^hlgh), and although derived from leukemic stem cells (LSCs), are devoid of stem/progenitor capacity. The inventors have here determined that RECs demonstrate the ability to augment or reduce leukemic regeneration in vivo based on transfusion or depletion, respectively. Furthermore, RECs are prognostic for patient survival and predictive of treatment failure in AML cohorts. The disclosure herein reveals RECs as a previously unknown functional catalyst of LSC driven regeneration contributing to the non-canonical framework of AML regeneration. [0008] Accordingly, in an aspect herein provided is a method for determining the presence of regeneration enriched cells (RECs) in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subj ecting the sample to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; and ii) determining the presence of CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells; wherein the presence of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells are indicative of the presence of RECs.

[0009] In another aspect, herein provided is a method of determining a ratio of regeneration enriched cells (RECs) to non-RECs in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subj ecting the sample to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells which are indicative of RECs; iii) measuring the amount of CD74’ CD68’ CD45^brigbt SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii).

[0010] In another aspect, herein provided is method of determining a prognosis for a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subjecting a sample obtained from the subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells, which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^brigbt SSC^high CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^big}1 CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the subject has an increased risk of relapse or treatment failure, and wherein when the ratio is at or below about 0.15, the subject has a decreased risk of relapse or treatment failure.

[0011] In another aspect, herein provided is a method of selecting a subject who has completed a treatment for Acute Myeloid Leukemia (AML) for further treatment, the method comprising: i) subjecting a sample obtained from the subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells, which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^brigbt SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^big}1 CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^big}1 CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the subject is selected for further treatment, and wherein when the ratio is at or below about 0. 15, the subject is not selected for further treatment.

[0012] In yet another aspect, herein provided is a method of treating a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subjecting a sample obtained from a subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells , which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^brigbt SSC^high CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the method further comprises administering a further treatment to the subject, and wherein when the ratio is at or below about 0.15, the method further comprises not administering a further treatment to the subject.

[0013] In a further aspect, herein provided is a use of a further treatment for treating a subject who has completed a treatment for Acute Myeloid Leukemia (AML), wherein the subject was previously determined to have a ratio of regeneration enriched cells (RECs) to non- RECs of at least about 0.20, wherein CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells are indicative of REC cells, and wherein CD74’ CD68’ CD45^brigbt SSC^bigb CD34’ cells, CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34" cells, or CD74⁺ CD68" CD45^bngbt SSC^blgb CD34" cells are indicative of non-RECs.

[0014] In an embodiment, the flow cytometry comprises fluorescence activated cell sorting (FACS).

[0015] In an embodiment, the treatment comprises a cytotoxic treatment. In an embodiment, the cytotoxic treatment comprises chemotherapy. In another embodiment, the cytotoxic treatment comprises a pyrimidine nucleoside analog. In yet another embodiment, the cytotoxic treatment comprises Cytarabine.

[0016] In an aspect, herein provided is a method for purifying regeneration enriched cells (RECs) in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) labelling cells in the sample with fluorescent markers for CD74, CD68, CD45, and CD34; ii) passing the labelled cells through a flow cytometer with fluorescence detection; iii) detecting side scatter (SSC) and fluorescence signals emitted by the fluorescent markers in i); and iv) sorting cells by the detected fluorescence signals and SSC in step iii), wherein the cells sorted in step iv) according to CD74⁺ CD68⁺ CD45^bnght SSC^hlgh CD34' are indicative of RECs.

[0017] In another aspect, herein provided is a method for generating regeneration enriched cells (RECs) in vitro, the method comprising: i) purifying a population of CD74' CD34⁺ cells or CD68' CD34⁺ cells by fluorescence activated cell sorting (FACS); and ii) incubating the purified population of cells in a medium containing erythropoietin (EPO), interleukin 3 (IL3), granulocyte colony stimulating factor 3 (GCSF), and stem cell factor (SCF); thereby generating CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells, which are indicative of RECs.

[0018] In an embodiment, the medium comprises semisolid methylcellulose medium.

[0019] In an embodiment, the incubating in step ii) is for 14 days. In another embodiment, the incubating in step ii) is at 37°C.

[0020] In an embodiment, the method further comprises a step of purifying the RECs by FACS.

[0021] In another aspect, herein provided is an assay for screening the efficacy of a test agent in reducing the number of regeneration enriched cells (RECs), the assay comprising: i) obtaining a population of cells comprising RECs by a method herein disclosed; ii) treating the population of cells comprising RECs with the test agent; iii) measuring the number and/or ratio of RECs to non-RECs within the population of cells comprising RECs; and iv) comparing the number and/or ratio in iii) to the number and/or ratio of RECs in a control population in the absence of test agent; wherein a reduced number or ratio of RECs in iii) compared to the control population is indicative that the test agent is effective for reducing the number of RECs. [0022] In yet another aspect, herein provided is a kit comprising a CD74 detection agent, a CD68 detection agent, a CD45 detection agent, and a CD34 detection agent.

[0023] In an embodiment, the kit further comprises instructions for use in a method herein disclosed or in an assay herein disclosed.

[0024] In an embodiment, the kit further comprises a CD 14 detection agent, a CD117 detection agent, a detection agent for CD15, and/or a detection agent for live/dead cells.

[0025] In another embodiment, the kit further comprises flow cytometry buffer and/or ammonium chloride.

[0026] Other features and advantages of the present disclosure will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating embodiments of the disclosure, are given by way of illustration only and the scope of the claims should not be limited by these embodiments but should be given the broadest interpretation consistent with the description as a whole.

DRAWINGS

[0027] Certain embodiments of the disclosure will now be described in greater detail with reference to the attached drawings. The drawings are not intended to limit the scope of the teachings described herein.

[0028] Figures 1A-1D show that scRNA seq data on frequently sampled AML xenografts following cytarabine treatment identifies unique kinetics and defines both responding and non-responding PDXs in example embodiments of the application. Figure 1 A: Disease burden (hCD45 Chimerism * Total Cells Harvested) represented by mean ± SD from PDXs at Day 0 (“Untreated”) and at the lowest disease burden timepoint (“Cytoreduced”) normalized to Untreated AML 1-6, sorted by ELN stratifications, clinical outcome, and responder status (N=6, n=31 untreated, n=18 treated). Figure IB: Experimental overview of data generating panels. Figures 1C-1D: De novo patient tissue andhCD45⁺ BM harvested from 3 patient matched PDXs at each noted timepoint underwent scRNA seq with cell multiplexing, immunophenotyping, functional assays, and cellularity assessments. UMAP plots of cells from all timepoints of AML 1-3 PDXs organized by (Figure 1C) transcriptionally defined cell cluster ID and by (Figure ID) timepoint. ****p<0.0001, *p<0.05, ns p>0.05 by unpaired t tests.

[0029] Figure 2A shows total number of cells (hCD45Chimerism * Total Cells Harvested) in PDXs throughout a 5-day AraC treatment time course normalized to Untreated control from AML1-6 PDXs (N=6, n = 9-22 per AML) in an example embodiment of the disclosure. Figure 2B shows total AML burden of AML1 PDXs at Day 0, Day 7 and Day 10 timepoints of a 5 day AraC treatment normalized to Untreated (Day 0) (n = 5-7 per timepoint) in an example embodiment of the disclosure. Figure 2C shows UMAP plot of all PDX cells in the analysis anchored by healthy BM, organized by tissue source (n = 78 500) in an example embodiment of the disclosure. Figures 2D-2F show experimental outline for generating scRNA and paired analyses from AML patient 1, AML patient 3 and a CB donor in an example embodiment of the disclosure. Figure 2G shows a metric of cluster volatility (1 - [Lowest Cluster%/Highest Cluster%]) representing the proportional change of the cluster throughout leukemic regeneration of each shared and substantive cluster graphed by each AML (n = 16) in example embodiments of the disclosure. Floating bar represents mean of each AML. ****p<0.0001, *p<0.05, ns p>0.05 by unpaired t tests (Figure 2B), or paired t tests (Figure 2G).

[0030] Figure 3A shows an illustration of the increase in progenitor frequency (light grey line) representing leukemic regeneration and the decrease in disease burden (dark grey line) representing a state of cytoreduction in an example embodiment of the disclosure. Figure 3B shows total # of AML Cells (%hCD45 * total cells harvested) and progenitor frequency (CFUs / cells seeded) from sorted CD33⁺ hCD45⁺ cells of AML1 PDX models over a AraC time course (students t test) in an example embodiment of the disclosure. Figure 3C shows total # of AML Cells (%hCD45 * total cells harvested) and progenitor frequency (CFUs / cells seeded) from sorted CD33⁺ hCD45⁺ cells of AML2 PDX models over a AraC time course (students t test) in an example embodiment of the disclosure. UMAPs of AML cells at different timepoints during leukemic regeneration in PDX models of Figure 3D shows AML1 and Figure 3E shows AML2 in a example embodiments of the disclosure. Figure 3F shows total number of cells belonging to each cluster at untreated and cytoreduced timepoints in AML1 and AML2 in an example embodiment of the disclosure. Figure 3G shows total number of cells belonging to each cell assignment at untreated and cytoreduced timepoints in AML1 and AML2 in an example embodiment of the disclosure. Figure 3H shows clusters enrichment at regeneration (Cluster % at regeneration normalized to untreated control) of AML2 biological triplicates in an example embodiment of the disclosure. Composition of REC Clusters 1 and 5 by (Figure 31) cell assignment and (Figure 3J) cell cycle phase in example embodiments of the disclosure. Figure 3K shows top 50 HALLMARK KEGG REACTOME and WIKI pathways enriched (ranked by NES Values of GSEA analyses) (FDR < 0.01) in REC clusters in example embodiments of the disclosure. [0031] Figures 4A-4F show that Cluster 5 and Cluster 1 are most enriched at functionally defined biologically relevant timepoints in AML1 and AML2 respectively in example embodiments of the disclosure. Disease burden (Total cells harvested * %hCD45; dark grey) and progenitor frequency (#colonies/cells seeded; light grey) during and following a five- day AraC treatment in (Figure 4A) AML1 PDXs and (Figure 4B) AML2 PDXs, overlayed with UMAP plots of scRNA sequencing data (n=3 per timepoint per AML sample [AML1 pooled]) from the same cell pool that derived the functional progenitor frequency and disease burden data throughout the time course. Figures 4C-4D show bar graphs of the fold enrichment of each substantive cluster at untreated vs regeneration timepoints for AML1 (n=3, pooled) and AML2 (n=3). Grayscale intensity of bars represents a metric of cytoreduction magnitude [LoglO(Cluster% Untreated / Cluster% Cytoreduced)]. Figure 4E shows correlation plots between fold enrichment of Clusters 1 and 5 at untreated vs regeneration timepoint (Cluster% at Regeneration/Cluster% at Untreated) compared to fold increase of total AML cell burden at Day 7 to Day 14. Figure 4F shows bar graph of the correlation coefficient (R2 value) from linear regression analysis from Figure 4E from all shared and substantive clusters. The linear regression reveals a correlation that is significantly non-zero (p>0.01, light grey bars) or not significant (p>0.05, dark grey bars).

[0032] Figure 5 A shows REC gene expression and demonstrate predictive capacity of AML patient survival and are defined by the CD74⁺ CD68⁺ immunophenotype in example embodiments of the disclosure. Patient specific UMAP plots of all PDX and de novo cells from AML1 highlighting RECs from PDXs from de novo tissue. The first UMAP plot highlights RECs in contrast to other PDX derived cells, while the third UMAP plot highlights the cluster of cells that RECs cluster with in contrast to other patient derived cells, demonstrating the overlap in transcriptional similarity between these populations. The middle UMAP plot is the concatenation of UMAP plots 1 and 3. Figure 5B shows the 71 shared DEGs that overlap between RECs of AML 1-3 in an example embodiment of the disclosure. Figure 5C shows multivariate cox regression analysis on Regen71, ELN stratifications, Age and WBC count with overall and event free survival of an independent AML cohort (TARGET- AML, N = 1914) in an example embodiment of the disclosure. Figure 5D shows flow of narrowing down the Regen71 gene score to CD68 as a biomarker for RECs Figure 5E UMAP plots of cells from del(7) AML patients throughout treatment timecourse (N = 3)25 with CD68 enriched Cluster 2 highlighted, grouped by cluster ID and clinical timepoint, respectively, in an example embodiment of the disclosure. Figure 5F shows bar plot of cluster composition of each timepoint of scRNA from Figure 5E. CD68⁺ Cluster 2 emerged post-chemotherapy in an example embodiment of the disclosure. Figure 5G shows UMAP plots of de novo AML38 cells at diagnosis and when refractory to treatment with CD68 enriched Cluster 6 highlighted, grouped by cluster ID and treatment timepoint, respectively, in an example embodiment of the disclosure. Figure 5H shows bar plot of cluster composition of each timepoint of scRNA from Figure 5G in an example embodiment of the disclosure. CD68⁺ Cluster 6 emerged postchemotherapy. Figure 51 shows UMAP plots of de novo REC Cluster 0’ where grayscale intensity is by tissue source (healthy BM: n = 1123, AML sample: n = 2696) and of PDX REC Clusters 1 and 5 where grayscale intensity is by tissue source (CB xenograft: n=2305, AML PDX: n=9403) in an example embodiment of the disclosure. Figure 5J shows logic flow of narrowing down the shared DEG from Figure 5G to CD74 as a biomarker for leukemia specific RECs in an example embodiment of the disclosure. Figure 5K shows LogCD74 expression by RNA seq represented by mean +/- SD of AML patients (N=542) compared to healthy BM cells (N=73) from leukemia MILE study (***p < 0.001, student’s t test), and CD74 expression by FC on AML patients (N=2) compared to healthy BM donations (N=2) in an example embodiment of the disclosure.

[0033] Figure 6A shows patient specific UMAP plots of all PDX and de novo cells from AML 2-3 highlighting RECs from the PDX system and from de novo tissue in an example embodiment of the disclosure. The first UMAP plot (furthest left) from each AML sample highlights RECs in contrast to other PDX derived cells, while the third UMAP plot (furthest right) highlights the cluster of cells that RECs cluster with in contrast to other patient derived cells, demonstrating the overlap in transcriptional similarity between RECs and certain de novo cell populations. The middle UMAP plot for each AML sample is the concatenation of UMAP plots 1 and 3. Figure 6B shows UMAP plots of all de novo samples (AML1-3, BM1-2, n = 25 996) highlighted by: Clusters 0’ - 17’, tissue source, AML1-3 RECs and Cluster 0’ (653 DEG), respectively, in an example embodiment of the disclosure. Figure 6C shows multivariate cox regression analyses on TARGET AML cohort (N = 1914) and a filtered version of the cohort with uniform induction treatments (N=311) assessing the association of EFS and OS with Regen71 and other molecular scores (LSC17, LSCR) and clinical covariates (WBC, ELN, age) in an example embodiment of the disclosure. Figure 6D shows multivariate cox regression analyses on TCGA LAML cohort (N = 151) and Metzeler 2008 (N = 79) assessing correlation to OS of Regen71 alongside other molecular scores (LSC17, LSCR) and clinical covariates (WBC, ELN, age) when available in an example embodiment of the disclosure. Figure 6E shows multivariate cox regression analyses on TARGET AML cohort subdivided by ELN stratifications (N = 890, 659, and 243 for low, intermediate, and adverse risk groups, respectively) assessing correlation to EFS of the Regen71 with other molecular scores (LSC17, LSCR) in an example embodiment of the disclosure. Figure 6F shows CD68 expression based on FC and scRNA seq on AML specific de novo REC clusters in AML1 and AML3 in an example embodiment of the disclosure. Figure 6G shows a Bar graph of NPM1 mutational profile of cells comprising each cluster of the Figure 6H scRNA seq data in an example embodiment of the disclosure. Figure 6H shows visuals of the cell clusters from the 10 NPM1 mutated AML patients from Naldini 2023 scRNA seq dataset, both as a UMAP plot, and a bar graph of clusters over a time in an example embodiment of the disclosure. CD68⁺ Cluster 1 emerged post-chemotherapy.

[0034] Figures 7A-7B show bar graphs (median +/- range) of %CD74/CD68 in remission (N = 9) vs relapse (N = 10) (Figure 7A), and remission vs. treatment failure (N = 21) (*p < 0.05, unpaired t test) (Figure 7B) in an example embodiment of the disclosure. Figure 7C shows representative flow plot of CD74⁺ CD68⁺ cells as compared to fluorescent minus one (FMO) controls in an example embodiment of the disclosure. Figure 7D shows representative ancestral gating strategy for CD74⁺ CD68⁺ REC population of Figure 7B in an example embodiment of the disclosure. Figure 7E shows representative flow plot of a CD45^bnght SSC high monocyte and blast populations of an AML sample and ancestral gating strategy in an example embodiment of the disclosure. Figure 7F shows three AML samples hCD45 vs. SSC flow cytometry gates with CD74⁺ CD68⁺ population backgated in an example embodiment of the disclosure. Location of CD74⁺ CD68⁺ cells is interpatient and interpatient heterogenous.

[0035] Figures 8A-8E show that RECs demonstrate clinical potential in example embodiments of the disclosure. Figure 8A shows a visual representation of experimental design (N=30). Figure 8B shows box plots of %CD74/CD68 population relative to monocytic population (CD45^hlghSSC^hlgh) of patients that entered remission (N=9) compared to patients that relapsed after remission (N=10). Figure 8C shows the %CD74/CD68 population relative to monocytic population (CD45^hlghSSC^hlgh) of patients that entered remission (N=9) compared to patients that experienced either form of treatment failure (relapse or refractory, N = 21). ROC curves comparing the predictive capacity of relapse (Figure 8D) and treatment failure (Figure 8E) of %CD74/CD68: Monocytes ratio, CD34, cKit, and blast%. The greatest AUC value for both clinical outcomes was %CD74/CD68:Monocytes. In Figures 8B-8C, ***p<0.001, **p<0.01 by unpaired students t tests. [0036] Figure 9A shows a line graph of CD34⁺ expression of AML samples (N=29) compared to the REC population in an example embodiment of the disclosure. Figure 9B shows a line graph of Tim3, CD90, CD117 CD38, CD123 expression of AML samples (N=4, AML3,6,38,39) compared to the REC population in an example embodiment of the disclosure. Figure 9C shows the composition of LSC17+ and LSC- patients by REC+/- profde in an example embodiment of the disclosure. LSC and REC (Regen71) high and low were decided based on the average normalized expression of the score being above or below the median of the data set. REC+ samples are enriched in LSC17+ samples (Fisher’s exact test ****p < 0.0001). Figure 9D shows survival of TARGET AML cohort (N = 1914) by Kaplan-Meier curve on the four populations subdivided by REC+/- LSC+/- in an example embodiment of the disclosure. REC+/LSC- patients had the lowest OS (p < 0.0001). Figure 9E shows the change in proportion of ELN stratifications between each REC+/- LSC+/- population (p<0.0001, chi square) in an example embodiment of the disclosure. Figure 9F shows whole H&E stained tissue of AML 10 and BM3 used for spatial UMAP plots (Figure 9G) of spatial transcriptomics data highlighting areas of CD34, CD68 and CD74 expression, respectively, in an example embodiment of the disclosure. Figure 9H shows violin plots of average normalized CD74 and CD68 expression of healthy and AML BM sections between CD34⁺ (n=112 AML n=13 healthy) and CD34" (n=1363 AML, n=2736 healthy) spots in an example embodiment of the disclosure. In Figures 9A and 9E, **p<0.01, ***p<0.001 by unpaired t tests).

[0037] Figure 10A shows that RECs demonstrate no sternness capacity and co-localize to CD34⁺ cells within leukemic tissue in example embodiments of the disclosure. Representative flow plot of hCD45 and CD33 expression in BM aspirates from PDXs eight weeks post intra-femoral injection with RECs (n=ll, N=3). Figure 10B shows a bar graph of mean+/-SD of CFU frequency (#colonies/cells seeded) of FACS purified CD74⁺ CD68⁺ cells and bulk AML patient MNCs normalized to average AML patient MNCs in an example embodiment of the disclosure. Figure 10C shows a bar graph of leukemic mutation VAF of FACS purified CD74⁺ CD68⁺ cells compared to control MNCs in an example embodiment of the disclosure. Figure 10D shows whole H&E stained tissue and representative images with and without spot overlays of BM tissue from AML patient 11 and BM donor 4 in an example embodiment of the disclosure. Scalebar=100uM. Figure 10E shows Spots of CD74⁺ CD68⁺ CD34⁺ co-expression overlayed onto whole tissue section of AML BM11, and four representative images each from areas with and without CD74⁺ CD68⁺ CD34⁺ co-expression in an example embodiment of the disclosure. Scalebars=50uM. Figure 10F shows Xenograft BM sections of engrafted CB and AML, with hCD74 hCD68 hCD34 immunofluorescent labels by MIBI-TOF methodology in an example embodiment of the disclosure. Examples of CD74⁺ CD68⁺ cells and CD34⁺ are highlighted in white. Figure 10G shows a bar graph of the average distance between CD74⁺ CD68⁺ and CD34⁺ cells in the AML vs CB xenograft BM (****p < 0.0001, students t test) in an example embodiment of the disclosure.

[0038] Figure 11A shows representative FACS gating of bulk AML cells (AML39) for the GOF and LOF experiments, where RECs (CD34⁺/CD68⁺) and REC depleted (either CD68' or CD74') are sorted from live single cells in an example embodiment of the disclosure. CD19 lymphoid cells were excluded to avoid graft vs. host disease in recipient mice. Figure 11B shows purity of sorted RECs (CD34⁺/CD68⁺) through the same gating system (-90%) in an example embodiment of the disclosure. Figure 11C shows Purity of sorted REC depleted (either CD68' or CD74') through the same gating system (-100%) in an example embodiment of the disclosure. Figure 11D shows Experimental visual and representative flow plot of injected RECs into non engrafted mice to control for REC autonomous regeneration in Figure 12C in an example embodiment of the disclosure. No engraftment (hCD45⁺/CD33⁺) was detected.

[0039] Figure 12A shows RECs catalyze leukemic regeneration by supporting LSCs (Figure 12A) in example embodiments of the disclosure. Experimental visual of REC loss of function limiting dilution analysis experiment. Figure 12B shows bar graph of estimated AML LSC frequency when RECs are depleted, present, and isolated (N=2) in an example embodiment of the disclosure. Figure 12C shows experimental visual of REC gain of function transfusion experiment in an example embodiment of the disclosure. Figure 12D shows growth of AML grafts before during and after REC or control transfusion (N=3) in an example embodiment of the disclosure. AML growth was greater in PDXs that received REC transfusions as compared to control conditions calculated by change in chimerism from transfusion timepoint to readout timepoint (13-16 days) (Figure 12E) and by growth rate (doubling time per day) calculated by exponential growth fits (Figure 12F) (N=3, students t test, **p<0.01, *p<0.05) in an example embodiment of the disclosure.

[0040] Figures 13A-13G show the proportion of CD74⁺ CD68⁺ cells within SSC^hlgh CD45^bnght monocytes varies among AML patients and is a refined prognostic measurement of relapsed/refractory disease and treatment failure in example embodiments of the disclosure. Figure 13 A: Representative flow plot of CD74 and CD68 expression in AML3. Figure 13B: Representative flow plot of CD14 and CD34 expression within CD74⁺ CD68⁺ SSC^hlgh CD45^bngbt and CD74⁺ CD68⁺ SSC^blgb CD45^dim cell populations from an AML patient. Figure 13C: Stacked bar graph of CD74⁺ CD68⁺ SSC^bigb CD45^brigbt and CD74⁺ CD68⁺ SSC^bigb CD45dim as a % of CD74⁺ CD68⁺ in N = 4 AML patient. Figure 13D: Stacked bar graph of CD74⁺ CD68⁺ SSC^high CD45^brigbt and CD74⁺ CD68⁺ SSC^bigb CD45dim as a % of CD74⁺/CD68⁺ in N = 28 AML patient. Figure 13E: A breakdown of the N = 30 AML patients assayed by flow cytometry for the % of CD74⁺ CD68⁺ in SSC^blgb CD45^bngbt, by disease status/treatment response. Figure 13F: Bar graphs of % of CD74⁺ CD68⁺ in SSC^blgb CD45^bngbt in remission v.s. relapse/refractory and remission v.s. relapse patients (**<0.01, ***p<0.001). Figure 13G: Bar graphs of % of CD74⁺ CD68⁺ in SSC^blgb CD45^bngbt in remission v.s. treatment failure and remission v.s. relapse patients (ns=not significant).

[0041] Figures 14A-14G show CD74⁺ CD68⁺ SSC^bigb CD45^brigbt populations in AML patients are transcriptionally distinct from CD74⁺ CD68⁺ SSC^blgb CD45^dim populations in example embodiments of the disclosure. Figure 14A: Analytical visual of our in-house single cell RNA sequencing dataset (N = 3 patients) integration with Jordan et al.’s CITE-seq dataset (N = 25 patients). Figure 14B: UMAP plot showing transcriptionally defined cell cluster IDs of the integrated dataset. Figure 14C: UMAP plot highlighting the abundance of regeneration enriched cells from our in-house dataset within Cluster 1 of the integrated dataset. Figure 14D: UMAP plots visualizing the protein phenotypic expression of CD45 and CD34 within the integrated dataset. Figure 14E: UMAP plot showing transcriptionally defined cell assignments of the integrated dataset. Figure 14F: Cluster 1 compared to all AML cells in the integrated dataset reveals 265 differentially expressed genes with a LogFC > 2, and 99 with a LogFC < - 2. Figure 14G: Cluster 1 compared to CD74⁺ CD68⁺ CD34⁺ (RNA) CD34⁺ (protein) cells in the integrated dataset reveals 350 differentially expressed genes with a LogFC > 2, and 341 with a LogFC < -2.

[0042] Figures 15A-15D show CD74⁺ CD68⁺ CD34’ SSC^bigb CD45^brigbt cells predict patient outcomes including relapse and treatment response in example embodiments of the disclosure. Figure 15A: Proportion of RECs and non-REC monocytes in 30 AML patients sorted by disease outcome. Figure 15B: Flow cytometry panels of SSC and CD45 expression demonstrating some of the monocytic heterogeneity between patients, and how the CD74⁺ CD68⁺ CD34' CD45^Bngbt compartment works as a prognostic despite this. Figure 15C: In a 30 patient cohort analyzed by FC, the percentage of CD74⁺ CD68⁺ CD34' as well as CD74' OR CD68' CD34' within the CD45^bngbt compartment tightly predicts both treatment failure and relapse, while CD45^bngbt percentage alone does not. Highlighted here is a prognostic threshold of 20% CD74⁺ CD68⁺ CD34’ to CD74’ OR CD68’ CD34’ within the CD45^brigbt compartment. Figure 15D: Event free survival of 19 AML patients stratified by the CD45^bngbt compartment being 20% CD74⁺ CD68⁺ CD34" or more compared to less than 20% RECs. *p < 0 .05, **p < 0.01, ***p < 0.001

[0043] Figures 16A-16G show that in AML patients, there is a skew of metastability towards CD74⁺ CD68⁺ CD34’ SSC^high CD45^bright populations from CD74’ CD68’ CD34⁺ and CD74" and/or CD68" CD34" SSC^blgb CD45^bngbt cells in example embodiments of the disclosure. Figure 16A: Bar graph showing SSC ^blgb CD45^bngbt % (stacked with proportions of CD74⁺ CD68⁺ to CD74' and/or CD64') of de novo AML (N = 3) and healthy (N = 3) patients (ns=not significant). Figure 16B: Bar graph showing SSC ^blgb CD45 ^bngbt % (stacked with proportions of CD74⁺ CD68⁺ to CD74’ and/or CD64’) of CD34⁺ CD74’ CD68’ FACS sorted cells into H4434 differentiation medium from AML (N = 3) and healthy (N = 3) patients (*p<0.05). Figure 16C: Visual depicting a working model of hematopoietic cellular dynamics with regards to the generation of CD74⁺ CD68⁺ CD34" SSC^blgb CD45^bngbt cells in AML and healthy patients based on (Figure 16A) and (Figure 16B). Figure 16D: Representative flow plot from an AML patient demonstrating the acquisition of CD74⁺ CD68⁺ CD34' SSC^blgb CD45^bngbt phenotype from a CD74' and/or CD68' CD34' SSC^blgb CD45^bngbt FACS purified population following culture in H4434 differentiation medium. Figure 16E: Representative flow plot from an AML patient demonstrating the acquisition of CD74' and/or CD68' CD34' SSC^blgb CD45^bngbt phenotype from a CD74⁺ CD68⁺ CD34' SSC^blgb CD45^bngbt FACS purified population following culture in H4434 differentiation medium. Figure 16F: Bar graphs showing the % of CD74" and/or CD68’ CD34’ SSC^bigbCD45^brigbtand CD74⁺ CD68⁺ CD34’ SSC^bigb CD45^brigbt from FACS purified CD74⁺ CD68⁺ CD34’ SSC^bigb CD45^brigbt and CD74’ and/or CD68’ CD34’ SSC^bigb CD45^bngbt cells respectively placed into H4434 differentiation medium for one day (*p<0.05). Figure 16G: Visual depicting an update of the working model of hematopoietic cellular dynamics with regards to the generation of CD74⁺ CD68⁺ CD34" SSC^blgb CD45^bngbt cells in AML and healthy patients based on (Figure 16D), (Figure 16E), and (Figure 16F).

[0044] Figure 17 shows a functional assay to create CD74⁺ CD68⁺ CD34' and CD45^bngbt cells in vitro in an example embodiment of the disclosure. Optimized on three healthy adult GCSF mobilized blood samples and 3 AML patient samples, CD74' CD34⁺ or CD68' CD34⁺ cells and CD74⁺ CD68⁺, CD45^bngbt, CD34' cells were FACS purified and plated in a semisolid differentiation medium for 14 days. CD74⁺ CD68⁺, CD45^bngbt, CD34" cells could not self sustain, leaving dwindling numbers by day 14. CD74' or CD68', CD34⁺ cells successfully derived substantial numbers of CD74⁺ CD68⁺ CD45^bngbt CD34' cells by day 14. [0045] Figure 18 shows a clinically compatible kit and protocol for detection of CD74⁺ CD68⁺ CD34" SSC^hlgh CD45^bngbt cells in an example embodiment of the disclosure. In brief, a blood sample was diluted and aliquoted into correct cell number and volume. The aliquot of cells was stained with cell surface markers and live/dead viability dye. Red blood cells were lysed in ammonium chloride for 10 minutes before washing. The resulting cells were ready to be analyzed by flow cytometry.

[0046] Figure 19 shows a use of a clinically compatible kit and protocol for detection of CD74⁺ CD68⁺ CD34" SSC^hlgh CD45^bngbt cells in an example embodiment of the disclosure. In brief, single cells were identified through FSC SSC size and complexity gating (pl) and two rounds of width to height doublet discrimination (p2-3). Live cells were selected through cells through negative gating on the live dead gate (live gate). Monocytes were next selected by gating on CD45^bngbt and CD34' (p4-5). RECs were selected as double positive on the CD74/CD68 gate, while non-REC monocytes were gated on the cells that express none or one of these markers.

[0047] Figure 20 shows use of a clinically compatible kit and protocol for detection of CD74⁺ CD68⁺ CD34" SSC^blgb CD45^bngbt cells in an example embodiment of the disclosure. In brief, single cells were identified through FSC SSC size and complexity gating (pl) and two rounds of width to height doublet discrimination (p2-3). Live cells were selected through cells through negative gating on the live dead gate (live gate). Monocytes were next selected by gating on CD45^bngbt and CD34' (p4-5). RECs were selected as double positive on the CD74/CD68 gate, while non-REC monocytes were gated on the cells that express none or one of these markers.

[0048] Figure 21 shows guidelines for gating out the monocyte SSC^blgb CD45^bngbt population in an example embodiment of the disclosure. 9 concatenated AML patient flow data highlighting the cellular compartments distinguishable using SSC^blgb CD45^bngbt gating. First, the highest CD45 peak was selected, defined as CD45^bngbt. In this example, the median fluorescent intensity of this peak was 31078 relative fluorescent units. From here, the highest SSC peak was selected (SSC^blgb) to eliminate low side scatter lymphocytes. In this context, the median arbitrary units of SSC was 294164. From here, the resulting cells were the monocytic SSC^bigb CD45^brigbt cluster.

[0049] Further aspects and features of the example embodiments described herein will appear from the following description taken together with the accompanying drawings. DETAILED DESCRIPTION

I. Definitions

[0050] Unless otherwise indicated, the definitions and embodiments described in this and other sections are intended to be applicable to all embodiments and aspects of the present disclosure herein described for which they are suitable as would be understood by a person skilled in the art. It is also to be understood that the terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting.

[0051] In understanding the scope of the present disclosure, the term “comprising” and its derivatives, as used herein, are intended to be open ended terms that specify the presence of the stated features, elements, components, groups, integers, and/or steps, but do not exclude the presence of other unstated features, elements, components, groups, integers and/or steps. The foregoing also applies to words having similar meanings such as the terms, “including”, “having” and their derivatives. The term “consisting” and its derivatives, as used herein, are intended to be closed terms that specify the presence of the stated features, elements, components, groups, integers, and/or steps, but exclude the presence of other unstated features, elements, components, groups, integers and/or steps. The term “consisting essentially of’, as used herein, is intended to specify the presence of the stated features, elements, components, groups, integers, and/or steps as well as those that do not materially affect the basic and novel characteristic(s) of features, elements, components, groups, integers, and/or steps.

[0052] Terms of degree such as “substantially”, “about” and “approximately” as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree should be construed as including a deviation of at least ±5% of the modified term if this deviation would not negate the meaning of the word it modifies.

[0053] As used in this disclosure, the singular forms “a”, “an” and “the” include plural references unless the content clearly dictates otherwise.

[0054] In embodiments comprising an “additional” or “second” component, the second component as used herein is chemically different from the other components or first component. A “third” component is different from the other, first, and second components, and further enumerated or “additional” components are similarly different.

[0055] The term “and/or” as used herein means that the listed items are present, or used, individually or in combination. In effect, this term means that “at least one of’ or “one or more” of the listed items is used or present. Multiple elements listed with "and/or" should be construed in the same fashion, i.e., "one or more" of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the "and/or" clause, whether related or unrelated to those elements specifically identified.

[0056] The abbreviation, “e.g.” is derived from the Latin exempli gratia and is used herein to indicate anon-limiting example. Thus, the abbreviation “e.g.” is synonymous with the term “for example.” The word “or” is intended to include “and” unless the context clearly indicates otherwise.

[0057] As used herein, the phrase "at least one," in reference to a list of one or more elements, should be understood to mean at least one element selected from anyone or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified.

[0058] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the description. Ranges from any lower limit to any upper limit are contemplated. The upper and lower limits of these smaller ranges which may independently be included in the smaller ranges is also encompassed within the description, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the description. The recitation of numerical ranges by endpoints herein includes all numbers and fractions subsumed within that range (e.g. 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, and 5). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term "about."

[0059] It will be understood that any component defined herein as being included may be explicitly excluded by way of proviso or negative limitation, such as any specific compounds or method steps, whether implicitly or explicitly defined herein. [0060] It should also be understood that, in certain methods described herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited unless the context indicates otherwise.

[0061] Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, examples of methods and materials are now described.

II. Methods and Uses

[0062] The inventors have identified a new population of cells, Acute Myeloid Leukemia (AML) Regeneration Enriched Cells (RECs), through temporal single cell transcriptomic characterization of AML hierarchical regeneration in response to chemotherapy, which can have use, for example, in in vitro assays.

[0063] Accordingly, in an aspect, herein provided is a method for determining the presence of regeneration enriched cells (RECs) in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subj ecting the sample to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; and ii) determining the presence of CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells; wherein the presence of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells are indicative of the presence of RECs.

[0064] In another aspect, herein provided is a method for determining the presence of regeneration enriched cells (RECs) in a sample from a subject who has or is suspected of having acute myeloid leukemia (AML) or pre- AML, the method comprising: i) subj ecting the sample to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; and ii) determining the presence of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells; wherein the presence of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells are indicative of the presence of RECs. [0065] As used herein the term “regeneration enriched cells” or “RECs” refers to a population of cells that are derived from leukemic stem cells (LSCs), but are devoid of stem/ progenitor capacity. RECs cause leukemic regeneration in patient derived xenograft models. RECs are characterized by a cell surface marker profde comprising CD74⁺ CD68⁺. Cells with a cell surface expression profile of CD74⁺ CD68⁺ CD45^bnght CD34', and having high side scatter (SSC^hlgh), for example, in flow cytometry applications, are a subpopulation of RECs with a large prognostic value.

[0066] As used herein, the term “status” refers to a measured characteristic of a cell surface marker or a flow cytometry metric that can be used to differentiate one cell type from another. The status can be indicative of a binary characteristic, a multimodal characteristic, or a characteristic within a spectrum, and is indicated herein by a status qualifier such as “+” , “bright”, and “high”.

[0067] As used herein, the qualifiers “+” and indicate a binary presence and absence of a cell surface marker, respectively. The qualifiers “High” and “Low” are used when there is a spectrum within a metric, such as granularity and cell size (side scatter and forward scatter, respectively). The qualifiers “bright” “dim” and “negative” are used when there are three distinct flow cytometry peaks, which are generally indicative of cells that express a cell surface marker at high levels, cells that express a cell surface marker at low levels, and cells that do not express the cell surface marker, respectively.

[0068] The term “CD45^bngbt” as used herein refers to the cells within the highest peak of CD45 fluorescence intensity as measured by flow cytometry.

[0069] The term “SSC^blgb” as used herein refers to the cells within the highest peak of side scatter intensity as measured by flow cytometry. Side scatter is a metric in flow cytometry that refers to a measurement of light scattered at an angle generally perpendicular to the laser beam. This metric can provide information about the internal complexity or granularity of a cell (e.g., organelles and/or granules) and can be used for differentiating between different cell types.

[0070] CD74 can be from any organism and optionally as defined by GenPept accession number P04233. CD68 can be from any organism, and optionally as defined by GenPept accession number P34810. CD45 can be from any organism, and optionally as defined by GenPept accession number P08575. CD34 can be from any organism, and optionally as defined by GenPept accession number P28906. [0071] As used herein, the term “acute myeloid leukemia” or “AML” refers to a cancer of the myeloid line of blood cells, characterized by the rapid growth of abnormal white blood cells that accumulate in the bone marrow and interfere with the production of normal blood cells, referred to clinically as blasts. The term “pre- acute myeloid leukemia” or “pre-AML” as used herein refers to conditions that may precede the development of AML, but that do not always progress to AML. Pre-AML conditions include, for example, myelodysplastic syndrome (MDS) and myeloproliferative neoplasms (MPNs).

[0072] In one embodiment of the methods and uses herein disclosed, pre-AML comprises

MDS or MPN. In another embodiment of the methods and uses herein disclosed, pre-AML comprises MDS. In yet another embodiment of the methods and uses herein disclosed, pre-AML comprises MPN.

[0073] The term "subject" as used herein refers to any member of the animal kingdom. In one embodiment, the subject is a mammal, such as a human. In an embodiment, the subject is presenting with, or is suspected of having non-canonical AML regeneration independent of stem and progenitor cells.

[0074] The term "sample" as used herein refers whole blood, mobilized whole blood, a fractionated blood sample, a leukapheresis sample or a bone marrow sample. In one embodiment, the sample comprises mononuclear cells. In one embodiment, the sample comprises leukemic cells, optionally AML cells. In one embodiment, the sample comprises CD45⁺ cells.

[0075] In another aspect, also provided is a method for determining a ratio of regeneration enriched cells (RECs) to non-RECs in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subj ecting the sample to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells which are indicative of RECs; iii) measuring the amount of CD74’ CD68’ CD45^bright SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii). [0076] In another aspect, also provided is a method for determining a ratio of regeneration enriched cells (RECs) to non-RECs in a sample from a subject who has or is suspected of having acute myeloid leukemia (AML) or pre-AML, the method comprising: i) subj ecting the sample to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells which are indicative of RECs; iii) measuring the amount of CD 74’ CD68’ CD45^bright SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii).

[0077] The term “non-RECs” or “non-REC monocytes” as used herein refers to a population of cells with a profde of CD74’ CD68’ CD45^brigbt SSC^big}1 CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’.

[0078] The inventors have here determined that RECs demonstrate the ability to augment or reduce leukemic regeneration in vivo based on transfusion or depletion, respectively. Furthermore, RECs are prognostic for patient survival and predictive of treatment failure in AML cohorts. The disclosure herein reveals RECs as a previously unknown functional catalyst of LSC driven regeneration contributing to the non-canonical framework of AML regeneration.

[0079] Accordingly, in another aspect, herein provided is a method of determining a prognosis for a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subjecting a sample obtained from the subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells , which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^brigbt SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the subject has an increased risk of relapse or treatment failure, and wherein when the ratio is at or below about 0.15, the subject has a decreased risk of relapse or treatment failure.

[0080] A REC to non-REC ratio between 0. 15 and 0.20 is considered inconclusive, and further testing should be considered.

[0081] In another aspect, herein provided is a method of determining a prognosis for a subject who has or is suspected of having acute myeloid leukemia (AML) or pre-AML, the method comprising: i) subjecting a sample obtained from the subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bnght SSC^hlgh CD34" cells , which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^bright SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the subject has an increased risk of relapse or treatment failure, and wherein when the ratio is at or below about 0.15, the subject has a decreased risk of relapse or treatment failure.

[0082] A REC to non-REC ratio between 0. 15 and 0.20 is considered inconclusive, and further testing should be considered.

[0083] The term “determining a prognosis” refers to a prediction of the likely progress and/or outcome of an illness, which optionally includes defined outcomes such as risk of relapsing disease. In some embodiments, determining a prognosis may involve a binary classification such as classifying a subject as having a high risk or a low risk of relapsing AML. In some embodiments, determining a prognosis refers to a prediction of the likely response to a treatment, such as a cytotoxic treatment. [0084] In one embodiment, a sample is obtained from a subject who has completed a treatment for AML in order to determine a prognosis for the subject. In one embodiment, the sample is from a subject who previously received and has completed chemotherapy and/or radiation therapy. In one embodiment, chemotherapy may comprise the use or administration of aDNA synthesis inhibitor, optionally cytarabine. In one embodiment, the sample is from a subject who previously received induction chemotherapy and/or consolidation chemotherapy. In one embodiment, a cytotoxic treatment is complete when no additional administrations of a cytotoxic agent and/or radiation are planned or anticipated for the treatment of AML in a subject.

[0085] In one embodiment, the sample is obtained from the subject at least 3 days, 5 days, 1 week or 10 days after completing the treatment for AML. In one embodiment, the sample is obtained from the subject between about 10 days and 40 days after completing the treatment for leukemia. In one embodiment, a sample is obtained from the subject at intervals, for example to provide ongoing monitoring of prognosis for a subj ect, optionally for up to 3 years or to remission status.

[0086] In another aspect, herein provided is a method of selecting a subject who has completed a treatment for Acute Myeloid Leukemia (AML) for further treatment, the method comprising: i) subjecting a sample obtained from the subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells, which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^brigbt SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb and CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the subject is selected for further treatment, and wherein when the ratio is at or below about 0. 15, the subject is not selected for further treatment.

[0087] A REC to non-REC ratio between 0. 15 and 0.20 is considered inconclusive, and further testing should be considered. [0088] In another aspect, herein provided is a method of selecting a subject who has or is suspected of having acute myeloid leukemia (AML) or pre-AML for treatment, the method comprising: i) subjecting a sample obtained from the subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells, which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^brigbt SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb and CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the subject is selected for treatment, and wherein when the ratio is at or below about 0. 15, the subject is not selected for treatment.

[0089] A REC to non-REC ratio between 0. 15 and 0.20 is considered inconclusive, and further testing should be considered.

[0090] In yet another aspect, herein provided is a method of treating a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subjecting a sample obtained from a subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells , which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^brigbt SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; iv) calculating the ratio of ii) to iii); and wherein when the ratio is at least about 0.20, the method further comprises administering a further treatment to the subject, and wherein when the ratio is at or below about 0.15, the method further comprises not administering a further treatment to the subject.

[0091] A REC to non-REC ratio between 0. 15 and 0.20 is considered inconclusive, and further testing should be considered.

[0092] In yet another aspect, herein provided is a method of treating a subject who has or is suspected of having acute myeloid leukemia (AML) or pre- AML, the method comprising: i) subjecting a sample obtained from a subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bnght SSC^hlgh CD34" cells , which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74’ CD68’ CD45^bright SSC^bigb CD34’; CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; iv) calculating the ratio of ii) to iii); and wherein when the ratio is at least about 0.20, the method further comprises administering a treatment to the subject, and wherein when the ratio is at or below about 0.15, the method further comprises not administering a treatment to the subject.

[0093] A REC to non-REC ratio between 0. 15 and 0.20 is considered inconclusive, and further testing should be considered.

[0094] In yet another aspect, herein provided is a use of a further treatment for treating a subject who has completed a treatment for Acute Myeloid Leukemia (AML), wherein the subject was previously determined to have a ratio of regeneration enriched cells (RECs) to non- RECs of at least about 0.20, wherein CD74⁺ CD68⁺ CD45^bngbt SSC^blgh CD34" cells are indicative of REC cells, and wherein CD74’ CD68’ CD45^brigbt SSC^bigb CD34’ cells, CD74’ CD68⁺ CD45^brigbt SSC^big}1 CD34’ cells, or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells are indicative of non-RECs.

[0095] In yet another aspect, herein provided is a use of a treatment for treating a subj ect who has or is suspected of having acute myeloid leukemia (AML), wherein the subject was previously determined to have a ratio of regeneration enriched cells (RECs) to non-RECs of at least about 0.20, wherein CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells are indicative of REC cells, and wherein CD74’ CD68’ CD45^brigbt SSC^bigb CD34’ cells, CD74’ CD68⁺ CD45^brigbt SSC^big}1 CD34’ cells, or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells are indicative of non-RECs.

[0096] In further aspect, herein provided is a further treatment for use in treating a subject who has completed a treatment for Acute Myeloid Leukemia (AML), wherein the subject was previously determined to have a ratio of regeneration enriched cells (RECs) to non- RECs of at least about 0.20, wherein CD74⁺ CD68⁺ CD45^bngbt SSC^blgh CD34" cells are indicative of REC cells, and wherein CD74’ CD68’ CD45^brigbt SSC^bigb CD34’ cells, CD74’ CD68⁺ CD45^brigbt SSC^big}1 CD34’ cells, or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells are indicative of non-RECs.

[0097] In further aspect, herein provided is a treatment for use in treating a subject who has or is suspected of having acute myeloid leukemia (AML), wherein the subject was previously determined to have a ratio of regeneration enriched cells (RECs) to non-RECs of at least about 0.20, wherein CD74⁺ CD68⁺ CD45^bngbt SSC^blgh CD34" cells are indicative of REC cells, and wherein CD74’ CD68’ CD45^brigbt SSC^bigb CD34’ cells, CD74’ CD68⁺ CD45^brigbt SSC^big}1 CD34’ cells, or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells are indicative of non-RECs.

[0098] In some embodiments, the subject was previously determined to have a ratio of RECs to non-RECs of at least about 0.20 by a method herein disclosed.

[0099] In one embodiment of a method or use herein disclosed, the treatment comprises the administration or use of chemotherapy such as cytoreductive chemotherapy. In one embodiment, the chemotherapy comprises the administration or use of a DNA synthesis inhibitor. In one embodiment, the treatment is a pyrimidine nucleoside analog. In one embodiment, the chemotherapy comprises the administration or use of cytarabine.

[00100] In one embodiment of a method or use herein disclosed, the further treatment comprises a new course of the previously administered treatment. In another embodiment of a method or use herein disclosed, the further treatment comprises a different treatment than the previously administered treatment. [00101] In one embodiment of a method or use herein disclosed, the use or administration comprises an effective amount of an agent that targets RECs, optionally a test agent herein disclosed. As used herein, the phrase "effective amount" or "therapeutically effective amount" means an amount effective, at dosages and for periods of time necessary to achieve the desired result. For example, in the context of treating AML, an effective amount is an amount that for example reduces the likelihood of relapsing disease compared to the response obtained without administration of the agent. An effective amount may also be an amount that decreases the ratio of RECs to non-RECs to or below about 0.15. Effective amounts may vary according to factors such as the disease state, age, sex, weight and/or species of the subject. The amount of a given agent that will correspond to such an amount will vary depending upon various factors, such as the given drug or compound, the pharmaceutical formulation, the route of administration, the type of disease or disorder, the identity of the subject being treated, and the like, but can nevertheless be routinely determined by one skilled in the art.

[00102] In one embodiment, an agent that targets RECs, such as a test agent herein disclosed, is formulated for use or administration to a subject in need thereof. Conventional procedures and ingredients for the selection and preparation of suitable formulations are described, for example, in Remington's Pharmaceutical Sciences (2003 - 20th edition) and in The United States Pharmacopeia: The National Formulary (USP 24 NF19) published in 1999.

[00103] Given that RECs are a new class of cells that can have use, for example, in in vitro assays, herein provided, in an aspect is a method for purifying regeneration enriched cells (RECs) in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) labelling cells in the sample with fluorescent markers for CD74, CD68, CD45, and CD34; ii) passing the labelled cells through a flow cytometer with fluorescence detection; iii) detecting side scatter (SSC) and fluorescence signals emitted by the fluorescent markers in i); and iv) sorting cells by the detected fluorescence signals and SSC in step iii), wherein the cells sorted in step iv) according to CD74⁺ CD68⁺ CD45^bnght SSC^hlgh and CD34' are indicative of RECs. [00104] In another aspect, there is a method for purifying regeneration enriched cells (RECs) in a sample from a subject who has or is suspected of having acute myeloid leukemia (AML) or pre- AML, the method comprising: i) labelling cells in the sample with fluorescent markers for CD74, CD68, CD45, and CD34; ii) passing the labelled cells through a flow cytometer with fluorescence detection; iii) detecting side scatter (SSC) and fluorescence signals emitted by the fluorescent markers in i); and iv) sorting cells by the detected fluorescence signals and SSC in step iii), wherein the cells sorted in step iv) according to CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh and CD34' are indicative of RECs.

[00105] The inventors have also determined that RECs are not self-sustaining as they do not demonstrate any self-renewal capabilities, and have therefore further developed a method for generating REC cells in vitro.

[00106] Accordingly, in another aspect, herein provided is a method for generating regeneration enriched cells (RECs) in vitro, the method comprising: i) purifying a population of CD74' CD34⁺ cells or CD68' CD34⁺ cells by fluorescence activated cell sorting (FACS); and ii) incubating the purified population of cells in a medium containing erythropoietin (EPO), interleukin 3 (IL3), granulocyte colony stimulating factor 3 (GCSF), and stem cell factor (SCF); thereby generating CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells, which are indicative of RECs.

[00107] The medium may be any known medium that would be suitable to one of skill in the art. In some embodiments, the medium comprises semisolid methylcellulose medium.

[00108] In some embodiments, the incubating in step ii) is for at least 11 days, at least 12 days, at least 13 days, at least 14 days, at least 15 days, at least 16 days, at least 21 days, or until significant apoptotic colony morphology. In an embodiment, the incubating in step ii) is at least 14 days. In another embodiment, the incubating in step ii) is about 14 days.

[00109] In some embodiments, the incubating in step ii) is at about 37°C.

[00110] EPO can be from any organism, and optionally as defined by GenPept accession number P01588. 1. IL3 can be from any organism, and optionally as defined by GenPept accession number P08700.2. GCSF can be from any organism, and optionally as defined by GenPept accession number P09919.1. SCF can be from any organism, and optionally as defined by GenPept accession number P21583.1.

III. Assays

[00111] Given at least the prognostic value of RECs, wherein a ratio of RECs to non-RECs in a sample of at least 0.20 is indicative of increased risk of relapse or treatment failure, the inventors have created an assay for testing agents for preventing or inhibiting relapsing AML.

[00112] Accordingly, in an aspect, herein provided is an assay for screening the efficacy of a test agent in reducing the number and/or proportion of regeneration enriched cells (RECs), the assay comprising: i) obtaining a population of cells comprising RECs by a method disclosed herein; ii) treating the population of cells comprising RECs with the test agent; iii) measuring the number of RECs and/or ratio of RECs to non-RECs within the population of cells comprising RECs; and iv) comparing the number and/or ratio in iii) to the number of RECs and/or ratio of RECs to non-RECs in a control population in the absence of test agent; wherein a reduced number or ratio in iii) compared to the control population is indicative that the test agent is effective for reducing the number of RECs.

[00113] In an embodiment, the obtaining in step i) comprises purifying RECs by a method herein disclosed. In another embodiment, the obtaining in step i) comprises generating the RECs by a method herein disclosed.

[00114] In an embodiment, the test agent comprises a small molecule drug, a drug targeting agent, a peptide-based agent, optionally an immunotherapeutic agent comprising CAR engineering or an agent for antibody-based inhibition, or an RNA-based agent, optionally for RNA-based therapy.

IV. Kits

[00115] In another aspect, herein provided is a kit comprising a CD74 detection agent, a CD68 detection agent, a CD45 detection agent, and a CD34 detection agent.

[00116] In some embodiments, the kit further comprises instructions for use in a method herein disclosed.

[00117] In some embodiments, the kit further comprises a CD14 detection agent, CD117 detection agent, a CD15 detection agent and/or a detection agent for live/dead cells. In an embodiment, the kit further comprises a detection agent for live/dead cells.

[00118] CD 14 can be from any organism, and optionally as defined by GenPept accession number P08571.2. CD15 can be from any organism, and optionally as defined by GenPept accession number P22083.3. CD117 can be from any organism, and optionally as defined by GenPept accession number P10721.1.

[00119] In some embodiments, the kit further comprises flow cytometry buffer and/or ammonium chloride.

[00120] In some embodiments, each of the detection agents comprise a fluorescent label that can be differentiated, for example by flow cytometry, from the fluorescent label of each other detection agent.

[00121] In some embodiments, one or more of the detection agents comprise fluorescently labelled antibodies.

[00122] In some embodiments, the flow cytometry buffer comprises bovine serum albumin (BSA), fetal bovine serum (FBS), and/or phosphate-buffered saline (PBS). Other suitable flow cytometry buffers and/or cell suspension buffers are known in the art.

[00123] Embodiment 1. A method of determining prognosis, relapse and/or treatment response in patients with Acute Myeloid Leukemia (AML), the method comprising: a) Obtaining a bone marrow (BM) and/or Trephine biopsy from a patient with AML; b) Using flow cytometry to measure the ratio of regeneration enriched cells (RECs) which express CD74+/CD68+ to clinically standardized monocytes; c) Predicting AML relapse or treatment failure based on the ratio of CD74+/CD68+ cells to clinically standardized monocytes.

[00124] Embodiment 2. The method of embodiment 1, wherein the patient has been treated with chemotherapy.

[00125] Embodiment 3. The method of embodiment 1, wherein the monocytes are defined by standard SSC^hlgh CD45^bnght gating.

[00126] Embodiment 4. The method of embodiment 1, wherein a ratio higher than 0.25 (1 REC per 4 monocytes) predicts relapse or treatment failure.

[00127] Embodiment 5. The method of embodiment 1 wherein a ratio lower than 0.25 (1 REC per 4 monocytes) predicts a higher probability of remission.

[00128] Embodiment 6. The method of embodiment 1, wherein the CD74+/CD68+ REC cells support Leukemia Stem Cell (LSC) driven regeneration of AML.

[00129] Embodiment 7. The method of embodiment 1, wherein REC detection is incorporated into measurable residual disease (MRD) alone or in conjunction with current MRD detection approaches.

[00130] Embodiment 8. A method of treating a patient with AML and/or non-canonical AML regeneration depends as independent of stem or progenitors cells, comprising administering a therapeutic agent targeting RECs to a patient with a ratio of CD74+/CD68+ cells to monocytes higher than 0.25 (1 REC per 4 monocytes), the agent comprising drug targeting, antibody -based inhibition, or immunotherapy using CAR engineering.

[00131] Embodiment 9. A method of identifying cells involved in non-canonical disease regeneration defined by independence of stem or progenitors cells, the method comprising: a) Obtaining patient derived xenograft models b) Completing single cell molecular or proteomic analysis combined with functional stem and progenitor cell assays.

Wherein immunophenotypically defined cells are identified.

[00132] Embodiment 10. The method of embodiment 9 wherein the single cell molecular proteomic analysis comprises transcriptomics.

EXAMPLES [00133] The following non-limiting examples are illustrative of the present disclosure:

[00134] Results

[00135] Temporal analysis of AML PDXs following chemotherapy captures cellular dynamics of disease regeneration.

[00136] Cytarabine (AraC) is the backbone of the standard of care in AML treatment (4,15) and has been used to model chemotherapy treatment in xenografts (11-13) as other agents given to AML patients, such as daunorubicin, are toxic in xenografts (13). AraC is a pyrimidine nucleoside analog and functions in a non-specific fashion to target highly proliferative cells (16). AraC treatment was modeled through a five-day administration mimicking clinical use in patients as shown previously (13) in AML patient derived xenografts (PDX) initiated from six patients with diverse European Leukemia Network (ELN) (1) stratification (AML1-6, Table 1). Once AML disease was established in recipient mice, the dynamics of AML response was analyzed at three sequential timepoints post treatment (Days 7, 10, and 14). Disease burden (total # of AML cells [hCD45⁺% * total harvested cells]) was analyzed throughout the follow-up timepoints post chemotherapy treatment in PDXs derived from all patient samples (Figure 1 A, 2A). The dynamics of these changes were variable across patients, consistent with expectations that multiple timepoints of analysis are required to capture the true cellular kinetics of AraC induced cytoreduction and subsequent regeneration. Absolute PDX response to chemotherapy was patient specific (p< 0.05, two-way ANOVA) and supported the separation of patients into categories of responders vs. non-responders based on the disease burden in the AML PDXs: responders being statistically reduced (PDXs: AML1, AML4), and non-responders being non-statistically reduced (PDXs: AML2-3, 5-6) following AraC treatment (Figure 1 A). This distinction correlated to AML patient outcomes, where PDX responders entered clinical remission (CR) after induction treatment, while PDX non-responders failed to enter induced CR, which broadly correlated with predicted outcome based on ELN classification (Figure 1A). An alignment of clinical metrics to chemosensitivity in PDX models has not been reported in experimental settings to date, and possibly observed here due to maximized chemosensitivity timepoints compared among patients. This is exemplified in AML patient 1 PDXs, where a single follow up at Day 10 would not capture the acute cytoreduction shown at Day 7 and would otherwise be incorrectly classified as a nonresponder (Figure 2B).

[00137] With kinetics of AML regeneration biologically established in vivo, this foundational data was used to understand the molecular dynamics of AML cells during regeneration. Using droplet based single cell RNA sequencing (scRNA seq), d libraries from nonresponder (AML2 and AML3, ELN intermediate and adverse, respectively) and responder (AML1, ELN intermediate) PDXs were generated at Day 0, 7, 10, and 14 post chemotherapy treatment, as well as at the ethical endpoint of each recipient xenograft (summarized and depicted in Figure IB, 2D-E). To assure AML specific features could be distinguished, parallel experiments were performed using healthy hematopoietic stem cells (HSCs) from umbilical cord blood (CB) engrafted in PDX mice to obtain healthy control cells before and after chemotherapy treatment in vivo (Figure 2F). A total of 42 resulting PDX samples used for scRNA-seq were merged by batch correcting and integrating using the Seurat integration package (17), with healthy BM scRNA seq data as a reference anchor (Figure 2C). Resulting individual cell gene expression could be summarized by stratification into 26 transcriptionally defined cell clusters (Figure 1C). Greater variability of clusters throughout timepoints post chemotherapy was qualitatively observed in AML1 PDXs (responders) as compared to AML 2 and 3 PDXs (nonresponders) (Figure ID). To provide quantitative temporal evaluation for each cluster over time, a metric of cluster volatility was utilized: 1 - (Lowest % over time / highest % over time) for each shared cluster from all three AML patients (Figure 2G). Cluster volatility over time was found to be significantly higher in the responding AMLs as compared to the non-responding AMLs, indicative of greater transcriptional change in AML disease cells that effectively respond to therapy, whereas non-responsive disease reflects reduced transcriptional changes accompanied with disease retention. These analyses provide a basis to dissect graded responses of AML disease to chemotherapy and transcriptionally define potential cell entities involved in the dynamic destruction and subsequent regeneration of the human AML hierarchy.

[00138] Identification of transcriptionally assigned cells that correlate to functional states of regeneration in PDX models: Regeneration Enriched Cells (RECs).

[00139] AML disease is suggested to be organized in a hierarchy sustained by LSCs at its apex. This same disease hierarchy can be established in immunodeficient mice by leukemic initiating stem cells upon transplantation of cells from AML patients, and the resulting surrogate murine recipient can be treated with chemotherapy (11-13). Aside from level of leukemic burden measured in PDX models by amount of human leukemic cells, the activation state of leukemic regeneration was shown to faithfully be represented by leukemic progenitor activity (13,18). Accordingly, both leukemic burden and progenitor activity were used as metrics to identify the biologically relevant time points of 1) cytoreduction and 2) subsequent regenerating AML (Figure 3A). [00140] In vivo biological readout of AML disease was matched to single cell transcript profiling derived from the same PDX for each timepoint to identify transcriptomic patterns and clusters associated with AraC resilience and leukemic hierarchical regeneration. Using deep functional and scRNA-seq analysis, AML1 and AML2 were selected as representative responding and non-responding patients, respectively. The greatest disease burden reduction was identified at Day 7, signifying the lowest threshold of cytoreduction, whereas peak leukemic AML progenitor frequency signifying regeneration, was detected at Day 10 in the responder and Day 14 in the non-responder (Figure 4A-B, 3B-E). Nearly all clusters were depleted following chemotherapy exposure after incorporating a disease burden metric (cluster proportion * total AML cells harvested; Figure 4SF) based on 3 PDX recipient mice for each timepoint for each patient (Figure 4A-B, 3F). Similar results were obtained using unbiased categorization methods to assign cell type assignment to each cell (19), which also revealed each cell type was diminished following chemotherapy (Figure 3G). Collectively, these results indicate that cytoreductive chemotherapy targets the complete array of cells of the AML hierarchy without prejudice. Despite cytoreduction of AML cells in response to chemotherapy, clusters with the least reduction after chemotherapy were identified in Cluster 5 in AML1 and Cluster 1 in AML2 and these same clusters were also the most enriched at corresponding regeneration timepoints (Figure 4C-D). Cluster 5 was preferentially enriched at functional regeneration by 68-fold in responding AML (Figure 4C, n=3), whereas Cluster 1 from the non-responding AML was enriched by only 1.3- fold (Figure 4D, n=3). To avoid artifacts from single PDXs, each timepoint consisted of three pooled PDX biological replicates and confirmed that cluster dynamics were consistent across pooled biological replicates by cell multiplexing analysis (Figure 3H). Cluster 1 was highly prevalent before and after chemotherapy in AML2 PDX recipients, consistent with the expected dampened response to chemotherapy in non-responders. In contrast, Cluster 5 emerged almost exclusively during AraC treatment in chemotherapy responsive AML patient xenografts.

[00141] Given the correlation of these clusters with AML regeneration kinetics in vivo, Clusters 1 and 5 were termed as Regeneration Enriched Cells (RECs). Transcriptionally, RECs resemble non-cycling monocyte-like profiles (Figure 3I-3J). Gene expression profiles of RECs showed enrichment of pro-inflammatory and oxidative phosphorylation profiles by gene score enrichment analysis (GSEA) of HALLMARK, REACTOME, WIKI and KEGG pathways (Figure 3K), a feature previously observed in cytarabine response and AML disease regrowth (11,12,20,21). To explore the applicability of REC Clusters 1 and 5 to functional AML regeneration, the analysis was expanded to an interpatient level to include all PDXs with paired scRNA and functional data sets, AML1-3 (Figure 1A, 2E; N=3, n=7). Fold enrichment of REC clusters directly correlates to the magnitude of graft regrowth, as measured by total leukemic cells from Day 7 to Day 14 (Figure 4E). Even though REC clusters were derived from distinct chemotherapy responses across patients, these findings show consistent correlation between emergence of RECs in all AML PDXs with functional leukemic growth. Using similar analysis, no other clusters were found to positively correlate to the magnitude of AML graft regeneration (Figure 4F). The magnitude of REC cluster emergence as a direct function of AML graft regrowth aggressivity suggests RECs represent unique cell types involved in AML regeneration.

[00142] RECs in patient derived diagnostic tissue predict clinical outcomes and are immunophenotypicallv defined by CD68/CD74 expression.

[00143] To evaluate the relevance of PDX generated REC profiles (Cluster 1 or 5), it was examined whether RECs could be observed in AML patients. scRNA seq analysis was applied to de novo samples of AML patients 1, 2 and 3, and scRNA-seq data was integrated and batch corrected with patient matched PDX scRNA-seq data for all AML xenograft timepoints used (n = 39) (17). This was performed on a per patient basis to reduce variability that may arise from inter-patient heterogeneity (22,23). In all AML patients examined, RECs previously identified in PDXs were highlighted (Figure 5 A, 6A) and primarily assigned to a single cluster for each AML patient. Transcriptionally similar cells from de novo sources were present within these clusters (Figure 5 A, 6A), indicating on a transcriptional level the RECs are present in patients at diagnosis. The ability to identify PDX derived RECs as a transcriptionally definable entity in AML patients demonstrates that RECs are not a generated artifact of AraC treatment or PDX modelling.

[00144] To characterize REC properties potentially shared among AML patients, differentially expressed genes (DEG) of de novo sourced RECs (Figure 5B) compared to all de novo cells were identified from each AML patient. This revealed a total of 71 DEGs that were common among AML1-3, which were termed the Regeneration-71 score (Regen71) (Figure 5B, Table 2). To be thorough and to ensure the same biological phenomena are captured in all three patients, de novo scRNA data sets from AML patients 1 to 3 were merged with two non-leukemic BM donor samples (BM1 and BM2) and re-clustered to best capture interpatient variability (Figure 6B). Accordingly, cluster numbers from this dataset were denoted with a prime symbol to differentiate from PDX data sets (Figures 1,4, 5 A); cells from all samples could be stratified into Clusters 0’ - 17’ (Figure 6B). Previously identified RECs from AML Patients 1, 2, and 3 at regeneration cluster together within newly assigned Cluster 0’ (Figure 6B). Cluster 0’ hosted 653 upregulated genes, which contained the previously identified Regen71. [00145] The Regen71 is a gene score that represents the biological process of leukemic regeneration. Based on this and its absence of primitive gene expression (Figure 3I-3K), this likely represents a distinct biological entity other than AML LSCs. To examine the value of the Regen71, multivariate cox regression analyses was performed on three publicly available gene expression datasets (24), separately accounting for both comparable molecular scores (LSCR (9), LSC17 (10) and clinical covariates (Age, ELN stratifications, white blood cell [WBC] count). In the TARGET AML cohort (N = 1914), the Regen71 correlated to both overall survival (OS) and event free survival (EFS) (Figure 5C, 6C). When filtering the cohort for patients that received the same induction treatments, prognostic capabilities improved (Figure 6C). In the TCGA-LAML cohort (N = 151), the Regen71 also correlated with OS in the molecular analysis and approached significance in the clinical analysis (Figure S3D). The Metzeler et al. (2008) (24) (N = 79) patient cohort had none of the gene scores achieve significance, although the LSC17 and Regen71 approach it (Figure 6D). In addition, the TARGET AML cohort was subdivided into the three ELN stratifications and the molecular multivariate analysis performed revealed Regen71 to be the most predictive in the ELN high risk group (Figure 6E). Notably, the Regen71 shows predictability of EFS and OS in both pediatric and adult AML, indicating this gene score may be independent of aging processes. Thus, the Regen71 provides a straightforward, and unweighted signature of genes to evaluate the dynamics of human AML regeneration that have not been explored to date and justifies further investigation into this score and related biological processes.

[00146] It was next aimed to use Regen71 gene profile to identify a putative REC immunotype, unlike LSC17 or LSC-R. Genes encoding for putative cell surface markers of RECs were identified and using the criteria of gene expression strength (Average Log2 FC > 1) and commercially available well-established flow cytometry (FC) antibodies. CD68, CD 163, and CD44 were revealed as candidate biomarkers (Figure 5D). To ensure gene expression of candidates translated to protein expression, positive population frequency by FC was cross referenced on AML3 cells and compared the frequency of de novo AML3 REC Cluster (Figure 6A,F) within scRNA seq data on AML3 (19%). CD68 was closest to the frequency at 13.3%, while the CD 163 and CD44 (Figure 5D) failed to achieve precise cellular identity. Similarly, in patient AML1, the proportion of the CD68 by FC reflected the proportion of the REC cluster in the scRNA seq data set (Figure 5A, 6F). Accordingly, CD68 was selected as a candidate biomarker for RECs.

[00147] To test whether the transient emergence of RECs is robust and reproducible in external cohorts, an additional 14 AML patient datasets were surveyed. In three del (7) AML patients from Naldini et al (25), the CD68 enriched cluster (Cluster 2) is temporally enriched 14 days following treatment initiation (Figure 5E-5F). In another AML patient (AML38), a CD68 enriched cluster (Cluster 6) expands after an unsuccessful treatment (refractory to treatment, Figure 5G-5H). In a ten patient NPM1 mutated AML cohort with paired NPM1 aberration mutational status (25), a CD68 positive cluster (Cluster 1) is temporally enriched 14 days following chemotherapy initiation (Figure 6F-6H). This dataset exhibited multiple CD68⁺ clusters wherein NPM1 mutational frequency varied (Figure 6H). Overall, the consistency of REC emergence in diverse patients from other studies post chemotherapy provides a promising pattern that necessitated further investigation.

[00148] As CD68⁺ populations with lower frequency of leukemic mutations were present in the NPM1+ AML ten-patient cohort (25) (Figure 6G), it is suggested that another biomarker should be included to deplete CD68⁺ cells of the healthy hematopoietic system from RECs. Using a de novo AML scRNA seq dataset (Figure 6B), DEG analysis was performed comparing cells within REC Cluster 0’ of AML1-3 to cells in REC Cluster 0’ from healthy BM donors (Figure 51). This revealed 543 enriched genes in the leukemic fraction of Cluster O’. Similar DEG analysis was performed on the PDX scRNA data sets (Figure 1C-1D, 2C), comparing the AML PDX sourced cells and CB donor xenografts (Cluster 1 and 5). A total of 1003 genes were upregulated in the AML PDX vs CB xenograft in Clusters 1 and 5, consisting of a 420 gene overlap with AML specific cells of Cluster O’. This revealed CD74, CD81, CD63, and CD47 as candidate cell markers, with CD74 having the highest Log2FC average value. The double positive CD74/CD68 protein expression profile was still consistent with the expected concentration of patient derived RECs of AML3 and is detectable as populations in newly diagnosed AML patients (Figure 5J, 7C, N=25). CD74 was validated as leukemia specific marker using RNA gene expression AML vs healthy gene expression data accrued by the MILE study (21,26) (N=543, N=73 AML Healthy BM respectively, p < 0.001 student’s t test) and by FC (N=2, for AML and healthy BM, Figure 3K). It was moved forward with defining RECs by the CD74/CD68 immunophenotype. The combined REC vs. malignant cells with AML vs. healthy cells comparison analyses provided a robust method of cell enrichment which accounts for intra-patient heterogeneity and specificity to AML disease vs. healthy hematopoiesis to provide a candidate leukemic-enriching immunophenotype for RECs. These studies allowed a departure from cell cluster profiling used to define LSC17 and LSC-R, to cell entity based on cell surface markers leading to cell isolation to purify cells for causal and functional characterization of RECs.

[00149] Prognostic value of RECs in AML patient survival and therapeutic response. [00150] Given the prognostic value of RECs by gene expression (Regen71) RECs defined by CD74⁺CD68⁺ cells were examined in clinical management and treatment response of AML patients. Specifically, due to the derivation through association with functional regeneration in response to cytarabine treatment alone, it is postulated that RECs may have clinical potential as a candidate relapse biomarker applicable to patients that were treated with standard 7+3 chemotherapy (cytarabine and daunorubicin). A biomarker detectable at diagnosis that predicts whether an AML patient will relapse after successful induction therapy would be a valuable clinical tool, as it could inform clinical decision making such as prompting follow-up rounds of consolidation therapy and hematopoietic stem cell transplants. REC frequency was analyzed using tissue samples from 30 independent AML patients from 3 distinct groups: Refractory to treatment (N = 11), entered CR but relapsed (N = 10), and entered long term CR (N = 9) (Figure 8A). REC frequency (gating strategies: Figure 7C-7D) successfully stratified those patients that remained in CR vs. those who eventually relapsed (p < 0.05, Figure 7A), demonstrating potential as a relapse marker detectable at diagnosis. RECs failed to stratify patients which suffered general treatment failure (both refractory and relapse) from patients that remained in remission (Figure 7B). As myeloid leukemias can occur at many stages of primitiveness within the leukemic hierarchy, including leukemias that are molecularly similar to RECs (monocytes), it was hypothesized that normalizing REC percentage to the monocytic compartment percentage of each respective AML will relieve some complexity of interpatient heterogeneity. Based on standard monocytic CD45^bnghtSSC^bnght gates within all live cells (Figure 7D-7E), the REC:monocyte ratio improved the prognostic value of RECs (Figure 8B) and expanded the prognostic capacity of RECs to stratify overall treatment failure from sustained remission (Figure 8C). Flow cytometric presentation of RECs were found to be heterogenous between patients in either the blast gate vs monocyte gate (SSC hCD45 gating, Figure 7F), and it is suggested that RECs play a vital role in AML biology support independent of AML differentiation status. To examine the value of the REC:monocyte measurement, receiver operating characteristic (ROC) analysis was conducted. ROC represents a well-established methodology for assessing viability of a clinical diagnostic tool, as it takes into consideration false positive/negative rates and eliminates biases of arbitrarily drawn thresholds to produce an area under the curve (AUC) value which summarizes the utility of the assay. Using AUC values from ROC curves REC:monocyte ratio predicted both relapse (Figure 8D) and treatment failure (Figure 8E) better than blast%, and primitive marker expression (CD34 and cKit) in this 30-patient cohort. The REC:monocyte ratio had an AUC of 0.867 and 0.857 for predicting relapse and treatment failure respectively, widely regarded as a strong predictive assay (27). Accordingly, these results indicate the REC immunophenotype has prognostic value for both overall survival and therapeutic response of AML patients. This is the first example of using RECs as a immunophenotypic biomarker for patient management.

[00151] RECs are devoid of intrinsic sternness capacity but have in situ proximity to putative CD34⁺ LSCs.

[00152] To examine the relationship of RECs to LSCs, flow cytometric analysis of RECs was performed alongside conventional primitive leukemic cell immunophenotypes (CD34, CD90, CD117, CD123, CD38, and Tim3). Apart from Tim3, there is no enrichment of these markers on REC cell populations (Figure 9A-9B), suggesting the RECs are a distinct biological entity. However, as features of sternness are defined functionally and not by correlative immunophenotypes, human CD74⁺CD68⁺ RECs were purified by fluorescent activated cell sorting (FACS) and showed depleted LSC activity by either CFU (progenitor) or xenotransplantation (LSC) assays (Figure 10A-10B). To characterize the origin of RECs, patient specific mutations were analyzed in purified RECs. These analyses demonstrated RECs are enriched for patient specific leukemic mutations (N=4, Figure 10C) revealing human AML derived RECs are of leukemic origin and are differentiation products of LSCs. Although dissimilar to LSCs by transcript, immunophenotype and function, an enrichment of REC high patients was observed in LSC 17 high patients compared to LSC 17 low (Figure 9C), suggesting patients with greater RECs also contain greater numbers of LSCs. Consistently, within the TARGET-AML cohort, patients that have over the median score ofRegen71 (REC+) and LSC 17 (LSC+) have dismal EFS and worse ELN stratifications compared to REC+/LSC-, LSC+/REC- and REC -/LSC- patients (Figure 9D-9E).

[00153] Based on these combined characteristic features of RECs, it was postulated that RECs may serve as localized accessory cells to support leukemic stem/progenitor cell driven disease regeneration. To examine this in situ, spatial transcriptomics analysis from 4 donated human trephine BM samples (2 from AML patients vs. 2 from healthy donors) was used. Tissues were H&E stained, and verified for expected differences between healthy donors and leukemic patients such as BM fat content (28), and BM blast invasion (Figure 10D, 9F). This approach has each transcriptionally defined area of 50um in diameter (Figure 10D) and was used to investigate whether there are areas that contain both RECs and LSCs. CD68⁺/CD74⁺ transcript expression was used as a surrogate probe to define RECs in this analysis. LSC 17 or LSCR gene scores do not represent single cell entities, but instead are powerful prognostic profiles and molecular definitions of putative LSCs derived through bulk cell assays and curated based on correlation to patient survival. As such, CD34 was chosen as a surrogate marker to represent LSCs, and was shown to correlate to LSC17 signatures in previous studies (18).

[00154] All tissues contained areas with CD74, CD68 and CD34 expression (Figure 9G) but only BM from AML patients had areas that overlapped CD74/CD68 and CD34 expression (Figure 10E). Representative images of REC/CD34⁺ proximity reveal consistency in tissue and morphology as compared to other regions of AML BM (Figure 10E). In sharp contrast, healthy BM sections were devoid of co-proximity of CD74⁺CD68⁺ and CD34⁺ cells. This suggested that CD74⁺/CD68⁺ cells may physically localize to CD34⁺ primitive cells in AML disease. To confirm that the proximity of RECs in AML patients was not a product of increased expression of markers within leukemic tissue, the amount of CD74/CD68 expression was examined within CD34⁺ and CD34' areas. CD34⁺ areas had higher CD74 and CD68 expression as compared to CD34' areas, while this pattern was absent in healthy tissues (Figure 9H), confirming a leukemia specific proximity of RECs and CD34⁺ cells. Last, to ensure proximity was not due to CD34⁺ cell proliferation and differentiation into RECs, the level of proliferation in relation to REC/CD34⁺ proximity was assessed. Three transcript markers of cell cycle (Ki67, CDK2 and PCNA) were combined as a measure of proliferative index (Figure 91). Using this index, proliferating vs. nonproliferating areas were quantitatively compared and compared to areas with and without REC/CD34 proximity. In two AML patients examined, no significant difference in distribution in locations of proliferation (p=0.27, Fishers Exact Test) was found, suggesting proximity of RECs to primitive leukemic cells in patients is independent of proliferative status of these cell types. As this analysis is limited to area and transcript, potential co-localization of RECs and CD34⁺ cells at the single cell protein level in PDX mice was further investigated. To achieve this, multiplexed ion beam imaging by time of flight (MIBI-TOF) (29) technology was applied to BM tissue sections of AML vs. CB xenografted mice (Figure 10F). RECs represented by CD74⁺CD68⁺ cells were found to be in closer proximity to CD34⁺ cells in the AML xenograft as compared to the CB xenografts (Figure 10G).

[00155] While it is historically the most robust surrogate marker of LSCs, using CD34 in this analysis has its shortcomings, as CD34' LSCs exist and CD34⁺ cells that are not LSCs have been identified. However, to maintain consistency between conditions preserving comparative values in this analysis, the same markers for LSCs and healthy HSCs were used mainly due to an absence of precise markers that effectively discriminate LSCs from HSCs. Nonetheless, these observations collectively suggest leukemia specific proximity of CD74⁺CD68⁺ cells and CD34⁺ primitive cells, and a potential support role for RECs in AML regeneration that forms the basis of functional testing.

[00156] RECs catalyze leukemic regeneration by supporting LSCs.

[00157] To functionally examine the properties of RECs, leukemic activity was assayed in both cellular loss and gain of function experiments. RECs were depleted from AML samples via FACS (representative FACS plots Figure 11 A-l 1C) and limiting dilution transplants (LDAs) were performed on the REC depleted AML samples as compared to the unfractionated samples to obtain LSC frequency with and without RECs present (N=2, Figure 6A). Notably, this experiment was designed by creating the unfractionated population by re-combining sorted RECs and REC purified populations to their initial proportions to avoid any biases introduced by FACS. Although RECs are devoid of any intrinsic LSC capacity, REC depleted AML samples contained less functional LSCs (Figure 12B), suggesting removal of RECs reduced supportive function to LSCs for survival and disease regeneration in vivo.

[00158] In cellular gain of function experimentation, the proportion of RECs present during leukemic regeneration was increased by transfusion of excess RECs. Between 50-80k FACS purified patient matched RECs were intrafemorally transfused (or cell number matched REC depleted cell control) into patient matched AraC treated PDXs two days post chemo exposure (N = 3, Figure 6C, representative FACS plots Figure 11 A-l 1C). As RECs are non-selfsustaining, it was postulated that a transient increase of hCD45% following transfusion and traced the leukemic graft growth for the next 14 days (Figure 12D) by comparing the hCD45% chimerism before intrafemoral transfusion and 14 days later. REC transfused grafts had a greater change in engraftment levels compared to REC depleted transfused grafts (Figure 12E), and a greater growth rate calculated by the doubling time of the exponential curve fit (Figure 12F). This increase in AML regeneration in the presence of additional RECs demonstrates RECs augment leukemic growth following chemotherapy exposure. To determine whether the increase in chimerism was cell-extrinsic in nature, RECs were transfused into non-engrafted AraC treated mice. No hCD45% chimerism was detected in these mice (Figure 1 ID) and therefore any growth from REC transfusion is non-cell autonomous. Notably, these experiments were performed using AML patient exposed to chemotherapy vs. untreated patients, with similar effects of augmenting AML regeneration with addition of RECs. Accordingly, although derived through enrichment following chemotherapy exposure in PDX models, these results indicate RECs play an LSC support cell function independent of chemotherapy exposure. [00159] Overall, these findings reveal RECs have prognostic and clinical utility beyond long term survival outcomes alone, including revealing new and more precise targets that are not restricted to various LSC populations transcriptionally profiled previously (30-32).

[00160] Investigating inter and intra patient heterogeneity of RECs reveals RECs are best defined as CD74⁺ CD68⁺ CD45^bright SSC^high and CD34~,

[00161] The next aim was to investigate how applicable the findings of RECs (CD74⁺ CD68⁺ cells) are to all AML patients. To do this, flow cytometry was performed with other cell type distinguishing markers (CD14, CD34, CD45) to investigate the cellular heterogeneity within and across patients of the CD74 and CD68 compartment. When gating the double positive CD74⁺ CD68⁺ cells (Figure 13 A), in some patients there was heterogeneity in the CD45 CD34 and CD14 profile of these cells (Figure 13B-13C), where some CD74⁺ CD68⁺ cells were CD45^brigbt SSC^bigb CD34 CD14⁺, while others were CD45^dim SSC^bigb CD34⁺ CD14’. Notably, all CD74⁺ CD68⁺ cells investigated functionally in Figure 12 were CD45^bngbt, SSC^blgb CD34' CD14⁺ (Figure 13B-13C), but as further AML patients were investigated, this heterogeneity became evident, and important to address (Figure 13D). It was next investigated whether the CD45^dim CD34⁺ CD14" fraction needed to be included within CD74⁺ CD68⁺ cells. The proportion of SSC^bigb CD45^brigbt CD74⁺ CD68⁺ within SSC^bigb CD45^brigbt cells in AML patients had the capacity to stratify the patient response and relapse rates, while CD45^dim CD74⁺ CD68⁺ within CD45^dim cells did not (Figure 13E-13G). This supports excluding the CD45^dim CD74⁺ CD68⁺ cells from the downstream analyses.

[00162] To validate this, the scRNA dataset that defined CD74⁺ CD68⁺ cells as RECs (N=3, Figure 6B) was merged with an external N = 25 CITEseq dataset (53) with simultaneous proteome and transcriptional profiling (Figure 14A). Once data was integrated and batch corrected, cells were observed as grouped into 16 transcriptionally defined cell clusters (Figure 14B). The cells previously identified as RECs as being correlative to leukemic regrowth in a PDX model (Cluster O’, Figure 6B), clustered primarily within Cluster 1 of this new analysis. Relying on the CITEseq data, Cluster 1 was positively enriched for cell surface CD45, negatively enriched for cell surface protein CD34 (Figure 14D), and were primarily transcriptional monocytes (Figure 14E), refining RECs as monocytic CD74⁺ CD68⁺ CD45^bngbt CD34' cells. By flow cytometry, CD45 brightness stratified monocytes and lymphocytes from other hematopoietic cells, and although not possibly represented in the scRNA datasets, side scatter (SSC) by flow cytometry further stratified lymphocytes from monocytes. As RECs cannot be lymphocytes, SSC^blgb was determined to be a flow cytometric descriptor of RECs. The N=28 patient cohort was used to get a better transcriptional description of RECs. There were 265 positively and 99 negatively differentially expressed genes in cluster 1 (Log2 FC > 2 and padj value < 0.01, Figure 14G, Table 3). To generate a transcriptional score of RECs compared to CD45^dim CD74⁺ CD68⁺ cells identified as not RECs, DEG analysis was performed on cluster 1 directly on cells that express CD34, CD74, CD68 and were within a cell cluster that was positive for CD34 cell surface markers. This revealed 350 positive and 341 negative DEG in RECs (Figure 14H, Table 3).

[00163] Notably, RECs were not the only cells assigned as monocytes present in the scRNA seq data set (Figure 14E), indicating that monocytes are not exclusively RECs. The proportion and number of RECs and non-REC monocytes varied between samples (Figure 15 A). This is consistent with no consensus on whether more differentiated leukemias have worse disease outcomes compared to primitive leukemias. As RECs were now immunophenotypically defined as a subset of monocytic cells (CD45^bnght, SSC^hlgh, CD34' ,CD74⁺, CD68⁺ cells), and it is known that monocytic leukemia cells are not a strong predictor of treatment failure or relapse, the inventors postulated that frequency of RECs within the CD45^bnght SSC^blgb (monocytic) compartment would be a successful outcome indicator, which they tested. Another way of achieving this was to look at the ratio between RECs (CD74⁺ CD68⁺ CD45^brigbt SSC^bigb CD34’ cells) to non-REC monocytes (CD74‘ and/or CD68’, CD45^brigbt SSC^blgb CD34 ). When plotted on a bar graph arranged by patient response (Figure 15A), this REC to non-REC monocyte ratio provided a level of stratification, with the patients that suffered treatment failure, relapse, or refractory disease having a higher frequency of RECs to non-REC monocytes. This ratio strategy alleviated the immense intra-patient heterogeneity demonstrated in AML, as based on these results, patients will present with orders of magnitude differences in monocytic compartments. These methods are not restricted by how many monocytes can be detected, as the ratio was still reliable with varying numbers of monocytes (Figure 15B). At diagnosis, the ratio between RECs (CD74⁺ CD68⁺ CD45^brigbt SSC^bigb CD34’ cells) to non-REC monocytes (CD74‘ or CD68', CD45^bngbt SSC^blgb CD34 ) successfully stratified patients that remained in CR vs. those who eventually relapsed (p < 0.01, Figure 15C), and patients that remained in remission compared to all patients that experienced treatment failure (relapse and refractory, p < 0.001, Figure 15C). The monocytic compartment (CD45^bngbt SSC^blgb) was insufficient in mirroring this stratification capacity. This demonstrates the second example of the potential use of RECs as a relapse marker detectable at diagnosis. The 1 REC to 4 non-REC monocytes ratio was shown to be a significant threshold of RECs to non-REC monocytes that tightly predicts treatment outcome, as shown in survival plots (Figure 15D). Accordingly, these results indicate the REC immunophenotype (CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34' cells) has prognostic value for both overall survival and therapeutic response of AML patients. The inclusion of REC (CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells) monitoring should be considered throughout disease progression, including clinical timepoints of diagnosis, before, during and after induction chemotherapy, during stages of clinical remission, before during and after consolidation therapy, during any relapse timepoints, and pre and post HSCT. Monitoring the ratio of RECs (CD74⁺ CD68⁺ CD45^brigbt SSC^bigb CD34’ cells) to non- REC monocytes (CD74‘ and/or CD68", CD45^bngbt SSC^blgb CD34 ) can inform the treating clinician if their current treatment regimen is effective, and if the patient’s REC to non-REC monocyte ratio remains above the 1 to 4 ratio, have the option to change the disease management strategy.

[00164] AML differentiation is primed toward REC generation.

[00165] The relationship of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells within the healthy hematopoietic system was investigated. As remission patients have a lesser REC to non-REC monocyte ratio, the hypothesis that this would also be true in healthy blood samples was investigated. Inconsistently, when investigating three healthy and three de novo blood samples, the ratio and absolute value of REC to non-REC monocytes ratio was not significantly different between healthy and AML samples (Figure 16A). However, when plating purified stem cell REC depleted cells (CD34⁺ CD74' CD68 ) from each sample into a differentiation medium (14 days in STEMCELL Tech H4434) the cells differentiated from the stem cell REC depleted fraction were significantly enriched for the REC fraction (Figure 16B). This demonstrated that in de novo recapitulation of the healthy and AML hierarchies through stem cell differentiation is skewed in AML towards generating RECs over non-REC monocytes (Figure 16C). The hierarchical relationship between RECs and non-REC monocytes was unknown and yet to be explored (Figure 16C). To investigate this, purified RECs and non-REC monocytes were plated in the same differentiation medium used above. Neither RECs nor non- REC monocytes produced any reliably detectable cell or colony growth at the normal readout time of 14 days. However, early in the differentiation process, plated non-REC monocytes had created RECs at hr6 and hr24 in all six samples tested (representative plots Figure 16D). Moreover, plated RECs created non-REC monocytes at hour 6 and 25 in all six samples tested (representative plots Figure 16E). Therefore, RECs and non-REC monocytes are non- hierarchically arranged, and appear to readily transition. At 24 hours, the transition of non-REC monocytes to RECs was significantly greater in AML samples compared to healthy (Figure 16F). This demonstrated a degree of metastability between these two states, which favoured the REC phenotype in AML contexts compared to healthy (Figure 16G).

[00166] Culturing CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34' cells would be valuable for downstream applications. However, RECs are not self-sustaining as they do not demonstrate any self-renewal capabilities. Instead, the inventors have created a method in which RECs can be derived from primitive hematopoietic populations of both the healthy and leukemic systems. First, REC depleted primitive progenitor populations CD74' or CD68' CD34⁺ cells were FACS purified and placed in a standard semisolid methylcellulose medium to grow progenitors that contains hematopoietic differentiation growth factors (EPO, IL3, GCSF, SCF) for 14 days at 37°C. The primitive CD74" CD34⁺ or CD68" CD34⁺ cells in this condition differentiated into many different cell types by day 14, including CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34' cells. Interestingly, CD74' CD34⁺ or CD68' CD34⁺ cells placed in these conditions provided orders of magnitude more CD74⁺ CD68⁺ CD45^brigbt SSC^bigb CD34’ cells than if CD74⁺ CD68⁺ CD45^brigbt SSC^bigb CD34’ cells were plated directly into these conditions (Figure 17). This application could be used for any downstream drug or small molecule screening on RECs.

[00167] The inventors have created a flow panel, protocol, and kit for detecting RECs (CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34' cells) by flow cytometry (Figure 18) for the purpose of both clinical applications (Figure 15), and research applications (Figure 17). The kit can encompass flow cytometry buffer (BSA/FBS), ammonium chloride, the near infrared live dead stain, and the seven fluorescently conjugated antibodies specified in Figure 18. A hematopoietic tissue sample of any sort can be used in the following protocol. After counting the cells per volume of the sample, the concentration of the sample was diluted to 10e9 cells/L using PBS/BSA. Cells were pelleted through density centrifugation and resuspended in PBS/BSA to achieve a final cell concentration of 10e9 cells/L. I OOUL of the sample was aliquoted and added the antibody cocktail. The cells were stained with the antibody cocktail for 20min @ room temperature in the dark. 2mL of NH4CI2 was added for lOminutes at room temperature before adding ImL of PBS/BSA mixture. Cells were pelleted through density centrifugation and resuspended in PBS/BSA, and then once again pelleted through density centrifugation before one final suspension in 800uL PBS/BSA. This sample was then ready to be run on clinical or research flow cytometers.

[00168] Inventors have also created a gating strategy for purification of RECs (CD74⁺ CD68⁺ CD45^brigbt SSC^bigb CD34’ cells) and non-REC monocytes (CD74‘ or CD68’, CD45^brigbt SSC^hlgh CD34' cells) by fluorescent activated cell sorting (FACS) (Figure 19). Once stained with the flow cytometry kit (Figure 18), forward and side scatter gating were used to eliminate debris. Forward scatter width (FSC-W) to forward scatter height (FSC-H) and side scatter width (SSC- W) to side scatter height (SSC-H) were then used to eliminate doublets. Next live cells were gated by taking events that do fluoresce with the Live/Dead Near Infrared dye. Events that were CD45^bngbtSSC^blgb (defined in Figure 21) and CD34' population were taken subsequently. To gate RECs, gate was drawn to purify the cells with positive CD68 and CD74 intensity, using fluorescence minus one (FMO) controls to guide the gate placement. An event with a fluorescence intensity above the fluorescence intensity of 99% of events from the FMO control is considered a positive event. A conservative gate placement approach was taken while drawing gates during purification to preserve the purity of the population. Gate placement was confirmed to be correct by re-running purified RECs back through the sorter and returning >90% cells above the FMO control based gates. To gate non-REC monocytes, a gate was drawn far separated from the REC gate, ensuring to capture cells that do not express both CD74 and CD68, using FMOs to guide the gate placement.

[00169] The inventors have further created a gating strategy for identification of RECs (CD74⁺ CD68⁺ CD45^brigbt SSC^bigb CD34’ cells) and non-REC monocytes (CD74’ or CD68; CD45^bngbt SSC^blgb CD34' cells) by flow cytometry (Figure 20). Once stained with the flow cytometry kit (Figure 18), forward and side scatter gating were used to eliminate debris. Forward scatter cell (FSC) area and FSC height were used to eliminate doublets. Next, the gate on live cells was determined by taking events that do fluoresce with the Live/Dead Near Infrared dye. Events that were CD45^bngbt SSC^blgb (defined in Figure 21) and CD34' (Defined by FMOs) were then taken. On the remaining events, a quadrant gate was drawn using fluorescence minus one (FMO) controls to guide the gate placement. Cells that were present in the upper right quadrant were CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34' cells, and the frequency of this gate was the prognostic indicator (Figure 15). To gate non-REC monocytes the frequencies of the upper left and the two bottom quadrants were combined, indicating the CD74' and/or CD68', CD45^bngbt SSC^bigb CD34' cell population

[00170] To define CD45^bngbt and SSC^blgb within the assay, the flow results of nine AML patient samples that had undergone the flow cytometry kit methodology (Figure 21) were concatenated. Once gated on the first three gates of Figure 20, all events were placed on SSC/CD45 expression. CD45^brigbtin this series of events referred to the cells within the highest peak, with a median fluorescent intensity of 31078. The highest peak of SSC was purified to effectively gate monocytes from lymphocytes, with a median intensity of 294164, effectively isolating the population of interest. In each sample’s case, there is room to maneuver the gate to include more anchor points around what is reasonably the same population as what is defined by SSC^hlgh CD45^bnght, as populations rarely present as square. For example, tracing the external most completely intact 2% contour line provides a framework for refining the SSC^hlgh CD45^bngbt population. Stringency of adjustments must be consistent within the sample cohort. For example, if one is using 2% contour lines to define SSC^blgb CD45^bngbt for a patient and comparing to a sample from the same patient at relapse, the relapse sample should also have their SSC^blgb CD45^bngbt population defined through an equally as stringent 2% contour line process.

[00171] Discussion

[00172] This work identified an accessory population of leukemic patient origin that acts like a catalyst for AML regeneration post chemotherapy. RECs have functional and prognostic value that was illustrated by the original approach used to identify RECs in this study that examines acute responses to regeneration following chemotherapy. These dynamics, originally defined in PDX models of AML regeneration, would be masked when performing molecular analysis on static patient samples obtained at defined stages of therapeutic management e.g. Dx, CR, and Relapse. Functional and molecular characterization of RECs is consistent with a non- autonomous role of leukemic RECs for stem cell driven AML regeneration. This adds to the mounting evidence for non-canonical cellular mechanisms of leukemic regeneration and disease relapse in response to therapy. The biological and clinical relevancy of anon-primitive gene score for RECs departs but collaborates with conventional canonical viewpoints of LSC based AML regeneration. These findings contribute to the foundational work from several studies identifying non-stem/progenitor gene expression properties that correlate to leukemic chemo-resilience and regeneration (11-13), and that more differentiated leukemias can have worse overall outcomes and treatment response to venetoclax (33,34), but these studies remained disconnected. As RECs are enriched for gene expression patterns identified in these separate reports, including cellular senescence, inflammation, oxidative phosphorylation, (Figure 3K) and the leukemic regenerating cell gene score, RECs provide a basis for consolidation of these previously unlinked observations in AML (11-13). Furthermore, RECs allow an immunophenotypically definable cellular entity to be investigated and utilized, thus extending beyond the limited use of gene expression profiling. As sources of gene expression upregulation may be ambiguous and could be due to cellular response to chemotherapy exposure and not innate chemo-resistance, functional demonstration of immunophenotypically purified REC regeneration augmentation (Figure 12) leaves less ambiguity for future study and REC translational impact. This appears to be the first AML single cell transcriptomics analysis which has resulted in the identification of an immunophenotypically defined cell involved in disease regeneration, as well as the initial demonstration of a role for non- stem/progenitor cells of leukemic origin to contribute to AML disease.

[00173] These findings are based on the study’s approach that incorporates multiple postchemotherapy timepoints and paired functional stem cell biology assays that define the unique transcriptomic profile of transiently cytoreduced leukemia and leukemic graft regeneration. AML regeneration post chemotherapy allowed temporal tracking of both disease burden and progenitor activity during leukemic graft recovery to obtain a non-patient specific gene score predictive of OS, EFS, and treatment response in independent patient cohorts. Notably, neither transcriptional REC clusters, nor their derived gene scores, exhibit similarity to primitive gene expression. As such, RECs serve a cellular therapeutic target with a defined phenotype that possesses prognostic value to AML patients as a biomarker of therapy response and long-term survival. These results uncover a principle in AML disease suggesting cancer stem cell driven regeneration is not solely cell autonomous in nature and involves other complex cellular elements exemplified by CD74⁺CD68⁺ CD45^bnght SSC^hlgh CD34 RECs. This emphasizes the importance of cell extrinsic factors that support chemotherapy resilience in stem cell driven relapsed disease, that may be applicable to other tumors sustained by cancer stem cells (13,35). RECs may represent a cellular bridge to the microenvironment for LSCs as RECs are products of LSC differentiation to sustain leukemia, and act as a survival response to chemotherapy injury that target LSCs. As such, the incorporation of REC detection into measurable residual disease (MRD) (36) approaches could aid in the precision and fidelity of MRD measurement by including RECs phenotype frequency alone or in combination with AML patient specific DNA mutations and existing MRD phenotype (36), as opposed to binary absence or presence of mutation detection currently used.

[00174] CD74 has been associated with cancer progression (37,38), proinfl ammatory immune response (39), has been implicated in regenerating tissues (40), and is considered a monocytic/macrophagic marker within the myeloid branch of the hematopoietic system. Along with other cell types, differentiated cells of the hematopoietic system including macrophages and monocytes help create the BM niche in a healthy hematopoietic system (35), supporting HSC survival and regulation. With an abundant role in tissue repair (40), the expression of CD74 in leukemic regeneration is consistent with the working theory that cancers are an over-healing wound (41), and suggest leukemic transformation hijacks healthy repair mechanisms for malignant growth and support by generating its own supportive cells. CD68 is a marker of macrophages and has been implicated with tumor associated macrophages (TAMs) in AML (42), Although perhaps providing a similar role, the leukemic origin of RECs (Figure IOC) separate these findings from TAMs in AML disease. RECs are a product of leukemic transformation that require altered states of differentiation from normal hematopoiesis and are needed to survive aggressive chemotherapy injury. This may signify a change in the BM niche during leukemogenesis that preferentially supports LSCs over HSCs (35). It is proposed that LSC generated monocyte and macrophage-like blast cells likely support leukemic regeneration, perhaps through a leukemic BM niche-like support system that remains to be understood. Nonetheless, directed therapies towards leukemic derived cells such as RECs via drug targeting, antibody-based inhibition, or immunotherapy using CAR engineering would provide a first in class approach for combating AML relapse and warrants further investigation by the biomedical community.

[00175] Many of these results were described in Hollands et al., Cell Rep Med. 2024 Apr 16;5(4): 101485, herein incorporated by reference in its entirety.

[00176] Methods

[00177] Droplet Based scRNA sequencing: 3’ scRNA sequencing experiments were performed directly on cells purified by the FACSAria II: hCD45⁺ CD33⁺ 7AAD- when from xenograft source; 7AAD- when directly from patient tissue. During all preparation steps, cells were kept at 2-6°C. Manufactures recommendation’s (User Guide, CG000388) were following to create single cell libraries. When cell multiplexing was utilized (PN# 1000262), up to three samples were multiplexed into a single run, and samples that were multiplexed together were always biological replicates. Gene expression and cell multiplexing libraries were sequenced on the NovaSeq SP flow cell (TCAG, SickKids) using the recommended parameters.

[00178] Spatial Transcriptomics: Prior to performing spatial transcriptomics experiment, DV200 assessment was done on all the samples with a minimum criterion set at 30%. RNA was extracted from paraffin curls (4 curls at 10 pm per tissue) using Qiagen’s RNeasy Mini Kit (Cat#74104). All working space and instruments were treated with RNase zap (Invitrogen, Cat#AM9780) prior to RNA extraction, and gentle scoring was performed around the tissue to minimize excess paraffin during curl collection. Spatial transcriptomics experiment was performed using Visium Spatial Transcriptomics (10X genomics, Cat#1000185), following materials and methods according to the manufacturer’s website (User Guide, CG000407). Gene expression libraries were sequenced on the NovaSeq SP flow cell (TCAG, SickKids) using the recommended parameters. Analysis and visualizations were performed by manufacturer provided Loupe Browser. Co-localization of putative LSCs to RECs was determined by areas of CD34⁺ expression AND areas that of CD74⁺ CD68⁺ expression (Expression > 0 reads).

[00179] Bioinformatics pipelines: ScRNA sequencing reads were counted and aligned using Cell Ranger software provided by 10X Genomics. Individual scRNA sequencing data sets were integrated, and batch corrected using Seurat’s integration protocol, and QCed using standard exclusion metrics (%mitochondrial genes > 3*SD+median_sampie, (cell features < median_sampie - (3*SD)). An integration anchor of healthy BM was used when samples of different patient backgrounds were being merged to minimize variation on interpatient heterogeneity. The K nearest neighbour clustering methodology was used to identify clusters in data sets described. Clusters were excluded from analyses on a per sample basis if the percent of the cluster within the sample of the integrated data set was less than the smallest cluster of the sample dataset when not integrated with other data. These clusters were considered bioinformatic products of the integration protocol, called them non-substantive clusters, and thus were excluded from downstream calculations. Visuals were created using the Seurat’s R package built-in visualization program. Manufacturers recommendations were followed when utilizing the celldex package for assigning cell types using data from the human primary cell atlas. Manufacturers recommendations were followed when utilizing the Seurat package for assigning cell cycle phase. The Seurat Package’s FindAllMarkers function was used to perform Wilcoxon rank sum test to derive DEG (defined by Log2FC>0.25, padj > 0.01). Sequencing reads from spatial transcriptomics protocols were counted, aligned, and aggregated using manufacturer provided Space Ranger and Loupe Browser, following pipeline provided by the manufacturer. Visualizations of these data sets were created using Loupe Browser and GraphPad Prism. Areas were considered positive for a transcript if expression was detected, and considered negative if no expression was detected. For CITE-seq data, the scRNA pipeline was followed above. Processed cell surface marker antigen data was added as metadata, processing described by authors (53).

[00180] Fluorescent activated cell sorting and flow cytometry: Immunophenotyping for human hematopoietic cell surface markers was carried out using the following antibodies: PE- conjugated anti-CD74 (1:500), PECy7-conjugated anti-CD68 (1:200), BV421 -conjugated anti- CD14 (1: 100), FITC-conjugated anti-CD15 (l:100)V450-conjugated anti-CD45 (1: 100; 2D1), APC-conjugated anti-CD33 (1:300; WM-33), PE-CF594-conjugated or PE-conjugated or APC- conjugated anti-CD34 (1:200; 581), FITC-conjugated anti-CD19 (1: 100; HIB19), APC- conjugated anti-CD117 (1:200), KromeOrange anti-CD45 (1: 100). To evaluate candidates from the REC gene signature at the protein level, gene targets were identified with available commercial antibodies that had been validated for flow cytometry. CD74 CD68, CD14, CD44, CD163. 7-aminoactinomycin D (7AAD, Beckman Coulter) or live dead NIR exclusion was used to discriminate live cells and was always used during cell sorting processes. As described, fluorescence minus one control were used to optimize gating strategies for target cell populations. For scRNA sequencing experiments directly from patient tissue, viable MNCs were purified based on side scatter and forward scatter gating and 7AAD exclusion. To FACS isolate cells from PDX models for downstream applications of scRNA sequencing and CFU assays, MNCs from human grafts were isolated by using forward scatter and side scatter gates, 7AAD exclusion, and hCD45⁺ CD33⁺ gates. RECs were purified from primary AML samples using forward scatter and side scatter gates, doublet exclusion, live dead exclusion, CD45/SSC monocyte gating, and CD74⁺ CD68⁺ gates. Post-sort purities were routinely >95%. FACS sorting was performed using a FACSAria II sorter, and flow cytometry analysis was performed with a LSRII Cytometer (BD), or CytoFlex LX (Backman Coulter). FACSDiva (BD) and CytExpert were used for data acquisition, and FlowJo software (Tree Star) was used for analysis.

[00181] MIBI-TOF Processing and Analysis: Tissues were sectioned 4pm in thickness onto gold coated MIBIslides. The slides baked at 65°C for 1 hour, followed by deparaffmization and rehydration in sequential washes in xylene (3x), 100% ethanol (3x), 95% ethanol (2x), 70% ethanol (2x), and MIBI-water. Antigen-retrieval in a pH 9 Target Antigen Retrieval Solution (DAKO Agilent) occurred at 125°C for 40 minutes in a Decloaking Chamber (BioCare Medical). After cooling to room temperature, the slides were washed twice in TBS-T (lonPath). The tissue was incubated in a blocking buffer consisting of 3% normal donkey serum (Jackson ImmunoResearch) in TBS-T for 20 minutes. The slides were incubated with blocking-bufier- diluted antibody panel consisting of metal-tagged antibodies supplied by lonPath overnight at 4°C in a moisture chamber. After overnight incubation, the tissues were fixed and dehydrated in sequential washes in TBS-T (3x), 2% glutaraldehyde (5min), Tris pH 8.5 (3x), MIBI-water (2x), 70% ethanol (2x), 90% ethanol (2x), 95x ethanol (2x), 100% ethanol (3x). The slides were stored in a desiccator prior to MIBIscope analysis. Spectral images of mouse femurs were collected using an lonPath MIBIscope with Multiplexed Ion-Beam Imaging technology. Xenon primary ions from a Hyperion™ ion gun rastered across the slide to sputter stained tissue into a plume of secondary ions detected by mass-spectrometry by time-of-flight to reconstruct the spectral images, on a pixel-by -pixel basis, of each channel consisting of a single stained antibody. A more detailed description of the Multiplexed Ion-Beam Imaging technology appears in Keren, et al (2018) (43) With the assistance of a pathologist, 400x400pm fields of view (FOVs) inside the lesions. Multiplexed raw image sets were denoised and aggregate filtered using lonPath's MIBI/0 and the default correction settings. These processed image TIFF files represented the dataset. Nuclei segmentation was performed with the input of the nuclear-stained and the membrane- stained marker channels using Mesmer (44) and the segmentation mask images were stored as TIFF files for further analysis in MATLAB and R Studio. Single-cell data was extracted for all cell objects defined by the segmentation masks using a custom R script and packages as previously described (45-47). Then, asinh-transformed with a cofactor of 1. To classify cell types based on their marker expression levels, the Bioconductor 'FlowSOM' R package was used (48). The algorithm clustered the 534,012 total cells from the cohort into 100 FlowSOM clusters. By inspecting a heatmap displaying normalized individual marker intensities, each of the 100 clusters were annotated into 16 meta-clusters, which included signatures that represented CD34⁺ cells and CD74⁺CD68⁺ cells for downstream proximity analysis. The CytoMAP software (49) was used to perform single-cell spatial analysis with the aim to determine the proximity of CD74⁺CD68⁺ cells to all CD34⁺ cells present in the FOVs. The algorithm achieved this by using the cell types and their positions in the image to calculate the distance between all CD34⁺ cells and the nearest cell for CD74 CD68⁺ cells.

[00182] Droplet Digital Polymerase Chain Reaction: Detection of NPM1 c.863_864insTCTG (COSMIC 17559), TP53c842A-T, and idh2 c515 was performed on the QX200 Droplet Digital PCR system (Bio-Rad Laboratories, Inc., Hercules, CA, USA) using TaqMan(tm) Liquid Biopsy dPCR Assay Hs000000064_rm (Life Technologies, Carlsbad, CA, USA). The 20 ml reaction mix consisted of 10 ml of 2x ddPCR SuperMix for Probes (Bio-Rad Laboratories), 0.5 ml of the 40X assay, 9.5 ml water and 1 ml of 30-50 ng/ml genomic DNA. The assay was tested by temperature gradient to ensure optimal separation of reference and variant signals. Cycling conditions for the reaction were 95°C for 10 min, followed by 45 cycles of 94°C for 30s and 60°C for 1 min, 98°C for 10 min and finally a 4°C hold on a Life Technologies Veriti thermal cycler. Data was analyzed using QuantaSoft Analysis Pro software vl.0.596 (Bio-Rad Laboratories).

[00183] Progenitor Frequency Assays: The clonogenic capacity of leukemic progenitors was evaluated by colony-forming unit (CFU) assays. Briefly, either FACS purified or bulk AML cells (500-25000 cells/well) were seeded in semisolid methylcellulose media (Methocult #H4434; Stemcell Technologies) according to established protocols. Progenitor assays of xenografted leukemic cells were performed following human cell purification as described above. Individual CFU wells were seeded from multiple mice. Colony units were counted at day 14. Differentiation products were observed as early as 6 hours post plating.

[00184] Multivariate Survival Analysis: TARGET AML and TCGA L-AML RNA sequencing and clinical data were accessed from the NIH National Cancer Institute GDC Data Portal: https://portal.gdc.cancer.gov/. Cohort sizes were adjusted due to the gene expression and survival (OS and EFS) data readily accessible through the GDC Data Portal. When filtering the TARGET AML patient cohort to achieve a cohort of patients with consistent induction treatment, the Gemtuzumab ozogamicin treatment tab was used and selected patients with no Gemtuzumab ozogamicin treatment from the linked AAML0513 (50) and AAML03P1 (51) trials. These patients received ADE 10 (cytarabine 100mg/m2/dose (3.3 mg/kg) every 12 hours on Days 1 to 10; dau-norubicin 50 mg/m2/dose once daily (1.67 mg/kg) on Days 1,3, and 5; and etoposide 100 mg/m2/dose (3.3 mg/kg) once daily on Days 1 to 5) without the use of Gemtuzumab ozogamicin (50). Gene scores were calculated as per quantification and statistical analysis section. Missing clinical data were replaced by Bayesian polytomous regression or logistic regression. R v3.5.1 (using packages survival v3.2-7, survminer vO.4.8, and mice v3.11.0) were used for this analysis.

[00185] Quantification And Statistical Analyses: Summarized data are represented as mean± standard deviation. Statistical comparisons were analyzed using unpaired student’s t-tests (two-tailed), paired t-tests, one-way analysis of variance tests (ANOVAs) followed by Tukey’s multiple comparison tests, two-way ANOVAs, Fisher’s exact test., chi square test, or Mantel Cox tests Any deviations from normal distribution or homogeneity of variances were corrected by loglO transformation prior to parametric statistical tests, unless transformation did not resolve heterogeneity in variances in which non-parametric tests were applied. Datasets that had negative or zero values prior to transformation to reduce heterogeneity of variances were translated by +1 prior to log transformation. Prism software (version 7.0; GraphPad), R v3.5.1 (using packages survival v3.2-7, survminer vO.4.8, and mice v3.11.0) and MedCalc software (v20.110) was used for statistical analysis and p < 0.05 was considered statistically significant unless otherwise specified in the figure captions. Survival times were calculated from the date of sample collection, using previously established criteria for OS and EFS by subtracting the date of sample collection from the date of death (OS) or from the date of relapse (EFS).52 The expression score from every patient within a specific probe/gene was normalized to the mean expression of that probe to adjust to inter-probe transcriptional variance. To assign a value of a gene score to each patient, this value was calculated and averaged for each probe/gene of a select gene score. When dividing patients into high and low scores, the cohort was stratified into gene score high and low by arrangement around the gene score median. The Kaplan Meier method was used for univariate survival analyses and multivariate Cox regression was used to evaluate independent predictors of survival. For multivariate survival analyses, missing data were replaced by Bayesian polytomous regression or logistic regression. Patients with primary refractory disease were assigned an EFS of 0 days.

[00186] While the present disclosure has been described with reference to examples, it is to be understood that the scope of the claims should not be limited by the embodiments set forth in the examples, but should be given the broadest interpretation consistent with the description as a whole.

[00187] All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. Where a term in the present disclosure is found to be defined differently in a document incorporated herein by reference, the definition provided herein is to serve as the definition for the term.

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Claims

Claims:

1) A method of determining the presence of regeneration enriched cells (RECs) in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subjecting the sample to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; and ii) determining the presence of CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells; wherein the presence of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells are indicative of the presence of RECs.

2) A method of determining a ratio of regeneration enriched cells (RECs) to non-RECs in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subjecting the sample to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells which are indicative of RECs; iii) measuring the amount of CD74" CD68" CD45^bngbt SSC^blgb CD34"; CD74" CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii).

3) A method of determining a prognosis for a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) subjecting a sample obtained from the subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells, which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74" CD68" CD45^bngbt SSC^blgb CD34"; CD74" CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the subject has an increased risk of relapse or treatment failure, and wherein when the ratio is at or below about 0.15, the subject has a decreased risk of relapse or treatment failure.

4) A method of selecting a subject who has completed a treatment for Acute Myeloid Leukemia (AML) for further treatment, the method comprising: i) subjecting a sample obtained from the subject to flow cytometry to determine the status of CD45, side scatter (SSC), CD34, CD74 and CD68 on cells of the sample; ii) measuring the amount of CD74⁺ CD68⁺ CD45^bngbt SSC^blgb CD34" cells, which are indicative of regeneration enriched cells (RECs); iii) measuring the amount of CD74" CD68" CD45^bngbt SSC^blgb CD34"; CD74" CD68⁺ CD45^brigbt SSC^bigb CD34’; or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells, which are indicative of non-RECs; and iv) calculating the ratio of ii) to iii); wherein when the ratio is at least about 0.20, the subject is selected for further treatment, and wherein when the ratio is at or below about 0.15, the subject is not selected for further treatment.

5) Use of a further treatment for treating a subject who has completed a treatment for Acute Myeloid Leukemia (AML), wherein the subject was previously determined to have a ratio of regeneration enriched cells (RECs) to non-RECs of at least about wherein CD74⁺ CD68⁺ CD45^bngbt SSC^hlgh CD34" cells are indicative of REC cells, and wherein CD74’ CD68’ CD45^bright SSC^bigb CD34’ cells, CD74’ CD68⁺ CD45^brigbt SSC^bigb CD34’ cells, or CD74⁺ CD68’ CD45^brigbt SSC^bigb CD34’ cells are indicative of non-RECs.

6) The method of any one of claims 1 to 4 wherein the flow cytometry comprises fluorescence activated cell sorting (FACS).

7) The method of any one of claims 1 to 4 and 6, or the use of claim 5, wherein the treatment comprises chemotherapy.

8) The method of any one of claims 1 to 4, 6 and 7, or the use of claim 5 or 7, wherein the treatment comprises a pyrimidine nucleoside analog.

9) The method of any one of claims 1 to 4 and 6-8, or the use of any one of claims 5, 7, and 8, wherein the treatment comprises Cytarabine.

10) A method for purifying regeneration enriched cells (RECs) in a sample from a subject who has completed a treatment for Acute Myeloid Leukemia (AML), the method comprising: i) labelling cells in the sample with fluorescent markers for CD74, CD68, CD45, and CD34; ii) passing the labelled cells through a flow cytometer with fluorescence detection; iii) detecting side scatter (SSC) and fluorescence signals emitted by the fluorescent markers in i); and iv) sorting cells by the detected fluorescence signals and SSC in step iii), wherein the cells sorted in step iv) according to CD74⁺ CD68⁺ CD45^bngbt SSC^blgh CD34' are indicative of RECs.

11) A method for generating regeneration enriched cells (RECs) in vitro, the method comprising: i) purifying a population of CD74' CD34⁺ cells or CD68' CD34⁺ cells by fluorescence activated cell sorting (FACS); and ii) incubating the purified population of cells in a medium containing erythropoietin (EPO), interleukin 3 (IL3), granulocyte colony stimulating factor 3 (GCSF), and stem cell factor (SCF); thereby generating CD74⁺ CD68⁺ CD45^bnght SSC^hlgh CD34" cells, which are indicative of RECs.

12) The method of claim 11, wherein the medium comprises semisolid methylcellulose medium.

13) The method of claim 11 or 12, wherein the incubating in step ii) is for 14 days.

14) The method of any one of claims 11 to 13, wherein the incubating in step ii) is at 37°C.

15) The method of any one of claims 11 to 14, further comprising a step of purifying the RECs by FACS.

16) An assay for screening the efficacy of a test agent in reducing the number of regeneration enriched cells (RECs), the assay comprising: i) obtaining a population of cells comprising RECs by a method as defined in any one of claims 10 to 15; ii) treating the population of cells comprising RECs with the test agent; iii) measuring the number and/or proportion of RECs within the population of cells comprising RECs; and iv) comparing the number and/or proportion in iii) to the number and/or proportion of RECs in a control population in the absence of test agent; wherein a reduced number or proportion of RECs in iii) compared to the control population is indicative that the test agent is effective for reducing the number of RECs. 17) A kit comprising a CD74 detection agent, a CD68 detection agent, a CD45 detection agent, and a CD34 detection agent.

18) The kit of claim 17, further comprising instructions for use in a method as defined in any one of claims 1-4 and 6-15 or in an assay as defined in claim 16.

19)The kit of claim 17 or 18, further comprising a CD14 detection agent, a CD117 detection agent, a detection agent for CD15, and/or a detection agent for live/ dead cells.

20) The kit of any one of claims 17 to 19, further comprising flow cytometry buffer and/or ammonium chloride.