# A Deeply Quiescent Subset of CML LSC depend on FAO yet Avoid Deleterious ROS by Suppressing Mitochondrial Complex I

Nyam-Osor Chimge, Min-Hsuan Chen, Cu Nguyen, Yuqi Zhao, Xiwei Wu, Paulina Garcia Gonzalez, Heather Ogana, Samantha Hurwitz, Jia-Ling Teo, Xiaolong Chen, et al.

► To cite this version:

Nyam-Osor Chimge, Min-Hsuan Chen, Cu Nguyen, Yuqi Zhao, Xiwei Wu, et al.. A Deeply Quiescent Subset of CML LSC depend on FAO yet Avoid Deleterious ROS by Suppressing Mitochondrial Complex I. Current Molecular Pharmacology, 2023, 17, 10.2174/1874467217666230906092236. hal-04948242

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# A Deeply Quiescent Subset of CML LSC depend on FAO yet Avoid Deleterious ROS by Suppressing Mitochondrial Complex I

Nyam-Osor Chimge<fnref n="1" />, Min-Hsuan Chen<fnref n="2" />, Cu Nguyen<fnref n="1" />, Yuqi Zhao<fnref n="2" />, Xiwei Wu<fnref n="2" />, Paulina Garcia Gonzalez<fnref n="3" />, Heather Ogana<fnref n="4" />, Samantha Hurwitz<fnref n="4" />, Jia-Ling Teo<fnref n="1" />, Xiaolong Chen<fnref n="5" />, Juan Du<fnref n="2" />, Victor Jin<fnref n="6" />, Yong-Mi Kim<fnref n="4" />, Masaya Ono<fnref n="7" />, Rafael J. Argüello<fnref n="3" /> and Michael Kahn<fnref n="1" />,<fnref n="8" />,*

**Abstract:**

**Background and Objective:**

Disease relapse and therapy resistance remain serious impediments to treating cancer. Leukemia stem cells (LSC) are therapy resistant and the cause of relapse. A state of deep quiescence appears to enable cancer stem cells (CSC) to acquire new somatic mutations essential for disease progression and therapy resistance. Both normal hematopoietic stem cells (HSC) and LSC share many common features, thereby complicating the safe elimination of LSC. A recent study demonstrated that long lived normal oocytes exist without mitochondrial complex I (MC-1), expressing it in a developmentally regulated fashion, thereby mitigating their vulnerability to ROS. Quiescent CSC rely on mitochondrial FAO, without complex I expression, thereby avoiding the generation of damaging ROS, similar to long lived normal human stem cells. A deeper understanding of the biology of therapy resistance is important for the development of optimal strategies to attain complete leukemia cures.

**Methods:**

Here, using scRNA-sequencing and ATAC-seq on primary chronic myelogenous leukemia (CML) patient samples, combined with bioinformatics analyses, we further examine the heterogeneity of a previously characterized *in vitro* imatinib-selected CD34 CD38 CML LSC population. We utilized a series of functional analyses, including single-cell metabolomic and Seahorse analyses, to validate the existence of the deepest quiescent leukemia initiators (LI) subset.

**Results:**

Current study revealed heterogeneity of therapy resistant LSC in CML patients and their existence of two functionally distinct states. The most deeply quiescent LI suppress the expression of MC-1, yet are highly dependent on fatty acid oxidation (FAO) for their metabolic requirements and ATAC-seq demonstrated increased chromatin accessibility in this population, all consistent with an extremely primitive, quiescent stemness transcriptional signature. Importantly, the specific CREB binding protein (CBP)/β-catenin antagonist ICG-001 initiates the differentiation of LSC, including LI, decreases chromatin accessibility with differentiation and increasing expression of MC-1, CD34, CD38 and *BCR-ABL1*, thereby sensitizing them to imatinib.

**Conclusion:**

We investigated the biological aspects related to LSC heterogeneity in CML patients and demonstrated the ability of specific small molecule CBP/β-catenin antagonists to safely eliminate deeply quiescent therapy resistant CSC. These observations may represent an attractive generalizable therapeutic strategy that could help develop better protocols to eradicate the quiescent LSC population.

**Keywords:** CML, LSC, LI, Metabolism, MC-1, CBP, ICG-001.

<footnote n="1">¹Department of Cancer Biology and Molecular Medicine, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA</footnote>

<footnote n="2">²The Integrative Genomics Core, Beckman Research Institute, City of Hope Medical Center, Duarte, CA 91010, USA</footnote>

<footnote n="3">³Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France</footnote>

<footnote n="4">⁴Department of Pediatrics, Division of Blood and Bone Marrow Transplantation, Children's Hospital Los Angeles, Los Angeles, CA 90027, USA</footnote>

<footnote n="5">⁵Center for Genetic Medicine Research, Children's National Hospital, Washington, DC 20010, USA</footnote>

<footnote n="6">⁶Institute of Health Equity and MCW Cancer Center, The Medical College of Wisconsin, Milwaukee, WI 53226, USA</footnote>

<footnote n="7">⁷Department of Clinical Proteomics, National Cancer Center Research Institute, Tokyo 104-0045, Japan</footnote>

<footnote n="8">⁸City of Hope, Comprehensive Cancer Center, Duarte, CA 91010, USA</footnote>
# 1. INTRODUCTION

Despite the remarkable clinical success in targeting the constitutively activated BCR-ABL tyrosine kinase in CML patients with the BCR-ABL antagonist imatinib, responses in advanced-phase CML are often short-lived, and patients generally undergo disease progression [1]. Resistance remains a significant concern, with approximately half of patients experiencing relapse within 12 months of therapy cessation [2 - 5]. Resistance and relapse in CML have been primarily attributed to the persistence of phenotypically defined quiescent LSC that are insensitive to Tyrosine Kinase Inhibitors (TKI) due in part to their low expression of BCR-ABL1 [6], and genomic instability in this subpopulation represents a significant concern in regards to the expansion of heterogeneity, therapy resistance and relapse [7]. Therefore, the ability to safely eliminate LSC is a critical therapeutic goal. Normal HSC and LSC have many more similarities than differences, which is not surprising as LSC likely arise via mutations in HSC, thereby complicating the safe elimination of LSC [8].

Although generally considered to reside within the CD34<sup>+</sup>CD38<sup>-</sup> population, the LSC population has been associated with various phenotypes [9 - 14]. Previously, utilizing an agnostic functional *in vitro* imatinib selection strategy with primary CML patient samples, we identified a deeply quiescent (96% in G0/G1), TKI therapy-resistant, LSC population that expressed very low levels of *BCR-ABL1*, and immunophenotypically were CD34<sup>+</sup>CD38<sup>-</sup>, which were functionally characterized as LSC *via* xenotransplantation into NOD.Prkdc<sup>scid</sup>.II2rg<sup>null</sup> (NSG) mice [14, 15]. We further demonstrated that forced differentiation of this population utilizing the Wnt modulating, CBP/β-catenin antagonist ICG-001, safely eliminated LSC both *in vitro* and *in vivo*, *via* activation from quiescence with increased *BCR-ABL1* expression, dramatically increasing the effectiveness of TKI chemotherapy [14, 15].

However, this TKI resistant population was still clearly heterogeneous [14]. Recent investigations based on single cell molecular approaches have revealed the existence of a distinct subset with a deep quiescence signature that persists during TKI-induced remission [16, 17]. However, these studies were performed on prospectively identified CML LSC based on cell surface CD34 expression.

In response to therapeutic pressure, leukemia cells and cancer cells more generally, can enter a drug-tolerant persister (DTP) state, a reversible epigenetic-driven phenotype, in which a subclonal population undergoes cell cycle arrest providing a source of non-genetic heterogeneity [18]. DTP cells undergo a metabolic shift to FAO and are phenotypically and transcriptionally related to the diapause state [18, 19]. Maintenance of quiescence *versus* activation is regulated both intrinsically and extrinsically, which includes metabolic modulation, with Wnt signaling playing an important regulatory role [20 - 22]. We now wish to report, using single-cell RNA-seq and functional single cell metabolomic profiling

that imatinib-resistant CML patient derived LSC contain a most deeply quiescent/persister subset, which is highly dependent on FAO, very similar to deeply quiescent normal HSC [23 - 25] and exhibits repressed mitochondrial complex I (MC-I) expression. These features allow for the persistence of LSC over decades [26].

# 2. MATERIALS AND METHODS

## 2.1. Patient Samples and Cell Lines

Bone marrow or blood samples from CML patients and AML patient samples were collected with informed consent according to procedures approved by the Institutional Review Board of the Keck School of Medicine, University of Southern California. Primary CML cells were cultured in RPMI1640 (GenClone, 25-506) supplemented with 5% FBS (VWR, 89510-186) for imatinib treatment and switched to QBSF-60 medium (ThermoFisher Scientific NC0823508) for treatment of drug-resistant cells with ICG-001. Dead cell removal (DCR) was performed using DCR kit (Miltenyi Biotec 130-090-101). De-identified list of patient samples used in the study is shown in Table S1.

Human CML cell lines K562 (ATCC CCL-243) and K562R (imatinib-resistant) (ATCC CRL-3344™) and mouse M2-10B4 stromal cells (ATCC CRL-1972) were purchased from the American Type Culture Collection (Manassas, VA) and cultured in RPMI1640 supplemented with 10% FBS. K562R cells were cultured with a medium containing 1 µM imatinib (LC Laboratories, I-5508).

## 2.2. scRNA-seq and Data Processing

scRNA-seq of CML patient imatinib-resistant cells was performed by the Integrative Genomics Core at COH. Raw sequencing data was processed using Cell Ranger V5.9.9 (10x Genomics). R package Seurat V2 was used for data analysis [27]. Pathway enrichment analysis was performed using Ingenuity Pathway Analysis (IPA) (QIAGEN). Metabolic differential activity using single-cell transcriptomes was conducted using the COMPASS package [28].

## 2.3. Bulk RNA-seq Analysis and Data Processing

CML patient cells were treated with 1 µM Nilotinib and/or DMSO for 7 days, respectively. After dead cell removal, drug-resistant cells were further treated with 10 µM ICG001 and/or DMSO for 24 h and collected for RNA-sequencing along with control cells. The data was analysed using CLC Genomics Workbench (QIAGEN) and downstream clustering analyses were performed using Partek Genomics Suite.

## 2.4. ATACseq Processing and Analysis

Approximately 50,000 cells from CML patient untreated sample, imatinib treated resistant population and column purified CCR7<sup>+</sup> cells along with CCR7<sup>+</sup> cells further treated with DMSO and ICG-001 for 6 days were submitted to the Integrative Genomics Core at COH for omni-ATACseq and data analysis. The transcription start site (TSS) heatmaps were generated using Partek Flow and chromosome view was generated using Partek Genomics Suite 7.0, respectively (Partek, Inc).
## 2.5. SCENITH and Flow Cytometry

SCENITH analysis was performed as described [29]. Briefly, cells were kept overnight in RPMI1640 media containing 5% FBS and pen/strep and next day pre-treated with the described inhibitors for 1h at 37°C. 10 µg/ml Puromycin was added to all conditions simultaneously with inhibitors. Cells were stained with a combination of Fc receptor blocking reagent and LIVE/DEAD Fixable Aqua stain (ThermoFisher Scientific, L34965) for 15' followed by incubation with primary antibodies CCR7-APC-eF780 (ThermoFisher Scientific, 47-1979-42) and CD74-PE (ThermoFisher Scientific, 12-0748-42) for 40'. Afterwards cells were fixed, permeabilized using Foxp3/Fixation Permeabilization solution (ThermoFisher Scientific, 00-5523-00), and incubated with intracellular anti-Puromycin-AF488 antibody, clone R4743L-E8 for translation staining.

Flow cytometry experiments were performed on Attune NxT (ThermoFisher) and analyzed using FlowJo (v10.6.1). For non-SCENITH flow cytometry analyses, live cells were selected on the basis of their non-permeability with 7-AAD (ThermoFisher Scientific, A1310). Additional antibodies used for flow cytometry analysis were anti-human CD34 APC (BD Biosciences 561209), anti-human CD38 BV450 (BD Biosciences 561378) and anti-human CD8a-PE (ThermoFisher Scientific, 12-0089-73).

## 2.6. MACS Enrichment of CCR7⁺ Cells

Patient samples were treated with 5 µM imatinib for 6-12 days to eliminate the S population. After DCR, approximately $4 \times 10^6 - 2 \times 10^7$ live resistant cells from bone marrow samples and $10^7 - 5 \times 10^8$ live cells from leukapheresis samples, respectively, were stained with biotinylated CCR7 antibody (ThermoFisher Scientific, 13-1979-82) and magnetically labeled with streptavidin microbeads (Miltenyi Biotec, 130-048-102). CCR7⁺ cells were positively selected using MS columns in combination with a MACS separator (Miltenyi Biotec) following the manufacturer's instructions.

## 2.7. Seahorse Metabolic Assays

Metabolic analysis of column enriched CCR7⁺ cells and flow-through fraction of CCR7⁺ cells was performed using Agilent XFe96 Analyzer (Agilent). Approximately 100K cells/well were seeded at least in triplicate in Cell-Tak (Corning, 324240) coated 96 well plates and Seahorse XF Long Chain FAO Stress Test analyses were performed both at basal conditions and after sequential injections of 4 µM etomoxir, 1.5 µM Oligomycin, 0.5 µM FCCP and 0.5 µM Rotenone + antimycin A using Seahorse XF Long Chain Fatty Acid Oxidation Stress Test Kit Agilent, (Agilent, 103672-100). Readouts were normalized against high contrast brightfield (HCBF) cell count imaged by Cytation 1 (Biotek) using Gen5 software (Agilent). Mitochondrial respiration and energy map at baseline were generated using Seahorse Analytics software (Agilent).

## 2.8. RNA Isolation and Real-time PCR

Total RNA was extracted using TRIzol reagent

(ThermoFisher Scientific, 10296028) and reverse transcribed using qScript cDNA Synthesis kit (QuantaBio, 95-047-100). Quantitative real-time PCR was carried out in triplicate using PerfeCTa SYBR Green SuperMix (QuantaBio, 95053-02K) on CFX Connect Real-Time PCR system (Bio-Rad). Relative transcript levels were normalized to those of *GUSB*. Primers used for qPCR are listed in Table S2.

## 2.9. Co-immunoprecipitation

200 µg of nuclear protein from each treated sample were diluted with 1mL of co-immunoprecipitation buffer and incubated with 1µg of CBP (Santa Cruz Biotechnology, sc-369) or normal rabbit IgG antibody (Santa Cruz Biotechnology, sc-3888) overnight at 4 °C. Then 50 µl of a 50% slurry of protein A-agarose beads were added to each diluted sample and incubated for 1h. After washing three times, agarose beads containing bound antibody/protein complex were suspended in 5% Sodium Deoxycholate and bound protein complexes were separated from the agarose beads using Illustra microspin columns (VWR, 95017-597) for proteomic analysis.

## 2.10. Proteomic Sample Preparation and Mass Spectrometry Analysis

Proteomics sample preparation was performed as previously described [30, 31]. The obtained peptide solution was subjected to nanoLC-Ultra 2D (AB SCIEX) coupled with a TripleTOF5600 (AB SCIEX) mass spectrometer. The masses of the peptides were determined using TripleTOF5600. MS peaks were detected and quantified using 2DICAL [32, 33]. The 2DICAL intensity data were converted to protein value averaging the intensity data of peptides derived from the protein.

## 2.11. Western Blotting

Cytoplasmic, nuclear fractions were isolated using NE-PER (ThermoFisher, 78835) per the manufacturer's protocol. Mitochondrial fractions were isolated using Mitochondrial Isolation Kit for Mammalian Cells (ThermoFisher, 89874) following manufacturer's instructions. Primary antibody dilutions used are the following: rat monoclonal anti-PHB2 (EMD Millipore, MABC953), mouse monoclonal α-tubulin (EMD Millipore, CP06-100UG), mouse monoclonal Lamin A/C (Santa Cruz Biotechnology, sc-376248), rabbit polyclonal NDUFS6 (Proteintech, 14417-1-AP). Secondary antibodies were HRP-conjugated donkey anti-goat (Abcam, ab6885) mouse IgGκ BP-HRP (Santa Cruz Biotechnology, sc-516102), and goat anti-rabbit IgG-HRP (Santa Cruz Biotechnology, sc-2357).

## 2.12. FISH Assay

Fluorescent *in situ* hybridization (FISH) was performed at the Cytogenetic Core facility at COH. *In vitro* imatinib selected resistant cells were column purified for CD8a using anti-human CD8a Biotin (Thermofisher Scientific, 12-0089-73) antibody. CD8a⁺ cells and the flowthrough as CD8a⁻ fraction were subjected to mini-harvest procedure.
<figure><img src="image_2.png" /><figcaption>**Fig. (1).** Imatinib-resistant CML cells are heterogeneous. (A) Schematic representation of the sample preparation for scRNA-sequencing. (B) *In vitro* imatinib selected population have CD34+CD38- immunophenotype. (C) and (D) Unsupervised hierarchical clustering and tSNE analysis of scRNA-seq data. (E) Dot plot depiction of feature genes.</figcaption></figure>

**2.13. Quantification and Statistical Analysis**

Statistical testing has been performed using GraphPad Prism. RNA-seq, scRNA-seq, ATAC-seq, proteomics data and published datasets were analysed using the respective packages and software described.

**3. RESULTS**

**3.1. scRNA-seq Reveals Heterogeneity in the Therapy-resistant CML Population**

To better assess the heterogeneity of therapy-resistant (R)

CML cells, we performed scRNA-seq analysis of imatinib-resistant cells from a CML patient sample (Fig. 1A). Consistent with our previous studies [14], imatinib treatment in vitro resulted in the elimination of the sensitive (S) and selection of the R cells, which were negative for the expression of both CD34 and CD38 (Fig. 1B). Overall 1279 cells passed quality control criteria, with a total of 12558 genes being expressed (Fig. S1A-C). Unsupervised hierarchical clustering revealed the existence of two major groups of cells, hereby termed Group 1 and Group 2, which could further be subdivided into clusters c0-c2 for Group 1 and c3 and c4 for Group2 (Fig. 1C). Frequencies of cells in each cluster were 37% (c0), 22% (c1),
13% (c2), 24% (c3) and 5% (c4), respectively, and c0 comprised 52% of all cells within Group 1. t-Distribution stochastic neighbour embedding (t-SNE) analysis supported the presence of 5 clusters (c0-c4) (Figs. 1D, S1D), each of which could be represented by specific marker genes as shown in the

dot plot (Fig. 1E). Noteworthy, the R population was characterized by overall strong expression of CD3, CD44 and CD52 in all clusters, however no expression of CD4 or CD19 was observed in any cluster, whereas expression of CD8a was restricted exclusively to the c4 cluster (Fig. S2).

<figure><img src="image_3.png" /><figcaption>**Fig. (2).** c0 cells show very limited transcriptional and translational activity. (A) Comparison of down- and upregulated genes (-1.2>FC>1.2, FDR<0.05) in clusters from scRNA-seq. (B) and (C) IPA pathway enrichment comparisons of differentially expressed genes in clusters. Red dotted lines in B represent threshold of significance (p<0.05). (D) Translation analysis of CML patient LSC (R) population subsets using SCENITH. Cells were gated for R fractions and puromycin intensities were analyzed in CCR7<sup>+</sup> LI vs CCR7<sup>+</sup> compartments refer to the deeply-quiescent, yet heterogeneous Group 1 population as Leukemia Initiators (LI).</figcaption></figure>
Thus, distinct expression profiles, yet with an interrelated continuum, suggested that drug-resistant LSC exist in two functional states represented by the cells of *c0-c2* and *c3*.

## 3.2. LI Exhibit Low Metabolic Profile and Protein Synthesis

To further investigate the dynamic changes in expression in LSC, we first excluded from the analysis the *c4* cells due to their non-CML status confirmed by the absence of BCR-ABL1 via FISH analysis (Fig. S3A-S3D). We subsequently identified the differentially expressed genes in each cluster (*c0-c3*). There is a gradual increase in gene expression from *c0* to *c3*, with the highest number of upregulated genes (N=1786) in *c3* and the lowest in *c0* (N=19) (Fig. 2A). This suggested that the *c0* cluster represents the most deeply quiescent subpopulation within the LSC, with the lowest level of transcriptional activity. Pathways associated with protein synthesis and mitochondrial activity were strikingly suppressed in *c0* (Fig. 2B). Furthermore, compared to *c0*, the *c1* population demonstrated an increase in stress response pathways, whereas the *c2* and, even more so, the *c3* cells exhibited a dramatic increase in pathways associated with metabolism and protein synthesis (Fig. 2C). Protein synthesis is a well-known indicator of global metabolic activity [29, 34], therefore the pathway analysis is fully consistent with the very low transcriptional activity exhibited by the deeply-quiescent *c0* cells. Henceforth, we refer to the deeply-quiescent, yet heterogeneous Group 1 population as Leukemia Initiators (LI).

To experimentally confirm the low metabolic activity of LI, we utilized the recently developed single cell energetic metabolism by profiling translation inhibition (SCENITH) analysis [29]. SCENITH uses puromycin incorporation as a readout for the level of protein synthesis to analyze the metabolic profiling of heterogeneous cells by flow cytometry. Based on the sc-RNAseq data, we selected the cell surface expression of CCR7 and CD74 as markers to differentiate two different populations within the R cells (Fig. 1C, 1E). We designated Group 1/LI as CCR7⁺ and CCR7⁺CD74⁺ and the more differentiated Group 2 fraction of LSC as CCR7⁻ and CCR7⁻CD74⁺ (Fig. 1C and 1E). Consistent with the computational analyses, results from 9 CML patient samples and K562 cells demonstrated increased protein synthesis in the CCR7⁺ compared to the CCR7⁺ fraction, and the use of CCR7⁺CD74⁺ versus CCR7⁻CD74⁺ further enhanced that difference (Fig. 2D). Gating strategies for flow cytometry are shown in the supplemental data (Fig. S4).

Taken together, our combined bioinformatics and functional studies denote a particularly low metabolic profile and deeper quiescence exhibited by LI compared to the Group 2 population. Furthermore, the differences in gene expression and pathway enrichments indicate that the *c0* cluster represents the most deeply quiescent non-cycling persister LI, which transitions through the *c1-c3* clusters.

## 3.3. LI Rely on FAO as Their Main Energy Source

FAO has been found to be a major regulator of quiescence in the context of both normal somatic stem cells (SSC) and cancer stem cells (CSC) [23, 35, 36]. Therefore, we anticipated

that LI would be more dependent on FAO as their source of energy. To test this hypothesis, we first analyzed our scRNA-seq data using Compass, a recently described algorithm that derives information on cellular metabolic states from single-cell transcriptomic data [28]. Compass score differences in glycolysis and fatty acid (FA) metabolism in the resistant LSC were compared between the most quiescent stem-like CCR7⁺CD74⁺ population in the *c0* cluster and the least quiescent CCR7⁻CD74⁺ population in the *c3* cluster. Compass analysis revealed that whereas glycolytic reactions were totally dominant in the CCR7⁺CD74⁺ compartment, FAO metabolism demonstrated a more balanced activity between the two populations, with long-chain fatty acyl-CoA synthetase (ACSL) and carnitine O-palmitoyltransferase (CPT) being more active in the deeply-quiescent CCR7⁺CD74⁺ LI (Fig. 3A). ANOVA analysis of scRNA-seq data further showed that the expression of both *CPT1A* and *CPT2* were higher in the CCR7⁺CD74⁺ LI compared to the CCR7⁻CD74⁺ cells (Fig. 3B). The differences revealed by both the Compass and ANOVA analyses, however, were not statistically significant, likely due to the overall quiescent nature of these drug-resistant cell populations and the continuum of their relationship.

Next, we used SCENITH to test whether LI are indeed more reliant on FAO/AAO (Amino Acid Oxidation) to meet their metabolic requirements. As anticipated, the S population was considerably more metabolically active compared to the R population (Fig. S5A). Consistent with their quiescent status, the R cells showed a greater capacity to use FAO/AAO as their source for ATP production, whereas the S population had higher glycolytic capacity and glucose dependence (Fig. S5B). To demonstrate that the LI within the R population is more dependent on FAO/AAO, we gated on R cells and compared the translation level in the CCR7⁺ versus CD74⁺ subsets. In line with their more deeply-quiescent nature, CCR7⁺CD74⁺ LI from 4 different patients demonstrated greater dependence on mitochondrial metabolism and FAO/AAO capacity, whereas the less quiescent CCR7⁻CD74⁺ cells exhibited higher glucose dependence and glycolytic capacity (Fig. 3C).

Finally, we investigated the metabolic profile of the LI versus CCR7⁺ cells by Seahorse (Fig. 3D). Cells were treated with imatinib for 6-12 days prior to column purification to ensure elimination of S cells (Fig. S6). Consistent with the overall low metabolic status of the R cells (Fig. S4A), Seahorse analysis revealed low activity in both the CCR7⁺ and CCR7⁻ fractions of imatinib-resistant cells. Nevertheless, energy maps from the 4 different patient samples consistently demonstrated lower glycolytic rates and the more deeply quiescent nature of CCR7⁺ LI (Fig. 3E). This is fully consistent with the previously observed lower metabolic rate (Fig. 2D) and the essentially non-glycolytic status of the LI compared with the CCR7⁻ cells that primarily utilize glycolysis (Fig. 3A and C). To further investigate the greater reliance of the LI on FAO, we used etomoxir, an inhibitor of CPT1A, to suppress mitochondrial FAO. Drug resistant CCR7⁺ and CCR7⁻ cells from 3 CML patient samples showed comparable acute responses to etomoxir, however, CCR7⁺ LI revealed a stronger response under conditions of maximal respiration (Fig. 3F). Thus, experimental analyses of the metabolic status of the respective
CCR7⁺ and CCR7⁻ R subpopulations using both SCENITH at the single cell level and Seahorse on the bulk populations

confirmed the deeply-quiescent nature of LI within the TKI-resistant LSC and their heavy reliance on FAO as their primary energy source.

<figure><img src="image_4.png" /><figcaption>**Fig. (3).** LI rely on FAO for their metabolic requirements. (A) Compass-score differential activity test between Group 1 (CCR7⁺CD74⁺) and Group 2 cells (CCR7⁻CD74⁺) from single cell transcriptomics data. (B) Comparison of *CPT1A* and *CPT2* expressions in Group 1/LI and Group 2 from scRNA-seq data. 2. (C) Metabolic profiles of Group 1/LI and Group 2 subsets of R population obtained by SCENITH. \* p<0.05, n=3. (D) Schematic representation of sample preparation for Seahorse analysis. Imatinib-resistant LSC from CML patient samples were isolated using biotin-labeled CCR7⁻ antibody and MACS column purification. (E) Energy maps of TKI resistant CCR7⁺ and CCR7⁻ cells from Seahorse analyses. (F) Seahorse Long chain FAO stress test of imatinib-resistant CCR7⁺ and CCR7⁻ cells. \*\* p<0.01, n=3.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. (4).** CBP/β-catenin antagonism initiates differentiation of quiescent CML LSC. (A) ICG-001 induces differentiation by selectively blocking the interaction between CBP and β-catenin. (B) Schematic representation of sample preparation for RNA-seq. (C) Semi-supervised hierarchical clustering of common genes affected by both TKI therapy and ICG-001 treatment in RNA-seq analysis of CML patient sample. (D) Pathway enrichment analysis of proteomics data from K562 cells treated with imatinib and/or DMSO followed by 10 µM ICG-001 for 24h. K562R cells were treated with 10 µM ICG-001 for 48h. (E) CPT1a and CPT2 expression changes in proteomics data of K562 cells treated with imatinib for 7 days followed by 10 µM ICG-001 treatment for 24h, and proteomics data of K562R cells treated with 10 µM ICG-001 for 24h and 48h (lower panel). (F) FACS profiling of CCR7 expression in LSC of patient sample treated with ICG-001 and/or DMSO for 24h (G FACS analysis) of the percentage of CCR7⁺ cells in LSC of patient samples treated with ICG-001 or DMSO for 24h. (H) Translation dynamics of vehicle (DMSO) and ICG-001 treated LSC using SCENITH. (I) FACS profiling of LSC after treatment with ICG-001 on stroma and under hypoxia for 14 days (upper panel) and 28 days (lower panel). (J) FACS analysis of CD34 and CD38 expressing cells after treatment of LSC with ICG-001 and/or DMSO for 28 days. (K) and (L) qPCR analysis of *BCR-ABL1* expression in LSC (R) (K) and CCR7⁺ LI (L) after 14 days of treatment with ICG-001 and/or DMSO.</figcaption></figure>
### 3.4. CBP/β-Catenin Antagonist ICG-001 Initiates the Differentiation Of Quiescent Therapy Resistant LSC Including LI

Inhibition of CBP/β-catenin mediated transcription, with a subsequent switch to its closely related paralog p300/β-catenin driven transcription, results in a concomitant increase in cellular differentiation at the expense of self-renewal (Fig. 4A) [37]. To further explore the effects of CBP/β-catenin antagonism on drug resistant LSC, we first utilized RNA-seq and proteomics analyses to examine the global effects of the antagonism at both the transcriptional and translational levels. For RNA-seq, in vitro therapy resistant CML patient cells were treated with either vehicle or Nilotinib (Fig. 4B). Semi-supervised hierarchical clustering of genes commonly regulated by both TKI and ICG-001 revealed a global reversal of therapy responsive genes with 24h ICG-001 treatment (Fig. 4C). For the proteomics analysis, we treated K562 cells with imatinib and/or DMSO, with subsequent treatment of the resistant cells with ICG-001 and/or DMSO for 24h. Separately, drug-resistant K562R cells were treated with ICG-001 and/or DMSO for 24h and 48h (Fig. S7A). Remarkably, the top enriched pathway for the proteins repressed by imatinib was Ribosome (Fig. 4D). Consistent with the differentiation promoting effect of CBP/β-catenin antagonists, Ribosome and Spliceosome were amongst the top pathways upregulated by ICG-001 in both the imatinib treated K562, as well as in the K562R cells (Fig. 4D and S7B). Furthermore, in line with an increased role for FAO in energy production (Fig. 3A-3C and 3F), proteomics data from K562 cells treated with imatinib increased their expression of both CPT1a and CPT2, whereas treatment with ICG-001 opposed the increase. Similarly, proteomics data from K562R cells treated with ICG-001 decreased the expression of CPT2 (Fig. 4E). No CPT1a protein was detected in the K562R proteomics. Consistent with the critical role of OXPHOS in therapy resistant quiescent LSC in myeloid leukemia [16], imatinib resistance was associated with an increase in an array of mitochondrial proteins while dramatically decreasing the levels of glycolytic enzymes and treatment with ICG-001 tended to reverse these changes (Fig. S7C and S7D). These effects on metabolism induced by differential Kat3 coactivator usage in CML cells are consistent with the roles of the Kat3 N-termini, providing highly evolutionary conserved hubs to integrate cellular metabolism with cellular differentiation and status [30].

To further demonstrate the differentiating effects of CBP/β-catenin antagonism on the continuum of LSC, we used several different CML patient samples maintained under different culture conditions. All patient samples were first treated with imatinib to eliminate the S fraction. As judged by FACS analysis, after 24h treatment with ICG-001 under normoxia, R cells showed a decrease in the percentage of CCR7⁺ cells (Fig. 4F and 4G). We next co-cultured therapy-resistant R cells on stroma and used SCENITH to demonstrate a gradual increase in translation over time following ICG-001 treatment (Fig. 4H). To more effectively mimic the bone marrow microenvironment in which the drug resistant LSC resides in vivo, we co-cultured R cells on stromal cells under hypoxia.

Treatment of R cells for 14 days resulted in a decreased R population and reemergence of the S population (Fig. 4I, upper panel) with a concomitant increase in translation and re-expression of CD34 and CD38 (Fig. S8A and S8B). Prolonging the duration of treatment to 28 days resulted in near complete elimination of the R cells, with most of the R cells transitioning to the S population (Fig. 4I, lower panel) accompanied by a small increase in the expression of CD34 with a more dramatic increase in CD38 (Fig. 4J) treatment. Consistent with our previous investigations [15] and (Fig. 1B), after prolonged imatinib treatment, no expression of CD34 and CD38 was detected prior to ICG-001 treatment (Fig. S8C).

TKI resistant LSC express very low or even undetectable levels of *BCR-ABL1* transcripts [38]. The detection of the *BCR-ABL1* transcript by qRT-PCR is the gold standard method for minimal residual disease monitoring [39]. In CML patients, continuous TKI inhibition of BCR-ABL1 can be effective for many years to prevent disease progression by preventing CML cell proliferation; however, it is insufficient to eradicate quiescent LSC [3]. Consistent with its differentiation promoting effects, 14-day treatment of R cells with ICG-001 on stroma under hypoxia increased the expression of *BCR-ABL1*, thereby re-sensitizing the cells to TKI treatment (Fig. 4K), similarly to what we have previously observed [15]. To further confirm that CBP/β-catenin antagonism can specifically activate and initiate the differentiation of even the most deeply quiescent LI, we treated MACS column enriched CCR7⁺ cells with ICG-001 or DMSO for 14 days. As compared to control treatment ICG-001 treatment significantly increased the expression of *BCR-ABL1* in cells derived from CCR7⁺ LI (Fig. 4L). Noteworthy, similar treatment of the MACS column enriched CD8a⁺c4 non-CML cells did not result in the expression of *BCR-ABL1*. Taken together, these results support that deeply quiescent CCR7⁺ CML cells are LI/persisters. Importantly, our results further confirm that specifically antagonizing the CBP/β-catenin interaction with ICG-001 initiates cellular differentiation and the transition from a quiescent to an activated state with depletion of LSC [15, 40-42].

### 3.5. LI are Elevated in the CD34⁺ Fraction of Both Myeloid LSC and Normal HSC

To further characterize the LI population, we investigated the expression of the most prominent transcripts from *c0,i.e.LRRN3*, *ACTN1*, *FHIT*, *SATB1* and *RGCC*, (Fig. 1C and 1E) in the LI from 2 CML patient samples and K562 cells. Cells were treated with imatinib and enriched for CCR7 as previously described (Fig. 3D). For patient #30, all *c0*-specific transcripts demonstrated increased expression in the CCR7⁺ LI. For patient #102, we observed increased expression of *LRRN3*, *SATB1* and *ACTN1*, but not for *RGCC* and *FHIT*. Noteworthy, although present at extremely low levels, *SATB1* expression was 7-fold higher in the CCR7⁺ fraction of K562 cells and *FHIT* expression was increased 29-fold in the CCR7⁺ fraction from patient #30 (Fig. 5A). Bulk RNA-seq from patient #30 demonstrated that imatinib treatment significantly eliminated *CD34⁺* expression and increased message for *CCR7*, *LRRN3* and *RGCC* (Fig. 5B). We further examined the expression of
c0 transcripts in CML patient samples from a previously published study [10]. Normalized expression values of sorted Lin⁻CD34⁺, Lin⁺CD34⁺ and Lin⁺CD34⁺ fractions from CML patients (pooled from 12 chronic phase CML patients) and healthy donors (pooled from 12 healthy donors) were extracted from GSE11675. Similar to what we have previously described [14], the phenotype of R cells was CD34⁺CD38⁻ (Figs. **1B**, **5B**)

and S7C) therefore we compared the Lin⁻CD34⁺ cells with the Lin⁻CD34⁺ cells. The expression levels of the five c0 transcripts were all increased in the Lin⁻CD34⁺ compared with the Lin⁺CD34⁺ cells (Fig. **5C**). Thus, the CD34⁺ CML LSC expression profile defined from our scRNA-seq data appears to be a commonly shared feature in CML patient samples.

<figure><img src="image_6.png" /><figcaption>**Fig. (5).** CCR7 is elevated in CD34⁺ fraction of myeloid LSC and HSC. (A-C) qPCR analysis of c0-specific transcripts in CCR7⁻ versus CCR7⁺ fractions of LSC (A), bulk RNA-seq data of LSC (B) and in GSE11675 (C). (D) FACS analysis of CCR7 expression in AML, CML patients and healthy donors. (E) CCR7 expression in CML and normal donor samples from GSE11675. (F) CCR7 expression in CD34⁺, CD38⁺ fractions of AML patients. ** p<0.01, *** p<0.001. (G) Metabolic profiling of AML patient sample using SENITH. (H) Mitotracker CMTMRos staining of AML, CML and healthy donor samples.</figcaption></figure>
Normal HSC and LSC share many characteristics, which is understandable, as LSC in both CML and acute myeloid leukemia (AML) originate from HSC *via* mutations [8]. Therefore, we anticipated that a similar $CCR7^+$ deeply quiescent LI would also exist in healthy donors as well as in AML patients, despite the fact that AML is genetically a more clonally diverse disease. To examine this hypothesis, we first confirmed that $CCR7^+$ cells reside in CD34$^+$CD38$^-$ fractions of AML and CML patients and healthy donors (Fig. 5D). Comparison of CML patients and healthy donors from the GSE11675 study [10] revealed higher expression of $CCR7$ in Lin$^+$CD34$^+$ cells compared to Lin$^+$CD34$^+$ fraction in CML and even more so in healthy donor cells (Fig. 5E). Next, we examined AML patient samples using the published data deposited to GSE76008. This dataset contains gene expression profiling from 83 primary AML patients, sorted based on the expression of CD34 and CD38 and functionally characterized as LSC$^+$ or LSC$^-$ by xenotransplantation into NOD.Pmkdc<sup>scid</sup>.Il2rg<sup>null</sup> (NSG) mice [11]. $CCR7$ expression was significantly associated with the CD34$^+$CD38$^-$ phenotype, regardless of their categorization as LSC$^+$ or LSC$^-$ (Fig. 5F). To further confirm the functional similarity of the $CCR7^+$ cell population in myeloid leukemias, we performed metabolic profiling of an AML patient sample using SCENITH. Interestingly, similar to what we observed in CML patient samples (Fig. 3C), the $CCR7^+$ cells from the CD34$^+$CD38$^-$ fraction demonstrated higher FAO/AAO capacity and mitochondrial dependence compared to the $CCR7^-$ cells. The $CCR7^+$ cells further demonstrated higher glucose dependence and glycolytic capacity (Fig. 5G). Finally, Mitotracker Orange CMTMRos staining revealed that $CCR7^+$CD34$^+$CD38$^-$ cells displayed evidence of elevated mitochondrial activity in both AML and CML patient samples, as well as in a healthy donor (Fig. 5H). Thus, results obtained from both CML and AML patients support the proposal that within a heterogeneous population of myeloid LSC, a deeply quiescent population of CD34$^+$ LI with elevated expression of $CCR7$ and enhanced mitochondrial activity exists, similar to normal quiescent HSC.

## 3.6. LI Exhibit Suppressed MC-1 Expression to Maintain ROS-Free Mitochondrial Metabolism

Next we speculated that deeply-quiescent LI increased their mitochondrial activity, yet without production of ROS, via suppression of MC-1, similar to a very recently described oocyte ROS-mediated toxicity evasion mechanism [26]. Indeed, RNA-seq analysis showed that drug-resistant CML cells exhibit decreased levels of MC-1 transcripts which showed a trend towards reversal when treated with ICG-001 for 24h (Fig. 6A). scRNA-seq data confirmed that within the R population, MC-1 transcripts were significantly decreased in the most deeply quiescent c0 cluster especially compared to the more differentiated and activated c3 cells (Fig. 6B). Next, we used ATAC-seq to measure chromatin accessibility dynamics in the drug-resistant population. As anticipated based on their “stem-like” status, ATAC-seq revealed increased global accessibility of imatinib treated R cells compared to untreated cells, which was particularly evident in the $CCR7$ enriched LI, the most deeply quiescent LSC subpopulation (Fig. 6C and 6D) [43]. Treatment of $CCR7^+$ LI with ICG-001 for 6 days resulted

in reduced global chromatin accessibility as compared to DMSO treated LI, reflecting enhanced differentiation (Fig. 6E). Consistent with mostly increased expression of MC-1 transcripts after 24h of ICG-001 treatment (Fig. 6A), LI from the same patient demonstrated elevated ATAC-seq peaks for numerous MC-1 transcripts after 6 days of treatment with ICG-001 (Fig. 6F). Finally, we assessed expression of MC-1 genes in published studies. Consistent with suppressed MC-1 expression in quiescent cell populations, mean expression of 28 MC-1 genes detected in GSE11675 study was lower in the Lin$^+$CD34$^+$ fraction of both CML patient and healthy donor samples (Fig. 6G) [10], and $CCR7$ was negatively correlated with the mean expression of 48 MC-1 transcripts in the CD34$^+$CD38$^-$ fraction of AML patients (Fig. 6H) [11].

Remarkably, supporting the generalizable differential role of the Kat3 coactivators CBP and p300 in stem cell maintenance and differentiation (Fig. 4A), proteomics data from p300 S89A knock-in mice brain (manuscript submitted) and intestinal tissue [31] also demonstrated decreased expression of MC-1 proteins. p300 Serine 89 is phosphorylated by several kinase cascades [44, 45] and the S89A point mutation affects the ratio of $\beta$-catenin associated with CBP versus p300, favoring CBP/$\beta$-catenin driven transcription with p300/$\beta$-catenin association being significantly decreased in S89A knock-in mice [30, 31]. All 6 differentially expressed MC-1 proteins detected in brain tissue and all 3 proteins detected in intestine (-1.2>FC>1.2, FDR<0.05) showed downregulation in p300 S89A knock-in mice compared to their wild-type littermates (Fig. 6I).

These data support the concept that remodeling the mitochondrial electron transport chain by eliminating MC-1 may be a universal feature utilized by long-lived quiescent stem cells to maintain their viability. Chromatin structure controls self-renewal and differentiation in the hematopoietic lineage and targeting chromatin accessibility utilizing CBP/$\beta$-catenin antagonists may be generally therapeutically beneficial in hematologic disorders [41, 46].

## 3.7. Imatinib Treatment Enhances the Binding of PHB2 to CBP in K562 Cells And ICG-001 Opposes this Effect

To further investigate the downstream mechanistic effects of CBP/$\beta$-catenin antagonism that are associated with the transition from quiescence to an activated state, we undertook a targeted analysis of CBP associated proteins in K562 cells (Fig. 7A). A total of 211 CBP associated proteins were detected with 20 proteins showing enhanced association and 34 proteins revealing decreased association with CBP after imatinib treatment. Subsequent treatment with ICG-001 resulted in the reduced association of 12 out of the 20 proteins (60%) with enhanced CBP association. Drug induced decreased association with CBP demonstrated lesser (27%) reversal with ICG-001 treatment (Fig. 7B). Among the proteins demonstrating increased CBP association after imatinib treatment that were reversed by ICG-001, we focused on PHB2 (Fig. 7C). PHBs are highly conserved scaffolding proteins with pleiotropic attributes mediated in a cell-compartment- and tissue-specific manner. They have been shown to shuttle between the nucleus and mitochondria, thereby assisting with the coordination of
<figure><img src="image_7.png" /><figcaption>**Fig. (6).** LI exhibit suppressed MC-1. (A and B) Heatmap of MC-1 transcripts in CML LSC from bulk RNA-seq (A) and scRNA-seq (B) data of CML patients. (C and D) Global chromatin accessibility tracks (C) and TSS plot (D) of all peaks. Bulk population, imatinib-treated LSC and column-purified CCR7+ LI fractions from CML patient samples were subjected to ATAC-seq. (E) TSS plot of LI treated with ICG-001 and DMSO for 6 days. (F) Gene tracks showing ATAC-seq reads at representative MC-1 loci. (G) MC-1 transcripts expression in pooled CML patient sample and healthy donors. (H) Correlation of MC-1 with CCR7 expression in CD34⁺CD38⁻ fraction of AML patients. (I) Expression of MC-1 proteins in proteomics data of p300 S89A knock-in mice (n=3) vs wild-type littermates (n=3). Shown are differentially expressed proteins (FDR * p<0.05, ** p<0.01, *** p<0.001).</figcaption></figure>

nuclear/mitochondrial events during differentiation [47]. Therefore, we proposed that PHB2, consistent with its known role as a nuclear receptor corepressor [48], is primarily nuclear localized during quiescence and *via* binding to CBP, transcriptionally represses target genes required for exit from

quiescence. ICG-001 induced differentiation and exit from quiescence, *via* binding to the N-terminus of CBP, results in the collapse of the repressive complex and PHB2 relocation, principally to the mitochondria, whereby it can stabilize the mitochondrial MC-1 complex [49] (Fig. 7D). Indeed,
immunoblot analysis revealed the presence of PHB2 in the cytosol, nucleus and mitochondria in control treated cells, whereas 1 µM imatinib treatment for 5 days significantly increased PHB2 nuclear localization (Fig. 7E). ICG-001 treatment of the imatinib treated K562 cells for 24h decreased nuclear PHB2, with almost exclusive relocalization to the mitochondria after 72h. Treatment of K562R cells with ICG-001 for 72h resulted in a similar mitochondrial

accumulation of PHB2 (Fig. 7E). Uncropped images and densitometry readings/intensity ratio of each band are provided in Fig. S9. Thus, the localization and function of the highly conserved PHB scaffolding proteins, which play an important role in the coordination of transcription and metabolic function during changes in cellular status, are regulated via association with Kat3 coactivators.

<figure><img src="image_8.png" /><figcaption>**Fig. (7).** Role of PHB2 in CBP/β-catenin antagonism mediated differentiation. (A) Schematic representation of the sample preparation for targeted proteomics analyses. K562 cells were treated with imatinib or DMSO for 5 days prior to ICG-001 treatments. Control (IgG) antibody and anti-CBP antibody were used for immunoprecipitation followed by proteomics 2DICAL LC/MS analyses (B) Pie-charts showing changes in CBP associated proteins after imatinib and subsequent ICG-001 treatments. Data has been normalized to control IgG precipitated samples. (C) Heatmap of CBP associated proteins reversed by ICG001. (D) Model illustrating role of PHB2 in quiescence and differentiation. (E) Western blot analysis of nuclear/mitochondrial shuttling of PHB2. 10 µg of protein loaded per lane.</figcaption></figure>
4. DISCUSSION

The majority of CML patients achieve remission with TKI
treatment, however only a minor subset of patients attains deep
molecular remissions and can discontinue TKI treatment
without disease recurrence. Yet the persistence of BCR-ABL+
cells can still be detected even in this select group [50], and the
resistance of quiescent, primitive LSC to TKIs remains a major
hurdle to curing CML [2, 4, 5, 51]. It is becoming increasingly
evident that drug-resistant cells that survive conventional
cancer therapies and evade anti-tumor immunity are
heterogeneous, including quiescent, senescent, and diapause-
like persister cells that collectively contribute to disease
recurrence [19, 52 - 54].

In the current study, we employed our previously utilized
agnostic pharmacological selection of CML patient cells to
identify LSC. Although essentially all clinical and
experimental protocols are targeted to enrich CD34⁺ cells,
recent studies in both the murine and human systems have
indicated that stem cells capable of multilineage repopulation
do not necessarily express detectable levels of cell surface
CD34. These studies challenge the prevailing dogma that all
human repopulating cells are CD34⁺ [55]. Contrary to
previously published studies where CML LSC have been
characterized as being CD34⁺CD38⁻, in our study, cells
surviving extended imatinib treatment were consistently CD34⁻
CD38⁻, in line with our previously published studies [14, 15].
CD34⁻ cells are known to represent a kinetically and
functionally “resting” subset of HSC that needs to be activated
to transition to a CD34⁺ population, which has higher
proliferative and engraftment potential [56, 57]. Thus, the
CD34⁺ fraction of LSC represents an activated, cycling stem
cell population, whereas the more deeply quiescent drug-
resistant CD34⁺ fraction makes critical contributions to disease
persistence and relapse. We previously robustly demonstrated
that the R population was indeed enriched in leukemia
initiating stem cells using multiple functional assays
(engraftment in NSG mice with subsequent re-expression of
BCR-ABL1, CFC assay, etc.) [15]. Similar Lin⁻CD34⁺ LSC with
intrinsic resistance to imatinib have been previously
characterized from CML patients at diagnosis by Lemoli et al.
[10].

We further analyzed the TKI resistant CD34<sup>+</sup>CD38<sup>-</sup> CML LSC population by sc-RNAseq and demonstrated the existence of two major subgroups, Group 1 (c0-c2) and Group 2 (c3-c4). Distinct expression profiles with an interrelated continuum suggested that the cells in Group 1/LI are the most deeply quiescent, akin to the G population found in normal SSC that includes long term HSC, which showed a decrease in the production of ribosomal RNA and proteins, leading to reduction of their volume and size [58]. The extremely limited protein synthesis in c0 suggests that c0 represents the most quiescent non-cycling persister cells that can transition to cycling persisters represented by cells in c1-c3. Indeed, compared to c0, c1-c3 cells demonstrated increased expression of stress response pathways, including the NRF2-mediated oxidative stress response pathway, akin to cycling persisters described in lung cancer cells, which also showed upregulated expression of genes involved in anti-oxidant defense [19].

Furthermore, a dramatic increase in pathways associated with
metabolism and protein synthesis in c3 (Group 2) cells is
reminiscent of the “alerting” mechanism described in SSC,
where mTORC activation was sufficient for the transition of
quiescent cells (G) into activated (G<sub>Alen</sub>) [58]. Similarly,
transition of cells from Group 1 to Group 2 is likely related to a
process used by HSC to respond rapidly under conditions of
injury and stress. Increased expression of CD74, the HLA class
II histocompatibility antigen gamma chain and MIF receptor, is
a marker of inflammation in the Group 2 cells. This is likely a
“signal” of their “alerted” state. Interestingly, Group 1 cells
were enriched in expression of the chemokine homing receptor
CCR7. Despite growing evidence of the role of CCR7 in the
pathophysiology of blood cancers [59], its role in the deeply-
quiescent Group 1 cells in relation to their microenvironmental
niche remains unclear. Recently, primitive imatinib resistant
LSC have been described in spleenic red pulp [60]. This
population resembled the Group 1 cells and it is plausible that
CCR7 contributes to niche formation for LSC maintenance in
the spleen and potentially other niches. Furthermore, we
speculate that the c4 population, whose non-CML status was
confirmed by FISH analysis, may represent an “exhausted”
NK/ILC1-like population based on their expression profile,
akin to cells that control LSC fate in AML [61], although this
requires further analysis. In both SSC and CSC, FAO has been
found to be the major energetic contributor to the maintenance
of their quiescent status [23, 35]. Our comparison at the single
cell level, both experimentally by SCENITH and
computationally *via* COMPASS and on the bulk populations by
Seahorse and proteomics, confirmed the deeply quiescent
nature of the LI in the TKI resistant LSC and reliance on FAO
as their primary energy source. It is fascinating that the c0 cells
do not express MC-1 genes and rely on FAO to minimize the
deleterious ROS effect to provide for long term quiescent
survival, a mechanism potentially utilized generally by long
lived stem cell populations [26]. Overall, our results show that
heterogeneity within the therapy resistant cell population
represents a transcriptionally and translationally dynamic phase
transition to a more activated state, in which protein synthesis
is tightly regulated to allow for the maintenance of a deeply
quiescent CCR7⁺ subset of LSC.

Imatinib resistance correlates with the emergence of LSC
associated with increased nuclear catenin levels and enhanced
Wnt/β-catenin transcription [62]. To activate gene
transcription, β-catenin must recruit one of the two Kat3
coactivators, CBP or p300, along with additional components
of the basal transcriptional apparatus [63 - 65]. Kat3
coactivators uniquely acetylate histone H3K27 [66] and CBP
and/or p300 occupancy, in conjunction with acetylated H3K27
(H3K27Ac) demarcates genomic regions possessing enhancer
(E) and super-enhancer (SE) architecture [67 - 69]. β-catenin,
independent of its interaction with TCF/LEF transcription
factors, rather *via* its N and C-terminal intrinsically disordered
regions, can be recruited into E/SE-driven complexes and
subsequently recruit either CBP or p300 [70]. Differential
recruitment of CBP or p300 to licensed enhancers was already
noted over twenty years ago [71]. This differential recruitment
perturbs the epigenetic landscape of stem cells and is
responsible for the distinct roles of the Kat3 coactivators in
self-renewal versus differentiation, with CBP, but not p300, being essential for stem cell maintenance, whereas p300 is critical for initiation of differentiation [30, 72 - 78]. We previously demonstrated that the small molecule CBP/β-catenin antagonist ICG-001, by specifically binding to the N-terminus of CBP and biochemically disrupting the CBP/β-catenin interaction, can safely eliminate CML LSC both in vitro and in vivovia forced symmetric differentiation [15]. We now further demonstrate that CBP/β-catenin antagonism can cause forced differentiation of LSC, including the most deeply quiescent drug resistant LI, even in a microenvironment mimicking the hypoxic bone marrow niche, with concomitant re-expression of CD34 and CD38, increased protein translation and BCR-ABL1 expression. Although biochemically very specific for the N-terminus of CBP, small molecule CBP/β-catenin antagonists elicit a host of downstream pleiotropic effects (43), one of them being nuclear to the mitochondrial shuttling of PHB2, which results in the collapse of a PHB2 repressive complex, thereby licensing differentiation and exit from quiescence [79]. Remarkably, very recently, PHB2 has been shown to directly interact with NDUFS1 in mitochondria to enhance the binding of NDUFS1 to NDUFV1, another core subunit of complex I, thereby enhancing OXPHOS by promoting MC-1 stability and activity [49]. Given the similar increased expression of a Wnt signature in both CML and AML [80 - 84], one may speculate that similar deeply quiescent CCR7$^+CD34^+CD38^-$ cells in AML could also be differentiated by CBP/β-catenin antagonist treatment. However, due to the much greater degree of heterogeneity in AML than CML, this will have to be the subject of further investigation.

## CONCLUSION

We recently demonstrated that the N-termini of the KAT3 coactivators provide a highly evolutionary conserved hub to integrate β-catenin, nuclear receptor family members via a conserved LXXLL sequence and Stat1, 2 [37] to coordinate cellular metabolism with cellular status and immune response [30]. Specific inhibition of the interaction between CBP and catenin with small molecule inhibitors, i.e. ICG-001 or PRI-724 modulates Wnt/catenin signaling [46, 85], and can safely eliminate CSC via forced symmetric division without any deleterious effect on long-lived normal quiescent SSC, which preferentially undergo asymmetric divisions [15, 37, 41, 86, 87]. Quiescent LSC and likely more generally CSC, appear to rely on mitochondrial FAO, without complex I expression, thereby mitigating vulnerability to ROS, similarly to long lived human primordial oocytes [26]. Therefore, the ability to safely eliminate deeply quiescent therapy resistant populations utilizing specific small molecule CBP/catenin antagonists may represent an attractive generalizable therapeutic strategy to overcome the problem of the elimination of deeply quiescent CSC therapy resistant persister cells [46].

## Abbreviations

**CSC** = cancer stem cells

**HSC** = hematopoietic stem cells

**MC-1** = mitochondrial complex I

**CML** = chronic myelogenous leukemia

**LI** = leukemia initiators

**FAO** = Fatty acid oxidation