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Nilotinib interferes with cell cycle, ABC transporters and JAK-STAT signaling pathway in CD34+/lin- cells of patients with chronic phase chronic myeloid leukemia after 12 months of treatment

  • Alessandra Trojani ,

    Roles Conceptualization, Data curation, Investigation, Supervision, Validation, Writing – original draft, Writing – review & editing

    alessandra.trojani@ospedaleniguarda.it

    Affiliation Division of Hematology, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy

  • Ester Pungolino,

    Roles Conceptualization, Project administration, Resources, Supervision, Validation

    Affiliation Division of Hematology, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy

  • Alessandra Dal Molin ,

    Contributed equally to this work with: Alessandra Dal Molin, Barbara Di Camillo, Giacomo Baruzzo

    Roles Data curation, Formal analysis, Software, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Information Engineering, University of Padova, Padova, Italy

  • Milena Lodola,

    Roles Data curation, Investigation

    Affiliation Division of Hematology, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy

  • Giuseppe Rossi,

    Roles Resources

    Affiliation Department of Hematology, ASST Spedali Civili, Brescia, Italy

  • Mariella D’Adda,

    Roles Resources

    Affiliation Department of Hematology, ASST Spedali Civili, Brescia, Italy

  • Alessandra Perego,

    Roles Resources

    Affiliation Internal Medicine-Haematology, Desio Hospital, Desio, Italy

  • Chiara Elena,

    Roles Resources

    Affiliation Hematology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy

  • Mauro Turrini,

    Roles Resources

    Affiliation Division of Hematology, Department of Internal Medicine, Valduce Hospital, Como, Italy

  • Lorenza Borin,

    Roles Resources

    Affiliation Hematology Division, San Gerardo Hospital, Monza, Italy

  • Cristina Bucelli,

    Roles Resources

    Affiliation Hematology Division, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milano, Italy

  • Simona Malato,

    Roles Resources

    Affiliation Hematology and Bone Marrow Transplantation Unit, San Raffaele Scientific Institute, Milano, Italy

  • Maria Cristina Carraro,

    Roles Resources

    Affiliation Hematology and Transfusion Medicine, Sacco Hospital, Milano, Italy

  • Francesco Spina,

    Roles Resources

    Affiliation Division of Hematology–Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy

  • Maria Luisa Latargia,

    Roles Resources

    Affiliation ASST Valle Olona Ospedale di Circolo, Busto Arsizio, Italy

  • Salvatore Artale,

    Roles Resources

    Affiliation ASST Valle Olona Sant’Antonio Abate, Gallarate, Italy

  • Pierangelo Spedini,

    Roles Resources

    Affiliation Division of Hematology, Hospital of Cremona, Cremona, Italy

  • Michela Anghilieri,

    Roles Resources

    Affiliation ASST Lecco, Lecco, Italy

  • Barbara Di Camillo ,

    Contributed equally to this work with: Alessandra Dal Molin, Barbara Di Camillo, Giacomo Baruzzo

    Roles Data curation, Formal analysis, Software, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Information Engineering, University of Padova, Padova, Italy

  • Giacomo Baruzzo ,

    Contributed equally to this work with: Alessandra Dal Molin, Barbara Di Camillo, Giacomo Baruzzo

    Roles Data curation, Formal analysis, Software, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Information Engineering, University of Padova, Padova, Italy

  • Gabriella De Canal,

    Roles Data curation, Investigation

    Affiliation Pathology Department, Cytogenetics, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy

  • Alessandra Iurlo,

    Roles Resources

    Affiliation Hematology Division, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milano, Italy

  • Enrica Morra,

    Roles Supervision

    Affiliation Executive Committee, Rete Ematologia Lombarda, Italy

  •  [ ... ],
  • Roberto Cairoli

    Roles Project administration, Supervision, Validation

    Affiliation Division of Hematology, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy

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Nilotinib interferes with cell cycle, ABC transporters and JAK-STAT signaling pathway in CD34+/lin- cells of patients with chronic phase chronic myeloid leukemia after 12 months of treatment

  • Alessandra Trojani, 
  • Ester Pungolino, 
  • Alessandra Dal Molin, 
  • Milena Lodola, 
  • Giuseppe Rossi, 
  • Mariella D’Adda, 
  • Alessandra Perego, 
  • Chiara Elena, 
  • Mauro Turrini, 
  • Lorenza Borin
PLOS
x

Abstract

Chronic myeloid leukemia (CML) is characterized by the constitutive tyrosine kinase activity of the oncoprotein BCR-ABL1 in myeloid progenitor cells that activates multiple signal transduction pathways leading to the leukemic phenotype. The tyrosine-kinase inhibitor (TKI) nilotinib inhibits the tyrosine kinase activity of BCR-ABL1 in CML patients. Despite the success of nilotinib treatment in patients with chronic-phase (CP) CML, a population of Philadelphia-positive (Ph+) quiescent stem cells escapes the drug activity and can lead to drug resistance. The molecular mechanism by which these quiescent cells remain insensitive is poorly understood. The aim of this study was to compare the gene expression profiling (GEP) of bone marrow (BM) CD34+/lin- cells from CP-CML patients at diagnosis and after 12 months of nilotinib treatment by microarray, in order to identify gene expression changes and the dysregulation of pathways due to nilotinib action. We selected BM CD34+/lin- cells from 78 CP-CML patients at diagnosis and after 12 months of first-line nilotinib therapy and microarray analysis was performed. GEP bioinformatic analyses identified 2,959 differently expressed probes and functional clustering determined some significantly enriched pathways between diagnosis and 12 months of nilotinib treatment. Among these pathways, we observed the under expression of 26 genes encoding proteins belonging to the cell cycle after 12 months of nilotinib treatment which led to the up-regulation of chromosome replication, cell proliferation, DNA replication, and DNA damage checkpoint at diagnosis. We demonstrated the under expression of the ATP-binding cassette (ABC) transporters ABCC4, ABCC5, and ABCD3 encoding proteins which pumped drugs out of the cells after 12 months of nilotinib. Moreover, GEP data demonstrated the deregulation of genes involved in the JAK-STAT signaling pathway. The down-regulation of JAK2, IL7, STAM, PIK3CA, PTPN11, RAF1, and SOS1 key genes after 12 months of nilotinib could demonstrate the up-regulation of cell cycle, proliferation and differentiation via MAPK and PI3K-AKT signaling pathways at diagnosis.

Introduction

CML results from unfaithful repaired DNA damage in a hematopoietic stem cell, but specific features of leukemic stem cells (LSCs) have not yet been fully understood. Several studies demonstrated that LSCs show a strong resistance to therapies in TKI-treated CML patients due to their ability to activate specific signaling biological pathways [1]. Although nilotinib is highly effective in the treatment of CML, multiple clinical trials showed that some patients could become refractory and develop drug resistance [2]. Therapeutic strategies aiming for a cure of CML will require full eradication of Ph+ CML stem cells. Previous studies demonstrated that the aberrant regulation of pathways involved in the self-renewal of stem cells is implicated in cancer [3]. Identifying such pathways and trying to exploit them therapeutically is important to achieve CML-LSC eradication and disease cure [4]. Altered cell cycle checkpoints and a low intracellular concentration of TKIs are among those mechanisms that can lead to drug resistance in CML stem cells [5].

Previous studies demonstrated an increased expression of BCR-ABL1 oncogenic fusion protein-kinase and the deregulation of cell cycle proteins that induced DNA damage in CML cells [6]. These findings highlighted the properties of LSCs which become insensitive and resilient to TKI treatments in the bone marrow niche [7]. In addition, stromal cells play an important role in the survival of LSCs inducing cell cycle arrest and promote cellular quiescence in marginal environments even after TKI therapies [1].

The ABC transporters represent the most abundant transmembrane protein family encoded in the human genome. These membrane proteins transport drugs/substances across the cell membrane by ATP hydrolysis, and their physiological role as a mechanism of defense against xenobiotics has been investigated in CML [8, 9]. An altered regulation of ABC transporter proteins induced multi drug resistance (MDR) in different types of cancer cells [10]. In particular, the over expression of specific ABC transporter proteins can promote drug resistance and the development of malignancy in CML CD34+ population [10]. Indeed, Porro et al, showed that high levels of c-MYC were associated with an increased expression of some members of ABC genes (including ABCC4) which were involved in drug resistance in promyeloid leukemia cells [11].

The MDR phenotype may arise not only through the efflux of ABC transporters, but also through several other mechanisms such as pathways involved in the cell growth and survival of LSCs.

In order to identify pathways which contribute to the LSCs survival, several investigations have identified JAK2 as a putative target for CML. Hematopoietic growth factors (HGFs) bind to specific cell surface receptors in the JAK2-STAT5 cell signaling pathway. Following the HGFs binding, STAT5 is phosphorilated by JAK2 protein within the nucleus. JAK2-STAT5 signaling is involved in the signaling network downstream of BCR-ABL1, playing a crucial role in the leukemogenesis in CML cells [12]. Recently, the existence of a JAK2/BCR-ABL1 protein complex, which helps to stabilize BCR/ABL1 kinase activity, has been demonstrated [13]. Gallipoli et al. concluded that the JAK2/STAT5 signaling pathway is an important therapeutic target in CML stem/progenitor cells, and that JAK2/STAT5 inhibition by nilotinib and ruxolitinib might contribute to obtain disease eradication [12]. Clinical studies combining ruxolinib and TKIs in CML are ongoing in an attempt to eliminate the leukemic stem cell population (EudraCT: NCT01702064).

Gene expression profiling studies have been performed to identify biomarkers predictive of TKI failure [1416]. In particular, analyses on CML CD34+ cells have revealed that some pathways were consistently deregulated in TKI non-responding patients [1].

The PhilosoPhi34 (EudraCT: 2012-005062-34) study aimed to verify the clearance of BM CD34+/lin- Ph+ cells in CML patients after 3, 6 and 12 months of nilotinib treatment. We investigated the transcriptome profiles and the consequent deregulation of genes and pathways in CD34+/lin- cells from 78 CP-CML patients at diagnosis vs. 12 months of nilotinib treatment by microarray analysis. We determined the deregulation of the cell cycle, the membrane drug-transporters and the JAK-STAT signaling pathway to provide new insight into the action of nilotinib in CP-CML patients.

Materials and methods

Patients

The PhilosoPhi34 study, which included 15 centers in Italy, collected samples from consenting patients on behalf of the Rete Ematologica Lombarda (REL). The participants provided their written consent to participate in this study. The study was approved by the Ethics Committee ASST Grande Ospedale Metropolitano Niguarda (Milan, Italy) and the following local Ethics Committees of the participants centers (Lombardia, Italy): EC ASST Spedali Civili Brescia, EC Desio Hospital, EC IRCCS Policlinico San Matteo (Pavia), EC Valduce Hospital (Como), EC Monza Brianza, EC IRCCS Ca’ Granda Ospedale Maggiore Policlinico (Milan), EC San Raffaele Scientific Institute (Milan), EC Sacco Hospital (Milan), EC IRCCS Istituto Nazionale dei Tumori (Milan), EC Valle Olona Ospedale di Circolo (Busto Arsizio), EC ASST Valle Olona Sant’Antonio Abate (Gallarate), EC Hospital of Cremona, and EC ASST Lecco. In this study, we enrolled 87 CP-CML patients [17]. Patients received first-line therapy with nilotinib 300 mg BID.

Isolation of BM CD34+/lin- cells using immunomagnetic beads

We collected BM samples from 87 patients at diagnosis. In addition, we collected BM samples after 3, 6 and 12 months of nilotinib therapy [17]. 80/87 patients were examined after 12 months of nilotinib. Among these 80 patients, only one relapsed at 12 months. Mononuclear cells (MNCs) from the bone marrow (BM) blood samples (range, 1–25 ml) of 80 CML patients were isolated using Ficoll density gradient centrifugation at 800 rpm for 20 minutes. Immediately afterwards, we selected BM CD34+/lin- cells using Diamond CD34 Isolation kit and autoMACs Pro separator (Miltenyi Biotec, Bologna, Italy) according to the manufacturer’s instructions (Miltenyi Biotec). Briefly, we labeled BM MNCs with a mix of biotin-conjugated antibodies against lineage-specific antigens. Immediately afterwards, these cells were labeled with Anti-Biotin Microbeads. We selected the lineage-negative stem and progenitor cells by the depletion of the magnetically labeled cells. BM CD34+/lin- cells were obtained from the lineage-negative stem and progenitor cells using CD34 Microbeads (Miltenyi Biotec). The purity of isolated BM CD34+/lin- cells was detected by flow cytometry.

The methods were described in http://dx.doi.org/10.17504/protocols.io.yncfvaw, and showed in our previous study [18].

FISH

Standard FISH tests were performed on isolated BM CD34+/lin- cells for 87 patients at diagnosis and for 80/87 patients after 3, 6 and 12 months of nilotinib treatment. For each patient, a small sample of selected CD34+/lin- cells (containing at least 103 cells fixed in Carnoy’s solution) was analyzed by FISH using standard method [18]. Samples were co-hybridized to XL BCR/ABL1 plus Translocation/Dual Fusion Probe (MetaSystems, Milan, Italy) on ThermoBrite Statspin Model (Leica Biosystems, US). FISH analyses were performed using fluorescence microscope Axioskop 2 (Carl Zeiss Microimaging GmbH, Göttingen, Germany), equipped with a UV 100-W lamp (Osram, Augsburg, Germany), ProgRes MF CCD camera (Jenoptik AG, Jena, Germany), and ISIS System Software (MetaSystems Hard & Software, Althlussheim, Germany).

Fine modulo.

At least, 200 interphase nuclei were counted from each suitable specimen (optimum: 300 nuclei). Each available interphase nucleus was read even in sub-optimal specimens. FISH analyses were performed as described by Trojani et al [18], and in http://dx.doi.org/10.17504/protocols.io.yncfvaw.

Cell cryopreservation and RNA extraction

Selected BM CD34+/lin- cells of 80 CP-CML patients were resuspended in 50 μl of RNAlater (Thermo Fisher Scientific, Milano, Italy) and stored at -20°C until RNA extraction was performed as previously described [18].

Total RNA was isolated from the BM CD34+/lin- cells stored in RNAlater using MagMAX 96 Total RNA Isolation Kit (Thermo Fisher Scientific) [18], according to the manufacturer’s instructions. The quality and the yield of the extracted RNA were measured using Nanodrop (Thermo Fisher Scientific) (see http://dx.doi.org/10.17504/protocols.io.yncfvaw).

GEP experiments

Microarray experiments were performed on the BM CD34+/lin- cells of 80 CP-CML patients at diagnosis as well as those who had undergone 12 months of nilotinib treatment. We prepared cDNA starting from the previously extracted RNA (50 ng) using Ovation Pico WTA System V2 kit (NuGEN) and Encore Biotin Module Kit (NuGEN) following the manufacturer’s instructions.

cDNA was hybridized to Affymetrix HTA 2.0 using the Gene Chip platform (Affymetrix, Santa Clara, Ca, USA) and signals were scanned by Affymetrix Gene Chip Scanner 3000 according to the manufacturer’s instructions as described in http://dx.doi.org/10.17504/protocols.io.yncfvaw, and in our previous manuscript [18].

Bioinformatic analyses of GEP data

The preprocessing of microarray raw data was performed using R software version 3.4.2 [19]. The Affymetrix HTA 2.0 probes were initially summarized into probe sets specific for a given gene using function RMA [20] of R package oligo [21], downloaded from Bioconductor repository version 3.4. Principal component analysis (PCA) has been performed using prcomp function of package stats version 3.4.2 [19]. MvA plots were generated using custom scripts. MvA plots show the relationship among the average log intensity of the gene expression (A value) and the log of intensity ratio (M value) between two samples. PCA and MvA plots were examined before and after microarray preprocessing as a quality checking procedure. PCA plots revealed the presence of batch effects due to the different protocols used for performing RNA extraction and GEP experiments. Batch effects have been corrected using function ComBat [22] of R package sva [23]. MvA plots showed the presence of bias in the distribution of intensities among samples, then data was normalized using function normalize.quantiles of R package preprocessCore [24].

The differential expression analysis was performed on the samples at 12 months vs. diagnosis using the two-classes SAM test [25], implemented in the homonym function in R package samr [25]. Benjamini-Hochberg procedure was applied to control the False Discovery Rate (FDR) and a cut-off value of 0.05 was applied to select for significant differential expression [26].

Functional clustering was performed on significant differentially expressed genes using online tool DAVID (https://david.ncifcrf.gov/) [27, 28], to classify them into functional groups based on their annotation term co-occurrence. For this analysis, 1,723 protein coding genes which have a unique EntrezID in the “Affymetrix NetAffx” annotation were used (HTA 2.0 Transcript Cluster Annotations, Release 36, 7/6/16). Groups that resulted significantly enriched were selected based on FDR value below 0.05 [29], (see http://dx.doi.org/10.17504/protocols.io.yncfvaw).

Results

FISH

At diagnosis, FISH analysis detected BM CD34+/lin- Ph+ cells in all 87 CP-CML patients. At 12 months, we could analyze 80/87 patients [17]. 79/80 patients were evaluable because they achieved at least a complete cytogenetic response whereas 1/80 patient relapsed at 12 months. No Ph+ nuclei were detected in 79/79 patients [17].

Purity of selected cells, quality and yield of total RNA

The purity of BM CD34+/lin- cells was > 97% as determined by flow cytometry (S1 Appendix). The purity of the extracted RNA was in the range of 1.7–1.8, determined by absorbance ratios of A(260)/A(280) using a NanoDrop Spectrophotometer (Thermo Fisher Scientific). The total RNA concentration isolated from 100,000 BM CD34+/lin- cells was about 300 ng.

Preprocessing of HTA 2.0 arrays of BM CD34+/lin- cells of CP-CML patients at diagnosis and after 12 months of nilotinib treatment

We performed the preprocessing and correction for batch effects for samples of 80 patients at diagnosis and after 12 months of nilotinib treatment. We conducted the analyses on 78 subjects. Due to experimental issues, two patients were not considered for differential expression analysis, as the microarray CEL files of the 12 months samples were corrupted and missed probe intensities for most of the probes. After correction for batch effects and normalization, no more batch effects or residual systematic differences were observed in all the 156 arrays.

Identification of genes and pathways deregulated between BM CD34+/lin- cells of CP-CML patients at diagnosis vs. 12 months of nilotinib treatment

The differential expression analysis detected 2,959 probes (corresponding to 2,726 unique genes and 1,740 unique gene symbols) differently expressed (DE) between 78 patients at diagnosis compared to 12 months of nilotinib treatment (S1 Table). Among the unique genes, 1,868 genes were annotated as “protein coding” and 858 as “non-coding” in the “Affymetrix NetAffx” annotation (HTA 2.0 Transcript Cluster Annotations, Release 36, 7/6/16). Most of non-coding DE genes (364 genes) consisted of long non-coding RNAs, while the remaining genes were annotated as snoRNAs, miRNAs, piRNAs, miscRNAs, tRNAs and rRNAs.

The functional clustering analysis revealed interesting functional groups of genes, involved in cell cycle, ATP-binding, and JAK-STAT pathway (Table 1 and S2 Table).

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Table 1. Genes with significant differential expression in BM CD34+/lin- cells from 78 CP-CML patients at diagnosis vs. 12 months of nilotinib treatment.

https://doi.org/10.1371/journal.pone.0218444.t001

Up-regulation of 26 genes of the Cell Cycle (G1, S, G2 and M phases), DNA damage and repair at diagnosis

Functional enrichment analysis demonstrated that 26/124 genes encoding proteins that belong to the cell cycle pathway were significantly over expressed at diagnosis compared to 12 months of nilotinib (Tables 1 and 2, Fig 1A). ORC5, ORC2, ORC4 (Origin Recognition Complex), MCM3, MCM6 (Mini-Chromosome Maintenance complex) and HDAC2 encoding proteins that belong to G1 phase of the cell cycle (Cell cycle control of Chromosome replication), were up-regulated at diagnosis. We demonstrated that CCNA2, CDK7, CDC6, DBF4, ORC5, ORC2, ORC4, MCM3, and MCM6 (S phase of the cell cycle) were over expressed at diagnosis. GEP results showed that CCNA2, CCNB1, WEE1, PRKDC, ATM, MDM2 (G2 phase of the cell cycle) as well as TTK, MAD2L1, BUB3, BUB1, ANAPC1, ANAPC4, ANAPC7, CDC27, SMC3, YWHAE, and YWHAZ (M phase of the cell cycle) were over expressed at diagnosis compared to 12 months of nilotinib.

thumbnail
Fig 1.

(A) Box plot of expression of genes of the Cell Cycle and Mitosis pathway. Twenty-six genes were significantly differentially expressed in BM CD34+/lin- cells from CP-CML patients at diagnosis vs. 12 months of nilotinib treatment. (B) Box plot of expression of genes of the ATP-binding cassette (ABC) pathway. The comparison between BM CD34+/lin- cells of CP-CML patients at diagnosis and 12 months of nilotinib treatment showed 3 genes significantly differentially expressed. (C) Box plot of expression of genes of the JAK-STAT pathway. Eight genes demonstrated a significant differential expression in BM CD34+/lin- cells from CP-CML patients at diagnosis vs. 12 months of nilotinib treatment.

https://doi.org/10.1371/journal.pone.0218444.g001

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Table 2. Genes of the Cell cycle and Mitosis pathway with significant differential expression in BM CD34+/lin- cells from 78 CP-CML patients at diagnosis vs.

12 months of nilotinib treatment.

https://doi.org/10.1371/journal.pone.0218444.t002

Over expression of ATP-binding ABC transporters genes in CD34+/lin- cells at diagnosis

GEP data demonstrated that ABCC4, ABCC5 and ABCD3 genes were significantly up-regulated at diagnosis (Tables 1 and 3, Fig 1B). We previously demonstrated the over expression of ABCC5 at diagnosis vs. 12 months of nilotinib treatment in 30 CP-CML patients [18].

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Table 3. Genes of the ATP-binding cassette (ABC) pathway with significant differential expression in BM CD34+/lin- cells from 78 CP-CML patients at diagnosis vs. 12 months of nilotinib treatment.

https://doi.org/10.1371/journal.pone.0218444.t003

Activation of JAK-STAT signaling pathway at diagnosis vs. 12 months of nilotinib

We analyzed JAK-STAT signaling pathway that is made up of 155 genes (Kegg Pathway Database). This pathway was deregulated in CD34+/lin- cells at diagnosis vs. 12 months of nilotinib treatment. SOS1, PIK3CA, RAF1, IL7, JAK2, STAM, and PTPN11 were up-regulated whereas IL22RA was down-regulated at diagnosis (Tables 1 and 4, Fig 1C).

thumbnail
Table 4. Genes of the JAK-STAT pathway with significant differential expression in BM CD34+/lin- cells from 78 CP-CML patients at diagnosis vs. 12 months of nilotinib treatment.

https://doi.org/10.1371/journal.pone.0218444.t004

Discussion

The resistance to TKIs remains one of the major causes of treatment failure and patient death in CML [30]. A better understanding of the molecular biology of LSCs is crucial to develop more effective treatments for advanced CML and prevent drug resistance [1].

To the best of our knowledge, we hereby report for the first time the results of a wide transcriptome analysis of BM CD34+/lin- cells of 78 CP-CML patients at diagnosis vs. 12 months of nilotinib treatment. We found 2,959 probes differently expressed at diagnosis compared to 12 months of nilotinib treatment. In particular, we focused on genes which are over expressed at diagnosis and which play a crucial role in the cell cycle, ATP-binding ABC transporters and JAK-STAT signaling pathway (Fig 1).

Gene expression and proteomic profile studies of CML LSCs drew attention to specific gene pathways that could represent both prognostic indicators as well as new targets for therapy that might eventually overcome resistance to the BCR-ABL TKIs [31, 32].

The alteration of different signaling pathways such as cell cycle, JAK-STAT, and the deregulation of ABC drug efflux transporters can promote the development of growth and survival of CML progenitor and stem cells [1]. Some authors showed that several genes encoding proteins involved in the cell cycle and chromosome segregation were up-regulated in CML LSCs [1]. We demonstrated that 26 genes representing phases of the cell cycle (G1, S, G2 and M), were over expressed at diagnosis compared to 12 months of nilotinib treatment in 78 CP-CML patients (Tables 1 and 2). The integrity of signaling pathways involved in cell cycle arrest, chromatin remodeling and DNA repair are critical to maintain the fidelity of replicated DNA. Mancini et al, demonstrated that normal cells repaired damaged DNA during G1 arrest whereas leukemic cells often had a deficient G1/S checkpoint and this depended on a functional G2/M checkpoint for DNA repair (Mancini M. et al. Blood. 2017; Abs. Suppl1 130:1588).

Among the 26 deregulated genes, we found that ORC5, ORC2, ORC4, MCM3, MCM6, and HDAC2 controlled G1 phase as well chromosome replication. The up-regulation of these genes was associated with the initiation of DNA replication [33]. Notably, some studies demonstrated that HDAC inhibitors treatment represented an effective strategy to target LSCs in CP-CML patients receiving tyrosine kinase inhibitors [34, 35].

Our GEP results demonstrated that genes encoding proteins involved in the S phase of cell cycle (CCNA2, CDK7, CDC6, DFB4, MCM3, and MCM6) were down-regulated after 12 months of nilotinib. Previous studies showed that these genes might promote the cell proliferation and DNA replication in CML CD34+/lin- cells at diagnosis [15, 36, 37].

We showed that CCNA2, CCNB1, WEE1, PRKDC, ATM and MDM2 (G2 phase) were down-regulated after 12 months of nilotinib. Notably, the study by Reynaud et al, demonstrated the over expression of CCNA2 and CCNB1 in CML-LSCs of transgenic mice [38].

Our study demonstrated that TTK, MAD2L1, BUB3, BUB1, ANAPC1, ANAPC4, ANAPC7, CDC27, SMC3, YWHAE, and YWHAZ (mitosis) were over expressed at diagnosis. In particular, TTK and MAD2L1 might increase cell proliferation in CML CD34+/lin- cells at diagnosis, and some researchers demonstrated that they were over expressed in CML leukemic stem cells compared to the same cell counterpart from normal subjects [15]. Moreover, previous studies showed the over expression of the mitotic checkpoint genes BUB1 and BUB2 in several solid tumors [3941].

In conclusion, we can reasonably speculate that all the 26 genes over expressed at diagnosis led to the up-regulation of the cell cycle in CML CD34+/lin- cells at diagnosis increasing their survival with respect to 12 months of nilotinib treatment.

Our results showed that ABCC5, ABCC4 and ABCD3 were significantly under expressed in CP-CML patients after 12 months of nilotinib treatment compared to diagnosis [18]. Previous studies demonstrated that drug transporters, particularly ATP-binding cassette (ABC) transporters, played a critical role in the intracellular levels of TKI and primary resistance [10]. Indeed, 48 genes represent the ABC transporters family (Kegg Pathway Database), and the up-regulation of some of them can lead to MDR by promoting the efflux of drugs out of the cells [9, 10, 11, 42]. Recent studies have investigated ABCC4 and ABCC5 to clarify the clinical significance of their altered function and expression in MDR. In particular, Chen et al, demonstrated that proteins encoded by ABCC4 and ABCC5 were expressed at low levels in all normal tissues [42]. Wang et al, demonstrated that both ABCC4 and ABCC5 regulated the efflux of purine analogues. In order to overcome the drug resistance, recent in vitro studies demonstrated that TKIs such as nilotinib and imatinib were able to inhibit the efflux actions of ABC transporter proteins [4346].

Several studies on CML demonstrated JAK-STAT signaling pathway as a potential survival mechanism of CML LSCs [4]. Recently, researchers focused on the function of the intracellular JAK2 in the survival and proliferation of CML LSCs and its putative role as a therapeutic target in CML [12]. The combination of JAK2 inhibitors with TKI showed to be effective against CML cell lines and primary cells. However, further work is still required to assess the effectiveness, toxicity and specificity of inhibitors [31, 4749]. Some studies are ongoing to identify other regulators of the JAK-STAT pathway and to design innovative therapeutic strategies. Our GEP data demonstrated an average up-regulation of 7 genes (JAK2, SOS1, PIK3CA, RAF1, IL7, STAM, and PTPN11) encoding proteins of JAK-STAT signaling pathway at diagnosis.

In addition, the JAK-STAT pathway plays a major role in the transfer of signals from cell-membrane receptors to the nucleus [50]. The interaction between the surface receptors and the cytokines activates JAK2 and the cascade of genes which lead to the proliferation, differentiation, cell cycle and survival of LSCs. Our study identified the dysregulation of MAPK and PI3K signaling pathways due to the over expression of PTPN11, SOS1, RAF1 and PIK3C, respectively [5153]. Moreover, JAK2 could promote the phosphorilation of PIK3CA via PI3K-AKT signaling pathway [53] that might be responsible to TKI resistance in Ph+ cell lines [54].

In summary, we identified gene expression changes in BM CD34+/lin- cells of a cohort of 78 CP-CML patients after 12 months of nilotinib therapy compared to diagnosis. The dysregulation of cell cycle and DNA repair, ABC transporters, and JAK-STAT signaling pathway after treatment with nilotinib are interesting, since previous studies highlighted the role of these pathways in CML.

We determined that the BM CD34+/lin- cells at diagnosis were all Ph-positive whereas the same cells after 12 months of nilotinib were Ph-negative by FISH analyses. We could suppose that BM CD34+/lin- cells of patients after 12 months of nilotinib were normal because of the cytogenetic results. To clarify this point, we will compare GEP of BM CD34+/lin- cells after 12 months of nilotinib with respect to the normal cell counterparts of healthy donors.

The potential suitability of the genes highlighted in our study as biomarkers in CML requires, however, further investigation to address their clinical relevance. We strongly believe that the identification of dysregulated signaling pathways in progenitor and stem cells in CML patients can significantly alter the presentation of the disease and its progression, and therefore might suggest the design of new therapeutic strategies in CML. Furthermore, the identification of pathways that might represent new drug-targets for elimination of LSCs, could improve the outcomes of CML patients.

Supporting information

S1 Appendix. A representative FACS plot of the purity of BM CD34+/lin- cells determined by flow cytometry.

https://doi.org/10.1371/journal.pone.0218444.s001

(PPTX)

S1 Table. Results of the differential expression analysis on 78 CML patients at diagnosis vs. 12 months of treatment with nilotinib.

https://doi.org/10.1371/journal.pone.0218444.s002

(XLSX)

S2 Table. Results of the functional clustering analysis performed with DAVID tool on differentially expressed genes.

https://doi.org/10.1371/journal.pone.0218444.s003

(XLSX)

Acknowledgments

We would like to thank ASST Grande Ospedale Metropolitano Niguarda which promoted the study on behalf of REL. The authors would like to thank Dr John Welch for his proof reading.

References

  1. 1. Zhou H, Xu R. Leukemia stem cells: the root of chronic myeloid leukemia. Protein Cell. 2015; 6:403–12. Epub 2015 Mar 10. pmid:25749979
  2. 2. Yang K, Fu L-W. Mechanisms of resistance to BCR–ABL TKIs and the therapeutic strategies: A review Critical Reviews in Oncology/Hematology. 2015; 93:277–292.
  3. 3. Matsui WH. Cancer stem cell signaling pathways. Send to Medicine (Baltimore). 2016; 95:S8–S19.
  4. 4. Holyoake TL, Vetrie D. The chronic myeloid leukemia stem cell: stemming the tide of persistence. Blood. 2017; 129:1595–1606. Epub 2017 Feb 3. Review. pmid:28159740
  5. 5. Quintás-Cardama A, Cortes J. Molecular biology of bcr-abl1-positive chronic myeloid leukemia. Blood. 2009; 113:1619–1630. Epub 2008 Sep 30. Review. pmid:18827185
  6. 6. Cramer K, Nieborowska-Skorska M, Koptyra M, Slupianek A, Penserga ET, Eaves CJ, et al. BCR/ABL and other kinases from chronic myeloproliferative disorders stimulate single-strand annealing, an unfaithful DNA double-strand break repair. Cancer Res. 2008; 68:6884–6888. pmid:18757400
  7. 7. Wang A, Zhong H. Roles of the bone marrow niche in hematopoiesis, leukemogenesis, and chemotherapy resistance in acute myeloid leukemia. Hematology. 2018; 14:1–11. [Epub ahead of print].
  8. 8. Ambudkar SV, Kim IW, Xia D, Sauna ZE. The A-loop, a novel conserved aromatic acid subdomain upstream of the Walker A motif in ABC transporters, is critical for ATP binding. FEBS Lett. 2006; 580:1049–1055. Epub 2005 Dec 22. Review. pmid:16412422
  9. 9. Giannoudis A, Davies A, Harris RJ, Lucas CM, Pirmohamed M, Clark RE. The clinical significance of ABCC3 as an imatinib transporter in chronic myeloid leukaemia. Leukemia. 2014; 28:1360–1363. Epub 2014 Jan 20. pmid:24441286
  10. 10. Wang YJ, Zhang YK, Kathawala RJ, Chen ZS. Repositioning of tyrosine kinase inhibitors as antagonists of ATP-binding cassette transporters in anticancer drug resistance. Cancers (Basel). 2014; 6:1925–1952. Review.
  11. 11. Porro A, Iraci N, Soverini S, Diolaiti D, Gherardi S, Terragna C, et al. c-MYC oncoprotein dictates transcriptional profiles of ATP-binding cassette transporter genes in chronic myelogenous leukemia CD34+ hematopoietic progenitor cells. Mol Cancer Res. 2011; 9:1054–1066. Epub 2011 Jun 21. pmid:21693596
  12. 12. Gallipoli P, Cook A, Rhodes S, Hopcroft L, Wheaton H, Whetton AD, at al. JAK2/STAT5 inhibition by nilotinib with ruxolitinib contributes to the elimination of CML CD34+ cells in vitro and in vivo. Blood. 2014; 124:1492–1501. Prepublished online 2014 Jun 23. pmid:24957147
  13. 13. Chen M, Gallipoli P, DeGeer D, Sloma I, Forrest DL, Chan M, et al. Targeting primitive chronic myeloid leukemia cells by effective inhibition of a new AHI-1-BCR-ABL-JAK2 complex. J Natl Cancer Inst. 2013; 105:405–423. Epub 2013 Feb 27. pmid:23446755
  14. 14. Singh N, Tripathi AK, Sahu DK, Mishra A, Linan M, Argente B, et al. Differential genomics and transcriptomics between tyrosine kinase inhibitor-sensitive and -resistant BCR-ABL-dependent chronic myeloid leukemia. Oncotarget. 2018; 13:30385–30418. eCollection 2018 Jul 13.
  15. 15. Avilés-Vázquez S, Chávez-González A, Hidalgo-Miranda A, Moreno-Lorenzana D, Arriaga-Pizano L, Sandoval-Esquivel MÁ, et al. Global gene expression profiles of hematopoietic stem and progenitor cells from patients with chronic myeloid leukemia: the effect of in vitro culture with or without imatinib. Cancer Med. 2017; 6:2942–2956. Epub 2017 Oct 13.
  16. 16. Arrigoni E, Del Re M, Galimberti S, Restante G, Rofi E, Crucitta S, et al. Concise Review: Chronic myeloid leukemia: stem cell niche and response to pharmacologic treatment. Stem Cells Transl Med. 2018; 7:305–314. Epub 2018 Feb 8. pmid:29418079
  17. 17. Pungolino E, Rossi G, De Canal G, Trojani A, D'Adda M, Perego A, et al. Nilotinib induced bone marrow CD34+/lin-Ph+ cells early clearance in newly diagnosed CP-chronic myeloid leukemia. Am J Hematol. 2018; 93:E162–E164. Epub 2018 May 17. No abstract available. pmid:29633310
  18. 18. Trojani A, Pungolino E, Rossi G, D'Adda M, Lodola M, Camillo BD, et al. Wide-transcriptome analysis and cellularity of bone marrow CD34+/lin- cells of patients with chronic-phase chronic myeloid leukemia at diagnosis vs. 12 months of first-line nilotinib treatment. Cancer Biomark. 2017; 21:41–53. pmid:29036785
  19. 19. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2017. Available from URL http://www.R-project.org/.
  20. 20. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 2003; 31:e15. pmid:12582260
  21. 21. Carvalho BS, Irizarry RA. A Framework for Oligonucleotide Microarray Preprocessing. Bioinformatics. 2010; 26:2363–2367. ISSN 1367-4803. R package version 1.42.0. pmid:20688976
  22. 22. Johnson WE, Li , Rabinovic A. Adjusting batch effects in microarray data using empirical bayes methods. Biostatistics 2007; 8:118–127. pmid:16632515
  23. 23. Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE, Storey JD, et al. 2018. sva: Surrogate Variable Analysis. R package version 3.22.0.
  24. 24. Bolstad B (2018). preprocessCore: A collection of pre-processing functions. R package version 1.36.0.
  25. 25. Tusher V, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. PNAS 2001; 98:5116–5121, R package version 2.0.0. pmid:11309499
  26. 26. Benjamini and Hochberg. Controlling the first discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995; 57:289–300.
  27. 27. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc. 2009; 4:44–57.
  28. 28. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37:1–13. pmid:19033363
  29. 29. Storey JD A direct approach to false discovery rates. J R Stat Soc. 2002; 3:479–498.
  30. 30. Wieczorek A, Uharek L. Management of chronic myeloid leukemia patients resistant to tyrosine kinase inhibitors treatment. Biomark Insights. 2015; 10:49–54. Published online 2016 Feb 18. pmid:26917943
  31. 31. Sinclair A, Latif AL, Holyoake TL. Targeting survival pathways in chronic myeloid leukaemia stem cells. Br J Pharmacol. 2013; 169: 1693–1707. Published online 2013 Jul 26. pmid:23517124
  32. 32. Ricciardi MR, Salvestrini V, Licchetta R, Mirabilii S, Forcato M, Gugliotta G, et al. Differential proteomic profile of leukemic CD34+ progenitor cells from chronic myeloid leukemia patients. Oncotarget. 2018; 9:21758–21769. eCollection 2018 Apr 24. pmid:29774100
  33. 33. Duncker BP, Chesnokov IN, McConkey BJ, The origin recognition complex protein family. Genome Biol. 2009; 10:214. Published online 2009 Mar 17. pmid:19344485
  34. 34. Zhang B, Strauss AC, Chu S, Li M, Ho Y, Shiang KD, et al. Effective targeting of quiescent chronic myelogenous leukemia stem cells by histone deacetylase inhibitors in combination with imatinib mesylate. Cancer Cell. Author manuscript; available in PMC 2011 May 18. Published in final edited form as: Cancer Cell. 2010; 17: 427–442. pmid:20478526
  35. 35. Stubbs MC, Kim W, Bariteau M, Davis T, Vempati S, Minehart J, et al. Selective inhibition of HDAC1 and HDAC2 as a potential therapeutic option for B-ALL. Clin Cancer Res. Author manuscript; available in PMC 2016 May Published in final edited form as: Clin Cancer Res. 2015; 21:2348–2358. Published online 2015 Feb 16. pmid:25688158
  36. 36. Wu Y, Chen C, Sun X, Shi X, Jin B, Ding K, et al. Cyclin-dependent kinase 7/9 inhibitor SNS-032 abrogates FIP1-like-1 platelet-derived growth factor receptor α and bcr-abl oncogene addiction in malignant hematologic cells. Clin Cancer Res. 2012 A; 18:1966–1978. Epub 2012 Mar 23.
  37. 37. Zhang JH, He YL, Zhu R, Du W, Xiao JH. Deregulated expression of Cdc6 as BCR/ABL-dependent survival factor in chronic myeloid leukemia cells. Tumour Biol. 2017; 39:1010428317713394. pmid:28639894
  38. 38. Reynaud D, Pietras E, Barry-Holson K, Mir A, Binnewies M, Jeanne M, et al. E. IL-6 controls leukemic multipotent progenitor cell fate and contributes to chronic myelogenous leukemia development. Cancer Cell. 2011; 20:661–673. pmid:22094259
  39. 39. Kalitsis P, Earle E, Fowler KJ, Choo KH. Bub3 gene disruption in mice reveals essential mitotic spindle checkpoint function during earl embryogenesis. Genes Dev. 2000; 14:2277–2282. PMC 316933. pmid:10995385.
  40. 40. Yuan B, Xu Y, Woo JH, Wang Y, Bae YK, Yoon DS, et al. Increased expression of mitotic checkpoint genes in breast cancer cells with chromosomal instability. Clin Cancer Res. 2006; 12:405–410. pmid:16428479
  41. 41. Grabsch H, Takeno S, Parsons WJ, Pomjanski N, Boecking A, Gabbert HE, et al. Overexpression of the mitotic checkpoint genes BUB1, BUBR1, and BUB3 in gastric cancer-association with tumour cell proliferation. J Pathol. 2003; 200:16–22. pmid:12692836
  42. 42. Chen Z-S, Tiwari AK. Multidrug resistance proteins (MRPs/ABCCs) in cancer chemotherapy and genetic diseases. FEBS J. Author manuscript; available in PMC 2012 Sep 1. Published in final edited form as: FEBS J. 2011; 278: 3226–3245. Published online 2011 Aug 1. pmid:21740521
  43. 43. Eadie LN, Hughes TP, White DL. ABCB1 overexpression is a key initiator of resistance to tyrosine kinase inhibitors in CML cell lines. PLoS One. 2016; 11: e0161470. Published online 2016 Aug 18. pmid:27536777
  44. 44. Marques DS, Sandrini JZ, Boyle RT, Marins LF, Trindade GS. Relationships between multidrug resistance (MDR) and stem cell markers in human chronic myeloid leukemia cell lines. Leuk Res. 2010; 34:757–762. Epub 2009 Dec 6. pmid:19969351
  45. 45. Galimberti S, Bucelli C, Arrigoni E, Baratè C, Grassi S, Ricci F, et al. The hOCT1 and ABCB1 polymorphisms do not influence the pharmacodynamics of nilotinib in chronic myeloid leukemia. Oncotarget. 2017; 8:88021–88033. eCollection 2017 Oct 20. pmid:29152138
  46. 46. Shen T, Kuang Y-H, Charles R. Ashby CR, Lei JY, Chen A, et al. Imatinib and Nilotinib Reverse Multidrug Resistance in Cancer Cells by Inhibiting the Efflux Activity of the MRP7 (ABCC10). PLoS One. 2009; 4: e7520. Published online 2009 Oct 20. Correction in: PLoS One. 2009; 4: pmid:19841739
  47. 47. Lin H, Chen M, Rothe K, Lorenzi MV, Woolfson A, Jiang X. Selective JAK2/ABL dual inhibition therapy effectively eliminates TKI-insensitive CML stem/progenitor cells. Oncotarget. 2014; 5:8637–8650. pmid:25226617
  48. 48. Sloma I, Mitjavila-Garcia MT, Feraud O, Griscelli F, Oudrhiri N, El Marsafy S, et al. Whole-genome analysis reveals unexpected dynamics of mutant subclone development in a patient with JAK2-V617F-positive chronic myeloid leukemia. Exp Hematol. 2017; 53:48–58. Epub 2017 Jun 8. pmid:28602946
  49. 49. Zhang X, Tu H, Yang Y, Wan Q, Fang L, Wu Q, Li J. High IL-7 levels in the bone marrow microenvironment mediate imatinib resistance and predict disease progression in chronic myeloid leukemia. Int J Hematol. 2016; 104:358–367. Epub 2016 Jun 6. pmid:27272942
  50. 50. Seif F, Khoshmirsafa M, Aazami H, Mohsenzadegan M, Sedighi G, Bahar M. The role of JAK-STAT signaling pathway and its regulators in the fate of T helper cells. Cell Commun Signal. 2017; 15:23. Review. pmid:28637459
  51. 51. Majoros A, Platanitis E, Kernbauer-Hölzl E, Rosebrock F, Müller M, Decker T. Canonical and non-canonical aspects of JAK-STAT signaling: lessons from interferons for cytokine responses. Front Immunol. 2017; 8:29. eCollection 2017.
  52. 52. Gu S, Sayad A, Chan G, Yang W, Lu Z, Virtanen C, et al. SHP2 is required for BCR-ABL1-induced hematologic neoplasia. Leukemia. 2018; 32:203–213. Epub 2017 Aug 14. pmid:28804122
  53. 53. Samanta AK, Chakraborty SN, Wang Y, Kantarjian H, Sun X, Hood J, et al. Jak2 inhibition deactivates Lyn kinase through the SET-PP2A-SHP1 pathway, causing apoptosis in drug-resistant cells from chronic myelogenous leukemia patients. Oncogene. 2009; 28:1669–1681. Epub 2009 Feb 23. pmid:19234487
  54. 54. Quentmeier H, Eberth S, Romani J, Zaborski M, Drexler HG. BCR-ABL1-independent PI3Kinase activation causing imatinib-resistance. J Hematol Oncol. 2011; 4:6. pmid:21299849