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Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia

Fontana, Diletta1; Ramazzotti, Daniele1; Aroldi, Andrea1,2; Redaelli, Sara1; Magistroni, Vera1; Pirola, Alessandra3; Niro, Antonio1; Massimino, Luca1; Mastini, Cristina1; Brambilla, Virginia4; Bombelli, Silvia1; Bungaro, Silvia5; Morotti, Alessandro6; Rea, Delphine7; Stagno, Fabio8; Martino, Bruno9; Campiotti, Leonardo10; Caocci, Giovanni11; Usala, Emilio12; Merli, Michele13; Onida, Francesco14; Bregni, Marco15; Elli, Elena Maria2; Fumagalli, Monica2; Ciceri, Fabio16; Perego, Roberto A.1; Pagni, Fabio4; Mologni, Luca1; Piazza, Rocco1,2; Gambacorti-Passerini, Carlo1,2

Author Information
doi: 10.1097/HS9.0000000000000497

Introduction

Atypical chronic myeloid leukemia (aCML) is a rare BCR-ABL1-negative clonal disorder belonging to the myelodysplastic/myeloproliferative group.1 Its incidence is 1% to 2% of t(9;22) BCR-ABL1-positive CML.2–5 This disorder affects elderly patients with a median age ranging between 60 and 76 years, with an apparent male predominance.6,7 Patients present clinical features in common with BCR-ABL1-positive CML including splenomegaly, elevated white blood cells (WBC) count with a predominance of granulocytes and immature myeloid cells, and moderate anemia. According to the 2016 revision of the WHO classification for myeloid neoplasms, the median overall survival for aCML patients is 24 months. Moreover, no established standards of care exist for its treatment.8 Until 2012, the molecular lesions responsible for the onset of this leukemia remained unknown. The use of Next Generation Sequencing techniques (NGS) allowed our and other groups to identify recurrent somatic mutations occurring in SETBP1 and ETNK1 genes,6,9,10 later confirmed by several other independent studies.11–18 The application of NGS technologies demonstrated the presence of several other mutations involving ASXL1, CBL, EZH2, NRAS, TET2, CSFR3R, and U2AF1 genes. The identification of somatic variants occurring in a large number of genes clearly indicates that the genetic basis of aCML is heterogeneous, in striking contrast with classical CML. This heterogeneity poses a great challenge to the dissection of the molecular steps required for aCML leukemogenesis. Here we report a comprehensive analysis including mutation profiling, gene expression analysis and clinical outcome in a cohort of 43 aCML patients. We experimentally validated actionable mutations and identified the clonal hierarchy of multiple mutated genes. For a patient carrying both ETNK1 G245 V and NRAS G12D mutations, in addition to targeting them ex vivo, we established a patient-derived xenograft in order to test the activity of the MEK inhibitor trametinib in vivo. Finally, we found 3 differentially expressed genes, allowing the clustering of our patients’ cohort into 2 groups based on their survival.

Materials and methods

Patients

Diagnosis of aCML was performed according to the World Health Organization 2016 classification. All patients provided written informed consent, which was approved by the institutional ethics committee. This study was conducted in accordance with the Declaration of Helsinki. Bone marrow (BM) samples were collected at diagnosis, and leukemic cells were obtained by separation on a Ficoll-Paque Plus gradient (GE Healthcare, Milan, Italy). Surface markers were evaluated by fluorescence-activated cell sorting (FACS) analysis, and myeloid cells (positive for CD33, CD13 or CD117 staining) made up > 80% of the total cells.

Whole exome sequencing

Ten million cells were used for genomic DNA extraction by using PureLink Genomic DNA kit (Thermo Fisher, Milan, Italy) according to manufacturer's instructions. 1 μg of gDNA was used to generate exome libraries (Galseq, Monza, Italy). Mean exon coverage was 80×. To identify somatically acquired mutations we compared DNA from leukocytes and constitutive DNA extracted from lymphocytes or buccal swabs. Bioinformatic analysis was performed as already described in.6

MethoCult™ colonies assay and combined treatment

One million peripheral blood (PB) or 2 × 105 BM cells were seeded in methylcellulose-based medium Methocult H4034 (StemCell Technologies, Meda, Italy), according to manufacturer's instructions, and plated in 6-well dishes. After 2 weeks of incubation at 37°C, 5% CO2, individual colonies were picked, washed in PBS, and lysed in 20 μL of the following buffer: 10 mM Tris-HCl, 50 mM NaCl, 6.25 mM MgCl2, 0.045% NP40, 0.45% Tween-20; pH 7.6. On average 50 colonies per sample were isolated. After adding 1 μL of 20 μg/mL proteinase K, the lysate was incubated at 56°C for 1 hour and at 95°C for 15 minutes. Subsequently, the sample was amplified using dedicated barcoded primers by PCR, and underwent deep-sequencing. For combined treatment, 2 × 105 BM-derived cells were seeded in methylcellulose-based medium in presence of phosphoethanolamine 1 mM (Merck Life Science, Milan, Italy), trametinib 10 nM (Selleck Chemicals, Rome, Italy), trametinib 100 nM and combination of them. After 2 weeks of incubation, colonies were counted. Expected additive effect of the combination viability is the product of the 2 singlet viabilities. For actionable mutations targeting, all the inhibitors used (crizotinib, dasatinib, imatinib, and ruxolitinib) were purchased from Selleck Chemicals.

Deep-sequencing

Amplicon libraries were generated starting from 500 ng PCR product, purified on agarose gel, end-repaired and adenylated at 3’ ends before ligation of Truseq DNA Adapter Indices, and then amplified with 6-cycles PCR. Libraries were sequenced on an Illumina HiSeq 2500 instrument with paired-end reads 150 bp long. Paired fastq were initially deindexed using a custom, home-made tool and subsequently aligned to the reference human genome (hg38) using BWA.19 Coverage was > 2000× for all samples. Bam alignment files were generated from SAM using Samtools.20 Variant calls were performed using CEQer2.21

Clonal architecture analysis

Each methylcellulose colony was sequenced to reconstruct the clonal architecture of the corresponding sample. The indexed (barcoded) amplicons underwent NGS sequencing and were analyzed after deindexing using dedicated in-house bioinformatics tools. A generic mutation A was considered to be an earlier event compared to mutation B if A was identified in individual colonies in absence of mutation B.

aCML patient-derived xenograft (PDX) establishment

Ten 6 weeks-old NOD.Cg-PrkdcSCIDIl2rgtm1Wjl/SzJ (NSG) mice were purchased from Charles River (Milan, Italy), kept under standard conditions following the guidelines of the University of Milano-Bicocca ethical committee for animal welfare, and treated in accordance with European Community guidelines as approved by the Italian Ministry of Health. The protocol was approved by the Italian Ministry of Health and by the Institutional Committee for Animal Welfare. Mice were sub-lethally irradiated with 200 cGy, and after 24 hours 107 CMLPh-042 ficoll-purified bone marrow cells were i.v. transplanted through tail vein injection. 80ug/ml gentamicin was added to drinking water to prevent infections. Mice body weight was evaluated three times a week for the whole experiment duration. Peripheral blood collection was carried out every 15 days after transplantation to check human bone marrow cells engraftment. Human CD45+ cells engraftment occurred after 45 days from transplantation, then mice were randomized in 2 groups to receive vehicle alone (3 mice) or 1 mg/kg trametinib suspended in 0.5% carboxymethylcellulose/0.1% Tween80 by oral gavage once a day for 66 days (4 mice). Animals showing signs of morbidity (weight loss, hunched posture, unsteady gait, respiratory distress) were sacrificed before the end of the experiment. Sternum, spleen, lung, heart, liver, bowel, kidney, femur, tibia, and vertebrae were surgically extracted and paraffin fixed in buffer neutral formalin for immunohistochemistry experiments.

Analysis of human cells in peripheral blood by flow cytometry

Human cells engraftment was check every 15 days by flow cytometry. For the blood collection, mice were placed under a heat lamp to promote peripheral vasodilatation and were mechanically restrained using a plexiglass chamber. Then, a small transverse cut was performed in the lateral tail vein with a sterile lancet. Blood drops were collected with a micropipette and mixed with 10 μl EDTA 0.5 M. A maximum volume of 100 μl of blood was collected. After blood collection, a slight pressure was applied to ensure the bleeding stop. 100 μl EDTA-anticoagulated peripheral blood were lysed twice in red blood cells lysis buffer (140 mM NH4Cl, 8 mM Tris, pH 7.2) at RT for 5 minutes, and then cells were washed and suspended in PBS. Subsequently, cells were stained with the Alexa Fluor® 700-conjugated anti-human CD45 (HI30; Biolegend, San Diego, CA, USA) and PE-conjugated anti-mouse CD45 (30-F11; Biolegend) antibodies, at RT for 30 minutes. Dual-color flow cytometry was performed on MoFlo Astrios cell sorter equipped with Summit 6.3 software (both from Beckman Coulter, Miami). The acquisition process was stopped when at least 5000 events were collected in the population gate. Off-line analysis was performed using Kaluza 1.3 software (Beckman Coulter). Engraftment occurred in 70% of mice.

Immunohistochemistry staining

Formalin fixed sections were processed using automated tissue processors and embedded in paraffin. Then we obtained 2 μm sections for haematoxylin and eosin staining and immunohistochemical staining with an anti-CD45 antibody (Anti-CD45 ab10559, Abcam). Immunohistochemistry was performed on a Dako Omnis platform (Glostrup, Denmark) with a dilution of 1:1500 of the antibody with an initial concentration of 1 mg/ml. Slides were digitally scanned using a ScanScope CS digital scanner (Aperio, Park Center Dr., Vista, CA, USA).

Cytogenetic analysis and western blot

For cytogenetic analysis and western blot details, please see Supplemental Digital Content, http://links.lww.com/HS/A105.

RNA-sequencing

Ten million cells were lysed in TRIzol (Thermo Fisher Scientific) and RNA was extracted according to manufacturer's instructions. 2 μg of RNA (concentration 400ng/μl) were used for library preparation (Galseq, Monza, Italy); the average per-sample read count was 35 M. For batch effect correction, please see Supplemental Digital Content, http://links.lww.com/HS/A105.

Patients’ stratification

We analyzed batch corrected RNA-sequencing expression data to assess the presence of any clinically relevant subtype within our cohort. To do so, we first removed genes at low variance (variance < 0.01) and mitochondrial gene counts and normalized the remaining counts across patients. We then performed clustering analysis using CIMLR.22 In short, this method constructs a set of multiple Gaussian Kernels from RNA-sequencing data and uses them to effectively reduce noise and separate patients presenting different profiles. In our cohort, CIMLR discovered two distinct subtypes (Supplementary Fig. 3, http://links.lww.com/HS/A105).

Features selection and pathway enrichment

To achieve an understanding of the genes that are differentiating the two discovered subtypes, we first consider a list of known cancer-related genes23–26 (Supplementary Table 1, http://links.lww.com/HS/A106) and performed t-test for each gene in the list to assess whether a significant difference in expression was present (t-test p value adjusted for false discovery rate p < 0.01). We then selected genes among the significant ones that were differentially expressed in more than 75% of the patients of a cluster and conjunctively less than 25% of the patients in the other cluster to obtain a final list of 38 genes. This further filter aims at ensuring the biological relevance of the selected genes in terms of differential gene expression between the two clusters. We finally performed pathway enrichment considering these 38 genes by using the Max Plank Institute for Molecular Genetics ConsensusPathDB-human CPDB tool, setting the original list of known cancer-related genes as a background reference.

Patients’ classification

We further reduced the list of 38 differentially expressed genes by considering only the top 3 genes. Briefly, we selected all the genes differentially expressed in more than 80% of the patients of a cluster and conjunctively less than 20% of the patients in the other cluster. We constructed a random forest classifier27 using these genes. This method is used for classification and specifically performs the construction of multiple decision trees at training time and estimate the resulting class as the mode of the classes inferred by the individual trees. These analyses were implemented using the caret R package (version 6.0–84).

Next generation sequencing data

The NGS data discussed in this publication have been deposited in NCBI's Sequence Read Archive (SRA) and are accessible through accession number PRJNA60458.

Results

Clinical characteristics of patients

The clinical characteristics of aCML patients are summarized in Supplementary Table 2, http://links.lww.com/HS/A107. The median age at diagnosis was 65 years (range 38–85). The average blast percentage was 2.8% (range 0–17.0; SD 4.1) in peripheral blood and 3.4% (range 0–19.0; SD 3.8) in bone marrow. Splenomegaly was observed in 69.8% and bone marrow fibrosis (mostly MF1) in 37.2% of cases.

Mutation profiling

Whole Exome Sequencing (WES) was performed on 37 aCML samples. All the somatic mutations identified were scored according to OncoScore.28 Six patients revealed the presence of a single somatic mutation, 14 patients carried 2 mutations, 8 patients showed the co-presence of 3 mutations, while 4 or 5 mutations were present in 4 patients. Interestingly, 5 patients showed no mutations in known oncogenes, splicing factors, epigenetic factors, and cancer-related genes, and they carried a normal karyotype (Fig. 1; Supplementary Table 3, http://links.lww.com/HS/A108; Supplementary Table 4, http://links.lww.com/HS/A109); however, in all cases we identified somatic, likely passenger mutations, which supports the existence of clonal hematopoiesis also for these patients (Supplementary Tables 5, http://links.lww.com/HS/A110, 6, http://links.lww.com/HS/A111, 7, http://links.lww.com/HS/A112, 8, http://links.lww.com/HS/A113, and 9, http://links.lww.com/HS/A114). The most frequent mutation in our cohort was represented by ASXL1 (43.2%). Other frequently mutated genes were SETBP1 (29.7%), TET2 (27.0%), and KRAS/NRAS (21.6%), confirming our previous results.6 Mutations in EZH2 occurred in 18.9% of patients, while ETNK1 mutations were present in 16.2% of cases, again supporting our previous findings.9 In 13.5% of patients RUNX1 or SRSF2 mutations were detected. Furthermore, 3 patients showed mutation of CBL or CREBBP (8.1% of cases), while CSF3R and KIT mutations occurred in one patient each.

Figure 1
Figure 1:
Oncoprint. Oncoprint showing somatic mutations for a panel of 12 genes in 37 patients.

Targeting of actionable mutations

In 27% of cases (10/37 patients) actionable mutations occurring in KIT, NRAS, KRAS, and CSF3R genes were found. All these mutated genes could be targeted with clinically available drugs, such as dasatinib, trametinib or ruxolitinib29 (Supplementary Table 4, http://links.lww.com/HS/A109). In order to test whether these drugs were able to affect the growth of the leukemic clones in aCML cases, we performed colony assays in presence or absence of selected inhibitors on bone marrow-derived cells from four RAS mutated patients (CMLPh-003, CMLPh-039, CMLPh-006 and CMLPh-042, carrying NRAS G12R, NRAS G12D, KRAS A146 V, and NRAS G12D mutations, respectively), from a KIT D816 V positive patient (CMLPh-010), and from a CSF3R T618I mutated patient (CMLPh-040) (Figs. 2 and 4A, C). Patients with RAS mutations showed sensitivity to the MEK inhibitor trametinib, with 50% growth inhibition around 10 nM for NRAS mutated patients and around 50 nM for the patients with KRAS mutation (Figs. 2A–C and 4A, C), while unrelated inhibitors used as negative controls, such as imatinib, dasatinib and crizotinib, were inactive. CMLPh-010 patient presented a KIT D816 V mutation which is known to be highly sensitive to dasatinib, but resistant to imatinib.30 Colony assays showed high sensitivity to dasatinib starting at 10 nM, while growth was almost completely abrogated at 0.1 μM; in contrast, imatinib was ineffective even at 3 μM (Fig. 2D). CMLPh-040 cells, bearing a CSF3R somatic mutation, were grown in presence of increasing concentrations of dasatinib, ruxolitinib, or crizotinib as negative control (Fig. 2E). Dasatinib completely inhibited cell growth already at 0.1 μM, while ruxolitinib showed a 50% inhibition at 0.1 μM and completely inhibited cell growth starting from 0.3 μM. Crizotinib did not affect colonies formation at any concentration. Taken together, these results indicate that the presence of KIT, RAS, and CSF3R mutations in aCML cells predicts sensitivity to clinically available inhibitors, at least ex vivo. Given the very poor prognosis of this disorder, these findings suggest a possible targeted treatment for a subset of aCML patients.

Figure 2
Figure 2:
Colony formation assay. X axis represents different treatments and Y axis represents total number of colonies formed, normalized to 100 (colony counts in control conditions). Results are shown as the mean± s.d. (n = 2). (A) Patient CMLPh-003 carrying NRAS G12R mutation. (B) Patient CMLPh-006 carrying KRAS A146 V mutation. (C) Patient CMLPh-039 carrying NRAS G12R mutation. (D) Patient CMLPh-010 carrying KIT D816 V mutation. (E) Patient CMLPh-040 carrying CSF3R T618I mutation.

Hierarchical architecture of aCML patients

The temporal reconstruction of the different mutations occurring in a clonal disorder can have important biological, prognostic and therapeutic repercussions. For these reasons, in patients whose bone marrow cells were available, we studied clonal evolution through the analysis of individual leukemic clones by methylcellulose assays (Fig. 3). The clonal architecture could be reconstructed in 7 patients. According to exome sequencing, patient CMLPh-003 was mutated in both SETBP1 and NRAS. Clonal analysis confirmed the presence of SETBP1 G870S in all the tested clones, while heterozygous NRAS G12R mutation was detected in 67% (Fig. 3A). Notably in the remaining 33% another heterozygous NRAS variant, G12D, was detected. Retrospective reanalysis of exome data confirmed the presence of the newly identified variant, which had been previously filtered-out from exome data because of the low frequency. Patient CMLPh-005 carried mutations in ASXL1, CBL and SETBP1 genes. Targeted analysis performed on 68 clones revealed a complex, branching evolution, with 63 clones carrying all the 3 variants. Of them, 47 (74.6%) had a heterozygous and 16 (25.4%) a homozygous CBL variant. Four clones (4.2%) carried ASXL1 and SETBP1 but not CBL mutations, while only a single clone was mutated in ASXL1 and CBL in absence of SETBP1 mutations, indicating that CBL mutations occurred independently in two different subclones (Fig. 3B). Allelic imbalance analysis of exome data using CEQer21 revealed that CBL homozygosity was caused by a telomeric somatic uniparental disomy. Patient CMLPh-008 was mutated in ETNK1 and EZH2, carrying both EZH2 D608G and R634H variants. All the 27 clones analyzed carried ETNK1 mutation, while one was WT for both EZH2 D608G and R634H, indicating that both mutations occurred late in the clonal evolution of this patient (Fig. 3C). Patient CMLPh-013 carried ASXL1, ETNK1, NRAS and SETBP1 mutations. Of the 39 clones analyzed, 34 (82.9%) showed the coexistence of all mutations, 4 were mutated in ASXL1, ETNK1 and NRAS and 1 in ETNK1 and NRAS, suggesting that ETNK1 and NRAS were early events, ASXL1 an intermediate one and SETBP1 a late occurring mutation (Fig. 3D). Patient CMLPh-019 was characterized by the presence of a complex mutational status, with mutations occurring in SETBP1, ETNK1, ASXL1 and CBL genes. Targeted resequencing analysis revealed the presence of all the 4 variants in 44/60 (73.3%) clones; in 15/60 (25%) we detected the presence of mutated ETNK1, ASXL1 and CBL. Of these 15 clones, 33% carried heterozygous and 67% homozygous CBL mutations. In one clone (1.7%) we detected heterozygous ETNK1 and homozygous CBL, indicating a strong selective pressure towards the acquisition of homozygous CBL mutations (Fig. 3E). Allelic imbalance analysis of CMLPh-019 exome revealed that CBL homozygosity was caused by a somatic uniparental disomy event occurring in the telomeric region of the long arm of chromosome 11 (Supplementary Fig. 4, http://links.lww.com/HS/A105). Patient CMLPh-022 was mutated in both SETBP1 and CSF3R. Clonal analysis found the presence of SETBP1 in all the 40 clones analyzed, while CSF3R mutation was detected in 47.5% of colonies, suggesting that CSF3R mutations occurred later than SETBP1 (Fig. 3F). Patient CMLPh-026 was mutated in ASXL1 and SETBP1. Targeted analysis revealed the presence of mutated ASXL1 in all the 58 clones analyzed, while heterozygous SETBP1 mutation was detected in 50 clones (86.2%), indicating that ASXL1 mutations occurred earlier than SETBP1 ones (Fig. 3G).

Figure 3
Figure 3:
Clonal architecture of aCML patients. Schematic representation of the clonal architecture of 7 aCML patients whose bone marrow mononuclear cells were grown in semisolid medium and underwent targeted resequencing based on previously identified somatic mutations. CBLhet indicates a heterozygous somatic mutation; CBLhom indicates a homozygous somatic CBL mutation.

Altogether, these results indicate that, when ETNK1 and SETBP1 mutations are co-present, ETNK1 variants occur earlier in the clonal evolution history of aCML, while SETBP1 mutations generally represent late events; interestingly, in two cases where ASXL1 was mutated together with SETBP1, ASXL1 mutations occupied an intermediate hierarchical position. CBL mutations, when present, showed a strong tendency toward reaching homozygosity through somatic uniparental disomy.

Combined targeting treatment of ETNK1 and NRAS mutations

In patient CMLPh-042, ETNK1 G245 V and NRAS G12D were found to be early mutational events (Supplementary Table 10, http://links.lww.com/HS/A115). From the available sequencing data, we could not determine which of the 2 mutations arose first. We decided to target both mutations ex vivo: trametinib was used to block the NRAS pathway: at 10 nM it was able to reduce ERK phosphorylation (Supplementary Fig. 5, http://links.lww.com/HS/A105). ETNK1 was targeted with the use of phosphoethanolamine (P-Et), which was found to abrogate the effects of ETNK1 mutations (Fontana et al Nat Commun., in press). Colonies grown from the patient showed a strong synergistic effect of the combination treatment of trametinib 10 nM and P-Et 1 mM, according to the Bliss Independence Principle31 (expected additive effect of P-Et 1 mM + trametinib 10 nM combination: 146 colonies; observed colonies: 0; Fig. 4A, C).

Figure 4
Figure 4:
Effects of trametinib and phosphoethanolamine in patient CMLPh-042. (A) Colony-forming assay at the onset: bone marrow derived cells were left untreated or treated with phosphoethanolamine 1 mm, trametinib 10 nM, trametinib 100 nM, or combination of the two drugs. Colonies were counted after 15 days. (B) Colony-forming assay performed at relapse after treatment with trametinib 1 mg/day.(C) Number of colonies at the onset. (D) Number of colonies at relapse.

Subsequently, a patient-derived xenograft (PDX) model was established. Treatment with 1 mg/kg trametinib allowed animals to live until the end of the experiment, while untreated animals showed signs of leukemia and were sacrificed (Fig. 5). Due to the limited number of animals transplanted, statistical significance was borderline (p = 0.07). However, analysis of human cells in peripheral blood assessed by flow cytometry showed a complete reduction of human CD45+ cells in the treated animals compared to untreated ones (Fig. 5), and immunohistochemistry revealed the absence of human CD45 cells in the treated animals in both spleen and bone marrow, at difference from untreated ones (Fig. 5).

Figure 5
Figure 5:
In-vivo experiments. (A) Overall survival (OS) of mice treated with 1 mg/kg trametinib by oral gavage once a day (red line) as compared with controls (black line). OS were analyzed using Kaplan-Meier plot and the log-rank test. (B) Analysis of human CD45 cells in peripheral blood assessed by flow cytometry in PDX models treated with 1 mg/kg trametinib compared to controls. Representative plots are shown. (C) Immunohistochemistry (human CD45 expression) of bone and spleen PDX models treated with 1 mg/kg trametinib by oral gavage once a day compared to controls. Representative images are shown. Scale bar: 200 μm.

CMLPh-042 was treated on a named patient protocol with trametinib, at 1 mg/day. Unfortunately, no clinical grade P-Et was available. A hematological response was obtained with normalization of WBC, reduction of approximately 50% in the volume of the spleen and discontinuation of hydroxyurea (2 g/day). After 3 months of treatment the patient became severely anemic, transfusion dependent, the spleen volume and leukocyte numbers started to grow again, and trametinib was discontinued at month 4. Colonies grown at this time point showed complete resistance to trametinib (Fig. 4B, D), a testimony to the selection process which developed over 3 months, in spite of only a transient clinical response. Conventional cytogenetic analysis performed at the time of relapse revealed the presence of isocromosome 17q (karyotype: 46,Y,i(17)(q10)[16]/46,XY[4]) with the loss of one copy of 17p, which includes the p53 locus, suggesting a possible mechanism of acquired resistance to MEK inhibition (Supplementary Fig. 6, http://links.lww.com/HS/A105).32 These data confirm that targeting the effects of a single mutation event can result in a clinical response, but of limited duration.

Patients stratification based on RNA-sequencing data

RNA-sequencing was performed on all 43 patients; no fusions were detected.33 Stratification based on whole-transcriptome data22 identified two clearly different populations (26 and 17 patients) in terms of Overall Survival (OS), with 2 year OS of 69.23% [95% IC: 48.21%-86.67%] and 35.29% [95% IC: 14.21%-61.67%] respectively (log-rank test for trend: p = 0.004, Fig. 6; Supplementary Table 11, http://links.lww.com/HS/A116). The group with better prognosis showed a higher frequency of ETNK1 mutations (22.7% vs 6.7%; hypergeometric test: p = 0.032). We performed differential gene expression analysis to detect genes that were differentially expressed between the two patients’ populations. Functional enrichment annotation of the differentially expressed genes revealed several biological processes involved (Supplementary Tables 12, http://links.lww.com/HS/A117–13, http://links.lww.com/HS/A118), including gene transcription and cell differentiation, cell cycle regulation, mitochondrial activity, DNA repair. From these lists, we selected cancer-related GO terms (Fig. 7A) as well as cancer-related pathways (Fig. 7B) to perform clustering analysis. These analyses clearly showed 2 distinct clusters based on patients’ outcome: cancer-related terms were highly enriched in patients with poor prognosis. Further analysis revealed 38 overexpressed genes in the group with negative clinical outcome (t-test p value adjusted for false discovery rate,34 padj < 0.01; Table 1). To further reduce the number of classifier genes, we then considered expression data for the 3 most significant genes from the previous list (namely DNPH1, GFI1B, and PARP1). Using these 3 genes, we built a classifier model that is able to separate patients according to the respective subtype (better vs worse prognosis). The results showed that overexpression of these 3 genes is highly predictive of poor prognosis, and a random forest algorithm27 applied to the 3 most significant genes achieves a 95.03% accuracy (out-of-bag error rate of 4.65%) assessed by means of 10 fold cross validation (Fig. 8).

Figure 6
Figure 6:
Overall survival curve (Kaplan-Meier curve). Overall survival curve censored at 24 months shows significantly different outcomes (low-rank p = 0.004).
Figure 7
Figure 7:
Gene ontology and pathway heatmaps. (A) Heatmap showing expression for a set of selected cancer-related GO terms is presented. (B) Heatmap showing expression for a set of selected cancer-related pathways is presented.
Table 1 - Differentially expressed genes between the two clusters of patients.
Gene Name Median Good Prognosis Median Bad Prognosis Log2 fold change Good prognosis/Bad prognosis p value Two-Sided OncoScore Is Oncogene?
AIMP2 54.50 185.00 −1.76 0.00004 50.49 1
AURKA 60.50 184.00 −1.60 0.00407 75.65 1
CDK4 203.00 802.00 −1.98 0.00074 74.80 1
CHST10 20.50 78.00 −1.93 0.00317 30.00 1
DNAJC11 199.50 532.00 −1.42 0.00006 33.33 1
DNPH1 18.00 95.00 −2.40 0.00003 45.99 1
EI24 138.00 430.00 −1.64 0.00200 45.75 1
ERCC2 119.00 304.00 −1.35 0.00048 72.57 1
EXTL2 24.50 99.00 −2.01 0.03693 42.60 1
FAM189B 78.50 227.00 −1.53 0.00029 0.00 0
GADD45GIP1 57.50 274.00 −2.25 0.00010 52.13 1
GATA1 26.50 318.00 −3.58 0.00188 44.21 1
GFI1B 31.00 348.00 −3.49 0.00203 44.22 1
GNA12 175.50 502.00 −1.52 0.00103 34.94 1
H2AFX 84.50 376.00 −2.15 0.00225 70.66 1
HSPD1 825.00 2040.00 −1.31 0.00686 31.55 1
IDH2 614.00 1830.00 −1.58 0.00136 78.73 1
KEAP1 160.50 445.00 −1.47 0.00005 41.29 1
KIT 136.50 722.00 −2.40 0.00880 31.28 1
MEN1 216.00 598.00 −1.47 0.00053 88.03 1
MYBBP1A 212.50 599.00 −1.50 0.00017 55.23 1
MYC 177.50 1031.00 −2.54 0.00095 69.59 1
NME1 13.00 79.00 −2.60 0.00165 82.83 1
PA2G4 323.50 960.00 −1.57 0.00224 61.57 1
PARP1 329.50 1437.00 −2.12 0.00227 56.94 1
PBX1 34.50 186.00 −2.43 0.00173 79.54 1
PDZK1IP1 3.00 27.00 −3.17 0.00371 50.88 1
PIR 3.50 23.00 −2.72 0.00122 12.07 0
POLRMT 196.00 485.00 −1.31 0.00005 14.73 0
RITA1 34.00 126.00 −1.89 0.00004 12.30 0
RNF43 22.50 58.00 −1.37 0.00460 80.49 1
SALL2 21.00 50.00 −1.25 0.00734 58.62 1
SLC39A4 29.50 101.00 −1.78 0.00059 18.08 0
SMARCB1 264.50 608.00 −1.20 0.00172 80.79 1
TAL1 62.50 525.00 −3.07 0.00057 74.33 1
TCF3 223.00 769.00 −1.79 0.00005 47.38 1
TIMP3 16.00 190.00 −3.57 0.00019 50.28 1
TP53 159.00 608.00 −1.94 0.00006 90.62 1
The table reports a list of 38 genes significantly higher expressed in the cluster with bad prognosis. Median expression values for the 2 clusters, Log2 fold change Good vs Bad prognosis, and t-tests to assess their differences are also reported. Their OncoScore as well as their classification as oncogenes (marked as 1 in the table) are presented.

Figure 8
Figure 8:
Heatmap showing fold change for the threetop differentially expressed genes used to classify good vs bad prognosis subtypes is presented.

Discussion

In the last decade, the application of NGS techniques dramatically improved our understanding of several issues in the biology of neoplasias. However, this knowledge has rarely been translated into better prognosis or treatment tools. In the present work, we analyzed both mutation profiles and RNA-sequencing expression data from a large cohort of aCML patients. aCML is a highly heterogeneous disorder characterized by both myelodysplastic and myeloproliferative features.1 Several mutations in different genes are responsible for the onset of the disease. In our cohort, mutations in ASXL1 gene were the most frequent alteration (16/37 cases), as already reported by other recent studies.35–37 Additional frequent mutations involved SETBP1, ETNK1, TET2 and RAS genes. This mutation profile is very close to that of a recent French report from Julien et al,38 where SETBP1 mutations occur in 30.3% of the aCML cases, ETNK1 in 7.4% of patients, while ASXL1 was found mutated in 68.8% of cases. Surprisingly, however, our profile is different from a recently published study,36 in which Zhang and colleagues report the frequency of SETBP1 mutations to be as low as 7.4% (2/27). The explanation for this difference is at present unclear; however, it may be caused by a different genetic background for the European/US aCML populations. In the context of the differential diagnosis of aCML and related clonal disorders such as CNL, CSF3R mutations are particular noteworthy.39–41 In our study, we found only one patient out of 37 carrying a CSF3R mutation. This result confirms the rarity of such event in the mutational landscape of aCML. In addition to the high and distinctive heterogeneity of this disease, the lack of detailed information regarding the clonal hierarchy of the mutations contributes to make treatment approaches to this disorder even more difficult. Given the genetic complexity of aCML, we tried to reconstruct the clonal hierarchy of the observed mutations in order to identify early versus late occurring mutations. The clonal architecture of 7 aCML patients was characterized by colony assays and targeted resequencing. Our findings suggest that, when present, ETNK1 variants occur at the initial stages of clonal evolution history of aCML. These results are in line with our recent findings regarding the role of ETNK1 in the induction of a mutator phenotype (Fontana et al Nat Commun., in press).

The knowledge of the clonal progression can be useful to suggest treatment priorities. The inclusion of early occurring mutations in the targeting strategy carries the advantage of hitting all leukemic cells. Surprisingly, it is important to note that, in contrast to gene expression results, the presence of mutations, either as single or combined events, and weighted by their respective OncoScore, failed to predict clinical outcome. This finding would seem to contradict what previously asserted by our group.6 Unfortunately, the rarity of the disease and the complexity and heterogeneity of the somatic drivers involved in this disorder, where SETBP1 is often present together with several other driver mutations such as ASXL1, ETNK1, TET2 or others, makes a clear dissection of the prognostic effect of individual variants extremely challenging. Probably, a larger cohort of aCML patients will be required to thoroughly analyze this point. In contrast, transcriptomic data demonstrated the presence of two distinct populations which significantly differed in terms of overall survival. Based on these findings, we identify a 3-genes signature capable of stratifying aCML patients according to their prognoses with high accuracy. The 3 identified genes are known oncogenes and play roles in cellular proliferation, DNA repair, tumor transformation (Supplementary Table 14, http://links.lww.com/HS/A119). A limitation of these results resides in the lack of a validation cohort which will need future work, and in the limited number of aCML patients under study, due to the rarity of the disease. The fact that mutation analysis did not prove clinically informative but gene expression profiles did, raises important considerations. While mutations certainly affect gene functions, several non-mutational mechanisms can also affect gene activity (eg, promoter methylation, histone modifications, gene amplifications and deletions). All these mechanisms result in altered gene expression, the analysis of which could provide clinical insights into the disease behavior. In addition, gene expression profiles also reflect the impact of a certain mutation on the genetic background of each individual patient.

Our ex vivo and in vivo results also indicate that the presence of actionable mutations could indeed inform therapy and drive synergistic combinations; in one case where NRAS and ETNK1 mutations were simultaneously present, trametinib gave evidence of clinical, albeit limited, therapeutic activity.

In conclusion, our work provides novel insights on aCML clonal evolution and suggests the presence of 2 subtypes of aCML characterized by distinct RNA expression profiles, showing different clinical outcomes. Further studies will be required to confirm our findings. In general, it will be important to obtain more insights into the molecular mechanisms governing aCML development and progression, and to convert them into better treatment strategy modalities, since no effective therapies are available to date for aCML and the outcome is almost invariably fatal.

Sources of Funding

This work was supported by Fondazione AIRC per la Ricerca sul Cancro 2018 (IG-22082) to RP, Fondazione AIRC per la Ricerca sul Cancro 2015 (IG-17727) to RP, Fondazione AIRC per la Ricerca sul Cancro 2017 (IG-20112) to CGP.

Acknowledgments

We kindly acknowledge the contributions of Michela Viltadi for technical help.

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