Abstract
Background
Most researches of chronic myeloid leukemia (CML) are currently focused
on the treatment methods, while there are relatively few researches on
the progress of patients’ condition after drug treatment. Traditional
biomarkers of disease can only distinguish normal state from disease
state, and cannot recognize the pre-stable state after drug treatment.
Results
A therapeutic effect recognition strategy based on dynamic network
biomarkers (DNB) is provided for CML patients’ gene expression data.
With the DNB criteria, the DNB with 250 genes is selected and the
therapeutic effect index (TEI) is constructed for the detection of
individual disease. The pre-stable state before the disease condition
becomes stable is 1 month. Through functional analysis for the DNB,
some genes are confirmed as key genes to affect the progress of CML
patients’ condition.
Conclusions
The results provide a certain theoretical direction and theoretical
basis for medical personnel in the treatment of CML patients, and find
new therapeutic targets in the future. The biomarkers of CML can help
patients to be treated promptly and minimize drug resistance, treatment
failure and relapse, which reduce the mortality of CML significantly.
Electronic supplementary material
The online version of this article (10.1186/s12859-019-2738-0) contains
supplementary material, which is available to authorized users.
Keywords: Chronic myeloid leukemia (CML), Dynamic network biomarkers
(DNB), Differentially expressed genes (DEGs), Therapeutic effect index
(TEI), Pre-stable state, Treatment time
Introduction
Chronic myeloid leukemia (CML) is a clonal myeloproliferative disorder
of a pluripotent stem cell. It is mainly caused by the disorder of
differentiation and maturation of hematopoietic stem cells. The annual
incidence rate is about 1.3 per 100,000, and it is slightly more common
in males than in females. The main hallmark is the presence of the
Philadelphia chromosome, which is resulted from the balanced
translocation of chromosome t(9;22) (q34; q11) [[33]1]. At present, the
use of ABL kinase inhibitors (e.g. imatinib) for the treatment of CML
can inhibit the activity of BCR-ABL kinase effectively, inhibit the
malignant proliferation of leukemia cells, and extend the survival time
of patients significantly. During the treatment, there will be a stable
point in CML drug response [[34]2]. The condition of patients gradually
eases before it comes, and stabilizes after it comes. It’s difficult to
find the stable point only through clinical medicine. Therefore, it’s
urgent to discover and validate stable points through bioinformatics
for CML drug therapy.
Increasing evidences suggest that many mathematical models can
contribute to elucidating mechanisms and providing quantitative
predictions for cancer research [[35]3], and the combination of model
and clinical information has provided useful suggestions for treatment
[[36]4]. Sasaki K et al. used the robust linear regression model to
define the best fit average molecular response, where the average
molecular levels were defined. Predicting the highest probability of
reaching optimal values proposed by the model to decide whether to
continue treatment [[37]5]. In addition, traditional biomarkers cannot
distinguish the state of critical point before the disease worsens.
Based on this situation, Chen LN et al. [[38]6] proposed a theory of
dynamic network biomarkers (DNB) to analyze the dynamic signals of DNB
when the system was in the critical point state, and put forward three
universal properties of DNB [[39]7, [40]8]. Markus AD et al. showed
that the critical point will enter the disease state quickly under
certain triggering factors, so the critical point was treated as an
early warning signal for complex diseases [[41]9]. Lesterhuis WJ et al.
found that the use of dynamic network biomarkers can identify critical
points in the state of the system by comparing dynamic biomarkers with
static biomarkers of complex diseases [[42]10]. Combined with the
advantages of high-throughput sampling of gene expression data, many
discussions have shown that DNB is promising candidate biomarker for
clinical trials and clinical detection of complex diseases [[43]11].
Based on the advanced high-throughput technology, gene or protein
expression data with dynamic measurements can be obtained. In order to
detect the therapeutic effect of CML medications from a small amount of
high-throughput data, a therapeutic effect recognition strategy is
provided based on DNB for CML patients’ gene expression data. In the
study, the datasets divided into the treatment group and the control
group are used to select differentially expressed genes (DEGs) by
t-test. DEGs are clustered into 60 categories by hierarchical
clustering. Then, according to the three criteria for the
identification of DNB proposed by Chen, a group of 250 genes is
selected as DNB. Therefore, the therapeutic effect index (TEI) is
constructed to observe the dynamic change, and it can be used to
predict and determine when it is in pre-stable state. Finally,
functional enrichment analysis is performed on the DNB, and the role of
the DNB in CML is studied by KEGG enrichment analysis and literature
mining.
Materials and methods
Datasets
Three datasets, including [44]GSE33075, [45]GSE12211, and [46]GSE24493
from the National Center for Biotechnology Information’s Gene
Expression Omnibus (GEO) database are used to analyze treatment time.
Initially, datasets in CEL files are standardized by Robust Multichip
Averaging (RMA) implemented in the affy package, and return the log2
conversion intensity [[47]12], and the probe sets are mapped to unique
gene symbols by the averaging method. This study doesn’t consider probe
sets without corresponding gene symbols. Due to limited experimental
data, multiple GEO data are combined to obtain 39 chips. The
information of dataset is shown in Table [48]1. In the study, samples
of CML diagnosed are defined as control groups. 8927 genes can be
obtained from the same gene of each GEO dataset. The COMBAT method is
used to adjust the batch effect [[49]13]. The experiment Information of
dataset is shown in Table [50]2. Figure [51]1 shows the distribution of
box plots before and after removing batch effects.
Table 1.
The information of dataset
Dataset Probe Gene Diagnosis Treatment for 16 h Treatment for 7 days
Treatment for 1 month Normal
[52]GSE33075 45782 23507 9 - - 9 9
[53]GSE12211 21225 13506 - - 6 - -
[54]GSE24493 45782 23507 3 3 - - -
[55]Open in a new tab
Table 2.
The experiment information of dataset
Dataset Platform Imatinib used in the experiment
[56]GSE33075 [57]GPL570 400 mg imatinib mesylate (IM)/day
[58]GSE12211 [59]GPL571 400mg Glivec/day
[60]GSE24493 [61]GPL570 10 μM STI571 (Imatinib) for 16 h
[62]Open in a new tab
Note: STI571’s generic name is imatinib mesylate and its trade name is
Glivec
Fig. 1.
[63]Fig. 1
[64]Open in a new tab
The box plots of data expression. The combined dataset is visually
displayed by the gene box plot. On the left side, the three datasets
are merged without any transformation. On the right side, the three
datasets are merged with the COMBAT method. After removing batch
effects, the distribution of genes is more similar than before
The student’s t-test applied in the selection of DEGs is used to assess
the significance of DEGs between the control group and the treatment
group. The p-value calculated by t-test is used for the subsequent
filtering analysis with multiple testing corrections directly. Set the
p-value of 0.05 and the fold change of 1.5. The volcano plot is shown
in Fig. [65]2.
Fig. 2.
[66]Fig. 2
[67]Open in a new tab
The volcano plot of DEGs
Identify pre-stable state based on DNB
We assume the reference sample data is C(t), where the n-dimensional
vector represents the observed value or molecular concentration (e.g.
gene expression or protein expression) at time t (t=0, 1,...), e.g.
minutes, hours or days. Therefore, the Pearson correlation coefficient
(PCC) [[68]14] between the two genes x, y in the data from reference
sample can be calculated as
[MATH: PCC(x,y)=∑i=1
n(xi−x¯)(<
/mo>yi−y¯)∑i=1
n(<
mi>xi−x¯)2∑i=1
n(<
mi>yi−y¯)2 :MATH]
1
Where x[i] and y[i] represent the i−th sample’gene expressions of gene
x and gene y in the reference sample, respectively.
[MATH: x¯
:MATH]
and
[MATH: y¯
:MATH]
represent the average gene expression of gene x and gene y in the
reference sample, respectively.
The reference sample data C(t) can be divided into two groups, the
control group C[control](t) and the treatment group C[treat](t), as
follows
[MATH: Ccontrol(t<
mo>)=(Ccontrol1(t),...,Ccontroln(t))
:MATH]
2
[MATH: Ctreat(t)=(Ctreat1(t),...,Ctreatn(t)) :MATH]
3
There are S[t] samples at time t for each gene or protein (see
Fig. [69]3). Due to large differences in the expression values of
various genes or proteins, the expression data is standardized as
follow
[MATH: C~=Ctreat−mean(Ccontrol)SD(C<
mrow>control) :MATH]
4
Fig. 3.
[70]Fig. 3
[71]Open in a new tab
Sampling time and samples for the measured high throughput data
Where
[MATH: C~
:MATH]
represents the standardized expression data for gene or protein of each
sample. mean(C[control]) and SD(C[control]) are the mean and standard
deviation in control samples, respectively. Then the standardized
matrix is showed
[MATH: C~=c11~c12~...c1t<
/mi>~c21~c22~...c2t<
/mi>~..
..........<
/mi>cn1<
/mn>~cn2<
/mn>~...cnt~<
/mtd> :MATH]
5
where
[MATH: cnt~
:MATH]
denotes the standardized data of the n−th reference sample at time t.
Potential DNB modules can be detected because of the gene expression
for a specific sample. For specific samples, DEGs are clustered by
hierarchical clustering analysis. According to the three criteria of
DNB identification proposed by Chen [[72]15], the optimal group of
genes or proteins is selected as DNB and is labeled as C[DNB], the rest
groups are labeled as C[other]. During disease treatment, a key point
is defined as pre-stable state, where the change of DNB is relatively
stable after treatment, and the state changes sharply before pre-stable
state. After identifying the DNB, the TEI at each time can be
constructed based on the following three criteria:
(i) As the system approaches the pre-stable state, the average
coefficient variation (CV) of molecules in this DNB group decreases
rapidly and then approaches the CV value of health.
(ii) The average PCCs of molecules in this DNB group decreases rapidly
in the absolute value and then approaches the PCC value of health.
(iii) The average PCCs of molecules between this DNB group and outside
of DNB group increases rapidly in the absolute value and then
approaches the OPCC value of health. Therefore, TEI at each time can be
constructed as:
[MATH: TEIt
=CVt×PCCt
OPCC
t :MATH]
6
where
[MATH: CVt
=SD(C<
mrow>DNB(t)
)mean(CDNB(t)
) :MATH]
7
[MATH: PCCt
=cov(c
i1t<
/mi>,ci2t)σ(ci1<
mi>t)σ(c<
/mi>i2t) :MATH]
8
[MATH: OPCC
t=cov(c
it,
cjt)σ(cit)σ(<
/mo>cjt)<
/mtd> :MATH]
9
(i=1, 2,..., the number of DNB)(j=1, 2,..., the number outside of
DNB)Where PCC[t] is the average PCC of the DNB group at time t in
absolute value. OPCC[t] is the average PCC between the DNB group and
the outside of DNB group at time t in absolute value. CV[t] is the
coefficient of variation of the DNB group at time t. According to the
characteristics of the treatment, the TEI value changes slowly at the
beginning of treatment, and decreases rapidly to be the lowest(i.e.,
reaches the pre-stable state) after treatment for a period of time,
then approaches the TEI value of health.
Result
Based on the gene expression of the control group and the treatment
group, 321 DEGs are selected by t-test and clustered into 60 categories
by correlation analysis. A group of 250 genes is identified as the DNB
(Additional file [73]1), where 43 genes relate to CML closely
(Additional file [74]2). In order to clarify the time in the treatment,
Fig. [75]4 shows the changes of four indices in detail. In the progress
of imatinib treatment for CML patients, the CV value of DNB decreases
gradually in Fig. [76]4a. The CV value is the lowest and closest to
health value at time 3 (i.e., imatinib treatment for 1 month). The PCC
value is the lowest at time 3, indicating the correlations of DNB
decreases gradually in the process of imatinib treatment and the
condition eases gradually in Fig. [77]4b. Although the change of the
OPCC is not obvious in Fig. [78]4c, the TEI value is the lowest at time
3 and closest to the TEI value of health in Fig. [79]4d. Therefore, the
most significant physiological effect occurs at time 3, indicating that
the condition of CML patients is relieved significantly and become
normal after imatinib treatment for 1 month.
Fig. 4.
[80]Fig. 4
[81]Open in a new tab
The therapeutic effect index of CML. The abscissa represents time t. On
the timeline, 1 represents imatinib for 16 h, 2 represents imatinib for
3 days, 3 represents imatinib for 1 month, and 4 represents normal. a
The average coefficient variation (CV) of DNB. b The average PCC of
DNB. c The average PCC between the DNB group and outside of the DNB
group. d The TEI of DNB
To analyze the DNB dynamics, we discusses the molecular mechanism of
disease from the perspective of the system by protein-protein
interactions (PPI) in Fig. [82]5. It can be found that most genes in
DNB interact strongly and most of the 43 DNB genes associated with CML
have been shown to be most interactive. We also graphically demonstrate
the dynamic changes in DNB with 4 sampling points in Fig. [83]6, which
clearly shows the significance of the DNB in terms of expression
variations and network structures near the pre-stable point (1 month).
Fig. 5.
[84]Fig. 5
[85]Open in a new tab
Protein-Protein interaction (PPI) network for part of DNB. PPI network
discusses the molecular mechanism of disease from the perspective of
the system. A PPI network is set up for 250 DNBs, an interaction score
of 0.7 is set, and genes not in the network are deleted. A PPI network
of 228 genes is obtained, and it is found that most genes in DNB
interact strongly and most of the 42 genes associated with CML have
been shown to be most interactive
Fig. 6.
[86]Fig. 6
[87]Open in a new tab
Dynamic changes in DNB (250 genes) subnetwork (43 genes) with 4
sampling points. For CML, we show the dynamic evolution of the network
structure of the identified DNB subnetwork with 4 sampling points. (a)
DNB at 16 h. 43 genes, 631 lines (b) DNB at 7 days. 43 genes, 413 lines
(c) DNB at 1 month (the pre-stable state). 43 genes, 385 lines (d) DNB
in normal. 43 genes, 457 lines. Each point represents a gene, which is
gradually colored according to the standard deviation of the gene.
Lines between genes indicate the correlation between genes, calculated
by PCC, and the lines with weak correlation (|PCC|≤0.4) are deleted.
From these dynamic evolution charts, it can be clearly seen that the
DNB group provides important signals when the system approaches the
pre-stable point, the standard deviation of DNB genes becomes smaller
and tends to be stable after treatment for 1 month, correlation of DNB
genes is gradually weakened and the condition has eased and stabilized.
So, a strongly correlated observable subnetwork is also formed in terms
of expression variations and network connections
To further analyze the biological function of the DNB, a bioinformatics
database DAVID [[88]16] with Gene Ontology (GO) analysis and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway analysis is provided.
GO analysis can be divided into three parts: molecular function,
biological process and cellular composition. Some enriched GO functions
based on the identified genes in the DNB are listed in Table [89]3.
Some genes have been shown to be associated with CML. For example, on
the cellular level, CML is associated with a specific chromosomal
abnormality, T (9;22) is reciprocally transposed to form the
Philadelphia (PH) chromosome, and the C−ABL proto-oncogene on
chromosome 9 and the BCR (breakpoint cluster region) gene on chromosome
22 lead to the PH chromosome. The fusion of C−ABL and BCR is considered
to be the main reason of CML. CRK is considered as the major tyrosine
phosphorylated protein on recognition of CML neutrophils. PI3K is a
heterodimer of regulatory and catalytic subunits, and the protein
encoded by PIK3R2 is a regulatory component of PI3K. The protein
encoded by TGFBR2 is a transmembrane protein that has a protein kinase
domain, forms a heterodimeric complex with TGF- β receptor type-1, and
binds TGF- β. This receptor/ligand complex phosphorylates proteins,
which then enter the nucleus and regulate the transcription of genes
related to cell proliferation, cell cycle arrest, wound healing,
immunosuppression, and tumorigenesis [[90]17]. The genes mentioned are
associated with the pathogenicity of CML and may also regulate and
provide an early warning signal for the process of CML treatment.
Table 3.
Functional enrichment of GO for part of DNB
Enriched items Genes p-value
Cell surface receptor signaling pathway (GO:0007166) GCD3G, CD3D, CD8A,
CD3E, CCR1, CD247, CXCR1, FADD, IL7R, IL17RA, IFNAR2, LILRB2, TNFSF10,
MYD88, CCR5, LILRB3, CD2, KLRD1, CD14, CD27, CD28 5.57E-21
Immune response (GO:0006955) IL18RAP, AQP9, CD8A, GZMA, CCR1, HLA-DMB,
GZMH, IL7R, HLA-DMA, LILRB2, TNFRSF1B, TNFSF10, CCR5, IL4R, IRF8,
ZAP70, HLA-DPA1, CD27, PTAFR, HLA-DRA 1.50E-13
T cell costimulation (GO:0031295) CD3G, TRAC, CD3D, CD3E, LGALS1,
CD247, LCK, HLA-DPA1, CD5, HLA-DRA, CD28 4.77E-12
T cell receptor signaling pathway (GO:0050852) CD3G, TRAC, CD3D, CD3E,
GATA3, CD247, LCK, ZAP70, HLA-DPA1, HLA-DRA, PIK3R2, CD28 1.61E-10
Apoptotic process (GO:0006915) PRF1, GZMA, LGALS1, LY86, TGFBR2, FADD,
GZMB, ZBTB16, GZMH, TNFSF10, MYD88, RIPK1, MAP3K1, CD2, CTSH, CD14
1.03E-07
[91]Open in a new tab
Functional enrichment analysis showed that DNB gene is involved in
biological processes such as cell surface receptor signaling pathway,
immune response, cell adhesion and apoptotic process. The specific
immune responses of CML contribute to the control of the disease. For
example, the low expression of antigens recognized by CD247 leads to
impaired immune response [[92]12], and is also associated with T cell
co-stimulation and cell surface receptor signaling pathways. TNF
receptor family member CD27 is expressed on bone marrow CML
stem/progenitor cells in the bone marrow of CML patients. CD27
signaling promotes the growth of BCR/ABL^+ leukemia cells by activating
the Wnt pathway. Therefore, adaptive immunity contributes to leukemic
progression. Targeting CD27 on the leukemia stem cells (LSCs) may
represent an attractive therapeutic approach in blocking the Wnt/
β-catenin pathway in CML [[93]13]. Changes in LGALS1 expression trigger
changes in MDR1 expression and resistance to cytotoxic drugs, and MDR1
shows high efficacy in the treatment of BCR-ABL-positive CML, so LGALS1
may be considered as a novel target for combination therapy, used to
improve the efficacy of imatinib in the treatment of CML [[94]18].
Also, it is involved in the process of apoptosis. TGFBR2 regulates cell
proliferation and participates in apoptotic processes.
According to KEGG pathway enrichment analysis, at least 50% of DNB
genes are closely related to hematopoietic cell lineage,
cytokine-cytokine receptor interaction, apoptosis, chronic myeloid
leukemia MAPK signaling pathway, PI3K-Akt signaling pathway and other
gene pathways. From the results, BCR, TGFBR2, ABL1, CRK, and PIK3R2
play a decisive role in the pathogenesis of CML from CML pathway in
Table [95]4. Hematopoietic cell lineage, apoptosis, MAPK signaling
pathway, and PI3K-Akt signaling pathway play a key role in the process
of CML treatment in Fig. [96]7. The PI3K-Akt signaling pathway is
activated by a variety of cellular stimuli or toxic insults and
regulates basic cellular functions such as transcription, translation,
proliferation, growth, and survival. The mitogen-activated protein
kinase (MAPK) cascade is a highly conserved module involved in a
variety of cellular functions, including cell proliferation,
differentiation, and migration. Apoptosis is a genetically programmed
process for the elimination of damaged or redundant cells by activation
of caspases (aspartate-specific cysteine proteases).
Table 4.
Functional enrichment of KEGG pathways for part of DNB
Term Description Genes p-value
hsa04640 Hematopoietic cell lineage CD3G, CD8A, CD3D, CD3E, IL7R,
FLT3LG, CD1D, IL4R, MS4A1, CD2, CD5, CD14, HLA-DRA 3.23E-11
hsa04060 Cytokine-cytokine receptor interaction IFNAR2, TNFRSF1B,
IL2RB, TNFSF10, IL18RAP, CCR5, IL4R, CCR1, TGFBR2, CXCR1, IL7R, CD27,
IL17RA, FLT3LG 3.87E-07
hsa04210 Apoptosis TNFSF10, NTRK1, RIPK1, FADD, CAPN2, PIK3R2 3.82E-04
hsa05220 Chronic myeloid leukemia BCR, TGFBR2, ABL1, CRK, PIK3R2
0.005920
hsa04010 MAPK signaling pathway DUSP4, RASGRP1, NTRK1, MAP3K1, TGFBR2,
CRK, CD14 0.043672
hsa04151 PI3K-Akt signaling pathway FGFR2, IFNAR2, IL2RB, CD19, IL4R,
RXRA, ITGB7, PIK3CD, RAC1, JAK3, IL7R, PIK3R2 0.059021
[97]Open in a new tab
Fig. 7.
[98]Fig. 7
[99]Open in a new tab
Key biological pathways with DNB genes in CML pathway. By splitting the
KEGG pathway map, a portion of the genes associated with DNB are
extracted and finally the sub-pathway is obtained, as shown in the
above figure. Among them, blue represents DNB, red represents genes in
the CML pathway, and yellow represents genes of CML pathway’s pathways.
Lines between genes represent various relationships between genes
According to literature mining, it has been found that the chemokine
receptor CCR5 plays a role in determining blast malignant properties
and localization of extramedullary infiltrations in acute myeloid
leukemia (AML) [[100]19]. The cell surface target CD52 is expressed on
neural stem cells (NSCs) in a group of patients with AML. CD52 is a
novel prognostic NSC marker and a potential NSC target in patients with
AML and may have clinical significance [[101]20]. GATA3 is a sensitive
and specific marker for diagnosing acute leukemia with T-cell
differentiation and may be a useful complement to the panel of
immunophenotypic markers for the diagnostic evaluation of acute
leukemia [[102]21]. In addition, genes such as CEBPD, FUT4, LILRB1 and
MVP play a role in the cure, the treatment, and clinical drug
resistance of AML [[103]22], providing theoretical directions for the
treatment of CML and finding new therapeutic targets in future.
Discussion
At present, most researches of CML are focused on the treatment, while
a few on the progression of patients after drug treatment. Traditional
biomarkers of disease can only distinguish normal state from disease
state, and cannot recognize pre-stable state after drug treatment. CML
patients are often resistant to conventional chemotherapeutic agents
and tyrosine kinase inhibitors. Therefore, the key of the treatment is
to control the progression of disease treatment. In order to detect the
therapeutic effects of imatinib from a small amount of high-throughput
data, a therapeutic effect recognition strategy based on DNB is
provided for CML patients’ gene expression data. In the study, the
student’s t-test applied in the selection of DEGs is used to assess the
significance of DEGs between the control group and the treatment group.
DEGs are clustered into 60 categories by hierarchical clustering, and a
group of 250 genes satisfies the three criteria of DNB. Besides, the
values of CV, PCC, and OPCC are calculated to construct TEI which is
used to detect pre-stable state of CML. TEI in treatment progression
shows 1 month is the best time for curative effect. In pre-stable
state, the OPCC is not obvious. The other three indices are
significantly related to the theory. After treatment for 1 month, the
CV of the DNB gene becomes smaller and closer to the CV value at the
time of health. The correlation between genes is gradually weakened,
the condition is relieved and tends to be stable.
Among the 250 genes of DNB, 43 genes have been shown in pathogenesis
maps of CML, and BCR, TGFBR2, ABL1, CRK, and PIK3R2 may be the key
genes leading to the progression of CML, and the remaining genes have
also been found in other types of leukemia like AML. It provides a
certain theoretical direction to search for target genes in the future.
In clinical medicine, imatinib treatment of CML is difficult to achieve
recovery. Most patients adhere to medication after the condition is
relieved, so that the patients can survive for a long time. Only a
small number of patients can be cured and discontinued.
Conclusions
The results of this study intend to provide a certain theoretical
direction and theoretical basis for medical personnel in the treatment
of CML patients, and find new therapeutic targets in the future. The
biomarkers of CML can help patients to be treated promptly and minimize
drug resistance, treatment failure and relapse, which reduce the
mortality of CML significantly. Due to the limited data, there are a
few sampling points for collection and it is impossible to predict the
pre-stable state fully. In the future we will focus on this important
topic and continue to refine the algorithm in later research.
Additional file
[104]Additional file 1^ (61.1KB, pdf)
DNB genes of CML. Based on the gene expression of the control group and
the treatment group, 321 DEGs are selected by t-test and clustered into
60 categories by correlation analysis. A group of 250 genes is
identified as DNB. Supporting Information includes all DNB genes, where
215 genes are down-regulated and 35 genes are up-regulated. (PDF 62 kb)
[105]Additional file 2^ (72.5KB, pdf)
Key genes in CML pathway. Among the DNB genes, there are 43 genes
related to CML closely. Supporting Information includes the key genes
and the pathway each gene belongs to. (PDF 73 kb)
Acknowledgements