Download BCR–ABL Transcript Dynamics Support the Hypothesis That

Published OnlineFirst September 8, 2011; DOI: 10.1158/1078-0432.CCR-11-0396
Clinical
Cancer
Research
Cancer Therapy: Clinical
See commentary by Smith and Shah, p. 6605
BCR–ABL Transcript Dynamics Support the Hypothesis That
Leukemic Stem Cells Are Reduced during Imatinib Treatment
Andrew M. Stein1, Dean Bottino2, Vijay Modur1, Susan Branford3, Jaspal Kaeda4, John M. Goldman5,
Timothy P. Hughes3, Jerald P. Radich6, and Andreas Hochhaus7
Abstract
Purpose: Imatinib induces a durable response in most patients with Philadelphia chromosome–positive
chronic myeloid leukemia, but it is currently unclear whether imatinib reduces the leukemic stem cell (LSC)
burden, which may be an important step toward enabling safe discontinuation of therapy. In this article, we
use mathematical models of BCR–ABL levels to make inferences on the dynamics of LSCs.
Experimental Design: Patients with at least 1 BCR–ABL transcript measurement on imatinib were
included (N ¼ 477). Maximum likelihood methods were used to test 3 potential hypotheses of the dynamics
of BCR–ABL transcripts on imatinib therapy: (i) monoexponential, in which there is little, if any, decline in
BCR–ABL transcripts; (ii) biexponential, in which patients have a rapid initial decrease in BCR–ABL
transcripts followed by a more gradual response; and (iii) triexponential, in which patients first exhibit a
biphasic decline but then have a third phase when BCR–ABL transcripts increase rapidly.
Results: We found that most patients treated with imatinib exhibit a biphasic decrease in BCR–ABL
transcript levels, with a rapid decrease during the first few months of treatment, followed by a more gradual
decrease that often continues over many years.
Conclusions: We show that the only hypothesis consistent with current data on progenitor cell turnover
and with the long-term, gradual decrease in the BCR–ABL levels seen in most patients is that these patients
exhibit a continual, gradual reduction of the LSCs. This observation may explain the ability to discontinue
imatinib therapy without relapse in some cases. Clin Cancer Res; 17(21); 1–10. 2011 AACR.
Introduction
Imatinib is a tyrosine kinase inhibitor (TKI) specifically
developed to inhibit BCR–ABL, the oncoprotein encoded
by the BCR–ABL fusion gene that results from the reciprocal
translocation of chromosomes 9 and 22 (1, 2), which is the
hallmark of chronic myeloid leukemia (CML). Despite the
high response rates in patients with CML treated with
imatinib (3), it is unknown whether responses can be
maintained indefinitely after treatment discontinuation in
most patients. To safely discontinue therapy, residual long-
Authors' Affiliations: 1Novartis Institutes for BioMedical Research, Inc.,
Cambridge, Massachusetts; 2Novartis Pharmaceuticals Corporation, East
Hanover, New Jersey; 3Department of Haematology, SA Pathology, Uni€matologie und Onkologie,
versity of Adelaide, Adelaide, Australia; 4Ha
€tsmedizin, Charite Virchow Klinikum, Berlin, Germany; 5HaemaUniversita
tology Department, Hammersmith Hospital, London, United Kingdom;
6
Clinical Research Division, Fred Hutchinson Cancer Research Center,
€matologie/Onkologie, Klinik fu
€r
Seattle, Washington; and 7Abteilung Ha
€tsklinikum Jena, Jena, Germany
Innere Medizin II, Universita
Note: Supplementary data for this article are available at Clinical Cancer
Research Online (http://clincancerres.aacrjournals.org/).
Corresponding Author: Andrew M. Stein, Oncology, Novartis Institutes for
BioMedical Research, Inc., 45 Sidney St. Cambridge, MA 02139. Phone:
617-256-7600; Fax: 609-981-4102; E-mail: [email protected]
doi: 10.1158/1078-0432.CCR-11-0396
2011 American Association for Cancer Research.
term leukemic stem cells (LSC) may need to be targeted by
the TKI (4). For the purpose of this analysis, LSCs are
defined as tumor-initiating cells (i.e., cells that have the
capacity for long-term self-renewal and are capable of
repopulating all hematopoietic cell compartments). This
definition applies regardless of whether the LSCs are derived
from normal stem cells or from transformed progenitors
(5).
Two contrasting sets of data pose a dilemma in understanding the effect of prolonged therapy on LSC populations in CML. First, in vitro experiments suggest that quiescent LSCs are not affected by imatinib, at least not detectably
over the 3- to 12-day duration of the experiments (6, 7). In
contrast, case reports (8–13) and data from a clinical trial
(14) have described successful discontinuation from imatinib treatment after the achievement of low or undetectable
levels of BCR–ABL in the blood, suggesting that long-term
control of LSCs was obtained. To understand the dynamics
of the LSC population over the course of therapy, we
modeled responses to imatinib among patients treated on
the landmark International Randomized Study of Interferon and STI571 (IRIS) trial.
Hematopoiesis is a process in which a relatively small
number of stem cells in the bone marrow produce a large
number of differentiated blood cells. An analogous process
happens in the leukemic cells of patients with CML (Fig. 1;
ref. 15). At diagnosis, patients with CML have an unknown
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Published OnlineFirst September 8, 2011; DOI: 10.1158/1078-0432.CCR-11-0396
Stein et al.
Translational Relevance
With the success of tyrosine kinase inhibitors (TKI) in
inducing sustained responses in patients with chronic
myeloid leukemia (CML), the question of whether
patients can safely discontinue therapy and achieve
"cure" has become of paramount importance in the
field. Although imatinib induces strong responses in
many patients with CML, it is unclear whether it targets
leukemic stem cells (LSC), which may be necessary for
patients to ultimately discontinue treatment and maintain their response. This article combines mathematical
modeling with recent clinical observations of progenitor
cell kinetics in the bone marrow to support the hypothesis that patients treated with imatinib exhibit a sustained, gradual reduction of LSCs. This model may
explain the sustained responses in many patients after
discontinuation of imatinib and may help to identify
patients who can safely discontinue TKI therapy.
number of LSCs that cycle between a proliferative and a
quiescent phase. Some proliferating LSCs differentiate and
become early progenitor cells (EPC), which themselves
proliferate but no longer have the capacity for long-term
self-renewal. After a series of approximately 20 cell divisions, the EPCs become late progenitor cells (LPC), which
undergo a series of maturation stages until they become
mature leukemic cells that migrate to the blood.
It has previously been reported that most patients who
undergo imatinib therapy exhibit an initial rapid decline in
the BCR–ABL levels, followed by a more gradual decline
(16, 17). A number of mathematical models have been
developed to explain the BCR–ABL dynamics. Some assume
that LSCs are reduced during imatinib therapy (18–20),
whereas others argue that the LSCs are not affected (16,
17, 21). In this work, we examine 3 different interpretations
of the biphasic BCR–ABL transcript dynamics that are
observed in most patients: (i) The proliferating-quiescent
hypothesis, in which initial response is due to the loss of
proliferating LSCs and long-term responses are due to
quiescent LSCs that die upon becoming proliferative (18,
19); (ii) The early stem cell hypothesis, in which initial
response is due to death of EPCs and the long-term response
is due to the reduction of LSCs that die upon becoming
proliferative (22); (iii) The late-early progenitors hypothesis, in which initial response is due to death of LPCs, longterm response is due to death of EPCs, and the LSCs are not
significantly depleted during imatinib therapy and may in
fact expand (refs. 16, 17; Fig. 2). The 3 hypotheses were
originally generated by 3 different modeling groups using
IRIS data from their respective cohorts: the proliferatingquiescent model used data from the German cohort (18,
19), the late-early progenitors model used data from the
Australian cohort (16, 17), and the early-stem model used
data from all IRIS patients who had imatinib blood level
concentration measurements (22).
The kinetic data analysis makes use of the ratio of BCR–
ABL transcripts to that of a control gene (typically BCR or
ABL) in peripheral blood (23). We assumed that the BCR–
ABL transcript level was directly proportional to the fraction
of mature leukemic cells in the blood (24). In an effort to
determine the effect of long-term imatinib therapy on LSCs,
we used nonlinear mixed effect (NLME) modeling techniques to describe the BCR–ABL dynamics in patients with
CML in chronic phase (CML-CP), with 7 years of follow-up
on the IRIS study. Each patient was described by 1 of 3
dynamic response models: (i) monophasic patients, who
exhibited a gradual BCR–ABL decline at rate m; (ii) biphasic
patients, who exhibited an initial rapid BCR–ABL decline
at rate a, followed by a more gradual decline at rate b;
(iii) triphasic patients, who also exhibited an initial rapid
BCR–ABL decline at rate a, followed by a gradual decline at
rate b, but then a rapid increase at rate g as the disease
relapses. We then used recent measurements of hematopoietic dynamics to make inferences on how the BCR–ABL
dynamics relate to changes in the LSC population (Fig. 3
and Supplementary Fig. S1).
Materials and Methods
Patients and samples
Samples for real-time quantitative PCR (RQ-PCR) were
collected from peripheral blood: (i) after achievement of
complete cytogenetic response (CCyR) in all patients; (ii) at
1, 2, and 3 months and then every 3 months thereafter in a
Late progenitor cells
Leukemic stem cells
LT-HSC
ST-HSC
Early progenitor cells
MPP
CMP
Self-renewal
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Clin Cancer Res; 17(21) November 1, 2011
GMP
White blood cells
Promonocyte
Monocyte
Neutrophilic myelocyte
Granulocyte
Basophilic myelocyte
Basophil
Figure 1. An illustration of leukemic
hematopoiesis and a
corresponding compartmental
model used to describe it (15). The
differential equations for this model
are given in the Supplementary
Appendix Fig. S2. CMP, common
myeloid progenitor; GMP,
granulocyte macrophage
progenitor; LT-HSC, long-term
hematopoietic stem cell; MPP,
multipotent progenitor; ST-HSC,
short-term hematopoietic stem cell.
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Published OnlineFirst September 8, 2011; DOI: 10.1158/1078-0432.CCR-11-0396
BCR–ABL Transcript Dynamics on Imatinib
A
β
PQ hypothesis
α ~ decline of LSC-Ps
β ~ decline of LSC-Qs
LSC-Q
LSC-P
EPC
LPC
WBC
EPC
LPC
WBC
LPC
WBC
α
β
B
ES hypothesis
α ~ decline of EPCs
β ~ decline of LSCs
LSC-Q
LSC-P
α
C
LE hypothesis
α ~ decline of LPCs
β ~ decline of EPCs
LSCs untouched
LSC-Q
LSC-P
EPC
β
α
Figure 2. A summary of 3 hypotheses for the biphasic response, in which each hypothesis is explored by using a different parameter set in the same structural
model. A, the proliferating quiescent (PQ) model assumes the LSCs themselves undergo a biphasic decline due to LSCs transitioning between 2 states,
proliferative (LSC-P) and quiescent (LSC-Q), such that they are only vulnerable to imatinib therapy in their proliferative state (18, 19). B, the early-stem (ES)
model assumes the initial response is due to rapid killing of the EPCs and the long-term response is due to a depletion of the LSCs (22). C, the late-early
progenitors (LE) hypothesis (17, 46, 47) assumes that the death rate of the progenitor pool is itself biphasic under imatinib treatment and the LSCs are not
depleted at all (17, 46).
gene), the U.K. measurement was used]. In all other cases,
the measurement of minimum value was used, consistent
with previous analyses (30–32).
The effective range for the IS is BCR–ABL (IS) 10% (23).
This upper limit is due to 2 sources of nonlinearity in the
BCR–ABL ratio estimation: (i) Depending upon the primer
design for the ABL control gene, both ABL and BCR–ABL are
amplified when the BCR–ABL levels are high (33); (ii) An
additional nonlinearity occurs for both the BCR and
ABL control genes because healthy cells have 2 copies of
the control gene (e.g., ABL), whereas leukemic cells have
subpopulation of patients treated in Germany (25); (iii)
every 3 months in subpopulations of patients treated in
Australia and New Zealand; and (iv) at the discretion of
physicians before achievement of CCyR. All RQ-PCR (BCR–
ABL/control gene 100%) measurements were then converted to the international scale (IS) for BCR–ABL transcripts (23, 26–29). When multiple measurements existed
within a 3-month interval, we followed the IRIS analysis
protocol for choosing a single unique measurement [i.e.,
when one measurement was carried out at the U.K. lab (BCR
control gene) and the other at the German lab (ABL control
A
B
2
2
µ´
0
10
-2
10
BCR–ABL (IS)%
BCR–ABL (IS)%
10
α´
0
10
β´
-2
10
4
Time (y)
6
8
ү´
10
0
2
4
Time (y)
D
6
8
0
2
4
6
Time (y)
8
E
2
2
10
10
BCR–ABL (IS)%
2
BCR–ABL (IS)%
0
α´
0
10
-2
10
α´
0
10
0
10
-2
-2
10
10
0
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C
2
10
BCR–ABL (IS)%
Figure 3. BCR–ABL (IS)% for all
477 patients in this analysis, divided
into 5 groups with an example
patient highlighted within each
group. A, slow-monophasic
patients (n ¼ 44). B, fast-biphasic
patients (n ¼ 282). C, fast-triphasic
patients (n ¼ 14). D, fast-unknown
(n ¼ 68). E, unknown (n ¼ 69). The
asterisks ( ) indicate when a BCR–
ABL (IS)% measurement fell below
0.0032, the approximate limit of
quantification of the assay for all
labs in the IRIS. The prime (0 ) symbol
after the
model parameters (m, a, b, and g)
denotes that the slopes in the graph
are the model parameters
multiplied by the constant log10 e ¼
0.43
(e.g., m0 ¼ 0.43 m).
2
4
Time (y)
6
8
0
2
4
Time (y)
6
8
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1 control gene and 1 BCR–ABL gene. In that case, even if the
expression rates of ABL and BCR–ABL are equal, the BCR–
ABL/ABL ratio is equal to L/(L þ 2H), in which L is the
number of leukemic white blood cells (WBC) and H is the
number of healthy WBCs. Because both L and H change
with time, and BCR–ABL and ABL expression and amplification levels are not necessarily identical, there is no simple
transformation that can be applied to remove this nonlinearity. Thus, although measurements above 10% are included in this analysis so that we can describe the full range of
BCR–ABL dynamics for all patients, we emphasize that
quantitative values of the parameters governing the dynamics at the start of treatment (A, a, m) should be used only in
a qualitative fashion.
Cell populations were described according to the presence of cell surface markers. We assumed proportional
relationships between LSCs and CD34þ/CD38 cells,
between EPCs and CD34þ/CD38þ cells, and between LPCs
and CD38 cells.
Model selection and parameter fitting
In this analysis, we employed NLME modeling methodology, as is the recommended practice in developing population models of clinical data (34, 35). This approach is
specifically designed to make maximal use of all available
data and to handle sparse data without introducing biases.
Therefore, all patients with at least 1 data point were
included in the initial model-fitting analysis. Using Monolix
(www.monolix.org, version 3.1) NLME software in Matlab
(The MathWorks), we used maximum likelihood methods
to fit the following mixture model to the log10 of the BCR–
ABL dynamics R(t) once treatment begins at t ¼ 0:
8
log10 ðAemt Þ þ ";
>
>
>
>
>
monoexponential; with probability p1
>
>
>
at
bt
>
>
log
< 10 ðAe þ Be Þ þ ";
biexponential ðtypical responseÞ; with probability p2
RðtÞ ¼
>
>
at
bt
gt
>
log
>
10 ðAe þ Be þ Ce Þ þ ";
>
>
>
>
> triexponential ðsecondary resistanceÞ;
>
:
with probability p3
An additional strength of the NLME mixture-modeling
approach is that all parameters (A, B, C, m, a, b, g, e, p1, p2, p3
¼ 1 p1 p2) can be fit to the entire patient population
simultaneously. The model represents 3 typical profiles: (i)
monoexponential, in which there was little, if any, decline
in BCR–ABL (IS)% at a rate m 0; (ii) biexponential, in
which patients had a rapid initial decrease (a) in the BCR–
ABL (IS)% followed by a more gradual response (b), which
could be either increasing or decreasing; and (iii) triexponential, in which patients first exhibit a biphasic decline but
then have a third phase when the BCR–ABL (IS)% increases
rapidly with rate g > 0. The constant terms (A, B, C) are
unitless and are restricted to be greater than zero because
BCR–ABL (IS)% is always positive.
Baseline BCR–ABL levels were approximately equal to the
A parameter. In the limiting case that a patient had only a
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Clin Cancer Res; 17(21) November 1, 2011
single measurement at baseline, data from that patient
informed the population average of the baseline measurement but did not influence the other parameter estimates.
The rate constants (m, a, b, and g) have units of 1 per year.
Before treatment starts, the BCR–ABL (IS)% is constant and
equal to its initial value at t ¼ 0. Each model has an additive
residual error term, e, that describes variability in the
measurement due to assay variability, interoccasion variability, and model error.
Due to limits of quantification of the PCR assay, we chose
0.0032% [a 4.5-log10 decrease in the BCR–ABL (IS)%] as the
minimal level of detection of the assay for all labs and
treated these measurements as left-censored data. In the
original IRIS study, this was the limit at which a patient was
defined as having achieved an undetectable number of
BCR–ABL transcripts, defined as a complete molecular
remission (CMR; ref. 32). This value has subsequently been
used in other studies and is the desired limit of detection,
although not always achievable because of differences in
sample quality. Rather than remove all measurements
below the limit of quantification or fix them at a certain
value, we used a maximum likelihood approach, treating
any measurement falling below the limit of detection as
having an upper boundary of 0.0032%. This approach,
referred to as method 3 (36), allows us to make optimal
use of the measurements that fall below the limit of
quantification.
The patient population was then divided into 5 categories as shown in Fig. 3 and Supplementary Fig. S1.
Three categories were based on the model categorization
above (slow monophasic, fast biphasic, and fast triphasic). However, if a patient had only 1 data point, fewer
than 2 months of follow-up, or no data in the first 2 years,
the patient was classified as unknown. If a patient had
fewer than 4 years of follow-up and was classified as
biexponential by the model, we noted that the patient
may in fact be triexponential; however, they were not
followed long enough to detect the third phase. Therefore, patients with a fast response and fewer than 4 years
of follow-up were classified as fast unknown because it
was unknown whether they were biphasic or triphasic. As
few as 2 data points were sufficient to classify many
patients. For example, a baseline measurement of
100% followed by a 6-month measurement of 90% was
indicative of slow-monophasic dynamics, whereas a 6month measurement of 1% indicated a fast initial
response.
To investigate whether there is a relationship between
response category and clinical effect, we looked for differences in event-free survival (EFS) among 4 response categories (excluding those patients without sufficient data for
classification). EFS was defined as the time between randomization and any of the following events on treatment:
death because of any cause, progression to accelerated phase
or blast crisis, increase in WBCs, loss of complete hematologic response, and loss of major cytogenetic response (37).
The time was censored at the date of last assessment (hematology, extramedullary disease, or cytogenetic evaluation)
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BCR–ABL Transcript Dynamics on Imatinib
Results
Of the 553 patients receiving imatinib at the start of
treatment on the IRIS study, 477 had at least 1 BCR–ABL
transcript measurement and were included in the analysis.
BCR–ABL was measured for a median of 6.4 years, with a
median of 8 data points for each patient.
Of the 477 patients on the IRIS trial with at least 1 BCR–
ABL measurement, 282 (59%) were classified as biphasic,
44 (9%) as shallow monophasic, and 14 (3%) as triphasic;
68 patients (14%) were fast responders but of undetermined type, and 69 patients (14%) had insufficient data
for classification. The BCR–ABL transcript dynamics according to the IS for all patients, along with fits to representative
patients in each category, are shown in Fig. 3. Histograms of
the rate constants m, a, b, and g are plotted in Fig. 4. The
median and the upper and lower quartiles of the NLME
model parameters are reported in the Supplementary
Appendix Table S1.
The EFS outcomes of the 4 groups are summarized
in Fig. 5. The biphasic patients had significantly better EFS
than all other groups (P < 0.0001). In particular, only 10 of
282 (4%) biphasic patients experienced an event and 6 died
for reasons unrelated to the disease. Of the 68 patients with
Number of patients
A
250
200
Fast (α)
89.2%
(364/408)
150
100
50
Slow (μ)
10.8%
(44/408)
0
0.1
1
10
100
Time (1/y)
B
Number of patients
140
Fast unknown (β)
18.7%
(68/364)
120
100
80
60
40
Fast biphasic (β)
77.5%
(282/364)
20
0
-1
0
1
2
Fast triphasic (γ)
3.8%
(14/364)
3
4
5
Time (1/y)
Figure 4. Distribution of patients for the A, initial reduction rate (m, a rate
constants; n ¼ 408) and B, long-term BCR–ABL (IS)% dynamics (b and g
rate constants; n ¼ 364).
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1
Fast biphasic
Probability of EFS
for patients without an event. The relationship between the
response category and EFS was then determined using a x2
2-sample test.
0.8
Unknown
0.6
Fast unknown
0.4
Slow monophasic
Fast triphasic
0.2
0
0
1
2
3
4
5
6
7
8
Time (y)
Figure 5. EFS according to patient group (P < 0.0001 for the comparison
between fast-biphasic and all other categories).
a fast-unknown response, 17 (25%) had an event. In contrast, 25 of 44 (57%) monophasic patients and 9 of 14
(64%) triphasic patients experienced an event.
We also explored whether the a and b slopes were
predictive of response by comparing the event rates of
biphasic patients in the top and bottom quartiles in a and
b using a log-rank test. The difference in survival was not
significant for a (P ¼ 0.22), whereas the same comparison
for b gave a significant difference (P ¼ 0.02). Among the
entire patient population, a rapid initial response did correlate with improved outcome because the rapid responders
excluded the slow-monophasic patients by definition.
Among the rapid responders (biphasic and triphasic
patients), however, a steeper a did not correlate with
improved outcome.
Given the ability of the model to subdivide the patient
population into groups with very different outcomes, we
next explored the biological interpretation of the model.
The monophasic patients often exhibit primary resistance,
though a small number of slow-monophasic patients do
exhibit a slow, gradual decrease in BCR–ABL transcript
levels over the course of therapy and obtain a CCyR. Only
17 of 44 monophasic patients (39%) achieved a CCyR on
imatinib, and 39 (89%) either experienced an event or
discontinued therapy because of an unsatisfactory response.
The triphasic patients correspond to those exhibiting secondary resistance to imatinib, which is defined by the
achievement and then loss of response. The third exponential (g) corresponds to an exponentially growing population
of cells that do not respond to imatinib treatment. Although
12 of 14 triphasic patients (86%) achieved a CCyR, 14
(100%) experienced an event or discontinued therapy
because of an unsatisfactory response. Finally, the biphasic
patients, as described above, exhibited a superior response
on imatinib. There are many possible interpretations of the
biphasic response, which we explored.
To interpret how b relates to leukemic hematopoiesis,
particularly in biphasic patients, we considered 3 hypotheses described previously as the proliferating-quiescent,
early-stem, and late-early progenitors hypotheses (Fig. 2).
The 3 hypotheses are described with a single structural
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A number of experimental observations indicate that
the EPCs are depleted on the faster (a) time scale, supporting the early-stem and proliferating-quiescent models
over the late-early progenitors model, which predicts that
EPCs decay on the slower (b) time scale. In healthy
subjects, it takes approximately 5 days to mature from
a myeloblast (the most immature differentiated cell) to
the last myelocyte (the most mature hematopoietic cell
that undergoes mitosis) and approximately 6.6 days to go
from a myelocyte to a mature blood cell (38, 39). Furthermore, of the 9 patients with CML on imatinib therapy
studied by Abe and colleagues (40), 7 exhibited a rapid
depletion of EPCs to undetectable levels within the first
18 months of treatment. The remaining 2 patients had
only 2 measurements, with 1 at baseline and 1 at year 4,
and thus the BCR–ABL dynamics in the first year cannot
be estimated. The 3 different models are depicted graphically alongside the data from Abe and colleagues (40)
in Fig. 6, in which it is seen that the EPC kinetics are most
consistent with the early-stem and proliferating-quiescent
models, but not the late-early progenitors model. This
evidence strongly suggests that EPC dynamics operate on
time scales of weeks, which supports the early-stem and
proliferating-quiescent hypotheses, both of which predict
a long-term depletion of the LSCs.
For all 3 models, the long-term dynamics of the WBCs
(and in fact all progenitor compartments) will be parallel to
model, with each hypothesis represented by a different set
of parameter values. Although all 3 models exhibit the
observed biphasic BCR–ABL response trajectories, the models make different assumptions in terms of LSC dynamics
under imatinib treatment. The proliferating-quiescent
model assumes that depletion of proliferating and quiescent stem cells, respectively, drive the a and b slopes of
BCR–ABL decline. The early-stem model assumes that
depletion of EPCs and stem cells drive the a and b slopes,
respectively. Finally, the late-early progenitors model
assumes that there is no significant LSC depletion with
imatinib therapy and that the a and b slopes correspond
instead to depletion of the LPCs and EPCs, respectively.
The choice of parameters for each model and the corresponding equations are described in more detail in the
Supplementary Appendix. As a consequence of adjusting
the biological parameters of each model to fit the observed
biphasic trajectories, the models make different predictions
with respect to EPC kinetics. In particular, the late-early
progenitors model predicts that EPCs have a half-life of
approximately 180 days, corresponding to the b slope,
whereas the early-stem and proliferating-quiescent models
predict a much shorter half-life of 5 to 17 days, corresponding to the a slope. As the models make different predictions,
we can use experimental observations on EPC kinetics to
distinguish between the late-early progenitors, early-stem,
and proliferating-quiescent models.
Model/data comparison
100
Monophasic,
decreasing
-2
10
C
LE hypothesis
α ~ decline of LPCs
β ~ decline of EPCs
LSCs untouched
EPC/LPC %
LSC/(LSC+HSC)
0
2
4
102
101
100
Monophasic,
increasing
10-1
10-2
0
2
Time (y)
4
WBC BCR–ABL (IS)%
10-1
10-2
0
4
102
101
10-1
100
2
102
101
100
10-1
-2
10
0
2
4
102
101
100
10-1
10-2
0
2
Time (y)
WBCs
102
101
100
10-1
10-2
4
WBC BCR–ABL (IS)%
LSC-P/EPC %
LSC/(LSC+HSC)
ES hypothesis
α ~ decline of EPCs
β ~ decline of LSCs
2
EPCs
102
101
WBC BCR–ABL (IS)%
Biphasic,
decreasing
10-2
EPC BCR–ABL (IS)%
10-1
0
B
EPC BCR–ABL (IS)%
100
EPC BCR–ABL (IS)%
A
PQ hypothesis
α ~ decline of LSC-Ps
β ~ decline of LSC-Qs
LSC-Q/LSC-P %
LSC/(LSC+HSC)
LSCs
102
101
4
0
2
4
0
2
4
0
2
Time (y)
4
102
101
100
10-1
10-2
102
101
100
10-1
10-2
Figure 6. Comparison between the mathematical models and patient data (from Abe and colleagues; ref. 40; N ¼ 9). Note that the late-early progenitors (LE)
hypothesis, in which LSCs are not affected by imatinib, does not match the observed EPC dynamics. The EPCs plotted are granulocyte macrophage
þ
þ
þ
þ
progenitors, characterized by the cell surface markers CD34 CD38 IL-3Ra CD45RA Lin . ES, early-stem; LSC-P, proliferating LSC; LSC-Q, quiescent
LSC; PQ, proliferating-quiescent; HSC, hematopoietic stem cell.
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BCR–ABL Transcript Dynamics on Imatinib
the long-term dynamics of the LSCs. In the proliferatingquiescent and early-stem models, the gradual decay of the
LSCs is matched by a gradual decay in the WBCs and EPCs
after year 1. In the late-early model, the leukemic WBC and
EPC levels will eventually plateau after year 4. This property—that the more mature cell population dynamics parallel the LSC dynamics after sufficient treatment duration—
is true for any generic model of hematopoiesis (Supplementary Appendix) and reflects the fundamental observation that all leukemic cells ultimately arise from LSCs. Thus,
although residual progenitor cells are also detected after
many years of continuous imatinib therapy (41), the models and the data from Abe and colleagues (40) suggest that
these progenitors arise from persistent LSCs, rather than
that particular progenitor cells persist in the bone marrow
for years. Therefore, under constant therapy, after long
durations, WBC dynamics serve as a surrogate measure for
LSC dynamics.
In the biphasic patient subset from the IRIS study presented above, 90% of patients were found to have b above
0.69 per year (Fig. 4B), meaning that the half-life of the
slowest observable compartment was more than 1 year. We
show above that this half-life is too long to describe the
EPCs. Therefore, the gradual rate of decay (b) in BCR–ABL
must correspond to an earlier hematopoietic population of
cells with the capacity to persist in the bone marrow for
many years. The only cells with the ability to persist in the
bone marrow for many years are the LSCs. Therefore, we
deduced that the gradual rate of reduction of the BCR–ABL
(b) is best explained by a reduction of the LSCs.
Discussion
We used maximum likelihood methods to fit 3 different
exponential functions to the BCR–ABL (IS)% data set for
477 patients with Philadelphia chromosome–positive
(Phþ) CML-CP in the imatinib arm of the IRIS trial, with
an average follow up of 7 years. Most patients (282 of 477;
59%) with Phþ CML-CP were best described by a biexponential model consisting of 2 slopes: an a slope, corresponding to the rapid initial decrease in the BCR–ABL
transcripts at the start of treatment, and a b slope, corresponding to the long-term BCR–ABL transcript dynamics.
A second population (44 of 477 patients; 9%) was best
described by a gradual monoexponential model with little
or no initial decrease in transcript levels, corresponding to
primary resistance or minor response to imatinib. Finally,
14 of 477 patients (3%) were best described by a triexponential model, corresponding to an initial biphasic decline
followed by a third increasing phase, which would be
clinically described as molecular relapse, likely due to
acquired resistance to imatinib. We note that 69 of 477
patients (14%) did not have sufficient data for classification
and that 68 of 477 (14%) could only be classified as having a
fast initial response, but whether they were biphasic or
triphasic could not be determined.
We argue that in those patients well described by the
biexponential model, a gradual decline of LSCs during
www.aacrjournals.org
imatinib treatment is the only hypothesis that can explain
(i) the observed gradual decline in the number of BCR–ABL
transcripts in peripheral blood and (ii) known time scales of
EPCs. The alternative explanation (late-early progenitors
hypothesis; ref. 17) is that a population of progenitor cells
persists in the marrow for many years, but this hypothesis
cannot simultaneously explain (i) and (ii). We emphasize
that although we use the 3 models to illustrate the different
biological hypotheses for the a and b slopes, the main
conclusion—that the LSCs are the only population of
leukemic cells that have the ability to persist in the bone
marrow for years and thus contribute to the gradual b
slope—is model-independent (Supplementary Appendix).
In the original paper, in which the late-early progenitors
hypothesis was proposed, 2 observations were provided
that supported the lack of depletion of the LSCs: (i) Three
patients received imatinib for at least 1 year and all relapsed
rapidly upon halting treatment, and (ii) in vitro, LSCs are
resistant to imatinib on short-time scales (17). Rapid
relapse upon discontinuation, however, occurs in all 3
models and, therefore, is not necessarily a function of lack
of effect on LSCs (Supplementary Appendix Fig. S3). Furthermore, this work addresses LSC dynamics over many
years of treatment, whereas the in vitro experiments that
show no effect of imatinib on LSCs (6, 7) represent only a
few days of treatment.
Although our modeling approach can fit existing data to
infer LSC kinetics over a long course of therapy, it provides
little insight into the mechanisms underlying the stem cell
depletion. It may be that in vivo LSCs are slightly susceptible
to imatinib treatment but only over long time periods,
which would explain why this susceptibility is not observed
in vitro (6, 7). It may also be that other mechanisms for LSC
depletion, not active in vitro, contribute to the decline, such
as an immune response (42, 43) or competition of LSCs
with residual normal hematopoietic stem cells for a limited
number of niches (19).
One potential limitation of this study is the simplified
definitions of stem cells that were used. Although it is
common practice, defining stem cells according to cell
surface markers is imprecise. For example, not all CD34þ/
CD38 cells are able to reconstitute an immune system or
generate a colony-forming unit. However, Abe and colleagues (40) evaluated the dynamics of several different
subpopulations of progenitor cells (granulocyte–macrophage progenitors characterized by the cell surface
markers CD34þCD38þIL-3RaþCD45RAþLin; common
myeloid progenitors characterized by CD34þCD38þIL3RaþCD45RA Lin ; and megakaryocyte/erythroid
progenitors characterized by CD34 þCD38 þIL3RaCD45RALin), and although our focus was on the
granulocyte–macrophage progenitors, the dynamics of all
3 populations were similar. The model described here is a
simplification of LSC biology. For example, it is possible
for differentiated hematopoietic cells to become less
differentiated, and proliferating leukemic cells can asymmetrically divide to produce quiescent leukemic cells or
more differentiated cells. These phenomena were not
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Stein et al.
included in the current model because the purpose of this
model was to illustrate the basic differences between the
existing hypotheses about the effect of imatinib on LSCs.
The conclusions from this work can be generalized to
more sophisticated models that more accurately capture
LSC dynamics.
The CML cell population is genetically heterogeneous.
Thus, b only provides an estimate for the depletion rate of
the most resistant, but still observable, population of LSCs.
In some patients, there may still be a small population of
LSCs in the marrow that is driven to quiescence under
imatinib treatment. Persistently quiescent LSCs may remain
undetectable with reverse transcriptase PCR based on BCR–
ABL transcripts, and it may be cells such as these that
account for rapid relapse in patients who discontinue
imatinib years after achieving undetectable levels of
BCR–ABL. Although both the early-stem and proliferating-quiescent hypotheses predict that the b slope is due
to LSC depletion, they differ in their explanation for the
initial BCR–ABL decline (a); the early-stem hypothesis
attributes a to EPCs, and the proliferating-quiescent
hypothesis attributes a to a decline in the proliferating
LSCs. Further modeling work and experimental observations may be necessary to distinguish between the 2 models.
The implications of this work are that b may be predictive of the curative potential of TKI therapy. This
analysis predicts that a steeper b at the time a patient
achieves CMR (defined as undetectable BCR–ABL) correlates with a better chance of maintaining a durable
response once treatment is discontinued. The model also
predicts that, after achieving CMR, patients with a more
gradual b should continue treatment for a longer duration
before discontinuation. For example, consider 2 patients
who achieve CMR at the same time, in which patient 1 has
a long-term response with b ¼ 1 per year and patient 2
has a steeper response with b ¼ 2 per year. The model
predicts that patient 1 would need to continue therapy for
twice as long as patient 2 to have the same likelihood of
maintaining a durable response upon discontinuation.
Testing of these hypotheses will require an analysis of
discontinuation trials in which BCR–ABL time course of
the patients is reported from initiation of therapy.
The mathematical modeling literature has made different
predictions for when a typical patient with CML may be
expected to achieve a "cure." Roeder and Glauche (44)
estimated that most patients will require 20 years to achieve
LSC eradication, whereas Lenaerts and colleagues (21)
predicted that the majority of patients will achieve eradication of minimal residual disease within 5 years on TKI
therapy. In our view, the discrepancy between current
models of LSC dynamics indicates that much is still
unknown about the hematopoietic dynamics of leukemia
and that further clinical trials will be necessary to understand how a model might provide further insight into
discontinuation studies.
Because there are numerous models that well describe
BCR–ABL dynamics in CML, the ability of a model to fit
existing data is not validation of the model but, rather, a
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Clin Cancer Res; 17(21) November 1, 2011
demonstration that the model is one potential quantitative
hypothesis for describing the known data. When models
lead to conflicting hypotheses, additional observations are
required. In this work, we have integrated existing models
with recent measurements of EPC dynamics.
In summary, we modeled the transcript numbers measured in 477 patients in the imatinib arm of the IRIS trial.
We found that a biexponential curve best described most of
the patient population. The patient response over 7 years is
well described with 2 slopes: an a slope corresponding to
the rapid initial decrease in BCR–ABL transcript levels after
the start of treatment, and the b slope corresponding to the
long-term BCR–ABL dynamics. Using recent measurements
of the EPC dynamics, in combination with known biology
of healthy hematopoiesis, we find that the most likely
biological interpretation of the b slope is that it corresponds
to the change in the LSC population. We believe that this
model is the simplest model that best describes the patient
population. This model may eventually be useful in interpreting data with second-generation BCR–ABL inhibitors
and quantifying patient progress toward eradication of
CML. We are currently evaluating the molecular response
dynamics in patients treated with nilotinib, an agent that
may be more likely to induce "cure" in patients, based on
the faster and deeper molecular responses observed in
newly diagnosed patients treated with nilotinib versus
imatinib (45).
Disclosure of Potential Conflicts of Interest
S. Branford received research support and honoraria from Novartis and
Bristol-Myers Squibb; J.M. Goldman received honoraria from and participated in speakers’ bureaus for Novartis and Bristol-Myers Squibb; T.P.
Hughes received research funding from Novartis and Bristol-Myers Squibb,
received honoraria from Novartis, Bristol-Myers Squibb, and Ariad, and
participated in speakers’ bureaus for Novartis; J.P. Radich acted as a consultant for and received honoraria from Novartis and Bristol-Myers Squibb, and
received research funding from Novartis; and A. Hochhaus received research
support from Novartis. The other authors disclosed no potential conflicts of
interest.
Acknowledgments
The authors thank Yen-Lin Chia, Ovidiu Chiparus, Jim Dunyak, Insa
Gathmann, Alan Hatfield, Gabriel Helmlinger, Tillmann Krahnke, Jerry
Nedelman, Elke Ortmann, and Elisabeth Wehrle for many useful discussions. The authors also thank Erinn Goldman for medical editorial assistance
with this article and, finally, Thea Kalebic for initiating and continually
supporting the application of modeling and simulation to optimize the
treatment of CML.
Grant Support
Financial support for medical editorial assistance was provided by Novartis Pharmaceuticals Corporation. A. Hochhaus was supported by the Bundesministerium f€
ur Bildung und Forschung (BMBF), Germany, grant no.
Haematosys 0315452D.
The costs of publication of this article were defrayed in part by the
payment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate
this fact.
Received February 11, 2011; revised July 19, 2011; accepted August 19,
2011; published OnlineFirst September 8, 2011.
Clinical Cancer Research
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Published OnlineFirst September 8, 2011; DOI: 10.1158/1078-0432.CCR-11-0396
BCR–ABL Transcript Dynamics on Imatinib
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Clinical Cancer Research
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Published OnlineFirst September 8, 2011; DOI: 10.1158/1078-0432.CCR-11-0396
BCR−ABL Transcript Dynamics Support the Hypothesis That
Leukemic Stem Cells Are Reduced during Imatinib Treatment
Andrew M. Stein, Dean Bottino, Vijay Modur, et al.
Clin Cancer Res Published OnlineFirst September 8, 2011.
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