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Individualized Systems Medicine (ISM) strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia
Authors: Tea Pemovska1†, Mika Kontro2†, Bhagwan Yadav1, Henrik Edgren1, Samuli Eldfors1, Agnieszka Szwajda1, Henrikki Almusa1, Maxim M. Bespalov1§, Pekka Ellonen1, Erkki Elonen2, Bjørn T. Gjertsen3, Riikka Karjalainen1, Evgeny Kulesskiy1, Sonja Lagström1, Anna Lehto1, Maija Lepistö1, Tuija Lundán4, Muntasir Mamun Majumder1, Jesus M. Lopez Marti1, Pirkko Mattila1, Astrid Murumägi1, Satu Mustjoki2, Aino Palva1, Alun Parsons1, Tero Pirttinen5, Maria E. Rämet5, Minna Suvela1, Laura Turunen1, Imre Västrik1, Maija Wolf1, Jonathan Knowles1, Tero Aittokallio1‡, Caroline A. Heckman1‡, Kimmo Porkka2‡, Olli Kallioniemi1‡, Krister Wennerberg1‡* Affiliations: 1 Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland. 2 Hematology Research Unit Helsinki, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland. 3 Department of Clinical Science, Hematology Section, University of Bergen, Bergen, Norway and Department of Internal Medicine, Hematology Section, Haukeland University Hospital, Bergen, Norway. 4 Department of Clinical Chemistry and TYKSLAB, University of Turku and Turku University Central Hospital, Turku, Finland. 5 Department of Internal Medicine, Tampere University Hospital, Tampere, Finland. † Shared first authorship ‡ Shared senior authorship § Current address: Stem cells and Neurogenesis Unit, Division of Neuroscience San Raffaele Scientific Institute, Milan, Italy. Running title: ISM approach to therapy selection
Keywords: leukemia; functional genomics; experimental molecular therapeutics; kinase inhibitors; molecular diagnosis and prognosis. Financial support: Academy of Finland (Aittokallio, Kallioniemi); Biocenter Finland (Kallioniemi, Wennerberg); ERDF: the European Regional Development Fund (Västrik); EU FP7 BioMedBridges (Kallioniemi); Finnish Cancer Societies (Kallioniemi, Porkka); Jane and Aatos Erkko Foundation (Wennerberg); Sigrid Jusélius Foundation (Kallioniemi); and Tekes: Finnish Funding Agency for Technology and Innovation (Kallioniemi, Knowles).
*Corresponding author: Krister Wennerberg, Institute for Molecular Medicine Finland, Tukholmankatu 8, 00290 Helsinki, Finland. Phone: +358 9 191 25764; Fax: +358 9 191 25737; E-mail: [email protected]
Potential conflicts of interest: Labcyte, Inc. and FIMM/University of Helsinki have a collaboration agreement on the utilization of Labcyte’s acoustic dispensing technologies.
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ABSTRACT
We present an individualized systems medicine (ISM) approach to optimize cancer
drug therapies one-patient-at-a-time. ISM is based on i) molecular profiling and ex
vivo drug sensitivity and resistance testing (DSRT) of patients’ cancer cells to 187
oncology drugs, ii) clinical implementation of therapies predicted to be effective and
iii) studying consecutive samples from the treated patients to understand the basis of
resistance. Here, application of ISM to 28 samples from patients with acute myeloid
leukemia (AML) uncovered five major taxonomic drug response subtypes based on
DSRT profiles, some with distinct genomic features (e.g. MLL gene fusions in
subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT
resulted in several clinical responses. After progression under DSRT-guided
therapies, AML cells displayed significant clonal evolution, novel genomic changes
potentially explaining resistance, while ex vivo DSRT data showed resistance to the
clinically applied drugs and new vulnerabilities to previously ineffective drugs.
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SIGNIFICANCE
Here we demonstrate an ISM strategy to optimize safe and effective personalized
cancer therapies for individual patients as well as to understand and predict disease
evolution and the next line of therapy. This approach could facilitate systematic drug
repositioning of approved targeted drugs as well as help to prioritize and de-risk
emerging drugs for clinical testing.
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INTRODUCTION
Adult acute myeloid leukemia (AML) is a prototype example of the challenges of
modern cancer drug discovery, development and patient therapy. With the exception
of the retinoic acid-sensitive acute promyelocytic leukemia (APL) subtype,
molecularly targeted therapeutic approaches for AML are yet to be translated into
clinical advances. The disease has traditionally been subdivided into different
subtypes (M0-M7) based on cellular lineage and biomarkers (1). Current WHO
classification reflects the fact that a growing number of AML cases can be
categorized on the basis of their underlying genetic abnormalities that define distinct
clinicopathologic entities (2). Genomic changes in AML are now relatively well
understood, with each AML sample containing roughly 400 genomic variants, of
which an average of 13 reside in coding regions (3, 4). Recurrent changes has
highlighted potential driver genes, including NPM1, CEBPA, DNMT3A, TET2,
RUNX1, ASXL1, IDH2 and MLL, with mutations in FLT3, IDH1, KIT and RAS genes
modifying the disease phenotype (5). Although several of the recurrent genetic
alterations link to tractable drug targets, genetic testing of AML patients has yet to
result in effective personalized or stratified therapies.
AML patients have a poor outcome with a 5-year survival of 30-40% (6, 7). The
standard therapy for most adult AML patients is conventional chemotherapy
consisting of the nucleoside analogue cytarabine combined with a topoisomerase II
inhibitor (8, 9). A number of second-line treatment options have been applied in AML
patients after relapse, but the response rates have remained low. Furthermore, AML
patients at relapse exhibit an increased number of genetic alterations, which can be
attributed to disease progression and/or DNA damaging agents used for routine
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chemotherapy (10). In light of the genomic and molecular diversity of AML, and its
continuous evolution in response to chemotherapy, it is important to better understand
the potential utility of all targeted cancer drugs that are already available in the clinic.
These drugs could be systematically repurposed as off-label indications to responding
subgroups of AML. Furthermore, comparative information on the efficacy of the
hundreds of emerging targeted anti-cancer agents, as well as their potential
combinations, in patient-derived ex vivo samples could dramatically help prioritize
clinical development of such agents.
To facilitate testing of already clinically available drugs as well as emerging targeted
inhibitors for AML patients, we undertook a comprehensive functional strategy to
directly determine the drug dependency of cancer cells based on ex vivo drug
sensitivity and resistance testing (DSRT). First, we applied a systematic large panel of
drugs covering both cancer chemotherapeutics, as well as many clinically available
and emerging molecularly targeted drugs. Second, we developed a new way to score
for differential drug response in AML cells as compared to the efficacy of these drugs
in normal bone marrow cells. Third, we verified DSRT-predictions in vivo by treating
AML patients with off-label drugs. Fourth, we assessed the molecular mechanisms
underlying development of cancer progression and drug resistance by repeat sampling
from relapsed patients, followed by genomic and transcriptomic profiling as well as a
new DSRT analysis to understand both co-resistance as well as new vulnerabilities.
Taken together, we term this approach individualized systems medicine (ISM) (Fig.
1).
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Here, we demonstrate that the ISM strategy made it possible to i) create a taxonomy
of comprehensive drug responses in AML, ii) identify clinically actionable AML-
selective targeted drugs, iii) clinically apply such therapies for individual
chemorefractory AML patients predicted to be sensitive to targeted drugs, and iv)
follow individually optimized therapies in patients by analysis of the clonal evolution
of leukemic cells and molecular profiling to understand mechanisms of drug
resistance.
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RESULTS
Individualized systems medicine strategy for personalized AML therapy
To uncover the mechanisms of drug response and resistance as well as to monitor
therapy response at the level of individual AML subclones we combined DSRT and
deep molecular profiling data. DSRT was implemented to AML blast cells ex vivo
using a comprehensive set of 187 drugs, consisting of conventional
chemotherapeutics and a broad range of targeted oncology compounds (Table 1 and
Supplementary Table S1). Each drug was tested over a 10,000-fold concentration
range, allowing for the establishment of accurate dose-response curves for each drug
in each patient and control sample with identification of half-maximal effective
concentrations and max responses (Supplementary Table S2). Although, these
responses reflect the drug-sample interaction, the detailed interpretation of curve
parameters is difficult. To overcome this issue, we found that the most informative
way to assess quantitative drug sensitivities was to convert the information to a Drug
Sensitivity Score (DSS), a metric used to determine the area under the dose-response
curves. Importantly, we developed a new scoring system for assessing leukemia-
selective effects by comparing the DSS in the AML cells to those of healthy donors
(selective DSS: sDSS). Analysis of the drug response data revealed that DSRT is
highly reproducible (r=0.98; Supplementary Fig. S1) and that all control samples
exhibited similar response profiles (Supplementary Fig. S1). The analysis of DSRT
results was completed in four days, rapid enough for clinical implementation. Indeed,
we carried out a pilot test for the implementation of optimized therapies for eight
chemorefractory AML patients. Patient treatment outcome was assessed by clinical
criteria, but also by genomic profiling to understand the clonal architecture underlying
drug response and emerging resistance. Data on recurrent, paired samples identified
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drugs that showed efficacy after the development of drug resistance and highlighted
drugs that could be applied in combination. This approach provided the framework
for a real-time continuous cycle of learning and optimization of therapies, one-
patient-at-a-time, thereby creating an individualized systems medicine (ISM) process
for improving cancer care.
DSRT identified signal transduction inhibitors as putative AML-selective drugs
Ex vivo DSRT was performed on 28 samples obtained from 18 AML patients
(Supplementary Table S3). Eighteen samples were collected at relapse and ten at
diagnosis, mainly from patients with adverse or intermediate cytogenetic risk
(according to European LeukemiaNet) (9). Out of the 187 drugs tested, sDSS
indicated the most selective ex vivo effective drugs for each individual patient, with a
focus on leukemia-specific efficacies by comparing responses to normal bone marrow
mononuclear cells. The results were expressed as a patient-specific waterfall plot
(Fig. 2A). Several targeted drugs exhibited a selective response in a subset of the
AML samples, with only a minimal response in the control samples (Figs. 2B-C and
Supplementary Fig. S2), suggesting that these AML samples were addicted to the
signaling pathways inhibited by the drugs. In contrast, the average sensitivity to
conventional chemotherapeutics did not significantly differ between the patient
samples and controls (Figs. 2B and 2D). This reflects the known limited therapeutic
window for these drugs and the difficulty in predicting their clinical efficacy based on
ex vivo testing. Interestingly, cytarabine, an established and effective AML drug
showed higher selective efficacy against AML cells than other cytotoxic agents (Fig.
2B and Supplementary Fig. S2), suggesting the feasibility of combining cytarabine
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with promising molecularly targeted drugs in the future clinical testing of relapsed or
primary AML.
Significant anti-cancer selective effects were observed for tyrosine kinase inhibitors
(TKIs) dasatinib in 10 of the AML patient samples (36%) and sunitinib in 10 (36%),
MEK inhibitors such as trametinib in 10 (36%), rapalogs such as temsirolimus in 9
(32%), foretinib in 9 (32%), ponatinib in 7 (25%), ruxolitinib in 7 (25%), dactolisib in
7 (25%), MK-2206 in 6 (21%), sorafenib in 6 (21%), and quizartinib in 5 (18%) (Fig.
2E). Thus, we identified several highly (ex vivo) AML-selective drugs that are not
currently approved for AML but that are approved for other cancer indications, and
would therefore be available in the clinic. Furthermore, a number of effective
investigational drug classes were seen, such as AKT inhibitors and ATP-competitive
mTOR inhibitors, which could be prioritized for future clinical studies of
chemorefractory AML.
Taxonomy of AML based on the comprehensive drug response profiles
Overall drug response patterns of the patient samples were visualized with
unsupervised hierarchical clustering. Although each individual sample showed a
unique leukemia-selective drug-response profile, the overall drug response profiles
segregated the AML patient samples into five robust functional subgroups (Fig. 3,
Supplementary Fig. S3 and S4). Thus, despite the underlying genomic and
phenotypic variability in AML, similar drug sensitivity patterns were observed among
the AML patient samples for certain drug classes. Compared to controls, all five
groups showed increased sensitivity to navitoclax, a Bcl-2/Bcl-XL inhibitor, HSP90
inhibitors and HDAC inhibitors. Group I exhibited a strong selective response to
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navitoclax and lack of sensitivity to the remainder of the tested compounds. Group II
AMLs were largely non-responsive to receptor tyrosine kinase inhibitors, but instead
showed a potential inflammatory signal-driven phenotype as seen by selective
responses to a group of immunosuppressive drugs (e.g. dexamethasone or
prednisolone), JAK-family kinase inhibitors and MEK inhibitors. Group III, IV and V
AMLs were selectively sensitive to a broad range of tyrosine kinase inhibitors (TKIs)
indicating that they were driven or addicted to receptor tyrosine kinase signaling
pathways. Group III AMLs displayed similar sensitivity pattern to HSP90, HDAC,
PI3K/mTOR inhibitors as Group IV and V, albeit with lower selectivity. Group IV
AMLs were especially sensitive to MEK and PI3K/mTOR inhibitors, whereas group
V AMLs showed selective responses to receptor tyrosine kinase inhibitors targeting
ABL, VEGFR, PDGFR, FLT3, KIT, PI3K/mTOR as well as topoisomerase II
inhibitors (Fig. 3). Overall, 19 of 28 samples were sensitive to tyrosine kinase
inhibition, correlating well with the findings in the recent study by Tyner and
coworkers (11).
Taxonomy of cancer drugs based on the comprehensive drug response profiles in
AML
The hierarchical clustering also stratified the drugs based on the variability of
responses among the AML patients (Supplementary Fig. S3). In this unsupervised
analysis, drugs with similar modes of action clustered together, such as the
PI3K/mTOR inhibitors, MEK inhibitors, HSP90 and HDAC inhibitors, VEGFR-type
tyrosine kinase inhibitors, PDGFR-inhibitors and ABL-like kinase inhibitors,
antimitotics, and topoisomerase II inhibitors. Thus, the unsupervised clustering of
drugs into subgroups defined by their intended targets strongly supports the
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consistency and reproducibility of the DSRT analysis as well as its ability to acquire
biologically and medically relevant information. However, there were also notable
deviations from the expected patterns. Importantly, the FLT3 inhibitor quizartinib,
clustered with the topoisomerase II inhibitors but not with other TKIs. Furthermore,
the recently approved BCR-ABL1 and FLT3-inhibitor ponatinib clustered with
cytarabine, HSP90 and HDAC inhibitors and not with other TKIs. These unexpected
links may represent underlying key molecular mechanisms of these drugs in the AML
context, including unexpected off-target effects. Furthermore, these drug clustering
patterns from human AML patient specimens ex vivo may in the future be critically
important in designing novel therapeutic combination strategies for clinical trials in
AML. Therefore, this provides new combinatorial possibilities that could not have
easily been discovered without the unbiased DSRT data.
Genomic and molecular findings underlying the drug response profiles
To test whether the molecular profiles of the patient samples correlated with the
overall drug responses we compared the distribution of significant AML mutations
and recurrent gene fusions (4) with the DSRT-driven clustering (Fig. 3). FLT3
mutated samples appeared in several different functional groups, but all patient
samples in Group V, the most tyrosine kinase-dependent group, carried these
mutations. Hence, the Group V drug response pattern is a strong indicator of driver
FLT3 mutations, and, as expected, a FLT3 mutation is an indicator that the cells are
likely to respond to tyrosine kinase inhibitor treatment. Several FLT3 inhibitors, such
as quizartinib, sunitinib and foretinib were among the most selective drugs for Group
V. Importantly, tyrosine kinase inhibitors without FLT3 inhibitory activity such as
dasatinib were highly effective in this group (p=0.03), suggesting that FLT3-driven
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AMLs are also dependent on other tyrosine kinase signals. Furthermore, mutations in
RAS genes correlated significantly with ex vivo sensitivity to MEK inhibitors
(p=0.001). Samples from two patients with MLL fusions clustered together in Group
IV, possibly linking MLL fusions with sensitivity to MEK inhibitor sensitivity. In
addition, an enrichment of TP53 mutations in Groups I and II was observed (3/4
cases) and all patient samples in taxonomic Group II were associated with adverse
karyotypes. Beyond these examples, the majority of the clustering of drug sensitivities
could not be attributed to obvious alterations in the AML tumor genomes.
DSRT was predictive of clinical responses and recapitulates acquisition of
resistance in vivo
The results of DSRT were considered to be therapeutically actionable if (1) a distinct
leukemia-selective response pattern was seen, (2) drugs showing sensitivity were
available for compassionate or off-label use without significantly delaying the
treatment and (3) no standard therapy was available. According to European
LeukemiaNet (9) response criteria three out of eight evaluable patients had a response
to DSRT guided therapy (Supplementary Table 4) (patient 600 (dasatinib-sunitinib-
temsirolimus): complete remission with incomplete platelet recovery (CRi), patients
718 (sorafenib-clofarabine) and 800 (dasatinib-clofarabine-vinblastine): morphologic
leukemia free state). Four other patients had responses, which did not meet the
European LeukemiaNet criteria. Patient 560 showed a rapid clearance of blasts in
peripheral blood after five days of treatment (dasatinib-sunitinib) after which therapy
was discontinued due to gastrointestinal toxicity. Patient 252 (AML with three prior
relapses) had an 8-week progression-free period during dasatinib monotherapy (bone
marrow blasts 65-40-70%). Patient 784 achieved a transient response with dasatinib-
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sunitinib-temsirolimus therapy, bone marrow blasts decreased from 70 to 35%, but
the treatment response was lost due to selection of a resistant clone. Patient 1145 had
hematologic improvement during ruxolitinib-dexamethasone therapy. Even patients
with partial or transitional clinical responses had a profound effect on the clonal
composition of the tumors, including selection of potential drug-resistance associated
mutations. Therefore, detailed genomic analysis of such cases is important to measure
the impact of therapy and to understand the potential mechanisms of response and
resistance.
Here we present in detail two clinical examples on the implementation of DSRT
results in AML patients including the consecutive sampling and serial monitoring of
drug sensitivity profiles and clonal evolution in vivo. In the first case, the bone
marrow cells from a relapsed and refractory 54-year-old patient (sample 600_2) with
a normal karyotype AML FAB M5 were subjected to DSRT and deep molecular
profiling. The patient had previously failed three consecutive induction therapies (Fig.
4A). The DSRT results highlighted dasatinib, sunitinib and temsirolimus among the
top 5 most selective approved drugs. In an off-label compassionate use setting, the
patient received a combination of these targeted drugs resulting in rapid reduction of
the bone marrow blast count and marked improvement in the poor performance status.
Concomitantly, the blood counts rapidly normalized resulting in CRi. However, 30
days after achieving the CRi response, resistance emerged. A new DSRT analysis
from the relapsed sample (600_3) showed that the drugs used in patient treatment
exhibited remarkably reduced anti-cancer activity as compared to the pre-treatment
sample (Fig. 4B), demonstrating a match between ex vivo and in vivo responses. In
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this patient, the ex vivo responses to many other drugs were also strongly reduced in
the relapsed sample (Fig. 4C).
A fusion transcript joining NUP98 exon 12 and NSD1 exon 6 resulting from a cryptic
chromosome translocation t(5;11)(q35;p15.5) was detected by RNA sequencing in
sample 600_2. This oncogenic fusion (12) is relatively common in cytogenetically
normal pediatric AML (13, 14) but relatively rare in adult AML (15). The NUP98-
NSD1 fusion was also detected in the diagnostic sample (600_0) and all follow-up
samples suggesting that this fusion was the initiating event in the development of the
patient’s disease. Exome sequencing revealed a diverse subclonal architecture
highlighted by two FLT3-ITD (Supplementary Figs. S5A-B) and four different WT1
mutations (Supplementary Fig. S6A-B, Supplementary Table S5, and
Supplementary Methods). After induction chemotherapy, the predominant FLT3-
ITD harboring subclone was no longer detectable. Instead, subclones containing WT1
mutations and a second FLT3-ITD emerged (Fig. 4D-E). The sensitivity to
quizartinib, sunitinib and other FLT3 inhibitors indicated FLT3 as a disease driver in
the 600_2 sample. In the DSRT-relapsed 600_3 sample, the dasatinib, sunitinib and
temsirolimus therapy had further selected a specific subclone still containing the
second FLT3-ITD even though the response to FLT3 inhibitors and other tyrosine
kinase inhibitors was lost. The broad loss in drug response in the 600_3 sample was
also accompanied with decreased phosphorylation of AKT (S473), CHK2 (T68),
CREB (S133), ERK1/2 (T202/Y204, T185/Y187), FAK (Y397), p38� (T180/Y182),
and STAT1 (Y701) (data not shown).
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DSRT and molecular profiling defined key oncogenic signals and mechanisms of
drug resistance
A second clinical case was a 37-year old patient (784_1) who was diagnosed with a
recurrent t(11;19)(q23;p13.1) translocation and corresponding MLL-ELL fusion gene.
The patient had relapsed from three previous rounds of conventional therapy. Initial
DSRT results (784_1) showed selective responses to MEK inhibitors, rapalogs and
several tyrosine kinase inhibitors, including dasatinib (Fig. 5A). This patient was also
treated with dasatinib, sunitinib and temsirolimus, which led to a rapid decrease in
both peripheral leukocytosis and bone marrow blast counts, but the effect was short-
lived. The ex vivo drug sensitivity of the resistant sample (784_2) revealed that the
cells had lost their response to dasatinib and rapalogs, but preserved the response to
ATP-competitive mTOR inhibitors (such as dactolisib and AZD8055). Interestingly,
the resistant sample gained sensitivity to a tyrosine kinase inhibitor that was
previously ineffective in the DSRT, BMS-754807 (IGF1R/Trk inhibitor), as well as
crizotinib (tyrosine kinase inhibitor) and tipifarnib (farnesyltransferase inhibitor),
several topoisomerase II inhibitors, immunomodulatory and differentiating
compounds (Fig. 5B).
Using the DSRT data on kinase inhibitors with published comprehensive biochemical
profiling data (16), we identified putative kinases to which the cells were addicted.
Importantly, a comparison between the first and the relapsed sample identified a
major switch in kinase addiction with a loss of addiction to Src family kinases, PI3-
kinases and p38 MAP kinases and a gain of addiction to ALK and Trk family receptor
tyrosine kinases (Fig. 5C).
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The resistance to the dasatinib, sunitinib and temsirolimus treatment in this patient
was not associated with any novel detected mutations or other genetic alterations, but
coincided with more than 1000-fold enrichment of two fusion transcripts, ETV6-
NTRK3 and STRN-ALK (Supplementary Fig. S7), suggesting that resistance emerged
from the selection of pre-existing small subclones. The ETV6-NTRK3 encodes for the
oncogenic fusion protein TEL-TrkC while the STRN-ALK fusion was out of frame
and therefore did not result in a functional fusion protein (Fig. 6A and
Supplementary Fig. S8). These genetic events were accompanied by increases in
p70S6 kinase (T389) and CREB (S133) phosphorylation (Fig. 6B), suggesting that
mTORC1 was hyperactivated, a known mechanism of resistance towards rapalogs
(17). The fusion gene MLL-ELL was detected from the diagnostic and subsequent
samples suggesting that this was an initiating driver event in this leukemia. Similar to
patient 600, patient 784 initially also had FLT3-ITD mutations (Supplementary Figs.
S9A-B) that were lost after induction chemotherapy, and a mutation to WT1
augmented by loss of heterozygosity that persisted throughout the course of the
disease (Fig. 6C-D and Supplementary Table S6). The gained sensitivity to the dual
IGF1R/TrkC inhibitor BMS-754807 fits with the model of the TEL-TrkC fusion
protein as a new driver since the oncogenic potential of this fusion has been shown to
be dependent of the activity of IGF1R (18, 19) and lead to hyperactivation of
mTORC1(20, 21). Thus, we predict that in this patient, the mechanism of resistance
involved TEL-TrkC mediated activation of IGF1R signaling, which promoted
hyperactivation of mTORC1. Supporting this hypothesis, we observed synergistic
activities between the BMS-754807 IGF1R/TrkC inhibitor and dactolisib, an ATP-
competitive mTOR inhibitor (Fig. 6E). Combination of BMS-754807 and the MEK
inhibitor trametinib, on the other hand, did not result in synergism, indicating that the
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TEL-TrkC/IGF1R/mTORC1 dependency represents a separate signal than the one
leading to addiction to MEK signaling (Fig. 6F). Taken together, these results
indicate how ISM strategy helps to identify not only the mechanisms of resistance,
but also potential ways to counteract it with combinatorial therapies.
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DISCUSSION
We present here an individualized systems medicine strategy based on the systematic
functional testing of patient-derived primary cancer cells to targeted anti-cancer
agents coupled with genomic and molecular profiling. Importantly, the intent is to
guide treatment decisions for individual cancer patients coupled with monitoring of
subsequent responses in patients to measure and understand efficacy and mechanism
of action of the drugs. The ISM strategy allows for an iterative adjustment of
therapies for cancer patients, one-patient-at-a-time, with repeated sampling playing a
major role in understanding and learning from each success and failure. Furthermore,
ISM facilitates learning by discovering molecular or functional patterns from past
patient cases to help therapeutic assessment of new patients.
Application of the DSRT technology to 28 AML patient samples identified effective
molecularly targeted compounds inducing selective toxic or inhibitory responses in
AML cells over normal bone marrow mononuclear cells. Our data suggest the
intriguing possibility that anti-cancer agents already in clinical use for other diseases,
such as dasatinib (current approved indications are CML and Ph+ ALL), sunitinib
(renal cell cancer and gastrointestinal stromal tumors), and temsirolimus (renal cell
cancer), could be repositioned for subsets of AML patients. Although we did not
achieve long-term cures in AML patients receiving DSRT-guided therapies, the
clinical responses seen are encouraging given the fact that most of the AML patients
studied here had complex chemorefractory, end-stage disease. Obviously, the clinical
results arise from a non-randomized setting, and need to be verified. However, the
clinical implementation of ISM in individual patients is a powerful way to create
hypotheses to be tested in systematic clinical studies, both for existing and emerging
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drugs. Indeed, we also identified several investigational oncology drug classes, such
as ATP-competitive PI3K/AKT/mTOR pathway inhibitors, MEK inhibitors and JAK
inhibitors, which deserve attention as drugs to prioritize for future clinical trials in
AML. Furthermore, the ISM approach could also help to identify effective drug
combinations based on associating drug sensitivities.
Unsupervised clustering of the patient samples identified five functional taxonomic
groups in AML based on ex vivo drug responses. FLT3 mutations were highly
enriched among the most tyrosine kinase inhibitor sensitive functional group (group
V) with FLT3 inhibitors being the most selective class of drugs for this group.
However, these samples were also selectively sensitive to other kinase inhibitors, such
as dasatinib, that lack FLT3 activity suggesting that oncogenic FLT3 signaling may
be dependent on the signaling of other tyrosine kinases whose inhibition may
synergize with the therapeutic effects of FLT3 inhibitors. Given that the clinical
implementation of FLT3 inhibitors has proven very challenging, the identification of
druggable synergistic kinase signals or drugs could be extremely important.
Furthermore, we observed a significant association between activating mutations in
RAS genes and sensitivity to MEK inhibitors in line with previous results showing
that trametinib exhibited favorable clinical responses in RAS mutated refractory AML
patients (22). Finally, we identified a tendency of clustering of TP53 mutations in the
two least responsive functional groups (groups I and II) and mutations and fusions
linking to epigenetic modulation in groups III and IV. However, most of the drug
response classifications and variabilities could not yet be attributed to the main
mutations detected in the patient samples. Such drug responses may arise from non-
genomic causes and complex combinatorial molecular pathways, and these findings
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therefore highlight the value of DSRT in i) functionally validating suggestions arising
from the genomic profiles and ii) discovering other drug dependencies that have yet to
be deduced from genomic or transcriptomic data. The relationship between genomic
changes and drug response may also be more complicated in the chemorefractory
patient samples studied here.
Despite multiple efforts over several decades, the direct prediction of cancer cell
chemosensitivity in the clinical setting has remained an elusive goal. As compared to
previously published approaches, the ISM approach presented here has distinct
differences. First, our studies focused on targeted drugs while most of the previous
efforts of ex vivo drug testing have focused on conventional chemotherapeutics (23-
27). The ex vivo responses to these agents are often non-selective and more difficult to
interpret and translate to clinical patient care. Second, we focused on leukemias while
many previous studies have focused on solid tumors (28-33), where representative
samples are difficult to acquire and consecutive samples from recurrent disease are
typically not available. Third, we measured selective anti-cancer effects as compared
to normal bone marrow mononuclear cells, which makes it possible to identify
cancer-selective drugs with potential for less systemic toxicity. Fourth, we performed
a rapid analysis of the in vivo effects of the drugs from consecutive samples. Our
novel endpoint for therapy efficacy in patients was an impact on the clonal
composition of the cancer sample.
The different types of responses in our clinically translated patient cases highlight the
difficulty in predicting mechanisms of resistance and support the importance of
repeated functional testing such as DSRT during disease progression in order to
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identify changes in drug sensitivities. In patient 600, the leukemic cells carried a
FLT3-ITD mutation and showed addiction to this oncogene based on highly selective
responses to FLT3-inhibitors such as quizartinib and sunitinib. Interestingly, the same
FLT3-ITD variant remained in the resistant cells, but the response to quizartinib,
sunitinib and other FLT3 inhibitors was lost and the cells acquired pan-resistance to
almost all agents tested. In contrast, in patient 784, resistance to dasatinib, sunitinib
and temsirolimus treatment was linked to enrichment of clonal populations carrying a
tyrosine kinase fusion gene that mediated resistance. The ETV6-NTRK3 fusion and
the resulting TEL-TrkC fusion protein is a known oncogenic driver in AML and other
cancers (34-36) and is dependent on IGF1R kinase activity (18-20, 37, 38). This
hypothesis is supported by the acquisition of sensitivity, based on ex vivo testing, to
the dual IGF1R/TrkC inhibitor BMS-754807 exclusively in the relapsed sample.
In conclusion, we present an individual-centric, functional systems medicine strategy
to systematically identify drugs to which individual AML patients are sensitive and
resistant, implement such strategies in the clinic, and learn from the integrated
genomic, molecular and functional analysis of drug sensitivity and resistance in
paired samples. ISM strategy provides a powerful way to create hypotheses to be
tested in formal clinical trials, both for existing drugs, emerging compounds and their
combinations. In the future, ISM may pave a path for routine individualized
optimization of patient therapies in the clinic.
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MATERIALS AND METHODS
Study patients and material
Twenty-eight bone marrow aspirates and peripheral blood samples (leukemic cells)
and skin biopsies (non-cancerous cells for germline genomic information) from 18
AML and high-risk (according to WPSS (39)) MDS patients, and 7 samples from
different healthy donors (controls) were obtained after informed consent with
approval (No. 239/13/03/00/2010, 303/13/03/01/2011). Patient characteristics are
summarized in Supplementary Table S3. Mononuclear cells were isolated by Ficoll
density gradient (Ficoll-Paque PREMIUM; GE Healthcare), washed, counted and
suspended in Mononuclear Cell Medium (PromoCell) supplemented with 0.5 �g/ml
gentamicin and 2.5 �g/ml amphotericin B. One sample from patient 393, a secondary
AML after MDS with 20% myeloblasts, was enriched for the CD34+ cell population
(sample 393_3 – corresponding to the blast cell population) using para-magnetic
beads according to manufacturer's instructions (Miltenyi Biotech).
Development of the compound collection
The oncology compound collection covers the active substances from the majority of
FDA/EMA approved anti-cancer drugs (n=123) as well as emerging investigational
and pre-clinical compounds (n=64) covering a wide range of molecular targets
(Supplementary Table S1). The compounds were obtained from the National Cancer
Institute Drug Testing Program (NCI DTP) and commercial chemical vendors: Active
Biochem, Axon Medchem, Cayman Chemical Company, ChemieTek, Enzo Life
Sciences, LC Laboratories, Santa Cruz Biotechnology, Selleck, Sequoia Research
Products, Sigma-Aldrich and Tocris Biosciences.
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Drug sensitivity and resistance testing
Ex vivo DSRT was performed on freshly isolated primary AML cells derived from
patient samples as well as mononuclear cells derived from healthy donors. The
compounds were dissolved in 100% dimethyl sulfoxide (DMSO) and dispensed on
tissue culture treated 384-well plates (Corning) using an acoustic liquid handling
device, Echo 550 (Labcyte Inc.). The compounds were plated in 5 different
concentrations in 10-fold dilutions covering a 10,000-fold concentration range (e.g. 1-
10,000 nM). The predrugged plates were kept in pressurized StoragePods (Roylan
Developments Ltd.) under inert nitrogen gas until needed. The compounds were
dissolved with 5 �l of MCM while shaking for 30 min. Twenty �l of single cell
suspension (10,000 cells) was transferred to each well using a MultiDrop Combi
(Thermo Scientific) peristaltic dispenser. The plates were incubated in a humidified
environment at 37°C and 5% CO2 and after 72 h cell viability was measured using
CellTiter-Glo luminescent assay (Promega) according to manufacturer’s instructions
with a Molecular Devices Paradigm plate reader. The data was normalized to negative
control (DMSO only) and positive control wells (containing 100 μM benzethonium
chloride, effectively killing all cells).
Generation of dose response curves and analysis of data
The plate reader data was uploaded to Dotmatics Browser/Studies software
(Dotmatics Ltd.) for a normalized calculation of % survival for each data point and
generation of dose response curves for each of the drugs tested. The dose response
curves were fitted based on a four parameter logistic fit function defined by the top
and bottom asymptote, the slope and the inflection point (EC50).
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In the curve fitting, the top asymptote of the curve was fixed to 100% viability, while
the bottom asymptote was allowed to float between 0 and 75% (i.e. drugs causing less
than 25% inhibition were considered inactive).
Scoring and clustering of drug sensitivity and resistance testing data
To quantitatively profile individual patient samples in terms of their DSRT-wide drug
responses, as well as to compare drug responses across various AML patient samples,
a single measure was developed, DSS. The curve fitting parameters were used to
calculate the area under the dose response curve (AUC) relative to the total area
between 10% threshold and 100% inhibition (TA). Further, the integrated response
was divided by a logarithm of the top asymptote (a). Formally, the DSS was
calculated by:
We scored for differential activity of the drugs in AML blast cells in comparison to
control cells, sDSS. Clustering of the drug sensitivity profiles across the AML patient
and control samples was performed using unsupervised hierarchical complete-linkage
clustering using Spearman and Euclidean distance measures of the drug and sample
profiles, respectively. Reproducibility of the clustering and the resulting drug
response subtypes detected was evaluated using the bootstrap resampling method with
the Pvclust R-package (40).
Prediction of kinase addictions
DSS = 100xAUCTAx loga
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sDSSs of kinase inhibitors were further used to predict sample-specific kinase
addictions. Sample-specific sDSS responses were compared with target profiles for 35
kinase inhibitors overlapping between our compound panel and the panel profiled by
Davies and coworkers (16). More specifically, for each kinase target, we calculated a
Kinase Inhibition Sensitivity Score (KISS) by averaging the sDSS values among
those compounds that selectively target the kinase. These putative selective kinases
were compared to gene expression to exclude non-expressed targets and the
remaining kinases defined a putative “kinaddictome” for each patient sample. For
displaying purposes, the resulting kinases were depicted in a target similarity network,
in which edges connect kinases with similar inhibitor specificity profiles (16).
(Spearman’s rank-based correlation > 0.5) (Szwajda et al, unpublished).
DNA sequencing
Genomic DNA was isolated using the DNeasy Blood & Tissue kit (Qiagen). Exome
sequencing was performed on the patient samples highlighted in Fig 3. In addition,
whole genome sequencing was performed using DNA from the skin and AML cells
from sample 784_2. 3 �g of DNA was fragmented and processed according to the
NEB Next DNA Sample Prep Master Mix protocol. Exome capture was performed
using the Nimblegen SeqCap EZ v2 capture kit (Roche NimbleGen). Sequencing of
exomes and genomes was done using HiSeq1500, 2000 or 2500 instruments
(Illumina). For germline control samples 4×107 and for tumor samples 10×107 2×100
bp paired-end reads were sequenced per sample. The leukemia DNA sample from
patient 1497 was sequenced using the Illumina TruSeq Amplicon Cancer Panel and
the MiSeq sequencer (Illumina).
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Somatic mutation calling and annotation from exome sequencing data
Sequence reads were processed and aligned to the reference genome as described
previously (41, 42). Somatic mutation calls were made for exome capture target
regions of the NimbleGen SeqCap EZ v2 capture kit (Roche NimbleGen) and the
flanking 500 bps. High confidence somatic mutations were called for each tumor
sample using the VarScan2 somatic algorithm (43) with the following parameters:
-strand-filter 1
-min-coverage-normal 8
-min-coverage-tumor 1
-somatic-p-value 0.01
-normal-purity 0.95
-min-var-freq 0
Mutations were annotated with SnpEff (44) using the Ensembl v68 annotation
database. To filter out false positive calls due to genomic repeats, somatic mutation
calls in regions defined as repeats in the RepeatMasker track obtained from the UCSC
Genome Browser were removed from the analysis. To filter out misclassified
germline variants, population variants included in dbSNP version 130 were removed.
Remaining mutations were visually validated using Integrated Genomics Viewer
(Broad Institute).
Analysis of mutation frequencies in serial samples
In order to examine frequencies of the identified mutations in samples where the
mutations did not pass the criteria for high confidence mutations, variant frequencies
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27
and read counts for each mutation were retrieved from a set of unfiltered variant calls
generated by VarScan2 with the following parameters:
-strand-filter 0
-min-coverage-normal 8
-min-coverage-tumor 1
-somatic-p-value 1
-normal-purity 1
-min-var-freq 0
In addition, we used variant allele frequencies from control-leukemia pairs to identify
regions of loss of heterozygosity.
FLT3-ITD detection by capillary sequencing and qPCR
For determination of patients´ FLT3-ITD status, genomic DNA was extracted from
bone marrow mononuclear cell fraction. Qualitative PCR was performed as described
by Kottaridis et al by using a FAM labeled forward primer (45). The PCR products
were separated on an agarose gel and in capillary electrophoresis with ABI3500Dx
Genetic Analyzer and sequenced using M13-tailed direct sequencing. Assessment of
minimal residual disease (MRD) level was performed with real-time quantitative PCR
(qPCR). Patient (ITD) specific ASO (allele-specific oligonucleotide) primer was
designed at the ITD junction region and used together with downstream TaqMan®
probe and reverse primer (primer sequences available upon request). Albumin gene
qPCR was additionally performed to normalize the variability in DNA quality in the
follow-up samples.
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Amplicon sequencing
Amplicons were amplified using locus specific PCR primers carrying Illumina
compatible adapter sequences, grafting sequences (P5 and P7) and an amplicon
specific 6 bp index sequence (Supplementary Table S7). The PCR reaction
contained 10 ng of sample DNA, 10 μl of 2x Phusion High-Fidelity PCR Master Mix
(Thermo Scientific Inc), and 0.5 μM of each primer. Following PCR amplification,
samples were purified using Performa® V3 96-Well Short Plate and QuickStep™2
SOPE™ Resin (EdgeBio). Sequencing of PCR amplicons was performed using the
Illumina HiSeq2000 instrument (Illumina). Samples were sequenced as 101 bp paired-
end reads and one 7 bp index read.
Library preparation, sequencing and data analysis of transcriptomes
2.5-5 μg of total-RNA was used for depletion of ribosomal-RNA (Ribo-Zero™ rRNA
Removal Kit, Epicentre), purified (RNeasy clean-up-kit, Qiagen) and reverse
transcribed to ds cDNA (SuperScript™ Double-Stranded cDNA Synthesis Kit, Life
Technologies). Random hexamers (New England BioLabs) were used for priming the
first strand synthesis reaction.
Illumina compatible Nextera™ Technology (Epicentre) was used for preparation of
RNAseq Libraries. HMW–buffer and 50 ng of cDNA was used for tagmentation as
recommended by manufacturer. After the tagmentation reaction the fragmented
cDNA was purified with SPRI beads (Agencourt AMPure XP, Beckman Coulter).
The RNAseq libraries were size selected (350-700 bp fragments) in 2% agarose gel
followed by purification with QIAquick gel extraction kit (Qiagen).
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Each transcriptome was loaded to occupy 1/3 of the lane capacity in the flow cell. C-
Bot (TruSeq PE Cluster Kit v3, Illumina) was used for cluster generation and
Illumina HiSeq2000 platform (TruSeq SBS Kit v3-HS reagent kit) for paired end
sequencing with 100 bp read length. Nextera Read Primers 1 and 2 as well as Nextera
Index Read Primer were used for paired end sequencing and index read sequencing,
respectively. RNA seq data analysis, such as fusion gene identification, mutation
calling and gene expression quantitation (Tophat and Cufflinks) was done as
described previously (46). Primers used to validate as well as quantify the fusion
genes detected are listed in Supplementary Tables S8-9.
Proteomic analysis
Phosphoproteomic analysis of the AML patient samples was performed using
Proteome Profiler antibody arrays (R&D Systems) according to the manufacturer’s
instructions. Lysates containing 300 μg of protein were applied to the arrays and
fluorescently labeled streptavidin (IRDye 800 CW streptavidin, LI-COR) and an
Odyssey imaging system (LI-COR) were used for detection.
Other statistical analyses
Statistical analysis was performed using GraphPad Prism 5. Pearson correlation test
was used to determine the correlations between drug sensitivity profiles of healthy
donor samples. A two-tailed student t-test was used to assess the correlation between
RAS or FLT3 mutations and MEK inhibitor or dasatinib sensitivity, respectively. A
correlation in sensitivity was considered statistically significant when P < 0.05.
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30
ACKNOWLEDGEMENTS
The authors wish to thank the patients in donating their samples for our research, Jani
Saarela and Ida Lindenschmidt (FIMM Technology Centre, High Throughput
Biomedicine Unit) for technical assistance, Biocenter Finland research infrastructures
(Drug Discovery and Chemical Biology platform as well as the Genome-Wide
Methods network), EATRIS and BBMRI ESFRI infrastructures for technical and
infrastructural support, and Labcyte Inc. for technical assistance in developing the
DSRT pipeline as part of an ongoing collaboration with FIMM.
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31
REFERENCES 1. Marcucci G, Haferlach T, Dohner H. Molecular genetics of adult acute
myeloid leukemia: prognostic and therapeutic implications. J Clin Oncol.
2011;29:475-86.
2. Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, et al. WHO
Classification of Tumours of Haematopoietic and Lymphoid Tissues, Fourth Edition.
Lyon, France: IARC Press; 2008.
3. Welch JS, Ley TJ, Link DC, Miller CA, Larson DE, Koboldt DC, et al. The
origin and evolution of mutations in acute myeloid leukemia. Cell. 2012;150:264-78.
4. Network CGAR. Genomic and epigenomic landscapes of adult de novo acute
myeloid leukemia. N Engl J Med. 2013;368:2059-74.
5. Dohner H, Gaidzik VI. Impact of genetic features on treatment decisions in
AML. Hematology Am Soc Hematol Educ Program. 2011;2011:36-42.
6. Rowe JM, Kim HT, Tallman MS. Reply to induction therapy and outcome in
acute myeloid leukemia. Cancer. 2011;117:2237-.
7. Rowe JM, Tallman MS. How I treat acute myeloid leukemia. Blood.
2010;116:3147-56.
8. Buchner T, Berdel WE, Haferlach C, Haferlach T, Schnittger S, Muller-Tidow
C, et al. Age-related risk profile and chemotherapy dose response in acute myeloid
leukemia: a study by the German Acute Myeloid Leukemia Cooperative Group. J Clin
Oncol. 2009;27:61-9.
9. Dohner H, Estey EH, Amadori S, Appelbaum FR, Buchner T, Burnett AK, et
al. Diagnosis and management of acute myeloid leukemia in adults: recommendations
from an international expert panel, on behalf of the European LeukemiaNet. Blood.
2010;115:453-74.
Research. on January 1, 2020. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350
32
10. Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, et al. Clonal
evolution in relapsed acute myeloid leukaemia revealed by whole-genome
sequencing. Nature. 2012;481:506-10.
11. Tyner JW, Yang WF, Bankhead A, 3rd, Fan G, Fletcher LB, Bryant J, et al.
Kinase Pathway Dependence in Primary Human Leukemias Determined by Rapid
Inhibitor Screening. Cancer Res. 2013;73:285-96.
12. Wang GG, Cai L, Pasillas MP, Kamps MP. NUP98-NSD1 links H3K36
methylation to Hox-A gene activation and leukaemogenesis. Nat Cell Biol.
2007;9:804-12.
13. Cerveira N, Correia C, Doria S, Bizarro S, Rocha P, Gomes P, et al.
Frequency of NUP98-NSD1 fusion transcript in childhood acute myeloid leukaemia.
Leukemia. 2003;17:2244-7.
14. Hollink IH, van den Heuvel-Eibrink MM, Arentsen-Peters ST, Pratcorona M,
Abbas S, Kuipers JE, et al. NUP98/NSD1 characterizes a novel poor prognostic group
in acute myeloid leukemia with a distinct HOX gene expression pattern. Blood.
2011;118:3645-56.
15. Fasan A, Haferlach C, Alpermann T, Kern W, Haferlach T, Schnittger S. A
rare but specific subset of adult AML patients can be defined by the cytogenetically
cryptic NUP98-NSD1 fusion gene. Leukemia. 2012.
16. Davis MI, Hunt JP, Herrgard S, Ciceri P, Wodicka LM, Pallares G, et al.
Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol.
2011;29:1046-51.
17. Dowling RJ, Topisirovic I, Alain T, Bidinosti M, Fonseca BD, Petroulakis E,
et al. mTORC1-mediated cell proliferation, but not cell growth, controlled by the 4E-
BPs. Science. 2010;328:1172-6.
Research. on January 1, 2020. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350
33
18. Lannon CL, Martin MJ, Tognon CE, Jin W, Kim SJ, Sorensen PH. A highly
conserved NTRK3 C-terminal sequence in the ETV6-NTRK3 oncoprotein binds the
phosphotyrosine binding domain of insulin receptor substrate-1: an essential
interaction for transformation. J Biol Chem. 2004;279:6225-34.
19. Morrison KB, Tognon CE, Garnett MJ, Deal C, Sorensen PH. ETV6-NTRK3
transformation requires insulin-like growth factor 1 receptor signaling and is
associated with constitutive IRS-1 tyrosine phosphorylation. Oncogene.
2002;21:5684-95.
20. Tognon CE, Martin MJ, Moradian A, Trigo G, Rotblat B, Cheng SW, et al. A
tripartite complex composed of ETV6-NTRK3, IRS1 and IGF1R is required for
ETV6-NTRK3-mediated membrane localization and transformation. Oncogene.
2012;31:1334-40.
21. Tamburini J, Chapuis N, Bardet V, Park S, Sujobert P, Willems L, et al.
Mammalian target of rapamycin (mTOR) inhibition activates phosphatidylinositol 3-
kinase/Akt by up-regulating insulin-like growth factor-1 receptor signaling in acute
myeloid leukemia: rationale for therapeutic inhibition of both pathways. Blood.
2008;111:379-82.
22. Borthakur G, Popplewell L, Boyiadzis M, Foran JM, Platzbecker U, Vey N, et
al. Phase I/II Trial of the MEK1/2 Inhibitor Trametinib (GSK1120212) in
Relapsed/Refractory Myeloid Malignancies: Evidence of Activity in Patients with
RAS Mutation-Positive Disease. ASH Annual Meeting Abstracts. 2012;120:677-.
23. Gustavsson A, Olofsson T. Prediction of response to chemotherapy in acute
leukemia by in vitro drug sensitivity testing on leukemic stem cells. Cancer Res.
1984;44:4648-52.
Research. on January 1, 2020. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350
34
24. Larsson R, Fridborg H, Kristensen J, Sundstrom C, Nygren P. In vitro testing
of chemotherapeutic drug combinations in acute myelocytic leukaemia using the
fluorometric microculture cytotoxicity assay (FMCA). Br J Cancer. 1993;67:969-74.
25. Larsson R, Nygren P. Laboratory prediction of clinical chemotherapeutic drug
resistance: a working model exemplified by acute leukaemia. Eur J Cancer.
1993;29A:1208-12.
26. Pieters R, Loonen AH, Huismans DR, Broekema GJ, Dirven MW, Heyenbrok
MW, et al. In vitro drug sensitivity of cells from children with leukemia using the
MTT assay with improved culture conditions. Blood. 1990;76:2327-36.
27. Yamada S, Hongo T, Okada S, Watanabe C, Fujii Y, Ohzeki T. Clinical
relevance of in vitro chemoresistance in childhood acute myeloid leukemia.
Leukemia. 2001;15:1892-7.
28. Iwadate Y, Fujimoto S, Namba H, Yamaura A. Promising survival for patients
with glioblastoma multiforme treated with individualised chemotherapy based on in
vitro drug sensitivity testing. Br J Cancer. 2003;89:1896-900.
29. Bosanquet AG, Bell PB. Ex vivo therapeutic index by drug sensitivity assay
using fresh human normal and tumor cells. J Exp Ther Oncol. 2004;4:145-54.
30. Villman K, Blomqvist C, Larsson R, Nygren P. Predictive value of in vitro
assessment of cytotoxic drug activity in advanced breast cancer. Anticancer Drugs.
2005;16:609-15.
31. Ehemann V, Kern MA, Breinig M, Schnabel PA, Gunawan B, Schulten HJ, et
al. Establishment, characterization and drug sensitivity testing in primary cultures of
human thymoma and thymic carcinoma. Int J Cancer. 2008;122:2719-25.
Research. on January 1, 2020. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350
35
32. Lehnhardt M, Muehlberger T, Kuhnen C, Brett D, Steinau HU, Jafari HJ, et al.
Feasibility of chemosensitivity testing in soft tissue sarcomas. World J Surg Oncol.
2005;3:20.
33. Brigulova K, Cervinka M, Tosner J, Sedlakova I. Chemoresistance testing of
human ovarian cancer cells and its in vitro model. Toxicol In Vitro. 2010;24:2108-15.
34. Knezevich SR, McFadden DE, Tao W, Lim JF, Sorensen PH. A novel ETV6-
NTRK3 gene fusion in congenital fibrosarcoma. Nat Genet. 1998;18:184-7.
35. Liu Q, Schwaller J, Kutok J, Cain D, Aster JC, Williams IR, et al. Signal
transduction and transforming properties of the TEL-TRKC fusions associated with
t(12;15)(p13;q25) in congenital fibrosarcoma and acute myelogenous leukemia.
EMBO J. 2000;19:1827-38.
36. Tognon C, Knezevich SR, Huntsman D, Roskelley CD, Melnyk N, Mathers
JA, et al. Expression of the ETV6-NTRK3 gene fusion as a primary event in human
secretory breast carcinoma. Cancer Cell. 2002;2:367-76.
37. Perl AE, Kasner MT, Tsai DE, Vogl DT, Loren AW, Schuster SJ, et al. A
phase I study of the mammalian target of rapamycin inhibitor sirolimus and MEC
chemotherapy in relapsed and refractory acute myelogenous leukemia. Clin Cancer
Res. 2009;15:6732-9.
38. Tognon CE, Somasiri AM, Evdokimova VE, Trigo G, Uy EE, Melnyk N, et
al. ETV6-NTRK3-mediated breast epithelial cell transformation is blocked by
targeting the IGF1R signaling pathway. Cancer Res. 2011;71:1060-70.
39. Malcovati L, Germing U, Kuendgen A, Della Porta MG, Pascutto C,
Invernizzi R, et al. Time-dependent prognostic scoring system for predicting survival
and leukemic evolution in myelodysplastic syndromes. J Clin Oncol. 2007;25:3503-
10.
Research. on January 1, 2020. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350
36
40. Suzuki R, Shimodaira H. Pvclust: an R package for assessing the uncertainty
in hierarchical clustering. Bioinformatics. 2006;22:1540-2.
41. Koskela HL, Eldfors S, Ellonen P, van Adrichem AJ, Kuusanmaki H,
Andersson EI, et al. Somatic STAT3 mutations in large granular lymphocytic
leukemia. N Engl J Med. 2012;366:1905-13.
42. Sulonen AM, Ellonen P, Almusa H, Lepisto M, Eldfors S, Hannula S, et al.
Comparison of solution-based exome capture methods for next generation
sequencing. Genome Biol. 2011;12:R94.
43. Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, et al.
VarScan 2: somatic mutation and copy number alteration discovery in cancer by
exome sequencing. Genome Res. 2012;22:568-76.
44. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A
program for annotating and predicting the effects of single nucleotide polymorphisms,
SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.
Fly. 2012;6:80-92.
45. Kottaridis PD, Gale RE, Frew ME, Harrison G, Langabeer SE, Belton AA, et
al. The presence of a FLT3 internal tandem duplication in patients with acute myeloid
leukemia (AML) adds important prognostic information to cytogenetic risk group and
response to the first cycle of chemotherapy: analysis of 854 patients from the United
Kingdom Medical Research Council AML 10 and 12 trials. Blood. 2001;98:1752-9.
46. Edgren H, Murumagi A, Kangaspeska S, Nicorici D, Hongisto V, Kleivi K, et
al. Identification of fusion genes in breast cancer by paired-end RNA-sequencing.
Genome Biol. 2011;12:R6.
Research. on January 1, 2020. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from
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37
Table 1. Drug classes and drugs represented in the DSRT screening platform Classes of drugs Drugs
Alkylating agents
altretamine, azacitidine, busulfan, carboplatin, carmustine, chlorambucil, cyclophosphamide, dacarbazine, ifosfamide, lomustine, pipobroman, procarbazine, streptozocin, temozolomide, thioTEPA, uracil mustard
Antimetabolites
allopurinol, capecitabine, cladribine, clofarabine, cytarabine, decitabine, floxuridine, fludarabine, fluorouracil, gemcitabine, mercaptopurine, methotrexate, nelarabine, pentostatin, thioguanine
Antimitotics ABT-751, docetaxel, indibulin, ixabepilone, paclitaxel, patupilone, S-trityl-L-cysteine, vinblastine, vincristine, vinorelbine
Antitumor antibiotics bleomycin, dactinomycin, mitomycin C, plicamycin Bcl-2 inhibitors navitoclax, obatoclax
HDAC inhibitors belinostat, CUDC-101, entinostat, panobinostat, tacedinaline, vorinostat
Hormone inhibitors abiraterone, aminoglutethimide, anastrozole, exemestane, finasteride, flutamide, fulvestrant, goserelin, letrozole, megestrol acetate, nilutamide, raloxifene, tamoxifen
HSP90 inhibitors alvespimycin, BIIB021, NVP-AUY922, tanespimycin
Immunomodulators celecoxib, dexamethasone, fingolimod, imiquimod, lenalidomide, levamisole, methylprednisolone, plerixafor, prednisolone, prednisone, tacrolimus, thalidomide
Kinase inhibitors, AGC alisertib, AT9283, AZD1152-HQPA, BI2536, bryostatin 1, danusertib, enzastaurin, fasudil, midostaurin, MK-2206, ruboxistaurin, sotrastaurin, UCN-01
Kinase inhibitors, CAMK AZD7762, PF-00477736 Kinase inhibitors, CK1 MK-1775
Kinase inhibitors, CMGC alvocidib, AZ 3146, doramapimod, palbociclib, seliciclib, SNS-032
Kinase inhibitors, PIKL AZD8055, dactolisib, idelalisib, OSI-027, PF-04691502, pictilisib, XL147, XL765
Kinase inhibitors, STE pimasertib, refametinib, selumetinib, trametinib
Kinase inhibitors, TK
afatinib, axitinib, BMS-754807, canertinib, cediranib, crizotinib, dasatinib, dovitinib, EMD1214063, erlotinib, foretinib, gandotinib, gefitinib, imatinib, lapatinib, lestaurtinib, linsitinib, masitinib, MGCD-265, motesanib, nilotinib, pazopanib, ponatinib, quizartinib, regorafenib, ruxolitinib, saracatinib, sorafenib, sunitinib, tandutinib, tivozanib, tofacitinib, vandetinib, vatalanib
Kinase inhibitors, TKL vemurafenib PARP inhibitors iniparib, olaparib, rucaparib, veliparib
Proteasome inhibitors bortezomib, carfilzomib Rapalogs everolimus, sirolimus, temsirolimus
Smothened (Hh) inhibitors erismodegib, vismodegib
Topoisomerase I/II inhibitors
amonafide, camptothecin, daunorubicin, doxorubicin, etoposide, idarubicin, irinotecan, mitoxantrone, teniposide, topotecan, valrubicin
Miscellaneous antineoplastics bexarotene, hydroxyurea, mitotane, tretinoin
Other 2-methoxyestradiol, anagrelide, bimatoprost, pilocarpine, Prima-1 Met, serdemetan, tarenflurbil, tipifarnib, XAV-939, YM155
AGC: PKA, PKG and PKC kinase group; CAMK: calcium/calmodulin-dependent protein kinase group; CK1: casein kinase group; CMGC-CDK, MAPK, GSK3 and CLK kinase group; PIKL: PI3 kinase-like (PI3K inhibitors and inhibitors of related atypical kinases: mTOR, DNA-PK, ATM, ATR); STE: sterile kinase group; TK: tyrosine kinase group; TKL: tyrosine like kinase group.
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38
FIGURE LEGENDS Figure 1. Functional individualized systems medicine platform for improved AML
therapy. The platform involves i) comprehensive direct drug sensitivity and resistance
testing (DSRT) of 187 approved and investigational oncology compounds in ex vivo
primary cells from serial AML samples ii) clinical implementation of testing results in
individual patients with relapsed and refractory disease iii) deep molecular and
genomic profiling of the AML patients from consecutive samples before and after
relapse and drug resistance for monitoring disease progression and the clonal
evolution, and iv) integrating drug sensitivity, next generation sequencing and clinical
follow up data to understand the biology of disease, drug sensitivity and resistance
that can lead to rapid introduction of novel therapies to the clinic.
Figure 2. Targeted compounds exhibit cancer selective ex vivo drug responses in
AML. (A) Waterfall plot that highlights the most potent cancer-selective drugs for
each individual patients as well as drugs that are most likely to exhibit resistance/no
sensitivity. (B) Comparison of cancer-selectivity of ex vivo drug responses (DSS) of
four clinically approved drugs, conventional chemotherapeutic agents (left panel), and
four signal transduction inhibitors (right panel). The DSS:s are compared between
healthy bone marrow samples (controls; n=7) and AML patient samples (n=28) with
the range and median DSS value depicted. (C-D) The distribution of the sensitivity to
and trametinib (MEK inhibitor) and idarubicin (Topoisomerase II inhibitor) in 28
AML patient and 7 control samples expressed as Z-score (standard deviation from the
average control DSS drug response). (E) Percentages of AML patient samples
selectively responding ex vivo to selected signal transduction inhibitors as assessed by
sDSS represented as a bar graph.
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39
Figure 3. Functional taxonomy of AML based on comprehensive drug response and
mutation profiles. A dendrogram of the DSRT responses shows clustering of the
AML patient samples in five functional groups (Group I, II, III, IV and V). Samples
were clustered with the complete linkage method using Euclidean distance measures.
This approach provides a data-driven way to classify samples based on drug efficacies
and drugs based on their differential bioactivity across patient samples. Sensitivity or
non-sensitivity to either navitoclax, ruxolitinib, MEK inhibitors, dasatinib,
quizartinib, sunitinib, PI3K/mTOR inhibitors and topoisomerase II inhibitors drives
the sample groupings. The molecular profiles (significant AML mutations and
recurrent gene fusions), disease stage, and adverse karyotype status of the patient
samples are also shown to illustrate the correlation between functional drug sensitivity
and somatic mutation and cytogenetics data. D - diagnosis; D* - secondary AML
diagnosis; R - relapsed and/or refractory.
Figure 4. Clinical implementation of DSRT predictions and clonal evolution analysis
in a heavily refractory AML patient. (A) Clinical follow up of patient 600 from
diagnosis through relapse depicting percentage of bone marrow blasts and number of
neutrophils. (B) Initial DSRT results from relapsed and refractory patient 600 showed
that the patient cells ex vivo exhibited sensitivity to several kinase inhibitors
(including dasatinib and sunitinib) and rapalogs and as a result was treated with a
combination of dasatinib, sunitinib and temsirolimus. The patient achieved complete
remission under this drug regimen, but relapsed five weeks later. Bar graphs show the
before and after relapse selective DSS for sensitivity to dasatinib, sunitinib and
temsirolimus. (C) Comparison of DSRT responses of the initial (600_2) and relapsed
sample (600_3), revealing the loss of the sensitivity to the majority of tested
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40
compounds. Drugs for which the DSS decreased with greater than 10 from 600_2 to
600_3 are marked in blue; other drugs with a selective DSS greater than 10 in 600_2
are marked in pale blue; and drugs with selective DSS greater than 10 in both 600_2
and 600_3 are marked in green. (D) Clonal progression of the disease in the patient
from diagnosis to relapse; further information in Supplementary Data and
Supplementary Table 5 in Supplementary Materials. (E) Summary heatmap
illustrating the key putative oncogenic genetic alterations in the clones depicted in
panel D.
Figure 5. Functional DSRT and kinase inhibitor sensitivity defined key oncologic
signals and mechanisms of resistance. (A) Initial DSRT results from refractory patient
784 highlighting selective sensitivity to MEK inhibitors, rapalogs, and several
tyrosine kinase inhibitors. Based on these results the patient was treated with
dasatinib, sunitinib and temsirolimus (marked with asterisks). (B) Correlation of the
DSRT results of the initial (784_1) and resistant (784_2) sample, illustrating that the
relapsed cells had lost sensitivity to dasatinib and rapalogs but retained sensitivity to
ATP-competitive mTOR inhibitors and gained sensitivity to BMS-754807, crizotinib,
danusertib and tipifarnib. Drugs for which the DSS decreased with greater than 10
from 784_1 to 784_2 are marked in blue; other drugs with a selective DSS greater
than 10 in 784_1 are marked in pale blue; drugs for which the DSS increased with
greater than 10 from 784_1 to 784_2 are marked in red; other drugs with a selective
DSS greater than 10 in 784_2 are marked in in pink and drugs with selective DSS
greater than 10 in both 600_2 and 600_3 are marked in green. (C) Kinase inhibitor
sDSS responses matched with target profiles described by Davis and coworkers (16)
yields putative kinase addiction sub-networks in the two patient samples.
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41
Figure 6. IGF1R and mTOR inhibition has a synergistic effect in ETV6-NTRK3
driven AMLs. (A) Validation sequencing and resulting predicted protein structure of
two fusions identified in this patient with RNA sequencing. The MLL-ELL fusion was
present throughout the disease, whereas the ETV6-NTRK3 (TEL-TrkC) fusion was
detected in the dasatinib-temsirolimus resistant sample. (B) Phosphoproteomic
profiling of the initial and resistant samples displayed a signaling switch in the
leukemic cells. (C) Clonal evolution of the AML from diagnosis to relapse. (D)
Summary heatmap illustrating the key putative oncogenic genetic alterations in the
clones depicted in panel C. (E) Combinatorial treatment of the patient cells with the
IGF1R/TrkC inhibitor BMS-754807 and either dactolisib (ATP-competitive mTOR
inhibitor) or trametinib (MEK inhibitor) revealed that there is a synergistic effect
between mTOR and IGF1R/TrkC inhibition. (F) A model of kinase driver switch and
drug resistance in this patient.
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Drug sensitivity and resistance testing (DSRT)
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A B
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A B
C
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0 5 10 15 20CUDC-101 - HDACiPictilisib - PI3KiIdelalisib - PI3Ki Dactolisib - mTOR/PI3KiDoramapimod - p38iTanespimycin - HSP90iTivozanib - TKI (VEGFR)Lestaurtinib - Broad TKIDasatinib - Broad TKI*BIIB021 - HSP90iSelumetinib - MEKiPlicamycin - RNA synth.iAZD8055 - mTORiTrametinib - MEKiPF-04691502 - mTOR/PI3KiEverolimus - RapalogTemsirolimus - Rapalog*Sirolimus - RapalogPimasertib - MEKiRefametinib - MEKi
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A
MLLELLAGTGAAACAGAATCCTTTTCTTTTGGT T
ETV6NTRK3ACAGCCACGGGACCTGCTATTCTCCCA A
Zinc finger, CXXC-type(IPR002857)RNA pol II elongation factor ELL (IPR019464)Occludin/RNA pol II elongation factor ELL domain (IPR010844)Pointed doman (IPR003118)Tyr kinase catalyticdomain (IPR020635)
MLL ELL
TEL TrkC
1 140646
1 336 466
622
825
BM
S-7
5480
7
Das-Sun-Tem
TEL-TrkC/IGF1R
hyperactivatedTORC1IG
F1R
/TrK
Ci
0 0.025 0.25 2.5 25 250
Trametinib
100 122.1147 93.40567
104.8524 89.97883 103.6878
117.0776
83.7311 81.26086
MEKi
Non-synergistic
Dasatinib target
TORC1
784_1 784_2
D
0 0.1 1 10 100 1000
DactolisibmTOR/PI3Ki
Synergistic
0
1
10
100
1000
10000
B
pp38
�
pGSK-3�
/�
pMEK1/2
pCREB
pp70
S6K0
1
2
3
4
5
Fold
cha
nge
784_1784_2
F
Figure 6. Pemovska et al
MLL
-ELL
fusi
onW
T1PD
CD
10(R
157H
)TP
63(I4
32T)
FLT3
-ITD
#1
FLT3
-ITD
#2
WT1
LO
HET
V6-N
TRK3
fusi
onST
RN
-ALK
fusi
on
Clone 1Clone 2Clone 3Clone 4Clone 5
E
C
Chemotherapy Das-Sun-Tem
784_1784_0 784_2
MLL-ELLWT1
Clone 1FLT3-ITD #1
Clone 2FLT3-ITD #2
Clone 3WT1 LOH
Clone 4ETV6-NTRK3
Clone 5STRN-ALK
RESISTANCE
therapy
Research. on January 1, 2020. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from
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Published OnlineFirst September 20, 2013.Cancer Discovery Tea Pemovska, Mika Kontro, Bhagwan Yadav, et al. leukemiatreatments for patients with chemorefractory acute myeloid Individualized Systems Medicine (ISM) strategy to tailor
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Research. on January 1, 2020. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350