Individualized Systems Medicine (ISM) strategy to tailor ...cancerdiscovery.aacrjournals.org/content/candisc/early/2013/09/18/2159... · 1 Individualized Systems Medicine (ISM) strategy

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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.

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

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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.




23

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).




24

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




25

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).




26

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




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.




28

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).




29

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.




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.




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.




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.




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.




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.




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.




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.




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.




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.




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




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.




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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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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