individualized systems medicine strategy to tailor ... · (i) create a taxonomy of comprehensive...

2013;3:1416-1429. Published OnlineFirst September 20, 2013.Cancer Discovery Tea Pemovska, Mika Kontro, Bhagwan Yadav, et al. Patients with Chemorefractory Acute Myeloid LeukemiaIndividualized Systems Medicine Strategy to Tailor Treatments for

Updated version

10.1158/2159-8290.CD-13-0350doi:

Access the most recent version of this article at:

Material

Supplementary

http://cancerdiscovery.aacrjournals.org/content/suppl/2013/09/18/2159-8290.CD-13-0350.DC1.html

Access the most recent supplemental material at:

Cited Articles

http://cancerdiscovery.aacrjournals.org/content/3/12/1416.full.html#ref-list-1

This article cites by 44 articles, 19 of which you can access for free at:

Citing articles

http://cancerdiscovery.aacrjournals.org/content/3/12/1416.full.html#related-urls

This article has been cited by 2 HighWire-hosted articles. Access the articles at:

E-mail alerts related to this article or journal.Sign up to receive free email-alerts

Subscriptions

Reprints and

[email protected]

To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department at

Permissions

[email protected]

To request permission to re-use all or part of this article, contact the AACR Publications Department at

Research. on February 5, 2014. © 2013 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from

Published OnlineFirst September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350



http://cancerdiscovery.aacrjournals.org/lookup/doi/10.1158/2159-8290.CD-13-0350

http://cancerdiscovery.aacrjournals.org/lookup/doi/10.1158/2159-8290.CD-13-0350







http://cancerdiscovery.aacrjournals.org/cgi/alerts

http://cancerdiscovery.aacrjournals.org/cgi/alerts

mailto:[email protected]




http://cancerdiscovery.aacrjournals.org/




RESEARCH ARTICLE

1416 | CANCER DISCOVERY�DECEMBER 2013 www.aacrjournals.org

Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia Tea Pemovska 1 , Mika Kontro 2 , Bhagwan Yadav 1 , Henrik Edgren 1 , Samuli Eldfors 1 , Agnieszka Szwajda 1 , Henrikki Almusa 1 , Maxim M. Bespalov 1 , Pekka Ellonen 1 , Erkki Elonen 2 , Bjørn T. Gjertsen 5 , 6 , Riikka Karjalainen 1 , Evgeny Kulesskiy 1 , Sonja Lagström 1 , Anna Lehto 1 , Maija Lepistö 1 , Tuija Lundán 3 , Muntasir Mamun Majumder 1 , Jesus M. Lopez Marti 1 , Pirkko Mattila 1 , Astrid Murumägi 1 , Satu Mustjoki 2 , Aino Palva 1 , Alun Parsons 1 , Tero Pirttinen 4 , Maria E. Rämet 4 , Minna Suvela 1 , Laura Turunen 1 , Imre Västrik 1 , Maija Wolf 1 , Jonathan Knowles 1 , Tero Aittokallio 1 , Caroline A. Heckman 1 , Kimmo Porkka 2 , Olli Kallioniemi 1 , and Krister Wennerberg 1

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 profi ling 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 fi ve major taxonomic drug-response sub-

types based on DSRT profi les, 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 signifi cant clonal evolution and

novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance

to the clinically applied drugs and new vulnerabilities to previously ineffective drugs.

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. Cancer Discov; 3(12);

1416–29. ©2013 AACR.

See related commentary by Hourigan and Karp, p. 1336.

RESEARCH ARTICLE

Authors’ Affi liations: 1 Institute for Molecular Medicine Finland, FIMM; 2 Hematology Research Unit Helsinki, Helsinki University Central Hospital, University of Helsinki, Helsinki; 3 Department of Clinical Chemistry and TYKSLAB, Turku University Central Hospital, University of Turku, Turku; 4 Department of Internal Medicine, Tampere University Hospital, Tampere, Finland; 5 Department of Clinical Science, Hematology Section, University of Bergen; and 6 Department of Internal Medicine, Hematology Section, Haukeland University Hospital, Bergen, Norway

Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/).

T. Pemovska and M. Kontro shared fi rst authorship of this article.

T. Aittokallio, C.A. Heckman, K. Porkka, O. Kallioniemi, and K. Wennerberg shared senior authorship of this article.

Current address for M.M. Bespalov: Stem cells and Neurogenesis Unit, Division of Neuroscience, San Raffaele Scientifi c Institute, Milan, Italy.

Corresponding Author: Krister Wennerberg, Institute for Molecular Medi-cine Finland, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland. Phone: 358-9-191-25764; Fax: 358-9-191-25737; E-mail: krister.wennerberg@fi mm.fi

doi: 10.1158/2159-8290.CD-13-0350

©2013 American Association for Cancer Research.





DECEMBER 2013�CANCER DISCOVERY | 1417

INTRODUCTION

Adult acute myeloid leukemia (AML) is a prototype exam-

ple of the challenges of modern cancer drug discovery, devel-

opment, and patient therapy. With the exception of the

retinoic acid–sensitive acute promyelocytic leukemia subtype,

molecularly targeted therapeutic approaches for AML are yet

to be translated into clinical advances. The disease has tra-

ditionally been subdivided into different subtypes (M0–M7)

based on cellular lineage and biomarkers ( 1 ). Current World

Health Organization classifi cation refl ects the fact that a grow-

ing number of AML cases can be categorized on the basis of

their underlying genetic abnormalities that defi ne distinct clin-

icopathologic 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 have 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 trac-

table drug targets, genetic testing of patients with AML has yet

to result in effective personalized or stratifi ed therapies.

Patients with AML have a poor outcome, with a 5-year

survival of 30% to 40% ( 6, 7 ). The standard therapy for most

adult patients with AML is conventional chemotherapy con-

sisting of the nucleoside analog cytarabine combined with

a topoisomerase II inhibitor ( 8, 9 ). A number of second-line

treatment options have been applied in patients with AML after

relapse, but the response rates have remained low. Furthermore,

patients with AML at relapse exhibit an increased number of

genetic alterations, which can be attributed to disease progres-

sion and/or DNA-damaging agents used for routine chemo-

therapy ( 10 ). In light of the genomic and molecular diversity of

AML, and its continuous evolution in response to chemother-

apy, 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 effi cacy of the hundreds of

emerging targeted anticancer agents, as well as their potential

combinations, in patient-derived ex vivo samples could dramati-

cally help prioritize clinical development of such agents.

To facilitate testing of already clinically available drugs as

well as emerging targeted inhibitors for patients with AML,

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 chemo-

therapeutics and many clinically available and emerging molec-

ularly targeted drugs. Second, we developed a new way to score

for differential drug response in AML cells as compared with

the effi cacy of these drugs in normal bone marrow cells. Third,

we verifi ed DSRT predictions in vivo by treating patients with

AML 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 profi ling as well as

a new DSRT analysis to understand both coresistance as well

as new vulnerabilities. Taken together, we term this approach

individualized systems medicine (ISM; Fig. 1 ).

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 chemorefrac-

tory AML patients predicted to be sensitive to targeted drugs,






Pemovska et al.RESEARCH ARTICLE

and (iv) follow individually optimized therapies in patients by

analysis of the clonal evolution of leukemic cells and molecular

profi ling to understand mechanisms of drug resistance.

RESULTS ISM 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 profi l-

ing 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 identifi cation of

half-maximal effective concentrations and maximal responses

(Supplementary Table S2). Although these responses refl ect the

drug–sample interaction, the detailed interpretation of curve

parameters is diffi cult. 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 with 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 con-

trol samples exhibited similar response profi les (Supplementary

Fig. S1). The analysis of DSRT results was completed in 4 days,

rapid enough for clinical implementation. Indeed, we carried

out a pilot test for the implementation of optimized therapies

for 8 patients with chemorefractory AML. Patient treatment

outcome was assessed by clinical criteria, but also by genomic

profi ling to understand the clonal architecture underlying drug

response and emerging resistance. Data on recurrent, paired

samples identifi ed drugs that showed effi cacy after the develop-

ment 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 individual-

ized systems medicine process for improving cancer care.

DSRT Identifi ed Signal Transduction Inhibitors as Putative AML-Selective Drugs

Ex vivo DSRT was performed on 28 samples obtained from

18 patients with AML (Supplementary Table S3). Eighteen sam-

ples were collected at relapse and 10 at diagnosis, mainly from

patients with adverse or intermediate cytogenetic risk (according

to European LeukemiaNet; ref. 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-specifi c effi cacies by

comparing responses with normal bone marrow mononuclear

cells. The results were expressed as a patient-specifi c water-

fall 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 ( Fig. 2B and C and Supplemen-

tary 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 signifi cantly differ between the patient samples and controls

( Fig. 2B and D ). This refl ects the known limited therapeutic

window for these drugs and the diffi culty in predicting their

Figure 1. Functional ISM platform for improved AML therapy. The platform involves (i) comprehensive direct 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 profi ling of the patients with AML from consecutive samples before and after relapse and drug resistance for monitoring disease progression and 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. DSS, Drug Sensitivity Score.

DSRT

Molecular profiling

Effective drugs

Resistant drugs

–9 –8 –7 –6 –5

0

20

40

60

80

100

Log conc (mol/L)

% S

urv

ival

Se

lective

DS

S

Genome Transcriptome Signalome

Individualized drug treatment selection

Result

Understanding

biology of disease

Understanding

drug sensitivity

and resistance

Rapid introduction

of therapies

Diagnosis

Relapse 1

Relapse 2






ISM Approach to Therapy Selection RESEARCH ARTICLE

clinical effi cacy based on ex vivo testing. Interestingly, cytarabine,

an established and effective AML drug, showed higher selective

effi cacy against AML cells than other cytotoxic agents ( Fig. 2B

and Supplementary Fig. S2), suggesting the feasibility of com-

bining cytarabine with promising molecularly targeted drugs in

the future clinical testing of relapsed or primary AML.

Signifi cant anticancer-selective effects were observed for the

tyrosine kinase inhibitors (TKI) dasatinib in 10 of the AML

patient samples (36%) and sunitinib in 10 (36%), MAP–ERK

kinase (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 identifi ed 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 investiga-

tional drug classes were seen, such as AKT inhibitors and ATP-

competitive mTOR inhibitors, which could be prioritized for

future clinical studies of chemorefractory AML.

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, fl oxuridine, fl udarabine,

fl uorouracil, 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, fi nasteride, fl utamide, fulvestrant, goserelin,

letrozole, megestrol acetate, nilutamide, raloxifene, tamoxifen

HSP90 inhibitors Alvespimycin, BIIB021, NVP-AUY922, tanespimycin

Immunomodulators Celecoxib, dexamethasone, fi ngolimod, 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, gefi tinib, imatinib, lapatinib, lestaurtinib, linsitinib, masitinib, MGCD-265,

motesanib, nilotinib, pazopanib, ponatinib, quizartinib, regorafenib, ruxolitinib, saracatinib, sorafenib,

sunitinib, tandutinib, tivozanib, tofacitinib, vandetanib, vatalanib

Kinase inhibitors, TKL Vemurafenib

PARP inhibitors Iniparib, olaparib, rucaparib, veliparib

Proteasome inhibitors Bortezomib, carfi lzomib

Rapalogs Everolimus, sirolimus, temsirolimus

Smoothened (Hh) inhibitors Erismodegib, vismodegib

Topoisomerase I/II inhibitors Amonafi de, 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, tarenfl urbil,

tipifarnib, XAV-939, YM155

Abbreviations : 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: phosphoinositide 3-kinase (PI3K)-like (PI3K inhibitors and inhibitors of related atypical kinases: mTOR, DNA-PK, ATM, ATR); STE: sterile kinase group; TK: tyrosine kinase group; TKL: tyrosine kinase-like group.







A B

C

AML samples

Controls

Idar

ubicin

Clofara

bine

Vincr

istin

e

Tram

etinib

AZD

8055

Sun

itinib

Cytar

abine

Das

atinib

Chemotherapy Targeted agents

D

Entinosta

tR

efa

metinib

Vorinosta

tP

anobin

osta

tB

IIB

021

Pim

asert

ibP

rim

a-1

Met

NV

P-A

UY

922

Selu

metinib

Navitocla

xLesta

urt

inib

Azacitid

ine

Alv

espim

ycin

Ruxolit

inib

Tra

metinib

CU

DC

-101

Tanespim

ycin

Dexam

eth

asone

Lenalid

om

ide

Ponatinib

Alis

ert

ibS

ara

catinib

Fin

golim

od

Tenip

osid

eD

actinom

ycin

Gem

citabin

eC

ladribin

eP

aclit

axel

Vin

bla

stine

Serd

em

eta

nD

oceta

xel

Danusert

ibV

inore

lbin

eY

M155

Mitoxantr

one

Cam

pto

techin

Topote

can

PF

-00477736

Idaru

bic

inC

lofa

rabin

e

−15

−10

−5

0

5

10

15

20

25

E

393_3

1064

600_3

1145_3

393_1

1145_1

1145_6

252_3

C2

800

252_2

1993

C7

1280

C5

C1

250

784_1

C4

C6

252_1

C3

560_1

252_5

784_2

600_2

1497

600_1

1867

560_9

718

2095

1690

4701001

2080

−10

−5

0

5

10

Z-s

co

re

Idarubicin

393_3

sD

SS

DS

S

Sig

nific

ant re

sponders

(%

)

0

5

10

15

20

25

30

35

Das

atinib

Tram

etinib

Sun

itinib

Temsiro

limus

Foret

inib

Pon

atinib

Rux

olitinib

Dac

tolis

ib

MK-2

206

Sor

afen

ib

Quiza

rtinib

0

10

20

30

40

C1

C4

C5

1690

718

1064

1145_1

1993

2095

1280

C2

252_3

C3

1497

600_3

C7

600_1

252_5

C6

1145_6

560_1

600_2

252_2

393_1

252_1

560_9

1867

393_3

4701001

784_2

250

800

784_1

1145_3

2080

−10

−5

0

5

10

Z-s

co

re

Trametinib

Taxonomy of AML Based on the Comprehensive Drug-Response Profi les

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 profi le, the overall drug-response

profi les segregated the AML patient samples into fi ve robust

functional subgroups ( Fig. 3 ; Supplementary Figs. 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 with controls, all fi ve groups showed

increased sensitivity to navitoclax, a BCL-2/BCL-XL inhibitor,

HSP90 inhibitors, and histone deacetylase (HDAC) inhibi-

tors. Group I exhibited a strong selective response to navito-

clax and lack of sensitivity to the remainder of the tested

compounds. Group II AMLs were largely nonresponsive to

receptor TKIs, but instead showed a potential infl ammatory

signal-driven phenotype as seen by selective responses to a

group of immunosuppressive drugs (e.g., dexamethasone or

prednisolone), Janus-activated kinase (JAK) family kinase

inhibitors, and MEK inhibitors. Group III, IV, and V AMLs

were selectively sensitive to a broad range of TKIs, indicating

that they were driven or addicted to receptor tyrosine kinase

signaling pathways. Group III AMLs displayed a similar

sensitivity pattern to HSP90, HDAC, and phosphoinositide

3-kinase (PI3K)/mTOR inhibitors as group IV and V, albeit

with lower selectivity. Group IV AMLs were especially sensi-

tive to MEK and PI3K/mTOR inhibitors, whereas group V

AMLs showed selective responses to receptor TKIs target-

ing ABL, VEGF receptor (VEGFR), platelet derived growth

factor receptor (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 fi ndings in the recent study by

Tyner and colleagues ( 11 ).

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 patient 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), and four signal transduction inhibitors (right). The DSSs 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 and D, the distribution of the sensitivity to trametinib (MEK inhibitor) and idarubicin (topoisomerase II inhibitor) in 28 AML patient and 7 control samples expressed as Z -score (SD from the average control DSS drug response). E, percentages of AML patient samples selectively respond-ing ex vivo to selected signal transduction inhibitors as assessed by sDSS, represented as a bar graph.







Taxonomy of Cancer Drugs Based on the Comprehensive Drug-Response Profi les in AML

The hierarchical clustering also stratifi ed the drugs based

on the variability of responses among the patients with

AML (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 TKIs, PDGFR inhibitors, and

ABL-like kinase inhibitors, antimitotics, and topoisomerase

II inhibitors. Thus, the unsupervised clustering of drugs

into subgroups defi ned by their intended targets strongly

supports the consistency and reproducibility of the DSRT

analysis as well as its ability to acquire biologically and medi-

cally 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 con-

text, 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 clini-

cal trials in AML. Therefore, this provides new combinatorial

possibilities that could not have easily been discovered with-

out the unbiased DSRT data.

Genomic and Molecular Findings Underlying the Drug-Response Profi les

To test whether the molecular profi les of the patient sam-

ples correlated with the overall drug responses, we compared

the distribution of signifi cant 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, an FLT3 mutation is an

indicator that the cells are likely to respond to TKI treatment.

Several FLT3 inhibitors, such as quizartinib, sunitinib, and

foretinib, were among the most selective drugs for group V.

Importantly, TKIs without FLT3 inhibitory activity, such as

dasatinib, were highly effective in this group ( P = 0.03), suggest-

ing that FLT3-driven AMLs are also dependent on other tyrosine

kinase signals. Furthermore, mutations in RAS genes correlated

signifi cantly 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 sensitiv-

ity to MEK inhibitor sensitivity. In addition, an enrichment of

TP53 mutations in groups I and II was observed (3 of 4 cases),

and all patient samples in taxonomic group II were associated

Figure 3. Functional taxonomy of AML based on comprehensive drug response and mutation profi les. A dendrogram of the DSRT responses shows clustering of the AML patient samples in fi ve functional groups (group I, II, III, IV, and V). Samples were clustered with the complete linkage method using Euclidean distance meas-ures. This approach provides a data-driven way to classify samples based on drug effi cacies and drugs based on their differential bioac-tivity across patient samples. Sensitivity or nonsensitivity to either navitoclax, ruxolitinib, MEK inhibitors, dasatinib, quizartinib, sunitinib, PI3K/mTOR inhibitors, and topoisomerase II inhibitors drives the sample groupings. The molecular profi les (signifi cant AML mutations and recurrent gene fusions), disease stage, and adverse karyotype status of the patient sam-ples 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.

D

1993

2095

1064

1145_3

393_1

393_3

800

1145_1

1145_6

718

1280

1497

600_3

252_2

252_3

1867

784_1

784_2

250

600_1

252_5

252_1

560_1

560_9

600_2

1690

4701001

NavitoclaxRuxolitinib

DexamethasoneMEKi

PI3K/mTORiQuizartinib

Topoisomerase IIi

II III IV VI

FLT3-ITD

WT1

TP53

NRAS/KRASDNMT3A

PTPN11

RUNX1

NUP98–NSD1

MLL-X fusions

ETV6–NTRK3

NPM1

M1M2FAB subtypes M2 M2M2M2 M5M5 M5M5 M5M5M5M5M5M5M5 M1M1

KIT

DasatinibSunitinib

IDH1/IDH2

Adverse karyotype

D D* R R D* D R D* R R D R R R D R R R R R R R R R D RDisease stage







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 (i) a distinct leukemia-selective response pattern

was seen, (ii) drugs showing sensitivity were available for com-

passionate or off-label use without signifi cantly delaying the

treatment, and (iii) no standard therapy was available. Accord-

ing to European LeukemiaNet ( 9 ) response criteria, 3 of 8

evaluable patients had a response to DSRT-guided therapy

[Supplementary Table S4; 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 that did not

meet the European LeukemiaNet criteria. Patient 560 showed

a rapid clearance of blasts in peripheral blood after 5 days

of treatment (dasatinib–sunitinib), after which therapy was

discontinued because of 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–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 hemato-

logic 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 patients with AML,

including the consecutive sampling and serial monitoring of

drug-sensitivity profi les and clonal evolution in vivo . In the

fi rst 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

profi ling. The patient had previously failed three consecutive

induction therapies ( Fig. 4A ). The DSRT results highlighted

dasatinib, sunitinib, and temsirolimus among the top fi ve

most selective approved drugs. In an off-label compassion-

ate 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 anticancer

activity as compared with the pretreatment sample ( Fig. 4B ),

demonstrating a match between ex vivo and in vivo responses.

In 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 cytogeneti-

cally 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, suggest-

ing 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 (Supple-

mentary Fig. S5A and S5B) and four different WT1 mutations

(Supplementary Fig. S6A and S6B; 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 and 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 tem-

sirolimus therapy had further selected a specifi c subclone still

containing the second FLT3 -ITD even though the response

to FLT3 inhibitors and other TKIs 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),

p38α (T180/Y182), and STAT1 (Y701; data not shown).

DSRT and Molecular Profi ling Defi ned 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) transloca-

tion 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 TKIs, including dasat-

inib ( 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 their

response to ATP-competitive mTOR inhibitors (such as dac-

tolisib and AZD8055). Interestingly, the resistant sample gained

sensitivity to a TKI that was previously ineffective in the DSRT,

BMS-754807 [insulin-like growth factor-I receptor (IGF-IR)/

Trk inhibitor], as well as crizotinib (TKI) and tipifarnib (farnesyl-

transferase inhibitor), several topoisomerase II inhibitors, and

immunomodulatory and differentiating compounds ( Fig. 5B ).

Using the DSRT data on kinase inhibitors with published

comprehensive biochemical profi ling data ( 16 ), we identifi ed

putative kinases to which the cells were addicted. Impor-

tantly, a comparison between the fi rst and the relapsed sam-

ple identifi ed a major switch in kinase addiction with a loss

of addiction to Src family kinases, PI3K and p38 mitogen-

activated protein kinases (MAPK), and a gain of addiction to

ALK and Trk family receptor tyrosine kinases ( Fig. 5C ).

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 1,000-fold enrichment of two fusion tran-

scripts, ETV6–NTRK3 and STRN-ALK (Supplementary Fig. S7),

suggesting that resistance emerged from the selection of







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 rapa-logs, and as a result, the patient was treated with a combination of dasatinib, sunitinib, and temsirolimus. The patient achieved complete remission under this drug regimen, but relapsed 5 weeks later. Bar graphs show the before and after relapse sDSS 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 sensitivity to the majority of tested com-pounds. Drugs for which the DSS decreased greater than 10 from 600_2 to 600_3 are marked in blue, other drugs with an sDSS greater than 10 in 600_2 are marked in pale blue, and drugs with an sDSS 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 is included in the Supplementary Data and Supplementary Table S5. E, summary heatmap illustrat-ing the key putative oncogenic genetic alterations in the clones depicted in D.

A

C D

0 50 100 150 180 200 2200

20

40

60

80

100

0.0

2.5

5.0

Chemotherapy Das–Sun–Tem

D600_0 600_2 600_3

%

10E

9

Chemotherapy Das–Sun–Tem Das–Tem

Bone marrow blasts (%)

Neutrophils (x10E9)

Temsirolimus

sD

SS

NUP98–NSD1

600_0 600_1 600_3600_2

74 %

6%

20%

33%

37%

15%

15%

90%

Clone1FLT3-ITD #1

Clone 3WT1 #2

FLT3-ITD #2WT1 #1Clone 2

WT1 #4Clone 5

WT1 #3Clone 4

<10%

<10%

B Dasatinib

sD

SS

600_2 600_30

5

10

15Sunitinib

sD

SS

600_2 600_30

5

10

15

20

600_2 600_3–5

0

5

10

15

0 5 10 15 200

5

10

15

20

Plicamycin

Cytarabine

Mitoxantrone

Navitoclax

Alvespimycin

BIIB021

Ponatinib

PF-04691502

Tivozanib

AZD7762

NVP-AUY922

Lestaurtinib

DasatinibAxitinib

Temsirolimus

Entinostat

Sorafenib

Sirolimus

Etoposide Sunitinib

Quizartinib

600_2 sDSS

600_3 s

DS

S

Tanespimycin

E

NU

P98–

NSD

1 fu

ssio

n

IST1

(MP2

43-)

FLT

3-IT

D #

1FL

T3-IT

D #

2W

T1(-3

79?)

(#1)

H2A

FZ(V

110G

)

SDR

42E1

(R30

3Q)

WT1

(-376

R?)

(#2)

WT1

(-370

?) (#

3)

WT1

(-380

?) (#

4)

Clone 1

Clone 2

Clone 3

Clone 4

Clone 5

preexisting small subclones. The ETV6–NTRK3 encodes for

the oncogenic fusion protein TEL–TrkC, whereas 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 toward 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

Fig. S9A and S9B) that were lost after induction chemo-

therapy, and a mutation to WT1 augmented by LOH that

persisted throughout the course of the disease ( Fig. 6C and

D and Supplementary Table S6). The gained sensitivity to

the dual IGF-IR/TrkC inhibitor BMS-754807 fi ts with the

model of the TEL–TrkC fusion protein as a new driver,

because the oncogenic potential of this fusion has been shown

to be dependent on the activity of IGF-IR ( 18, 19 ) and lead

to hyperactivation of mTORC1 ( 20, 21 ). Thus, we predict

that in this patient, the mechanism of resistance involved







Figure 5. Functional DSRT and kinase inhibitor sensitivity defi ned key oncologic signals and mechanisms of resistance. A, initial DSRT results from refractory patient 784, highlighting selective sensitivity to MEK inhibitors, rapalogs, and several TKIs. On the basis of 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 greater than 10 from 784_1 to 784_2 are marked in blue, other drugs with an sDSS greater than 10 in 784_1 are marked in pale blue, drugs for which the DSS increased greater than 10 from 784_1 to 784_2 are marked in red, other drugs with an sDSS greater than 10 in 784_2 are marked in pink, and drugs with an sDSS greater than 10 in both 600_2 and 600_3 are marked in green. C, kinase inhibitor sDSS responses matched with target profi les described by Davis and colleagues ( 16 ) yield putative kinase addiction subnetworks in the two patient samples.

A B

C

Top selective drugs in the 784_1 sample

0 5 10 15 20

CUDC-101 - HDACiPictilisib - PI3Ki

Idelalisib - PI3KiDactolisib - mTOR/PI3Ki

Doramapimod - p38iTanespimycin - HSP90i

Tivozanib - TKI (VEGFR)Lestaurtinib - Broad TKI

Dasatinib - Broad TKI*BIIB021 - HSP90i

Selumetinib - MEKiPlicamycin - RNA synth.i

AZD8055 - mTORiTrametinib - MEKi

PF-04691502 - mTOR/PI3KiEverolimus - Rapalog

Temsirolimus - Rapalog*Sirolimus - Rapalog

Pimasertib - MEKiRefametinib - MEKi

sDSS

784_1 784_2

CK1ε

JNK2

CK1δBTK

EPHB4

p38γ

p110α

p110γp110β

p110δ

CDK2

ULK2

CDK16

MEK2

YES1

MEKK2

TrkA

DAPK1

MEK1

LTK

CHK2

PhKγ2

ALK

MER

PhKγ1

TrkC

IRAK3

p38β

HCKCSK

EPHA4

TXK

FYNBMX

p38α

EPHB2

CDK4CDK9 CDK18

CDK13CDK11B

GSK3A CDK11A

CK1ε

JNK2

CK1δBTK

EPHB4

p38γ

p110α

p110γ

CDK17

p110δ

CDK2

ULK2

CDK16

MEK2

YES1

MEKK2

TrkA

DAPK1

ITK

MEK1

LTK

CHK2

BIKE

PhKγ2

ALK

MER

PhKγ1

AAK1

TrkC

IRAK3

p38β

HCKCSK

EPHA4

TXK

FYNBMX

p38α

EPHB2

CDK4CDK9 CDK18

CDK13CDK11B

GSK3A CDK11A

AAK1

ITK

BIKE

p110β

78

4_

2 s

DS

S

784_1 sDSS

Paclitaxel

0 5 10 15 200

5

10

15

20

Sirolimus

TemsirolimusEverolimus

Dasatinib

PF-04691502

AZD8055

Lestaurtinib

Docetaxel

Tipifarnib

Dactinomycin

BMS-754807

Navitoclax

Danusertib

Dexamethasone

RefametinibPimasertib

TrametinibSelumetinib

BIIB021

Plicamycin

Vinblastine

Dactolisib

VincristineTopotecan

Mitoxantrone

CDK17

TEL–TrkC-mediated activation of IGF-IR signaling, which pro-

moted hyperactivation of mTORC1. Supporting this hypothe-

sis, we observed synergistic activities between the BMS-754807

IGF-IR/TrkC inhibitor and dactolisib, an ATP-competitive

mTOR inhibitor ( Fig. 6E ). The combination of BMS-754807

and the MEK inhibitor trametinib, on the other hand, did not

result in synergism, indicating that the TEL–TrkC/IGF-IR/

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 iden-

tify not only the mechanisms of resistance, but also potential

ways to counteract it with combinatorial therapies.

DISCUSSION

We present here an ISM strategy based on the systematic func-

tional testing of patient-derived primary cancer cell sensitivity to

targeted anticancer agents coupled with genomic and molecu-

lar profi ling. Importantly, the intent is to guide treatment

decisions for individual cancer patients coupled with monitor-

ing of subsequent responses in patients to measure and under-

stand the effi cacy and mechanism of action of the drugs. The

ISM strategy allows for an iterative adjustment of therapies for

patients with cancer, one patient at a time, with repeated sam-

pling 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 identifi ed 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 anticancer agents already in clini-

cal use for other diseases, such as dasatinib (current approved

indications are chronic myeloid leukemia and Ph+ acute lym-

phoblastic leukemia), sunitinib (renal cell cancer and gastroin-

testinal stromal tumors), and temsirolimus (renal cell cancer),

could be repositioned for subsets of AML patients. Although

we did not achieve long-term cures in patients with AML receiv-

ing DSRT-guided therapies, the clinical responses seen are

encouraging, given the fact that most of the patients with AML

studied here had complex chemorefractory, end-stage disease.

Obviously, the clinical results arise from a nonrandomized

setting and need to be verifi ed. However, the clinical implemen-

tation of ISM in individual patients is a powerful way to create

hypotheses to be tested in systematic clinical studies, both for

existing and emerging drugs. Indeed, we also identifi ed 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 priori-

tize for future clinical trials in AML. Furthermore, the ISM

approach could also help to identify effective drug combina-

tions based on associating drug sensitivities.

Unsupervised clustering of the patient samples identifi ed

fi ve functional taxonomic groups in AML based on ex vivo

drug responses. FLT3 mutations were highly enriched among

the most TKI-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, sug-

gesting that oncogenic FLT3 signaling may be dependent on the

Figure 6. IGF-IR and mTOR inhibition has a synergistic effect in ETV6 – NTRK3 -driven AMLs. A, validation sequencing and resulting predicted protein structure of two fusions identifi ed 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 profi ling 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 C. E, combinatorial treatment of the patient cells with the IGF-IR/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 IGF-IR/TrkC inhibition. F, a model of kinase driver switch and drug resistance in this patient.

A

MLLELL

ETV6NTRK3

Zinc finger, CXXC-type(IPR002857)

RNA pol II elongationfactor ELL (IPR019464)

Occludin/RNA pol II elongationfactor ELL domain (IPR010844)

Pointed doman(IPR003118)

Tyr kinase catalyticdomain (IPR020635)

MLL ELL

TEL TrkC

1 140646

1 336 466

622

825

BM

S-7

54807

Das–Sun–Tem

TEL-TrkC/IGF-IR

HyperactivatedTORC1IG

F-I

R/T

rKC

i

0 0.025 0.25 2.5 25 250

Trametinib

MEKi

Nonsynergistic

Dasatinib

target

TORC1

784_1 784_2

D

0 0.1 1 10 100 1,000

Dactolisib

mTOR/PI3Ki

Synergistic

0

1

10

100

1,000

10,000

B

pp38α

pGSK-3

α/β

pMEK1/

2

pCREB

pp70

S6K

0

1

2

3

4

5

Fo

ld c

ha

ng

e

784_1784_2

F

MLL

–ELL

fusi

onW

T1

PD

CD

10(R

157H

)TP

63(I4

32T)

FLT3

-ITD

#1

FLT3

-ITD

#2

WT1

LO

HE

TV6–

NTR

K3

fusi

onS

TRN

–ALK

fusi

on

Clone 1

Clone 2

Clone 3

Clone 4

Clone 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







signaling of other tyrosine kinases whose inhibition may syner-

gize with the therapeutic effects of FLT3 inhibitors. Given that

the clinical implementation of FLT3 inhibitors has proven very

challenging, the identifi cation of druggable synergistic kinase

signals or drugs could be extremely important. Furthermore, we

observed a signifi cant association between activating mutations

in RAS genes and sensitivity to MEK inhibitors, in line with pre-

vious results showing that trametinib exhibited favorable clini-

cal responses in patients with RAS -mutated refractory AML ( 22 ).

Finally, we identifi ed 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 classifi ca-

tions and variabilities could not yet be attributed to the main

mutations detected in the patient samples. Such drug responses

may arise from nongenomic causes and complex combinatorial

molecular pathways, and these fi ndings therefore highlight the

value of DSRT in (i) functionally validating suggestions aris-

ing from the genomic profi les 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 chem-

orefractory patient samples studied here.

Despite multiple efforts over several decades, the direct pre-

diction of cancer cell chemosensitivity in the clinical setting has

remained an elusive goal. Compared with previously published

approaches, the ISM approach presented here has distinct differ-

ences. First, our studies focused on targeted drugs, whereas 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 nonselective and more diffi cult to inter-

pret and translate to clinical patient care. Second, we focused

on leukemias, whereas many previous studies have focused on

solid tumors ( 28–33 ), where representative samples are diffi cult

to acquire and consecutive samples from recurrent disease are

typically not available. Third, we measured selective anticancer

effects as compared with 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 effi cacy 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 diffi culty in predicting mecha-

nisms of resistance and support the importance of repeated

functional testing such as DSRT during disease progression

to 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. Inter-

estingly, 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, resist-

ance to dasatinib, sunitinib, and temsirolimus treatment was

linked to enrichment of clonal populations carrying a tyro-

sine 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 IGF-IR kinase activity ( 18–20 , 37, 38 ).

This hypothesis is supported by the acquisition of sensitivity,

based on ex vivo testing, to the dual IGF-IR/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 patients with AML are sensitive and resistant,

implement such strategies in the clinic, and learn from the inte-

grated genomic, molecular, and functional analysis of drug sen-

sitivity and resistance in paired samples. ISM strategy provides

a powerful way to create hypotheses to be tested in formal clini-

cal trials, 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.

METHODS Study Patients and Material

Twenty-eight bone marrow aspirates and peripheral blood samples

(leukemic cells) and skin biopsies (noncancerous cells for germline

genomic information) from 18 AML and high-risk [according to the

WHO classifi cation-based Prognostic Scoring System (WPSS); ref. 39 ]

patients with myelodysplastic syndromes (MDS) and 7 samples from

different healthy donors (controls) were obtained after informed con-

sent 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 (MCM; PromoCell) supplemented with

0.5 μg/mL gentamicin and 2.5 μg/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, cor-

responding to the blast cell population) using paramagnetic beads

according to the manufacturer’s instructions (Miltenyi Biotech).

Development of the Compound Collection The oncology compound collection covers the active substances from

the majority of U.S. Food and Drug Administration/European Medi-

cines Agency ( FDA/EMA)–approved anticancer drugs ( n = 123), as well

as emerging investigational and preclinical compounds ( n = 64) covering

a wide range of molecular targets (Supplementary Table S1). The com-

pounds 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.

DSRT 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 fi ve different concentra-

tions in 10-fold dilutions covering a 10,000-fold concentration range

(e.g., 1–10,000 nmol/L). The predrugged plates were kept in pressu-

rized StoragePods (Roylan Developments Ltd.) under inert nitrogen

gas until needed. The compounds were dissolved with 5 μL of MCM

while shaking for 30 minutes. Twenty microliters of single-cell sus-

pension (10,000 cells) was transferred to each well using a Multi Drop

Combi (Thermo Scientifi c) peristaltic dispenser. The plates were incu-

bated in a humidifi ed environment at 37°C and 5% CO 2 , and after

72 hours cell viability was measured using CellTiter-Glo luminescent

assay (Promega) according to the manufacturer’s instructions with a







Molecular Devices Paradigm plate reader. The data were normalized to

negative control (DMSO only) and positive control wells (containing

100 μmol/L benzethonium chloride, effectively killing all cells).

Generation of Dose–Response Curves and Analysis of Data The plate reader data were uploaded to Dotmatics Browser/Studies

software (Dotmatics Ltd.) for a normalized calculation of percentage

survival for each data point and generation of dose–response curves

for each of the drugs tested. The dose–response curves were fi tted on

the basis of a four-parameter logistic fi t function defi ned by the top

and bottom asymptote, the slope, and the infl ection point (EC 50 ).

In the curve fi tting, the top asymptote of the curve was fi xed to 100%

viability, whereas the bottom asymptote was allowed to fl oat between 0%

and 75% (i.e., drugs causing < 25% inhibition were considered inactive).

Scoring and Clustering of DSRT Data To quantitatively profi le 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 fi tting parameters were used to calculate

the area under the dose–response curve (AUC) relative to the total

area (TA) between 10% threshold and 100% inhibition . Furthermore,

the integrated response was divided by a logarithm of the top asymp-

tote ( a ). Formally, the DSS was calculated by:

DSSAUC

TA log=

××

100

a

We scored for differential activity of the drugs in AML blast cells

in comparison with control cells, sDSS. Clustering of the drug sen-

sitivity profi les across the AML patient and control samples was per-

formed using unsupervised hierarchical complete-linkage clustering

using Spearman and Euclidean distance measures of the drug and

sample profi les, 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 sDSSs of kinase inhibitors were further used to predict sample-

specifi c kinase addictions. Sample-specifi c sDSS responses were

compared with target profi les for 35 kinase inhibitors overlapping

between our compound panel and the panel profi led by Davies

and colleagues ( 16 ). More specifi cally, 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 with

gene expression to exclude nonexpressed targets, and the remaining

kinases defi ned a putative “kinaddictome” for each patient sample.

For displaying purposes, the resulting kinases were depicted in a tar-

get similarity network, in which edges connect kinases with similar

inhibitor specifi city profi les (ref. 16 ; Spearman rank-based correla-

tion > 0.5; Szwajda and colleagues, unpublished data).

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. DNA (3 μg) was fragmented and processed according to the

NEBNext 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 germ-

line control samples 4 × 10 7 and for tumor samples 10 × 10 7 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).

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 fl anking 500 bps. High con-

fi dence somatic mutations were called for each tumor sample using

the VarScan2 somatic algorithm ( 43 ) with the following parameters:

– strand-fi lter 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 fi lter out false-positive calls due to genomic

repeats, somatic mutation calls in regions defi ned as repeats in the

RepeatMasker track obtained from the University of California, Santa

Cruz (UCSC) Genome Browser were removed from the analysis. To fi l-

ter out misclassifi ed germline variants, population variants included

in dbSNP (Single Nucleotide Polymorphism database) version 130

were removed. Remaining mutations were visually validated using the

Integrated Genomics Viewer (Broad Institute).

Analysis of Mutation Frequencies in Serial Samples To examine frequencies of the identifi ed mutations in samples

where the mutations did not pass the criteria for high confi dence

mutations, variant frequencies and read counts for each mutation

were retrieved from a set of unfi ltered variant calls generated by

VarScan2 with the following parameters:

– strand-fi lter 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–leuke-

mia pairs to identify regions of LOH.

FLT3-ITD Detection by Capillary Sequencing and qPCR For determination of patients’ FLT3 -ITD status, genomic DNA was

extracted from the bone marrow mononuclear cell fraction. Qualita-

tive PCR was performed as described by Kottaridis and colleagues ( 45 )

by using a 6-carboxyfl uorescein (FAM)-labeled forward primer. The

PCR products were separated on an agarose gel and in capillary elec-

trophoresis with an ABI3500D× Genetic Analyzer and sequenced using

M13-tailed direct sequencing. Assessment of minimal residual disease

(MRD) level was performed with real-time quantitative PCR (qPCR). A

patient ITD-specifi c ASO (allele-specifi c oligonucleotide) primer was

designed at the ITD junction region and used together with a down-

stream 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.

Amplicon Sequencing Amplicons were amplifi ed using locus-specifi c PCR primers carrying

Illumina compatible adapter sequences, grafting sequences (P5 and

P7), and an amplicon-specifi c 6-bp index sequence (Supplementary

Table S7). The PCR reaction contained 10 ng of sample DNA, 10 μL

of 2× Phusion High-Fidelity PCR Master Mix (Thermo Scientifi c),







and 0.5 μmol/L of each primer. Following PCR amplifi cation, samples

were purifi ed using Performa V3 96-Well Short Plate and QuickStep2

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

Total RNA (2.5 to 5 μg) was used for depletion of rRNA (Ribo-Zero

rRNA Removal Kit; Epicentre), purifi ed (RNeasy clean-up-kit; Qia-

gen), and reverse transcribed to double-stranded cDNA (SuperScript

Double-Stranded cDNA Synthesis Kit; Life Technologies). Random

hexamers (New England BioLabs) were used for priming the fi rst-

strand synthesis reaction.

Illumina-compatible Nextera Technology (Epicentre) was used for

preparation of RNAseq Libraries. High Molecular Weight (HMW)

buffer and 50 ng of cDNA were used for tagmentation as recommended

by the manufacturer. After the tagmentation reaction, the fragmented

cDNA was purifi ed with SPRI beads (Agencourt AMPure XP, Beckman

Coulter). The RNAseq libraries were size selected (350–700 bp frag-

ments) in 2% agarose gel followed by purifi cation with QIAquick gel

extraction kit (Qiagen).

Each transcriptome was loaded to occupy one third of the lane

capacity in the fl ow cell. C-Bot (TruSeq PE Cluster Kit v3, Illumina)

was used for cluster generation, and an Illumina HiSeq2000 platform

(TruSeq SBS Kit v3-HS reagent kit) was used for paired-end sequenc-

ing 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 identifi cation, mutation calling, and gene expression

quantitation (Tophat and Cuffl inks), was done as described previ-

ously ( 46 ). Primers used to validate as well as quantify the fusion

genes detected are listed in Supplementary Tables S8 and S9.

Proteomic Analysis Phosphoproteomic analysis of the AML patient samples was per-

formed using Proteome Profi ler antibody arrays (R&D Systems)

according to the manufacturer’s instructions. Lysates containing 300

μg of protein were applied to the arrays, and fl uorescently 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. A

Pearson correlation test was used to determine the correlations between

drug sensitivity profi les 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 signifi cant when P < 0.05.

Disclosure of Potential Confl icts of Interest S. Mustjoki has honoraria from the Speakers Bureaus of Novartis

and Bristol-Myers Squibb and is a consul tant/advisory board member

of the same. O. Kallioniemi has received commercial research support

from IMI-Project Predect (a consortium of pharmaceutical compa-

nies) and is a consultant/advisory board member of Medisapiens. No

potential confl icts of interest were disclosed by the other authors.

Authors’ Contributions Conception and design: T. Pemovska, M. Kontro, E. Elonen, I. Västrik,

J. Knowles, C.A. Heckman, K. Porkka, O. Kallioniemi, K. Wennerberg

Development of methodology: T. Pemovska, M. Kontro, B. Yadav,

H. Edgren, S. Eldfors, M.M. Bespalov, R. Karjalainen, E. Kulesskiy,

S. Lagström, M. Lepistö, M.M. Majumder, P. Mattila, L. Turunen, M. Wolf,

T. Aittokallio, C.A. Heckman, K. Porkka, O. Kallioniemi, K. Wennerberg

Acquisition of data (provided animals, acquired and managed

patients, provided facilities, etc.): M. Kontro, M.M. Bespalov, P. Ellonen,

E. Elonen, B.T. Gjertsen, R. Karjalainen, E. Kulesskiy, A. Lehto, M. Lepistö,

T. Lundán, P. Mattila, A. Murumägi, S. Mustjoki, A. Parsons, M.E. Rämet,

M. Suvela, L. Turunen, M. Wolf, C.A. Heckman, K. Porkka

Analysis and interpretation of data (e.g., statistical analysis,

biostatistics, computational analysis): T. Pemovska, B. Yadav,

H. Edgren, S. Eldfors, A. Szwajda, H. Almusa, E. Elonen, T. Lundán,

M.M. Majumder, J.M.L. Marti, S. Mustjoki, I. Västrik, T. Aittokallio,

C.A. Heckman, K. Porkka, O. Kallioniemi, K. Wennerberg

Writing, review, and/or revision of the manuscript: T. Pemovska,

M. Kontro, H. Edgren, S. Eldfors, E. Elonen, B.T. Gjertsen, T. Lundán,

A. Murumägi, S. Mustjoki, J. Knowles, T. Aittokallio, C.A. Heckman,

K. Porkka, O. Kallioniemi, K. Wennerberg

Administrative, technical, or material support (i.e., report-

ing or organizing data, constructing databases): T. Pemovska,

R. Karjalainen, A. Lehto, A. Palva, A. Parsons, T. Pirttinen, M. Suvela,

L. Turunen, I. Västrik, C.A. Heckman, K. Porkka

Study supervision: E. Elonen, J. Knowles, T. Aittokallio, C.A. Heck-

man, K. Porkka, O. Kallioniemi, K. Wennerberg

Acknowledgments The authors thank the patients for donating their samples for

research, Jani Saarela and Ida Lindenschmidt (FIMM Technology

Centre, High Throughput Biomedicine Unit) for technical assist-

ance, 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.

Grant Support This study was fi nancially supported by the Academy of Finland (to

T. Aittokallio and O. Kallioniemi); Biocenter Finland (to O. Kallioniemi

and K. Wennerberg); ERDF: the European Regional Development

Fund (to I. Västrik); EU FP7 BioMedBridges (to O. Kallioniemi);

Finnish Cancer Societies (to O. Kallioniemi and K. Porkka); the Jane

and Aatos Erkko Foundation (to K. Wennerberg); the Sigrid Jusélius

Foundation (to O. Kallioniemi); and Tekes: Finnish Funding Agency

for Technology and Innovation (to O. Kallioniemi and J. Knowles).

Received July 8, 2013; revised September 12, 2013; accepted Sep-

tember 13, 2013; published OnlineFirst September 20, 2013.

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 classifi cation of tumours of haematopoietic and lymphoid tis-

sues . 4th ed . 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 out-

come 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 profi le 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 .

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 III, 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 , Prat-

corona M , Abbas S , Kuipers JE , et al. NUP98/NSD1 characterizes a

novel poor prognostic group in acute myeloid leukemia with a dis-

tinct 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 specifi c subset of adult AML patients can be defi ned by the cytoge-

netically cryptic NUP98–NSD1 fusion gene . Leukemia 2013 ; 27 : 245 – 8 .

16. Davis MI , Hunt JP , Herrgard S , Ciceri P , Wodicka LM , Pallares G , et al.

Comprehensive analysis of kinase inhibitor selectivity . Nat Biotech-

nol 2011 ; 29 : 1046 – 51 .

17. Dowling RJ , Topisirovic I , Alain T , Bidinosti M , Fonseca BD , Petrou-

lakis E , et al. mTORC1-mediated cell proliferation, but not cell

growth, controlled by the 4E-BPs . Science 2010 ; 328 : 1172 – 6 .

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 phospho-

rylation . 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 localiza-

tion 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 phos-

phatidylinositol 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 . In: Proceed-

ings of the 54th ASH Annual Meeting and Exposition ; 2012 Dec 8–11 ;

Atlanta, GA : Georgia World Congress Center . p. 120 . Abstract nr 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 .

24. Larsson R , Fridborg H , Kristensen J , Sundstrom C , Nygren P . In vitro

testing of chemotherapeutic drug combinations in acute myelocytic

leukaemia using the fl uorometric microculture cytotoxicity assay

(FMCA) . Br J Cancer 1993 ; 67 : 969 – 74 .

25. Larsson R , Nygren P . Laboratory prediction of clinical chemothera-

peutic drug resistance: a working model exemplifi ed by acute leukae-

mia . Eur J Cancer 1993 ; 29A : 1208 – 12 .

26. Pieters R , Loonen AH , Huismans DR , Broekema GJ , Dirven MW , Hey-

enbrok 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 . Clini-

cal 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 .

32. Lehnhardt M , Muehlberger T , Kuhnen C , Brett D , Steinau HU , Jafari

HJ , et al. Feasibility of chemosensitivity testing in soft tissue sarco-

mas . 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 fi brosarcoma . 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 fi brosarcoma and

acute myelogenous leukemia . EMBO J 2000 ; 19 : 1827 – 38 .

36. Tognon C , Knezevich SR , Huntsman D , Roskelley CD , Melnyk N , Math-

ers 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 transforma-

tion 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 syn-

dromes . J Clin Oncol 2007 ; 25 : 3503 – 10 .

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 lym-

phocytic 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 nucle-

otide 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 prognos-

tic information to cytogenetic risk group and response to the fi rst 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. Identifi cation of fusion genes in breast cancer by

paired-end RNA-sequencing . Genome Biol 2011 ; 12 : R6 .





individualized systems medicine strategy to tailor ... · (i) create a taxonomy of comprehensive...

Documents