an integrated analysis of heterogeneous drug responses in...

Therapeutics, Targets, and Chemical Biology

An Integrated Analysis of Heterogeneous DrugResponses in Acute Myeloid Leukemia ThatEnables the Discovery of Predictive BiomarkersWeihsu C. Chen1, Julie S. Yuan2, Yan Xing3, Amanda Mitchell1, Nathan Mbong1,Andreea C. Popescu1, Jessica McLeod1, Gitte Gerhard3, James A. Kennedy1,4,GoceBogdanoski2, Stevan Lauriault2, Sofie Perdu3,Yulia Merkulova3, Mark D.Minden1,4,5,6,Donna E. Hogge3, Cynthia Guidos2,7, John E. Dick1,8, and Jean C.Y.Wang1,4,5

Abstract

Many promising new cancer drugs proceed through preclinicaltesting and early-phase trials only to fail in late-stage clinicaltesting. Thus, improved models that better predict survival out-comes and enable the development of biomarkers are needed toidentify patients most likely to respond to and benefit fromtherapy. Here, we describe a comprehensive approach in whichwe incorporated biobanking, xenografting, and multiplexedphospho-flow (PF) cytometric profiling to study drug responseand identify predictive biomarkers in acute myeloid leukemia(AML) patients. To test the efficacy of our approach, we evaluatedthe investigational JAK2 inhibitor fedratinib (FED) in 64 patientsamples. FED robustly reduced leukemia in mouse xenograftmodels in 59% of cases and was also effective in limiting theprotumorigenic activity of leukemia stem cells as shown byserial transplantation assays. In parallel, PF profiling identified

FED-mediated reduction in phospho-STAT5 (pSTAT5) levels as apredictive biomarker of in vivo drug response with high specificity(92%) and strong positive predictive value (93%). Unexpectedly,another JAK inhibitor, ruxolitinib (RUX), was ineffective in 8 of10 FED-responsive samples. Notably, this outcome could bepredicted by the status of pSTAT5 signaling, which was unaf-fected by RUX treatment. Consistent with this observed dis-crepancy, PF analysis revealed that FED exerted its effectsthrough multiple JAK2-independent mechanisms. Collectively,this work establishes an integrated approach for testing novelanticancer agents that captures the inherent variabilityof response caused by disease heterogeneity and in parallel,facilitates the identification of predictive biomarkers thatcan help stratify patients into appropriate clinical trials. CancerRes; 76(5); 1214–24. �2016 AACR.

IntroductionHistorically, improvements in long-term survival of cancer

patients due to new therapeutic approaches have been incremen-tal. Promising preclinical studies or early-phase clinical trialsfrequently do not translate into survival benefits in phase III trials

(1), implying that traditional preclinicalmodels and endpoints inearly-phase trials are insufficient surrogates for predicting long-term outcomes. Moreover, the mechanistic basis for variabletreatment responses in clinical trials is often unknown and canresult in rejection of a drug that may be of benefit to a subset ofpatients. Thus, a re-examination of traditional drug developmentmodels and parallel identification of drug response biomarkersfor patient selection are required in order to improve the successrate in bringing forward new effective oncologic drugs.

Current preclinical models seldom reflect the disease statewithin humans. For example, new drugs are frequently screenedfor their antiproliferative activity against cancer cell lines in vitro.However, cell lines and in vitro cultures do not fully capture theintrinsic and extrinsic diversity of human disease. Moreover,proliferation in culture measures drug effects on the bulk popu-lation and not the cancer stem cells (CSC), which inmany tumorshave been linked to therapy failure and disease recurrence (2).Tumor heterogeneity is also not well modeled by (frequentlynonorthotopic) injection of human cancer cell lines into mice, oreven by engineered mouse models; the low variability and goodreproducibility of the latter are actually disadvantageous for drugtesting as they do not reflect intratumor and interpatient hetero-geneity (3, 4). Xenotransplantation of primary cancer cells iscurrently the best functional assay for both normal andmalignantadult human stem cells, and in the context of human acutemyeloid leukemia (AML) reads out clinically relevant properties

1PrincessMargaret CancerCentre, UniversityHealthNetwork,Toronto,Ontario, Canada. 2Program in Developmental and Stem Cell Biology,Hospital forSickChildrenResearch Institute,Toronto,Ontario,Canada.3Terry Fox Laboratory, BC Cancer Agency,Vancouver, British Colum-bia, Canada. 4Department of Medicine, University of Toronto,Toronto,Ontario, Canada. 5Division of Medical Oncology and Hematology,University Health Network,Toronto, Ontario, Canada. 6Department ofMedical Biophysics, University of Toronto, Toronto, Ontario, Canada.7Department of Immunology, University of Toronto, Toronto, Ontario,Canada. 8Department of Molecular Genetics, University of Toronto,Toronto, Ontario, Canada.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

W.C. Chen and J.S. Yuan contributed equally to this article.

C. Guidos, J.E. Dick, and J.C.Y. Wang contributed equally to this article.

Corresponding Author: Jean C.Y. Wang, Princess Margaret Cancer Centre, 101College Street, Toronto Medical Discovery Tower, 8th Floor, Rm 8-301, Toronto,ON M5G 1L7, Canada. Phone/Fax: 416-581-7475; E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-15-2743

�2016 American Association for Cancer Research.

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of repopulating cells (5–7). Numerous previous studies haveevaluated the efficacy of antileukemia drugs in the setting ofxenotransplantation assays (8–12); however, the number of pri-mary patient samples tested has generally been small, thus pre-cluding biomarker development.

Here, we describe a comprehensive approach that combinesdrug testing of a large cohort of primary patient samples inxenotransplantation assays with parallel phospho-flow (PF) cyto-metric single-cell profiling of short-term drug responsivenessin vitro to develop companion drug response biomarkers. To testthis approach, we studied the efficacy of fedratinib (FED, alsoknown as SAR302503 or TG101348), an investigational Januskinase 2 (JAK2) inhibitor, against leukemia stem cells (LSC) inAML. JAK2 inhibitors including ruxolitinib (RUX) and FED havedemonstrated efficacy in clinical trials for the treatment of mye-loproliferative neoplasms (MPN; refs. 13–15), but have not beenemployed in AML, where activating JAK2 mutations are rare.Nevertheless, downstreamSTAT transcription factors are activatedin the majority of AML cases (16, 17). Furthermore, high levels ofphosphorylated JAK2 (pJAK2) expression have been associatedwith worse outcome in AML, and in vitro studies suggest that JAK2could be a therapeutic target in this disease (18). We demonstratehere that our approach effectively captures the variability intreatment response that is generally seen in patient cohorts andallowed identification of a PF signature that correlated with drugresponses in xenografts.

Materials and MethodsXenotransplantation assay and drug studies

Xenotransplantation and in vivo drug treatment experimentswere carried out in Toronto (T) and Vancouver (V) using localoptimized protocols. Employed protocols yielded similar engraft-ment results at both sites for patient samples tested in pilotstudies. NOD.SCID (NS) and NOD.SCID-IL2Rgnull (NSG) micewere bred and housed at the University Health Network (UHN)Animal Facility (T) or the BC Cancer Research Centre AnimalResource Centre (V). Eight- to 10-week-old mice were sublethallyirradiated (T: 225 cGy; V-NS: 325 cGy; V-NSG: 315 cGy) 24 hoursbefore transplantation. NS mice (T) received 200 mg anti-CD122mAbby subcutaneous injection immediately after irradiation. ForNSG experiments, T-cell depletion was carried out by treatingmice with 12.5 mg/kg anti–CD3-diphtheria toxin by i.p. injection24 hours after AML transplantation for 2 consecutive days (V), orby using the EasySep CD3 Positive Selection Kit (StemCell Tech-nologies) prior to intrafemoral (IF) transplantation (T). AMLsamples were injected IF except for two Vancouver samples thatwere transplanted intravenously as indicated in Table 1. AMLsamples were transplanted at a dose of 2 to 5 � 106 cells/mouse.Treatment with FED (Sanofi) or RUX (Selleck Chemicals), both ata dose of 60 mg/kg, or vehicle (0.5% methylcellulose) was giventwice daily by oral gavage for 14 days starting 2 to 3 weeks aftertransplantation. For serial transplantation studies, equal numbersof humanCD45þ cells harvested from thepooledbonemarrowofFED-treated or vehicle-treated mice were injected into untreatedsecondary recipients and engraftment evaluated 10 to 12 weeksafter transplantation. For cytarabine combination studies, micewere treated with cytarabine 80mg/kg/d i.p.�5 days prior to FEDor vehicle treatment. For DAS combination studies, mice received60mg/kg FED twice daily, 50mg/kg DAS once daily, or both for 2weeks by oral gavage. For all drug studies, mice were sacrificed the

day after the final dose, and the level of human leukemic engraft-ment in the injected femur and noninjected bones (other femurplus two tibias) was evaluated by flow cytometry using human-specific mAbs (for details, see Supplementary Methods).

Definition of drug response for xenotransplantation studiesFor in vivo drug studies, definition of response was based on the

relative reduction (RR) in human leukemic engraftment in drug-treated versus vehicle-treated mice. RR was calculated as [(mean-%engraftment of vehicle-treated mice) � (mean%engraftment ofdrug-treatedmice)]/(mean%engraftment of vehicle-treatedmice).We distinguished effects in the injected right femur (RF) versusnoninjected bones (BM) as leukemic burden is usually higher inthe injected RF, and as such a significant reduction in leukemicengraftment in the RF is more difficult to achieve than in non-injected BM. Patients were classified as responders (R) if RR in theRF was >50%, partial responders (PR) if we observed 20% to 50%RR in the RF or >20%RR in the BMonly, and nonresponders (NR)if there was no statistically significant difference in engraftmentlevels between vehicle- and drug-treated mice or RR was <20% inboth RF and BM.

PF cytometric analysisAML patient samples tested in vivowere subjected to PF analysis

following short-term drug treatment in vitro. Viably frozen sam-ples were thawed and serum starved for 1 hour at 37�C, thentreated with DMSO (vehicle), FED (100 nmol/L), AC220 (5nmol/L; Selleck Chemicals), RUX (300 nmol/L), or DAS (100–200 nmol/L; Toronto Research Chemicals) for another hour.During the last 30 m, cells were incubated with viability dye,then fixed, washed, and permeabilized. Phosphomarker andextracellular staining was carried out for 30' with optimizedconcentrations of antibodies (for details, see SupplementaryMethods). Data were acquired on a BD LSRFortessa and analyzedusing FlowJo and Cytobank software (http://cytobank.org/;ref. 19). Ba/F3 cells were obtained from R. Rottapel in 2006. ThisIL3-dependent hematopoietic cell line remains exquisitely IL3dependent for survival and proliferation, as assessed by cellviability assays following IL3 withdrawal (ongoing). OCI-AML5cells were obtained from M.D. Minden in 2010. This patient-derived AML cell line was authenticated by short-tandem repeatsanalysis in 2014 at the Centre for Applied Genomics (Hospital forSick Children, Toronto, Canada).

Statistical analysisSixty-four independent patient samples were used in the study

to capture the diversity of AML. Comparison of engraftment indrug- versus vehicle-treatedmicewas performedusing two-tailed ttests. For PF analysis, two-group comparisons were performedusing the Mann–Whitney U test. Correlations were assessed bytwo-tailed Spearman correlation. All data were analyzed withGraphPad Prism software, version 5.0, for Mac OS X.

Study approvalPeripheral blood cells were collected from patients with newly

diagnosed or relapsed AMLat the PrincessMargaret Cancer Centreor Vancouver General Hospital after obtaining informed consentaccording to procedures approved by the UHN and University ofBritish Columbia (UBC) Research Ethics Boards. Thawed viablyfrozen samples were prescreened for engraftment ability in

Primary Xenografting Reveals Novel Response Biomarkers

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xenotransplanted mice, and engrafting samples were used indrug studies. All animal experiments were performed in accor-dance with institutional guidelines approved by the UHNor UBCAnimal Care Committee.

ResultsFED targets LSCs in primary AML xenografts withheterogeneous responses

We tested the potential efficacy of FED in AML in an initialcohort of 34 patient samples obtained at diagnosis or relapse andrepresenting multiple cytogenetic and molecular subtypes (Sup-plementary Table S1). Samples were transplanted into cohorts ofimmune-deficientmice (n¼ 5–8/group); following a 2- to 3-weekengraftment period,micewere treatedwith FEDor vehicle controlfor another 2 weeks (Fig. 1A). For 17 of 34 samples, leukemicengraftment in the injected femurs was 56% to 94% lower inFED-treated relative to vehicle-treated mice (P < 0.05, Table 1and Fig. 1B). These samples also showed a 30% to 95% RR inleukemic engraftment of noninjected bones (P < 0.05). Five addi-tional samples responded less robustly (<50%RR in injected femurand 31%–64% RR in noninjected bones; P < 0.05; Table 1). These22 samples were classified as xenograft responders (X-R). By con-trast, FEDhad a small ornegligible effect on the leukemic graft in12

of 34 samples (<20% RR, P < 0.05 or any RR, P > 0.05); these wereclassified as xenograft nonresponders (X-NR; Table 1 and Fig. 1C).

We also evaluated whether treatment with a standard agentsuch as cytarabine could potentiate the effects of FED, usingsamples from 3 AML patients that were partial- or nonrespondersto FED alone (Supplementary Fig. S1A). In 2 of 3 samples tested,cytarabineþFED treatment significantly reduced leukemiaburdenin treated mice compared with either drug alone (SupplementaryFig. S1B). These findings suggest that combining the targetedtherapeutic FED with cytarabine may increase overall efficacy.

To evaluatewhether FED targets LSCs, AML cells were harvestedfrom primary mice and transplanted into untreated secondaryrecipients; two X-NR and seven X-R samples were evaluable (cellsfrom vehicle-treated primary mice generated >10%mean engraft-ment levels in secondary mice). For the two X-NR samples, theFED-treated and control groups yielded similar engraftment levelsin secondary mice (Fig. 1D, left and Supplementary Fig. S1C). Bycontrast, in five of seven X-R samples, the FED-treated group gaverise to smaller grafts compared with controls (Fig. 1D, right andSupplementary Fig. S1C), although this only reached statisticalsignificance in two samples due to small numbers of transplantedmice. These results suggest that in responding samples, FEDtreatment may impair the function and/or survival of LSCsexposed to drug in the primarymice, although variable sensitivity

Table 1. Efficacy of FED against xenotransplanted AML samples (initial cohort)

Injected RF Noninjected BMMean engraftment (%) Mean engraftment (%)

Site Patient ID FAB Sample Vehicle FED P value RR (%) Vehicle FED P value RR (%)In vivo

responsea

T 1 M1 Relapse 90.7 7.2 0.001 92 67.9 5.8 0.001 91 RT 2 M0 Diagnosis 21.6 1.2 0.001 94 5.9 0.3 0.004 95 RT 3 Unclassified Diagnosis 50.9 7.4 0.002 85 19.9 3.6 0.001 82 RT 4 M4 Relapse 34.3 6.3 0.008 82 7.7 1.5 0.002 81 RT 5 Unclassified Diagnosis 36.4 8.6 0.002 76 15.5 4.1 0.001 74 RT 6 M4 Relapse 17.1 4.2 0.001 75 6.8 0.5 0.001 92 RT 7 ND Diagnosis 76.9 19.3 0.001 75 46.8 7.9 0.001 83 RT 8 M4 Relapse 75.7 19.4 0.001 74 50.9 9.3 0.001 82 RV 9 M5 Diagnosis i.v. i.v. 27.4 6.7 0.0001 76 RV 10 M4 Diagnosis i.v. i.v. 60.5 18.3 0.0001 70 RT 11 M5a Diagnosis 22.6 7.3 0.004 68 13.5 3.7 0.001 73 RT 12 ND Diagnosis 86.9 28.3 0.001 67 40.4 9.6 0.001 76 RT 13 M5 Diagnosis 36.6 12.7 0.02 65 3.6 1.8 0.05 50 RV 14 M0 Diagnosis 18.7 7.1 0.0001 62 8.5 1.8 0.004 79 RT 15 M5b Diagnosis 25.6 10.0 0.01 61 11.5 3.6 0.01 69 RV 16 ND Diagnosis 66.1 27.4 0.0008 59 6.7 4.7 NS 30 RT 17 M1 Diagnosis 27.9 12.4 0.05 56 0.6 0.2 NS 67 RT 18 M5 Diagnosis 28.0 15.2 0.001 46 15.2 8.1 0.01 47 PRV 19 M2 Diagnosis 55.7 32.5 NS 42 12.3 5.3 0.044 57 PRV 20 M4 Diagnosis 84.9 64.1 0.009 24 57.1 33.7 0.045 41 PRT 21 M2 Diagnosis 86.1 84.5 NS 2 53.7 37.1 0.03 31 PRV 22 M0 Diagnosis 93.6 93.7 NS 0 90.9 32.3 0.0001 64 PRV 23 M5b Diagnosis 17.9 3.3 NS 82 2.8 1.3 NS 54 NRT 24 ND Relapse 41.0 26.5 NS 35 9.6 1.7 NS 82 NRV 25 M4 Diagnosis 24.6 19.4 NS 21 5.2 2.5 0.017 52 NRV 26 M4Eo Diagnosis 16.0 13.6 NS 15 1.0 1.5 NS �56 NRV 27 M4 Diagnosis 25.7 22.7 NS 12 2.8 1.3 NS 54 NRT 28 M2 Diagnosis 68.0 64.9 NS 5 25.1 5.6 NS 78 NRV 29 M4 Diagnosis 20.7 20.2 NS 2 2.3 3.0 NS �30 NRT 30 Unclassified Diagnosis 82.2 80.9 NS 2 84.3 81.0 NS 4 NRT 31 M4 PD 96.4 95.0 NS 1 92.3 80.7 0.001 13 NRV 32 M4 Diagnosis 98.4 97.8 NS 1 98.7 87.8 0.014 11 NRV 33 M1 Diagnosis 79.8 82.2 NS �3 55.8 32.4 NS 42 NRV 34 M4Eo Diagnosis 46.2 52.0 NS �13 14.8 19.3 NS �30 NR

Abbreviations: FAB, French-American-British; ND, not determined; PD, persistent disease; NS, not statistically significant.aIn vivo response criteria: R: >50%RR inRF; PR: 20% to50%RR inRFor>20%RR inBMonly; NR, no significant difference between FED- andvehicle-treatedmice (NS)or <20% RR in both RF and BM.

Chen et al.

Cancer Res; 76(5) March 1, 2016 Cancer Research1216




Figure 1.Heterogeneous responses to FED treatment in primary AML xenografts. A, schematic illustrating the experimental protocol for in vivo drug testing. B andC, flow cytometric analysis of human CD45þCD33þ AML engraftment in the injected femur of mice transplanted with AML cells followed by treatment with FED orvehicle control (left) and summary of human CD45þCD33þ AML engraftment in the injected femur and noninjected bones of engrafted mice after FED or vehicletreatment (right). B, representative responsive samples. C, representative nonresponsive samples. D, summary of human leukemic engraftment in untreatedsecondary recipients 10 to 12 weeks posttransplantation of equal numbers of human CD45þ cells harvested from the pooled bone marrow of FED� or vehicle-treated mice. Representative X-NR (left) and X-R (right) patient samples are shown. Data from injected RF and noninjected bones were combined for theanalysis. Bars indicate mean values. � , P < 0.05; �� , P < 0.01; �� , P < 0.001.






was observed as in the primary mice. Overall, by testing a largecohort of patient samples in a clinically relevant model, weevaluated drug effects against LSCs and captured the heteroge-neous treatment response that is often seen in clinical trials.

FED-sensitive pSTAT5 provides a biomarker of in vivo responseto FED

Given the observed heterogeneity of response in xenograftassays, we sought to identify a biomarker of FED responsiveness.As FED is a tyrosine kinase inhibitor, we used a multiplexed PFcytometry assay (20) to profile, with single-cell resolution, theimpact of short-term (30–600) in vitro FED treatment on thebasal activity of the SYK, BCR, JAK/STAT, MAPK, PI3K/mTOR,and NF-kB signaling pathways in primary AML blasts (Supple-mentary Fig. S2A–S2C). We used 100 nmol/L FED in thesestudies, because this concentration greatly reduced IL3-inducedSTAT5 phosphorylation in the FLT3L-responsive OCI-AML5 cellline (Supplementary Fig. S2D). As expected (5), the AML sam-ples in this cohort displayed highly variable expression of CD34,CD45, CD123, and CD33 (Supplementary Fig. S3A and S3B).Therefore, we costained each sample with antibodies directedagainst these markers to evaluate signaling in immunopheno-typic cell subsets within individual samples. Basal levels ofMAPK p38, pSTAT5, pAKT(T), pSRC, and IkBa showed thehighest variance across this cohort (Fig. 2A, top). As would beexpected, a 600 treatment with FED in vitro decreased basal levelsof pSTAT5 to a greater extent than other phosphoproteins(Fig. 2A, bottom). Interestingly, not all samples with high basalpSTAT5 levels were FED-responsive in the PF assay (R2 ¼ 0.54),but samples with the highest basal levels of pSTAT5 showed thegreatest FED-mediated decrease in pSTAT5 (Supplementary Fig.S3C). In most cases, only a fraction of the AML blast populationshowed elevated basal pSTAT5; the level of CD34 expression onpSTAT5hi cells was highly variable (Supplementary Fig. S3D).Thus, we identified considerable signaling heterogeneity withinand between AML samples in the cohort.

By comparing the in vivo drug response and PF data for eachpatient sample, we found that basal pSTAT5 levels were signifi-cantly higher in X-R compared with X-NR samples (Fig. 2B). FEDtreatment also led to a significantly greater decrease in basalpSTAT5 levels inX-R relative toX-NR samples (Fig. 2B), suggestingthat the PF assay represents a biomarker of AML responsiveness toFED in vivo. This conclusion was supported by unsupervisedclustering and heatmap analysis of FED-mediated decreases inbasal levels of 12 phosphoproteins, which divided the cohort intotwo major groups (Fig. 2C). One cluster contained primarily X-Rsamples (14/15) that showed robust FED-mediated decreases inpSTAT5 [0.47 to 2.78 fold change (FC), log2 scale]; there wereinconsistent but significant decreases in several other phospho-proteins in this sample cluster following FED treatment. Thesecond cluster contained a mixture of X-R and X-NR sampleswhere FED treatment did not cause changes in the measuredphosphoproteins. Considering all of the X-R samples in this initialpatient cohort, 64% (14/22) exhibited �0.4-fold (log2) FED-mediated decrease in pSTAT5 (Supplementary Fig. S3E), whereasthe remaining X-R samples lacked this biomarker, suggesting atleast two different mechanisms of response to FED. Importantly,92% (11/12) of X-NR samples did not show loss of pSTAT5 whentreated with FED. This assay thus provides a highly specificpredictor of treatment sensitivity to FED in vivowith high-positivepredictive value (14/15 ¼ 93%).

To confirm these findings, we tested the suitability of FED-sensitive decrease in pSTAT5 as a biomarker of in vivo response toFED in an independent validation cohort of 30 additional AMLpatient samples. In this cohort, 16 and 14 samples were classifiedas X-R and X-NR, respectively, based on RR of leukemic engraft-ment in treated xenotransplanted mice (Table 2 and Supplemen-tary Fig. S4). A FED-mediated reduction in pSTAT5 signaling wasobserved in 9 of 16 X-R and was absent in 12 of 14 X-NR patients,giving a sensitivity of 56% and specificity of 86% for this responsebiomarker in the validation cohort. Collectively, these data indi-cate that FED-sensitive pSTAT5may provide a phosphoproteomicbiomarker to identify AML patients whose leukemia cells areunlikely to respond to FED treatment in the in vivo model.

To examine whether the effects of FED against LSCs in AMLwere indeedmediated by JAK2 inhibition, we tested the efficacy ofRUX, a JAK1/2 inhibitor approved for the treatment of MPNs,against 10 AML samples classified as FED X-R. Surprisingly, eightof ten samples did not respond to RUX treatment in xenograftassays, and the remaining two showed only a partial response(Fig. 3A and Table 3). Furthermore, in vitro treatment with RUXhad minimal or no impact on pSTAT5 levels, in marked contrastwith strong FED-mediated effects. As cytokine-induced JAK2activation is accompanied by autophosphorylation of Y1007 andY1008 (Fig. 3B; ref. 21), we also examined the abundance ofpJAK2Y1007/Y1008 in samples from both cohorts. Interestingly,when detected, pJAK2 was predominantly expressed in CD34�

CD45hi AML blasts, and levels were not affected by in vitrotreatmentwith FED(Fig. 3C). Sampleswith FED-sensitive pSTAT5either had little pJAK2 (Fig. 3C, AML43), or expressed pJAK2 andpSTAT5 in phenotypically distinct cellular subsets (Fig. 3C,AML50), suggesting that STAT5 phosphorylation was JAK2 inde-pendent in most cases. Finally, samples with the highest basalpJAK2 typically had lowpSTAT5 (Fig. 3C,AML53), suggesting thatpJAK2 did not induce STAT5 phosphorylation in these samples.Collectively, these data suggest that FED exerts its effects in AMLprimarily through JAK2-independent mechanisms.

Although FED was developed as a JAK2 inhibitor, like all othertyrosine kinase inhibitors, it has significant effects on othertyrosine kinases. Specifically, FED also inhibits FLT3 (Supplemen-tary Fig. S5A; ref. 22), albeit with 5-fold lower potency (23).Therefore, we examined whether the observed FED effectsmight be mediated by FLT3 inhibition, because activatingFLT3-ITD mutations are common in AML. In accordance withstudies showing that FLT3-ITD causes STAT5 activation (24),basal pSTAT5 levels were significantly higher in FLT3-ITDþ com-pared with FLT3-ITD�AML samples (Supplementary Fig. S5B), aspreviously reported (25). Treatment in vitro with the FLT3 inhib-itor AC220 or FED decreased pSTAT5 to a greater extent in FLT3-ITDþ samples (Supplementary Fig. S5B and S5C). Nonetheless,FED also robustly decreased pSTAT5 levels in 3 FLT3-ITD� AMLsamples (Supplementary Fig. S5C), and 15/38X-R samples lackedFLT3-ITD (Supplementary Fig. S5D). These data argue that in vitroand in vivo responses to FED are not restricted to FLT3-ITDþ

samples.

Identification of subpopulations with distinct pSRC/pSTAT5signaling and drug sensitivity

Many AML samples from both cohorts exhibited small FED-mediated reductions in basal pSRC levels (Fig. 2A and Supple-mentary Fig. S4A), suggesting that inhibition of SRC signalingmight partly account for FED's efficacy in vivo. Indeed, prior

Chen et al.





reports have suggested that several SRC family kinases are aber-rantly activated in AML (26, 27). In vitro treatment with dasatanib(DAS), a dual SRC/ABL kinase inhibitor, robustly decreased SRCphosphorylation in our initial patient cohort (Fig. 4A). Interest-ingly, most AML samples contained phenotypically defined cel-lular subsets that exhibited distinct signaling profiles, including adiscrete pSRChi subset that was typically CD45hiCD34�/lo (Fig.4B) andCD33hi (data not shown), and a pSTAT5hi subset thatwasCD45lo (CD34þ or CD34�). In most cases, pSTAT5 levels wererobustly decreased by in vitro treatment with FED but not DAS,whereas pSRC was reduced to a greater extent by DAS than FED

(Fig. 4B). One exception was sample AML21, in which pSTAT5and pSRC, although clearly present in different subsets, were bothmore sensitive to inhibition byDAS than by FED (SupplementaryFig. S6). Indeed, DAS treatment reduced global tyrosine phos-phorylation in this sample; of note, this sample contains a BCR-ABL1 gene rearrangement. This unusual PF profile was alsoobserved in six chronic phase CML samples tested (Supplemen-tary Fig. S6 and data not shown).

The identification of distinct cell populations with differentialpSTAT5/pSRC signaling and the prediction from in vitro analysisthat FED and DAS might be efficacious when used together

Figure 2.pSTAT5 levels predict response to FED treatment in the initial AML patient cohort. A, box and whisker plots of normalized basal median fluorescence intensities(MFI; top) and log2 FC ratios (bottom) showing the impact of FED treatment on phosphoprotein levels analyzed by flow cytometry. Horizontal lines in boxesindicate medians, boxes span interquartile range, and whiskers extend to the minimum and maximum values. n ¼ 34 for all markers except pSTAT1 (n ¼ 26),pSTAT3(S) (n ¼ 27), pSRC (n ¼ 26), pAkt(T) (n ¼ 33), pAkt(S) (n ¼ 27), IkB (n ¼ 26), p4EBP1 (n ¼ 14), pNFkB (n ¼ 15), pSHP2 (n ¼ 19). Normalized basalMFI was calculated by subtracting the MFI of the phospho-antibody–stained sample minus the MFI of the fluorescence minus one control (stained withsurface markers but without phospho-antibodies). The log2 FC ratio was calculated as the log2 of the ratio of (MFI of drug-treated sample)/(MFI of vehicle-treatedsample). B, basal andpost-FED treatment changes inpSTAT5 levels in patient samples classified as nonresponders (X-NR)or responders (X-R) in xenograft assays. C,unsupervised hierarchical clustering analysis (Euclidean distance) of AML patient samples based on log2 FC ratios of FED-treated/vehicle-treatedsamples for each phosphoprotein as described above. White areas indicate missing values.






prompted us to test the efficacy of FEDþDAS combinationtherapy in vivo with 3 patient samples (Fig. 4C–E and data notshown). Response to single-agent therapy (either FED or DAS) inxenotransplanted mice was concordant with drug sensitivity pre-dicted by PF analysis of in vitro treatment effects (Fig. 4B and D),and the combination of FEDþDASwasmore effective in reducingleukemic engraftment in treatedmice compared with either agentalone. In addition, although a limited number of mice weretested, serial transplantation of AML8 (a relapse sample) sug-gested that combination therapymaymore effectively impair LSCfunction compared with the single agents (Fig. 4E). Thus, com-bining multiparameter PF analysis with in vivo drug testing ofprimary patient samples enabled the rational design and evalu-ation of a combination regimen to target LSCs in an animalmodelof AML.

DiscussionHere, we developed an approach that combines drug testing of

large numbers of primary patient sampleswithmultiparameter PFanalysis. Using FED as a prototype, we demonstrated heteroge-neity of treatment response in our xenograft model, with efficacyobserved in 38 of 64 (59%) samples. In parallel, we identified adrug response biomarker that predicts which patients are likely tobenefit from FED treatment based on the responses observed inxenografts. To date, this is the largest patient cohort tested with asingle drug. This scale of testing allowed inclusion of patientswith

refractory/relapsed disease and heterogeneous molecular andcytogenetic abnormalities. The similar concordance of in vivo drugresponsewithPF response in Toronto andVancouver points to therobustness and feasibility of our approach for future preclinicaldrug development in AML.

Although xenograft models are often employed in anticancerdrug development, timing and sample availability generallypreclude contemporaneous analysis of patients in a clinical trialand xenografts derived from the same patients' samples, lead-ing to uncertainty as to the validity of the latter for predictingtreatment response. In the absence of such paired analyses,characterization of a predictive biomarker derived from xeno-graft treatment responses represents an ideal means to correlatethe heterogeneous responses seen in the two settings. Valida-tion of predicted drug responses in patients would stronglysupport the utility and applicability of large-scale xenograftingfor preclinical drug development, which if carried out in par-allel with studies to identify response biomarkers as describedhere, will increase the success rate of anticancer drugs thatproceed to trial, especially for drugs that may be beneficial toonly a subset of patients.

In the case of AML, this approach can only be applied to theapproximately 50% of patient samples that can generate xeno-grafts using currentmethods,whichmay raise questions about theuniversality of drug-testing results. However, AML patients whosecells are engraftment-capable haveworse outcomes (Kennedy andcolleagues; unpublished data; ref. 7), and thus are in greatest

Table 2. Efficacy of FED against xenotransplanted AML samples (validation cohort)

Injected RF Distal BMMean engraftment (%) Mean engraftment (%)

Site Patient ID FAB Sample Vehicle FED P value RR (%) Vehicle FED P value RR (%)In vivo

responsea

T 35 M4 Diagnosis 45.3 8.6 0.001 81 24.8 7.3 0.01 71 RT 36 M5a Diagnosis 20.4 4.0 0.001 80 6.9 3.3 NS 52 RT 37 Unclassified Diagnosis 39.6 8.6 0.001 78 23.4 4.0 0.001 83 RT 38 M5 Diagnosis 42.4 9.7 0.001 77 15.4 3.7 0.02 76 RV 39 M4 Diagnosis 51.4 12.6 0.0001 75 4.3 0.7 0.029 84 RT 40 M4 Diagnosis 30.0 8.8 0.003 71 16.2 5.6 0.001 65 RT 41 M5a Diagnosis 27.1 10.9 0.005 60 6.2 2.0 0.03 68 RT 42 M5a Diagnosis 21.5 9.1 0.001 58 16.0 8.0 0.01 50 RT 43 Unclassified Diagnosis 79.5 39.8 0.001 50 52.2 9.3 0.001 82 RV 44 M4Eo Diagnosis 25.5 13.9 0.024 46 12.9 4.5 0.001 65 PRT 45 M5a Diagnosis 72.4 45.1 0.06 38 50.2 16.6 0.001 67 PRV 46 Unclassified Diagnosis 56.8 35.8 0.0057 37 15.5 6.4 0.013 59 PRT 47 M1 Diagnosis 48.7 30.9 0.03 37 13.8 9.8 NS 29 PRT 48 M0 Diagnosis 42.6 28.4 NS 33 9.8 5.1 0.05 48 PRT 49 M4 Diagnosis 97.8 78.9 0.001 19 88.4 55.9 0.001 37 PRT 50 Unclassified Diagnosis 72.7 63.4 NS 13 63.8 49.1 0.04 23 PRV 51 M5 Diagnosis 44.2 19.5 NS 56 71.8 61.7 NS 14 NRV 52 M2 Diagnosis 19.0 10.4 NS 45 5.4 2.0 0.017 64 NRT 54 M5b Diagnosis 54.3 43.4 NS 20 36.8 24.8 NS 33 NRV 54 Unclassified Diagnosis 24.4 20.0 NS 18 24.1 17.1 NS 29 NRT 55 Unclassified Diagnosis 78.0 68.3 NS 12 55.5 49.5 NS 11 NRT 56 M1 Diagnosis 92.2 81.0 NS 12 55.9 53.7 NS 4 NRT 57 Unclassified Diagnosis 46.9 42.2 NS 10 39.8 31.0 NS 22 NRT 58 M5 Diagnosis 75.1 71.0 NS 5 53.5 39.9 NS 25 NRV 59 M4Eo Diagnosis 43.4 41.2 NS 5 8.6 7.6 NS 11 NRT 60 M1 Relapse 93.1 90.1 NS 3 76.7 65.2 NS 15 NRT 61 M1 Diagnosis 75.6 73.6 NS 3 20.4 25.6 NS �25 NRT 62 Unclassified PD 95.8 93.3 0.001 3 89.4 79.6 0.05 11 NRV 63 M1 Diagnosis 74.1 72.9 NS 2 69.7 58.6 0.014 16 NRV 64 M4 Diagnosis 30.0 36.7 NS �22 38.4 49.4 NS �29 NR

Abbreviations: FAB, French-American-British; PD, persistent disease; NS, not statistically significant.aIn vivo response criteria: R: >50% RR in RF; PR: 20% to 50% RR in RF or >20% RR in BM only; NR, no significant difference between drug- and vehicle-treated mice(NS) or <20% RR in both RF and BM.

Chen et al.





need. Furthermore, there is substantive evidence that the prop-erties of LSCs assayed in AML xenotransplantmodels have clinicalrelevance across a broader set of samples beyond those that cangenerate xenografts (5–7). For example, a functionally definedLSC-specific gene expression signature is highly prognostic inmultiple independent AML cohorts (5, 6), suggesting that com-mon pathways that are operative within LSC-enriched (i.e., xeno-graft-initiating) cell fractions are linked to outcome in all patients.This link also implies that, regardless of a patient's mutationalspectrum and even when the subclonal composition of a xeno-graft does not reflect the dominant leukemic clone in a patient(28), the common stemness pathways that govern engrafting cellsrepresent potentially relevant therapeutic targets. Thus, drugs thatimpair leukemic engraftment may affect outcomes, a possibilitythat must ultimately be tested in clinical trials.

Patient-derived tissues that have been serially propagated asxenografts (distinct from primary patient samples) have beenused previously by the Pediatric Preclinical Testing Program (29)and others (30) to test a panel of therapeutic agents against alimited number of individual tumors. Although effective inscreening potential new drugs, this approach does not capture

interpatient tumor heterogeneity nor allow for identification ofdrug response biomarkers. Furthermore, xenografts that havebeen extensively propagated may not mirror the patient's tumoraccurately. The largest study in a single tumor type examined theefficacy of Sagopilone against a panel of 22 primary non–small

Table 3. Summary of in vivo and PF response of AML samples treated with FEDor RUX

In vivoresponse

Log2 FC drug/vehpSTAT5

Sample ID FLT3-ITD FED RUX FED RUX

AML48 þ PR NR �1.22 �0.2AML37 þ R NR �1.52 �0.48AML38 þ R PR �0.52 �0.07AML40 þ R NR �0.76 0.15AML2 þ R NR �2.09 NDAML12 þ PR NR �3.03 �0.44AML43 þ R NR �1.6 �0.21AML1 � R NR �0.35 �0.01AML36 � R NR �0.03 0.04AML7 � R PR �0.56 �0.64

Abbreviation: ND, not determined.

Figure 3.Discordant responses to FED andRUX treatment in AML xenograftassays. A, summary of human CD45þ

CD33þ AML engraftment in theinjected femur and noninjectedbones of engraftedmice treated withvehicle, FED, or RUX. Results areshown for two representativesamples of ten tested. Each symbolrepresents one mouse. Barsindicate mean values. �� , P < 0.01;�� , P < 0.001. B, histogram showingpJAK2 levels in Ba/F3 cells (a pro-Bcell line) stimulated with mIL-3 (10ng/mL) for 5 minutes. C, PF analysisof pSTAT5 and pJAK2 levels incombination with surface CD34 orCD45 expression in representativeAML samples after in vitro treatmentwith vehicle (VEH) or FED.






Figure 4.PF analysis reveals subpopulations with distinct pSRC/pSTAT5 signaling and drug sensitivity. A, log2 FC ratios (calculated as described in Fig. 2) showing impact oftreatmentwith FED or DASon each phosphoprotein in initial cohort of AML patient samples, analyzed by flow cytometry. B, PF analysis of pSRC and pSTAT5 levels incombination with CD34 or CD45 expression in representative AML samples after in vitro treatment with vehicle, FED, or DAS. C, schematic illustrating theexperimental protocol for FED/DAS testing. D, summary of human CD45þCD33þ AML engraftment in the injected femur and noninjected bones of engrafted micetreated with vehicle, FED, DAS, or FEDþDAS. Results from two representative patient samples are shown. E, summary of human CD45þCD33þ AML engraftment inthe injected femur and noninjected bones of untreated secondary recipients 10 to 12 weeks posttransplantation of equal numbers of human CD45þ cells fromthe pooled bone marrow of primary mice transplanted with AML8 and treated as indicated. For D and E, each symbol represents one mouse. Bars indicatemean values. �� , P < 0.01; �� , P < 0.001.

Chen et al.





cell lung cancer (NSCLC) xenografts (31). Fifty percent ofsamples exhibited partial responses defined by RECIST (32),but only 5% of patients had a partial response at clinical trial(33). The incongruence between success at preclinical stagesand failure in clinical trials seen in this and other studies maybe explained by the inability of previous preclinical models ofNSCLC and other tumors both to reflect disease heterogeneityand to read out CSC function (2). Drugs that reduce tumor bulkmay not necessarily have activity against CSCs, and vice versa.As evaluation of drug effects against CSCs in clinical trials ischallenging, it is important to evaluate CSC function in well-characterized preclinical models of human cancer, of which theAML xenograft is the best example.

The use of a large number of primary AML samples in ourstudy enabled us to capture the response heterogeneity that iscommonly seen in the clinic. The mechanistic basis for vari-ability in treatment responses in clinical trials is often unknownbut is likely related at least in part to disease heterogeneityamong patients. Both inter- and intrapatient tumor heteroge-neity exist on functional and genetic levels, and cell lines oreven a small number of patient-derived xenografts do notsufficiently capture this heterogeneity. This precludes identifi-cation of response biomarkers and confounds efforts to under-stand mechanisms of action of candidate drugs and correlatethese to response. For example, treatment with AZD1480, anATP-competitive inhibitor of JAK kinases, reduced leukemicengraftment in xenograft studies of a small number of AMLsamples (n ¼ 7; ref. 34). In vitro analysis of 48 AML samplesdemonstrated inhibition of both pSTAT3 and pSTAT5 in thephenotypically defined CD34þ LSC/progenitor blast popu-lation, but the degree of inhibition was quite variable(0%–100%). Thus, it is difficult to draw firm conclusions aboutwhether inhibition of pSTAT3/5 signaling is the critical mech-anism by which AZD1480 mediates its in vivo effects againstLSCs. In our study, PF profiling provided some mechanisticinsights into how FED exerts its anti-LSC effects in vivo; spe-cifically, that FED is not acting solely through inhibition ofJAK2 or FLT3 activity. Unexpectedly, we observed very fewin vivo responses to another JAK inhibitor (RUX), highlightingthe fact that tyrosine kinase inhibitors usually have multiplespecificities (23), and that the mechanism of action againstLSCs may not be related to their most potent inhibitor activity.The discordance between the in vivo and in vitro responsesobserved with FED and RUX also demonstrates that responsebiomarkers are likely tied to specific-drug actions and cannot beextrapolated to a drug class. Parallel assessment of in vivoresponse and in vitro analysis of affected pathways also providesopportunity for uncovering drug synergy, as demonstrated herefor FEDþDAS.

Predictive response biomarkers are increasingly being soughtto guide patient selection in clinical trials to maximize thechances of demonstrating true drug effectiveness. Even whenpatients are selected for a trial based on the presence of a putativedrug target (mutation and/or pathway), variability in responsemay be related to off-target effects, as seen in the current study,and thus unpredictable. The drug-responsive pSTAT5 biomarkerthat we identified based on correlation with treatment responsesin xenografts was highly specific (few false positives) withstrong positive predictive value in an independent validationcohort. Although this biomarker correlated with FLT3-ITDpositivity, it also correctly predicted in vivo response to FED in

3 FLT3-ITD� xenografted patient samples. The biomarker did notidentify all patients who exhibited drug responsiveness in xeno-graft assays; however, reserving treatment for patients who havethe response biomarker avoids exposing those unlikely to derivetherapeutic benefit to potential drug toxicities. Indeed, whenour study was initiated, FEDwas in clinical trials for the treatmentofMPNs. However, Sanofi recently halted these trials after reportsof Wernicke's encephalopathy in a small number of treatedpatients (35).

Our study provides a modern paradigm for preclinical drugdevelopment that improves the chances of correctly identifyingdrugs with LSC activity to move forward into clinical trials. Theevidence that xenotransplantation assays detect properties of AMLpatient samples that correlate with outcome (5–7) increases theconfidence that candidate drugs that ablate LSCs in this preclinicalmodel will also be effective when administered to patients.Clinical trials evaluating novel anticancer therapies are expensive,time consuming, and expose patients to risk. We anticipate thatadoption of this approach for preclinical evaluation and bio-marker development will lead to improved patient selection andclinical trial outcomes.

Disclosure of Potential Conflicts of InterestM.D. Minden is a consultant/advisory board member for Celgene. No

potential conflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: W.C. Chen, M.D. Minden, C. Guidos, J.E. Dick,J.C.Y. WangDevelopment of methodology: W.C. Chen, J.S. Yuan, A. Mitchell, C. Guidos,J.E. Dick, J.C.Y. WangAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): W.C. Chen, Y. Xing, N. Mbong, G. Gerhard,G. Bogdanoski, S. Lauriault, Y. Merkulova, M.D. Minden, D.E. Hogge,C. Guidos, J.C.Y. WangAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): W.C. Chen, J.S. Yuan, Y. Xing, A. Mitchell,J.A. Kennedy, G. Bogdanoski, S. Lauriault, Y. Merkulova, C. GuidosWriting, review, and/or revision of the manuscript: W.C. Chen, J.S. Yuan,A. Mitchell, M.D. Minden, D.E. Hogge, C. Guidos, J.E. Dick, J.C.Y. WangAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): W.C. Chen, J.S. Yuan, Y. Xing, A.C. Popescu,J.A. Kennedy, G. Bogdanoski, S. Perdu, Y. Merkulova, D.E. HoggeStudy supervision: D.E. Hogge, C. Guidos, J.E. Dick, J.C.Y. WangOther (performed experiments): J. McLeod

AcknowledgmentsThe authors thank Jaime Claudio and Amanda Kotzer for project manage-

ment support and Sherry Zhao andmembers of the SickKids-UHN Flow Facilityfor technical support.

Grant SupportAll authors were supported by the Cancer Stem Cell Consortium with

funding from the Government of Canada through Genome Canada and theOntario Genomics Institute (OGI-047), and through the Canadian Institutes ofHealth Research (CSC-105367). J.E. Dick is also supported by grants from theCanadian Cancer Society, Terry Fox Foundation, Ontario Institute for CancerResearchwith funds from the province ofOntario, a Canada Research Chair andthe Ontario Ministry of Health and Long Term Care (OMOHLTC). The viewsexpressed do not necessarily reflect those of the OMOHLTC.

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received October 1, 2015; revised December 14, 2015; accepted December17, 2015; published OnlineFirst February 1, 2016.






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2016;76:1214-1224. Published OnlineFirst February 1, 2016.Cancer Res Weihsu C. Chen, Julie S. Yuan, Yan Xing, et al. BiomarkersMyeloid Leukemia That Enables the Discovery of Predictive An Integrated Analysis of Heterogeneous Drug Responses in Acute

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