Personalized Preclinical Trials in BRAF Inhibitor ... · deﬁneeffective double and triple combination therapies, leading to complete tumor regression in all tumors of one PDX model

Personalized Medicine and Imaging

Personalized Preclinical Trials in BRAF Inhibitor–Resistant Patient-Derived Xenograft ModelsIdentify Second-Line Combination TherapiesClemens Krepler1, Min Xiao1, Katrin Sproesser1, Patricia A. Brafford1, Batool Shannan1,Marilda Beqiri1, Qin Liu1,Wei Xu2, Bradley Garman2, Katherine L. Nathanson2,Xiaowei Xu2, Giorgos C. Karakousis2, Gordon B. Mills3, Yiling Lu3, Tamer A. Ahmed4,Poulikos I. Poulikakos4, Giordano Caponigro5, Markus Boehm5, Malte Peters5,Lynn M. Schuchter2, Ashani T.Weeraratna1, and Meenhard Herlyn1

Abstract

Purpose: To test second-line personalized medicine combina-tion therapies, based on genomic and proteomic data, in patient-derived xenograft (PDX) models.

Experimental Design: We established 12 PDXs from BRAFinhibitor–progressed melanoma patients. Following expansion,PDXs were analyzed using targeted sequencing and reverse-phaseprotein arrays. By using multi-arm preclinical trial designs, weidentified efficacious precision medicine approaches.

Results: We identified alterations previously described as dri-vers of resistance: NRAS mutations in 3 PDXs, MAP2K1 (MEK1)mutations in 2, BRAF amplification in 4, and aberrant PTEN in 7.At the protein level, re-activation of phospho-MAPK predomi-nated, with parallel activation of PI3K in a subset. Second-lineefficacy of the pan-PI3K inhibitor BKM120 with either BRAF(encorafenib)/MEK (binimetinib) inhibitor combination or the

ERK inhibitor VX-11ewas confirmed in vivo. Amplification ofMETwas observed in 3 PDXmodels, a higher frequency than expectedand a possible novel mechanism of resistance. Importantly, METamplification alone did not predict sensitivity to the MET inhib-itor capmatinib. In contrast, capmatinib as single agent resulted insignificant but transient tumor regression in a PDXwith resistanceto BRAF/MEK combination therapy and high pMET. The triplecombination capmatinib/encorafenib/binimetinib resulted incomplete and sustained tumor regression in all animals.

Conclusions: Genomic and proteomic data integration identi-fies dual-core pathway inhibition as well as MET as combinatorialtargets. These studies provide evidence for biomarker developmentto appropriately select personalized therapies of patients and avoidtreatment failures. Clin Cancer Res; 22(7); 1592–602. �2015 AACR.

See related commentary by Hartsough and Aplin, p. 1550

IntroductionThe treatment of advanced melanoma has been significantly

improved in recent years, enabled by BRAF andMEK inhibitors asnew standard therapies inmelanomas,withBRAF-V600E/Kmuta-tions (1, 2) and immune checkpoint inhibitors showing remark-ably durable responses in a subset of patients (3–5). Although amajority of patients treated with BRAF or BRAF/MEK inhibitorsexperience a robust initial response, the excitement about thetherapeutic success is dampened by the relapse of most patients.This is due to the development of acquired (secondary) resistance

mediated by multiple mechanisms (6–10). Therefore, rationalsecond-line combination therapies are urgently needed, and weexpect that these therapies require individualization to the spec-trum of resistance mechanism of each patient (11). There is a lackof translationalmodels to studyprecisionmedicine approaches toresistance mechanisms found in patients, although a range ofpreclinical mouse melanoma models, including patient-derivedxenografts (PDX), are in use (12). PDXs have been successfullyestablished for solid tumors including melanoma by implantingfresh tumormaterial from patients directly into immunodeficientmice (13). Success rates vary significantly between tumor types,yet melanoma is highly suited to this experimental approachpossibly due to the fact that even a few melanoma cells aresufficient to establish a tumor in NSG (NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ) mice (14). Tumor grafts generated in this way,and used as "avatars", can predict therapeutic responses in cancerpatients (15). Melanoma PDXs recapitulate the tumor architec-ture and genotype of the patient tumor (16), and metastaticbehavior of these PDXs correlates with clinical outcome inpatients (17). In this study, we developed PDXs from a cohortof patients who became resistant to and progressed on BRAFinhibitors. Using genomic and proteomic analysis, we were ableto identify targets and test combinations of compounds in clinicaldevelopment. However, we had an added advantage in thatwe were able to test multiple combinations in parallel due to anin vivo expansion strategy. These "preclinical trials" allowed us to

1Molecular and Cellular Oncogenesis Program, Tumor Microenviron-ment and Metastasis Program, and Melanoma Research Center, TheWistar Institute, Philadelphia, Pennsylvania. 2University of Pennsylva-nia Abramson Cancer Center, Philadelphia, Pennsylvania. 3UniversityofTexasMDAndersonCancerCenter,Houston,Texas. 4IcahnSchoolofMedicine at Mount Sinai, New York, New York. 5Novartis OncologyTranslational Medicine, Cambridge, Massachusetts.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Corresponding Author: Clemens Krepler, The Wistar Institute, 3601 SpruceStreet, Philadelphia, PA 19104. Phone: 215-898-0002; Fax: 215-898-0980;E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-15-1762

�2015 American Association for Cancer Research.

ClinicalCancerResearch

Clin Cancer Res; 22(7) April 1, 20161592

Research. on September 11, 2020. © 2016 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Published OnlineFirst December 16, 2015; DOI: 10.1158/1078-0432.CCR-15-1762

http://clincancerres.aacrjournals.org/

define effective double and triple combination therapies, leadingto complete tumor regression in all tumors of one PDX modeltreated. This translational approach towards improving person-alizedmedicine inmelanoma highlights the potential use ofMETinhibitor combination therapy in a defined subset of melanomapatients.

MethodsPatient samples and generation of PDXs

Biopsies from patients with a BRAF-V600E mutation whohad progressed by RECIST on either vemurafenib or dabrafenibwere included in this study. Tissue collection was approved byThe Wistar Institute Institutional Review Board. Sterile tumorsamples were placed in transport media (DMEM, Fungizone0.1%, and 2mL gentamicin 0.2%) on wet ice and processedwithin 24 hours under sterile conditions. Tumor tissue wasfinely minced using the cross blade technique, digested incollagenase IV for 20 minutes at 37�C with repeated trituration,followed by 2-minute incubation in trypsin. The tumor slurrywas implanted with Matrigel (Corning Life Sciences) subcuta-neously in NSG mice. When tumors reached a volume of 1,000mm3 (determined by weekly caliper measurements using theformula W � W � L/2), animals were sacrificed and tumorsharvested. Tumor grafts were digested as above and eitherreimplanted within 24 hours or banked. All animal experi-ments were approved by The Wistar Institute InstitutionalAnimal Care and Use Committee.

Targeted next-generation sequencingPDX tumors were massively parallel DNA sequenced by Foun-

dationMedicine (http://foundationone.com) for 315 cancer geneexons and 28 cancer gene introns for base pair change, insertions,deletions, copy number changes, and select fusions by next-generation sequencing (18). Copy number changes in genesknown to be recurrently amplified in cancer were called ashigh-level (CN>8) and focal (CN>5) amplifications, nonfocallow-level (CN�8) amplifications, and homozygous deletions ofgenes known to be recurrently deleted in cancer.

Reverse-phase protein arraysFrozen tumor tissue was ground in a mixer mill (Retsch)

and lysed with 200-mL ice-cold lysis buffer [1% Triton X-100,

50mmol/LHEPESpH7.4, 150mmol/LNaCl, 1.5mmol/LMgCl2,1 mmol/L EGTA, 100 mmol/L NaF, 10 mmol/L Na pyrophos-phate, 1 mmol/L Na3VO4, 10% glycerol, freshly added protease,and phosphatase cocktail tablets (Roche Applied Science Cat.#05056489001 and #04906837001)]. After two flash-freezecycles, samples were centrifuged at 13,000 rpm for 15 minutesat 4�C, and supernatants were collected. Protein concentrationwas determinedby Bio-Rad protein assay (#500-0006). About 40-mL cell lysate (protein adjusted to1–1.5mg/mL)wasmixedwith 4�SDS sample buffer (40% glycerol, 8% SDS, 0.25 mol/L Tris_HCLpH 6.8; b-mercaptoethanol at 1/10 of volume without bromo-phenol added before use). The samples were then heated for 10minutes at 100�C in a heat block and submitted for reverse-phaseprotein array (RPPA) processing. RPPAwas performed by the MDAnderson Center RPPA core facility (Houston, TX) as previouslydescribed (19) and data reported as normalized log2. SeveralRPPA datasets were successfully merged using replicates-basednormalization (RBN; ref. 20). Unsupervised hierarchical cluster-ing was performed on RBN log2 median-centered protein valuesusing Cluster 3.0 software (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm#ctv). Results were visualized usingTreeView software (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm#ctv).

CompoundsPLX4720 200 ppm chemical additive diet was irradiated and

heat-sealed (Research Diets) and fed to mice once tumors wereestablished. PLX4720 was provided by Plexxikon. Encorafenib,binimetinib, VX-11e, capmatinib, and BKM120were provided byNovartis. For in vivo oral gavage, compounds were suspended in0.5% carboxymethylcellulose sodium (MC), 0.5% Tween 80(encorafenib); 1% MC, 0.5% Tween 80 (binimetinib); 5% eth-anol, 20% propylene glycol, 7.4% Tween 80 (VX-11e); 0.5%MC,0.1% Tween 80 (capmatinib); 1% MC, 1% Tween 80 (BKM120)in water and dosed using feeding tubes (Instech Laboratories,Inc).

In vivo experimentsHuman melanoma RPDX tumors were expanded in vivo

using NSG mice prior to the therapy experiments. Pooledtumor chunks banked from early mouse passages wereimplanted into 50 NSG mice (1:10 expansion; refs. 3–5).These tumors were harvested when reaching the maximumvolume allowed on the protocol (1,000 mm3), digested, andbanked as live cells. The larger part of this stock was retainedas a master bank, and the other part was implanted at a 1:5ratio into NSG mice to use in the therapy experiments. Theexpansion phase was under continuous drug pressure withPLX4720 200 ppm chemical additive diet at approximatelyclinical plasma levels. The plasma levels of PLX4720 (103.7mg/mL �3.2 after 7 days) were similar to steady-statelevels in patients treated with vemurafenib 960 mg twice aday (130.6 mg/mL � 71.78; ref. 21). When tumors havereached 200 mm3 per caliper measurement, animals wererandomized into treatment groups followed by a 3-day wash-out phase. Tumor size was assessed twice weekly per calipermeasurement. Mice were sacrificed after two weeks of treat-ment or when necessary for animal welfare. Dosing was pro-longed when tumor control was achieved as indicated. Tumortissue was conserved in formalin (for FFPE) and snap-frozen in

Translational Relevance

Basket trials to assign patients into treatment arms accord-ing to targetable alterations are an important development inpersonalized cancer medicine. However, selecting the appro-priate combinations for each patient can be challenging.Especially, in patients with acquired resistance to kinase inhi-bitors, intrapatient heterogeneity and concurrent mutationswithin the same lesion occur frequently. In this study, we usedPDXs from targeted therapy–progressed patients to test per-sonalized combinations based not only on genomic data, butvalidated by proteomic analysis. By using a multi-arm pre-clinical trial design, we were able to identify efficacious pre-cision medicine approaches and highlight the importance ofassessing pathway activation status at the protein level.

Personalized Therapies in BRAF Inhibitor–Resistant PDX

www.aacrjournals.org Clin Cancer Res; 22(7) April 1, 2016 1593




liquid N2 for protein extraction. Treatment groups were sacri-ficed 4 hours after last dose.

Immunohistochemical analysisTumor tissue was fixed in 10% formalin, dehydrated, and

embedded in paraffin. The immunohistochemical staining pro-cedure followed the manufacturer's protocol (Vector R.T.U Vec-tastain Kit, Universal Elite ABC kit #PK-7200). Primary antibody(Ki67: Vector #VP-RM04, 1:500; cleaved caspase-3: Cell SignalingTechnology #9664s, 1:300) was added to each section and incu-bated overnight at 4�C in a humidity chamber. The color visual-ization was Vector Impact DAB kit (SK-4105), followed by coun-terstaining with hematoxylin.

Western blot analysisProtein extraction was performed as described for RPPA.

Fifteen microgram of protein extracts was subjected to electro-phoresis on 10% SDS-PAGE gels and transferred on nitrocel-lulose membranes in the Bio-Rad Trans-Blot Turbo TransferSystem. The membranes were blocked with Odyssey BlockingBuffer (#:927-40000, 1:1 diluted in TBS; LI-COR) for 1 hour atroom temperature and incubated at 4�C overnight with thefollowing primary antibodies: pMet #3129, pAKT (s473)#4060s, pAKT (Thr308) #13038P, pERK #4370s, pMEK#9121S, MEK #2352, RSK #8408, pRSK #9344 (all Cell Signal-ing Technology), and b-Actin (Sigma #A5441). All primaryantibodies were diluted 1:1,000 in 5% BSA TBS-0.1%Tween20 buffer except b-actin, which was diluted 1:10,000. Afterwashing and incubating with secondary antibodies (ThermoScientific #PI35571, Thermo Scientific #PI35518, diluted1:10,000 in 1:4 Odyssey Blocking Buffer), the bands werevisualized by the LI-COR Odyssey Infrared Imaging System.

Statistical analysisProgression-free survival (PFS) and overall survival (OS) of the

patient were calculated using the Kaplan–Meier method. For invivo experiments, statistical significance was determined using thetrends of mean tumor volume over time. Treatment groups werecompared using linear mixed models, and a likelihood ratiotesting nested model was used to examine whether trends wereoverall significantly different among groups. P < 0.05 was con-sidered to be significant.

ResultsEstablishment of PDXs fromBRAF inhibitor progressed patientsamples

We collected 12 tumor samples from 10 melanoma patientspostprogression on a BRAF inhibitor (Fig 1A). In one patient,tissue from the same lesion was collected twice at different timepoints, and in another patient, a bowel and a brain metastasissample was collected. The distribution between male andfemale was 6:4, and the median age at biopsy was 64.5 years.All patients, except one with an unknown primary, had cuta-neous primaries, and all had distant metastatic disease, fromwhich the PDX models were established, with 7 biopsies fromsubcutaneous metastases, one each from the parotid gland andbowel and 3 from brain metastases (Supplementary Table S1).Two patients had surgical complete responses after excision oftheir progressing lesions, 5 had partial responses, and 3 patientshad stable disease as best response to BRAF inhibitor therapy.

The median PFS of all patients in this set was 39 weeks, with awide SD of 7.3 weeks (Fig. 1B). Median OS was 97.57 weekswith a SD of 45.64 weeks.

Whereas the majority of samples were from patients pro-gressed on vemurafenib, 2 samples were from patients who hadprogressed on dabrafenib, a drug with similar clinical efficacy(22). All samples were successfully established as tumor graftswith a median latency until palpable of 5.75 weeks (Fig. 1C).The median growth rate was 120.3 mm3/weeks to sacrifice,measured from palpability to last follow-up (Fig. 1D). We didnot observe any significant growth delay between untreatedand chronically PLX4720–treated tumor grafts (SupplementaryFig. S1). The histologic examination of the original patienttumor and the tumor grafts grown in mice showed similaritieswith respect to morphologic and histopathologic criteria. Fur-thermore, PDX serially transplanted up to 5 passages in micestill resembled the initial lesion, even when these were grownunder continuous drug pressure (Fig. 1E and SupplementaryFig. S2).

Identification of targetable resistance mechanismsTo characterize the resistance mechanisms in these models

and assess how well they would recapitulate the known biologyof resistance in patients, targeted next-generation sequencingwas performed on all PDX expanded under BRAF inhibitionwith a median exon coverage of 713 using the FoundationOnepanel (Foundation Medicine, Cambridge, MA). A median of11.5 somatic short variants of known, likely, and unknownsignificance was identified, with one PDX containing 111 variantsin the 343 exons and introns assessed, and complete results areprovided in (Supplementary Fig. S3). The BRAF V600E variantwas confirmed in all samples. Importantly, at least 2 and up to9 known deleterious concomitant alterations (mutations, ampli-fications, and deletions) were found in each of the 12 PDXsamples (Fig. 2A), including genes in the MAPK and PI3K path-ways, the receptor tyrosine kinase (RTK) family, transcriptionregulators, and DNA repair genes. The most common alterationwas loss of CDKN2A in 9 of 12 samples (23). Many geneticaberrations found through this approach were previously asso-ciated with resistance to BRAF inhibitors. For instance, 3 of 12PDX had activating NRAS mutations (24), 2 of 12 had activatingMAP2K1 mutations (Q56P and K57E; refs. 25, 6), 4 of 12 hadBRAF amplification (8), and 7 of 12 samples had deletion ormutation of PTEN (8). Moreover, in several cases, multiplecandidate resistance mechanisms co-occurred (e.g., PIK3CA andNRAS). Finally, some potentially actionable alterations detectedwere not previously described in the context of BRAF inhibitorresistance, such as MET amplification in 3 PDX models(WM3965-2 with a calculated copy number of 16, WM3983 witha copy number of 9, and WM4071-1 with a copy number of 93).

Matched samples were collected from several patients:WM4007 is a pretreatment lesion to WM3901 and does not haveamplified BRAF; WM3936-1 and -2 are both from the samerelapsed lesion at different timepoints and after aggressive growthunder therapy, but are both remarkably similar; finally,WM4071-1 and -2 are from therapy-resistant bowel and brain metastases,respectively, and although the 2 lesions have distinct mutationprofiles, pERK, pAKT, and other protein levels were concordant inboth PDX.

BRAF short-splicing variants have been reported in BRAF inhib-itor–progressed patient samples at a frequency of 13% to 32%

Krepler et al.

Clin Cancer Res; 22(7) April 1, 2016 Clinical Cancer Research1594




(6, 26, 8). However, all of our PDXmodels were determined to benegative for BRAF splice variants by protein and RNA analysis(Supplementary Fig. S4).

To complement genomic profiling with an assessment ofpathway activation status, RPPAs were run for all PDX. To differ-entiate between genomic/epigenomic changes versus signalingfeedback loops due to continued BRAF inhibition, an analysis ofdifferential protein signaling between all untreated PDX byunsupervised hierarchical clustering was performed (Supple-mentary Fig. S2A). Principal component analysis (PCA) wasperformed on three groups identified in the clustering butfailed to distinguish among the groups due to the lack ofsimilarly expressed proteins (Supplementary Fig. S2B). Further-more, attempting to identify signaling feedback loops, weanalyzed protein fold changes between treated and untreatedtumors using unsupervised hierarchical clustering (Supple-mentary Fig. S2C). Again, PCA did not succeed in definingcommonly changed pathway. Instead, it highlighted the het-erogeneity of resistance mechanisms within our relativelysmall tumor subset (Supplementary Fig. S2D). However,MAPK pathway reactivation was identified as a putative mech-anism of resistance in the majority of PDX (Fig. 2B). Fold

change in pAKT levels between BRAF inhibitor treated versusuntreated PDX tumors indicated the PI3K pathway as a pos-sible compensatory mechanism in 5 PDX models (Fig. 2C).Although we did not see a negative correlation between pERKand pAKT, the increase of pAKT while on drug indicates thatcontinued pathway inhibition in the resistant setting mightlead to upregulation of PI3K signaling through cross-talkbetween these two pathways (27).

Rational dual MAPK and PI3K pathway inhibition inhibitstumor growth in vivo

To test thehypothesis of dual-core pathway inhibitionbasedongenomic and proteomic data, we selected a MAPK and PI3Khyperactivated model for a multi-arm PDX in vivo study. Thepatient whose tumor tissue was used in this study had receiveddabrafenib in a clinical trial with an excellent clinical response butdeveloped a new subcutaneous thigh lesion after 9 months oftherapy, which was then biopsied (WM3936-1). The patient wastransitioned to commercial vemurafenib, but aggressive growth ofthat same lesion was observed under therapy so that this progres-sing thigh lesion was surgically excised after 3 months on vemur-afenib (WM3936-2). We found that both PDX had similar

Figure 1.Establishment of 12 PDX fromBRAF inhibitor–progressed patients. A, schematic of BRAF inhibitor–resistant PDXgeneration, expansion, characterization, and in vivotesting. The arrow shown in blue outline denotes possible future clinical translation. B, PFS of patients is measured from first day on drug (vemurafenib/solid bar ordabrafenib/hatched bar) to progression by RECIST. Best response is denoted by the color of each bar: stable disease, light blue; partial response, blue; surgicalcomplete response, green. Each bar represents one patient, 1/2 denote two samples collected from the same patient. C, PDX tumor grafts are followed fromimplantation until palpable; blue bars are untreatedmice; and red bars aremice continuously dosedwith BRAF inhibitor (PLX4720) diet. Mean ofmouse cohorts (n >5) is shown; error bars are SEM. D, growth rate of the same tumor grafts from palpable until sacrifice is calculated bymaximum tumor volume inmm3/time inweeks,thus higher values indicate a faster growth rate; blue bars are untreatedmice; and red bars aremice continuously dosedwith BRAF inhibitor (BRAFi; PLX4720) diet.Mean ofmouse cohorts (n>5) is shown; error bars are SEM. E, histologic examination of patient tumor tissue used to generate the PDXand subsequent PDXpassagesharvested;WM3965-2 is shown as a representativemodel, all others are shown in Supplementary Fig. S2; MP1, first mouse passage; MP4, 4th serial transplantation inmice; on BRAFi, Rel., relative; mice were continuously dosed with BRAF inhibitor diet; H&E staining except PDX S100, a melanoma marker.






mutation profiles and had acquired NRAS and PIK3CA muta-tions. Available next-generation sequencing data of a pretherapylesion biopsy indicated NRAS wild-type and PIK3CA wild-typestatus at least to the depth of sequencing performed. WM3936-1and -2were both derived from the samepatient lesion progressingondabrafenib and subsequently vemurafenib, andbothharboredNRAS Q61K heterozygous, PTEN C105Y homozygous, andPIK3CA H1047Y heterozygous mutations as potential resistancemechanisms that would be expected to lead to re-activation of theMAPK and compensatory activation of the PI3K pathway, asconfirmed in the RPPA data. Neither the PIK3CA or NRAS muta-tions were detected in a pretherapy patient lesion; PTEN statuscouldnot be assessed.On thebasis of genomic and RPPA (Fig. 2Aand C) data, we designed a rational second-line combinationtherapy to target all candidate resistance mechanisms centeredon the pan-PI3K inhibitor BKM120 (28) in combination witheither a BRAF/MEK inhibitor combination using encorafeniband binimetinib currently in clinical trials (NCT01543698 andASCO 2015 abstract 9007) or the ERK inhibitor VX-11e (29).For this 6-arm in vivo study, the WM3936-2 PDX model wasexpanded until tumor grafts could be implanted simultaneous-ly into a cohort of 60 NSG mice. Once tumors were established,animals were dosed for 2 weeks. As expected, the tumors wereresistant to BRAF/MEK inhibitor combination therapy as wellas to PI3K inhibition alone. The ERK inhibitor inhibited tumor

growth as a single agent compared with control (P < 0.0001)but targeting both MAPK and PI3K signaling using either of the2 strategies: triple combination encorafenib/binimetinib/BKM120 (P < 0.0001) or double combination VX-11e/BKM120(P < 0.0001) resulted in significantly improved tumor growthcontrol (Fig. 3A). The difference between triple and doubletherapy was not significant. This result confirmed the utility ofrationally designed MAPK/PI3K inhibitor combination therapybased on genomic and proteomic data. Tumor tissue harvestedat the end of study was assessed for pathway inhibition;BKM120 as a single agent did not decrease pAKT signaling,and this difficulty in assessing PI3K inhibition at the level ofAKT has been previously described. The combination of encor-afenib/binimetinib resulted in a modest inhibition of pRSK as adownstream target of the MAPK pathway and pS6, downstreamof MAPK and PI3K pathways. Similarly, ERK inhibition by VX-11e resulted in robust inhibition of pRSK reflected in tumorgrowth inhibition. However, only the encorafenib/binimeti-nib/BKM120 triple combination led to a complete inhibition ofboth pRSK and pS6 (Fig. 3B).

MET amplification alone is not sufficient to predicteffective therapy

We then analyzed the activation status of RTKs present on theRPPA (Fig. 3C), as these have been reported as potential resistance

Figure 2.Targeted sequencing to identifytargetable alterations. A, DNAalterations identified in 12 PDX samples.Only known somatic short-variants,deletions, and amplifications found in atleast one PDX are shown; the fulldataset of alterations in 343 exons andintrons can be found in SupplementaryFig. S3 as an excel file. PDXs sorted bynumber of concomitant alterations arein columns, genes sorted by biologicpathway are in rows. Mutations are ingreen, amplifications in red, deletions inblue, and black squares indicate thenumber of concomitant alterations.B, levels of ERK/MAPK proteinphosphorylation, measured by RPPA,as a surrogate for MAPK pathwayactivation; BRAFi mice were undercontinuous BRAF inhibitor therapy atthe time of harvest; mean of 3 biologicreplicates is shown; error bars are SD.C, fold change in AKT phosphorylationbetween untreated andBRAF inhibitor–treated tumors as a surrogate for PI3Kpathway reactivation under therapy;mean of 3 biologic replicates is shown.

Krepler et al.





Figure 3.Dual pathway inhibition controls tumor growth. A, tumor growth curves of WM3936-2 PDX. Animals were treated with vehicle control, encorafenib 20 mg/kgevery day þ binimetinib 3 mg/kg every day (EncþBin), BKM120 30 mg/kg every day, the triple combination encorafenibþbinimetinibþBKM120, VX-11e 50mg/kg twice a day (VX), or VX-11eþBKM120. Dosing was started with established tumors; all compounds were administered orally, n ¼ 10/group;error bars are SEM; � indicates P < 0.0001. B, immunoblot of tumors harvested at the end of study, 4 hours post last dose. The membrane was probed withindicated antibodies. b-Actin was included to ensure equal loading. C, levels of RTK proteins assessed by RPPA, red higher than median, green lower thanmedian, unsupervised hierarchical clustering; data is mean of 3 biologic replicates. D, relative tumor growth (final volume/days to maximum volume) ofWM3983 PDX relative to vehicle control. In two separate experiments, animals were treated with (i) vehicle control, encorafenib 20 mg/kg every day(EncþBin) 3 mg/kg every day and (ii) vehicle control, capmatinib 25 mg/kg every day (Cap). In both experiments, dosing was started with established tumors.All compounds were administered orally for 14 days, n ¼ 10/group; error bars are SEM. E, immunoblot of tumor grafts harvested after 3 days of dosing 4 hourspost last dose. The membrane was probed with indicated antibodies. b-Actin was included to ensure equal loading. þ denotes the WM3965 tumor graft tissueincluded as a positive control with elevated levels of MET and pMET. F, immunohistochemical staining for MET of melanoma lesion of the patient. Thepre-BRAF inhibitor biopsy shows a strong membrane stain for MET, but only in a subpopulation. The post-progression biopsy is negative for MET; the positivecells are macrophages (this lesion was used to establish the PDXs).






mechanisms in melanoma (30). Clusters of PDX with upregula-tion in the EGFR/HER2/HER3 family of RTKs, c-Kit upregulation,as well as MET were identified. We selected the proto-oncogeneMET (31, 32) as another promising target for second-linetherapies. The 25% (3/12) incidence of MET amplifications inthis set of BRAF inhibitor–progressed PDX was significantlyhigher than in the melanoma Cancer Genome Atlas (TCGA) at8/129 BRAF hotspot mutated, 0/17 BRAF non-hotspot, and 3/149 BRAF wild-type yielding a total of 11/299 or 3.7%. Thus,MET amplification and BRAF hotspot mutation had a signifi-cant tendency towards cooccurrence (P ¼ 0.035). In the TCGAdatabase, out of 9 MET-amplified melanomas with availableRPPA data, only 2 showed more than 2-fold increase in pMET.Indeed, in one of the three MET-amplified PDXs, an isolatedprogression of a scalp lesion, pMET was not increased com-pared with the median. This was possibly due to being part of amuch broader amplicon including EGFR and possibly theentire chromosomal arm 7, and despite being amplified 9-fold.In a study of 1,115 patients, MET amplifications were detectedin 2.5% of solid tumors (melanoma 2/61) and these patientspresented with more metastatic sites then non-MET–amplifiedtumors (26). This led us to hypothesize that MET amplifica-tions represent either preexisting or acquired mechanisms ofresistance and would predict response to the MET antagonistcapmatinib (33). Indeed, MET pY1235 (pMET) levels signifi-cantly higher than the median were confirmed in 2 of 3 PDXmodels with MET amplification (Fig. 3C). High levels of pMETwere seen in WM3965 and WM4071-1 PDX tumors, with thelatter derived from a bowel metastasis. A brain metastasis fromthe same patient and also collected post progression (WM4071-2) did not have amplified MET or increased pMET, althoughthis could relate to difference in sequencing depth and anability to make amplification calls.

To test whether genomic data alone would be sufficientto design a rational second-line therapy in a MET-amplifiedsetting, we expanded the WM3983 PDX in vivo and confirmedit to be completely resistant to encorafenib (even exhibitingincreased proliferation as compared with vehicle control)and encorafenib/binimetinib combination therapy (Fig. 3D).Importantly, while WM3983 demonstrated amplified MET, itdid not have pMET signaling (Fig. 3C). Thus, the MET inhibitorcapmatinib had no antitumor effect in this model in vivo (Fig.3D), concordant with undetectable levels of pMET in controltumors harvested at the end of study (Fig. 3E). The lack of pERKand pMEK inhibition in encorafenib/binimetinib–treated ver-sus untreated animals (Fig. 3E) supported a MAPK reactivationmechanism of resistance in line with the NRAS-mutant; BRAF-amplified genotype of this tumor. To rule out the possibilitythat the PDX had lost its pMET phenotype due to changes in themurine environment, we analyzed patient samples from beforeand after BRAF inhibitor progression. Although MET-positivesubpopulations of tumor cells were found in the pretherapylesion, these had disappeared in the progression biopsy used togenerate the PDX (Fig. 3F).

Integrating genomic and proteomic data for the design ofsecond-line combination therapies

On the basis of thesefindings, we hypothesized that integrationof genomic and protein data may provide greater informationcontent than either alone and selected the MET amplified andhigh pMET signaling PDX model WM3965 for a multi-arm

combination study centered on capmatinib (Fig. 4A). The patientwhose tumor was used to generate the PDX had received vemur-afenib in a neoadjuvant setting, but after only 3 months, devel-oped early progressive disease (PD) in the right parotid gland,which was surgically excised and used to generate the PDX. The 6-armdesign included a vehicle control group showing rapid tumorgrowth, encorafenib single agent (accelerated tumor growth, P ¼0.020) and encorafenib/binimetinib combination arms (no anti-tumor effect) confirming the aggressive and MAPK pathwayinhibitor resistant phenotype of this PDX model. Remarkably,the tumors in the other 3 dosing groups (capmatinib single agent,capmatinib/encorafenib, and capmatinib/encorafenib/binimeti-nib) all rapidly regressed after as short as 3 days of dosing. Thistrend continued untilmore than 2weeks of daily dosing, at whichpoint a separation of the growth curves became apparent, whereasthe tumor grafts on capmatinib single agent and capmatinib/encorafenib developed therapy resistance, albeit with variabletumor growth kinetics, the capmatinib/encorafenib/binimetinib(triple combination) treated tumors showed complete tumorregression in 10 of 10 animals after 21 days of dosing with noevidence of therapy resistance (Fig. 4A).

High levels of pMET protein were confirmed in vehicle treatedtumors byWestern blot, and high pERK andpAKT levels indicatedactive signaling through both MAPK and PI3K pathways, respec-tively (Fig. 4B). Furthermore, the highly proliferative phenotypecould be demonstrated by strong Ki67 staining (SupplementaryFig. S6A). In contrast, animals dosed for 3 days with capmatinibshowed complete abrogation of pMET (Fig. 4B). Importantly,capmatinib single agent did not lead to meaningful decreasesin pAKT or pERK signaling, whereas the triple combinationresulted in almost complete inhibition of pAKT and pERK. Thisobservation correlated with increased apoptosis as measured bycleaved caspase-3 staining at this early time point (SupplementaryFig. S6B).

Tumor tissue of vehicle-treated, capmatinib � encorafenib–responding and progressing animals was submitted for RPPAanalyses. Intriguingly, three distinct clusters could be observed:one containing all early responders and the other two randomlydistributed untreated and progressed tumors (Fig. 4C). The cap-matinib-responding tumor cluster was predominately defined bypMET, pEGFR, and pHER2 downregulation in association withdownregulation of their downstream effectors pMAPK, pRB,cyclin D1, pAKT, p4E-BP1, IGFBP2, and FOXM1. Interestingly,glycogen synthase (GYS) phosphorylation was inversely upregu-lated, indicating a decrease in glycogen production ability in thesetumors. The observation that the control and progression sampleswere interspersed is consistent with a loss ofMET inhibitory effectand reversal of the signaling profile back to the untreated state.Still, two distinct populations were apparent in this PDXmodel asdefined by protein expression profile, confirming the heteroge-neity found in PDX tumors. We then performed PCA comparingthe 2 clusters and found evidence for differences in metabolism,PI3K signaling, and RTKs (Supplementary Fig. S7), although thesedid not correspond with time to progression. As resistanceoccurred at roughly the same time and relatively quickly afterinitial regression, this may suggest a common (adaptive) resis-tance mechanism.

Finally, patient biopsies from the therapy-na€�ve primarylesion (Fig. 4D) and a postprogression metastasis, used togenerate the PDXs (Fig. 4E), both stained highly positive forMET, indicating that the amplification of MET might have been

Krepler et al.





preexisting in the primary melanoma and thus acted as anintrinsic mechanism of resistance leading to early relapse withonly 3 months PFS.

DiscussionPatient-derived xenografts provide sustainable models for per-

sonalized therapy. The key advantage of these models is theirsurprising biologic and genetic stability when implanted intomice, as reflected in our current study. This allowed for a com-prehensive analysis of drivers of resistance to targeted therapy andthe design of effective second-line combination therapies tailored

to each model. Although patient-specific real-time "co-clinical"trials are feasible, obstacles such as timing and regulatory issuesmay hinder progress. Instead, the development of biomarkersusing this approach might offer a higher benefit ratio. A singlepatient–based approach is further complicated by the fact thatPDX models currently do not allow for a comprehensive assess-ment of immunotherapies, which are rapidly becoming first-linetherapy for melanoma patients. Although efforts are ongoing tomove this technology into "humanized" mice, which have areconstituted human immune system, this will prove both chal-lenging and extremely costly. Our studies are therefore restrictedto direct targeting of signaling pathways in melanoma cells and

Figure 4.Integrating genomic and protein signaling results in an effective triple therapy preclinical in vivo trial. A, tumor growth curves of WM3965 PDX (MET amplified, highpMET); dosing was started withwell-established tumors expanded on BRAF inhibitor diet followed by awashout period before the start of dosing. Animals receivedeither vehicle control, encorafenib (Enc) 20 mg/kg every day, binimetinib (Bin) 3 mg/kg every day, capmatinib (Cap) 25 mg/kg every day as single agents orcombinations as indicated. All compounds were administered orally, n ¼ 10/group; error bars are SEM; � indicates P < 0.05. B, immunoblot of tumorsharvested after 3 daysof dosing and4hours post last dose. Themembranewas probedwith indicated antibodies andb-Actinwas included to ensure equal loading. C,RPPA analysis of tumors harvested either at the end of the efficacy experiments (A) or 3 days of dosing (B). Mice without palpable tumors were not included(all triple combo animals). Color coding on the lower x axis denotes the following groups: blue are control animals (vehicle), orange are the 6 animals treatedwith Cap or Enc/Cap for 3 days (early responders), and pink were treated with Cap or Enc/Cap in the efficacy cohorts and progressed after initial response(progression). Proteinswith similar expression along all sampleswere excluded. The proteins that vary across the samples over a cutoff of 0.4 SDs are shownon the yaxis. Green is downregulated; red is upregulated. Unsupervised hierarchical clustering of the log2 median-centered data is shown on the top x axis. D, METimmunohistochemical staining (brown) of FFPE patient tissue. The pre-BRAF inhibitor sample is the safety margin around the primary lesion and shows residualmelanoma nests in the dermal layer (black circle). E, lymph node metastasis of the same patient after progression (this biopsy was used to establishthe PDX).






possibly the murine stroma. These PDX models with acquiredresistance to targeted therapy provide an effective way to expandtissue for multiple methods (sequencing, RPPA, etc.) that do notface the same challenges as growing these cells on plastic (loss ofarchitecture and single cell clonality), and as we show in thisstudy, provide alternative therapeutic targets in relapsed patients.Overall, PDX can play a major role in exploring novel targets andcombination therapies based on the increasingly detailed pictureof genomic and proteomic aberrations in cancer cells.

In this study, we utilized tumor samples from patients whoreflected the wide range of responses seen in the clinic (34). Therelative uniformity in tumor grafting in vivo and invariableresistance to dosing with a BRAF inhibitor indicated that onceresistance had occurred, all tumors were capable of tumorinitiation in vivo irrespective of time to progression in patients.Next-generation sequencing of patient lesions using targetedplatforms is transforming personalized cancer therapy by unco-vering actionable genomic alterations in a majority of solidtumors such as lung cancer (35). We used the same approach touncover possible mechanisms of intrinsic and acquired resis-tance. Many of these alterations such as NRAS (24) andMAP2K1 (25, 36) mutations as well as PTEN deletion andBRAF amplification (8) were previously described to conferresistance to BRAF inhibitors. MAPK pathway–hyperactivatingalterations (BRAF or RTK amplification, NRAS and MAP2K1mutations) were found in 11 of 12 samples (most oftenmutually exclusive), and RPPA analyses confirmed active MAPKsignaling under drug pressure in the majority of PDX concor-dant with recently published results (6, 37). Interestingly, allthree brain metastasis–derived PDX had aberrant PTEN, where-as only one of the extracerebral metastases had a PTEN dele-tion, which had been previously described as a poor prognosticmarker (38). Taking into account the intrapatient heterogeneityand possible clonal selection in the generation of a PDX model,it is likely that not the whole spectrum of tumor cell populationin a patient will be represented in a mouse avatar. Thus,mapping of patients for driver mutations and their possibleconvergence on the same biologic pathways (39) can be studiedby generating multiple PDX from the same patient.

The identification ofmultiple concomitant alterations per PDXfurther emphasized the challenges of personalized therapy selec-tion in the clinic and was consistent with published reports (27).To determine whether multiple genomic aberrations integratedinto a limited number of pathways, we extended our analysisto signaling on the phospho protein level using the RPPA plat-form. RPPA relies on validated antibodies and well characterizedtargets, allowing us to confirm sustained signaling through theMAPKpathway in themajority of PDX and increased activation ofthe PI3K pathway in a smaller subset. We did not focus our studyon the discovery of novel targets as studies of patient samplesdirectly are much more suited to this approach. Rather, thestrength of our PDX platform lies in translating findings to invivo target validation.

Indeed, using either a BRAF/MEK inhibitor or ERK inhibitorcombination strategy with the pan-PI3K inhibitor BKM120proved effective in abrogating tumor growth in a MAPK and PI3Kpathway–activatedmouse avatar. This confirmedprevious reportsin cell lines with acquiredNRASmutations resistant to dabrafenib(40) and in those with increased RTK signaling leading toincreased PI3K signaling (41). Therefore, we could establish thatgenomic profiling and assessment of associated signaling path-

way activity is a viable strategy to design rational second-linecombination therapies in vivo. Also, we can postulate that thecorresponding patient likely would not have benefited fromdabrafenib/trametinib combination therapy, but that the inhibi-tion of bothMAPK and PI3Kpathwayswould have been critical inachieving a response. This combination efficacy may be the firstclear example of co-occurrence of redundant mechanisms ofresistance.

On the other hand, a MET-amplified BRAF and combinedBRAF/MEK inhibitor–resistant model did not respond to METinhibition in vivo, and we therefore concluded that amplifica-tion of MET is not sufficient to define it as a driver of resistanceand that a second readout, such as pMET protein levels wouldbe necessary. This was in line with a previously publishedstudy of a large cohort of over 1,000 patients where METamplification did not correlate with response to a MET inhib-itor and where MET protein levels were not assessed (42).Targeting MET has proved effective in MET-amplified gastriccancer using the inhibitor volitinib (43), and MET has beendescribed as a novel target for adjuvant therapy for melanoma(44). In our study, the triple combination of MET, BRAF, andMEK inhibition was exceptionally effective in vivo, with pro-found MAPK pathway inhibition. This observation might beexplained by HGF/MET–mediated RAS activation (45) leadingto BRAF dimerization and thus resistance to vemurafenib (46).We therefore propose that increased MET protein phosphor-ylation with or without MET amplification should be assessedas a biomarker of response to MET inhibitor combinationtherapies. This will be of highest priority in MAPK pathwayinhibitor–resistant patients as increases in MET RNA levelshave been described at an increased frequency in this patientcohort (47). However, as targeted genomic sequencing iscurrently the gold standard of personalized therapies, a pre-selection of patients for protein signaling analysis currently notin widespread use for clinical treatment selection will beadvantageous.

Althoughwe did not extend our study to patient treatment afterdetermining efficacious second-line therapies in the PDXmodels,a clinical trial with parallel assignment into 5 treatment armsbased on sequencing data is currently ongoing in relapsed mel-anoma patients (ClinicalTrials.gov: NCT02159066). The resultsfrom the PDX preclinical studies clearly argue that genomic andproteomic approaches shouldbe integrated to increase the successrates of personalized cancer therapies, as this approach allowed usto outline and confirm personalized medicine strategies. Thesemodels can be used to refine precision medicine approaches andto develop biomarkers of response for future clinical trials andavoid treatment failures for patients. Future studies using human-ized mouse models with reconstituted T-cell function will be ofmajor importance to integrate the findings described here into animmunotherapy landscape of melanoma.

Disclosure of Potential Conflicts of InterestG.B. Mills reports receiving commercial research grants from Adelson Med-

ical Research Foundation, AstraZeneca, Critical Outcome Technologies, KomenResearch Foundation, and Nanostring; speakers bureau honoraria from Astra-Zeneca, ISIS Pharmaceuticals, Nuevolution, and Symphogen; holds ownershipinterest (including patents) in Catena Pharmaceuticals, Myriad Genetics, PTVVentures, and Spindletop Ventures; and is a consultant/advisory boardmemberfor Adventist Health, AstraZeneca, Blend, Catena Pharmaceuticals, CriticalOutcome Technologies, HanAl Bio Korea, ImmunoMET, Millenium Pharma-ceuticals, Nuevolution, Precision Medicine, Provista Diagnostics, Signalchem

Krepler et al.





Lifesciences, and Symphogen. M. Peters holds ownership interest (includingpatents) in Novartis. No potential conflicts of interest were disclosed by theother authors.

DisclaimerThe content is solely the responsibility of the authors and does not neces-

sarily represent the official views of the NIH.

Authors' ContributionsConception and design: C. Krepler, K. Sproesser, G.B. Mills, G.C. Caponigro,M. Boehm, M. Peters, M. HerlynDevelopment of methodology: C. Krepler, K. Sproesser, X. Xu, T.A. Ahmed,M. HerlynAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): C. Krepler, M. Xiao, K. Sproesser, P.A. Brafford,B. Shannan, M. Beqiri, W. Xu, K.L. Nathanson, X. Xu, G.C. Karakousis,G.B. Mills, L.M. Schuchter, A.T. Weeraratna, M. HerlynAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): C. Krepler, P.A. Brafford, B. Shannan, Q. Liu,B. Garman, K.L. Nathanson, X. Xu, G.B. Mills, G. Caponigro, M. Boehm,M. Peters, M. HerlynWriting, review, and/or revision of themanuscript:C. Krepler, M. Xiao,W. Xu,K.L. Nathanson, X. Xu, G.C. Karakousis, G.B. Mills, G. Caponigro, M. Boehm,L.M. Schuchter, A.T. Weeraratna, M. HerlynAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): K. Sproesser, B. Shannan, M. Beqiri, W. Xu,X. Xu, P.I. Poulikakos, M. Herlyn

Study supervision: C. Krepler, W. Xu, G. Caponigro, M. Boehm, M. Peters,M. HerlynOther (performed RPPA and provided the raw data): Y. Lu

AcknowledgmentsThe authors thank the Animal and Imaging Core Facilities at the Wistar

Institute and the Tumor Tissue and Biospecimen Bank at the University ofPennsylvania Abramson Cancer Center. The authors also thank Gideon Bollagat Plexxikon for providing PLX4720.

Grant SupportSupport for shared resources utilized in this study was provided by Cancer

Center Support Grant (CCSG) P30CA010815 to the Wistar Institute. Thiswork was also supported by NIH grants PO1 CA114046, P01 CA025874,P30 CA010815, R01 CA047159, and a research grant by Novartis (toM. Herlyn), CCSG grant P30CA016672 (to G.B. Mills), RO1 CA174746-01 (to A.T. Weeraratna), and the Dr. Miriam and Sheldon G. AdelsonMedical Research Foundation.

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 July 23, 2015; revised November 16, 2015; accepted December 3,2015; published OnlineFirst December 16, 2015.

References1. Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, et al.

Improved survival with vemurafenib in melanoma with BRAF V600Emutation. N Engl J Med 2011;364:2507–16.

2. Robert C, Karaszewska B, Schachter J, Rutkowski P, Mackiewicz A, Stroia-kovski D, et al. Improved overall survival in melanoma with combineddabrafenib and trametinib. N Engl J Med 2015;372:30–9.

3. Hodi FS,O'Day SJ,McDermott DF,Weber RW, Sosman JA,Haanen JB, et al.Improved survivalwith ipilimumab in patients withmetastaticmelanoma.N Engl J Med 2010;363:711–23.

4. Wolchok JD, Kluger H, Callahan MK, Postow MA, Rizvi NA, Lesokhin AM,et al. Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med2013;369:122–33.

5. HamidO, Robert C,DaudA,Hodi FS,HwuW-J, Kefford R, et al.. Safety andtumor responses with lambrolizumab (Anti–PD-1) inmelanoma. N Engl JMed 2013;369:134–44.

6. Rizos H,Menzies AM, Pupo GM, CarlinoMS, Fung C, Hyman J, et al. BRAFinhibitor resistance mechanisms in metastatic melanoma: spectrum andclinical impact. Clin Cancer Res 2014;20:1965–77.

7. Van Allen EM, Wagle N, Sucker A, Treacy DJ, Johannessen CM, Goetz EM,et al. The genetic landscape of clinical resistance to RAF inhibition inmetastatic melanoma. Cancer Discov 2013;4:94–109.

8. Shi H, Hugo W, Kong X, Hong A, Koya RC, Moriceau G, et al. Acquiredresistance and clonal evolution in melanoma during BRAF inhibitortherapy. Cancer Discov 2013;4:80–93.

9. Villanueva J, Vultur A, Lee JT, Somasundaram R, Fukunaga-Kalabis M,Cipolla AK, et al. Acquired resistance to BRAF inhibitorsmediated by a RAFkinase switch in melanoma can be overcome by cotargeting MEK and IGF-1R/PI3K. Cancer Cell 2010;18:683–95.

10. O'Connell MP, Marchbank K, Webster MR, Valiga AA, Kaur A, Vultur A,et al. Hypoxia induces phenotypic plasticity and therapy resistance inmelanoma via the tyrosine kinase receptors ROR1 and ROR2. CancerDiscov 2013;3:1378–93.

11. Kwong LN, Davies MA. Targeted therapy for melanoma: rational combi-natorial approaches. Oncogene 2014;33:1–9.

12. Merlino G, Flaherty K, Acquavella N, Day C-PP, Aplin A, Holmen S,et al. Meeting report: The future of preclinical mouse models inmelanoma treatment is now. Pigment Cell Melanoma Res 2013;26:E8–E14.

13. Hidalgo M, Amant F, Biankin AV, Budinsk�a E, Byrne AT, Caldas C, et al.Patient-derived xenograft models: an emerging platform for translationalcancer research. Cancer Discov 2014;4:998–1013.

14. Quintana E, Shackleton M, Sabel MS, Fullen DR, Johnson TM, MorrisonSJC. Efficient tumour formation by single human melanoma cells. Nature2008;456:593–8.

15. Stebbing J, Paz K, Schwartz GK, Wexler LH, Maki R, Pollock RE, et al..Patient-derived xenografts for individualized care in advanced sarcoma.Cancer 2014;120:2006–15.

16. Einarsdottir BO, Bagge RO, Bhadury J, JespersenH,Mattsson J, Nilsson LM,et al.. Melanoma patient-derived xenografts accurately model the diseaseand develop fast enough to guide treatment decisions. Oncotarget2014;5:9609–18.

17. Quintana E, Piskounova E, Shackleton M, Weinberg D, Eskiocak U,Fullen DR, et al.. Human melanoma metastasis in NSG mice corre-lates with clinical outcome in patients. Sci Transl Med 2012;4:159ra49.

18. Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, et al..Development and validation of a clinical cancer genomic profiling testbased on massively parallel DNA sequencing. Nat Biotechnol 2013;31:1023–31.

19. Tibes R,Qiu Y, Lu Y,Hennessy B, AndreeffM,Mills GB, et al.. Reverse phaseprotein array: validation of a novel proteomic technology and utility foranalysis of primary leukemia specimens and hematopoietic stem cells.MolCancer Ther 2006;5:2512–21.

20. Akbani R, Ng PK, Werner HM, Shahmoradgoli M, Zhang F, Ju Z, et al.. Apan-cancer proteomic perspective on The Cancer Genome Atlas. NatCommun 2014;5:3887.

21. Grippo JF, Zhang W, Heinzmann D, Yang KH, Wong J, Joe AK, et al.. Aphase I, randomized, open-label study of the multiple-dose pharma-cokinetics of vemurafenib in patients with BRAF V600E mutation-positive metastatic melanoma. Cancer Chemother Pharmacol 2014;73:103–11.

22. Jang S, Atkins MB. Which drug, and when, for patients with BRAF-mutantmelanoma? Lancet Oncol 2013;14:e60-e9.

23. Bastian BC. The molecular pathology of melanoma: an integratedtaxonomy of melanocytic neoplasia. Ann Rev Pathol 2014;9:239–71.






24. Nazarian R, Shi H, Wang Q, Kong X, Koya RC, Lee H, et al.. Melanomasacquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregula-tion. Nature 2010;468:973–7.

25. Emery CM, Vijayendran KG, Zipser MC, Sawyer AM, Niu L, Kim JJ, et al..MEK1mutations confer resistance toMEK and B-RAF inhibition. Proc NatlAcad Sci U S A 2009;106:20411–6.

26. Poulikakos PI, Persaud Y, JanakiramanM, Kong X, Ng C,MoriceauG, et al..RAF inhibitor resistance is mediated by dimerization of aberrantly splicedBRAF(V600E). Nature 2011;480:387–90.

27. Mendoza MC, Er EE, Blenis J. The Ras-ERK and PI3K-mTOR path-ways: cross-talk and compensation. Trends Biochem Sci 2011;36:320–8.

28. Burger MT, Pecchi S, Wagman A, Ni ZJ, Knapp M, Hendrickson T, et al..Identification of NVP-BKM120 as a potent, selective, orally bioavailableclass I PI3 kinase inhibitor for treating cancer. ACS Med Chem Lett2011;2:774–9.

29. Aronov AM, Tang Q, Martinez-Botella G, Bemis GW, Cao J, Chen G, et al..Structure-guided design of potent and selective pyrimidylpyrrole inhibi-tors of extracellular signal-regulated kinase (ERK) using conformationalcontrol. J Med Chem 2009;52:6362–8.

30. Wilson TR, Fridlyand J, Yan Y, Penuel E, Burton L, Chan E, et al.. Wide-spread potential for growth-factor-driven resistance to anticancer kinaseinhibitors. Nature 2012;487:505–9.

31. Gentile A, Trusolino L, Comoglio P. The Met tyrosine kinase receptor indevelopment and cancer. Cancer Metastasis Rev 2008;27:85–94.

32. Boccaccio C, Comoglio PM. MET, a driver of invasive growth and cancerclonal evolution under therapeutic pressure. Cur Opin Cell Biol 2014;31C:98–105.

33. Liu X, Wang Q, Yang G, Marando C, Koblish HK, Hall LM, et al.. A novelkinase inhibitor, INCB28060, blocks c-MET-dependent signaling, neoplas-tic activities, and cross-talk with EGFR and HER-3. Clin Cancer Res2011;17:7127–38.

34. McArthur GA, Chapman PB, Robert C, Larkin J, Haanen JB, Dummer R,et al.. Safety and efficacy of vemurafenib in BRAF(V600E) and BRAF(V600K) mutation-positive melanoma (BRIM-3): extended follow-upof a phase 3, randomised, open-label study. Lancet Oncol 2014;15:323–32.

35. Drilon A, Wang L, Arcila ME, Balasubramanian S, Greenbowe JR, Ross JS,et al.. Broad, hybrid capture-based next-generation sequencing identifiesactionable genomic alterations in "driver-negative" lung adenocarcino-mas. Clin Cancer Res 2015;21:3631–9.

36. Villanueva J, Infante JR, Krepler C, Reyes-Uribe P, Samanta M, Chen HY,et al.. Concurrent MEK2 mutation and BRAF amplification conferresistance to BRAF and MEK inhibitors in melanoma. Cell Rep 2013;4:1090–9.

37. Kwong LN, Boland GM, Frederick DT, Helms TL, Akid AT, Miller JP, et al.Co-clinical assessment identifies patterns of BRAF inhibitor resistance inmelanoma. J Clin Invest 2015;125:1459–70.

38. Bucheit AD, Chen G, Siroy A, Tetzlaff M, Broaddus R, Milton D, et al.Complete loss of PTEN protein expression correlates with shorter time tobrain metastasis and survival in stage IIIB/C melanoma patients withBRAFV600 mutations. Clin Cancer Res 2014;20:5527–36.

39. Gerlinger M, Horswell S, Larkin J, Rowan AJ, Salm MP, Varela I, et al.Genomic architecture and evolution of clear cell renal cell carcinomasdefined by multiregion sequencing. Nat Genet 2014;46:225–33.

40. Greger JG, Eastman SD, Zhang V, BleamMR, Hughes AM, Smitheman KN,et al. Combinations of BRAF, MEK, and PI3K/mTOR inhibitors overcomeacquired resistance to the BRAF inhibitor GSK2118436 dabrafenib, medi-ated by NRAS or MEK mutations. Mol Cancer Ther 2012;11:909–20.

41. Villanueva J, Vultur A, Lee JT, Somasundaram R, Fukunaga-Kalabis M,Cipolla AK, et al. Acquired resistance to BRAF inhibitorsmediated by a RAFkinase switch in melanoma can be overcome by cotargeting MEK and IGF-1R/PI3K. Cancer Cell 2010;18:683–95.

42. Fontes Jardim DL, Tang C, De Melo-Gagliato D, Falchook GS, Hess KR,Janku F, et al. Analysis of 1,115 patients tested for MET amplification andtherapy response in the MD Anderson Phase I clinic. Clin Cancer Res.2014;20:6336–45.

43. Gavine PR, Ren Y, Han L, Lv J, Fan S, ZhangW, et al. Volitinib, a potent andhighly selective c-Met inhibitor, effectively blocks c-Met signaling andgrowth in c-MET amplified gastric cancer patient-derived tumor xenograftmodels. Mol Oncol 2015;9:323–33.

44. Etnyre D, Stone AL, Fong JT, Jacobs RJ, Uppada SB, Botting GM, et al.Targeting c-Met in melanoma: mechanism of resistance and efficacy ofnovel combinatorial inhibitor therapy. Cancer Biol Ther 2014;15:1129–41.

45. Graziani A, GramagliaD, dalla Zonca P, Comoglio PM.Hepatocyte growthfactor/scatter factor stimulates the Ras-guanine nucleotide exchanger. J BiolChem 1993;268:9165–8.

46. Poulikakos PI, Rosen N. Mutant BRAF melanomas–dependence andresistance. Cancer Cell 2011;19:11–5.

47. Hugo W, Shi H, Sun L, Piva M, Song C, Kong X, et al. Non-genomic andimmune evolution of melanoma acquiring MAPKi resistance. Cell2015;162:1271–85.


Krepler et al.




2016;22:1592-1602. Published OnlineFirst December 16, 2015.Clin Cancer Res Clemens Krepler, Min Xiao, Katrin Sproesser, et al. Combination TherapiesPatient-Derived Xenograft Models Identify Second-Line

Resistant−Personalized Preclinical Trials in BRAF Inhibitor

Updated version

10.1158/1078-0432.CCR-15-1762doi:

Access the most recent version of this article at:

Material

Supplementary

http://clincancerres.aacrjournals.org/content/suppl/2015/12/16/1078-0432.CCR-15-1762.DC1

Access the most recent supplemental material at:

Cited articles

http://clincancerres.aacrjournals.org/content/22/7/1592.full#ref-list-1

This article cites 47 articles, 11 of which you can access for free at:

Citing articles

http://clincancerres.aacrjournals.org/content/22/7/1592.full#related-urls

This article has been cited by 12 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

Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://clincancerres.aacrjournals.org/content/22/7/1592To request permission to re-use all or part of this article, use this link



http://clincancerres.aacrjournals.org/lookup/doi/10.1158/1078-0432.CCR-15-1762

http://clincancerres.aacrjournals.org/content/suppl/2015/12/16/1078-0432.CCR-15-1762.DC1

http://clincancerres.aacrjournals.org/content/22/7/1592.full#ref-list-1

http://clincancerres.aacrjournals.org/content/22/7/1592.full#related-urls

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

mailto:[email protected]

http://clincancerres.aacrjournals.org/content/22/7/1592


Documents

Personalized Preclinical Trials in BRAF Inhibitor ... · deﬁneeffective double and triple combination therapies, leading to complete tumor regression in all tumors of one PDX model