Tumor Stromal Architecture Can De ne the Intrinsic Tumor ... · Tumor Stromal Architecture Can...

Predictive Biomarkers and Personalized MedicineSee related article by Rini, p. 6647

Tumor Stromal Architecture Can Define the Intrinsic TumorResponse to VEGF-Targeted Therapy

Neil R. Smith1, Dawn Baker1, Matthew Farren1, Aurelien Pommier1, Ruth Swann1, Xin Wang1, Sunita Mistry1,Karen McDaid1, Jane Kendrew1, Chris Womack1, Stephen R. Wedge1,2, and Simon T. Barry1

AbstractPurpose: The aim of the study was to investigate the vascular and stromal architecture of preclinical

tumor models and patient tumor specimens from malignancies with known clinical outcomes to VEGFi

treatment, to gain insight into potential determinants of intrinsic sensitivity and resistance.

Experimental Design: The tumor stroma architecture of preclinical and clinical tumor samples were

analyzed by staining for CD31 and a-smooth muscle actin (a-SMA). Tumor models representative of each

phenotype were then tested for sensitivity to the VEGFR2-blocking antibody DC101.

Results:Human tumor typeswithhigh response rates to VEGF inhibitors (e.g., renal cell carcinoma) have

vessels distributed amongst the tumor cells (a "tumor vessel" phenotype, TV). In contrast, those malig-

nancies where single-agent responses are lower, such as non–small cell lung cancer (NSCLC), display a

complex morphology involving the encapsulation of tumor cells within stroma that also supports the

majority of vessels (a "stromal vessel" phenotype). Only 1 of 31 tumor xenograft models displayed the

stromal vessel phenotype. Tumor vessel models were sensitive to VEGFR2-blocking antibody DC101,

whereas the stromal vessel models were exclusively refractory. The tumor vessel phenotype was also

associated with a better Response Evaluation Criteria in Solid Tumors (RECIST) response to bevacizumab

þ chemotherapy in metastatic colorectal cancer (CRC).

Conclusion: The tumor stromal architecture can differentiate between human tumor types that respond

to a VEGF signaling inhibitor as single-agent therapy. In addition to reconciling the clinical experiencewith

these agents versus their broad activity in preclinical models, these findings may help to select solid tumor

types with intrinsic sensitivity to a VEGFi or other vascular-directed therapies. Clin Cancer Res; 19(24);

6943–56. �2013 AACR.

IntroductionThe early promise of agents that target the VEGF signaling

axis has failed to translate widely in the clinic. AlthoughVEGF signaling plays a pivotal role in angiogenesis, andsome patients receive benefit, not all patients are responsiveto VEGF inhibitor (VEGFi) therapy, and those who dorespond will eventually become refractory to treatment(1, 2). The expectation that the use of VEGFi therapy wouldtransform the way in which many solid human tumors aremanaged clinically was based largely upon the broad effi-

cacy of these agents in a variety of preclinical tumor modelsin vivo (3, 4). Given this discrepancy, further insight intopotential determinants of VEGFi efficacy is warranted.

The discordance between preclinical and clinical respo-nses and the initial lack of markers identifying intrinsicresistance to a VEGFi has been a significant focus of research.Additional studies in preclinical models have revealed anumber of putativemechanisms thatmay contribute tobothintrinsic and acquired VEGFi resistance. One of the firstmechanisms proposed as mediating resistance to VEGF-Aneutralization was the presence or recruitment of inflam-matory infiltrate, specifically CD11b, GR-1–positive cellsderived from the bone marrow, in response to upregulationof tumor-derived factors (5, 6). A complementary resistancemechanism is the expression of factors that drive alternativeangiogenic pathways such as high expression of the potentangiogenic stimulus FGF-2 (7). Structural features of thevessel have also been shown to influence the response toVEGF signaling inhibitors, withmature vessels supported bypericytes or myofibroblast-like cells being less sensitive toVEGFi treatment. Furthermore, models of acquired resis-tance to VEGFi are characterized by the recruitment ofpericytes, possibly through an elevated EGFR signalingresponse (8), or platelet-derived growth factor (PDGF)-

Authors' Affiliations: 1Oncology Innovative Medicines, AstraZeneca,Alderley Park, Macclesfield, Cheshire; and 2Northern Institute for CancerResearch, Newcastle University, Newcastle upon Tyne, United Kingdom

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

Corresponding Authors: Neil R. Smith, Oncology iMed, AstraZeneca,Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG, UnitedKingdom. Phone: 440-1625233731; Fax: 440-1625510097; E-mail:[email protected]; and Simon T. Barry, Oncology iMed,AstraZeneca, Mereside, Alderley Park, Macclesfield, Cheshire SK104TG, United Kingdom. Phone: 440-1625513350; Fax: 440-1625519749;E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-13-1637

�2013 American Association for Cancer Research.

ClinicalCancer

Research

www.aacrjournals.org 6943

on June 14, 2020. © 2013 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst September 12, 2013; DOI: 10.1158/1078-0432.CCR-13-1637

http://clincancerres.aacrjournals.org/

mediated signals (9–11). Determining the potential clinicalrelevance of such resistance mechanisms requires a closerexamination of the preclinical models to determine whichaspects are representative of human cancer.

Wehavepreviously studied thediversity in the angiogenicresponse represented by a panel of preclinical tumor xeno-graft models and found that while the human tumor cellsmay showdifferent expression of angiogenic genes, the hostresponse is similar between models (12). To build on thisstudy, we have profiled a number of different humantumors to examine the histologic relationship betweentumor and stroma, to determine whether these features arepresent within a panel of histologically diverse humantumor xenografts. We found that based on the tumorstromal architecture human disease could be broadly sub-divided into 2 phenotypes that have different sensitivity toVEGFi therapy as a single agent. The significance of thephenotype is explored in the context of both preclinicalmodeling and clinical samples.

Materials and MethodsHuman and xenograft tumor tissues

Formalin-fixed, paraffin-embedded (FFPE) human pri-mary cancer resection blocks, both whole and formattedinto tumor microarrays (TMA), were sourced underapproved legal contract from commercial tissue suppliers,Asterand, Cytomyx, and TriStar Technology Group andWales Cancer Bank. Appropriate consents, licensing, andethical approval were obtained for this research. The suit-ability of each specimen for immunohistochemical (IHC)analyses was determined by pathology assessment of tissuemorphology and preservation [hematoxylin and eosin(H&E)] and the general extent of antigen preservation (panp-Tyr immunostains). Tumor xenograft tissue was derived

fromexperiments carried out as described (13)with licensesissued under the UK Animals (Scientific Procedures) Act1986.

DC101 tumor growth studiesCalu-6 and Calu-3 human lung tumor xenografts estab-

lished in nude and severe combined immunodeficient(SCID) female mice, respectively. HT-29 and SW620 tumors were established in nude mice. Once tumors reached amean volume of about 0.2 to 0.3 cm3 (Calu-3), mice werethen intraperitoneally injected twice weekly with either 15mg/kg of DC101 (Cell Essentials Inc.) or an isotype controlantibody for the times indicated. DC101 is a monoclonalneutralizing antibody raised to murine VEGF receptor-2(flk-1) that was chosen on the basis of its specificity as aVEGFi. Following treatment, tumors from each group wereexcised and divided; one half being snap frozen in liquidnitrogen and stored at �80�C until required, and the otherfixed in neutral-buffered formalin for 24 hours.

Histopathologic stainingThe following antibodies were used in IHC and immu-

nofluorescent (IF) analyses: rabbit anti-mouse CD31(AstraZeneca, CHG-CD31-P1; ref. 13); mouse anti-human a-smooth muscle actin (a-SMA; Sigma, 1A4);rabbit anti-human PDGFRb (Epitomics 1469-1); mouseanti-human CD68 (Dako, M0876); mouse anti-humanNeutrophil Elastase (Dako, M0752); rabbit anti-humanE-cadherin (Cell Signaling Technology, 3195); andmouse anti-human vimentin (Dako, M0725), rat anti-mouse Gr-1 (BD Pharmingen, 550291), rat anti-mouseF4/80 (Serotec, MACP497), and mouse anti-human Ki67(Dako, M7240). Tissues were sectioned onto glass slides,dewaxed, and rehydrated. For both IHC and IF, all incu-bations were conducted at room temperature and TBScontaining 0.05% Tween (TBST) used for washes. Antigenretrieval was conducted in pH 6 retrieval buffer (S1699,Dako) at 110�C for 5 minutes in an RHS-1 microwavevacuum processor (Milestone), then endogenous biotin(Vector, SP-2002, neutrophil elastase, E-cadherin, Gr-1and F4/80 only), peroxidase activity (3% hydrogen per-oxide for 10 minutes), and nonspecific binding sites(Dako, X0909) blocked.

For single marker analyses, antibodies raised to CD31,a-SMA, PDGFRb, CD68, neutrophil elastase, E-cadherin,vimentin, Gr-1, F4/80, and Ki67 were diluted, 1:400,1:1,000, 1:2,000,1:200, 1:200, 1:100, 1:100, 1:1,000,1:100, and 1:100, respectively, in antibody diluent (Dako,S0809) and applied to sections for 1 hour. Mouse Envisionsecondary (Dako, K4007) for a-SMA, vimentin, CD68, andKi67, rabbit Envision secondary (Dako, K4003) for CD31,biotinylated rabbit anti-mouse IgG (Dako, E0464) for neu-trophil elastase, goat anti-rabbit IgG (Dako, E0432) forPDGFRb, and E-cadherin or rabbit anti-rat IgG (Dako,E0488) for Gr-1 and F4/80 were added for 30 minutes. Forneutrophil elastase, vimentin, Gr-1, and F4/80, VectastainElite ABC solution (Vector, PK-6100), diluted as instructedin kit, was added for 30minutes. Sections were washed and

Translational RelevanceVEGF pathway inhibitors (VEGFi) are approved for the

treatment of advanced cancer. Although these deliversomeclinical benefit, questions remainas towhich tumortypes are most sensitive, which patients receive greatestbenefit, and why the broad preclinical activity seen withthese agents did not translate to the clinic. Here, wedifferentiate human tumor types based on their stromalarchitecture into tumor vessel or stromal vessel pheno-types. The tumor vessel phenotype is associated withtumor types (renal and thyroid cancer) with better sin-gle-agent clinical response to a VEGFi than the stromalvessel phenotype (colorectal and non–small cell lungcancers). The tumor vessel phenotype is also evident inmany human tumor xenograft models commonly usedto evaluate efficacy, whereas a model displaying thestromal vessel phenotype was found to be refractory totreatment with the VEGFR2-blocking antibody DC101.These data suggest an association between stromal archi-tecture and intrinsic tumor sensitivity to VEGFi therapy.

Smith et al.

Clin Cancer Res; 19(24) December 15, 2013 Clinical Cancer Research6944




developed in diaminobenzidine for 10 minutes (Dako,K3466) and then counterstained with Carazzi’s hematoxy-lin. Appropriate no primary antibody and isotype controlswere conducted for each antibody. Chromogenic CD31-a-SMA costain was conducted using the Envision G/2doublestain System (Dako, K5361) following the manu-facturer’s recommendations with antigen retrieval and pri-mary antibody dilution as described above. CD31-a-SMAco-IF was conducted as previously described (13).

Pathology scoring and computer-assisted imageanalysisChromogenic or fluorescent images were captured using

the �20 objective of either an Aperio image scan (LeicaBiosystems) or Pannoramic SCAN (3DHISTECH), respec-tively. Scanned images were scored by a trained pathologistand 2 scientists using simple subjective reporting proce-dures. Both human and xenograft tumors were scored forstromal vessel and tumor vessel (tumor vessel) phenotypesusing H&E or CD31-a-SMA stains. The tumor vessel phe-notype was defined as a tumor structure where vessels areembedded throughout the tumor cell mass and the stromalvessel phenotype classed as tumor cell nests surrounded bywell-developed stromal structures which contain the major-ity of the vessels. A tumorwas classified as either tumor vesselor stromal vessel based on the predominant phenotype(cutoff of >60% tumor area), whereas a tumor composedof 40% to 60% of both phenotypes was scored as interme-diate. These criteria were prospectively defined by an expertpathologist to clearly differentiate between tumors that werepredominantly tumor vessel (>60%) or stromal vessel(>60%) and those that were intermediate (40%–60%) anddifficult to classify as one or the other phenotype. On thebasis of CD68 marker immunostaining, macrophage infil-trate into the tumor compartment was classified as negative,low (>0but�1%CD68þ cells: tumor cells) ormedium-high(>1% CD68þ cells: tumor cells). Tumor epithelial-to-mes-enchymal transition (EMT) statuswas scoredusing epithelial(E-cadherin) and mesenchymal (vimentin) markers todefine epithelial (E-cadherinþvimentin�), mesenchymal(E-cadherin�vimentinþ), or intermediate phenotypes (E-cadherinþvimentinþ).Computer-assisted image analysis was conducted on

digitally acquired chromogenic images. For images ofhuman tumors immunostained for CD31-a-SMA, thetumor compartment, including associated stroma, wasselected by hand using the Aperio Image Viewer Software(Leica Biosystems). Downstream image analyses of theannotated areas were conducted using Aperio image anal-ysis software (Leica Biosystems).Microvessel density (MVD,number of vessels per mm2 viable tumor), using CD31 as amarker, was determined using the Aperio microvessel anal-ysis algorithm (Leica Biosystems). Myofibroblast levelsbased on a-SMA were determined using the Aperio colordeconvolution algorithm tomeasure percentage ofa-SMA–positive pixels per total number of pixels. Genie (LeicaBiosystems), a pattern recognition software tool, wastrained to segment stained images of the Calu-3 xenograft

into tumor, stroma, and necrotic regions and used todetermine the percentage of each compartment per tumor.This Genie classifier was combined with the appropriateAperio image analysis algorithm to analyze biomarkerparameters in stromal- or tumor-classified regions ofCalu-3 tumor s. Calu-3 stromal or tumor MVD, based onCD31 immunostain, was determined using themicrovesselanalysis algorithm. Stromal myofibroblast content (a-SMAand PDGFRb) or macrophage (F4/80) and neutrophil (Gr-1) infiltrate into the stromaor tumorwas analyzed using thecolor deconvolution algorithm (Leica Biosystems) to deter-mine the percentage of positive pixels per total number ofpixels. Tumor proliferative index [number of Ki67-positivetumor cells/total number of tumor cells (hematoxylin-pos-itive nuclei)] and tumor cell density [total number of tumorcells (hematoxylin-positive nuclei)/tumor area] were deter-mined using a nuclear algorithm (Leica Biosystems). Tomeasure tumor nest size, the diameter of 44 to 220 distinctnests were measured for each a-SMA–immunostainedCalu-3 section (to highlight hematoxylin-positive tumornests) using the Aperio image viewer measuring tool (LeicaBiosystems). Biomarker data were analyzed using the Stu-dent 2-tailed t test to determine statistical significancebetween treatment groups.

TaqMan fluidigm gene expression profilingRNAwas isolated from30 to50mg frozen tumorusing an

RNeasy Lipid Tissue Mini Kit (QIAGEN, 74104), accordingto manufacturer’s protocol. On-column DNase digestionwas conducted using the RNase-free DNase Kit (QIAGEN,79254). RNA concentration was measured using the Nano-Drop ND1000 (Thermo Fisher Scientific). Human- andmouse-specific assays were designed and supplied byApplied Biosystems, whereas eukaryotic 18S rRNAwas usedas the endogenous control (Supplementary Table S4). TotalRNA (50 ng) was converted into cDNA using the HighCapacity cDNA Reverse Transcription Kit (Applied Biosys-tems, 4368814) in final volume of 20 mL, according to themanufacturer’s instruction. cDNA (1.25 ml) was pre-ampli-fied using a pool of TaqMan primers at a final dilution of 1in 100 and Pre-amplification Master Mix (Applied Biosys-tems, 4391128) in a final volume of 5 mL. Samples werediluted 1 in 5 with 1� TE and stored at�20�C. Sample andassay preparation for 48.48 dynamic arrays was conductedaccording to the manufacturer’s instruction (Fluidigm).

Data were collected and analyzed using the FluidigmReal-Time PCR Analysis 2.1.1 software (v.2.1.3). Species-specific normalization of the expression data to 18S rRNAwas conducted as reported (12). Gene expression valueswere calculatedusing the comparativeCT (�DCT)methodaspreviously described in User Bulletin #2 ABI PRISM 7700Sequence Detection System 10/2001, using the corrected18S rRNA Ct values for normalization of the tumor tran-script and the original values for the stroma. To determinegenes altered byDC101 treatment student anddifferentiallyexpressed between Calu-3 versus the others models, t testsand fold change were calculated with significantly alteredgenes identified by having a P < 0.05 and a fold change >1.5.

Tumor Stromal Phenotypes Define VEGF Sensitivity

www.aacrjournals.org Clin Cancer Res; 19(24) December 15, 2013 6945




ResultsTumor stromal architecture defines human tumortypes that respond to VEGF signaling inhibitors asmonotherapy

To assess the tumor stromal architecture of different solidhuman malignances, we used a number of tumor TMAscomprising renal cell carcinoma (RCC), glioblastoma mul-tiforme (GBM), head and neck squamous cell carcinoma

(HNSCC), non–small cell lung carcinoma (NSCLC), ovar-ian (OvC), thyroid (ThC) subtypes, colorectal (CRC), pros-tate (PC), and breast (BC) cancers (Fig. 1; SupplementaryTable S2). For the purposes of this analysis, we have viewedresponsive disease as high incidence of tumor response byResponse Evaluation Criteria in Solid Tumors (RECIST)assessment (tumor shrinkage) and/or a large increase inprogression free survival on long-term therapy reported

100μM

A

100μM

100μM

HCC

CD31ααSMA

DAPI

ThC

100 μm

CD31αSMA

DAPI

RCC

100 μm

CD31αSMADAPI

100 μm

200 μm

C

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

RC

C

GB

M

OvC

HC

C

ThC

HN

SC

C

CR

C

NS

CL

C

PC

BC

SVTV

TV

SV

Phenoty

pes (

% r

atio)

SV

TV

200 μm

B

NSCLC

100 μm

CD31αSMA

DAPI

PC

100 μm

CD31αSMA

DAPI

100 μm

CRC

CD31αSMA

DAPI200 μm

200 μm

200 μm

200 μm

TV SV

n =

44

n =

49

n =

61

n =

26

n =

49

n =

37

n =

30

n =

36

n =

56

n =

50

Figure 1. Two dominant phenotypesbased on tumor stromalarchitecture stratify tumor types.Tumor vessel phenotype (A; a tumorstructure where vessels areembedded throughout the tumorcell mass) and stromal vesselphenotype (B; a tumor structuredominated by a pattern of tumor cellnests surrounded by well-developed stromal structures whichcontain the majority of the vessels).Also shown are IF-stained FFPEhuman tumors [CD31-AF488(green), a-SMA -AF555 (red), andDAPI counterstain (blue)], indicativeof tumor vessel (RCC, GBM, HCC,ThC, OvC, andHNSCC) and stromalvessel (CRC, NSCLC, PC, and BC).C, percentage ratios of tumorvessel: stromal vessel phenotypesfor RCC, GBM, OvC, HCC, ThC,HNSCC,CRC,NSCLC, PC, andBC.Phenotypes were scored fromCD31-a-SMAorH&E-stainedTMAs(1 TMA per tumor type, number ofpatient samples per tumor type areshown).

Smith et al.





with different therapies targeting VEGF signaling. RCC (14)and GBM (15, 16) are diseases we have taken as represen-tative of cancers known to respond to VEGFi as a single-agent therapy (Supplementary Table S1). CRC (17) andNSCLC (18) are diseases where VEGF inhibitors show littlesingle-agent responses andhence are trialed in combinationwith cytotoxic therapy. Tumors were screened by IF tovisualize the relative distribution ofmyofibroblast-like cells(a-SMA), blood vessels (CD31), and tumor cells [40,6-diamidino-2-phenylindole (DAPI)]. This revealed 2 dom-inant morphologies: (i) a "tumor vessel" phenotype whichdescribes a tumor structurewith vessels embedded through-out the tumor cell mass represented in RCCs, GBMs, andhepatocellular carcinoma (HCC; Fig. 1A, SupplementaryFig. S1 and Supplementary Table S2); (ii) a "stromal-vessel" phenotype in which the tumor structure is dom-inated by a pattern of tumor cell nests surrounded by well-developed stromal structures containing the majority ofthe vessels (Fig. 1B), as evident in CRCs, NSCLCs, and PC(Fig. 1B, Supplementary Fig. S1 and Supplementary TableS2). Using this classification, cores from multiple tumortypes were scored for either a tumor vessel or stromalvessel phenotype (Fig. 1C). The tumor vessel phenotypewas observed in RCCs, GBMs, OvC, HCC, ThC, and headand neck squamous cell carcinoma (HNSCC). The stro-mal vessel phenotype was dominant in CRC, NSCLC, PCs,and BCs.As the phenotype ratios for each tumor type were derived

initially from TMA cores, full tumor sections were alsoclassified for each phenotype to ensure the results wererepresentative of larger diagnostic tumor samples [RCCs,HCCs and PC (n ¼ 10); GBM, ThC, CRC, and BC (n ¼ 9);and NSCLC (n ¼ 19); Fig. 2A, Supplementary Fig. S2 andSupplementary Table S3]. Where sections exhibit regions ofboth phenotypes, the predominant phenotype (>60%) wasscored (Supplementary Fig. S2a). The frequency of thephenotypes representing diagnostic specimens agreed withthe original TMA dataset (Supplementary Fig. S2b and S2c).These data establish that the tumor stromal architectureof the tumor can be used to cluster human tumor typesinto 2 groups. Significantly, those clinical tumor typesassociated with response to a VEGFi alone [RCC (14),ThC (19, 20), GBN (15, 16)] were dominated by a tumorvessel phenotype. In contrast, tumors where a VEGFi hasshown modest activity in combination [CRC (17), PC(21), NSCLC (18), BC (22)] were dominated by thestromal vessel phenotype.Additional biomarker analyses were conducted on the

diagnostic tumorpanel (n¼32 tumor vessel,n¼35 stromalvessel) to further examine the properties of the two phe-notypes (Fig. 2B–D). Although immature and mature peri-cyte covered vessels (CD31þ endothelial cells with tightlyassociated a-SMAþ cells) were associated with both phe-notypes, tumor-embedded vasculature of the tumor vesselphenotype tended to be pericyte-free, whereas stromalvessels in the stromal vessel phenotype were often pericytecovered or associated with a-SMAþ fibroblasts (Fig. 2B).Computer-assisted image analysis ofCD31 immunostained

lesions revealed that the tumor vascular density in thecombined tumor and stromal compartments was higherin tumor vessel (mean � SEM ¼ 105.3 � 21.3) than instromal vessel (47.7 � 7.5; Fig. 2C and Supplementary Fig.S3b). a-SMA analysis showed myofibroblasts were greaterin stromal vessel (mean� SEM¼ 10.0� 1.8) than in tumorvessel (1.4 � 0.3) phenotype tumors (Fig. 2C and Supple-mentary Fig. S3b) tumors. EMT status was determined byimmunostaining serial sections for E-cadherin and vimen-tin (Fig. 2D and Supplementary Fig. S3b). Tumor vesseltumors were generally more mesenchymal (E-cadherin�/vimentinþ) than stromal vessel tumors (E-cadherinþ/vimentin�). Qualitative histologic assessment of CD68-immunostained tumors from the panel showed that thetumor vessel phenotype has higher levels of macrophagetumor infiltrate (Supplementary Fig. S3b and S3c).

Neuroendocrine pancreatic cancer has recently beenshown to respond to the VEGFi sunitinib (23), whereaspancreatic adenocarcinomahas proven comparatively resis-tant to such approaches (24–26). Comparing availablesamples of neuroendocrine pancreatic cancer with pancre-atic adenocarcinoma revealed them to represent tumorvessel and stromal vessel phenotypes, respectively (Supple-mentary Fig. S3a), consistent with our hypothesis on VEGFisensitivity.

The tumor vessel phenotype is common to tumorxenograft models

To evaluate the relevance of the 2 phenotypes in preclin-ical models, a panel of human tumor xenografts grownsubcutaneously and representing a broad range of differenttumor types of origin [i.e., lung (8), colon (5), breast (4),prostate (3), brain (2), stomach (1), ovary (1), pancreas (1),skin (1), pharynx (1), uterus (1), vulva (1), and blood (1)tumor and fibrosarcoma (1) cell lines] were classified for the2 phenotypes based on the approach used for humantumor s (Fig. 3). The majority (30 of 31) exhibited atumor vessel phenotype (Fig. 3A and B). Only Calu-3(lung adenocarcinoma) had a stromal vessel phenotype(Fig. 3A and C). These data indicate that the tumor vesselphenotype is common to subcutaneously grown tumorxenograft models.

Intrinsic gene expression differences between stromalvessel and tumor vessel xenograft tumors

Gene expression analysis of human transcripts was usedto investigate the relationship between Calu-3, Calu-6,SW620, and HT-29 tumor s. The difference in expressionof a range of 180 genes associated with angiogenesis,inflammation, and invasive growth was determined (Fig.3DandE). This gene set differentiatedCalu-3 from theothermodels; genes showing the highest differential expressionin Calu-3 were MMP7, LPAR3, CT55, IL1RN, and SPP1(osteopontin). Compared toothermodels analyzed,Calu-3xenograft tumor s were enriched in genes associated withrecruitment of the stromal cells, in particular fibroblastgrowth factor (FGF) and PDGF ligands (Fig. 3D). To vali-date that Calu-3 tumors showed a different expression






profile, they were also compared to a broader panel oftumor xenograft models, again the same genes were over-expressed relative to other models. This expression analysissupports the conclusion thatCalu-3 tumor cells exhibit highexpression of a number of transcripts thatmay contribute totheir phenotype.

The tumor vessel phenotype has greater sensitivity toVEGFR2 inhibitor, DC101, than the stromal vesselphenotype, in vivo

To determine whether the tumor stromal architecturecould influence tumor response to VEGFi, we used themurine VEGFR2-blocking antibody, DC101 to assess the

A

Phenoty

pe (

% r

atio)

Intermediate

SV

TV

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100% n = 1

0n

= 9

n = 1

0n

= 9

n = 9

n = 1

9n

= 1

0n

= 9

RC

C

GB

M

HC

C

Th

C

CR

C

NS

CL

C

PC

BC

SVTV

MV

D

(num

ber

vessels

per

mm

2)

Intermediate

SV

TV

α-S

MA

po

sitiv

ity (

%)

RCC

ThC

CRC

NSCLC

CD31ααSMADAPI

CD31αSMADAPI

50 μm 50 μm

50 μm 50 μm

B

C D

EM

T s

tatu

s (

% r

atio

) Indeterminable

Epithelial

Mesenchymal

n = 3

8

n = 4

1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

TVSV

Phenotype

500

450

400

350

300

250

200

150

100

50

0

40

35

30

25

20

15

10

5

0

RC

C

GB

M

HC

C

Th

C

CR

C

NS

CL

C (

Ad

en

o)

NS

CL

C (

SC

C)

PC

BC

RC

C

GB

M

HC

C

Th

C

CR

C

NS

CL

C (

Ad

en

o)

NS

CL

C (

SC

C)

PC

BC

Figure 2. Biomarker analyses of the 2 phenotypes in whole tumor samples. A, percentage ratios of tumor vessel:stromal vessel:intermediate phenotypes forRCC, GBM, HCC, ThC, HNSCC, CRC, PC, and BC. Phenotypes were scored from CD31-a-SMA costained whole FFPE tumor sections (number ofpatient samplesper tumor type are shown). B, pericyte coverageof vessels associatedwith tumor vessel (RCCandThC) and stromal vessel (CRCandNSCLC)phenotypes, white or gray arrows indicate naked and pericyte covered vessels, respectively. Samples were analyzed for: (C) MVD (number of CD31þ vesselsper mm2) and myofibroblast content (percentage number of a-SMA þ pixels/total number pixels) and (D) EMT status [percentage ratio epithelial(E-cadherinþvimentin�):intermediate (E-cadherinþvimentinþ):mesenchymal (E-cadherin�vimentinþ)].

Smith et al.





A Tumour xenograft models (n = 31)

B

U11

8MG

U87

MG

A278

0

Col

o205

HC

T116

HT2

9

LoVo

SW62

0

A549

Cal

u3

Cal

u6

HX1

47

NC

I H19

75

NC

IH46

0

NC

IH52

6

PC9

CW

R22

Rv1

Du1

45

PC3

BT47

4cl

MC

F-7

MD

AMB2

31

ZR-7

5-1

A375

A431

FaD

u

HL-

60

HPA

C

HT1

080

MES

-SA

NC

IN87

TV SVC

D

TVSV

Calu-6dd

CT

dd

CT

HT29 SW620 Calu-3

Calu-6 HT29 SW620 Calu-3

200 μm 200 μm

MMP7

LPAR3

CTSS

lL1RN

SPP1

CCL5

FGF2

lL1R1

PDGFA

PDGFD

CDH1

CDH3

CXCL1

CX3CL1

CXCR7

PLAT

NOTCH3

PLAU

SERPINB5

PDGFC

THBS1

TIMP3

MGP

IL8

MST1R

LPAR1

CCL22

BMP2

BMPR1B

CTSL1

CTSD

ID4

SGK2

IL6R

EDG3

IL6R

CTSL2

IL1B

IL1A

8.27.57.47.26.86.86.56.56.05.95.65.25.24.94.74.64.64.34.03.93.73.73.63.53.43.33.33.02.52.52.42.22.21.91.91.91.81.81.7

−9.1

−8.9

−6.6

−6.3

−6.1

−6.0

−5.7

−5.2

−4.5

−4.2

−3.9

−3.7

−3.2

−3.1

−3.0

−3.0

−2.9

−2.7

−2.6

−2.6

−2.6

−2.5

−2.4

−2.3

−2.0

−1.9

−1.7

−1.7

−1.7

−1.6

PGF

GAS6

MMP2

FGF19

DLL4

ENG

S100A4

ACVR1B

HEY1

MERTK

KISS1

MMP13

TYRO3

ACVR1C

ENPP2

F2R

SERPINE1

ANGPT1

LPAR1

EFNB2

NOTCH1

BMPR1A

FGF1

NRP2

SPHK2

FGF18

CST3

ITGA5

FGF17

BMP4

E

Figure 3. Prevalence and intrinsic gene expression differences between the 2 phenotypes in tumor xenograft models. A, classification of 31 subcutaneouslyimplanted human tumor xenograft models for tumor vessel and stromal vessel phenotypes. Data were derived from the scoring of CD31-a-SMA–immunostained TMAs (4FFPE tumors permodel, 3 coresper tumor). B andC, diagrams andfluorescent immunostained [CD31-AF488 (green),a-SMA -AF555(red), and DAPI counterstain (blue)] images representing preclinical xenograft. B, tumor vessel (Calu-6—low myofibroblast content) and (C) stromalvessel (Calu-3). D and E, transcript profiling of tumor vessel (Calu-6, HT-29, and SW620) and stromal vessel (Calu-3) tumor xenograft models. Geneswith high (D) and low (E) expression levels in Calu-3 compared to Calu-6, HT-29 and SW620 are presented. ddCT ¼ [(average dCT Calu-3) � (average dCT

(Calu-6 þ SW620 þ HT-29)].






effects of pruning neovasculature on tumor growth in tumorvessel (Calu-6, HT-29 and SW620) compared to stromalvessel (Calu-3) tumor xenograft models. DC101 was dosedintraperitoneally at 15 mg/kg twice weekly. Calu-6, HT-29,and SW620 xenografts exhibited the classic tumor growthresponse to VEGF signaling inhibitors (Fig. 4A) seen inmultiple models (27–29). In contrast, Calu-3 tumors exhib-ited a poor response, withDC101 having no effect on tumorgrowth (experiment 1) or inducing a small initial reductionin growth (0–3 days, experiment 2) followed by a rapidreturn to a growth rate similar to the controls (Fig. 4B).

The gross vascular response to DC101 is similarbetween tumor vessel and stromal vessel tumorxenografts

To compare the total vascular response of tumor vesseland stromal vessel models to DC101, MVD analysis wasconducted on CD31-immunostained control and DC101-treated samples from the Calu-6 and Calu-3 (replicates 1and 2) growth studies. The effect of DC101 on MVDreduction was similar between Calu-6 and Calu-3 models(Fig. 4C). Furthermore, species-specific reverse transcrip-tion quantitative polymerase chain reaction (RT-qPCR)

C

A

B

D

504030201000.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Geom

etr

ic m

ean t

um

our

volu

me (

cm

3 ±

SE

M)

Days of treatment

353025201510500.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Geom

etr

ic m

ean t

um

our

volu

me (

cm

3 ±

SE

M)

Days of treatment

Calu-3 (2)Calu-3 (1)

SV

28242016128400.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Ge

om

etr

ic m

ean

tum

ou

r

vo

lum

e (

cm

3 ±

SE

M)

Days of treatment

2016128400.0

0.5

1.0

1.5

2.0

Ge

om

etr

ic m

ea

n t

um

our

volu

me

(cm

3±

SE

M)

Days of treatment

16128400.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Geom

etr

ic m

ean t

um

our

volu

me (

cm

3 ±

SE

M)

Days of treatment

TV

SW-620 HT-29 Calu-6

Calu-3 (2)

TV SV

MV

D(n

um

ber

vessels

per

mm

2)

P = 0.0001

Calu-6 Calu-3 (1)

0

50

100

150

200

250

300

MV

D(n

um

ber

vessels

per

mm

2)

MV

D(n

um

ber

vessels

per

mm

2)

P = 0.004 P = 0.03

0

10

20

30

40

50

0

50

100

150

200

250

300

Con

trol

DC10

1 tre

ated

Con

trol

DC10

1 tre

ated

Con

trol

DC10

1 tre

ated

Calu-3 (1)Calu-6

Humanfold

changeGene P value

Mousefold

changeGene P value

Humanfold

changeGene P value

Mousefold

changeGene P value

Con

trol

DC10

1 tre

ated

Con

trol

DC10

1 tre

ated

CDH8 5.17 1.77

1.611.531.51

−1.69

0.0320.0090.037

0.0360.033

0.0050.0290.0160.028

0.0340.0140.010

2.572.381.701.68

−1.98−4.06

SGK2LPAR1GAS6LPAR1SPP1CSTA

PLATVEGFABMP2MST1FGF2

Notch3

Cxcl2 7.01 0.0060.0270.0090.0100.0050.004

0.0030.0080.0020.0010.001

2.552.111.95

−1.63−1.64−1.66−1.80−1.92−2.02−3.32

Mmp9

ll1bltgamTie1Flt4

PdgfbGpr116Efnb2Kdr

Dll4

Mmp9Cdh3Flt1

Mmp13Gpr116

Tie1

Efnb2Flt4

Angpt2

Dll4Cdh8

−1.50 0.0340.0200.0340.0050.0310.0300.0210.0420.001

0.0210.0000.009

−1.52−1.65−1.70

−1.72−1.76−1.85−1.88−1.91−2.03−2.77

−4.03

Figure 4. The tumor vesselphenotype is more sensitive toVEGFR2 signaling inhibitor,DC101, than the stromal vesselphenotype in vivo. A and B,geometric mean tumor volumegrowth curves (cm3) for DC101 (15mg/kg twice weekly; red) and IgG1

(control; blue) treated arms of (A)Calu-6, HT-29, SW620 and (B)Calu-3 (growth curve 1, growthcurve 2) tumor xenograft studies.Geometric mean�SEMare shownfor growth curves. C,MVD (numberof CD31þ vessels per mm2) of IgG1

control and DC101-treated Calu-6and Calu-3 tumor studies (growthcurves 1 and 2). D, significant genechanges induced in the tumor(human) and stromal (stromal)vascular modulation and invasiontranscript profile on DC101treatment of Calu-6 and Calu-3(growth curve 1) xenograft tumors.Tumor transcripts are normalizedto human 18S, whereas stromaltranscripts are normalized to total18S. Only significant gene changesare included (P > 0.05 and foldchange > 1.5), Each column is anindividual tumor and each colorgradient is scaled independentlywithin each gene.

Smith et al.





using genes associated with angiogenesis and tumor cellinvasion (Fig. 4D) revealed that human tumor gene changeswere unique to each tumor xenograft model, but a rangeof murine genes associated with endothelial cells, werereduced in both. Therefore, while there is a clear vascularresponse toDC101 in bothmodels this does not translate togrowth inhibition in the Calu-3 xenograft.

DC101 targets the stromal vasculature in a stromalvessel phenotype model, which leads to a reduction inthe stromal compartment but with negligible effect ontumor sizeTumor-embedded vessels were not detected in the Calu-3

model; however, vasculaturewas detected at a highdensity inthe stroma (Fig. 5A and B). Stromal vasculature was reducedby >50% in response to DC101 with the remaining stromalvessels exhibited a greater mean vessel area than those inuntreated Calu-3 tumors (Fig. 5A). Qualitative assessmentof CD31-a-SMA IF-stained tumors revealed the proportionof pericyte-covered stromal vessels increased in treated tu-mors (Fig. 5B). These data indicate that while treatment withDC101 reduces small immature vessels, it fails to reduce thelarger more mature vasculature within the stroma.To examine the influence of the stromal vessel pheno-

type on the response to DC101, we conducted a detailedbiomorphometric analysis of DC101-treated Calu-3xenograft tumors. In Calu-3 tumors, DC101 treatmentled to reduction in the area of the stromal compartmentand an increase in the area occupied by tumor cells withnegligible effect on tumor size (Fig. 5C). Analysis of thestromal compartment for myofibroblasts using a-SMAand PDGFRb staining revealed a significant reduction instromal myofibroblast levels (Fig. 5D). DC101 treatmentalso led to a significant increase in the mean diameter oftumor nests (from 200 to 450 mm, P ¼ 0.04); however,effects on the proliferative index and density of the tumorwere negligible at this time point (Supplementary Fig. S4aand S4c). The recruitment of inflammatory cells was alsoassessed using F4/80 (macrophages) and Gr-1 (neutro-phils). DC101 treatment resulted in a small but signifi-cant increase in the tumor macrophage content and asignificant accumulation of Gr-1–positive cells in thestroma (Supplementary Fig. S4b and S4c). In conclusion,the reduction of stromal angiogenic vasculature byDC101 in the Calu-3 model had the greatest impact onthe architecture and cellular composition of the stroma,rather than the tumor compartment, but with a negligibleeffect on the overall tumor mass.

The two phenotypes correlate with different clinicalefficacies to bevacizumab and FOLFIRI in metastaticCRCThe relationship between phenotype and RECIST

response to a combination of VEGFi and oxaliplatin-basedchemotherapy in patients with metastatic CRCs was inves-tigated. Two TMAs consisting of surgical tumor samplesfrom 56 patients with metastatic CRC (Tristar) were chro-mogenically stained for CD31-a-SMA and scored for the

tumor vessel and stromal vessel phenotypes by 2 observers.Forty-two patient samples were classified as the stromalvessel phenotype and 14 as the tumor vessel phenotype(Fig. 6). RECIST response information for FOLFIRI andbevacizumab (Avastin) treatment as first- or second-linetherapy post-surgery was available for each patient. Fisherexact test was used to determine the significant associationsbetween the phenotype and best RECIST response catego-ries [progressive disease (PD) and other RECIST outcome]during patient treatment periods (for this dataset, bestRECIST responses also had longest duration). The proba-bility of the stromal vessel phenotype grouphaving a poorerresponse (PD category) to bevacizumab and FOLFIRI thanthat of the tumor vessel phenotype group was statisti-cally significant [P¼ 0.0487; 95% confidence interval (CI),1.24–¥]. Although it is not possible to conclude that thedifferential response is a direct result of the treatment withbevacizumab alone, these data suggest that the tumor vesselphenotype appears to bemore sensitive to the combinationtherapy than the stromal vessel phenotype.

DiscussionThis study suggests that human tumor types can be

broadly categorized according to the spatial distributionof their blood vessels in relation to the tumor cells andother stromal components. Across a broad panel of humantumor types, independent of disease type, tumors largelyadopted either a tumor vessel or stromal vessel phenotype.The tumor vessel phenotype appears to be indicative oftumors that are likely to show greater single-agentresponses to anti-angiogenic drugs targeting the VEGFsignaling axis. Although we were able to separate differenttumor types using this approach, no tumor type studiedwas exclusively one type of architecture. For example,although RCC tumors are predominantly of the tumorvessel phenotype, some exhibit a stromal vessel architec-ture. Conversely, while CRC tumors are dominated by thestromal vessel phenotype, a subset exhibits the tumorvessel architecture. Heterogeneity is also evident withinindividual tumors. In particular, tumor types with a stro-mal vessel architecture have regions with tumor vesselarchitecture, whichmay representmore angiogenic regionsof the tumor. Tumor vessel vasculature is embeddedwithinthe tumor cell mass. This forms a major structural featureof the tumor, facilitating intratumoral blood flow. Incontrast, in stromal vessel tumors, the vascular supplydevelops almost exclusively in the stromal compartment,which separates the tumor cells from the vessels, poten-tially creating more mature vessels.

The tumor vessel phenotype was associated with differ-ences in the distribution of other cells, specifically thepresence of high levels of macrophages. In stromal vesseltumors, these macrophages did not commonly associatewith the tumor cells perhaps indicating a different role forthemacrophage in these tumors. A possible explanation forthese observations is that the intratumoral vasculatureobserved in the tumor vessel phenotype, but absent from






BA Calu-3 (1)S

tro

ma

l M

VD

(n

um

be

r ve

sse

ls p

er

mm

2)

Str

om

al m

icro

ve

sse

l a

rea

(μm

2p

er

ve

sse

l) P = 0.04

020406080

100120140160180200

Stromal MVA

CD

31

CD

31/α

-SM

A

C

TumourStroma

Necrotic space

Re

lative

pro

po

rtio

ns o

f xe

no

gra

ft c

om

pa

rtm

en

ts (

%)

0

10

20

30

40

50

60

70 P = 0.09

P = 0.001

P = 0.35

Calu-3 (1) Calu-3 (1)

Com

part

ment

overlay

Haem

ato

xylin

P = 0.001

D

10

20

30

40

50

60

70

80

90

100

P = 0.0005

P = 0.009

α-SMA

PDGFRβ

Calu-3 (1)

% P

ositiv

ity

Contr

ol

DC

101 t

reate

d

50 μm 50 μm

100 μm100 μm

200 μm 200 μm

200 μm

100 μm 100 μm

100 μm100 μm

200 μm

700

600

500

400

300

200

100

0

P = 0.0001

Con

trol

DC10

1 tre

ated

Con

trol

DC10

1 tre

ated

Con

trol

DC10

1 tre

ated

Con

trol

DC10

1 tre

ated

Control DC101 treated

Control DC101 treated

0

Smith et al.





the stromal vessel phenotype, would facilitate extravasationof immune infiltrate into the tumor. In contrast, in thestromal vessel, phenotype macrophages would extravasateinto the dense stroma surrounding the tumor nests whichmay impair their migration into the tumor. On the basis ofE-cadherin loss and vimentin gain, the tumor vessel phe-notype was more associated with tumor cells of a mesen-chymal phenotype. It is conceivable that the tissue of originor subsequent progression of tumor to a more mesenchy-mal phenotype could influence reciprocal signalingbetween the tumor and the tumor microenvironment,thereby influencing the extent of stromal recruitment andits subsequent architecture. However, we do not currentlyhave a mechanism to explain this.While it remains challenging to acquire human tumor

samples with associated outcome data following treatmentwith VEGF signaling inhibitors, we were able to obtainTMAs of samples from patients with CRCs treated withbevacizumab þ FOLFIRI. Although these samples werelimited in number, and only RECIST response categoryinformation was available, they did allow initial explora-tion of the hypothesis that tumor vessel and stromal vesseltumor s may respond differently to therapy. The analysisfrom the TMA is valid as previous data on baseline samplessets showed good concordance between the whole tumorsample and the TMA. The distribution of tumor vesselversus stromal vessel was largely representative of the largerdatasets for CRCs. Interestingly, in this dataset, the tumorvessel phenotype was associated with good response, withno examples of PD associated with this phenotype, suggest-ing that the tumor vessel phenotype may be more sensitiveto the combination therapy. This supports the idea that thetumor vessel or stromal vessel should be considered whendifferentiating response of these tumors to angiogenic ther-apy. It would be interesting to understand in late-stageCRCs, where VEGF inhibitors can be used as a single agent,whether differential response or disease control is associ-ated with the differences in phenotype. As progression-freesurvival (PFS) and overall survival (OS) information wasnot available for these samples, further analyses to correlatephenotypes with these survival outcomes are required tounderstand the use of this approach to predict long-termoutcome in diseases dominated by stromal vessel morphol-ogy. Only primary tumor samples were analyzed in thisstudy. Often archival diagnostic samples from the primarytumor are the only tissue available to evaluate the tumorbiomarker status of a patient. It will be important to

establish whether the phenotypes are maintained duringdisease progression (e.g., metastatic lesions) and on pro-gression following therapeutic intervention to determinethe use of stratifying tumor types based on phenotype usingarchival samples.

Preclinical tumor xenografts have been used to definemany of the mechanisms with the potential to influenceresponse to VEGFi, other angiogenic factors (7), bonemarrow–derived cells (5, 6), or by recruitment of stromalfibroblasts/pericytes into the tumor which drive the matu-ration of tumor vessels (8–10). Our analysis suggests thatthosemechanismsof resistance defined inpreclinical tumorxenografts may be most relevant to those diseases thatdisplay a tumor vessel phenotype. It is probable that inselecting tumor models that grow quickly to facilitate drugtesting, stromal-rich tumors have been severely underrep-resented by virtue of their growth characteristics.

0

5

10

15

20

25

30

PD

SD

PR

CR

Patient num

ber

(n =

56)

Phenotype

TV SV

Figure6. Correlationof phenotypewithRECIST response tobevacizumaband FOLFIRI in metastatic CRC. Data were derived from a CD31-a-SMAimmunostained TMA consisting of 56 metastatic CRC samples (n ¼ 2cores per tumor) from patients before treatment. Tumors were classifiedas either tumor vessel or stromal vessel phenotypes by 2 observers andcompared to best RECIST outcome for the treatment period of eachpatient. CR, complete response; PR, partial response; SD, stabledisease. On the basis of the dataset, Fisher exact test showed that thestromal vessel phenotype group has higher probability of having a poorresponse (PD category) to bevacizumab and FOLFIRI than that of thetumor vessel phenotype group with statistical significance (P ¼ 0.0487;95% CI, 1.24–¥).

Figure 5. The stromal neovasculature is targeted by DC101 in a tumor model of the stromal vessel phenotype, Calu-3 which leads to stromal changes withnegligible effect on tumor size. A, MVD (number of CD31þ vessels per mm2) and microvessel area (MVA, total CD31þ area (mm2)/total number of CD31þ

vessels) of Genie classified stromal compartment of IgG1 control and DC101-treated Calu-3 tumor s (growth curve study 1). B, chromogenic (CD31,DAB, brown) and fluorescent [CD31-AF488 (green), a-SMA -AF555 (red), and DAPI counterstain (blue)] immunostained control and DC101-treated Calu-3tumor s (growth curve 1). For CD31 images, black or gray arrows point to small and large stromal vessels, respectively (S, stroma; T, tumor). For CD31-a-SMAimages,white or gray arrows indicate nakedandpericyte-covered vessels, respectively.C,Genie (Aperio Technologies Ltd) classification andquantification oftumor, stromal, and necrotic areas (% number of tumor, stromal, or necrotic pixels/total number of pixels) using digitally captured images of hematoxylin-stained sections for DC101 (15 mg/kg twice weekly, n ¼ 6) and IgG1 (control, n ¼ 6) treated arms of Calu-3 growth studies [1 and 2 (data not shown)].Hematoxylin (blue) staining of control andDC101-treatedCalu-3 tumors andoverlay ofGenie classified tumor, stromal, and necrotic compartments for Calu-3growth curves 1 and2 (data not shown). D, stromalmyofibroblast content (percentage number ofaSMAþorPDGFRbþpixels/total number pixels) withinGenieclassified regions of control and DC101-treated tumor s (growth curve 1). Mean � SEM and P values are shown for all biomarkers.






Understanding the implications of the stromal vesselarchitecture on tumor cell function and response to drugswill be important. The Calu-3 model (stromal vesselphenotype), which exhibited a slower growth rate andshowed a poor response to the specific VEGFR2-blockingantibody DC101, expressed higher levels of human genesthought to play a role in recruitment of stroma thanmodels representative of the tumor vessel phenotype.These genes, PDGF-A, C and D, FGF-2, interleukin (IL)-8 are known to influence fibroblast function as well asresistance to VEGF signaling inhibitors (9–12, 27–30). Thehigh stromal content would generate a distinct microen-vironment where the mature vessels in the stroma may beresistant to therapy, an effect observed with VEGF signal-ing inhibitors in preclinical tumor xenografts (3, 11), thestromal phenotype may be a more extreme representationof this resistance mechanism. Interestingly, Calu-3 tumorstroma had a high local vessel density that was reducedfollowingDC101 treatment in this model, but a significantnumber of established mature residual vessels remained.In addition, although treatment with DC101 led to adecrease in the density of the stroma, the general archi-tecture of the stroma was maintained. This suggests that inCalu-3 tumors, there may be a role for the vasculature inmaintaining the stroma or that changes in the tumorcompartment following a reduction in vessels lead to areduction in reactive stroma. Treating human stromalvessel tumors with VEGFi may change the apparent vesselpermeability, perfusion or blood flow as determined byimaging or reduces vessels asmeasured by other biomarkerapproaches, but without reaching a threshold that issufficient to impact upon tumor cell growth or survival.In stromal vessel tumors, pruning of immature vasculatureand retention of larger mature vessels in the stroma byVEGFi could alter interstitial pressure within the tumorconsistent with the vascular normalization hypothesis(31) and influence drug delivery. Tumor types that arepredominantly stromal vessel (e.g., CRC, NSCLC, BC) donot exhibit dramatic responses to VEGFi monotherapy inthe clinic but alterations in stromal density may givebenefit in combination or with single-agent therapy inlate-stage disease. Although we have only conducted alimited analysis, it will be interesting to examine howreductions in tumor vasculature influence tumor cell sur-vival or growth in other models with a high stromalcontent. Here, we focused on testing VEGF signalinginhibition by using an antibody that specifically antago-nized VEGFR2 on the murine vasculature. Many small-molecule VEGFR inhibitors have also been developed, butthese compounds have broader pharmacology profilesand inhibit additional kinase such as PDGFR and relatedfamily members which impart additional effects in thestroma or tumor. It will be of future interest to also modelsuch small-molecule inhibitors preclinically taking intoaccount their clinical exposure profiles, to determinewhether any differential effects are delivered with thesemixed pharmacology agents on tumor vessel and stromalvessel tumors.

Our findings offer an alternative way to interpret theeffects we have seen with VEGFi therapy in the clinic. Iftumors with either a tumor vessel or stromal vesselarchitecture are viewed as distinct from the point of viewof tumor angiogenesis, then it also challenges whether wehave interpreted current biomarkers appropriately. Forexample, while biomarker changes in vessel numbers orvascular function in response to VEGFi treatment couldconceivably be observed in tumors with either a tumorvessel or stromal vessel phenotype, it is possible that thesemay only translate into therapeutic benefit in the former.Given that predominantly stromal vessel diseases (e.g.,CRC) will still have a small proportion of tumors with atumor vessel phenotype, it would also be interesting todetermine whether differences in response are seen with-in these tumor subsets. This segmentation of disease mayoffer an approach to prioritize tumor types that are likelyto show a clinical response to VEGFi therapy and poten-tially other vascular directed agents. We suggest thatdiseases that predominantly display a tumor vessel phe-notype would show good single-agent response to suchagents.

This study has a number of limitations. Although thepreclinical evaluation of the concept uses several tumorvesselmodels treatedwithDC101, because of the scarcity oftumor xenografts that represent the stromal vessel pheno-type, we were limited to one stromal vessel model (Calu-3).Studies using additional VEGR antagonists at differentexposures in models that represent a range of phenotypes,including additional stromal vessel phenotypes andmodelsheterogeneous for both phenotypes, would be important ininvestigating this concept further. In addition, a morecomprehensive evaluation of the phenotypes in specifictumor types is required to better understand (i) prevalenceand heterogeneity, (ii) prognostic and predictive value, and(iii) phenotype changes associatedwith disease progressionor following therapy.

The vascular architecture is not the only parameter that islikely to determine the response of an individual tumor toVEGFi therapy.Within the context of each phenotype, otherfactors may further influence the outcome following ther-apy. For example, with a vascular targeting therapy, it isreasonable to expect factors that determine the metabolicstatus of the cells, or the degree of environmental stress thetumor cell can withstand, would also influence the likeli-hood of obtaining an objective tumor response. In partic-ular, the stromal architecturemay influence features such asthe intrinsic metabolic status of the tumor cells eitherdirectly, or by creating a dependency onmetabolic couplingbetween the stromal fibroblast and tumor to promoteanabolic growth (the "reverse Warburg effect"; ref. 32). Itis unclear what features of the tumor cell determine thevessel phenotypes, but a better understanding of the under-lyingmechanisms couldhelp further refinepatient selectionstrategies for VEGFi therapy.

In conclusion, we suggest that consideration of the tumorstromal architecture may be an important determinant ofwhether tumors will be therapeutically susceptible to

Smith et al.





treatment with VEGFi monotherapy and potentially othervascular modulating agents. The ability to stratify VEGFiresponses in preclinical models and human disease on thebasis of a tumor vessel or stromal vessel phenotypewarrantswider evaluation.

Disclosure of Potential Conflicts of InterestAll authors are current or former AstraZeneca employees and share-

holders. M. Farren was employed as a Post Doc and S. Wedge as SeniorPrincipal Scientist. J. Kendrew and S.T. Barry have Ownership Interest(including patents) as AstraZeneca Shareholder. No potential conflicts ofinterest were disclosed.

Authors' ContributionsConception and design: N.R. Smith, J. Kendrew, S.T. BarryDevelopment of methodology: N.R. Smith, M. Farren, A.J.C. Pommier,S. Mistry, S.T. Barry

Acquisitionofdata (provided animals, acquired andmanagedpatients,provided facilities, etc.):N.R. Smith,M. Farren, A.J.C. Pommier, R. Swann,K. McDaid, J. Kendrew, C. WomackAnalysis and interpretation of data (e.g., statistical analysis, biosta-tistics, computational analysis): N.R. Smith, M. Farren, A.J.C. Pommier,X. Wang, S. Mistry, J. Kendrew, C. Womack, S. Wedge, S.T. BarryWriting, review, and/or revision of the manuscript: N.R. Smith, A.J.C.Pommier, J. Kendrew, C. Womack, S. Wedge, S.T. BarryAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): N.R. Smith, D. Baker, A.J.C. Pom-mier, K. McDaid, S.T. BarryStudy supervision: N.R. Smith, S.T. Barry

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received June 17, 2013; revised August 28, 2013; accepted September 4,2013; published OnlineFirst September 12, 2013.

References1. Crawford Y, Ferrara N. Tumor and stromal pathways mediating refrac-

toriness/resistance to anti-angiogenic therapies. Trends PharmacolSci 2009:30:624–30.

2. Sitohy B, Nagy JA, Dvorak HF. Anti-VEGF/VEGFR therapy for cancer:reassessing the target. Cancer Res 2012;72:1909–14.

3. Bergers G, Hanahan D. Modes of resistance to anti-angiogenic ther-apy. Nat Rev Cancer 2008;8:592–603.

4. Ivy SP, Wick JY, Kaufman BM. An overview of small-molecule inhibi-tors of VEGFR signaling. Nat Rev Clin Oncol 2009;6:569–79.

5. Muramatsu M, Yamamoto S, Osawa T, Shibuya M. Vascular endo-thelial growth factor receptor-1 signaling promotes mobilization ofmacrophage lineage cells from bone marrow and stimulates solidtumor growth. Cancer Res 2010;70:8211–21.

6. Shojaei F, Wu X, Qu X, Kowanetz M, Yu L, Tan M, et al. G-CSF-initiated myeloid cell mobilization and angiogenesis mediate tumorrefractoriness to anti-VEGF therapy in mouse models. PNAS 2009;106:6742–7.

7. Welti JC, Gourlaouen M, Powles T, Kudahetti SC, Wilson P, BerneyDM, et al. Fibroblast growth factor 2 regulates endothelial cell sensi-tivity to sunitinib. Oncogene 2011;30:1183–93.

8. Cascone T, Herynk MH, Xu L, Du Z, Kadara H, Nilsson MB, et al.Upregulated stromal EGFR and vascular remodeling in mouse xeno-graft models of angiogenesis inhibitor-resistant human lung adeno-carcinoma. J Clin Invest 2011;121:1313–28.

9. Kitadai Y, Sasaki T, Kuwai T, Nakamura T, Bucana CD, Fidler IJ, et al.Targeting the expression of platelet-derived growth factor receptor byreactive stroma inhibits growth and metastasis of human colon car-cinoma. Am J Pathol 2006;169:2054–65.

10. Pietras K, Pahler J, Bergers G, Hanahan D. Functions of paracrinePDGF signaling in the pro-angiogenic tumor stroma revealed bypharmacological targeting. PLoS Med 2008;5:e19.

11. Crawford Y, Kasman I, Yu L, Zhong C,Wu X,Modrusan Z, et al. PDGF-C mediates the angiogenic and tumorigenic properties of fibroblastsassociatedwith tumors refractory to anti-VEGF treatment. Cancer Cell2009;15:21–34.

12. Farren M, Weston S, Brown H, Broadbent N, Powell S, Shaw R, et al.Expression of stromal genes associated with the angiogenic responseare not differentiated between human tumour xenografts with diver-gent vascular morphologies. Angiogenesis 2012;15:555–68.

13. Smith NR, Baker D, JamesNH, Ratcliffe K, JenkinsM, Ashton SE, et al.Vascular endothelial growth factor receptors VEGFR-2 and VEGFR-3are localized primarily to the vasculature in human primary solidcancers. Clin Cancer Res 2010;16:3548–61.

14. Sternberg CN, Davis ID, Mardiak J, Szczylik C, Lee E,Wagstaff J, et al.Pazopanib in locally advanced or metastatic renal cell carcinoma:results of a randomized phase III trial. J Clin Oncol 2010;28:1061–8.

15. Batchelor TT, Duda DG, di Tomaso E, Ancukiewicz M, Plotkin SR,Gerstner E, et al. Phase II study of cediranib, an oral pan-vascular

endothelial growth factor receptor tyrosine kinase inhibitor, in patientswith recurrent glioblastoma. J Clin Oncol 2010;28:2817–23.

16. Friedman HS, Prados MD, Wen PY, Mikkelsen T, Schiff D, Abrey LE,et al. Bevacizumab alone and in combination with irinotecan in recur-rent glioblastoma. J Clin Oncol 2009;27:4733–40.

17. Winder T, Lenz HJ. Vascular endothelial growth factor and epidermalgrowth factor signaling pathways as therapeutic targets for colorectalcancer. Gastroenterology 2010;138:2163–76.

18. Das M, Wakelee H. Targeting VEGF in lung cancer. Expert Opin TherTargets 2012;16:395–406.

19. Lam ET, Ringel MD, Kloos RT, Prior TW, Knopp MV, Liang J, et al.Phase II clinical trial of sorafenib in metastatic medullary thyroidcancer. J Clin Oncol 2010;28:2323–30.

20. Bible KC, Suman VJ, Molina JR, Smallridge RC, Maples WJ, MenefeeME, et al. Efficacy of pazopanib in progressive, radioiodine-refractory,metastatic differentiated thyroid cancers: results of a phase 2 consor-tium study.; Endocrine Malignancies Disease Oriented Group; MayoClinic Cancer Center; Mayo Phase 2 Consortium. Lancet Oncol2010;11:962–72.

21. Antonarakis ES, Carducci MA. Targeting angiogenesis for thetreatment of prostate cancer. Expert Opin Ther Targets 2012;16:365–76.

22. Mackey JR, Kerbel RS, Gelmon KA, McLeod DM, Chia SK, Rayson D,et al. Controlling angiogenesis in breast cancer: a systematic review ofanti-angiogenic trials. Cancer Treat Rev 2012;38:673–88.

23. Raymond E, Dahan L, Raoul JL, Bang YJ, Borbath I, Lombard-BohasC, et al. Sunitinib malate for the treatment of pancreatic neuroendo-crine tumors. N Engl J Med 2011;364:501–13.

24. Kindler HL, Wroblewski K, Wallace JA, Hall MJ, Locker G, Nattam S,et al. Gemcitabine plus bevacizumab compared with gemcitabine plusplacebo in patients with advanced pancreatic cancer: phase III trial ofthe Cancer and Leukemia Group B (CALGB 80303). J Clin Oncol2010;28:3617–22.

25. Kindler HL, Ioka T, Richel DJ, Bennouna J, L�etourneau R, OkusakaT, et al. Axitinib plus gemcitabine versus placebo plus gemcita-bine in patients with advanced pancreatic adenocarcinoma: adouble-blind randomised phase 3 study. Lancet Oncol 2011;12:256–62.

26. O'Reilly EM, Niedzwiecki D, Hall M, Hollis D, Bekaii-Saab T, Pluard T,et al. ACancer andLeukemiaGroupBphase II studyof sunitinibmalatein patients with previously treated metastatic pancreatic adenocarci-noma (CALGB 80603). Oncologist 2010;15:1310–9.

27. Prewett M, Huber J, Li Y, Santiago A, O'Connor W, King K, et al.Antivascular endothelial growth factor receptor (fetal liver kinase 1)monoclonal antibody inhibits tumor angiogenesis and growth of sev-eral mouse and human tumors. Cancer Res 1999;59:5209–18.

28. Wedge SR, Kendrew J, Hennequin LF, Valentine PJ, Barry ST, BraveSR, et al. AZD2171: a highly potent, orally bioavailable, vascular






endothelial growth factor receptor-2 tyrosine kinase inhibitor for thetreatment of cancer. Cancer Res 2005;65:4389–400.

29. Ebos JM, Lee CR, Christensen JG, Mutsaers AJ, Kerbel RS. Multiplecirculating proangiogenic factors induced by sunitinib malate aretumor-independent and correlate with antitumor efficacy. Proc NatlAcad Sci U S A 2007;104:17069–74.

30. Casanovas O, Hicklin D, Bergers G, Hanahan D. Drug resistance byevasion of antiangiogenic targeting of VEGF signaling in late stagepancreatic islet tumors. Cancer Cell 2005;8:299–309.

31. ChauhanVP, StylianopoulosT,Martin JD,Popovi�cZ,ChenO,KamounWS, et al. Normalization of tumour blood vessels improves the deliveryof nanomedicines in a size-dependent manner. Nat Nanotechnol2012;7:383–8.

32. Whitaker-Menezes D, Martinez-Outschoorn UE, Lin Z, Ertel A,Flomenberg N, Witkiewicz AK, et al. Evidence for a stromal-epithe-lial "lactate shuttle" in human tumors: MCT4 is a marker ofoxidative stress in cancer-associated fibroblasts. Cell Cycle 2011;10:1772–83.

Smith et al.





2013;19:6943-6956. Published OnlineFirst September 12, 2013.Clin Cancer Res Neil R. Smith, Dawn Baker, Matthew Farren, et al. Response to VEGF-Targeted TherapyTumor Stromal Architecture Can Define the Intrinsic Tumor

Updated version

10.1158/1078-0432.CCR-13-1637doi:

Access the most recent version of this article at:

Material

Supplementary

http://clincancerres.aacrjournals.org/content/suppl/2013/09/13/1078-0432.CCR-13-1637.DC1

Access the most recent supplemental material at:

Cited articles

http://clincancerres.aacrjournals.org/content/19/24/6943.full#ref-list-1

This article cites 32 articles, 13 of which you can access for free at:

Citing articles

http://clincancerres.aacrjournals.org/content/19/24/6943.full#related-urls

This article has been cited by 9 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/19/24/6943To 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-13-1637

http://clincancerres.aacrjournals.org/content/suppl/2013/09/13/1078-0432.CCR-13-1637.DC1

http://clincancerres.aacrjournals.org/content/19/24/6943.full#ref-list-1

http://clincancerres.aacrjournals.org/content/19/24/6943.full#related-urls

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

mailto:[email protected]

http://clincancerres.aacrjournals.org/content/19/24/6943


Tumor Stromal Architecture Can De ne the Intrinsic Tumor ... · Tumor Stromal Architecture Can...

Documents

Transcript of Tumor Stromal Architecture Can De ne the Intrinsic Tumor ... · Tumor Stromal Architecture Can...