Patient-Derived Xenograft Models Reveal Intratumor ... · Translational Science Patient-Derived Xenograft Models Reveal Intratumor Heterogeneity and Temporal Stability in Neuroblastoma

Translational Science

Patient-Derived Xenograft Models RevealIntratumor Heterogeneity and TemporalStability in NeuroblastomaNo�emie Braekeveldt1, Kristoffer von Stedingk2,3, Susanne Fransson4,Angela Martinez-Monleon4, David Lindgren1, Ha

�kan Axelson1, Fredrik Levander5,

Jakob Willforss5, Karin Hansson5, Ingrid �ra2, Torbj€orn Backman6, Anna B€orjesson6,Siv Beckman1, Javanshir Esfandyari1, Ana P. Berbegall7, Rosa Noguera7,Jenny Karlsson8, Jan Koster3, Tommy Martinsson4, David Gisselsson8,9,Sven Pa

�hlman1, and Daniel Bexell1,9

Abstract

Patient-derived xenografts (PDX) and the Avatar, a singlePDX mirroring an individual patient, are emerging tools inpreclinical cancer research. However, the consequences ofintratumor heterogeneity for PDX modeling of biomarkers,target identification, and treatment decisions remain under-explored. In this study, we undertook serial passagingand comprehensive molecular analysis of neuroblastomaorthotopic PDXs, which revealed strong intrinsic genetic,transcriptional, and phenotypic stability for more than 2years. The PDXs showed preserved neuroblastoma-associ-ated gene signatures that correlated with poor clinical out-come in a large cohort of patients with neuroblastoma.Furthermore, we captured spatial intratumor heterogeneityusing ten PDXs from a single high-risk patient tumor. We

observed diverse growth rates, transcriptional, proteomic,and phosphoproteomic profiles. PDX-derived transcription-al profiles were associated with diverse clinical character-istics in patients with high-risk neuroblastoma. These datasuggest that high-risk neuroblastoma contains elements ofboth temporal stability and spatial intratumor heterogene-ity, the latter of which complicates clinical translation ofpersonalized PDX–Avatar studies into preclinical cancerresearch.

Significance: These findings underpin the complexity ofPDX modeling as a means to advance translational applica-tions against neuroblastoma. Cancer Res; 78(20); 5958–69.�2018AACR.

IntroductionMalignant tumors are often made up of distinct subclonal

populations, which can evolve both spatially, that is, in differentregions of the tumor, and temporally as a consequence of tumorgrowth or treatment. This evolving intratumor heterogeneity(ITH) has been implicated as fundamental in cancer progression,metastasis, treatment resistance, and cancer survival (1, 2), par-ticularly in adult cancers. Recent work suggests that pediatrictumors also exhibit genomic diversity.

Neuroblastoma, a childhood cancer originating from the devel-oping sympathetic nervous system, is characterized by a diverseclinical phenotype (3). Patients with high-risk neuroblastomasoften have a poor prognosis despite heavymultimodal treatment,and survivors can suffer from long-term, severe side-effects.Emerging data suggest that clonal evolution and genetic ITH alsois a feature in neuroblastoma (4–11) and is involved in neuro-blastoma treatment response (8, 9, 11). We have previouslyshown that high genetic diversity is common in childhood cancerafter chemotherapy and correlates to poor prognosis (4, 11). Lessis known about transcriptional, proteomic, and functional ITH inneuroblastoma; yet, understanding this is important as it mostlikely influences biomarker discovery, target identification, treat-ment decisions, and treatment response.

1Department of Laboratory Medicine, Division of Translational Cancer Research,Lund University, Lund, Sweden. 2Department of Clinical Sciences, Division ofPediatric Oncology, Lund University, University Hospital, Lund, Sweden.3Department of Oncogenomics, Amsterdam UMC, University of Amsterdam,the Netherlands. 4Department of Pathology and Genetics, University of Gothen-burg, Gothenburg, Sweden. 5Department of Immunotechnology, Lund Univer-sity, Lund, Sweden. 6Division of Pediatric Surgery, Department of ClinicalSciences, Lund University, University Hospital, Lund, Sweden. 7Department ofPathology, Medical School, University of Valencia/INCLIVA/CIBERONC, Madrid,Spain. 8Division of Clinical Genetics, Department of Laboratory Medicine, LundUniversity, Lund, Sweden. 9Department of Pathology, Laboratory Medicine,Medical Services, University Hospital, Lund, Sweden.

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

N. Braekeveldt and K. Stedingk contributed equally to this article.

Corresponding Authors: Daniel Bexell, Lund University, Medicon Village 404:C3, SE-223 81 Lund, Sweden. Phone: 46-46-222-64-23, E-mail: [email protected]; and Kristoffer von Stedingk, Department of Oncogenomics,Academic UMC, University of Amsterdam, the Netherlands. E-mail:[email protected]

doi: 10.1158/0008-5472.CAN-18-0527

�2018 American Association for Cancer Research.

CancerResearch

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Patient-derived xenografts (PDX) have emerged as promisingpreclinical cancer models because PDXs can retain many of themolecular and functional features of their ancestral tumors inpatients (12). Thousands of PDX models have thus been estab-lished in major academic and industrial preclinical drug testingprograms. The PDX–Avatar and the coclinical trial conceptsdepend on the establishment of a single PDX model mirroringa patient tumor. By simultaneously establishing and treating aPDX–Avatar of a patient enrolled in a clinical trial with a newagent, underlyingmechanisms canbe studied, novel combinationstrategies can be evaluated, and potential biomarkers identified.The use of well-characterized PDX models thus holds promise toimprove the transition of preclinical drug evaluation data to theclinics. However, the functional consequences of ITH for PDX–Avatar and coclinical trial studies have not been addressed. Thiscould be crucial because clonal evolution and ITH are essentialfactors in the response to anticancer drugs and the development oftreatment resistance (2).

We have previously established neuroblastoma patient-derivedorthotopic xenografts (PDOX), which retain the histopathologic,stromal, and metastatic features of aggressive patient tumors(13, 14). We prefer to use PDOXs instead of subcutaneous PDXs,because orthotopic tumors have more relevant biology (15) andretain spontaneous metastatic capacity (16). In this study, up toeight in vivo generations of neuroblastoma PDOXs were estab-lished through serial passaging in NSG mice. PDOXs were trans-planted as undissociated tumor fragments to avoid genetic aber-rations and clonal selections associated to culturing procedures(17). Comprehensive molecular analyses of multiple in vivopassages from each PDOX model were performed to examinetemporal evolution of human high-risk neuroblastoma.We dem-onstrate high genetic, transcriptional and phenotypic stabilityover time, and identify PDOX-derived gene signatures, whichcorrelate with neuroblastoma-associated processes and with clin-ical characteristics including patient prognosis. These gene signa-tures are preserved for more than 2 years of in vivo serial passagingin mice. However, using multiple PDOXs derived from a singlehigh-risk patient tumor, we uncovered diverse transcriptional,proteomic, and phosphoproteomic profiles, suggesting a signif-icant spatial ITH of the patient tumor. Our findings highlightfunctional and molecular spatial ITH as an element of high-riskneuroblastoma. This complicates the interpretation of PDX–Avatar studies (1 mouse, 1 patient) for biomarker discovery aswell as target identification and treatment-guiding decisions inpersonalized oncology.

Materials and MethodsAnimal procedures

Animal procedures were performed as described previously(13, 14). Briefly, 4-to-6-week-old female or male NSG mice werehoused under pathogen-free conditions and received autoclaved

water and food. We have previously established neuroblastomaPDOXs through the implantation of undissociated patient tumorfragments into the paraadrenal space of immunodeficient NSGmice (13, 14). Established PDOXs underwent serial orthotopictransplantation into next generation NSG mice to establish up toeight in vivo generations for each PDOX model. The study wasapproved by the Regional Ethics Board of Southern Sweden (289-2011). The Malm€o–Lund Ethical Committee for the use of lab-oratory animals approved all animal experiments (M146-13). SeeSI Appendix for further information on animal procedures.

IHC and microscopyXenograft tumors andmice organswere formalin-fixed, embed-

ded in paraffin, and 4 mm tissue sections were cut and analyzed.See SI Appendix for further information.

SNP analysisPDOXs were snap frozen and stored at �80�C for SNP array

analysis. Briefly, DNA was extracted from the tissues using theDNeasy Blood and Tissue Kit (Qiagen) according to the manu-facturer's instructions. The Affymetrix CytoScan HD platformwasused for SNP array analysis as described previously (13).

Exome sequencingDNAwas extracted from frozen tumors or constitutional blood

fromneuroblastomapatients and from thePDOXsusing standardprocedures prior to fluorometric quantitation and DNA integrityassessment on Agilent Tapestation (Agilent). Exome sequencingwas performed on tumors from 5 patients and correspondingconstitutional DNA for 3 of the patients, and on PDOXs fromdifferent in vivo generations (ranging fromG1–G8). See SI Appen-dix for further information.

AmpliSeq transcriptome human gene expressionFor RNA sequencing (RNA-seq), 50 ng of RNA was reverse

transcribed according to Ion AmpliSeq Transcriptome HumanGene Expression Kit Preparation protocol (Thermo Fisher Scien-tific). See SI Appendix for further information.

Proteomics and phosphoproteomicsProteomic andphosphoproteomic analyseswereperformedon

10 different tumors of PDOX #5 (G1). See SI Appendix for furtherinformation.

Neuroblastoma patient cohort analysesA publically available RNA-seq patient cohort of 498 neu-

roblastoma tumors was analyzed using R2: Genomics Analysisand Visualization Platform (http://r2.amc.nl; Tumor Neuro-blastoma - SEQC - 498 - RPM - seqcnb1). See SI Appendix forfurther information.

Statistical analysisSee SI Appendix.

Table 1. Patient characteristics

Patient Age Sample type Chemotherapy Stage Histology SNP profile

1 1y4m Primary (AG) No IV Un NB MYCN amp, 1p-, þ17q2 2y2m Metastasis Yes IV Un NB MYCN amp, 1p-, þ17q3 2y9m Primary (AG) No III Pd NB MYCN amp, 1p-, þ17q4 3y Primary Yes III Pd NB MYCN amp, þ17q5 3y Metastasis No IV Pd NB þ17q

Abbreviations: AG, adrenal gland; Un NB, Undifferentiated neuroblastoma; Pd NB, poorly differentiated neuroblastoma; y, years; m, months.

Intratumor Heterogeneity in Neuroblastoma

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ResultsSerial passaging of neuroblastoma PDOXs

Neuroblastoma PDOXs were previously established throughthe implantation of undissociated patient tumor fragments intothe paraadrenal space of immunodeficient NSG mice (13, 14).Patient characteristics are presented in Table 1. Here, we per-formed serial passaging of these PDOXs to establish up to eightin vivo generations (G) of tumor-bearing NSG mice from eachestablished model. PDOXs from G1–G2 were defined as low invivo G PDOXs (lowG-PDOX) and PDOXs from G5–G8 weredefined as high in vivo G PDOXs (highG-PDOX; Fig. 1A).Although the engraftment rate from patient tumor to mousewas around 50%, as reported previously (13), the in vivopropagation rate once a PDOX has been established, is muchhigher (220 of 233 mice; 94.4%). The in vivo growth time forthe generations of various PDOX models is shown in Fig. 1A.PDOXs #1–3 and 5 showed a stable growth pattern duringserial in vivo passaging, but, in contrast, PDOX # 4 presentedwith dynamic growth between the lowG-PDOXs and highG-PDOXs (Fig. 1A). This likely reflects that the correspondingpatient tumor sample showed signs of chemotherapy-induceddifferentiation and slow tumor-cell proliferation. Thus, the

growth rates of the PDOXs remained mostly stable upon serialpassaging.

Neuroblastoma lowG-PDOXs retain cellular morphology andprotein marker expression of the patient tumors (13, 14). WhenhighG-PDOXs were analyzed within this study, they were foundto retain the poorly differentiated morphology as well as positivefocal-to-regional expression of the neuroblastomamarkers synap-tophysin, chromogranin A, and tyrosine hydroxylase. There wasalso retention of strong diffuse expression of the neural celladhesion molecule (NCAM; CD56; Fig. 1B). HighG-PDOXs alsoretained spontaneous metastatic capacity to bone/bone marrow,liver, and lungs, as shown by NCAM IHC of tissue from theseorgans of the mice (Fig. 1C). Thus, the histopathologic andclinical hallmarks of patient tumors and lowG-PDOXs are pre-served in highG-PDOXs.

Genomic stability through serial in vivo passagingWe performed whole-exome sequencing of the original patient

tumors and their corresponding lowG-PDOXs and highG-PDOXs. Most of the somatic mutations found in the patienttumors were retained in the corresponding lowG-PDOXs andhighG-PDOXs (Fig. 1D). The exception was # 4, where PDOXs

Figure 1.

Genotypic and phenotypic stability of neuroblastoma PDOXs. A, Time intervals (days) for establishment and serial orthotopic passaging of neuroblastomaPDOXs up to eight in vivo generations (G). B, Hematoxylin and eosin (H&E) staining, expression of neuroblastoma markers, synaptophysin (SYP), chromogranin A(CHGA), tyrosine hydroxylase (TH), and neural cell adhesion molecule (NCAM) in patient tumors and corresponding PDOXs from low and high in vivo generations,exemplified by PDOX #1. Scale bar, 50 mm. C,NCAMþmetastatic neuroblastoma cells in liver, bone marrow, and lungs following serial in vivo passaging. Exemplifiedby PDOXs #2 and 3. Scale bar, 50 mm. D, The numbers of somatic mutations/Mb in patient tumors (P) and in PDOX in vivo generation (G) 1, 4, and 6/8.Each color represents a unique set of mutations (Supplementary Table S1). E, Mutations previously implicated in neuroblastoma progression.

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displayed several uniquemutations as compared with the patienttumor sample. The number of common mutations were 18(PDOX #1), 27 (PDOX #2), 17 (PDOX #3), 6 (PDOX #4), and11 (PDOX #5). A number of unique mutations were found inpatient tumors (0–6), and in the PDOXs (0–19) as well asmutations unique for individual PDOX generations (0–6;Fig. 1D; Supplementary Table S1). We identified mutations ingenes previously implicated in neuroblastoma progression (Fig.1E). Specifically, NRAS p.Q61K (PDOX #3), ALK p.F1174L(PDOX #4) and p.L1240V (PDOX #5), ARID1B mutations(PDOX #4), andMYCN p.P44L (PDOX #5) mutations have beenimplicated in aggressive neuroblastoma growth and/or treatmentrelapse (5, 7, 18–22). MYCN p.P44L is a potentially activatingmutation, which could lead to high MYCN levels despite noMYCN amplification (20, 23). A complete list of all mutationsis shown in Supplementary Table S1.

SNP array analysis was performed to determine how allelicimbalances evolve in PDOXs following serial in vivo passaging.Comparing patient tumors with their corresponding PDOXs, wefound both similarities and PDOX-unique allelic imbalances(Fig. 2A; Supplementary Table S2). However, the latter were aminority of the total number of imbalances and there was a highlevel of consistency regarding the total number of aberrationsbetween patient tumors and their corresponding lowG andhighG

PDOXs (Spearman r ¼ 0.94, P ¼ 1.3 � 10�7; Fig. 2B and C).Chromosomal changes typical of neuroblastoma such as 1p36deletion,MYCN amplification (MNA) and 17q gainwere retainedin both lowG- and highG-PDOXs of PDOXs #1 to 4 (Fig. 2D).Most structural changes that differentiated the non-MNA(N-MNA) patient tumor #5 and the corresponding PDOXs #5were located to chromosome regions affected by complex chro-mothripsis–like changes, a type of chromosomal rearrangementassociated with poor prognosis in neuroblastoma (21). Detaileddata on the allelic imbalances are presented in SupplementaryTable S2.

Transcriptional stability of neuroblastoma PDOXs throughserial passaging

We performed whole-transcriptome analysis (RNA-seq) on theprimary patient tumors (n¼ 1 for each tumor), and lowG-PDOXsand highG-PDOXs (n ¼ 6–8 for each PDOX model, and n ¼ 2–4for each G). In total, RNA-seq was performed on 46 tumorsamples.

t-SNE clusteringwasperformedonall samples (primary tumors– n ¼ 5; PDOX – n ¼ 41) revealing distinct separation of thedifferent PDOX models based on their overall gene expression(Fig. 3A). The primary tumor samples, however, seemed to form acluster away from their respective PDOX models. Gene ontology

Figure 2.

Genomic stability through serial in vivo passaging. A, Genome profiling showing the number of allelic imbalances in patient tumors (P) and in PDOX in vivogeneration (G) 1, 4, 6/8. Each color represents a unique set of imbalances (Supplementary Table S2). The numbers of amplifications/gains, losses, and copy numberneutral imbalances are indicated. The majority of alterations differing between patient tumor #5 and PDOXs #5 were part of chromothripsis-like rearrangements.B, The total number of copy number alterations (CNA) in patient tumors and PDOXs are presented with r signifying Spearman correlation coefficient.C,Box-plot demonstrating CNAs in PDOXs versus corresponding patient tumors (red).D,Neuroblastoma-associated copy number changes. A, amplification; L, loss;C, copy number neutral imbalance; P, patient tumor; G, PDOX In vivo generation.






Figure 3.

PDOX transcriptional patterns following serial passaging. A, t-SNE clustering analysis of 5 primary tumor samples and their corresponding PDOX models. Individualmodels are displayed (left) as well as primary versus PDOX samples (right). B, Gene Ontology (GO) analysis of significantly differentially downregulated genes in allPDOX models compared with primary samples. �Log(P) values and proportion of ontology overlap are displayed. C, Hierarchical clustering of the top 3,000differentially expressed genes across all samples (ANOVA FDR < 0.001). Gene clusters displaying similar variation across samples are defined on the left side ofthe heatmap. MYCN status is displayed aswell as primary tumor samples are labeledwith "P".D, t-SNE clustering of all samples based on expression of the top varyinggenes defined in C. Individual models (left) and primary versus PDOX samples (right). E, Significant Gene Ontology results derived from analysis of the geneclustersdefined inC.�Log(p)-valuesandproportionofontologyoverlaparedisplayed.F, t-SNEclusteringof498publicly availableneuroblastoma tumor samples. INSSstaging (left) andMYCN status (right) are displayed.G andH,K-means clustering of the 498 tumor samples based on cluster 2 (G) and cluster 6 (H) genes defined inC.Corresponding k-means cluster subgroup Kaplan–Meier overall survival analysis with accompanying log-rank P value (bottom left) and overlaid t-NSE clustering(bottom right) are displayed. t-SNE, t-distributed stochastic neighbor embedding.

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analysis on the differentially expressed genes between the primarytumors and their corresponding PDOXs (ANOVA FDR < 0.01)displayed enrichment of genes involved in primarily stromal-associated processes including immune response, extracellularstructure organization, and angiogenesis (Fig. 3B; SupplementaryTable S3). This suggests that the reason for the clustering ofprimary tumors away from the PDOX models was due to thepresence of human stromal components, which is expected asPDOX tumors lack human immune and stromal cells after severalin vivo passages.

To focus on differences within and between the differentPDOXs, we again performed hierarchical and t-SNE clustering ofall samples following multi-group ANOVA analysis across thedifferent PDOX models (top 3,000 genes, FDR < 0.001; Fig. 3Cand D). Each PDOXmodel displayed distinct expression patternsof multiple gene clusters. The different expression patternsbetween the PDOX models remained stable regardless of serialpassaging generation, indicating that the inherent differencesbetween models are retained over time. Furthermore, focusingon the varying genes between the PDOX models also resulted inthe concurrent clustering of the primary tumor samples with theirrespective PDOX models (Fig. 3C and D) showing that thetemporally stable gene signatures between models are alsoreflected in their respective tumors of origin.

Across thedifferent tumormodels,we identified6main clustersof genes (Fig. 3C; Supplementary Table S4). Cluster 2 genesdisplayed highest expression in PDOXs #1 to 4, all of which areMNA. Ontology analysis of cluster 2 genes indeed confirmed anenrichment of MYC and MYC-MAX target genes as well as genesinvolved in general transcription and translation (Fig. 3E). Incontrast, N-MNA PDOX #5 displayed enrichment of genesinvolved in cell cycle regulation as well as indications of signal-ing via WNT and NGF (cluster 6). Interestingly, PDOX #1displayed a significant enrichment of genes involved in embry-onic morphogenesis (cluster 4), which may reflect the young ageof the patient (16 months) relative to the others. Furthermore,both PDOX #1 and 4 displayed enrichment of genes involved inneurogenesis and related terms, suggesting potentially varyingdifferentiation states across the different PDOX models. Addi-tional detailed ontology findings are presented in Fig. 3E andSupplementary Table S5.

To investigate the clinical relevance of the identified geneclusters, we analyzed their expression patterns in a publiclyavailable cohort of 498 neuroblastoma patients (Fig. 3F; ref. 24).K-means clustering of patients based on cluster 2 genes, which areassociated with the MNA PDOXs #1 to 4, displayed 3 distinctgroups of patients (Fig. 3G) with the highest expression of thegenes being associated with a poor prognosis and specifically theMNA subset of stage 4 patients (Fig. 3F andG). In contrast, cluster6 genes that were specific to the N-MNA PDOX #5 displayed theirhighest expression amongst patients with stage 4N-MNA andwasalso associated with a poor prognosis (Fig. 3F and H). Interest-ingly, patients with MNA displayed the lowest expression ofcluster 6 genes, reflecting the mutually exclusive pattern of thissignature in the PDOX models. Overall, these data support theclinical relevance of the transcriptional phenotypes observedacross the different PDOXmodels. Expression patterns of all geneclusters in the patient cohort are shown in Supplementary Fig. S1.

Here, we have shown that tumors of patients with neuroblas-toma contain higher expression of genes involved in humanstromal composition including immune infiltration, ECM pro-

cesses, and angiogenesis than their corresponding PDOXs.Furthermore, the defining clusters of the different neuroblas-toma PDOXs reflect those of their respective primary tumorsand these features remain stable and transplantable acrossmultiple generations of serial orthotopic passaging (greaterthan 2 years of in vivo growth). In addition, these PDOX-derivedgene clusters correlate to clinically relevant subgroups ofpatients with neuroblastoma.

Multiple PDOXs established from a single patient tumor revealfunctional and molecular intratumor heterogeneity

To shed light on functional and molecular spatial ITH, weimplanted ten different tumor fragments from a single patienttumor (the high-risk, N-MNA tumor in patient #5) into 10 NSGmice. PDOXs were established in all the 10 mice. Unexpectedly,the time period from implantation until significant tumor burdenvaried dramatically among the mice. We classified the 10 micebased on the tumor engraftment time into three groups; group 1(97–149days in vivo, n¼4), group2 (198–201days in vivo, n¼3),and group 3 (317–342 days in vivo, n ¼ 3; Fig. 4A).

SNParray analysis revealed that the various PDOXs retained thetypical neuroblastoma-associated chromosomal copy numberchanges of the patient tumor (e.g., 1p del, chromothripsis-likecomplex rearrangements of chromosome 5 and17q gain), andwefound no recurrent differences at copy number level between thegroups that could explain the differences in growth rate (Fig. 4B).Exome sequencing revealed that all ten PDOXs contained neu-roblastoma-associated mutations, ALK p.L1240V and MYCNp.P44L (Fig. 4C). We found no evidence of mutations that wereconsistently different among the groups 1 to 3.

t-SNE clustering performed on RNA-seq data from the 10PDOX models displayed intermingled clustering of the 3 groupsof samples (Fig. 4D). Unsupervised hierarchical clustering (top1,000 SD genes) displayed transcriptional heterogeneity betweensamples, where we could identify 5 gene clusters (Fig. 4E; Sup-plementary Table S6). Gene ontology analysis of the differentgene clusters revealed significant enrichment of processesinvolved in neurogenesis and neuronal differentiation, mesen-chymal transition, and cell cycle regulation as well as stromalcomposition and angiogenesis (Fig. 4F; Supplementary Table S7).Expression of these clusters identified different subgroups of thePDOXs, suggesting functional and phenotypic heterogeneityamongst the different samples. When these gene clusters wereexamined between the different groups defined by time-to-tumorburden, although finding trends that may hint at differencesbetween the groups, no significant differences of cluster expres-sion were found (Fig. 4G and H). Furthermore, multigroupANOVA analysis (FDR < 0.05) between time-to-tumor burdengroups revealed no significant differences in gene expression.Taken together, this suggests that transcriptional and phenotypicheterogeneity does exist within biopsies from the same parentaltumor, however this heterogeneity does not account for differencein time-to-tumor take. In addition, we examined expression of the5 gene clusters derived from PDOX #5 biopsies in all primary andPDOX samples (n ¼ 46; Supplementary Fig. S2). Using theseclusters, each PDOX model (#1–5) could be distinctly separatedfrom each other, suggesting that differentially regulated pro-cesses defined within a given tumor (PDOX #5 in this case) mayreflect processes that differ between different tumor models.The fact that the PDOX #5 biopsies did still cluster together,






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however, supports the notion that ITH is not as prominent asintertumor heterogeneity.

Toagainaddress the clinical relevanceof the geneclusters definedwithin theN-MNA stage 4 PDOX #5,we examined their expressionin the corresponding subset of patients from the public datasetdescribed above (24). K-means clustering displayed that expressionof these gene clusters could identify two subgroups of patientswithin theN-MNAstage4 cohort (Fig. 4I–K). These subgroupswereprimarily definedbydifferences in cluster 4 and cluster 5 expression(Fig. 4J), with subgroup 2 displaying significantly higher levelsof both clusters. In addition, these subgroups differed with signif-icantly with regard to patient prognosis (Fig. 4K).

Proteomic and phosphoproteomic analysis reveal molecularspatial intratumor heterogeneity

In addition to genetic and RNA-seq analyses, we performedmass spectrometry–based proteomic and phosphoproteomicanalysis to further examine spatial ITH in the ten PDOXs frompatient #5. Unsupervised clustering of the top varying peptides inboth the proteomic (Fig. 5A; Supplementary Table S8) andphosphoproteomic (Fig. 5B; Supplementary Table S9) analysesrevealed heterogeneity amongst the samples. Gene ontologyanalysis again revealed varying expression of proteins and phos-phoproteins involved in stromal contribution as well as neuronaldifferentiation and axon guidance amongst others (Fig. 5C andD;Supplementary Tables S10 and S11). These results are in line withthose obtained from the RNA-seq analysis, suggesting that het-erogeneity does exist within PDOXmodels derived from the sametumor, and that may reflect different states of differentiation aswell as varying interaction with and composition of tumor stro-mal components. Of note, correlation analysis between RNA-seqand proteomic data revealed a lack of correlation between sam-ples derived from the same PDOX, however from different piecesof the tumor (Supplementary Fig. S3). This may be an indicationthat the heterogeneity observed within the primary tumor alsoexists within the subsequent PDOXs.

As was observed in the RNA-seq data, varying protein expres-sion patterns were not correlated with time-to-tumor burden, andmultigroup ANOVA analysis did not reveal significant differencesin protein expression between groups. When grouping the morelatent groups together (group 2þ3), however, we did identifydifferentially expressed proteins and phosphoproteins betweengroup 1 and group 2þ3 (Supplementary Fig. S3; SupplementaryTables S12 and S13). Notably, protein expression of dihydropyr-imidinase-like 3 (DPYSL3) was high in the fast-growing group 1;this has previously been implicated in neuroblastoma progres-sion (25). Additional proteins with higher levels in group 1included: Ras GTPase–activating protein-binding protein 2(G3BP2), involved inRas signal transduction; stathmin (STMN1),involved in neuroblastoma metastasis (26); cofilin-1 (CFL1)

implicated in neuroblastoma susceptibility (27), and microtu-bule-associated protein (MAP2; ref. 28). Among the phospho-proteins, more highly expressed in group 1 were microtubule-binding or -associated proteins (e.g., MAP2, DCX, MAPRE1,DPYSL5, and CLASP1) and proteins linked to neural develop-ment processes (e.g., DPYSL3, DCX, DBNL, and DPYSL5). Inaddition, Ser-9 phosphorylation of GSK-3b is associated to phos-phorylation of DPYSL3 at sites Thr-509, Thr-514, and Ser-518(29), all of which were upregulated in our group 1 dataset(Supplementary Fig. S3; Supplementary Table S13). Notably,Ser-9 phosphorylation of GSK-3b in neuroblastoma leads toinactivationofGSK-3b,which in turn stabilizes theMYCNprotein(30). The proteomic differences between the time-to-tumor bur-den groups were validated with targeted mass spectrometry toconfirm these results with anorthogonalmethod (SupplementaryMethods þ Supplementary Table S14).

In summary, mass spectrometry–based analysis identifiedITH within multiple PDOXs from a single patient tumor. Thepatterns of ITH found by protein analyses resembled thepatterns found across samples using RNA-seq analysis, includ-ing variation in genes involved in neuronal differentiation andstromal composition. Although these results did not explaindifferential times to tumor burden, they do shed light onpotential targets in subsets of neuroblastoma cells, all of whichare tumorigenic.

DiscussionThe functional consequence of ITH for PDX modeling and its

subsequent impact on biomarker discovery and treatment deci-sion–making, is not well understood. Although we and othershave previously shown that genetic ITH is a feature of neuroblas-toma, studies of transcriptional, proteomic, phosphoproteomic,and functional ITH has been less of a focus. Here, we performedserial orthotopic in vivo passaging, multi-sample implantation,and comprehensivemolecular characterization of neuroblastomaPDOXs and their corresponding patient tumors. By establishingten PDOXs from a single tumor from a patient with high-risk N-MNA, we found distinct growth patterns and diverse molecularprofiles suggesting substantial spatial ITH of the patient tumor.Furthermore, we show that neuroblastoma PDOXs can displaystable phenotypic, genetic, and transcriptional features despitegrowing for more than 2 years in NSG mice. Thus, high-riskneuroblastoma can display elements of both intrinsic genetic,transcriptional, and phenotypic stability as well as strikingmolec-ular and functional ITH, resulting in diverse tumor phenotypeswithin a single tumor.

ITH has been highlighted as an important feature of variousadult cancers (1) and is implicated in the variable response totreatment. More recently, genetic data have shown that ITH is also

Figure 4.Intratumor transcriptional heterogeneity.A,Graphic of 10biopsies taken fromoneprimary tumor (Patient #5) used to create 10 individual PDOXmodels. ThedifferentPDOX models displayed different time-to-tumor burden and were grouped on the basis of this feature. B and C, SNP analysis (B) and mutational status of twoneuroblastoma-associated variants (C) from a representative PDOX from each of the groups. D, PCA plot of each of the PDOX models, grouped by time-to-tumor burden. E, Hierarchical clustering of the top 1,000 varying genes across all samples derived from Patient #5. Gene clusters displaying similar variationacross samples are defined on the left side of the heatmap. F, Heatmap of gene clusters defined in E separated by time-to-tumor burden groups. Cluster colorannotations are visible on the right side of the plot. G, Boxplots of signature scores (z-score–based) of the gene clusters defined in E across the time-to-tumorburden groups. H, Significant Gene Ontology (GO) results derived from analyses of the gene clusters defined in E. �Log(p)-values and proportion of ontologyoverlap are displayed. I, K-means clustering of the stage 4, MYCN-nonamplified subset of the publicly available 498 tumor dataset based on expression ofthe top 1,000 varying genes displayed in E. J,Boxplots of themost differentially expressed cluster 4 and cluster 5 genes between the k-means subgroups defined in I.K, K-means defined subgroups displayed on the patient t-SNE clustering presented in Fig. 3F (top) and accompanying Kaplan–Meier overall survival analysiswith accompanying log-rank P value (bottom). T-SNE, t-distributed stochastic neighbor embedding.






Figure 5.

Proteomic and phosphoproteomic heterogeneity. A and B, Hierarchical clustering of the top 250 varying proteins (A) and top 250 varying phosphoproteins(condensed to common protein name, n ¼ 125; B) across Patient #5–derived PDOXs. Protein and phosphoprotein clusters displaying similar variationacross samples are defined on the left side of the respective heatmaps. C and D, Gene Ontology (GO) analyses of the clusters defined in A and B, respectively.�Log(p)-values and proportion of ontology overlap are displayed. E, The implications of the results include the need for multiple biopsies, biomarker diversity,combination/generalized treatment strategies, and stress the need for multiple PDOXs per patient to reliably cover the molecular and functional diversityof the corresponding patient tumors.

Braekeveldt et al.





found in neuroblastoma (4–11) and is implicated in neuroblas-toma treatment response (8, 9, 11). Our study builds on this workto further demonstrate diverse growth patterns and molecularprofiles inmultiple PDOXs derived from a single patient tumor. Itis conceivable that the different main biological processes (neu-ronal/mesenchymal/stroma and angiogenesis) found in thePDOXs reflect different subpopulations within the patient tumor.Interestingly, time-to-tumor burden did not correlate stronglywith genetic or transcriptional variation. Although this maysuggest that the strongest representative signals of the tumor'sheterogeneity do not directly affect time-to-tumor take, this mayalso be an indication that different growth rates may be a result oftechnical variation. Nonetheless, molecular ITH is observedamongst PDOX models derived from a single tumor, which isconsistent with findings from colorectal cancer, where diversetumor cell populations within genetically uniform tumor celllineages have been observed (31). Further studies examining thedifferences in response to therapeutics across the different PDOXbiopsies are, of course, of extreme interest.

The findings stress the need for multiple biopsy sampling tocover the diversity of high-risk tumors. Considering that ITH is acritical factor involved in the development of treatment resistance(32), and our previous findings suggest that high genomic diver-sity in childhood cancer correlates with poor prognosis (4, 11),our current results complicate the direct translation of resultsobtained frompersonalized PDX–Avatar studies to clinical testingof the corresponding patient. This is particularly important fortumors with high ITH, as these are likely to be the aggressivetumors that we need better treatment for. Given the diversityobserved in our studies using multiple samples from a singletumor, one can expect increased geno- and phenotypic diversitywhen comparing PDXs derived from treatment na€�ve versusrelapsed samples, and from primary versus metastatic sites.

One attempt to handle the issue of high spatial ITH would beto establish multiple PDXs from different regions of the samepatient tumor and perform drug testing on each individualPDX. This could be demanding and time-consuming, andsometimes not possible due to shortage of available tissue;however, it could open up possibilities to track and treatsubclonal tumor populations involved in chemotherapy resis-tance. Implantation of heterogeneous patient-derived cell cul-tures derived from different tumor regions could theoreticallyalso create PDXs with retained ITH. A third option is toestablish PDXs through cells derived from liquid biopsies withthe assumption that these blood-borne tumor cells wouldcapture the intratumor diversity of the patient tumor.

The results emphasize the need for combination treatmentstrategies of high-risk neuroblastoma. Furthermore, strategies totarget general neuroblastoma oncoproteins, as recently shownusing a GPC2-directed antibody-drug conjugate (33), should befurther pursued in light of the striking functional and molecularITH shown in this paper. The phosphoproteomic analysis iden-tified proteins and phosphosites previously implicated in cancerandneuroblastomaprogression [e.g., phosphorylation ofGSK-3bat Ser9 that stabilizes the MYCN protein in neuroblastoma (30)],as well as novel phosphosites, which provide potential targetopportunities for neuroblastoma. Importantly, these findingsalso highlight the need for mass spectrometry for decipheringITH and for discovery of novel therapeutic targets. Interestingly,RNA and proteomic data from the same PDOX models did notdisplay significant correlation. The observed varying processes

across samples, however, were similar. As extractions for each ofthese protocols were performed on different pieces of the tumor,this may suggest that the ITH present within the primary tumormay be reestablished within each of the derived PDOX models.Further experiments involving multiple biopsies in both theprimary tumor and each of the resulting PDOX tumors may shedlight onto this issue.

In contrast to the spatial ITH originating from the ancestralpatient tumor, we also found that neuroblastoma can displaycertain features of temporal stability. We identified stable geneexpression signatures for different PDOX models related to neu-roblastoma processes, for example, N-MYC signature, neurogen-esis, and embryonal morphogenesis, and these signatures weretransplantable and correlated to poor clinical outcome in a largecohort of patients with neuroblastoma. These findings reflect anintrinsic temporal stability of a subset of neuroblastoma-associ-ated genes when tumors are grown in a controlled in vivo envi-ronment without external selection pressure. Nevertheless, it isstill conceivable that neuroblastoma PDX serial passaging alsocan lead to clonal selection, as recently shown in PDXs derivedfrom adult tumors (34). Technical factors that could influenceclonal selection processes in vivo include implantation site (ortho-topic vs. subcutaneous), passaging procedure (in vitro culturedcells vs. undissociated tumor fragments), and choice of mousestrain. Furthermore, it is likely that addition of an externalselection pressure (e.g., chemotherapy or radiotherapy) wouldalter the observed temporal stability. For instance, genomicchanges following neuroblastoma chemotherapy relapse includemutations predicted to activate the RAS-MAPK pathway and/orinduce epithelial–mesenchymal transition (5, 6). The identifica-tion of ALK F1174L and ARID1B mutations only in the PDOXsand not in the tumor from patient #4 illustrates the problem oftaking only one tumor sample from a patient, as these alterationscould have been present in other regions of the original tumor.Other explanations that cannot be ruled out include very lowallele frequency of themutations in the patient samples or de novoappearance of the mutations in the PDOXs.

Our study highlights molecular and functional spatial ITH inhigh-risk neuroblastoma and demonstrates the importance ofassessing ITH, tomaximize theusefulness of thedata inbiomarkerdiscovery, preclinical target identification and drug testing studies(Fig. 5E). A limitation of our study is that only one patient tumorwas included in the analysis of ITH. Future studies should extendon our findings to additional neuroblastomas including thedifferent subtypes.Nevertheless, thefindings shouldbe importantfor other pediatric and adult aggressive tumors with high ITH, asITH has been implicated as a key player in cancer treatmentresponse and resistance. Indeed, recent comprehensive geneticanalyses of adult tumors and their corresponding PDXs revealedthat PDXsoverall resemble the genetic landscapes of their parentaltumors, but PDXs can also diverge genetically from the parentaltumors, in part due to genomic instability and spatial ITH (34).PDX models can still serve as important tools for tumor assess-ment and treatment planning, aswell as for identification of noveltargets. However, our results suggest that PDXs should be used inthe context of larger cohorts, including multiple patient tumors,rather than drawing individual conclusions from 1 mouse–1patient Avatar studies.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.






Authors' ContributionsConception anddesign:N. Braekeveldt, K. Stedingk, D.Gisselsson, S. Pa

�hlman,

D. BexellDevelopment of methodology: N. Braekeveldt, K. StedingkAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): N. Braekeveldt, K. Hansson, I. �ra, A. B€orjesson,R. Noguera, T. Martinsson, D. BexellAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis):N. Braekeveldt, K. Stedingk, S. Fransson, A. Martinez-Monleon, D. Lindgren, H. Axelson, F. Levander, J. Willforss, R. Noguera,J. Koster, T. Martinsson, D. Gisselsson, S. Pa

�hlman, D. Bexell

Writing, review, and/or revision of the manuscript:N. Braekeveldt, K. Stedingk,S. Fransson, D. Lindgren, H. Axelson, F. Levander, J. Willforss, K. Hansson, I.�ra,A.P. Berbegall, R. Noguera, T. Martinsson, D. BexellAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): N. Braekeveldt, K. Hansson, T. Backman,S. Beckman, J. Esfandyari, A.P. Berbegall, J. Karlsson, T. Martinsson, D. BexellStudy supervision: N. Braekeveldt, J. Koster, T. Martinsson, D. Bexell

AcknowledgmentsThis work was supported by grants from the Swedish Cancer Society (CAN

2017/376 to D.Bexell), Swedish Childhood Cancer Foundation (PR2017-0003to D. Bexell), the Swedish Research Council (2017-01304 to D. Bexell), MrsBerta Kamprad Foundation (to S.Pa

�hlman), the SSF Strategic Center for Trans-

lational Cancer Research-CREATE Health (to S.Pa�hlman), the Strategic Cancer

Research Program BioCARE (to D. Bexell), Crafoord Foundation (to D. Bexell),Jeanssons stiftelser (to D. Bexell), Mary B�eves Stiftelse f€or Barncancerforskning(to D. Bexell), Ollie och Elof Ericssons stiftelser (to D. Bexell), Berth vonKantzows stiftelse (to D. Bexell), The Royal Physiographic Society in Lund (toD.Bexell), Åke Wibergs stiftelse (to D. Bexell), The Tegger Foundation (to KStedingk), Gyllenstiernska Krapperupsstiftelsen (toD. Bexell), Ska

�neUniversity

Hospital research funds (to D. Bexell), grants from the Scientific FoundationSpanish Association Against Cancer 2015; FIS contract PI17/01558 and CB16/12/00484 (to R.Noguera), and grants from the ISCIII & FEDER (EuropeanRegional Development Fund), Spain (to R.Noguera). The authors would like toacknowledge the support of Science for Life Laboratory/Clinical Genomics,Gothenburg and of the National Genomics Infrastructure (NGI)/UppsalaGenome Center and UPPMAX for providing assistance in massive parallelsequencing and computational infrastructure. Work performed atNGI/UppsalaGenome Center has been funded by RFI/VR and Science for Life Laboratory,Sweden.

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 February 20, 2018; revised July 6, 2018; accepted August 23, 2018;published first August 28, 2018.

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2018;78:5958-5969. Published OnlineFirst August 28, 2018.Cancer Res Noémie Braekeveldt, Kristoffer von Stedingk, Susanne Fransson, et al. and Temporal Stability in NeuroblastomaPatient-Derived Xenograft Models Reveal Intratumor Heterogeneity

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