Therapeutic Targeting of the Premetastatic Stage in Human Lung-to-Brain … · colonizing cancer cells could be targeted before they initiate the secondary brain tumor. Here we use

Translational Science

Therapeutic Targeting of the Premetastatic Stagein Human Lung-to-Brain MetastasisMohini Singh1,2, Chitra Venugopal1,3, Tomas Tokar4, Nicole McFarlane1,3,Minomi K. Subapanditha3, Maleeha Qazi1,2, David Bakhshinyan1,2, Parvez Vora1,3,Naresh K. Murty3, Igor Jurisica4,5,6, and Sheila K. Singh1,2,3

Abstract

Brain metastases (BM) result from the spread of primarytumors to the brain and are a leading cause of cancer mortalityin adults. Secondary tissue colonization remains the mainbottleneck inmetastatic development, yet this "premetastatic"stage of themetastatic cascade, when primary tumor cells crossthe blood–brain barrier and seed the brain before initiating asecondary tumor, remains poorly characterized. Current stud-ies rely on specimens from fully developed macrometastasesto identify therapeutic options in cancer treatment, overlook-ing the potentially more treatable "premetastatic" phase whencolonizing cancer cells could be targeted before they initiatethe secondary brain tumor. Here we use our established brainmetastasis initiating cell (BMIC) models and gene expressionanalyses to characterize premetastasis in human lung-to-BM.Premetastatic BMIC engaged invasive and epithelial develop-mental mechanisms while simultaneously impeding prolifer-

ation and apoptosis. We identified the dopamine agonistapomorphine to be a potential premetastasis-targeting drug.In vivo treatment with apomorphine prevented BM formation,potentially by targeting premetastasis-associated genesKIF16B, SEPW1, and TESK2. Low expression of thesegenes was associated with poor survival of patients with lungadenocarcinoma. These results illuminate the cellular andmolecular dynamics of premetastasis, which is subclinical andcurrently impossible to identify or interrogate in humanpatients with BM. These data present several novel therapeutictargets and associated pathways to prevent BM initiation.

Significance: These findings unveil molecular features ofthe premetastatic stage of lung-to-brain metastases and offer apotential therapeutic strategy to prevent brain metastases.Cancer Res; 78(17); 5124–34. �2018 AACR.

IntroductionMetastases to the brain (BM) are the most common neoplasm

to affect the adult central nervous system, occurring in up to 40%of patients with cancer and at a rate 10 times greater than that ofprimary neural neoplasms (1). Survival of patients with BM islimited to mere weeks, extended to months upon administrationof multimodal treatment (2). Despite the devastating clinicaloutcomes, the genetic and molecular events that govern meta-static development remain frustratingly difficult to predict. Theprocess of metastasis is both complicated and extremely ineffi-cient, where only aminute percentage of disseminated tumor cellsare capable of surviving the lymphovascular system to establishmetastatic tumors.Metastatic cellsmustfirst adapt to and seed this

secondary environment, termed "premetastasis"; this tissue-col-onization stage directly precedes formation of small microme-tastases, and establishment of vasculature will promote largermacrometastatic growth. Understanding premetastasis is the larg-est barrier to metastatic development and tissue colonization, yetthis stage remains poorly characterized in solid tumor-derived BM(3). Clinically, current diagnostic techniques require tumors to beof a certain size before they can be detected; theoretically, thedelay between primary tumor formation and clinical diagnosis ofmetastatic growth, evenwith early tumor dissemination, providesa potential window for therapeutic intervention (4).

Significant investigation into the cancer genome has led togreater understanding of the evolving clonal architecture oftumors, exposing the coexistence of a dominant originatingprimary tumor clone along with multiple genetically distinctsubclones that can give rise to recurrence and metastases (5, 6).Further lineage analyses have identified early and initiating con-ditions that define a "precancerous" stage in the progression ofseveral primary cancers (7–9). Initiating events have similarlybeen explored for metastatic growth, identifying the conditionalimplementation of various mechanisms such as epithelial–mesenchymal transitions and angiogenesis by metastasis initiat-ing cells (10). Unfortunately, there remains a dearth of knowledgeof the mechanisms that promote "premetastatic" initiation andthe tissue-colonization stage prior to establishment of tumormasses (11). Though many solid tumors undergo metastasis tothe brain, the ability to recapitulate every intricate stage of thisprocess in vivo is very difficult; as such currentmodels are only ableto mimic individual or partial stages at once. Additionally, themajority of current in vivo and clinical studies utilizes or analyzes

1Stem Cell and Cancer Research Institute, McMaster University, Hamilton,Ontario, Canada. 2Department of Biochemistry and Biomedical Sciences,McMaster University, Hamilton, Ontario, Canada. 3Department of Surgery,McMaster University, Hamilton, Ontario, Canada. 4Krembil Research Institute,University Health Network, Toronto, Ontario, Canada. 5Departments of MedicalBiophysics and Computer Science, University of Toronto, Toronto, Ontario,Canada. 6Institute of Neuroimmunology, Slovak Academy of Sciences,Bratislava, Slovakia.

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

Corresponding Author: Sheila K. Singh, McMaster University, 1280 Main StreetWest, MDCL 5027, Hamilton, ON L8S 4K1, Canada. Phone: 905-521-2100ext. 75237; Fax: 905-521-9992; E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-18-1022

�2018 American Association for Cancer Research.

CancerResearch

Cancer Res; 78(17) September 1, 20185124

on March 7, 2021. © 2018 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from

Published OnlineFirst July 9, 2018; DOI: 10.1158/0008-5472.CAN-18-1022

http://crossmark.crossref.org/dialog/?doi=10.1158/0008-5472.CAN-18-1022&domain=pdf&date_stamp=2018-8-15

http://cancerres.aacrjournals.org/

established macrometastasis samples, failing to properly capturethis temporally sensitive premetastatic stage. Systematic charac-terization of this premetastatic stage could provide more relevantavenues for therapeutic options in BM prevention as opposed totreating existing BM.

In the present work, we utilized our established patient-derivedmodels of lung-to-brain metastasis to elucidate the molecularvariances that underlie premetastatic initiation through focusedstudy of human BMICs injected into immunocompromised micevia the intrathoracic route. Importantly, the premetastatic phasecaptures a stage of the metastatic cascade that can never beroutinely biopsied or captured in humans, as metastatic cellsseeding the brain without yet initiating a secondary tumor wouldrepresent subclinical disease that cannot be detected by eitherclinical symptoms or current surveillance neuroimaging techni-ques. We found these premetastatic BMICs (termed BMIT) topossess over 7,000 dysregulated genes, many of which are activein invasive but not in proliferative mechanisms; similar datahave only recently been shown in C. elegans (12). Interestingly,these BMIT genes were also enriched in neural neoplasm andneurodegenerative pathways; studies have implicated an inversecorrelation of genes involved in cancer development and neuro-degenerative disorders, and where the gene expression profilesof our established lung and tumors and BM appear to supportthis, our premetastatic BMIT genes do not (13). Through Con-nectivity Map analysis (CMAP) of these BMIT genes and prelim-inary in vivo validation, we demonstrated that the dopamineagonist apomorphine inhibited BMdevelopment in vivo, presum-ably by inhibiting the premetastatic state. Further pharmacoge-nomic interrogation of the BMIT gene list identified 3 down-regulated genes that are directly targeted by apomorphine,KIF16B, SEPW1, and TESK2, where administration of apomor-phine restores expression. Lastly, interrogation of lung adenocar-cinoma patient databases showed that decreased expression ofthese genes is associated with poor disease-free survival.

With this work we have successfully characterized a noveltemporal genetic profile of premetastatic growth and have func-tionally validated the efficacy of targeting this stage in BM devel-opment through administration of apomorphine. The ability toprevent metastatic progression to the brain can transform anunvaryingly lethal systemic disease into one that is eminentlymore treatable.

Materials and MethodsPatient sample collection and cell culture

Human lung-derived BM were obtained with written consentfrom patients, as approved by the Hamilton Health Sciences/McMaster Health Sciences Research Ethics Board (REB # 07366),in compliance with Canada's Tri-Council Policy Statement on theEthical Conduct for Research Involving Humans and the Inter-national Ethical Guidelines for Biomedical Research InvolvingHuman Subjects.

BMs were processed and maintained in tumor sphere media(TSM) as previously described (14, 15). BMICs were grown astumorspheres that were maintained at 37�C with a humidifiedatmosphere of 5% CO2.

In vivo modeling of metastasisAll experimental procedures involving animals were reviewed

and approved by McMaster University Animal Research Ethics

Board. NOD-SCIDmice were used for all experiments. Mice wereanesthetizedusing gas anesthesia (isoflurane: 5% induction, 2.5%maintenance) before minimally invasive surgery. Injectionswere performed as previously described for intracranial (ICr),intrathoracic (IT), and intracardiac (ICr) routes (SupplementaryTable S1A; ref. 14).

Mice were monitored weekly, and upon reaching endpointbrains and lungs were harvested and underwent two separateanalyses:

1. Hemotoxylin and eosin staining. For ICr injections, 100,000cells of BT478 (n¼2) andBT530 (n¼2)were utilized, for ICainjections 250,000 cells of BT478 (n¼ 6) and BT530 (n¼ 2),and for IT injections 500,000 cells of BT478 (n ¼ 7) andBT530 (n ¼ 2). Whole brains (and lungs from IT injections)were sectioned and paraffin-embedded for hematoxylin andeosin. Images were scanned using an Aperio Slide Scannerand analyzed by ImageScope v11.1.2.760 software (Aperio).

2. In vitro culture and expansion. For ICr injections, 50,000 cells ofBT478 (n ¼ 3) and BT530 (n ¼ 4) were utilized, for ICainjections 250,000 cells of BT478 (n¼ 9) and BT530 (n¼ 6),and for IT injections 500,000 cells of BT478 (n ¼ 17) andBT530 (n¼ 9). BMICs were reisolated from ICr brain tumors(BT), IT lung tumors (LT), and premetastatic brain tumors(BMIT), and ICa brain tumors (BMIC) as follows: Wholebrains and lungs (IT injections) were dissociated into singlecell suspensions (15) and cultured in DMEMwith decreasingconcentrations of FBS—the first 2 days in 20% FBS, 10% FBSfor 2 to3days, 5%FBS, andfinally in TSMwithpuromycin fora minimum of 1 week prior to any analyses to select out anyresidual contamination of mouse cells as well as to enrich forthe BMICs. Duplicate samples per BT, LT, BMIT, and BMIC

were collected per BMIC line, RNA isolated, and submittedfor microarray analyses (BT478) or RNA sequencing analyses(BT478 and BT530).

For drug treatments, mice were injected through IT (control,n ¼ 5; apo tx, n ¼ 10) and IC route (control, n ¼ 5; apo tx, n ¼10), and cells were allowed to engraft for 2 weeks. R-(�)-Apo-morphine hydrochloride hemihydrate (Sigma) was resus-pended in sterile saline at 0.5 mg/mL and administered bysubcutaneous (s.c.) injections to give a final dose of 5 mg/kg,3 times weekly for 1 month. Control mice received only saline.Control mice were culled as they succumbed to endpoint, and2 corresponding apomorphine treatment mice were culled foreach control mouse to complete a matched set.

IC50 curve generationBMICs were dissociated into a single cell suspension, and

2,000 cells/well were plated into a 96-well plate at a volume of200 mL/well in increasing concentrations (5–25 mmol/L) ofapomorphine, GW-8510, lomustine (Sigma), acacetin (Sigma),thioridazine (Sigma), trifluoroperazine (Sigma), and prochlor-perazine (Sigma). DMSO was used as a control. Cells wereincubated for 4 days. Presto Blue (20 mL; Invitrogen) was addedto each well approximately 2 hours prior to the readout timepoint. Fluorescence was measured using a FLUOstar OmegaFluorescence 556 Microplate reader (BMG LABTECH) at excita-tion and emission wavelengths of 535 nm and 600 nm, respec-tively. Readings were analyzed using Omega analysis software.Dose–response curves were fitted to the data.

Targeting of Premetastasis

www.aacrjournals.org Cancer Res; 78(17) September 1, 2018 5125




Reverse transcription and quantitative PCR of mRNATotal RNA was isolated using Norgen RNA extraction kit

(Biotek) and reverse transcribed using qScript cDNA Super Mix(Quanta Biosciences) and a C1000 Thermo Cycler (Bio-Rad).qRT-PCR was performed using the Cfx96 (Bio-Rad) withSsoAdvanced SYBR Green (Bio-Rad) using gene specific primers(Supplementary Table S2) and GAPDH as the internal control.

Flow-cytometric characterizationAdherent BMICs were detached through application of TryplE

(Invitrogen) and single cells were resuspended in PBSþ2mmol/LEDTA. Cell suspensions were stained with human anti-TRA-1-85(CD147, Miltenyi) and incubated for 30 minutes on ice. Sampleswere run on a MoFlo XDP Cell Sorter (Beckman Coulter). Deadcells were excluded using the viability dye 7AAD (1:10; BeckmanCoulter). Compensation was performed using mouse IgGCompBeads (BD). Surface marker expression was defined aspositive or negative based on the analysis regions establishedusing the isotype control.

Microarray data analysesBT478 samples were prepared, processed, and run as per

Illumina protocol as previously described (16). Illumina sum-mary probe profiles along with associated control probe profileswere read using a Bioconductor package limma v3.30.13 (17).Data were then background corrected using negative controlprobes and subsequently normalized applying quantile normal-ization using all the available control probes. After normalization,expression of the genes was averaged across the technical repli-cates obtained from the same biological sample.

To provide qualitative assessment of the dissimilarity of theBMIT against BT, LT, and BMIC, scatterplots were plotted depictingexpression of the genes as obtained from individual samples. ThePearson coefficient of correlation between the individual sampleswas calculated and plotted to generate a heat map of the obtainedcorrelations.

RNA sequencingIllumina sequencing was performed by the Farncombe Meta-

genomics Facility (McMaster University). RNA integrity was firstverified using the Agilent BioAnalyzer, followed bymRNA enrich-ment and library prep using the NEBNext Ultra Directional RNALibrary Prep Kit alongwith theNEBNext Poly(A)mRNAMagneticIsolation Module. Libraries were subject to further BioAnalyzerQC and quantified by qPCR. Sequencingwas performed using theHiSeq Rapid v2 chemistry with paired end 2 � 50 bp read lengthconfigurations.

Raw RNA sequencing data were preprocessed and normalizedas follows: RNA-seq data were aligned against hg38 referencegenome, using bowtie2. Reads counts per gene were obtainedusing R packages GenomicRange andGenomicFeatures and usingUCSC hg38 KnowGene database as a reference for genomiclocations (TxDb.Hsapiens.UCSC.hg38). Counts were first nor-malized to counts per million, and then additional quantilenormalization was applied. Expressions were averaged acrosspairs of technical replicates. Counts were then log2 transformed,and genes whose expression was <0 across all the 18 samples wereremoved. Principal component analysis (PCA) was then con-ducted and all the samples were depicted in the space definedby the two most principal components. Additionally, a heat mapdepicting sample differences, as quantified by Euclidean distance

of the gene expression, was generated along the dendrogramdepicting hierarchical clustering of the samples. Sample 16 wasthen excluded from further analysis as an outlier.

Differential expression analysis was performed to identifygenes whose expression was significantly different when compar-ing: (i) BMIT against BT, LT, and BMIC from BT478 and (ii) BMIT

against BT, LT, and BMIC of BT530. Using Bioconductor packagelimma v3.30.13 (17), log2-fold change of the gene expressionwascalculated for both comparisons along the associated P value andfalse discovery rate.

Enrichment analysisTwo types of enrichment analysis were conducted, gene set

enrichment analysis (GSEA) as described by Subramanian andcolleagues (18), along with overrepresentation analysis usinghypergeometric test to assess significance of overlap between theselected group of genes and given pathway or biological process.In both cases, enrichment against the 5 major ontologies wasassessed, including Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways (19), Gene Ontology (GO)–biological pro-cesses, GO–cellular components, GO–molecular functions (20),and disease ontology (DO; ref. 21). All the enrichment analyseswere performed using functions implemented within the Biocon-ductor package ClusterProfiler v3.2.14 (22).

CMAP analysisCMAP analysis was used to predict effects of the drugs on the

expression of the deregulated genes (23). In this analysis, drugs(comprising 1,289 chemical substances) were assessed withrespect to their ability to invert expression changes of the deregu-lated genes obtained from above described differential geneexpression analysis. CMAP analysis was conducted using Biocon-ductor package PharmacoGx (24). Drugswere first filtered accord-ing to resulting connectivity score (connectivity score < 0) andassociated significance (P < 0.01). Finally, drugs were selected forpreliminary in vitro screening based on the criteria of novelty inmetastasis treatment, ability to cross the blood–brain barrier, andpotential to target neural developmental systems or associateddisorders.

To further explore effects of apomorphine on gene expression,we constructed a protein–protein interaction (PPI) network usingapomorphine gene targets obtained from DrugBank v5.0.11 (25)and The Comparative Toxicogenomics Database vJan-2018 (26).Genes transcriptionally modified by apomorphine were identi-fied using CMAP ver. 1 (23). Using the three gene lists, we thenidentified PPIs connecting individual genes in the list usingIntegrated InteractionsDatabase IID v2017-04 (27). The resultingPPI network was visualized using NAViGaTOR v3 (28). As perlegend, node color representsGO–Molecular Function; edge colorcorresponds to tissue specificity, specifically highlighting lung andbrain tissue, as obtained from IID. Themost important BMIT genetargets of apomorphine were identified by applying PharmacoGxframework for sensitivity modeling (for more details see Phar-macoGx user's guide). Genes were filtered according to the drug'sestimated effect on their expression (upregulation of the down-regulated genes anddownregulation of the upregulated ones) andassociated significance (P < 0.01).

Kaplan–Meier analysisPrognostic potential of the genes targeted by the selected

drugs was assessed through SurvExpress v2.0—web resource

Singh et al.

Cancer Res; 78(17) September 1, 2018 Cancer Research5126




for validation of cancer gene expression biomarkers (http://bioinformatica.mty.itesm.mx:8080/Biomatec/SurvivaX.jsp; ref. 29) andlungmodule of Kaplan–Meier plotter (KMplotter)—tool formeta-analysis-based biomarker assessment (http://kmplot.com; ref. 30).Prognostic significance of the 3 target genes (KIF16B, SEPW1, andTESK2) was first tested in SurvExpress using The Cancer GenomeAtlas (TCGA) lung adenorcarcinomagene expressiondata set (June2016) and then validated in KMplotter using all available lungadenocarcinoma data sets. In both cases, survival analysis wasconducted under default parametrization.

Statistical analysisReplicates from at least 3 samples were used for IC50 and

RT-PCR experiments. Respective data represent mean � SD withn values listed in figure legends. Student t test and two-wayANOVA analyses were conducted using GraphPad Prism 5. P <0.05 was considered statistically significant.

Data availabilityThe authors declare that all the Supplementary Data of the

findings of this study are available within the article, its Supple-mentary information files, and from the corresponding authorupon reasonable request. RNA sequencing files are available asGEO data set GSE110495 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc¼GSE110495 upon request.

ResultsCapturing the pre- andmacrostages of metastatic growth in BMdevelopment

We utilized early-passage BM cell lines derived from primarypatient samples of lung-to-BM in our work, as these samples areenriched for BMICs that have already successfully completed themetastatic process. Previous work in our lab successfully estab-lished preclinicalmodels of lung-to-brain BM (14, 31). Briefly, weinjected mice through 3 different injection routes: (i) intracranial(ICr), (ii) intrathoracic injections (IT), and (iii) intracardiacinjections (ICa), where wewere able to replicate the premetastaticand macrometastatic stages from IT and ICa injections respec-tively (14). Here, we have further isolated and characterizedBMICs at each metastatic stage. BMIC lines transduced with GFPwere injected into our BM models and were shown to reformtumors at each stage of the metastatic cascade, from primarylung (LT) and secondary brain (BT) tumor formation to thepremetastatic (BMIT) andmacrometastasis (BMIC) stages of tumorgrowth (Fig. 1). Approximate timeframes for tumor develop-ment (endpoint) varied between models and cell line injection(Supplementary Table S1A); however, there was approximately10 to 14 days difference between ICa and IT endpoints. BMICswere isolated from BT, BMIT, and BMIC tumors and minimallycultured, and retained the ability to reform secondary spheres,suggesting a preservation of their stem-like and tumor initia-tion properties (Fig. 1).

To characterize the genetic profiles of each stage of metastaticprogression, we performed preliminary microarray analysis ofBT478 BMICs from BT, LT, BMIT, and BMIC samples. Intriguingly,we found that genes from BMIT cells clustered separately from BT,LT, and BMIC samples (Supplementary Fig. S1A and S1B). Tocorroborate this unique premetastatic BMIT genetic profile, weanalyzed RNA sequencing data obtained across two separateBMIC lines. Hierarchical clustering along PCA showed that BMIT

from both BMIC lines cluster together, irrespective of the cell line

origin, whereas established metastatic tumors (BT, LT, BMIC)group into cell line–specific clusters (Fig. 2A and B; Supplemen-tary Fig. S1C). We then performed differential expression analysiscomparing expression profiles of BMIT with non-BMIT samplesfrom both cell lines separately. We identified �7,000 differen-tially expressed genes in the premetastatic BMIT stage (Supple-mentaryData Set S1). These results indicate temporal evolution ofBMICs throughmetastasis, during which a distinct genetic profileemerges prior to the initiation of the secondary brain metastasis,while established tumors retain a genetically similar profiledespite tissue of origin.

Premetastatic BMICs retain a unique genetic profileUsingGSEA, we assessed association of BMIT deregulated genes

with biological processes (GO), cellular components (GO),molecular functions (GO), biological pathways (KEGG) or dis-eases (DO). We found increased expression of genes regulatingcytoskeletal structures and epithelial tumor invasion, as well asdecreased expression in processes of cell division and apoptosis(Fig. 3A and B; Supplementary Data Set S2). These data suggestthat premetastatic BMIT are not dormant, but have concurrentlyincreased activation of invasive mechanisms while repressingprogrammed cell death and growth mechanisms. We also foundenrichment within several neurodegenerative pathways (Supple-mentary Fig. S2; Supplementary Data Set S3) and neural neo-plasm components (Supplementary Fig. S3, Supplementary DataSet S4). We also performed enrichment analysis (overrepresen-tation analysis) of the gene clusters obtained by hierarchicalclustering of BT, LT, BMIT, and BMIT genes (Fig. 3C).We identifiedclusters of BMIT deregulated genes to be significantly (P < 0.01)enriched in pathways of cancer and neuroactive ligand–receptorinteraction. Interestingly, enrichment analysis of the instances ofthe DO revealed enrichment of the autonomic nervous systemneoplasm (Supplementary Data Set S5).

Therapeutic targeting of premetastatic BMIT

CMAP was performed on the dysregulated BMIT gene setto identify potential targeting therapeutics (SupplementaryTable S3; Supplementary Data Set S6). Drugs were selected forpreliminary in vitro screening based on the criteria of novelty inmetastasis treatment, ability to cross the blood–brain barrier, andpotential targeting of neural developmental systems or associateddisorders, fromwhich the DRD2 agonist apomorphine proved tohave amoderately low IC50 for both BT478 andBT530BMIC lines(Fig. 4). We repeated the drug screening with other dopamine-specific psychological therapeutics,which failed to affect BMICs tothe same extent as apomorphine (Fig. 4).

To assess the efficacy of apomorphine inhibiting BMIT in vivo,we performed ICa injections with BMIC line BT478, following amodified protocol utilized for in vivo Alzheimer models treatedwith apomorphine (32). BMICs were allowed to engraft for 2weeks prior to starting a month-long administration of apomor-phine, 3 times weekly along with saline for controlmice (Fig. 5A).Despite apomorphine being a known emetic, the treated micedisplayed no significant weight loss, whereas there was a slightdecrease in control mice weights (Supplementary Table S1B).Mice were culled at endpoint (approximately 2.5 months post-tumor injection for ICa, and2months posttumor injection for IT),and their brains minimally cultured to remove the bulk of mousecellular debris. We then performed FACS for human-Tra-1-85 toisolate human BMICs. apomorphine greatly attenuated BM





http://bioinformatica.mty.itesm.mx:8080/Biomatec/SurvivaX.jsp

http://bioinformatica.mty.itesm.mx:8080/Biomatec/SurvivaX.jsp

http://kmplot.com

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110495




development through the ICaBMmodel, as definedby a completeabsence of BMICs in apomorphine-treated brains (Fig. 5B;Supplementary Fig. S4), suggesting that apomorphine does targetBMIT cells to prevent BM initiation anddevelopment, both in silicoand in vivo. The efficacy of apomorphine in inhibiting BM devel-opment in the IT model was indeterminable, as the relatively lownumber of BMICs that were reisolated from both the controland apomorphine-treated mice made it difficult to determine adifference (Supplementary Fig. S5A).

Premetastatic BMIT genes are predictive of lung cancerpatient survival

We attempted to elucidate the biological context of apomor-phine to determine possible mechanisms of actions. We firstgenerated an interactome to identify overall genes targeted byapomorphine (Fig. 6A). Application of a targeted PharmacoGx

framed CMAP on apomorphine focusing on the premetastaticBMIT genes identified 3 genes downregulated as direct targets,KIF16B, SEPW1, and TESK2 (Fig. 6B). In vitro analyses determinedtranscript levels of these 3 genes to be moderately increased inBMICs treated with apomorphine (Fig. 6C). These 3 genes werethen interrogated for prognostic value using transcriptomic datafrom a lung adenocarcinoma patient cohort. The genes takenindividually as well as a refined collective signature comprised ofTESK2, SEPW1, andKIF16Bwere found to have significant impactonpatient survival,where lowexpressionof these genes correlatedwith poor patient survival (Fig. 6C; Supplementary Fig. S5B).

DiscussionOur limited mechanistic understanding of metastatic disease

greatly hinders therapeutic discovery and improvement of the

Figure 1.

Isolation and characterization of in vivo BMICS, BT, LT, BMIT, and BMICs. Top, BT478 and BT530 BMICs were tagged with a GFP-expressing vectorcontaining a puromycin-resistant cassette. GFPþ BMICswere injected via ICr, ICa, and IT routes and characterized via hematoxylin and eosin staining. BMICs are ableto recapitulate metastatic stages of primary lung (LT) and secondary orthotopic brain (BT) tumors, micrometastases (BMIT) and macrometastases (BMIC).Bottom, whole organs (brain or lung) were isolated from each metastatic stage and cultured under TSM conditions with puromycin to select for only GFPþ BMICs,where recovered BMICs were able to reform spheres. Scale bar, 400 mm.

Singh et al.





dismal patient outcome of BM (33). Despite advancements inpreventative and treatment modalities for primary tumors thathave resulted in increased patient survival, the inability of thesetreatments to target residual CSC and BMIC populations leavespatients with cancer vulnerable and prone to relapse and metas-tases (34).

Significant study of the genome evolution of cancer has iden-tified precancerous events in several primary cancers (7, 8, 35);unfortunately, the molecular mechanisms that drive premeta-static cells in the brain remain poorly defined. A significantdisadvantage with currently available in vivomodels is the inabil-ity to capture the premetastatic stage of brain tissue colonization,instead focusing on the easier to collect macrometastatic stage.Recent studies with C. elegans led by Matus and colleagues (12)determined that cellular invasion and proliferation are mutuallyincompatible stages, where both stages are representative ofpremetastasis andmacrometastasis progression, respectively. Thiswork substantiates the inefficient targeting of invasive cells by

current chemotherapies that tend to target rapidly dividing cells,perhaps at the expense of invasive cells (36).

Previous work in our lab successfully established clinicallyrelevant models of lung-derived BM representing the differentstages of metastasis, where we captured both the premetastaticand macrometastatic stages of tumor growth via our IT and ICroutes, respectively (14). From our intrathoracic BM model, wefound thatmice characteristically die of lung tumor burden just asBMICs cross the blood–brain barrier and colonize the brain,giving us a time point to isolate these premetastatic BMICs.Through isolation and comparison of BMICs at various stagesof metastatic progression in our established BM models, weidentified a genetic pattern unique only to BMICs undergoingpremetastasis, termed BMIT, whereas establishedmacrometastatictumors (BT, LT, and BMIC) were genetically similar. These BMIT-BMICs possess �7,000 dysregulated genes, active in mechanismsthat promote invasion and repress apoptosis and division,corroborating results by Matus and colleagues in our more rele-vant patient-related modeling systems (12). Where the use ofNOD-SCID mice encourages increased rates of engraftment ofpatient BMIC lines, it is possible that the lack of a full immunesystem does not provide information on the full scope of met-astatic progression. Current studies concerning the interaction ofthe immune system and metastatic cells suggest an intricaterelationship, where immune cells can mediate metastatic cellentry into the CNS as well as modulate BM growth (37). Theaddition of an active immune system may likely reduce the rateof BMIC engraftment in our BM models, possibly requiringinoculation of higher cell numbers or longer incubation timesto tumor development.

The role of neurotransmitters in cancer has drawn varyinginterest over the years, where they have been found to exert astrong influence over external and internal cellular factors incancer progression (38). Breast cancer BMICs have been foundto exhibit GABAergic properties, mimicking neuronal phenotypesthat appear to aid their colonization of the brain (39). Dopaminereceptors (DR) and dopamine have been revealed to exhibitvarious pleiotropic properties through dependent and indepen-dent pathways, and their modulation has enhanced the efficiencyof anticancer drugs in preclinical cancer models (40, 41). Inparticular, DRD2 agonists have recently been shown to suppressproliferation, angiogenesis, and invasion in several cancers andtumors (42–44). Such studies paired with epidemiologic dataimplicate a relationship between lower rates of cancer develop-ment in patients with Parkinson, intimating a possible linkbetween DR agonists and cancer (45, 46).

Through enrichment analyses, we determined that BMIT

dysregulated gene sets enrich pathways that regulate autonomicnervous system neoplasms and neural system dysregulation,implying a possible relation between neurodevelopmental path-ways and promotion of cancer invasion. CMAP interrogationof the dysregulated BMIT genes identified a list of targetingtherapeutics, of which several of the top hits are currently applied,or are being investigated, as antineoplastic agents against variouscancers (47–49). We selected drugs for preliminary in vitro screen-ing based on the ability to pass the BBB, treatment of neurologicdisorders, and overall novelty as a cancer therapeutic, fromwhich apomorphine was selected for further validation. Apomor-phine is a nonselective dopamine agonist of the morphine deriv-ative, primarily activating dopamine-like receptor 2 (DRD2).Among its multiple uses, apomorphine administration reduced

Figure 2.

Characterization of the individual stages of brain metastasis progression.A, Heat maps depicting Pearson correlation coefficient of gene expressionacross the samples as measured initially by RNA-seq, along with associatedhierarchical clustering of the samples using Euclidean distance betweensamples expression profiles. B, PCA plot depicting samples in the planedefined by two main components (percentage indicates variance explained;"original" denotes BMIC samples collected prior to injection).






Figure 3.

Cellular processes and biological pathways associated with BMIT. A, Visualization of the GSEA across GO–cellular components ontology and KEGGpathways database, using BMIT deregulated genes ordered according to their expression fold change [y-axis, statistical significance; point size, size ofthe gene set (cellular component/pathway); color, normalized enrichment score (NES)].B,Heatmaps depicting Pearson correlation coefficient of gene expression inselect cellular processes across the samples asmeasured initially by RNA sequencing. C,Heatmap depicting expression of the BMIT-deregulated genes across all thesamples along the dendrogram obtained by hierarchical clustering of these genes. Enrichment (overrepresentation) analysis of BMIT genes across individualbranches of the dendrogram revealed enrichment of several KEGGpathways, aswell as DO instances, GObiological processes, cellular compartments, andmolecularfunctions ("original" denotes BMIC samples collected prior to injection).

Singh et al.





amyloid b degradation in patients with Alzheimer (32) andhas recently shown efficacy in the treatment of Parkinson disease(50) as well as a potential targeting of tumor cell invasion (51).Further screening against other dopamine-specific psychologicaltherapeutics validated the specific efficacy of apomorphine intargeting premetastatic BMICs.

To further validate the ability of apomorphine to target BMIT,we applied the drug in vivo in our BM models. Initial trialsadministering apomorphine against our IT model drew incon-

clusive results, where the relatively low number of BMICs wewereable to capture at the premetastatic stage made it difficult toconfidently determine the efficacy of apomorphine (Supplemen-tary Fig. S6A). Thus, we utilized our ICa model to properlyinterrogate the efficacy of apomorphine against BMdevelopment,collecting samples at early time points that follow themicrometa-static time course of our IT model as well as at survivalendpoint to confirm macrometastatic growth. Apomorphineproved to be successful at inhibiting micrometastatic growth as

GW8510

% C

ontro

l pro

lifer

atio

n

150

100

50

0

1,000 10,000

(nmol/L)100,000

Acacetin

% C

ontro

l pro

lifer

atio

n

150

100

50

0

1,000 10,000

(nmol/L)100,000

Lomustine Prochlorperazine Thioridazine

% C

ontro

l pro

lifer

atio

n

150

100

50

01,000 10,000

(nmol/L)100,000

% C

ontro

l pro

lifer

atio

n

200

150

50

100

01,000 10,000

IC50 = 11.9 µmol/LIC50 = 14.6 µmol/LIC50 = 15 µmol/L

(nmol/L)100,000

% C

ontro

l pro

lifer

atio

n

150

100

50

01,000 10,000

IC50 = 11.5 µmol/L

(nmol/L)100,000

Trifluoroperazine

% C

ontro

l pro

lifer

atio

n

150

200

100

50

01,000 10,000

IC50 = 20.4 µmol/LIC50 = 13 µmol/L

(nmol/L)100,000

Apomorphine

% C

ontro

l pro

lifer

atio

n

150

100

50

01,000100 10,000

(nmol/L)100,000

478 - IC50 = 8.5 µmol/L

530 - IC50 = 3.6 µmol/L

Figure 4.

In vitro IC50 screening of potential brain metastasis targeting drugs. IC50 curves of selected BMIT-targeted drugs. (n ¼ 3).

Figure 5.

Preclinical testing of apomorphine to prevent brainmetastasis. A, Schematic representation of dosingregimen for apomorphine. B, Scatter plot graphdepicting percentage of human-Tra-1-85–positive GFP-tagged BMIC cells reisolated from apomorphine (Apo)treatment and control (CNTL) ICa BM model (control,n ¼ 3; treatment, n ¼ 6; �� , P < 0.0001).






well as subsequent macrometastases. We utilized a treatmentprotocol modified from in vivo Alzheimer models being treatedwith apomorphine, as these models proved apomorphine to be

effective and tolerable at the administered dosages. However,future studies will look to tailor the apomorphine dosage todetermine the lowest concentration for BM inhibition.

Figure 6.

Novel gene targets of apomorphine. A, PPI network identifying common gene targets of apomorphine. B, BMIT genes directly targeted by apomorphine, asdetermined by CMAP analysis; negative direction values depict low gene expression, which is correlated with poor prognosis. Relative transcript levels of KIF16B,SEPW1, andTESK2 inBMICs treatedwith apomorphine (n¼ 3; ns, not significant; � ,P<0.05; �� ,P<0.01; �� ,P<0.001).C,Kaplan–Meier curves depictingexpressionofapomorphine-targeted genes by risk group as obtained from SurvExpress using TCGA data from patients with lung adenocarcinoma.

Singh et al.





PharamcoGx directed CMAP analysis determined 3 downre-gulated BMIT genes specifically targeted by apomorphine—KIF16B, SEPW1, and TESK2—where in silico application of thedrug would activate their expression. SEPW1 belongs to a familyof selenoproteomes, which have been increasingly implicated inaspects of neurobiology and neurodegenerative disorders (52).TESK2 is a serine/threonine protein kinase (53). KIF16B is akinesin-like motor protein that may be involved in intracellulartrafficking (54), where defects in this family of proteins has beenassociated with neurodegenerative, developmental, and cancerdiseases (55). In vitro analysis of apomorphine-treated BMICsdetermined transcript levels of these 3 genes to be moderatelyincreased as compared with the control. When these genes wereapplied both individually and as a collective signature in a cohortof patients with lung adenocarcinoma, their low expressionwas correlated with poorer patient survival. Further interrogationof data that follow patients with lung cancer progression intoBM development will be required to validate the predictive valueof TESK2, SEPW1, and KIF16B. It is anticipated that, with thediscovery of our novel premetastatic gene set, we could predict oridentify the potential for metastasis in either primary lung canceror circulating tumor cells prior, thus any treatment to be admin-isteredwouldbe on apreventative basis andhopefully circumventthe need for the current dismal treatment options. We are wellaware that any therapeutic administered could alter the nature ofthe tumor andpromotemetastasis through a resistant population;however, we are optimistic that our preventative treatment wouldextend patient survival long enough to determine an alternativetreatment if necessary.

ConclusionWe present an in-depth genetic characterization of the pre-

viously uncaptured stage of premetastasis in BM progression.We further identified apomorphine to be a novel BMIT targetingtherapeutic to prevent BM development. Continuing studieswill further characterize the role and related mechanisms of DRagonists in BM development. The ability to inhibit BMICs from

initiating metastasis would target BM at the ideal stage, pre-venting the need for more toxic and possibly detrimentaltreatments. Our identification of this premetastatic stage in thedevelopment of BM can be mined to provide further criticaltherapeutic targets in all cancers that metastasize to the brain,offering a paradigm shift for the current state of BM treatment.

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

Authors' ContributionsConception and design: M. Singh, C. Venugopal, T. Tokar, S.K. SinghDevelopment of methodology: M. Singh, C. Venugopal, T. Tokar,M.K. Subapanditha, M. Qazi, D. Bakhshinyan, S.K. SinghAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): M.K. Subapanditha, M. Qazi, P. Vora, N.K. Murty,S.K. SinghAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): M. Singh, C. Venugopal, T. Tokar, I. Jurisica,S.K. SinghWriting, review, and/or revision of the manuscript: M. Singh, C. Venugopal,T. Tokar, I. Jurisica, S.K. SinghAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): N. McFarlaneStudy supervision: C. Venugopal, I. Jurisica, S.K. Singh

AcknowledgmentsM. Singh was supported by the Brain Canada PhD Studentship. This work

was supported by funds from the Department of Surgery at McMaster Univer-sity, Canadian Cancer Society Innovation to Impact Grant (i2I16-1) and TheBoris Family Fund for Brain Metastasis Research awarded to S.K. Singh, andOntario Research Fund (GL2-01-030), Canada Research Chair Program (CRC#225404), and Canada Foundation for Innovation (CFI #29272, #225404,#30865) awarded to I. Jurisica.

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

Received April 5, 2018; revised May 29, 2018; accepted June 29, 2018;published first July 9, 2018.

References1. Patchell RA. The management of brain metastases. Cancer Treat Rev

2003;29:533–40.2. Sjobakk TE, Vettukattil R, Gulati M, Gulati S, Lundgren S, Gribbestad IS,

et al.Metabolic profiles of brainmetastases. Int JMol Sci 2013;14:2104–18.3. Chambers AF, Groom AC, MacDonald IC. Dissemination and growth of

cancer cells in metastatic sites. Nat Rev Cancer 2002;2:563–72.4. Vanharanta S, Massague J. Origins of metastatic traits. Cancer Cell

2013;24:410–21.5. Brastianos PK, Carter SL, Santagata S, Cahill DP, Taylor-Weiner A, Jones RT,

et al. Genomic characterization of brain metastases reveals branchedevolution and potential therapeutic targets. Cancer Discov 2015;5:1164–77.

6. Anderson K, Lutz C, van Delft FW, Bateman CM, Guo Y, Colman SM, et al.Genetic variegation of clonal architecture and propagating cells in leukae-mia. Nature 2011;469:356–61.

7. Hong D, Gupta R, Ancliff P, Atzberger A, Brown J, Soneji S, et al. Initiatingand cancer-propagating cells in TEL-AML1-associated childhood leukemia.Science 2008;319:336–9.

8. Shlush LI, Zandi S, Mitchell A, Chen WC, Brandwein JM, Gupta V, et al.Identification of pre-leukaemic haematopoietic stem cells in acute leukae-mia. Nature 2014;506:328–33.

9. Wistuba II, Mao L, Gazdar AF. Smoking molecular damage in bronchialepithelium. Oncogene 2002;21:7298–306.

10. Celia-Terrassa T, Kang Y. Distinctive properties of metastasis-initiatingcells. Genes Dev 2016;30:892–908.

11. Massague J, Obenauf AC. Metastatic colonization by circulating tumourcells. Nature 2016;529:298–306.

12. MatusDQ, Lohmer LL, Kelley LC, Schindler AJ, KohrmanAQ, BarkoulasM,et al. Invasive cell fate requires G1 cell-cycle arrest and histone deacetylase-mediated changes in gene expression. Dev Cell 2015;35:162–74.

13. Klus P, Cirillo D, Botta Orfila T, Gaetano Tartaglia G. Neurodegenerationand cancer: where the disorder prevails. Sci Rep 2015;5:15390.

14. Singh M, Venugopal C, Tokar T, Brown KR, McFarlane N, Bakhshinyan D,et al. RNAi screen identifies essential regulators of human brainmetastasis-initiating cells. Acta Neuropathol 2017 134:923–40.

15. Venugopal C,McFarlane NM,Nolte S,Manoranjan B, Singh SK. Processingof primary brain tumor tissue for stem cell assays and flow sorting. J Vis Exp2012.

16. Venugopal C, Hallett R, Vora P, Manoranjan B, Mahendram S, Qazi MA,et al. Pyrvinium targets CD133 in human glioblastoma brain tumor-initiating cells. Clin Cancer Res 2015;21:5324–37.

17. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powersdifferential expression analyses for RNA-sequencing and microarray stud-ies. Nucleic Acids Res 2015;43:e47.

18. SubramanianA, TamayoP,Mootha VK,Mukherjee S, Ebert BL,GilletteMA,et al. Gene set enrichment analysis: a knowledge-based approach for






interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A2005;102:15545–50.

19. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes.Nucleic Acids Res 2000;28:27–30.

20. AshburnerM, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Geneontology: tool for the unification of biology. The gene ontology consor-tium. Nat Genet 2000;25:25–9.

21. Schriml LM, Arze C, Nadendla S, Chang YW, Mazaitis M, Felix V, et al.Disease ontology: a backbone for disease semantic integration. NucleicAcids Res 2012;40(Database issue):D940–6.

22. Yu G,Wang LG, Han Y, HeQY. clusterProfiler: an R package for comparingbiological themes among gene clusters. OMICS 2012;16:284–7.

23. Lamb J. The connectivity map: a new tool for biomedical research. Nat RevCancer 2007;7:54–60.

24. Smirnov P, Safikhani Z, El-Hachem N, Wang D, She A, Olsen C, et al.PharmacoGx: an R package for analysis of large pharmacogenomic data-sets. Bioinformatics 2016;32:1244–6.

25. WishartDS, Feunang YD,GuoAC, Lo EJ,MarcuA,Grant JR, et al.DrugBank5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res2018;46(D1):D1074–D82.

26. Davis AP, Grondin CJ, Johnson RJ, Sciaky D, King BL, McMorran R, et al.The comparative toxicogenomics database: update 2017. Nucleic Acids Res2017;45(D1):D972–D8.

27. Kotlyar M, Pastrello C, Sheahan N, Jurisica I. Integrated interactionsdatabase: tissue-specific view of the human and model organism inter-actomes. Nucleic Acids Res 2016;44(D1):D536–41.

28. Brown KR, Otasek D, Ali M, McGuffin MJ, Xie W, Devani B, et al.NAViGaTOR: network analysis, visualization and graphing Toronto.Bioinformatics 2009;25:3327–9.

29. Aguirre-Gamboa R, Gomez-Rueda H, Martinez-Ledesma E, Martinez-Torteya A, Chacolla-Huaringa R, Rodriguez-Barrientos A, et al.SurvExpress: an online biomarker validation tool and database forcancer gene expression data using survival analysis. PLoS One 2013;8:e74250.

30. Gyorffy B, Surowiak P, Budczies J, Lanczky A. Online survival analysissoftware to assess the prognostic value of biomarkers using transcriptomicdata in non-small-cell lung cancer. PLoS One 2013;8:e82241.

31. Nolte SM, Venugopal C, McFarlane N, Morozova O, Hallett RM, O'FarrellE, et al. A cancer stem cell model for studying brain metastases fromprimary lung cancer. J Natl Cancer Inst 2013;105:551–62.

32. Himeno E, Ohyagi Y, Ma L, Nakamura N, Miyoshi K, Sakae N, et al.Apomorphine treatment in Alzheimer mice promoting amyloid-betadegradation. Ann Neurol 2011;69:248–56.

33. Steeg PS. Targeting metastasis. Nat Rev Cancer 2016;16:201–18.34. Langley RR, Fidler IJ. The biology of brain metastasis. Clin Chem

2013;59:180–9.35. Sgroi DC. Preinvasive breast cancer. Annu Rev Pathol 2010;5:193–221.36. Hurst RE, Bastian A, Bailey-Downs L, Ihnat MA. Targeting dormant micro-

metastases: rationale, evidence to date and clinical implications. Ther AdvMed Oncol 2016;8:126–37.

37. Hamilton A, Sibson NR. Role of the systemic immune system in brainmetastasis. Mol Cell Neurosci 2013;53:42–51.

38. Jobling P, Pundavela J, Oliveira SM, Roselli S, Walker MM, HondermarckH. Nerve-cancer cell cross-talk: a novel promoter of tumor progression.Cancer Res 2015;75:1777–81.

39. Neman J, Termini J, Wilczynski S, Vaidehi N, Choy C, Kowolik CM, et al.Human breast cancermetastases to the brain display GABAergic propertiesin the neural niche. Proc Natl Acad Sci U S A 2014;111:984–9.

40. Minami K, Liu S, Liu Y, Chen A, Wan Q, Na S, et al. Inhibitory effects ofdopamine receptor D1 agonist on mammary tumor and bone metastasis.Sci Rep 2017;7:45686.

41. Wang S, Mou Z, Ma Y, Li J, Li J, Ji X, et al. Dopamine enhances theresponse of sunitinib in the treatment of drug-resistant breast cancer:involvement of eradicating cancer stem-like cells. Biochem Pharmacol2015;95:98–109.

42. Peverelli E, Giardino E, Treppiedi D, Locatelli M, Vaira V, Ferrero S, et al.Dopamine receptor type 2 (DRD2) inhibits migration and invasion ofhuman tumorous pituitary cells through ROCK-mediated cofilin inacti-vation. Cancer Lett 2016;381:279–86.

43. Hoeppner LH, Wang Y, Sharma A, Javeed N, Van Keulen VP, Wang E, et al.Dopamine D2 receptor agonists inhibit lung cancer progression by reduc-ing angiogenesis and tumor infiltrating myeloid derived suppressor cells.Mol Oncol 2015;9:270–81.

44. Roy S, Lu K, Nayak MK, Bhuniya A, Ghosh T, Kundu S, et al. Activation ofD2 dopamine receptors in CD133þve cancer stem cells in non-small celllung carcinoma inhibits proliferation, clonogenic ability, and invasivenessof these cells. J Biol Chem 2017;292:435–45.

45. FengDD,CaiW,ChenX. The associations between Parkinson's disease andcancer: the plot thickens. Transl Neurodegener 2015;4:20.

46. Bajaj A, Driver JA, Schernhammer ES. Parkinson's disease and cancerrisk: a systematic review and meta-analysis. Cancer Causes Control2010;21:697–707.

47. Webster WS, Leibovich BC. Exisulind in the treatment of prostate cancer.Expert Rev Anticancer Ther 2005;5:957–62.

48. TomodaR, SetoM,Hioki Y, Sonoda J,Matsumine A, Kusuzaki K, et al. Low-dose methotrexate inhibits lung metastasis and lengthens survival in ratosteosarcoma. Clin Exp Metastasis 2005;22:559–64.

49. Fujita K, Kubota Y, Ishida H, Sasaki Y. Irinotecan, a key chemotherapeuticdrug for metastatic colorectal cancer. World J Gastroenterol 2015;21:12234–48.

50. Boyle A, Ondo W. Role of apomorphine in the treatment of Parkinson'sdisease. CNS Drugs 2015;29:83–9.

51. Jung YS, Lee SO. Apomorphine suppresses TNF-alpha-induced MMP-9expression and cell invasion through inhibition of ERK/AP-1 signalingpathway in MCF-7 cells. Biochem Biophys Res Commun 2017;487:903–9.

52. Zhang X, Ye YL, Zhu H, Sun SN, Zheng J, Fan HH, et al. Selenotranscrip-tomic analyses identify signature selenoproteins in brain regions in amouse model of Parkinson's disease. PLoS One 2016;11:e0163372.

53. Rosok O, Pedeutour F, Ree AH, Aasheim HC. Identification and char-acterization of TESK2, a novel member of the LIMK/TESK family ofprotein kinases, predominantly expressed in testis. Genomics 1999;61:44–54.

54. Farkhondeh A, Niwa S, Takei Y, Hirokawa N. Characterizing KIF16B inneurons reveals a novel intramolecular "stalk inhibition" mechanism thatregulates its capacity to potentiate the selective somatodendritic localiza-tion of early endosomes. J Neurosci 2015;35:5067–86.

55. Hirokawa N, Niwa S, Tanaka Y. Molecular motors in neurons: transportmechanisms and roles in brain function, development, and disease.Neuron 2010;68:610–38.


Singh et al.




2018;78:5124-5134. Published OnlineFirst July 9, 2018.Cancer Res Mohini Singh, Chitra Venugopal, Tomas Tokar, et al. Lung-to-Brain MetastasisTherapeutic Targeting of the Premetastatic Stage in Human

Updated version

10.1158/0008-5472.CAN-18-1022doi:

Access the most recent version of this article at:

Material

Supplementary

http://cancerres.aacrjournals.org/content/suppl/2018/07/07/0008-5472.CAN-18-1022.DC1

Access the most recent supplemental material at:

Cited articles

http://cancerres.aacrjournals.org/content/78/17/5124.full#ref-list-1

This article cites 53 articles, 10 of which you can access for free at:

Citing articles

http://cancerres.aacrjournals.org/content/78/17/5124.full#related-urls

This article has been cited by 1 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://cancerres.aacrjournals.org/content/78/17/5124To request permission to re-use all or part of this article, use this link



http://cancerres.aacrjournals.org/lookup/doi/10.1158/0008-5472.CAN-18-1022

http://cancerres.aacrjournals.org/content/suppl/2018/07/07/0008-5472.CAN-18-1022.DC1

http://cancerres.aacrjournals.org/content/78/17/5124.full#ref-list-1

http://cancerres.aacrjournals.org/content/78/17/5124.full#related-urls

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

mailto:[email protected]

http://cancerres.aacrjournals.org/content/78/17/5124


Documents

Therapeutic Targeting of the Premetastatic Stage in Human Lung-to-Brain … · colonizing cancer cells could be targeted before they initiate the secondary brain tumor. Here we use