Reduced Expression of PROX1 Transitions Glioblastoma Cells Into a Mesenchymal

Author Manuscript Published OnlineFirst on August 22, 2018; DOI: 10.1158/0008-5472.CAN-18-0320 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Title: Reduced expression of PROX1 transitions glioblastoma cells into a mesenchymal

gene expression subtype

Running title: PROX1 regulation of glioblastoma subtype

Author list: Kaveh M. Goudarzi1, Jaime A. Espinoza1, Min Guo2, Jiri Bartek1&3,

Monica Nistér2, Mikael S. Lindström1, Daniel Hägerstrand2

Author affiliations: 1SciLifeLab, Division of Genome Biology,

Department of Medical Biochemistry and Biophysics, Karolinska Institutet,

SE-171 21 Stockholm, Sweden. 2Cancer Center Karolinska, Department of Oncology-

Pathology, Karolinska Institutet and Karolinska University Hospital at Solna, SE-171 76

Stockholm, Sweden. 3The Danish Cancer Society Research Centre, DK-2100,

Copenhagen, Denmark.

Corresponding author contact information: email: [email protected] phone:

+46 70 569 87 36 address: Department of Oncology-Pathology, Cancer Center

Karolinska, CCK R8:05, Karolinska Institutet and Karolinska University Hospital at

Solna, SE-171 76 Stockholm, Sweden.

Conflict of Interest: The authors have no conflicts of interest to declare.

Downloaded from cancerres.aacrjournals.org on September 28, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 22, 2018; DOI: 10.1158/0008-5472.CAN-18-0320 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Abstract

The homeodomain transcription factor PROX1 has been linked to several cancer types

including gliomas, but its functions remain to be further elucidated. Here we describe a

functional role and the prognostic value of PROX1 in glioblastoma. Low expression of

PROX1 correlated with poor overall survival and the mesenchymal glioblastoma subtype

signature. The latter finding was recapitulated in vitro where suppression or

overexpression of PROX1 in glioma cell cultures transitioned cells to a mesenchymal or

to a non-mesenchymal glioblastoma gene expression signature, respectively. PROX1

modulation affected proliferation rates that coincided with changes in protein levels of

CCNA1 and CCNE1 as well as the cyclin inhibitors CDKN1A, CDKN1B, and

CDKN1C. Overexpression of SOX2 increased PROX1 expression, but treatment with a

CDK2 inhibitor subsequently decreased PROX1 expression, which was paralleled by

decreased SOX2 levels. The THRAP3 protein was a novel binding partner for PROX1,

and suppression of THRAP3 increased both transcript and protein levels of PROX1.

Together these findings highlight the prognostic value of PROX1 and its role as a

regulator of glioblastoma gene expression subtypes, intratumoral heterogeneity,

proliferation, and cell cycle control.

Introduction

Glioblastoma represents the most common and aggressive primary brain tumor type in

adults (1). Its intrusive growth and the plastic nature of the tumor make complete surgical resection impossible and the tumor cells prone to evade chemo- and radiation therapy (2).

Stem cell regulatory pathways are shown activated in gliomas supporting self-renewal,

tumor maintenance and survival under stress (3). Furthermore, glioblastomas are very

heterogeneous on an intratumoral level, composed of tumor cells displaying different

gene expression signatures constituting the different glioblastoma tumor subtypes (4).

Thus, a glioma stem-like phenotype, cell motility and tumor cell heterogeneity are

considered significant hurdles to overcome for developing new treatment against these

tumors (5,6).

Glioblastoma may arise from adult neural stem cells or multipotent neural progenitor

cells that persist in proliferative niches in the human central nervous system, or

alternatively from differentiated cells of different lineages as for example astrocytes or

oligodendrocytes (7). The human glioblastoma transcriptome has been found to resemble

normal outer radial glial cells and intermediate progenitors (8). In support of this, it was

recently shown that glioblastoma initiation is associated to aberrant reactivation of a

normal developmental program in the brain (9). Therefore, a better understanding of

developmental pathways and their involvement in glioblastoma is thought to lead to new

therapeutic possibilities.

PROX1 (Prospero-Related Homeobox 1) is a transcription factor that mediates cell fate

decisions of neuroblasts, as reviewed in (10). In several instances PROX1 has been

shown to play an active role in cancer. For example, PROX1 suppresses the growth of

neuroblastoma (11), whereas it enhances colorectal cancer progression as a -

catenin/TCF/LEF target gene contributing to a transition from early to a dysplastic stage

(12). Prox1 regulates the number of cancer stem cells, by promoting cell proliferation and

thereby expanding the cancer stem cell population in intestinal adenomas and colorectal

cancer after activation of the Wnt-pathway (13). In human astrocytic brain tumors, the percentage of PROX1+ cells has been shown to increase with tumor grade (14).

Furthermore, level of PROX1 predicted survival in grade II gliomas, where a percentage

of PROX1+ cells over 10% correlated with worse outcome (15). Moreover, PROX1 has

been proposed as a novel pathway-specific prognostic biomarker for high-grade

astrocytomas (16). Here we sought further understanding of the role of PROX1 in glioblastoma by analyzing publicly available gene expression data from tumor samples

and by performing in vitro experiments.

Material and methods

Cell culture

Glioblastoma cell lines U-343 MG, U-343 MGa, U-343 MGa Cl2:6, U118MG, U178MG,

U373MG, and U1242MG have previously been characterized (17-21) and references

therein. The high-grade glioma cultures 4, 11 and 18 were initially characterized based on

gene expression and phenotypes (22), and have in other studies also been referred to as

U2975, U2982 and U2987 (23), respectively. For unity, these cultures are referred to as

U2975(4), U2982(11) and U2987(18) in this study. The cell lines and cultures above

were retrieved from authors’ lab stocks (22) and subsequently cultured for less than 3

months. All cell lines were maintained in Dulbecco’s modified Eagle’s medium (DMEM)

(Thermo Fisher Scientific) with 10% FBS, 2mM L-glutamine and penicillin-streptomycin

at 37°C with 5% CO2, and 5% O2 and >95% humidity. The other glioma cell lines

(U87MG, U251MG, M059J and M059K), as well as the osteosarcoma line U2-OS and

colon cancer line SW480 were purchased from ATCC and cultured per standard

guidelines for less than a month. SOX2 and YFP (control) overexpressing U-343 MG

cultures were generated by lentiviral transduction with pLEX-Blast-V5-SOX2 and pLEX-

Blast-V5-YFP by methods previously described (24). To confirm the relationship

between the U-343 clones they were subjected to STR profiling at NGI-Uppsala,

SciLifeLab, Uppsala University, using the AmpFISTR Identifiler PCR Amplification kit

(Thermo Fisher) (Table S1). The cultures used in this study were screened for

mycoplasma using the MycoAlert™ Mycoplasma Detection Kit (LT07-218, Lonza).

Stock solution of CVT-313 (Santa Cruz) was prepared by dissolving powder in DMSO,

stored at -20 ℃, and used by dissolving stock solution in cell culture media and

incubating with cells at concentration and time indicated.

Lentiviral transduction and transfection of plasmids or siRNA constructs

To generate a PROX1 overexpressing cell culture, U-343 MG-PROX1, purified lentivirus

(Lentifect™) for PROX1 transduction into U-343 MG was purchased (PROX1 containing virus LPP-F0925-Lv105-100-S and negative control virus LPP-NEG-LV105-100-C,

GeneCopoeia), and used according to manufacturer’s instructions. To study PROX1

suppression in U-343 MGa, the cell line U-343 MGa-shPROX1 was generated using

lentiviral vectors against PROX1 (TRCN0000232123, Sigma-Aldrich) or unrelated

control shRNA targeting GFP (TRCN0000072178, clonetechGfp_438s1c1, previously

described (24)). Virus production was performed by calcium-phosphate-mediated co-

transfection of HEK 293T cells with packaging plasmids (MISSION Lentiviral

Packaging Mix, SHP001, Sigma-Aldrich). 24 hours after transfection the different

supernatants were collected two times with 24-hour intervals, filtered and then used to

infect U-343 MGa cells cultured under optimal conditions. Lentiviral transductions of

glioblastoma cells were conducted at MOI of 1-2. The cells were analyzed and selected in

the presence of 1 μg/mL Puromycin for 72 hours or until all non-transduced control cells

were killed. Transduced cells were kept in culture for 3 days, prior to experiments. For

RNAi experiments SMARTpool mixes, ON-TARGETplus siRNA (GE Healthcare

Dharmacon), were used to target PROX1 (L-016913-00-0005), SOX2 (L-011778-00-

0005) or THRAP3 (L-019907-00-0010) or non-targeting control pool (D-001810-10-05)

according to the manufacturer’s instructions. Lipofectamine® 2000 (Thermo Fisher

Scientific), and RNAiMAX (Thermo Fisher Scientific) reagents were used for the

transfection of plasmid DNA and siRNA, respectively, according to the manufacturer´s

instructions.

Publicly available gene expression data

The Cancer Genome Atlas (TCGA) data from 539 glioblastoma samples, published in

2013 (25) was downloaded from UCSC University of California Santa Cruz Xena portal

(www.xenabrowser.net). Data for glioblastoma gene expression based subtypes and

patient survival were downloaded at cBioPortal for Cancer Genomics at

http://cbioportal.org and merged with the expression data. Samples categorized as G-

CIMP or taken from relapse tumors were removed yielding a final set of 375 tumors.

Gene expression data for cell cultures was downloaded from the CCLE (26) database at

www.broadinstitute.org/ccle. Cell cultures described to be derived from glioblastomas

were selected, which generated a set of 45 cell cultures. Gene expression data for 48

glioblastoma cell cultures was downloaded from the Human Glioblastoma Cell Culture

(HGCC) portal at www.hgcc.com (27).

Survival analysis of glioblastoma patients

Analyses of overall and disease-free survival were conducted using data available for 206

glioblastoma patients, published in 2008 (28), extracted from http://cbioportal.org.

Statistical analysis of the data was performed using an unpaired two-tailed t-test

assuming normal distributions (Prism 6.0, Graphpad software Inc.). Kaplan–Meier

survival curves were plotted for patients grouped according to PROX1 expression levels

above or below 1, and THRAP3 above or below 0.3. Log-rank (Mantel–Cox) tests were

used to determine differences between survival curves.

Gene Set Enrichment Analysis

For the Gene Set Enrichment Analysis (GSEA) (29), we used the software on the

GenePattern online server (www.genepattern.broadinstitute.org) module version 18 and

the Molecular Signatures Database (MSigDB) (30) version 6.0. Overlaps of PROX1

correlated gene sets were computed with the following categories in MSigDB collections

using the default setting (FDR q-value below 0.05). For GSEA of the CCLE data for 45

glioblastoma cell cultures we divided the cultures into two groups based on the

expression of PROX1. To determine at what cutoff to divide the sample data into two

groups according to PROX1 expression the cultures were first ranked per expression level

of PROX1. T-tests were then performed between high and low PROX1 expression in the

samples for all the genes. The T-test was performed for all permutations starting with the

2 cultures with the highest PROX1 versus the rest, the 3 cultures with the highest PROX1

level versus the rest, and so on. For each permutation, the T-test significance was

determined for all genes and internally compared to the p-value based rank position of

PROX1. The cutoff for high versus low PROX1 expressing cultures was set to where

PROX1 had the highest relative p-value ranking compared to other genes. In other words,

the cut-off was set to where PROX1 had the lowest relative p-value compared to other

genes. We found the optimal cutoff when the cultures were divided into 25 low versus 20

high PROX1 expressing cultures where PROX1 was the 11th most significant gene to

distinguish the samples. We then used the 25 low versus 20 high PROX1 expressing

culture division for further GSEA analyses. The analyses were run with 1000

permutations, phenotype as permutation type, collapsed data set and the AFFYMETRIX

chip platform file option. In this case, we investigated the gene set databases that

included all subcategories including positional, curated, motif, computational, gene

ontology, oncogenic signatures, immunologic signatures and hallmarks.

RNA purification and quantitative real-time PCR

Total cellular RNA was extracted from 70-80% confluent cultures using PureLink® kits

(Ambion® RNA extraction products) according to manufacturer’s instructions (Thermo

Fisher Scientific). RNA concentrations were measured with a NanoDrop

spectrophotometer and samples were stored at -80 °C. For gene expression analysis, real-

time quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) was

performed. The Power SYBR® Green RNA-to-CT™ 1-Step kit (Thermo Scientific) was

used according to the manufacturer’s instructions in conjunction with a 7500

StepOnePlus Real-Time PCR system (Thermo Scientific). GAPDH was used as the

internal standard reference. Gene expression levels were calculated using the relative

ΔΔCt method. The primers used for qRT-PCR are listed in Table S2. Statistical analysis

of the data was performed using an unpaired Student’s two-tailed t-test (Prism 6.0,

Graphpad software Inc.). P-values <0.05 were considered statistically significant.

RNA sequencing and gene expression analysis

RNA quality was assessed by electrophoresis on the Tapestation instrument (Agilent

Technologies) and samples with a RIN score above 8 were used in further analysis. RNA

library preparation, sequencing, raw data processing and quality control were performed

at the National Genomics Infrastructure at Science for Life Laboratory, Stockholm).

Reads were mapped using Tophat (2.0.4.) and FPKM values were generated with

Cufflinks (2.1.1.). For further gene expression based analyses we filtered transcripts from

the expression data (FPKM values) that were present for the CCLE gene-centric RMA-

normalized Affymetrix data, retaining about 12042 genes. Significant difference in

expression was calculated in Prism applying a t-test to two biological groups: 1) U-343

MG-control versus U-343 MG-PROX1 with 2 replicates in each group and 2) U-343

MGa-control versus U-343 MGa-shPROX1 with 3 replicates in each group. Volcano

plots were generated by using genes with FPKM values over 0.01, a 2-fold change in

expression and a p-value below 0.05. Venn diagrams were generated using GeneVenn

(http://genevenn.sourceforge.net) to identify PROX1 induced or suppressed genes in an

extended list only with more than 2-fold difference in expression between groups of

replicates, where the average of either replicate group was not 0. The PROX1-induced

and -repressed genes were investigated at MSigDB (30) to identify enriched gene sets.

RNA-seq data has been deposited at EBI ArrayExpress database with the accession

number E-MTAB-6991. To generate gene expression heat maps the Heat map module at the GenePattern site was used (31).

Glioblastoma gene expression based subtype analysis

To assess changes of glioblastoma gene expression subtypes due to PROX1

overexpression in U-343 MG-PROX1 or suppression in U-343 MGa-shPROX1, we

counted the number of subtype defining genes whose expression were altered in our

RNA-seq experiment following modulation of PROX1. To assess the change of each

gene expression subtype we used the subtype defining centroid genes previously

described by (32), where each subtype was described by a set of 210 centroid genes with

either high or low expression as compared to the others. For instance, in our analysis

corresponding to U-343 MG-PROX1 versus U-343 MG for the mesenchymal subtype, 35

out of 128 genes with available measurements were increased more than 1-fold, while 12

out of 33 genes decreased more than 1-fold. This resulted in a rescaled index of -42 for

the mesenchymal subtype, when -100 is full negative change of subtype, 0 is neutral, and

100 is full positive change.

Cell proliferation assay

Cell proliferation assay was performed by cell counting using Countess™ automated cell

counter (Thermo Fisher Scientific). For proliferation analysis cells were seeded in 12-

well plates and the total cell number was determined at the indicated time points. The

experiments were repeated three times using triplicate samples. Statistical analysis of the

data was performed using an unpaired Student’s two-tailed t-test. p-values <0.05 were

considered statistically significant.

Immunoblotting, co-immunoprecipitation and mass spectrometry analysis

For detection of specific antigens the following antibodies were used; PROX1 (R&D

Systems AF2727), PROX1 (Abcam, ab37128), p53 (Santa Cruz, sc-6243), p53 (Santa

Cruz, sc-6243-G), p57 Kip2 (Abcam, ab75974), P21 (Abcam, ab7960), P21 (Santa Cruz, sc-397), Cyclin B1 (Santa Cruz, sc-245), Cyclin D1 (Santa Cruz, sc-8396), Cyclin E

(Abcam, ab7959-1), CyclinA2 (Abcam, ab16726), Cdk2 (Santa Cruz, sc-6248),

TRAP150 (Santa Cruz, sc-48779), β-actin (Sigma-Aldrich, clone AC15), SOX2 (EDM

Millipore, AB5603), GFAP (Abcam, ab10062), FN1 (BD Biosciences, 610078). For

analysis of detergent soluble proteins, we used 0.5% Nonidet P-40 lysis buffer with

complete protease and phosphatase inhibitors (PhosSTOP™, cOmplete™ ULTRA)

(Roche). Analysis of detergent soluble proteins, co-immunoprecipitation (co-IP), and IB were conducted as described (33). For co-IP (“large-scale”) followed by mass spectrometry, the cells were harvested and prepared according to the nuclear complex co-

IP kit instructions (Nuclear Complex Co-IP Kit) (Nordic Biolabs), using the high

stringency buffer option (Active Motif). For the initial co-IP in U2-OS cells we used an antibody against the FLAG tag (Flag M2, F1804, Sigma-Aldrich). Immunoprecipitates

were separated by SDS-PAGE (NuPAGE™ Novex™ 4-12% Bis-Tris Protein Gels) (Life

Technologies), the gels stained with colloidal CBB kit (Life Technologies), bands unique to the PROX1-FLAG antibody lane were cut out, and identified by mass spectrometry

(LC-MS/MS) (Alphalyse A/S, Denmark). Protein samples were reduced and alkylated with iodoacetamide, i.e. carbamidomethylated, and trypsin digested. Peptides were concentrated on a ZipTip micropurification column and eluted onto an anchorchip target to be used for analysis on a Bruker Autoflex III MALDI TOF/TOF instrument. MALDI

MS/MS was performed on 15 peptides for peptide fragmentation analysis. Peptide tolerance was set to 60 ppm (1 miscleavage allowed). The MS and MS/MS spectra were combined and Mascot software version 2.2.03 was used. The database NRDB was used for protein identification.

Immunofluorescence staining

Procedures for immunofluorescence staining and microscopy analysis have previously been published (33).

Results

Low PROX1 mRNA level in glioblastoma tumors correlates with mesenchymal gene

expression subtype and with shorter survival

Since PROX1 has been shown to have diagnostic and prognostic value in gliomas, we

utilized gene expression datasets to further explore this. First we investigated a combined

low- and high-grade glioma dataset composed of 226 grade II, 244 grade III and 150

grade IV gliomas from the GlioVis portal (http://gliovis.bioinfo.cnio.es/) to assess

PROX1 expression levels (34). PROX1 transcripts were significantly less in grade IV

tumors as compared to grade II (p-value <0.0001) and III (p-value <0.0001) tumors (Fig.

1A). In a survival analysis glioma patients with high PROX1 expression levels survived

longer, with a median survival of 64.6 months versus 40.5 for the subgroup with low

PROX1 expression levels (p-value <0.0001) (Fig. 1B). To investigate the prognostic

value of PROX1 exclusively in glioblastomas, we extracted gene expression data

available for 206 glioblastoma patients from The Cancer Genome Atlas (TCGA) at the

cBio Cancer Genomics Portal (http://cbioportal.org). Survival differences for different

PROX1 expression z-score cutoffs (-2 to +2 thresholds) were analyzed and values

between 0.8-1.3 for overall survival, and 0.9-1.3 for disease-free survival yielded

significant differential survival (Fig. 1C). Specifically, the median overall survival for

patients with a PROX1 expression higher than z-score 1 was 33.6 months as compared to

12.3 months for the subgroup with lower expression (16 versus 190 patients; p-

value=0.0015), while the median disease free survival for patients with a PROX1

expression higher than z-score 1 was 16 months compared to 6.5 (13 versus 149 patients;

p-value=0.0118) (Fig. 1D and E). To investigate the correlation between PROX1

expression and molecular subtypes in glioblastoma, we explored a TCGA dataset (25)

containing 375 glioblastoma patients with available subtype calling. We divided the

patients into 4 equally sized groups based on PROX1 high to low expression values (Fig.

1F). We found that the group with highest PROX1 expression, patients 1 to 94 (group 1),

was mostly represented by tumors of classical and proneural subtypes (Fig. 1G). On the

contrary, in tumors with lowest PROX1 transcript levels, patients 283 to 375 (group 4),

mesenchymal and neural subtypes were the most abundant. Specifically, significant lower

PROX1 expression (z-score) was found in mesenchymal subtype compared to proneural

(p-value <0.0001) or classical subtype (p-value <0.0001) (Fig. 1H). Together, the

proneural and classical have overall a higher PROX1 expression, whereas neural and

mesenchymal cases have lower PROX1 expression levels. Thus, low PROX1 expression

defines a subset of patients enriched for mesenchymal and neural glioblastoma subtypes

that display shorter disease free and overall survival. Of note, the neural subgroup has

been associated with contamination of normal neuroepithelial cells and non-tumor

specific expression signature and should therefore be considered with caution (35).

Expression analysis of PROX1 in different glioblastoma cell lines identifies co-

expressed genes and functional implications

To assess gene expression differences between glioblastoma cultures with high or low

PROX1 expression levels we performed Gene Set Enrichment Analysis (GSEA) for gene

expression data from 45 glioblastoma cell lines in the Cancer Cell Line Encyclopedia

(CCLE) (26). 25 cultures were defined to contain high PROX1 levels versus 20 with low.

A ranked list of genes was generated and queried against gene sets deposited at MSigDB

(29). 137 gene sets were significantly enriched for glioma cultures with relatively high

PROX1 expression and 71 gene sets for low PROX1 expressing glioblastoma cultures

(Table S3).

Analysis of protein and gene expression in glioma cell cultures and generation of

stable cell lines to assess PROX1 function

To evaluate PROX1 expression we compared protein levels in a set of glioma cancer cell cultures including the clonal model U-343 (19) by immunoblotting and

immunofluorescence staining. We found that U-343 MGa 31L, U-343 MG, U87MG,

U2982(11), U2975(4), M059J, and M059K had undetectable levels of PROX1 protein,

whereas it was readily detected in U-343 MGa Cl2:6, U-343 MGa, U373MG, U251MG

and U2987(18) (Fig. 2A and B). PROX1 also displayed a nuclear localization as

previously described in the positive control colorectal cancer cell line SW480 (12) (Fig.

2B). U-343 MG and U-343 MGa (and its derivatives U-343 MGa Cl2:6 and U-343 MGa

31L) were originally derived from one glioblastoma tumor, and have previously been

characterized (17,19). The U-343 MG cells express Fibronectin (FN1) but not Glial

Fibrillary Acidic Protein (GFAP), and display a bipolar fibroblastic cell shape. The U-

343 MGa cells on the other hand express GFAP but not FN1, and they display a

polygonal morphology. Corresponding levels of glioma cell protein markers SOX2,

GFAP and FN1 were also determined in the panel of cell lines by immunoblotting. The

astroglial stem cell related proteins SOX2 and GFAP were present in all the PROX1

expressing cells, whereas FN1 was expressed in all PROX1 negative cultures, except U-

343 MGa 31L and U87MG (Fig. 2A). Thus, PROX1 is differentially expressed in glioma cell lines and is often co-expressed with SOX2 and GFAP (Fig. 2A). The relatively high

expression of FN1 in U-343 MG, and high expression of GFAP in U-343 MGa cultures was confirmed by qRT-PCR (Fig. 2C). Given that the two cell lines U-343 MG and U-

343 MGa are derived from the same tumor but differ in levels of PROX1 protein (Fig.

2A), we chose these two for further analysis of PROX1 function. Using lentivirus, we

generated stable cell lines with either PROX1 overexpression or shRNA-mediated

PROX1 suppression. In U-343 MG-PROX1 or MGa-shPROX1, we achieved stable

overexpression or suppression of PROX1 at the mRNA and protein level, respectively

(Fig. 2D and E). Experiments using double-immunofluorescence staining have previously

shown the existence of GFAP and PROX1 double positive cells in grade IV brain tumors

(14). To investigate if PROX1 is a marker or driver of the U-343 phenotype defined by

high GFAP expression we performed qRT-PCR analysis. We found increased GFAP

mRNA levels upon overexpression of PROX1 in U-343 MG, and decreased levels in U-

343 MGa upon suppression of PROX1 (Fig. 2F). Accordingly, immunofluorescence

staining and immunoblotting of U-343 MGa-shPROX1 samples revealed reduced GFAP

protein (Fig. 2G and 2H). Similar to U-343 MGa the high-grade glioma culture

U2987(18) expresses GFAP endogenously (22). We found that a transient suppression of

PROX1 in this line was sufficient to decrease GFAP transcript levels as determined by

qRT-PCR (Fig. 2I). Taken together, PROX1 correlates with and regulates GFAP

expression in human glioblastoma cells, thus could be a driver of the U-343 MGa

phenotype.

Global gene expression analysis by RNA-seq upon PROX1 overexpression or

suppression in glioblastoma cells

To gain a more comprehensive understanding of PROX1 as a transcription factor in glioblastoma, gene expression analysis was conducted by RNA sequencing. We analyzed differential gene expression upon PROX1 overexpression in U-343 MG or suppression in

U-343 MGa cells (Fig. 3A). 394 genes were significantly up-regulated more than 2-fold and 139 down-regulated significantly more than 2-fold after PROX1 overexpression. In the same way, 137 versus 93 genes were altered upon PROX1 suppression (Fig. 3A). For

further analyses, we compared intersectional genes between PROX1 overexpression and

suppression using an extended gene list applying only a cut-off of 2-fold expression

change. We selected 1472 genes whose expression was increased more than 2-fold upon

PROX1 overexpression in U-343 MG and 733 that were decreased more than 2-fold upon

PROX1 suppression in U-343 MGa. From these, we identified 237 overlapping genes,

which we defined as PROX1-induced genes (Fig. 3B). Conversely, the expression of 855

genes increased more than 2-fold upon PROX1 suppression in U-343 MGa, and 1199

genes decreased more than 2-fold upon PROX1 overexpression in U-343 MG. Out of

these, 286 overlapped and are from here on referred to as PROX1-repressed genes (Fig.

3C).

From an MSigDB analysis of the 237 PROX1-induced genes we found enrichment of GO

terms including neurogenesis and regulation of cell proliferation (Fig. 3D) and overlaps

with hallmark gene categories (Fig. 3E). Similarly, results from the analysis performed

for the PROX1-repressed genes are presented in Figure 3F and G. Of note, both PROX1-

induced and PROX1-repressed gene sets were enriched for hallmark genes implicated in

epithelial to mesenchymal transition.

To assess the relevance of our identified PROX1-induced and -repressed genes in vivo we

performed further analysis of these by comparing them with PROX1 correlated genes in

glioblastoma TCGA expression data (Fig. 3H and I). The Venn diagram in Figure 3H

presents that 16% (38 out of 237) of PROX1-induced genes identified here overlap with

the top 2000 genes positively correlated to PROX1 expression in TCGA glioblastoma

cases. Conversely, 32% of PROX1-repressed genes overlapped with the top 2000

negatively correlated to PROX1 expression (Fig. 3I). GO terms and hallmark gene

categories were also identified for a merged list of the intersecting genes from Figure 3H

and 3I, as illustrated in Figure 3J. Collectively, our analysis of RNA-seq data facilitated

the identification of PROX1-regulated functional gene networks, including those

controlling neurogenesis, regulation of cell proliferation, and epithelial to mesenchymal

transition.

PROX1 regulates gene expression-based subtypes in glioblastoma

Since PROX1 expression in our survey of TCGA data correlated with glioblastoma

subtypes (Fig. 1G and H) we used our gene expression data to assess its regulation of

these subtypes. We calculated a support index for each signature by counting the number

of classifier genes (32) with altered expression for each corresponding subtype. We found

that PROX1 overexpression decreased the support index for mesenchymal subtype and

increased it for non-mesenchymal subtypes in U-343 MG cells. Upon a Chi-square test

the change of genes supporting a mesenchymal subtype was found significant with a p-

value less than 0.000095 (Fig. 4A and Fig. S1). Conversely, PROX1 suppression

significantly (Chi-square, p-value 0.017) increased the mesenchymal support index in U-

343 MGa cells (Fig. 4B and Fig. S1). Thus, PROX1 modulation altered the glioblastoma

gene expression subtype in cell cultures, where overexpression shifted cells into a non-

mesenchymal and suppression into a mesenchymal subtype.

To pinpoint a system independent core set of PROX1 regulated genes we filtered genes

that were correlated with PROX1 in TCGA, CCLE and the HGCC (Human Glioblastoma

Cell Culture) resource (27) (Fig. 4C). In an extended exploration of these datasets, we

found similarities between gene expression patterns in glioblastoma tumor material and

cell cultures regarding PROX1 correlated genes, and identified GO terms suggesting

involvement of PROX1 in several biological processes including regulation of nervous

system development and chromatin modifications (Fig. S2). The analysis revealed 82

intersecting PROX1 correlated genes between CCLE, HGCC and TCGA (Fig. 4C).

Strikingly, of these the top 10 PROX1-upregulated genes, ranging from a 203- to a 15-

fold increase, all have been reported to be involved in neuroglial development, including

SEMA6A, NES, PTPRZ1, FABP7, POU3F2 and GAP43 (Fig. 4C). Moreover, the

intersect genes that were upregulated more than 2-fold were enriched for gene sets

including the “classical” and “proneural” subsets, “neural development” and “upregulated in glioblastoma cells with capacity to form neurospheres” (Table S4). We also noted that

FABP7, PTPRZ1 and GAP43 were among the top stemness signature genes in the single-

cell gene expression data described by Patel et al. (4). Upon analysis of the Patel et al. stem cell signature genes, we found that PROX1 increased the expression level of 27 out of the 44 genes (61%) with available expression values, highlighting the connection

between PROX1 and stemness regulation in glioblastoma (Fig. 4D). To investigate

PROX1 expression pattern at a single-cell level we analyzed the single-cell RNA-seq data

available for 5 glioblastomas (4). Since a PROX1 expression measurement was not

available in the dataset, we developed a PROX1 proxy profile to assess PROX1 related

expression patterns. Specifically, we calculated a proxy profile based on the average

expression of the top 5 PROX1-regulated genes from the Patel et al. stemness signature,

composed of SPARCL1, PTPRZ1, FABP7, GLUL and GAP43. We filtered and ranked the top 100 positively correlated genes to the proxy profile in the Patel et al. dataset. In each

of the tumors we observed cell populations with varying expression levels of genes

positively correlated with the PROX1 proxy (Fig. 4E). By analyzing single-cell data, we

demonstrate cell heterogeneity within tumors regarding genes correlated to the PROX1

proxy profile. Furthermore, we found that 71% of genes with a fold change more than 2

(30 out of 42; among the top 100) could be upregulated by PROX1 overexpression, based

on the RNA-seq analysis of U-343 MG-PROX1 (Fig. 4E). Together, the combined

analysis of functional effects of PROX1 in cell cultures, co-expression in multiple gene expression datasets, including both bulk tumor and single-cell measurements, describes

PROX1 as a regulator of glioblastoma tumor evolution that distinguishes subpopulations of cells in heterogeneous tumors and whose loss of expression enables a switch from a non-mesenchymal to a mesenchymal phenotype.

PROX1 overexpression induced cell cycle regulators and increased cell proliferation

It has previously been shown that SOX2+/GFAP+ glioblastoma cultures have higher rates

of proliferation and tumorigenicity, as compared to those with mesenchymal profile

characterized by high expression of FN1 (22). We explored data from the HGCC biobank and found that high expression of PROX1 correlated with high tumorigenicity of the

HGCC cultures as previously assessed by intracranial injection into NOD-SCID mice

(27) (Fig. 5A). We thus compared the proliferation rates between U-343 MG-control and

U-343 MG-PROX1, and between U-343 MGa-control and U-343 MGa-shPROX1 cell

lines by cell counting. Overexpression of PROX1 increased proliferation of U-343 MG-

PROX1 cultures (Fig. 5B), whereas PROX1 suppression decreased the proliferation rate

of U-343 MGa (Fig. 5C). In the RNA-seq experiment we noticed changes in the levels of

several cyclin and cyclin dependent kinase (CDK) genes following PROX1 modulation

(Fig. 5D). To investigate if the altered proliferation was paralleled by changes in levels of

cell cycle regulatory proteins, we performed immunoblotting analysis of cyclins. We

found increased levels of cyclins A1 (CCNA1), B1 (CCNB1), D1 (CCND1) and E1

(CCNE1) proteins in U-343 MG-PROX1. PROX1 suppression resulted in decreased cyclins A1 and E1 in U-343 MGa-shPROX1, whereas no noticeable change was observed

for cyclin D1 or cyclin B1 (Fig. 5E). Additionally, we observed decreased p53 (TP53)

and increased p21 (CDKN1A), p27 (CDKN1B), and p57 (CDKN1C) protein levels after

PROX1 overexpression, while the levels remained unchanged in U-343 MGa-shPROX1

compared to the controls (Fig. 5E). In summary, we found increased or decreased

proliferation upon PROX1 overexpression or suppression, respectively, which coincided

with changes of cell cycle regulators.

PROX1 interacts directly and co-localizes with THRAP3 in the nucleus

The function of PROX1 and its regulation remains poorly understood in molecular

details. We thus performed experiments to identify proteins that interact with PROX1.

Initially we used an osteosarcoma cell line that stably expresses PROX1-FLAG or empty-

FLAG. A nuclear complex co-IP followed by mass spectrometry analysis identified

proteins associated with PROX1, of which none has previously been described as a

PROX1 interacting protein (Fig. S3). One of the identified PROX1 associated proteins

was THRAP3 (thyroid hormone receptor associated protein 3, aka TRAP150). THRAP3

peptides detected by MS/MS sequencing are shown (Fig. 6A). Subsequently, we

confirmed the interaction of endogenous PROX1 with THRAP3 protein in the U-343

MGa cells via a reciprocal co-IP experiment (Fig. 6B). Given the important role of

THRAP3 in several cellular processes related to transcriptional activities, we decided to

investigate a functional link between THRAP3 and PROX1. We found that both proteins

localize to the nucleus (Fig. 6C). Upon THRAP3 suppression following five days of

siRNA treatment PROX1 levels were found to have increased (Fig. 6D). Similarly, two-

day THRAP3 siRNA treatment also led to increased PROX1 protein levels in U-343

MGa as well as in U-343 MG-PROX1 cells (Fig. 6E). In line with the result that PROX1 increased GFAP transcript and protein levels (Fig. 2F), we found that GFAP protein

levels also increased upon THRAP3 suppression, though seemingly independent of

PROX1 (Fig. 6F). To investigate if the increase in PROX1 and GFAP levels was due to

transcriptional effects we also assessed transcript levels and found an increase of PROX1

and GFAP expression upon THRAP3 suppression (Fig. 6G). In parallel, no transcript

changes were observed for the oligodendroglial and neuronal makers OLIG2 and MAP2,

respectively (Fig. 6G). To investigate the association between THRAP3 and PROX1

transcripts in glioma, we explored the TCGA data. THRAP3 expression was significantly less in glioma grade IV as compared to grade II (p<0.0001) and grade III (p<0.0001)

tumors (Fig. 6H), displaying a similar pattern to PROX1 expression in the same samples

(cf. Fig. 1A). Moreover, a positive correlation was found between THRAP3 and PROX1

transcript levels in glioblastoma (Pearson r =0.58) (Fig. 6I). Finally, patients with high or

low THRAP3 transcript levels differed with regard to overall and disease-free survival

(Fig. 6J, K and L). A survey of genes with the highest correlation to both PROX1 and

THRAP3 expression in glioblastoma samples revealed overlaps with hallmark gene sets

including oxidative phosphorylation, biological processes including nucleoside

triphosphate metabolic process, and cellular compartments such as respiratory chain and

mitochondria (Fig. S3). Taken together, these results identify THRAP3 as a novel

interacting partner for PROX1, and suggest its involvement in the regulation of PROX1

and GFAP transcript and protein levels.

Correlation and regulatory connection of PROX1 and SOX2 in glioblastoma

SOX genes are key regulators of embryonic development of the CNS, and its

maintenance in adults (36). Specifically, SOX2 has been reported to bind to the PROX1

promoter in a global ChIP-seq analysis (37), which suggests that SOX2 could be a determinant for PROX1 regulation. It was recently shown that SOX2 is stabilized through

phosphorylation by cyclin E-CDK2 complexes in glioma (38). Given that PROX1 was

found to affect expression of cyclins in glioma cells, we investigated the connection

between PROX1, SOX2 and cyclins. As illustrated in Figure 2A we noted a positive

correlation between PROX1 and SOX2 protein levels in a panel of glioma cell lines. In

addition, we found a significant positive correlation between expression of PROX1 and

SOX2 in 375 cases from TCGA clinical data (r=0.43) (Fig. 7A), in 48 glioblastoma cell

cultures from HGCC (r=0.38), and in 45 glioblastoma cell lines from CCLE (r=0.55)

(Fig. 7B). In line with SOX2 being an upstream regulator of PROX1 we found that SOX2

overexpression in U-343 MG caused elevated PROX1 transcript and protein levels (Fig.

7C and 7D), and SOX2 suppression in U-343 MGa decreased PROX1 protein levels (Fig.

7E). On the other hand, suppression of PROX1 in U-343 MGa did not decrease SOX2

protein levels (Fig. 7D). Given that CDK2 activity can stabilize SOX2 (38), we

investigated if treatment of U-343 MGa cells with the CDK2 inhibitor CVT-313

compound would result in decreased PROX1 protein levels and found that CVT-313

treatment decreased both SOX2 and PROX1 protein levels within 24 hours (Fig. 7F). It was confirmed that treatment of cells with 10 µM of the compound for 24 hours also decreased Cyclin E1 levels (Fig. 7F). Together, these data indicate that SOX2 controls

PROX1 expression and that CDK2 inhibition leads to decreased levels of both SOX2,

PROX1 and Cyclin E1. It was recently reported that THRAP3 binds to SOX9 and

regulates transcription (39). Also, we have previously reported a subset of glioblastoma

cell cultures with high expression of SOX2 and SOX9 (22). We therefore investigated the

correlation of expression between SOX2, SOX9 and THRAP3 in glioblastoma samples

and found that THRAP3 was highly correlated with SOX2 and SOX9 (Fig. 7G and H).

Including the results presented in figure 6I, this connects the expression levels of SOX2,

THRAP3, PROX1 and SOX9 in glioblastoma samples.

Discussion

Results presented in this work indicate that PROX1 mRNA level fluctuates with tumor

grade of gliomas, and is expressed at a lower level in grade IV gliomas. In particular, for

grade IV gliomas we found that the mesenchymal subtype was associated with relatively

low PROX1 levels. In the light of recent findings about tumor heterogeneity and the intermixture of mesenchymal and non-mesenchymal gene expression subtype cells (4), the data present a mechanism of how these subtypes may be regulated by PROX1 during tumor cell evolution. In the present analysis PROX1 is less expressed in the

mesenchymal-like U-343 MG cell line as compared to its sibling line U-343 MGa, and

the clonal derivatives thereof, U-343 MGa Cl2:6 and U-343 MGa 31L. According to a

suggested model of tumor progression, from a non-mesenchymal to a mesenchymal

subtype (40), the U-343 MG cells should then have arisen from a U-343 MGa ancestry

clone where PROX1 expression has been lost. Per our gene expression analysis, the U-

343 MGa culture became more mesenchymal-like upon PROX1 suppression, and

conversely U-343 MG shifted to a non-mesenchymal subtype profile upon PROX1

overexpression. Thus, our data show that PROX1 can regulate the glioblastoma subtype

in a bi-directional manner. In extension, the switch between glioblastoma transcriptional

subtypes is reversible and thus allows glioblastoma cells to fluctuate between subtypes

and give rise to heterogeneous tumor cell populations. The subtype gene expression

signature is relatively stable for the U-343 cultures in vitro, suggesting an underlying

genetic or epigenetic cause. Moreover, whether external queues from neighboring cancer

cells and tumor microenvironment also may affect PROX1 levels remains to be studied.

Beyond strictly clonal differences, maintained by genetic or epigenetic events, this could

be an underlying explanation for previous observations where not all cancer cells within

single tumors displayed similar PROX1 protein levels (14). Therefore, our findings

underscore PROX1 function in gliomas where its decreased mRNA level is connected

with tumor cell evolution and intratumoral heterogeneity.

PROX1 is not an established oncogene or tumor suppressor, but results presented here

place it downstream of SOX2 that is a prominent glioblastoma cancer driver due to its

focal amplification and tumor initiating capacity (41,42). The high correlation of PROX1

and SOX2 expression in tissue, and previous reports of SOX2 binding to the PROX1

promoter identified by ChIP-seq analysis of glioblastoma (37) highly suggest that PROX1

is regulated by SOX2. Of note, our GSEA of glioblastoma cell cultures from CCLE with

high or low PROX1 expression identified a significant enrichment of SOX2 target genes.

Thus, in addition to PROX1 being part of a normal CNS developmental program, we

propose that abnormally regulated PROX1 is an important component of astrocytic tumor

formation and growth. The decreased proliferation rate observed upon PROX1

suppression in U-343 MGa raises the question if PROX1 has a growth promoting effect

in early stage tumors; whether it decreases with increasing grade and potentially regulates

transcriptional subtypes during tumor progression. It has been reported that the tumor-

initiating capacity in stem-like glioma cell cultures with high growth rate, as opposed to

mesenchymal-like cultures with low proliferative rate and tumor-initiating capacity, is

maintained by SOX2 (22). Consistent with this, in our analysis of HGCC data (27),

PROX1 expression was also associated with higher tumor initiating capacity. On the other

hand, we found that glioblastomas with lower PROX1 levels, mesenchymal subtype, have

worse outcome. Similarly, Patel et al. demonstrated that glioblastomas with a high

representation of proneural cells were associated with longer survival, compared to those with a high intermixture of mesenchymal cells (4). Furthermore, the glioblastoma cancer

stem cell signature was found strongest in individual cells conforming to the proneural

and classical subtypes, but underrepresented in cells of the mesenchymal subtype. This

presents a conceptual contradiction, where tumor data connects the mesenchymal subtype

with poor outcome, whereas in an experimental setting non-mesenchymal stem-like cells

display higher tumorigenic potential. We speculate that as gliomas become more

mesenchymal when they progress, mesenchymal subtype glioblastomas may represent

more progressed tumors that per se have shorter survival. Many experimental models do

not reflect this and thus may be the underlying cause for this discordance. Furthermore,

the intermixture of non-mesenchymal and mesenchymal cells may have tumorigenic

effects that are not observed in tumor models with low clonal heterogeneity.

The present results show that PROX1 and GFAP are positively correlated in

glioblastomas and cell lines datasets, and GFAP protein is decreased upon PROX1

suppression in two independent experimental settings. Immunohistochemistry analysis by

Elsir et al. identified up to 80% PROX1+/MAP2+ and around 30% PROX1+/GFAP+, but

only 1-2% MAP2+/GFAP+ cells in high-grade gliomas, indicating that tumor cells

represent either neuronal or glial differentiation pathways in vivo (14). Furthermore,

Prox1 depletion has been shown to reduce the number of stem cells and the rate of cell

proliferation in intestinal tumor growth (13). According to the present data analysis of

glioblastoma samples, PROX1 expression correlates with GFAP and is frequent in the

proneural and classical subtypes. Based on these observations, we speculate that PROX1

marks a transitory stage in the evolution of gliomas, possibly coinciding with the choice

between neuronal and glial cell developmental paths. Similarly, studies on the role of

Prox1 in mouse CNS development indicate a crucial function at this stage of CNS

development (10,43).

PROX1 is part of several complexes involved in development and tumorigenesis

including NCoR and HDAC3 (44), as well as PGC-1α and ERRα, which together with

BMAL1 play central roles in the transcriptional control of energy homeostasis and

biological clocks in the mice liver (45,46). Here we identified THRAP3 as a novel

binding partner for ectopic and endogenous PROX1 in U2-OS and U-343 MGa cells,

respectively. Suppression of THRAP3 in the glioblastoma culture U-343 MGa resulted in

increased PROX1 and GFAP protein levels, findings that physically and functionally

connects PROX1 and THRAP3. Moreover, THRAP3 suppression increased PROX1 and

GFAP transcripts. Interestingly, Sono et al. recently demonstrated THRAP3 as an

interactor with SOX9, where the two proteins together inhibited transcriptional activity

during chondrogenesis (39). In the analysis of glioma we found high correlation between

PROX1, THRAP3, SOX2 and SOX9. In addition to PROX1, SOX2 binds to the promoter

region of THRAP3 and SOX9 (37). Together, these results imply that SOX2 acts upstream

of transcriptional complexes composed of PROX1 or SOX9, and that THRAP3 is a

modulator of their transcriptional activity. Further, THRAP3 is known as a clock-

regulated component of CLOCK-BMAL1 complexes that promotes CLOCK-BMAL1

transcriptional activity and is important for circadian clock function (47). In the GSEA

analysis we identified low PROX1 levels to be associated with increased levels of ERRα

target genes, described in (48). Based on our and others’ results this ties PROX1, through

THRAP3, to ERRα and BMAL1. We thus speculate that THRAP3 interacts with PROX1

to modulate PROX1 transcriptional function and may take part in regulation of

developmental pathways and of the cell cycle machinery by direct or indirect control of

metabolic pathways, for example via changes of estrogen signaling (49).

PROX1 has previously been shown to control CCNE1 by direct binding to its promoter

(50). Adding to this, we show that PROX1 affects cyclin protein levels and growth rate in

glioblastoma. Berger et al. unraveled a critical function for CCNE1 during neural cell fate

specification in Drosophila, through interfering with Prospero localization to the nucleus

and thereby regulating its function (50,51). Based on mutant analysis in the CNS of flies, it has been proposed that cyclin E function in specifying a neuronal as opposed to glial cell fate is independent of its established role in G1-to-S phase transition (51).

Furthermore, CCNE1/CDK2 has recently been shown to stabilize pluripotency factors, on

a protein level, including SOX2, NANOG, and POU5F1 (OCT4) (38). These observations together with data presented here suggest a feed forward loop where

PROX1 enhances CCNE1 transcription, CCNE1 in complex with CDK2 stabilizes SOX2, and finally SOX2 enhances PROX1 transcription. This places PROX1 as a link between cell proliferation and stemness regulation in glioblastoma. The factors that might govern

such a loop remain to be studied, but one could speculate that the regulation of such a

feed forward loop is out of control in glioblastoma.

The characteristic intratumoral heterogeneity of glioblastoma is suggested to reflect

neural development and is likely a key to understanding treatment failure (4). Based on

the results in this paper PROX1 can be decreased by the CDK inhibitor CVT-313 and its

SOX2 protein destabilizing effect (38). This places CVT-313 as a growth inhibitory drug that would target glioblastomas with high PROX1 expression. In contrast, a CVT-313 induced decrease of PROX1 would be predicted to transition cells from a non- mesenchymal to a mesenchymal subtype and thus potentially promote tumor progression.

Further experiments are required to understand how the balance between the growth

inhibitory and transition regulation of PROX1 translates in a tumor setting. In extension,

the benefit of transitioning cells from a non-mesenchymal to a mesenchymal subtype or

vice versa as a therapeutic approach remains to be studied.

In summary, this work highlights the utility of PROX1 as a prognostic marker and adds

biological insight to its role in glioblastoma, where it maintains a neural stemness gene

expression profile and a proliferative state with high levels of G1-cyclins. Furthermore,

loss of PROX1 transitions proneural cells into a mesenchymal subtype, which during

tumor evolution would yield heterogeneous cell populations. Finally, CDK2-inhibitors

may pose an opportunity to target cell growth of glioblastoma cells with high PROX1

expression.

Acknowledgements

The authors appreciate discussions with the members of the Nistér laboratory, as well as

the Bartek group and the SciLifeLab research community who helped make this article

possible. This work was supported by KID-funding (KMG), Åke Wiberg stiftelse (ML,

390316483), Karolinska Institutet (ML, 1885/12-226) and (DH, 2014fobi41302), Magnus

Bergvall's stiftelse (ML) and (DH, 2014-00600), King Gustaf V's Jubilee Foundation

(ML, 164102) and the Swedish Research Council (ML, K2012–99X-21969–01–3). MG

was supported by the Chinese Scholarship Council. MN was supported by grants from

the Swedish Cancer Society (CAN 2014/836, contract 160334; CAN 2017/737, contract

170659), the Cancer Society in Stockholm (2015-151213), the Swedish Research Council

(K2014-67X-15399-10-4), the Swedish Childhood Cancer Foundation (PR2014-0021;

2017-0002), Karolinska Institutet (2016fobi47658) and the Stockholm County Council

(SLL), and JB by grants from the Swedish Cancer Society (contract 170176) and the

Swedish Research Council (K2014-46602-117891-30).

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Figure Legends

Figure 1. Comparison of PROX1 expression in glioma tumors of different grades and

PROX1 correlation with survival and gene expression subtype. (A) Comparison of

PROX1 transcript levels in grade II-IV gliomas in a combined TCGA dataset (226 grade

II, 244 grade III and 150 grade IV gliomas) (http://gliovis.bioinfo.cnio.es/). (B) Kaplan–

Meier curves showing that grade II-IV gliomas with high PROX1 levels had longer

survival: median 67.5 versus 34.9 months for the subgroup with low PROX1 (p-value <

0.0001). (C) Analysis of PROX1 expression z-scores cutoffs (-2 and +2 threshold) in

TCGA data for 206 glioblastoma patients (http://cbioportal.org). Values between 0.8-1.3

for overall survival, and 0.9-1.3 for disease-free survival yielded significant results for

differential survival. (D-E) Kaplan–Meier curves showing significantly higher disease-

free and overall survival for PROX1 expression over 1 z-score. Patients with a PROX1

expression higher than z-score 1 had significantly higher overall survival (log-rank test,

p-value 0.00115), with the median survival 33.6 months as compared to 12.3 months for

those with z-score ≤1 in the dataset. (F) Distribution of PROX1 expression values for 375

glioblastoma patients with available molecular subtypes in TCGA. There were no

recurrent tumors included among the cases. Cases were divided into 4 equally sized

groups based on PROX1 high to low expression values as indicated in the figure. 1-94

with high, 95-188 medium high, 188-282 medium low and 283-375 low PROX1

expression. Corresponding subtypes are color-labeled in blue (proneural, PN), beige

(neural, N), black (classical, CL), and red (mesenchymal, MES). (G) Distribution of the

percentage of each glioblastoma subtype between patients with high, medium high,

medium low and low expression of PROX1, according to the groups 1, 2, 3 and 4 in panel

F, respectively. (H) Scatter plot showing significantly lower PROX1 expression (z-score) in mesenchymal subtype compared to proneural (p-value < 0.0001) or classical subtype

(p-value < 0.0001) in glioblastoma patients from the same TCGA dataset as used in panel

Figure 2. Expression analysis of PROX1 in different glioma sample collections and

generation of stable cell lines to assess PROX1 function in glioblastoma. (A) Screening

of glioma cell lines for PROX1, as well as SOX2, GFAP, and FN1 protein levels.

Immunoblot of β actin protein was used as loading control. (B) PROX1 protein was

detected by immunofluorescence staining in U-343 MGa Cl2:6, indicating a nuclear localization, while in U-343 MG and U87MG the signal was undetectable. Colorectal cancer cell line SW480 was used as a positive control for PROX1 staining. (C)

Comparisons of endogenous GFAP and FN1 transcript levels in U-343 MG and U-343

MGa clones. (D) Analysis of PROX1 mRNA levels in U-343 MG-PROX1 and U-343

MGa-shPROX1 as determined by qRT-PCR. (E) Analysis of PROX1 protein levels in U-

343 MG-PROX1 and U-343 MGa-shPROX1, as determined by immunoblotting. (F)

qRT-PCR analysis indicating enhanced GFAP mRNA expression in U-343 MG-PROX1 line as compared to U-343 MG, and suppression of PROX1 in U-343 MGa-shPROX1 cells. (G) Immunofluorescence staining of GFAP (green) in U-343 MGa-shPROX1 cells shows a reduction of the protein. Cell nuclei were stained using DAPI (blue). (H)

Immunoblots indicating decreased GFAP protein in U-343 MGa-shPROX1 as compared to U-343 MGa, where U2-OS lysate was used as negative control. (I) qRT-PCR data indicating a decline in expression of GFAP mRNA in U2987(18) cultures transiently

transfected with siPROX1 oligomers. All gene expression analyses were normalized to

GAPDH.

Figure 3. Global gene expression analysis by RNA-seq upon PROX1 overexpression

and suppression. (A) Volcano plots depicting differential gene expression upon PROX1

overexpression or suppression in U-343 MG or U-343 MGa, respectively. (B) Venn diagram illustrating 237 genes the expression of which was increased in U-343 MG-

PROX1 and decreased in U-343 MGa-shPROX1 more than two-fold (PROX1 induced).

MG-PROX1 and increased expression in U-343 MGa-shPROX1 (PROX1 repressed). (D)

Top 10 enriched GO terms (C5: biological process) for the 237 induced genes identified by double filtering in B. Red line represents p-value of 0.05 as visual threshold. (E)

Overlap of these 237 genes found in B with hallmark gene categories processed by

GenePattern. (F) GO analysis for the 286 gene intersect found in C. (G) Overlaps of

genes identified in C with the hallmark gene categories (H-I). 38 genes intersect found to

be positively regulated by PROX1 and correlated in TCGA, and 93 PROX1 repressed

genes found to be correlated in TCGA data. (J) Depicted here are GO analyses and

identification of overlaps with hallmark genes categorized for the combined gene list of

the overlapping genes from H and I.

Figure 4. PROX1 regulation of glioblastoma expression-based subtypes. (A) Changes

of subtype defining genes upon PROX1 overexpression in U-343 MG compared to

control assessed by RNA-seq. Genes in the pink or blue areas are regulated in the

corresponding direction supporting the subtype change and add to a positive index score.

Index value of -100 is full negative change of subtype, 0 is neutral, and 100 is a full

positive subtype change. Genes with no available measurement are colored gray. PN:

proneural; CL: classical; MES: mesenchymal; N: neural. (B) The same illustration as for

panel A, but corresponding to suppression of PROX1 in U-343 MGa versus control. (C)

Venn diagram representing number of genes positively regulated by PROX1 more than

two-fold according to the RNA-seq data and found to have positive correlation with

PROX1 in TCGA clinical data (r > 0.3). Highlighted are the genes with the highest

increased expression levels by PROX1 overexpression (top 10). (D) Bar graph depicting

regulation of stemness signature genes from Patel et al. by PROX1 overexpression in our

in vitro system. (E) Heatmaps representing gene expression of top 100 correlated genes

with a PROX1 proxy profile in single-cell analysis data from 5 glioblastomas presented

in Patel et al. (4). The cells are arranged (from high to low) according to their correlation

to the proxy profile in each tumor. Red indicates high gene expression; blue, low

expression. Bar graphs represent PROX1 regulation of these genes based on the RNA-seq

analysis of PROX1 overexpression in U-343 MG.

Figure 5. PROX1 overexpression increased cell proliferation coinciding with changes

of cell cycle regulators. (A) Boxplot data extracted from HGCC (http://www.hgcc.se)

showing higher PROX1 expression in glioblastoma cultures with tumor initiating capacity

assessed by intracranial tumor formation (shown in red) according to (27). (B) Increased

rate of cell proliferation in U-343 MG-PROX1 as compared to U-343 MG parental line, determined by cell counting at the days indicated. (C) Bar graphs showing decreased cell proliferation rate upon PROX1 suppression in U-343 MGa-shPROX1 compared to control. (D) Heatmap illustration of RNA-seq experiment data indicating changes of cyclins and CDK gene expression following PROX1 modulation (red, high expression;

blue, low expression). (E) Immunoblots showing levels of Cyclin A1, Cyclin B1, Cyclin

D1, Cyclin E1, p21, p27, p57 and p53 in U-343 MG upon PROX1 overexpression and

upon suppression in U-343 MGa cells. Immunoblot of β actin protein was used as loading

controls.

Figure 6. PROX1 interacts directly with THRAP3 in the nucleus and is upregulated

following THRAP3 suppression. (A) THRAP3 peptides detected by MS/MS sequencing,

following a nuclear complex co-IP. (B) Double nuclear complex co-IP experiment

confirming interaction of endogenous PROX1 with THRAP3 protein using nuclear

fraction from U-343 MGa glioma cells. Immunoprecipitation was conducted with a rabbit

antibody against THRAP3, and the IP and input fractions were blotted with a goat

PROX1 antibody. Shown is also a THRAP3 immunoblot (on the left). No band was

detected in lanes corresponding to the negative control (no antibody), nor in IgG control

lanes. Shown on the right side are immunoprecipitates with two different antibodies

against PROX1 (Ab1 and Ab2) and the input fraction blotted with THRAP3 antibody.

THRAP3 was found present in PROX1 immunoprecipitates captured by either antibody

and more efficiently with Ab 1. Also shown is an immunoblot of PROX1. (C) Co-

localization of PROX1 and THRAP3 in the nucleus. (D) Immunoblots showing increased

PROX1 levels following THRAP3 suppression evaluated after 120 hours of siRNA

treatment. (E) Immunoblots showing PROX1 levels following THRAP3 suppression

evaluated after 48 hours of siRNA treatment. (F) Immunoblots showing GFAP protein

levels following THRAP3 suppression evaluated after 48 hours of siRNA treatment. (G)

qRT-PCR analysis in U-343 MGa upon 48 hours of siTHRAP3 treatment as compared to

control. (H) Comparison of THRAP3 expression in glioma grades II-IV. (I) THRAP3 correlation with PROX1, extracted from (http://cbioportal.org), TCGA glioblastoma data.

(J) Analysis of THRAP3 expression z-scores cutoffs in TCGA data for 206 glioblastoma

patients (http://cbioportal.org). Z-score 0.3 yielded significant results for differential

survival. (K and L) Kaplan–Meier curves using z-score cutoff 0.3 for THRAP3 for disease-free and overall survival.

Figure 7. Correlation and regulatory connection of PROX1 and SOX2 in glioblastoma.

(A) Scatterplot for co-expression of PROX1 and SOX2 (r = 0.43) in TCGA glioblastoma

clinical data, extracted from (http://cbioportal.org). (B) Scatterplots depicting expression

of PROX1 and SOX2 in 45 glioblastoma lines from CCLE (r = 0.55), and in 48 cultures from HGCC (r = 0.3827). (C) qRT-PCR analysis indicating induction of PROX1 mRNA

expression in a U-343 MG cell line with stable overexpression of SOX2, as compared to

the control. (D) Immunoblots indicating overexpression of PROX1 protein in U-343 MG-

SOX2. (E) Immunoblots indicating decreased PROX1 levels following 48 hours of

siSOX2 treatment of U-343 MGa cells. (F) Treatment of U-343MGa cells with CDK2

inhibitor CVT-313 (10 µM) for 24 hours resulted in decreased PROX1, SOX2, and

Cyclin E1 levels, assesses by immunoblotting. Shown is also an immunoblot of CDK2.

(G and H) Correlation analysis for THRAP3 expression levels versus SOX2 and SOX9 in

TCGA glioblastoma samples.

Reduced expression of PROX1 transitions glioblastoma cells into a mesenchymal gene expression subtype

Kaveh M Goudarzi, Jaime A Espinoza, Min Guo, et al.

Cancer Res Published OnlineFirst August 22, 2018.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-18-0320

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