# PAK3 is a key signature gene of the glioma proneural subtype and affects its proliferation, differentiation and growth

Nathalie Magne, Véronique Rousseau, Kévin Duarte, Sandrine Poëa-Guyon, Vincent Gleize, Alexandre Mutel, Charlotte Schmitt, Hélène Castel, Ahmed Idbaih, Emmanuelle Huillard, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Nathalie Magne, Véronique Rousseau, Kévin Duarte, Sandrine Poëa-Guyon, Vincent Gleize, et al. PAK3 is a key signature gene of the glioma proneural subtype and affects its proliferation, differentiation and growth. Cellular Oncology, 2021, 44 (6), pp.1257-1271. 10.1007/s13402-021-00635-8. hal-03439238

HAL Id: hal-03439238

https://hal.science/hal-03439238

Submitted on 22 Nov 2021

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
1

ORIGINAL ARTICLE

<figure><img src="image_3.png" /></figure>

2

# PAK3 is a key signature gene of the glioma proneural subtype and affects its proliferation, differentiation and growth

3

4

Nathalie Magne¹ · Véronique Rousseau¹ · Kévin Duarte¹ · Sandrine Poëa-Guyon¹ · Vincent Gleize² ·

5

Alexandre Mutel³ · Charlotte Schmitt² · Hélène Castel³ · Ahmed Idbaih² · Emmanuelle Huillard² · Marc Sanson² ·

6

Jean-Vianney Barnier¹ ID

7

Accepted: 1 September 2021

8

© Springer Nature Switzerland AG 2021

9

## Abstract

AQ1

**Purpose** Gliomas are the most lethal adult primary brain cancers. Recent advances in their molecular characterization have contributed to a better understanding of their pathophysiology, but there is still a need to identify key genes controlling glioma cell proliferation and differentiation. The p21-activated kinases PAK1 and PAK2 play essential roles in cell division and brain development and are well-known oncogenes. In contrast, the role of PAK3 in cancer is poorly understood. It is known, however that this gene is involved in brain ontogenesis and has been identified as a gene of the proneural subtype signature in glioblastomas.

11

12

13

14

15

16

**Methods** To better understand the role of PAK kinases in the pathophysiology of gliomas, we conducted expression analyses by querying multiple gene expression databases and analyzing primary human glioma samples. We next studied *PAK3* expression upon differentiation in patient-derived cell lines (PDCLs) and the effects of *PAK3* inhibition by lentiviral-mediated shRNA on glioma cell proliferation, differentiation and tumor growth.

17

18

19

20

**Results** We show that contrary to *PAK1* and *PAK2*, high *PAK3* expression positively correlates with a longer survival of glioma patients. We also found that *PAK3* displays differential expression patterns between glioma sub-groups with a higher expression in 1p/19q-codeleted oligodendrogliomas, and is highly expressed in tumors and PDCLs of the proneural subtype. In PDCLs, high *PAK3* expression negatively correlated with proliferation and positively correlated with neuronal differentiation. Inhibition of *PAK3* expression increased PDCL proliferation and glioma tumor growth in nude mice.

21

22

23

24

25

**Conclusions** Our results indicate that *PAK3* plays a unique role among PAKs in glioma development and may represent a potential therapeutic target.

26

27

**Keywords** Gliomas · Proneural signature · p21-activated kinase · Patient-derived cell line · Proliferation and neuronal

28

differentiation · Glioma tumor growth

29

## 1 Introduction

Diffuse gliomas, including glioblastomas (GBMs), are the most common malignant primary brain tumors and contain diverse populations of cells ranging from highly tumorigenic stem-like cells to more differentiated cells [1, 2]. Molecular studies have related genetic alterations, gene expression profiles, and DNA methylation signatures to distinct glioma subtypes with prognostic value [3–7]. Those studies have also led to the deciphering of key signaling pathways involved in glioma biology, and inlightened the altered differentiation and multipotency of the tumor cells [8]. They also helped to characterize potential therapeutic targets relevant for each subtype of

30

31

32

33

34

35

36

37

38

39

40

41

Jean-Vianney Barnier

A1

jean-vianney.barnier@cnrs.fr

A2

1 Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay, 91190 Gif-sur-Yvette, France

A3

A4

2 Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau, ICM, AP-HP, Hôpitaux Universitaires La Pitié, Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, 75013 Paris, France

A5

A6

A7

A8

3 Normandie Univ, UNIROUEN, INSERM, U1239, Laboratoire Différenciation Et Communication Neuronale

A9

A10

Et Neuroendocrine, Institut de Recherche Et D'Innovation Biomédicale de Normandie, 76000 Rouen, France

A11

A12

<figure><img src="image_4.png" /></figure>
glioma. p21-activated kinases (PAKs) play important func-
tional roles in neuronal differentiation and cancer biology.
The three group I PAKs (PAK1, PAK2 and PAK3) form a
small gene family encoding the main effectors of the Rac
and Cdc42 GTPases, which play crucial roles in cell pro-
liferation and migration [9]. These kinases are expressed
in the developing and adult brain where they have unique
functions during brain development [10]. PAK1 regulates
cortical development via the control of progenitor cell pro-
liferation and neuronal differentiation [11]. PAK2 is an
effector of the Rheb/TSC complexes in benign tuberous
sclerosis in neuronal tissues [12].

Within this family, PAK3 may have unique signaling
properties since its mutations have been found to be respon-
sible for several neurological disorders including intellec-
tual disability [10]. PAK3 is involved in brain development,
regulating cell cycle exit of neuronal precursors during pri-
mary neurogenesis, and in neurite outgrowth and neuronal
differentiation during telencephalon ontogenesis [13–15].
PAK3 also regulates oligodendroglial lineage rate specifi-
cation [16] PAK1 and PAK2 are oncogenes often found to
be overexpressed or activated in different cancers such as
breast and colon cancer [17, 18]. Their over-activation and
over-expression favour cell proliferation, inhibit apoptosis,
enhance invasion and metastasis, and sustain angiogenesis,
all cell functions that trigger oncogenesis [17]. In contrast,
PAK3 overexpression or mutation have only been associated
with adrenocorticotropic hormone-secreting tumors [19]. It
has also been found, however, that this gene belongs to the
proneural transcriptomic signature in glioblastomas (GBMs)
[6, 20].

As yet, the role of *PAKs* in gliomas is scarcely described
in the literature [21]. To investigate this further, we ana-
lyzed group *PAK1-3* expression according to histological
and molecular criteria in a well-characterized patient cohort
and in data deposited in public cancer databases. Contrary
to *PAK1* and *PAK2*, high *PAK3* expression was found to
be positively correlated with a longer patient survival. We
also found that *PAK3* was differentially expressed between
glioma subgroups, with a higher expression in 1p/19q-code-
leted oligodendrogliomas. Comparison of *PAK* expression in
GBM patient-derived cell lines (PDCLs) and in their paren-
tal tumors indicated that *PAK3* expression may serve as one
of the more reliable markers of the proneural subtype. In
PDCLs, increasing *PAK3* expression upon a set of differ-
entiation paradigms was correlated with those of neuronal
markers. Interestingly, short hairpin RNA-mediated inhibi-
tion of *PAK3* expression increased PDCL proliferation and
astrocytic marker expression, and also accelerated in vivo
tumor growth. Together, these data indicate that *PAK1-2* and
*PAK3* play different roles in the pathophysiology of gliomas.
*PAK3* exhibits an inhibitory role in glioma tumor growth and
may be of potential interest for glioma therapy.

## 2 Materials and methods

2.1 In silico tumor database analysis

RNA-seq data were obtained from the transcriptomic database of Zhang et al. [22] and are expressed in FPKM (Fragments Per Kilobase of exon per Million reads mapped) values. RNAs were extracted from different cell types purified from the postnatal mouse cerebral cortex (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html). Gene expression analyses from primary tumors were performed employing the Academic Medical Center (AMC) database, using the R2 genomics analysis and visualization platform (http://r2.amc.nl) and across different tumor datasets for each analyzed gene [23]. Kaplan-Meier survival curves of patients were derived from the Tumor Glioma—French—284 -dataset, composed of 276 glioma samples of all histologies and 8 control samples [24]. Samples were sorted according to the expression of each PAK and divided into two groups on the basis of a cutoff expression value giving the higher probability of confidence, using the log-rank test with Bonferroni correction for multiple comparisons. The clinical and molecular data of lower grade gliomas (LGGs) and GBM samples for this study were downloaded from The Cancer Genome Atlas database (https://tcga-data.nci.nih.gov/docs/publications/tcga/) generated by the TCGA Research Network (http://cancergenome.nih.gov/), firstly in 2014 and subsequently in February 2016 (cancer genome). mRNA expression levels of PAK1 and PAK3 were obtained from RNAseq analyses using Illumina TruSeq Kit Paired-end Sequencing on an Illumina HiSeq2000. Detection of somatic variants (IDH-mut) from TCGA whole-exome sequencing and RNAseq data was performed using RADIA [25]. The mRNA levels were represented as “normalized count” corresponding to a transformation of the “raw_count”. For each gene of interest, all “raw_count” values were divided by the 75th percentile of the column patient (after removing zeros) and multiplied by 1000.

## 2.2 Glioma sample analyses

Patient samples (from the OncoNeuroTek tissue bank) and
informed consent, RNA extraction and gene expression
analyses have been described before [26].

2.3 GBM cell culture, shRNA and lentiviral infection

Primary sphere cultures were initially derived from patient
tumors as described before [27]. Glioblastoma-derived
spheres were cultured in DMEM/F12 medium supplemented
with penicillin/streptomycin and B27 (Life Technologies),

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

<figure><img src="image_5.png" /></figure>
referred to as basal medium, supplemented with EGF (20 ng/ml; Peprotech) and FGF (20 ng/ml; Miltenyi), referred to as proliferative medium. Spheres were dissociated using Stem-Pro Accutase (Life technologies) when they reached a size of 100–200 µm. For cell growth analysis, dissociated cells were seeded at 5 × 10⁴ cells/ml in triplicates, and on day 1, 4 and 7, live cells from dissociated spheres were counted. Viability was tested by Trypan blue exclusion. For differentiation experiments, single-cell suspensions were seeded on a laminin substrate (10 µg/ml; Life technologies) in the proliferative medium, as described before [28]. Twenty-four hours later, the medium was replaced with basal medium containing 0.5% fetal calf serum, or in some experiments, with basal medium containing Bone Morphological Protein-4 (BMP-4, 10 ng/ml, Peprotech) or all-trans-retinoic acid (ATRA, 10 µM, Sigma). For PAK3 expression inhibition, viral particles containing TRC human PAK3 shRNA (Thermo Scientific) cloned in the pLK0.1 lentiviral vector were produced at the ICM Vectorology platform and used to transduce PDCLs. After puromycin selection, the efficiency of PAK3 expression inhibition was analyzed in proliferative and differentiative conditions by qRT-PCR.

## 2.4 Quantitative real-time PCR (qRT-PCR)

Total RNA was isolated from cells using TRIzol Reagent (Life Technologies) according to the manufacturer's protocol. RNA quality analysis was performed using a Bio-Rad's Experion system and nanodrop, and cDNAs were generated from 0.5 µg mRNAs using an iScript Reverse Transcription Supermix (Biorad). Real-time PCR was performed on a CFX96 Touch Real-Time PCR Detection System (Biorad) using a SsoAdvanced Universal SYBRGreen Supermix (Biorad), Primers were designed by retrieving nucleotide sequences from the NCBI gene database using the Primer-3Plus program (http://www.bioinformatics.nl/cgi-bin/prime r3plus/primer3plus.cgi/). Gene-specific primers were synthesized by Eurofins Genomics and their sequences are listed in Supplementary Materials. Samples were amplified in triplicate and relative copy numbers were determined using the comparative ΔΔCt method, with PPIA and GAPDH as normalization genes, and expression in a human embryonic brain mRNA sample as exogenous reference (Clinisciences).

## 2.5 Western blotting

For Western blot analysis, protein samples (10 µg) were separated by 10% SDS-PAGE and transferred to PVDF membranes (Millipore), as previously described [29]. Immunoblot analyses were performed using antibodies described in Supplementary Materials. Immunodetection was performed using a Luminata Crescendo Western HRP substrate (Merck Millipore). Quantification of chemiluminescence signals was performed using NIH ImageJ software.

188

189

190

## 2.6 Immunocytochemistry

191

Monolayer PDCL cultures were PFA-fixed and immunolabeled for native proteins using antibodies described in Supplementary Materials. Images were acquired in a sequential mode, using suitable filter cubes on a Leica DMI6000 microscope controlled by SimplePCI Software (Compix-Hamamatsu).

192

193

194

195

196

197

## 2.7 Animal experimentation

198

All animal experiments were conducted in accordance with the European Communities Council Directive for animal care and experimentation (Directive 2010/63/UE), and following the guidelines of the animal facility in Orsay (France) approved by the Ethical Committee (France, CEEA 59) and the Direction of veterinary services (France, agreement D91-471-104). 1 × 10⁶ dissociated cells were subcutaneously injected into the left flank of 8-weeks-old female nude mice. Tumor growth was monitored regularly and measured twice a week using a caliper as soon as tumors reach 25 mm³.

199

200

201

202

203

204

205

206

207

208

209

## 2.8 Statistical analysis

210

Patient survival was defined as the time between histological diagnosis and either death or the last follow-up. Differences between Kaplan-Meier survival curves were assessed using the log-rank test. For samples from the Pitié-Salpêtrière Hospital's cohort, gene expression levels were measured by qRT-PCR and compared between different molecular subgroups of patients using a one-way analysis of variance (ANOVA) and Mann-Whitney test (two-tailed) included in GraphPad Prism software. In the TCGA datasets, gene expression was compared between glioma subgroups with ANOVA and Tukey's comparison tests using R software. For cellular and molecular assays, experiments were performed in triplicates and repeated independently at least three times. Whenever relevant, Kruskal-Wallis or Mann-Whitney Wilcoxon tests were performed using R software. Differences were considered significant below *p* < 0.05, **p** < 0.01 and ***p** < 0.001 thresholds.

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228
229 **3 Results**

230 **3.1 A higher PAK3 expression in gliomas**
231 is associated with a longer patient survival

We first analyzed PAK1-3 expressions in postnatal brain cell types by interrogating the database from the Barres group [22]. We found that PAK1 is highly expressed in neurons, oligodendrocyte lineage cells, microglia and to a lesser extent in astrocytes (Fig. 1a). PAK2 was expressed in all cell types and represented the main PAK isoform in astrocytes and endothelial cells. PAK3 expression, overall lower than that of the two other PAK genes, was restricted to neurons and cells of the oligodendroglial lineage. We next compared the expression levels of each PAK isoform in tumors from the nervous system and other organs, by mining the Academic Medical Center (AMC) database (Fig. 1b) [23]. PAK1 was almost equally expressed in all tumor types, but at a slightly lower level in gliomas compared with other tumors, and less than in normal brain tissue. PAK2 was expressed at higher levels in all tumor types including gliomas, compared with normal brain tissue. Interestingly, PAK3 levels were higher in gliomas and neuroblastomas compared with other cancers, with a heterogeneous distribution in gliomas. Interestingly, we found that PAK3 displayed the same pattern of expression as DCX in neural cell types and also in tumors [19, 22] (Fig. 1a,b; Supplementary Fig. S1). The relatively lower expression of PAK1 and PAK3 in gliomas compared to normal brain tissue is likely due to the high proportion of astrocyte-like cells, which express the lowest levels of PAK1 and PAK3.

We next investigated the correlation between PAKs expression and patient overall survival in a large cohort of glioma samples of diverse histological types and grades described by French and colleagues [24] (Fig. 1c). As we found that PAK2 expression was not significantly correlated with survival (p = 0.084), we decided to focus on PAK1 and PAK3. Interestingly, high PAK1 expression was found to be associated with a shorter survival and, conversely, high PAK3 expression with a longer survival. For PAK3, similar results were obtained from the Kawaguchi and REMBRANT glioma databases, underscoring a correlation between PAK3 expression and patient survival (Supplementary Fig. S2). For PAK1 expression, similar results were obtained from most databases but not all, suggesting that the relationship between PAK1 expression and tumor progression is weaker, or may depend on the different histological subtypes composing the cohort (data not shown). Using R2 software, we found that the PAK1 and PAK3 expression levels did not correlate with each other (r = 0.54), supporting an independent expression

for each gene during cancer progression. Together, these data point to distinct roles for PAK1 and PAK3 in glioma development.

279

280

281

282 **3.2 PAK3 is highly expressed in IDH-mutant**
283 and 1p/19q-codeleted oligodendrogliomas

To better understand the roles of PAK1 and PAK3 in the pathophysiology of gliomas, we next set out to investigate more precisely their expression levels in glioma subtypes, using qRT-PCR in 223 tumor samples from the OncoNeuroTek bank (Fig. 2 a-c). By doing so, we did not observe any significant difference in PAK1 expression between grade II/III gliomas (lower grade gliomas: LGGs) and GBMs, whereas PAK3 was significantly higher expressed in LGGs than in GBMs (Fig. 2a). More specifically, PAK3 expression was higher in oligodendrogliomas compared with astrocytomas or oligoastrocytomas. In contrast, PAK1 expression was not significantly different among the LGG subtypes (Fig. 2b). In view of the new molecular classification for gliomas, we examined PAK1 and PAK3 expression according to the major alterations IDH1/2 and 1/19q codeletion. Analysis of PAK expression in IDH-WT and IDH-mutant LGGs revealed opposite results for each isoform, with a tendency towards increased PAK1 expression in IDH-WT-LGGs and a significantly higher expression of PAK3 in IDH-mutant LGGs (Fig. 2c). Moreover, in contrast to PAK1, PAK3 expression was significantly higher in gliomas harboring the 1p/19q codeletion compared with non-codeleted tumors. To validate these results, we performed a meta-analysis of data from the TCGA including 582 human glioma samples (Fig. 2 d-f). For PAK3, we detected a lower expression in GBMs than in LGGs, a higher expression in oligodendrogliomas than in other histological types, and a higher expression in IDH-mutant LGGs compared with non-mutant LGGs. In line with the PSH data, the TCGA analysis revealed a higher expression of PAK1 in oligodendrogliomas and a lower expression in IDH-mutant LGGs. Together, these results indicate a higher PAK3 expression in less aggressive gliomas.

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

3.3 High PAK3 expression is a reliable marker
for proneural subtypes of tumors and PDCLs

GBMs can be classified in molecular subtypes according to their gene expression profiles. Interestingly, PAK3 was found to be enriched in the proneural glioma subtype signature (Fig. S3) [6, 20]. We next examined this relationship using the transcriptional data obtained from tumors and matched cell lines described by Rosenberg [30]. We focused on a panel of selected genes that define the proneural signature, as established by Crisman [20]. We were able to confirm the higher expression of PAK3 in samples classified as

319

320

321

322

323

324

325

326

327

327

<figure><img src="image_6.png" /></figure>
a

PAK1

<figure><img src="image_7.png" /></figure>

PAK2

<figure><img src="image_8.png" /></figure>

PAK3

<figure><img src="image_9.png" /></figure>

b

PAK1

<figure><img src="image_10.png" /></figure>

PAK2

<figure><img src="image_11.png" /></figure>

PAK3

<figure><img src="image_12.png" /></figure>

c

PAK1

<figure><img src="image_13.png" /></figure>

PAK2

<figure><img src="image_14.png" /></figure>

PAK3

<figure><img src="image_15.png" /></figure>

Fig. 1 PAK expression in CNS cellular population and correlation with survival of glioma patients. a, RNA-seq analysis of PAK expression in the postnatal mouse cerebral cortex shows specific patterns in different cell types. Data are mined from the transcriptomic database of Zhang et al. [22] and expressed in FPKM (Fragments Per Kilobase of exon per Million reads mapped) values. Astrocyte: As; neuron: Ne; oligodendrocyte progenitor cell: OP; newly formed oligodendrocyte: NF; myelinating oligodendrocyte: MO; microglia: Mi; endothelial cell: En. b, PAK1 is similarly expressed in all types of tumors, whereas PAK3 is highly expressed in gliomas and neuroblastomas. Box plots show log2 gene expression levels in tumors from the Affymetrix u133p2 microarray, and from R2 platform analysis.

Academic Medical Center (AMC). The data are derived from the following cohorts: normal brain (NB, Berchtold, n=172); glioma (Gl, French, n=284); neuroblastoma (Ne, Versteeg, n=88); bladder (Bl, Riester, n=93); colon (Co, Sieber Smith, n=355); lung (Lu, Expo, n=114); breast (Br, Bos, n=204); kidney (Ki, Expo, n=261). c, Contrary to PAK1, high PAK3 expression in gliomas is associated with a longer patient survival. Kaplan-Meier survival plot split-ted according to PAK expression level, AMC, French database. For PAK1, high expression n=185, low expression n=88; for PAK2, high expression n=113, low expression n=160; for PAK3, high expres-sion n=186, low expression n=87
qPCR
Pitié-Salpêtrière

<figure><img src="image_16.png" /><figcaption>**Fig. 2** *PAK3* is highly expressed in *IDH-mutant* and *1p/19q-codeleted* glioma subtypes. *a-c:* *PAK1* and *PAK3* gene expression levels were analyzed by qRT-PCR in glioma samples from the OncoNeuroTek bank. *PAK1* and *PAK3* mRNA expression levels are presented as ΔΔCq; *a*, in lower grade gliomas (LGGs, n=122) versus glioblastomas (GBMs, n=101); *b*, in the various histological classes of LGG (A: astrocytoma, n=15; O: oligodendroglioma, n=54; OA: oligoastrocytoma, n=53) and including II and III tumor grades; *c*, according*</figcaption></figure>

RNA seq
TCGA database

<figure><img src="image_17.png" /></figure>

to IDH status and to 1p/19q-codeletion (IDH wild type: WT, n=28; IDH mutant: mut; no codeletion 1p/19q: -, n=34; codeletion 1p/19q: +, n=47). **d-f**, *PAK1* and *PAK3* gene expression analyzed from the TCGA database; **d**, in lower grade gliomas (LGGs, n=417) versus glioblastomas (GBMs, n=165); **e**, in the various histological classes of LGG (A, n=149; O, n=159; OA, n=109) and including II and III tumor grades; **f**, according to IDH status (wild type: WT, n=98; mutant: mut, n=434)

<figure><img src="image_18.png" /></figure>
<figure><img src="image_19.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**Fig. 3** High PAK3 expression in primary tumors and their PDCLs correlates with proneural subtypes. Data were extracted from a transcriptome analysis performed by Rosenberg et al. [30]. a, Expression of Crisman proneural signature genes in proneural or</figcaption></figure>

non proneural subtypes (16 samples corresponding to 8 tumors and 8 PDCLs); **b**, *PAK1* and *PAK3* expression levels in tumors and their corresponding PDCLs. **c**, expression levels of proneural signature genes in paired tumors and corresponding PDCLs
proneural, regardless of sample type (tumors or PDCLs) (Fig. 3a). PDCLs have been developed both for analysing the biology of human tumors and as preclinical models for screening potential therapeutic agents. The analysis of phe-notype transition may be relevant with regard to tumor evo-lution towards a more aggressive mesenchymal phenotype [3]. Although in most cases PDCLs maintain the molecu-lar subgroup signature of the parental tumor, some PDCLs acquire characteristics of other phenotypic groups [30]. We asked whether the PAK3 expression level may represent a marker for the proneural subtype during tumor/PDCL phe-notypic transition. Previously, GBM subtypes were found to be maintained in 5/9 of the paired tumors/PDCLs, whereas 4/9 cases exhibited subtype changes [30]. Interestingly, when we analysed these cases, we consistently observed higher PAK3 levels in the proneural samples, regardless of the kind of change and tissue (Fig. 3b,c). Note that PAK1 expression was always lower in PDCLs compared to tumors, without subtype correlation (Fig. 3b). In case the subtype changes during cell line establishment, increased expres-sion of PAK3 was observed in PDCLs which acquire this proneural subtype and, conversely, its expression was found to decrease when the PDCLs lost this proneural subtype. Expression comparisons of the other proneural genes, such as ASCL1, DCX, MAP2, NKX2.2, OLIG2, PDGFRA and SOX2 selected from the Vehraak list, and BCAN, CDC25A, CHD7, CLAPS2, DCX, DDX42, GSK3, PPM1D, WASF1 and ZEB2 selected from the shortlist defined by Crisman, we found that tumors and cell samples attributed to the proneu-ral subtype compared to other subtypes clearly indicated that among the genes of the proneural signature, only PAK3 and DCX displayed a higher expression in proneural tumors and PDCLs (Fig. 3c). Thus, these data indicate that PAK3 serves as a reliable marker for the proneural characterization of tumors and PDCLs.

## 3.4 High PAK3 expression negatively relates to PDCL proliferation

In order to better characterize the association between PAK3 expression and glioma cell biology, we analyzed PAK3 expression in 3 PDCL cultures named 3731a, 3731b and 4371 [27] that are growing as spheroids in EGF-FGF defined medium (Fig. 4a). These PDCLs harbor EGFR amplifica-tions and 3731a and b, derived from the same initial 3731 cell line, express the mutated oncogene EGFRvIII [27]. We found that the two 3731 PDCLs displayed a transcriptomic profil with a more prounnonced mesenchymal/classical signature compared to the 4371 PDCL, which displays a proneural expression phenotype, as established by qRT-PCR analysis of genes representative of the four Verhaak GBM subtypes (heatmap: Fig. 4b, Fig. S4).

In cultured spheroids, PAK3 expression was significantly higher in 4371 cells compared to the two other cell lines, as measured by qRT-PCR (Fig. 4c). Cell growth analysis showed that 3731a and 3731b exhibit faster proliferation rates than 4371, with fold increases after one week, of 16, 17 and 12, respectively (Fig. 4d). Accordingly, the expres-sion levels of CCND1 and of CDKN1B were the lowest and highest, respectively, in the slow proliferative 4371 cells compared with the 3731a/b cells (Fig. 4e). We conclude that PAK3 expression is higher in slower growing PDCLs.

378
379
380
381
382
383
384
385
386
387

## 3.5 High PAK3 expression positively associates with neuronal differentiation

388
389

We next analyzed PAK3 expression during PDCL differen-tiation induced after growth factor withdrawal in monolayer cultures as previously described [31]. In these conditions, no cell death was observed in the three PDCL cultures (data not shown). PAK3 expression was analyzed after 3 days (t3) and 8 days (t8), compared with proliferative conditions (t0). We found that in the two 3731 PDCL cultures PAK3 expression was strongly induced at both the mRNA and protein levels, in contrast to that in 4371 cells (Fig. 5a-b, Fig. S5a). In pro-liferative conditions (t0), the proportion of KI67+ cycling cells varied between PDCL cultures, from 90% (3731b) to 60% (4371) (Fig. 5c and S5b, first column), in accordance with the rates observed in sphere cultures (Fig. 4b). After growth factor withdrawal, the proportion of KI67+ cells was largely reduced for 3731a and 4371 (t8, 22%), and to a lesser extent for 3731b, which remained highly proliferative (t8, 65%). Accordingly, p27-KIP1 expression increased after induction of growth arrest in the three PDCL lines (Fig. 5d). We next analyzed GFAP and DCX expression in monolayer cultures by immunolabeling and mRNA and protein quanti-fication (Fig. S5c, Fig. 5e-f). For 3731b cells, growth factor withdrawal had little effect on the expression of the cell dif-ferentiation markers DCX and GFAP. In contrast, we found that the 3731a and 4371 cell lines displayed a reduced pro-liferation with an increased expression of both GFAP and DCX.

390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428

Next, PDCL cultures were exposed to bone morpho-genetic proteins 4 (BMP4) and all-trans retinoic acid (ATRA) in order to induce astroglial and neuronal differ-entiation, respectively [32, 33]. We found that BMP4 or ATRA treatment did not modify the proportion of prolif-erating cells compared to growth factor withdrawal alone (Fig. S6a). Cell lineage differentiation was assessed using qRT-PCR expression analysis of DCX GFAP, OLIG2, NG2, PDGFRa, S100beta and TUBB3 (Fig. 5, Fig. S6b). BMP4 treatment had a relatively little effect on PAK3 expression in the three PDCL cultures, except for a strong GFAP transcriptional activation in 3731b cells and mod-erate in 3731a cells, suggesting astroglial differentiation

<figure><img src="image_22.png" /></figure>
<figure><img src="image_23.png" /><figcaption>**Fig. 4** High PAK3 expression negatively correlates with PDCL proliferation. a, Representative images showing spheroids grown for 7 days. b, Heat map representation of subtype signature gene expression in 3731a, 3731b and 4371 cells. Expression is shown relatively to the mean (red: higher expression level; green: lower expression; white for the mean). Genes are organized according to gene expression signatures: Mesenchymal (MES), Classical (CL), Proneural</figcaption></figure>

(PN) and Neural (NL). c, qRT-PCR analysis of PAK3 expression in PDCL spheres. Expression levels are presented as ΔΔCq, relatively to human fetal brain as external reference. d, Fold increase in cell number over time, day1 to 7 (d1, d4 d7). e, qRT-PCR analysis of cell cycle genes. Expression levels are presented as ΔΔCq, relatively to human fetal brain as external reference. For all analyses, data represent at least three independent experiments

(Fig. 5g). ATRA treatment induced a strong transcriptional activation of PAK3 in 4371 cells and, to a lesser extent, in 3731a cells, associated with expression of the neuronal markers DCX (Fig. 5h) or TUBB3 (Fig. S6b). In summary, we found that growth factor withdrawal induced an increase in PAK3 expression associated with GFAP and DCX expression in mesenchymal/classical 3731 cell

lines, whereas ATRA treatment induced PAK3 and DCX up-regulation mainly in the proneural PDCLs (Fig. 5i). In contrast, BPM4 induced glial differentiation in mesenchymal cell lines without affecting PAK3 expression. This highlights a link between PAK3 expression and neuronal differentiation.

436

437

438

439

440

441
<caption>**Fig. 5** High PAK3 expression is positively correlated to neuronal differentiation. a-f:</caption>

PDCLs were grown as monolayers in proliferative medium (t0) or without growth factors in 0.5% FCS for 3 (t3) or 8 (t8) days. a, qRT-PCR analysis of PAK3 expression during the time course after growth factor withdrawal, expressed relatively to that in normal human fetal brain as external reference. b, Western blot analysis of PAK3 protein expression with actin as loading control. c, Percentage of Ki67-positive cells relative to DAPI staining. d, qRT-PCR analysis of p27 expression relative to that in normal human fetal brain as external control. e, qRT-PCR analysis of DCX and GFAP expression relative to that in normal human fetal brain as external reference. f, Western blot analysis of DCX and GFAP expression with actin as control, in PDCLs over time. g-i: Cells were grown as monolayers in proliferative medium and then without growth factors in the absence of FBS (0%), or in the presence of BMP4, DMSO or *all trans* retinoic acid (ATRA) for 3 (t3) or 8 (t8) days. g, h, qRT-PCR analysis of gene expression at t3 and t8, expressed relative to the t0 value of each PDCL culture, in the absence (0%) or presence of BMP4 (g), and in the presence of DMSO as a control or ATRA (h). i, Diagram showing the variations in gene expression levels after growth factor withdrawal, BMP4 or ATRA treatment. For all these qRT-PCR (expressed as ΔΔCq) or Western blot analyses, data represent at least three independent experiments.

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>
<figure><img src="image_26.png" /><figcaption>**Fig. 6** ShRNA-mediated inhibition of PAK3 expression modifies glioma cell proliferation, differentiation and tumor growth. **a-c:** 3731a PDCLs stably transduced with Control-shRNA (Ctrl) or PAK3-shRNA (03A, 04A) lentiviral vectors were grown as monolayers in proliferative medium (t0) or three days after growth factor removal and in 0.5% FCS (t3). **a.** qRT-PCR analysis of PAK3 expression relative to that in normal human fetal brain (n=3). **b.** Percentage of Ki67-positive cells. **c.** qRT-PCR analysis of GFAP and DCX expression relatively to that in normal human fetal brain (n=3). **d.** **e:** Mice</figcaption></figure>

were subcutaneously injected with *PAK3*-shRNA-03A-transduced 3731a cells (03A) (n=9) or the Control-shRNA-transduced 3731a cells as control (Ctrl) (n=8). **d.**, Tumor growth over time analyzed after tumors reached 25 mm³; mean curves were obtained by LOESS method. **e.**, Mean doubling time ± SD of Control and Sh03A tumor growth. The doubling time parameter was extracted after exponential growth curve modeling. Each individual data point represents one mouse

## 3.6 PAK3 knock-down alters glioma cell proliferation, differentiation and tumor growth

442

443

444

445

446

447

448

449

To more precisely define the role of PAK3 in glioma physiopathology, we knocked down PAK3 expression using PAK3-specific shRNAs. We selected two shRNAs (03A and 04) expressed from lentiviral vectors in COS7-infected cells, based on their efficiency in inducing PAK3 expression decrease. The greatest efficacy of PAK3 expression

inhibition was obtained in 3731a cells with Sh-03A, and to a lesser extent with Sh-04A, in proliferative conditions (t0) and after growth factor withdrawal (t3), compared to the Control-shRNA (Fig. 6a). In proliferative conditions (t0), inhibition of PAK3 expression by the two shRNAs had no effect on cell proliferation, as revealed by Ki67 analysis (Fig. 6b). However, after three days without growth factors (t3), the proportion of Ki67-positive cells increased in the PAK3-knock down compared to control cells. We next

450

451

452

453

454

455

456

457

458
evaluated *GFAP* and *DCX* expression in PAK3-shRNA-3731a cells, in proliferative conditions (t0) and after growth factor withdrawal (t3), compared to Control-shRNA expressing cells. Inhibition of *PAK3* expression had no effect on *GFAP* expression in proliferative medium (t0) whereas the *GFAP* level increased notably after growth factor withdrawal (t3) (Fig. 6c). Furthermore, no significative effect was observed on *DCX* expression under the same experimental conditions. These results indicate that inhibition of *PAK3* expression in GBM cells induces astroglial differentiation.

Finally, to analyze the effect of *PAK3* inhibition on glioma cell tumorigenicity in vivo, we decided to choose the 3731a PDCL that forms subcutaneous tumors in mice, whereas 4371 does not (data not shown). Sh-03A or Control-shRNA transduced 3731a cells were injected subcutaneously in nude mice. Monitoring of tumor sizes clearly indicated that tumors grew faster in the Sh-03A-cells injected mice compared to the Control-shRNA injected mice (Fig. 6d). Doubling times were extracted from the exponential growth curves. Compared to the control (15.38 ± 3.23 days), the doubling time for the Sh-03A-cells injected mice was shorter (6.43 ± 1.64 days), indicating an increase in tumor growth (Fig. 6e). PAK3 expression levels were found to be lower in tumors isolated from the four mice injected with *PAK3*-shRNA transduced cells, compared with tumors isolated from mice injected with Control-shRNA-cells, indicating that Sh-mediated *PAK3* silencing is maintained over time (Fig. S7). This allows us to confirm the correlation between *PAK3* expression in GBM cells and tumour growth. However, we did not observe any increase in the expression of glial markers, such as GFAP or SOX9, in tumors derived from cells transduced with *PAK3*-shRNA, compared to Control-shRNA tumors (Fig. S7). These results strongly suggest that inhibition of *PAK3* expression in GBM cells impacts their differentiation fate towards an astroglial phenotype in vitro and promotes their tumor growth in vivo.

## 4 Discussion

We here report correlations between PAK expression in gliomas and patient survival that are specific of each isoform. We found that *PAK1* expression in tumors is negatively correlated to patient survival, suggesting that *PAK1* may favor glioma tumor growth, in accordance with its reported oncogenic functions in other cancers [17, 18]. We found that the *PAK2* expression level is not related to survival but that, in contrast, a high *PAK3* expression is associated with a better survival. These results clearly indicate distinct and unique roles for each *PAK* isoform in the pathophysiology of gliomas. Interestingly, we found a higher *PAK3* expression level in 1p/19q-codeleted tumors, which are tumors with oligodendrocyte characteristics associated with a better

prognosis, raising the question whether *PAK3* acts as tumor suppressor or simply as a marker of the differentiated status of these tumors. Indeed, during brain development *PAK3* possesses differentiating functions, being involved in neuronal cell specification, neuroblasts differentiation and oligodendrocyte differentiation [13–16]. These functions may act during tumor development and favor neuronal differentiation of glioma cells.

To address this question, we performed a comparative study on several PDCLs. The different cell cultures used displayed distinct characteristics, particularly regarding *PAK3* expression. The two 3731 PDCL clones grew fast, exhibited classical/mesenchymal transcriptomic signatures, formed tumors after subcutaneous engraftment and displayed a low *PAK3* expression, compared to the 4371 PDCL. Following growth factor withdrawal or ATRA treatment the 3731a subclone differentiated towards an immature neuronal state associated with an increase in *PAK3* expression, whereas the 3731b subclone showed upregulated *GFAP* expression upon BMP4 treatment, without any change in *PAK3* expression. This suggests that these cells undergo an astroglial differentiation or acquire stem cell-like properties, independently of *PAK3* expression. The 4371 PDCL culture possesses opposite characteristics: it grew more slowly, expressed a more proneural transcriptomic profile and did not form tumors after xenografting. Interestingly, it expressed a relatively higher level of *PAK3* and neuroglial differentiation markers. Growth factor withdrawal and BMP4 treatment induced only a small increase in neuroglial differentiation, whereas treatment with ATRA induced a strong increase in the expression of neuronal markers such as *DCX*, as well as *PAK3* expression. These results are in agreement with distinct differentiation characteristics of PDCLs upon different differentiation-inducing regimens [34]. However, our data clearly show that an increased *PAK3* expression was associated with neuronal differentiation in two PDCL cultures, suggesting that *PAK3* favors neuronal differentiation and decreased proliferation of glioma cells, as *PAK3* does during normal brain development.

We found that *PAK3* expression knockdown resulted in an increase in PDCL proliferation associated with an increase in GFAP expression in vitro and an increase in tumor growth rate in engrafted mice. These results strongly suggest that *PAK3* can promote neuronal differentiation and/or inhibit astroglial differentiation, leading to slow tumor growth. In our setting, we did not observe any increase in astrocytic markers in *PAK3*-shRNA tumors. Among several possible explanations, the most likely is that the analysis of the cellular phenotype was carried out relatively late, i.e., mice were euthanized after having reached a tumor stage at which cellular regulations and phenotypes are radically modified. Some brain-specific factors present in culture media of PDCLs, but absent in the xenograft environment, could be

<figure><img src="image_27.png" /></figure>
562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

necessary for inducing an astrocytic phenotype shift. Astroglial cell fate inhibition may be due to a direct inhibition of astrocyte differentiation or an indirect effect of the activation of neuronal differentiation triggered at the expense of astrocytic differentiation, as observed earlier during Xenopus embryogenesis [13].

The *PAK3* gene belongs to a group of genes characteristic of a proneural signature. This was established in the princeps study of Verhaak and collaborators and also in the shortlist of 48 genes for this same subtype, established by different algorithms in the study of Crisman and colleagues [6, 20]. More recently, scRNA-seq allowed a delineation of the transcriptomes of single glioblastoma cells, their intratumoral heterogeneity and their phenotypic plasticity. The main identified subgroups of cells are characteristic of neuronal and glial lineages or their progenitors. Interestingly, the *PAK3* gene belongs to the top 30 genes that define neural progenitor cells (NPCs) specifically involved in the neuronal lineage (NPC2) [35]. Here, we confirmed a key status of the *PAK3* gene for a proneural signature in tumors as well as in PDCLs. This underlines its weight as a subgroup indicator, but at the same time raises the question of its role in the biology of these tumors. In particular, it remains to be determined how *PAK3* could have opposite roles to those of *PAK1* in the biology of gliomas. A first possibility may be that *PAK3* has its own substrates. Currently, most *PAK* substrates involved in cancer have been identified for *PAK1*, but these have not been validated for other group I kinases [19, 36]. A hypothesis may be that *PAK3* uses one or more yet unknown substrates that are not phosphorylated by *PAK1* and are involved in the inhibition of cell division, or in the initiation of neuronal differentiation. Another hypothesis may be that *PAK3* belongs to a signaling pathway involved in a negative regulation of tumor growth. We have previously shown that *PAK3* signaling differs from that of *PAK1* at several points, *PAK3* being activated by the Rho GTPase Cdc42 and preferentially binding to the Nck2/Grb4 adapter [37, 38]. These pathways are known to act in tumor proliferation, differentiation and progression, but their role in the biology of gliomas is still poorly understood. Another possibility may be that *PAK3* can act on cell proliferation by inhibiting *PAK1*. Indeed, the *PAK* kinases have been reported to form dimers that allow their trans-inhibition [39, 40]. However, we have previously shown that the *PAK3* protein prefers to form heterodimers with *PAK1* rather than homodimers, leading to inhibition of *PAK1* kinase activity [29]. Thus, a transcriptional increase of *PAK3* in gliomas could lead to a decrease in *PAK1* activity.

Although *PAK1* and *PAK2* are well characterized oncogenes, our data strongly suggest that *PAK3* acts as a tumor suppressor gene [17, 36, 41]. *PAK* genes are rarely mutated in cancer and the analysis of mutation databases does not allow to discriminate mutational patterns between *PAK1*

and *PAK3* (CBioPortal) [42]. On the other hand, *PAK1/2* genes are often overexpressed in cancer, as recently reported for breast cancer, in contrast to *PAK3* [43]. *PAK1* is mainly involved in cancer development through functional activation, due to transcriptional or post-translational processes. Here, our transcriptomic analysis showed a strong correlation between *PAK3* expression and patient survival, and the functional experiments we performed indicate that *PAK3* may indeed be considered as a tumor suppressor. One consequence of our results relates to possible therapeutic approaches. They stress the need to analyze, in different tumor types, the expression and activation of oncogenic *PAK* kinases such as *PAK1* or *PAK2*, but also that of *PAK3*. Indeed, the use of non-selective *PAK* kinase inhibitors could have deleterious effects by inhibiting the *PAK3* tumor suppressor gene. To date, there are no specific *PAK* inhibitors that do not inhibit *PAK3* [44]. Our data do suggest, however, that differential therapies may be developed depending on *PAK3* expression level. A therapeutic strategy for tumors with a low *PAK3* level could be to target the transcription of *PAK3* in order to force post-mitotic cell differentiation by targeting the proneural Notch/Neurogenin/NeuroD pathway previously identified in Xenopus [8, 13, 45]. Differentiation strategies have already been considered for GBM cancer stem cells [8, 34, 46]. However, this approach is complex and requires a more precise description of the function of the gene in different cellular contexts. This was recently illustrated by data showing that the effects of overexpression of *ASCL1*, which acts as a proneural gene during embryonic development and belongs to the proneural signature gene set that includes *PAK3*, depends on the GBM subtype [47]. Increasing *PAK3* expression may be a therapeutic goal in poorly differentiated tumors, since several factors such as neurogenin positively regulate its transcription [48].

We have provided omics and experimental data suggesting a unique role of *PAK3* in the pathophysiology of gliomas. These data need to be consolidated by other approaches such as orthotopic grafts of different PDCLs, in which the expression of *PAK3* and its kinase activity can be experimentally modified, in order to analyze tumor growth within the brain environment. Our results strongly indicate that each group I-PAK plays a different role in cancer progression, in particular in breast and brain cancer, and that *PAK3* acts as a tumor suppressor in gliomas and, as such, may represent a potential therapeutic target.

## 5 Grant support

This work was supported by the Centre National de la Recherche Scientifique (CNRS), the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC, Paris, Ile de France), the Ligue Nationale

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664
<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>

<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /></figure>

<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>

Contre le Cancer (Comité départemental Essonne-Ile de France) and the Association de Recherche sur les Tumeurs Cérébrales (ARTC, France). This work was supported by funding from the Ligue Nationale contre le Cancer (to EH), the Association de Recherche sur le cancer (Fondation ARC) (PJA 20,131,200,481 and PJA 20,151,203,259 to EH) and FP7 Marie Curie CIG (to EH). VG is a recipient of a Fondation ARC fellowship. The research leading to these results has received funding from the programs "investissements d'avenir" ANR-10IAIHU-06 and ANR-11-INBS-0011- NeurATRIS: Translational Research Infrastructure for Biotherapies in Neurosciences.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s13402-021-00635-8.

**Acknowledgements** We thank OncoNeuroTek for sharing data and samples from their banks. We are also grateful to C. Levasseur for assistance in mice inoculation and to A. Bonilla, C. Dubois and C. Rousseau for animal care. We also acknowledge the technical support from the ICM facility iGenSeq.

## Declarations

**Conflict of interest** The authors declare no potential conflict of interest.

## References

1. S.K. Singh, C. Hawkins, I.D. Clarke, J.A. Squire, J. Bayani, T. Hide, R.M. Henkelman, M.D. Cusimano, P.B. Dirks, Identification of human brain tumour initiating cells. Nature 432, 396 (2004)

2. M. Westphal, K. Lamszus, The neurobiology of gliomas: from cell biology to the development of therapeutic approaches. Nat Rev Neurosci 12, 495 (2011)

3. H.S. Phillips, S. Kharbanda, R. Chen, W.F. Forrest, R.H. Soriano, T.D. Wu, A. Misra, J.M. Nigro, H. Colman, L. Soroceanu, P.M. Williams, Z. Modrusan, B.G. Feuerstein, K. Aldape, Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157 (2006)

4. H. Noushmehr, D.J. Weisenberger, K. Diefes, H.S. Phillips, K. Pujara, B.P. Berman, F. Pan, C.E. Pelloski, E.P. Sulman, K.P. Bhat, R.G.W. Verhaak, K.A. Hoadley, D.N. Hayes, C.M. Perou, H.K. Schmidt, L. Ding, R.K. Wilson, D. Van Den Berg, H. Shen, H. Bengtsson, P. Neuvial, L.M. Cope, J. Buckley, J.G. Herman, S.B. Baylin, P.W. Laird, K. Aldape, Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma. Cancer Cell 17, 510 (2010)

5. D. Sturm, H. Witt, V. Hovestadt, D.-A. Khuong-Quang, D.T.W. Jones, C. Konermann, E. Pfaff, M. Tönjes, M. Sill, S. Bender, M. Kool, M. Zapatka, N. Becker, M. Zucknick, T. Hielscher, X.-Y. Liu, A.M. Fontebasso, M. Ryzhova, S. Albrecht, K. Jacob, M. Wolter, M. Ebinger, M.U. Schuhmann, T. van Meter, M.C. Frühwald, H. Hauch, A. Pekrun, B. Radlwimmer, T. Niehues, G. von Komorowski, M. Dürken, A.E. Kulozik, J. Madden, A. Donson, N.K. Foreman, R. Drissi, M. Fouladi, W. Scheurlen, A. von Deimling, C. Monoranu, W. Roggendorf, C. Herold-Mende, A. Unterberg, C.M. Kramm, J. Felsberg, C. Hartmann, B. Wiestler, W. Wick, T. Milde, O. Witt, A.M. Lindroth, J. Schwartzentruber, D. Faury, A. Fleming, M. Zakrzewska, P.P. Liberski, K. Zakrzewski, P. Hauser, M. Garami, A. Klekner, L. Bognar, S. Morrissy, F. Cavalli, M.D. Taylor, P. van Sluis, J. Koster, R. Versteeg, R. Volckmann, T. Mikkelsen, K. Aldape, G. Reifenberger, V.P. Collins, J. Majewski, A. Korshunov, P. Lichter, C. Plass, N. Jabado, S.M. Pfister, Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22, 425 (2012)

6. R.G.W. Verhaak, K.A. Hoadley, E. Purdom, V. Wang, Y. Qi, M.D. Wilkerson, C.R. Miller, L. Ding, T. Golub, J.P. Mesirov, G. Alexe, M. Lawrence, M. O'Kelly, P. Tamayo, B.A. Weir, S. Gabriel, W. Winckler, S. Gupta, L. Jakkula, H.S. Feiler, J.G. Hodgson, C.D. James, J.N. Sarkaria, C. Brennan, A. Kahn, P.T. Spellman, R.K. Wilson, T.P. Speed, J.W. Gray, M. Meyerson, G. Getz, C.M. Perou, D.N. Hayes, Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98 (2010)

7. M. Vitucci, D.N. Hayes, C.R. Miller, Gene expression profiling of gliomas: merging genomic and histopathological classification for personalised therapy. Br J Cancer 104, 545 (2011)

8. P.-O. Guichet, I. Bieche, M. Teigell, C. Serguera, B. Rothhut, V. Rigau, F. Scamps, C. Ripoll, S. Vacher, S. Taviaux, H. Chevassus, H. Duffau, J. Mallet, A. Susini, D. Joubert, L. Bauchet, J.-P. Hugnot, Cell death and neuronal differentiation of glioblastoma stem-like cells induced by neurogenic transcription factors. Glia 61, 225 (2013)

9. G.M. Bokoch, Biology of the p21-Activated Kinases. Annu. Rev. Biochem. 72, 743 (2003)

10. P. Kreis, J.-V. Barnier, PAK signalling in neuronal physiology. Cell. Signal. 21, 384 (2009)

11. X. Pan, X. Chang, C. Leung, Z. Zhou, F. Cao, W. Xie, Z. Jia, PAK1 regulates cortical development via promoting neuronal migration and progenitor cell proliferation. Mol Brain 8, 36 (2015)

12. M. M. Alves, G. M. Fuhler, K. C. S. Queiroz, J. Scholma, S. Goorden, J. Anink, C. Arnold Spek, M. Hoogeveen-Westerveld, M. J. Bruno, M. Nellist, Y. Elgersma, E. Aronica, and M. P. Pepelenbosch, PAK2 is an effector of TSC1/2 signaling independent of mTOR and a potential therapeutic target for Tuberous Sclerosis Complex. Sci Rep 5, 14534 (2015).

13. J. Souopgui, M. Sölter, T. Pieler, XPak3 promotes cell cycle withdrawal during primary neurogenesis in Xenopus laevis. EMBO J. 21, 6429 (2002)

14. I. Cobos, U. Borello, J.L.R. Rubenstein, Dlx transcription factors promote migration through repression of axon and dendrite growth. Neuron 54, 873 (2007)

15. X. Dai, H. Iwasaki, M. Watanabe, S. Okabe, Dlx1 transcription factor regulates dendritic growth and postsynaptic differentiation through inhibition of neuropilin-2 and PAK3 expression. Eur J Neurosci 39, 531 (2014)

16. M. R. L. Maglorius Renkilaraj, L. Baudouin, C. M. Wells, M. Doulazmi, R. Wehrlé, V. Cannaya, C. Bachelin, J.-V. Barnier, Z. Jia, B. Nait Oumesmar, I. Dusart, and L. Bouslama-Oueghlani, The intellectual disability protein PAK3 regulates oligodendrocyte precursor cell differentiation. Neurobiology of Disease 98, 137 (2017).

17. M. Radu, G. Semenova, R. Kosoff, J. Chernoff, Pak signaling in the development and progression of cancer. Nat Rev Cancer 14, 13 (2014)

18. R. Kumar, R. Sanawar, X. Li, F. Li, Structure, biochemistry, and biology of PAK kinases. Gene 605, 20 (2017)

19. R. Liu, W. Wang, L. Ye, Y. Bi, H. Fang, B. Cui, W. Zhou, M. Dai, J. Zhang, X. Li, G. Ning, p21-Activated kinase 3 is overexpressed in thymic neuroendocrine tumors (carcinoids) with ectopic ACTH syndrome and participates in cell migration. Endocr 38, 38 (2010)

<figure><img src="image_78.png" /></figure>
785

786

787

788

789

790

791

792

793

794

795

796

797

798

799

800

801

802

803

804

805

806

807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828

829

830

831

832

833

834

835

836

837

838

839

840

841

842

843

844

845

846

847

848

849

20. T. J. Crisman, I. Zelaya, D. R. Laks, Y. Zhao, R. Kawaguchi, F. Gao, H. I. Kornblum, and G. Coppola, identification of an efficient gene expression panel for glioblastoma classification. PLoS ONE 11, e0164649 (2016).

21. A. Venu, B. Archana, R. Kanumuri, V. K. Vuttaradhi, L. D'Cruze, S. Murugan, K. Ganesh, D. Prathiba, M. A. Dymova, S. K. Rayala, and G. Venkatraman, Clinical Evaluation of P21 Activated Kinase 1 (PAK1) Activation in gliomas and its effect on cell proliferation. Cancer Investigation 0, 1 (2020).

22. Y. Zhang, K. Chen, S.A. Sloan, M.L. Bennett, A.R. Scholze, S. O'Keeffe, H.P. Phatnani, P. Guarnieri, C. Caneda, N. Ruderisch, S. Deng, S.A. Liddelow, C. Zhang, R. Daneman, T. Maniatis, B.A. Barres, J.Q. Wu, An RNA-Sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929 (2014)

23. R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl http://r2platform.com).

24. L. A. M. Gravendeel, M. C. M. Kouwenhoven, O. Gevaert, J. J. de Rooi, A. P. Stubbs, J. E. Duijm, A. Daemen, F. E. Bleeker, L. B. C. Bralten, N. K. Kloosterhof, B. De Moor, P. H. C. Eilers, P. J. van der Spek, J. M. Kros, P. A. E. Sillevis Smitt, M. J. van den Bent, and P. J. French, Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology. Cancer Research 69, 9065 (2009).

25. A. J. Radenbaugh, S. Ma, A. Ewing, J. M. Stuart, E. A. Collisson, J. Zhu, and D. Haussler, RADIA: RNA and DNA Integrated Analysis for Somatic Mutation Detection. PLoS One 9, (2014).

26. POLA Network, K. Labreche, I. Simeonova, A. Kamoun, V. Gleize, D. Chubb, E. Letouzé, Y. Riazalhosseini, S. E. Dobbins, N. Elarouci, F. Ducray, A. de Reyniès, D. Zelenika, C. P. Wardell, M. Frampton, O. Saulnier, T. Pastinen, S. Hallout, D. Figarella-Branger, C. Dehais, A. Idbaih, K. Mokhtari, J.-Y. Delattre, E. Huillard, G. Mark Lathrop, M. Sanson, and R. S. Houlston, TCF12 is mutated in anaplastic oligodendroglioma. Nat Commun 6, 7207 (2015).

27. S.-N. Bikeye, C. Colin, Y. Marie, R. Vampouille, P. Ravassard, A. Rousseau, B. Boisselier, A. Idbaih, C. Calvo, P. Leuraud, M. Lasalle, S. El Hallani, J.-Y. Delattre, M. Sanson, ASPM-associated stem cell proliferation is involved in malignant progression of gliomas and constitutes an attractive therapeutic target. Cancer Cell Int 10, 1 (2010)

28. S.M. Pollard, K. Yoshikawa, I.D. Clarke, D. Danovi, S. Stricker, R. Russell, J. Bayani, R. Head, M. Lee, M. Bernstein, J.A. Squire, A. Smith, P. Dirks, Glioma stem cell lines expanded in adherent culture have tumor-specific phenotypes and are suitable for chemical and genetic screens, Cell Stem Cell 4, 568 (2009)

29. G. Combeau, P. Kreis, F. Domenichini, M. Amar, P. Fossier, V. Rousseau, J.-V. Barnier, The p21-activated kinase PAK3 forms heterodimers with PAK1 in brain implementing trans-regulation of PAK3 activity. J. Biol. Chem. 287, 30084 (2012)

30. S. Rosenberg, M. Verreault, C. Schmitt, J. Guegan, J. Guehennec, C. Levasseur, Y. Marie, F. Bielle, K. Mokhtari, K. Hoang-Xuan, K. Ligon, M. Sanson, J.-Y. Delattre, A. Idbaih, Multi-omics analysis of primary glioblastoma cell lines shows recapitulation of pivotal molecular features of parental tumors. NEUONC 2, 219 (2016)

31. V. Lamour, A. Henry, J. Kroonen, M.-J. Nokin, Z. von Marschall, L.W. Fisher, T.-L. Chau, A. Chariot, M. Sanson, J.-Y. Delattre, A. Turtoi, O. Peulen, B. Rogister, V. Castronovo, A. Bellahcène, Targeting osteopontin suppresses glioblastoma stem-like cell character and tumorigenicity in vivo. Int. J. Cancer 137, 1047 (2015)

32. S.G.M. Piccirillo, B.A. Reynolds, N. Zanetti, G. Lamorte, E. Binda, G. Broggi, H. Brem, A. Olivi, F. Dimeco, A.L. Vescovi, Bone morphogenetic proteins inhibit the tumorigenic potential of human brain tumour-initiating cells. Nature 444, 761 (2006)

33. M. Ying, S. Wang, Y. Sang, P. Sun, B. Lal, C.R. Goodwin, H. Guerrero-Cazares, A. Quinones-Hinojosa, J. Laterra, S. Xia, Regulation of glioblastoma stem cells by retinoic acid: role for Notch pathway inhibition. Oncogene 30, 3454 (2011)

34. V. Balasubramaniyan, B. Vaillant, S. Wang, J. Gumin, M.E. Butalid, K. Sai, F. Mukheef, S.H. Kim, H.W.G.M. Boddeke, F. Lang, K. Aldape, E.P. Sulman, K.P. Bhat, H. Colman, Aberrant mesenchymal differentiation of glioma stem-like cells: implications for therapeutic targeting. Oncotarget 6, 31007 (2015)

35. C. Neftel, J. Laffy, M.G. Filbin, T. Hara, M.E. Shore, G.J. Rahme, A.R. Richman, D. Silverbush, M.L. Shaw, C.M. Hebert, J. Dewitt, S. Gritsch, E.M. Perez, L.N.G. Castro, X. Lan, N. Druck, C. Rodman, D. Dionne, A. Kaplan, M.S. Bertalan, J. Small, K. Pelton, S. Becker, D. Bonal, Q.-D. Nguyen, R.L. Servis, J.M. Fung, R. Mylvaganam, L. Mayr, J. Gojo, C. Haberler, R. Geyeregger, T. Czech, I. Slavic, B.V. Nahed, W.T. Curry, B.S. Carter, H. Wakimoto, P.K. Brastianos, T.T. Batchelor, A. Stemmer-Rachamimov, M. Martinez-Lage, M.P. Frosch, I. Stamenkovic, N. Riggi, E. Rheinbay, M. Monje, O. Rozenblatt-Rosen, D.P. Cahill, A.P. Patel, T. Hunter, I.M. Verma, K.L. Ligon, D.N. Louis, A. Regev, B.E. Bernstein, I. Tirosh, M.L. Suvà, An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835 (2019)

36. R. Kumar, A.E. Gururaj, C.J. Barnes, p21-activated kinases in cancer. Nat Rev Cancer 6, 459 (2006)

37. P. Kreis, E. Thévenot, V. Rousseau, B. Boda, D. Muller, J.-V. Barnier, The p21-activated kinase 3 implicated in mental retardation regulates spine morphogenesis through a Cdc42-dependent pathway. J. Biol. Chem. 282, 21497 (2007)

38. E. Thévenot, A.W. Moreau, V. Rousseau, G. Combeau, F. Domenichini, C. Jacquet, O. Goupille, M. Amar, P. Kreis, P. Fossier, J.-V. Barnier, p21-activated Kinase 3 (PAK3) protein regulates synaptic transmission through its interaction with the Nck2/Grb4 protein adaptor. J. Biol. Chem. 286, 40044 (2011)

39. M. Lei, W. Lu, W. Meng, M.-C. Parrini, M.J. Eck, B.J. Mayer, S.C. Harrison, Structure of PAK1 in an autoinhibited conformation reveals a multistage activation switch. Cell 102, 387 (2000)

40. M.C. Parrini, M. Lei, S.C. Harrison, B.J. Mayer, Pak1 kinase homodimers are autoinhibited in trans and dissociated upon activation by Cdc42 and Rac1. Mol. Cell 9, 73 (2002)

41. D. Yao, C. Li, M.S.R. Rajoka, Z. He, J. Huang, J. Wang, J. Zhang, P21-Activated kinase 1: emerging biological functions and potential therapeutic targets in cancer. Theranostics 10, 9741 (2020)

42. E. Cerami, J. Gao, U. Dogrusoz, B.E. Gross, S.O. Sumer, B.A. Aksoy, A. Jacobsen, C.J. Byrne, M.L. Heuer, E. Larsson, Y. Antipin, B. Reva, A.P. Goldberg, C. Sander, N. Schultz, The Cbio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401 (2012)

43. Y. Dang, Y. Guo, X. Ma, X. Chao, F. Wang, L. Cai, Z. Yan, L. Xie, X. Guo, Systemic analysis of the expression and prognostic significance of PAKs in breast cancer. Genomics 112, 2433 (2020)

44. J.J. Crawford, K.P. Hoeflich, J. Rudolph, p21-Activated kinase inhibitors: a patent review. Expert Opin. Ther. Pat. 22, 293 (2012)

45. R. I. Martinez-De Luna, R. Y. Ku, Y. Lyou, and M. E. Zuber, Maturin is a novel protein required for differentiation during primary neurogenesis. Developmental Biology 384, 26 (2013).

46. M. Santra, S. Santra, B. Buller, K. Santra, A. Nallani, M. Chopp, Effect of doublecortin on self-renewal and differentiation in brain tumor stem cells. Cancer Sci 102, 1350 (2011)

47. A. Narayanan, F. Gagliardi, A.L. Gallotti, S. Mazzoleni, M. Cominelli, L. Fagnocchi, M. Pala, I.S. Piras, P. Zordan, N. Moretta, E. Tratta, G. Brugnara, L. Altabella, G. Bozzuto, P. Gorombei, A. Molinari, R.-A. Padua, A. Bulfone, L.S. Politi, A. Falini, A. Castellano, P. Mortini, A. Zippo, P.L. Poliani, R. Galli, The proneural gene ASCL1 governs the transcriptional subgroup

850

851

852

853

854

855

856

857

858

859

860

861

862

863

864

865

866

867

868

869

870

871

872

873

874

875

876

877

878

879

880

881

882

883

884

885

886

887

888

889

890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

914
915

916

917

918

919

920

affiliation in glioblastoma stem cells by directly repressing the mesenchymal gene NDRG1. Cell Death Differ **26**, 1813 (2019)

48. J. Piccand, A. Meunier, C. Merle, Z. Jia, J.-V. Barnier, G. Gradwohl, Pak3 promotes cell cycle exit and differentiation of β-cells in the embryonic pancreas and is necessary to maintain glucose homeostasis in adult mice. Diabetes **63**, 203 (2014)

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

921

922

923

<figure><img src="image_79.png" /></figure>

UNCORRECTED PROOF
Journal: **13402**

Article: **635**

Author Query Form

Please ensure you fill out your response to the queries raised below and return this form along
with your corrections

Dear Author

During the process of typesetting your article, the following queries have arisen. Please check your typeset proof carefully against the queries listed below and mark the necessary changes either directly on the proof/online grid or in the 'Author's response' area provided below

<table><thead><tr><td>Query</td><td>Details Required</td><td>Author's Response</td></tr></thead><tbody><tr><td>AQ1</td><td>Please check Affiliations if captured correctly.</td><td></td></tr></tbody></table>