Valproic acid treatment response in vitro is

Child's Nervous System https://doi.org/10.1007/s00381-018-3817-7

ORIGINAL PAPER

Valproic acid treatment response in vitro is determined by TP53 status in medulloblastoma Bruna Mascaro-Cordeiro 1,2 & Indhira Dias Oliveira 1 & Francine Tesser-Gamba 1 & Lorena Favaro Pavon 3 & Nasjla Saba-Silva 1 & Sergio Cavalheiro 1,3 & Patrícia Dastoli 1 & Silvia Regina Caminada Toledo 1,2 Received: 18 August 2017 / Accepted: 25 April 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018

Abstract Purpose Histone deacetylate inhibitors (HDACi), as valproic acid (VA), have been reported to enhance efficacy and to prevent drug resistance in some tumors, including medulloblastoma (MB). In the present study, we investigated VA role, combined to cisplatin (CDDP) in cell viability and gene expression of MB cell lines. Methods Dose-response curve determined IC50 values for each treatment: (1) VA single, (2) CDDP single, and (3) VA and CDDP combined. Cytotoxicity and flow cytometry evaluated cell viability after exposure to treatments. Quantitative PCR evaluated gene expression levels of AKT, CTNNB1, GLI1, KDM6A, KDM6B, NOTCH2, PTCH1, and TERT, before and after treatment. Besides, we performed next-generation sequencing (NGS) for PTCH1, TERT, and TP53 genes. Results The most effective treatment to reduce viability was combined for D283MED and ONS-76; and CDDP single for DAOY cells (p < 0.0001). TERT, GLI1, and AKT genes were overexpressed after treatments with VA. D283MED and ONS-76 cells presented variants in TERT and PTCH1, respectively and DAOY cell line presented a TP53 mutation. Conclusions MB tumors belonging to SHH molecular subgroup, with TP53MUT, would be the ones that present high risk in relation to VA use during the treatment, while TP53WT MBs can benefit from VA therapy, both SHH and groups 3 and 4. Our study shows a new perspective about VA action in medulloblastoma cells, raising the possibility that VA may act in different patterns. According to the genetic background of MB cell, VA can stimulate cell cycle arrest and apoptosis or induce resistance to treatment via signaling pathways activation. Keywords Medulloblastoma . HDACi . NGS . Gene expression . TP53

Introduction Medulloblastoma (MB) is a primitive neuroepithelial tumor originating from the hindbrain and represents one of the

leading causes of pediatric tumor-related death. MB is thought to arise from neural stem cell precursors in the granular cell layer of the cerebellum, and the incidence of MB in patients up to 19 years old ranges from 0.49 in girls to 0.75 in boys, per

* Silvia Regina Caminada Toledo [email protected]

Sergio Cavalheiro [email protected]

Bruna Mascaro-Cordeiro [email protected] Indhira Dias Oliveira [email protected]

Patrícia Dastoli [email protected] 1

Pediatrics Oncology Institute-GRAACC (Grupo de Apoio ao Adolescente e à Criança com Câncer) /UNIFESP (Federal University of Sao Paulo), Rua Botucatu, 743, Floor 8 – Genetics Laboratory, Vila Clementino, São Paulo, SP Zip Code 04023-062, Brazil

Lorena Favaro Pavon [email protected]

2

Department of Morphology and Genetics, Division of Genetics, Federal University of São Paulo, São Paulo, SP, Brazil

Nasjla Saba-Silva [email protected]

3

Departament of Neurology, Federal University of Sao Paulo, Sao Paulo, SP, Brazil

Francine Tesser-Gamba [email protected]

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100,000 patients-years, accounting for 16% of all pediatric brain tumors [1]. Despite current radical treatment, MB is associated to 30% of lethality. Survivors usually develop severe neurological side effects, underscoring the importance of developing alternative therapeutic strategies [2]. The 2007 WHO classification of CNS tumors recognized four MB histological subtypes: classic, desmoplastic/nodular, extensive nodularity (MBEN), and large cell anaplastic (LCA) [3]. In 2012, four molecular subgroups of MB were identified: WNTactivated, SHH-activated, and the numerically designated Bgroup 3^ and Bgroup 4^ [4]. A new risk classification was proposed in a consensus, in 2016, based in MB molecular subgroups and TP53 status [5]. Standard treatment for MB includes surgery, chemotherapy, and depending on the age of the patient, radiation therapy. Chemotherapy in addition to radiotherapy treatment has increased survival rates and allowed lowering of radiotherapy doses for standard risk patients [6, 7]. Cisplatin, vincristine, carboplatin, etoposide, and cyclophosphamide are used in various combinations, in most drug regimens for MB [8]. Also, synergistic drugs are used to enhance efficacy and to prevent drug resistance [9]. Histone deacetylase inhibitors (HDACi) are considered a promising cancer therapeutics, and are a key component of the epigenetic machinery, regulating gene expression though increased histone acetylation, and behaving as oncogenes in some cancer types. The first HDACi were initially characterized for their antitumor activity in vitro before the discovery that they were known to inhibit HDAC [10, 11]. Within HDACi, valproic acid (VA) inhibits both class I and II HDACs with resultant hyperacetylation of histone H3 and H4 [12]. VA is a short branched–chain carboxylic acid, FDA approved anti-seizure and antidepressant drug, a welltolerated anticonvulsant, with several established properties, an extensively characterized toxicity profile and well-defined pharmacokinetic properties [12]. Functional studies have confirmed that HDACi and histone hyperacetylation are essential for VA antitumor activity, once VA inhibits growth of human cancers [12–14], including pediatric solid tumors [15, 16]. Previous studies have reported that VA efficiently prevented MB growth, cell cycle arrest, apoptosis, senescence and differentiation, and suppressing colony forming [17–19]. Additionally, VA significantly inhibited the in vivo growth of DAOY and D283-MED cell lines [20], and presented radiosensitizing capacity, suggesting an adjuvant administration to improve MB therapy [21]. Previous studies have also provided evidences that VA sensitization works in conjunction with chemotherapy inducing apoptosis in MB cells [11]. Improving MB therapy would rely on a rational approach considering consecutive in vitro studies, followed by in vivo experiments [22]. In vitro studies, using long-established continuous cell lines have the advantages of being easy to expand

and relatively uniform [23]. In the present study, we evaluated the role of VA, combined to cisplatin (CDDP), in cytotoxicity, viability, and gene expression profile in three well-established MB cell lines (DAOY, D283MED, and ONS-76). Additionally, we investigated PTCH1, TERT, and TP53 genes trough next-generation sequencing (NGS).

Material and methods Cell lines selection and culture For the present study, we used three well-characterized MB cell lines: DAOY (ATCC® HTB-186™), D283MED (ATCC ® HTB-185™), and ONS-76 (JCRB® IFO50355). DAOY MB cell line was grown and maintained in Dulbecco’s Modified Eagle’s Medium (DMEM; Cultilab®) with 10% fetal bovine serum (FBS, Cultilab®). D283MED cell line was grown in Minimum Essential Medium (MEM, GibcoTM, Thermo Fisher Scientific®), adding 2.2 g of NaHCO3 and 2.5 g of HEPES for each 1000 mL of medium. ONS-76 cells were gown in RPMI-1640 Medium (Cultilab®), containing 10% of FBS. Cells were kept at a temperature of 37 °C, a minimum relative humidity of 95%, and an atmosphere of 5% CO2 in air.

DNA and RNA extraction, and cDNA synthesis DNA and RNA were extracted from untreated cell lines, and treated cells, submitted to the three groups of treatment: (1) VA single, (2) CDDP single, and (3) VA and CDDP combined, after 24, 48, and 72 h. For DNA and RNA extraction, we used NucleoSpin TriPrep Kit (Macherey-Nagel®). DNA was quantified in Qubit™ (ThermoFisher Scientific®), and RNA was quantified in NanoDrop™ (ThermoFischer Scientific®). cDNA was synthetized from 1 μg of RNA, using SuperScript Vilo™, following manufacture’s manual.

Cell lines authentication Cell lines were authenticated using STR (short tandem repeat) profiling analysis, as published in ANSI/ATCC ASN-00022011 normative, which determines that eight loci are enough to authenticate a cell line, with correspondence > 80% [24]. For this genotyping, we utilized GlobalFiler® PCR Amplification Kit (Applied Biosystems™/Thermo Fisher Scientific®), in ABI PRISM 3500 Genetic Analyzer (Life Technologies™/Thermo Fisher Scientific®). Results were analyzed in GeneMapper ID-X software (Life TechnologiesTM/ Thermo Fisher Scientific®). Correspondence index (CI) was calculated as suggested by Cosme et al. 2017 [25], and genotype profile was compared to ATCC STR database (www. atcc.org/str; last accessed February 15, 2017).

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Fluorescence in situ hybridization

Flow cytometry

Fluorescence in situ hybridization (FISH) was performed in DAOY, D283MED, and ONS-76 methanol acetic acid-fixed cells using Vysis LSI N-MYC (2p24) SpectrumGreen/Vysis CEP 2 SpectrumOrange Probe (Abbott®). FISH procedure was performed according to manufacturer’s instructions. Slides were visualized by fluorescence microscopy, and images were captured using the ISIS software (Zeiss, Jena, Germany).

To evaluate cells viability after 48 h of exposure to the three groups of treatment, we selected DAOY and D283MED cells. We considered seven conditions for each cell line: (1) cells without treatment or DNA binding dye; (2) cells without treatment, marked with anexin; (3) cells without treatment, marked with 7aminoactinomycin D (7-AAD); (4) death control (cells exposed to methanol); (5) cells treated for 48 h with VA; (6) cells treated for 48 h with CDDP; (7) cells treated for 24 h with VA, and after this, we added CDDP for 48 h. Following exposure of cells to drugs, after 48 h, cells were resuspended in 0.5 ml 1X PBS. Calibration of the markers was performed using controls 2 and 3. At least 10,000 events were considered for each study condition. Analysis was performed on the cell-flow analysis equipment BD LSRFortessa™ (BD Biosciences®). Results were analyzed using FlowJo® software (http://flowjo.com).

Genomic variants investigation We performed the investigation of MB molecular subgroupsrelated genes for DAOY, D283MED, and ONS-76 cell lines. For this investigation, we developed an AmpliSeq® custom panel, for Ion Torrent PGM® (ThermoScientific®) nextgeneration sequencer (NGS), comprising the complete genomic regions (CDS) of CTNNB1, PTCH1, DDX3X, and TP53 genes, and the CDS + UTR region for TERT gene. DNA from untreated cell lines was submitted to Ion PGM™ library preparation. The resulting template was submitted to a micelles clonal amplification, using Ion PGM™ Hi-Q™ OT2 kit (Thermo Fisher Scientific®) in Ion OneTouch 2 machine (ThermoFisher Scientific®). Then, the template was enriched and applied to the 316 Ion Chip (Thermo Fisher Scientific®). The chip was inserted into the Ion Torrent PGM™ sequencer (Thermo Fisher Scientific®) to start sequencing.

Dose-response curves Dose-response curve was used to determine the potential of VA and CDDP in promoting MB cells death. Treatments were distributed in three groups, during 24, 48, and 72 h: (1) VA single, (2) CDDP single, and (3) 24 h pretreatment with VA, and after 24 h, addition of CDDP (combined treatment). Untreated cells were the control for the assays. We plated 104 cells in a 96-wells plate. After 24 h, drugs were added individually or in combination at a serial dilution (0.078– 200 mM). Every 24 h, cells were incubated for 2 h with 10 μl of the PrestoBlue™ cell viability reagent (Invitrogen, Carlsbad, CA) and then analyzed on the ELISA reader M3 SpectraMax (Molecular Devices). PrestoBlue™ reagent is reduced by metabolically active cells, providing a quantitative measure of cytotoxicity and cell viability, which allows the calculation of the inhibitory concentration (IC50) of the cells. IC50 values were calculated using GraphPad Prism version 6.0 software (San Diego, CA). Values for concentrations were normalized, converted to log10 and plotted as a non-linear regression curve, following software manual. Once the IC50 values were determined for all treatments in each cell line, viability, migration, and invasion experiments were performed after the exposure to the treatments.

Quantitative real-time PCR Quantitative real-time PCR (qPCR) evaluated expression levels of MB key genes AKT, CTNNB1, GLI1, KDM6A, KDM6B, NOTCH2, PTCH1, and TERT. Amplification was performed in triplicate, in a 96-well-plate, using 12-μl final volume of a reaction containing the following mixture: 6 μl of SYBR Green (ThermoFisher Scientific®), 3 μl of nucleasefree water and 1.5 μl of each qPCR primers. Primers were chosen with the assistance of Primer Express Software (ThermoFisher Scientific®). We conducted a BLAST search to confirm the total gene specificity of the nucleotide sequences chosen for primers. For all reactions, we used an ABI Prism 7500 thermal cycler (ThermoFisher Scientific®). To determine the relative quantification of gene expression, the comparative Ct (cycle threshold) method was used. Briefly, this method uses arithmetic formulas to determine relative quantification. To normalize the varying cDNA quantities, a gene housekeeping ACTB runned concurrently with the target gene. The Ct was determined by subtracting the ACTB Ct from the specific target gene Ct. The Ct of the control references was then subtracted from the ΔCt of the target gene, yielding the Ct and the relative quantitative value was expressed as 2−ΔΔCt [19, 26].

Data and statistical analysis After removing adaptor sequences, we evaluated the quality of raw reads obtained through NGS. Following a laboratorydeveloped pipeline, we selected amplicons through the coverage data analysis with the ratio between forward and reverse strands as ≥ 0.6 and ≤ 1.4, and a sequencing coverage of 200X reads was used as a minimum requirement. After processing the reads to meet a certain quality standard, they were aligned against human references (NCBI build 37, human genome 19).

Childs Nerv Syst Table 1 Cell lines authentication results

Locus

DAOY (ATCC® HTB186™)

D283MED (ATCC® HTB-185™)

Observed Reference

ONS-76 (JCRB No. IFO50355)

Observed Reference

Observed Reference

vWA

14, 20

14, 20

16, 18

16, 18

14, 16

D16S539

10

10

11

11

9, 10

14, 16 9, 10

CSF1PO TPOX

11 8, 10

11 8

9, 12 8, 11

9, 12 8, 11

10, 12 8

10, 12 8

AMEL TH01

X 9

X 9

X, Y 7, 7

X, Y 7, 7

X 9, 9

X 9, 9

D5S818

11, 13

11, 13

11

11

9, 10

9, 10

D13S317 D7S820

13, 14 8, 10

13, 14 8, 10

8, 10 10

8, 10 10

8, 13 11, 12

8, 13 11, 12

Correspondence index (CI)

ATCC®

Cosme et al. 2016

ATCC®

Cosme et al. 2016

ATCC®

Cosme et al. 2016

93%

96%

100%

100%

N.A

100%

N.A, not avaliable

We performed gene expression statistical analyses using GraphPad Prism version 5.0 software (California). The efficacy of the treatments was compared using the BExtra-sum-of-squares F test^, which assesses whether or not the null hypothesis (in this case, the fact that the treatments are equally effective) should be rejected. Comparisons between the median values of gene expression in the treated and untreated cell lines (control) were evaluated using the Wilcoxon rank test. Continuous data on gene and protein expression were evaluated and compared using nonparametric tests: Mann-Whitney, Wilcoxon, Kruskal-Wallis, correlation coefficient (R), Spearman and Friedman, followed by Dunns post-test for multiple comparisons and two-way ANOVA. Results were considered statistically significant when p < 0.005.

Results

Fig. 1 MB cell lines characteristics, somatic variants, pathogenic predictors (S = SIFT, P = PolyPhen, MT = mutation taster) and human genetic variation (HGV; 1G = 1000 genomes, Ex = ExAC) obtained

through NGS sequencing, considering exonic variants with coverage > 200×, for our cohort

Cell lines authentication DAOY, D283MED, and ONS-76 cell lines were authenticated, with > 90% of CI. Genotype profile and CI are described in Table 1.

Fluorescence in situ hybridization We did not observe MYCN gene amplification in DAOY, D283MED, and ONS-76 cell lines.

Childs Nerv Syst Table 2 IC50 values for each treatment, in each cell line, in 24, 48, and 72 h of exposure

Genomic variants investigation

DAOY

DAOY cell line presented six variants. Of these, three exonic variants: one missense variant in exon 12 of TERT gene (c.2915G > A:p.Arg972His), and two missense variants in TP53 gene, both in exon 3: c.215C > G:pPro72Arg variant, also known as codon 72 polymorphism of TP53 gene, and c.329G > T:p.Cys110Phe variant. D283MED cell line presented 10 variants. One of these variants is c.215C > G:pPro72Arg variant, of TP53 gene, and the other is the synonymous variant in exon 13 of TERT gene (c.2850C > T:p.His950=). ONS76 cell line presented 15 variants. Of these, we observed two synonymous variants in TERT gene, one in exon 2 (c.915G > A:p.Ala305=), and another in exon 14 (c.2850C > T:p.His950=). Also, we observed one missense variant in exon 22 of PTCH1 gene: c.3583A > T:p.Thr1195Ser. The relation of variants observed is described in Fig. 1.

24 h

VA (mM) 3

CDDP (mM) 97

Combined 3 mM AV + 12.9 mM CDDP

48 h

2.7

6

3 mM AV + 4,7 mM CDDP

72 h 2.5 D283MED

2

3 mM AV + 2,6 mM CDDP

24 h

VA (mM) 5

CDDP (mM) 9.2

Combined 0.5 mM AV +5.7 mM CDDP

48 h

2.7

6

0.5 mM AV + 4.7 mM CDDP

72 h 2.5 ONS-76

2

0.5 mM AV + 2,6 mM CDDP

VA (mM)

CDDP (mM)

Combined

24 h

18,598

6356

18,598 mM AV + 9261 mM CDDP

48 h 72 h

8116 6277

4044 4739

8116 mM AV + 5564 mM CDDP 6277 mM AV + 4421 mM CDDP

Fig. 2 Graphics comparing viability rates for DAOY cell line after exposure do single VA and CDDP, and VA and CDDP combined after 24 h (a), 48 h (b), and 72 h (c)

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Fig. 3 Graphics comparing viability rates for D283MED cell line after exposure do single VA and CDDP, and VA and CDDP combined after a 24, b 48, and c 72 h of treatment

Dose-response curves

Flow cytometry

VA and CDDP were used single and in combination. We obtained the IC50 value for each MB cell lines, in each group of treatment (single VA and CDDP, and VA and CDDP combined), in each time (24, 48, and 72 h) (Table 2). The best time for dose-effect curve and IC50 was achieved after 48 h. For DAOY cells, we observed that single CDDP treatment significantly reduced cells viability, when compared to single VA and combined treatments. This result was observed in 24 (p = 0.0418), 48, and 72 h (p < 0.001) (Fig. 2). For D283MED cells, we observed that combined treatment significantly reduced cells viability, when compared to single VA and CDDP treatments. This result was observed when treatments were compared in 24 (p = 0.0154) and 48 h (P < 0.0001) (Fig. 3). ONS-76 cells presented significant reduction of viability in the presence of VA treatment (single or combined) (p < 0.0001) (Fig. 4).

For DAOY cell line, we observed that the highest percentage of viable cells was present in single VA treatment (33%), while the highest percentage of cell death was present in single CDDP treatment (92%). For combined treatment, we observed 9% of viability and 81% of cell death. For D283MED cell line, we observed that single VA treatment resulted in 42% of viable cells. However, we observed that combined treatment presented the lowest percentage of viable cells (11%), less than half of the percentage of viable cells observed in single CDDP treatment (26%). Also, we observed that combined treatment presented 50% of cell death, double that observed in single VA (26%) and CDDP (25%) treatments. Results are demonstrated in Fig. 5.

Quantitative real-time PCR We observed an important gene expression modulation in relation to treatments with combined and single VA.

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Fig. 4 Graphics comparing viability rates for ONS-76 cell line after exposure do single VA and CDDP, and VA and CDDP combined after a 24, b 48, and c 72 h of treatment

Cell lines isolated analysis demonstrated that KDM6A, KDM6B, CTNNB1, TERT, GLI1, AKT, PTCH1, and NOTCH2 genes expression, when submitted to the three groups of treatment proposed, varied significantly for D283MED (p < 0.0001) and ONS-76 cells (p < 0.0001) (Fig. 6). For D283MED cells, we observed that TERT gene expression was significantly increased in groups of treatment with VA single and combined, in 48 h, compared to control (Fig. 6a). For ONS-76 cells, we observed that GLI1 gene expression was significantly increased in groups of treatment with VA single and combined (Fig. 6b). When comparing gene expression and treatments, individually, we observed that in single VA treatment, TERT gene was significantly overexpressed in D283MED cells (p = 0.05) (Fig. 7a). In addition, for single VA treatment, AKT gene was overexpressed in ONS-76 cells (p = 0.0286) when compared to control (Fig. 7b). GLI1 gene presented significant overexpression in group of treatments with VA (single or combined), when comparing the three cell lines (p = 0.0176) (Fig. 7c), and after 48 h (Fig. 7d).

Discussion The current challenge in MB treatment is to identify which of the conventional chemotherapy agents and which newtargeted drugs might be expected to work best in each of the MB molecular subtypes. In this way, it is important to ascertain the subtype each cell line belongs to in order to link preclinical data to tumor type of the patients. For the present study, we selected three MB cell lines: DAOY and ONS-76, both classified as SHH subtype, carrying mutated (TP53MUT), and wild-type TP53 (TP53WT), respectively, and D283MED classified as either group 4 or group 3, with TP53WT [23]. To validate our findings, first we authenticated the cell lines using STR profiling analysis. As previously reported, we did not observe MYCN amplification in DAOY, ONS-76, and D283MED cells [27]. Also, corroborating to literature, D283MED and ONS-76 cell lines did not present TP53 mutations, and DAOY cell line, in turn, has a pathogenic mutation in TP53 gene (c.329G > T:p.Cys110Phe). It has been proposed that VA leads to a specific DNA damage [28], the double strands breakage (DSB), which can result in altered gene expression and induction of apoptosis

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Fig. 5 Graphics demonstrating DAOY and D283MED cells behavior after 48 h of treatment with single VA (a, d) and CDDP (b, e), and combined VA and CDDP (c, f)

[29]. The histone hyperacetylation induced by VA causes structural alterations in chromatin, exposing portions of DNA to DNA-damaging agents, that are normally protected by heterochromatin [30, 31]. HDACi, as VA, function by increasing histone acetylation, but also by enhancing acetylation of nonhistone proteins as TP53 [32]. The outcome of an anticancer chemotherapy can depend on the TP53 status of the tumor [33]. The exact mechanisms of HDACi anti-tumoral action under influence of the status of TP53 are not yet fully understood [34–36]. In our study, MB cell lines investigated presented different response to VA treatment. DAOY has TP53MUT while ONS76 and D283MED have TP53WT. This difference can be explained by the fact that DAOY and ONS-76 belongs to SHH molecular meduloblastoma subgroup, and D283MED belongs to molecular subgroup 3/4 [23]. SHH subgroup is characterized by activation of the SHH pathway, and tumors commonly harbor mutations in components of the SHH pathway, specifically PTCH, SMO, and SUFU [37]. A proportion of SHH tumors exhibits amplification of MYCN and GLI2, and mutations in TP53, frequently associated with anaplastic morphology [38]. In addition to belonging to the subgroup SHH, the DAOY cell presents TP53MUT. It was recognized that

group 3 is more related to group 4 than WNT and SHH, with some overlapping features, and therefore these two groups were introduced as provisional entities within non-SHH/nonWNT medulloblastomas, where the subgrouping is unequivocal [38]. In addition to the genetic background, another striking difference between the MB cells investigated is the site of origin. DAOY and ONS-76 cell lines originated from a primary tumor site, and D283MED cell line originated from a metastatic tumor site. Recent studies described that medulloblastoma metastasis are different from primary tumor, showing only around 10% of molecular targets presented at diagnosis [15]. Probably, due to cell lines origins and molecular subgroups, it might presents different genetic backgrounds, and so, other confounding genes related to different responses to VA treatment, as observed in our study [39]. ONS-76 cells has a more aggressive behavior, presenting higher proliferation and migration capability, greater division rate, and is more immature (undifferentiated) than other medulloblastoma cell lines [13, 14, 23, 26]. Fujiki et al. (2013) demonstrated proapoptotic and antiproliferative effects of VA on neural progenitor cells (NPCs) in the embryonic

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Fig. 6 Expression profile for all investigated genes, for D283MED (a), ONS-76 (b), and DAOY (c) cell lines, after treatments with single VA and CDDP, and combined VA and CDDP, for 24, 48, and 72 h

development. Also, the authors reported that cells apoptosis after VA exposure was exerted transiently only during early embryonic brain development [40]. Catalano et al. (2005) reported that VA strongly inhibits cell growth through induction of apoptosis and cell cycle arrest in poorly differentiated cancer cells, using the intrinsic pathway of apoptosis, activating caspase 9, and leading to recruitment of caspase 3. The authors concluded that the intrinsic pathway is a selective pathway of VA-induced apoptosis, and that the effect on cells apoptosis is VA dose-dependent [41]. In our study, we used higher VA concentrations in ONS-76 cells than in the other two cell lines investigated to reach cytotoxic levels (Table 2). Fujiki et al. (2013) reported that VA had dose-dependent effects both on apoptosis and hyperacetylation of histone H3 in NPCs [40], Yagi et al.

(2010) described that inhibition of VA in gastric cancer cells was dependent on the dose and incubation time [42], and Jung et al. (2015) reported that the rate of cell death was found to increase in a dose-dependent manner in undifferentiated human cells [43]. We suggest that the accentuated VA cytotoxicity observed in ONS-76 cell line compared to CDDP is due to its ability to induce apoptosis and cell cycle arrest in poorly differentiated cancer cells, like ONS-76, possibly by caspase 9 activation and histone hyperacetylation in a dependent doseresponse mechanism. Some studies point to a largely p53-independent action of HDACis [3, 34], while other studies, however, suggest an essential role of TP53 in the response of tumor cells to HDACi treatment [44, 45]. In our study, as observed by other authors, the use of VA as a single agent did not decreased

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Fig. 7 Multiple comparisons Bonferroni analysis for gene expression of MB cell lines. a TERT overexpression of D283MED cells, after treatment with VA. b AKT overexpression observed in cell lines after treatment with

VA. c GLI1 overexpression observed after VA and combined treatments. d AKT overexpression observed after 48 h of exposure to VA in relation to control

DAOY cell viability [32], and DAOY cells treated with single CDDP had a large drop in the viability rates. We suggest that treatment response observed in combined VA and CDDP was not similar to single CDDP due to TP53 status. DAOY cells presents TP53MUT, making us suggest a possible selection and treatment resistance for CDDP, after previous exposition to VA. Altered expression of multiple genes has been reported in cells exposed to VA treatment [46]. Indeed, excessive histone acetylation at transcribed regions could destabilize chromatin, thus leading to an increase of transcription at erroneous starting sites [10, 31]. In our study, we observed that TERT, GLI1, and AKT genes were, four to ten times, overexpressed after VA treatment (p < 0.0001). GLI1 and PTCH1 belongs to SHH signaling pathway, and while PTCH1 is a suppressor of this pathway, GLI1 is an important transcription factor and final product of SHH pathway [47]. GLI1 overexpression observed in ONS-76 cells, after treatment with single and combined VA, might be related to the presence of c.3583A > T:p.Thr1195Ser missense PTCH1 gene variant, observed in heterozygosis. Once this variant is related to PTCH1 loss of

function [48], SHH pathway may become activated, and this might be the reason of GLI1 overexpression in ONS-76 cell line. Another hypothesis for GLI1 overexpression after treatment with VA, observed in ONS-76 cell line, might be through AKT overexpression observed in these cells. Studies have shown that the chronic use of VA is associated with reactivation of the AKT pathway, which may be involved with mechanisms of treatment resistance. Furthermore, AKT signaling pathway activates SHH pathway independently [49]. In transformed cells, as tumor cells, epigenetic regulators are required to maintain the expression of a few key target genes, and a slight tip in this regulation balance is sufficient to result in a cell catastrophe [50]. This result suggests an activation of GLI1 by AKT, and this activation may trigger a treatment resistance mechanism. Our study shows a new perspective about VA action in medulloblastoma cells, raising the possibility that VA may act in different patterns. According to the genetic background of MB cell, VA can stimulate cell cycle arrest and apoptosis or induce resistance to treatment via signaling pathways

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activation. VA may help in treatment, acting as a sensitizer to conventional therapies, however, this is a tiny balance and we believe that VA chronic use may select resistant cells through the high expression levels of genes related to proliferation and undifferentiation. Mechanisms that regulate gene expression and resistance after exposition to HDACi, including VA, are really complex and little understood. Inhibition of HDACs show many chances of interfering on cells fundamental functions, and should be expected positive and negative effects upon tumorigenic and oncosupression processes [16]. Based on our results, the main take home message about MB cells and VA is about MB tumors belonging to SHH molecular subgroup, with TP53MUT, that would be the ones presenting high risk in relation to VA use during the treatment. When TP53MUT cells were exposed previously to VA, CDDP effect was not as efficient as expected in the treatment with CDDP single agent. On the other hand, TP53WT MBs can benefit from VA therapy, both SHH and groups 3 and 4. Some additional studies are necessary to understand the precise VA mechanism of action upon dose-dependent proapoptotic effects and resistant-selected cells, to establish VA safety when prescribed to MB patients. Acknowledgments We thank Prof. Dr. Reinado Salomão and Milena Brunialti, from Department of Medicine-Imunology, Laboratory of Imunology - Federal University of São Paulo, that made available cellflow analysis equipment BD LSRFortessa™ (BD Biosciences®), and FlowJo® software. Funding FAPESP (Fundação de Amparo à Pesquisa do Estado de Sao Paulo) project number 2013/12281-4, and Pediatric Oncology Institute – IOP/GRAACC.

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Compliance with ethical standards Conflict of interest The authors declare no conflict of interests.

References Crawford JR, MacDonald TJ, Packer RJ (2007) Medulloblastoma in childhood: new biological advances. Lancet Neurol 6(12):1073– 1085 2. Massimino M, Biassoni V, Gandola L, Garrè ML, Gatta G, Giangaspero F, Poggi G, Rutkowski S (2016) Childhood medulloblastoma. Crit Rev Oncol Hematol 105:35–51 3. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114(2):97–109 4. Taylor MD, Northcott PA, Korshunov A, Remke M, Cho YJ, Clifford SC, Eberhart CG, Parsons DW, Rutkowski S, Gajjar A, Ellison DW, Lichter P, Gilbertson RJ, Pomeroy SL, Kool M, Pfister SM (2012) Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol 123(4):465–472 5. Ramaswamy V, Remke M, Bouffet E, Bailey S, Clifford SC, Doz F, Kool M, Dufour C, Vassal G, Milde T, Witt O, von Hoff K, Pietsch T, Northcott PA, Gajjar A, Robinson GW, Padovani L, André N,

17.

18.

1.

19.

20.

21.

Massimino M, Pizer B, Packer R, Rutkowski S, Pfister SM, Taylor MD, Pomeroy SL (2016) Risk stratification of childhood medulloblastoma in the molecular era: the current consensus. Acta Neuropathol 131(6):821–831 Packer RJ, Goldwein J, Nicholson HS, Vezina LG, Allen JC, Ris MD, Muraszko K, Rorke LB, Wara WM, Cohen BH, Boyett JM (1999) Treatment of children with medulloblastomas with reduceddose craniospinal radiation therapy and adjuvant chemotherapy: a Children’s Cancer Group Study. J Clin Oncol 17(7):2127–2136 von Hoff K et al (2009) Long-term outcome and clinical prognostic factors in children with medulloblastoma treated in the prospective randomised multicentre trial HIT’91. Eur J Cancer 45(7):1209– 1217 von Hoff K, Rutkowski S (2012) Medulloblastoma. Curr Treat Options Neurol 14(4):416–426 Rutkowski S, von Hoff K, Emser A, Zwiener I, Pietsch T, FigarellaBranger D, Giangaspero F, Ellison DW, Garre ML, Biassoni V, Grundy RG, Finlay JL, Dhall G, Raquin MA, Grill J (2010) Survival and prognostic factors of early childhood medulloblastoma: an international meta-analysis. J Clin Oncol 28(33):4961–4968 Ceccacci E, Minucci S (2016) Inhibition of histone deacetylases in cancer therapy: lessons from leukaemia. Br J Cancer 114(6):605– 611 Hacker S et al (2011) Histone deacetylase inhibitors prime medulloblastoma cells for chemotherapy-induced apoptosis by enhancing p53-dependent Bax activation. Oncogene 30(19):2275–2281 Gottlicher M et al (2001) Valproic acid defines a novel class of HDAC inhibitors inducing differentiation of transformed cells. EMBO J 20(24):6969–6978 Sun L, Moritake T, Zheng YW, Suzuki K, Gerelchuluun A, Hong Z, Zenkoh J, Taniguchi H, Tsuboi K (2013) In vitro stemness characterization of radio-resistant clones isolated from a medulloblastoma cell line ONS-76. J Radiat Res 54(1):61–69 Ranger A, McDonald W, Moore E, DelMaestro R (2010) The invasiveness of five medulloblastoma cell lines in collagen gels. J Neuro-Oncol 96(2):181–189 Garzia L et al (2018) A hematogenous route for medulloblastoma leptomeningeal metastases. Cell 172(5):1050–1062.e14 Minucci S, Pelicci PG (2006) Histone deacetylase inhibitors and the promise of epigenetic (and more) treatments for cancer. Nat Rev Cancer 6(1):38–51 Furchert SE, Lanvers-Kaminsky C, Juürgens H, Jung M, Loidl A, Frühwald MC (2007) Inhibitors of histone deacetylases as potential therapeutic tools for high-risk embryonal tumors of the nervous system of childhood. Int J Cancer 120(8):1787–1794 Li XN, Shu Q, Su JM, Perlaky L, Blaney SM, Lau CC (2005) Valproic acid induces growth arrest, apoptosis, and senescence in medulloblastomas by increasing histone hyperacetylation and regulating expression of p21Cip1, CDK4, and CMYC. Mol Cancer Ther 4(12):1912–1922 Ecke I, Petry F, Rosenberger A, Tauber S, Monkemeyer S, Hess I, Dullin C, Kimmina S, Pirngruber J, Johnsen SA, Uhmann A, Nitzki F, Wojnowski L, Schulz-Schaeffer W, Witt O, Hahn H (2009) Antitumor effects of a combined 5-aza-2'deoxycytidine and valproic acid treatment on rhabdomyosarcoma and medulloblastoma in Ptch mutant mice. Cancer Res 69(3):887–895 Shu Q, Antalffy B, Su JMF, Adesina A, Ou CN, Pietsch T, Blaney SM, Lau CC, Li XN (2006) Valproic acid prolongs survival time of severe combined immunodeficient mice bearing intracerebellar orthotopic medulloblastoma xenografts. Clin Cancer Res 12(15): 4687–4694 Patties I, Kortmann RD, Menzel F, Glasow A (2016) Enhanced inhibition of clonogenic survival of human medulloblastoma cells by multimodal treatment with ionizing irradiation, epigenetic modifiers, and differentiation-inducing drugs. J Exp Clin Cancer Res 35(1):94

Childs Nerv Syst 22.

Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, Pangalos MN (2014) Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13(6):419–431 23. Ivanov DP, Coyle B, Walker DA, Grabowska AM (2016) In vitro models of medulloblastoma: choosing the right tool for the job. J Biotechnol 236:10–25 24. Barallon R, Bauer SR, Butler J, Capes-Davis A, Dirks WG, Elmore E, Furtado M, Kline MC, Kohara A, Los GV, MacLeod RAF, Masters JRW, Nardone M, Nardone RM, Nims RW, Price PJ, Reid YA, Shewale J, Sykes G, Steuer AF, Storts DR, Thomson J, Taraporewala Z, Alston-Roberts C, Kerrigan L (2010) Recommendation of short tandem repeat profiling for authenticating human cell lines, stem cells, and tissues. In Vitro Cell Dev Biol Anim 46(9):727–732 25. Cosme B, Falagan-Lotsch P, Ribeiro M, Napoleão K, Granjeiro JM, Moura-Neto R (2017) Are your results valid? Cellular authentication a need from the past, an emergency on the present. In Vitro Cell Dev Biol Anim 53:430–434 26. Zanini C, Ercole E, Mandili G, Salaroli R, Poli A, Renna C, Papa V, Cenacchi G, Forni M (2013) Medullospheres from DAOY, UW228 and ONS-76 cells: increased stem cell population and proteomic modifications. PLoS One 8(5):e63748 27. Snuderl M, Batista A, Kirkpatrick ND, Ruiz de Almodovar C, Riedemann L, Walsh EC, Anolik R, Huang Y, Martin JD, Kamoun W, Knevels E, Schmidt T, Farrar CT, Vakoc BJ, Mohan N, Chung E, Roberge S, Peterson T, Bais C, Zhelyazkova BH, Yip S, Hasselblatt M, Rossig C, Niemeyer E, Ferrara N, Klagsbrun M, Duda DG, Fukumura D, Xu L, Carmeliet P, Jain RK (2013) Targeting placental growth factor/neuropilin 1 pathway inhibits growth and spread of medulloblastoma. Cell 152(5):1065–1076 28. Lee JH, Choy ML, Ngo L, Foster SS, Marks PA (2010) Histone deacetylase inhibitor induces DNA damage, which normal but not transformed cells can repair. Proc Natl Acad Sci U S A 107(33): 14639–14644 29. Chen CS, Wang YC, Yang HC, Huang PH, Kulp SK, Yang CC, Lu YS, Matsuyama S, Chen CY, Chen CS (2007) Histone deacetylase inhibitors sensitize prostate cancer cells to agents that produce DNA double-strand breaks by targeting Ku70 acetylation. Cancer Res 67(11):5318–5327 30. Abreu PA, Dellamora-Ortiz G, Leão-Ferreira LR, Gouveia M, Braggio E, Zalcberg I, Santos DO, Bourguinhon S, Cabral LM, Rodrigues CR, Castro HC (2008) DNA methylation: a promising target for the twenty-first century. Expert Opin Ther Targets 12(8): 1035–1047 31. Wang Z, Zang C, Cui K, Schones DE, Barski A, Peng W, Zhao K (2009) Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes. Cell 138(5):1019–1031 32. Buchwald M, Kramer OH, Heinzel T (2009) HDACi—targets beyond chromatin. Cancer Lett 280(2):160–167 33. Soussi T, Beroud C (2001) Assessing TP53 status in human tumours to evaluate clinical outcome. Nat Rev Cancer 1(3):233–240 34. Ruefli AA, Ausserlechner MJ, Bernhard D, Sutton VR, Tainton KM, Kofler R, Smyth MJ, Johnstone RW (2001) The histone deacetylase inhibitor and chemotherapeutic agent suberoylanilide hydroxamic acid (SAHA) induces a cell-death pathway characterized by cleavage of bid and production of reactive oxygen species. Proc Natl Acad Sci U S A 98(19):10833–10838 35. Sonnemann J, Hartwig M, Plath A, Saravana Kumar K, Müller C, Beck JF (2006) Histone deacetylase inhibitors require caspase activity to induce apoptosis in lung and prostate carcinoma cells. Cancer Lett 232(2):148–160 36. Lindemann RK, Newbold A, Whitecross KF, Cluse LA, Frew AJ, Ellis L, Williams S, Wiegmans AP, Dear AE, Scott CL, Pellegrini M, Wei A, Richon VM, Marks PA, Lowe SW, Smyth MJ, Johnstone RW (2007) Analysis of the apoptotic and therapeutic

activities of histone deacetylase inhibitors by using a mouse model of B cell lymphoma. Proc Natl Acad Sci U S A 104(19):8071–8076 37. Kool M, Jones DT, Jäger N, Northcott PA, Pugh TJ, Hovestadt V, Piro RM, Esparza LA, Markant SL, Remke M, Milde T, Bourdeaut F, Ryzhova M, Sturm D, Pfaff E, Stark S, Hutter S, Seker-Cin H, Johann P, Bender S, Schmidt C, Rausch T, Shih D, Reimand J, Sieber L, Wittmann A, Linke L, Witt H, Weber UD, Zapatka M, König R, Beroukhim R, Bergthold G, van Sluis P, Volckmann R, Koster J, Versteeg R, Schmidt S, Wolf S, Lawerenz C, Bartholomae CC, von Kalle C, Unterberg A, Herold-Mende C, Hofer S, Kulozik AE, von Deimling A, Scheurlen W, Felsberg J, Reifenberger G, Hasselblatt M, Crawford JR, Grant GA, Jabado N, Perry A, Cowdrey C, Croul S, Zadeh G, Korbel JO, Doz F, Delattre O, Bader GD, McCabe M, Collins VP, Kieran MW, Cho YJ, Pomeroy SL, Witt O, Brors B, Taylor MD, Schüller U, Korshunov A, Eils R, Wechsler-Reya RJ, Lichter P, Pfister SM, ICGC PedBrain Tumor Project (2014) Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition. Cancer Cell 25(3):393–405 38. Zhukova N, Ramaswamy V, Remke M, Pfaff E, Shih DJH, Martin DC, Castelo-Branco P, Baskin B, Ray PN, Bouffet E, von Bueren AO, Jones DTW, Northcott PA, Kool M, Sturm D, Pugh TJ, Pomeroy SL, Cho YJ, Pietsch T, Gessi M, Rutkowski S, Bognar L, Klekner A, Cho BK, Kim SK, Wang KC, Eberhart CG, FevreMontange M, Fouladi M, French PJ, Kros M, Grajkowska WA, Gupta N, Weiss WA, Hauser P, Jabado N, Jouvet A, Jung S, Kumabe T, Lach B, Leonard JR, Rubin JB, Liau LM, Massimi L, Pollack IF, Shin Ra Y, van Meir EG, Zitterbart K, Schüller U, Hill RM, Lindsey JC, Schwalbe EC, Bailey S, Ellison DW, Hawkins C, Malkin D, Clifford SC, Korshunov A, Pfister S, Taylor MD, Tabori U (2013) Subgroup-specific prognostic implications of TP53 mutation in medulloblastoma. J Clin Oncol 31(23):2927–2935 39. Kool M, Korshunov A, Remke M, Jones DTW, Schlanstein M, Northcott PA, Cho YJ, Koster J, Schouten-van Meeteren A, van Vuurden D, Clifford SC, Pietsch T, von Bueren AO, Rutkowski S, McCabe M, Collins VP, Bäcklund ML, Haberler C, Bourdeaut F, Delattre O, Doz F, Ellison DW, Gilbertson RJ, Pomeroy SL, Taylor MD, Lichter P, Pfister SM (2012) Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, group 3, and group 4 medulloblastomas. Acta Neuropathol 123(4):473–484 40. Fujiki R, Sato A, Fujitani M, Yamashita T (2013) A proapoptotic effect of valproic acid on progenitors of embryonic stem cellderived glutamatergic neurons. Cell Death Dis 4(6):e677 41. Catalano MG, Fortunati N, Pugliese M, Costantino L, Poli R, Bosco O, Boccuzzi G (2005) Valproic acid induces apoptosis and cell cycle arrest in poorly differentiated thyroid cancer cells. J Clin Endocrinol Metab 90(3):1383–1389 42. Yagi Y, Fushida S, Harada S, Kinoshita J, Makino I, Oyama K, Tajima H, Fujita H, Takamura H, Ninomiya I, Fujimura T, Ohta T, Yashiro M, Hirakawa K (2010) Effects of valproic acid on the cell cycle and apoptosis through acetylation of histone and tubulin in a scirrhous gastric cancer cell line. J Exp Clin Cancer Res 29:149 43. Jung EM, Choi YU, Kang HS, Yang H, Hong EJ, An BS, Yang JY, Choi KH, Jeung EB (2015) Evaluation of developmental toxicity using undifferentiated human embryonic stem cells. J Appl Toxicol 35(2):205–218 44. Bajbouj K, Mawrin C, Hartig R, Schulze-Luehrmann J, WilischNeumann A, Roessner A, Schneider-Stock R (2012) P53dependent antiproliferative and pro-apoptotic effects of trichostatin A (TSA) in glioblastoma cells. J Neuro-Oncol 107(3):503–516 45. Henderson C, Mizzau M, Paroni G, Maestro R, Schneider C, Brancolini C (2003) Role of caspases, bid, and p53 in the apoptotic response triggered by histone deacetylase inhibitors trichostatin-A (TSA) and suberoylanilide hydroxamic acid (SAHA). J Biol Chem 278(14):12579–12589

Childs Nerv Syst 46.

47.

48.

Phiel CJ, Zhang F, Huang EY, Guenther MG, Lazar MA, Klein PS (2001) Histone deacetylase is a direct target of valproic acid, a potent anticonvulsant, mood stabilizer, and teratogen. J Biol Chem 276(39):36734–36741 Kataoka S, Takuma K, Hara Y, Maeda Y, Ago Y, Matsuda T (2013) Autism-like behaviours with transient histone hyperacetylation in mice treated prenatally with valproic acid. Int J Neuropsychopharmacol 16(1):91–103 Wolter M, Reifenberger J, Sommer C, Ruzicka T, Reifenberger G (1997) Mutations in the human homologue of the Drosophila

49.

50.

segment polarity gene patched (PTCH) in sporadic basal cell carcinomas of the skin and primitive neuroectodermal tumors of the central nervous system. Cancer Res 57(13):2581–2585 Juengel E, Makarević J, Tsaur I, Bartsch G, Nelson K, Haferkamp A, Blaheta RA (2013) Resistance after chronic application of the HDAC-inhibitor valproic acid is associated with elevated Akt activation in renal cell carcinoma in vivo. PLoS One 8(1):e53100 Dawson MA, Kouzarides T (2012) Cancer epigenetics: from mechanism to therapy. Cell 150(1):12–27