Abstract

   Diffusely infiltrating gliomas (DIGs) are difficult to completely
   resect and are associated with a high rate of tumor relapse and
   progression from low- to high-grade glioma. In particular, optimized
   short-term culture-enriching patient-derived glioma stem cells (GSCs)
   are essential for customizing the therapeutic strategy based on
   clinically feasible in vitro drug screening for a wide range of DIGs,
   owing to the high inter-tumoral heterogeneity. Herein, we constructed a
   novel high-throughput culture condition screening platform called
   ‘GFSCAN’, which evaluated the cellular growth rates of GSCs for each
   DIG sample in 132 serum-free combinations, using 13 previously reported
   growth factors closely associated with glioma aggressiveness. In total,
   72 patient-derived GSCs with available genomic profiles were tested in
   GFSCAN to explore the association between cellular growth rates in
   specific growth factor combinations and genomic/molecular backgrounds,
   including isocitrate dehydrogenase 1 (IDH1) mutation, chromosome arm 1p
   and 19q co-deletion, ATRX chromatin remodeler alteration, and
   transcriptional subtype. GSCs were clustered according to the
   dependency on epidermal growth factor and basic fibroblast growth
   factor (E&F), and isocitrate dehydrogenase 1 (IDH1) wild-type GSCs
   showed higher E&F dependencies than IDH1 mutant GSCs. More importantly,
   we elucidated optimal combinations for IDH1 mutant glioblastoma and
   lower grade glioma GSCs with low dependencies on E&F, which could be an
   aid in clinical decision-making for these DIGs. Thus, we demonstrated
   the utility of GFSCAN in personalizing in vitro cultivation to nominate
   personalized therapeutic options, in a clinically relevant time frame,
   for individual DIG patients, where standard clinical options have been
   exhausted.

   Keywords: diffuse infiltrating glioma, personalized medicine, glioma
   stem cells, short-term cultivation screening platform, growth factor,
   genomic profiling

1. Introduction

   Diffusely infiltrating gliomas (DIGs) are categorized by the World
   Health Organization (WHO) into diffuse astrocytomas,
   oligodendrogliomas, and isocitrate dehydrogenase 1 and 2 (IDH1/2)
   mutant (mut) and wild-type (wt) glioblastomas, according to their
   histological grades, IDH1/2 status, and chromosome arm 1p and 19q
   (1p19q) co-deletion [[50]1,[51]2]. DIGs are among the most devastating
   tumor types and are characterized by extensive infiltrative growth, a
   low probability of complete resection, and resistance to combined
   therapies [[52]3,[53]4]. Glioblastoma is a WHO grade IV disease with
   poor prognosis, and only about 10% of these malignancies possess IDH1/2
   mutation without 1p19q co-deletion [[54]2]. On the other hand, diffuse
   astrocytomas (IDH mutant and 1p19q intact) and oligodendrogliomas (IDH
   mutant and 1p19q co-deleted) belong to WHO grades II and III, which
   represent "lower-grade gliomas" (LGGs) in The Cancer Genome Atlas
   (TCGA) [[55]1,[56]2,[57]3,[58]4]. Most importantly, LGGs not only recur
   over time, but also progress to high-grade gliomas (HGGs) that are
   characterized by the high inter- and intra-tumoral heterogeneity
   associated with rapid clinical deterioration [[59]3,[60]4,[61]5].

   Recently, the three-dimensional (3D) organoid culture system has been
   reported to recapitulate the biology of parental tumors and their
   microenvironment in glioblastomas [[62]6,[63]7]. Organoid culture
   methods have definite advantages in mimicking the in vivo
   microenvironment and biological complexities of original tumors
   [[64]8], but the two-dimensional (2D) spheroid culture system is still
   preferred as it is more reproducible, flexible and suitable for
   elucidating cancer drug responses and, in particular, the response of
   glioma stem cells (GSCs) with a high-throughput large scale
   [[65]9,[66]10,[67]11] compared to other 3D organotypic spheroids
   [[68]12,[69]13,[70]14,[71]15,[72]16]. As tumor evolution and inevitable
   recurrence after therapy are likely attributed to GSCs [[73]17],
   short-term cultured patient-derived GSCs could facilitate real-time
   precision treatment decisions via high-throughput drug screening
   assays, and would, therefore, be reliable tools for translational
   research. Thus, a variety of efforts have been made to enrich GSCs from
   patient tumor tissues while maintaining the heterogeneity of mature
   tumors and retaining their major genomic/molecular characteristics
   [[74]14,[75]16,[76]18].

   As GSCs share similarities with neural stem cells (NSCs), the neural
   basal medium supplemented with epidermal growth factor (EGF) and basic
   fibroblast growth factor (bFGF) (E&F; referred to as NBE) has been
   widely used for GSCs [[77]14,[78]16,[79]18,[80]19,[81]20]. However,
   GSCs are distinguished from NSCs by their chromosomal alterations,
   cytologic and nuclear atypia, aberrant differentiation, failure to
   respond to growth inhibitory cues and tumor growth [[82]19]. For
   example, NBE is effective only for GSCs in patients with IDH/2 wt
   glioblastomas [[83]14,[84]18]. Although informing precision treatment
   decisions against IDH1-mutated (mut) glioblastomas and LGGs has been
   limited by the lack of clinically relevant translational models, some
   of them have become refractory to standard therapies after recurrence
   [[85]3,[86]4,[87]5,[88]21], therefore, a short-term culture system
   providing appropriate strategies within the time window of the first
   therapy is urgently needed.

   Hence, to personalize in vitro culture conditions for GSCs derived from
   individual patients with distinct genomic traits, we constructed a cell
   growth screening platform, named ‘Growth Factor SCAN (GFSCAN).’ GFSCAN
   provides 132 distinct culture conditions generated by 11 oncogenic
   growth factors (GFs) and E&F combinations in a 384-well high-throughput
   screening format in a fast and cost-effective manner ([89]Figure 1 and
   [90]Table S1). In this study, GFSCAN and next-generation sequencing
   (NGS) were performed on 53 glioblastoma and 19 LGG stem cells from 60
   patients, including multi-sector samples ([91]Table S2). The obtained
   GFSCAN and genomic data were combined to determine the best culture
   conditions according to IDH1 mutations, 1p19q co-deletion, and ATRX
   chromatin remodeler (ATRX) gene mutations, which are key alterations in
   DIG ([92]Figure 1).

Figure 1.

   [93]Figure 1
   [94]Open in a new tab

   Schematic workflow of the Growth Factor SCAN (GFSCAN) to identify
   optimal growth factor conditions for DIGs. For 19 LGG and 53
   glioblastoma GSCs, GFSCAN was performed to evaluate the effects of 132
   growth factor combinations on cell growth, and their genomic
   characteristics were determined by next-generation sequencing.

2. Results

2.1. Reproducibility and Robustness of the GFSCAN Platform

   The biological stability of each GF and cell doubling time should be
   considered when evaluating the ability of GFs to promote in vitro cell
   growth. As each GSC shows variable doubling times ranging from 2 to 9
   days, we determined the GFSCAN screening period to be 6 days, so that
   cells could grow without media addition ([95]Figure 1). To measure cell
   proliferation rates under each culture condition, we defined a new cell
   growth index as (D6 - N6)/D0, where D0, D6, and N6 indicate the
   quantitative GFSCAN readout on the day of initial cell seeding (D0),
   after 6 days of incubation under each culture condition (D6), and after
   6 days of incubation in basal media (N6), respectively ([96]Figure 1).
   The cell growth index represented cell growth 6 days after initial cell
   seeding and also the effects of growth factors on cell growth compared
   to cell viability in the basal media without GFs. We screened two cases
   (BT-S07T and BT-S08T) on GFSCAN plates by seeding dissociated cells
   from the same patient, thawed from two independently frozen vials. The
   cell growth indices under each condition were highly correlated in both
   cases (R^2 = 0.9957 and 0.9898, respectively) ([97]Figure S1).
   Furthermore, GSCs from LGGs and glioblastomas showed distinct
   transcriptome profiles ([98]Figure S2A,B) and the cell growth rates
   (D6/D0) under the NBE condition of glioblastoma GSCs were significantly
   higher compared to those of LGG GSCs (p = 0.0027) ([99]Figure S2C),
   which was consistent with the up-regulated cell cycle activity in
   glioblastomas compared to that of LGGs ([100]Figure S2D), indicating
   the reproducibility and robustness of our GFSCAN platform.

2.2. Genomic Profiles of 72 DIGs in the GFSCAN Cohort

   To determine the genomic and expression profiles of 72 DIGs, tissues or
   cultured GSCs were subjected to whole exome sequencing (WES),
   glioma-specific targeted DNA sequencing (GliomaSCAN^TM), and whole
   transcriptome sequencing (WTS) ([101]Tables S2 and S3). Recently
   performed large-scale studies reported that the genomic profiles of
   LGGs and glioblastomas are distinct; glioblastomas show frequent
   alterations in the epidermal growth factor receptor (EGFR), phosphatase
   and tensin homolog (PTEN), and cyclin-dependent kinase inhibitor 2A
   (CDKN2A), whereas genes encoding IDH1/2, ATRX, tumor protein P53
   (TP53), capicua transcriptional repressor (CIC), and far upstream
   element binding protein 1 (FUBP1) are frequently altered in LGGs with
   or without 1p19q co-deletion [[102]1,[103]3,[104]4,[105]22]. We
   assessed genomic alterations in these previously reported genes for the
   72 DIGs subjected to GFSCAN ([106]Figure 2A and [107]Table S3). In our
   cohort, all LGGs (n = 19) and six out of 53 (11.3%) of the
   glioblastomas harbored IDH1 mutations; among the LGGs, nine (47.4%)
   showed 1p19q co-deletion, while no samples showed 1p19q co-deletion in
   glioblastomas.

Figure 2.

   [108]Figure 2
   [109]Open in a new tab

   Genomic landscape of 72 diffusely infiltrating gliomas (DIGs) and
   distinct genomic characteristics between IDH1-mutated (mut) and
   wild-type (wt) glioma stem cells (GSCs). (A) The alterations in the
   frequently mutated genes in DIGs for 19 lower-grade gliomas (LGGs) and
   53 glioblastomas. On the left, the alteration frequency of each gene
   was compared between LGGs and glioblastomas. (B) The principle
   component analysis (PCA) of the gene expression profiles of 72 DIGs
   (IDH1-wt (n = 47) and IDH-mut DIGs (n = 25), using newly curated
   tumor-intrinsic genes (refer to Materials and Methods). The blue and
   orange dots indicate IDH1-wt and IDH1-mut GSCs, respectively. (C)
   Differentially expressed genes between GFSCAN IDH1-wt and -mut GSCs (p
   value ≤ 0.05 and Benjamini q value ≤ 0.1). (D) The cell growth index of
   IDH1-mut and -wt GSCs in neural basal medium supplemented with EGF and
   bFGF (NBE) (p = 0.0027). (E) The gene-set enrichment analysis (GSEA)
   plot of a cell cycle-associated pathway
   (GO_CELL_CYCLE_PHASE_TRANSITION) between IDH1-mut and -wt GSCs.

   Other frequently mutated genes in glioblastomas, such as EGFR and PTEN,
   displayed similar mutation frequencies as in previous reports
   [[110]1,[111]3,[112]4,[113]22], although IDH1, TP53, and ATRX mutations
   occur more frequently in this glioblastoma cohort ([114]Figure 2A and
   [115]Table S3). Additionally, FUBP1, CIC, Notch homolog 1,
   translocation-associated (Drosophila) (NOTCH1), ATRX, and TP53
   mutations were accompanied by IDH1 mutation in GFSCAN LGGs ([116]Figure
   2A and [117]Table S3), which is consistent with the results of previous
   studies [[118]1,[119]3,[120]4]. To summarize, the DIG samples used in
   the present study showed similar genomic characteristics as an external
   dataset analyzed in recent studies, indicating that our cohort was
   relevant and representative, despite the small number of cases.

2.3. Molecular and Biological Characteristics of Patient-Derived GSCs with
IDH1 Mutation

   In general, IDH1 mutation is a critical biomarker that is used to
   classify DIGs into clinically and biologically similar subgroups
   [[121]3,[122]4]. Mutant IDH is frequent in LGGs but also detected in
   50–88% of secondary glioblastomas and in 5% of primary glioblastomas,
   resulting in altered 2-hydroxyglutarate production and altered DNA
   methylation [[123]4,[124]22,[125]23]. More importantly, IDH1-mut GSCs
   are difficult to propagate in vitro and even in orthotopic xenografts
   compared to IDH1-wt GSCs [[126]24,[127]25,[128]26]. As expected, GSCs
   from IDH1-wt (n = 47) and -mut DIGs (n = 25) showed remarkable
   differences in their gene expression profiles ([129]Figure 2B,C), and
   IDH1-wt GSCs showed significantly higher cell growth indices under
   conventional NBE conditions (p = 0.0036; [130]Figure 2D), in addition
   to more dysregulated cell proliferation signatures ([131]Figure 2E).

   To optimize culture media conditions for GSCs that did not show
   adequate cell growth under NBE conditions, all GSCs were first
   clustered according to their cell growth indices in GFSCAN, resulting
   in four subgroups ([132]Figure 3 and [133]Table S4). GSCs in Clusters 1
   and 4 showed increased cell growth indices in the presence of E&F
   compared to E&F-absent conditions (E&F-dependent), while GSCs in
   Clusters 2 and 3 were less affected by E&F (E&F-independent). Most GSCs
   from LGGs were included in the E&F-independent group (E&F-independent;
   n = 14, 73.7% vs. E&F-dependent; n = 5 26.3%) and, consistently, 72% of
   IDH1-mut GSCs were present in the E&F-independent group.

Figure 3.

   [134]Figure 3
   [135]Open in a new tab

   Unsupervised clustering of cell growth indices evaluated by GFSCAN in
   72 DIGs. The unsupervised clustering revealed that DIGs are categorized
   according to their E&F dependencies.

2.4. Pigment Epithelium-Derived Factor (PEDF)/Midkine (MDK)/E&F-Containing
Media Promoted In Vitro GSC Propagation from IDH1-wt Glioblastomas

   IDH1-wt GSCs showed higher growth indices in NBE than IDH1-mut GSCs
   ([136]Figure 2D), but not all GSCs from IDH1-wt glioblastomas were
   dependent on E&F for cell growth ([137]Figure 3 and [138]Figure 4A).
   For example, all four IDH1-wt glioblastoma GSCs with BRAF hotspot
   mutations (V600E and D594H) were classified into the E&F-independent
   group ([139]Figure 3 and [140]Table S4), although BRAF alterations are
   rarely observed in adult HGG or other diffusely infiltrating gliomas
   (2–5%) [[141]22].

Figure 4.

   [142]Figure 4
   [143]Open in a new tab

   Identification of new culture conditions in E&F-independent GSCs among
   IDH1-wt GSCs. (A) The 6-day cell proliferation rates of each cluster
   without or with E&F. The E&F-dependent group differed significantly in
   6-day cell growth rates according to whether E&F were present (Clusters
   1 and 4, p = 0.0145 and p < 0.0001, respectively), while
   E&F-independent 6-day cell growth rates were not affected by the
   absence of E&F (Clusters 2 and 3). (B) The enrichment of a cell
   cycle-associated gene set (KONG_E2F3_TARGETS, the left panel) in
   E&F-dependent group and enrichment of a glioblastoma mesenchymal gene
   set (VERHAAK_GLIOBLASTOMA_MESENCHYMAL, the right panel) in the
   E&F-independent group. (C) The distribution of glioblastoma subtypes
   into independent and dependent groups in IDH1-wt DIGs. (D–E) The
   culture conditions with higher growth success rates than the NBE
   condition for E&F-independent group (D) and mesenchymal IDH1-wt GSCs
   (E). The bars and dots indicate growth success rates and cell growth
   indices, respectively. (F) The cell growth indices of IDH1-wt GSCs from
   a validation cohort under epidermal growth factor (EGF) and basic
   fibroblast growth factor (bFGF) (E&F; referred to as NBE) and pigment
   epithelium-derived factor (PEDF)/midkine (MDK)/E&F conditions. The dots
   represent the values from technical duplicates.

   Interestingly, gene-set enrichment analysis (GSEA) revealed that genes
   related to cell proliferation were enriched in E&F-dependent IDH1-wt
   glioblastoma GSCs, while genes related to a mesenchymal subtype
   signature [[144]27] were enriched in the E&F-independent group
   ([145]Figure 4B). Similarly, when we determined the tumor-intrinsic
   molecular subtypes of IDH1-wt GSCs based on gene expression profiles
   [[146]28], IDH1-wt GSCs with a proneural subtype (n = 11) were more
   prevalent in the E&F-dependent group (10% (2/20) in E&F-independent vs.
   34.6% (9/26) in E&F-dependent), while the tumor-intrinsic mesenchymal
   subtype (n = 16) was more dominant in the E&F-independent group (45%
   (9/20) in E&F-independent vs. 26.9% (7/26) in E&F-dependent)
   ([147]Figure 4C). The tumor-intrinsic classical subtype (n = 19)
   occurred in the independent and dependent groups at comparable rates
   (45% (9/20) in E&F-independent vs. 38% (10/26) in E&F-dependent).

   Based on these results, we determined the most efficient GF combination
   for the expansion of IDH1-wt GSCs with mesenchymal traits that were
   closely associated with the most aggressive DIG subset [[148]28].
   First, we calculated the growth success rate for each condition, where
   growth success rate was defined as the ratio of the number of GSCs with
   D6/D0 > 1 to the number of total GSCs. IDH1-wt GSCs within the
   E&F-independent group showed the highest growth success rate under
   PEDF/MDK/E&F conditions ([149]Figure 4D), which was identical to the
   most optimal growth factor composition for cell growth in the
   mesenchymal subtype ([150]Figure 4E). Previously, MDK has been reported
   as a secretion protein that promotes glioma cell proliferation
   [[151]29]. We evaluated the effects of an MDK-neutralizing antibody on
   an E&F-independent GSC from our GFSCAN cohort (BT-030T) using gene
   expression profiling, and cell cycle and proliferation-associated gene
   sets were significantly down-regulated in the MDK-neutralizing
   antibody-treated sample compared to the control sample ([152]Figure
   S3).

   To verify our findings in independent IDH1-wt glioblastoma cohorts, we
   first obtained the differentially expressed genes (DEGs) between
   E&F-dependent and -independent IDH1-wt GSCs in the GFSCAN cohort
   ([153]Table S5), and then we defined the DEGs as surrogate markers to
   classify IDH1-wt glioblastomas with gene expression profiles into
   either the E&F-dependent or -independent groups. Using the defined
   surrogate markers, we categorized an additional 202 in-house IDH1-wt
   glioblastomas into E&F-dependent or -independent samples that were not
   included in the screening set ([154]Figure S4A). Consistent with the
   original screening dataset, the mesenchymal subtype and BRAF mutations
   were dominant in the E&F-independent group, and over 90% of proneural
   glioblastomas belonged to the E&F-dependent group ([155]Figure S4B,C).
   Among the E&F-independent samples, we selected three IDH1-wt GSCs
   (BT-S01T and BT-S02T: mesenchymal subtype; BT-S03T: classical subtype)
   and evaluated their growth rates with GFSCAN. This confirmed that the
   cell growth indexes of the three GSCs were 1.6-fold, 1.8-, and 1.5-fold
   higher, respectively, under PEDF/MDK/E&F conditions than under the NBE
   condition, as expected ([156]Figure 4F).

2.5. Newly Identified Conditions for the Cultivation of IDH1-mut GSCs

   It has been reported that patient-derived GSCs with IDH1 mutation were
   hardly grown in conventional culture conditions and scarcely
   established in preclinical platforms previously
   [[157]24,[158]26,[159]30,[160]31,[161]32]. IDH1/2 mutations have been
   estimated to occur in 54–100% of diffuse astrocytomas (WHO II), 66.1%
   of anaplastic astrocytomas (WHO III), and 64–93% of oligodendrogliomas
   (WHO II and III) cases [[162]22]. In addition, 1p19q co-deletion is a
   genetic marker that can distinguish oligodendroglioma from astrocytoma
   [[163]1,[164]3,[165]4,[166]22]. LGGs with an IDH1 mutation and 1p19q
   co-deletion show oligodendroglioma histological characteristics and
   arise from TERT activation, mutations in CIC and FUBP1, and activating
   alterations in the phosphoinositide-3-kinase (PI3K) pathway
   [[167]1,[168]3,[169]4,[170]22]. On the other hand, LGGs with IDH1
   mutation and no 1p19q co-deletion accompanied by ATRX and TP53
   mutations represent astrocytomas. Unsupervised clustering using the
   cell growth index from GFSCAN revealed that IDH1-mut GSCs were
   dominantly classified into the E&F-independent group ([171]Figure 3, p
   = 0.0253) consistent with previous findings
   [[172]10,[173]24,[174]25,[175]26]. Therefore, it is important to define
   individualized culture conditions for cultivating IDH1-mut GSCs
   according to the presence of these major alterations.

   When the cell growth index in each condition was evaluated depending on
   the 1p19q co-deletion status in IDH1-mut GSCs ([176]Figure 5A,B),
   1p19q-intact IDH1-mut GSCs were the most responsive to placental growth
   factor (PlGF)/interleukin-6 (IL-6)/E&F, and showed 1.44-fold higher
   cell growth index than under NBE conditions ([177]Figure 5A, upper
   panel). On the other hand, the PEDF/sonic hedgehog (SHH)/E&F condition
   was the most effective for promoting cell proliferation in 1p19q
   co-deleted IDH1-mut GSCs, and the cell growth index of this condition
   was 1.75-fold higher than that of NBE condition ([178]Figure 5B, upper
   panel). The effectiveness of these newly identified culture conditions
   was validated in additional GSCs ([179]Figure 5A,B, lower panels).

Figure 5.

   [180]Figure 5
   [181]Open in a new tab

   Newly selected culture conditions for IDH1-mut DIGs. (A,B) The culture
   conditions with higher growth success rates than the NBE condition for
   the IDH1-mut and 1p19q intact cells (A) and IDH1-mut and 1p1/9q
   co-deleted cell (B) (upper panels). The bars and dots indicate growth
   success rates and cell growth indices, respectively. The culture
   condition with the highest growth success rate and highest cell growth
   index was validated in additional samples (lower panels). The dots
   represent the values from technical duplicates. (C) A volcano plot
   showing culture conditions with significant differences in cell growth
   indices between IDH1-wt and -mut GSCs for frequently mutated genes in
   DIGs. The X-axis represents the magnitude (cell growth index of
   IDH1-mut GSCs–that of IDH1-wt GSCs), and the Y-axis represents the
   significance (−log10(Wilcoxon rank-sum P-value)) of the association
   between mutations and culture conditions. Each circle represents a
   single mutation-culture condition interaction, and the size is
   proportional to the mutation frequencies. (D) The GSEA plot of
   transforming growth factor-beta (TGF-β)-associated gene set
   (HALLMARK_TGF_BETA_SIGNALING) between ATRX-mut and -wt GSCs in IDH1-mut
   DIGs from the TCGA LGG dataset.

   Furthermore, to improve culture conditions for IDH1-mut GSCs with
   specific mutations, we examined the association between the genomic
   mutations and cell growth indices of all growth factor combinations in
   IDH1-mut GSCs ([182]Figure 5C). This revealed that IDH1-mut GSCs with
   ATRX mutations showed higher cell propagation levels in culture media,
   which included transforming growth factor-beta (TGF-β)-containing
   including ‘neurTGF-β/E&F’, ‘platelet-derived growth factor
   (PDGF)/TGF-β/E&F’, and ‘MDK/TGF-β/E&F’ conditions compared to ATRX-wt
   IDH1-mut GSCs ([183]Figure 5C and [184]Table S4); this finding is
   supported by the observation that a TGF-β-dependent pathway was
   significantly enriched in IDH1-mut DIGs with ATRX alterations compared
   to those without ATRX alterations ([185]Figure 5D, NES = 1.994, false
   discovery rate < 0.001) in the TCGA LGG dataset. Moreover, astrocytomas
   are significantly enriched for the TGF-β-dependent pathway compared to
   oligodendrogliomas and normal tissues in the Repository for Molecular
   Brain Neoplasia Data (REMBRANDT) dataset as well ([186]Figure S5).

3. Discussion

   Owing to their relatively favorable prognosis compared to IDH1-wt
   glioblastomas, IDH1-mut glioblastomas and LGGs have not been
   extensively investigated [[187]33]. However, all DIGs are characterized
   by extensive infiltrative growth, neovascularization and resistance to
   various combined therapies and, despite variable intervals, the
   remaining tumors after neurosurgical resection result in tumor
   evolution to a more refractory and aggressive form
   [[188]3,[189]4,[190]5]. GSCs are critical therapeutic targets, because
   these cells are responsible for DIG progression and evolution
   [[191]17]. The act of passaging cells itself has been shown to activate
   cellular plasticity, altering the gene expression and phenotype of
   patient-derived glioblastoma cells [[192]14,[193]34,[194]35]. Time is
   also a critical component for the utility of our functional pipeline to
   inform and succeed in precision oncology. Therefore, short-term
   cultured patient-derived GSCs continue to be a preferred model as they
   retain the capacity to differentiate into multiple lineages and
   recapitulate the genetic/molecular/biological profiles of parental
   gliomas, offering a unique opportunity for streamlining drug testing
   and evaluating pathophysiology [[195]9,[196]10,[197]14].

   As current GSC culture methods have many limitations for various
   high-throughput applications due to limited supply, culture instability
   and genetic background variability, it is imperative to improve cell
   culture format based on concrete scientific rationales for the
   expansion and storage of a DIG patient-derived spheroid biobank. Slight
   changes in the concentration of various additive growth factors can
   alter cell behavior and drug sensitivity [[198]35]. As culture
   conditions are a key factor limiting the efficacy of current in vitro
   models, improved GF combinations based on scientific rationale are
   needed for expanding and storing DIG patient-derived GSCs. Herein, we
   describe a precision approach that combines genomic characterization,
   patient-derived GSCs isolated from the diverse grades of DIGs, and a
   newly-constructed GFSCAN that is a fast, cost-effective, and reliable
   screening platform for overcoming the scarcity of translational
   research for diverse grades of DIGs with genetic background
   variability. We evaluated the 6-day cell growth rates of 72 GSCs
   against 132 distinct combinations, using 13 tumor-associated GFs in a
   384-well GFSCAN plate. The 13 GFs, including E&F, MDK, PlGF, IL-6,
   PEDF, SHH, IGF1, TGF-β, neuregulin 1 (NRG-1), hepatocyte growth factor
   (HGF), PDGF and semaphorin 3A (Sema3A) have been linked to the
   aggressive phenotypes of gliomas, such as cell proliferation, survival,
   invasion, cancer stemness and the resistance to standard therapies
   [[199]14,[200]16,[201]18,[202]27,[203]29,[204]36,[205]37,[206]38,[207]3
   9,[208]40,[209]41,[210]42,[211]43,[212]44,[213]45,[214]46,[215]47,[216]
   48,[217]49,[218]50,[219]51,[220]52,[221]53,[222]54,[223]55,[224]56,[225
   ]57,[226]58,[227]59,[228]60,[229]61,[230]62,[231]63,[232]64,[233]65,[23
   4]66,[235]67,[236]68,[237]69,[238]70,[239]71,[240]72,[241]73,[242]74,[2
   43]75,[244]76,[245]77,[246]78,[247]79,[248]80,[249]81,[250]82,[251]83,[
   252]84].

   As IDH is a component of the Krebs cycle and converts isocitrate and
   cofactor NAD^+ to carbon dioxide, NADH and α-ketoglutarate; IDH
   mutations cause a buildup of the onco-metabolite D-2-hydroxyglutarate,
   resulting in the distinct expression of a GSC phenotype [[253]22]. LGGs
   and glioblastomas within our cohort demonstrated distinct gene
   expression patterns and GSCs from glioblastomas were more dependent on
   E&F than LGG PDCs. Especially among glioblastomas, GSCs with an IDH1
   mutation were less dependent on E&F than those with wildtype IDH1,
   indicating that GSCs could show distinct gene expression patterns and
   dependency on E&F according to their IDH1 mutation status. First, we
   identified PEDF/MDK/E&F as the best GF combination for mesenchymal
   IDH1-wt GSCs that are less dependent on E&F for cell growth and have
   relatively low cell-cycling activity. In contrast, most IDH1-wt GSCs
   with proneural subtype traits were dependent on E&F. Consistent with
   our findings, gliomas with molecular profiles comprised of a
   mesenchymal signature represent an invasive phenotype with a low
   proliferating activity. This phenotype is regulated by PI3K/AKT,
   nuclear factor-κB, TGF-β, MET, Wnt/β-catenin activation, and metabolic
   reprogramming of glycolysis by triggering the expression of epithelial
   to mesenchymal transition (EMT) activators [[254]85]. Mechanistically,
   PEDF is involved in the self-renewal and maintenance of infiltrating
   GSCs as an autocrine factor by activating a signal transducer, an
   activator of transcription 3 (STAT3) and the Notch-Sox2 signaling axis
   [[255]60]. Additionally, MDK, which is upregulated by Wnt/β-catenin in
   gliomas, contributes to tumor progression and metastasis by enhancing
   the growth, survival, and EMT of cancer cells; this is because MDK
   activates a Notch signaling that cross-talks with PI3K/AKT and STAT3
   signaling [[256]64,[257]65,[258]86,[259]87]. These previous reports are
   consistent with the growth-promoting effects of MDK on E&F-independent
   IDH1-wt glioblastomas with high mesenchymal activity.

   The recent WHO 2016 criteria utilized 1p19q co-deletion to distinguish
   IDH-mutated DIGs with oligodendroglial phenotypes from astrocytomas
   [[260]1,[261]3,[262]4]. We tried to figure out the best culture
   condition for glioblastoma with IDH1 mutation and LGG with/without
   1p19q co-deletion, previously known as hard to grow and easily
   eliminated in standard NBE culture conditions
   [[263]24,[264]26,[265]30,[266]31,[267]32]. The differences in the
   culture success rate, depending on clinical and biological aggressive
   tumors, supported the difficulty in the generation of the GSCs from
   these subsets [[268]10,[269]24,[270]26,[271]88,[272]89]. There is a
   compelling need for more reliable in vitro GSC libraries that mimic the
   biology of human LGGs and IDH1-mut glioblastomas in situ, which can be
   expanded to include considerable numbers of patients, and can
   facilitate the identification of drug sensitivity profiles for small
   subsets of patients with significant results. In this study, the
   PEDF/SHH/E&F combination was critical for the steady propagation of a
   subset of IDH1-mut GSCs harboring 1p19q co-deletion and PlGF/IL-6/E&F
   was essential for another subset of IDH1-mut GSC spheroids with intact
   1p19q. On the other hand, ATRX mutations are mutually exclusive from
   1p19q co-deletion, but are associated with IDH1 mutation, suggesting
   that ATRX drives the lineage-specific formation of astrocytomas
   [[273]1,[274]3,[275]4]. Among all the 132 conditions, three containing
   TGF-β (IGF1/TGF-β/E&F, PDGF/TGF-β/E&F, and MDK/TGF-β/E&F) were
   particularly effective for cultivating IDH1-mut GSCs harboring ATRX
   mutations compared to ATRX-wt GSCs. Interestingly, we also confirmed
   that TGF-β signaling was up-regulated in ATRX-mut DIGs or astrocytomas
   in the independent public dataset, highlighting the tumor-promoting
   role of TGF-β in DIGs with ATRX mutations.

   In summary, we have identified the optimal culture conditions for
   propagating GSCs, according to their major genetic alterations in
   spheroid culture, using large-scale GF screens. Our versatile GFSCAN
   tool could figure out the optimal culture conditions for other cancer
   types, for which robust and reliable in vitro culture conditions need
   to be determined. Most importantly, the complex cross-talk between a
   variety of signaling pathways associated with GSCs support our findings
   that ‘PDGF/IL6/E&F’, ‘PDGF/PlGF/E&F’ and ‘PEDF/SHH/E&F’ could be
   alternative supplements to standard E&F alone, by their synergistic
   promoting effects on the establishment of GSCs from IDH1-mut
   glioblastomas and LGGs. These newly identified GFs do not function
   alone, but rather their combinations generate complex and dynamic
   interactions that can synergistically promote the propagation of
   patient-derived GSCs in vitro. The findings from the present study
   suggest that the mechanisms underlying the association between specific
   genomic alterations and specific GF combinations, and the more in-depth
   elucidation of biomarkers tailoring optimal culture media, should be
   further explored using a larger panel of GSCs and machine learning.
   Additional GFs and combinations should also be tested and validated in
   external GSC sample sets from DIGs in prospective cohort studies.

4. Materials and Methods

4.1. Preparation of Patient-Derived GSCs from Human DIGs

   Fresh tumor specimens and corresponding clinical records were obtained
   from 60 patients with DIGs ([276]Figure 2 and [277]Table S2) who
   underwent surgery at the Samsung Medical Center (Seoul, Korea), in
   accordance with the guidelines of the institutional review board (No.
   2005-05-001, 2010-04-004). Informed consent was obtained from each
   patient prior to the study. Portions of surgical samples were
   enzymatically dissociated into single cells using Liberase^TM (Roche
   Diagnostics, Basel, Switzerland) and red blood cells were removed by
   density gradient centrifugation with Percoll for GFSCAN and stored at
   −80 °C for further genomic characterization and GFSCAN. The dissociated
   cells were centrifuged for 3 minutes at 1200 rpm after counting and the
   cell pellet was gently suspended with 1 mL of CELLBANKER2 (a serum-free
   cell cryopreservation medium with a unique complete formulation for the
   broad spectrum of mammalian cell cultures, ZENOAQ, #BLC-2)
   cryopreservation medium by 1–2×10^6 cells per vial on average. The vial
   was stored in freezing tank, frozen slowly at −80 °C for one day and
   then stored in a liquid nitrogen tank. For cell thawing, we devised a
   new process, which requires no growth factors and improves early cell
   viability ([278]Figure S6); Cells were thawed at 37 °C and placed
   slowly in a culture flask containing pre-warmed (37 °C) media, so that
   the dissolved cells contained less than 1% of DMSO in the culture
   flask. The flask was incubated at 37 °C for 4 h and then cells were
   centrifuged. The centrifuged cell pellet was re-suspended in fresh
   media and incubated at 37 °C for a day. We compared the 1-day cell
   viability of the new cell thawing method with conventional thawing and
   confirmed that the new method resulted in a higher 1-day cell viability
   ([279]Figure S6).

4.2. Manufacturing GFSCAN Based on Patient-Derived GSCs

   We established a novel screening platform to identify advanced in vitro
   culture conditions by systematically evaluating the pro-survival and
   pro-proliferation effects of various oncogenic GFs on GSCs. The frozen
   dissociated cells were thawed and seeded into 384-well plates (1000
   cells in 50 μL media/well) in technical duplicates. The cells were
   cultured in the GFSCAN plate for 6 days and cell growth was measured
   using ATPlite^TM (PerkinElmer, Waltham, MA, USA) ([280]Figure 1).
   First, we selected 11 growth factors previously reported to enhance
   cell survival, proliferation, cancer stemness, and invasion,
   particularly in malignant glioma, in addition to E&F. These included
   IGF1, NRG-1, HGF, PDGF, PlGF, PEDF, Sema3A, TGF-β, IL-6, SHH, and MDK.
   GSCs were cultured in neurobasal medium with N2 and B27 supplements
   (0.5× each; Gibco, Grand Island, NY, USA) and various combinations of
   11 GFs and E&F (5 ng/mL EGF; 5 mg/mL bFGF; 1 ng/mL IGF1; 1 ng/mL NRG-1;
   1 ng/mL HGF; 1 ng/mL PEDF; 1 ng/mL PDGF; 1 ng/mL PlGF; 1 ng/mL SHH; 1
   ng/mL Sema3A; 1 ng/mL TGF-β; 1 ng/mL IL-6; 1 ng/mL MDK). Detailed
   information of growth factors tested in the present study is summarized
   in [281]Table S1. Except for E&F, two of the 11 growth factors were
   selected and mixed to form 55 combinations. These 11 growth factors
   were also used alone under 11 single conditions. As the E&F combination
   is the basic condition required for GSC culture, E&F were added to the
   55 combinations (culture conditions with E&F), while another 55
   combinations did not contain E&F (culture conditions without E&F).
   GFSCAN assays containing these 132 conditions and two control
   conditions (no growth factor and NBE) were set up by the JANUS
   ‘cherry-picking system’ (PerkinElmer). A schematic workflow is
   summarized in [282]Figure 1.

4.3. GFSCAN QC Criteria

   The ATPlite^TM-based cell viability data obtained from the GFSCAN
   platform were analyzed only when they met the following quality control
   criteria: 1) the ATPlite^TM value must be more than 5000, as we assume
   that a sinal intensity lower than 5000 indicates that the number of
   viable tumor cells are very low and negligible and 2) the ‘6 day/0 day’
   ratio must be more than 0.5.

4.4. Genomic and Transcriptomic Analysis

   For all 72 DIG samples (19 LGGs and 53 glioblastomas from 60 patients;
   summarized in [283]Table S2) that were subjected to GFSCAN, either WES
   (n = 35) or glioma-specific targeted DNA sequencing (GliomaSCAN^TM, n =
   37) was performed to reveal single-nucleotide variations, insertions
   and deletions, and copy number alterations (CNAs) ([284]Tables S2 and
   S3). Data from WTS were also obtained to determine the gene expression
   profiles and structural variants of the 72 DIGs. For WTS, total RNA was
   extracted from patient-derived cells and tumor tissues using the RNeasy
   Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer’s
   instructions. A 2100 Bioanalyzer (Agilent Technologies, Santa Clara,
   CA, USA) was used to assess RNA integrity. RNA-Seq libraries were
   prepared using the TruSeq RNA Library Prep kit (Illumina, San Diego,
   CA, USA). From the DNA and RNA sequencing data, mutation calling, CNA
   estimation, exon skipping detection, and gene expression profiling were
   processed using the methods reported in our previous publication
   [[285]90].

   The gene expression levels were summarized in reads per kilobase
   million (RPKM) values, and the log2-transformed (RPKM + 1) values were
   used for further analyses. Moreover, although high levels of similarity
   in mutations and gene expression patterns have been reported between
   parental tumor tissues and matched patient-derived GSCs [[286]14], we
   selected genes showing highly positive correlations (Spearman
   correlation coefficient values ≥ 0.4) between GSCs and their parental
   tissues using our in-house DIG WTS dataset. Then, we removed
   unannotated genes and the genes with maximum log2(RPKM + 1) values = 0
   in more than half of tumor tissues or half of GSCs. Finally, 3208 genes
   were left and assumed to be tumor-intrinsic genes, and we used only
   these genes for further analysis due to the lack of
   microenvironment-specific genes in GSCs, with the exception of GSEA
   analysis and subtype assignment ([287]Figure S7). DEGs between
   E&F-dependent and -independent samples were extracted by DEGseq (p
   value ≤ 0.05 and Benjamini q value ≤ 0.1) [[288]91]. These DEGs were
   subjected to the Nearset Template Prediction (NTP) algorithms (R
   package, NTPez) to classify additional DIG samples with RNA sequencing
   into either E&F-dependent or -independent groups [[289]92].

4.5. Unsupervised Hierarchical Clustering of Cell Growth Index from GFSCAN

   Based on cell growth indices of 72 DIGs, unsupervised hierarchical
   clustering was performed in R with Ward linkage and Euclidean distance
   methods.

4.6. Pathway Enrichment Analysis and GSEA

   GSEA was conducted using the GenePattern software of the Broad
   Institute
   ([290]http://software.broadinstitute.org/cancer/software/genepattern)
   [[291]93]. The statistics to measure gene set enrichment were estimated
   by 1000 gene set permutations.

4.7. Glioblastoma Molecular Subtype Classification

   To determine the molecular subtype of IDH1-wt GSCs based on their gene
   expression profiles, R package GSVA (gene set variation analysis) was
   used to calculate single-sample GSEA (ssGSEA) scores for each subtype
   (three glioblastoma-intrinsic subtypes) of GSCs [[292]28,[293]94].
   Then, ssGSEA scores were normalized across the GSCs, and the subtype
   with the highest normalized score was assigned to each sample.

4.8. External Gene Expression Data Sets

   Gene expression data and ATRX mutation status from TCGA LGG dataset
   were downloaded from the Genomic Data Commons (GDC)
   ([294]https://portal.gdc.cancer.gov/) using the R package TCGAbiolinks
   [[295]95]. Microarray raw data (CEL files) and clinical information
   from the Repository of Molecular Brain Neoplasia Data (REMBRANDT) were
   downloaded and processed from the REMBRANDT data portal in 2011 (now
   available in ArrayExpress E-MTAB-207) [[296]96].

4.9. Statistical Analysis

   GraphPad Prism software was used for all statistical analyses
   (GraphPad, Inc., La Jolla, CA, USA). Column statistics were used to
   display the differences in growth rate between each group. Data are
   expressed as the mean ± standard error of mean (SEM), two-tailed
   t-tests of variance were performed whenever appropriate and p values <
   0.05 were considered as significant. Spearman rank correlation
   coefficients were also calculated. Spearman’s correlation coefficients
   and significance (two-tailed) were calculated for each pair of genes.

5. Conclusions

   In this study, we successfully established a robust system called
   GFSCAN that evaluates 6-day cell growth rates under 132 culture
   conditions. Using GFSCAN, we evaluated the cell growth indices of 72
   DIG patient-derived cells and also obtained their matched genomic and
   transcriptomic profiles to explore the association between cell growth
   and genomic characteristics. Finally, it was found that mesenchymal
   IDH-wt glioblastomas were less proliferative under current standard
   culture conditions compared to proneural and classical glioblastomas,
   and that the PDEF/MDK/E&F combination could be an alternative to NBE
   for improving the propagation ability of mesenchymal GSCs. IDH1-mutant
   GSCs also showed significantly lower cell growth indexes than IDH1-wt
   GSCs under NBE. Therefore, we identified new GF combinations for
   IDH1-mut GSCs. Furthermore, PIGF/IL-6/E&F and PEDF/SHH/E&F effectively
   increased cell growth indexes for IDH1-mut/1p19q intact GSCs and
   IDH1-mut/1p19q co-deleted GSCs, respectively. Moreover,
   TGF-β-containing culture conditions with E&F were shown to promote the
   growth of IDH1/ATRX-mut GSCs.

6. Patents

   A patent covering GFSCAN has been filed under publication number
   WO2019050314A1.

Acknowledgments