Purpose: Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors.
Experimental Design: Here, we used 33 single cell–derived subclones generated from five clinical glioblastoma specimens for exploring intra- and interindividual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacologic sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors.
Results: Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential antiglioblastoma compounds. A more comprehensive intratumoral analysis revealed that stable genetic and phenotypic characteristics of coexisting subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naïve parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacologic profiles could be shown for establishing rational strategies for individualized secondary lines of treatment.
Conclusions: Our data provide a previously unrecognized strategy for revealing functional consequences of intratumor heterogeneity by enabling predictive modeling of treatment-related subclone dynamics in human glioblastoma. Clin Cancer Res; 23(2); 562–74. ©2016 AACR.
Inevitably recurring tumor growth complicates even the most promising pharmacotherapies in glioblastoma. Arguing that coexisting cellular hierarchies contributing to pharmaco-resistance are extractable from clinical samples, we here show that phenotypic and genetic stability of intratumor subclones enables controlled and discriminative drug profiling ex vivo. Our data imply that the respective profiles are directly applicable to predict intratumoral treatment-induced clonal population shifts in vitro and in vivo, and thus to predict the cellular composition of relapsing human cancer tissue at the time of primary diagnosis. In addition, we show that pharmacologic profiles could serve as a valuable asset for defining combinatorial secondary lines of treatment. Further development of this strategy may be key to the understanding of therapeutic failure, and it may become a sophisticated evidence-based planning tool for personalizing therapy in glioblastoma.
Cellular heterogeneity has traditionally been viewed as a result of hyperproliferation and increasing genetic instability that, at late stages of tumor progression, leads to the spawning of subclones (1, 2). Their phylogeny can be recapitulated, for example, by applying single-nucleus deep sequencing, regional dissections, or visualization of specific genetic hallmarks (3–5). On a practical note, increasing degrees of intratumor heterogeneity are acknowledged as an indicator for unfavorable disease progression/prognosis (6–8), and it is thought that heterogeneity data could have the potential to influence clinical decision making (9), but this is not routinely applied in the field yet. One aspect is a lack of preclinical model systems that could help to better understand the impact of chemotherapy on clonal heterogeneity. Ideal models would have to implement genetic/phenotypic identity of subclones with the respective cellular function for monitoring drug effects over time in a given tumor (10).
In many of the particularly malignant cancers, for example, the primary brain tumor glioblastoma, (stem-like) subclones with intrinsic drug resistance are considered to account for treatment failure and relapse that inevitably occur during the course of disease (11–13). Recent insights from in vitro studies on intraindividual drug response suggest that subclones with differential resistance profiles coexist in glioblastoma (14). Here, we found that single cell–derived subclones of clinical glioblastoma samples maintain their distinct phenotypic and genetic identities ex vivo and that their pharmacologic profiles enable experimental access to model specific subclone targeting in vitro and in vivo. As a consequence, drug-related polyclonal population dynamics becomes predictable. A major further benefit of this approach is the previously unrecognized feature to identify subclone-specific drug combinations suited for sequential targeting of coexisting tumor cell hierarchies, which reflect the foundation of intratumor heterogeneity.
Materials and Methods
Tumor tissue was obtained from glioblastoma surgery at the University of Bonn (BN035-BN118, Bonn, Germany) and the University of Florida (GNV019, Gainesville, FL, patient details: Supplementary Table S1). Local ethics committees at both sites approved the studies, and patients or their guardians provided informed consent. Tissue diagnosis/grading is based on the WHO classification (15, 16).
Tissue handling and cell culture
Handling of tissue and cell derivation protocols (BN035-BN118/GNV019) were described previously (17, 18). Samples were analyzed at in vitro passages 5 to 13. Subclones derived from passage 5/6 parental cells were investigated at subsequent in vitro passages 2 to 8. With the exception of neurosphere and extreme limiting dilution assays (ELDA), samples were grown adherently on laminin (Life Technologies)/poly-L-ornithine (Sigma-Aldrich)–coated (PO) plasticware (17, 18). Culture methods for reference/control cells were described: U87(MG) glioma cell line, hnNCs (human nonmalignant neural cells: short-term expanded hippocampus-derived adult human neural progenitors; ref. 18), and hESCdNPs (human ES cell–derived neural progenitor cells; ref. 19). Cell line authentication was conducted by the DSMZ using STR analysis, last tested in September 2015 [U87(MG)]. Human primary fibroblasts were provided by Dr. Phillip Koch and expanded in DMEM/F12 media (Life Technologies) supplemented with 10% FCS (Hyclone, GE Healthcare) and 1% antibiotic-antimycotic. All investigated cells tested mycoplasma negative per standard cell lysate PCR detection. For in vitro growth kinetics, populations doublings (PD) were calculated: n = 3.32(log UCY – log l) + X, where n = final PD number at the end of given subculture; UCY = cell yield at that point; I = cell number used as inoculum of subculture; X = doubling level of inoculum used to initiate the quantified subculture. The neurosphere assay testing cellular differentiation was applied as described previously (17, 20). For the ELDA, GNV019 cells plated in a volume of 150 μL on ultralow attachment 96-well plates (Corning) in decreasing numbers for 7 days were incubated with Calcein AM viability dye (5 μmol/L) for 30 minutes to label vital cells and for fluorescence-based quantification of tumor spheres.
Derivation of tumor subclones
Passage 5 GNV019 cells were plated at 15 cells/cm2 on five PO-coated 10-cm dishes. Twenty individual cells per dish were randomly selected on the subsequent day, marked with pen at the bottom of the dish, and followed for 30 to 60 days. Seven of these formed clonal colonies, were selected using 8-mm cloning cylinders (Corning), trypsinized, and transferred to a 6-cm dish for expansion. CL1/2/3/6/7 cells were depicted from these based on their distinctive morphologies. BN samples were plated at 0.5 cells per well in up to eight 96-well plates, validated, and monitored throughout clonal expansion by automated image-based analysis (Cellavista, Roche). Eleven to 26 single cell–derived subclones were selected per case and expanded. For generation of pilot data, at least 5 subclones were used per patient sample.
Compound screening and treatment
Reference/control cells, parental tumor cells, and subclones were seeded in 96-well plates at 5–13 × 103 cells/cm2 in triplicates. Twenty-four hours later, drugs were applied as 10× stock dilutions. Cellular viability was determined as the ratio of background-subtracted alamarBlue (Life Technologies) fluorescence intensities of treated and vehicle control cells. Compounds of the pilot drug screen (Supplementary Table S2) were applied in six different concentrations to determine dose–response levels. Four days later, cellular viability was compared with vehicle applications [0.55% DMSO for compounds combined with 50 μmol/L temozolomide; 1.5% DMSO for temozolomide treatment alone; 0.5% ethanol (EtOH) for perifosine, and 0.5% DMSO for all other drugs]. The “Killer Plates” compound library (MicroSource) was applied to GNV019 cells at 1 μmol/L concentrations each and compared with vehicle controls (0.01% DMSO) at 5 days after treatment. IC50 evaluation for selected compounds was performed as described previously (18).
For coculture, subclones were labeled with green (CellTracker Green CMFDA, 5 μmol/L, or Vybrant DiO, 1:200) or red fluorescent dyes (CellTracker Red CMTPX, 25 μmol/L, or Vybrant DiD, 1:200, all Life Technologies) for 30 minutes. Equal quantities of green- and red-labeled cells were seeded on 12-well plates. CL1/2/3/6/7 cells were treated with 10 μmol/L thioguanine, 2 μmol/L oridonin, 4 μmol/L sorafenib, 1 μmol/L cantharidin, or 0.1% DMSO for 5 days. BN035 subclones were treated with 0.4 nmol/L bortezomib, 8 μmol/L lonafarnib (+50 μmol/L temozolomide), or 0.25% DMSO. BN046 subclones were treated with 3 μmol/L 17-AAG, 10 μmol/L etoposide (+50 μmol/L temozolomide), or 0.15% DMSO for 3 days. Challenged cells were trypsinized for flow cytometry using 15–20 × 103 cells (FACSCalibur Cell Analyzer; BD Biosciences) to determine drug effects.
For subclone selection from parental GNV019 cells, increasing concentrations of thioguanine or 0.1% DMSO were applied for 5 days, followed by a 4-day growth factor withdrawal-induced differentiation period, before quantifying (giant) multinucleated cells. CL2-like cells were selected by single- or repeated treatment with 4 μmol/L sorafenib for 5 days. For subclone selection from BN035 and BN046 parental cells, drugs were applied in two 3-day cycles at concentrations indicated (Supplementary Figs. S5B and S6B). Sequential treatment (5 + 5 days for all) of GNV019 cells (Fig. 6) was either conducted with 10 μmol/L thioguanine/0.1% DMSO (first line), followed by 20 μmol/L perifosine, 1.5 μmol/L SAHA (+50 μmol/L temozolomide), 3 μmol/L sunitinib, 0.5 nmol/L bortezomib, 100 nmol/L dasatinib, or 0.12% DMSO (second line). The alternative course included 4 μmol/L sorafenib/0.02% DMSO (first line), followed by 1 μmol/L cantharidin, 20 μmol/L imatinib, 0.5 μmol/L etoposide (+50 μmol/L temozolomide), 0.2% DMSO, 1 mmol/L temozolomide, or 1% DMSO (control condition for temozolomide alone; second line).
Orthotopic xenograft experiments and animal treatments
Ethical Committees of the Universities of Bonn and Florida approved all animal studies. For engraftment, cells were harvested, counted, and resuspended in 0.1% DNase I (Worthington)/PBS (Life Technologies). Cell vitality was confirmed via Trypan blue exclusion. Two microliters encompassing 1 × 105 cells were stereotactically applied to the brains of Fox Chase SCID/beige mice (females, 9–13 weeks old; 1.6 mm anterior, 1.9 mm lateral to the bregma, 1.4 mm deep from the dura; Charles River Laboratories). In addition to presented data, tumorigenicity and cellular characteristics of GNV019 parental and subclonal cells were confirmed in NMRI nu/nu mice (females, 6–10 weeks old; n = 2, each; 2.2 mm anterior, 1.3 mm lateral, 1.7 mm deep; Janvier Labs). Mice were monitored daily and euthanized when signs of neurologic impairment or significant weight loss (≥20% from preoperative weight) were noted. For routine histology, brains were fixed by vascular perfusion (4% formaldehyde). Coronal gradient echo, T2-weighted, and fluid-attenuated inversion recovery MRI data were obtained from formaldehyde-fixed whole brains using the core facility of the McKnight Brain Institute (University of Florida, Gainesville, FL) under standard imaging protocols with a 15-mm birdcage coil and 11-T horizontal-bore magnet (Bruker).
In vivo analysis of subclone enrichment commenced at day 42 postorthotopic xenotransplantation of GNV019 parental cells. A 2.5 mg/mL stock solution of thioguanine (50 mg thioguanine/20 mL of 0.02 mol/L NaOH) generated doses of 10 mg/kg per injection and was applied for 3 consecutive days (21). A total of 100 mg sorafenib dissolved in 2.5 mL Kolliphor/EtOH (50:50, Sigma-Aldrich) to obtain a 4× stock solution was further diluted in ddH2O. Treatments (100 mg/kg) were conducted on 5 days per week (22). Sorafenib-induced population shifts were assessed using DNA from 5 of 8 animals of the experimental series (2× Kolliphor/EtOH, 3× sorafenib). Three of 8 samples were excluded because sufficient DNA quantity/quality could not be obtained: two samples representing prenecrotic brain tissue of animals that died over night; one sample failed DNA extraction.
Paraformaldehyde-fixed cells and formaldehyde-fixed paraffin-embedded tissues were supplied with primary antibodies against βIII-tubulin (Promega; monoclonal mouse, clone 5G8, 1:1,000), GFAP (DAKO; polyclonal rabbit, #Z0334, 1:600), pan-cadherin (Thermo Fisher Scientific, polyclonal rabbit; #PA5-19479, 1:200), and α-tubulin (Sigma-Aldrich, monoclonal mouse; clone DM1A; 1:1,000) overnight at 4°C. Respective antigens were labeled by incubation with fluorophore-conjugated secondary antibodies (Alexa Fluor 488 goat anti-mouse IgG 1:800 and Alexa Fluor 555 goat anti-rabbit IgG 1:500, Life Technologies) for 1 hour at room temperature. Cell nuclei were exposed with 2 μg/mL DAPI (Sigma Aldrich) for 10 minutes. Fluorescence microscopy was performed on a Zeiss Axioskop2 or Axio Imager.Z1 upright microscope. Frequencies of multinucleated giant cells (mGC; in vivo) and mGC-like multinucleated cells (mnCells; in vitro) were determined by quantifying mono- versus multinucleated cells in respective samples using DAPI and α-tubulin (in vitro) or pan-cadherin (in vivo) labeling. Quantification was conducted by averaging the results of at least two investigators (R. Reinartz, L. Rauschenbach, and B. Scheffler) blinded to the experimental conditions.
DNA/RNA was isolated using the AllPrep DNA/RNA Mini Kit (Qiagen) following the manufacturer's instructions. Chromosomal aberrations were analyzed using Illumina's BeadChips (HumanHap550/Human610-Quad). Sample preparation was performed according to Illumina's Infinium protocols. For whole-genome gene expression profiling, total RNA of biological triplicates was extracted using QIAzol Lysis Reagent (Qiagen) and analyzed using HumanHT-12 v3 expression BeadChips (Illumina). Gene expression and genotyping data were deposited at GEO (Gene Expression Omnibus: accession numbers GSE72927, GSE72732). TP53 mutation screening was performed according to the direct sequencing protocol of the International Agency for Research on Cancer (Lyon, France). DNA copy numbers were quantified on a ViiA 7 Real-Time PCR System (Life Technologies) using SYBR Green. PCR cycling conditions are as follows: 95°C for 3 minutes, followed by 40 cycles at 95°C for 20 seconds, 60°C for 20 seconds, and 72°C for 30 seconds. Ct values for target genes on Chr. 1 (CDKN2C), Chr. 5 (SCAMP1, CHD1), and Chr. 22 (BID, NF2) were normalized using Ct values of the Chr. 2 reference genes (MEMO1 and ASB3). Copy number values were calculated using the ΔΔCt method on tumor samples and human leukocyte DNA as reference. Gene expression analysis of signature neural stem-like genes was performed as described previously (18) using 1 μg total RNA (for primers, see Supplementary Table S3). Custom multiplex ligation-dependent probe amplification (MLPA) kits (P345-X1 & P346-X1; MRC-Holland) were used to detect copy number alterations in BN035 and BN046 cells. A total of 90 ng DNA from tumor cells or from (two individual) reference human leukocyte DNA was used for each MLPA reaction. Fragment separation was performed on a 3130xl Genetic Analyzer (Applied Biosystems) and MLPA ratios determined using Coffalyser software (MRC-Holland).
Bioinformatics and statistical analysis
Hierarchical clustering, calculation of Pearson correlation coefficients, heatmaps, and logR ratio plots were performed using R-project statistical software (v3.0.2; ref. 23). Molecular subtypes of GNV019 samples were classified as “neural” (according to ref. 24). Cluster dendrograms were created using Euclidean distance and average linkage analysis. All other computations were carried out using GraphPad Prism 6.0f and Microsoft Excel. Where applicable, the two-tailed Student t test (assuming equal variances), the one-way ANOVA with Tukey correction for multiple comparisons, or the two-way ANOVA with Bonferroni correction for multiple comparisons were performed for statistical analysis. Standard distribution of data was applied for respective tests. Data analysis is based on biological triplicates, unless otherwise specified. Unless otherwise indicated, data are presented as mean ± SD (levels of significance: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001). Cartoons were produced using SERVIER Medical Art.
Drug–response profiles of tumor subclones reflect intra- and interindividual tumor heterogeneity
In pilot experiments, we explored single cell–derived (subclonal) cultures from clinical patient samples to display heterogeneity of drug–response patterns. On the basis of reported transcriptome analysis of 430 single cells from 5 glioblastoma patients (25), we expected patterns of strong interindividual differences and a considerable degree of intratumor heterogeneity. In a parallel constellation, we applied short-term expanded primary cell cultures from five glioblastoma patients and, additionally, a total of 33 respective subclones for analysis of differential drug response. All cells were maintained under adherent in vitro conditions suited for the expansion of neural stem- and precursor cells (Materials and Methods). Twenty clinical trial grade drugs and compounds (Supplementary Table S2) were used to determine IC50 values, that is, the individual concentrations that reduced cellular viability by 50% compared with vehicle control treatments. Hierarchical clustering of data revealed an extent and pattern of heterogeneity comparable with the transcriptome data presented by Patel and colleagues (25). A strong interindividual variability was noted as well as substantial intraindividual differences of drug–response patterns (Fig. 1). Notably, for each of the five cases, the median drug concentration difference between the most and least resistant subclone revealed a 2-fold intraindividual variability of drug response among all investigated compounds (Supplementary Fig. S1, mean factor, 2.32; range, 1.86–2.92).
Thus, data from our test system indicated considerable variation of drug responsiveness among intratumor subclones, adding another level of complexity to the interindividual differences that are commonly acknowledged in the biology of glioblastoma (24, 26). These pilot data, however, also raised questions: on one hand related to the overall range of potentially coexisting subclones and intraindividual drug–response profiles; on the other hand, it was unclear to what degree the subclones maintain their distinct phenotypic and genetic identities ex vivo and whether the determined variability of drug response would impact on the cellular composition of a tumor bulk upon treatment. We prioritized investigation of the latter complex of questions, because the range of intratumor response profiles would be irrelevant if significant alterations to the cellular composition of the tumor bulk would not occur.
Morphologic, genetic, and functional traits of tumor subclones are preserved in vitro and in vivo
Previous studies already demonstrated that patient- and disease-specific hallmarks of glioblastoma could be mirrored ex vivo for experimental investigation (17, 18, 27). For experimental access to studying consequences of clonal diversity, we selected the case GNV019 that presented with a characteristic morphologic trait of heterogeneity: mGCs. Their presence is not an obligatory finding, but rare mGCs are frequently observed in glioblastoma (16). Histopathology of the patient's tumor accordingly revealed very few (≈1%) mGCs intermixed with other pleomorphic smaller cell phenotypes (Fig. 2A). Morphologic heterogeneity was similarly observed when primary GNV019 cells were isolated and propagated under adherent conditions (Fig. 2B; ref. 20). The cellular expansion rate remained stable, and the disease- and patient-specific gene expression-/copy number profiles were conserved in vitro (Fig. 2C–E; refs. 28, 29). Orthotopic xenotransplantation demonstrated a tumorigenic potential replicating the original tumor's glioblastoma histology, including the presence of intermixed, rare mGCs (2.1 ± 1.1%; n = 11; Fig. 2F, G, and I). We next investigated subclones (CL1/2/3/6/7) derived from early-passage parental GNV019 cells (Fig. 2H, top). They presented common genomic profiles with only a few differential copy number alterations (Supplementary Fig. S2), and similar to the parental samples, they each classified as “neural” subtype (24). Notably, however, orthotopic xenotransplantation revealed distinct categories of in vivo behavior. CL2 cells were not tumorigenic (n = 11/11). The other subclones consistently developed histopathologic features of glioblastoma (16), yet their cellular composition varied. CL7-derived tumors appeared “small cell-enriched”: most tumor cells were uniformly small and round, mGCs extremely rare (0.2 ± 0.2%; n = 8). In contrast, CL1/3/6-derived tumors presented high mGC frequencies (32.8 ± 9.3%; n = 8/7/10; Fig. 2H, bottom, and I).
We concluded that GNV019 disease- and patient-specific characteristics were preserved ex vivo and that isolation of tumor subclones enabled functional access to distinguishable morphologic characteristics of the parental tumor.
Subclone phenotypes present distinct developmental and genetic traits
We next explored whether the distinct categories of in vivo behavior reflected distinct patterns of cellular plasticity, gene expression, and genetic aberrations. In vitro, the subclones consistently revealed stem-like developmental potentials, that is, 5 of 5 were able to self-renew (Supplementary Fig. S3) and to differentiate into neuronal and glial progeny (Fig. 3A, top). Notably, a pronounced capacity to also generate (giant) mnCells upon spontaneous differentiation in vitro that resembled mGCs in vivo was only observed in CL1/3/6 cells matching their developmental potential in xenografts (Fig. 3A, bottom). Moreover, analysis of signature neural stem-like genes (FABP7, OTX2, SOX9, BMI1, SOX2, VIM, NOTCH2, VCAM1, NES, NCAM1, SOX8, and FGFR4) indicated distinct expression motives separating nontumorigenic CL2 cells from tumorigenic mGC-forming CL1/3/6, and from tumorigenic “small cell-enriching” CL7 cells (Supplementary Fig. S4). Unsupervised clustering of correlation data from genome-wide gene expression and in-depth genotype analysis further confirmed the specific hierarchic alignment of GNV019 subclones (Fig. 3B).
In synthesis, findings indicated consistently distinct morphologic, developmental, and genetic traits of the investigated GNV019 subclones in vitro and in vivo, serving as an ideal basis to reveal functional consequences of intratumor heterogeneity.
Distinct pharmacologic response patterns enable selection of subclones by in vitro drug challenge
The specific alignment of CL1/3/6, CL2, and CL7, as determined in the functional and molecular classification experiments above, corresponded directly to the drug response profiles of GNV019-derived subclones of our pilot data (Fig. 1). This encouraged further investigation toward pharmacologic targeting of distinct subclones. To broaden the approach and for experimental validation, we additionally applied a commercial library comprising 160 synthetic/natural compounds, previously used for identification of new drug candidates in glioblastoma (18). Hierarchical clustering of the respective cellular viability data further confirmed the specific alignment of CL1/3/6, CL2, and CL7 (Fig. 4A).
Together, data suggested that the five investigated GNV019 subclones represented three independent intratumor cell hierarchies separating coexisting precursor cells and their descendants by functional, phenotypic, and genetic traits. We hypothesized that these hierarchies needed to be considered as functionally distinct intratumor cell populations with independent drug resistance profiles. On the basis of this assumption and the noted stability of subclones ex vivo, we opted for discriminative investigation and modeling of intratumor population dynamics using GNV019 cells as an experimental system. The approach was initiated by determining the most suitable drugs for pharmacologic hierarchy selection, chosen from both sets of compound screening data (Fig. 4B). Subsequent pharmacodynamic analysis established drug concentrations with most pronounced effects. The highest differential level of intrinsic drug resistance was defined at 10 μmol/L thioguanine (synthetic guanosine analogue antimetabolite, inhibits nucleic acid synthesis) for CL1/3/6 and at 2 μmol/L oridonin (mechanism of action not yet fully understood) for CL7 cells. CL2 cells could be discriminated from all others by their sensitivity to 1 μmol/L cantharidin (PP2A inhibitor) and by their resistance to 4 μmol/L sorafenib (multikinase inhibitor, see Supplementary Table S2; Fig. 4C). To test the drugs' applicability for pharmacologic selection, defined mixtures of fluorescently prelabeled subclones were exposed in coculture to a single drug dose, and their respective population shifts were evaluated by flow cytometry 5 days later (Fig. 4D and E). Comparison with vehicle controls confirmed the predicted targeting of distinct subclones/hierarchies. The resulting mean population shifts were determined at 20 ± 3% in response to in vitro drug challenge (Fig. 4F).
Intratumoral, treatment-related population dynamics can be predicted in vitro
The aforementioned, reductionist coculture experiments suggested that the ratios of intratumoral subclone fractions could be selectively modulated depending on the choice of drug used for pharmacologic challenge. In the next series of experiments, we aimed to show that this could be applied to the more complex setting of parental GNV019 cells.
In the first approach, parental cells were considered as a polyclonal collection of precursors either responsible for (e.g., thioguanine-resistant CL1/3/6-like cells) or incapable of mGC/mnCell generation (e.g., thioguanine-sensitive CL2/7-like cells; Fig. 5A). The morphologic trait of mGC/mnCell generation was used as a read-out parameter to quantify the extent of pharmacologic selection. Application of thioguanine to GNV019 parental cells and subsequent differentiation in vitro indeed yielded up to 5-fold concentration-dependent increases of mnCell fractions (Fig. 5B). The corresponding increases correlated with differential viability effects recorded at pharmacodynamic investigation of CL1/3/6 and parental cells (Fig. 5C and D, compare Fig. 4C). In a control setting, we confirmed that thioguanine alone did not induce the mnCell phenotype, as its application did not alter the capability of CL2 or CL7 cells to develop the phenotype in vitro (Fig. 5E). We concluded that, as predicted, thioguanine selectively enriched CL1/3/6-like cells from the GNV019 parental cells. The second approach was based on the presence of a distinctive, 36-megabase deletion in CL2 cells on chromosome 5q (Fig. 5F). Used as a read-out parameter, a decreased abundance of this chromosomal region would indicate an increased fraction of CL2-like cells within the GNV019 parental cells (Fig. 5G). The extent of pharmacologic selection was then determined by quantifying copy numbers of genes within the CL2-specific deletion. As predicted, sorafenib exposure led to dose-dependent copy number decreases, indicating an enrichment of CL2-like cells from GNV019 parental cells (Fig. 5H, compare Fig. 4C).
These data implied that pharmacologic profiling of subclones could blueprint post hoc identification of individual cell hierarchies in a heterogeneous parental tumor sample. To validate whether this insight could be applied to other glioblastoma specimens, we investigated 14 additional subclones from two more clinical samples of our pilot dataset (Fig. 1), adopting the experimental course established on GNV019 cells. Briefly, BN035- and BN046 subclones underwent MLPA analysis for identification of specific genetic marks (Materials and Methods), selection of subclone-specific drugs from pharmacologic profiles, respective validation in coculture, and successful tracking of predicted subclone enrichment in parental tumor samples (Supplementary Figs. S5 and S6). The consistency of experimental results from all three investigated clinical samples led us conclude that intratumoral, treatment-related cellular population dynamics is predictable, based on discriminative investigation of drug responses under controlled in vitro conditions.
Intratumoral, treatment-related population dynamics can be predicted in vivo
Next, orthotopic xenotransplantation validated predictability of drug-induced polyclonal dynamics in vivo. Treatments of animals engrafted with 105 GNV019 parental cells commenced at day 42, when intracerebral glioblastoma characteristics had already developed (compare Fig. 2F and G). Intraperitoneal vehicle injections yielded a median overall survival (mOS) of 60.5 days (n = 3, each; Fig. 5I).
The established morphologic/genetic read-out parameters were then used to verify thioguanine/sorafenib–induced enrichment of distinct hierarchies, that is, CL1/3/6-like or CL2-like cells. Both drugs indeed induced the predicted population shifts, independent of their influence on mOS. Thioguanine applications were limited to three injections due to potential myelotoxicity (n = 6 animals; intraperitoneal; 21) and did not extend the mOS of engrafted animals (Fig. 5I). Nevertheless, a significant 2.8-fold increase of mGCs indicated a treatment-related population shift as predicted (P = 0.0149; Fig. 5J). In the parallel experiment, sorafenib applications (n = 5 animals; intraperitoneal, dose according to ref. 22) extended the mOS significantly to 74 days (P = 0.0042; Fig. 5I). Microdissected tumor tissue from these animals then provided evidence for an increasing population size of CL2-like cells, as predicted. Quantifying copy numbers of genes in the CL2-specific region of deletion on chromosome 5q revealed a significant decrease from 2.03 ± 0.05 (vehicle) to 1.83 ± 0.06 (sorafenib; P = 0.029; Fig. 5K).
Implications of predictable population dynamics for second-line treatment strategies
In the last series of experiments, we investigated whether information from ex vivo pharmacologic profiling and prediction of subclone enrichment could be applied to the design of rational drug combinations. We hypothesized that one drug could be used in a first-line setting to drive the heterogeneous parental tumor bulk toward enrichment of particular subclones/hierarchies with distinct sensitivities to a choice of second-line drugs. The hypothesis was tested in two settings on GNV019 parental cells, implementing pharmacologic profiles as planning tools (Fig. 6A and C). In setting one, we observed enrichment of CL1/3/6-like cells with thioguanine, rendering the parental tumor bulk significantly more vulnerable to perifosine, SAHA + temozolomide, or sunitinib, which were predicted particularly effective on CL1/3/6 cells by the initial profiling. In contrast, drugs with minor inhibitory effects on CL1/3/6 cells in the initial profiling, for example, bortezomib and dasatinib, showed a less pronounced effect on bulk tumor cells secondary to thioguanine application (Fig. 6A and B). In setting two, enrichment of CL2-like cells with sorafenib required cantharidin for more effective second-line inhibition. As predicted from CL2 profiling, imatinib, etoposide + temozolomide, and temozolomide were less appropriate second-line combinations for sorafenib (Fig. 6C and D). In a control arm for both sets of experiments, we could furthermore show that these effects were exclusive to the parental bulk of tumor cells (Supplementary Fig. S7).
We conclude that ex vivo drug profiling of tumor subclones can facilitate predictions on treatment-related population dynamics and that rational drug combinations for sequential application in glioblastoma can be identified.
Our study considers glioblastoma as a polyclonal collection of potent cellular hierarchies, at least at clinical manifestation of disease. The concept combines classic stochastic and cancer stem cell models (2, 12), implying that subclones with distinct intrinsic resistance profiles coexist in tumor specimens. Although poor or short-lasting therapy responses still occur in most glioblastoma patients, we here show the applicability of discriminative ex vivo drug profiling of subclones from clinical samples. A previously unrecognized feasibility is revealed, enabling predictions on intratumoral drug response and the resulting population shifts in bulk tumor samples. This note has important implications, particularly in light of the additional finding that pharmacologic profiles could serve as a valuable asset for defining appropriate drug combinations for individualized sequential application.
The roots of our work are classic cell culture studies describing intratumoral diversity of karyotypes, phenotypes, and pharmacologic responses in human glioma (30–32), recently revisited by investigating patterns of receptor tyrosine kinase amplifications and their respective functional dependence in vitro (33). Taking advantage of stem/precursor culture conditions that enable maintenance of phenotypic and genetic properties, we demonstrate that isolated subclones are amenable for profiling and for predictions on drug-related intratumoral dynamics. The importance of investigating alterations to the cellular composition of solid cancers before and after treatment is being increasingly revealed (e.g., refs. 34, 35). Drug-related enrichments of primary resistant cell types may even include minority cellular hierarchies of the original tumor (36, 37). This could be recapitulated in our model by the significant drug-related enrichment of CL1/3/6-like or CL2-like GNV019 hierarchies. We observed the effect even occurring independent of a survival benefit in xenografts. A logical next step for future follow-up would be a study on matched clinical samples obtained before and after treatment to provide unequivocal evidence that predictable drug-related clonal selection occurs in glioblastoma patients. We already know that successful eradication of targeted neoplastic cells cannot prevent the cells that were not targeted to develop fatal relapse (e.g., ref. 38). Predictable population dynamics could serve as a valuable asset on these grounds for defining appropriate secondary lines of treatment. It might even be essential, because secondary surgery cannot always be performed in glioblastoma, and current practice of care frequently considers rechallenge of first-line pharmacotherapy for treating relapsed disease (39).
It needs to be emphasized, however, that our approach focused on the feature of primary/intrinsic resistance. Further investigation may be directed, for example, toward acquired drug resistance and potential subclone interactions, as well as toward environmental and immunologic cues for a more comprehensive view (12, 40–42). Some of these factors, in addition to animal model–inherent obstacles, including potentially suboptimal drug dosage/pharmacokinetics/distribution, might already explain the lower efficacy of in vivo versus in vitro subclone enrichment observed in our study. Another restriction may apply to conditions for derivation and expansion of clinical samples. Although our work shows how cellular heterogeneity can be captured and how cellular identity can remain stable under adherent conditions, it is known that any setting of primary cell culture may inflict selection bias on patient tissue and cells (e.g., ref. 43). Thus, even though we present data on drug-mediated enrichment of 19 subclones from three clinical specimens, sufficient data are not available for a statistically valid extrapolation of clonal diversity. This could be addressed in future, for example, by introducing artificial DNA barcodes to the entire population, facilitating quantification of individual subclones in the tumor bulk (44).
Our study furthermore provides tools for individualizing medicine, for example, specifying how subclone drug profiles could help to foster translational studies and more personalized approaches addressing the functional consequences of intratumor heterogeneity (see Supplementary Fig. S8). In our study, ternary plots serve as schematic representations of subclone drug profiles. They correlate the degree of drug sensitivity among coexisting tumor cell hierarchies in a two-dimensional plot. Applied as a planning tool, suited pairs of drugs can be identified that drive the heterogeneous parental tumor bulk toward enrichment of a particular subclone/hierarchy during first-line application and that aim for depletion of this hierarchy in the second-line approach. Granted that future developments in clinical medicine will improve upon many of the technical aspects of our work, for example, toward a more complete mirroring of subclone heterogeneity from clinical specimens, our approach could provide the rationale for personalizing sequential therapy in glioblastoma. For basic sciences, the approach could enable investigation of potential relationships between drug-specific mechanisms of actions, differential drug sensitivities, and the various molecular signatures provided by intratumor hierarchies. Also, preclinical limitations, for example, inflicted by combinatorial versus sequential drug application, could be studied in a reductionist setting.
Taken together, our observations support accumulating evidence from the study of many malignant types of cancer and the notion that population-level methods could underestimate clinically relevant information (3–5, 14, 36, 45). Pharmacologic predictions on tumor dynamics would smoothly integrate into the intense ongoing search for new and alternative pharmacotherapy options, particularly needed in the setting of defining individualized second-line treatment strategies. Combined with advanced genetic diagnostics and driven by the high medical need in glioblastoma, this strategy might become an essential tool for precision medicine and clinical trial design (9).
Disclosure of Potential Conflicts of Interest
O. Brüstle has ownership interest (including patents) in LIFE & BRAIN GmbH, D.A. Steindler has ownership interest (including patents) in Prana Tx. B. Scheffler has conducted contract-based research for LIFE & BRAIN GmbH. No potential conflicts of interest were disclosed by the other authors.
Conception and design: R. Reinartz, M. Glas, B.A. Reynolds, D.A. Steindler, B. Scheffler
Development of methodology: R. Reinartz, S. Wang, D.J. Silver, A. Wieland, T.M. Shepherd, M. Glas, Y. Liu, M. Simon, D.A. Steindler, B. Scheffler
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): R. Reinartz, S. Wang, S. Kebir, D.J. Silver, A. Wieland, T. Zheng, T.M. Shepherd, N. Schäfer, M. Glas, A.M. Hillmer, S. Cichon, A.A. Smith, T. Pietsch, A. Yachnis, M. Simon, D.A. Steindler, B. Scheffler
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): R. Reinartz, S. Wang, S. Kebir, D.J. Silver, M. Küpper, L. Rauschenbach, R. Fimmers, T.M. Shepherd, N. Schäfer, M. Glas, A.M. Hillmer, S. Cichon, T. Pietsch, Y. Liu, O. Brüstle, D.A. Steindler, B. Scheffler
Writing, review, and/or revision of the manuscript: R. Reinartz, S. Kebir, D.J. Silver, T. Zheng, L. Rauschenbach, T.M. Shepherd, D. Trageser, A. Till, N. Schäfer, M. Glas, A.M. Hillmer, S. Cichon, A.A. Smith, T. Pietsch, Y. Liu, B.A. Reynolds, A. Yachnis, D.W. Pincus, M. Simon, O. Brüstle, D.A. Steindler, B. Scheffler
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S. Kebir, M. Glas, T. Pietsch, M. Simon, O. Brüstle, B. Scheffler
Study supervision: D.A. Steindler, B. Scheffler
We would like to thank Heike Höfer, Mihaela Keller, Anke Leinhaas, Sabine Normann, and Ramona Schelle for technical assistance, Dr. Philipp Koch for providing study material, Stefan Herms and the group of Dr. J. Schultze (LIMES-Institute, University of Bonn) for help with bioinformatics in this study. The authors thank the Departments of Neurosurgery and Neuropathology at the University of Bonn Medical Center for their assistance in tumor procurement and for the processing of paraffin-embedded samples. We thank Robert Schuit and Suvi Savola (MRC Holland) for providing P345-X1 & P346-X custom MLPA kits.
This study was mainly supported by the Lichtenberg program of the VW foundation (to B. Scheffler); additional funds were provided by the Federal Ministry of Education and Research, Germany (B. Scheffler/M. Glas; BMBF, VIP initiative, FKZ 03V0785), and NIH/NINDS grant NS055165 (to D.A. Steindler). O. Brüstle support included EU FP7-HEALTH-2010-266753-SCR&Tox, COLIPA, BIO.NRW z0911bt027i, StemCellFactory, and Hertie Foundation.
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