Medulloblastoma is among the most common malignant brain tumors in children. Recent studies have identified at least four subgroups of the disease that differ in terms of molecular characteristics and patient outcomes. Despite this heterogeneity, most patients with medulloblastoma receive similar therapies, including surgery, radiation, and intensive chemotherapy. Although these treatments prolong survival, many patients still die from the disease and survivors suffer severe long-term side effects from therapy. We hypothesize that each patient with medulloblastoma is sensitive to different therapies and that tailoring therapy based on the molecular and cellular characteristics of patients' tumors will improve outcomes. To test this, we assembled a panel of orthotopic patient-derived xenografts (PDX) and subjected them to DNA sequencing, gene expression profiling, and high-throughput drug screening. Analysis of DNA sequencing revealed that most medulloblastomas do not have actionable mutations that point to effective therapies. In contrast, gene expression and drug response data provided valuable information about potential therapies for every tumor. For example, drug screening demonstrated that actinomycin D, which is used for treatment of sarcoma but rarely for medulloblastoma, was active against PDXs representing Group 3 medulloblastoma, the most aggressive form of the disease. Functional analysis of tumor cells was successfully used in a clinical setting to identify more treatment options than sequencing alone. These studies suggest that it should be possible to move away from a one-size-fits-all approach and begin to treat each patient with therapies that are effective against their specific tumor.
These findings show that high-throughput drug screening identifies therapies for medulloblastoma that cannot be predicted by genomic or transcriptomic analysis.
Medulloblastoma is a highly malignant brain tumor that occurs predominantly in children. Genomic and epigenomic studies have revealed four major subgroups of the disease—WNT, Sonic hedgehog (SHH), Group 3, and Group 4—that differ in terms of molecular characteristics, demographics, and patient outcomes (1–4). Recently, heterogeneity within these subgroups has been recognized, and it has been suggested that medulloblastoma may consist of up to 14 molecular subtypes (5–7). But so far the impact of this heterogeneity on therapy has been limited, with trials testing Smoothened (SMO) antagonists for patients with SHH-associated medulloblastoma (8), and efforts to reduce therapy for patients with WNT-driven tumors (9), who have a relatively favorable prognosis. Outside of these trials, most patients with medulloblastoma still receive similar therapies, including surgery, craniospinal radiation (except in young children, for whom radiotherapy has devastating neurocognitive side effects) and multiagent chemotherapy. For patients who fail frontline treatment, there are few curative options, and recurrent medulloblastoma is frequently lethal. Overall, approximately 1/3 of patients with medulloblastoma die from the disease, and survivors suffer severe long-term side effects from treatment. We hypothesize that tailoring therapy based on characteristics of individual patients with medulloblastoma will result in improved outcomes.
The notion of tailoring therapies based on genomic characteristics is not new (10). “Basket trials,” in which a targeted therapy is matched to patients with a specific genetic lesion across a variety of tumor types, have led to FDA approval of treatments such as pembrolizumab in mismatch repair-deficient cancers and BRAF/MEK inhibition in metastatic BRAF V600E–mutated non–small cell lung cancer. However, heterogeneity in overall response rate has been observed on the basis of tumor type or histology (10). “Umbrella trials” such as NCI's Molecular Analysis for Therapy of Choice (MATCH) trial include multiple targeted therapies, and assign patients to a therapy based on the presence of specific genetic lesions in their tumors (11). Although umbrella trials have shown some promise (12), adequate enrollment of patients into each therapeutic arm, heterogeneity of patients within each arm, and efficacy of single-agent therapy in highly pretreated patients, have been major challenges (10). “N of 1” trials use genomic and molecular characteristics to guide therapy on an individualized basis without a predetermined set of therapies but rather a host of therapies that may be chosen based on molecular predictors (13). In one such trial, tumors were sequenced and up to five targeted agents were chosen for therapy; targeting a larger fraction of molecular alterations was associated with better outcomes (14). Although such trials have improved responses in select patients, in many cases gains have been modest.
Precision medicine has also been evaluated in pediatric oncology. For example, a basket trial using larotrectinib showed promising results in Trk fusion–positive tumors of a variety of histologies (15). Pediatric MATCH, an umbrella trial with 10 arms into which patients are assigned on the basis of genetic lesions, has demonstrated the feasibility of this approach, but the prevalence of target lesions in the pediatric population is low, and evidence for efficacy of recommended drugs has been limited (16). The value of looking at genetic lesions on an individualized basis was previously shown by Gröbner and colleagues (17). However, in the individualized Cancer Therapy (iCAT) trial, an N of 1 trial for advanced, extracranial, solid tumors, 31 of 100 patients received a recommendation based on genetic alterations, but only three received a matched therapy and none of these showed an objective response (18). This and other evidence has emphasized the paucity of targetable genetic lesions in the pediatric population. Thus, the use of DNA alone to identify targeted therapies has been disappointing.
Other types of data that could point to appropriate therapies include gene expression, protein expression, epigenetic analysis, and empirical drug screening. Although these approaches have been evaluated extensively in the lab (17, 19), their application in the clinic has been much more limited. Here, using a panel of medulloblastoma patient-derived xenografts (PDX), we show that considering gene expression and drug response along with DNA variants may better inform therapeutic decisions than sequencing alone. In addition, we demonstrate that these approaches are feasible in a clinical setting, raising the possibility that they could be incorporated into current precision medicine protocols.
Materials and Methods
NOD-SCID IL2Rγ null (NSG) mice used for intracranial tumor transplantation were purchased from Jackson Labs. Mice were maintained in the animal facilities at the Sanford Consortium for Regenerative Medicine. All experiments were performed in accordance with national guidelines and regulations, and with the approval of the animal care and use committees at the Sanford Burnham-Prebys Medical Discovery Institute and University of California San Diego (UCSD, San Diego, CA).
Establishment and maintenance of PDXs
PDX lines were generated by implanting 0.5–1 × 106 dissociated patient cells directly into the cerebellum of NSG mice, and propagated from mouse to mouse without in vitro passaging. The identity and subgroup of each line was validated by DNA methylation analysis. PDX lines used for this study include BT084 (SHH) from the Milde lab (20); MB002 and MB009 (G3) from the Cho lab (21, 22); ICb-984 (SHH), ICb-1572 (G3), ICb-1487 (G3), and ICb-1299 (G3), from the Li lab (23); Med-1712FH (SHH), Med-411FH (G3), Med-211FH (G3), Med-1911FH (G3), and Med-2312FH (G4) from the Olson lab (24, 25); and RCMB28 (G3), RCMB18 (SHH), RCMB32 (SHH), RCMB38 (G4), RCMB20 (G3), RCMB40 (G3), RCMB24 (SHH), and DMB006 (G4) from the Wechsler-Reya lab (26, 27). No WNT subgroup PDXs were available for these studies. For all experiments, cells were isolated from tumor-bearing mice, resuspended in NeuroCult with proliferation supplement and penicillin/streptomycin (StemCell Technologies, catalog no. 05702), and assayed as described below. All PDXs described in this study will be made available to investigators at other institutions upon reasonable request.
Short-read sequencing data are available at the European Genome-phenome Archive (http://www.ebi.ac.uk/ega/), hosted by the European Bioinformatics Institute, under accession number EGAS00001004698. Methylation and gene expression data have been deposited in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under accession numbers GSE151344 and GSE151343, respectively. Gene expression analyses (Affymetrix processing, gene expression quantification, and gene expression analyses using DiSCoVER) can be reproduced following the documentation and using the code in the GenePattern GitHub Repository.
Identification of actionable mutations from DNA
To determine what drugs would be predicted on the basis of mutational analysis, we merged two databases containing mutation-drug-outcome associations: CIViC (28) and OncoKB (29). This created a searchable list of drugs targeting specific genes. We then ran PHIAL (30) to cross-reference the mutations found in our PDXs with mutations reported in the TARGET database (https://software.broadinstitute.org/cancer/cga/target as of February 2015). PHIAL returned a list of actionable mutations present in our samples. For each mutated gene, drugs identified by OncoKB and CIViC were added to our list of drug candidates. This compiled list of drug-mutation associations was reduced to include only genes mutated in our PDX lines.
Identification of drug candidates from RNA analysis
The DiSCoVER method (31) extracts a highly expressed gene expression signature from a tumor sample and leverages publicly available databases to identify cell lines with a similar signature, and the compounds to which they are sensitive. Briefly, the method proceeds as follows:
The signature of the tumor consists of the 150 most highly differentially expressed genes in that tumor compared with a control. Here the tumors are PDXs and human cerebellar stem cells are used as the control.
A single sample version of gene set enrichment analysis (32, 33) is used to score the activation of signature genes in the expression profiles from two cell line collections: the Cancer Cell Line Encyclopedia (CCLE; ref. 34) and the Sanger Cell Line Project (35). The expression activation scores are then compared with the cell lines' corresponding viability profiles for compounds in the appropriate screening databases: the Cancer Therapeutics Response Portal (CTRP; ref. 36) for the CCLE cell lines and the Genomics of Drug Sensitivity in Cancer (GDSC) database for the Sanger Cell lines.
The association of the viability and the signature activation of the cell lines yields a score from which the probability of a compound's effectiveness on the PDX can be inferred. The compounds are ranked according to the quantification of these associations.
High-throughput drug screening
The screen used 7,729 compounds from the following libraries: StemSelect, InhibitorSelect Pathway, Kinase Inhibitors (all from EMD,), Spectrum, US and International Drug Collections (both from MicroSource), LOPAC (Sigma), Prestwick Chemical Library (Prestwick), LeadGen Collection, Epigenetics library (both from Enzo Life Sciences), NIH Clinical Collection (NIH), NCI Oncology Drugs (NCI), Kinase Inhibitors (Cayman), Kinase Inhibitors (SelleckChem). Many drugs were represented in multiple libraries; thus only 4,476 unique compounds were tested.
Prior to screening, 2.5 nL of compound-containing solution was pin transferred into 384-well plates (Greiner Bio-One, catalog no. 781098). Tumor tissue was harvested from tumor-bearing mice and dissociated using 10 U/mL papain (Worthington, catalog no. LS003126) to create a single cell suspension. Into each drug-containing well, we plated 104 tumor cells in 25 μL Neurocult with proliferation supplement, resulting in a final drug concentration of 1 μmol/L. Each drug was tested in triplicate, with tumor cells from at least 3 separate mice. Because testing the entire library required approximately 80 million cells, and because the yield of tumor cells from a given mouse ranged from 10 to 150 million cells, testing the entire library in triplicate required 3 to 24 mice for each PDX line. Every set of plates included 12 wells of DMSO (negative control) and each tumor replicate included 12 wells of 1 μmol/L YM155 (positive control, Cayman, catalog no. 11490). Viable cell number in each well (as indicated by ATP content) was determined using the CellTiter-Glo assay (Promega, catalog no. G7571) and read in an automated Envision plate reader (Perkin-Elmer) after 48-hour incubation. Percent inhibition was calculated using the formula (sample result/mean value of the entire plate containing cells and compound × 100).
Treatment of tumor-bearing mice
For treatment of intracranial tumor-bearing mice in pilot experiments, actinomycin D, bortezomib, gambogic acid, idarubicin (all from Cayman, catalog nos. 11421, 10008822, 14761, 14176 respectively), and oleandrin (MedChemExpress, catalogno. HY-13719) were dissolved in 10% DMSO, 10% Tween80, and sterile water (vehicle, n = 8). Actinomycin D was administered at 0.03 mg/kg daily or 0.06 mg/kg biweekly (intraperitoneal, i.p., n = 8 for each). Bortezomib was administered at 1 mg/kg biweekly (i.p., n = 8). Oleandrin was administered at 2 mg/kg daily or 3 mg/kg biweekly (i.p., n = 8 for each). Idarubicin was administered at 1 mg/kg daily or 1.5 mg/kg biweekly (i.p., n = 8 for each). Gambogic acid was administered at 2 mg/kg daily or 4 mg/kg biweekly (i.p., n = 8 for each).
For in vivo comparison with standard-of-care drugs, actinomycin D (Cayman), vincristine, and cisplatin (both from Sigma, catalog nos. V8879, PHR1624) were dissolved in 5% DMSO in PBS and cyclophosphamide (Sigma, catalog no. C7397) was dissolved in 10% DMSO in PBS. Pilot experiments demonstrated that the maximum tolerated weekly doses were 130 mg/kg cyclophosphamide (intraperitoneal) for 13 weeks with one week off in the 7th week, 4.5 mg/kg cisplatin (intraperitoneal) for 8 weeks with 2 weeks off the 6th and 7th week, 1 mg/kg vincristine (intraperitoneal) for 8 weeks, and 0.3 mg/kg actinomycin D (retro-orbital intravenous) for 10 weeks. Two weeks after transplantation, mice were randomly separated into five groups of 8, and treated with vehicle (10% DMSO); cyclophosphamide, cisplatin, vincristine, or actinomycin D. Mice that received actinomycin D were given 1 million allogenic bone marrow cells intravenously every week, 2 days postdrug treatment. Animals were drug treated every 7 days, with weeks off as described above, until they displayed signs of morbidity or toxicity (>30% weight loss), whereupon they were euthanized.
Primary patient samples
Studies were conducted in accordance with recognized ethical guidelines and were approved by an institutional review board Written informed consent was obtained from patients.
Molecular characterization of PDXs
To identify novel therapies for medulloblastoma, we assembled a panel of 20 PDX lines that had been passaged only in vivo. To determine which subgroups of medulloblastoma these lines most closely resemble, we performed DNA methylation profiling and analyzed the data using the Heidelberg brain tumor classifier (www.molecularneuropathology.org; ref. 37), which calculates a score between 0 and 1, indicating the similarity of that sample to one of the 82 central nervous system tumor classes present in the reference cohort. Samples classified as Group 3 or Group 4 medulloblastoma were further subtyped using the Group 3/4 classifier described by Sharma and colleagues (7), which distinguishes between eight subtypes, designated I–VIII. Scores for each PDX, and when available, the primary tumor from which it was generated, are shown in Supplementary Table S1.
Methylation analysis identified six SHH, 10 Group 3, and four Group 4 lines within our cohort. In most cases, the subgroup of primary tumors was consistent with that of the corresponding PDX models. One exception was Med-2312FH, for which the primary tumor was classified as Group 4 and the PDX was classified as Group 3. Subtype analysis indicated that both samples belonged to subtype V, which consists of tumors that are intermediate between Groups 3 and 4 (6). Given the high score supporting Group 4 classification of the primary tumor (0.936) and the relatively low score supporting the Group 3 classification of the PDX (0.627), for the analyses below we considered it a Group 4 tumor. t-distributed stochastic neighbor embedding (t-SNE) clustering of the DNA methylation profiles of the PDX models with a reference cohort of medulloblastomas representing all four subgroups (25) shows that PDX models cluster with the subgroups predicted by the classifier (Fig. 1A).
To identify the genetic lesions present in our PDX cohort, we performed deep whole-exome sequencing and low-depth whole-genome sequencing (WGS; Fig. 1B; Supplementary Table S2A–S2C). Among the six SHH lines, three (Med-1712FH, RCMB24, and RCMB32) had alterations in PTCH1, which occurs in about 43% of SHH-MB patients (27). The remaining three SHH lines, RCMB18, ICb-984, and BT084, exhibited loss or inactivation of both alleles of TP53, amplification of MYCN and/or GLI2, and chromothripsis, a genotype associated with extremely poor prognosis (38). Notably, alterations in TP53 account for only 13% of all SHH-MB cases, but were found in three out of six (50%) of our SHH PDXs, consistent with the notion that more aggressive tumors are more likely to take in mice (23, 25). Likewise, among the 10 Group 3 lines, eight (80%) exhibited MYC amplification, a biomarker of poor prognosis that is present in only 17% of Group 3 patients. Mutations in CREBBP, PIKC3A, KMT2D (MLL2), CHD7, and activation of GFI1B by enhancer hijacking were among the additional lesions observed in our Group 3 lines. Among the four Group 4 lines, one (ICb-1487) exhibited GFI1 enhancer hijacking and CDK6 amplification, and another (DMB006) had PRDM6 enhancer hijacking and a mutation in KDM6A. Gender, metastatic (M) status, histology, age, and prevalence of each genetic lesion within the medulloblastoma population for all 20 PDX lines are shown in Fig. 1B.
Identification of candidate therapies based on genetic data
Most precision medicine trials use DNA sequencing as a basis for recommending therapies. To determine what therapies would be predicted to be effective using this approach, we analyzed DNA sequencing data from our PDXs (Supplementary Table S2A–S2C) using mutation-drug-outcome associations from three databases: PHIAL2 (Precision Heuristics for Interpreting the Alteration Landscape; ref. 30), CIViC (28), and OncoKB (29), and additional relevant publications (39–41). This allowed us to identify candidate therapies for several of our PDX lines (Fig. 2A). We categorized the strength of each of these mutation-drug predictions based on criteria used for the iCAT study: alterations associated with response to a therapeutic agent in clinical trials are considered Tier 1 (if the trial was in the same disease) or Tier 2 (for a different disease), and alterations associated with drug responses based on preclinical studies are considered Tier 3 (for studies in the same disease) or Tier 4 (for a different disease; ref. 18).
Using this framework, the only Tier 1 mutations were in PTCH1 (in the SHH lines RCMB24, RCMB32 and Med-1712FH), which has been shown in clinical trials of patients with medulloblastoma to predict responsiveness to SMO antagonists. Notably, one PDX line (RCMB18) had a SMO mutation (R145C). However, it is unknown if the resulting protein remains sensitive to SMO antagonists. Moreover, this line also had amplification of MYCN, a downstream target of the SHH pathway that would be expected to render the tumor resistant to this therapy (27). Thus, we did not consider RCMB18 to have a Tier 1 lesion.
The only Tier 2 lesion we observed was a PIK3CA (E545K) mutation in MB009, which could be susceptible to PI3K, mTOR, or AKT inhibitors. Phase I–III clinical trials in various cancers have suggested that PI3K pathway inhibitors have greater clinical benefit in PIK3CA mutant than in PIK3CA wild-type (WT) tumors (42). However, there is evidence that E545K mutations have an equivalent response to WT PI3K (43), raising questions about the predictive value of this lesion. Although preclinical studies have suggested that PI3K inhibitors are effective against Group 3 as well as SHH medulloblastoma (44, 45) no clinical data are available for medulloblastoma, so this lesion was considered Tier 2.
The majority of lesions were Tier 3 or Tier 4. Among these were the CDK4/6 amplifications found in ICb-1487 and RCMB18. In principle, amplification of these genes should lead to overexpression of the corresponding proteins, and this could reflect an increased dependency of the tumor on the kinases. On the other hand, higher levels of the kinases could necessitate increased levels of inhibitors to block kinase activity, suggesting that amplified tumors would be more resistant to these inhibitors. In fact, both phenomena have been observed clinically (46, 47). Although preclinical studies have suggested that palbociclib is effective in SHH and Group 3 medulloblastoma (31, 48), these studies were done using models lacking amplification of CDK4/6. Thus, there is limited evidence to suggest that CDK4/6 amplification represents a biomarker for sensitivity to CDK4/6 inhibitors in medulloblastoma.
Additional mutation-drug associations assigned Tier 3 status were Aurora kinase inhibitors for MYC, MYCN, and MYCL amplification (49), BET inhibitors for PTCH1 and SMO mutation and GLI2 amplification (40) and MYC, MYCN, and MYCL amplification (21) and arsenic trioxide for GLI2 amplification (39). HDAC inhibitors for CREBBP mutations (50) and EZH2 inhibitors for KDM6A mutations (41) were each assigned Tier 4 status, as preclinical evidence has been shown in other cancer(s) with a similar lesions (Fig. 2A).
To examine the validity of the DNA-based predictions, we tested some of the suggested drugs on PDX lines in vitro. Although SMO inhibitors have been shown to be effective against PTCH1-mutant medulloblastoma tumors in vivo (25, 27), several studies have suggested that medulloblastoma cells lose dependency on the SHH pathway when they are placed in culture (51). Consistent with this, the PTCH1-mutant PDXs in our study were not inhibited by the SMO antagonist NVP-LDE225. PI3K inhibitors such as NVP-BGT226, BKM120, and GSK2126458, which were predicted to work on the PIK3CA-mutant line MB009, were found to inhibit survival of all Group 3 lines, regardless of PIK3CA mutational status (Fig. 2B–D; Supplementary Fig. S1A–S1F). These drugs were active against some SHH and Group 4 PDXs, but notably, were also toxic to the nontransformed hepatocyte line, HepaRG. These results suggest that PI3K inhibitors are effective on Group 3 medulloblastoma, but are not selectively active against PIK3CA-mutant medulloblastoma cells, and may also be toxic to normal cells.
A similar trend was seen with other drugs predicted to work based on genetic lesions. For example, the CDK4/6 inhibitor palbociclib was predicted to work on the CDK4/6-amplified lines RCMB18 and ICb-1487, but showed minimal activity against these lines. Conversely, we observed inhibition of most Group 3 and SHH lines, including several lines previously shown to be sensitive to these inhibitors (Med-211FH, Med-411FH, and Med-1712FH; Fig. 2E–G; ref. 48). BET inhibitors have been suggested to be effective for tumors with PTCH1, SMO, or MYC/MYCN/MYCL alterations (among our PDXs, this includes all SHH lines and all Group 3 lines except for ICb-1572 and ICb-1299). However, these drugs were not effective against several of the amplified or mutant lines as well as all Group 4 lines, but were effective against the non-MYC-amplified line ICb-1572 (Fig. 2H–J; Supplementary Fig. S1G–S1I). The EZH2 inhibitor, GSK126, worked similarly in all Group 4 lines and two Group 3 lines, despite being predicted to work only on DMB006, the Group 4 line with a KDM6A mutation (Supplementary Fig. S1J–S1L). Finally, HDAC inhibitors, which were predicted to be particularly effective against the CREBBP-mutated lines Med1712, MB009, and ICb1572, were effective against all lines tested, regardless of CREBBP mutation status (Fig. 2K–M; Supplementary Fig. S1M–S1R). Thus, for our medulloblastoma PDXs, mutations were not predictive of drug response.
Identification of candidate therapies using gene expression data
Changes in gene expression can also provide insight into pathways that drive tumor growth, and suggest therapies that might be used to disrupt these pathways (19, 31). To determine whether this approach might be applicable to our PDXs, we performed gene expression profiling, and then used the DiSCoVER algorithm (31) to generate predictions of drug sensitivity (Fig. 3A). Briefly, the gene expression profile of each PDX (see Supplementary Table S3A) was compared with that of normal cerebellar stem cells, and the most highly differentially expressed genes were used as a signature for that PDX (see Supplementary Table S3B). DiSCoVER was then used to predict drug sensitivity, by comparing the signature of each PDX with the gene expression profiles of cell lines for which drug response data are publicly available (for details, see Materials and Methods). For each PDX, each drug was given a score that reflects the PDX's predicted sensitivity to that drug: high scores suggest the PDX is likely to be sensitive and low or negative scores suggest that it is likely to be resistant.
Using the GDSC and CTRP databases, DiSCoVER analysis suggested drugs that would be effective for each PDX (Fig. 3B; Supplementary Table S4A and S4B). Notably, the top 30 drugs predicted to be effective for each PDX were highly similar across most PDXs (Fig. 3B; Supplementary Table S4). These included inhibitors of BCL2, Aurora kinases, receptor tyrosine kinases (RTK; IGF1R, EGFR, PDGFR), histone deacetylases (HDAC), retinoic acid receptors (RAR) and PARP. Nonetheless, some drugs were predicted to be selectively active against a subset of PDXs. For example, GSK319347a and tpca1, both of which target IKK family members, were predicted to be more effective against SHH PDXs, whereas vx702, a p38 MAPK inhibitor, and xmd1499, an inhibitor of ALK, CDK7, and LTK, were predicted to work more selectively on Group 3 tumors. Rucaparib, a PARP inhibitor, was predicted to work on SHH and Group 4, and axitinib, an inhibitor of VEGFR and other RTKs, was predicted to work selectively on SHH and G3.
To determine whether the drugs predicted on the basis of gene expression were effective on the corresponding PDXs, we tested several of the top ranked drugs in vitro. On the basis of this analysis, some of the drugs predicted to work on all subgroups were confirmed to be effective on most PDX lines tested. For example, the aurora kinase inhibitor GSK1070916 was among the top ranked predictions for all three subgroups (Supplementary Table S4A), and in our in vitro studies we found it to be effective on all Group 3 and most SHH lines tested. It did not however, meet our efficacy criteria (IC50s were > 5 μmol/L) for Group 4 lines and for the SHH lines RCMB18 and ICb-984 (Fig. 3C–E). In contrast, the aurora kinase inhibitors alisertib and barasertib, suggested to work on all lines (Supplementary Table S4B), showed efficacy on a subset of Group 3 lines, but were ineffective on PDXs derived from SHH and Group 4 tumors (Supplementary Fig. S2A–S2F). BCL2 inhibitors, including ABT-737, navitoclax, and brd-m00053801, were predicted to work on all medulloblastoma subgroups based on GDSC and CTRP (Supplementary Table S4A and S4B), and efficacy was observed on all lines with ABT-737 and TW-37 (Fig. 3F–H; Supplementary Fig. S2G–S2I). Notably, these drugs showed minimal activity on control HepaRG cells, suggesting that they did not exhibit nonspecific toxicity at the doses tested.
In contrast, several drugs predicted to work on all PDXs or on specific subgroups showed no efficacy on most lines tested. Linsitinib, an IGFR1 inhibitor with high rank in all three subgroups (Supplementary Table S4A and S4B), showed efficacy on all Group 3 lines but was less effective (maximal inhibition <50%) against SHH and Group 4 PDXs (Supplementary Fig. S2J–S2L). SB52334, a TGFβ receptor I (ALK5) inhibitor, was a top ranked prediction for all three subgroups (Supplementary Table S4A) but showed very little activity in all lines tested except the Group 3 line, ICb-1572, which was modestly inhibited (Supplementary Fig. S2M–S2O). Likewise, the PARP inhibitors olaparib, rucaparib, and talazoparib were predicted to work on all subgroups (Supplementary Table S4A and S4B), but in our assays, olaparib showed variable efficacy on Group 3 lines and minimal effect on lines from the other subgroups (Supplementary Fig. S2P–S2R).
These results show that gene expression can be used to suggest drugs for most tumors. However, the majority of the top ranked drugs were predicted to be effective on most PDXs despite their divergent molecular profiles, and responses in vitro did not consistently match predictions.
Identification of candidate therapies based on high-throughput drug screening
The strategies discussed above were used to infer sensitivity to drugs based on genomic or transcriptomic characteristics. To test for responsiveness empirically, we performed a drug screen on our cohort of PDXs using a library of 7,729 compounds, including 4,476 unique agents (Fig. 4A). Cells were cultured in the presence of compounds at a concentration of 1 μmol/L for 48 hours, and cell viability was assessed using the CellTiter-Glo assay. Each drug was tested in triplicate, and average viability scores were calculated (Supplementary Table S5). A drug was classified as effective against a particular PDX if average cell viability scores for that drug were in the 0.1th percentile for the drug's null distribution (see Supplementary Methods for details).
This analysis identified 375 drugs that were effective against at least one PDX line (Supplementary Table S6A–S6C). Group 3 lines were generally sensitive to the highest number of drugs, whereas Group 4 lines tended to show sensitivity to fewer drugs (Fig. 4B and C). Efficacy in each subgroup was quantified using a binomial distribution (Supplementary Table S7). We sorted the effective drugs for each subgroup into different drug classes (Fig. 4D). Among the most common classes of drugs effective against medulloblastoma PDXs were cardiac glycosides (e.g., digoxin, digoxigenin, ouabain), inhibitors of DNA and RNA synthesis (e.g., idarubicin, daunorubicin, mitoxantrone), epigenetic regulators (e.g., apicidin) and regulators of protein homeostasis (e.g., bortezomib, MG132, gambogic acid). Interestingly, some classes of drugs showed specificity for certain subgroups. For example, SHH and Group 3 PDXs responded to cardiac glycosides and ion regulators whereas Group 4 PDXs did not. Likewise, Group 3 and Group 4 tumors responded to DNA/RNA synthesis inhibitors but SHH lines tended not to.
Because Group 3 medulloblastoma is associated with particularly poor outcomes, we performed dose-response studies on several drugs predicted to be effective against Group 3 PDXs (Fig. 5A–E; Supplementary Fig. S3A–S3O). Some of these drugs (e.g., Tyrphostin A9, PF-04691502, GSK2126458) were found to be toxic to HepaRG cells at doses similar to those that induced killing of Group 3 PDXs, and were therefore deprioritized. Among the remaining agents, we focused on those that are clinically available and have been used in patients, those for which there was available dosing information in mice, and those with novel mechanisms of action [e.g., having already demonstrated efficacy of PI3K inhibitors and HDAC inhibitors in Group 3 medulloblastoma (44), we did not focus on this class of drugs]. On the basis of these criteria, we selected five agents—actinomycin D, oleandrin, gambogic acid, idarubicin, and bortezomib—for further study.
To test the efficacy of these agents in vivo, we performed pilot experiments (n = 8 mice/drug) on mice orthotopically transplanted with Med-211FH, which our drug screen had suggested was the most sensitive PDX line (see Fig. 4B). Notwithstanding their potency in vitro, idarubicin, gambogic acid, and oleandrin had no effect on survival of tumor-bearing mice, and bortezomib only marginally increased survival (Supplementary Fig. S4A–S4D). In contrast, actinomycin D significantly prolonged survival (Supplementary Fig. S4E), and was prioritized for further investigation. Notably, actinomycin D was much more effective on Group 3 PDXs (IC50s ranging from 0.02 to 0.31 nmol/L; Fig. 5A) than on PDXs derived from Group 4 medulloblastoma (10–57 nmol/L) or SHH medulloblastoma (IC50s from 9 to 33 nmol/L; Supplementary Fig. S4F–S4G). To optimize in vivo treatment with actinomycin D, we tested multiple dosing regimens, and found that weekly intravenous administration was the most effective. We also tested combinations of actinomycin D with cyclophosphamide or radiation; while actinomycin D consistently prolonged survival as a single agent, toxicity was an issue when combined with other therapies.
We then compared the effects of actinomycin D with the standard chemotherapies used for medulloblastoma—cisplatin, vincristine, and cyclophosphamide—using three Group 3 PDX models. As shown in Fig. 5F–I, cyclophosphamide was generally the most effective drug in all PDX lines. Vincristine also prolonged survival in all PDX lines, but was particularly effective in Med-411FH, in which its activity was similar to cyclophosphamide. In contrast, cisplatin was highly effective on ICb-1572, but showed little activity on the other two lines. These studies demonstrated that drug response, even among standard-of-care therapies, is variable between PDX lines. Notably, actinomycin D was more effective than cisplatin and/or vincristine in all lines tested (Fig. 5F–I; Supplementary Fig. S5A–S5C). These data suggest that actinomycin D could be an effective therapy for Group 3 medulloblastoma.
Use of multimodal analysis for primary patient samples
The studies described above demonstrate the feasibility of using DNA sequencing, gene expression profiling, and empirical drug screening for predicting responses to therapeutic agents. To determine whether this approach could be used for patients in the clinic, we established a pipeline for acquiring DNA, RNA, and drug response data on samples from brain tumor patients at Rady Children's Hospital. WGS was done at 94×, and RNA sequencing was done at >30 million 150-base read pairs. Drug screening used a library of 120 drugs that are FDA approved or in clinical trials for treating cancer (52).
One case analyzed in this manner was an 8-year-old male with newly diagnosed, metastatic medulloblastoma. The primary tumor was in the lateral cerebellum, as shown on T2-weighted MRI (Fig. 6A). Histopathology showed a densely cellular proliferation of small polygonal cells with high nuclear to cytoplasmic ratio, scant amphophilic cytoplasm, nuclear pleomorphism, molding, occasional cell–cell wrapping, and brisk mitotic activity, consistent with large cell/anaplastic medulloblastoma (Fig. 6B). A majority of tumor cells were immunopositive for GAB1 (Fig. 6C), consistent with the immunophenotype of a SHH tumor (53). Moreover, subsequent DNA methylation analysis classified it as a SHH medulloblastoma, subclass A (children and adults; Fig. 6D).
Genomic DNA sequencing of the tumor and SNP microarray analysis detected multiple complex copy-number abnormalities and chromothripsis of chromosomes 1p, 7, and 17 (Fig. 6E; Supplementary Table S8). Furthermore, a somatic p53 missense variant that translated to a p.Cys176Tyr substitution in the DNA binding domain was identified; this variant has been described in other cancer types (Fig. 6F; ref. 54). The variant was not present in the patient's germline DNA following targeted sequencing analysis. Copy-number analysis by SNP microarray suggested loss of chromosome 17p (Supplementary Table S8), which was confirmed by methylation profiling. Together, these data suggested that the patient had a TP53-mutated SHH tumor.
The patient's tumor was also subjected to RNA sequencing and DiSCoVER analysis, and to high-throughput drug screening. The top 20 drugs predicted by DiSCoVER included BCL2 family inhibitors, and inhibitors of BRAF, PI3K/mTOR, HDACs, and RTKs (Fig. 6G; Supplementary Tables S9 and S10). Drug screening identified distinct but overlapping classes of effective drugs, including RTK inhibitors, BCL2 inhibitors, HDAC inhibitors, a BRAF inhibitor, and mTOR inhibitors (Fig. 6G; Supplementary Table S11). Our analysis suggested that it would be feasible to design a personalized treatment plan including drugs predicted by both gene expression and drug screening. However, consistent with standard practice, the patient was treated with high-dose craniospinal proton therapy and adjuvant chemotherapy including cisplatin, cyclophosphamide, and vincristine.
Genomic studies over the last decade have revealed that medulloblastoma is a highly heterogeneous disease (4–6). However, most patients with medulloblastoma still receive the same therapy, consisting of surgical resection, craniospinal radiation, and intensive chemotherapy. These treatments allow many patients to survive for 5 years or more, but approximately 1/3 of patients still die of their disease, and survivors suffer severe long-term side effects from therapy. Novel therapeutic approaches have been proposed for Group 3 tumors (24, 31, 44), but these have not yet shown efficacy in the clinic. Here, we test the hypothesis that DNA sequencing, RNA sequencing, and empirical drug screening can help identify therapies that may be effective for a heterogeneous group of patients with medulloblastoma.
Most current efforts to find personalized therapies focus on genomic alterations. This offers the opportunity for targeted therapy and can sometimes suggest multiple therapies that can be used in combination. However, most pediatric cancers, including medulloblastoma, have few actionable mutations. Instead, medulloblastoma is often driven by copy-number changes, structural variants, or epigenetic changes (4), for which targeted therapies are often not available. Finally, many mutations that are considered actionable do not confer responsiveness to a therapy when tested empirically. For example, we observed that tumors with CDK4 or CDK6 amplifications do not necessarily show increased sensitivity to palbociclib, and tumors with CREBBP mutations are no more sensitive to HDAC inhibitors than those that lack such mutations. Thus, while sequencing may point to targeted therapies for some patients with medulloblastoma, most patients will not benefit from sequencing alone.
Expression-based strategies offer an alternative approach for identifying targeted therapies. Commonly used approaches include Connectivity map, DiSCoVER, and Ingenuity Pathway Analysis (19, 31). One advantage of these approaches is that they almost always generate predictions for a given patient. However, as we have shown in our studies, drugs identified using expression-based approaches are not always validated when those drugs are tested empirically. One reason for this could be the discordance between the transcriptome (which is used to infer drug responsiveness) and the proteome (which is likely to mediate response, and resistance, to most anticancer drugs). In fact, recent studies have suggested that in Group 3 and 4 medulloblastoma, in particular, pathway activity predicted from RNA does not correlate well with that predicted from proteomic analysis (55, 56). Another concern with expression-based prediction methods is that they are often based on the responses of cell lines that have been in culture for many years, and that are derived from other cancer types; for example, among the 1171 lines used for DiSCoVER analysis, only three (D283-Med, Daoy, and ONS-76) are derived from patients with medulloblastoma. Because drug sensitivity may be context-specific, predictions based on a given tumor type may not be generalizable to other tumors, and this may explain why some RNA-based predictions were not validated by empirical drug testing on medulloblastoma PDXs. Going forward, it may be helpful to generate medulloblastoma-specific drug response datasets (from medulloblastoma PDXs or patient samples) that can be used to predict responses in this disease.
Drug screening provides functional information about the susceptibility of tumor cells to therapeutic agents, and thus could be valuable for identifying effective therapies for patients in the clinic. But there are a number of caveats to this approach. For example, freshly isolated cells from a patient or PDX may not proliferate extensively in vitro, so the conditions used in our assay may underestimate the efficacy of drugs that inhibit proliferation without causing cell death. Furthermore, responses to drugs in vitro may not predict responses in vivo. This may be due to differences in drug metabolism or the ability of a drug to reach its target (e.g., due to the blood brain barrier), or to features of the tumor microenvironment (such as hypoxia, pH, or the presence of other cell types) that modulate drug response. However, the fact that drug screening empirically measures responses rather than inferring them from other characteristics of tumor cells makes it a valuable source of information, particularly in cases where other approaches are not informative.
An example of the power of this approach is our identification of actinomycin D as a drug that is effective against the majority of Group 3 medulloblastomas. Actinomycin D was originally discovered as an antibiotic, but has been used for treatment of cancer—including childhood cancer—since the 1950s (57). Although it has been widely used for pediatric sarcomas (58, 59), its use for medulloblastoma has been very limited (60). One reason for this is the commonly held view that actinomycin D does not cross the blood–brain barrier (BBB; ref. 61). While this might be true, it is worth noting that many of the compounds we found to be active in vitro were not effective in orthotopic tumor-bearing mice (Fig. 5B–E). Although there could be other reasons for this, it does suggest that the tumors in our mice are not permeable to all compounds, and that actinomycin D might have some selective ability to accumulate in these tumors. It is also possible that actinomycin D does not cross the BBB efficiently, but that in our models the BBB is sufficiently disrupted to allow the drug to enter the tumor. To the extent that this is true, it is important to note that many patients with medulloblastoma exhibit compromised blood–brain (or blood–tumor) barriers, so our models may accurately reflect how actinomycin D would behave in a patient.
A notable finding from our studies was that response to medulloblastoma standard-of-care therapies varied among PDXs. Surprisingly, vincristine, whose efficacy has been called into question by some investigators (62), showed significant antitumor activity in all of the models we tested, and in one model appeared to be as effective as cyclophosphamide. Cisplatin, on the other hand, showed no activity in one line, modest activity in another, and was highly effective in only 1 of 3 lines tested. Actinomycin D outperformed vincristine and/or cisplatin in all of our models. These findings call into question the utility of standard therapeutic regimens, and suggest that tailoring chemotherapies based on an individual patient's response profile may be more effective. Our studies, along with recent work demonstrating the efficacy of actinomycin D in RelA-positive ependymoma, embryonal tumor with multilayered rosettes and glioblastoma (63–65), suggest the importance of reevaluating this agent in the context of pediatric brain tumors.
One of the most important conclusions from our study is that multimodal analysis, including DNA sequencing, RNA sequencing, and drug screening, is feasible in a clinical setting. The patients studied at our center underwent surgical resection of their tumors, and excess tissue (beyond what was needed for diagnosis) was rapidly obtained and processed for sequencing and drug screening. Even for patients with recurrent tumors, there was usually sufficient tissue to perform all of these analyses. Moreover, while sequencing data were not available for a few weeks, drug screening was completed within a few days, making it one of the quickest sources of information regarding therapeutic responsiveness. Once all the data were collected, they were shared with a multidisciplinary molecular tumor board, and implications for diagnosis and therapy were discussed. As noted above, even for cases in which standard of care had been exhausted, multimodal analysis suggested possible therapies. Because most of the data were not obtained in a Clinical Laboratory Improvement Amendments–certified setting, this information could not be used for clinical decision making. But our results using this approach suggest that clinical grade versions of these tests could be extremely valuable and lead to significant improvements in therapy for both newly diagnosed and recurrent disease.
Disclosure of Potential Conflicts of Interest
S.K. Tacheva-Grigorova reports other compensation from Allogene Therapeutics and Pfizer outside the submitted work. D. Finlay reports grants from NIH/NCI during the conduct of the study and grants from NIH/NCI outside the submitted work. M. Snuderl reports personal fees from Illumina, Inc. outside the submitted work. D.P. Dimmock reports personal fees from Biomarin, Audentes, Ichorion, and Complete Genomics outside the submitted work; in addition, D.P. Dimmock has a patent for US8718950B2 licensed to HUDSONALPHA INSTITUTE FOR BIOTECHNOLOGY. E.M. Van Allen reports personal fees from Tango Therapeutics, Genome Medical, Invitae, Monte Rosa Therapeutics, Manifold Bio, Illumina, and Enara Bio, and grants from Novartis and BMS outside the submitted work. S.M. Pfister reports grants from IMI-2 Grant during the conduct of the study. M. Kool reports grants from Deutsche Krebshilfe during the conduct of the study. R.J. Wechsler-Reya reports grants from NCI, National Institute of Neurological Disorders and Stroke, Pediatric Brain Tumor Foundation, Accelerate Brain Cancer Cure, Ian's Friends Foundation, Alex's Lemonade Stand Foundation, William's Superhero Fund, McDowell Charity Trust, and California Institute for Regenerative Medicine during the conduct of the study. Outside the submitted work, R.J. Wechsler-Reya serves as Director of the Clayes Center for Neuro-Oncology and Genomics at Rady Children's Institute for Genomic Medicine and receives personal fees for this service. No potential conflicts of interest were disclosed by the other authors.
J.M. Rusert: Conceptualization, data curation, formal analysis, investigation, writing-original draft, writing-review and editing. E.F. Juarez: Conceptualization, data curation, formal analysis, investigation, writing-original draft, writing-review and editing. S. Brabetz: Conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, writing-original draft, writing-review and editing. J. Jensen: Conceptualization, data curation, formal analysis, investigation, writing-original draft. A. Garancher: Data curation, investigation. L.Q. Chau: Investigation. S.K. Tacheva-Grigorova: Investigation. S. Wahab: Investigation. Y.T. Udaka: Data curation, investigation. D. Finlay: Resources, data curation, formal analysis. H. Seker-Cin: Resources, data curation, formal analysis. B. Reardon: Resources, data curation, software, formal analysis. S. Gröbner: Resources. J. Serrano: Resources, data curation, formal analysis. J. Ecker: Resources. L. Qi: Resources. M. Kogiso: Resources. Y. Du: Resources. P.A. Baxter: Resources. J.J. Henderson: Resources. M.E. Berens: Supervision. K. Vuori: Supervision. T. Milde: Resources. Y.-J. Cho: Resources, supervision. X.-N. Li: Resources, supervision. J.M. Olson: Resources, supervision. I. Reyes: Resources, data curation, project administration. M. Snuderl: Resources, data curation, formal analysis, funding acquisition. T.C. Wong: Data curation, formal analysis. D.P. Dimmock: Resources, data curation, supervision. S.A. Nahas: Data curation, formal analysis, supervision, investigation, writing-original draft. D. Malicki: Resources, supervision, investigation, writing-original draft. J.R. Crawford: Resources, supervision, writing-original draft. M.L. Levy: Resources, supervision. E.M. Van Allen: Resources, software, formal analysis, supervision. S.M. Pfister: Resources, formal analysis, supervision, funding acquisition, visualization, methodology, writing-review and editing. P. Tamayo: Data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology. M. Kool: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing. J.P. Mesirov: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing. R.J. Wechsler-Reya: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing.
The authors gratefully acknowledge the Animal Facilities at UCSD and SBP for help with animal care and husbandry. We would also like to acknowledge the DKFZ Genomics and Proteomics Core Facility, DKFZ Heidelberg, Germany, and the AMC Department of Oncogenomics, Amsterdam, the Netherlands, for performing high-throughput sequencing and microarray analyses to a very high standard. We also thank the DKFZ data management group for their excellent support in processing the sequencing data. We are also indebted to the Conrad Prebys Center for Chemical Genomics, including Fu-Yue Zeng, Luis Orozco, and Michael Jackson, for their help with drug library selection and plating compounds and Sumeet Salaniwal for assistance with bioinformatics. We are most appreciative of Anthony Pinkerton and Robert Ardecky for their advice on in vivo drug testing. We gratefully acknowledge Brian James of the Genomics-DNA Analysis core at SBP for his help with PDX fingerprinting. This work was supported by funding from the NCI (2R01 CA159859 to R.J. Wechsler-Reya; P30 CA30199 to R.J. Wechsler-Reya; U01CA184898 to J.P. Mesirov; U24CA194107 to J.P. Mesirov; U24CA220341 to J.P. Mesirov and P. Tamayo; U01CA217885 to J.P. Mesirov and P. Tamayo); the National Institute for Neurological Disorders and Stroke (R01 NS096368 to R.J. Wechsler-Reya); the National Institute of General Medical Sciences (R01GM074024 to J.P. Mesirv), Deutsche Krebshilfe (111537 to M. Kool and S.M. Pfister), BMBF (01KT1605 to S.M. Pfister), and the Helmholtz International Graduate School for Cancer Research (to S. Brabetz). SBP's Shared Resources are supported by SBP's NCI Cancer Center Support Grant P30 CA030199. Methylation profiling at NYU was supported in part by a grant from the Friedeberg Charitable Foundation (to M. Snuderl). Work in R.J. Wechsler-Reya's laboratory was also funded by the Pediatric Brain Tumor Foundation, Accelerate Brain Cancer Cure, Ian's Friends Foundation, Alex's Lemonade Stand Foundation, William's Superhero Fund, the McDowell Charity Trust, and the California Institute for Regenerative Medicine.
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