High-Throughput Drug Screening of Primary Tumor Cells Identifies Therapeutic Strategies for Treating Children with High-Risk Cancer

Abstract

For one-third of patients with pediatric cancer enrolled in precision medicine programs, molecular profiling does not result in a therapeutic recommendation. To identify potential strategies for treating these high-risk pediatric patients, we performed in vitro screening of 125 patient-derived samples against a library of 126 anticancer drugs. Tumor cell expansion did not influence drug responses, and 82% of the screens on expanded tumor cells were completed while the patients were still under clinical care. High-throughput drug screening (HTS) confirmed known associations between activating genomic alterations in NTRK, BRAF, and ALK and responses to matching targeted drugs. The in vitro results were further validated in patient-derived xenograft models in vivo and were consistent with clinical responses in treated patients. In addition, effective combinations could be predicted by correlating sensitivity profiles between drugs. Furthermore, molecular integration with HTS identified biomarkers of sensitivity to WEE1 and MEK inhibition. Incorporating HTS into precision medicine programs is a powerful tool to accelerate the improved identification of effective biomarker-driven therapeutic strategies for treating high-risk pediatric cancers.

Significance: Integrating HTS with molecular profiling is a powerful tool for expanding precision medicine to support drug treatment recommendations and broaden the therapeutic options available to high-risk pediatric cancers.

Graphical abstract

Figure 1.

Drug response profiles are independent of tumor cell expansion. A, Workflow for in vitro HTS on tumor cells derived from patients enrolled in the ZERO childhood cancer precision medicine program. HTS is performed on freshly dissociated tumor cells or tumor cells expanded by in vitro culturing and/or in vivo growth. Samples are authenticated by STR profiling and validated by at least one of the following methods: SNP array, IHC, flow cytometry (FC) or, for screens on freshly dissociated tumor cells only, WGS. B, Hematoxylin and eosin (H&E) staining of representative patient tumor samples (left) and matching 3D primary cultures (middle), and IHC of tumor markers in the 3D primary cultures (right). Tumor markers used: CD99 for Ewing sarcoma sample zcc38, inhibin for adrenocortical carcinoma (ACC) sample zcc292, and WT1 for Wilms tumor (WT) sample zcc384. Bottom images show Ki67 staining results for matching tumor biopsy and 3D primary culture of WT zcc384. C, Cancer types and subtypes with successful in vitro HTS (N = 125). D, Number of patient-derived samples for main types of pediatric cancer that underwent in vitro HTS. Percentages indicate samples for which HTS, authentication by STR profiling and validation by SNP array profiling, IHC, flow cytometry, or WGS was successful. E, Successful in vitro HTS on freshly dissociated and expanded samples highlighted by cancer types. F, Number of samples screened by different sized libraries and the associated methodology used to generate the samples for HTS (i.e., fresh dissociation of tumor cells or in vitro and/or in vivo expansion of tumor cells). G, Heatmap of the targeted drug response profiles across the 125-sample cohort. Drug response profiles were established by calculating the median AUC Z scores for targeted drugs grouped according to MOA. Median AUC Z scores are represented by a color scale from blue (resistant) to red (sensitive). MOAs and tumor samples are ordered by unsupervised hierarchical clustering. Top annotations indicate tumor type and type of sample. Tumor type key: brain tumors (BT), hematologic malignancies (HM), neuroblastoma (NB), sarcoma tumors (SAR), solid other (SO). Cancer subtype key: diffuse midline glioma (DMG), high-grade glioma (HGG), medulloblastoma (MB), B-precursor acute lymphoblastic leukemia (Pre-B-ALL), acute myeloid leukemia (AML), T-lymphoblastic leukemia (T-LBL), non-MYCN-amplified (NonMYCNA), MYCN-amplified (MYCNA), alpha thalassemia/mental retardation syndrome X-linked (ATRX), Ewing sarcoma (ES), rhabdomyosarcoma (RMS), osteosarcoma (OS), Wilms tumor (WT), adrenocortical carcinoma (ACC), malignant rhabdoid tumor (MRT).

Figure 2.

Correlations between drug response profiles. Pearson correlation coefficient heatmap of the relationship between drug AUC Z scores across the 125-sample cohort. Pearson r values are represented by a color scale from red (positive linear relationship) to blue (negative linear relationship). Chemotherapeutics (gray; top) and targeted drugs (black; bottom) are ordered by unsupervised hierarchal cluster analysis using Euclidian distance. Drugs are annotated by their MOA.

Figure 3.

In vitro PARP inhibitor responses correlate with in vivo responses and correlated drug response profiles identify effective combinations for individual samples. A, AUC Z score correlations across the cohort between PARP inhibitors: olaparib versus veliparib (left), talazoparib versus veliparib (middle), and talazoparib versus olaparib (right). B, Comparison of veliparib, olaparib, and talazoparib AUC values shows increased efficacy with increasing PARP DNA entrapment potency. C, Correlation between in vitro talazoparib AUC Z scores and in vivo responses in matching PDX models as established by calculating T/C values for each model using the following formula: [medium EFS time after talazoparib treatment (=T)]/[medium EFS time when untreated (=C)]. D, Correlation between the AUC Z scores of AURKA inhibitor alisertib and irinotecan metabolite SN-38. Samples sensitive to both alisertib and SN-38 that are used for in vivo combination testing in D are depicted in orange. E,In vivo effects of vehicle control (black), irinotecan plus temozolomide (green), monotherapy alisertib (blue), and alisertib in combination with irinotecan plus temozolomide (red) in PDX models for MRT (left), Wilms tumor (middle), and neuroectodermal tumor (NET; right). Top graphs show percentage change in tumor model for each PDX and bottom graphs show EFS. MRT, malignant rhabdoid tumor; WT, Wilms tumor; NET, neuroectodermal tumor. *, P < 0.05; ***, P < 0.001; ****, P < 0.0001. *, versus vehicle control; *, versus alisertib; *, versus irinotecan (IRN) plus temozolomide (TMZ).

Figure 4.

In vitro HTS on patient-derived tumor samples confirms known associations between drug sensitivity and driver aberrations. A, AUC distribution across all samples for targeted drugs. Drugs are ordered from most (left) to least (right) effective based on lowest median AUC, followed by lowest quartile AUC values. Colors indicate the mechanism of action of each drug. B, Volcano plots of tumor type-specific sensitivity or resistance to drugs based on the AUC Z scores for each drug, using the following formula: [average AUC Z score for tumor type of interest] − [average AUC Z score for the remaining cohort]. A difference in average AUC Z score ≤ 0.5 and adjusted P < 0.01 is considered relatively resistant (blue) and a difference in average AUC Z score > 0.5 and adjusted P < 0.01 is considered relatively sensitive (red). Only significant targeted drugs are named in the figure. Circle size represents the number of samples within the given tumor type on which the indicated drug has been tested. C, AUC values for TRK inhibitor larotrectinib sulfate, BRAF inhibitors dabrafenib and vemurafenib, and ALK inhibitors ceritinib, crizotinib, and alectinib with samples harboring an NTRK fusion, BRAF V600E mutation, or ALK aberration (i.e., ALK F1245I mutation or EML4-ALK fusion) highlighted in red, respectively. Arrows indicate the BRAF V600E–mutated HGG and ALK F1245I–mutated NB sample used for in vivo efficacy testing in D and E, respectively. D,In vivo effects of vehicle control (saline; black), dabrafenib monotherapy (green), trametinib monotherapy (blue), and dabrafenib plus trametinib combination therapy (red) on EFS in the matching HGG PDX model of the BRAF V600E–mutated sample with in vitro sensitivity to BRAF inhibition. E,In vivo effects of vehicle control (black) and ALK inhibitors ceritinib (green), crizotinib (blue), alectinib (orange), and lorlatinib (red) in the matching NB PDX model of the ALK F1245I–mutated NB sample with in vitro sensitivity to ALK-targeting inhibitors. Left graph shows percentage change in tumor model for each PDX and right graph EFS. F, AUC values for MEK inhibitors trametinib (left), cobimetinib (middle), and selumetinib (right) in samples with (colored) and without (gray) driver aberrations in RAS–MAPK signaling. Shapes of the symbols indicate tumor type. Tumor type key: brain tumors (BT), hematologic malignancies (HM), neuroblastoma (NB), sarcoma tumors (SAR), solid other (SO). *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. *, versus vehicle control.

Figure 5.

Sensitivity to mTORC1 and CDK4/6 inhibitors is not predicted by clinically applied predictive biomarkers. A, Dot plots of the AUC Z scores of PI3K-AKT-mTOR inhibitors for samples with (colored) and without (gray) bona fide genomic alterations in PI3K–AKT–mTOR signaling. Arrows indicate the sarcoma samples harboring a TSC2 mutation (green arrow; average sensitivity to temsirolimus) and a PIK3CA mutation (purple arrow; high sensitivity to temsirolimus) used for in vivo and clinical validation in B and C, respectively. B,In vivo effects of temsirolimus on EFS in matching sarcoma PDX models for the two sarcoma samples indicated in A (N = 4 mice/treatment arm). C, Observed responses for temsirolimus addition to backbone chemotherapy (irinotecan plus temozolomide) in matching sarcoma patients. Top images, ¹⁸F-FDG PET/CT images demonstrating progressive disease in the anterior chest wall of the patient with a TSC2-mutated osteosarcoma. Bottom images, computed tomography (CT) scans of the chest demonstrating partial regression of pleural metastases in the patient with a PIK3CA-mutated Ewing sarcoma. D, Dot plots of CDK4/6 inhibitors for samples with (colored) and without (gray) bona fide genomic alterations in cell-cycle regulation. Dot sizes in A and D indicate log₂[IC₅₀] Z score values. *, P < 0.05.

Figure 6.

Integrative analysis identifies novel biomarkers predictive of adavosertib efficacy. A, GSEA enrichment plots for top enriched hallmark, GO biological processes (GO:BP), and curated gene sets for the positively correlating genes with adavosertib efficacy. Gene sets enriched among the genes for which log₂[TPM] values negatively correlated with adavosertib AUC Z scores (high expression correlates with increased efficacy) with FDR q value < 0.01, list > 10%, and NES ≤ −2 were selected as most relevant. Genes with |r| ≥ 0.3 are highlighted in the barcode regions. B, Clustering of the MSigDB GO:BP gene sets enriched in the adavosertib-sensitive nonhematologic tumor samples with FDR q value < 0.01, list > 10%, and NES ≤ −2. Nodes are grouped and colored based on same or similar functional indication and their size indicates the NES. The two largest clusters are shown. C, Twenty-five top correlating genes with adavosertib efficacy based on Pearson correlation coefficient between adavosertib AUC Z scores and gene log₂[TPM] values across the 63-sample tumor cohort. Negatively (high expression correlates with sensitivity) and positively (high expression correlates with resistance) correlating genes with P < 0.01 are indicated in red and blue, respectively. D, Principal component (PC) analysis of the adavosertib response based on AUC Z score with red being relatively sensitive and blue relatively resistant. Shapes of the symbols indicate tumor type. E, Twenty top GO, hallmark, and KEGG gene sets identified from the 25-gene set shown in C correlating with adavosertib efficacy (FDR q value < 0.01). Blue, GO biological process; black, GO cellular component; green, hallmark. F, Adavosertib dose–response curves for SHEP-21N neuroblastoma cells with MYCN on [green; −doxycycline (Dox)] versus MYCN off (red; +Dox). Effects on cell viability were established after 24-hour treatment in four independent experiments with three technical replicates in each experiment. Dots indicate the average cell viabilities ± SD and lines represent the fitted dose-response curves using nonlinear regression. Western blot on top shows MYCN repression upon doxycycline treatment after 0, 24, and 96 hours. G, Adavosertib AUC (left) and log₂[IC₅₀] (right) values in SHEP-21N neuroblastoma cells with MYCN off (−Dox) versus MYCN on (+Dox) after 24-hour treatment. Horizontal lines indicate median values. Tumor type key: brain tumors (BT), neuroblastoma (NB), sarcoma tumors (SAR), solid other (SO). *, P < 0.05.

Figure 7.

PIK3R1 mutations predict sensitivity in pediatric HGG and DMG samples to MEK inhibition. A, Trametinib AUC (left) and log₂[IC₅₀] (right) values for noncancerous brain samples versus pediatric HGG and DMG samples without bona fide alterations in RAS–MAPK signaling. Horizontal lines represent median values. B, GSEA of MSigDB GO:BP gene sets on the genes positively correlated with trametinib sensitivity (NES < 0). Dot size indicates NES and dots above the dotted line (FDR q value > 0.01) are not significant. Biological processes associated with RAS–MAPK and PI3K signaling are highlighted in red and orange, respectively. C, Activation status of RAS–MAPK and PI3K signaling pathways in trametinib-sensitive DMG samples zcc372 and zcc135 harboring a PIK3R1 mutation. DMG sample zcc135 harbors an additional activating mutation in PIK3CA and both DMG samples zcc372 and zcc135 harbor gain of AKT3. RAS–MAPK and PI3K pathway activation was established after 40-minute treatment with DMSO (= baseline levels) or 50 nmol/L trametinib by Western blot analysis of phosphorylated and total levels of ERK1/2 and AKT and S6K, respectively. Trametinib-sensitive DMG sample zcc116 harboring a BRAF V600E mutation and trametinib-insensitive DMG sample zcc92 without bona fide alterations in RAS–MAPK or PI3K signaling have been included as controls. β-Actin was used as loading control. The color of the crosses indicates the sensitivity of the sample to trametinib. Red, sensitive; blue, insensitive. D, Lollipop diagram of the PIK3R1 mutations in brain tumors in our cohort (indicated in black) that are associated with trametinib sensitivity. nSH2 domain mutations associated with neomorphic-activated RAS–MAPK signaling and increased sensitivity to MEK inhibition are indicated in gray. Genome coordinates are in hg19.