The paper explained Problem

Next-generation sequencing can identify molecular targets and permit personalized choice of drug treatment for children with cancer. However, the clinical uptake of therapeutic recommendations has been low, suggesting the need for orthogonal proofs using other techniques to support the molecular recommendation and thus clinical decision-making. A diagnostic platform integrating genomics and transcriptomics with drug testing of patient’s primary tumor cells in high-throughput drug screening (HTS) and patient-derived xenografting (PDX) has the potential to improve identification of targeted therapies and clinical uptake.

Results

A precision medicine platform incorporating rapid HTS and PDX of primary tumor tissue was developed and evaluated in 56 high-risk pediatric cancer patients with an expected survival of less than 30%. Across the whole testing platform, treatment options were identified for 70% of patients. Fresh tumor tissue allowed HTS and/or PDX for 52% of patients. Drug hits were present in the majority of HTS, which provided orthogonal proof of drug efficacy suggested by molecular analyses and negative results for some molecular findings. Effective treatments were also observed in over half of PDX models. Therapeutic options were found in 10 patients for whom no targetable molecular lesions could be identified by genomics and transcriptomics. Only molecular therapeutic recommendations were provided to treating oncologists and led to a change in therapy in 53% of patients, of whom 29% had a clinical benefit. There was a strong correlation between HTS and PDX results, and the clinical responses in patients.

Impact

This study represents the first pediatric precision oncology study which has integrated genomics, transcriptomics, in vitro and in vivo drug efficacy testing to derive therapeutic options for high-risk pediatric cancer patients. This comprehensive approach is feasible and has the potential to expand therapeutic options, increase clinical uptake, and improve clinical outcomes for these patients.

Introduction

The development of targeted anticancer therapies has suggested the goal of more personalized treatment approaches aimed at better matching the patient’s driver genes to therapeutics. Two recent pediatric cancer genomic landscape studies highlight the differences between pediatric and adult cancers, including a contrasting spectrum of genomic driver events (Grobner et al, 2018; Ma et al, 2018). Evaluation of next-generation sequencing in pediatric cancers reports targetable molecular findings in 34–87% of cases (Mody et al, 2015; Chang et al, 2016; Harris et al, 2016; Oberg et al, 2016; Parsons et al, 2016; Worst et al, 2016; Harttrampf et al, 2017; Pincez et al, 2017; Khater et al, 2019). However, the clinical uptake of therapeutic recommendations was only 10–38% of patients (Mody et al, 2015; Harris et al, 2016; Worst et al, 2016). There are many reasons for this relatively low clinical uptake, including drug access and insufficient evidence supporting the recommendation. One strategy to improve confidence in personalized therapeutic recommendations is the inclusion of functional analyses of primary patient tumor cells exposed to potential therapeutics (Pemovska et al, 2013; Friedman et al, 2015; Letai, 2017; Pauli et al, 2017). We hypothesized that a diagnostic platform integrating tumor genomics and transcriptomics with in vitro and in vivo drug sensitivity testing of the patient’s primary tumor cells would improve identification of targeted therapies and clinical uptake.

Here, we report the first pediatric cancer study evaluating therapeutic recommendations derived from high-throughput drug screening (HTS) and patient-derived xenografting (PDX) of primary tumor tissues from high-risk pediatric cancer patients. The addition of in vitro and in vivo drug testing to a genome-only analysis significantly increased the proportion of patients with treatment options.

Results Patients and tumor samples

A total of 56 children with high-risk cancer and an estimated cure rate < 30% were consecutively enrolled in the TARGET pilot study of the Australian ZERO Precision Childhood Cancer Program between June 2015 and October 2017 at the two pediatric hospitals (Sydney Children’s Hospital and Children’s Hospital at Westmead) in Sydney, Australia. The molecular results of 47 of these patients have been reported in conjunction with the follow-up national trial (Wong et al, 2020). There was an equal distribution of patients at diagnosis and relapse/refractory, and included 48% central nervous system (CNS) tumors, 38% non-CNS solid tumors, and 14% hematologic malignancies (HMs) (Appendix Table S1). The median survival of the cohort was 13.1 months, with 80% of patients surviving beyond 6 months from enrollment.

We specified a preference for the submission of fresh tissue for HTS and PDX. Of the 56 samples, 46 were received fresh and triaged by the amount of available tissue. In the case of small solid samples (< 30 mg), the priorities after molecular (genomics and transcriptomics) analysis were primary culture for CNS tumors and PDX for non-CNS tumors. Primary culture and/or PDX were attempted in 44 of the 46 fresh samples (Fig 1A). Hence, our study demonstrated that it was feasible to collect fresh tumor samples for a pediatric precision oncology platform.

Figure 1. Development of preclinical models from 46 fresh samples

Flow diagram of sample allocation for molecular profiling (M), primary culture, high-throughput drug screening (HTS), and patient-derived xenograft (PDX) drug studies. Outcome of in vitro expansion in 46 fresh samples. Outcome of PDX establishment in 46 fresh samples. Outcome of in vitro and in vivo drug testing attempts in 46 fresh samples. In vitro and in vivo drug screening of tumor-derived cells is feasible in the majority of patients

Of the 46 fresh samples, adequate cell numbers allowing upfront HTS were available in only three. We therefore proceeded to in vitro expansion of primary tumor cells in 31 fresh samples, and developed a platform for rapid authentication by short tandem repeat (STR) profiling (i.e., confirming a culture was from the correct patient) and confirmation of the presence of tumor cells using histopathology, flow cytometry, or single-nucleotide polymorphism (SNP) array. Of the 31 samples subjected to in vitro expansion, seven were successfully expanded to proceed to HTS (Fig 1B). The culture failed to proliferate in 12. Importantly, we found an absence of tumor cells in proliferating culture in nine and failed authentication in one. Culture material was not available for authentication in two. This finding illustrates the importance of culture authentication and tumor cell content validation.

We attempted PDX murine models in 42 of the 46 fresh patient samples, with successful engraftment of 22 samples (Fig 1A and C; Appendix Table S2). Non-CNS solid tumors were subcutaneous flank models, whereas leukemia and CNS tumors were orthotopic models. Engraftment rates and time to engraftment showed variability according to tumor type. PDX engraftment was most successful in non-CNS solid tumors/lymphoma (15/18 attempts) followed by leukemia (3/4 attempts) and CNS tumors (4/20 attempts). Successfully engrafted tumors were harvested after a median of 3.2 months (range 1.0–10.9 months; CNS 2.7 months, solid tumors 4.2 months, leukemia 1.1 months). CNS orthotopic PDX proved the most challenging to establish. Importantly, ex vivo expansion of cells from engrafted PDXs allowed later HTS to be performed in seven patients for whom the initial primary sample could not be cultured. Drug testing was not feasible in one patient where the PDX was very slow growing. In summary, we were able to conduct HTS and/or PDX testing (7 PDX only, 5 HTS only, and 12 PDX and HTS) in 24 of the 46 patients who provided a fresh sample (Fig 1D; Appendix Table S3).

In vitro drug screening identifies therapies additional to those found on molecular analyses

We hypothesized that integration of HTS would enhance the identification of therapeutic options beyond those identified by molecular testing. We designed a 111-compound screening library (63 targeted agents; 48 chemotherapeutic drugs) (Table EV1) to examine this hypothesis. These agents were chosen because they were either US Federal Drug Administration (FDA) approved (n = 92) or in late clinical development (n = 19) and were considered potentially useful in treating pediatric cancer patients based on prior clinical and preclinical evidence. Ninety of these 111 agents had existing pediatric dose and safety data.

HTS was performed on 17 patient-specific, STR-authenticated cultures, of which 14 were non-adherent cultures. Tumor cell sources included primary cells without expansion (n = 3), expanded primary cultures (n = 7), and tumor cells derived from a PDX (n = 7) (Appendix Table S4). Tumor cell content was confirmed in 12 of 17 samples. We used rigid criteria for identifying a drug hit, defined as a z score ≤ −2 for both area under the dose–response curve (AUC) and IC50. The HTS for each patient was completed at a median of 4.3 months (7 days–21.4 months) from sampling (Appendix Table S4). Drug hits were identified in 13 of 17 patients. The median number of drug hits per patient was 2 (range: 0–7). In total, 45 drug hits (32 targeted and 13 chemotherapy) involving 37 compounds were identified (Fig 2A; Appendix Table S5). Seventy-one percent of hits had an IC50 lower than the published maximum or steady-state plasma concentration, suggesting the IC50 was clinically achievable.

Figure 2. Overview of drug hits identified by high-throughput drug screening in 13 patient-derived samples

Z score for area under the dose–response curve (AUC) and IC50 of 37 different drugs (shown along the horizontal axis) identified as hits in 13 of 17 samples screened. A drug hit is defined as z score of less than −2 for both AUC and IC50. Each dot in a column represents a sample screened for that drug. The size of the dot corresponds to the IC50 z score for that sample (the larger the dot, the smaller the IC50). Dots below the black horizontal line represent sample with AUC z score of less than −2. Dots are color coded for drug hit types. All color dots below the black line represent a hit for the corresponding drug. Plots of AUC z score against IC50 z score for each of the drugs screened in the 13 samples with drug hits.

Source data are available online for this figure.

Of the 32 targeted drug hits, 10 correlated with molecular targets known to confer drug sensitivity to that agent found in the same patient sample (Fig 2A and B; Table 1; Appendix Table S5). This included seven hits which correlated with DNA mutations (e.g., an mTORC1 (mammalian target of rapamycin complex 1) inhibitor hit correlated with the TSC1 Asp769Ter mutation in patient RA-002 (Tsoli et al, 2018) and three correlating with the copy number variation (CNV) (e.g., sensitivity to nintedanib, a tyrosine kinase inhibitor (TKI) with anti-PDGFRA (anti-platelet-derived growth factor receptor alpha) and anti-VEGFR2 (anti-vascular endothelial growth factor receptor 2) activity, correlated with high copy number gains of PDGFRA (55 copies) and VEGFR2 (28 copies) in patient RA-055) (Fig 2B).

Table 1. Correlation between high-throughput drug screening (HTS) drug hits and prior molecular analysis. Patient ID Diagnosis Drug hit Drug target Molecular target HTS correlated with molecular RA-002 HGG Everolimus mTOR TSC1 mutation with LOH Yes Sirolimus mTOR Yes Temsirolimus mTOR Yes Sorafenib Multi TKI BRAF 6 copies and high RNA Yes RA-028 HGG Crenolanib Multi TKI PDGFRA mutation Yes Ponatinib Multi TKI Yes RA-056 HGG Dasatinib Multi TKI PDGFRA mutation Yes Pazopanib Multi TKI Yes RA-034 CPC Dasatinib Multi TKI High SRC RNA Yes RA-055 DMG Nintedanib Multi TKI PDGFRA and VEGFR2 amp Yes RA-019 EWS Cabozantinib Multi TKI High KIT RNA Yes WE-012 EWS Gefitinib EGFR EGFR 6 copies and high RNA Yes RA-017 OST Dinaciclib CDK1/2/5/9 High CCNE1 RNA Yes PRI-724 CTNNB1 High CTNNB1 RNA Yes Panobinostat HDAC High HDAC6 RNA Yes RA-013 NBL Ceritinib ALK, IGF1R High ALK and IGF1R RNA Yes Venetoclax BCL2 High BCL2 RNA Yes WE-005 OST Crizotinib ALK, MET, ROS1 NA Temsirolimus mTOR RA-002 HGG Axitinib multi TKI HTS drug responses without prior molecular hallmarks for sensitivity to that drug Lapatinib ERBB2, EGFR Vandetanib multi TKI RA-028 HGG Lapatinib ERBB2, EGFR RA-056 HGG Abiraterone CYP17A1 Fulvestrant ESR1 Pinometostat DOT1L RA-019 EWS Alectinib ALK Dinaciclib CDK1/2/5/9 Panobinostat HDAC RA-054 RMS Buparlisib PI3K Voxtalisib PI3K, mTOR RA-017 OST Crenolanib multi TKI Of the 17 HTS performed, 32 molecular drug hits were identified in 11 samples. amp, amplification; CPC, choroid plexus carcinoma; DMG, diffuse midline glioma H3 K27M mutant; EWS, Ewing’s sarcoma; HGG, high grade glioma; LOH, loss of heterozygosity; TKI, tyrosine kinase inhibitor; NA, molecular data not available; NBL, neuroblastoma; OST, osteosarcoma; RMS, rhabdomyosarcoma.

Intriguingly, we found seven examples from four patients where specific gene expression aberrations detected by bulk tumor RNA-sequencing, independent of mutations, structural variants, or CNV, appeared to correlate with drug hits. One patient sample (RA-013) demonstrated elevated B-cell lymphoma 2 (BCL-2) expression and was sensitive in HTS to the BCL-2 inhibitor, venetoclax. Additionally, in the absence of anaplastic lymphoma kinase (ALK) fusion or mutation, this same sample with elevated ALK expression demonstrated sensitivity to the ALK inhibitor, ceritinib, with an IC50 well below the published maximum plasma concentration. Of interest, the sample also had increased expression of insulin-like growth factor 1 receptor (IGF1R), a known off-target response to ceritinib (Kuenzi et al, 2017).

These data from HTS provided orthogonal confirmation of a targetable molecular abnormality (mutation, copy number, expression) in 17 of the 32 targeted hits (Table 1). Importantly, the remaining 15 hits represented drug responses without prior molecular hallmarks of sensitivity to that drug. In contrast, HTS did not predict sensitivity of five samples to drugs suggested by molecular analyses. This suggests HTS might also be utilized to avoid ineffective therapy.

Combination and single agent sensitivities are revealed on in vivo drug testing

We next evaluated the feasibility of incorporating PDX drug efficacy testing to increase therapeutic options. As PDX drug testing could not feasibly include the full 111-compound library, drugs used in PDX testing were prioritized by: (i) supporting molecular and/or HTS findings; (ii) prior published preclinical or clinical evidence of drug efficacy for the specific tumor type; or (iii) potential patient eligibility for an open clinical trial at the treating institution. Combination treatments were also included whenever possible, in particular combinations shown to be feasible and effective in prior clinical trials. Whenever mouse pharmacokinetics data were available, animals were dosed to achieve the most efficacious target plasma drug concentrations reported in humans.

PDX drug efficacy testing was conducted in 19 PDX models (16 non-CNS and 3 CNS) and combination treatments were included in 16 models (Appendix Tables S6 and S7). A total of 75 treatments were tested in these 19 PDXs and 22 of these treatments were based on prior molecular findings (Table 2). The number of treatment arms for each patient’s PDX ranged from 1 to 10 (median: 4). The duration of experiments (inoculation to predefined endpoint) ranged from 2.0 to 12.1 months (median 4.1 months). The median duration was 4.1 months (range 4.0–4.5) for the three CNS patients, 2.5 months (range 2.0–10.7) for the three leukemia patients, and 4.1 months (range 2.4–12.1) for the 13 solid tumor patients. Thus, the PDX results would have been clinically available at a median of 7.9 months (range 2.0–19.1) from the time of sampling, including establishing the PDX, secondary in vivo expansion and drug testing, well below the median survival duration for the cohort.

Table 2. Correlation between drug sensitivity predicted by molecular testing and patient-derived xenograft (PDX) responses. Patient ID Diagnosis Molecular target PDX treatment PDX responsea PDX correlated with molecular RA-001 EWS TP53, STAG2 mut IRN + TMZ + talazoparib MCR Yes Talazoparib PD No WE-012 EWS TP53, STAG2 mut IRN + TMZ + talazoparib CR Yes Talazoparib PD No RA-039 NBL ALK amplification Ceritinib CR Yes Cyclo/Topo/ceritinib MCR Yes RA-049 ALCL NPM1 - ALK Ceritinib CR Yes Alectinib MCR Yes Brentuximab + ceritinib MCR Yes RA-002 HGG TSC1 mut (LOH) Temsirolimus R Yes RA-045 T-ALL CDKN2A/B loss Palbociclib SD No RA-054 RMS

CDK4 amplification

High FGFR4 RNA

Palbociclib PD No Palbociclib + temsirolimus PD No Ponatinib PD No RA-029 RMS High FGFR4 RNA Ponatinib PD No RA-017 OST CCNE1 amplification Dinaciclib PD No Dinaciclib + cisplatin PD No RA-013 NBL High BCL RNA Venetoclax PD No RA-027 NBL NF1 mutation with LOH Trametinib PD No Trametinib + isotretinoin PD No RA-028 HGG

PDGFRA mutation

CDKN2A/B loss

Palbociclib PD No Temsirolimus + palbociclib PD No ALCL, anaplastic large cell lymphoma; CR, complete response; Cyclo/Topo, cyclophosphamide/topotecan; EWS, Ewing’s sarcoma; HGG, high grade glioma; IRN, irinotecan; LOH, loss of heterozygosity; MCR, maintained complete response; NBL, neuroblastoma; OST, osteosarcoma; PD, progressive disease; R, response; RMS, rhabdomyosarcoma; SD, stable disease; T-ALL, T-cell acute lymphoblastic leukemia; TMZ, temozolomide.

Objective responses using the Pediatric Preclinical Testing Consortium (PTCC) criteria (Houghton et al, 2007) were demonstrated in 10 of 16 non-CNS solid tumor and leukemia models (Fig 3A). This included responses to chemotherapy in six, targeted monotherapy in four, and combination therapy in six patients (Fig 3A–K; Appendix Table S6). Drugs resulting in significantly prolonged survival (median event-free survival (EFS) of treatment group at least twice longer than control and P < 0.001) were seen for three targeted agents in one of the three CNS models (RA-002) (Fig 3L). No activity was observed for gemcitabine monotherapy in the group 3/MYC-amplified medulloblastoma model (RA-021) (Appendix Table S6). This is consistent with other studies reporting the lack of efficacy of gemcitabine as a single agent but synergies with other drugs such as pemetrexed (folate pathway inhibitor) and prexasertib (checkpoint kinase 1/2 (CHK1/2) inhibitor) (Morfouace et al, 2014; Endersby et al, 2021). Of the 22 treatments suggested by prior molecular testing, 8 led to an objective response (Table 2). As negative controls, targeted agents were included for seven PDXs where no direct molecular aberrations for these agents were identified, and no objective responses were seen. The additional benefit of combination treatment could be assessed in 12 models. An improved objective response was observed for drug combinations compared to monotherapy for four patients [RA-001 (Fig 3F), RA-012 (Fig 3G), RA-039 (Fig 3J), and RA-049 (Fig 3E)]. Furthermore, PDX could have facilitated prioritization of different treatment options in eight patients for whom PDX identified both effective and non-effective treatments. Examples include an anaplastic large cell lymphoma PDX (RA-049) (Fig 3E) demonstrating no response to brentuximab but complete response (CR) to ceritinib and alectinib, and a T-cell acute lymphoblastic leukemia (T-ALL) PDX (RA-045) (Fig 3B) with no response to venetoclax but complete response to nelarabine and carfilzomib/chemotherapy.

Figure 3. In vivo drug efficacy studies in patient-derived xenografts

A. Treatment response in 16 hematologic malignancy (HM) and non-CNS (central nervous system) solid patient-derived xenograft (PDX) models. Objective responses including maintained complete response (MCR), complete response (CR), and partial response (PR) were observed in 10 of 16 models. Drugs are indicated as chemotherapy (Ch), targeted agent (T), or combination treatment (C). B–D. Event-free survival (EFS) and percentage of human CD45+ leukocytes in peripheral blood in three acute lymphoblastic leukemia (ALL) orthotopic models. An event is defined as human CD45 cells above 25% in the peripheral and is represented by the dotted line. E–K. EFS and tumor volume in seven non-CNS subcutaneous PDX models which demonstrated objective response in one or more treatments. L. EFS in a CNS orthotopic model in which drug sensitivity was observed. EFS is time of inoculation of tumor cells to event (defined by neurologic symptoms or weight loss).

Data information: Survival curves were estimated for each treatment group using the Kaplan–Meier method and compared with the untreated control group in each PDX model statistically using log rank test. P value for log rank test for comparison of EFS: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001. The exact P values are provided in Appendix Table S6. ALCL, anaplastic large cell lymphoma; Cyclo, cyclophosphamide; IRN, irinotecan; PD, progressive disease; SD, stable disease; Topo, topotecan; TMZ, temozolomide; VXL, vincristine/dexamethasone/L-asparaginase.

Source data are available online for this figure.

Together, PDX modeling for 19 patients confirmed drug sensitivities seen in prior HTS or molecular analyses in five patients, identified new treatment options which were not informed by HTS or molecular analyses in seven patients, and provided useful negative results for treatment prioritization in 17 patients for whom alternative treatments or other effective options identified by PDX could be considered.

Overall clinical impact of a four-part diagnostic platform

We then evaluated whether our four-part testing platform with molecular (DNA and RNA), HTS, and PDX would increase treatment options for the overall group of 56 high-risk pediatric cancer patients, compared to molecular alone. Of the 56 patients, 32 had only molecular analyses, 23 had molecular, HTS and/or PDX drug testing conducted, and one patient had only HTS and PDX testing with no molecular analysis performed (Appendix Table S3). We used five tiers of therapy evidence as described in the Individualized Cancer Therapy (iCAT) study (Harris et al, 2016) for molecular, HTS or PDX drug sensitivity. The overall rate of identification of treatment options was high, with 55 treatment options identified for 70% of patients across the testing platform (Fig 4A; Table EV2).

Figure 4. Individualized therapeutic options in 56 pediatric high-risk cancers

A. Overview of each patient’s precision oncology platform results. The highest tier of therapy options for each patient is shown. A total of 55 recommendations were made in 39 patients. B. Tier of therapy and related molecular alterations. Structural variant (SV), single-nucleotide variant (SNV) with loss of heterozygosity (LOH) in a tumor suppressor gene, copy number variant (CNV). C, D. Treatment response by therapy tier. Fourteen of 29 patients with molecular-based therapeutic options received the treatment. E. Tests contributing to the identification of treatment by tier.

Molecular profiling was performed in 55 patients. The results of 47 patients have been described in conjunction with the follow-up national trial (Wong et al, 2020). The key molecular aberrations of this cohort are provided in Fig EV1A–D; Tables EV2 and EV3). DNA and RNA profiling provided therapeutic options for 29 of 55 patients (Fig 4A; Table EV2). This included targetable fusions (n = 4) and CNV (n = 7) in nine patients among whom no targetable DNA mutations were present (Fig 4A and B). Five patients also had reportable germline mutation. Fourteen of 55 patients received the personalized treatment, with a clinical benefit rate in 4 (1 complete response (CR), 2 partial responses (PRs), 1 stable disease (SD)) (Fig 4C and D; Table 3). When we correlated the clinical response with the prediction of response by either molecular, HTS or PDX (Table 3), we found 4/14 molecular, 4/5 HTS, and 4/8 PDX predictions correctly forecast a response in the patient receiving that specific drug. This included the prediction of response or non-response and strongly supports the clinical relevance of HTS and PDX testing.

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Figure EV1. Molecular aberrations in 55 pediatric high-risk cancers

Genes with somatic and germline DNA mutations (single-nucleotide variant (SNV) and indel) considered to be pathogenic or likely pathogenic by whole genome sequencing (WGS) and/or panel sequencing. Thirty of 55 samples were found to have 1 or more pathogenic or likely pathogenic mutations. T