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Review

Genomics-Driven Precision Medicine in Pediatric Solid Tumors

by
Praewa Suthapot
1,2,3,
Wararat Chiangjong
4,
Parunya Chaiyawat
3,5,
Pongsakorn Choochuen
2,6,
Dumnoensun Pruksakorn
3,5,
Surasak Sangkhathat
2,6,7,
Suradej Hongeng
1,
Usanarat Anurathapan
1,* and
Somchai Chutipongtanate
8,*
1
Division of Hematology and Oncology, Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
2
Department of Biomedical Science and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
3
Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
4
Pediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
5
Musculoskeletal Science and Translational Research Center, Department of Orthopedics, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
6
Translational Medicine Research Center, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
7
Department of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
8
Division of Epidemiology, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
*
Authors to whom correspondence should be addressed.
Cancers 2023, 15(5), 1418; https://doi.org/10.3390/cancers15051418
Submission received: 18 January 2023 / Revised: 10 February 2023 / Accepted: 15 February 2023 / Published: 23 February 2023
(This article belongs to the Special Issue Childhood Cancer in the Genomic Era: Experimental and Theoretical Approaches)
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Review Reports Versions Notes

Abstract

:

Simple Summary

The detection of genomic aberrations in cancers has yielded a wealth of information to discover oncogenic drivers or pathogenic variants that are relevant for the development of precise treatment strategies. Recent studies have shown promising outcomes in adult cancer patients with well characterized cancer genetic biomarkers. However, the development of precise treatments for pediatric cancers is difficult due to the limited number of accessible samples and the fact that well-defined target genetic aberrations are limited. Here, we review the current landscape of pediatric precision oncology compared to adults and highlight the examples of single-arm and multiple-arm designs of pediatric precision treatments.

Abstract

Over the past decades, several study programs have conducted genetic testing in cancer patients to identify potential genetic targets for the development of precision therapeutic strategies. These biomarker-driven trials have demonstrated improved clinical outcomes and progression-free survival rates in various types of cancers, especially for adult malignancies. However, similar progress in pediatric cancers has been slow due to their distinguished mutation profiles compared to adults and the low frequency of recurrent genomic alterations. Recently, increased efforts to develop precision medicine for childhood malignancies have led to the identification of genomic alterations and transcriptomic profiles of pediatric patients which presents promising opportunities to study rare and difficult-to-access neoplasms. This review summarizes the current state of known and potential genetic markers for pediatric solid tumors and provides perspectives on precise therapeutic strategies that warrant further investigations.

Graphical Abstract

1. Introduction

Cancer occurrence before the age of 20 years is rare, but it is one of the leading causes of disease-related mortality in children and adolescents globally [1,2]. Approximately 300,000 children aged 0–19 years old worldwide are diagnosed with cancer each year [1], and 80% of these patients live in low- and middle-income countries (LMCs). Hematologic malignancies are more common among pediatric cancers, comprising about half of all cases. Solid malignancies are rarer and heterogenous as following an age-specific pattern. In early childhood, embryonal-type solid tumors are common, such as neuroblastoma, retinoblastoma, medulloblastoma, hepatoblastoma, and Wilms tumor [3]. The prognosis for childhood cancer has improved dramatically over the past four decades, particularly for hematologic malignancies [2]. Nonetheless, treatment outcomes for childhood solid malignancies remain unsatisfactory, especially in LMCs [4,5].
Genetic sequencing studies have led to the identification of somatic gene alterations as cancer hallmarks and germline predisposition and targeted the molecular abnormalities for the development of precise treatment [6,7,8]. Dramatic differences in the genetic repertoire between normal and cancer cells provide advantages of molecular targeted therapies over traditional strategies based on the target selectivity [9,10,11]. Several components in cellular signaling pathways, i.e., tyrosine receptor kinase (TRK), mitogen-activating protein kinase (MAPK) and phosphoinositide 3-kinases (PI3K)-mammalian target of rapamycin (mTOR), have been commonly identified as actionable mutations that would recommend appropriately targeted therapies [12,13]. These generic biomarker-driven precise treatments have been investigated in several pre-clinical and clinical trials since the early 2000s [14].
Progress in designing treatments targeting molecular alterations specific to pediatric cancers is considerably slow due to the rare and unique genetic alterations in children compared to adults [15]. A report from the European Union (E.U.) revealed that up to 26 anticancer drugs approved for adults might be also effective in pediatric malignancies; however, only four of these drugs have been approved for childhood cancers [16]. Nishiwaki S. and Ando Y. reported that only 3 out of 66 drugs with adult indications have been approved for pediatrics in the E.U., United States, and Japan [17]. Thus far, larotrectinib and entrectinib have been two of the most successful molecularly targeted therapies for children with solid tumors and have shown their promising responses in patients with NTRK-fusion [9]. In 2018, larotrectinib became the first drug to receive FDA approval to treat NTRK fusion-positive solid tumors in children and adults [18]. Similarly, entrectinib, a multi-kinase inhibitor, also received approval for the treatment of TRK fusion solid tumors in patients aged ≥ 12 years [19]. Combinatorial treatment of dabrafenib and trametinib has been recently approved by FDA (June 2022) for use in adult and pediatric patients > 6 years of age with unresectable or metastatic solid tumors with BRAF V600E mutation [New Drug Application (NDA): 202806 and 204114]. Note that abnormalities in NRAS, ABL1, JAK2, KIT, ALK and BRAF were among the group of common genetic variants found in adult and childhood cancers. In this review, we summarize the progress in the identification of actionable mutations in pediatric malignancies, FDA-approval status for pediatric and childhood treatment, and the recent update from clinical studies to explore the feasibility and utility of genomics-driven precision medicine.

2. Genetic Alterations on Cancer Hallmarks

2.1. Cancer Hallmarks and Common Targeted Signaling Pathways

Cancers are driven by changes in cellular DNA which further promote the transition of genetic landscape, especially in cell survival programs, leading to unstoppable cell growth with abnormal cellular characteristics [20]. In contrast to normal tissues, cancer cells can dysregulate their own signaling cascades autonomously, thus controlling their own cell fate [21]. Besides their proficiency in cancer hallmarks in evading growth suppressors, resisting cell death, reprogramming cellular mechanisms, and avoiding immune destruction, cancer cells can also acquire the capability to sustain proliferative signaling in several alternative ways [22,23]. Cancer cells may send signals to activate normal cells within the tumor parenchyma, which reciprocally communicate to supply cancer cells with various growth-promoting factors [24,25]. Furthermore, common downstream components in distinct signaling cascades also allowed cancer cells to control cell fate in a growth factor-independent manner by triggering the downstream molecules directly, negating the need for ligand-mediated receptor activation [23,26]. Hence, the vast majority of different cancers are coordinately modulated by canonical oncogenic drivers, including KRAS, MYC, NOTCH, and TP53. This factors highlights the need to fully elucidate their regulatory networks for further therapeutic development [27].

2.2. Tumor Cells Have Both Germline and Somatic Variants in Their Genome

Cancer gene mutations can be either inherited or acquired. Hereditary or germline mutations refer to the genomic changes that occur in germ cells and can be detected in all cells of the offspring and are passed inter-generationally [28,29]. Genetic predisposition has been described by certain characteristics, including [30];
  • Familial history of the same or related cancers;
  • Occurrence of bilateral or multifocal cancers;
  • Earlier age at disease onset;
  • Physical suggestive of a predisposition syndrome;
  • Appearance of specific tumor types corresponding to the genetic predisposition.
Several studies have described germline mutations in cancer including BRCA1/2, TP53, ATM, CHEK2, MSH2 and PALB2 [31,32,33]. Cancer cells harboring these germline predispositions are prone to increase cancer susceptibility, developing cancers at younger ages than usual. Using the 565 cancer-predisposing gene (CPG) panel for germline mutation analysis in children and adolescents with pan-cancer (n = 1120), Zhang et al. [31] reported that 95 pathogenic variants were detected in 21 of the 60 autosomal dominant CPGs in 94/1120 patients. Interestingly, the prevalence of germline mutation was greatest among patients with non-CNS solid tumors (16.7%), followed by brain tumors (8.6%) and leukemia (4.4%) [31]. Genetic predisposition syndromes associated with rare cancers of pediatric solid malignancies are provided in Table 1 [34,35,36]. Cancer predisposition syndrome such as Li–Fraumeni syndrome (LFS) with TP53 mutation generally promotes the onset of various benign and malignant neoplasms, such as neuroblastoma (NB), osteosarcoma (OS), soft tissue sarcomas (STS), and brain tumors [37]. Mutations in NF1 are associated with neurofibromatosis (NF), low- and high-grade gliomas (L/HGGs), and malignant peripheral nerve sheath tumors. Mutations in SUFU or PTCH1 in Nevoid basal cell carcinoma are relevant to the development of the sonic hedgehog (SHH) subgroup-medulloblastoma (MB) [38].
Somatic mutations are de novo genetic alterations that spontaneously develop in an individual cell over time and play a vital role in cancer development and progression [51]. Studies have shown that the number of genetic abnormalities identified in each cancer patient may increase over time, leading to tumor survival against the selective pressure of drug actions, thereby acquiring resistance and causing disease progression [13,52]. Commonly identified somatic mutations include those involved in RTK signaling (PDFGRA, ERBB2 and EGFR), MAPK signaling (NF1, KRAS, and MAP2K1), PI3K-mTOR signaling (PIK3CA, MTORC1/2 and PTEN), cell cycle (CDKN2A/B, RB1 and ATM), DNA maintenance (TP53), transcriptional regulators (MYC and MYCN), and epigenetic modifiers (SMARCB1 and ATRX) [12,53]. Cancers usually involve a different spectrum of mutation which are strongly associated with pathogenesis and disease prognosis. A pan-cancer analysis reported by Grobner et al. [33] showed that 93% of adult cancer patients harbor at least one significantly mutated gene, while only 47% presented such mutations in pediatric tumors. However, approximately 30% of recurrent hot-spot mutations in pediatrics overlapped with adult cancers, highlighting some potential druggable targets based on finding from adult cancers. Hence, advances in identifying and understanding oncogenic drivers and actionable mutations would further improve the current therapeutic strategies for the development of precision medicine in cancers.

2.3. Germline and Somatic Variants Classified as Druggable

In the context of defining mutational actionability, the relevant effects of genomic aberration participating in cancer phenotypes are considered. DNA aberrations include missense, nonsense, frameshift mutations, and chromosome rearrangements, with some changes affecting only a single DNA base that may or may not alter the protein’s property and some point mutations completely abrogating protein expression. A wide variety of gene alterations have been detected such as activating point mutation in BRAF, ALK, EGFR and FGFR1 genes, high copy number gains in PDGFRA and ERBB2, loss-of-function mutation affecting PTEN, PTPN11, PIK3R1, and MTORC1, CDKN2A/2B deletions, or in-frame expression of large indels (NOTCH1 and FOXA1) [12]. Other changes involving larger stretches of DNA may include rearrangements, deletions, or duplications of long stretches of DNA [54]. For example, exon skipping on MET exon 14 proto-oncogenes resulting from intronic mutation increases the protein lifespan and promotes MET activation in lung carcinogenesis [55].
The significance of genetic variants may vary depending upon their potential effects on cellular functions. An “actionable” mutation is defined as a genetic aberration that is potentially responsive to targeted therapy, while a “driver” mutation refers to variants that confer a growth advantage to cancer cells but may not be targetable with a specific treatment yet. Passenger mutation is used to designate cancer-neutral variations and is unlikely to be under selective pressure during the evolution of the cancerous cells [56,57]. The “passenger” mutation has the lowest tendency to impact protein function, most of which are synonymous substitutions; however, these mutations occur more frequently than driver or actionable mutations. Unraveling the passenger mutational paradigm has otherwise revealed the existence of pre-existing latent driver mutations in which certain combinations of the passenger mutations could indeed be functional drivers. One example is the non-hotspot, passenger mutation of the Akt1 gene at position L52R, C77F, and Q79K, which promotes its membrane localization similarly to the E17K driver. In contrast, the co-existence of D32Y, K39N, and P42T passenger mutations can lead to Akt conformational inactivation, suggesting that treatment decisions based only on genetics may overlook crucial actionable components [56,58]. In addition, silent mutations occurring near the donor splice junction could contrarily affect exon splicing. For example, T125T mutation in TP53 is a recurrent mutation that is generally considered a non-functional passenger event; however, its existence at the −1 donor site of exon 4 raises the possibility that this mutation affects splicing. Further integration with RNA-seq data demonstrated that T125T mutation resulted in the retention of intron 4 and introduced a premature stop codon such as nonsense-mediated decay [59]. Thus, aberrant splicing caused by silent mutations should be carefully evaluated during interpretation of the sequencing results.
The accumulated data of genetic composition data from the tumors of patients has become a growing compendium of molecular biomarkers for precise treatment with FDA-approved drugs. Figure 1 summarizes the actionable mutations currently approved by FDA consortium for targeted therapy in adult cancers and pediatric solid tumors. Common actionable genetic aberrations associated with the National Comprehensive Cancer Network (NCCN) guidelines or FDA-approved targeted therapies are extensively summarized in Table 2. The data were predominantly gathered from the OncoKB database and the representative cancer types, and levels of evidence were included [60].

3. Pediatric Cancer Genome

3.1. Pediatric vs. Adult Cancer Development

Pediatric cancers reflect a heterogeneous group of disorders distinct from adult cancers in terms of cellular origins, genetic complexity, and specific driver alterations [62,63]. Pediatric malignancies typically occur in developing mesoderm rather than adult epithelia (ectoderm) and are often induced by inherited or sporadic errors during development [33]. Studies have quantified the mutation burden in many pediatric cancers, identifying approximately 5 to 10 protein-coding variants identified across multiple tumor types except in osteosarcoma, which showed an average of 25 protein-affecting mutations. In contrast, the average number of mutations in adult cancers ranges between 33 to 66 in pancreatic, colon, breast, and brain cancers while mutagen-caused adult tumors (such as melanoma and lung cancers) can include up to 200 protein-coding variants [64,65,66]. At diagnosis, patients with pediatric cancers tend to have less complexity on mutational spectra than those in adult cancers; however, with treatment-refractory tumors and recurrence—the mutation rates in pediatric tumors have increased to be comparable to adult tumors [67,68]. Moreover, the rare occurrence of pediatric cancers and the low frequency of recurrent genomic alterations have a great impact on the investigations and the availability of targeted agents. Thus, there is an urgent need to accelerate the pace of genomic data acquisition and clinical trials in children to design more effective strategies for pediatric precision oncology.

3.2. Somatic and Germline Mutations Identified in Pediatric Cancer Cohorts

Single nucleotide variations (SNVs) and small indels are the usual mutations identified in adult cancers. In contrast, childhood cancers show a relatively high prevalence of copy number aberrations (CNAs) and specific structural variations (SVs). Note that insertion and deletion lead to adding and removing at least one nucleotide to the gene, respectively, which can affect protein functions and contribute to carcinogenesis. Current data suggest that approximately 10% of pediatric cancers are caused by genetic predisposition [32]. Zhang et al. [31] revealed that 95 out of 1120 (8.5%) patients younger than 20 years of age harbor germline mutations in cancer-predisposing genes. Diets et al. [69] performed trio-based whole-exome sequencing on the germline DNA of 40 selected children with cancer and their parents. Of these, germline pathogenic mutations were identified in 20% (8/40) of children with cancer [69]. Similarly, Grobner et al. [33] reported that most germline variants were related to DNA repair genes from mismatch (MSH2, MSH6, PMS2) and double-stranded break (TP53, BRCA2, CHEK2) repair.
Using combined somatic and germline sequencing for children with solid tumors, Parsons et al. [32] identified actionable mutations in up to 40% (47/121) of pediatric solid tumor tissues. Likewise, Wong et al. [12] performed the combination of tumor and germline sequencing (WGS) and RNA sequencing (RNA-seq) to identify 968 reportable molecular aberrations (39.9% in both WGS and RNA-seq; 35.1% in WGS only and 25.0% in RNA-seq only) in 247 high-risk pediatric cancer patients with 252 tumor tissues. Interestingly, 93.7% of these patients had at least one germline or somatic aberration, 71.4% had therapeutic targets, and 5.2% had a change in diagnosis [12].
These cohort studies emphasized that comprehensive molecular profiling could resolve molecular aberration in high-risk pediatric cancer and provide clinical benefits in a significant number of patients. In the era of next-generation sequencing, publicly genomic data access is considered one of the keys to accelerate research. The St. Jude Cloud is one of the most promising data-sharing ecosystems, with genomic data from >10,000 pediatric patients with cancer and long-term survivors. When exploring the mutational profile of pediatric solid tumors, the resource has revealed common genetic alterations among the different cancer types, as shown in Table 3. This integrative view of genomic data could be further used to expedite studies of pediatric cancer-associated risk factors and initiate novel therapeutic investigations for improving treatment outcomes.

3.3. Predictive and Common Genetic Variant Abnormalities Identified in Pediatric Tumors

The reports of actionable mutations identified in various studies have ranged from 27% to 100%, depending on the study design [6]. Several methods have been adopted for comprehensive molecular analysis to discover the actionable mutations that result in the targeting of cancer-associated elements. Table 4 contains a comprehensive, up-to-date summary of genomic aberrations found in pediatric solid tumors, together with potential targeted treatments, based on several public databases [60,70,71,72,73]. We systemically reviewed genomic alterations with high prevalence in pediatric cancers using comprehensive WES and RNA-seq data via the St. Jude Cloud (www.stjude.cloud; accessed on 26 September 2022) [70]. Importantly, the genomic point mutations and gene fusions reported by this public domain are unique and different from those variants identified in the OncoKB database (the mutational collection of adult cancers) [60]. In addition, the potential druggable targets of these significant genomic alterations required further testing in pediatric solid tumor patients. A significant number of studies [60,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85] were reported by the Clinical Interpretation of Variants in Cancer (CIViC) database (https://civicdb.org; accessed on 18 September 2022) [71] which matched genomic alteration and molecularly targeted therapies tested in pediatric patients. These treatment designs were translated from the clinical care of adults across different tumor types but harboring the same genetic dysregulation, which gave satisfactory clinical outcomes. For pediatric solid tumors with no clinical evident support or undruggable genomic alterations, we listed the potential targeted therapies based on the knowledge from adult cancers as suggested by cBioPortal (www.cbioportal.org; accessed on 30 April 2022) [72,73] and OncoKB (https://www.oncokb.org; accessed on 17 April 2022) [60] that should be considered for further investigation and optimization for pediatric treatments. As of now, fewer number of patients could hinder the availability of molecular characterization and statistically meaningful preclinical/clinical outcomes. However, this challenge can be overcome by the initiation of multi-institutional cooperation and international data sharing, which would enable clinicians to effectively explore optimized therapeutic interventions toward pediatric precision oncology.

4. Current Progress in Clinical Trials for Pediatric Precision Oncology

Genomic precision medicine has demonstrated preferential outcomes among ongoing genomic-driven clinical trials in adult cancers. Yet, clinical investigations based on pediatric tumor genetics are still lacking. Based on the patient genetic profile screening, scattered reports on molecularly defined pediatric patients are showing prominent responses to some targeted therapies. For example, targeting ALK has shown success in treatments of ALK(+) non-small cell lung cancers and also in childhood anaplastic large cell lymphoma (ALCL) and inflammatory myofibroblastic tumor using the ALK inhibitor crizotinib [92]. While ALK mutation is the most common somatic mutation in neuroblastoma, crizotinib was compromised due to the interference by common ALK mutation F1174 [93]. Since then, ceritinib, alectinib, brigatinib, and lorlatinib have been approved against advanced ALK+ NSCLC [94,95,96,97]. Intriguingly, the third-generation TKI that targets both ALK and ROS1, lorlatinib, has recently shown promise in patients with ALK mutated neuroblastoma, but most of the studies are still at phase I clinical trial. [98]. Nonetheless, repotrectinib, a next-generation ROS1/TRK inhibitor with >90-fold potency against ROS1 than crizotinib in NSCLC patients is also being tested for dose escalation in phase II clinical trial with patients aged ≥ 12 years [99]. Another promising example is the targeted therapy against Ras-Raf-MEK-ERK signaling cascade which include somatic BRAF alterations (BRAF V600E and BRAF fusions). The prototype for targeting BRAF V600E/K is cutaneous melanoma, where 40–60% of patients with these mutations are eligible for the FDA-approved BRAF-inhibitor, vemurafenib [100]. Low-grade-gliomas have been identified to contain multiple alterations in Ras-Raf-MEK-ERK pathway, and a single treatment of vemurafenib in malignant glioma resulted in tumor regression [85,101]. Recently, Jain et al. [102] reported that a combination of BRAF-inhibitor dabrafenib and MEK-inhibitor trametinib enhanced treatment efficacies in pediatric low-grade-glioma carrying KIAA1549-BRAF fusion. Additionally, several studies have utilized the combination of molecularly targeted agents and traditional chemotherapy or radiation to reduce the severe side effects caused by an intensive dose of chemo/radiotherapy while minimizing acquired drug resistance due to selective pressure (Table 5).
The following large-scale pediatric and young-adult precision oncology programs have been launched with multiple-arm trials for patients with matched molecular profiles: TAPUR (ClinicalTrials.gov identifier NCT02693535), NCI-COG Pediatric MATCH (NCT03155620), the Tumor-Agnostic Precision Immuno-Oncology and Somatic Targeting Rational for You (TAPISTRY) (NCT04589845). These global, multicenter, open-label, multi-cohort studies are now at phase II, and the treatment assignment has relied on the basis of relevant onco-genotypes as identified by a Clinical Laboratory Improvement Amendments (CLIA)-certified or a validated next-generation sequencing (NGS) assay. While the eligible criteria of TAPUR are open for patients aged 12 years old or older, most of the patients enrolled are reported to have adult cancer phenotypes [103,104,105]. In contrast, the NCI-COG Pediatric MATCH aims to evaluate the molecular-targeted therapies with selected biomarkers of childhood and young adult patients with a reported detection rate of actionable alterations of 31.5% from the first 1000 tumors screened. Assignments to treatment arms were made for 28% of patients screened and 13% of patients enrolled in the treatment trial [106]. In the TAPISTRY study, nine targeted treatments are being examined, and eleven non-randomized treatment arms are available for participants of all ages with locally advanced/metastatic solid tumors. The purpose of this study is to evaluate the safety and efficacy of different targeted therapies and immunotherapies in patients as single agents, but the results of the study are still to be released. Overall, the advancements in high-throughput sequencing technology have closed the gap between the current treatment paradigm and precision medicine, markedly improving rates of response, progression-free survival (PFS), and overall survival (OS) compared to traditional randomized trials. Moreover, the multicenter, open-label, multi-arm treatment designs can further benefit treatment strategies by yielding efficacy and toxicity data in a timely manner with cost-effectiveness. Therefore, in the future, international coordination will be crucial to generate a database to inform rational trial design and to evaluate the combination of treatments/interventions that ensure more favorable outcomes.
The current applications of precision study designs for pediatric cancers (summarized from clinicaltrials.gov; accessed on 17 August 2022) are shown as single-arm and multiple-arm designs in Table 5 and Table 6, respectively.

5. Challenges and Perspectives

Large-scale cancer sequencing studies such as the 1000 Genomes Project [107], The Cancer Genome Atlas (TCGA) [108], and the International Cancer Genome Consortium (ICGC) [109] provide an extensive landscape of tumor genomic profiles which substantially facilitate the predication of recurrent hot-spot mutations on the selected type of cancers. Other large databases aim to collect the profile of childhood cancers include St. Jude/Washington University Pediatric Cancer Genome Project (PCGP) [110] and NCI’s Therapeutically Applicable Research to Generate Effective Treatments (TARGET) [53] which are accessible via the St. Jude Cloud (https://www.stjude.cloud, accessed on 26 September 2022) public data repository. These large-scale studies have confirmed that the spectra of genomic alterations and their relevant mechanisms differ in childhood tumors from those predominantly occurring in adult cancer—at least by half. Thus, the actionability of pediatric-driven mutations needs to be carefully interpreted before translating into a targeted treatment option.
Several challenges need to be addressed when researchers launch the study/trial for pediatric cancer treatment. Many pediatric cancers are rare, and finding the right patient population for the drugs is challenging. In fact, a small patient population and a prolonged trial duration are not uncommon issues in the settings of rare diseases and low-incidence pediatric cancers [111,112,113,114]. Optimal statistical designs for less stringent comparisons, for example, by relaxing type I error (higher than 5%) or power (lower than 80%) can still provide meaningful results from small but faster trials [111,112,113,114]. Implementing multi-arm multi-stage trial design would allow patients with poor prognosis to be stratified into multiple phase II arms; receiving the window-of-opportunity/experimental therapies and restaging by serial biopsies and molecular characterizations to inform ongoing treatment choices [113,114]. These approaches remain useful to increase the overall feasibility for rare disease trials, i.e., keeping the sample size as small as possible while maintaining the power and ability to address the trial objectives.
Only 45% of pediatric cancer driver genes are shared with adult cancers, suggesting that novel therapeutic agents are required for pediatric cancer. Additionally, pediatric cancers are often driven by structural variants that can be challenging to identify and target. Nonetheless, children with cancers have accumulated fewer genetic mutations, thus making genomic targeting simpler than adults [113]. In a broad view, cancer intrinsic targets (e.g., mutated oncogene, tumor suppressor, epigenetics, synthetic lethal, and DNA damage) play crucial roles in cancer pathogenesis and thus could serve as the key stones for drug development against childhood cancers [115]. Another approach in drug development strategy is a mechanisms-of-action (MoA)-driven approach which successfully exemplified the efficiency of nivolumab and larotrectinib as targeted anticancer drugs against programmed cell death protein-1 (PD1) and TRK receptors, respectively [116]. Nonetheless, lessons learned from adult cancers have warned us that many pediatric cancers would have failed to express mutated kinase targets, and resistance to targeted therapies would rapidly occur. Recently, newly emerging cancer targets have been discovered upon multidimensional complexity of the dynamic oncogenic states, for example, tumor archetypes, master regulators, cancer-associated protein–protein interactions, and metabolic vulnerabilities [115,117,118,119,120]. The development of drugs against the emerging classes of cancer targets may deliver adjunct/complementary agents for combination with targeted therapeutic regimens [115]. The emergence of gene editing technologies such as transcription activator-like effector nucleases (TALENS) and clustered regularly interspaced palindromic repeats (CRISPR) paired with the CRISPR-associated endonuclease 9 (CRISPR-CAS9) offer the powerful customizable therapeutic options to precisely edit the targeted genes [121,122,123], thus providing hope to all pediatric cancers to be benefited from genomic-driven precision medicine approach.
Comprehensive molecular profiling of the genetic variants/mutations, gene expression at both transcripts and protein levels, and perhaps information on post-translational modifications and metabolites are coordinately utilized to improve the accuracy of molecularly targeted agents. Challenges in this grand scheme, besides big data sharing and multi-omics integration, are interpreting complex high-dimensional data in the biological sense, prioritizing findings into actionable targets/pathways, and achieving the candidate compounds/drugs for precise treatment. Aberrant expression of messenger RNA associated with genomic changes could contribute to the biology of tumor progression. In most cases, RNA-seq analysis can increase the coverage number of variant curations, especially the comprehensive gene fusion discovery and tumor expression subgroup analysis, when compared to WGS alone [124]. A novel molecularly guided approach, so-called transcriptomic connectivity analysis, utilizes the power of RNA-seq to detect aberrant gene expression and employs transcriptomic reversal of cancer cells/tissues for repurposing FDA-approved drugs [125,126,127]. This molecularly guided therapeutic approach could be an asset for prioritizing the approved drugs for off-label use in childhood cancer trials.
Despite the promising demonstration of ongoing genomic-driven clinical trials of targeted anticancer small molecules, cancer immunotherapies have become significant advances for pediatric solid tumors [128,129]. Ganglioside GD2 is a sialic acid-containing glycosphingolipid that highly expressed on the surface of multiple pediatric solid tumors, i.e., neuroblastoma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, and brain tumors including diffuse intrinsic pontine glioma (DIPG) and medulloblastoma [128,129]. Thus, GD2 is recognized as one of the most promising targets for pediatric cancer immunotherapy. Dinutuximab, anti-GD2 monoclonal antibody, has been approved as the first-line therapy for high-risk pediatric neuroblastoma [128,129,130], while GD2-specific chimeric antigen receptor (CAR) T cell therapy is under investigation in the early phase trials for children with neuroblastoma, osteosarcoma, and brain tumors (ClinicalTrials.gov identifier NCT03721068, NCT04539366, NCT04099797, NCT04196413). Besides GD2, newly emerging targets for pediatric cancer immunotherapy, including PD1/PD-L1 (NCT04544995, NCT04796012), B7-H3 (CD276; NCT04864821, NCT04743661), HER2 (NCT00902044, NCT04616560) and CD47 (NCT04525014, NCT04751383), have been actively investigated for pediatric sarcomas and brain tumors.
Last but not least, it should be noted that new therapeutics often lack dosage guidelines for children [12]. Acknowledging children have different drug responses and tolerance profiles compared to adults, it is crucial to define the optimal dosages of new drugs/biologics (and the off-label use of FDA-approved medications) to achieve preferred therapeutic outcomes. Recent innovations in study designs (i.e., phase I dose-finding design for pediatric population, the potential inclusion of children in adult trials, cooperative group trials) [131,132,133,134], together with the regulatory initiatives in the United States (US) and the E.U. which encourage the development of novel anticancer therapies in children [134,135], provide guidance to address this challenge while accelerating the pace of genomic-driven precision medicine in pediatric oncology.

6. Conclusions

Essential questions that need to be addressed in applications of precision therapeutic program include the applicability of the genetic testing, the significance of the mutation variant, and the existence of an approved targeted therapy. Although targeted agents are approved for a set of tumors harboring specific mutations, future development of clinical guidelines may recommend these agents to be used off-label in different tumor types with the same mutations. Identifying the mutational signatures of pediatric solid tumors will open opportunities for new targeted therapeutic strategies since their malignant origin manifests differently from the adults. Similar genomic-driven precision medicine approaches have been launched by several institutes, while the long-term effects of many of those novel agents are just beginning to be evaluated. These treatments could improve survival and reduce toxicity in pediatric patients and maximize therapeutic advantages when incorporated into standard care.

Author Contributions

Conceptualization, S.C.; methodology, P.S., W.C., P.C. (Parunya Chaiyawat), P.C. (Pongsakorn Choochuen), D.P., S.S., S.H., U.A. and S.C.; resources, D.P., S.S., S.H. and U.A.; data curation, P.S.; writing—original draft preparation, P.S.; writing—review and editing, W.C., P.C. (Parunya Chaiyawat), P.C. (Pongsakorn Choochuen), D.P., S.S., S.H., U.A. and S.C.; visualization, P.S. and S.C.; supervision, D.P., S.S., S.H., U.A. and S.C.; funding acquisition, D.P., S.S. and U.A. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Genomic Thailand Project of the Health Systems Research Institute, Thailand, grant number HSRI64-130 (to D.P., S.S., S.H., U.A.). The APC was funded by Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand.

Acknowledgments

The graphical abstract was created with BioRender.com (accessed on 29 November 2022).

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

TRKTyrosine receptor kinase
MAPKMitogen-activating protein kinase
PI3KPhosphoinositide 3-kinases
NTRKNeurotrophic tyrosine receptor kinase
CPG Cancer predisposing gene
CNSCentral nervous system
RTKReceptor tyrosine kinase
WGSWhole genome sequencing
WESWhole exome sequencing
TGFBTransforming growth factor beta
NSCLCNon-small cell lung cancer

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Cancers 15 01418 g001 550
Figure 1. Oncogenic drivers identified in adult and pediatric solid tumors. These selective biomarkers are predicted to be responsive to various levels of FDA-approved drugs (detailed in Table 1). Note that targeted therapies against PTCH1 and ALK in medulloblastoma and neuroblastoma are currently undergoing clinical assessment and awaiting further approval.
Figure 1. Oncogenic drivers identified in adult and pediatric solid tumors. These selective biomarkers are predicted to be responsive to various levels of FDA-approved drugs (detailed in Table 1). Note that targeted therapies against PTCH1 and ALK in medulloblastoma and neuroblastoma are currently undergoing clinical assessment and awaiting further approval.
Cancers 15 01418 g001
Table
Table 1. Mutated genes and dysregulated signaling pathways in selected cancer predisposition syndromes.
Table 1. Mutated genes and dysregulated signaling pathways in selected cancer predisposition syndromes.
Cancer Predisposition SyndromeCommon Solid TumorsMutated Genes (Inheritance)Dysregulated
Pathways		Reference
Beckwith–Wiedemann syndromeWilms tumor, hepatoblastoma,
neuroblastoma, rhabdomyosarcoma		CDKN1C (AD)Cell cycle[39,40]
Constitutional mismatch repair deficiencyBrain tumor, neuroblastoma, Wilms tumor, osteosarcoma, rhabdomyosarcomaMLH1, MSH2, MSH6, PMS2 (AR)DNA mismatch
repair		[36,41]
Hereditary retinoblastomaRetinoblastoma, melanoma,
osteosarcoma, pineoblastoma		RB1 (AD)Cell cycle[39,42]
Li-Fraumeni syndromeBrain tumor, sarcoma, neuroblastoma, rhabdomyosarcoma, retinoblastomaTP53 (AD)Cell cycle,
apoptosis		[39,43,44]
NeurofibromatosisGlioma, astrocytoma, ependymoma, malignant peripheral nerve sheath tumors, neuroblastoma, rhabdomyosarcomaNF1, NF2 (AD)RAS/MAPK[39,45]
Rhabdoid tumor
predisposition syndrome		Atypical teratoid/rhabdoid tumor,
malignant rhabdoid tumor		SMARCB1, SMARCA4 (AD)Wnt/β-catenin, Sonic hedgehog[39,46]
Multiple endocrine
neoplasia		Ependymoma, Medullary thyroid cancerMEN1, RET (AD)Transcriptional
activity		[39,47]
Nevoid basal cell
carcinoma		Medulloblastoma, rhabdomyosarcomaPTCH1, PTCH2, SUFU (AD)Sonic hedgehog[39,46]
Familial adenomatous polyposisMedulloblastoma, hepatoblastomaAPC (AD)Wnt/β-catenin[39,48]
Tuberous sclerosisSubependymal giant cell astrocytoma, rhabdomyosarcomaTSC1, TSC2 (AD)mTOR[39,49]
Bloom syndromeOsteosarcoma, Wilms tumorBLM (AR)DNA double-strand repair[34,35]
Rubinstein–Taybi
syndrome		Medulloblastoma, neuroblastoma,
rhabdomyosarcoma		CREBBP (AD)Transcriptional
regulation		[34,35]
Noonan syndromeRhabdomyosarcoma, neuroblastoma,
glioma, hepatoblastoma		PTPN11, SOS1, RAF1, KRAS, MAP2K1 (AD)RAS/MAPK[50]
Abbreviations: AD, autosomal dominant; AR, autosomal recessive.
Table
Table 2. Targeted therapies recommended for the selected genetic alterations according to FDA-approved or NCCN guidelines [60].
Table 2. Targeted therapies recommended for the selected genetic alterations according to FDA-approved or NCCN guidelines [60].
GeneAlterationsTargeted TherapiesCancer TypesFDA-Approved Level a
AKT1E17KAZD5363Breast Cancer, Ovarian Cancer; Endometrial CancerLv.3
ALKFusionsAlectinib; Brigatinib; Ceritinib; CrizotinibNon-Small Cell Lung CancerLv.1
Brigatinib; Ceritinib; CrizotinibInflammatory Myofibroblastic TumorLv.2
Oncogenic MutationsLorlatinibNon-Small Cell Lung Cancer; Neuroblastoma cLv.1
CrizotinibNon-Small Cell Lung Cancer; Neuroblastoma cLv.R2
ARAFOncogenic MutationsSorafenibNon-Small Cell Lung CancerLv.3
ARID1ATruncating MutationsPLX2853; TazemetostatAll Solid TumorsLv.4
ATMOncogenic MutationsOlaparibProstate CancerLv.1
BRAFV600EDabrafenib + TrametinibMelanoma; Non-Small Cell Lung Cancer;
Low grade glioma b; High grade glioma b		Lv.1
Encorafenib + CetuximabColorectal Cancer
Fusions or V600ESelumetinibPilocytic AstrocytomaLv.2
V600EDabrafenib + Trametinib,
Vemurafenib + Cobimetinib		Diffuse Glioma; Encapsulated Glioma;
Ganglioglioma
FusionsTrametinib; CobimetinibOvarian CancerLv.3
V600EDabrafenib + TrametinibBiliary Tract Cancer
G464, G469A, G469R, G469V, K601, L597PLX8394All Solid TumorsLv.4
BRCA1/2Oncogenic MutationsNiraparib; Olaparib; Olaparib + Bevacizumab; RucaparibOvarian Cancer; Peritoneal Serous CarcinomaLv.1
Olaparib; RucaparibProstate Cancer
Olaparib; TalazoparibBreast CancerLv.3
BRIP1Oncogenic MutationsOlaparibProstate CancerLv.1
CDK4AmplificationPalbociclib; AbemaciclibDedifferentiated Liposarcoma;
Well-Differentiated Liposarcoma		Lv.4
CDK12Oncogenic MutationsOlaparibProstate CancerLv.1
CDKN2AOncogenic MutationsPalbociclib; Ribociclib; AbemaciclibAll Solid TumorsLv.4
CHEK1/2Oncogenic MutationsOlaparibProstate CancerLv.1
EGFRExon 19 deletion, L858RAfatinib; Dacomitinib; Erlotinib; Erlotinib + Ramucirumab; Gefitinib; OsimertinibNon-Small Cell Lung CancerLv.1
Exon 20 insertionAmivantamab; Mobocertinib
G719, L861Q, S768IAfatinib
T790MOsimertinib
A763_Y764insFQEAErlotinibLv.2
E709_T710delinsDAfatinibLv.3
Exon 19 insertionErlotinib; Gefitinib
Exon 20 insertionPoziotinib
Kinase Domain DuplicationAfatinib
A763_Y764insFQEA or Exon 19 insertion or L718V, L747PAfatinibLv.4
D761YOsimertinib
Kinase Domain DuplicationErlotinib; Gefitinib
Amplification or A289V, R108K, T263PLapatinibGlioma
Exon 20 insertion, T790MErlotinib; Gefitinib; AfatinibNon-Small Cell Lung CancerLv.R1
C797S, D761Y, G724S, L718VOsimertinib; GefitinibLv.R2
ERBB2AmplificationAdo-Trastuzumab; Emtansine; Lapatinib + Capecitabine; Lapatinib + Letrozole,
Margetuximab + Chemotherapy; Neratinib; Neratinib + Capecitabine; Trastuzumab + Pertuzumab + Chemotherapy; Trastuzumab + Tucatinib + Capecitabine; Trastuzumab Deruxtecan; Trastuzumab, Trastuzumab + Chemotherapy		Breast CancerLv.1
Pembrolizumab + Trastuzumab + Chemotherapy; Trastuzumab + Chemotherapy; Trastuzumab DeruxtecanEsophagogastric CancerLv.1
Trastuzumab + Lapatinib; Trastuzumab + Pertuzumab; Trastuzumab DeruxtecanColorectal CancerLv.2
Oncogenic MutationsAdo-Trastuzumab; Emtansine; Trastuzumab DeruxtecanNon-Small Cell Lung CancerLv.2
NeratinibBreast Cancer; Non-Small Cell Lung CancerLv.3
ESR1Oncogenic MutationsAZD9496; FulvestrantBreast CancerLv.3
FANCLOncogenic MutationsOlaparibProstate CancerLv.1
FGFR1AmplificationDebio1347; Infigratinib; ErdafitinibLung Squamous Cell CarcinomaLv.3
Oncogenic MutationsDebio1347; Infigratinib; Erdafitinib; AZD4547All Solid TumorsLv.4
FGFR2FusionsErdafitinibBladder CancerLv.1
Infigratinib; PemigatinibCholangiocarcinoma
Oncogenic MutationsDebio1347; Infigratinib; Erdafitinib; AZD4547All Solid TumorsLv.4
FGFR3Fusions or G370C, R248C, S249C, Y373CErdafitinibBladder CancerLv.1
G380R, K650, S371CErdafitinibLv.3
Oncogenic MutationsDebio1347; Infigratinib; Erdafitinib; AZD4547All Solid TumorsLv.4
FLI1EWSR1-FLI1 FusionTK216Ewing SarcomaLv.4
HRASOncogenic MutationsTipifarnibBladder Urothelial Carcinoma; Head and Neck Squamous Cell CarcinomaLv.3
IDH1R132IvosidenibCholangiocarcinomaLv.1
Oncogenic MutationsChondrosarcomaLv.2
R132GliomaLv.3
KDM6AOncogenic MutationsTazemetostatBladder CancerLv.4
KITA502_Y503dup, K509I, N505I, S476I, S501_A502dup, A829P and 5 other alterations, D572A and 65 other alterations, K642E, T670I, V654AImatinib; Regorafenib; Ripretinib; SunitinibGastrointestinal Stromal TumorLv.1
A829P and 5 other alterationsSorafenibGastrointestinal Stromal TumorLv.2
KRASG12CSotorasibNon-Small Cell Lung CancerLv.1
AdagrasibNon-Small Cell Lung CancerLv.3
Adagrasib; Adagrasib + CetuximabColorectal Cancer
Oncogenic MutationsCobimetinib; Trametinib; BinimetinibAll Solid TumorsLv.4
MAP2K1Oncogenic MutationsCobimetinib; TrametinibMelanoma; Non-Small Cell Lung Cancer;
Low grade glioma c		Lv.3
MDM2AmplificationMilademetanDedifferentiated Liposarcoma;
Well-Differentiated Liposarcoma		Lv.4
METD1010, Exon 14 deletion, Exon 14 splice mutationCapmatinib; TepotinibNon-Small Cell Lung CancerLv.1
Amplification or D1010, Exon 14 deletion, Exon 14 splice mutationCrizotinibLv.2
Y1003mutTepotinib; Capmatinib; CrizotinibLv.3
FusionsCrizotinibAll Solid TumorsLv.4
MTORE2014K, E2419KEverolimusBladder CancerLv.3
Q2223KEverolimusRenal Cell Carcinoma
L2209V, L2427QTemsirolimus
Oncogenic MutationsEverolimus; TemsirolimusAll Solid Tumors, Rhabdomyosarcoma cLv.4
NF1Oncogenic MutationsSelumetinibNeurofibroma bLv.1
Trametinib; CobimetinibAll Solid TumorsLv.4
NRG1FusionsZenocutuzumabAll Solid TumorsLv.3
NTRK1/2/3FusionsEntrectinib; LarotrectinibAll Solid Tumors bLv.1
PALB2Oncogenic MutationsOlaparibProstate CancerLv.1
PDGFBCOL1A1-PDGFB FusionImatinibDermatofibrosarcoma ProtuberansLv.1
PDGFRAExon 18 in-frame deletions or insertions, Exon 18 missense mutationsAvapritinibGastrointestinal Stromal TumorLv.1
Oncogenic MutationsRegorafenibGastrointestinal Stromal Tumor; Medullary thyroid cancer c, Hepatocellular carcinomacLv.2
Imatinib; Ripretinib; SunitinibGastrointestinal Stromal Tumor
D842VDasatinib
D842VImatinibGastrointestinal Stromal TumorLv.R1
PIK3CAC420R and 10 other alterationsAlpelisib + FulvestrantBreast CancerLv.1
Oncogenic Mutations (excluding C420R, E542K, E545A, E545D, E545G, E545K, Q546E, Q546R, H1047L, H1047R and H1047Y)Alpelisib + FulvestrantLv.2
PTCH1Truncating MutationsSonidegib; VismodegibMedulloblastomaLv.3
PTENOncogenic MutationsGSK2636771; AZD8186All Solid TumorsLv.4
RAD51B,
RAD51C,
RAD51D,
RAD54L		Oncogenic MutationsOlaparibProstate CancerLv.1
RETFusions or Oncogenic MutationsPralsetinib; SelpercatinibNon-Small Cell Lung Cancer,
Thyroid Cancer, Medullary Thyroid Cancer b		Lv.1
FusionsCabozantinibNon-Small Cell Lung Cancer; Sarcoma cLv.2
VandetanibNon-Small Cell Lung CancerLv.3
ROS1FusionsCrizotinibNon-Small Cell Lung CancerLv.1
EntrectinibBiomarker (+), solid and brain b
SMARCB1DeletionTazemetostatEpithelioid SarcomaLv.1
STK11Oncogenic MutationsBemcentinib + PembrolizumabNon-Small Cell Lung CancerLv.4
TSC1/2Oncogenic MutationsEverolimusEncapsulated Glioma; Subependymal giant cell astrocytoma bLv.1
a FDA-approved level 1 = FDA-recognized biomarker predictive of response to an FDA-approved drug in this indication; level 2 = Standard care biomarker recommended by the NCCN or other professional guidelines predictive of response to an FDA-approved drug in this indication; level 3 = Standard care or investigational biomarker predictive of response to an FDA-approved or investigational drug in another indication; level 4 = Compelling biological evidence supports the biomarkers as being predictive of response to a drug; level R1 = Standard care biomarker predictive of resistance to an FDA-approved drug in this indication; level R2 = Compelling clinical evidence supports the biomarker as being predictive of resistance to a drug. b FDA-approved for pediatrics used [61]. c Clinical trial in pediatrics.
Table
Table 3. Somatic and germline mutated genes of selected pediatric tumors.
Table 3. Somatic and germline mutated genes of selected pediatric tumors.
TumorSignificantly Mutated Genes (# Prevalence)
MedulloblastomaDDX3X (5.8%), KMT2D (5.8%), CTNNB1 (5.5%), PTCH1 (5.1%),
TP53 (4.0%), SMARCA4 (3.6%), KDM6A (3.1%), SUFU (1.3%),
SMO (1.5%), KMT2C (1.4%), CREBBP (1.3%), APC † (0.6%), IDH1 (0.4%)
High grade gliomaTP53†‡(28.5%), ATRX (11.3%), PIK3CA (5.6%), PDGFRA‡(5.1%),
BCOR (3.0%), PPM1D‡(3.9%), CREBBP‡(1.8%), NF1†(0.8%),
EGFR‡(0.6%)
EpendymomaRELA‡(25.0%), IGF2R†(20.0%)
Low grade gliomaFGFR1‡(33.3%), BRAF (8.7%), NF1†(3.9%), KIAA1549 (1.9%)
NeuroblastomaMYCN (36.2%), MYCNOS (33.0%), ATRX (22.2%), DDX1 (22.3%),
ALK (1.4%), RYR1 (0.5%), PTPN11 (0.7%)
Wilms tumorMYCN (12.4%), MYCNOS (12.4%), TP53 (3.2%), DROSHA‡(1.8%), WT1 (1.6%), CTNNB1 (1.5%), DGCR8 (1.1%)
OsteosarcomaTP53†(30.0%), RB1†(15.4%), ATRX (9.7%)
Ewing’s sarcomaEWSR1 (29.6%), FLI1 (25.9%), ERG (4.7%), STAG2 (2.4%)
RetinoblastomaRB1†(51.6%), BCOR (3.2%)
RhabdomyosarcomaPAX3‡(28.6%), FOXO1‡(25.9%), PAX7‡(16.7%), TP53†‡(12.3%),
FGFR4‡(7.7%), NRAS‡(4.6%)
# Prevalence of mutated genes in the selected pediatric tumor. Data from cBioPortal for cancer genomics (www.cbioportal.org; accessed on 30 April 2022). † Germline, ‡ Relapse. Data from St. Jude Cloud public data repository (www.stjude.cloud; accessed on 18 September 2022).
Table
Table 4. Significant genomic alterations of actionable genetic mutations in pediatric solid tumors.
Table 4. Significant genomic alterations of actionable genetic mutations in pediatric solid tumors.
Signaling
Pathway		GeneAlterationsEffected DomainPediatric CANCER TypesPotentially Targeted Therapy
(Level of Evidence) 		Additional References for Targeted Therapy
Tyrosine KinaseALKFusion NBLCrizotinib, Ceritinib, Alectinib, LorlatinibcBioPortal
F1174L ‡CAD exon23NBLCrizotinib (B)[74,75]
F1245VCAD exon24NBL
R1275Q/L †‡CAD exon25NBL
NTRK1TPM3::NTRK1 HGGLarotrectinib (A)[18,76,77]
NTRK2Fusion HGG, LGGLarotrectinib (A)[77,78,79]
NTRK3ETV6::NTRK3 HGG, LGGLarotrectinib (A)[76,77]
PDGFRAY288CExon6HGG Imatinib, sunitinib, regorafenib and ripretinibcBioPortal
E311_E7spliceExon7HGG
N659K ‡PKD exon14HGGImatinib, sunitinib, regorafenib and ripretinibcBioPortal
D842YPKD exon18HGGAvapritinib, Imatinib, SunitinibcBioPortal
ROS1Fusion OS, HGGCrizotinib, EntrectinibcBioPortal
MAPK signalingNF1Fusion OS, NBL, MB, HGGTrametinib, CobimetinibcBioPortal
Mutation LGG, NBLSelumetinib (B)[80,81,82]
BRAFKIAA1549::BRAF LGG, PASelumetinib (B), Sorafenib (C)[81,83,84]
V600E LGG, HGG, PA, NBLSelumetinib (B), Vemurafenib (B), Dabrafenib (B)[81,85,86]
KRASG12DGTPase exon2LGG, NBLTrametinib, Cobimetinib, BinimetinibcBioPortal
NRASG12SGTPase exon2HGGBinimetinib, Binimetinib + RibociclibcBioPortal
Q61K ‡/RGTPase exon3RHB, NBL
PTPN11E69KExon3NBL, PA
A72T/DExon3NBL
E76AExon3NBL, PA
Notch signalingNOTCH2Fusion OS, NBL
R5_P6fsExon1OS, NBL, RHB
P6fsExon1NBL, MB, PA, WLM
Sonic hedgehog signalingPTCH1Mutation MBSonidegib (B)[87]
A300fsExon6MBSonidegib, VismodegibcBioPortal
Y804fsExon15MBSonidegib, VismodegibcBioPortal
SMOL412F MBVismodegib # (C)[88]
W535L MBVismodegib #cBioPortal
Wnt signalingCTNNB1D32Exon3MB
S33Exon3MB
G34Exon3MB, RHB, ACT, HB
S37Exon3MB
T41A/NExon3WLM, MB, RHB
N387K ‡Exon8WLM
PI3K signalingPTENFusion OSGSK2636771, AZD8186cBioPortal
R130CAD exon5HGG
R233 *Exon7HGG
PIK3CAR88QSBD exon2HGGAlpelisib + FulvestrantcBioPortal
N345K ‡Exon5MB, RHB, EPD
E545KExon10HGG
Q546KExon10HGG, MB
E888 *CAD exon18NBL
H1047R/LCAD exon21HGG, MB, RHB, NBL
FGFR1Fusion LGGErdafitinib, InfigratinibcBioPortal
Internal tandem
duplication		CADLGG
N546KCAD exon12LGG, NBL, PA, WLM, HGGPemigatinib (C)[89]
K656ECAD exon14PA, HGG, WLMErdafitinib, InfigratinibcBioPortal
FGFR4V550L ‡CAD exon13RHB
EGFRA289VExon7HGGLapatinibcBioPortal
TGFB signalingACVR1R206HCAD exon6HGG
R258GCAD exon7HGG
G328E/VCAD exon8HGG
G356_E9spliceCAD exon9HGG
Cell cycle and DNA repairRB1Fusion OS
W78 *Exon2OS
R320 *Exon10RB, HGG
R445 *†Exon14RB
R552 *Exon17RB, OS, HGG
R579 *Exon18RB
TP53Mutation HGG, WLM, OS, MBVismodegib (C)[90]
T125T/R †DBD exon4HGG, WLM, ACT
R175H †‡DBD exon5HGG, WLM, MB, RHB, ACT
C176FDBD exon5RHB, EWS, NBL
R213 *†DBD exon6HGG, MB
G245SDBD exon7HGG, MB
R248Q/W †DBD exon7MB, HGG, OS, WLM
R273C †/HDBD exon4HGG, EWS, ACT, MB, OS
R282W †DBD exon8OS, HGG, MB
R337H †Exon10ACT
R342 */PExon10HGG, WLM
CDK1V124GCAD exon5MB
PPM1DW427 *Exon6HGG
S516 *Exon6HGG, NBL
E525 *Exon6HGG, MB
Transcriptional regulationEWSR1FLI1::EWSR1 EWSTK216cBioPortal
ERG::EWSR1 EWS
BCORR1164*Exon7HGG
H1481fsExon11HGG
SIX1Q177RDBD exon1WLM
MYCNFusion NBL
P44LExon2WLM, NBL, MB
PAX7FOXO1::PAX7 RHB
PAX3FOXO1::PAX3 RHB
RNA processingDROSHAE1147KRibonuclease exon29WLM
D1151Ribonuclease exon29WLM, NBL
DGCR8E518KRBM exon7WLM
DDX1DDX1::DDX1 NBL
MYCN::DDX1 NBL
DDX3XR351WHD exon11MB
M380IHD exon11MB
R534HD exon14MB
EpigeneticsATRXATRX::ATRX NBL
N294fsExon9OS
ASXL1R643fsExon13WLM
R693 *Exon13HGG, EPD
H3-3A
(H3F3A)		K28MExon2HGG, LGG
G35RExon2HGG
KMT2CT1636PExon33MB
E2798fsExon38MB
I4084LExon48MB
SMARCA4T910MHD exon19MB
H3C2
(HIST1H3B)		K28M ‡Exon1HGG
KDM6AS54_E2spliceExon2MB
R1351 *Exon28MB
IDH1R132C/HExon4MB, HGG, LGGBevacizumab and Sunitinib (B)[91]
R222C/HExon6HGG, EWS
RELAFusion EPD, HGG
STAG2R216 *STAG domain exon8EWS
R259 *STAG domain exon9MB, HGG
E1209QExon33OS
FLI1EWSR1::FLI1 EWS
ERGEWSR1::ERG EWS
† Germline, ‡ Relapse, # Reduce treatment activity, * Termination codon. Abbreviations: ACT, adrenocortical carcinoma; CAD, Catalytic domain; ECD, extracellular domain; DBD, DNA binding domain; EPD, ependymoma; EWS, Ewing sarcoma; HB, hepatoblastoma; HD, Helicase domain; HGG, high grade glioma; LGG, low grade glioma; MB, medulloblastoma; NBL, neuroblastoma; OS, osteosarcoma; PA, pilocytic astrocytoma; PKD, Protein kinase domain; RB, retinoblastoma; RBM, RNA binding motif; RHB, rhabdosarcoma; SBD, Substrate binding domain; WLM, Wilms’ tumor; Level of evidence: A, validated association; B, clinical evidence; C, case study; D, preclinical evidence; E, inferential association.
Table
Table 5. Precision study designs for pediatric cancer: Single-arm design.
Table 5. Precision study designs for pediatric cancer: Single-arm design.
Gene Involved in Trial DesignNCT
(Recruitment
Status)		PhaseSpecificationIntervention(s)Cancer Type(s)EligibilityEnrollment (Number)
ALKNCT01742286 (D)IALK alterationsCeritinibALK-activated Tumors1–17 years83
NCT02465528 (C)IIALK alterationsCeritinibTumors With Aberrations in ALK, Glioblastoma≥18 years22
NCT02780128 (A)IALK mutationCeritinib + RibociclibNeuroblastoma1–21 years131
NCT03107988 (A)IALK alterationsLorlatinib + ChemotherapyNeuroblastoma≥1 year65
NCT03194893 (B)IIIALK alterationsAlectinib or CrizotinibNeoplasmsall200
NCT04774718 (A)I, IIALK fusionAlectinibALK Fusion-positive Solid or CNS Tumors≤17 years42
NCT05384626 (A)I, IIALK alterationsNVL-655Solid Tumor, NSCLC≥12 years214
BRAFNCT01089101 (B)I, IIBRAF V600E mutation or BRAF-KIAA1549 fusionSelumetinibLow Grade Glioma, Recurrent Childhood Pilocytic Astrocytoma, Recurrent Neurofibromatosis Type 13–21 years220
NCT01596140 (D)IBRAF mutationVemurafenib + Everolimus or TemsirolimusAdvanced Cancer, Solid Tumorall27
NCT01636622 (D)IBRAF mutationVemurafenib + ChemotherapyAdvanced Cancers≥12 years21
NCT01677741 (D)I, IIBRAF V600 mutationDabrafenibNeoplasms, Brain1–17 years85
NCT02124772 (D)I, IIBRAF V600 mutationDabrafenib + TrametinibSolid Tumors, neuroblastoma, low grade glioma, neurofibromatosis Type 11 month to 17 years139
NCT02684058 (B)IIBRAF V600 mutationDabrafenib + Trametinib + RadiationSolid Tumors, CNS Tumors, high grade glioma, low grade glioma1–17 years149
NCT03919071 (A)IIBRAF V600 mutationDabrafenib + Trametinib + RadiationAnaplastic Astrocytoma, Glioblastoma, Malignant Glioma1–21 years58
NCT04576117 (A)IIIBRAF rearrangementSelumetinib + ChemotherapyLow Grade Astrocytoma, Glioma2–25 years18
EGFRNCT00198159 (C)IIEGFR expressionGefitinibRefractory Germ Cell Tumors Expressing EGRF≥15 years21
NCT00418327 (D)IEGFR mutationErlotinib + RadiationMalignant Brain Tumor, Glioma1–21 years48
NCT01182350 (C)IIEGFR overexpressionErlotinib + Bevacizumab + Temozolomide + RadiationDiffuse Intrinsic Pontine Glioma3–18 years53
NCT01962896 (C)IIEGFR/mTOR pathway activationErlotinib + SirolimusRelapsed/Recurrent Germ Cell Tumors1–50 years4
EWSR1NCT03709680 (A)IIEWSR1-ETS or FUS-ETS rearrangementPalbociclib + ChemotherapyEwing Sarcoma, Rhabdomyosarcoma, Neuroblastoma, Medulloblastoma, Diffuse Intrinsic Pontine Glioma2–20 years184
NCT04129151 (B)IIEWSR1 or FUS translocationPalbociclib + GanitumabEwing Sarcoma12–50 years18
FGFRNCT04083976 (A)IIFGFR alterationErdafitinibAdvanced Solid Tumor≥6 years336
NCT05180825 (A)IIFGFR1 and MYB/MYBL1 alterations, 7q34 duplicationTrametinib or VinblastineGrade 1 Glioma, Mixed Glio-neuronal Tumors, Pleomorphic Xanthoastrocytoma1 month to 25 years134
H3NCT02525692 (B)IIH3 K27M mutationONC201Glioblastoma, Glioma≥16 years89
NCT03416530 (A)IH3 K27M mutationONC201Diffuse Intrinsic Pontine Glioma, Glioma, Malignant2–18 years130
NCT05009992 (A)IIH3 K27M mutationONC201 + Paxalisib or RadiationDiffuse Intrinsic Pontine Glioma, Diffuse Midline Glioma, H3 K27M-Mutant2–39 years216
IDHNCT03749187 (A)IIDH1/2 mutationPARP Inhibitor BGB-290 + ChemotherapyGlioblastoma, Glioma13–39 years78
MYCNNCT02559778 (A)IIMYCN amplificationCeritinib, Dasatinib, Sorafenib or Vorinostat + ChemotherapyNeuroblastoma≤22 years500
NCT03126916 (A)IIIMYCN amplificationLorlatinib + Standard therapyGanglioneuroblastoma, Neuroblastoma1–30 years658
NFNCT01158651 (D)IINF1 mutationEverolimusGlioma1–21 years23
NCT03095248 (A)IINF2 mutationSelumetinibNeurofibromatosis 2, Vestibular Schwannoma, Meningioma, Ependymoma, Glioma3–45 years34
NCT03326388 (A)I, IINF1 positiveSelumetinibNeurofibromatosis Type 1, Plexiform Neurofibroma, Optic Nerve Glioma3–18 years30
NCT03871257 (A)IIINF1 positiveSelumetinib + ChemotherapyLow Grade Glioma, Neurofibromatosis Type 1, Visual Pathway Glioma2–21 years290
NTRKNCT02637687 (A)I, IINTRK-fusionLarotrectinibSolid Tumors Harboring NTRK Fusion≤21 years155
NCT03834961 (A)IINTRK-fusionLarotrectinibSolid Tumor, CNS Tumor≤30 years70
NCT04879121 (A)IINTRK amplificationLarotrectinibSolid Neoplasm≥16 years13
PDGFRNCT00417807 (D)I, IIPDGFR expressionImatinibRefractory Desmoplastic Small Round Cell Tumors≥16 years9
NCT03352427 (C)IIPDGFR alterationDasatinib + EverolimusGlioma, High Grade Glioma, Pontine Tumors1–50 years3
Rb1NCT02255461 (C)IRb1 positivePalbociclibCNS Tumors, Solid Tumors4–21 years35
NCT03355794 (B)IRb1 positiveEverolimus + RibociclibDiffuse Intrinsic Pontine Glioma, Malignant Glioma of Brain, High Grade Glioma, Glioblastoma, Anaplastic Astrocytoma1–30 years24
NCT03387020 (D)IRb1 positiveEverolimus + RibociclibCNS Tumors1–21 years22
ALK
c-MET
ROS		NCT00939770 (D)I, IIALK or MET alterationsCrizotinibRecurrent Neuroblastoma1–21 years122
NCT01524926 (B)IIALK or MET pathway activationCrizotinibLymphoma, Sarcoma, Rhabdomyosarcoma≥1 year582
NCT02034981 (B)IIALK, MET or ROS1 alterationsCrizotinibSolid Tumors≥1 year246
NCT02650401 (A)I, IIALK, ROS1, or NTRK1-3 RearrangementsEntrectinibSolid Tumors, CNS Tumors, Neuroblastoma≤18 years68
NCT03093116 (A)I, IIALK, ROS1, or NTRK1-3 RearrangementsRepotrectinibSolid tumor, CNS tumor≥12 years500
RAS
RAF
MEK
ERK
NF1		NCT02285439 (B)I, IIBRAF truncated fusion or NF1 mutationMEK162Low-Grade Gliomas, Brain, Soft Tissue Neoplasms1–18 years105
NCT02639546 (D)I, IIRAS/RAF/MEK/ERK pathway activationCobimetinibSolid Tumors6 months to 30 years56
NCT03363217 (A)IIBRAF-KIAA1549 fusion, NF1 mutation, MAPK/ERK pathway activationTrametinibLow-grade Glioma, Plexiform Neurofibroma, Central Nervous System Glioma1 month to 25 years150
NCT04201457 (A)I, IIBRAF V600 mutation or truncated fusion, NF1 mutationDabrafenib + Trametinib + hydroxychloroquineLow Grade Glioma, High Grade Glioma1–30 years75
NCT04216953 (A)I, IIMAPK pathway status and Tumor Mutational BurdenCobimetinib + AtezolizumabSarcoma, Soft Tissue≥6 months120
SHH
WNT		NCT00822458 (D)ISHH or WNT signaling activationVismodegibRecurrent Childhood Medulloblastoma3–21 years34
NCT01239316 (D)IISHH signaling activationVismodegibRecurrent Childhood Medulloblastoma3–21 years12
NCT01878617 (A)IISHH or WNT signaling activationVismodegib + chemotherapyMedulloblastoma3–39 years660
OthersNCT01396408 (B)IIMutations in sunitinib targets such as VEGFR, PDGFR, KIT, RET or mutations in mTOR pathway such as PTEN, TS1/2, LKB1, NF1/2Sunitinib or temsirolimusAdvanced Rare Tumors≥16 years137
NCT03654716 (A)IMDM2, MDMX, PPM1D or TET2 amplificationALRN-6924Solid Tumor, CNS Tumor1–21 years69
Recruitment status: (A) Recruiting, (B) Active, not recruiting, (C) Terminated, (D) Completed.
Table
Table 6. Precision study designs for pediatric cancer: Multiple-arm design.
Table 6. Precision study designs for pediatric cancer: Multiple-arm design.
Gene Involved in Trial DesignNCT
(Recruitment Status)		PhaseSpecificationIntervention(s)Cancer Type(s)EligibilityEnrollment (Number)
Testing the Use of Food and Drug Administration (FDA)-Approved Drugs
(TAPUR)		NCT02693535 (A)IIALK, ROS1, METCrizotinibAdvanced Solid Tumors≥12 years3581
CDKN2A, CDK4, CDK6Palbociclib or Abemaciclib
CSF1R, PDGFR, VEGFRSunitinib
mTOR, TSCTemsirolimus
BRAF V600E/D/K/RVemurafenib and Cobimetinib
RET, VEGFR1/2/3, KIT, PDGFRβ, RAF-1, BRAFRegorafenib
BRCA1/2, ATMOlaparib
NRG1Afatinib
BRCA1/2, PALB2Talazoparib
ROS1 fusionEntrectinib
NTRK amplificationLarotrectinib
NCI-COG Pediatric MATCH ScreeningNCT03155620 (A)IINTRK1, NTRK2, or NTRK3 gene fusionLarotrectinibRefractory or Recurrent Advanced Solid Tumors1–21 years2316
FGFR1, FGFR2, FGFR3, or FGFR4 gene mutationErdafitinib
EZH2, SMARCB1, or SMARCA4 gene mutationTazemetostat
TSC1, TSC2, or PI3K/mTOR gene mutationSamotolisib
activating MAPK pathway gene mutationSelumetinib
ALK or ROS1 gene alterationEnsartinib
BRAF V600 gene mutationVemurafenib
ATM, BRCA1, BRCA2, RAD51C, RAD51D mutationsOlaparib
Rb positive, alterations in cell cycle genesPalbociclib
MAPK pathway mutationsUlixertinib
HRAS gene alterationsTipifarnib
RET activating mutationsSelpercatinib
TAPISTRY Platform Study NCT04589845 (A)IIROS1 fusionEntrectinibSolid Tumorall770
NTRK1/2/3 fusionEntrectinib
ALK fusionAlectinib
AKT1/2/3 mutationIpatasertib
PIK3CA multiple mutationInavolisib
BRAF mutation or fusion-positiveBelvarafenib
RET fusion-positivePralsetinib
Recruitment status: (A) Recruiting.
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Suthapot, P.; Chiangjong, W.; Chaiyawat, P.; Choochuen, P.; Pruksakorn, D.; Sangkhathat, S.; Hongeng, S.; Anurathapan, U.; Chutipongtanate, S. Genomics-Driven Precision Medicine in Pediatric Solid Tumors. Cancers 2023, 15, 1418. https://doi.org/10.3390/cancers15051418

AMA Style

Suthapot P, Chiangjong W, Chaiyawat P, Choochuen P, Pruksakorn D, Sangkhathat S, Hongeng S, Anurathapan U, Chutipongtanate S. Genomics-Driven Precision Medicine in Pediatric Solid Tumors. Cancers. 2023; 15(5):1418. https://doi.org/10.3390/cancers15051418

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Suthapot, Praewa, Wararat Chiangjong, Parunya Chaiyawat, Pongsakorn Choochuen, Dumnoensun Pruksakorn, Surasak Sangkhathat, Suradej Hongeng, Usanarat Anurathapan, and Somchai Chutipongtanate. 2023. "Genomics-Driven Precision Medicine in Pediatric Solid Tumors" Cancers 15, no. 5: 1418. https://doi.org/10.3390/cancers15051418

APA Style

Suthapot, P., Chiangjong, W., Chaiyawat, P., Choochuen, P., Pruksakorn, D., Sangkhathat, S., Hongeng, S., Anurathapan, U., & Chutipongtanate, S. (2023). Genomics-Driven Precision Medicine in Pediatric Solid Tumors. Cancers, 15(5), 1418. https://doi.org/10.3390/cancers15051418

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