Review Article | Open | Published:

Liquid biopsy for pediatric central nervous system tumors

Abstract

Central nervous system (CNS) tumors are the most common solid tumors in children, and the leading cause of cancer-related death. Over the past decade, molecular profiling has been incorporated into treatment for pediatric CNS tumors, allowing for a more personalized approach to therapy. Through the identification of tumor-specific changes, it is now possible to diagnose, assign a prognostic subgroup, and develop targeted chemotherapeutic treatment plans for many cancer types. The successful incorporation of informative liquid biopsies, where the liquid biome is interrogated for tumor-associated molecular clues, has the potential to greatly complement the precision-based approach to treatment, and ultimately, to improve clinical outcomes for children with CNS tumors. In this article, the current application of liquid biopsy in cancer therapy will be reviewed, as will its potential for the diagnosis and therapeutic monitoring of pediatric CNS tumors.

Introduction

Pediatric central nervous system (CNS) tumors are the leading cause of cancer-related death in children under the age of 19.1 Classically, these tumors were diagnosed by magnetic resonance imaging (MRI) and immunohistochemical assessment of surgically removed tissue. More recently, the World Health Organization (WHO) has begun incorporating molecular findings into the diagnosis of specific tumor types.2 Over time, it is likely that the detection and monitoring of molecular alterations will be critical for the clinical management of these tumors.3

While tumor resection for a subset of pediatric CNS tumors (e.g., frontal low-grade gliomas) can be both diagnostic and curative, many CNS tumors (e.g., diffuse midline gliomas) are not amenable to extensive surgical resection, either due to the infiltrating nature of the tumor or to its sensitive neuroanatomical location. For these tumors, stereotactic or open biopsy is often performed, but even these less invasive procedures carry the risk of serious surgical complications, and provide limited amounts of tumor tissue for pathologic and molecular diagnoses.4,5 Tissue biopsy is also subject to sampling bias, and tissue from a single tumor location may fail to capture intratumor heterogeneity.6,7,8,9 Longitudinal monitoring and assessment of tumor molecular events throughout the course of treatment also remains a challenge, since tumor resection or biopsy is often performed at diagnosis or recurrence, but not throughout the course of disease.

Current methods of monitoring pediatric CNS tumor response to therapy (MRI and clinical evaluation), are also limited in both sensitivity and specificity. For example, pseudoprogression (transient inflammation of the tumor region) resembles tumor progression on MR imaging, and may falsely inform treatment decisions.10,11,12,13 In addition, MR imaging cannot detect very small tumors and does not provide information about molecular changes that may be taking place within the tumor.

Pediatric CNS tumors demonstrate high need for minimally invasive molecular profiling of the tumor, and molecularly driven monitoring of tumor response and progression. Liquid biopsy, where the liquid biome is interrogated for detection of tumor-associated molecules, is a promising platform that can be used to address these limitations. In this review, liquid biopsy will first be described, and then its application and potential for providing next generation precision medicine for children diagnosed with CNS tumors will be discussed.

Liquid biome

The “liquid biome” refers to biological fluids (biofluids) including blood, urine and cerebrospinal fluid (CSF). Biofluids may contain small amounts of tumor cells or biomolecules, including circulating tumor cells (CTCs), cell free DNA (cfDNA), circulating tumor DNA (ctDNA) and RNA (ctRNA), microRNAs (miRNAs), fragmented peptides, and intact proteins. The concept of the liquid biome for detection of circulating tumor molecules is not new. In fact, CTCs were reported in the blood of a deceased cancer patient as early as 1869,14 and in 1996, ctDNA was discovered in the plasma of lung cancer patients.15 Despite the scientific community’s awareness of tumor biomarkers in circulation, technologies necessary to exploit the full potential of the liquid biome in cancer management have not been available until recently.

For circulating biomarkers to be clinically useful, they must be highly tumor-specific and present in detectable concentrations. One example is CTCs, which detach from the solid tumor mass and flow through circulation.16 CTCs represent 1 in 109 cells in peripheral blood,16 and are often detected through either positive or negative selection of specific tumor cell markers, which distinguish them from non-tumor cells.16,17,18,19 Because CTCs harbor tumor DNA, RNA, and proteins, they provide a rich source of information on the molecular biology of single tumor cells, and can shed light on mechanisms of clonal evolution, invasion and metastasis.20 Quantification of CTC abundance in peripheral blood may be used to monitor disease burden and progression, as shown in several recent publications.16,21,22 This is particularly useful for assessing a patient’s risk (i.e., identifying patients with metastasis), tumor recurrence, and response to therapy, which would be essential in the clinical management of children with CNS tumors.

cfDNA refers to fragments of DNA that are shed primarily by dying cells into biofluids.23 High levels of cfDNA in plasma of patients with advanced or progressive tumors has been associated with lower survival when compared to patients with lower cfDNA yield.24,25 Since cfDNA levels can be influenced by multiple factors, including increased white blood cell production, impaired renal function, and increased normal cell turnover, longitudinal monitoring of cfDNA levels will likely be more beneficial than a single time-point.26

The fraction of cfDNA that arises from tumor cells is called ctDNA.27 ctDNA are shorter and more fragmented than non-tumor DNA,28 and represent 0.01–10% of total cell free DNA in the blood, depending on tumor mitotic activity, treatment and tumor access to biofluids.27 ctDNA can be distinguished from background cfDNA based on the presence of tumor-specific mutations not found in the DNA of healthy cells, which provides a highly specific biomarker. ctDNA is thought to arise mainly from apoptotic and necrotic tumor cells, but may be released by any cell in the primary tumor and its metastatic lesions.27,29 As tumors grow, they undergo higher rates of cell turnover, releasing greater quantities of ctDNA.29 Thus, longitudinal monitoring of ctDNA abundance provides a traceable biomarker for assessing tumor burden. Given the relatively short half-life (~ 2 h) of ctDNA, real-time monitoring of tumor response by ctDNA profiling becomes a possibility.27 Indeed, highly sensitive assays have enabled detection of ctDNA harboring disease-specific mutations in the blood of patients diagnosed with colorectal,30,31,32 lung,33,34 and breast cancers,35 and in the CSF of adult36,37 and pediatric brain tumor patients.38,39 In these publications, ctDNA not only aids in diagnosis, but is also used to follow treatment response and progression. An advantage of ctDNA compared to CTC analysis is the relatively low amount of biofluid required to assess tumor mutation burden. For example, ongoing studies have shown that only 1 mL of plasma is sufficient for monitoring tumor response to treatment (further discussed below).39 This makes ctDNA longitudinal monitoring more feasible in pediatric patients, for whom it can be challenging to obtain large sample volumes.

ctRNA is also present in the liquid biome, and can be analyzed to inform potential fusion variants and changes in gene expression.40,41 This would be ideal for pediatric CNS tumors that are molecularly characterized by fusions, including KIAA1549-BRAF fusion positive low-grade gliomas, and by elevated transcript levels due to changes in gene expression or copy number alterations (CNAs), such as those occurring in medulloblastoma.8,42 However, due to vulnerability of single-stranded mRNA to degradation, ctRNA analysis is often more challenging than ctDNA.43

Unique, tumor-derived miRNA profiles have been identified in biofluids of adult glioblastoma, glioma, meningioma, adult medulloblastoma, and pediatric medulloblastoma.44,45,46,47 In adult glioma, strong downregulation of miR-122 in plasma is shown to be associated with disease progression and poorer prognosis.48 miRNA can also be used to detect patients with cancer and distinguish between cancer types. For example, high levels of miR-21 and miR-10b in the CSF of patients with glioblastoma or metastases from breast and lung cancer separates these patients from those who are either in remission or have non-neoplastic conditions, while miR-200a, miR-200b, miR-200c, and miR-141 are highly expressed in the CSF of patients with metastatic cancer but not primary brain tumors such as glioblastoma.47

Extracellular vesicles (EVs), including exosomes and microvesicles, also contain tumor-specific DNA, RNA and proteins, and therefore serve as a promising source of tumor biomarkers.49 Some studies have suggested that up to 93% of detectable cfDNA in plasma may be contained in exosomes, representing a potent source of tumor DNA for analysis.49 In serum of adult patients with glioblastoma, signature exosomal small non-coding RNA profiles have been identified and associated with glioblastoma diagnosis.50 miRNAs detected in plasma or serum are also often encapsulated into extracellular vesicles, protecting them from RNase digestion and enhancing their usefulness in determining diagnosis, prognosis, and therapeutic response in patients with CNS tumors.51 As such, overexpression of miR-21, mir-222 and miR-124-3p in serum exosomes has been shown to distinguish between high and low-grade gliomas, and to decrease post-surgical tumor resection, emphasizing the utility of EV-derived biomarkers as a means to determine tumor size and behavior.52 Proteins in extracellular vesicles can also provide information about tumor behavior. For example, high levels of proteins associated with cell invasion, including several proteins involved in regulation of invadopodia production, were found in patients with high-grade gliomas when compared to patients with low-grade gliomas.53

Proteins (intact proteins and peptide fragments) may also be directly secreted by tumor cells into the liquid biome. Proteins are stable and detectable in many types of biofluids, making them ideal biomarkers for the diagnosis and monitoring of many types of adult cancer.54,55,56,57,58,59 Proteins also show promise as biomarkers in pediatric CNS tumors. For example, studies have shown successful detection of tumor-associated proteins, including Cyclophillin A (CypA) and dimethylarginase 1 (DDAH1), in biofluids (urine, blood, and CSF) of children diagnosed with DIPG.60 Candidate biomarker proteins such as procollagen C-endopeptidase enhancer 1 (PCOLCE) have also been identified and linked to metastatic spread in CSF of children with CNS tumors,61 and enrichment of the protease inhibitor TIMP3 and growth factor bFGF in urine can help distinguish juvenile pilocytic astrocytomas from medulloblastoma and glioblastomas.62 Studying secreted peptides can also inform posttranslational modifications (PTMs), which may infer tumor response to treatment. Indeed, PTMs including lysine acetylation and arginine methylation of histone proteins have been detected in plasma and serum of patients with leukemia, breast and lung cancers.63

The tumor immunosignature is another potential biomarker that can be used for diagnosis and to inform targeted therapy for children with CNS tumors.64 During cancer development, the immune system produces antibodies against tumor-specific antigens that can be detected in the blood using methods such as high density peptide microarrays.65,66 Different adult cancer types have demonstrated unique immunosignatures that can be detected with high sensitivity and specificity in plasma or serum samples.64 Immunosignature profiles have been used in glioblastoma multiforme to detect tumor grade and MGMT promoter methylation status, which is predictive of response to temozolomide treatment in adults.67 Immunogenic profiling also provides the opportunity for targeted therapy, such as antigen-specific vaccines, that can adapt to changes within the tumor without requiring surgical resection.

These examples highlight several key nervous system tumor biomarkers present in the liquid biome, and strongly suggest the utility of these biomarkers in diagnosing, monitoring, and understanding tumor molecular alterations in various types of cancer. Although primarily for research, commercially available kits have been developed for biomarker isolation from biofluids including CSF, plasma, serum, and urine.16,18,68,69,70 These methods of isolation are continuing to evolve as more detailed characterizations of each biomarker comes to light, allowing for rapid, sensitive, and specific analysis of each biomarker, and greatly expanding the clinical potential of biomarkers in pediatric patients.

Methods of analysis

Major sources of CNS tumor biomarkers are CSF, blood (plasma or serum), and urine. CSF is the most abundant source of biomarkers for CNS tumors, due to its close proximity to the tumor mass.38,71 Blood and urine have lower levels of ctDNA and other tumor biomarkers when compared to CSF,72 but this can be overcome through the use of highly sensitive platforms, suitable to detect and quantify low levels of tumor-derived molecules. When identifying a liquid source of biomarkers, it is important to find a balance between discomfort and risk to the patient, and obtaining sufficient levels of tumor biomarkers for analysis. Therefore, while CSF may serve as a better upfront medium for preliminary diagnosis and to detect commonly occurring mutations (using targeted sequencing and/or digital droplet PCR (ddPCR)), blood or urine may be a preferable source for long-term monitoring of variants associated with tumor response and progression.

The method of analysis will also be important to consider, depending on the liquid source. Highly sensitive methods are required for the detection and analysis of circulating tumor DNA, RNAs, and proteins within the liquid biome. Each biomarker is unique, requiring the presence of multiple different analytical methods, which can be adapted for different tumor types. For example, in some cases ctRNA is needed for detection of fusion variants that characterize a certain tumor type. In other instances, single gene mutations in ctDNA can be used to diagnose tumor subclass. Others may require detection of multiple mutation sites across the genome, or methods such as genome bisulfite sequencing or chromatin immunoprecipitation sequencing (ChIP-seq) to identify protein-DNA interactions or differential methylation patterns within CTCs.73 Different methods of analysis that can be applied to pediatric CNS tumors are highlighted below and in Tables 1 and 2.

Table 1 Examples of what has been accomplished using digital droplet PCR, alone or in combination with BEAMing PCR or custom amplicon sequencing, with a focus on CNS cancers
Table 2 Examples of liquid biopsy applications, including analysis of CTCs, cell free DNA and protein biomarkers, with a focus on CNS cancers

Digital droplet PCR

ddPCR is capable of sensitively detecting and quantifying the allelic frequency of circulating tumor DNA fragments against a backdrop of non-tumor DNA.74 By using sequence-specific probes and primers that selectively bind to the mutant and wild type alleles of a target gene, ddPCR allows for the detection of very low frequency variants, and can provide an assessment of mutational burden. This is particularly useful for the serial monitoring of mutation allelic frequency (MAF) to track changes related to tumor clonal evolution, as well as tumor progression/regression (reflected in the abundance of mutant DNA detected). To maximize diagnostic utility of a single sample, these reactions can be multiplexed to detect up to four target sequences (two mutant, two wild type), using targeted primers and probes.75

Due to its ability to detect and quantify rare mutant alleles using very low levels of ctDNA, ddPCR has increasingly become the platform of choice in preclinical studies of colorectal,76 breast,35 melanoma,77 lymphoma,78 brain,75,79 and other cancer types80 (Table 1). In a recent publication, our group showed that ddPCR monitoring of single nucleotide variants associated with histone H3 (H3K27M mutation), both in blood and CSF, can be effective for tumor subtyping and following tumor response in DIPG and other midline tumors.39 In patients with Langerhans Cell Histiocytosis (LCH) and Erdheim-Chester Disease (ECD), BRAF V600E-positive cells can be detected in the urine and plasma, which has assisted with diagnosis and monitoring of patients treated with molecularly targeted therapy.81,82 Given the success of this method in LCH and ECD patients, this could potentially be expanded for other pediatric patients with BRAF V600E-positive tumors, such as gliomas (Table 3).

Table 3 Examples of key molecular alterations identified in the most common pediatric nervous system tumors, which could potentially be the focus of liquid biopsy-based mutation screening. Detection of these alterations in the liquid biome could facilitate diagnosis, classification of patients into molecular subgroups, qualification or disqualification from clinical trials, prediction of prognosis and detection of clonal evolution

In addition to single nucleotide variants, ddPCR can detect differentially methylated cfDNA, which has been particularly useful for colorectal cancer in adults.83,84,85,86 This has clear application to pediatric CNS cancers such as pediatric high grade gliomas, ependymomas, and embryonal tumors, which harbor relatively low mutation load but have distinct methylation patterns.87,88 As such, ddPCR provides a rapid, affordable, and sensitive method for detecting distinct CNS tumor markers in pediatric patients.

BEAMing PCR

Another highly sensitive platform for detecting tumor mutations in cfDNA is beads, emulsification, amplification and magnetics PCR (BEAMing PCR). In BEAMing PCR, individual DNA sequences are amplified, fluorescently-labeled, and quantified using flow cytometry. Like ddPCR, BEAMing assays can be multiplexed for identification of multiple mutations in a single sample, maximizing diagnostic utility and requiring fewer samples for analysis.89 These PCR-based approaches are highly sensitive and detect tumor-specific variants at a frequency as low as 0.001%, provide a measurement of mutation load, and enable identification of residual disease post-treatment.90 BEAMing PCR has shown promise in detecting mutations in preclinical studies of adult colorectal,27,30,31,32 lung,33 breast,91 and glioma92 (Table 1). Similar to ddPCR, BEAMing PCR has fast turn-around time and is more cost effective compared to next generation sequencing, making this an attractive method for real-time diagnosis and therapeutic monitoring for children with CNS tumors.

Next generation sequencing

Although PCR-based methods are sufficient at detecting mutations in very low levels of ctDNA, they are limited by the number of mutations that can be detected, and perform best when targeting a single nucleotide variant. However, multiple genes may be mutated in pediatric CNS cancers, each with multiple alterations rather than a unique nucleotide change (Table 3).93 Detecting mutations in these genes using a PCR-based approach would require dozens of probes and primers, which is both costly and inefficient. Next generation sequencing (NGS) of ctDNA allows for the detection of a wide range of alterations, including unknown variants, typically through targeted exon sequencing. This approach has been used in neuroblastoma, and in adult cancers including glioblastoma, gastrointestinal, genitourinary, breast, and lung cancers.36,94,95,96 NGS would be ideal for the discovery of mutations acquired at the time of progression, or the detection of mutations in ctDNA from patients where the molecular profile of the tumor is unknown.

Compared to PCR, NGS is more expensive and time-consuming, and less sensitive, detecting variants as low as 0.1–10% frequency, compared to 0.001% in ddPCR and BEAMing PCR.90 In contrast to tissue-derived DNA, ctDNA is highly fragmented and short, and requires higher sequencing depth. To accommodate this, new NGS platforms that amplify and sequence specific regions of the genome are being developed and optimized for use with low input, fragmented ctDNA (Table 2). Thus, NGS technology has the potential to revolutionize the approach to diagnosis and clinical management of pediatric CNS cancers.

Alternative technologies

Massively parallel bisulfite sequencing is being used to detect genome-wide methylation changes in plasma cfDNA. In this method, unmethylated cytosine is converted to uracil, and uracil is then converted to thymine during PCR amplification, allowing methylated cytosines to be identified.97 This has been used to detect hypomethylation in cfDNA in plasma of cancer patients with high sensitivity and specificity.97 For example, in esophageal cancers, genome-wide DNA methylation profiling of serum cfDNA has shown the methylation profile of cfDNA to be highly concordant with tumor tissue DNA.98 As noted previously, this method would be beneficial for detecting and monitoring levels of ctDNA marked by distinct methylation patterns in children with CNS tumors.

For cell free RNA, miRNA and exosomal RNA isolation, whole blood is collected in tubes containing RNA-stabilizing preservatives.40,41,99 RNA can then be reverse transcribed to complementary DNA, and used to detect fusion variants via next generation sequencing or ddPCR of cDNA. This has been useful for the detection of EGFR(v)III amplification in high-grade gliomas, and IDH1 G395A mutant transcripts in adult glioma (Table 1).40,41,92 Some pediatric CNS tumors, such as gliomas and ependymomas, are primarily characterized by fusion variants (i.e., KIAA1567-BRAF, RELA, NTRK fusions (Table 3)). For these tumors, RNA would likely be more informative as a biomarker than ctDNA.

Liquid chromatography-mass spectrometry (LC-MS) is a commonly used method for protein biomarker discovery and quantification (including quantification of tumor-specific missense mutant proteins) in specimens including plasma, serum, urine and tissue.61,100 LC-MS methods are being developed to minimize protein loss and degradation, and to achieve deep proteome profiling in plasma.101 LC-MS following immunoaffinity purification has been used to identify tumor HLA antigens in plasma of adults with glioblastoma.102 In addition to high resolution mass spectrometry, methods such as ELISAs (enzyme linked immunosorbent assays) can quantify protein levels in blood. For example, programmed cell death ligand-1 (PD-L1) has been quantified in pre-treatment serum of patients with hepatocellular carcinoma, and shown to be predictive of disease-free and overall survival.103 Additional approaches to protein analysis use a top-down approach, in which the entire proteoform (the diversity of forms that can arise from a single protein-coding gene, including those produced by posttranslational modifications and alternative splicing) is analyzed in its undigested form.104 This approach was used to analyze intact proteins in the CSF of pediatric patients with posterior cranial fossa brain tumors, leading to identification of LVV- and VV-hemorphin-7 as potential biomarkers for this tumor type.105 Despite the potential, there remains a need to develop more robust bioinformatics pipelines for proteomic analyses,106 particularly for complex proteoforms isolated through the top-down approach.107 With further optimization, quantification and longitudinal monitoring of protein biomarkers have clear application to pediatric CNS tumors.

Liquid biopsy for pediatric CNS tumors: challenges and future directions

There are several key challenges in the implementation of liquid biopsy for the clinical care of pediatric CNS tumor patients. These platforms are not certified by CLIA (Clinical Laboratory Improvement Amendments) regulations, and there are a limited number of facilities that have the training and tools required for this type of liquid biopsy analysis. Additionally, methods of collecting and processing liquid biopsy specimens have not been standardized and optimized for downstream analysis of each biomarker type. For example, ctDNA integrity is affected by factors such as temperature and storage conditions,108,109 and time-dependent increases in cfDNA quantity have been observed in blood samples due to leukocyte lysis, which can increase total levels of cfDNA and dilute the fraction of tumor-specific DNA below the level of detection.110 In addition to the pre-analytic conditions such as collection, processing, and storage, other variables such as patient gender, age, lifestyle, and exposure to medications may also affect protein analysis, and should be considered during interpretation of the results.106 Therefore, before liquid biopsy can become a standard-of-care test, collection and preparation conditions for different biofluids and biomarker types will need to be standardized across institutions.

Another challenge is the sample volume required by many assays currently used to analyze liquid biopsy samples. Due to the low levels of tumor biomarkers in the liquid biome, in order to achieve a sufficient quantity of CTCs, ctDNA, ctRNA and other biomarkers, high sample volumes may be necessary. For example, up to 10 mL of blood and 7.5 mL of CSF have been used for biomarker discovery in adult patients (Table 2), which can be difficult to obtain in young children. High sensitivity assays, designed to detect and amplify low levels of tumor biomarkers, can be used in the pediatric population to overcome this challenge. For example, using ddPCR for commonly occuring mutations, or the top-down approach for protein isolation, can improve biomarker detection and recovery, allowing for the collection of smaller sample volumes.38,104 Careful selection of the liquid source can also decrease volume requirements in pediatric patients. Since tumor biomarker concentrations are highest in close proximity to the tumor,71 CSF may be the preferred liquid source for biomarkers that are present in low concentrations, such as ctDNA and ctRNA, while blood and urine could be used for CTCs, EVs, or protein biomarkers.

As mentioned above, sequencing fragmented ctDNA also poses a challenge. The short size of the DNA fragments makes sequencing more difficult, and individual read lengths can be subjected to adapter contamination, resulting in misalignment of the DNA molecule to the reference genome or a low alignment score, causing the read to be discarded.111 Because of this, low frequency variants could be missed due to their small size and incompatibility with current sequencing methods intended for larger DNA fragments. cfDNA requires extremely sensitive, high-throughput sequencing platforms that reduce background noise, detect low frequency variants, and encompass the diversity of tumor molecular alterations.111 Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq, Table 2) is an example of a new sequencing platform that optimizes library preparation methods for low DNA input, allowing for increased sensitivity despite low fluid volumes.112

Disruption of epigenetic mechanisms that affect DNA methylation, nucleosome positioning, long non-coding RNA and miRNA profiles, and chromatin accessibility, are also known to play a major role in early tumor development for pediatric CNS tumors such as medulloblastoma and gliomas.113,114 As noted previously, methods such as ChIP-seq and genome bisulfite sequencing are already being used to identify protein-DNA interactions or differential methylation patterns within CTCs of adult cancer patients,73 and could potentially be applied to the pediatric population. Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq), micrococcal nuclease-assisted isolation of nucleosomes sequencing (MNase-seq) and DNase I hypersensitive sites sequencing (DNase-seq) are being developed for adult tumor tissue to detect chromatin accessibility, particularly at regulatory sites such as promoters, enhancers, and insulators.115,116,117 Although not currently being used in the context of liquid biopsy, these novel approaches could potentially allow for rapid detection of epigenetic biomarkers in CTCs, and should be considered in pediatric CNS tumor biomarker research.

While each biomarker may play an independent role in tumor management, it is likely that biomarker combinations, such as protein plus ctDNA as seen in pancreatic cancer,118 may be even more useful for the early detection and monitoring of pediatric CNS tumors. Further studies that examine the role of each biomarker, both alone and in combination with other biomarkers, will be required to fully recognize the potential of liquid biopsy in children with CNS tumors. Finally, in order to capture the variety of alterations in pediatric CNS tumors (Table 3), distinct and specialized methods will need to be developed for accurate analysis of different types of alterations, including single nucleotide variants, fusions, copy number alterations, differential methylation patterns and miRNA profiles. The relatively small numbers of patients, and the limited number of institutions prioritizing pediatric CNS tumor biomarker research, pose a challenge in the discovery and technical validation of liquid biopsy platforms for this patient population. Multi-institutional collaborations will be important for combining liquid biopsy data (including data from CTC, ctDNA, ctRNA, exosome and protein analyses) together with clinical data, to more tightly define the predictive value of liquid-based biomarkers, and to correlate this data to clinical outcome—with a specific focus on the pediatric population.

Conclusion

Over the past ten years, knowledge of common molecular alterations in pediatric central nervous system tumors has greatly expanded (Table 3). This data has come largely from sequencing of tumor tissue DNA, and has led to classification of tumors into molecular subgroups with distinct clinical outcomes,8,119,120 empowering development of therapeutics that target tumor subtype-specific alterations with greater precision. With increased understanding of the genomic landscape of pediatric CNS tumors, liquid biopsy will serve as a powerful tool to complement our current methods of diagnosis and management.

When used alongside conventional diagnostic methods, liquid biopsy can fill in information gaps that are otherwise not available. At diagnosis, liquid biopsy has the potential to (1) provide information about prognosis in conjunction with tumor histology and MRI characteristics; (2) supplement tumor biopsy by sampling multiple tumor cells, revealing mutations that may not be detected in biopsy tissue due to tumor heterogeneity; and/or (3) provide a diagnosis for patients who were not able to undergo biopsy, but have specific diagnostic alterations (such as an H3K27M mutation in a patient with a midline tumor) noted in liquid samples. During the treatment course, biomarker levels can be used alongside MRI to assess treatment response and help differentiate between tumor pseudoprogression and true progression. At progression, molecular profiling of ctDNA can detect new mutations, possibly before changes in the tumor are seen on MRI, allowing for earlier intervention and overall improved outcomes. Finally, molecular and epigenetic changes that occur in the tumor at progression can be identified in liquid samples, offering critical information that can be used to develop a new treatment plan without requiring a tissue biopsy.

Despite the challenges facing the implementation of liquid biopsy as a clinical tool for pediatric CNS tumor diagnosis and surveillance, these challenges can be overcome with continuing optimization of analytical methods, and with increases in sensitivity and specificity. Once clinically validated, the successful utilization of liquid biopsy in pediatric CNS tumors will address unmet needs in pediatric neuro-oncology, and will ultimately improve outcomes for these patients.

Data availability

No datasets were generated or analyzed during the current study.

Ethics declarations

Competing interests

The authors declare no competing interests.

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References

  1. 1.

    Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2016. CA Cancer J. Clin. 66, 7–30 (2016).

  2. 2.

    Louis, D. N. et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131, 803–820 (2016).

  3. 3.

    Kline, C. N. et al. Targeted next-generation sequencing of pediatric neuro-oncology patients improves diagnosis, identifies pathogenic germline mutations, and directs targeted therapy. Neuro. Oncol. 19, 699–709 (2017).

  4. 4.

    Jackson, R. J. et al. Limitations of stereotactic biopsy in the initial management of gliomas. Neuro. Oncol. 3, 193–200 (2001).

  5. 5.

    Hamisch, C., Kickingereder, P., Fischer, M., Simon, T. & Ruge, M. I. Update on the diagnostic value and safety of stereotactic biopsy for pediatric brainstem tumors: a systematic review and meta-analysis of 735 cases. J. Neurosurg. Pediatr. 20, 261–268 (2017).

  6. 6.

    Chicard, M. et al. Genomic copy number profiling using circulating free tumor dna highlights heterogeneity in neuroblastoma. Clin. Cancer Res. 22, 5564–5573 (2016).

  7. 7.

    Salloum, R. et al. Characterizing temporal genomic heterogeneity in pediatric high-grade gliomas. Acta Neuropathol. Commun. 5, 78 (2017).

  8. 8.

    Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).

  9. 9.

    Vinci, M. et al. Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells. Nat. Med. 8, 1204–1215 (2018).

  10. 10.

    Aquino, D., Gioppo, A., Finocchiaro, G., Bruzzone, M. G. & Cuccarini, V. MRI in glioma immunotherapy: evidence, pitfalls, and perspectives. J. Immunol. Res. 2017, 5813951 (2017).

  11. 11.

    Carceller, F. et al. Pseudoprogression in children, adolescents and young adults with non-brainstem high grade glioma and diffuse intrinsic pontine glioma. J. Neurooncol. 129, 109–121 (2016).

  12. 12.

    Calmon, R. et al. Multimodal magnetic resonance imaging of treatment-induced changes to diffuse infiltrating pontine gliomas in children and correlation to patient progression-free survival. Int. J. Radiat. Oncol. Biol. Phys. 99, 476–485 (2017).

  13. 13.

    Calmon, R. et al. Cerebral blood flow changes after radiation therapy identifies pseudoprogression in diffuse intrinsic pontine gliomas. Neuro. Oncol. 20, 994–1002 (2018).

  14. 14.

    Ashworth, T. A case of cancer in which cells similar to those in the tumours were seen in the blood after death. Aust. Med J. 14, 146–149 (1869).

  15. 15.

    Chen, X. Q. et al. Microsatellite alterations in plasma DNA of small cell lung cancer patients. Nat. Med. 2, 1033–1035 (1996).

  16. 16.

    Mansilla, C., Soria, E. & Ramirez, N. The identification and isolation of CTCs: a biological Rubik’s cube. Crit. Rev. Oncol. Hematol. 126, 129–134 (2018).

  17. 17.

    Ferreira, M. M., Ramani, V. C. & Jeffrey, S. S. Circulating tumor cell technologies. Mol. Oncol. 10, 374–394 (2016).

  18. 18.

    Zhang, W. et al. Liquid biopsy for cancer: circulating tumor cells, circulating free DNA or exosomes? Cell. Physiol. Biochem. 41, 755–768 (2017).

  19. 19.

    Schilling, D. et al. Isolated, disseminated and circulating tumour cells in prostate cancer. Nat. Rev. Urol. 9, 448–463 (2012).

  20. 20.

    Ortiz, V. & Yu, M. Analyzing circulating tumor cells one at a time. Trends Cell Biol. 28, 764–775 (2018).

  21. 21.

    Ignatiadis, M., Lee, M. & Jeffrey, S. S. Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin. Cancer Res. 21, 4786–4800 (2015).

  22. 22.

    Hashimoto, M. et al. Positive correlation between postoperative tumor recurrence and changes in circulating tumor cell counts in pulmonary venous blood (pvCTC) during surgical manipulation in non-small cell lung cancer. J. Thorac. Dis. 10, 298–306 (2018).

  23. 23.

    Volik, S., Alcaide, M., Morin, R. D. & Collins, C. Cell-free DNA (cfDNA): clinical significance and utility in cancer shaped by emerging technologies. Mol. Cancer Res. 14, 898–908 (2016).

  24. 24.

    Mehrotra, M. et al. Detection of somatic mutations in cell-free DNA in plasma and correlation with overall survival in patients with solid tumors. Oncotarget 9, 10259–10271 (2018).

  25. 25.

    Valpione, S. et al. Plasma total cell-free DNA (cfDNA) is a surrogate biomarker for tumour burden and a prognostic biomarker for survival in metastatic melanoma patients. Eur. J. Cancer 88, 1–9 (2018).

  26. 26.

    Gorgannezhad, L., Umer, M., Islam, M. N., Nguyen, N. T. & Shiddiky, M. J. A. Circulating tumor DNA and liquid biopsy: opportunities, challenges, and recent advances in detection technologies. Lab. Chip. 18, 1174–1196 (2018).

  27. 27.

    Diehl, F. et al. Circulating mutant DNA to assess tumor dynamics. Nat. Med. 14, 985–990 (2008).

  28. 28.

    Underhill, H. R. et al. Fragment length of circulating tumor DNA. PLoS. Genet. 12, e1006162, https://doi.org/10.1371/journal.pgen.1006162 (2016).

  29. 29.

    Jahr, S. et al. DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 61, 1659–1665 (2001).

  30. 30.

    Tabernero, J. et al. Analysis of circulating DNA and protein biomarkers to predict the clinical activity of regorafenib and assess prognosis in patients with metastatic colorectal cancer: a retrospective, exploratory analysis of the CORRECT trial. Lancet Oncol. 16, 937–948 (2015).

  31. 31.

    Grasselli, J. et al. Concordance of blood- and tumor-based detection of RAS mutations to guide anti-EGFR therapy in metastatic colorectal cancer. Ann. Oncol. 28, 1294–1301 (2017).

  32. 32.

    Klein-Scory, S. et al. Significance of liquid biopsy for monitoring and therapy decision of colorectal cancer. Transl. Oncol. 11, 213–220 (2018).

  33. 33.

    Oxnard, G. R. et al. Association between plasma genotyping and outcomes of treatment with osimertinib (AZD9291) in advanced non-small-cell lung cancer. J. Clin. Oncol. 34, 3375–3382 (2016).

  34. 34.

    Krug, A. K. et al. Improved EGFR mutation detection using combined exosomal RNA and circulating tumor DNA in NSCLC patient plasma. Ann. Oncol. 29, 700–706 (2018).

  35. 35.

    Olsson, E. et al. Serial monitoring of circulating tumor DNA in patients with primary breast cancer for detection of occult metastatic disease. EMBO Mol. Med. 7, 1034–1047 (2015).

  36. 36.

    Schwaederle, M. et al. Detection rate of actionable mutations in diverse cancers using a biopsy-free (blood) circulating tumor cell DNA assay. Oncotarget 7, 9707–9717 (2016).

  37. 37.

    Martinez-Ricarte, F. et al. Molecular diagnosis of diffuse gliomas through sequencing of cell-free circulating tumor dna from cerebrospinal fluid. Clin. Cancer Res. 24, 2812–2819 (2018).

  38. 38.

    Huang, T. Y. et al. Detection of Histone H3 mutations in cerebrospinal fluid-derived tumor DNA from children with diffuse midline glioma. Acta Neuropathol. Commun. 5, 28 (2017).

  39. 39.

    Panditharatna, E. et al. Clinically relevant and minimally invasive tumor surveillance of pediatric diffuse midline gliomas using patient-derived liquid biopsy. Clin. Cancer Res. https://doi.org/10.1158/1078-0432.CCR-18-1345 (2018).

  40. 40.

    Manda, S. V. et al. Exosomes as a biomarker platform for detecting epidermal growth factor receptor-positive high-grade gliomas. J. Neurosurg. 128, 1091–1101 (2018).

  41. 41.

    Figueroa, J. M. et al. Detection of wild-type EGFR amplification and EGFRvIII mutation in CSF-derived extracellular vesicles of glioblastoma patients. Neuro. Oncol. 19, 1494–1502 (2017).

  42. 42.

    Zhang, J. et al. Whole-genome sequencing identifies genetic alterations in pediatric low-grade gliomas. Nat. Genet. 45, 602–612 (2013).

  43. 43.

    Rapisuwon, S., Vietsch, E. E. & Wellstein, A. Circulating biomarkers to monitor cancer progression and treatment. Comput. Struct. Biotechnol. J. 14, 211–222 (2016).

  44. 44.

    Drusco, A. et al. A differentially expressed set of microRNAs in cerebro-spinal fluid (CSF) can diagnose CNS malignancies. Oncotarget 6, 20829–20839 (2015).

  45. 45.

    Shalaby, T. F. G., B. S., Gerber, N. U., Baumgartner, M. & Grotzer, M. A. Detection and quantification of extracellular microRNAs in medulloblastoma. J. Cancer Metastas-. Treat. 1, 67–75 (2015).

  46. 46.

    Akers, J. C. et al. A cerebrospinal fluid microRNA signature as biomarker for glioblastoma. Oncotarget 8, 68769–68779 (2017).

  47. 47.

    Teplyuk, N. M. et al. MicroRNAs in cerebrospinal fluid identify glioblastoma and metastatic brain cancers and reflect disease activity. Neuro. Oncol. 14, 689–700 (2012).

  48. 48.

    Tang, Y. et al. Plasma miR-122 as a potential diagnostic and prognostic indicator in human glioma. Neurol. Sci. 38, 1087–1092 (2017).

  49. 49.

    Fernando, M. R., Jiang, C., Krzyzanowski, G. D. & Ryan, W. L. New evidence that a large proportion of human blood plasma cell-free DNA is localized in exosomes. PLoS. One. 12, e0183915, https://doi.org/10.1371/journal.pone.0183915 (2017).

  50. 50.

    Manterola, L. et al. A small noncoding RNA signature found in exosomes of GBM patient serum as a diagnostic tool. Neuro. Oncol. 16, 520–527 (2014).

  51. 51.

    Wang, H., Peng, R., Wang, J., Qin, Z. & Xue, L. Circulating microRNAs as potential cancer biomarkers: the advantage and disadvantage. Clin. Epigenetics 10, 59 (2018).

  52. 52.

    Santangelo, A. et al. A microRNA signature from serum exosomes of patients with glioma as complementary diagnostic biomarker. J. Neurooncol. 136, 51–62 (2018).

  53. 53.

    Mallawaaratchy, D. M. et al. Comprehensive proteome profiling of glioblastoma-derived extracellular vesicles identifies markers for more aggressive disease. J. Neurooncol. 131, 233–244 (2017).

  54. 54.

    Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

  55. 55.

    Di Meo, A., Bartlett, J., Cheng, Y., Pasic, M. D. & Yousef, G. M. Liquid biopsy: a step forward towards precision medicine in urologic malignancies. Mol. Cancer 16, 80 (2017).

  56. 56.

    Jimenez-Luna, C. et al. Proteomic biomarkers in body fluids associated with pancreatic cancer. Oncotarget 9, 16573–16587 (2018).

  57. 57.

    Kuppusamy, P., Govindan, N., Yusoff, M. M. & Ichwan, S. J. A. Proteins are potent biomarkers to detect colon cancer progression. Saudi J. Biol. Sci. 24, 1212–1221 (2017).

  58. 58.

    Van Raemdonck, G. A., Tjalma, W. A., Coen, E. P., Depuydt, C. E. & Van Ostade, X. W. Identification of protein biomarkers for cervical cancer using human cervicovaginal fluid. PLoS. One. 9, e106488, https://doi.org/10.1371/journal.pone.0106488 (2014).

  59. 59.

    Humphries, J. M. et al. Identification and validation of novel candidate protein biomarkers for the detection of human gastric cancer. Biochim. Biophys. Acta 1844, 1051–1058 (2014).

  60. 60.

    Saratsis, A. M. et al. Insights into pediatric diffuse intrinsic pontine glioma through proteomic analysis of cerebrospinal fluid. Neuro. Oncol. 14, 547–560 (2012).

  61. 61.

    Spreafico, F. et al. Proteomic analysis of cerebrospinal fluid from children with central nervous system tumors identifies candidate proteins relating to tumor metastatic spread. Oncotarget 8, 46177–46190 (2017).

  62. 62.

    Pricola Fehnel, K. et al. Using urinary bFGF and TIMP3 levels to predict the presence of juvenile pilocytic astrocytoma and establish a distinct biomarker signature. J. Neurosurg. Pediatr. 18, 396–407 (2016).

  63. 63.

    Gu, H. et al. Quantitative profiling of post-translational modifications by immunoaffinity enrichment and LC-MS/MS in cancer serum without immunodepletion. Mol. Cell. Proteom. 15, 692–702 (2016).

  64. 64.

    Stafford, P., Cichacz, Z., Woodbury, N. W. & Johnston, S. A. Immunosignature system for diagnosis of cancer. Proc. Natl. Acad. Sci. USA 111, E3072–E3080 (2014).

  65. 65.

    Chapoval, A. I. et al. Immunosignature: serum antibody profiling for cancer diagnostics. Asian Pac. J. Cancer Prev. 16, 4833–4837 (2015).

  66. 66.

    O’Donnell, B., Maurer, A., Papandreou-Suppappola, A. & Stafford, P. Time-frequency analysis of peptide microarray data: application to brain cancer immunosignatures. Cancer Inform. 14, 219–233 (2015).

  67. 67.

    Hughes, A. K. et al. Immunosignaturing can detect products from molecular markers in brain cancer. PLoS. One. 7, e40201, https://doi.org/10.1371/journal.pone.0040201 (2012).

  68. 68.

    Li, J., Hu, J., Newman, M., Liu, K. & Ge, H. RNA-seq analysis pipeline based on oshell environment. IEEE/Acm. Trans. Comput. Biol. Bioinform. 11, 973–978 (2014).

  69. 69.

    Lowes, L. E. et al. Circulating tumor cells (CTC) and cell-free DNA (cfDNA) workshop 2016: scientific opportunities and logistics for cancer clinical trial incorporation. Int. J. Mol. Sci. 17, 1505 (2016).

  70. 70.

    Lucchetti, D., Fattorossi, A. & Sgambato, A. Extracellular vesicles in oncology: progress and pitfalls in the methods of isolation and analysis. Biotechnol. J. e1700716, https://doi.org/10.1002/biot.201700716 (2018).

  71. 71.

    Stallard, S. et al. CSF H3F3A K27M circulating tumor DNA copy number quantifies tumor growth and in vitro treatment response. Acta Neuropathol. Commun. 6, 80 (2018).

  72. 72.

    De Mattos-Arruda, L. et al. Cerebrospinal fluid-derived circulating tumour DNA better represents the genomic alterations of brain tumours than plasma. Nat. Commun. 6, 8839 (2015).

  73. 73.

    Ortiz, V. & Yu, M. Analyzing circulating tumor cells one at a time. Trends Cell Biol. 28, 764–775 (2018).

  74. 74.

    Hindson, C. M. et al. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat. Methods 10, 1003–1005 (2013).

  75. 75.

    Pan, W., Gu, W., Nagpal, S., Gephart, M. H. & Quake, S. R. Brain tumor mutations detected in cerebral spinal fluid. Clin. Chem. 61, 514–522 (2015).

  76. 76.

    Spindler, K. G. Methodological, biological and clinical aspects of circulating free DNA in metastatic colorectal cancer. Acta Oncol. 56, 7–16 (2017).

  77. 77.

    Busser, B. et al. Plasma circulating tumor DNA levels for the monitoring of melanoma patients: landscape of available technologies and clinical applications. Biomed. Res. Int. 2017, 5986129 (2017).

  78. 78.

    Hiemcke-Jiwa, L. S. et al. The use of droplet digital PCR in liquid biopsies: a highly sensitive technique for MYD88 p.(L265P) detection in cerebrospinal fluid. Hematol. Oncol. 36, 429–435 (2018).

  79. 79.

    Hirano, M. et al. A novel high-sensitivity assay to detect a small fraction of mutant IDH1 using droplet digital PCR. Brain. Tumor Pathol. 35, 97–105 (2018).

  80. 80.

    Olmedillas-Lopez, S., Garcia-Arranz, M. & Garcia-Olmo, D. Current and emerging applications of droplet digital PCR in oncology. Mol. Diagn. Ther. 21, 493–510 (2017).

  81. 81.

    Hyman, D. M. et al. Prospective blinded study of BRAFV600E mutation detection in cell-free DNA of patients with systemic histiocytic disorders. Cancer Discov. 5, 64–71 (2015).

  82. 82.

    Heritier, S. et al. Circulating cell-free BRAF(V600E) as a biomarker in children with Langerhans cell histiocytosis. Br. J. Haematol. 178, 457–467 (2017).

  83. 83.

    Kim, H., Wang, X. & Jin, P. Developing DNA methylation-based diagnostic biomarkers. J. Genet. Genom. 45, 87–97 (2018).

  84. 84.

    Boeckx, N. et al. Mutation and methylation analysis of circulating tumor DNA can be used for follow-up of metastatic colorectal cancer patients. Clin. Colorectal Cancer 17, e369–e379 (2018).

  85. 85.

    Garrigou, S. et al. A study of hypermethylated circulating tumor DNA as a universal colorectal cancer biomarker. Clin. Chem. 62, 1129–1139 (2016).

  86. 86.

    Barault, L. et al. Discovery of methylated circulating DNA biomarkers for comprehensive non-invasive monitoring of treatment response in metastatic colorectal cancer. Gut 67, 1995–2005 (2017).

  87. 87.

    Ahsan, S. et al. Increased 5-hydroxymethylcytosine and decreased 5-methylcytosine are indicators of global epigenetic dysregulation in diffuse intrinsic pontine glioma. Acta Neuropathol. Commun. 2, 59 (2014).

  88. 88.

    Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).

  89. 89.

    Denis, J. A., Guillerm, E., Coulet, F., Larsen, A. K. & Lacorte, J. M. The role of BEAMing and digital PCR for multiplexed analysis in molecular oncology in the era of next-generation sequencing. Mol. Diagn. Ther. 21, 587–600 (2017).

  90. 90.

    Stewart, C. M. & Tsui, D. W. Y. Circulating cell-free DNA for non-invasive cancer management. Cancer Genet. https://doi.org/10.1016/j.cancergen.2018.02.005 (2018).

  91. 91.

    Higgins, M. J. et al. Detection of tumor PIK3CA status in metastatic breast cancer using peripheral blood. Clin. Cancer Res. 18, 3462–3469 (2012).

  92. 92.

    Chen, W. W. et al. BEAMing and droplet digital PCR analysis of mutant IDH1 mRNA in glioma patient serum and cerebrospinal fluid extracellular vesicles. Mol. Ther. Nucleic Acids 2, e109 (2013).

  93. 93.

    Buczkowicz, P. & Hawkins, C. Pathology, molecular genetics, and epigenetics of diffuse intrinsic pontine glioma. Front. Oncol. 5, 147 (2015).

  94. 94.

    Chicard, M. et al. Whole-exome sequencing of Cell-Free DNA reveals temporo-spatial heterogeneity and identifies treatment-resistant clones in neuroblastoma. Clin. Cancer Res. 24, 939–949 (2018).

  95. 95.

    Ikeda, S. et al. Next-generation sequencing of circulating tumor DNA reveals frequent alterations in advanced hepatocellular carcinoma. Oncologist 23, 586–593 (2018).

  96. 96.

    Jiang, B. Y. et al. Detection of driver and resistance mutations in leptomeningeal metastases of NSCLC by next-generation sequencing of cerebrospinal fluid circulating tumor cells. Clin. Cancer Res. 23, 5480–5488 (2017).

  97. 97.

    Chan, K. C. et al. Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing. Proc. Natl Acad. Sci. USA 110, 18761–18768 (2013).

  98. 98.

    Zhai, R. et al. Genome-wide DNA methylation profiling of cell-free serum DNA in esophageal adenocarcinoma and Barrett esophagus. Neoplasia 14, 29–33 (2012).

  99. 99.

    Qu, Z. et al. Exosomal miR-665 as a novel minimally invasive biomarker for hepatocellular carcinoma diagnosis and prognosis. Oncotarget 8, 80666–80678 (2017).

  100. 100.

    Wang, H. et al. The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification. Expert. Rev. Proteom. 13, 99–114 (2016).

  101. 101.

    Xue, L. et al. Mixed-mode ion exchange-based integrated proteomics technology for fast and deep plasma proteome profiling. J. Chromatogr. A. 1564, 76–84 (2018).

  102. 102.

    Shraibman, B. et al. Identification of tumor antigens among the HLA peptidomes of Glioblastoma tumors and plasma. Mol. Cell. Proteom. 17, 2132–2145 (2018).

  103. 103.

    Han, X. et al. Pre-treatment serum levels of soluble programmed cell death-ligand 1 predict prognosis in patients with hepatitis B-related hepatocellular carcinoma. J. Cancer Res. Clin. Oncol. https://doi.org/10.1007/s00432-018-2758-6 (2018).

  104. 104.

    Toby, T. K., Fornelli, L. & Kelleher, N. L. Progress in top-down proteomics and the analysis of proteoforms. Annu Rev. Anal. Chem. (Palo Alto Calif.) 9, 499–519 (2016).

  105. 105.

    Desiderio, C. et al. Cerebrospinal fluid top-down proteomics evidenced the potential biomarker role of LVV- and VV-hemorphin-7 in posterior cranial fossa pediatric brain tumors. Proteomics 12, 2158–2166 (2012).

  106. 106.

    Borrebaeck, C. A. Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer. Nat. Rev. Cancer 17, 199–204 (2017).

  107. 107.

    Chen, B., Brown, K. A., Lin, Z. & Ge, Y. Top-down proteomics: ready for prime time? Anal. Chem. 90, 110–127 (2018).

  108. 108.

    Parpart-Li, S. et al. The effect of preservative and temperature on the analysis of circulating tumor DNA. Clin. Cancer Res. 23, 2471–2477 (2017).

  109. 109.

    Medina Diaz, I. et al. Performance of streck cfDNA blood collection tubes for liquid biopsy testing. PLoS. One. 11, e0166354, https://doi.org/10.1371/journal.pone.0166354 (2016).

  110. 110.

    van Dessel, L. F. et al. Application of circulating tumor DNA in prospective clinical oncology trials - standardization of preanalytical conditions. Mol. Oncol. 11, 295–304 (2017).

  111. 111.

    Chan, L. L. & Jiang, P. Bioinformatics analysis of circulating cell-free DNA sequencing data. Clin. Biochem. 48, 962–975 (2015).

  112. 112.

    Newman, A. M. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 20, 548–554 (2014).

  113. 113.

    Gomez, S. et al. A novel method for rapid molecular subgrouping of medulloblastoma. Clin. Cancer Res. 24, 1355–1363 (2018).

  114. 114.

    Panditharatna, E., Yaeger, K., Kilburn, L. B., Packer, R. J. & Nazarian, J. Clinicopathology of diffuse intrinsic pontine glioma and its redefined genomic and epigenomic landscape. Cancer Genet 208, 367–373 (2015).

  115. 115.

    Zaret, K. Micrococcal nuclease analysis of chromatin structure. Curr. Protoc. Mol. Biol. Chapter 21, Unit 21 21 (2005).

  116. 116.

    Song, L. & Crawford, G. E. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. 2010, pdbprot5384 (2010).

  117. 117.

    Chen, X. et al. ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat. Methods 13, 1013–1020 (2016).

  118. 118.

    Cohen, J. D. et al. Combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancers. Proc. Natl Acad. Sci. USA 114, 10202–10207 (2017).

  119. 119.

    Mackay, A. et al. Integrated molecular meta-analysis of 1,000 pediatric high-grade and diffuse intrinsic pontine glioma. Cancer Cell. 32, 520–537 e525 (2017).

  120. 120.

    Castel, D. et al. Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes. Acta Neuropathol. 130, 815–827 (2015).

  121. 121.

    Chen, J. et al. Alu methylation serves as a biomarker for non-invasive diagnosis of glioma. Oncotarget 7, 26099–26106 (2016).

  122. 122.

    Bratman, S. V., Newman, A. M., Alizadeh, A. A. & Diehn, M. Potential clinical utility of ultrasensitive circulating tumor DNA detection with CAPP-Seq. Expert. Rev. Mol. Diagn. 15, 715–719 (2015).

  123. 123.

    Chaudhuri, A. A. et al. Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling. Cancer Discov. 7, 1394–1403 (2017).

  124. 124.

    Przybyl, J. et al. Combination approach for detecting different types of alterations in circulating tumor DNA in leiomyosarcoma. Clin. Cancer Res. 24, 2688–2699 (2018).

  125. 125.

    Scherer, F. et al. Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA. Sci. Transl. Med 8, 364ra155 (2016).

  126. 126.

    Juratli, T. A., Qin, N., Cahill, D. P. & Filbin, M. G. Molecular pathogenesis and therapeutic implications in pediatric high-grade gliomas. Pharmacol. Ther. 182, 70–79 (2018).

  127. 127.

    Wu, G. et al. The genomic landscape of diffuse intrinsic pontine glioma and pediatric non-brainstem high-grade glioma. Nat. Genet. 46, 444–450 (2014).

  128. 128.

    Fruhwald, M. C., Biegel, J. A., Bourdeaut, F., Roberts, C. W. & Chi, S. N. Atypical teratoid/rhabdoid tumors-current concepts, advances in biology, and potential future therapies. Neuro. Oncol. 18, 764–778 (2016).

  129. 129.

    Guerreiro Stucklin, A. S., Ramaswamy, V., Daniels, C. & Taylor, M. D. Review of molecular classification and treatment implications of pediatric brain tumors. Curr. Opin. Pediatr. 30, 3–9 (2018).

  130. 130.

    Bornhorst, M., Frappaz, D. & Packer, R. J. Pilocytic astrocytomas. Handb. Clin. Neurol. 134, 329–344 (2016).

  131. 131.

    Merino, D. M. et al. Molecular characterization of choroid plexus tumors reveals novel clinically relevant subgroups. Clin. Cancer Res. 21, 184–192 (2015).

  132. 132.

    Ramaswamy, V. et al. Risk stratification of childhood medulloblastoma in the molecular era: the current consensus. Acta Neuropathol. 131, 821–831 (2016).

  133. 133.

    Pajtler, K. W. et al. Molecular classification of ependymal tumors across all CNS compartments, histopathological grades, and age groups. Cancer Cell. 27, 728–743 (2015).

  134. 134.

    Spence, T. et al. CNS-PNETs with C19MC amplification and/or LIN28 expression comprise a distinct histogenetic diagnostic and therapeutic entity. Acta Neuropathol. 128, 291–303 (2014).

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Acknowledgements

The authors would like to acknowledge the generosity of all patients and their families. This work was supported by funding from the Smashing Walnuts Foundation (Middleburg, VA), The Gabriella Miller Kids First Data Resource Center, Clinical and Translational Science Institute at Children’s National (5UL1TR001876-03), Musella Foundation (Hewlett, NY), Mathew Larson Foundation (Franklin Lake, NJ), The Lilabean Foundation for Pediatric Brain Cancer Research (Silver Spring, MD), and the Childhood Brain Tumor Foundation (Germantown, MD).

Author information

E.R.B. and M.B. reviewed articles, prepared manuscript text and created tables (co-first authors). R.J.P. edited and revised the manuscript text. J.N. also edited and revised the manuscript text and tables, and served as corresponding author.

Competing interests

The authors declare no competing interests.

Correspondence to Javad Nazarian.

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