Electronic DNA Analysis of CSF Cell-free Tumor DNA to Quantify Multi-gene Molecular Response in Pediatric High-grade Glioma

Pediatric high-grade glioma (pHGG), including diffuse intrinsic pontine glioma (DIPG), is a group of aggressive brain cancers with <10% of patients surviving at 2 years from diagnosis (1). Overall median survival for pHGG is 13.5 months (21.4% 2-year survival) in midline gliomas and 11 months (5.2% 2-year survival) for DIPG specifically (2, 3). Unlike in adult HGG, pHGG is most commonly midline or infratentorial in location (1). Midline HGGs are not usually surgically resectable and standard treatment relies on palliative radiation and chemotherapy. Improved, targeted treatment with rapid time to diagnosis is desperately needed for pHGG.

The World Health Organization reclassified most gliomas to include molecular features, including the H3K27M mutational status of diffuse midline gliomas. This mutation occurs in >50% of pHGGs and in >80% of DIPGs, and portends a universally poor prognosis (4, 5). pHGGs also frequently have mutations in tumor-driving genes such as ATRX, TP53, ACVR1, BRAF, EGFR, and PIK3CA.

Historically, diagnosis has been made on the basis of clinical and imaging findings. Prior to the early 2000s, surgical biopsy for DIPGs and other midline tumors was considered unsafe, especially when therapeutic options were lacking (6). While MRI has been important for diagnosis and assessing response to treatment of brain tumors, accurate assessment may be confounded by radiation-related swelling (pseudoprogression). Molecular diagnosis of pHGG through biopsy carries major drawbacks (7–9). Liquid biopsy with cerebrospinal fluid (CSF) sampling offers a safer and less expensive alternative to determine molecular diagnosis than surgical biopsy. Brain tumors shed their DNA into the CSF (hereafter referred to as cell-free tumor DNA, cf-tDNA), and this cf-tDNA can be detected via CSF sampling (10–12).

Prior work has demonstrated the feasibility of detecting the H3K27M mutation in CSF using droplet digital PCR (ddPCR) with high sensitivity and specificity (11, 13, 14). However, ddPCR requires expensive and time-consuming assay design, and is best used for recurring point mutations such as H3K27M. For genes that have clinically significant mutations throughout multiple exons (e.g., TP53 and PIK3CA), ddPCR has significant limitations. In addition, technical complexity limits the use of ddPCR for characterization of a larger quantity of alterations.

As liquid biopsy technology advances, alternative methods to detect and monitor cf-tDNA from CSF are being explored, including sequencing. Next-generation sequencing (NGS) has been successfully employed to isolate patient-specific alterations in CSF cf-tDNA (12, 15). These studies have solely examined adult tumors (primary brain tumors or metastases) and required considerable amounts of CSF (3.5–5 mL) to obtain sequencing results. For the pediatric population, especially with brainstem or midline tumors, this volume can be risky or impossible to obtain. In addition, NGS methods remain relatively expensive with sophisticated and time-intensive informatics; these have limited its widespread use for CSF cf-tDNA diagnostics (16). In addition, few studies have explored the serial monitoring of CSF cf-tDNA for HGG (in children or adults) with comparison with clinical and radiographic outcomes. Confirmed feasibility of a clinical trial design with safe serial lumbar puncture and rapid cf-tDNA analysis at time of MRI would represent an important step for future clinical trials in HGG.

Here, we explore the use of an alternative platform for sequencing with the goal of tackling some of the issues that have limited the use of CSF cf-tDNA in HGG serial monitoring. Oxford Nanopore Technology (Nanopore) is an inexpensive, handheld device that sequences through direct, electronic analysis of nucleic acids fed through >1,000 biological pores that sequence DNA fragments simultaneously. Previous work has confirmed its feasibility and efficiency (same-day) in molecular characterization of adult glioma tumor samples (17). We hypothesized that the low DNA input required for Nanopore sequencing would be optimal for CSF cf-tDNA, which is often in the nanogram range for an entire sample; this is especially important for pediatric patients, where larger volume samples are difficult to obtain safely. To improve clinical utility, we were interested to see whether the sequencing efficiency and fairly straightforward informatics previously associated with Nanopore could be applied to liquid samples.

Here, we show that Nanopore sequencing of ultra-short pHGG CSF cf-tDNA fragments is feasible, efficient, and sensitive with very low-input samples. In addition, we describe the feasibility of a clinical trial design with serial correlate lumbar puncture in pHGG, which allowed us to track multi-gene molecular response in two patients with H3K27M-mutant HGG that correlated with long-term radiological response.

Pediatric patients with HGG who were seen at the University of Michigan (Ann Arbor, MI) and registered into the institutional review board (IRB)-approved studies: Brain Tumor CSF Registry (HUM0013228) or ONC201 phase I clinical trial in pediatric patients with H3K27M-mutant glioma (HUM00144364) were included in the study. Patients with tissue were also enrolled in the IRB-approved Pediatric MiOncoseq study (HUM00056496) for next-generation DNA (1,700 gene panel) and RNA sequencing (Illumina) of tumor and germline samples using methods established previously (18).

Patients enrolled in the ONC201 trial underwent sedated MRI with and without contrast plus CSF draw by lumbar puncture (LP) at the time of study initiation, 2 months into treatment, and 6 months into treatment. For patients who underwent clinical CSF collection (Brain Tumor CSF Registry or ONC201 trial), the amount of CSF collected ranged from 1 to 5 mL at each draw.

Normal control CSF was obtained from patients with normal pressure hydrocephalus through the IRB-approved Negative Controls for CSF Studies protocol (HUM00128299).

The study was conducted in accordance with the Declaration of Helsinki and International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS). All patients provided written consent and the study protocols were IRB approved.

Collected CSF was immediately processed by centrifuging at 4°C for 10 minutes at 2,000 rpm, then aliquoted as 0.5–1 mL into separate vials, and stored at −80°C. Control CSF samples were processed and stored in 2–10 mL aliquots at −80°C.

For samples obtained through research autopsy, CSF was collected by lumbar puncture, cisterna magna puncture, and/or lateral ventricle puncture. The brain (including tumor) was then extracted, placed on ice, and immediately transported for processing. Fresh brain and tumor were sectioned into 2-mL cryovials, flash-frozen with liquid nitrogen, and then placed at −80°C. CSF was immediately processed as described above.

DNA was extracted from approximately 20 mg of tissue (either normal brain or tumor specimens depending on the experiment) using DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer's protocol. DNA was extracted from 1 mL CSF using the QIAamp Circulating Nucleic Acid Extraction Kit (Qiagen) following the manufacturer's protocol.

For DNA fragment size analysis, 1 mL each of normal CSF and tumor CSF was used. Following extraction, as described above for CSF, the DNA was sent to the University of Michigan (Ann Arbor, MI) Advanced Genomic Core for High Sensitivity DNA ScreenTape (TapeStation) analysis.

Using information obtained by TapeStation analysis, primers were designed to amplify approximately 100- to 200-bp sequence around mutations of interest (see Supplementary Table S1 for gene panel and primer sequences) and ordered from Integrated DNA Technologies with attached Nanopore barcoding tags. Primers were reconstituted to 100 μmol/L stock with Buffer AE (Qiagen) and stored at −20°C, while working solutions were diluted to 10 μmol/L and stored at 4°C.

Extracted DNA from patient samples were subjected to PCR followed by gel electrophoresis to confirm amplification of target genes. All amplicons (by patient) were then pooled, underwent 1× bead clean-up with AMPure XP Beads (Beckmann-Coulter), and eluted in molecular biology grade Nuclease-free Water (Corning). The concentration of pooled samples was again checked via Qubit.

Patient samples were run in triplicate and underwent library preparation according to Nanopore. For experiments requiring multiple replicates or multiple patients in one sequencing run, all replicates underwent library preparation, barcoding, and adapter ligation using the Native Barcoding Amplicons (with EXP-NBD104, EXP-NBD114, and SQK-LSK109) protocol (Nanopore, https://dx.doi.org/10.17504/protocols.io.bgzxjx7n). For experiments not requiring barcoding, samples underwent library preparation and adapter ligation according to the Amplicons by Ligation (SQK-LSK109) protocol (Nanopore, https://dx.doi.org/10.17504/protocols.io.bgzxjx7n). For all experiments, after adapter ligation, the concentration of the sample was checked and the DNA library was prepared by adding 37.5-μL Sequencing Buffer (SQB) and 25.5-μL Loading Beads (Nanopore) to the DNA library. The DNA library was then run on a FLO-MIN106 (r9.4.1) flow cell using the Nanopore MinION handheld sequencing device. Sequencing runs were allowed to progress until 100,000 reads per sample was achieved, assuming equal distribution of samples across the flow cell. In general, approximately 80%–90% of reads were deemed mappable (e.g., 86.4% for patient UMPED57 tumor and 80.3% for patient UMPED22B CSF), and only mapped reads were included for variant allele fraction (VAF) calculation. For detailed materials and methods, see Oxford Nanopore Technology Library Preparation section in Supplementary Materials and Methods.

Once the sequencing was complete on the MinION flow cell, Nanopore MinKNOW sequencing software output FAST5 files were basecalled to FASTQ files and separated by barcode using Oxford Nanopore's Guppy basecaller (version 2.3.5 or other where noted). The reads in the barcoded FASTQ files were aligned to human genome 19 using minimap2 version 2.17 (19) and resulting BAM files were sorted using SAMtools version 1.9 (20). VAF for each sample and gene of interest was then determined using Integrated Genome Viewer (IGV, version 2.61). VAF was calculated as mutant reads divided by the sum of mutant and wild-type reads at that chromosomal location (see Supplementary Table S1 for chromosomal locations for SNPs of interest). When possible, up to three replicates were performed for sequencing. Reported VAF represents the mean of replicate values. Positive values were those with mean replicate values greater than our lower limit VAF (see Results).

cf-tDNA was subjected to PCR using an amplicon library designed for patient-specific SNPs and optimized for Illumina sequencing following the manufacturer's protocol (Swift Biosciences). Final library quality was assessed using the TapeStation 4200 (Agilent Technologies) and libraries were quantified by Kapa qPCR (Roche). Pooled libraries were subjected to 150-bp paired-end sequencing according to the manufacturer's protocol (Illumina MiSeq, University of Michigan Sequence Core Facility, Ann Arbor, MI). Bcl2fastq2 Conversion Software (Illumina) was used to generate demultiplexed FASTQ files.

Both normal control and tumor cf-DNA were amplified with H3F3A primers and underwent gel electrophoresis to verify amplification. Bead clean-up and library preparation (including barcoding) proceeded as described in Supplementary Materials and Methods. Prior to sequencing, the concentration was determined for both conditions. Ten separate samples with 75 femtomole (fmol) of normal DNA plus ever-decreasing amount of tumor DNA (Supplementary Table S2) were run independently on the flow cell and sequencing was progressed to about 200,000 reads. The data for each sample were then analyzed using the computational pipeline described above to determine H3F3A K27M VAF.

PCR was performed for both samples using H3F3A primers with the same PCR settings as described in Supplementary Materials and Methods, followed by gel electrophoresis and bead clean-up. Library preparation proceeded with the Oxford Nanopore SQK-LSK109 Kit without barcoding. In the Nanopore MinKNOW software, automatic basecalling was turned off. The sequencing runs progressed until >100,000 reads were obtained per sample, as estimated by the MinKNOW sequencer software. The resulting signal data from each sample were basecalled using the GPU accelerated version of Oxford Nanopore's Guppy basecaller (version 3.2.1 and 3.6.0 for Supplementary Fig. S2). Reads were then aligned to Gh37/hg19 human assembly using minimap2 version 2.17 (19), and sorted by read timestamp added by the basecaller. We then generated bins of 250 consecutive reads, considering only those reads that mapped to the target region (e.g., chr1:226252135 for H3F3A K27M). Each time-sorted bin was then realigned to hg19 and a pileup was generated using samtools pileup command (20). A custom python script was used to parse the pileup and extract VAF corresponding to the locus for the target mutation. VAF was computed as described earlier.

Multiplexed PCR was performed to identify the presence of HIST1H3B or H3F3A K27M mutation in CSF for sample with known H3K27M status. Control DNA included DNA from a patient with known H3F3A K27M mutation and from a patient with known HIST1H3B K27M mutation (by Illumina sequencing upon biopsy). Each of the three patient samples were individually amplified using H3F3A and HIST1H3B primers and following the Q5 High-Fidelity DNA Polymerase Protocol (New England Biolabs). The PCR tubes were prepared by combining 5× Q5 Reaction Buffer (New England Biolabs), 10 μmol/L dNTPS (New England Biolabs), Q5 DNA Polymerase (New England Biolabs), GC Enhancer (New England Biolabs), H3F3A and HIST1H3B primers (for final concentration 0.15 μmol/L), and DNA. The samples then underwent PCR amplification, gel electrophoresis, bead clean-up, DNA end repair, barcoding, and adapter ligation as described, and were then sequenced on the Nanopore MinION device.

Statistical significance in all experiments was defined as a two-sided P ≤ 0.05, with comparisons between groups made using Welch t test. All analyses were conducted with GraphPad Prism software (version 7.00).

To improve limit of detection for low-input samples, such as pediatric CSF samples, we utilized Nanopore sequencing because of its low input requirements. Prior to sequencing, we characterized the cf-DNA fragment distribution in the CSF of both patients with pHGG and controls.

TapeStation analysis of cf-DNA extracted from normal control CSF demonstrated two noticeable peaks of cf-DNA at 139 and 636 bp, while from tumor CSF there were three peaks at 184, 338, and 551 bp, with 184 bp being the largest peak (Fig. 1). Previous work has demonstrated that, early in apoptosis, DNA is cleaved into very small fragments of about 180–200 bp (21). On the basis of the notable approximately 180-bp distribution that was enriched in CSF from patients with tumors, we designed primers to amplify DNA fragments of 100–200 bp in length to enrich for analysis of apoptotic tumor DNA from CSF samples. We have previously designed and tested primers that amplify larger sequences (900–3,000 bp) with notably low amplification efficiency in CSF samples despite excellent amplification efficiency in tissue samples (data not shown).

We compiled CSF samples from patients with pHGG through multiple IRB-approved clinical protocols and research autopsies. Samples from 12 patients were included, with average age of 10 years (range, 3–17 years) at time of first sample donation (CSF or tissue; Table 1). Eleven of our patients had biopsy-confirmed diffuse midline glioma (brainstem, seven patients; thalamic, three patients; and spinal cord, one patient); nine of these were H3K27M positive and one patient had radiographic DIPG that did not undergo biopsy, but did undergo CSF collection at autopsy. All patients had undergone standard treatment prior to or during enrollment, including radiation and chemotherapy. We collected and stored specimens using identical methods regardless of protocol and sample timepoint. We then designed 100- to 200-bp primer sets for a panel of recurrent and actionable gene mutations found across our pHGG samples (Supplementary Table S1).

We determined the minimum amount of cf-tDNA that was required to calculate reliable VAF. In our pediatric patient population, the amount of CSF safely obtained by lumbar puncture ranged from 1–5 mL per CSF draw. When receiving only 1 mL of CSF, the amount of cf-tDNA was limited to about 15 ng per sample on average. When amplifying the DNA for sub–200-bp fragments, 15 ng of input DNA usually resulted in picomolar amounts of amplified cf-tDNA (see Materials and Methods section for PCR settings).

Given that Nanopore MinION flow cells are able to withstand a maximum of 200 fmol of input DNA, we hypothesized that we could obtain reliable results with 1 mL of CSF from pediatric patients with HGG. We were able to detect the H3F3A K27M mutation at a nonsignificantly different VAF in the tumor CSF down to 0.1 fmol of input cf-tDNA (Fig. 2A). This represented a depth of only 139 reads (Fig. 2B) for a sample with VAF of 24%.

We then determined the error rate of Nanopore CSF cf-tDNA sequencing. We compared CSF cf-DNA from UMPED57, a 9-year-old female with established (solid) tumor H3F3A (H3.3) K27M mutation VAF (47%), with CSF cf-DNA from a patient without tumor (normal). We examined the H3.3 K27M VAF in bins of 250 reads each (plotted in bins of 1,000× for ease of visualization) and found that at even low depth of <1,000×, the normal CSF cf-DNA maintained a false-positive error rate of about 1% VAF for H3F3A K27M mutation; this is consistent with established false-positive rates for recent high-accuracy basecallers targeting Oxford Nanopore sequencers (Fig. 2C; refs. 17, 22–24). Using the same data and separating by 1,000× depth bins, only the smallest bin (0–1,000× depth) was significantly different for our 100,000× data VAF (Supplementary Fig. S2A), which is consistent with the depth range from our limit of detection analysis (Fig. 2B). For normal CSF, the mean VAF was 0.98% (SD, 0.44%; range, 0.17%–1.58%) when depth was >1,000×, across all point mutations listed in Table 1.

To explore depth requirements for lower VAFs, we used a statistical analysis to estimate the coverage required to call a mutation with a desired confidence interval (95% CI) in relation to our established false-positive rate of approximately 1% (Fig. 2D). We used Cochran equation to estimate a sample size (total wild-type and mutant reads) required to call a VAF within a 95% CI. For each VAF, we set the error margin so that the bottom bound of the tumor sample CI is equal to the top bound of our nontumor false-positive samples. The required read depth increased with proximity of underlying VAF to the false-positive rate (Fig. 2D). Our analysis indicates that at 1,000× (wild-type + mutant) depth with our error rate, Nanopore sequencing can call mutations with a VAF as low as 2.5% with 95% CI. On the basis of these data, we considered our lower limit VAF to be 2.5% for all samples with at least 1,000× depth. Sequencing of an additional sample (UMPED22, H3.1 K27M) demonstrated a definitive VAF of 8%, with statistical significance from negative control even at lowest bin (first 250 mapped reads; Supplementary Fig. S2B).

We performed Nanopore sequencing of liquid and solid samples from our pHGG cohort (n = 188 total replicates and 127 CSF replicates). All samples were run with normal control (either normal brain tissue or normal CSF). Compared with diagnostic NGS-based tumor results, Nanopore sequencing detected clinically relevant genetic mutations in pHGG with 83% sensitivity and 100% specificity (Supplementary Table S3A and S3B). In CSF alone, the sensitivity was 85% and the specificity 100% (Supplementary Table S3A and S3B). Patient status did not influence sensitivity (living, 83% and autopsy, 82%; Supplementary Table S3A and S3B). For autopsy samples, ventricular CSF sample location showed the highest cf-DNA yield (7.42 ng/μL, n = 1), compared with lumbar puncture (mean 1.41 ng/μL, n = 3) and cisterna magna (mean 0.47 ng/μL, n = 2). Lumbar puncture samples obtained from living patients yielded lowest average yield (mean, 0.14 ng/mL). However, 1 mL of CSF was sufficient to generate >200 fmol in all samples. Within the limits of our sample size, sample type did not influence sensitivity for Nanopore sequencing.

To determine the efficiency of Nanopore sequencing for CSF cf-tDNA using ultra-short amplicons, we compiled the time to results for samples analyzed immediately after sample collection. We sequenced a sample using Nanopore immediately after collection from patient UMPED60. We performed Nanopore library preparation using Nanopore SQK-LSK109 and EXP-NBD104 kits and sequenced on Nanopore MinION device using an R9.4.1 flow cell in under 1.5 hours to total >300,000 DNA fragment reads, which was consistent with other Nanopore sequencing runs. Using our software pipeline (see Materials and Methods), we calculated VAF and detected the desired gene mutations with statistical significance (Fig. 3). From time of CSF draw to results, provided as VAF of the gene mutations, the total time required was around 12 hours (Fig. 3). Furthermore, we found that time to 1,000× depth (minimum depth suggested to achieve VAF with statistical certainty) was less than 10 minutes for multiple gene mutations of interest (Fig. 3), consistent with other comparable studies (17). These results suggested that Nanopore sequencing was both rapid and reliable in detecting mutations with oncogenic significance in CSF of patients with HGG.

To further establish the feasibility and reliability of Nanopore sequencing, we performed comparison NGS (Illumina) of CSF and tumor samples for patients with matched samples at diagnosis and autopsy (n = 6). Patient-specific driver mutations were determined through NGS, and amplicon-based Nanopore sequencing and NGS were performed on CSF and tumor samples (Fig. 4).

Sample volumes were scaled to optimal machine input requirements. For Nanopore sequencing, 1 mL of CSF was used for each patient. To achieve the required nanogram input required for NGS (Illumina NextSeq), 2–3 mL CSF was required. Nanopore sequencing detected patient-specific mutations in 67.9% (19/28) of the samples and was concordant with Illumina in 70.0% (14/20) of those. Nanopore sequencing detected the patient-specific mutation in five samples that Illumina did not detect, while Illumina detected a patient-specific mutation in one sample that Nanopore did not detect. In CSF, Nanopore found 64.3% (9/14) of the correct mutations, while Illumina detected 50% (7/14) of the correct mutations (Fig. 4; Supplementary Table S4). For three of the mutations, neither Illumina nor Nanopore was able to detect the mutation at autopsy, raising question of tumor evolution from diagnosis.

While the majority of our patient samples had Illumina-based molecular analysis results available, by which to compare and confirm our results, we had one pediatric patient (UMPED55) who was radiographically diagnosed with DIPG and who donated CSF by LP at time of death. H3K27M mutation can denote a K27M mutation affecting either the H3.1 (HIST1H3B) or H3.3 (H3F3A) protein. We performed multiplexed PCR on CSF from our patient with unknown H3 status alongside positive control CSF samples from patients with H3F3A K27M mutation and a patient with known HIST1H3B K27M mutation, amplifying with both H3F3A and HIST1H3B primers in all three samples. All samples were run together and in triplicate on the Nanopore MinION device. We found that the patient with unknown H3 status had about 20% VAF for H3F3A K27M and about 1% VAF for HIST1H3B (background error rate of Nanopore; Supplementary Fig. S1). This result was later confirmed on ddPCR analysis of H3F3A (data not shown) using methods established previously (18).

We next explored whether Nanopore sequencing could be employed for serial monitoring of CSF cf-tDNA for patients with pHGG and compared with clinical and radiographic outcomes. We established a clinical trial design with serial lumbar puncture and rapid cf-tDNA analysis at time of MRI, and this was employed as arm D of the multi-site ONC201 phase I trial for children with H3K27M-mutant glioma (NCT03416530). ONC201 is a dopamine receptor DRD2 antagonist and allosteric ClpP agonist, which has early reports of efficacy in patients with H3 K27M-mutant diffuse midline glioma (DMG) (25–27). Patients undergo LP at time of trial initiation (baseline), 2 months into treatment, and 6 months into treatment (Fig. 5A). We performed Nanopore sequencing on serial samples from 2 patients with pHGG.

Patient UMPED77 is a 13-year-old male with a large cortical and thalamic H3K27M-mutant diffuse midline glioma (Fig. 5B). He was enrolled on arm D of the ONC201 trial shortly after radiotherapy. Unfortunately, tumor fixation did not allow for baseline tumor DNA library preparation/sequencing, but IHC analysis confirmed H3K27M mutation in tumor. The patient has remained on treatment for 6 months with clinical and radiographic stability. This was mirrored on CSF cf-tDNA Nanopore analysis, which showed overall stability of H3K27M VAF in CSF obtained from lumbar puncture, with slight trend toward increase at 6-month timepoint (Fig. 5C).

Patient UMPED60 is a 17-year-old female with a unilateral thalamic H3K27M-mutant diffuse midline glioma. She was enrolled on arm D of ONC201 trial shortly after radiotherapy after initial concerns for early progression. NGS at time of biopsy (prior to radiation) demonstrated four clinically relevant mutations, including TP53 R158G, TP53 R248Q, PIK3CA E545G, and H3F3A K27M (Supplementary Table S5). The patient demonstrated clinical and radiographic stability at 2 months, with sustained left-sided mild hemiparesis and tremor. During 6 months on the trial she had symptomatic improvement as well as 40% decrease in tumor size on MRI compared with baseline, suggesting that the tumor was responding to ONC201 (Fig. 5D). CSF cf-tDNA Nanopore sequencing demonstrated stable-to-increased VAF of her four patient-specific alterations at 2 months, but reduction to 10% or less VAF for all at the 6-month timepoint CSF sample (Fig. 5E). This molecular response predicted a sustained clinical and radiographic response, which has continued for nearly 2 years on therapy at the time of this report. These results suggest that Nanopore sequencing can be used to track the multi-gene molecular response of a tumor to ONC201.

Here, we present a novel use of Nanopore sequencing as a way to detect molecular alterations in CSF cf-tDNA of patients with pHGG for both diagnosis and monitoring treatment response. Our data demonstrate that Nanopore sequencing of ultra-short pHGG CSF cf-tDNA fragments is feasible, efficient, and sensitive with low-input samples thus, overcoming many of the barriers restricting wider use of CSF cf-tDNA diagnosis and monitoring in this patient population. We also present a unique clinical trial design with standardized, serial CSF collections by lumbar puncture and establish feasibility for serial treatment monitoring via Nanopore sequencing of CSF in patients with HGG.

Nanopore MinION sequencing has recently been established as an alternative option to complement or improve some of the constraints of NGS, including in adult brain tumor samples (17). Euskirchen and colleagues found that detection of mutations by Nanopore was reliable compared with standard methods, including NGS or Sanger sequencing, in agreement with our results. We built upon this to highlight some of the features of Nanopore that are particularly well-suited for CSF cf-tDNA analysis, such as low tumor DNA input. Our work also establishes a rational framework for amplicon design for cf-tDNA using ultra-short (100–200 bp) sequences.

Although published research on detecting glioma cf-tDNA in CSF is limited, there are three studies in particular that have shown the feasibility of detecting HGG cf-tDNA in CSF (12, 15, 23). Unlike our studies, Miller and colleagues and Pentsova and colleagues examined adult patients. Pentsova and colleagues found 63% cf-tDNA positivity in solid tumor metastasis and 50% positivity for primary brain tumors using 5-mL CSF per patient and NGS-based assay (15). Miller and colleagues were able to detect cf-tDNA in 49.4% of adults with gliomas and in 58.7% of patients with grade IV glioma, also using NGS-based assay (12). Another study by Huang and colleagues had sensitivity of 87.5% for HGG in CSF from pediatric patients using Sanger sequencing with sub–2-mL sample inputs (23). Our data compare favorably with these alternate platforms. Even when utilizing low-volume samples (1 mL or less), Nanopore sequencing demonstrated 83% sensitivity and 100% specificity for pediatric patient-specific alterations.

While difficult to compare Nanopore sequencing and NGS directly, our data highlight some potential benefits of Nanopore for cf-tDNA analysis. Nanopore demonstrated sensitivity at an extremely low limit of detection, at 0.1 fmol DNA input. For comparison, Illumina devices generally require picomolar to nanomolar amounts of DNA input for reliable results, which equates to 2–3 mL of CSF at a minimum (28). Even for our comparison samples that used optimal amounts of input DNA for each platform, we found Nanopore to provide a slightly higher rate of detection of patient-specific mutations than matched NGS analysis. In addition, we have shown that Nanopore sequencing can provide results in as little as 12 hours from the time of CSF draw by LP, with less than 30 minutes of sequencing time for suitable depth. It is important to note that NGS can offer significant advantages, including well-established whole-exome or whole-genome sequencing methodology and a much lower error rate. While amplicon-based Nanopore sequencing is suitable for calling recurrent hotspot mutations or regions, NGS is less error-prone and more suitable for isolating novel mutations or loss-of-functions mutations in large genes such as ATRX. In our work, we examined error only at specific codons (such as the H3F3A K27M mutation) rather than the entire read region, which may affect error rate. Future studies may employ both platforms for liquid and solid tumor diagnostics to take advantage of complementary features. Additional future work may examine error rate of short-read sequencing versus long-read sequencing.

Importantly, we established and implemented serial spinal fluid collection within a glioma clinical trial. While serial LP is not typically standard of care for the management of glioma, our study demonstrates that timed CSF collection by LP has potential clinical use across multiple high-grade brain tumors. We showed that Nanopore sequencing allows for serial monitoring of patient-specific mutations and that this information correlates with radiographic and clinical response to treatment. Our patients tolerated the procedure well, with procedure being performed at time of clinically indicated anesthesia. Multi-timepoint CSF assessment in conjunction with MRI has the potential to clarify molecular changes over time and to track clinical response and pseudoresponse. We are now adopting this trial platform for multiple early-phase studies for HGG. With further validation, real-time results of serial CSF analysis via Nanopore/NGS could be used to generate an integrated radiographic/molecular response assessment in future clinical trials.

Limitations of the study include the anecdotal nature of our serial and multi-focal CSF assessments. In addition, our sensitivity may have been influenced favorably by the number of autopsy samples, which may have been enriched for apoptotic cf-tDNA. Future studies will build on our initial results in prospective clinical trials.

In summary, our data establish Nanopore sequencing of ultra-short pHGG CSF cf-tDNA fragments as an efficient and sensitive alternative to NGS. In addition, we describe the feasibility of a clinical trial design with serial correlate lumbar puncture in pHGG, which allowed us to track multi-gene molecular response. We believe this platform will push the field toward improved diagnostic and therapeutic assessments for patients with HGG.

A. Franson reports personal fees from Boehringer Ingelheim International (consultant) outside the submitted work. No potential conflicts of interest were disclosed by the other authors.

A.K. Bruzek: Conceptualization, resources, data curation, formal analysis, investigation, methodology, writing-original draft, writing-review and editing. K. Ravi: Software, formal analysis, writing-review and editing. A. Muruganand: Resources, formal analysis, investigation, writing-review and editing. J. Wadden: Data curation, software, formal analysis, methodology, writing-review and editing. C.M. Babila: Software, formal analysis, writing-review and editing. E. Cantor: Visualization. L. Tunkle: Software, formal analysis, methodology. K. Wierzbicki: Validation. S. Stallard: Conceptualization, investigation. R.P. Dickson: Conceptualization, investigation. I. Wolfe: Data curation, investigation. R. Mody: Resources, data curation, investigation. J. Schwartz: Investigation. A. Franson: Investigation. P.L. Robertson: Investigation. K.M. Muraszko: Resources. C.O. Maher: Resources. H.J.L. Garton: Resources. T. Qin: Software, formal analysis. C. Koschmann: Conceptualization, resources, formal analysis, supervision, investigation, methodology, writing-original draft, writing-review and editing.

The authors thank the patients and their families for participation in this study. C. Koschmann was supported by NIH/NINDS K08-NS099427–01, the University of Michigan Chad Carr Pediatric Brain Tumor Center, Catching Up With Jack, the Evans family, the Chad Tough Foundation, Hyundai Hope on Wheels, the Prayers from Maria Foundation, the U CAN-CER VIVE Foundation, the Morgan Behen Golf Classic, and the DIPG Collaborative.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.