Intraoperative DNA methylation classification of brain tumors impacts neurosurgical strategy

Long-read sequencing for brain tumors

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

Rapid DNA methylation-based classification of pediatric brain tumors from ultrasonic aspirate specimens

BACKGROUND

Although cavitating ultrasonic aspirators are commonly used in neurosurgical procedures, the suitability of ultrasonic aspirator-derived tumor material for diagnostic procedures is still controversial. Here, we explore the feasibility of using ultrasonic aspirator-resected tumor tissue to classify otherwise discarded sample material by fast DNA methylation-based analysis using low pass nanopore whole genome sequencing.

METHODS

Ultrasonic aspirator-derived specimens from pediatric patients undergoing brain tumor resection were subjected to low-pass nanopore whole genome sequencing. DNA methylation-based classification using a neural network classifier and copy number variation analysis were performed. Tumor purity was estimated from copy number profiles. Results were compared to microarray (EPIC)-based routine neuropathological histomorphological and molecular evaluation.

RESULTS

19 samples with confirmed neuropathological diagnosis were evaluated. All samples were successfully sequenced and passed quality control for further analysis. DNA and sequencing characteristics from ultrasonic aspirator-derived specimens were comparable to routinely processed tumor tissue. Classification of both methods was concordant regarding methylation class in 17/19 (89%) cases. Application of a platform-specific threshold for nanopore-based classification ensured a specificity of 100%, whereas sensitivity was 79%. Copy number variation profiles were generated for all cases and matched EPIC results in 18/19 (95%) samples, even allowing the identification of diagnostically or therapeutically relevant genomic alterations.

CONCLUSION

Methylation-based classification of pediatric CNS tumors based on ultrasonic aspirator-reduced and otherwise discarded tissue is feasible using time- and cost-efficient nanopore sequencing.

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.

Assessing the efficacy of target adaptive sampling long-read sequencing through hereditary cancer patient genomes

Innovations in sequencing technology have led to the discovery of novel mutations that cause inherited diseases. However, many patients with suspected genetic diseases remain undiagnosed. Long-read sequencing technologies are expected to significantly improve the diagnostic rate by overcoming the limitations of short-read sequencing. In addition, Oxford Nanopore Technologies (ONT) offers adaptive sampling and computationally driven target enrichment technology. This enables more affordable intensive analysis of target gene regions compared to standard non-selective long-read sequencing. In this study, we developed an efficient computational workflow for target adaptive sampling long-read sequencing (TAS-LRS) and evaluated it through application to 33 genomes collected from suspected hereditary cancer patients. Our workflow can identify single nucleotide variants with nearly the same accuracy as the short-read platform and elucidate complex forms of structural variations. We also newly identified several SINE-R/VNTR/Alu (SVA) elements affecting the APC gene in two patients with familial adenomatous polyposis, as well as their sites of origin. In addition, we demonstrated that off-target reads from adaptive sampling, which is typically discarded, can be effectively used to accurately genotype common single-nucleotide polymorphisms (SNPs) across the entire genome, enabling the calculation of a polygenic risk score. Furthermore, we identified allele-specific MLH1 promoter hypermethylation in a Lynch syndrome patient. In summary, our workflow with TAS-LRS can simultaneously capture monogenic risk variants including complex structural variations, polygenic background as well as epigenetic alterations, and will be an efficient platform for genetic disease research and diagnosis.

Recent advances in molecular profiling of bone and soft tissue tumors

The molecular characterization of soft tissue and bone tumors is a rapidly evolving field that has changed the perspective of how these tumors are diagnosed today. Morphology and clinico-radiological context still represent the cornerstone of diagnostic considerations but are increasingly complemented by molecular data that aid in objectifying and confirming the classification. The spectrum of analyses comprises mutation or gene fusion specific immunohistochemical antibodies, fluorescence in situ hybridization, DNA and RNA sequencing as well as CpG methylation profiling. This article provides an overview of which tools are presently available to characterize bone and soft tissue neoplasms molecularly, what limitations should be considered, and what conclusions can be drawn from the individual findings.

Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations

Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons' involvement in model development and validation.

Ultra-fast deep-learned CNS tumour classification during surgery

Central nervous system tumours represent one of the most lethal cancer types, particularly among children1. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of resection and minimizing risk of neurological damage and comorbidity2,3. However, surgeons have limited knowledge of the precise tumour type prior to surgery. Current standard practice relies on preoperative imaging and intraoperative histological analysis, but these are not always conclusive and occasionally wrong. Using rapid nanopore sequencing, a sparse methylation profile can be obtained during surgery4. Here we developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular subclassification of central nervous system tumours based on such sparse profiles. Sturgeon delivered an accurate diagnosis within 40 minutes after starting sequencing in 45 out of 50 retrospectively sequenced samples (abstaining from diagnosis of the other 5 samples). Furthermore, we demonstrated its applicability in real time during 25 surgeries, achieving a diagnostic turnaround time of less than 90 min. Of these, 18 (72%) diagnoses were correct and 7 did not reach the required confidence threshold. We conclude that machine-learned diagnosis based on low-cost intraoperative sequencing can assist neurosurgical decision-making, potentially preventing neurological comorbidity and avoiding additional surgeries.

Advances in computational and translational approaches for malignant glioma

Gliomas are the most common primary brain tumors in adults and carry a dismal prognosis for patients. Current standard-of-care for gliomas is comprised of maximal safe surgical resection following by a combination of chemotherapy and radiation therapy depending on the grade and type of tumor. Despite decades of research efforts directed towards identifying effective therapies, curative treatments have been largely elusive in the majority of cases. The development and refinement of novel methodologies over recent years that integrate computational techniques with translational paradigms have begun to shed light on features of glioma, previously difficult to study. These methodologies have enabled a number of point-of-care approaches that can provide real-time, patient-specific and tumor-specific diagnostics that may guide the selection and development of therapies including decision-making surrounding surgical resection. Novel methodologies have also demonstrated utility in characterizing glioma-brain network dynamics and in turn early investigations into glioma plasticity and influence on surgical planning at a systems level. Similarly, application of such techniques in the laboratory setting have enhanced the ability to accurately model glioma disease processes and interrogate mechanisms of resistance to therapy. In this review, we highlight representative trends in the integration of computational methodologies including artificial intelligence and modeling with translational approaches in the study and treatment of malignant gliomas both at the point-of-care and outside the operative theater in silico as well as in the laboratory setting.

The Current State of Nanopore Sequencing

Nanopore sensing is a disruptive, revolutionary way in which to sequence nucleic acids, including both native DNA and RNA molecules. First commercialized with the MinION^TM sequencer from Oxford Nanopore Technologies^TM in 2015, this review article looks at the current state of nanopore sequencing as of June 2022. Covering the unique characteristics of the technology and how it functions, we then go on to look at the ability of the platform to deliver sequencing at all scales-from personal to high-throughput devices-before looking at how the scientific community is applying the technology around the world to answer their biological questions.

Early ependymal tumor with MN1-BEND2 fusion: a mostly cerebral tumor of female children with a good prognosis that is distinct from classical astroblastoma

PURPOSE

Review of the clinicopathologic and genetic features of early ependymal tumor with MN1-BEND2 fusion (EET MN1-BEND2), classical astroblastomas, and recently described related pediatric CNS tumors. I also briefly review general mechanisms of gene expression silencing by DNA methylation and chromatin remodeling, and genomic DNA methylation profiling as a powerful new tool for CNS tumor classification.

METHODS

Literature review and illustration of tumor histopathologic features and prenatal gene expression timelines.

RESULTS

Astroblastoma, originally descried by Bailey and Cushing in 1926, has been an enigmatic tumor. Whether they are of ependymal or astrocytic derivation was argued for decades. Recent genetic evidence supports existence of both ependymal and astrocytic astroblastoma-like tumors. Studies have shown that tumors exhibiting astroblastoma-like histology can be classified into discrete entities based on their genomic DNA methylation profiles, gene expression, and in some cases, the presence of unique gene fusions. One such tumor, EET MN1-BEND2 occurs mostly in female children, and has an overall very good prognosis with surgical management. It contains a gene fusion comprised of portions of the MN1 gene at chromosomal location 22q12.1 and the BEND2 gene at Xp22.13. Other emerging pediatric CNS tumor entities demonstrating ependymal or astroblastoma-like histological features also harbor gene fusions involving chromosome X, 11q22 and 22q12 breakpoint regions.

CONCLUSIONS

Genomic DNA profiling has facilitated discovery of several new CNS tumor entities, however, traditional methods, such as immunohistochemistry, DNA or RNA sequencing, and cytogenetic studies, including fluorescence in situ hybridization, remain necessary for their accurate biological classification and diagnosis.

Neurooncology: 2023 update

This article presents some of the author's neuropathological highlights in the field on neuro-oncology research encountered in 2022. Major advances were made in the development of more precise, faster, easier, less invasive and unbiased diagnostic tools ranging from immunohistochemical prediction of 1p/19q loss in diffuse glioma, methylation analyses in CSF samples, molecular profiling for CNS lymphoma, proteomic analyses of recurrent glioblastoma, integrated molecular diagnostics for better stratification in meningioma, intraoperative profiling making use of Raman effect or methylation analysis, to finally, the assessment of histological slides by means of machine learning for the prediction of molecular tumor features. In addition, as the discovery of a new tumor entity may also be a highlight for the neuropathology community, the newly described high-grade glioma with pleomorphic and pseudopapillary features (HPAP) has been selected for this article. Regarding new innovative treatment approaches, a drug screening platform for brain metastasis is presented. Although diagnostic speed and precision is steadily increasing, clinical prognosis for patients with malignant tumors affecting the nervous system remains largely unchanged over the last decade, therefore future neuro-oncological research focus should be put on how the amazing developments presented in this article can be more sustainably applied to positively impact patient prognosis.

Efficient real-time selective genome sequencing on resource-constrained devices

BACKGROUND

Third-generation nanopore sequencers offer selective sequencing or "Read Until" that allows genomic reads to be analyzed in real time and abandoned halfway if not belonging to a genomic region of "interest." This selective sequencing opens the door to important applications such as rapid and low-cost genetic tests. The latency in analyzing should be as low as possible for selective sequencing to be effective so that unnecessary reads can be rejected as early as possible. However, existing methods that employ a subsequence dynamic time warping (sDTW) algorithm for this problem are too computationally intensive that a massive workstation with dozens of CPU cores still struggles to keep up with the data rate of a mobile phone-sized MinION sequencer.

RESULTS

In this article, we present Hardware Accelerated Read Until (HARU), a resource-efficient hardware-software codesign-based method that exploits a low-cost and portable heterogeneous multiprocessor system-on-chip platform with on-chip field-programmable gate arrays (FPGA) to accelerate the sDTW-based Read Until algorithm. Experimental results show that HARU on a Xilinx FPGA embedded with a 4-core ARM processor is around 2.5× faster than a highly optimized multithreaded software version (around 85× faster than the existing unoptimized multithreaded software) running on a sophisticated server with a 36-core Intel Xeon processor for a SARS-CoV-2 dataset. The energy consumption of HARU is 2 orders of magnitudes lower than the same application executing on the 36-core server.

CONCLUSIONS

HARU demonstrates that nanopore selective sequencing is possible on resource-constrained devices through rigorous hardware-software optimizations. The source code for the HARU sDTW module is available as open source at https://github.com/beebdev/HARU, and an example application that uses HARU is at https://github.com/beebdev/sigfish-haru.

Epigenetic tumor heterogeneity in the era of single-cell profiling with nanopore sequencing

Nanopore sequencing has brought the technology to the next generation in the science of sequencing. This is achieved through research advancing on: pore efficiency, creating mechanisms to control DNA translocation, enhancing signal-to-noise ratio, and expanding to long-read ranges. Heterogeneity regarding epigenetics would be broad as mutations in the epigenome are sensitive to cause new challenges in cancer research. Epigenetic enzymes which catalyze DNA methylation and histone modification are dysregulated in cancer cells and cause numerous heterogeneous clones to evolve. Detection of this heterogeneity in these clones plays an indispensable role in the treatment of various cancer types. With single-cell profiling, the nanopore sequencing technology could provide a simple sequence at long reads and is expected to be used soon at the bedside or doctor's office. Here, we review the advancements of nanopore sequencing and its use in the detection of epigenetic heterogeneity in cancer.

Characterization of ex vivo and in vivo intraoperative neurosurgical confocal laser endomicroscopy imaging

Background

The new US Food and Drug Administration-cleared fluorescein sodium (FNa)-based confocal laser endomicroscopy (CLE) imaging system allows for intraoperative on-the-fly cellular level imaging. Two feasibility studies have been completed with intraoperative use of this CLE system in ex vivo and in vivo modalities. This study quantitatively compares the image quality and diagnostic performance of ex vivo and in vivo CLE imaging.

Methods

Images acquired from two prospective CLE clinical studies, one ex vivo and one in vivo, were analyzed quantitatively. Two image quality parameters – brightness and contrast – were measured using Fiji software and compared between ex vivo and in vivo images for imaging timing from FNa dose and in glioma, meningioma, and intracranial metastatic tumor cases. The diagnostic performance of the two studies was compared.

Results

Overall, the in vivo images have higher brightness and contrast than the ex vivo images (p < 0.001). A weak negative correlation exists between image quality and timing of imaging after FNa dose for the ex vivo images, but not the in vivo images. In vivo images have higher image quality than ex vivo images (p < 0.001) in glioma, meningioma, and intracranial metastatic tumor cases. In vivo imaging yielded higher sensitivity and negative predictive value than ex vivo imaging.

Conclusions

In our setting, in vivo CLE optical biopsy outperforms ex vivo CLE by producing higher quality images and less image deterioration, leading to better diagnostic performance. These results support the in vivo modality as the modality of choice for intraoperative CLE imaging.

DNA methylation subclasses predict the benefit from gross total tumor resection in IDH-wildtype glioblastoma patients

BACKGROUND

DNA methylation-based tumor classification allows an enhanced distinction into subgroups of glioblastoma. However, the clinical benefit of DNA methylation-based stratification of glioblastomas remains inconclusive.

METHODS

Multicentric cohort study including 430 patients with newly diagnosed glioblastoma subjected to global DNA methylation profiling. Outcome measures included overall survival (OS), progression-free survival (PFS), prognostic relevance of EOR and MGMT promoter methylation status as well as a surgical benefit for recurrent glioblastoma.

RESULTS

345 patients (80.2%) fulfilled the inclusion criteria and 305 patients received combined adjuvant therapy. DNA methylation subclasses RTK I, RTK II, and mesenchymal (MES) revealed no significant survival differences (RTK I: Ref.; RTK II: HR 0.9 [95% CI, 0.64-1.28]; p = 0.56; MES: 0.69 [0.47-1.02]; p = 0.06). Patients with RTK I (GTR/near GTR: Ref.; PR: HR 2.87 [95% CI, 1.36-6.08]; p < 0.01) or RTK II (GTR/near GTR: Ref.; PR: HR 5.09 [95% CI, 2.80-9.26]; p < 0.01) tumors who underwent gross-total resection (GTR) or near GTR had a longer OS and PFS than partially resected patients. The MES subclass showed no survival benefit for a maximized EOR (GTR/near GTR: Ref.; PR: HR 1.45 [95% CI, 0.68-3.09]; p = 0.33). Therapy response predictive value of MGMT promoter methylation was evident for RTK I (HR 0.37 [95% CI, 0.19-0.71]; p < 0.01) and RTK II (HR 0.56 [95% CI, 0.34-0.91]; p = 0.02) but not the MES subclass (HR 0.52 [95% CI, 0.27-1.02]; p = 0.06). For local recurrence (n = 112), re-resection conveyed a progression-to-overall survival (POS) benefit (p < 0.01), which was evident in RTK I (p = 0.03) and RTK II (p < 0.01) tumors, but not in MES tumors (p = 0.33).

CONCLUSION

We demonstrate a survival benefit from maximized EOR for newly diagnosed and recurrent glioblastomas of the RTK I and RTK II but not the MES subclass. Hence, it needs to be debated whether the MES subclass should be treated with maximal surgical resection, especially when located in eloquent areas and at time of recurrence.

Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing

The Oxford Nanopore (ONT) platform provides portable and rapid genome sequencing, and its ability to natively profile DNA methylation without complex sample processing is attractive for point-of-care real-time sequencing. We recently demonstrated ONT shallow whole-genome sequencing to detect copy number alterations (CNAs) from the circulating tumor DNA (ctDNA) of cancer patients. Here, we show that cell type and cancer-specific methylation changes can also be detected, as well as cancer-associated fragmentation signatures. This feasibility study suggests that ONT shallow WGS could be a powerful tool for liquid biopsy.

Ultra-rapid somatic variant detection via real-time targeted amplicon sequencing

Molecular markers are essential for cancer diagnosis, clinical trial enrollment, and some surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform due to time-consuming DNA extraction, targeted amplification, and library preparation times. In this work, we present a proof-of-principle approach for sub-1 hour targeted variant detection using real-time DNA sequencers. By modifying existing protocols, optimizing for diagnostic time-to-result, we demonstrate confirmation of a hot-spot mutation from tumor tissue in ~52 minutes. To further reduce time, we explore rapid, targeted Loop-mediated Isothermal Amplification (LAMP) and design a bioinformatics tool-LAMPrey-to process sequenced LAMP product. LAMPrey's concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Using LAMPrey, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.

MGMT and Whole-Genome DNA Methylation Impacts on Diagnosis, Prognosis and Therapy of Glioblastoma Multiforme

Epigenetic changes in DNA methylation contribute to the development of many diseases, including cancer. In glioblastoma multiforme, the most prevalent primary brain cancer and an incurable tumor with a median survival time of 15 months, a single epigenetic modification, the methylation of the O⁶-Methylguanine-DNA Methyltransferase (MGMT) gene, is a valid biomarker for predicting response to therapy with alkylating agents and also, independently, prognosis. More recently, the progress from single gene to whole-genome analysis of DNA methylation has allowed a better subclassification of glioblastomas. Here, we review the clinically relevant information that can be obtained by studying MGMT gene and whole-genome DNA methylation changes in glioblastomas, also highlighting benefits, including those of liquid biopsy, and pitfalls of the different detection methods. Finally, we discuss how changes in DNA methylation, especially in glioblastomas bearing mutations in the Isocitrate Dehydrogenase (IDH) 1 and 2 genes, can be exploited as targets for tailoring therapy.

An Integrated Epigenomic and Genomic View on Phyllodes and Phyllodes-like Breast Tumors

Fibroepithelial lesions (FL) of the breast, in particular, phyllodes tumors (PT) and fibroadenomas, pose a significant diagnostic challenge. There are no generally accepted criteria that distinguish benign, borderline, malignant PT and fibroadenomas. Combined genome-wide DNA methylation and copy number variant (CNV) profiling is an emerging strategy to classify tumors. We compiled a series of patient-derived archival biopsy specimens reflecting the FL spectrum and histological mimickers including clinical follow-up data. DNA methylation and CNVs were determined by well-established microarrays. Comparison of the patterns with a pan-cancer dataset assembled from public resources including “The Cancer Genome Atlas” (TCGA) and “Gene Expression Omnibus” (GEO) suggests that FLs form a methylation class distinct from both control breast tissue as well as common breast cancers. Complex CNVs were enriched in clinically aggressive FLs. Subsequent fluorescence in situ hybridization (FISH) analysis detected respective aberrations in the neoplastic mesenchymal component of FLs only, confirming that the epithelial component is non-neoplastic. Of note, our approach could lead to the elimination of the diagnostically problematic category of borderline PT and allow for optimized prognostic patient stratification. Furthermore, the identified recurrent genomic aberrations such as 1q gains (including MDM4), CDKN2a/b deletions, and EGFR amplifications may inform therapeutic decision-making.