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 comorbidity^2,3. 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.

The human copper transporter 1 gene (hCTR1) was previously identified by functional complementation in ctr1-deficient yeast. Overexpression of hCTR1 in wild-type yeast leads to increased sensitivity to copper toxicity, and mice with a homozygous disruption at the Ctr1 locus die early during embryogenesis. It is proposed that hCTR1 is responsible for high-affinity copper uptake into human cells, but the underlying molecular mechanisms are unknown. To begin to investigate the biochemical characteristics of hCTR1, a polyclonal antiserum was raised against recombinant hCTR1-fusion peptides. Biosynthetic studies using this antiserum revealed that hCTR1 was synthesized as a precursor protein of 28kDa containing N-linked oligosaccharides, and is then converted to a mature protein of approx. 35kDa, which is ubiquitously expressed. Immunofluorescence studies showed that subcellular hCTR1 localization differed markedly between cell types. In some cell lines, hCTR1 was located predominantly in an intracellular vesicular perinuclear compartment, and in others hCTR1 was located predominantly at the plasma membrane. In contrast with the copper export P-type ATPases mutated in Wilson disease and Menkes disease, the localization of hCTR1 was not influenced by copper concentrations. Inhibition of endocytosis by methyl-β-cyclodextrin caused a partial redistribution of hCTR1 to the cell surface of HeLa cells. Taken together, the results in this study suggest a cell-specific control of copper uptake, which involves subcellular localization of the hCTR1 protein.

In the 5th edition of the WHO CNS tumor classification (CNS5, 2021), multiple molecular characteristics became essential diagnostic criteria for many additional CNS tumor types. For those tumors, an integrated, “histomolecular” diagnosis is required. A variety of approaches exists for determining the status of the underlying molecular markers. The present guideline focuses on the methods that can be used for assessment of the currently most informative diagnostic and prognostic molecular markers for the diagnosis of gliomas, glioneuronal and neuronal tumors. The main characteristics of the molecular methods are systematically discussed, followed by recommendations and information on available evidence levels for diagnostic measures. The recommendations cover DNA and RNA next-generation-sequencing, methylome profiling, and select assays for single/limited target analyses, including immunohistochemistry. Additionally, because of its importance as a predictive marker in IDH-wildtype glioblastomas, tools for the analysis of MGMT promoter methylation status are covered. A structured overview of the different assays with their characteristics, especially their advantages and limitations, is provided, and requirements for input material and reporting of results are clarified. General aspects of molecular diagnostic testing regarding clinical relevance, accessibility, cost, implementation, regulatory, and ethical aspects are discussed as well. Finally, we provide an outlook on new developments in the landscape of molecular testing technologies in neuro-oncology.