Purpose: Surgery for infiltrative gliomas aims to balance tumor removal with preservation of functional integrity. The usefulness of intraoperative stimulation mapping (ISM) has not been addressed in randomized trials. This study addresses glioma surgery outcome on the basis of a meta-analysis of observational studies. Methods: A systematic search retrieved 90 reports published between 1990 and 2010 with 8,091 adult patients who had resective surgery for supratentorial infiltrative glioma, with or without ISM. Quality criteria consisted of postoperative neurologic examination details and follow-up timing. New postoperative neurologic deficits were categorized on the basis of timing and severity. Meta-analysis with a Bayesian random effects model determined summary event rates of deficits as well as gross total resection rate and eloquent locations. Meta-regression analysis explored heterogeneity among studies. Results: Late severe neurologic deficits were observed in 3.4% (95% CI, 2.3% to 4.8%) of patients after resections with ISM, and in 8.2% (95% CI, 5.7% to 11.4%) of patients after resections without ISM (adjusted odds ratio, 0.39; 95% CI, 0.23 to 0.64). The percentages of radiologically confirmed gross total resections were 75% (95% CI, 66% to 82%) with ISM and 58% (95% CI, 48% to 69%) without ISM. Eloquent locations were involved in 99.9% (95% CI, 99.9% to 100%) of resections with ISM and in 95.8% (95% CI, 73.1% to 99.8%) of resections without ISM. Relevant sources of heterogeneity among studies were ISM, continent, and academic setting. Conclusion: Glioma resections using ISM are associated with fewer late severe neurologic deficits and more extensive resection, and they involve eloquent locations more frequently. This indicates that ISM should be universally implemented as standard of care for glioma surgery.

The neocortex is disproportionately expanded in human compared with mouse^1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth³. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer’s disease^4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.

WEE1 kinase is a key molecule in maintaining G₂–cell-cycle checkpoint arrest for premitotic DNA repair. Whereas normal cells repair damaged DNA during G₁-arrest, cancer cells often have a deficient G₁-arrest and largely depend on G₂-arrest. The molecular switch for the G₂–M transition is held by WEE1 and is pushed forward by CDC25. WEE1 is overexpressed in various cancer types, including glioblastoma and breast cancer. Preclinical studies with cancer cell lines and animal models showed decreased cancer cell viability, reduced tumor burden, and improved survival after WEE1 inhibition by siRNA or small molecule inhibitors, which is enhanced by combination with conventional DNA-damaging therapy, such as radiotherapy and/or cytostatics. Mitotic catastrophe results from premature entry into mitosis with unrepaired lethal DNA damage. As such, cancer cells become sensitized to conventional therapy by WEE1 inhibition, in particular those with insufficient G₁-arrest due to deficient p53 signaling, like glioblastoma cells. One WEE1 inhibitor has now reached clinical phase I studies. Dose-limiting toxicity consisted of hematologic events, nausea and/or vomiting, and fatigue. The combination of DNA-damaging cancer therapy with WEE1 inhibition seems to be a rational approach to push cancer cells in mitotic catastrophe. Its safety and efficacy are being evaluated in clinical studies. Clin Cancer Res; 17(13); 4200–7. ©2011 AACR.

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.