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Background The initial randomized, double‐blinded, actively controlled, phase III ANEAS study (NCT03849768) demonstrated that aumolertinib showed superior efficacy relative to gefitinib as first‐line therapy in epidermal growth factor receptor (EGFR)‐mutated advanced non‐small cell lung cancer (NSCLC). Metastatic disease in the central nervous system (CNS) remains a challenge in the management of NSCLC. This study aimed to compare the efficacy of aumolertinib versus gefitinib among patients with baseline CNS metastases in the ANEAS study. Methods Eligible patients were enrolled and randomly assigned in a 1:1 ratio to orally receive either aumolertinib or gefitinib in a double‐blinded fashion. Patients with asymptomatic, stable CNS metastases were included. Follow‐up imaging of the same modality as the initial CNS imaging was performed every 6 weeks for 15 months, then every 12 weeks. CNS response was assessed by a neuroradiological blinded, independent central review (neuroradiological‐BICR). The primary endpoint for this subgroup analysis was CNS progression‐free survival (PFS). Results Of the 429 patients enrolled and randomized in the ANEAS study, 106 patients were found to have CNS metastases (CNS Full Analysis Set, cFAS) at baseline by neuroradiological‐BICR, and 60 of them had CNS target lesions (CNS Evaluable for Response, cEFR). Treatment with aumolertinib significantly prolonged median CNS PFS compared with gefitinib in both cFAS (29.0 vs. 8.3 months; hazard ratio [HR] = 0.31; 95% confidence interval [CI], 0.17‐0.56; P < 0.001) and cEFR (29.0 vs. 8.3 months; HR = 0.26; 95% CI, 0.11‐0.57; P < 0.001). The confirmed CNS overall response rate in cEFR was 85.7% and 75.0% in patients treated with aumolertinib and gefitinib, respectively. Competing risk analysis showed that the estimated probability of CNS progression without prior non‐CNS progression or death was consistently lower with aumolertinib than with gefitinib in patients with and without CNS metastases at baseline. No new safety findings were observed. Conclusions These results indicate a potential advantage of aumolertinib over gefitinib in terms of CNS PFS and the risk of CNS progression in patients with EGFR‐mutated advanced NSCLC with baseline CNS metastases. Trial registration ClinicalTrials.gov number, NCT03849768

Newly diagnosed glioblastoma multiforme with unmethylated MGMT promoter has a poor prognosis, with a median survival of 12 months. This phase II study investigated the efficacy and safety of combining the selective integrin inhibitor cilengitide with a combination of metronomic temozolomide and procarbazine for these patients. Eligible patients (newly diagnosed, histologically confirmed supratentorial glioblastoma with unmethylated MGMT promoter) were entered into this multicentre study. Cilengitide (2000 mg IV twice weekly) was commenced 1 week prior to radiotherapy combined with daily temozolomide (60 mg/m 2 ) and procarbazine (50 or 100 mg) and, after 4 weeks’ break, followed by six adjuvant cycles of temozolomide (50–60 mg/m 2 ) and procarbazine (50 or 100 mg) on days 1–20, every 28 days. Cilengitide was continued for up to 12 months or until disease progression or unacceptable toxicity. The primary endpoint for efficacy was a 12-month overall survival rate of 65 %. Twenty-nine patients completed study treatment. Sixteen patients survived for 12 months or more, an overall survival rate of 55 %. The median overall survival was 14.5 months (95 % CI 11.1–19.6) and the median progression-free survival was 7.4 months (95 % CI 6.1–8). Cilengitide combined with metronomic temozolomide and procarbazine in MGMT-promoter unmethylated glioblastoma did not improve survival compared with historical data and does not warrant further investigation.

Lung cancer remains the leading cause of cancer deaths in the world with 1.69 million deaths in 2015. A total of 85% of lung cancer cases are non-small-cell lung cancers (NSCLCs). Driver mutations associated with anaplastic lymphoma kinase (ALK) have been identified in a variety of malignancies, including NSCLC. An ALK inhibitor (crizotinib, ceritinib and alectinib) is the preferred therapeutic approach to those advanced ALK fusion variant-positive NSCLC patients. Brigatinib, a next-generation ALK inhibitor, shows promising activity in ALK-rearranged NSCLC that have previously received crizotinib with response rates in ALTA ranging from 42-50%, intracranial response 42-67% and median progression-free survival 9.2-12.9 months. Randomized Phase III trial, ALTA-1 L is investigating brigatinib in ALK inhibitor-naive patients.

Background Metastatic breast cancer (MBC) behaviour differs depending on hormone receptors (HR) and human epidermal growth factor receptor (HER2) statuses. Methods The kinetics of central nervous system (CNS) metastases (CNS metastasis-free survival, CNSM-FS) and subsequent patient’s prognosis (overall survival, OS) according to the molecular subtype were retrospectively assessed in 16703 MBC patients of the ESME nationwide multicentre MBC database (Kaplan–Meier method). Results CNS metastases occurred in 4118 patients (24.6%) (7.2% at MBC diagnosis and 17.5% later during follow-up). Tumours were HER2−/HR+ (45.3%), HER2+/HR+ (14.5%), HER2+/HR− (14.9%) and triple negative (25.4%). Median age at CNS metastasis diagnosis was 58.1 years (range: 22.8–92.0). The median CNSM-FS was 10.8 months (95% CI: 16.5–17.9) among patients who developed CNS metastases. Molecular subtype was independently associated with CNSM-FS (HR = 3.45, 95% CI: 3.18–3.75, triple-negative and HER2−/HR+ tumours). After a 30-month follow-up, median OS after CNS metastasis diagnosis was 7.9 months (95% CI: 7.2–8.4). OS was independently associated with subtypes: median OS was 18.9 months (HR = 0.57, 95% CI: 0.50–0.64) for HER2+/HR+ , 13.1 months (HR = 0.72, 95% CI: 0.65–0.81) for HER2+/HR−, 4.4 months (HR = 1.55, 95% CI: 1.42–1.69) for triple-negative and 7.1 months for HER2−/HR+ patients ( p  <0.0001). Conclusions Tumour molecular subtypes strongly impact incidence, kinetics and prognosis of CNS metastases in MBC patients. Clinical trial registration NCT03275311.

Abstract Neoplasms of the peripheral nervous system represent a heterogenous group with a wide spectrum of morphological features and biological potential. They range from benign and curable by complete excision (schwannoma and soft tissue perineurioma) to benign but potentially aggressive at the local level (plexiform neurofibroma) to the highly malignant (malignant peripheral nerve sheath tumors [MPNST]). In this review, we discuss the diagnostic and pathologic features of common peripheral nerve sheath tumors, particularly those that may be encountered in the intracranial compartment or in the spine and paraspinal region. The discussion will cover schwannoma, neurofibroma, atypical neurofibromatous neoplasms of uncertain biological potential, intraneural and soft tissue perineurioma, hybrid nerve sheath tumors, MPNST, and the recently renamed enigmatic tumor, malignant melanotic nerve sheath tumor, formerly referred to as melanotic schwannoma. We also discuss the diagnostic relevance of these neoplasms to specific genetic and familial syndromes of nerve, including neurofibromatosis 1, neurofibromatosis 2, and schwannomatosis. In addition, we discuss updates in our understanding of the molecular alterations that represent key drivers of these neoplasms, including neurofibromatosis type 1 and type 2, SMARCB1, LZTR1, and PRKAR1A loss, as well as the acquisition of CDKN2A/B mutations and alterations in the polycomb repressor complex members (SUZ12 and EED) in the malignant progression to MPNST. In summary, this review covers practical aspects of pathologic diagnosis with updates relevant to neurosurgical practice.

Congenital melanocytic nevi (CMN) can be associated with neurological abnormalities and an increased risk of melanoma. Mutations in NRAS, BRAF, and Tp53 have been described in individual CMN samples; however, their role in the pathogenesis of multiple CMN within the same subject and development of associated features has not been clear. We hypothesized that a single postzygotic mutation in NRAS could be responsible for multiple CMN in the same individual, as well as for melanocytic and nonmelanocytic central nervous system (CNS) lesions. From 15 patients, 55 samples with multiple CMN were sequenced after site-directed mutagenesis and enzymatic digestion of the wild-type allele. Oncogenic missense mutations in codon 61 of NRAS were found in affected neurological and cutaneous tissues of 12 out of 15 patients, but were absent from unaffected tissues and blood, consistent with NRAS mutation mosaicism. In 10 patients, the mutation was consistently c.181C>A, p.Q61K, and in 2 patients c.182A>G, p.Q61R. All 11 non-melanocytic and melanocytic CNS samples from 5 patients were mutation positive, despite NRAS rarely being reported as mutated in CNS tumors. Loss of heterozygosity was associated with the onset of melanoma in two cases, implying a multistep progression to malignancy. These results suggest that single postzygotic NRAS mutations are responsible for multiple CMN and associated neurological lesions in the majority of cases.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging—with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology. An online approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups has been developed to help to improve current diagnostic standards. Classifying tumour types for better diagnoses Precise cancer diagnoses are essential to ensure the best treatment plans for patients, but standardization of the diagnostic process has been challenging. The authors present a comprehensive approach for DNA-methylation-based classification of brain tumours. The tool improves diagnostic precision of standard methods, and is made available online for broad accessibility. The results illustrate the potential applications of molecular diagnosis tools.

Key Points The 2016 WHO Classification of Tumours of the Central Nervous System reflects a paradigm shift, replacing traditional histology-based glioma diagnostics with an integrated histological and molecular classification system that enables more-precise tumour categorization The requisite diagnostic biomarkers in the 2016 WHO classification of gliomas are IDH1/2 (IDH) mutations, 1p/19q codeletion, H3F3A or HIST1H3B/C K27M (H3-K27M) mutations and C11orf95–RELA fusions Additional diagnostically relevant biomarkers include loss of nuclear ATRX expression, TERT -promoter mutations, KIAA1549–BRAF fusions, BRAF -V600E mutation, H3F3A -G34 mutation, and several other alterations associated with rare glioma entities MGMT -promoter methylation is predictive of benefit from alkylating chemotherapy in patients with IDH-wild-type glioblastoma; predictive biomarkers for targeted therapies, such as IDH1 and BRAF mutations, are also emerging Novel methods for large-scale DNA-methylation, copy-number and mutational profiling will further advance the assessment of glioma-associated molecular biomarkers Clinical trials require assessment of molecular biomarkers as criteria for study entry and/or patient stratification; predictive DNA sequencing followed by targeted therapy will support the implementation of precision medicine in neuro-oncology In 2016, a revised WHO classification of glioma was published, in which molecular data and traditional histological information are incorporated into integrated diagnoses. Herein, the authors highlight the developments in our understanding of the molecular genetics of gliomas that underlie this classification, and review the current landscape of molecular biomarkers used in the classification of disease subtypes. In addition, they discuss how these advances can promote the development of novel pathogenesis-based therapeutic approaches, paving the way to precision medicine. Genome-wide molecular-profiling studies have revealed the characteristic genetic alterations and epigenetic profiles associated with different types of gliomas. These molecular characteristics can be used to refine glioma classification, to improve prediction of patient outcomes, and to guide individualized treatment. Thus, the WHO Classification of Tumours of the Central Nervous System was revised in 2016 to incorporate molecular biomarkers — together with classic histological features — in an integrated diagnosis, in order to define distinct glioma entities as precisely as possible. This paradigm shift is markedly changing how glioma is diagnosed, and has important implications for future clinical trials and patient management in daily practice. Herein, we highlight the developments in our understanding of the molecular genetics of gliomas, and review the current landscape of clinically relevant molecular biomarkers for use in classification of the disease subtypes. Novel approaches to the genetic characterization of gliomas based on large-scale DNA-methylation profiling and next-generation sequencing are also discussed. In addition, we illustrate how advances in the molecular genetics of gliomas can promote the development and clinical translation of novel pathogenesis-based therapeutic approaches, thereby paving the way towards precision medicine in neuro-oncology.

Central nervous system tumours represent one of the most lethal cancer types, particularly among children 1 . 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 surgery 4 . 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. Sturgeon is a pretrained neural network that uses incremental results from nanopore sequencing to rapidly classify central nervous system tumours and can be used to aid critical decision-making during surgery.