4.5 Review

Molecular predictors of outcome in low-grade glioma

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CURRENT OPINION IN NEUROLOGY
卷 25, 期 6, 页码 767-773

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/WCO.0b013e32835a0217

关键词

biomarker; low-grade glioma; outcome; predictor; prognosis

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Purpose of review Recent and ongoing translational studies in neurooncology have investigated the role of molecular markers as potential predictors of outcome in patients with WHO grade I and II gliomas, commonly summarized as low-grade gliomas (LGGs). Here, we seek to highlight the most relevant molecular aberrations associated with these tumour types and update on recent findings on their potential prognostic and predictive value. Recent findings So far, no biomarker discussed has any relevance for the postoperative course of disease without genotoxic treatment. Isocitrate dehydrogenase (IDH) mutations, 1p deletion or 1p/19q codeletion have the strongest prognostic impact on survival of patients with LGG, given a genotoxic treatment is provided. Recent findings from phase III clinical trials on anaplastic oligodendroglial tumours conducted in North America and Europe suggest that the addition of procarbazine, lomustine and vincristine to radiotherapy is beneficial in the treatment of anaplastic gliomas with 1p/19q codeletion. To decipher the role of 1p/19q codeletion in LGG will be challenging. Recent developments in v-raf murine sarcoma viral oncogene homolog B1 (BRAF)(V600E)-specific small molecule inhibitors and their clinical approval for other cancer types could turn BRAF(V600E) into a promising molecular predictor of outcome in pilocytic astrocytomas, given a treatment with a mutation-specific BRAF inhibitor is applied. Summary Clinical prognostic factors such as age, tumour size and the presence or absence of clinical symptoms have long been recognized in the management of patients with LGGs. Molecular biomarkers are increasingly evolving as additional factors that facilitate diagnostics and therapeutic decision-making. However, further prospective randomized studies including multivariate analyses are needed to clearly distinguish between prognostic and predictive effects.

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