Techna Institute for the Advancement of Technology for Health, University Health Network, Toronto, Ontario, Canada.
Department of Medical Biophysics, University of Toronto, Ontario, Canada.
Cancer Res. 2019 May 1;79(9):2426-2434. doi: 10.1158/0008-5472.CAN-18-3411. Epub 2019 Mar 19.
Medulloblastoma (MB) is a pediatric malignant brain tumor composed of four different subgroups (WNT, SHH, Group 3, Group 4), each of which are a unique biological entity with distinct clinico-pathological, molecular, and prognostic characteristics. Although risk stratification of patients with MB based on molecular features may offer personalized therapies, conventional subgroup identification methods take too long and are unable to deliver subgroup information intraoperatively. This limitation prevents subgroup-specific adjustment of the extent or the aggressiveness of the tumor resection by the neurosurgeon. In this study, we investigated the potential of rapid tumor characterization with Picosecond infrared laser desorption mass spectrometry (PIRL-MS) for MB subgroup classification based on small molecule signatures. One hundred and thirteen MB tumors from a local tissue bank were subjected to 10- to 15-second PIRL-MS data collection and principal component analysis with linear discriminant analysis (PCA-LDA). The MB subgroup model was established from 72 independent tumors; the remaining 41 de-identified unknown tumors were subjected to multiple, 10-second PIRL-MS samplings and real-time PCA-LDA analysis using the above model. The resultant 124 PIRL-MS spectra from each sampling event, after the application of a 95% PCA-LDA prediction probability threshold, yielded a 98.9% correct classification rate. Post-ablation histopathologic analysis suggested that intratumoral heterogeneity or sample damage prior to PIRL-MS sampling at the site of laser ablation was able to explain failed classifications. Therefore, upon translation, 10-seconds of PIRL-MS sampling is sufficient to allow personalized, subgroup-specific treatment of MB during surgery. SIGNIFICANCE: This study demonstrates that laser-extracted lipids allow immediate grading of medulloblastoma tumors into prognostically important subgroups in 10 seconds, providing medulloblastoma pathology in an actionable manner during surgery.
髓母细胞瘤(MB)是一种由四个不同亚组(WNT、SHH、Group 3、Group 4)组成的小儿恶性脑肿瘤,每个亚组都是具有独特生物学特性的实体,具有不同的临床病理、分子和预后特征。虽然基于分子特征对 MB 患者进行风险分层可能提供个性化治疗,但传统的亚组识别方法耗时过长,无法在手术中提供亚组信息。这一限制阻止了神经外科医生根据肿瘤切除的程度或侵袭性对特定亚组进行调整。在这项研究中,我们研究了皮秒红外激光解吸质谱(PIRL-MS)快速肿瘤特征分析技术在基于小分子特征的 MB 亚组分类中的应用潜力。对当地组织库中的 113 例 MB 肿瘤进行了 10-15 秒的 PIRL-MS 数据采集和主成分分析(PCA)与线性判别分析(LDA)。MB 亚组模型是基于 72 例独立肿瘤建立的;其余 41 例未识别的未知肿瘤进行了多次、10 秒的 PIRL-MS 采样,并使用上述模型进行实时 PCA-LDA 分析。在应用 95%的 PCA-LDA 预测概率阈值后,从每个采样事件获得的 124 个 PIRL-MS 光谱,得出了 98.9%的正确分类率。术后组织病理学分析表明,在激光消融部位进行 PIRL-MS 采样之前,肿瘤内异质性或样本损伤能够解释分类失败的原因。因此,在转化后,10 秒的 PIRL-MS 采样足以允许在手术期间对 MB 进行个性化、亚组特异性治疗。意义:这项研究表明,激光提取的脂质可以在 10 秒内立即将髓母细胞瘤肿瘤分为具有重要预后意义的亚组,为手术期间提供可操作的髓母细胞瘤病理。