Imaging signatures of genetic mutations in glioblastoma using machine learning

胶质母细胞瘤（GB）是最常见和最具侵袭性的成人恶性脑肿瘤， 预后和异质性分子和成像谱。虽然目前适用的 治疗选择（即，手术、放疗、化疗）在过去的20年中已经扩大 年，OS率没有实质性改善。治疗GBM的主要障碍 患者的最大问题是其分子景观的异质性。分子靶标的确定 需要离体术后组织分析，这在评估肿瘤的空间分布方面是有限的。 异质性（由于单一样本组织病理学和分子分析导致的采样误差）和 时间异质性（不可能连续评估 治疗期间的肿瘤）。在此，我们建议开发定量成像表型（QIP） GB中一系列感兴趣的突变的标记物。我们将建立在EGFR，IDH1的先前工作的基础上 突变和MGMT甲基化QIP签名，并开发一个广泛的成像面板， 10个基因突变的签名，以及MGMT启动子甲基化，使用机器学习 方法适用于相对常规的临床mpMRI（标准加扩散张量和灌注 协议）。此类生物标志物的可用性有助于无创i）患者分层 适当的治疗，ii）测量个体分子特征。特别是， 我们建议实现以下具体目标： 具体目标1（SA1）：为构建量化的 GB突变的表型特征成像 特定目标2（SA2）：建立神经胶质瘤中10种感兴趣突变的QIP特征， MGMT启动子状态，使用下一代测序（NGS）。我们将使用709个数据集。 具体目标3（SA3）：使用QIP特征表征GB的分子异质性， 利用SA1的NGS样本，以及我们将进行基因分型的新样本， 从来自150名患者的每名患者的4个不同位置获得总共600个肿瘤样品。的 作为正在进行的工作的一部分，已经对第一批150个组织样本进行了分析。 具体目标4（SA4）：将我们的方法整合到癌症成像表型组学工具包中 （CaPTk），以便用户轻松访问它们

Glioblastoma (GB) is the most common and aggressive malignant adult brain tumor, with grim prognosis and heterogeneous molecular and imaging profiles. Although the currently applicable treatment options (i.e., surgery, radiotherapy, chemotherapy) have expanded during the last 20 years, there is no substantial improvement in the OS rates. The major obstacle in treating GBM patients is the heterogeneity of their molecular landscape. Determination of molecular targets requires ex vivo postoperative tissue analyses, which are limited in assessing the tumor's spatial heterogeneity (sampling error due to single sample histopathological and molecular analysis) and temporal heterogeneity (not possible to continuously assess the molecular transformation of the tumor during treatment). Herein we propose to develop quantitative imaging phenomic (QIP) markers of a range of mutations of interest in GB. We will build on prior work on EGFR, IDH1 mutations and MGMT methylation QIP signatures, and develop an extensive panel of imaging signatures of 10 gene mutations, as well as MGMT promoter methylation, using machine learning methods applied to relatively routine clinical mpMRI (standard plus diffusion tensor and perfusion protocols). Availability of such biomarkers can contribute to non-invasive i) patient stratification into appropriate treatments, ii) measurement of individual molecular characteristics. In particular, we propose to carry out the following specific aims: Specific Aim 1 (SA1): To develop the enabling methodologies for constructing Quantitative Imaging Phenomic signatures of GB mutations Specific Aim 2 (SA2): Establish QIP signatures of 10 mutations of interest in gliomas, plus MGMT promoter status, using next generation sequencing (NGS). We will use 709 datasets. Specific Aim 3 (SA3): Characterize the molecular heterogeneity of GB using QIP signatures, leveraging the NGS samples of SA1, as well as a new sample that we will genotype, adding to a total of 600 tumor samples obtained from 4 different locations per patient from 150 patients. The first 150 tissue samples are already analyzed as part of ongoing work. Specific Aim 4 (SA4): Integrate our methods into the Cancer Imaging Phenomics Toolkit (CaPTk), in order to allow easy access to them by users