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Abstract：
Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.
摘要：
瘤内异质性（ITH）使胶质瘤的诊断和治疗复杂化，部分原因是由潜在的基因组改变驱动的不同代谢谱。虽然多参数成像通过捕获空间和功能变化来增强ITH的表征，它不足以直接评估支撑这些表型差异的代谢活动。这种差距源于易于集成的挑战，具有代谢见解的共位病理学和详细的基因组数据。这项研究提出了一种多方面的方法，将立体定向活检与标准的临床开颅手术相结合，用于样本收集。MR图像的逐体素分析，基于回归的GAM，和全外显子组测序。这项工作旨在证明机器学习算法预测细胞和分子肿瘤特征变化的潜力。这项回顾性研究招募了10例经放射学证实的神经胶质瘤未接受治疗的患者。每位患者都进行了多参数MR扫描（T1W，T1W-CE,T2W,T2W-FLAIR,DWI）手术前。在标准开颅手术中，每位患者至少采集1次立体定向活检,并保存样本位置的屏幕截图，以便与手术前MR数据进行空间配准。对快速冷冻的肿瘤样本进行全外显子组测序，优先考虑五个神经胶质瘤相关基因的特征：IDH1，TP53，EGFR，PIK3CA,NF1。使用GAM对每个预测因子使用单变量形状函数来实现回归。标准接收器工作特性(ROC)分析用于评估检测，计算每个基因靶标和MR对比剂组合的AUC(曲线下面积)。5个基因靶标和31个MR对比剂组合的平均AUC为0.75±0.11；IDH1和TP53在T2W-FLAIR和ADC下的单个AUC高达0.96，对于具有T2W和ADC的EGFR和0.99。这些结果表明，预测外显子组全突变事件的可能性来自非侵入性，通过结合胶质瘤样本的立体定向定位和半参数深度学习方法进行体内成像。确定的基因组改变，特别是在IDH1，TP53，EGFR，PIK3CA,和NF1已知在驱动神经胶质瘤异质性的代谢途径中起关键作用。我们的方法论,因此,通过这些关键基因组标记的镜头间接揭示了神经胶质瘤的代谢景观，提示肿瘤基因组学和代谢之间复杂的相互作用。这种方法具有通过更好地解决神经胶质瘤肿瘤的基因组异质性来完善靶向治疗的潜力。
