Early Detection of Cancer Patients with Germline SDH Deficiency

初始训练集将包括40名患有SDH缺陷型GIST的患者，40名患者 SDH缺陷型副神经节瘤患者，以及40例性别和SDH亚基突变匹配的患者 参加罕见肿瘤自然史研究。将采集外周血和尿液样本 在一天的第二个空白时在三个独立的时间点采集，间隔至少为 每例患者一个月，以最大限度地减少代谢正常变异性的影响， 侧写所有患者将继续使用当前的标准筛选进行随访 建议.将从血浆中分离无细胞DNA。将分离尿液cfDNA 使用文献中已经显示的Q-Sepharose层析方案， 用于跨肾cfDNA的其他已发表和商业化的尿液cfDNA分离方法 产率已证明PDL-1甲基化调节PDL-1表达和DNA合成。 甲基化模式已显示匹配或超过TMB对PDL-1的预测值 抑制作用当分离到足够的cfDNA时，将使用以下方法评估PDL-1甲基化状态： 先前公开的探针和数字PCR增强的Methylight技术。该功能对应的标准 最小DNA输入量为3 ng，检测限接近0.03%。PDL-1甲基化状态 将与客观缓解率进行比较，作为独立测定的预测值， 以及当使用协同指数与cfDNA衍生的TMB整合时。虽然基因组的 SDH缺陷型GIST通常含有很少的体细胞突变，其他癌症相关基因 包括p53和RB，特别是在侵袭性肿瘤患者中， 行为除了甲基化分析之外，cfDNA变体等位基因检测和拷贝数分析也是一种有效的方法。 还将使用CAPP-seq方法评估改变。先前已验证， 将优化的探针序列掺入靶向杂交捕获NGS组中 （通过深度测序的癌症个性化分析（CAPP-Seq））。使用积分数字误差 抑制，CAPP-seq对罕见变异体的检测限接近0.0025%（2.5/105 分子）。代谢组学分析将与Dr. Naomi Taylor通过NCI的质谱（蛋白质和小分子）核心， 弗雷德里克。将采集配对血清和尿液样本，并在以下2小时内冷冻 收藏.将通过HPLC-Mass进行批次样品的代谢特征分析 包括TCA循环、磷酸戊糖分流和糖酵解定量的光谱分析 代谢物。目的2：将与NCI的癌症合作进行模型构建 数据科学实验室DNA甲基化和代谢组学数据的结合将提供 使用标准算法进行无监督聚类的模型开发的强大数据集 分析.如果可能的话，将对模型进行细化和简化， 更好的识别特征。目标3：将通过正在进行的 在罕见肿瘤自然史研究中招募生殖系SDH缺乏的患者。 代谢组学和cfDNA甲基化数据将从新入组的患有以下疾病的患者中收集： GIST、PHEO/PGL和无癌症证据（每组20例）。该模型将被测试 并使用这个数据集进行了改进。

The initial training set will include 40 patients with SDH-deficient GIST, 40 patients with SDH-deficient paraganglioma, and 40 patients matched for gender and SDH subunit mutation enrolled on the Rare Tumor Natural History Study. Peripheral blood and urine samples will be collected at the second void of the day at three separate time points separated by at least one month for each patient to minimize the impact of normal variability in the metabolic profiling. All patients will continue to be followed using the current standard screening recommendations. Cell free DNA will be isolated from plasma. Urine cfDNA will be isolated using a Q-Sepharose chromatography protocol which has been shown in the literature to outperform other published and commercial urine cfDNA isolation methods for trans-renal cfDNA yield. PDL-1 methylation has been demonstrated to regulate PDL-1 expression and DNA methylation patterns have been shown to match or exceed TMBs predictive value for PDL-1 inhibition. When sufficient cfDNA is isolated, PDL-1 methylation status will be assessed using previously published probes and digital PCR enhanced Methylight technology. This technology's minimal DNA input is 3ng with limits of detection approaching 0.03%. PDL-1 methylation status would be compared to objective response rates for predictive value as a stand-alone assay as well as when integrated with cfDNA derived TMB using a synergistic index. While the genome of SDH-deficient GIST typically contain very few somatic mutations, other cancer-associated genes including p53 and RB have been observed, particularly in patients with more aggressive tumor behavior. In addition to methylation analysis, cfDNA variant allele detection and copy number alterations will also be assessed using a CAPP-seq approach. Previously validated and optimized probe sequences will be incorporated into a targeted hybrid capture NGS panel (cancer personalized profiling by deep sequencing (CAPP-Seq)). Using integrated digital error suppression, CAPP-seq limit of detections for rare variants approaches 0.0025% (2.5 in 105 molecules). Metabolomic analysis will be performed in collaboration with the laboratory of Dr. Naomi Taylor through the Mass Spectrometry (Protein and Small Molecule) core at NCI at Frederick. Paired serum and urine samples will be collected and frozen within 2 hours of collection. Metabolic profiling of batched samples will be performed via HPLC-Mass spectrometry including quantitation of TCA cycle, pentose phosphate shunt, and glycolysis metabolites. AIM 2: Model building will be performed in collaboration with the NCI's Cancer Data Science Laboratory. The combination of DNA methylation and metabolomic data will provide a robust data set for model development using standard algorithms for unsupervised cluster analysis. If possible, the model will be refined and simplified to use only better-discriminating features. AIM 3: A testing data set will be generated through ongoing enrollment of patients with germline SDH deficiency on the Rare Tumor Natural History Study. Metabolomic and cfDNA methylation data will be collected from newly enrolled patients with GIST, PHEO/PGL, and without evidence of cancer (20 in each group). The model will be tested and refined using this dataset.