Outcomes in AYA survivors of pediatric medulloblastoma.

摘要 这项临床研究确定了有助于认知结果的强大因素， 儿童、青少年和年轻成人髓母细胞瘤（MB）脑肿瘤幸存者（PAYAS） 位于小脑。儿童脑肿瘤幸存者的认知风险增加 和相对于那些健康状况不佳和残疾的可能性很大， 没有癌症史，相对于没有接受救生的幸存者， 神经毒性放化疗治疗然而，认知结果的个体差异 在MB的PAYAS中，即使最可能的临床贡献者 是相同的（即，治疗时的年龄、放化疗风险水平、MB肿瘤亚型）。这导致我们的 一个研究小组研究了遗传素质和其他可能解释大范围遗传病的因素。 结果，从轻度到重度CI。除了遗传学之外，还需要填补以下方面的重要空白： 到目前为止，确定临床风险因素和环境资源的研究表明， CI风险最高的人这个多站点项目在佐治亚州，AL， 和OH将检查多因素的环境资源（例如，邻里社会经济 逆境，物质困难，承受能力和学校质量），临床因素（例如，年龄 治疗时，放化疗风险水平，MB肿瘤类型），和个体遗传素质（即， 候选单核苷酸多态性变体（SNP））。首先，在目标1中，研究将审查 三个领域中的每一个都独立地使用最先进的方法和创新的分析 找出CI的最佳预测因子。在其他人群中，多基因风险评分（PRS） 提供了比单一SNP更强的预测结果。因此，在基因 素质域，我们将检查有针对性的SNP和相邻的相互作用的突变，以创建 利用机器学习工具对调节RNA表达的SNP进行加权的鲁棒PRS 与CI相关，并结合了已知的表观遗传学、遗传学和遗传学的功能影响。 proteins.其次，在目标2中，我们将创建一个多域风险算法，使用最敏感的 跨三个域的CI预测因子（即，临床风险、环境资源和遗传 素质）。这种经验得出的风险算法将通过识别 风险适应性放化疗治疗和预防性干预的条件， 缓解和管理CI。符合2018年星星法案和精准医学 主动，这些研究结果将允许早期识别CI风险个体，并提供 治疗目标，以改善PAYAS的整体生活质量和适应功能 儿童期癌症

Abstract This clinical research identifies the robust factors that contribute to cognitive outcomes in pediatric, adolescent, and young adult survivors (PAYAS) of medulloblastoma (MB) brain tumors located in the cerebellum. Pediatric brain tumor survivors are at increased risk of cognitive impairment (CI) and have substantial likelihood of poor health and disability relative to those who do not have a cancer history, and relative to survivors who did not have to undergo lifesaving neurotoxic chemoradiation treatment. However, individual differences in cognitive outcomes among PAYAS of MB are diverse and wide ranging, even when the most likely clinical contributors are the same (i.e., age at treatment, chemoradiation level of risk, MB tumor subtype). This led our team to examine genetic diatheses and other contributors that may explain the wide range of outcomes, from mild to severe CI. In addition to genetics, there is a need to fill important gaps in the research to date to identify both clinical risk factors and environmental resources that identify those at greatest risk of CI. This multisite project at NCI-designated cancer centers in GA, AL, and OH will examine multifactorial environmental resources (e.g., neighborhood socioeconomic adversity, material hardships, caregiving capacity, and school quality), clinical factors (e.g., age at treatment, chemoradiation level of risk, MB tumor type), and individual genetic diathesis (i.e., candidate single nucleotide polymorphism variants (SNPs)). First, in Aim 1, the study will examine each of the three domains independently using state of the art methods and innovative analyses to identify the best predictors of CI. In other populations, polygenetic risk scores (PRS) have provided a stronger prediction of outcomes than single SNPs alone. Therefore, within the genetic diathesis domain, we will examine targeted SNPs and neighboring interactive mutations to create a robust PRS utilizing machine learning tools that weights SNPs regulating RNA expression associated with CI, and incorporates known functional impact of epigenetics, transcriptions and proteins. Second, in Aim 2, we will create a multi-domain risk algorithm, using the most sensitive predictors of CI across the three domains (i.e., clinical risks, environmental resources, and genetic diathesis). This empirically derived risk algorithm will inform precision medicine, by identifying conditions for risk-adapted chemoradiation treatment and prophylactic interventions to prevent, mitigate and manage CI. Consistent with the STAR Act of 2018 and the Precision Medicine Initiative, these findings will allow for early identification of individuals at risk for CI and provide targets for treatments in order to improve overall quality of life and adaptive functioning in PAYAS of childhood cancer.