Grant Number:	1R01CA261887-01A1 Interpret this number
Primary Investigator:	King, Tricia
Organization:	Georgia State University
Project Title:	Outcomes in Aya Survivors of Pediatric Medulloblastoma.
Fiscal Year:	2022

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.





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