Grant Number:	1R01CA138836-01A2 Interpret this number
Primary Investigator:	Plon, Sharon
Organization:	Baylor College Of Medicine
Project Title:	Genomic Approaches to Defining Inherited Basis of Childhood Cancer
Fiscal Year:	2010

DESCRIPTION (provided by applicant): Identification of mutations in specific genes responsible for cancer susceptibility impacts decisions about treatment of the patient as well as allowing increased surveillance and prevention approaches for at-risk family members. In addition, knowledge of inherited predisposition to pediatric malignancies has provided important insights into the mechanisms of cancer and potential therapeutic targets in both children and adults with cancer in the general population. Current utilization of high-throughput sequencing methodologies for coding changes has been focused on somatic mutations in tumor tissue, and conversely; genome-wide association studies of cancer susceptibility have focused on common variations that individually have small impacts on cancer risk. We propose a project at the intersection of these two different approaches. We plan to use massively parallel sequence analyses of constitutional DNA from childhood cancer patients with medical and family history suggestive of a cancer susceptibility syndrome. In Aim 1 we will focus on cohorts of families with consistent and specific phenotypes, for example, probands with childhood sarcomas and second malignancies by age 40 where thorough analysis of well-characterized cancer-associated genes has been negative. The molecular analyses of germline DNA will include two comprehensive approaches (1) capture of all coding exons followed by sequencing at dense coverage to identify mutations pathogenic mutations (deletion, nonsense, frameshift or splice site) that impact coding sequence (whole exome) and (2) whole genome sequencing of paired-ends libraries at lower coverage to identify copy number changes and novel rearrangements. In Aim 2, we will carry out informative statistical and bioinformatics analysis of rare or novel missense alleles, using original methodology involving optimization of alignment depths for evolution-based studies of functionality. In Aim 3, analyses of copy number change and rearrangement by whole genome sequencing will include an additional cohort of subjects with childhood cancer and congenital anomalies or developmental delay to uncover novel contiguous gene or rearrangement syndromes. Results from these initial cohorts will then be validated by targeted sequencing of implicated genes in additional childhood cancer probands and families. To complete this translational, cross-disciplinary project we include collaborators from pediatric cancer genetics programs at Texas Children's Hospital, University of Texas MD Anderson Cancer Center and Health Science Center at San Antonio and the Children's Hospital of Philadelphia, the Human Genome Sequencing Center and the Department of Statistics at Rice University. The clinical centers enroll families from ethnically diverse populations with linkage to medical data, and establishment of immortalized cell lines to facilitate the generation of functionally and clinically relevant data. Our plan to thoroughly interrogate the constitutional genome will advance the goals of the National Cancer Institute's Strategic Plan on gaining a fuller understanding of the spectrum of genetic susceptibility to cancer in highly affected childhood cancer families. PUBLIC HEALTH RELEVANCE: Identification of mutations cancer susceptibility genes impacts decisions about treatment of the patient as well as allowing increased surveillance and prevention approaches for at-risk family members. We plan to use massively parallel sequence analyses of all coding regions in constitutional DNA from childhood cancer patients with medical and family history suggestive of a cancer susceptibility syndrome to identify novel cancer susceptibility genes and gain a fuller understanding of the spectrum of genetic susceptibility to cancer.




Framework for microRNA variant annotation and prioritization using human population and disease datasets.
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