Familial Investigations of Childhood Cancer Predisposition
This research study aims to find new genes that increase the risk of childhood cancers like Acute Leukemia and Adrenocortical Carcinoma. It is an observational study, meaning no specific interventions are being tested. Researchers will collect blood or other tissue samples, as well as medical and family histories, from up to 1500 participants. The goal is to identify new cancer-predisposing genes, with results potentially available up to 20 years after the study starts. All ages and genders can join. This is a research study and not a substitute for clinical genetic testing; you may not receive results, or they may take many years.
- Study design
- This is an observational study planning to enroll up to 1500 participants. It is not a randomized or blinded study.
- What's involved
- You would provide blood or saliva samples, and potentially a skin sample. You would also share your medical and family history, and may be asked to update this information yearly if you agree to be re-contacted.
- Compensation
- Not stated in the trial record.
- Follow-up
- The primary endpoint, identification of novel cancer predisposing genes, is measured up to 20 years following study activation.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Familial Investigations of Childhood Cancer Predisposition
At a glance
Conditions
Where it's being run
1 sites across 1 statesStudy leadership
- Kim E. Nichols, MD · PRINCIPAL_INVESTIGATOR · St. Jude Children's Research Hospital
Who to contact
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Do you actually qualify for this trial?
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Inclusion
Exclusion
What this trial measures
- Identification of novel cancer predisposing genesUp to 20 years following study activation
Probands and cancer affected and unaffected relatives from selected families will be sequenced using Whole Genome Sequencing (WGS) or possibly Whole Exome Sequencing (WES) and analyzed to identify new predisposing genetic variants that co-segregate with the tumor phenotype. Data will be analyzed using annotation and filtering strategies to identify potentially deleterious germline mutations that co-segregate with disease.