Lisa Cannon-Albright, Ph.D.
Research Overview
This study will identify possible melanoma predisposition genes and variants that may be responsible for higher rates of melanoma in some families by analyzing their pedigrees (familial lines of traits). Pedigree analysis traces the inheritance of an abnormal trait or disease. Analyzing the inheritance patterns seen in large families has been shown to be a powerful way to identify the predisposition genes that are responsible for this type of skin cancer.
We have used this approach in Utah to identify major cancer gene mutations—BRCA1 そして BRCA2, as well as CDKN2A [the most common predisposition gene for cutaneous melanoma (CM)]. We aim to use this same approach for a large available resource of already sequenced, CM-related cases from high-risk Utah pedigrees.
This project will:
- Apply a unique approach that includes identifying the rare genetic variants that are shared among these familial cases to identify a set of strong possible CM predisposition genes and variants.
- Validate our gene set by testing for association with CM risk in independent sets of CM cases and controls.
- Investigate the top 10 candidates in the set of additional previously sampled CM-affected relatives of the affected cousin carriers to confirm segregation of the variants with CM in pedigrees.
Identification of additional genes and variants responsible for CM will improve identification of those people most at risk, expand our understanding of the causes of CM and allow the application of powerful screening and prevention strategies for this deadly cancer.
My “Why”
As a graduate student, I met Dr. Mark Skolnick who proposed studying high-risk pedigrees to understand the inherited contribution to cancer (in the late 1970s, when everyone thought all cancer was viral or environmental). I am a statistician and decided then to focus my tools on inherited cancer predisposition.
Why Funding Matters
Funding [from the Prevent Cancer Foundation] allows my team and I to efficiently analyze a powerful resource that already exists. We will be able to apply our method to analyze a set of sequenced melanoma cases to identify genetic changes that increase the risk of cancer in an effort to ultimately predict and prevent melanoma.