This proposal addresses Provocative Question #2. We will use innovative approaches to investigate howCDKN2A (which encodes p16) mutation carriers develop different cancer phenotypes (pancreatic cancer vsmelanoma vs no cancer), and include both genetic and non-genetic factors. We have identified 4 large, multi-generational kindreds with a founder CDKN2A deleterious mutation (L16R, 47T>G). Our preliminaryobservations demonstrate that this mutant has lower expression and decreased ability to regulate cell cycleprogression compared to wild type protein. Our sequencing studies of kindred members with different cancerphenotypes have identified potential variants in novel genes that modify risk (LGR6, a co-receptor of Wntsignaling and COL11A1, which participates in oncogenic signaling, including TGFbeta). We will determine theability of the p16 mutant to promote transformation and how it is influenced by interaction with the abovecandidate modifier genes, LGR6 or COL11A1, in pancreatic cancer and melanoma. We will also develop novelcomputational models using machine deep learning, to generate networks that capture high dimensionalfeatures to integrate gene, biology, and cancer phenotype. This approach will be extended to kindreds withother CDKN2A mutations. Our Specific Aims are to: (1) Identify genotypes of potential modifier genes inmultiple kindreds that feature pancreatic cancer and melanoma and known to carry CDKN2A germlinemutations. We will use genome wide variant coverage of germline DNA from CDKN2A carriers from the 4 largeL16R kindreds, plus additional members in 42 other similar CDKN2A kindreds. We will identify candidatemodifier genes in the kindreds by rule-based statistical genetic analysis of genotypes. (2) Define the impact ofCDKN2A L16R mutation on the function of p16 and its interplay with candidate modifier genes. We willelucidate the biological significance of mutations in CDKN2A and candidate modifier genes using functional andhigh throughput methodologies by analyzing the mechanism underlying the interplay between p16 and modifiergenes; define new pathways cooperating with this interplay using a combination of genome wide studies toassess transformation in cells carrying p16 mutant or wild-type background using well established in vitro and invivo models. (3) Develop a deep learning network model to integrate genetic, biological andepidemiological data to accurately infer pancreatic cancer and melanoma phenotypes and age of onsetin mutation carriers. We will apply a convolutional neural network, a deep learning algorithm in the trainingdataset, develop a back-propagation algorithm to fine tune ?weights,? and construct mutation-gene networks tocapture high-dimensional features for each disease subclass. We will acquire and disseminate new knowledgeand tools to the scientific community. Our integrated methods and approach will bring insight into how differentcancer phenotypes can occur with identical predisposing mutations, which can be applied to other cancersyndromes with similar challenges.
|Effective start/end date||8/17/16 → 7/31/21|
- National Institutes of Health: $578,620.00
Gene Regulatory Networks
Transforming Growth Factor beta
Weights and Measures