This proposal addresses Provocative Question #2. We will use innovative approaches to investigate how
CDKN2A (which encodes p16) mutation carriers develop different cancer phenotypes (pancreatic cancer vs
melanoma 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 preliminary
observations demonstrate that this mutant has lower expression and decreased ability to regulate cell cycle
progression compared to wild type protein. Our sequencing studies of kindred members with different cancer
phenotypes have identified potential variants in novel genes that modify risk (LGR6, a co-receptor of Wnt
signaling and COL11A1, which participates in oncogenic signaling, including TGFbeta). We will determine the
ability of the p16 mutant to promote transformation and how it is influenced by interaction with the above
candidate modifier genes, LGR6 or COL11A1, in pancreatic cancer and melanoma. We will also develop novel
computational models using machine deep learning, to generate networks that capture high dimensional
features to integrate gene, biology, and cancer phenotype. This approach will be extended to kindreds with
other CDKN2A mutations. Our Specific Aims are to: (1) Identify genotypes of potential modifier genes in
multiple kindreds that feature pancreatic cancer and melanoma and known to carry CDKN2A germline
mutations. We will use genome wide variant coverage of germline DNA from CDKN2A carriers from the 4 large
L16R kindreds, plus additional members in 42 other similar CDKN2A kindreds. We will identify candidate
modifier genes in the kindreds by rule-based statistical genetic analysis of genotypes. (2) Define the impact of
CDKN2A L16R mutation on the function of p16 and its interplay with candidate modifier genes. We will
elucidate the biological significance of mutations in CDKN2A and candidate modifier genes using functional and
high throughput methodologies by analyzing the mechanism underlying the interplay between p16 and modifier
genes; define new pathways cooperating with this interplay using a combination of genome wide studies to
assess transformation in cells carrying p16 mutant or wild-type background using well established in vitro and in
vivo models. (3) Develop a deep learning network model to integrate genetic, biological and
epidemiological data to accurately infer pancreatic cancer and melanoma phenotypes and age of onset
in mutation carriers. We will apply a convolutional neural network, a deep learning algorithm in the training
dataset, develop a back-propagation algorithm to fine tune “weights,” and construct mutation-gene networks to
capture high-dimensional features for each disease subclass. We will acquire and disseminate new knowledge
and tools to the scientific community. Our integrated methods and approach will bring insight into how different
cancer phenotypes can occur with identical predisposing mutations, which can be applied to other cancer
syndromes with similar challenges.
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