Skip to main content

COVID-19 is an emerging, rapidly evolving situation.

What people with cancer should know:

Guidance for cancer researchers:

Get the latest public health information from CDC:

Get the latest research information from NIH:

Grant Details

Grant Number: 5R01CA208517-05 Interpret this number
Primary Investigator: Petersen, Gloria
Organization: Mayo Clinic Rochester
Project Title: Determinants of Pancreatic Cancer and Malignant Melanoma Phenotypes in Cdkn2a Hereditary Kindreds
Fiscal Year: 2020


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.


Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility.
Authors: Xing Q.R. , Farran C.A.E. , Zeng Y.Y. , Yi Y. , Warrier T. , Gautam P. , Collins J.J. , Xu J. , Dröge P. , Koh C.G. , et al. .
Source: Genome research, 2020 Jul; 30(7), p. 1027-1039.
EPub date: 2020-07-22.
PMID: 32699019
Related Citations

Pancreatic cancer and melanoma related perceptions and behaviors following disclosure of CDKN2A variant status as a research result.
Authors: Leof E.R. , Zhu X. , Rabe K.G. , McCormick J.B. , Petersen G.M. , Radecki Breitkopf C. .
Source: Genetics in medicine : official journal of the American College of Medical Genetics, 2019 11; 21(11), p. 2468-2477.
EPub date: 2019-04-17.
PMID: 30992552
Related Citations

Regulostat Inferelator: a novel network biology platform to uncover molecular devices that predetermine cellular response phenotypes.
Authors: Ung C.Y. , Ghanat Bari M. , Zhang C. , Liang J. , Correia C. , Li H. .
Source: Nucleic acids research, 2019-08-22; 47(14), p. e82.
PMID: 31114928
Related Citations

CDKN2A Germline Rare Coding Variants and Risk of Pancreatic Cancer in Minority Populations.
Authors: McWilliams R.R. , Wieben E.D. , Chaffee K.G. , Antwi S.O. , Raskin L. , Olopade O.I. , Li D. , Highsmith W.E. , Colon-Otero G. , Khanna L.G. , et al. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2018 11; 27(11), p. 1364-1370.
EPub date: 2018-07-23.
PMID: 30038052
Related Citations

Mitofusin 2 Regulates Axonal Transport of Calpastatin to Prevent Neuromuscular Synaptic Elimination in Skeletal Muscles.
Authors: Wang L. , Gao J. , Liu J. , Siedlak S.L. , Torres S. , Fujioka H. , Huntley M.L. , Jiang Y. , Ji H. , Yan T. , et al. .
Source: Cell metabolism, 2018-09-04; 28(3), p. 400-414.e8.
EPub date: 2018-07-12.
PMID: 30017354
Related Citations

Global H3.3 dynamic deposition defines its bimodal role in cell fate transition.
Authors: Fang H.T. , El Farran C.A. , Xing Q.R. , Zhang L.F. , Li H. , Lim B. , Loh Y.H. .
Source: Nature communications, 2018-04-18; 9(1), p. 1537.
EPub date: 2018-04-18.
PMID: 29670118
Related Citations

Psychological Impact of Learning CDKN2A Variant Status as a Genetic Research Result.
Authors: Zhu X. , Leof E.R. , Rabe K.G. , McCormick J.B. , Petersen G.M. , Radecki Breitkopf C. .
Source: Public health genomics, 2018; 21(3-4), p. 154-163.
EPub date: 2019-04-18.
PMID: 30999302
Related Citations

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.
Authors: Ghanat Bari M. , Ung C.Y. , Zhang C. , Zhu S. , Li H. .
Source: Scientific reports, 2017-08-01; 7(1), p. 6993.
EPub date: 2017-08-01.
PMID: 28765560
Related Citations

Detection of early pancreatic ductal adenocarcinoma with thrombospondin-2 and CA19-9 blood markers.
Authors: Kim J. , Bamlet W.R. , Oberg A.L. , Chaffee K.G. , Donahue G. , Cao X.J. , Chari S. , Garcia B.A. , Petersen G.M. , Zaret K.S. .
Source: Science translational medicine, 2017-07-12; 9(398), .
PMID: 28701476
Related Citations

Back to Top