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Determinants of pancreatic cancer and malignant melanoma phenotypes in CDKN2A hereditary kindreds

CDKN2A 遗传家族中胰腺癌和恶性黑色素瘤表型的决定因素

基本信息

批准号：
9978727
负责人：
Martin Ernesto Fernandez-Zapico
金额：
$ 59.06万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-17 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9978727
关键词：
Address Affect Age Age of Onset Algorithms Alleles Alzheimer&apos s disease brain Back Biological Biology Brain Neoplasms CDKN2A gene Cancer Gene Mutation Cancer-Predisposing Gene Cell Cycle Progression Cells Code Communities Computer Models Cyclin-Dependent Kinase Inhibitor 2A DNA Data Data Set Development Diagnosis Discrimination Disease Environmental Exposure Etiology Gene Expression Gene Frequency Gene Mutation Gene-Modified Generations Genes Genetic Genome Genotype Germ-Line Mutation In Vitro Individual Inherited Knowledge Light Malignant Neoplasms Malignant neoplasm of pancreas Measures Methodology Methods Minor Modeling Molecular Mutation Oncogenic Pathway interactions Pattern Penetrance Performance Phenotype Process Proteins Proteomics Risk Signal Transduction Smoking Sun Exposure Syndrome Testing Training Transforming Growth Factor beta Variant WNT Signaling Pathway Weight base bead chip cancer genome cell growth convolutional neural network deep learning deep learning algorithm disease phenotype epidemiologic data epigenomics genetic analysis genetic variant genome-wide genome-wide analysis high dimensionality in vivo in vivo Model innovation insight interest kindred leukemia melanoma member metaplastic cell transformation mutant mutation carrier network models non-genetic novel novel diagnostics novel therapeutic intervention overexpression protein expression receptor tool transcriptome sequencing transmission process tumorigenesis

项目摘要

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.

这个提议解决了挑衅性问题#2。我们将使用创新的方法来研究如何

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Ribosomal proteins regulate 2-cell-stage transcriptome in mouse embryonic stem cells.

DOI：
10.1016/j.stemcr.2022.12.007
发表时间：
2023-02-14
期刊：
STEM CELL REPORTS
影响因子：
5.9
作者：
Yi, Yao;Zeng, Yingying;Sam, Tsz Wing;Hamashima, Kiyofumi;Tan, Rachel Jun Rou;Warrier, Tushar;Phua, Jun Xiang;Taneja, Reshma;Liou, Yih-Cherng;Li, Hu;Xu, Jian;Loh, Yuin-Han
通讯作者：
Loh, Yuin-Han