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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：
9172003
负责人：
Martin Ernesto Fernandez-Zapico
金额：
$ 57.86万
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 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9172003
关键词：
Address Affect Age Age of Onset Algorithms Alleles Alzheimer&apos s Disease Back Biological Biological Neural Networks Brain Neoplasms CDKN2A gene Cancer Biology Cancer Gene Mutation Cell Cycle Progression Cells Code Communities Computer Simulation Cyclin-Dependent Kinase Inhibitor 2A DNA Data Data Set Development Diagnosis Discrimination Disease Environmental Exposure Epidemiology Etiology Gene Expression Gene Frequency Gene Mutation Gene-Modified Genes Genetic Genome Genotype Germ-Line Mutation Group Processes In Vitro Individual Inherited Knowledge Learning Light Malignant Neoplasms Malignant neoplasm of pancreas Measures Methodology Methods Minor Modeling Molecular Mutation Oncogenic Pathway interactions Pattern Penetrance Performance Phenotype Processed Genes Proteins Proteomics Risk Signal Transduction Smoking Sun Exposure Syndrome Testing Training Transforming Growth Factor beta Variant Weight base bead chip cancer genome cell growth disease phenotype epigenomics feeding genetic analysis genetic variant genome-wide genome-wide analysis in vivo in vivo Model innovation insight interest kindred learning network 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.

该提案解决了争议性问题#2。我们将使用创新方法来研究如何 CDKN2A（编码 p16）突变携带者会发展出不同的癌症表型（胰腺癌与黑色素瘤与非癌症），并包括遗传和非遗传因素。我们已经确定了 4 个大型、多具有创始人 CDKN2A 有害突变（L16R，47T>G）的世代亲属。我们的初步观察结果表明，该突变体的表达较低，调节细胞周期的能力下降与野生型蛋白质相比的进展。我们对患有不同癌症的亲属进行的测序研究表型已识别出改变风险的新基因中的潜在变异（LGR6，Wnt 的共同受体）信号传导和 COL11A1（参与致癌信号传导，包括 TGFbeta）。我们将确定 p16突变体促进转化的能力以及它如何受到与上述相互作用的影响胰腺癌和黑色素瘤中的候选修饰基因 LGR6 或 COL11A1。我们还将开发新颖的使用机器深度学习的计算模型，生成捕获高维的网络整合基因、生物学和癌症表型的功能。这种方法将推广到有亲属关系的人其他 CDKN2A 突变。我们的具体目标是：（1）鉴定潜在修饰基因的基因型具有胰腺癌和黑色素瘤特征且已知携带 CDKN2A 种系的多个亲属突变。我们将使用来自 4 大基因组的 CDKN2A 携带者的种系 DNA 的全基因组变异覆盖率。 L16R 亲属，加上 42 个其他类似 CDKN2A 亲属中的其他成员。我们将确定候选人通过基于规则的基因型统计遗传分析来确定亲属中的修饰基因。 (2) 定义影响 CDKN2A L16R 突变对 p16 功能及其与候选修饰基因的相互作用。我们将使用功能和功能阐明 CDKN2A 和候选修饰基因突变的生物学意义通过分析 p16 和修饰剂之间相互作用的机制实现高通量方法基因；通过结合全基因组研究来定义与这种相互作用相配合的新途径使用体外和体内成熟的方法评估携带 p16 突变体或野生型背景的细胞中的转化体内模型。（3）开发深度学习网络模型，整合遗传、生物和流行病学数据可准确推断胰腺癌和黑色素瘤表型以及发病年龄在突变携带者中。我们将在训练中应用卷积神经网络，一种深度学习算法数据集，开发反向传播算法来微调“权重”，并构建突变基因网络以捕获每个疾病子类的高维特征。我们将获取并传播新知识和科学界的工具。我们的综合方法和方法将深入了解不同的癌症表型可能会出现相同的诱发突变，这可以应用于其他癌症具有类似挑战的综合症。