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

本提案针对挑衅性问题#2。我们将使用创新的方法来研究如何 CDKN 2A（编码p16）突变携带者发展不同的癌症表型（胰腺癌与 黑色素瘤vs无癌症），并且包括遗传和非遗传因素。我们已经确定了4个大的，多- 具有创始者CDKN 2A有害突变（L16 R，47 T>G）的世代激酶。我们的初步 观察结果表明，该突变体具有较低的表达和细胞周期调节能力下降 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 表型已经确定了改变风险的新基因（LGR 6，Wnt的共受体）的潜在变体 信号传导和COL 11 A1，其参与致癌信号传导，包括TGF β）。康贝特人将以 p16突变体促进转化的能力以及它如何受到与上述物质相互作用的影响 候选修饰基因LGR 6或COL 11 A1在胰腺癌和黑素瘤中的作用。我们还将开发新的 使用机器深度学习的计算模型，以生成捕获高维数据的网络， 整合基因、生物学和癌症表型的特征。这种方法将被扩展到kinetics与 其他CDKN 2A突变我们的具体目标是：（1）确定潜在的修饰基因的基因型， 以胰腺癌和黑色素瘤为特征的多种激酶，已知携带CDKN 2A种系 突变。我们将使用来自4个大的CDKN 2A携带者的种系DNA的全基因组变异覆盖率， L16 R激酶，加上其他42个类似的CDKN 2A激酶的额外成员。我们将确定候选人 通过基于规则的基因型统计遗传分析，在运动中的修饰基因。(2)定义影响 CDKN 2A L16 R突变对p16功能的影响及其与候选修饰基因的相互作用我们将 使用功能性和特异性抗体阐明CDKN 2A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潜在机制， 基因;使用全基因组研究的组合来定义与这种相互作用合作的新途径， 使用良好建立的体外和体内转化方法评估携带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，以准确推断胰腺癌和黑色素瘤的表型和发病年龄 突变携带者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，开发一个反向传播算法来微调“权重”，并构建突变基因网络， 捕获每个疾病亚类的高维特征。我们将获取和传播新知识 and tools工具to the scientific科学community社区.我们的综合方法和方法将使人们深入了解 癌症表型可以发生相同的诱发突变，这可以应用于其他癌症， 类似挑战的综合征。

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.