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

这项建议解决了挑衅性的问题2。我们将使用创新的方法来调查 CDKN2A(编码p16)突变携带者会患上不同的癌症表型(胰腺癌与 黑色素瘤与非癌症)，包括遗传和非遗传因素。我们已经确定了4个大型的，多个- 具有创立者CDKN2A有害突变(L16R，47T&gt；G)的一代人。我们的预赛 观察表明，该突变体的表达水平较低，调节细胞周期的能力降低。 与野生型蛋白相比，进展更快。我们对患有不同癌症的亲属的测序研究 表型已经在改变风险的新基因(Wnt的共同受体LGR6)中发现了潜在的变异 信号转导和COL11A1，参与致癌信号转导，包括TGFbeta)。我们将确定 P16突变体促进转化的能力及其与上述因素相互作用的影响 胰腺癌和黑色素瘤中的候选修饰基因LGR6或COL11A1。我们还将开发小说 使用机器深度学习的计算模型，以生成捕获高维的网络 整合基因、生物学和癌症表型的功能。这种方法将扩展到有以下特点的家庭 其他CDKN2A突变。我们的具体目标是：(1)确定潜在的修饰基因的基因类型 以胰腺癌和黑色素瘤为特征的多个家族，已知携带CDKN2A胚系 突变。我们将使用来自CDKN2A携带者的全基因组变异覆盖来自4个 L16R家系，外加42个其他类似CDKN2A家系的额外成员。我们将确定候选人 通过基于规则的基因型别统计遗传分析来修饰家系中的基因。(2)界定 CDKN2A L16R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会 用功能和分子生物学方法阐明CDKN2A和候选修饰基因突变的生物学意义 通过分析p16和修饰物之间相互作用的机制来实现高通量方法 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 评估携带p16突变或野生型背景的细胞的转化 活体模型。(3)开发深度学习网络模型，将遗传、生物和 准确推断胰腺癌和黑色素瘤表型和发病年龄的流行病学数据 在突变载体中。我们将应用卷积神经网络，一种深度学习算法在训练中 数据集，开发一种反向传播算法来微调“权重”，并构建突变基因网络以 捕捉每个疾病亚类的高维特征。我们将获取和传播新的知识 并向科学界提供工具。我们的集成方法和方法将使您深入了解 癌症表型可以与相同的易感突变发生，这也可以应用于其他癌症 具有类似挑战的综合症。

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.