Genetic mechanisms underlying sexual dimorphism in cancer and response to therapy

癌症由多种疾病组成，发病率和死亡率都很高。几乎所有常见的 癌症表现出某种形式的性二态，例如在发病率、预后或对治疗的反应方面。 这种二形性被认为源于男性和女性在荷尔蒙上的差异， 性染色体和环境暴露；然而，这些差异的分子基础仍然 很大程度上是未知的。对这种二形性的理解是癌症精确医学的基础，并且 可能导致发现新的生物标记物、治疗靶点和改善结果。我们建议 通过以下目标发现癌症中性二型性的分子基础：目标1将 性二型性基因表达的特征及其在肿瘤类型内部和之间的调节 NCI的癌症基因组图谱(TCGA)。利用TCGA，我们将在 转录组水平及其潜在的基因组和表观遗传学原因在癌症内部和跨癌症。目标2将 描述癌症易感性的可遗传遗传成分中的性别二型性 常见的癌症。利用癌症全基因组关联研究的数据，我们将测试多个基因 这些模型可以解释个体癌症的性二态特征。我们将测试一个多因素模型 遗传风险，即相同的等位基因影响两性，但风险较低的性行为具有更高的阈值和 需要更多或更强的遗传风险因素才能发展成疾病。我们将测试特定的风险或 X或Y染色体上编码的保护性因素对性别有不同的影响。我们会检测基因- 性交互作用，常染色体基因以一种性别依赖的方式影响风险。我们将测试它的存在 男性和女性由于激素调节而具有不同外显性或影响的常染色体危险因素 或其他在基因表达水平上起作用的性别二型性。目标3将描述性别二形性 对治疗学的反应，并确定导致这种现象的分子特征和机制 两面性。使用来自1000多个癌细胞系的分子和表型药物反应数据 癌症基因组计划(CGP)，我们将建立特定性别的药物反应预测模型，然后我们将 应用于全套TCGA肿瘤样本的基因表达数据，以推断性别特异性的药物反应 对于每个TCGA样本。我们将把药物对TCGA肿瘤分子特征的反应与 重点关注目标1和目标2中确定的那些。我们将从功能上验证预测的性二型性反应 使用细胞模型进行治疗，包括淋巴母细胞系、人类肝细胞和癌细胞 台词。这项研究的结果将确定在性别二型性基础上的遗传和基因组特征 癌症生物学、敏感性和对治疗方法的反应。

Cancer comprises a diverse group of diseases with significant morbidity and mortality. Nearly all common cancers exhibit some form of sexual dimorphism, for example in incidence, prognosis, or response to therapy. This dimorphism has been hypothesized to derive from differences between males and females in hormones, sex chromosomes, and environmental exposures; however the molecular basis of these disparities remains largely unknown. An understanding of this dimorphism is fundamental to precision medicine in cancer, and may lead to discovery of novel biomarkers, therapeutic targets, and improved outcomes. We propose to discover the molecular basis of sexual dimorphism in cancer though the following Aims: Aim 1 will characterize sexual dimorphism in gene expression and its regulation within and between tumor types of NCI's The Cancer Genome Atlas (TCGA). Leveraging TCGA, we will characterize sexual dimorphism at the transcriptome level and its potential genomic and epigenetic causes within and across cancers. Aim 2 will characterize sexual dimorphism in the heritable genetic component of cancer susceptibility across common cancers. Utilizing data from genome-wide association studies of cancer, we will test multiple genetic models that may explain sexually dimorphic traits of individual cancers. We will test a multifactorial model for genetic risk, whereby the same alleles affect both sexes, but the lower-risk sex has a higher threshold and would require more or stronger genetic risk factors to develop disease. We will test for specific risk or protective factors encoded on the X or Y chromosome that affect the sexes differentially. We will test for gene- sex interactions whereby autosomal loci act on risk in a sex-dependent manner. We will test for the presence of autosomal risk factors with different penetrance or effects in males and females due to hormonally-mediated or other sexual dimorphism acting at the gene expression level. Aim 3 will characterize sexual dimorphism in response to therapeutics and define the molecular features and mechanisms contributing to that dimorphism. Utilizing molecular and phenotypic drug response data from over 1000 cancer cell lines from the Cancer Genome Project (CGP), we will build sex-specific predictive models of drug response, that we will then apply to gene expression data from the full set of TCGA tumor samples to impute a sex-specific drug response for each TCGA sample. We will correlate imputed drug responses to TCGA tumor molecular features, with focus on those identified in Aims 1 and 2. We will functionally validate predicted sexually dimorphic response to therapy using cell models, including panels of lymphoblastoid cell lines, human hepatocytes and cancer cell lines. The results of this study will define genetic and genomic features that underlie sexual dimorphism in cancer biology, susceptibility, and response to therapeutics.