Impact of cancer predisposition on oncogenic process, microenvironment, and treatment

我们之前对33种癌症类型的1万多种肿瘤进行的研究确定了800多个种系。 肿瘤抑制基因和癌基因的易感性变异。尽管其中一些因素的影响 关于癌症发病、发病率和克隆性的变异已经被记录在案，这些变异的影响 (尤其是与体细胞突变相比)对癌细胞分子特征的贡献 具有生殖系变异和治疗反应的非癌症细胞的研究要少得多。我们最近的单曲 癌症样本的细胞(Sc)/单核(Sn)rna-seq分析提供了全面的表达谱。 并揭示了关键的癌症易感基因的表达，如BRCA 112， VHL、BAP1和c-Met在肿瘤微环境(TME)中的许多非肿瘤间质和免疫细胞中表达。 我们的初步分析还显示，在癌症和非癌症细胞中存在显著的基因表达差异 司机事件的性质是生殖系的还是躯体的。我们假设具有某些生殖系的肿瘤 肿瘤抑制基因和癌基因的易感性变异可能在肿瘤进展和 差异基因体细胞突变肿瘤的治疗反应 对癌细胞的突变、转录和蛋白质组学的影响以及潜在的独特性 来自具有这些生殖系变化的非癌症细胞的贡献。为了利用可观的 过去几年的进展，即新积累的癌症测序数据，单细胞研究进展 组学、患者来源的异种移植(PDX)和肿瘤遗传模型，我们建议通过以下方式来检验这些假设 执行以下操作：比较种系易感变异和体细胞突变以剖析其 使用计算分析的不同生物影响和相互作用(目标1)；执行 人类肿瘤的核RNA-seq/atac-seq、空间转录和多重成像分析 揭示种系易感变异和体细胞突变在肿瘤中的不同作用的样本 细胞和TME(AIM 2)；使用PDX和遗传癌症模型来研究潜在的功能 肿瘤细胞、TME和TME中种系易感变异和体细胞突变的差异 治疗反应(目标3)。这项研究的结果将促进我们对独特的 生殖系变异对癌细胞和TME的贡献，改善遗传咨询和预后， 并为KEY中携带生殖系变异和体细胞突变的肿瘤的鉴别治疗提供指导 癌症驱动基因。

Our previous study of more than 10 thousand tumors across 33 cancer types identified over 800 germline predisposition variants in both tumor suppressors and oncogenes. Although the impact of some of these variants on cancer onset, incidence rate, and clonality have been documented, the effects of these variants (especially when compared to somatic mutations) on molecular characteristics of cancer cells, contributions of non-cancer cells having germline variants, and treatment responses, are far less studied. Our recent single cell(sc) /single nucleus(sn) RNA-seq analyses of cancer samples provided comprehensive expression profiles at a single cell resolution and revealed the expression of key cancer predisposition genes, such as BRCA 112, VHL, BAP1, and c-MET in many non-cancer stromal and immune cells in the tumor microenvironment (TME). Our pilot analyses also revealed significant differential gene expression in cancer and non-cancer cells based on the nature of the driver event being germline or somatic. We hypothesize that tumors with certain germline predisposition variants in tumor suppressors and oncogenes may show differences in tumor progression and treatment responses from those tumors with somatic mutations in the same genes due to differential influence on mutational, transcriptomic, and proteomic profiles of the cancer cells and potentially distinctive contributions from non-cancer cells having those germline changes. To take advantage of the considerable progress over the last few years, namely newly accumulated cancer sequencing data, advances in single cell omics, patient-derived xenografts (PDX), and tumor genetic models, we propose to test these hypotheses by performing the following: compare germline predisposition variants and somatic mutations to dissect their differential biological impacts and interactions using computational analysis (Aim 1); perform single nucleus RNA-seq/ATAC-seq, spatial transcriptomics, and multiplex imaging analysis of human cancer samples to reveal the differential roles of germline predisposition variants and somatic mutations in tumor cells and the TME (Aim 2); use PDX and genetic cancer models to investigate potential functional differences between germline predisposition variants and somatic mutations in tumor cells, TME, and treatment responses (Aim 3). Results from this study will advance our understanding of the unique contributions of germline variants to cancer cells and TME alike, improve genetic counseling and prognosis, and provide guidance for differential treatment of tumors carrying germline variants vs somatic mutations in key cancer driver genes.