Systems analysis of mechanisms driving response to immunotherapy in clear cell cancers

透明细胞癌免疫疗法驱动反应机制的系统分析

• 批准号: 10554766
• 负责人: Andrew J. Gentles
• 金额: $ 53.91万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-13 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10554766
• 关键词: Address; Algorithms; Antibodies; Appearance; Architecture; Automobile Driving; Bayesian learning; Behavior; Biopsy; CD8-Positive T-Lymphocytes; Cancer Patient; Carcinoma; Cell Communication; Cells; Clear Cell; Clear cell renal cell carcinoma; Clinical; Clinical Investigator; Conventional (Clear Cell) Renal Cell Carcinoma; Data; Development; Disease; Dissection; Environment; Gene Expression; Genetic Transcription; Goals; Human; Image; Immune; Immune checkpoint inhibitor; Immunotherapy; Intervention; Kidney; Knowledge; Learning; Malignant - descriptor; Malignant Epithelial Cell; Malignant Neoplasms; Malignant neoplasm of ovary; Methods; Microsatellite Instability; Modeling; Molecular; Molecular Analysis; Morphology; Mus; Outcome; Ovarian; Ovarian Clear Cell Tumor; Ovarian Endometrioid Adenocarcinoma; Ovarian Serous Adenocarcinoma; Patient Selection; Patients; Phenotype; Property; Proteomics; Sample Size; Signal Transduction; Stromal Cells; Systems Analysis; Systems Biology; TP53 gene; Testing; Tissue Microarray; Transgenic Model; Translations; Tumor Tissue; Validation; Work; base; cancer cell; cancer type; cell type; cellular imaging; checkpoint therapy; computer framework; data resource; experience; imaging platform; immunogenic; improved; indexing; innovation; learning strategy; man; mouse model; novel; novel marker; objective response rate; pembrolizumab; predicting response; predictive marker; preservation; rare cancer; response; single-cell RNA sequencing; transcriptome sequencing; treatment response; tumor; tumor behavior; tumor microenvironment; tumor-immune system interactions

Clear cell ovarian cancer (ccOC) is a rare and lethal cancer with few treatment options. Based on molecular analysis ccOC appears intrinsically immunogenic but with an immunosuppressive tumor microenvironment, similar to other ovarian cancer types. However, ccOC is very distinct from high grade serous ovarian carcinoma. Strikingly, it is similar in gene expression profiles to more frequent clear cell renal cell carcinomas (ccRCC), suggesting that clear cell cancers share intrinsic mechanistic or microenvironment properties, not just morphological appearance. Around 25% of ccRCC respond well to immune checkpoint inhibitors (ICIs), but markers for predicting response are lacking. The objective response rate for monotherapy pembrolizumab in one study was 33.3% for ccOC patients; but, in general, it is unknown which clear cell cancer patients could benefit from ICI treatment. Recent work has shown that tumor behavior is driven not just by cellular composition, but also by the spatial organization of different cell types including immune and stromal cells, as well as malignant cells themselves. Knowledge of clear cell cancer tumor microenvironments and their spatial architecture is lacking. Addressing this gap will improve our understanding of mechanisms of response to ICIs in clear cell cancers, including rare ones like ccOC, and improve selection of patients for immunotherapy. This study will use systems biology approaches to (i) elucidate and compare the cell types and their transcriptional states present in ccOC and ccRCC; (ii) characterize the spatial architecture of these cells within tumors using the CODEX (CODetection by indEXing) single cell proteomic imaging platform; and (iii) model and validate cell-cell interactions in the spatial tumor microenvironment that drive clear cell cancer response to immunotherapy through extensions of causal signaling inference algorithms to incorporate spatial context, and to optimize experimental validations in mouse models that maximize the information gain about interaction networks. Similar intrinsic and tumor microenvironmental features shared by ccOC and ccRCC, will nominate common mechanisms of immunotherapy response, and identify the subset of both who might benefit from treatment with ICIs. Successful development and application of these methods to clear cell cancers will establish a framework that can be applied to other cancer types, notably to rare ones. The expected outcome of this proposal is a comprehensive definition and dissection of the tumor microenvironment of ccRCC and ccOC. It will identify common features and mechanisms between these clear cell cancers, providing a basis to extend the approach to other classes of cancer, opening new avenues for treatment, particularly in rare cancer types.

透明细胞卵巢癌 (ccOC) 是一种罕见且致命的癌症，治疗选择很少。基于分子 分析 ccOC 具有内在的免疫原性，但具有免疫抑制性肿瘤微环境， 与其他卵巢癌类型相似。然而，ccOC 与高级浆液性卵巢非常不同 癌。引人注目的是，它的基因表达谱与更常见的透明细胞肾细胞癌相似 （ccRCC），表明透明细胞癌具有内在的机制或微环境特性，而不仅仅是 形态外观。大约 25% 的 ccRCC 对免疫检查点抑制剂 (ICIs) 反应良好，但是 缺乏预测反应的标记。帕博利珠单抗单药治疗的客观缓解率 一项针对 ccOC 患者的研究为 33.3%；但总的来说，尚不清楚哪些透明细胞癌患者可以 受益于 ICI 治疗。最近的研究表明，肿瘤行为不仅是由细胞驱动的 组成，还受不同细胞类型（包括免疫细胞和基质细胞）的空间组织的影响，如 以及恶性细胞本身。了解透明细胞癌肿瘤微环境及其空间 缺乏架构。解决这一差距将提高我们对 ICI 响应机制的理解 透明细胞癌，包括像 ccOC 这样的罕见癌症，并改善免疫治疗患者的选择。 这项研究将使用系统生物学方法来（i）阐明和比较细胞类型及其 ccOC 和 ccRCC 中存在的转录状态； (ii) 描述这些细胞的空间结构 使用 CODEX (CODetection by indEXing) 单细胞蛋白质组成像平台对肿瘤进行检测； (iii) 型号 并验证空间肿瘤微环境中细胞与细胞的相互作用，从而驱动透明细胞癌的反应 通过扩展因果信号推理算法以纳入空间背景来进行免疫治疗，以及 优化小鼠模型中的实验验证，最大化交互信息增益 网络。 ccOC 和 ccRCC 具有相似的内在和肿瘤微环境特征，将提名 免疫治疗反应的常见机制，并确定可能受益于的两者的子集 ICI 治疗。这些方法的成功开发和应用将有助于清除细胞癌 建立一个可应用于其他癌症类型，特别是罕见癌症的框架。 该提案的预期结果是对肿瘤的全面定义和解剖 ccRCC 和 ccOC 的微环境。它将确定这些明确的共同特征和机制 细胞癌，为将该方法扩展到其他类型的癌症提供了基础，开辟了新的途径 治疗，特别是罕见癌症类型。