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）是一种罕见的致命癌症，几乎没有治疗选择。基于分子 分析CCOC在本质上是免疫原性的，但具有免疫抑制性肿瘤微环境， 类似于其他卵巢癌类型。但是，CCOC与高级浆液卵巢非常不同 癌。引人注目的是，基因表达谱与更频繁的清晰细胞肾细胞癌相似 （CCRCC），表明透明细胞癌具有内在的机械或微环境特性，而不仅仅是 形态学外观。大约25％的CCRC对免疫检查点抑制剂（ICI）的反应良好，但 缺乏预测响应的标记。单一疗法pembrolizumab的客观响应率 CCOC患者的一项研究为33.3％。但是，总的来说，尚不清楚哪些明确的细胞癌患者可以 受益于ICI治疗。最近的工作表明，肿瘤行为不仅是由细胞驱动的 组成，也是由不同细胞类型的空间组织，包括免疫和基质细胞，作为 以及恶性细胞本身。了解透明细胞癌肿瘤微环境及其空间 缺乏建筑。解决这一差距将提高我们对ICIS反应机制的理解 在清晰的细胞癌中，包括罕见的CCOC，以及改善患者免疫疗法的选择。 这项研究将使用系统生物学方法来阐明和比较细胞类型及其 CCOC和CCRC中存在的转录状态； （ii）表征这些单元格的空间结构 使用法典（通过索引编码）单细胞蛋白质组学成像平台的肿瘤；和（iii）模型 并在空间肿瘤微环境中验证细胞 - 细胞相互作用，以驱动清晰的细胞癌对 通过因果信号传导推理算法扩展的免疫疗法，以融合空间环境和 在鼠标模型中优化实验验证，以最大程度地提高有关相互作用的信息 网络。 CCOC和CCRC共享的类似的内在和肿瘤微环境特征将提名 免疫疗法反应的常见机制，并确定两者可能从中受益的子集 ICIS治疗。这些方法的成功开发和应用清除细胞癌将 建立一个可以应用于其他癌症类型的框架，特别是稀有癌症类型。 该提案的预期结果是肿瘤的全面定义和解剖 CCRC和CCOC的微环境。它将确定这些清晰的特征和机制 细胞癌，提供了将方法扩展到其他类别癌症的基础，为 治疗，特别是在罕见的癌症类型中。