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: 10704140
• 负责人: Andrew J. Gentles
• 金额: $ 50.25万
• 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/10704140
• 关键词: Address; Algorithms; Antibodies; Appearance; Architecture; Automobile Driving; Bayesian learning; 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; 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; Mutation; 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; cancer cell; cancer type; cell type; cellular imaging; checkpoint therapy; computer framework; data resource; experience; human disease; imaging platform; immunogenic; improved; indexing; innovation; learning strategy; 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表现出固有的免疫原性但具有免疫抑制性肿瘤微环境， 与其他类型的卵巢癌相似。然而，ccOC与高级别浆液性卵巢癌非常不同， carcinoma.引人注目的是，它与更常见的肾透明细胞癌的基因表达谱相似 （ccRCC），表明透明细胞癌共享内在机制或微环境特性，而不仅仅是 形态学外观大约25%的ccRCC对免疫检查点抑制剂（ICI）反应良好，但 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 一项研究是33.3%的ccOC患者;但一般来说，尚不清楚哪些透明细胞癌患者可能 从ICI治疗中获益。最近的研究表明，肿瘤的行为不仅是由细胞驱动的， 组成，而且还取决于不同细胞类型（包括免疫细胞和基质细胞）的空间组织，例如 以及恶性细胞本身。了解透明细胞癌肿瘤微环境及其空间分布 建筑缺乏。解决这一差距将提高我们对国际刑事法院的反应机制的理解 透明细胞癌，包括罕见的ccOC，并改善免疫治疗患者的选择。 本研究将使用系统生物学方法（i）阐明和比较细胞类型及其 存在于ccOC和ccRCC中的转录状态;（ii）表征这些细胞在 使用CODEX（CODetection by indEXing）单细胞蛋白质组成像平台的肿瘤;和（iii）模型 并验证空间肿瘤微环境中的细胞-细胞相互作用， 通过扩展因果信号推理算法以纳入空间背景的免疫疗法，以及 优化小鼠模型中的实验验证，最大限度地提高有关相互作用的信息增益 网络. ccOC和ccRCC具有相似的内在和肿瘤微环境特征， 免疫治疗反应的共同机制，并确定两者的子集谁可能受益于 用ICI治疗。成功开发和应用这些方法来清除细胞癌， 建立一个可以应用于其他癌症类型的框架，特别是罕见的癌症。 这一建议的预期成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机制之间的共同特点和机制 细胞癌症，为将该方法扩展到其他类型癌症提供基础，开辟新的途径 治疗，特别是在罕见的癌症类型。