Identification and characterization of cancer cell states by novel computational and experimental technologies - Resubmission - 1

通过新颖的计算和实验技术识别和表征癌细胞状态 - 重新提交 - 1

• 批准号: 10650774
• 负责人: Richard Mark White
• 金额: $ 56.41万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-21 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10650774
• 关键词: Ablation; Adopted; Atlas of Cancer Mortality in the United States; Biological Assay; Biological Models; Cancer Biology; Cancer Model; Cells; Clinical; Collaborations; Communities; Complex; Computing Methodologies; Data; Dependence; Detection; Development; Drug resistance; Elements; Environment; Fishes; Genes; Genetic; Genetic Engineering; Genetic Transcription; Goals; Heterogeneity; Human; Image; Immune; Individual; Maintenance; Methods; Modeling; Molecular; Neoplasm Metastasis; Oncologist; Pharmaceutical Preparations; Play; Publications; Reporter; Research; Role; Sampling; Sorting; System; Systems Biology; T-Cell Depletion; T-Lymphocyte; Technology; Testing; Time; Zebrafish; cancer cell; cancer therapy; cancer type; cell type; experimental study; flexibility; genetic manipulation; imaging capabilities; improved; in vivo; innovation; insight; lens; melanoma; mortality; novel; response; single-cell RNA sequencing; transcriptomics; treatment strategy; tumor; tumor heterogeneity; tumor initiation; tumor microenvironment; tumor progression; tumorigenesis

SUMMARY Tumors are complex systems composed of genetically and transcriptionally heterogeneous cells, and this heterogeneity has been implicated as a cause of drug resistance and overall mortality. Understanding intra- tumor heterogeneity is therefore likely to have widespread impact, both fundamental and clinical. The advent of single-cell RNA-Seq has led to the detection of transcriptionally distinct states among cancer cells across a wide range of tumor types and stages. However, the field lacks robust computational and experimental technologies to functionally identify and characterize these cancer cell states. In this project, we take a gene module-centric view to define cell states in a rigorous and widely applicable manner. We will validate the importance of each cell state using human samples and the zebrafish melanoma model, which has exceptional capabilities for imaging and perturbation of cell states throughout tumor progression and metastasis. To systematically characterize these cell states experimentally, we propose methods to assay their interdependencies within the tumor and with elements of the microenvironment. Our genetically engineered zebrafish will mark each cancer cell state with a fluorescent reporter and an ablation cassette, providing a flexible experimental platform to study and perturb each cell state. The reporter will enable us to sort cancer cell states and study them individually, with a particular emphasis on their plasticity. By systematically disrupting cancer cell states, we will further elucidate their individual contributions to tumor initiation, progression and metastasis. Using a spatial transcriptomics approach and integrating with single-cell RNA-seq data, we will map cancer cell states in relation to their microenvironment to screen for putative interactions. Finally, we will directly test predicted interactions between specific cancer cell states and the immune compartment using T cell-deficient fish. Throughout our three Aims, we adopt a systems biology workflow that iteratively cycles through modes of observations, perturbations, and refinement of our model of the functional role of cancer cell states during tumor progression. Our proposal collectively integrates the complementary expertise of the White and Yanai labs and sets out to significantly improve our understanding of intratumoral heterogeneity through the lens of cancer cell states.

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