Quantifying heterocellular communication and spatial intratumoral heterogeneity from high dimensional spatial proteomics data

从高维空间蛋白质组数据量化异细胞通讯和空间瘤内异质性

• 批准号: 10331796
• 负责人: Samantha A. Furman
• 金额: $ 4.55万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10331796
• 关键词: Algorithms; Architecture; Biological Markers; Biology; Cell physiology; Cells; Communication; Complex; Consensus; DNA Probes; Data; Data Set; Databases; Dimensions; Disease; Disease Progression; Drug resistance; Dyes; Environment; Epithelial; Evolution; Explosion; Funding; Gene Expression; Generations; Goals; Heterogeneity; Homeostasis; Human BioMolecular Atlas Program; Image; Imaging technology; Immune; Immune Evasion; Immunofluorescence Immunologic; Immunooncology; International Agencies; Isotopes; Label; Large Intestine Carcinoma; Learning; Literature; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Measures; Mesenchymal; Metastatic to; Methods; Modeling; Morphology; Mutation; Neoplasm Metastasis; Nerve Degeneration; Non-Malignant; Outcome; Pathway interactions; Patients; Pattern; Phenotype; Population; Property; Proteomics; RNA Probes; Recurrence; Research; Research Personnel; Resolution; Sampling; Signal Transduction; Spatial Distribution; System; Systems Biology; Testing; Therapeutic; Therapeutic Intervention; Tissue Sample; Tissues; Transitional Cell; Tumor Biology; United States National Institutes of Health; Validation; Visualization; Work; adaptive immunity; base; cancer type; cell type; cohort; design; differential expression; fluorescence imaging; high dimensionality; imaging modality; in silico; insight; interest; learning algorithm; macrophage; malignant breast neoplasm; malignant phenotype; molecular subtypes; multiplexed imaging; network models; next generation; open source tool; pathogen; precision medicine; predictive modeling; prognostic; prognostic model; prognostic value; protein expression; spatial relationship; statistics; stem cell differentiation; treatment response; tumor; tumor heterogeneity; tumor initiation; tumor microenvironment; tumor progression; tumorigenesis; unsupervised learning

The tumor microenvironment (TME) is composed of malignant and non-malignant cells, each contributing to spatial intratumoral heterogeneity (ITH) and heterocellular communication altering the composition and architecture of the TME. A high degree of ITH is correlated to metastatic progression and therapeutic response. Previous studies investigating spatial ITH have been limited due to a steep trade-off between cellular resolution, spatial context, and dimensionality of biomarkers. A recent explosion of multi to hyperplexed imaging modalities (e.g., fluorescence imaging, mass-spec imaging) enable the quantification of greater than 7 and up to > 100 biomarkers through sequentially multiplexed imaging of 2 to 3 biomarkers using iterative cycles of label-image-dye inactivation. The generation of this new type of data poses both unique opportunities and challenges. There are no state-of-the-art methods for harnessing the complexity of spatial data to infer tumor biology with a high dimensionality of biomarkers. In this project, we will probe the spatial complexity of a TME in hyperplexed immunofluorescence (HxIF) based spatial proteomics colorectal carcinoma (CRC) data (51 biomarkers + DAPI, 356 patient samples) to elucidate the heterocellular communication networks promoting spatial ITH through cellular phenotyping, microdomain extraction, and network biology inference algorithms. We will demonstrate the applicability of our algorithms to cancer types beyond CRC with multiplexed immunofluorescence breast cancer tissue samples In Aim 1, we will continue to develop unsupervised learning algorithm for cellular phenotypic heterogeneity (LEAPH) to identify specialized, rare, and transitional cell populations. Initial results applying LEAPH on the HxIF CRC data have revealed cellular heterogeneity patterns consistent with CRC literature (STEM cell differentiation, immune evasion, macrophage evolution). We will incorporate machine learning- based methods into LEAPH to measure spatial distribution patterns of each phenotype and correlate them with CRC progression (e.g., recurrence). In Aim 2, we will quantify spatial ITH in greater detail by identifying differentially expressed pair- or group-wise spatial relationships based on outcome data (e.g., recurrence vs no-recurrence within 5 years) to reveal phenotypic domains, microdomains, with prognostic potential. We expect improvement of prognostic power with pair- or group-wise spatial interactions in comparison to the single-phenotype based spatial ITH characterization of Aim 1. In Aim 3, we will dissect the microdomain- specific heterocellular communication dynamics with causal inference network models. We expect to identify emergent signaling networks conferring malignant phenotypes, such as known features from CRC consensus molecular subtypes. The algorithms constructed in this project will be implemented and disseminated through the Tumor Heterogeneity Research Interactive Visualization Environment (THRIVE), an open source tool to assist cancer researchers in interactive hypotheses testing and guiding the design of therapeutic strategies.

肿瘤微环境（tumor microenvironment， TME）由恶性细胞和非恶性细胞组成