Defining therapeutic strategies for boosting T-cell infiltration into cold tumors with spatial proteomics and machine learning

利用空间蛋白质组学和机器学习确定促进 T 细胞浸润冷肿瘤的治疗策略

• 批准号: 10743501
• 负责人: Matthew W. Thomson
• 金额: $ 42.31万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-05 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10743501
• 关键词: Address; Adhesions; Adjuvant; Algorithms; Architecture; Biological; Biological Markers; Breast; CCL19 gene; CCL8 gene; CD8-Positive T-Lymphocytes; CXCL9 gene; Cell Therapy; Cellular immunotherapy; Clinical; Collaborations; Colon Carcinoma; Computing Methodologies; Cues; Cytometry; Data; Data Collection; Data Set; Data Sources; Dependence; Disease; Engineering; Environment; Exhibits; Human; Image; Imaging technology; Immune checkpoint inhibitor; Immunologics; Immunotherapy; Infiltration; Intervention; Libraries; Ligands; Machine Learning; Malignant neoplasm of liver; Malignant neoplasm of prostate; Mammary Neoplasms; Maps; Measures; Medical center; Methods; Modeling; Molecular; Mutation; Pathologist; Patients; Pattern; Phenotype; Prostate; Prostatic Neoplasms; Proteins; Proteomics; Research; Resolution; Sampling; Signal Transduction; Signaling Molecule; Solid Neoplasm; Stromal Cell-Derived Factor 1; Stromal Cells; T cell infiltration; T cell response; T-Lymphocyte; Technology; Testing; Therapeutic; Therapeutic Intervention; Tissues; Training; Urologic Surgeon; Work; base; cancer cell; cancer imaging; cancer immunotherapy; cancer infiltrating T cells; cancer type; cell behavior; chemokine; chimeric antigen receptor; combinatorial; convolutional neural network; design; exhaustion; human data; human tissue; immune checkpoint; improved; learning strategy; machine learning framework; malignant breast neoplasm; melanoma; molecular imaging; neural network; neural network architecture; novel; patient subsets; precision medicine; receptor; response; success; therapeutic target; trafficking; transcriptomics; triple-negative invasive breast carcinoma; tumor; tumor microenvironment; tumor progression; tumor-immune system interactions

Project Summary Immunotherapies such as immune checkpoint inhibitors and chimeric antigen receptor (CAR-T) cell therapy have been highly successful in reversing cancer progression in a subset of patients. However, immunotherapies fail in patients with “cold tumors,” where T-cell infiltration and function are suppressed by inhibitory signaling environments generated by cancer and stromal cells. Poor CD8+ T-cell infiltration due to suppressive signaling environments is a primary obstacle to effective immunotherapy in many solid tumors including breast, liver, prostate, and colon cancer. Recent advances in high-resolution molecular imaging technologies, known as spatial proteomic methods, now allow micron-resolution profiling of signaling environments in cold and hot human tumors across up to 50 molecular channels providing a new data source for identifying signaling cues that promote or suppress T-cell infiltration. There is an urgent unmet need for computational strategies that can analyze large-scale, spatial proteomic data sets collected from human patient data to identify features of the tumor microenvironment that promote cold vs hot tumor phenotypes. Computational methods must be designed to extract concrete and specific therapeutic strategies that can be tested clinically for reprogramming the tumor microenvironment to promote T-cell infiltration and function. In this project, we develop a machine learning framework that uses cutting-edge spatial proteomic data to identify signaling molecules and guidance cues that promote the infiltration and function of T-cells into a tumor microenvironment. Our approach first trains a neural network on spatial proteomic data to predict T-cell infiltration using signaling and guidance cues. We, then, apply “counterfactual reasoning” to the classifier to predict optimal signaling perturbations for increasing CD8 T-cell infiltration into tumors. In preliminary data, we applied our strategy to melanoma and identified a therapeutic strategy that involves manipulation of five chemokine and signaling molecules in melanoma based on spatial proteomic data from 300 patients. In the work to be performed here, we aim to generalize our approach to a broader range of cancer types and larger patient data sets. We will systematically test neural network architectures to identify optimal architectures for different cancer types. Since spatial proteomic training data is currently limited, we will collect new training data from human patients across a broader set of tumors, for which we will profile chemokine and signaling molecules through a collaboration between Cedars-Sinai Medical Center and Caltech. We will generalize our counterfactual reasoning strategy to breast and prostate cancer to identify optimal therapeutic targets and to compare targets for different base tumor types. Broadly, our work will develop a novel machine learning approach for converting large-scale spatial proteomic data into specific molecular hypotheses for increasing T-cell infiltration into cold tumors across a range of solid tumor types.

项目概要 免疫检查点抑制剂和嵌合抗原受体（CAR-T）细胞疗法等免疫疗法已 在逆转部分患者的癌症进展方面取得了巨大成功，但免疫疗法却失败了。 患有“冷肿瘤”的患者，其中 T 细胞浸润和功能受到抑制信号的抑制 由于抑制性信号传导，癌症和基质细胞产生的 CD8+ T 细胞浸润不良。 环境是许多实体瘤（包括乳腺癌、肝癌、 前列腺癌和结肠癌。高分辨率分子成像技术的最新进展，称为 空间蛋白质组学方法，现在可以对冷和热的信号环境进行微米分辨率的分析 跨越多达 50 个分子通道的人类肿瘤，为识别信号线索提供新的数据源 促进或抑制 T 细胞浸润的计算策略的迫切需求尚未得到满足。 分析从人类患者数据中收集的大规模空间蛋白质组数据集，以确定蛋白质组的特征 必须设计促进冷肿瘤表型与热肿瘤表型的肿瘤微环境。 提取可在临床上测试的具体和具体的治疗策略，以重新编程肿瘤 促进 T 细胞浸润和功能的微环境在这个项目中，我们开发了一种机器学习。 该框架使用尖端的空间蛋白质组数据来识别信号分子和指导线索 促进 T 细胞在肿瘤微环境中的浸润和功能 我们的方法首先训练神经元。 然后，我们应用空间蛋白质组数据网络来预测 T 细胞浸润。 分类器的“反事实推理”可预测增加 CD8 T 细胞的最佳信号扰动 在初步数据中，我们将我们的策略应用于黑色素瘤并确定了一种治疗方法。 该策略涉及基于空间的黑色素瘤中五种趋化因子和信号分子的操纵 来自 300 名患者的蛋白质组数据在此处进行的工作中，我们的目标是将我们的方法推广到 我们将系统地测试神经网络。 由于空间蛋白质组训练数据是确定不同癌症类型的最佳架构的。 目前有限，我们将从更广泛的肿瘤人类患者中收集新的训练数据，为此 我们将通过 Cedars-Sinai 医疗中心之间的合作来分析趋化因子和信号分子 我们将把我们的反事实推理策略推广到乳腺癌和前列腺癌，以识别癌症。 总的来说，我们的工作将会发展 一种新颖的机器学习方法，用于将大规模空间蛋白质组数据转换为特定的分子 增加 T 细胞浸润到一系列实体瘤类型冷肿瘤中的假设。