A multimodal approach for precision immuno-oncoloy in lymphoma treated with CAR-T cells

CAR-T 细胞治疗淋巴瘤的精准免疫肿瘤多模式方法

• 批准号: 10722590
• 负责人: Roni Shouval
• 金额: $ 27.77万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-07 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10722590
• 关键词: Address; Antigens; Autologous; Automobile Driving; B-Cell Lymphomas; BCL2 gene; Bioinformatics; Biological Markers; Biology; Biopsy; CAR T cell therapy; CD19 gene; Cell Death Induction; Cell Therapy; Cells; Cessation of life; Clinical; Code; Complement; DNA Sequence Alteration; Data; Decision Support Systems; Disease; Flow Cytometry; Genes; Genetic; Genomics; Genotype; Goals; Heterogeneity; Image; Immune; Immune checkpoint inhibitor; Immunology; Immunotherapy; Individual; Inflammatory; Interferon Type II; Knowledge; Laboratories; Lymphoma; Machine Learning; Malignant Neoplasms; Measures; Mediating; Medical Imaging; Mentorship; Methodology; Modality; Modeling; Molecular; Mutation; Outcome; Pathology; Pathway interactions; Patient-Focused Outcomes; Patients; Phenotype; Prognostic Factor; Prognostic Marker; Publishing; Radiology Specialty; Receptor Signaling; Recurrent disease; Refractory; Relapse; Resistance; Risk; Role; Sampling; Shapes; Signal Transduction; Stimulus; TP53 gene; Treatment Efficacy; Treatment Failure; Tumor Burden; Work; acquired treatment resistance; biobank; biomarker discovery; biomarker identification; cancer cell; chimeric antigen receptor T cells; clinical decision-making; cohort; combinatorial; computer infrastructure; computerized tools; cytotoxic; cytotoxicity; experience; genomic biomarker; high risk; improved; individual patient; interpatient variability; machine learning algorithm; multidisciplinary; multimodal data; multimodality; multiple data sources; multiple omics; new therapeutic target; novel; novel marker; personalized approach; personalized care; personalized medicine; predicting response; predictive marker; predictive modeling; pressure; radiological imaging; radiomics; resistance mechanism; response; success; therapy resistant; transcriptomic profiling; transmission process; tumor

PROJECT SUMMARY/ABSTRACT Autologous CD19-directed chimeric antigen receptor T-cells (CAR-T) have resulted in extraordinary response rates in relapsing and refractory large B-cell lymphoma (LBCL). However, over 60% of CD19-CAR-T recipients will experience disease recurrence or progression. Most of these patients will die from their disease. Mechanisms of CAR-T treatment failure are partially understood and biomarkers informing patient outcomes and management have limited clinical utility. Our central hypothesis is that orthogonal modalities (e.g., clinical, molecular, genomic, and radiomic [quantitative measures from medical images]) complement one another, together providing information on resistance mechanisms and patient outcomes beyond that accessible through any individual modality. We present results suggesting that machine learning is an effective methodology for synthesizing and modeling multiple sources of data together. Cancer cells harness genomic heterogeneity to evade pressure applied by immunotherapies, such as immune checkpoint inhibitors. Our preliminary data also demonstrate that TP53 genomic alterations strongly determine response to CAR-T. Furthermore, using transcriptomic profiling, we found that cancer cellular pathways required for effective transmission of CAR-T cytotoxic signals are distorted in TP53-altered lymphoma. These early findings provide a proof-of-concept for the utility of genomics to inform disease biology and risk after CAR-T. We hypothesize that tumor genetic aberrations in cellular pathways used by CAR-T cells to exert cytotoxicity drive treatment resistance by rendering cancer cells insensitive to CAR-T stimuli and supporting immune escape. In Aim 1, we will use comprehensive genotypic and phenotypic tumor profiling before and after CAR-T to study the role of a priori determined genes and pathways in mediating inherent and acquired treatment resistance. We also hypothesize that orthogonal modalities for patient and tumor profiling are complementary, and their integration into a unified, multimodal model could accurately predict CAR-T outcomes. In Aim 2, we will synthesize data from multiple modalities and use machine learning algorithms to predict CAR-T response and identify novel biomarkers. To meet our goals, we have compiled one of the largest CAR-T patient and sample biobanks. A group of leading experts in immunology, genetics, pathology, radiology, machine learning, and bioinformatics will guide the candidate in this multidisciplinary work. If successful, we expect our combinatorial approach to uncover genetic features underlying inherent and acquired CAR-T resistance and identify new druggable targets. Furthermore, our machine learning approach will support treatment personalization by establishing decision support systems and identifying biomarkers of high-risk patients. Finally, we will introduce novel methodologies for modeling CAR-T outcomes, which are extendable to other forms of treatment.

项目总结/摘要 自体CD 19定向嵌合抗原受体T细胞（CAR-T）导致了非凡的反应 复发性和难治性大B细胞淋巴瘤（LBCL）的发病率。然而，超过60%的CD 19-CAR-T受体 将经历疾病复发或进展。大多数患者将死于这种疾病。 CAR-T治疗失败的机制得到部分理解，生物标志物告知患者结局 和管理具有有限的临床效用。我们的中心假设是正交模态（例如，临床上， 分子、基因组和放射组[来自医学图像的定量测量]）彼此互补， 一起提供关于耐药机制和患者结局的信息， 通过任何个人模式。我们目前的结果表明，机器学习是一种有效的 综合和建模多个数据源在一起的方法。癌细胞利用基因组 免疫检查点抑制剂等免疫疗法所施加的压力。我们 初步数据还表明，TP 53基因组改变强烈决定了对CAR-T的应答。 此外，使用转录组学分析，我们发现，有效的癌症细胞通路所需的 CAR-T细胞毒性信号的传递在TP 53改变的淋巴瘤中被扭曲。这些早期发现提供了 基因组学在CAR-T后告知疾病生物学和风险的实用性的概念验证。 我们假设CAR-T细胞用于发挥细胞毒性的细胞通路中的肿瘤遗传畸变 通过使癌细胞对CAR-T刺激不敏感和支持免疫， 逃跑在目标1中，我们将在CAR-T前后使用全面的基因型和表型肿瘤分析。 研究先天确定的基因和途径在介导固有和获得性治疗中的作用 阻力我们还假设用于患者和肿瘤分析的正交模式是互补的， 将它们整合到一个统一的多模式模型中，可以准确预测CAR-T的结果。在目标2中， 将综合来自多种模式的数据，并使用机器学习算法来预测CAR-T反应 并鉴定新的生物标志物。 为了实现我们的目标，我们编制了最大的CAR-T患者和样本生物库之一。一群 免疫学、遗传学、病理学、放射学、机器学习和生物信息学方面的顶尖专家将指导 在这个多学科的工作候选人。如果成功的话，我们希望我们的组合方法能揭示 遗传特征潜在的固有和获得性CAR-T耐药性，并确定新的药物靶点。 此外，我们的机器学习方法将通过建立决策来支持治疗个性化 支持系统和识别高风险患者的生物标志物。最后，我们将介绍新的方法 用于模拟CAR-T结果，可扩展到其他形式的治疗。

项目成果

Fecal microbiota transplantation in capsules for the treatment of steroid refractory and steroid dependent acute graft vs. host disease: a pilot study.

胶囊中粪便微生物移植用于治疗类固醇难治性和类固醇依赖性急性移植物抗宿主病：一项试点研究。

• DOI: 10.1038/s41409-024-02198-2
• 发表时间: 2024
• 期刊: Bone marrow transplantation
• 影响因子: 4.8
• 作者: Youngster,Ilan;Eshel,Adi;Geva,Mika;Danylesko,Ivetta;Henig,Israel;Zuckerman,Tsila;Fried,Shalev;Yerushalmi,Ronit;Shem-Tov,Noga;Fein,JoshuaA;Bomze,David;Shimoni,Avichai;Koren,Omry;Shouval,Roni;Nagler,Arnon
• 通讯作者: Nagler,Arnon