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.

项目摘要/摘要 自体CD19导向的嵌合抗原受体T细胞（CAR-T）导致了非凡的响应 复发和难治性大B细胞淋巴瘤（LBCL）的速率。但是，超过60％的CD19-CAR-T接收者 会经历疾病复发或进展。这些患者大多数都会死于他们的疾病。 CAR-T治疗衰竭的机制已部分理解，并告知患者预后的生物标志物 管理层的临床公用事业有限。我们的中心假设是正交方式（例如临床， 分子，基因组和放射素[医学图像的定量度量]）相互补充， 共同提供有关阻力机制和患者结果的信息 通过任何个人方式。我们提出结果表明机器学习是有效的 合成和建模多个数据源的方法。癌细胞利用基因组 免疫疗法施加的逃避压力的异质性，例如免疫检查点抑制剂。我们的 初步数据还表明，TP53基因组改变强烈确定对CAR-T的反应。 此外，使用转录组学分析，我们发现有效的癌细胞途径 CAR-T细胞毒性信号的传播在TP53改变的淋巴瘤中扭曲。这些早期发现提供了 基因组学效用的概念证明，以告知疾病生物学和CAR-T之后的风险。 我们假设CAR-T细胞用来发挥细胞毒性的细胞途径中的肿瘤遗传像差 通过使癌细胞对CAR-T刺激不敏感并支持免疫来驱动抗药性 逃脱。在AIM 1中，我们将在CAR-T之前和之后使用全面的基因型和表型肿瘤分析 研究先验确定的基因和途径在介导固有和获得治疗中的作用 反抗。我们还假设患者和肿瘤分析的正交方式是互补的， 它们整合到统一的多模型模型中可以准确预测CAR-T结果。在AIM 2中，我们 将从多种模式中综合数据，并使用机器学习算法来预测CAR-T响应 并确定新颖的生物标志物。 为了实现我们的目标，我们已经编译了最大的CAR-T患者和样品生物库之一。一组 免疫学，遗传学，病理学，放射学，机器学习和生物信息学专家将指导 这项多学科工作的候选人。如果成功，我们希望我们的组合方法可以揭开 固有和获得的CAR-T耐药性的遗传特征并确定新的可药靶标。 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 支持系统并识别高危患者的生物标志物。最后，我们将介绍新方法 用于建模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