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.

项目概要/摘要 自体 CD19 定向嵌合抗原受体 T 细胞 (CAR-T) 产生了非凡的反应 复发性和难治性大 B 细胞淋巴瘤 (LBCL) 的发生率。然而，超过 60% 的 CD19-CAR-T 接受者 会经历疾病复发或进展。这些患者中的大多数将死于他们的疾病。 CAR-T 治疗失败的机制已部分了解，生物标志物可告知患者结果 和管理的临床实用性有限。我们的中心假设是正交模式（例如，临床、 分子、基因组和放射组学[医学图像的定量测量]）相互补充， 共同提供有关耐药机制和患者结果的信息，超出了可获得的范围 通过任何个人方式。我们提出的结果表明机器学习是一种有效的 将多个数据源综合和建模的方法。癌细胞利用基因组 异质性以逃避免疫疗法（例如免疫检查点抑制剂）施加的压力。我们的 初步数据还表明，TP53 基因组改变强烈决定对 CAR-T 的反应。 此外，使用转录组分析，我们发现有效的癌症细胞途径需要 TP53 改变的淋巴瘤中 CAR-T 细胞毒性信号的传输发生扭曲。这些早期发现提供了 基因组学在 CAR-T 后为疾病生物学和风险提供信息的实用性的概念验证。 我们假设 CAR-T 细胞利用细胞通路中的肿瘤遗传畸变来发挥细胞毒性 通过使癌细胞对 CAR-T 刺激不敏感并支持免疫来驱动治疗耐药性 逃脱。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 研究先验确定的基因和途径在介导固有和后天治疗中的作用 反抗。我们还假设患者和肿瘤分析的正交方式是互补的， 将它们集成到统一的多模式模型中可以准确预测 CAR-T 结果。在目标 2 中，我们 将综合多种模式的数据并使用机器学习算法来预测 CAR-T 反应 并识别新的生物标志物。 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领域的顶尖专家将指导 这项多学科工作的候选人。如果成功，我们期望我们的组合方法能够发现 固有和获得性 CAR-T 耐药性背后的遗传特征，并确定新的药物靶标。 此外，我们的机器学习方法将通过建立决策来支持个性化治疗 支持系统并识别高危患者的生物标志物。最后，我们将介绍新颖的方法 用于对 CAR-T 结果进行建模，该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。

• 发表时间: 2024-03
• 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;Fein, Joshua A;Bomze, David;Shimoni, Avichai;Koren, Omry;Shouval, Roni;Nagler, Arnon
• 通讯作者: Nagler, Arnon