Therapeutic Targeting a Non-Hodgkin Lymphoma Driver Using AI

使用人工智能针对非霍奇金淋巴瘤驱动者进行治疗

• 批准号: 10585717
• 负责人: Yong Li
• 金额: $ 65.61万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-16 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585717
• 关键词: Abnormal Cell; Adaptor Signaling Protein; Address; Adoption; Affect; Agammaglobulinaemia tyrosine kinase; Age; Amino Acid Substitution; Artificial Intelligence; Artificial Intelligence platform; Attenuated; B-Cell Activation; B-Cell NonHodgkins Lymphoma; B-Lymphocytes; Binding Sites; Biochemical Reaction; Biological Assay; Blood typing procedure; Body part; Cancer Biology; Cell Survival; Cells; Cessation of life; Classification; Clinical; Clinical Trials; Collaborations; Cultured Cells; Data; Drug Kinetics; Extranodal; Genes; Growth; Hematopoietic Neoplasms; Hodgkin Disease; Immune response; Immune system; Immunity; In Vitro; In complete remission; Indolent; Industrialization; Inflammation; Innate Immune Response; Investigational Drugs; Knowledge; Leucine; Link; Lymphocyte; Lymphoma; Lymphoma cell; Lysine; Malignant Neoplasms; Mediating; Medicine; Missense Mutation; Mutate; Mutation; Myelogenous; Natural Immunity; Non-Hodgkin' s Lymphoma; Nuclear; Oncogenes; Oncogenic; Oncoproteins; Outcome; Pathogenesis; Patients; Phosphotransferases; Physiological; Polyubiquitin; Positioning Attribute; Proline; Proteins; Publishing; Reed-Sternberg Cells; Ring Finger Domain; Signal Transduction; Site; Solid; Structure; Technology; Testing; Therapeutic Agents; Toll-like receptors; Toxic effect; Transgenic Organisms; Ubiquitination; United States; Waldenstrom Macroglobulinemia; Work; Xenograft procedure; adaptive immune response; adaptive immunity; cancer therapy; causal variant; college; deep learning; drug candidate; drug development; drug discovery; in vivo; inhibitor; large cell Diffuse non-Hodgkin' s lymphoma; mouse model; multicatalytic endopeptidase complex; neural network; novel therapeutics; precision medicine; protein degradation; response; screening; small molecule; success; therapeutic development; therapeutic target; tumorigenesis; virtual

Therapeutic Targeting a Non-Hodgkin Lymphoma Driver using AI PROJECT SUMMARY Baylor College of Medicine (BCM) and Atomwise Incorporation have partnered to discover, optimize, and test inhibitors to undruggable oncoproteins using artificial intelligence (AI). Both Hodgkin lymphoma and non- Hodgkin lymphoma (NHL) are cancers that start in lymphocytes, which are part of the body’s immune system. The main difference between Hodgkin lymphoma and NHL is in the specific lymphocyte each involves: in the presence of abnormal cells called Reed-Sternberg cells, the lymphoma is classified as Hodgkin’s; otherwise, it is classified as NHL. NHL is the most common blood cancer and causes over 20,000 deaths every year in the United States. There are about 90 types of NHL, which usually develop when mutations occur within a lymphocyte. The gene MYD88 encodes myeloid differentiation primary response 88 protein, a critical universal adapter with essential functions in inflammation and immunity. Following stimulation of toll-like receptors, MYD88 transduces the signal to activate genes responsible for innate and adaptive immune responses. MYD88 is a driver oncogene that is frequently mutated in B-cell NHLs. The most frequent missense mutation is L265P, which changes leucine at position 265 to proline and accounts for ~90% of all MYD88 mutations. MYD88 L265P is found in ~90% of Waldenström macroglobulinemia (WM, a rare NHL), >50% of primary extranodal lymphomas, and ~29% of activated B-cell diffuse large B-cell lymphomas (DLBCL). WM is considered incurable. DLBCL can be cured in about 40% of the patients, but those with MYD88 L265P have poorer survival than those without. BCM collaborates with Atomwise, the inventor of the first deep learning AI technology based on neural networks and a leader in AI-assisted drug discovery, to virtually screen 2.7 million compounds. We identified scores of AI-selected compounds targeting a binding site near L265P in MYD88. We validated these hits by evaluating their inhibition of MYD88 L265P ubiquitination and xenograft tumorigenesis. One compound attenuated lymphoma growth from NHL cells with MYD88 L265P, but not that with WT MYD88. We hypothesize that adaptor oncoproteins such as MYD88 L265P can be targeted by AI. In this application, we propose two specific aims to develop drug candidates that target MYD88 L265P for NHL therapy. In Aim 1, we will use AI to virtually screen billions of compounds to discover novel drug candidates targeting a binding site near L265P in MYD88. In Aim 2, we will optimize validated hit compounds targeting MYD88 L265P. Data generated from this partnership will provide a solid scientific platform for therapeutic development targeting the oncogenic MYD88 L265P while sparing WT MYD88, which is critical for both innate and adaptive immunity. This work addresses the unmet clinical need to target MYD88 L265P directly and advances drug development against mutation-specific drivers.

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