A Software Platform for the Identification of Cell Surface Antigens Using RNA-seq Data

使用 RNA-seq 数据识别细胞表面抗原的软件平台

基本信息

批准号：
9909639
负责人：
MARTIN AKERMAN
金额：
$ 30.15万
依托单位：
ENVISAGENICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-12 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9909639
关键词：
Acute Myelocytic Leukemia Adult Acute Myeloblastic Leukemia Alternative Splicing Antibodies Aspirate substance Autoimmunity Automobile Driving Binding Bone Marrow Cell Line Cell surface Cessation of life Childhood Childhood Acute Myeloid Leukemia Chronic Collaborations Computer Simulation Computer software Consumption Data Databases Development Diagnosis Disease Drug Targeting Epitopes Event FDA approved Grant High-Throughput Nucleotide Sequencing Immune response Immunologics Immunooncology Immunotherapy In Vitro Infection Inflammation Inflammatory Knowledge Lead Leukemic Cell Lymphocyte Machine Learning Malignant Neoplasms Memorial Sloan-Kettering Cancer Center Methods Modality Monoclonal Antibodies Morbidity - disease rate Mutation Nonsense-Mediated Decay Pathogenicity Patients Peptides Performance Pharmacologic Substance Phase Population Probability Protein Isoforms Proteomics RNA Databases RNA Splicing Regulation Relapse Resistance Resources Role Sampling Small Business Innovation Research Grant Source Spliceosomes Surface Antigens Techniques Technology Tertiary Protein Structure Therapeutic Time Training Transcript Translations Tumor-Derived World Health Organization base clinically relevant cost drug development drug discovery high throughput technology human disease human monoclonal antibodies immunogenic innovative technologies knowledge base machine learning algorithm melanoma mortality new therapeutic target novel novel therapeutics patient biomarkers patient stratification response success therapeutic development therapy resistant transcriptome sequencing tumor tumor heterogeneity

项目摘要

Human monoclonal antibodies are among the fastest growing therapeutic modalities, with over sixty compounds approved by FDA to treat infections, autoimmunity, chronic inflammation and cancer. In combination, these diseases are responsible for the deaths of 50 million people annually, according to the World Health Organization. However, the advent of therapeutic immunologics is expected to significantly reduce the associated morbidity and mortality, particularly for oncologic diseases. Currently, 15 immuno-oncologic (IO) treatments are commercially available and comprise a growing market that is expected to reach $100B by 2022. IO therapeutics effectively attack cancer by selectively binding tumor-specific protein domains on the cell surface, referred to as tumor-associated ectodomains (TAEs). However, many cancers remain insensitive to available IO as effective and safe TAEs are difficult to identify. Standard methods to detect TAEs are costly, time-consuming and limited in their ability to discover novel targets, necessitating the development of innovative technologies to circumvent this burden. RNAseq is currently the most effective method to discover novel splicing isoforms, is high-throughput, sensitive and inexpensive. Envisagenics has been at the forefront of RNAseq- based splicing characterization since the release of its SpliceCore® platform. Here, we propose to develop SpliceIO, a novel drug discovery platform that integrates the Envisagenics’ SpliceCore knowledge base with machine learning algorithms to enable rapid identification of aberrant splicing-derived TAEs using RNAseq data. In this Phase I SBIR proposal, we will develop and apply SpliceIO in the context of Acute Myeloid Leukemia, a cancer particularly resistant to IO but highly associated with splicing mis-regulation and mutations within key spliceosome components. We will identify and validate TAEs in vitro using established leukemia cell lines and patient-derived bone marrow aspirates in collaboration with Dr. Omar Abdel-Wahab from Memorial Sloan Kettering Cancer Center. Collectively, the aims outlined herein will allow us to both develop and validate a novel splicing-dependent TAE identification platform to provide new sources of drug targets while dramatically reducing the time and cost associated with their development. In addition, this will allow Envisagenics to create new partnership opportunities for IO co- development with pharmaceutical companies. If successful, this pipeline can be used to identify drug targets and/or biomarkers for patient stratification in cancer and inflammatory diseases in the context of an SBIR Phase II grant.

人单克隆抗体是增长最快的治疗方式之一， FDA 批准超过 60 种化合物用于治疗感染、自身免疫、慢性炎症这些疾病加在一起导致 5000 万人死亡。然而，每年，根据世界卫生组织的数据，治疗方法的出现。免疫学预计将显着降低相关的发病率和死亡率，特别是针对肿瘤疾病，目前有 15 种免疫肿瘤 (IO) 治疗方法。商业可用，并包含一个不断增长的市场，预计到 2022 年将达到 $100B。 IO 疗法通过选择性结合肿瘤特异性蛋白结构域来有效攻击癌症细胞表面，称为肿瘤相关胞外域 (TAE)。由于难以识别有效且安全的 TAE，因此对可用的 IO 仍然不敏感。检测 TAE 的标准方法成本高昂、耗时且检测能力有限发现新的目标，需要开发创新技术来规避 RNAseq 是目前发现新型剪接亚型最有效的方法。 Envisagenics 具有高通量、灵敏且廉价的优势，一直处于 RNAseq 的前沿。自 SpliceCore® 平台发布以来，我们提出了基于熔接表征的方法。开发 SpliceIO，这是一个集成了 Envisagenics 的 SpliceCore 的新型药物发现平台具有机器学习算法的知识库，能够快速识别异常在第一阶段 SBIR 提案中，我们将开发和使用 RNAseq 数据进行剪接衍生的 TAE。将 SpliceIO 应用于急性髓系白血病，这是一种对 IO 特别耐药的癌症，但与关键剪接体成分内的剪接错误调节和突变高度相关。我们将使用已建立的白血病细胞系和患者来源的细胞在体外识别和验证 TAE 与纪念斯隆管理学院的 Omar Abdel-Wahab 博士合作进行骨髓抽吸总的来说，本文概述的目标将使我们能够发展和发展。验证新型剪接依赖性 TAE 识别平台以提供新的药物来源目标，同时大大减少与其开发相关的时间和成本。此外，这将使 Envisagenics 能够为 IO co- 创造新的合作机会如果成功，该管道可用于识别制药公司。用于癌症和炎症性疾病患者分层的药物靶点和/或生物标志物 SBIR 第二阶段拨款的背景。