The Role of Immature Tumor Subpopulations In Pediatric Rhabdomyosarcoma

未成熟肿瘤亚群在小儿横纹肌肉瘤中的作用

• 批准号: 10445963
• 负责人: Xiang Chen
• 金额: $ 41.63万
• 依托单位: ST. JUDE CHILDREN'S RESEARCH HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-04 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10445963
• 关键词: Adult; Aftercare; Archives; Bar Codes; Bayesian Analysis; Biopsy; Biopsy Specimen; Cell Cycle; Cell Nucleus; Cells; Characteristics; Child; Childhood; Childhood Rhabdomyosarcoma; Childhood Solid Neoplasm; Chromatin; Clinical; Collaborations; Computer Analysis; DNA Methylation; Data; Development; Diagnosis; Disease; Disease remission; Enhancers; Epigenetic Process; Evaluation; Formalin; Freezing; Gene Expression; Genes; Genetic Transcription; Genomics; Glioma; Goals; Government; Hematologic Neoplasms; Heterogeneity; Individual; Machine Learning; Malignant Childhood Neoplasm; Malignant Neoplasms; Measures; Mesoderm Cell; Methods; Modeling; Molecular; Monitor; Morphology; Muscle Cells; Myoblasts; Network-based; Normal Cell; Orthoptics; Outcome; Paraffin Embedding; Paraxial Mesoderm; Pathway interactions; Patients; Persons; Pilot Projects; Population; Positioning Attribute; Rare Diseases; Recurrence; Recurrent tumor; Relapse; Research; Research Proposals; Resistance; Rhabdomyosarcoma; Risk; Risk Factors; Role; Saint Jude Children' s Research Hospital; Sampling; Seeds; Skeletal Muscle; Small Nuclear RNA; Solid; Specimen; Testing; Work; activity marker; anticancer research; base; biomarker development; chemotherapy; computerized tools; convolutional neural network; data mining; deep learning; disorder risk; epigenomics; experience; improved; improved outcome; in vivo; in vivo Model; innovation; leukemia; machine learning algorithm; methylation pattern; methylome; multidisciplinary; myogenesis; neoplastic cell; novel; patient derived xenograft model; preservation; prognostic significance; promoter; prototype; reconstitution; response; single-cell RNA sequencing; soft tissue; therapy resistant; transcriptome sequencing; transcriptomics; tumor; tumor DNA; tumor heterogeneity

PROJECT SUMMARY / ABSTRACT Rhabdomyosarcoma (RMS) is a devastating pediatric soft tissue cancer with morphological features of develop- ing skeletal muscles. Although most patients with RMS achieve a complete remission, one third will develop disease recurrence which is associated with a dismal clinical outcome. These clinical challenges underscore an urgent need to identify patients at risk for resistance and develop better therapy to reduce the risk of recurrence. Our pilot study revealed that rhabdomyosarcoma tumors have developmental intratumoral heterogeneity: differ- ent cells in a single tumor harbor transcriptomic feature of different myogenic stages. Moreover, tumor cells with developmentally immature characteristics are enriched in post-therapy specimens and have the potential to rep- licate and reconstitute the entire developmental trajectory after therapy. In this application, we will develop com- putational analysis methods to unambiguously identify immature tumor subpopulations in single cell RNA-seq data and to reveal their master deregulated genes (Aim 1); In Aim 2, we will characterize the changes in those transcriptional networks and cellular states during treatment in orthoptic patient derived xenograft models (O- PDXs) in vivo. And in Aim 3, we will develop an innovative deep-learning data mining approach to evaluate the prognostic significance of the myogenic transcriptional networks that underly RMS cellular heterogeneity in pa- tient tumors. The proposed study integrates computational, statistical and experimental approaches to study the role of immature cell populations in rhabdomyosarcoma recurrence. Building upon our computational expertise, research experience in rhabdomyosarcoma, robust preliminary results and highly productive collaborations with multi-disciplinary expertise in genomics/epigenomics, machine learning, Bayesian statistics and translational re- search in rhabdomyosarcoma, we are in a unique position to achieve the goals of this research proposal. The proposed research will be impactful because it will potentially change how we treat children with rhabdomyosar- coma and the developed computational/statistical approaches will be broadly applicable to cancer research.

项目摘要/摘要 横纹肌肉瘤(RMS)是一种严重的儿科软组织癌，其形态特征主要表现为进展性软组织肉瘤和恶性软组织癌。 骨骼肌。虽然大多数RMS患者都能完全缓解，但仍有三分之一的患者会发展 与令人沮丧的临床结果相关的疾病复发。这些临床挑战强调了一种 迫切需要确定有耐药风险的患者，并开发更好的治疗方法，以降低复发风险。 我们的初步研究显示，横纹肌肉瘤具有发育性的瘤内异质性： 单个肿瘤中的ENT细胞具有不同肌发生阶段的转录特征。此外，肿瘤细胞与 发育不成熟的特征在治疗后的标本中丰富，并有可能代表 切割并重建治疗后的整个发育轨迹。在本应用程序中，我们将开发COM- 在单细胞RNA-SEQ中明确识别未成熟肿瘤亚群的假设分析方法 数据，并揭示它们主要的非调控基因(目标1)；在目标2中，我们将描述这些基因的变化 直视患者来源的异种移植模型治疗期间的转录网络和细胞状态 PDXs)在体内。在目标3中，我们将开发一种创新的深度学习数据挖掘方法来评估 生肌性转录网络对PAS患者RMS细胞异质性的预后意义 乳头肿瘤。拟议的研究整合了计算、统计和实验方法，以研究 未成熟细胞群在横纹肌肉瘤复发中的作用。以我们的计算专业知识为基础， 横纹肌肉瘤的研究经验、稳健的初步结果以及与 在基因组学/表观基因组学、机器学习、贝叶斯统计和翻译再研究方面的多学科专业知识 在横纹肌肉瘤的研究中，我们处于独特的地位，可以实现本研究提案的目标。这个 拟议中的研究将产生影响，因为它可能会改变我们治疗横纹肌综合症儿童的方式。 Coma和开发的计算/统计方法将广泛适用于癌症研究。