Molecular Signatures of Biologic Behavior in Pediatric Osteosarcoma

儿童骨肉瘤生物学行为的分子特征

• 批准号: 10588388
• 负责人: Kelly M Makielski
• 金额: $ 12.53万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10588388
• 关键词: Address; Adolescent and Young Adult; Affect; Award; Behavior; Binding; Bioinformatics; Biological; Biological Markers; Biology; Blood; Blood Tests; Bone neoplasms; Categories; Child; Childhood Osteosarcoma; Clinical; Clinical Trials; Data; Dedications; Development; Diagnosis; Diagnostic tests; Disease; Disease Progression; Disease remission; Funding; Future; Gene Cluster; Genes; Goals; Human; Immune response; Incidence; Knowledge; Leadership; Machine Learning; Malignant Neoplasms; Membrane; Mentors; Messenger RNA; Methods; Molecular Profiling; Morbidity - disease rate; Neoplasm Metastasis; Noise; Normal Cell; Operative Surgical Procedures; Outcome; Patients; Pattern; Pediatric cohort; Probability; Prognosis; Progression-Free Survivals; Research; Resources; Risk Marker; Sampling; Scientist; Second Primary Cancers; Sensitivity and Specificity; Serum; Severities; Survivors; Systemic Therapy; Testing; Time; Toxic effect; Treatment Failure; Tumor Biology; Work; Xenograft Model; Xenograft procedure; aggressive therapy; bioinformatics pipeline; biomarker discovery; biomarker identification; biomarker selection; biomarker signature; career; chemotherapy; clinically relevant; differential expression; exosome; experience; experimental study; genetic signature; improved; machine learning algorithm; microvesicles; minimally invasive; mouse model; multidisciplinary; negative affect; next generation sequencing; novel; osteosarcoma; patient stratification; patient subsets; predictive marker; predictive signature; predictive test; primary bone cancer; prognostic indicator; risk minimization; skills; standard of care; transcriptome sequencing; translational model; translational scientist; treatment response; treatment risk; tumor; tumor behavior; young adult

PROJECT SUMMARY/ABSTRACT Osteosarcoma, the most common primary tumor of bone, primarily affects children, adolescents, and young adults. A diagnosis of osteosarcoma is devastating, as approximately half of pediatric osteosarcoma patients experience metastasis and ultimately succumb to the disease within 10 years of their diagnosis. Currently there is no diagnostic test to predict prognosis, so all patients are treated with aggressive surgery and intense chemotherapy with high rates of toxicity. However, a subset of patients may not require as aggressive therapy to achieve remission. Additionally, those that survive have a high incidence of lifelong morbidities, including treatment-related secondary malignancies. Accurate prognostic indicators could be integrated into the standard of care for osteosarcoma to guide therapy. Children with more favorable prognoses could be treated more conservatively, reducing the need for aggressive surgery, and decreasing the intensity of systemic therapy. This would decrease the likelihood and severity of long-term morbidities, and reduce the probability of secondary, treatment-related malignancies without negatively affecting prognosis. Conversely, patients with a worse prognosis could receive more aggressive treatments or be guided to experimental clinical trials to improve their long-term survival. In this project, we will develop a serum exosomal gene signature associated with prognosis in pediatric osteosarcoma. Exosomes are membrane-bound microvesicles containing cargo associated with tumor biology and disease state. We will first identify biomarkers by sequencing serum exosomes from a large cohort of pediatric osteosarcoma patients with known clinical outcomes. We will then identify genes associated with metastatic propensity using xenograft mouse models established from pediatric osteosarcomas with distinct biologic behavior. We will analyze co-regulated gene clusters and apply machine learning, improving sensitivity and specificity, and ultimately resulting in a more robust gene signature. The osteosarcoma gene signature developed in this project can be utilized in the clinical setting to predict prognosis, stratifying patients into more appropriate treatment categories, and having the potential to improve management of this devastating disease. Additionally, these biomarkers will contribute to our understanding of the biological behavior and progression of osteosarcoma, allowing us to infer mechanisms of host response, metastasis, and response to therapy. Importantly, this K01 is critical to advancing my career as a translational scientist by providing the necessary protected time and dedicated resources to perform high-quality, clinically relevant research, under the guidance of an exceptional multidisciplinary mentor team. This award will facilitate my transition to independence, as the data procured in this project will allow me to be competitive for future independent funding applications. Additionally, as I complete these aims, I will develop the necessary knowledge and leadership skills of a successful independent research scientist specializing in exosome biology and translational models of pediatric osteosarcoma and ultimately expanding to other cancers.

项目概要/摘要 骨肉瘤是最常见的原发性骨肿瘤，主要影响儿童、青少年和年轻人 成年人。骨肉瘤的诊断是毁灭性的，因为大约一半的儿童骨肉瘤患者 在诊断后 10 年内经历转移并最终死于该疾病。现在 没有诊断测试来预测预后，因此所有患者都接受积极的手术和强烈的治疗 化疗的毒性率很高。然而，一部分患者可能不需要积极治疗 以达到缓解的目的。此外，那些幸存者的终生发病率很高，包括 与治疗相关的继发性恶性肿瘤。准确的预后指标可以集成到标准中 骨肉瘤的护理指导治疗。预后较好的儿童可以接受更多治疗 保守治疗，减少激进手术的需要，并降低全身治疗的强度。 这将降低长期发病的可能性和严重程度，并降低 继发性、与治疗相关的恶性肿瘤，不会对预后产生负面影响。相反，患有以下疾病的患者 预后较差可以接受更积极的治疗或指导进行实验性临床试验 提高他们的长期生存率。在这个项目中，我们将开发与相关的血清外泌体基因特征 与小儿骨肉瘤的预后有关。外泌体是含有货物的膜结合微泡 与肿瘤生物学和疾病状态相关。我们将首先通过血清测序来识别生物标志物 来自一大群具有已知临床结果的儿科骨肉瘤患者的外泌体。我们随后将 使用从儿科建立的异种移植小鼠模型来识别与转移倾向相关的基因 骨肉瘤具有独特的生物学行为。我们将分析共同调控的基因簇并应用机器 学习，提高敏感性和特异性，并最终产生更强大的基因特征。这 该项目开发的骨肉瘤基因特征可用于临床环境来预测 预后，将患者分为更合适的治疗类别，并有可能改善 控制这种毁灭性的疾病。此外，这些生物标志物将有助于我们理解 骨肉瘤的生物学行为和进展，使我们能够推断宿主反应的机制， 转移和对治疗的反应。重要的是，这台 K01 对于推进我作为翻译人员的职业生涯至关重要 科学家通过提供必要的受保护时间和专用资源来执行高质量的临床 在杰出的多学科导师团队的指导下进行相关研究。该奖项将有利于 我向独立的过渡，因为在这个项目中获得的数据将使我在未来具有竞争力 独立的资助申请。此外，当我完成这些目标时，我将开发必要的 专门从事外泌体生物学的成功独立研究科学家的知识和领导技能 和小儿骨肉瘤的转化模型，并最终扩展到其他癌症。