Integrated models for metastatic phenotype and outcome prediction in osteosarcoma

描述（由申请人提供）：本申请提出了骨肉瘤的基因组预后研究，骨肉瘤是一种研究相对不足的癌症，在年轻患者中发病率较高，其分子基础尚未完全了解。结果是高度可变的，缺乏充分研究的预后因素，将允许分层管理决策。一个主要的挑战是相对罕见的疾病和缺乏广泛可用的注释良好的冷冻组织资源。我们最近发表的初步数据分析了福尔马林固定石蜡包埋（FFPE）骨肉瘤组织中的miRNA表达和测序谱，产生了与骨肉瘤临床结局密切相关的可检验假设。特别是，已经鉴定出强预后性miRNA谱，有趣的是，其似乎主要聚集在一个染色体位置。在具体目标1中，我们建议利用来自基于儿童肿瘤学小组的大型队列的数据来验证成人队列和儿科队列中的候选miRNA预后特征。此外，我们将测试与相关临床病理数据的独立性和潜在协同作用，例如化疗诱导的坏死。在目标2中，将测试一种新的方法，旨在开发动态miRNA表达模型，通过分别利用来自活检和切除的配对化疗前和化疗后标本来捕获化疗对肿瘤的影响。假设它们将提供额外的强有力的预后见解，特别是与化疗耐药患者相关的。在目标3中，将研究深层miRNA序列模式，以探索它们与表达谱的关系和潜在的预后意义。在Aim 4中，miRNA测序将扩展到来自活检、化疗抗性切除和转移的系列标本，以鉴定表明转移表型的演变序列模式，由于丰度低，所述转移表型可能不容易通过诊断时的“快照”测试检测到。这些独特的模式可能最终与总体生存结局相关，并与许多复发性肿瘤患者在进一步治疗后仍能存活很长时间的观察结果相关。 治疗我们的建议利用了骨肉瘤的独特治疗模式，包括原发性和转移性疾病化疗后的连续活检和切除，从而在常规护理期间获得连续的患者标本。此外，还将在FFPE标本中研究这些目标中揭示的模式，以便为将来确定的大规模临床应用提供基础。这个多机构和跨学科的建议利用尖端的基因组技术，并汇集了来自达纳/法伯哈佛癌症中心内所有机构的骨肉瘤，基因组学，计算生物学和生物统计学专家团队。

DESCRIPTION (provided by applicant): This application proposes a genomic prognostic study in osteosarcoma, a relatively understudied cancer with high incidence in younger patients, the molecular underpinnings of which are incompletely understood. Outcome is highly variable and there is a lack of well-studied prognostic factors that would allow stratified management decisions. A major challenge is the relative disease rarity and the lack of widely available well annotated frozen tissue resources. We recently published preliminary data analyzing miRNA expression and sequencing profiles from formalin fixed paraffin embedded (FFPE) osteosarcoma tissues generating testable hypotheses with strong relevance to osteosarcoma clinical outcome. In particular, a strong prognostic miRNA profile has been identified which, interestingly, appears to be clustered mainly in one chromosomal location. In Specific aim 1, we propose to validate the candidate miRNA prognostic profile in an adult cohort, and a pediatric cohort utilizing data from a large Children's Oncology Group based cohort In addition, we will test for independence from, and potential synergy with, relevant clinicopathologic data such as chemotherapy induced necrosis. In Aim 2 a novel approach will be tested aiming to develop dynamic miRNA expression models capturing the effect of chemotherapy on the tumor, by utilizing paired pre and post chemotherapy specimens from biopsy and resection, respectively. The hypothesis is that they will offer additional powerful prognostic insights specifically relevan to chemoresistant patients. In Aim 3, deep miRNA sequence patterns will be studied in order to explore their relation to expression profiles and potential prognostic significance. In Aim 4 miRNA sequencing will be extended to serial specimens from biopsy, chemoresistant resection and metastasis in order to identify evolving sequence patterns signifying the metastatic phenotype which may not be easily detectable via a "snapshot" test at diagnosis, due to low abundance. These unique patterns may ultimately be relevant to overall survival outcome and to the observation that many patients with recurrent tumors can still survive a long time with further treatment. Our proposal takes advantage of the unique treatment patterns in osteosarcoma, which include serial biopsies and resections following chemotherapy administration in both primary and metastatic disease, resulting in availability of serial patient specimens during routine care. Additionally, patterns revealed in these Aims will also be studied in FFPE specimens in order to provide the basis for definitive large scale clinical application in the futue. This multi institutional and interdisciplinary proposal utilizes cutting edge genomic technologies and brings together a team of experts in osteosarcoma, genomics, and computational biology and biostatistics from all institutions within the Dana/Farber Harvard Cancer Center.