Validation of a risk model for stage I lung adenocarcinoma

描述（由申请人提供）：预测I期肺腺癌复发的预后模型的开发和验证。 随着最近国家肺癌筛查试验结果的披露，表明每年胸部CT扫描可以减少20%的高危人群肺癌死亡，通过筛查CT扫描检测到的早期拉克及其切除术的数量预计将增加。虽然I期LAC根治性手术切除后的5年复发率为18% -29%， 切除术后无复发生存率的个体差异。目前，外科医生和肿瘤学家指导治疗决策的唯一公认的预后因素是肿瘤大小，其预后表现仍不清楚。需要更好的预后工具来提供高质量的个体风险预测，识别高复发风险的患者，并帮助指导肿瘤学家和胸外科医生的治疗决策。 为了确定准确预测不良结局风险的预后标志物，我们的研究小组对迄今为止最大的I期LAC队列进行了广泛的临床和病理检查。我们开发了一种预测评分，该评分使用了临床、组织学和细胞学标准，可以在任何医院的H&E载玻片上进行常规评估，并以高准确度预测复发或死亡的风险。在这项提案中，我们的目标是通过microRNA分析（miRNA）进一步提高我们评分的预测能力。包括我们小组在内的几个研究者已经独立地确定了使用microRNA诊断早期肺癌的潜力。我们已经开发出一种能够预测早期肺癌的microRNA特征。我们的方法的一个明显优势是使用福尔马林固定石蜡包埋（FFPE）组织，类似于我们的临床病理标准，因此， 避免了复杂的组织处理方案的要求。 我们假设，富含miRNA表达数据的综合临床病理评分可以组合成一种简单、经济有效的预测工具，该工具将准确确定I期LAC根治性手术切除后复发或死亡的风险，并将确定哪些患者是积极监测和辅助治疗的主要候选人。在这项提案中，我们试图验证和完善我们现有的LAC miRNA签名，并检查其丰富临床病理评分预测质量的能力。 为 模型建立在简单的临床、病理特征（在H&E载玻片上评估）和来自FFPE的miRNA分析的基础上，我们的建议的结果是立即可实施的、及时的并且具有高度的翻译意义。 公共卫生相关性：预计早期肺癌的检测将增加，需要更准确的预测工具来识别复发风险较高的患者。在这项提案中，我们联合收割机结合了我们在病理学、细胞学和microRNA分析方面的专业知识，开发并验证了一个综合风险模型，以识别复发风险较高的患者，进行积极的监测或干预。

DESCRIPTION (provided by applicant): Development and validation of a prognostic model for predicting recurrence in stage I lung adenocarcinoma. With the recent disclosure of results from the National Lung Cancer Screening Trial, suggesting that annual chest CT scans can reduce 20% of lung cancer deaths in high-risk individuals, the number of early stage LACs detected by screening CT scans and their resection is expected to increase. While the 5-year recurrence rate following curative-intent surgical resection in stage I LAC is 18% - 29%, there is substantial individual variation in post-resection recurrence free survival. Currently, the only accepted prognostic factor guiding treatment decisions for both surgeons and oncologists is tumor size, and its prognostic performance remains unclear. Better prognostic tools are needed to provide good quality individual risk prediction, identify patients at high risk of recurrence and help to guide treatment decisions by both oncologists and thoracic surgeons. In an attempt to identify prognostic markers that accurately predict the risk of unfavorable outcome; our group has performed extensive clinical and pathological examination of the largest cohort of stage I LAC to date. We have developed a prediction score that uses combined clinical, histological and cytological criteria that can be assessed routinely on an H&E slide in any hospital, and predicts the risk of recurrence or death with high accuracy. In this proposal, we aim to further improve the predictive ability of our score by microRNA analysis (miRNA). The potential of using microRNAs for prognostication of early lung cancer has been established by several investigators independently including our group. We have developed a microRNA signature capable of prognostication of early lung cancer. A distinct advantage of our methods is the use of formalin-fixed paraffin-embedded (FFPE) tissue, similar to our clinico-pathologic criteria, thus avoiding the requirement of complex tissue processing protocols. We hypothesize that the comprehensive clinico-pathological score, enriched with miRNA expression data, can be combined into a simple, cost-effective predictive instrument that will accurately determine the risk of recurrence or death following curative-intent surgical resection for stage I LAC, and will identify those patients who are primary candidates for aggressive surveillance and adjuvant therapy. In this proposal, we seek to validate and refine our existing miRNA signature for LAC and examine its ability to enrich the predictive quality of our clinico-pathological score. As the model is built upon simple clinical, pathological characteristics (assessed on H&E slide) and miRNA analysis from FFPE, the results of our proposal are immediately implementable, timely and are of high translational significance. PUBLIC HEALTH RELEVANCE: Detection of early stage lung cancer is anticipated to increase necessitating more accurate predictive instruments to identify patients at higher risk for recurrence. In this proposal, we combine our expertise of pathological, cytological and microRNA analyses to develop and validate a comprehensive risk model to identify patients at a higher risk for recurrence for aggressive surveillance or intervention.