Transcriptomic classification of non-muscle invasive bladder cancer and its clinical and prognostic implication

这项研究解决了非肌肉浸润性膀胱癌治疗中未得到满足的临床需求。 (NMIBC)。NMIBC约占膀胱癌病例的75%，具有良好的五年生存率，但 通常复发(5年内50-70%)并进展为肌肉侵袭性疾病(MIBC，10%-30%)。膀胱内插管 卡介苗(BCG)是预防NMIBC复发和进展的最有效的疗法， 然而，BCG有30%的失败率，各种不良反应导致不耐受。存在两个关键需求：1) 一种风险分层工具，用于识别早期和侵袭性复发和进展的高风险患者 治疗，以及2)反应预测工具，以识别对卡介苗无反应或不耐受的患者 其他治疗方案。我们的目标是通过以下方式开发和验证风险分层和卡介苗应对工具 将分子标记纳入目前的病理系统，为NMIBC提供最佳的临床护理。 转录组分析是识别基因的有力手段，更重要的是，可以确定癌症分子亚型。 与不同的治疗和预后结果相关。该方法已在膀胱术中得到应用。 癌症，但主要集中在MIBC。对NMIBC的研究是一个未得到满足的需求。建立在膀胱癌的基础上 流行病学、健康和生活方式研究(BE-WELL)，NMIBC最大的预期队列之一 患者，我们建议进行一项全面的NMIBC转录分析，并检查 附加预后基因的分子亚型预测NMIBC治疗疗效和预后 结果。我们的中心假设是分子签名(即分子亚型和/或其他 基因)可以揭示NMIBC的异质性，从而改善目前的病理分类 为NMIBC量身定制的护理系统。我们将：目标1.开发NMIBC预后风险分层工具 纳入分子亚型、基因、临床病理和人口学因素的结果。主要 结果将是疾病的复发和进展，并探讨存活率。我们将定义分子 根据NMIBC预后结果(1a)进行亚型和识别基因，建立BE-Well风险预测模型 (n=928)(1b)，在独立验证队列中验证分子签名和风险分层工具 (n=959)(1c)，并在合并队列中的性别和种族特定群体中探索该工具(1d)。目标2.制定 通过结合分子亚型、免疫信号、基因、 临床病理和人口学因素。主要结果将是卡介苗对卡介苗不耐受无反应 探索过了。鉴于膀胱癌的高免疫原性和卡介苗疗法，我们将开发卡介苗- 反应预测工具，进一步考虑了BE中接受卡介苗治疗的患者的免疫特征。 Well(n=426)和验证队列(n=691)。我们将探索分子亚型的性别特征， 种族/民族，以及致病风险因素。临床翻译将加快，因为新一代 NMIBC-在大型医疗保健传递系统设置中使用纳米字符串的特定分子签名。

This study addresses the unmet clinical needs for management of non-muscle invasive bladder cancer (NMIBC). NMIBC represents ~75% of bladder cancer cases and has a favorable five-year survival rate, but typically recurs (50-70% in 5 years) and progresses to muscle-invasive disease (MIBC, 10-30%). Intravesical Bacillus Calmette-Guerin (BCG) is the most effective therapy to prevent NMIBC recurrence and progression, yet BCG has a 30% failure rate, with various adverse effects leading to intolerance. Two critical needs exist: 1) a risk stratification tool to identify patients at high risk of recurrence and progression for early and aggressive treatment, and 2) a response prediction tool to identify patients who are unresponsive or intolerant to BCG for other treatment options. Our goal is to develop and validate risk-stratification and BCG-response tools by incorporating molecular signatures into the current pathological system for optimal clinical care of NMIBC. Transcriptome analysis is powerful to identify genes, and importantly, to define cancer molecular subtypes associated with different therapeutic and prognostic outcomes. This approach has been applied in bladder cancer but primarily focused on MIBC. Research on NMIBC is an unmet need. Building on the Bladder Cancer Epidemiology, Wellness, and Lifestyle Study (Be-Well), one of the largest prospective cohorts of NMIBC patients, we propose to conduct a comprehensive transcriptomic analysis of NMIBC and examine the utility of molecular subtypes with additional prognostic genes in predicting NMIBC treatment response and prognostic outcomes. Our central hypothesis is that molecular signatures (i.e., molecular subtypes and/or other genes) could unveil NMIBC heterogeneity and thus improve the current pathological classification system for tailored NMIBC care. We will: Aim 1. Develop a risk stratification tool for NMIBC prognostic outcomes by incorporating molecular subtypes, genes, clinicopathological and demographic factors. Primary outcomes will be disease recurrence and progression, with survival explored. We will define molecular subtypes and identify genes by NMIBC prognostic outcomes (1a), build risk prediction models in Be-Well (n=928) (1b), validate molecular signatures and the risk-stratification tool in an independent validation cohort (n=959) (1c), and explore the tool in sex and race specific groups in the pooled cohorts (1d). Aim 2. Develop a response prediction tool for BCG outcomes by incorporating molecular subtypes, immune signatures, genes, clinicopathological and demographic factors. Primary outcomes will be BCG unresponsive with BCG intolerant explored. Given the high immunogenic nature of bladder cancer and BCG therapy, we will develop the BCG- response prediction tool with further consideration of immune signatures in patients who received BCG in Be- Well (n=426) and a validation cohort (n=691). We will explore characterization of molecular subtypes by sex, race/ethnicity, and etiological risk factors. Clinical translation will be accelerated given the generation of NMIBC-specific molecular signatures using NanoString in a large health care delivery system setting.