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

非肌层浸润性膀胱癌的转录组学分类及其临床和预后意义

• 批准号: 10388707
• 负责人: Marilyn L Kwan
• 金额: $ 131.84万
• 依托单位: ROSWELL PARK CANCER INSTITUTE CORP
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10388707
• 关键词: Address; Adjuvant; Adverse effects; BCG Live; Bacillus Calmette-Guerin Therapy; Biological Assay; Cancer Patient; Classification; Clinical; Clinical Management; Consensus; Data; Demographic Factors; Diagnosis; Disease; Early treatment; Ethnic Origin; Etiology; Event; Failure; Generations; Genes; Goals; Healthcare Systems; Heterogeneity; Immune; Individual; Intravesical Administration; Investigation; Knowledge; Life; Life Style; Malignant Neoplasms; Malignant neoplasm of urinary bladder; Modeling; Molecular; Molecular Profiling; Muscle; Nature; Newly Diagnosed; Occupational; Outcome; Pathologic; Patient-Focused Outcomes; Patients; Prospective cohort; Race; Recurrence; Research; Research Priority; Risk; Risk Factors; Sample Size; Smoking; Specimen; Survival Rate; System; Taxonomy; Therapeutic; Translating; Translations; Treatment outcome; Tumor Subtype; Validation; aggressive therapy; base; cancer care; cancer epidemiology; cancer heterogeneity; cancer recurrence; cancer subtypes; clinical care; clinical practice; clinical prognostic; clinical translation; cohort; cost; effective therapy; environmental chemical exposure; follow-up; health care delivery; high risk; immunogenic; improved; individualized medicine; intravesical; meetings; molecular marker; molecular subtypes; nano-string; non-muscle invasive bladder cancer; population based; predicting response; predictive modeling; predictive tools; prevent; primary outcome; prognostic; prognostic value; response; risk prediction model; risk stratification; sex; tool; transcriptome; transcriptomics; treatment response; tumor; tumor progression

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.

这项研究解决了非肌层浸润性膀胱癌治疗中未满足的临床需求 （NMIBC）。 NMIBC 约占膀胱癌病例的 75%，并且具有良好的五年生存率，但 通常会复发（5 年内 50-70%）并进展为肌肉侵袭性疾病（MIBC，10-30%）。膀胱内 卡介苗 (BCG) 是预防 NMIBC 复发和进展的最有效疗法， 但卡介苗的失败率高达30%，并有多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高风险的患者 治疗，以及 2) 反应预测工具，用于识别对 BCG 无反应或不耐受的患者 其他治疗选择。我们的目标是通过以下方式开发和验证风险分层和 BCG 响应工具： 将分子特征纳入当前的病理系统中，以实现 NMIBC 的最佳临床护理。 转录组分析对于识别基因非常有用，更重要的是，对于定义癌症分子亚型 与不同的治疗和预后结果相关。该方法已应用于膀胱 癌症，但主要关注 MIBC。 NMIBC 的研究是一个未满足的需求。以膀胱癌为基础 流行病学、健康和生活方式研究 (Be-Well)，NMIBC 最大的前瞻性队列之一 对于患者，我们建议对 NMIBC 进行全面的转录组分析并检查其效用 具有额外预后基因的分子亚型可预测 NMIBC 治疗反应和预后 结果。我们的中心假设是分子特征（即分子亚型和/或其他 基因）可以揭示 NMIBC 异质性，从而改善当前的病理分类 定制 NMIBC 护理系统。我们将： 目标 1. 开发用于 NMIBC 预后的风险分层工具 通过结合分子亚型、基因、临床病理学和人口统计学因素来确定结果。基本的 结果将是疾病复发和进展，并探讨生存率。我们将定义分子 亚型并通过 NMIBC 预后结果识别基因 (1a)，在 Be-Well 中构建风险预测模型 (n=928) (1b)，在独立验证队列中验证分子特征和风险分层工具 (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. 开发 通过结合分子亚型、免疫特征、基因、 临床病理和人口因素。主要结局是卡介苗无反应且卡介苗不耐受 探索过。鉴于膀胱癌和卡介苗治疗的高免疫原性，我们将开发卡介苗- 反应预测工具，进一步考虑接受 BCG 的患者的免疫特征 嗯（n=426）和验证队列（n=691）。我们将探索按性别划分的分子亚型特征， 种族/民族和病因学危险因素。鉴于产生的，临床转化将加速 在大型医疗保健服务系统环境中使用 NanoString 的 NMIBC 特定分子特征。