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: 10693811
• 负责人: Marilyn L Kwan
• 金额: $ 128.98万
• 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/10693811
• 关键词: Acceleration; Address; Adjuvant Therapy; 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 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; Recurrent Malignant Neoplasm; Recurrent disease; Research; Research Priority; Risk; Risk Factors; Sample Size; Smoking; Specimen; Survival Rate; System; Taxonomy; Therapeutic; Translating; Translations; Treatment outcome; Tumor Subtype; Validation; aggressive therapy; cancer care; cancer epidemiology; cancer heterogeneity; cancer recurrence; cancer subtypes; clinical care; clinical practice; 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.

本研究旨在解决非肌层浸润性膀胱癌管理的未满足的临床需求 （NMIBC）。NMIBC占膀胱癌病例的约75%，具有良好的五年生存率，但 通常复发（5年内50-70%）并进展为肌肉浸润性疾病（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， 然而，卡介苗有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，用于识别复发和进展风险高的患者， 治疗，以及2）反应预测工具，用于识别对卡介苗无反应或不耐受的患者， 其他治疗选择。我们的目标是开发和验证风险分层和BCG反应工具， 将分子特征整合到当前的病理系统中，用于NMIBC的最佳临床护理。 转录组分析在识别基因方面非常有效，重要的是，它可以定义癌症的分子亚型 与不同的治疗和预后结果相关。该方法已应用于膀胱 癌症，但主要集中在MIBC。对NMIBC的研究是一个未满足的需求。膀胱癌的治疗 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 我们建议对NMIBC进行全面的转录组学分析，并检查 在预测NMIBC治疗反应和预后中具有额外预后基因的分子亚型 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预后的风险分层工具 通过整合分子亚型、基因、临床病理学和人口统计学因素来评估预后。初级 结果将是疾病复发和进展，并探讨生存率。我们将定义分子 通过NMIBC预后结果（1a）进行亚型和识别基因，在Be-Well中建立风险预测模型 （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），并在汇总队列中的性别和种族特定组中探索该工具（1d）。目标二。开发一个 通过整合分子亚型、免疫特征、基因 临床病理学和人口统计学因素。主要结局为BCG无反应且BCG不耐受 探讨了鉴于膀胱癌和BCG治疗的高免疫原性，我们将开发BCG- 反应预测工具，进一步考虑了在Be-10接受BCG的患者中的免疫特征， 孔（n=426）和验证队列（n=691）。我们将探索性别分子亚型的特征， 种族/民族和病因风险因素。临床翻译将加快， 在大型医疗保健提供系统环境中使用NanoString的NMIBC特异性分子签名。