Genetic Programming to Predict Clinical Outcome from Transcript Quantification

描述（申请人提供）：这项I期研究的总体目标是建立我们提出的方法的技术优点和可行性，以开发并随后商业化一对用于新诊断的非肌肉浸润性Ta T1尿路上皮癌（UC）的伴随药物。准确预测初次经尿道肿瘤切除术后5年内肿瘤复发和进展的相对机会电切术（TURBT）。到II期结束时，将使用独立设盲的Ta T1膀胱UC组织集对I期中创建的预后分类器进行外部验证。利用Genetics Squared专有的遗传编程（GP）分析方法（Evolver（TM））来定义和量化编程选择的肿瘤基因之间的相互关系，我们的目标是创建比现有的最佳临床/病理预后因素在整个Ta T1膀胱UC人群中更准确和通用的预后特征。具体而言，我们的目标是：1。证明从可用的存档冷冻非肌肉浸润性Ta T1膀胱UC组织（n=178）中成功提取足量的完整RNA，用于进一步评价。2.建立使用Evolver（tm）平台分析基线人类全基因组表达数据的可行性，并整合相关的患者特异性临床和病理数据，以无偏见地识别“关键肿瘤基因”，当一起使用时，最好地预测TURBT后5年内非肌层浸润性膀胱UC首次复发时间（TTR）和进展时间（TTP）。3.通过对先前鉴定的“关键肿瘤基因”的定量RT-PCR（qRT-PCR）衍生表达谱进行Evolver（tm）分析，生成最佳预测肿瘤复发和进展的候选预后函数。4.从生成的候选分类器中，选择非肌肉侵入性膀胱UC TTR的最准确预测因子和TTP的最准确预测因子，并确认两者具有统计学显著的预后能力。整个项目将与南加州大学病理学系Richard Cote博士的实验室合作进行，该实验室位于加州洛杉矶。公共卫生相关性：对这项测试的需要源于膀胱癌患者及其临床医生必须做出的影响其生存，健康和生活质量的艰难决定。手术后更困难的决定之一是是否接受不愉快的和潜在的毒性化疗，甚至在肿瘤在发展的早期阶段被切除后进行化疗。做出避免这些程序的决定可以大大提高患者的生活质量。然而，对于某些人来说，这个决定可能会大大缩短他们的寿命。这种诊断的主要好处是减少接受不必要的化疗和/或化疗的患者数量，并确定那些极有可能进展并需要更积极治疗和/或随访的肿瘤患者。

DESCRIPTION (provided by applicant): The overall objective of this Phase I study is to establish the technological merit and feasibility of our proposed approach to developing and then commercializing a pair of companion prognostics for newly diagnosed non- muscle invasive Ta T1 urothelial carcinoma (UC) of the bladder that accurately predicts the relative chances of tumor recurrence and progression within 5 years after initial transurethral tumor resection (TURBT). By the end of Phase II, the prognostic classifiers created in Phase I will be externally validated using an independent blinded set of Ta T1 bladder UC tissues. Utilizing Genetics Squared's proprietary genetic programming (GP) analytic approach (Evolver(tm)) to define and quantify the interrelationships of programmatically selected tumor genes, we aim to create prognostic signatures that are more accurate and universal across the entire population of Ta T1 bladder UC than the best existent clinical/pathologic prognostic factors. Specifically, we aim to: 1. Demonstrate the successful extraction of sufficient amounts of intact RNA from the available archival frozen non-muscle invasive Ta T1 bladder UC tissue (n=178) for further evaluation. 2. Establish the feasibility of using the Evolver(tm) platform to analyze baseline human whole-genome expression data and integrate correlative patient-specific clinical and pathological data to unbiasely identify 'key tumor genes' that, when used together, best predict non-muscle invasive bladder UC time-to-first recurrence (TTR) and time-to-progression (TTP) within 5 years post-TURBT. 3. Generate candidate prognostic functions that best predict tumor recurrence and progression by Evolver(tm) analysis of the quantitative RT-PCR (qRT-PCR)-derived expression profiles of the 'key tumor genes' previously identified. 4. From amongst the candidate classifiers generated, select the most accurate predictor of non-muscle invasive bladder UC TTR and the most accurate predictor of TTP and confirm that both have statistically significant prognostic abilities. The entire project will be conducted in collaboration with Dr. Richard Cote's laboratory in the Department of Pathology, University of Southern California, Los Angeles, CA. PUBLIC HEALTH RELEVANCE:The need for this test emanates from the difficult decisions that bladder cancer patients and their clinicians must make that affect their survival, health and quality of life. One of the more difficult decisions after surgery is whether or not to undergo unpleasant and potentially toxic chemotherapy or even cystectomy after the tumor is removed at an early stage of development. Making a decision to avoid these procedures could dramatically improve a patient's quality of life. However, for certain individuals, that decision could drastically shorten their life-span. The key benefit of this diagnostic then would be to reduce the number of patients undergoing unnecessary chemotherapy and/or cystectomy and identify those patients with tumors that are highly likely to progress and would need more aggressive treatment and/or follow-up.