Genetic Programming to Predict Clinical Outcome from Transcript Quantification

通过转录定量预测临床结果的基因编程

• 批准号: 7539010
• 负责人: WILLIAM WORZEL
• 金额: $ 16.23万
• 依托单位: GENETICS SQUARED, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-15 至 2010-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7539010
• 关键词: Affect; Algorithms; Blinded; California; Cancer Patient; Clinical; Collaborations; Companions; Cystectomy; Data; Development; Diagnostic; Disease regression; Evaluation; Excision; Freezing; Gene Expression Profiling; Genes; Genetic; Genetic Programming; Genome; Health; Human; Individual; Invasive; Laboratories; Longevity; Los Angeles; Malignant neoplasm of urinary bladder; Methods; Molecular Profiling; Muscle; Newly Diagnosed; Numbers; Operative Surgical Procedures; Outcome; Pathologic; Pathology; Patients; Performance; Phase; Phase I Clinical Trials; Population; Procedures; Prognostic Factor; Public Health; Quality of life; RNA; Recurrence; Relative (related person); Research; Research Design; Reverse Transcriptase Polymerase Chain Reaction; Risk; Sampling; Staging; Statistically Significant; Testing; Time; Tissues; Training; Transcript; Transitional Cell Carcinoma; Tumor Tissue; Universities; Work; base; bladder transitional cell carcinoma; chemotherapy; follow-up; hazard; improved; prognostic; simulation; tumor

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.

描述（由申请人提供）：本I期研究的总体目标是确定我们提出的方法的技术优点和可行性，以开发和商业化新诊断的膀胱非肌肉侵袭性Ta T1尿路上皮癌（UC）的两种伴随预后，准确预测首次经尿道肿瘤切除术（TURBT）后5年内肿瘤复发和进展的相对机会。在II期结束时，I期创建的预后分类器将使用一组独立的盲法Ta T1膀胱UC组织进行外部验证。利用Genetics Squared专有的遗传编程（GP）分析方法（Evolver(tm)）来定义和量化程序选择肿瘤基因的相互关系，我们的目标是创建比现有最佳临床/病理预后因素更准确和普遍的预后标记，在整个Ta T1膀胱UC人群中。具体来说，我们的目标是：1。演示从可用的档案冷冻非肌肉侵入性Ta T1膀胱UC组织（n=178）中成功提取足够数量的完整RNA，用于进一步评估。2. 建立使用Evolver（tm）平台分析基线人类全基因组表达数据并整合相关患者特异性临床和病理数据的可行性，以无偏差地识别“关键肿瘤基因”，当这些基因一起使用时，可以最好地预测非肌肉侵袭性膀胱UC在turt后5年内的首次复发时间（TTR）和进展时间（TTP）。3. 通过Evolver（tm）对先前确定的“关键肿瘤基因”的定量RT-PCR （qRT-PCR）衍生表达谱进行分析，生成最能预测肿瘤复发和进展的候选预后功能。4. 从产生的候选分类器中，选择最准确的非肌肉侵入性膀胱UC TTR预测器和最准确的TTP预测器，并确认两者具有统计学上显著的预后能力。整个项目将与南加州大学洛杉矶分校病理学系Richard Cote博士实验室合作进行。公共卫生相关性：膀胱癌患者及其临床医生必须做出影响其生存、健康和生活质量的艰难决定，因此需要进行这项测试。手术后更困难的决定之一是是否要进行不愉快的、有潜在毒性的化疗，甚至在肿瘤在早期发展阶段被切除后进行膀胱切除术。决定避免这些手术可以极大地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了接受不必要的化疗和/或膀胱切除术的患者数量，并确定了那些极有可能进展的肿瘤患者，这些患者需要更积极的治疗和/或随访。