Comprehensive Tumor Microenvironment Based Prediction Models in Prostate Cancer

基于肿瘤微环境的前列腺癌综合预测模型

• 批准号: 8109106
• 负责人: Gustavo Enrique Ayala
• 金额: $ 32.47万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2011-12-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8109106
• 关键词: Address; Adjuvant; Adjuvant Therapy; Algorithms; Biochemical; Biological Markers; Biopsy; Biopsy Specimen; Cancer Patient; Caring; Cessation of life; Clinical; Combined Modality Therapy; Computer software; Data; Development; Diagnosis; Disease; Epithelium; Future; Gene Expression Profiling; Genes; Goals; Healthcare; Heterogeneity; Human; Image; Incidence; Lasers; Localized Disease; Malignant Neoplasms; Malignant neoplasm of prostate; Medicine; Modeling; Morbidity - disease rate; Neoadjuvant Therapy; Operative Surgical Procedures; Organ; Outcome; Pathologic; Pathway interactions; Patient Selection; Patients; Population; Positioning Attribute; Predictive Value; Predictive Value of Tests; Process; Prognostic Marker; Prostate; Prostatectomy; Proteins; Protocols documentation; Public Health; Radiation; Radiation therapy; Radical Prostatectomy; Recurrence; Reporting; Reproducibility; Resources; Serum; Staging; Technology; Testing; Tissue Banking; Tissue Banks; Tissue Microarray; Tissues; Tumor Angiogenesis; Universities; Validation; Work; Xenograft procedure; aggressive therapy; base; clinical decision-making; clinically relevant; cohort; college; enhancing factor; follow-up; improved; in vivo; in vivo Model; mortality; new therapeutic target; novel; outcome forecast; predictive modeling; prognostic; prospective; response; segmentation Image analysis; standard care; standard of care; tool; tumor; tumor growth; tumor progression

DESCRIPTION (provided by applicant): Developing new prognostic tools for prostate cancer (PCa) is vital owing to the disease's high incidence and mortality as well as morbidity resulting from current forms of therapy. Moreover, there are problematic limitations in our ability to predict patients who are most likely to die from PCa. If identified, these patients would benefit from additional therapies along with standard of care therapies. The ability to identify these patients in both in the biopsy setting and post-surgically would represent an important advancement in the field. The Baylor tumor microenvironment group has been at the forefront of establishing the predictive value of microenvironment-derived biomarkers. Our previous studies have shown that the tumor microenvironment reactive stroma promotes angiogenesis, tumor incidence and rate of tumor growth. Furthermore, we have reported that reactive stroma grading (RSG) is a significant predictor of PSA recurrence and PCa-specific mortality. Reliability and reproducibility are critical issues in biomarker development. To overcome this, it is essential to convert a biomarker to a truly quantitative test by focusing on analytical conditions. The overall goal of the proposed project is to produce a rigorous quantitative tumor microenvironment-based test, reproducible across populations that will supplement and improve currently used predictive tools and models. This test will be useful for both prostatectomy and biopsy specimens, to predict clinical outcomes and contribute to clinical decision-making. Our group is uniquely positioned to carry this work forward. Baylor College of Medicine has unique tissue and data resources. This will represent a major advancement. We hypothesize that we can improve on current standard-of-care predictive models, through the addition of novel tumor microenvironment-based biomarkers that are quantitative and reproducible, to support clinical decisions on whether a PCa patient would benefit by receiving more aggressive additional therapies. We propose three Specific Aims to address this hypothesis: Specific Aim 1: To develop predictive tools that will select patients who need adjuvant treatments above surgical therapy standard of care Specific Aim 2: To develop pre-treatment predictive tools, using biopsy tissues, for the selection of patients who could potentially benefit from additional treatments along with surgery or radiation Specific Aim 3: To identify key regulators of the microenvironment response in human PCa. PUBLIC HEALTH RELEVANCE: There is an imperative need to develop additional prognostic tools to identify prostate cancer patients who would benefit from receiving more aggressive therapies. Development of makers within the tumor microenvironment and use of advanced imaging and analytical technology will enable this process. Development of the proposed predictive models will benefit public health.

描述（由申请人提供）：由于前列腺癌（PCa）的高发病率和死亡率以及由当前治疗形式引起的发病率，开发新的预后工具至关重要。此外，我们预测最有可能死于前列腺癌的患者的能力也存在局限性。如果确定，这些患者将受益于额外的治疗和标准护理治疗。在活检设置和术后识别这些患者的能力将代表该领域的重要进步。贝勒肿瘤微环境小组一直处于建立微环境衍生生物标志物预测价值的前沿。我们前期的研究表明，肿瘤微环境反应性基质促进血管生成、肿瘤发病率和肿瘤生长速度。此外，我们报道了反应性基质分级（RSG）是PSA复发和PSA特异性死亡率的重要预测因子。可靠性和可重复性是生物标志物开发中的关键问题。为了克服这一点，必须通过关注分析条件，将生物标志物转化为真正的定量测试。拟议项目的总体目标是产生一种严格的基于肿瘤微环境的定量测试，可在人群中重现，这将补充和改进目前使用的预测工具和模型。该测试对前列腺切除术和活检标本都很有用，可以预测临床结果并有助于临床决策。我们集团在推进这项工作方面具有独特的优势。贝勒医学院拥有独特的组织和数据资源。这将是一个重大的进步。我们假设我们可以通过添加新的基于肿瘤微环境的生物标志物来改进当前的标准治疗预测模型，这些生物标志物是定量的和可重复的，以支持临床决策，即PCa患者是否会通过接受更积极的额外治疗而受益。我们提出了三个特定目标来解决这一假设：特定目标1：开发预测工具，以选择需要手术治疗标准以上辅助治疗的患者；特定目标2：开发治疗前预测工具，使用活检组织，以选择可能从手术或放疗的额外治疗中获益的患者；特定目标3：确定人类前列腺癌微环境反应的关键调节因子。