Comprehensive Tumor Microenvironment Based Prediction Models in Prostate Cancer

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现有治疗形式导致的发病率，开发前列腺癌(PCA)的新预后工具至关重要。此外，我们预测最有可能死于PCA的患者的能力存在问题的局限性。如果确诊，这些患者将受益于额外的治疗以及标准的护理治疗。无论是在活组织检查中还是在手术后，识别这些患者的能力都将是该领域的重要进步。贝勒肿瘤微环境小组一直站在建立微环境衍生生物标记物预测价值的前沿。我们以前的研究表明，肿瘤微环境反应性间质促进了血管生成、肿瘤发病率和肿瘤生长速度。此外，我们已经报道，反应性间质分级(RSG)是PSA复发和PCa特异性死亡率的重要预测因子。可靠性和重复性是生物标记物开发中的关键问题。要克服这一点，必须将生物标记物转化为真正的定量测试，重点放在分析条件上。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，可在人群中重复进行，以补充和改进目前使用的预测工具和模型。这项测试将对前列腺切除术和活检标本都有用，可以预测临床结果，并有助于临床决策。我们的团队处于独特的地位，可以推进这项工作。贝勒医学院拥有独特的组织和数据资源。这将是一个重大进步。我们假设我们可以通过增加新的基于肿瘤微环境的生物标记物来改进目前的护理标准预测模型，这些生物标记物是定量的和可重复性的，以支持临床决策，即PCa患者是否将从接受更积极的额外治疗中受益。我们提出了三个具体目标来解决这一假设：具体目标1：开发预测工具，将选择需要手术治疗的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，用于选择可能从手术或放射治疗的额外治疗中潜在受益的患者。目标3：确定人体PCA微环境反应的关键调节因素。 公共卫生相关性：迫切需要开发额外的预后工具，以确定哪些前列腺癌患者将从接受更积极的治疗中受益。肿瘤微环境中标志物的开发以及先进成像和分析技术的使用将使这一过程成为可能。拟议的预测模型的开发将有利于公众健康。

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.