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当前治疗形式导致的发病率，开发新的前列腺癌预后工具至关重要。此外，我们预测最有可能死于PCa的患者的能力存在问题限制。如果确定，这些患者将从附加治疗沿着标准治疗中获益。在活检环境和手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最前沿。我们以前的研究表明，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，我们还报道了反应性间质分级（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换为真正的定量检测。拟议项目的总体目标是产生一个严格的基于肿瘤微环境的定量测试，在人群中可重复，这将补充和改进目前使用的预测工具和模型。该测试将对乳腺切除术和活检标本都有用，以预测临床结果并有助于临床决策。我们的团队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。这将是一个重大进步。 我们假设，我们可以通过添加新的基于肿瘤微环境的定量和可重复的生物标志物来改善当前的标准治疗预测模型，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。我们提出了三个具体目标来解决这一假设：具体目标1：开发预测工具，选择需要手术治疗标准以上辅助治疗的患者具体目标2：开发治疗前预测工具，使用活检组织，选择可能从沿着手术或放疗的额外治疗中受益的患者具体目标3：确定人前列腺癌微环境反应的关键调节因子。

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.