Prostate Needle Biopsies: Impact of Preanalytical Procurement and Processing Variables on the Detection of Gene Expression Signatures of Prostate Cancer Aggressiveness

项目摘要 前列腺癌风险评估管理着一系列局部区域疾病的决策，从是否 活检以确定是否使用多模式治疗加强治疗。基于RNA转录物的签名具有 快速地被并入到这样的测定中，沿着格里森评分（现在称为等级组或GG）。 建议工作的意义在于，迫切需要优化和规范 测定最高GG和前列腺攻击性转录本表达的分析前条件 （PTA），特别是在使用活检生物标本时。这些预分析变量从组织 采购到保存转录标志物完整性，以便为每个患者分配适当的风险。 在多参数（mp）MRI引导下获得前列腺活检已迅速获得认可， 关于前列腺mpMRI如何影响必须识别的分析前变量的信息很少， 在临床试验和临床实践中进行管理。在地点和数量上有很大的差异， 收集的前列腺活组织检查和从固定到包埋再到处理组织以进行检测 的PTA。随着转录组学测试越来越多地纳入临床试验和实践， 在标准品的形式化中必须考虑常规遇到的分析前变量的测定 操作程序（SOP）。我们将评价从以下组织中获得的mpMRI靶向前列腺活检标本： 入组5项现有临床试验的患者，在这些临床试验中，使用 前列腺护理报告系统（PIRADSv2.1）和更先进、自动化和便携式定量 mpMRI逐像素评分系统称为“栖息地风险评分”（HRS）。虽然PIRADS提供了 前列腺mpMRI图像采集和解释的至关重要的框架，受读者限制 定义活检区域的主观性和可变性。在这个项目中， 采购与组织处理变量的描述配对，用于最具侵略性的分类。 由GG和PTA定义的前列腺癌属性。解密基因组分类器（GC）评分为 在签名中包含的PTA的代表，并在临床实践中常规使用，包括在当前的 NCCN建议，并且是GG/GC组合风险模型（Spratt模型）的一部分。我们的本金证明 关于预分析变量影响的研究检查了最高的GG和GC评分，以及对>400的影响。 家长教师协会我们假设mpMRI HRS定向组织采集将产生更能代表 前列腺癌风险，如通过组合GG/GC评分所确定的，与通过 mpMRI PIRADS怀疑评分（目标1），GG/Decipher GC风险将通过2个核心充分定义， 与4（目标2）相比，预分析处理变量的定义和修订的分析方法 将提高PTA的完整性（目标3）。

PROJECT SUMMARY Prostate cancer risk assessment governs decisions across a spectrum of local-regional disease from whether to biopsy to whether to intensify treatment using multimodality therapy. RNA transcript-based signatures have rapidly been incorporated into such determinations, along with Gleason Score (now termed Grade Group or GG). The significance of the proposed work is that there is a pressing need for optimization and standardization of preanalytic conditions for determination of highest GG and expression of Prostate Transcripts of Aggressiveness (PTAs), especially when working with biopsy biospecimens. These preanalytic variables extend from tissue procurement to preservation of transcript marker integrity such that appropriate risk is assigned for each patient. Obtaining prostate biopsies with multiparametric (mp) MRI-guidance has rapidly gained acceptance but there is little information about how prostate mpMRI contributes to the preanalytic variables that must be recognized and managed in clinical trials and clinical practice. There are considerable variances in the location and number of prostate biopsies collected and in the handling of the tissue from fixation to embedding to processing for testing of PTAs. As transcriptomic tests become more incorporated into clinical trials and practice, the tolerance of an assay for routinely encountered preanalytic variables must be considered in the formalization of standard operating procedures (SOPs). We will evaluate mpMRI targeted prostate biopsy specimens obtained from patients enrolled in five existing clinical trials in which the MR images are interpreted using both the standard-of- care reporting system for prostate (PIRADSv2.1) and a more advanced, automated, and portable quantitative mpMRI pixel by pixel-based scoring system termed the “Habitat Risk Score” (HRS). While PIRADS provides a critically important framework for prostate mpMRI image acquisition and interpretation, it is limited by reader subjectivity and variability in defining biopsy regions. In this project the impact of refined prostate biopsy procurement is paired with delineation of tissue processing variables for the classification of the most aggressive prostate cancer attributes defined by GG and PTAs. The Decipher Genomic Classifier (GC) score is representative of PTAs incorporated into signatures and is routinely used in clinical practice, is included in current NCCN recommendations, and is part of a combined GG/GC risk model (Spratt model). Our proof-of-principal studies on the impact of preanalytic variables examine highest GG and GC score, as well as effects on >400 PTAs. We hypothesize that mpMRI HRS directed tissue procurement will result in tissue more representative of prostate cancer risk, as determined by combined GG/GC score, as compared to tissue procurement directed by mpMRI PIRADS suspicion scores (Aim 1), that GG/Decipher GC risk will be adequately defined by 2 cores, as compared to 4 (Aim 2) and that the definition of preanalytic processing variables and revised analytic methods will result in improved integrity of PTAs (Aim 3).