Identifying lethal prostate cancer at diagnosis with advanced proteoglycomic, radiomic, and genomic approaches

项目摘要 传统前列腺腺癌（PCa）是美国男性癌症死亡的第二大原因。 患有器官局限性疾病的患者是通过根治性治疗的候选人。 直肠切除术或放射治疗。然而，20-40%的接受根治性膀胱切除术的患者和30-50%的患者 的接受放射治疗的患者可能在10年内出现生化复发。这些发现 表明在诊断时存在致死性PCa的次优鉴定。因此，识别 诊断时的侵袭性疾病可以对患者进行分层，开发更有效的治疗方案， 延长生存。在临床环境中，非侵入性成像生物标志物常规测量， 多参数磁共振成像（mpMRI）。然而，mpMRI具有多种限制，导致 PCa的敏感性和特异性降低，部分原因是由于炎症或基质细胞的遮蔽， 前列腺该提案推进了临床磁共振成像（MRI）序列、扩散 基础光谱成像（DBSI），能够检测PCa中的结构和细胞变化 微环境（例如，间质、炎症、肿瘤），否则不能用常规方法确定。 mpMRI，一个重大的进步。同时，我们的团队发现了一组细胞外蛋白多糖， PCa致命形式中的生物标志物（即，岩藻糖基化聚糖和修饰的胶原-“FuCol”生物标志物）， 组织学标本的基质辅助激光解吸电离（MALDI）质谱成像。这些 分子标记提供了对致死PCa结构紊乱的深入了解， 影响水扩散，这表明这些结构变化实际上可以用DBSI检测到。我们 假设MALDI检测到的蛋白多糖标志物（以FuCol评分表示）与以下因素相关： 致死性PCa的结构和代谢变化可以用DBSI可视化，以更好地识别侵袭性， 在诊断时可能致命的前列腺癌。在第一个目标中，我们将继续验证我们的FuCol评分， 大型机构生物储存库中疾病复发和转移的预测因素。在这个目标中，我们将 研究种族和饮食对FuCol评分的影响及其预测不良结局的能力。我们还将 建立测量FuCol评分的能力，作为“非侵入性液体活检”的一部分，以预测结果。在 目的2，我们将招募一个前瞻性队列的直肠癌切除术患者，以开发“扩散分子成像 (DMI)“;一种AI驱动的工具，其在椎间盘切除术之前使用体内DBSI作为其输入来生成体内FuCol评分， 因此是致命疾病的非侵入性成像读出。在目标3中，我们将开发一个增强的风险预测 该模型结合了新型DBSI成像、临床Decipher基因组学平台和常规临床 指标（分级，分期，PSA），以更好地预测肿瘤切除术时的致死性疾病。总之，这些实验 将导致临床准备工作流程的快速加速，以检测与不良反应相关的分子生物标志物。 结果。这将与我们小组正在开发的治疗这些患者的平行策略相吻合 致命的前列腺癌变异

PROJECT SUMMARY Conventional prostate adenocarcinoma (PCa) is the second leading cause of cancer death in American men. Patients with organ-confined disease are candidates for potentially curative treatment by either radical prostatectomy or radiation therapy. However, 20-40% of patients undergoing radical prostatectomy and 30-50% of patients undergoing radiation therapy can experience biochemical recurrence within 10 years. These findings indicate that there is suboptimal identification of lethal PCa at the time of diagnosis. Therefore, identification of aggressive disease at the time of diagnosis could stratify patients, develop more effective therapy options, and extend survival. In the clinical setting, noninvasive imaging biomarkers are routinely measured with multiparametric magnetic resonance imaging (mpMRI). However, mpMRI has multiple limitations that result in reduced sensitivity and specificity for PCa, in part from obscuration from inflammatory or stromal cells in the prostate. This proposal advances the use of a clinical magnetic resonance imaging (MRI) sequence, diffusion basis spectral imaging (DBSI), that has the ability to detect structural and cellular changes in the PCa microenvironment (e.g., stroma, inflammation, tumor), that cannot otherwise be determined with conventional mpMRI, a significant advancement. In parallel, our team has discovered a panel of extracellular proteoglycomic biomarkers in lethal forms of PCa (i.e., fucosylated glycans and modified collagens—“FuCol” biomarkers) with Matrix Assisted Laser Desorption Ionization (MALDI) mass spectrometry imaging of histologic specimens. These molecular markers provide insight into the structural derangements of lethal PCa and because structural changes affect water diffusion, it suggests that these structural changes may actually be detectable with DBSI. We hypothesize that MALDI-detected proteoglycomic markers, expressed as the FuCol score, are associated with structural and metabolic changes in lethal PCa that can be visualized with DBSI to better identify aggressive, potentially lethal PCa at the time of diagnosis. In the first Aim, we will continue to validate our FuCol score as a predictor of disease recurrence and metastasis in a large institutional biorepository. In this Aim, we will investigate the effects of race and diet on the FuCol score and its ability to predict poor outcomes. We will also establish the ability to measure a FuCol score as part of a “noninvasive liquid biopsy” to predict outcomes. In Aim 2, we will enroll a prospective cohort of prostatectomy patients to develop “Diffusion Molecular Imaging (DMI)”; an AI-driven tool that generates in vivo FuCol scores using in vivo DBSI as its input prior to prostatectomy, hence a non-invasive imaging readout of lethal disease. In Aim 3, we will develop an augmented risk prediction model that incorporates novel DBSI imaging, the clinical Decipher genomics platform, and conventional clinical metrics (grade, stage, PSA) to better predict lethal disease at prostatectomy. In summary, these experiments will result in rapid acceleration of a clinically-ready workflow to detect molecular biomarkers associated with poor outcomes. This will dovetail with parallel strategies that our group is developing to treat these cohorts of patients with lethal prostate cancer variants.