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

利用先进的蛋白糖组学、放射组学和基因组学方法在诊断时识别致命的前列腺癌

• 批准号: 10718530
• 负责人: Joseph Edward Ippolito
• 金额: $ 63.79万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-07 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10718530
• 关键词: 3D Print; Acceleration; Affect; American; Artificial Intelligence; Benign; Biochemical; Biological Assay; Biological Markers; Biopsy; Blood Banks; Cancer Etiology; Cessation of life; Clinical; Collagen; Data; Diagnosis; Diet; Diffusion; Disease; Enrollment; Extracellular Matrix; Fucose; Genomic approach; Genomics; Goals; Histologic; Histology; Image; Imaging Device; Imaging technology; Inflammation; Inflammatory; Information Systems; Institution; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Metabolic; Metabolism; Methods; Modeling; Molds; Molecular; Morbidity - disease rate; Neoplasm Metastasis; Nomograms; Organ; Outcome; Pathology; Patient-Focused Outcomes; Patients; Polysaccharides; Prognosis; Prospective cohort; Prostate; Prostate Adenocarcinoma; Prostatectomy; Prostatic Neoplasms; Provider; Race; Radiation therapy; Radical Prostatectomy; Recurrence; Recurrent disease; Reporting; Sensitivity and Specificity; Serum; Severities; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Stromal Cells; Stromal Hyperplasia; Testing; Time; Tissues; Training; Variant; Visualization; artificial intelligence algorithm; biobank; clinic ready; clinical risk; clinical translation; cohort; curative treatments; effective therapy; experience; experimental study; extracellular; genomic platform; genomic signature; genomic tools; histological image; histological specimens; imaging biomarker; imaging modality; in vivo; in vivo imaging; insight; liquid biopsy; mass spectrometric imaging; men; molecular imaging; molecular marker; mortality; multidisciplinary; non-invasive imaging; novel; outcome prediction; patient stratification; patient subsets; prognostic; prognostic model; prospective; racial disparity; radiomics; risk prediction model; risk stratification; spectrograph; structural imaging; survival outcome; tool; tumor; tumor microenvironment; water diffusion

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.

项目摘要 常规前列腺腺癌（PCA）是美国男性癌症死亡的第二大原因。 有器官疾病的患者是通过两种自由基治疗的候选者 前列腺切除术或放射治疗。但是，接受根治性前列腺切除术的患者中有20-40％和30-50％ 接受放射疗法的患者可以在10年内经历生化复发。这些发现 表明在诊断时对致命PCA有次优鉴定。因此，识别 诊断时侵略性疾病可以使患者分层，发展更有效的治疗选择，并 扩展生存。在临床环境中，常规测量非侵入性成像生物标志物 多参数磁共振成像（MPMRI）。但是，mpMRI有多个限制，导致 PCA的敏感性和特异性降低，部分原因是从炎症或基质细胞中观察到 前列腺。该建议推进使用临床磁共振成像（MRI）序列的使用，扩散 基础光谱成像（DBSI），具有检测PCA结构和细胞变化的能力 微环境（例如，基质，炎症，肿瘤），否则不能用常规确定 mpmri，这是一个重大进步。同时，我们的团队发现了一个细胞外蛋白聚物的面板 具有致命形式的PCA形式的生物标志物（即岩藻糖基化的聚糖和改性胶原蛋白 - “ Fucol”生物标志物） 基质辅助激光解吸电离（MALDI）的组织学标本质谱成像。这些 分子标记物可深入了解致命PCA的结构演化，因为结构变化 影响水扩散，这表明这些结构变化实际上可以通过DBSI检测到。我们 假设MALDI检测的蛋白聚糖标记（表示为FUCOL评分）与 致命PCA的结构和代谢变化，可以用DBSI可视化，以更好地识别侵略性， 诊断时可能会致命的PCA。在第一个目标中，我们将继续验证我们的FUCOL分数 在大型机构生物库中的疾病复发和转移的预测指标。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预后的能力。我们也会 确定作为“无创液体活检”预测结果的一部分的FUCOL评分的能力。在 AIM 2，我们将注册前列腺切除术患者的前瞻性队列，以发展“扩散分子成像 （DMI）”；一种AI驱动的工具，该工具在前列腺切除术之前使用体内DBSI作为其输入，生成体内FUCOL评分， 因此，致命疾病的非侵入性成像读数。在AIM 3中，我们将制定增强的风险预测 结合了新型DBSI成像，临床解密基因组平台和常规临床的模型 指标（等级，阶段，PSA）可以更好地预测前列腺切除术的致命疾病。总之，这些实验 将导致临床上准备工作流程的快速加速，以检测与差有关的分子生物标志物 结果。这将与我们小组正在制定治疗这些患者的平行策略相吻合 具有致命的前列腺癌变体。