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.

项目总结 传统前列腺癌(PCA)是美国男性癌症死亡的第二大原因。 器官受限疾病的患者可能是两种根治方法中的一种。 前列腺摘除或放射治疗。然而，接受根治性前列腺切除术的患者中有20%-40%，接受根治性前列腺切除术的患者中有30%-50% 接受放射治疗的患者可能在10年内经历生化复发。这些发现 表明在诊断时存在致死性PCA的次优识别。因此，身份识别 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，以及 延长生存时间。在临床环境中，非侵入性成像生物标志物的常规测量方法是 多参数磁共振成像(MpMRI)。然而，mpMRI有多个限制，导致 Pca的敏感性和特异性降低，部分原因是炎性细胞或间质细胞在 前列腺。这项提议推进了临床磁共振成像(MRI)序列扩散的使用 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，间质、炎症、肿瘤)，以其他方式无法用常规方法确定 MpMRI，一项重大的进步。与此同时，我们的团队发现了一组细胞外蛋白糖链 致死形式的前列腺癌的生物标记物(即岩藻糖化多聚糖和修饰的胶原蛋白-“FuCol”生物标记物) 基质辅助激光解吸电离(MALDI)组织标本的质谱学成像。这些 分子标记提供了对致死性前列腺癌结构紊乱的洞察，因为结构变化 影响水的扩散，这表明这些结构的变化实际上可能是可以检测到的DBSI。我们 假设MALDI检测到的蛋白糖化标志物，表达为FuCol分数，与 致死性前列腺癌的结构和代谢变化可以用DBSI可视化，以更好地识别攻击性、 确诊时可能致命的自发性前列腺癌。在第一个目标中，我们将继续验证我们的FuCol分数作为 大型机构生物信息库中疾病复发和转移的预测因子。为了实现这一目标，我们将 调查种族和饮食对FuCol评分及其预测不良结果的能力的影响。我们还将 建立测量FuCol评分的能力，作为“无创液体活组织检查”的一部分，以预测结果。在……里面 目的2，我们将招募一组前列腺癌切除患者的预期队列来开发“弥散分子成像” (DMI)；一种人工智能驱动的工具，它在前列腺切除术前使用体内DBSI作为其输入来生成体内FuCol分数， 因此，这是一种致命疾病的非侵入性成像读数。在目标3中，我们将开发一个增强的风险预测 融合了新型DBSI成像、临床破译基因组学平台和传统临床的模型 指标(分级、分期、PSA)更好地预测前列腺切除术中的致命性疾病。总而言之，这些实验 将导致快速加速临床就绪的工作流程，以检测与穷人相关的分子生物标记物 结果。这将与我们小组正在开发的治疗这些患者队列的平行策略相吻合 致命的前列腺癌变种。