Characterizing Prostate Cancer By ex vivo MRS Signatures

通过离体 MRS 特征表征前列腺癌

• 批准号: 8530894
• 负责人: Leo L Cheng
• 金额: $ 57.6万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-27 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8530894
• 关键词: Address; Adjuvant Therapy; African American; Age; Androgens; Benign; Biochemical; Biological; Biopsy; Cancer Detection; Cancer Diagnostics; Cancer Patient; Cancerous; Clinic; Clinical; Clinical Management; Diagnosis; Diagnostic Errors; Disease; Early Diagnosis; Enzymes; Evaluation; Exploratory/Developmental Grant for Diagnostic Cancer Imaging; Fresh Tissue; Funding; Gland; Gleason Grade for Prostate Cancer; Growth; Histologic; Histology; Human; Incidence; Individual; Indolent; Institution; Location; Magic; Magnetic Resonance Spectroscopy; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Measures; Messenger RNA; Metabolic; Metabolic Marker; Molecular Profiling; Operative Surgical Procedures; Outcome; PSA Velocity; Pathological Staging; Pathology; Patients; Process; Prospective Studies; Prostate; Prostate-Specific Antigen; Prostatectomy; Prostatic Neoplasms; Protocols documentation; Protons; Quality of life; Recruitment Activity; Recurrence; Resolution; Sampling; Sampling Errors; Sensitivity and Specificity; Serum; Site; Spatial Distribution; Staging; Structure of base of prostate; Survival Rate; Techniques; Testing; Therapeutic; Time; Tissue Sample; Tissues; Translations; Work; base; cancer recurrence; cancer risk; cancer statistics; cancer therapy; clinically significant; deprivation; follow-up; genetic profiling; human study; improved; laser capture microdissection; metabolomics; neoplastic; novel diagnostics; pre-clinical; prognostic; public health relevance; response; sample collection; screening; success; treatment strategy; tumor; tumor growth

DESCRIPTION (provided by applicant): Serum prostate specific antigen testing improved early detection of prostate tumors, increased diagnosed prostate cancer (PCa) incidence, and shifted newly detected PCa treatment to earlier stages. These successes also generated controversies for effective management of earlier-stage PCa, chiefly: (1) the all-too-common false-negative biopsy finding through tissue sampling errors in asymptomatic patients who harbor small, heterogeneously distributed PCa; and (2) the uncertain malignant potential of most newly detected tumors, with PCa pathologies similar at biopsy but which PCa statistics indicate will trace drastically different disease paths. Greater sensitivity/specificity for PCa diagnostic and prognostic approaches would address both these urgent needs. Our original R01 studies used high resolution magic-angle-spinning proton magnetic resonance spectroscopy (HRMAS 1HMRS), which we developed to permit intact tissue analysis and correlation with pathology, in order to produce proof-of-concept PCa metabolic markers. We then developed PCa metabolomics and demonstrated that PCa metabolomic profiles improve accuracy in PCa detection, diagnosis, and characterization. Profile analyses of histologically-defined benign prostate tissue from PCa patients allowed us to identify PCa pathological stage and predict PCa recurrence by showing the existence of delocalized PCa metabolomic field-effects, or metabolomic fields. These PCa metabolomic fields: 1) yield measures of PCa signatures in histo-benign tissue and thus are likely to reduce histological sampling errors by indicating PCa presence for patients with false-negatives biopsies, and 2) have the capacity to predict PCa malignant potential for patients with positive biopsies. Thus PCa metabolomic fields are likely to contribute most significantly to the PCa clinic by distinguishing aggressive from indolent disease and informing treatment strategies through markers that support active surveillance for indolent tumors or suggest that the patient harbors an aggressive PCa and needs timely institution of adjuvant therapies. These significant outcomes from our original R01 studies comprise the basis for our renewal application, in which we propose: (1) to systematically investigate spatial distributions of PCa metabolites and metabolomic profiles localized at and delocalized beyond their pathological origins in PCa glands to precisely define PCa metabolomic field markers; (2) to use banked PCa tissue samples with known clinical outcomes to test PCa metabolomic profile predictions of PCa growth rate and biochemical recurrence and therapy response, as well as to analyze different pathological components associated with genetic profiles from these same samples via laser capture microdissection and real-time quantitative PCR; and (3) to establish, through longitudinal patient follow-up, the prognostic ability of PCa metabolites, metabolomic profiles, and metabolomic fields as metabolomic criteria that answer the ultimate challenges of the current PCa clinic by predicting PCa risk for biopsy-negative patients and the suitability of entering active surveillance for biopsy-positive patients.

描述（由申请人提供）：血清前列腺特异性抗原检测提高了前列腺肿瘤的早期发现，增加了诊断的前列腺癌（PCa）发病率，并将新发现的前列腺癌治疗转移到早期阶段。这些成功也对早期前列腺癌的有效管理产生了争议，主要是：(1)在患有小的、分布不均的前列腺癌的无症状患者中，通过组织采样错误发现的假阴性活检太常见了；(2)大多数新发现的肿瘤的恶性潜力不确定，活检时PCa病理相似，但PCa统计表明，它们将追踪截然不同的疾病路径。提高前列腺癌诊断和预后方法的敏感性/特异性将解决这两个紧迫的需求。我们最初的R01研究使用了高分辨率魔角旋转质子磁共振波谱（HRMAS 1HMRS），我们开发了它来允许完整的组织分析和与病理的相关性，以产生概念验证的PCa代谢标志物。然后，我们开发了PCa代谢组学，并证明了PCa代谢组学谱提高了PCa检测、诊断和表征的准确性。通过对前列腺癌患者的组织学定义的良性前列腺组织的分析，我们可以通过显示局部前列腺癌代谢组场效应或代谢组场的存在来确定前列腺癌的病理分期和预测前列腺癌的复发。这些前列腺癌代谢组学场：1)产生组织良性组织中前列腺癌特征的测量，从而有可能通过提示假阴性活检患者存在前列腺癌来减少组织学采样误差，2)有能力预测活检阳性患者的前列腺癌恶性潜力。因此，前列腺癌代谢组学领域可能对前列腺癌临床做出最重要的贡献，通过区分侵袭性和惰性疾病，并通过支持主动监测惰性肿瘤的标志物告知治疗策略，或提示患者患有侵袭性前列腺癌，需要及时进行辅助治疗。这些来自我们原始R01研究的重要结果构成了我们更新应用的基础，我们提出：(1)系统地研究PCa代谢物的空间分布以及在PCa腺体中定位和非定位于其病理起源的代谢组学特征，以精确定义PCa代谢组学领域标记；(2)利用已知临床结局的存库PCa组织样本，测试PCa代谢组学预测PCa生长速度、生化复发和治疗反应，并通过激光捕获显微解剖和实时定量PCR分析这些样本中与遗传谱相关的不同病理成分；(3)通过对患者的纵向随访，建立PCa代谢物、代谢组学谱和代谢组学场作为代谢组学标准的预后能力，通过预测活检阴性患者的PCa风险以及对活检阳性患者进行主动监测的适用性，来回答当前PCa临床面临的最终挑战。