Prognostic Radiomic Signatures in Prostate Cancer Patients on Active Surveillance

积极监测的前列腺癌患者的预后放射学特征

• 批准号: 10724101
• 负责人: Jose Marcio Luna Castaneda
• 金额: $ 16.11万
• 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: 2023-09-11 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724101
• 关键词: Affect; Algorithms; Applied Research; Award; Biological Markers; Biopsy; Biopsy Specimen; Cancer Patient; Cancer Prognosis; Career Transition Award; Cellular Structures; Cessation of life; Clinical; Data; Data Science; Derivation procedure; Diagnosis; Diffusion; Disease; Ecosystem; Extracellular Matrix; Extracellular Matrix Proteins; Gleason Grade for Prostate Cancer; Goals; Image; Imaging Techniques; Immune system; Inflammatory; Link; Location; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Methods; Molecular; Monitor; Morbidity - disease rate; Neoplasm Metastasis; Patient Selection; Patients; Pattern; Performance; Polysaccharides; Population Surveillance; Positioning Attribute; Prediction of Response to Therapy; Prognosis; Prognostic Marker; Prostate; Prostate-Specific Antigen; Prostatectomy; Proteins; Recommendation; Research; Research Personnel; Resources; Retrospective cohort; Risk; Screening for Prostate Cancer; Specificity; Specimen; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Staging System; Students; Symptoms; System; Talents; Technology; Testing; Time; Training; Visualization; Work; anticancer research; cancer cell; cancer diagnosis; candidate selection; career; cell type; clinical decision-making; clinical imaging; clinical investigation; curative treatments; diagnostic biomarker; early detection biomarkers; genomic signature; histological slides; improved; innovation; male; mass spectrometric imaging; men; molecular imaging; mortality; novel diagnostics; overtreatment; patient stratification; predictive marker; predictive signature; prognostic; prognostic assays; prospective; prostate cancer progression; radiological imaging; radiomics; randomized trial; recruit; spectrograph; synergism; therapeutic biomarker; translational medicine; tumor; tumor progression

PROJECT SUMMARY/ABSTRACT With prostate cancer (PCa) being among the most common cancers in men worldwide (estimated 1,600,000 cases, 366,000 deaths annually), the need for new biomarkers for early detection, diagnosis, monitoring, and prognosis remains urgent. Among the available management alternatives for PCa, active surveillance (AS) is recommended as an initial treatment for males with very low-, low- and favorable intermediate-risk. AS relies on serial monitoring over time to identify progression, so that the patient receives timely curative treatment, while reducing morbidities related to definite treatment delivered at time of diagnosis. However, identifying ideal candidates for AS is challenging. Despite its limited specificity, the prostate-specific antigen (PSA) is the most used test for early detection of PCa. Other factors based on biopsies such as the Gleason Group (GG), are affected by limited biopsy sampling, while the non-invasive magnetic resonance imaging (MRI) has been connected to false positives and false negatives. Finally, the implementation of molecular prognostic tests, such as Decipher in AS populations has been limited due to the lack of randomized trials using actual AS patients. Previous work suggests that PCa progression can be dependent on the interactions between extracellular matrix (ECM) proteins in the stroma with various cell types including the immune system and cancer cells. On this regard, my team of collaborators has developed clinical imaging techniques such as, diffusion basis spectrum imaging (DBSI), and matrix-assisted laser desorption ionization (MALDI) mass spectrometry, that visualize inflammatory, stromal/ECM, and cancer cell components of the tumor that could be associated with cancer progression. Therefore, I propose to leverage radiomics, a method based on data-characterization algorithms to extract imaging features, to detect patterns in pre-op MRI with the guidance of DBSI and MALDI toward optimally selecting AS candidates. My central hypothesis is that the spatial analysis of structural components in the ECM extracted from the co-registration of molecular and radiological imaging, accurately predicts tumor upgrading and upstaging in PCa. In Aim 1 I will identify a baseline radiomic signature derived from pre-op MRI to accurately predict tumor upgrading (i.e., from GG1 to GG2 or higher) by augmenting well-established biomarkers (i.e., PSA, GG, Decipher), with an exploratory SubAim co-registering DBSI and MRI to improve the prediction. In Aim 2, I will use MALDI co-registered with pre-op MRI to guide the derivation of the radiomic signature to accurately predict tumor upstaging (i.e., from T1/T2 stage to T3 or higher). This research is innovative because, to date, no distinct spatial signatures linked with the ECM and derived from co-registered molecular and radiological imaging have been associated with prediction of tumor progression in PCa. Furthermore, this K22 career transition award will provide me with the training and resources needed to advance my career as an independent researcher in the field of data sciences applied to cancer research, and also to support my goal of recruiting the nation’s most talented students from backgrounds nationally underrepresented in cancer research.

项目总结/摘要 前列腺癌（PCa）是全世界男性最常见的癌症之一（估计有1，600，000例 病例，每年366，000例死亡），需要新的生物标志物进行早期检测，诊断，监测， 预后仍然紧迫。在PCa的可用管理替代方案中，主动监测（AS）是 推荐作为极低、低和有利的中等风险男性的初始治疗。AS依赖于 随着时间的推移进行连续监测，以确定进展，以便患者及时接受治愈性治疗， 减少与诊断时提供的明确治疗相关的发病率。然而，确定理想 作为候选人是具有挑战性的。尽管其有限的特异性，前列腺特异性抗原（PSA）是最 用于PCa的早期检测。其他基于活检的因素，如格里森集团（GG），是 受有限的活检采样的影响，而非侵入性磁共振成像（MRI）已被 与假阳性和假阴性有关最后，实施分子预后测试，如 由于缺乏使用实际AS患者的随机试验，AS人群中的Decipher受到限制。 以前的工作表明，PCa的进展可能依赖于细胞外基质之间的相互作用， (ECM)基质中的蛋白质与各种细胞类型，包括免疫系统和癌细胞。On this 关于这一点，我的合作者团队已经开发出了临床成像技术，如扩散基础光谱 成像（DBSI）和基质辅助激光解吸电离（MALDI）质谱， 可能与癌症相关的肿瘤的炎性、基质/ECM和癌细胞成分 进展因此，我建议利用放射组学，一种基于数据表征算法的方法， 提取成像特征，在DBSI和MALDI的指导下检测术前MRI中的模式， 选择候选人。我的中心假设是，ECM中结构组件的空间分析 从分子和放射成像的共同登记中提取，准确预测肿瘤升级 在PCa抢风头在目标1中，我将识别从术前MRI获得的基线放射组学特征，以准确 预测肿瘤升级（即，从GG 1到GG 2或更高）通过增加已确立的生物标志物（即，PSA， GG，Decipher），探索性SubAim共配准DBSI和MRI以提高预测。在目标2中，我 将使用MALDI与术前MRI配准，以指导放射组学签名的推导， 预测肿瘤升级（即，从T1/T2期到T3期或更高）。这项研究是创新的，因为迄今为止，还没有 与ECM相关并源自共同配准的分子和放射成像的不同空间特征 与预测前列腺癌的肿瘤进展有关。此外，K22职业转型奖 将为我提供所需的培训和资源，以促进我作为一个独立的研究人员， 数据科学领域应用于癌症研究，并支持我招募全国最多的目标。 有才华的学生来自全国范围内在癌症研究中代表性不足的背景。