Pathomics biomarkers for stratification of clear cell kidney cancers

用于透明细胞肾癌分层的病理组学生物标志物

• 批准号: 10578582
• 负责人: BEATRICE S KNUDSEN
• 金额: $ 21.57万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-06 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10578582
• 关键词: Adipose tissue; Adjuvant; Adjuvant Therapy; Algorithms; Architecture; Biological Markers; Blood Vessels; Cancer Prognosis; Cellular Morphology; Classification; Clear Cell; Clear cell renal cell carcinoma; Clinical; Clinical Practice Guideline; Clinical Trials; Correlation Studies; Data; Detection; Diagnosis; Disease Progression; Disease-Free Survival; Fatty acid glycerol esters; Funding; Future; Grant; Growth; Guidelines; High-Risk Cancer; Histologic; Histology; Immunotherapy; Invaded; Machine Learning; Malignant Neoplasms; Manuals; Maps; Metastatic Neoplasm to Lymph Nodes; Modeling; Morphology; National Comprehensive Cancer Network; Necrosis; Nephrectomy; Nuclear Grade; Operative Surgical Procedures; Pathologic; Pathologist; Pathology; Patient Selection; Patient-Focused Outcomes; Patients; Pattern; Performance; Pharmacotherapy; Phase III Clinical Trials; Practice Guidelines; Probability; Prognosis; Prognostic Marker; Recommendation; Recurrence; Recurrent Malignant Neoplasm; Recurrent disease; Recurrent tumor; Regional Cancer; Renal Cell Carcinoma; Renal carcinoma; Risk; Risk Assessment; Selection Criteria; Slide; Source; Staging; Stratification; Systemic Therapy; Testing; The Cancer Genome Atlas; Thrombus; Tissue imaging; Tissues; Training; Tumor Cell Invasion; Tumor Expansion; Tumor Tissue; Tumor stage; Universities; Utah; Venous; Work; anti-PD-L1 antibodies; biomarker development; cancer biomarkers; cancer diagnosis; cancer recurrence; cancer surgery; cancer type; cohort; computer generated; convolutional neural network; digital; draining lymph node; experience; high risk; improved; innovation; kidney surgery; large datasets; novel; pathology imaging; performance tests; predictive marker; predictive modeling; prognostic; prognostic assays; prognostication; random forest; risk stratification; side effect; tissue biomarkers; tumor; tumor growth; tumor progression

Project Summary Pathologic attributes of cancers, such as histology and tumor growth patterns are not quantitatively assessed to date. In every cancer type these parameters effect patient outcomes and are included in risk models of tumor recurrence and overall survival. Algorithms using machine learning and convolutional neural networks allow us to quantify pathology and develop Pathomics biomarkers. Here, we propose to obtain pathomics biomarkers of cancer recurrence/progression that enumerate histology growth patterns (HGPs) in clear cell renal cell cancer (ccRCC). ccRCC is the most common subtype of kidney cancer. In its localized stage, it is treated by nephrectomy. However, about 30% of patients experience disease progression after surgery and may benefit from adjuvant treatment. Deciding whether or not treatment is warranted requires identifying patients who are at a high risk of recurrence. Here, we hypothesize that quantitative biomarkers will improve the risk assessment of patients with ccRCC and propose to develop computer-generated features of tumor growth patterns. We previously defined 13 HGPs and demonstrated their ability to predict overall survival in patients treated for ccRCC. Distinctive features for each HGP will be generated and validated using frameworks of convolutional neural networks that produce probabilities of expression across cancer regions. Further, the distribution of probabilities will be used to obtain biomarkers of expression of each HGP. Using parametric and non-parametric models, HGP-biomarkers will be examined for their association with tumor stage and local mechanisms of ccRCC progression, such as formation of tumor thrombi, regional lymph node metastases or invasion into perinephric adipose tissues. The performance of each algorithm in the project will be evaluated. Altogether, biomarkers developed in this project will provide a starting point to select patients with ccRCC for adjuvant treatment after surgery.

项目摘要 癌症的病理学属性，如组织学和肿瘤生长模式没有定量评估 迄今在每一种癌症类型中，这些参数都会影响患者的预后，并被纳入风险模型， 肿瘤复发和总生存率。使用机器学习和卷积神经网络的算法 使我们能够量化病理学并开发病理学生物标志物。在这里，我们建议获得病理组学 肿瘤复发/进展的生物标志物，其列举透明细胞中的组织学生长模式（HGP） 肾细胞癌（ccRCC）。ccRCC是肾癌最常见的亚型。在其本地化阶段， 通过肾切除术治疗。然而，约30%的患者在手术后经历疾病进展， 可以从辅助治疗中获益。决定是否需要治疗需要确定 复发风险高的患者。在这里，我们假设定量生物标志物将改善 ccRCC患者风险评估，并建议开发计算机生成的肿瘤特征 增长模式。我们以前定义了13个HGP，并证明了它们预测总生存率的能力。 接受ccRCC治疗的患者每个HGP的独特特征将使用 卷积神经网络的框架，产生跨癌症区域的表达概率。 此外，概率分布将用于获得每种HGP表达的生物标志物。使用 在参数和非参数模型中，将检查HGP-生物标志物与肿瘤的相关性 ccRCC进展的分期和局部机制，例如肿瘤血栓的形成、局部淋巴结转移、局部淋巴结转移和局部淋巴结转移， 转移或侵入肾周脂肪组织。项目中每个算法的性能将 被评价。总而言之，本项目开发的生物标志物将为选择患者提供一个起点 ccRCC用于术后辅助治疗。