Dissecting and Predicting Lethal Prostate Cancer using Biologically Informed Artificial Intelligence

使用生物学信息人工智能剖析和预测致命性前列腺癌

• 批准号: 10628274
• 负责人: Eliezer M Van Allen
• 金额: $ 48.53万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10628274
• 关键词: Address; Adjuvant; Adjuvant Study; Architecture; Artificial Intelligence; Binding; Biological; Biological Markers; Cancer and Leukemia Group B; Cessation of life; Characteristics; Clinical; Clinical Data; Clinical Trials; Complex; Computer Vision Systems; DNA Repair; DNA Repair Gene; Dana-Farber Cancer Institute; Data Analyses; Development; Disease; Event; Future; Genetic Transcription; Genomics; Germ-Line Mutation; Histopathology; Image; Immune; Indolent; Learning; Localized Disease; Malignant neoplasm of prostate; Mediating; Medical Oncology; Methodology; Modeling; Molecular; Molecular Profiling; Mutation; Neoplasm Metastasis; Operative Surgical Procedures; Outcome; Outcome Measure; Pathologic; Pathologist; Pathway interactions; Patients; Pattern; Phase; Phenotype; Property; Prostate; Radiation; Radiation therapy; Radical Prostatectomy; Recurrence; Recurrent disease; Relapse; Retrospective cohort; Risk; Risk Factors; Somatic Mutation; Specimen; Techniques; Therapeutic; Tissues; Urologic Oncology; advanced disease; anticancer research; artificial intelligence algorithm; cancer care; cancer genomics; cancer type; candidate validation; clinical biomarkers; clinical predictive model; clinical prognostic; clinical translation; cohort; computer science; deep learning; deep learning model; digital; digital pathology; genome-wide; high risk; high risk men; hormone therapy; imprint; improved; innovation; men; molecular modeling; molecular subtypes; neural network; novel; novel therapeutics; patient population; phenotypic data; point of care; precision oncology; predictive modeling; prognostic; prognostic model; prognostic performance; progression risk; prostate cancer model; prostate cancer risk; standard of care; survival outcome; translational potential; treatment strategy; tumor

PROJECT SUMMARY – PROJECT THREE Treatment strategies for intermediate and high-risk localized prostate cancer (PCa) include surgery or radiation with or without hormonal therapy. Multiple molecular factors, including germline and somatic alterations in DNA repair genes and tissue-based transcriptional biomarkers, have biological and prognostic relevance in these clinical settings yet are rarely used today to guide treatment decisions. Determination of the interacting and co- occurring molecular features that jointly drive indolent or aggressive clinical outcomes in this setting is urgently needed to enable molecularly guided therapeutic strategies and biologically grounded predictive models for clinical use. Furthermore, complex molecular states may converge on histopathological patterns to augment these predictions, but these properties are difficult to quantify, integrate, and generalize across diverse patient populations. The advent of large and diverse patient cohorts with clinically embedded molecular characterization, digital histopathology techniques, and key outcome measures, along with innovations in computation and deep learning to analyze and interpret these data, has created an opportunity to profoundly expand the discovery and translational potential of molecular, pathologic, and phenotypic data for patients with localized PCa. Our overarching hypothesis is that interacting molecular, pathologic, and phenotypic features define prognostic outcomes in intermediate and high-risk localized PCa after surgery, and that biologically guided interpretable deep learning, paired with harmonized cohorts representative of PCa diversity, will transform our understanding of indolent versus potentially lethal localized PCa and deliver on the promise of precision cancer medicine. Toward that end, the specific aims of this proposal are: 1) Dissect the interacting germline and somatic properties that mediate localized PCa using biologically guided neural networks; 2) Determine the convergent spatial histopathologic properties of molecularly and clinically distinct forms of PCa; 3) Develop and validate a clinical grade molecular prognostic model guided by biological networks in real-world and clinical trial settings. For these aims, we will build on our team’s extensive expertise in PCa genomics, computer science, and medical and urologic oncology. Critically, we will embed our approaches in the context of harmonized and representative PCa cohorts. The ability to understand why some intermediate and high-risk localized prostate cancers are phenotypically aggressive, and therefore predict which PCa will progress following curative-intent treatment in this manner, would significantly advance basic PCa research and clinical translation. Broadly, this project will strive to transform precision cancer medicine for prostate cancer and serve as a model for the creation, development, and application of these emerging methodologies across cancer types and contexts.

项目概要-项目三 中高危局限性前列腺癌（PCa）的治疗策略包括手术或 放疗加或不加激素治疗。多种分子因素，包括生殖系和体细胞 DNA修复基因和基于组织的转录生物标志物的改变，具有生物学和预后 这些临床环境中的相关性，但今天很少用于指导治疗决策。测定 共同驱动惰性或侵袭性临床结果的相互作用和共同发生的分子特征 在这种情况下，迫切需要使分子指导的治疗策略和生物学 用于临床的接地预测模型。此外，复杂的分子状态可以收敛于 组织病理学模式来增强这些预测，但这些特性难以量化， 整合并推广到不同的患者群体。大型和多样化患者队列的出现 临床嵌入式分子表征，数字组织病理学技术， 沿着计算和深度学习方面的创新来分析和解释这些数据， 创造了一个机会，深刻扩大分子的发现和翻译潜力， 病理和表型数据的患者与局限性PCa。我们的首要假设是 相互作用的分子、病理和表型特征决定了中间型和中间型的预后结果。 手术后高风险的局部PCa，以及生物学引导的可解释的深度学习， 代表PCa多样性的协调队列，将改变我们对惰性与 潜在致命的局部PCa，并实现精准癌症医学的承诺。为此， 这项建议的具体目标是：1）解剖介导的相互作用的种系和体细胞特性 使用生物学引导的神经网络定位PCa; 2）确定会聚的空间组织病理学 PCa的分子和临床不同形式的性质; 3）开发和验证临床分级 在现实世界和临床试验环境中由生物网络指导的分子预后模型。为这些 目标，我们将建立在我们的团队在PCa基因组学，计算机科学和医疗和 泌尿肿瘤学重要的是，我们将把我们的方法嵌入到协调和有代表性的背景下， PCa队列。了解为什么一些中度和高度风险的局限性前列腺癌 表型侵袭性，因此预测哪些PCa将在治愈性治疗后进展 以这种方式，将显著推进基础PCa研究和临床转化。总的来说，这个项目 将致力于前列腺癌的精准癌症医学转型，并作为创建的典范， 这些新兴的方法学在癌症类型和背景中的发展和应用。