Dissecting and Predicting Lethal Prostate Cancer using Biologically Informed Artificial Intelligence

项目摘要--项目三 中高危局限性前列腺癌(PCA)的治疗策略包括手术或 放射治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记物的变化具有生物学和预后意义 在这些临床环境中的相关性，但今天很少用于指导治疗决定。的决定 共同驱动惰性或侵袭性临床结果的相互作用和共生的分子特征 在这种情况下迫切需要实现分子引导的治疗策略和生物学上的 临床使用的接地预测模型。此外，复杂的分子态可能会收敛到 组织病理学模式来增强这些预测，但这些特性很难量化， 整合，并在不同的患者群体中推广。大量和多样化的患者队列的出现 具有临床嵌入的分子特征、数字组织病理学技术和关键结果 随着在分析和解释这些数据的计算和深度学习方面的创新，这些措施已经 创造了一个机会，深刻地扩大了分子的发现和翻译潜力， 局限性前列腺癌患者的病理和表型数据。我们最重要的假设是 相互作用的分子、病理和表型特征决定了中期和晚期的预后。 手术后高风险的局部性PCA，以及生物引导的可解释性深度学习，与 代表PCA多样性的协调队列，将改变我们对懒惰与 潜在致命的局部PCA，并兑现了精准癌症药物的承诺。为此， 这项建议的具体目标是：1)剖析相互作用的生殖系和体细胞特性 基于生物引导神经网络的局部PCA；2)确定收敛的空间组织病理学 分子和临床上不同类型的前列腺癌的特性；3)制定和验证临床分级 现实世界和临床试验环境中生物网络引导的分子预后模型。为了这些 目标，我们将建立在我们的团队在PCA基因组学，计算机科学，医学和 泌尿外科肿瘤学。至关重要的是，我们将把我们的做法嵌入到协调一致和具有代表性的背景下。 PCA队列。能够理解为什么一些中高风险的局限性前列腺癌 表型具有侵袭性，因此可以预测哪个PCA在治疗意向治疗后会进展 以这种方式，将显著促进基础PCA研究和临床翻译。总的来说，这个项目 将致力于转化前列腺癌的精准癌症医学，并成为创建的典范， 这些新兴方法在癌症类型和背景中的开发和应用。

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.