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
基本信息
- 批准号:10628274
- 负责人:
- 金额:$ 48.53万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressAdjuvantAdjuvant StudyArchitectureArtificial IntelligenceBindingBiologicalBiological MarkersCancer and Leukemia Group BCessation of lifeCharacteristicsClinicalClinical DataClinical TrialsComplexComputer Vision SystemsDNA RepairDNA Repair GeneDana-Farber Cancer InstituteData AnalysesDevelopmentDiseaseEventFutureGenetic TranscriptionGenomicsGerm-Line MutationHistopathologyImageImmuneIndolentLearningLocalized DiseaseMalignant neoplasm of prostateMediatingMedical OncologyMethodologyModelingMolecularMolecular ProfilingMutationNeoplasm MetastasisOperative Surgical ProceduresOutcomeOutcome MeasurePathologicPathologistPathway interactionsPatientsPatternPhasePhenotypePropertyProstateRadiationRadiation therapyRadical ProstatectomyRecurrenceRecurrent diseaseRelapseRetrospective cohortRiskRisk FactorsSomatic MutationSpecimenTechniquesTherapeuticTissuesUrologic Oncologyadvanced diseaseanticancer researchartificial intelligence algorithmcancer carecancer genomicscancer typecandidate validationclinical biomarkersclinical predictive modelclinical prognosticclinical translationcohortcomputer sciencedeep learningdeep learning modeldigitaldigital pathologygenome-widehigh riskhigh risk menhormone therapyimprintimprovedinnovationmenmolecular modelingmolecular subtypesneural networknovelnovel therapeuticspatient populationphenotypic datapoint of careprecision oncologypredictive modelingprognosticprognostic modelprognostic performanceprogression riskprostate cancer modelprostate cancer riskstandard of caresurvival outcometranslational potentialtreatment strategytumor
项目摘要
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.
项目摘要——项目三
中危和高危局限性前列腺癌 (PCa) 的治疗策略包括手术或
有或没有激素治疗的放射治疗 多种分子因素,包括种系和体细胞。
DNA 修复基因和基于组织的转录生物标志物的改变,具有生物学和预后意义
这些临床环境中的相关性目前很少用于指导治疗决策。
相互作用和同时发生的分子特征共同驱动惰性或侵袭性临床结果
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
此外,复杂的分子状态可能会趋于一致。
组织病理学模式来增强这些预测,但这些特性很难量化,
整合并推广不同的患者群体 庞大且多样化的患者群体的出现。
具有临床嵌入式分子表征、数字组织病理学技术和关键结果
措施以及用于分析和解释这些数据的计算和深度学习方面的创新,已经
创造了一个机会来深刻扩展分子的发现和转化潜力,
局限性前列腺癌患者的病理和表型数据我们的总体假设是:
分子相互作用、病理学和表型特征定义了中期和中期的预后结果
手术后的高风险局部 PCa,以及生物引导的可解释深度学习,搭配
代表 PCa 多样性的统一队列将改变我们对惰性与惰性的理解
潜在致命的局部前列腺癌并兑现精准癌症医学的承诺。
该提案的具体目标是:1)剖析介导的种系和体细胞特性
使用生物引导神经网络进行局部 PCa;2) 确定收敛的空间组织病理学
PCa 的分子和临床不同形式的特性;3) 制定并验证临床分级
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。
为了实现这一目标,我们将利用我们团队在 PCa 基因组学、计算机科学以及医学和
至关重要的是,我们将把我们的方法纳入协调和代表性的背景中。
PCa 队列能够理解为什么一些中度和高风险的局限性前列腺癌。
表型具有侵袭性,因此可以预测哪些 PCa 在治疗后会进展
通过这种方式,将显着推进该项目的基础 PCa 研究和临床转化。
将努力改变前列腺癌的精准癌症医学,并成为创造的典范,
这些新兴方法在癌症类型和背景下的开发和应用。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Eliezer M Van Allen其他文献
Eliezer M Van Allen的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Eliezer M Van Allen', 18)}}的其他基金
Molecular Origins and Evolution to Chemoresistance in Germ Cell Tumors
生殖细胞肿瘤化疗耐药的分子起源和进化
- 批准号:
10773483 - 财政年份:2023
- 资助金额:
$ 48.53万 - 项目类别:
Molecular origins and evolution to chemoresistance in germ cell tumors
生殖细胞肿瘤中化学耐药性的分子起源和进化
- 批准号:
10443070 - 财政年份:2023
- 资助金额:
$ 48.53万 - 项目类别:
The Cellular Geography of Therapeutic Resistance in Cancer
癌症治疗耐药的细胞地理学
- 批准号:
10819853 - 财政年份:2023
- 资助金额:
$ 48.53万 - 项目类别:
A statistical framework to systematically characterize cancer driver mutations in noncoding genomic regions
系统地表征非编码基因组区域中癌症驱动突变的统计框架
- 批准号:
10260680 - 财政年份:2019
- 资助金额:
$ 48.53万 - 项目类别:
Integrative Somatic and Germline Computational Biology to Redefine Clinical Actionability in Solid Tumors
综合体细胞和种系计算生物学重新定义实体瘤的临床可操作性
- 批准号:
9913487 - 财政年份:2018
- 资助金额:
$ 48.53万 - 项目类别:
Molecular origins and evolution to chemoresistance in germ cell tumors
生殖细胞肿瘤中化学耐药性的分子起源和进化
- 批准号:
10379230 - 财政年份:2018
- 资助金额:
$ 48.53万 - 项目类别:
Molecular origins and evolution to chemoresistance in germ cell tumors
生殖细胞肿瘤中化学耐药性的分子起源和进化
- 批准号:
10084830 - 财政年份:2018
- 资助金额:
$ 48.53万 - 项目类别:
Integrative Somatic and Germline Computational Biology to Redefine Clinical Actionability in Solid Tumors
综合体细胞和种系计算生物学重新定义实体瘤的临床可操作性
- 批准号:
10160834 - 财政年份:2018
- 资助金额:
$ 48.53万 - 项目类别:
Integrative Somatic and Germline Computational Biology to Redefine Clinical Actionability in Solid Tumors
综合体细胞和种系计算生物学重新定义实体瘤的临床可操作性
- 批准号:
9517271 - 财政年份:2018
- 资助金额:
$ 48.53万 - 项目类别:
Integrative Somatic and Germline Computational Biology to Redefine Clinical Actionability in Solid Tumors
综合体细胞和种系计算生物学重新定义实体瘤的临床可操作性
- 批准号:
10396664 - 财政年份:2018
- 资助金额:
$ 48.53万 - 项目类别:
相似国自然基金
肿瘤微环境多层次调控的功能化纳米佐剂用于增强膀胱癌放疗疗效的机制研究
- 批准号:82303571
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
靶向FPPS的双磷酸疫苗佐剂的开发
- 批准号:82341040
- 批准年份:2023
- 资助金额:100 万元
- 项目类别:专项基金项目
皮内接种抗原佐剂复合疫苗跨器官诱导呼吸道黏膜免疫反应
- 批准号:82341042
- 批准年份:2023
- 资助金额:100 万元
- 项目类别:专项基金项目
双重生物响应性自佐剂聚多肽载体构建高效mRNA癌症疫苗
- 批准号:52373299
- 批准年份:2023
- 资助金额:50 万元
- 项目类别:面上项目
新型免疫调节复合佐剂的机制研究及在疫苗开发中的应用
- 批准号:82341039
- 批准年份:2023
- 资助金额:95 万元
- 项目类别:专项基金项目
相似海外基金
Intra-Articular Drug Delivery Modulating Immune Cells in Inflammatory Joint Disease
关节内药物递送调节炎症性关节疾病中的免疫细胞
- 批准号:
10856753 - 财政年份:2023
- 资助金额:
$ 48.53万 - 项目类别:
Anti-flavivirus B cell response analysis to aid vaccine design
抗黄病毒 B 细胞反应分析有助于疫苗设计
- 批准号:
10636329 - 财政年份:2023
- 资助金额:
$ 48.53万 - 项目类别:
Sonodynamic therapy using MRI-guided focused ultrasound in combination with 5-aminolevulinic acid to treat recurrent glioblastoma multiforme
使用 MRI 引导聚焦超声联合 5-氨基乙酰丙酸的声动力疗法治疗复发性多形性胶质母细胞瘤
- 批准号:
10699858 - 财政年份:2023
- 资助金额:
$ 48.53万 - 项目类别:
Project 2: Mechanisms of Resistance to Neoantigen Vaccines in PDAC
项目2:PDAC新抗原疫苗耐药机制
- 批准号:
10708575 - 财政年份:2023
- 资助金额:
$ 48.53万 - 项目类别: