Urine and serum biomarkers for early diagnosis and risk assessment of pancreatic cancer

用于胰腺癌早期诊断和风险评估的尿液和血清生物标志物

基本信息

项目摘要

ABSTRACT Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, primarily due to most cases being diagnosed at an advanced, incurable stage. While 5-year survival of metastatic PDAC is <5%, outcomes dramatically improve for localized PDAC. Poor prognosis is due to a lack of biomarkers for diagnosing PDAC at an early, asymptomatic stage when cure is possible. Effective diagnosis of early stage PDAC depends on identification of accurate, non-invasive biomarkers in combination with a strategy for screening increased risk populations. Our primary objective is to identify non-invasive protein biomarkers in serum, urine, and exosomes that accurately distinguish between patients with and without early stage resectable PDAC that is amenable to curative surgery. Novel diagnostics would also improve discrimination between PDAC and benign pancreatic pathologies. The goal of the proposed research is to develop clinically translatable noninvasive biomarkers- based tests for screening (in high risk groups) and differential diagnosis of PDAC. Our central hypothesis is that combinations of urinary, serum, and exosome derived biomarkers could be synergistic offering a superior classification power. We have used urine and serum samples from retrospective and prospective cohort studies to identify a range of strong candidate combinatorial multimarker algorithms for early detection and diagnosis of PDAC. In Aim 1, we will optimize the performance of a PDAC differential diagnosis algorithm and will validate the optimized algorithm in samples collected prior to clinical diagnosis in the Pancreatic Adenocarcinoma Gene Environment Risk (PAGER) study. In Aim 2, we will optimize the performance of an early detection algorithm for resectable PDAC in pre-diagnostic samples from three prospectively collected cohorts and validate the optimized EDA in blinded parallel serum/urine samples from the Southern Community Cohort Study (SCCS). If successful, our project will yield novel, validated algorithms for risk assessment and early detection and for differential diagnosis of PDAC. These algorithms when combined will result in a new pioneering screening paradigm for PDAC allowing for timely live-saving interventions. Our strong preliminary data, powerful and synergistic investigative team, and the availability of parallel urine and serum samples from unique prospective cohorts contribute to the high probability of successful accomplishing the proposed studies.
摘要 胰腺导管腺癌(PDAC)是最致命的癌症之一,主要是由于大多数病例是在胰腺癌中发生的。 在晚期无法治愈的阶段被确诊虽然转移性PDAC的5年生存率<5%,但结局 显著改善局部PDAC。预后差是由于缺乏诊断PDAC的生物标志物, 可能治愈的早期无症状阶段。早期PDAC的有效诊断取决于 识别准确的非侵入性生物标志物,并结合筛查风险增加的策略 人口。我们的主要目标是确定血清、尿液和外泌体中的非侵入性蛋白质生物标志物 准确区分有和没有早期可切除PDAC的患者, 治疗性手术新的诊断方法也将提高PDAC和良性胰腺癌之间的区分 病理学拟议研究的目标是开发临床可翻译的非侵入性生物标志物- 用于筛查(高危人群)和PDAC鉴别诊断的基础测试。我们的核心假设是, 尿、血清和外来体来源的生物标志物的组合可以是协同的,提供上级生物标志物。 分类能力我们使用了来自回顾性和前瞻性队列研究的尿液和血清样本 鉴定一系列强候选组合多标记算法用于早期检测和诊断 PDAC。在目标1中,我们将优化PDAC鉴别诊断算法的性能,并将验证 胰腺癌基因临床诊断前采集样本的优化算法 环境风险(PAGER)研究。在目标2中,我们将优化早期检测算法的性能, 可切除的PDAC在三个前瞻性收集的队列的诊断前样本,并验证优化的 来自南方社区队列研究(SCCS)的设盲平行血清/尿液样本中的EDA。如果成功, 我们的项目将产生新的,有效的算法,用于风险评估和早期检测, PDAC的诊断这些算法结合在一起,将产生一个新的开拓性的筛选范式, PDAC允许及时采取挽救生命的干预措施。我们强大的初步数据,强大的协同作用 研究团队,以及来自独特前瞻性队列的平行尿液和血清样本的可用性 有助于成功完成拟议研究的高概率。

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 }}

Surinder K. Batra其他文献

Functions of tumorigenic and migrating cancer progenitor cells in cancer progression and metastasis and their therapeutic implications
  • DOI:
    10.1007/s10555-007-9052-4
  • 发表时间:
    2007-02-02
  • 期刊:
  • 影响因子:
    8.700
  • 作者:
    Murielle Mimeault;Surinder K. Batra
  • 通讯作者:
    Surinder K. Batra
Wolfram 症候群の実態調査
关于 Wolfram 综合征的事实调查
  • DOI:
  • 发表时间:
    2011
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yukihiro Tamura;Michiyo Higashi;Sho Kitamoto;Seiya Yokoyama;Masahiko Osako;Michiko Horinouchi;Takeshi Shimizu;Mineo Tabata;Surinder K. Batra;Masamichi Goto;Suguru Yonezawa;松永仁恵,田部勝也,太田康晴,奥屋 茂,和田安彦,山田祐一郎,雨宮 伸,杉原茂孝,岡 芳知,谷澤幸生
  • 通讯作者:
    松永仁恵,田部勝也,太田康晴,奥屋 茂,和田安彦,山田祐一郎,雨宮 伸,杉原茂孝,岡 芳知,谷澤幸生
Mo1138 COMPARATIVE PROTEOMICS REVEALS TRANSLATIONAL POTENTIAL OF TARGETS FOR PANCREATIC DUCTAL ADENOCARCINOMA
  • DOI:
    10.1016/s0016-5085(23)02780-4
  • 发表时间:
    2023-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Mathilde Resell;Hanne-Line Rabben;Manoj Amrutkar;Lars Hagen;Surinder K. Batra;Caroline S. Verbeke;Timothy C. Wang;Duan Chen;Chun-Mei Zhao
  • 通讯作者:
    Chun-Mei Zhao
PP01.05 Targeting MUC16-Mediated KRASi Resistance in NSCLC
PP01.05 针对非小细胞肺癌中 MUC16 介导的 KRASi 耐药性
  • DOI:
    10.1016/j.jtho.2025.03.013
  • 发表时间:
    2025-04-01
  • 期刊:
  • 影响因子:
    20.800
  • 作者:
    Ashu Shah;Shamema Salam;Sanjib Chaudhary;Iniyan A. Muthamil;Imayavaramban Lakshmanan;Surinder K. Batra;Apar K. Ganti
  • 通讯作者:
    Apar K. Ganti
MUC5AC in stromal heterogeneity and the Progression of Pancreatic Cancer
  • DOI:
    10.1016/j.canlet.2023.216541
  • 发表时间:
    2024-01-28
  • 期刊:
  • 影响因子:
  • 作者:
    Surinder K. Batra
  • 通讯作者:
    Surinder K. Batra

Surinder K. Batra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('Surinder K. Batra', 18)}}的其他基金

Truncated O-glycan-dependent mechanisms inducing metastatic dissemination in pancreatic cancer
截短的O-聚糖依赖性机制诱导胰腺癌转移扩散
  • 批准号:
    10683305
  • 财政年份:
    2022
  • 资助金额:
    $ 62.24万
  • 项目类别:
Molecular Imaging Probe(s) for Optical Surgical Navigation of Pancreatic Cancer
用于胰腺癌光学手术导航的分子成像探针
  • 批准号:
    10557180
  • 财政年份:
    2022
  • 资助金额:
    $ 62.24万
  • 项目类别:
Novel Therapy to Inhibit IPMN Progression
抑制 IPMN 进展的新疗法
  • 批准号:
    10446455
  • 财政年份:
    2022
  • 资助金额:
    $ 62.24万
  • 项目类别:
Molecular Imaging Probe(s) for Optical Surgical Navigation of Pancreatic Cancer
用于胰腺癌光学手术导航的分子成像探针
  • 批准号:
    10367553
  • 财政年份:
    2022
  • 资助金额:
    $ 62.24万
  • 项目类别:
Novel Therapy to Inhibit IPMN Progression
抑制 IPMN 进展的新疗法
  • 批准号:
    10640955
  • 财政年份:
    2022
  • 资助金额:
    $ 62.24万
  • 项目类别:
Connectivity mapping identified novel combination therapy for glioblastoma
连接映射确定了胶质母细胞瘤的新型联合疗法
  • 批准号:
    10504826
  • 财政年份:
    2022
  • 资助金额:
    $ 62.24万
  • 项目类别:
Connectivity mapping identified novel combination therapy for glioblastoma
连接映射确定了胶质母细胞瘤的新型联合疗法
  • 批准号:
    10686268
  • 财政年份:
    2022
  • 资助金额:
    $ 62.24万
  • 项目类别:
Truncated O-glycan-dependent mechanisms inducing metastatic dissemination in pancreatic cancer
截短的O-聚糖依赖性机制诱导胰腺癌转移扩散
  • 批准号:
    10503433
  • 财政年份:
    2022
  • 资助金额:
    $ 62.24万
  • 项目类别:
Urine and serum biomarkers for early diagnosis and risk assessment of pancreatic cancer
用于胰腺癌早期诊断和风险评估的尿液和血清生物标志物
  • 批准号:
    10156494
  • 财政年份:
    2021
  • 资助金额:
    $ 62.24万
  • 项目类别:
Urine and serum biomarkers for early diagnosis and risk assessment of pancreatic cancer
用于胰腺癌早期诊断和风险评估的尿液和血清生物标志物
  • 批准号:
    10339431
  • 财政年份:
    2021
  • 资助金额:
    $ 62.24万
  • 项目类别:

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 62.24万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了