Generalizable biomedical informatics strategies for predictive modeling of treatment response

用于治疗反应预测建模的通用生物医学信息学策略

基本信息

批准号：
10463755
负责人：
ANTONINA MITROFANOVA
金额：
$ 32.56万
依托单位：
RUTGERS BIOMEDICAL AND HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-09 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10463755
关键词：
Acute Myelocytic Leukemia Affect Age Algorithms Alternative Splicing Androgen Antagonists Androgens Atlases Big Data Big Data Methods Bioinformatics Cancer Institute of New Jersey Case Study Clinical Clinical Data Clinical Trials Communities Complex Computers Consult Data Data Analyses Decision Making Development Disease Disease Management Disease Outcome Distributed Systems Engineering Ensure Event Foundations Generations Genes Genetic Transcription Genomics Gleason Grade for Prostate Cancer Goals Institution Investigation Machine Learning Malignant neoplasm of prostate Methods Modeling Molecular Molecular Analysis Molecular Profiling Multiomic Data National Heart, Lung, and Blood Institute Oncology Online Systems Pathway Analysis Patient risk Patients Positioning Attribute Prediction of Response to Therapy Prostate Adenocarcinoma Publishing Race Regimen Research Personnel Resistance Resources Risk Sample Size Statistical Data Interpretation Statistical Models Testing Therapeutic Therapeutic Intervention Time Training Transcriptional Regulation Translations Treatment Failure Tumor stage United States National Institutes of Health Validation Work androgen deprivation therapy base biomedical informatics cancer genome chemotherapy clinical decision-making clinical sequencing cohort cost deprivation design genome-wide improved ineffective therapies innovation molecular marker multitask novel open source patient response personalized therapeutic predictive modeling profiles in patients response standard of care statistical learning targeted treatment therapeutic candidate therapy development therapy resistant tool transcriptomics treatment response treatment risk tumor unnecessary treatment web portal

项目摘要

Identification of patients with poor and favorable treatment response prior to therapy administration is invaluable for improving patient survival and disease management. We propose to build an open-source scalable generalizable method that would assist experimentalists and clinicians on assessing patient's risk of developing therapy resistance and would establish a foundation for our long-term goal to build a platform for patient-centric clinical decision making, personalized therapeutic advice, and disease management. We propose to develop a generalizable versatile bioinformatics paradigm that will use patient molecular profiles to PREDICT their Therapy Response, PREDICTTR, which combines network analysis, statistical modeling, and ensemble machine learning in a unique innovative way that allows accurate elucidation of complex multi-level relationships that govern treatment response. The objective of our proposed approach is two-fold: (i) uncover molecular markers and valuable candidates for therapeutic intervention, which can potentially be targeted to preclude or overcome resistance; and (ii) predict patient's response to therapy administration, which holds a long-term promise to improve disease outcome and reduce the cost of unnecessary and ineffective treatments. Motivated by increasing cases of treatment resistance in oncology, we will apply our algorithm to elucidate (i) response to androgen targeting in prostate cancer and (ii) response to standard-of-care chemotherapy in acute myeloid leukemia. We will disseminate our approach through a web-based decision- making tool, which will be implemented through a Hadoop-oriented solution to (i) broaden its practical impact and (ii) establish clinical utility. Taken together, this multi-task resource is a unique innovative effort of its kind in the therapeutic resistance space with a direct broad impact on personalized therapeutic advice and disease management. Even though we will train our model in prostate cancer and acute myeloid leukemia, our approach can be easily and broadly applicable to other therapies and diseases. This effort will be led by an Early Stage Investigator, Antonina Mitrofanova (PI) who has extensive training and expertise in biomedical informatics and big data analytics. Her collaborative team includes Dr. Shantenu Jha (Rutgers, co-I) who is an expert in distributed systems and will advise on Hadoop development and validation; Dr. Shridar Ganesan (Rutgers, co-I) who will provide clinical and sequencing patient data and incorporate the utilization of our method into the Rutgers CINJ Molecular Tumor Board; Dr. Isaac Kim (Rutgers, co-I) who will provide additional data for validation in prostate cancer; Dr. Christopher Hourigan (NHLBI , NIH, Significant Collaborator), who will provide data for clinical validation in acute myeloid leukemia and is committed to test our web-based portal; and Dr. Scott Parrott (Rutgers, co-I), who is an expert in statistical analysis and will consult on power calculations and multiple testing corrections.

在治疗前识别治疗反应不佳和良好的患者对于改善患者生存和疾病管理具有不可估量的价值。我们建议建立一个开源的可扩展的通用方法，可帮助实验者和临床医生评估患者的风险发展治疗耐药性，并将为我们的长期目标奠定基础，即建立一个平台以患者为中心的临床决策、个性化治疗建议和疾病管理。我们建议开发一种通用的多功能生物信息学范式，该范式将使用患者分子谱来预测他们的治疗反应，PREDICTTR，它结合了网络分析，统计建模和集成机器学习以独特的创新方式实现准确的阐明控制治疗反应的复杂多层次关系。我们的目标所提出的方法有两个方面：（i）发现分子标记和有价值的治疗候选物干预，可能有针对性地排除或克服阻力； (ii) 预测患者的对治疗管理的反应，这有望改善疾病结果并减少不必要和无效治疗的费用。由于肿瘤学中治疗耐药病例不断增加，我们将应用我们的算法阐明 (i) 对前列腺癌中雄激素靶向的反应和 (ii) 对标准护理的反应急性髓系白血病的化疗。我们将通过基于网络的决策来传播我们的方法 - 制作工具，该工具将通过面向 Hadoop 的解决方案实施，以 (i) 扩大其实际影响 (ii) 建立临床实用性。总而言之，这种多任务资源是同类中独特的创新成果在治疗抵抗领域对个性化治疗建议和疾病产生直接广泛的影响管理。尽管我们将在前列腺癌和急性髓性白血病中训练我们的模型，但我们的模型该方法可以轻松且广泛地应用于其他疗法和疾病。这项工作将由早期研究员 Antonina Mitrofanova (PI) 领导，她拥有广泛的研究经验生物医学信息学和大数据分析方面的培训和专业知识。她的合作团队包括博士。 Shantenu Jha（罗格斯大学联合创始人）是分布式系统专家，将为 Hadoop 开发提供建议和验证； Shridar Ganesan 博士（罗格斯大学联合分校）将提供临床和测序患者数据以及将我们的方法的使用纳入罗格斯 CINJ 分子肿瘤委员会；艾萨克·金博士（Rutgers，co-I）谁将提供额外的数据以验证前列腺癌；克里斯托弗·胡里根博士（NHLBI、NIH、重要合作者），将为急性髓系白血病的临床验证提供数据并致力于测试我们的网络门户；斯科特·帕罗特 (Scott Parrott) 博士（罗格斯大学联合分校）是该领域的专家统计分析，并将就功率计算和多重测试校正进行咨询。