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

• 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
• 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

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.

在治疗给药前识别治疗应答较差和良好的患者， 对提高患者生存率和疾病管理非常宝贵。我们建议建立一个开源的 一种可扩展的可推广的方法，可以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，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生物信息学范式， 分子谱预测其治疗反应，预测，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， 阐明控制治疗反应的复杂多层次关系。我们的目标 所提出的方法是双重的：（i）发现用于治疗的分子标记物和有价值的候选物。 干预，这可能是有针对性的，以排除或克服阻力;和（ii）预测患者的 对治疗管理的反应，这具有改善疾病结局和减少 不必要和无效的治疗费用。 由于肿瘤学中治疗耐药病例的增加，我们将把我们的算法应用于 阐明（i）对前列腺癌雄激素靶向治疗的反应和（ii）对标准治疗的反应 急性髓细胞白血病的治疗我们将通过一项基于网络的决定传播我们的做法- 制作工具，将通过面向Hadoop的解决方案实施，以（i）扩大其实际影响 及（ii）建立临床效用。总的来说，这种多任务资源是一种独特的创新努力 在治疗阻力空间中，对个性化治疗建议和疾病 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， 这种方法可以容易地和广泛地应用于其他治疗和疾病。 这项工作将由早期研究者Antonina Mitrofanova（PI）领导，他拥有广泛的 生物医学信息学和大数据分析方面的培训和专业知识。她的团队包括博士。 Shantenu Jha（Rutgers，co-I），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，共同I）将提供临床和测序患者数据， 将我们方法的利用纳入罗格斯大学CINJ分子肿瘤委员会; Isaac Kim博士 （罗格斯大学，co-I）谁将提供额外的数据，用于验证前列腺癌;克里斯托弗Hourigan博士 （NHLBI，NIH，重要合作者），将为急性髓性白血病的临床验证提供数据 并致力于测试我们的基于网络的门户网站;和博士斯科特帕罗特（罗格斯大学，co-I），谁是专家， 统计分析，并将提供关于功效计算和多重测试校正的咨询。