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：
10259888
负责人：
ANTONINA MITROFANOVA
金额：
$ 32.56万
依托单位：
RBHS-SCHOOL/ HEALTH RELATED PROFESSIONS
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 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10259888
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.

在治疗之前，鉴定患有较差且有利治疗反应的患者是对于改善患者生存和疾病管理的宝贵价值。我们建议建造一个开源可扩展的可扩展方法，可以帮助实验者和临床医生评估患者的风险制定耐药性，并将为我们的长期目标建立基础，以建立一个平台以患者为中心的临床决策，个性化的治疗建议和疾病管理。我们建议开发可使用患者的可推广的多功能生物信息学范式分子谱以预测其治疗反应，预测TRET，结合了网络分析，统计建模和集合机器以独特的创新方式学习，可以准确阐明控制治疗反应的复杂多层关系。我们的目标提议的方法是两个方面：（i）发现分子标记和有价值的治疗候选者干预措施有可能针对排除或克服抵抗力；（ii）预测患者的对治疗给药的反应，这具有改善疾病结果并减少的长期承诺不必要和无效治疗的成本。通过增加肿瘤学治疗抗药性病例的动机，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，以及（ii）对标准护理的反应急性髓样白血病的化学疗法。我们将通过基于网络的决策来传播我们的方法 - 制造工具，该工具将通过面向Hadoop的解决方案来实施（i）扩大其实际影响（ii）建立临床公用事业。综上所述，这种多任务资源是同类的独特创新努力在治疗抗性领域，对个性化治疗建议和疾病有直接影响管理。即使我们将在前列腺癌和急性髓样白血病中训练我们的模型，我们方法可以轻松，广泛地适用于其他疗法和疾病。这项努力将由早期舞台调查员安东尼娜·米特罗法诺瓦（Antonina Mitrofanova）（PI）领导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（Rutgers，Co-I）是分布式系统专家，将为Hadoop开发提供建议和验证； Shridar Ganesan博士（Rutgers，Co-I）将提供临床和测序的患者数据以及将我们的方法的利用纳入Rutgers Cinj分子肿瘤板；艾萨克·金博士（Rutgers，Co-I）将提供其他数据以验证前列腺癌； Christopher Hourigan博士（NHLBI，NIH，重要合作者），他将提供急性髓样白血病临床验证的数据并致力于测试我们基于网络的门户；以及Scott Parrott博士（Rutgers，Co-I），他是专家统计分析，并将咨询功率计算和多个测试校正。