Generalizable biomedical informatics strategies for predictive modeling of treatment response

在接受治疗前确定治疗反应差和良好的患者 对于改善患者存活率和疾病管理来说，这是非常宝贵的。我们建议构建一个开源的 可扩展的可推广的方法，将帮助实验者和临床医生评估患者的风险 开发治疗抵抗力，并将为我们的长期目标奠定基础，为 以患者为中心的临床决策、个性化的治疗建议和疾病管理。 我们建议开发一种通用的、多功能的生物信息学范式，它将使用患者 预测他们治疗反应的分子图谱，结合了网络分析的PREDICTTR， 统计建模和集成机器学习以一种独特的创新方式，允许准确 阐明控制治疗反应的复杂的多层次关系。我们的目标是 建议的方法有两个方面：(1)发现分子标记和有价值的候选治疗方法 干预，这可能是有针对性的，以防止或克服阻力；和(Ii)预测患者的 对治疗用药的反应，这是一种改善疾病结局和减少 不必要和无效治疗的成本。 在肿瘤治疗耐药案例增加的激励下，我们将把我们的算法应用于 阐明(I)前列腺癌患者对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，将通过面向Hadoop的解决方案实施，以(I)扩大其实际影响 以及(Ii)建立临床实用程序。综上所述，这种多任务资源是同类产品中独一无二的创新成果 在治疗阻力领域，对个性化治疗建议和疾病有直接广泛的影响 管理层。尽管我们将训练我们的模型治疗前列腺癌和急性髓系白血病，但我们的 这种方法可以很容易和广泛地适用于其他疗法和疾病。 这项工作将由早期调查员Antonina Mitrofan ova(PI)领导，她拥有广泛的 生物医学信息学和大数据分析方面的培训和专业知识。她的合作团队包括Dr。 Shantenu Jha(罗格斯大学，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，co-I)，他将提供临床和测序患者数据 将我们方法的使用纳入罗格斯大学分子肿瘤委员会 谁将为前列腺癌的验证提供额外的数据；克里斯托弗·胡里根博士 (NHLBI，NIH，重要合作者)，世卫组织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基于网络的门户网站；以及斯科特·帕罗特博士(罗格斯大学，co-I)，他是 统计分析，并将就功率计算和多次测试修正进行咨询。

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.