Assessing the power of primary drug-screens to predict clinical response.

项目总结 精确肿瘤学依赖于这样一个假设，即进一步描述患者的肿瘤将导致更好的 治疗反应的预测。虽然这种方法有效地将诊断分解为越来越小的诊断 亚型可能对给定的靶向抑制剂有反应，缺点是它会导致高度分散的临床 数据分布在多个治疗部门。该领域在分配新疗法方面取得的进展越大， 就越难积累足够的样本量来测试另一种疗法。癌细胞的直接筛选 靶向抑制剂面板上的线条和初级样品是解决这一问题的唯一有希望的方法， 将每个患者样本变成100个迷你实验，但体外药物反应的临床验证 由于接受筛查和实际治疗的患者数量有限，预测受到了阻碍。 任何特定的药物。这样的评估对于确定从药物筛查中获得的预测值至关重要。 病人样本。 在过去的七年里，奈特癌症研究所与基因组一起进行了药物筛查 和/或对600多个原发白血病样本进行RNA测序。在两年内，我们将积累 200多名患者不仅进行了体外药物反应筛查，还进行了匹配的靶向治疗 抑制剂。我将利用这个现有的和不断增长的数据集来询问体外药物筛选的力量 使用回顾数据来预测临床反应的数据。我将为初级药物建立一个强有力的框架 筛选和分析，为临床决策建立可解释的模型，并探索机制 控制药物反应。该项目将改进高通量药物筛选，a 彻底计算体外药物筛选的预测能力，以及治疗的候选对象 耐药肿瘤中的联合作用。 我的目标是成为患者样本多组学领域的独立研究员和跨学科领导者。 简介、靶向治疗选择和转化性肿瘤学。在我的指导阶段，我将收到 系统生物学计算建模专家埃梅克·德米尔博士、杰弗里·泰纳博士指导 患者样本药物筛选和验证领域的领导者，靶向癌症的先驱布莱恩·德鲁克博士 他是奈特癌症研究所的主任和治疗专家。我还将提高我对复杂程度的统计理解 通过与我的顾问委员会成员Tomi Mori博士合作，学习集成大型数据集和 从Shannon McWeeney博士那里预测患者的结果，并改进现有的药物筛选 平台和分析方法与Laura Heiser博士。我决心成为一名独立的教师。 我的职位和我的导师都承诺在申请和过渡过程中帮助我。

PROJECT SUMMARY Precision oncology relies on the hypothesis that further characterizing a patient's tumor will lead to better predictions of treatment response. While this approach effectively breaks diagnoses into smaller and smaller subtypes likely to respond to a given targeted inhibitor, the downside is it results in highly fragmented clinical data spread across multiple treatment arms. The more progress the field makes in assigning new therapies, the harder it will be to accrue adequate sample size to test another therapy. Direct screening of cancer cell lines and primary samples on panels of targeted inhibitors is a uniquely promising approach to this problem, turning every patient sample into a hundred mini experiments, but clinical validation of in vitro drug-response predictions have been hampered by limited numbers of patients who are screened and actually treated with any given drug. Such an evaluation is critical to determine the predictive value gained from drug screening of patient samples. Over the past seven years the Knight Cancer Institute has performed drug screening paired alongside genomic and/or RNA sequencing for over 600 primary leukemic samples. Within two years, we will have accumulated over 200 patients not only screened for in vitro drug response but then treated with matched targeted inhibitors. I will leverage this existing and growing dataset to interrogate the power of in vitro drug screening data to predict clinical response using retrospective data. I will establish a robust framework for primary drug screening and analysis, build interpretable models for clinical decision making, and explore mechanisms controlling drug response. This project will result in improvements to high-throughput drug screening, a thorough accounting of the predictive power of in vitro drug screening, and candidates for treatment combinations in resistant tumors. My goal is to become an independent investigator and cross-disciplinary leader in patient sample multi-omic profiling, targeted therapy selection, and translational oncology. During my mentored phase I will be receiving guidance from Dr. Emek Demir, an expert in computational modeling of systems biology, Dr. Jeffery Tyner, a leader in patient sample drug screening and validation, and Dr. Brian Druker, a pioneer of targeted cancer therapy and director of the Knight Cancer Institute. I will also improve my statistical understanding of complex systems by working with my advisory committee member Dr Tomi Mori, learn to integrate large datasets and predicting patient outcomes from Dr. Shannon McWeeney, and improve upon existing drug screening platforms and analysis methods with Dr. Laura Heiser. I am determined to attain an independent faculty position and my mentors have committed to assisting me in the application and transition process.