Research Project 1: Development of PI3K Inhibitors as Single Agents or in Combination with MEK Inhibitors for Breast Cancer

研究项目 1：开发 PI3K 抑制剂作为单一药物或与 MEK 抑制剂联合治疗乳腺癌

• 批准号: 10005320
• 负责人: Cynthia X Ma
• 金额: $ 0.92万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10005320
• 关键词: AKT1 gene; Aftercare; Bioinformatics; Biological Markers; Biopsy; Breast Cancer Model; Breast Cancer Patient; Candidate Disease Gene; Catalytic Domain; Categories; Characteristics; Classification; Clinical; Clinical Trials; Combined Modality Therapy; Data; Data Collection; Data Discovery; Dependence; Development; Dose; Dose-Limiting; Drug Combinations; Drug effect disorder; Drug resistance; ERBB2 gene; Endocrine; Estrogen receptor positive; Evaluation; Event; FRAP1 gene; Feedback; Future; Gene Proteins; Genetic; Genome Stability; Goals; Immune; Individual; Infrastructure; Interruption; Investigation; Lipids; Literature; MEK inhibition; MEKs; Mass Spectrum Analysis; Mediating; Modeling; Molecular; Mus; Mutation; PIK3CA gene; PIK3CB gene; PTEN gene; Pathway interactions; Patient-derived xenograft models of breast cancer; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Phosphatidylinositide 3-Kinase Inhibitor; Phosphotransferases; Play; Population; Prediction of Response to Therapy; Progression-Free Survivals; Protein Isoforms; Proteomics; Proto-Oncogene Proteins c-akt; Publishing; Randomized; Research; Research Project Grants; Resistance; Resources; Role; SDZ RAD; Sampling; Signal Transduction; Standardization; Target Populations; Therapeutic Agents; Time; Tissues; Toxic effect; Treatment-related toxicity; Tumor Volume; Tumor-Derived; Universities; Validation; Washington; Xenograft Model; Xenograft procedure; antitumor effect; base; biomarker development; biomarker discovery; cancer subtypes; candidate marker; clinical development; differential expression; early phase trial; exome sequencing; genetic signature; genomic data; hormone receptor-positive; improved; inhibitor/antagonist; interest; malignant breast neoplasm; member; mutant; novel; predicting response; predictive marker; primary endpoint; research clinical testing; resistance mechanism; response; secondary endpoint; success; targeted agent; therapeutic target; therapy resistant; transcriptome sequencing; treatment group; triple-negative invasive breast carcinoma; tumor; tumor growth; tumorigenesis

Research project 1 focuses on identifying response predictors and resistance mechanisms for phosphatidylinositide 3-kinase (PI3K) inhibitors through the conduct of a PDX trial using a broad selection of breast cancer models of hormone receptor positive (HR+)/HER2- and triple negative breast cancer (TNBC) subtypes. PI3K inhibitors are attractive therapeutic agents for breast cancer because of the frequent occurrence of PIK3CA mutations in hormone receptor positive (HR+) and PTEN loss in TNBC. However single agent PI3K inhibitors have yielded only modest anti-tumor activity and the efficacy of pan-PI3K inhibitors are often limited by dose limiting toxicities in clinical trials. Isoform-specific PI3K inhibitors are therefore of great interest to maximize target inhibition with improved tolerability. A major challenge in the development of PI3K inhibitors have been the identification of patients who may benefit the most from these agents and for whom isoform specific inhibitors are appropriate. Clinical trials have focused on limited markers including PIK3CA mutation and PTEN status, which do not consistently predict response. In-depth biomarker research is needed, but is often difficult in the clinical trial setting due to the quantity and quality of the study material and the inability to obtain serial tumor biopsies from patients. In contrast, patient-derived xenograft (PDX) models provide nearly unlimited tumor resources for pre- and post-treatment in-depth genoproteomic analysis for biomarker development and determination of resistance mechanisms for rational drug combinations. We hypothesize that a breast cancer PDX trial of pan- and isoform-specific PI3K inhibitors, will discern isoform dependency of individual tumors to derive predictive biomarkers. We will use copanlisib, a potent pan-PI3K inhibitor with activity against predominantly PI3Kα versus PI3Kβ, and AZD8186, a specific PI3Kβ inhibitor, both agents are in the NCI-IND portfolio. In addition, based on literature evidence and our preliminary data from a PDX trial of buparlisib, we hypothesize that adding the MEK inhibitor selumetinib, also an NCI IND agent, will improve response to copanlisib and/or AZD8186 based on literature evidence and the synergistic anti-tumor effects we observed when combining a pan-PI3K inhibitor with a MEK inhibitor in our PDX models of breast cancer. Aim 1 will conduct the PDX trial. 100 breast cancer PDX models comprised of ER+HER2- and TNBC subtypes available from the PDX core will be selected based on passage number, genomic stability and tumor characteristics. To enrich for the target population, at least 30 PDX models with PIK3CA mutation and at least 30 PDX models with PTEN null phenotype will be included in this PDX trial. Each PDX model will be passaged to 12 immune-deficient mice and randomly assigned to 6 treatment groups (n=2 each treatment group) to receive either vehicle, copanlisib, AZD8186, selumatinib, copanlisib + selumetinib or AZD8186 + selumetinib. Several tumor response criteria will be used for classification of sensitive vs resistant to drug(s) therapy. These include the recently published mRECIST criteria and the traditional % tumor growth inhibition which includes a vehicle treated group. Aim 2 will identify candidate and novel genoproteomic predictors of response for each single agent or combination therapy by analyzing global genomic data (whole exome and RNA Seq) and proteomic data generated by multi-kinase inhibitor bead mass spectrometry (MIB-MS) for unbiased discovery of candidate biomarkers. The analysis will include a focus on identifying outlying, differentially expressed biomarkers between sensitive and resistant tumors. Aim 3 will perform kinome profiling (MIB-MS) of post treatment samples to assess drug-induced signaling changes in order to discern mechanisms of action of the study treatments and to identify intrinsic and treatment-induced adaptive survival mechanisms. This project will be supported by the infrastructure established by the WU-PDTC for data collection, including drug treatment, mice tolerability and tumor volumes changes over time. In addition, the bioinformatics capability provided by the WU-PDTC for trial interpretation and genoproteomic analysis will be leveraged to derive predictors of response and resistance mechanisms to PI3K inhibitors. In addition, team members of Project 1 will interact regularly with team members of Project 2 for information exchange to improve research approach and results sharing. The long term goal of this research is to set up a standardized PDX trial platform to identify promising drug(s) and biomarker pairs for clinical testing.

研究项目1专注于确定响应预测因素和阻力机制 磷脂酰肌醇3-激酶（PI3K）抑制剂通过PDX试验使用广泛的选择 马酮受体阳性（HR+）/HER2-和三重阴性乳腺癌（TNBC）的乳腺癌模型（TNBC） 亚型。 PI3K抑制剂是有吸引力的乳腺癌治疗剂，因为经常发生 TNBC中同一个受体阳性（HR+）和PTEN损失中的PIK3CA突变。但是单位代理PI3K 抑制剂仅产生适度的抗肿瘤活性，PAN-PI3K抑制剂的效率通常受到 剂量限制临床试验中的毒性。因此，同工型特异性的PI3K抑制剂引起了极大的兴趣 目标抑制作用，可提高耐受性。 PI3K抑制剂开发的主要挑战是 鉴定可能从这些药物中受益最大的患者，并为此特异性抑制剂 合适。临床试验集中于有限标记，包括PIK3CA突变和PTEN状态， 这不能始终如一地预测响应。需要深入的生物标志物研究，但通常很难 由于研究材料的数量和质量以及无法获得串行肿瘤而导致的临床试验设置 来自患者的活检。相比之下，患者衍生的Xenograpon（PDX）模型几乎提供了几乎无限的肿瘤 用于生物标志物开发和生物标志物发展前和后处理前和后期治疗的资源 确定合理药物组合的耐药机制。我们假设乳腺癌 PDX和同工型特异性PI3K抑制剂的PDX试验将识别单个肿瘤对的同工型依赖性 得出预测性生物标志物。我们将使用Copanlisib，这是一种潜在的PAN-PI3K抑制剂，其活性反对 主要是PI3Kα与PI3Kβ，而AZD8186（一种特定的PI3Kβ抑制剂）都在NCI IND中 文件夹。此外，根据文献证据和我们的PDX试验初步数据，我们 假设添加MEK抑制剂Selumetinib（也是NCI IND剂）将改善对 我们观察到的基于文献证据和协同抗肿瘤效应的Copanlisib和/或AZD8186 将PAN-PI3K抑制剂与MEK抑制剂组合在我们的PDX乳腺癌模型中时。 AIM 1将进行PDX试验。 100个乳腺癌PDX模型包括ER+HER2-和TNBC亚型 将根据密码，基因组稳定性和肿瘤选择从PDX核心获得 特征。为了丰富目标人群，至少30个具有PIK3CA突变的PDX模型 该PDX试验将包括30个具有PTEN NULL表型的PDX模型。每个PDX模型将通过 至12只免疫缺陷小鼠，并随机分配给6个治疗组（n = 2个治疗组）接受 Copanlisib，AZD8186，Selumatinib，Copanlisib + Selumetinib或AZD8186 + Selumetinib的车辆。一些 肿瘤反应标准将用于对抗药性治疗的敏感性与抗药性分类。这些包括 最近发表的MRECIST标准和传统％肿瘤生长抑制作用，包括车辆 治疗组。 AIM 2将确定每个单个药物的响应的候选者和新颖的基因蛋白质组学预测指标 或通过分析的全球基因组数据（整个外显子和RNA SEQ）和蛋白质组学数据组合疗法 由多激酶抑制剂珠质谱法（MIB-MS）生成，用于无偏见的候选者 生物标志物。该分析将包括关注识别外围，不同表达的生物标志物 敏感和抗性肿瘤。 AIM 3将对治疗后样品进行Kinome分析（MIB-MS）进行评估 药物诱导的信号变化，以辨别研究治疗的作用机制并确定 固有和治疗诱导的适应性生存机制。 该项目将由WU-PDTC建立的数据收集的基础架构支持，包括 药物治疗，小鼠的耐受性和肿瘤量会随着时间而变化。此外，生物信息学能力 WU-PDTC提供的试验解释和基因蛋白质组学分析将被利用以推导 预测对PI3K抑制剂的反应和抗性机制。此外，项目1的团队成员 将定期与项目2的团队成员进行信息交流以改善研究方法 和结果共享。这项研究的长期目标是建立一个标准化的PDX试用平台以识别 有希望的药物和生物标志物对进行临床测试。