INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER

在临床联合试验中整合组学和定量成像数据来预测三阴性乳腺癌的治疗反应

• 批准号: 10020941
• 负责人: Michael T. Lewis
• 金额: $ 63.29万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-19 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10020941
• 关键词: Address; Algorithms; Animals; Biological; Biological Markers; Biopsy; Blood Vessels; Breast Cancer Patient; Breast Cancer Treatment; Cancer Biology; Cancer Patient; Carboplatin; Caring; Cellularity; Clinical; Clinical Trials; Communities; Consensus; Coupled; Data; Development; Disease; Disease Progression; Enrollment; Evaluation; Funding; Genomics; Goals; Healthcare; Human; Image; Imaging technology; Immunotherapy; In complete remission; Informatics; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Mass Spectrum Analysis; Methodology; Methods; Modeling; Molecular; Molecular Target; Mus; Neoadjuvant Therapy; Online Systems; Pathologic; Patient-Focused Outcomes; Patients; Phenotype; Physiological; Prediction of Response to Therapy; Proteins; Recurrence; Regimen; Reproducibility; Research; Research Personnel; Resource Informatics; Resources; Science; Signal Transduction; Surrogate Endpoint; Testing; Time; Treatment Efficacy; Treatment Protocols; Work; Xenograft Model; Xenograft procedure; animal data; arm; base; bioinformatics resource; candidate identification; chemotherapy; cohort; contrast enhanced; data resource; data sharing; diffusion weighted; docetaxel; exome; high resolution imaging; human data; human model; imaging biomarker; imaging modality; improved; indexing; informatics tool; innovation; interest; mRNA Expression; machine learning algorithm; magnetic resonance imaging biomarker; malignant breast neoplasm; molecular marker; novel; online resource; optimal treatments; outcome forecast; personalized therapeutic; pre-clinical; preclinical trial; predicting response; predictive marker; predictive modeling; prospective; quantitative imaging; repaired; response; response biomarker; targeted treatment; tool; transcriptome sequencing; treatment response; triple-negative invasive breast carcinoma; tumor

Project Summary Triple negative breast cancer (TNBC) is a very challenging disease because it is biologically aggressive, there are no targeted therapies, and, consequently, patients have poor prognosis. Although immunotherapy is promising for treating many cancers, TNBC lacks specific molecular targets, no predictive biomarkers to chemotherapy response have yet been identified, and treatment response is difficult to evaluate using current biomarker assessments. Patient-derived xenograft (PDX) models of TNBC offer the exciting opportunity of evaluating this disease in terms of molecular features (e.g., genomic copy number, whole exome sequence, and mRNA expression) to identify candidate “omic” biomarkers that best predict the ultimate response to treatment and could provide surrogate endpoints to validate novel imaging biomarkers in co-clinical trial human trails. Moreover, emerging quantitative MRI methods, such as dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted MRI (DW-MRI), contain rich physiological signals in the images for predicting treatment response, but it is challenging to integrate both animal and human data to reliably predict the treatment response. A paradigm of “co-clinical trials” is emerging in which new treatments are evaluated in animals, and the results guide treatments in clinical trials, but there is a paucity of informatics tools and resources to enable analyses in such animal-to-human work. We believe that an informatics-based methodology that integrates molecular `omics' and imaging data will propel advances in TNBC by enabling development of machine learning models to predict the response to therapies. In order to develop research resources that will encourage consensus on how quantitative imaging methods are optimized to improve the quality of imaging results for co-clinical trials, we will leverage an ongoing co-clinical trial we are undertaking to pursue the following specific aims: (1) Identify molecular biomarkers that predict response in TNBC patient-derived xenografts (PDX); (2) Identify quantitative MRI biomarkers that predict response in TNBC patient-derived xenografts; and (3) Evaluate our informatics tools in a prospective co-clinical trial. Our proposed research is significant and innovative because it leverages advances in basic cancer biology, state-of-the-art imaging technologies, and informatics methods to develop a resource to catalyze discovery in this important disease. Our PDX-based approach will provide the cancer community with a rational, iterative, combined pre-clinical and clinical methodology and supporting data resource for making progressively more refined and personalized therapeutic regimens for TNBC patients. Our methods and tools will likely also generalize to other cancers and could, therefore, substantially benefit the care of all cancer patients.

项目概要 三阴性乳腺癌（TNBC）是一种非常具有挑战性的疾病，因为它具有生物学侵袭性， 目前尚无靶向治疗，因此患者预后较差。虽然免疫疗法是 TNBC 有望治疗多种癌症，但缺乏特定的分子靶标，也没有预测性生物标志物 化疗反应尚未确定，治疗反应很难用目前的方法来评估 生物标志物评估。 TNBC 患者来源的异种移植 (PDX) 模型提供了令人兴奋的机会 从分子特征（例如基因组拷贝数、整个外显子组序列和 mRNA 表达）来识别最能预测治疗最终反应的候选“组学”生物标志物 并可以提供替代终点来验证临床联合试验人体试验中的新型成像生物标志物。 此外，新兴的定量 MRI 方法，例如动态对比增强磁共振 成像（DCE-MRI）和扩散加权磁共振成像（DW-MRI），图像中包含丰富的生理信号 预测治疗反应，但整合动物和人类数据来可靠地预测治疗反应具有挑战性 治疗反应。 “联合临床试验”的范例正在出现，其中对新疗法进行评估 动物，其结果指导临床试验中的治疗，但缺乏信息学工具和资源 以便能够对此类动物到人类的工作进行分析。我们相信基于信息学的方法 整合分子“组学”和成像数据将通过促进 TNBC 的发展来推动 TNBC 的进步 机器学习模型来预测对治疗的反应。为了开发研究资源 鼓励就如何优化定量成像方法以提高成像质量达成共识 根据联合临床试验的结果，我们将利用正在进行的联合临床试验来追求以下目标 具体目标： (1) 确定预测 TNBC 患者来源异种移植物 (PDX) 反应的分子生物标志物； (2) 确定预测 TNBC 患者异种移植反应的定量 MRI 生物标志物；和（3） 在前瞻性联合临床试验中评估我们的信息学工具。我们提出的研究意义重大 创新是因为它利用了基础癌症生物学的进步、最先进的成像技术和 信息学方法开发资源以促进这一重要疾病的发现。我们基于 PDX 的 方法将为癌症界提供合理的、迭代的、组合的临床前和临床 方法论和支持数据资源，使治疗逐渐更加精细和个性化 TNBC 患者的治疗方案。我们的方法和工具也可能推广到其他癌症，并且可以， 因此，大大有利于所有癌症患者的护理。