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: 10478972
• 负责人: Michael T. Lewis
• 金额: $ 62.02万
• 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/10478972
• 关键词: Address; Algorithms; Animals; 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; Molecular; Molecular Target; Mus; Neoadjuvant Therapy; Online Systems; Pathologic; Patient-Focused Outcomes; Patient-derived xenograft models of breast cancer; Patients; Phenotype; Physiological; Prediction of Response to Therapy; Prognosis; Proteins; Recurrence; Regimen; Reproducibility; Research; Research Personnel; Resource Informatics; Resources; Science; Signal Transduction; Surrogate Endpoint; Testing; Time; Treatment Efficacy; Treatment Protocols; Work; animal data; arm; base; bioinformatics resource; candidate identification; chemotherapy; co-clinical trial; 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; machine learning model; magnetic resonance imaging biomarker; malignant breast neoplasm; molecular marker; novel; online resource; optimal treatments; patient derived xenograft model; 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.

项目总结