Integrating multi-omics, imaging, and longitudinal data to predict radiation response in cervical cancer

整合多组学、成像和纵向数据来预测宫颈癌的放射反应

基本信息

批准号：
10734702
负责人：
Jin Zhang
金额：
$ 52.15万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-07 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10734702
关键词：
3-Dimensional Aftercare American Aorta Biological Biological Assay Biological Markers Biological Models Biology Biopsy Cancer Biology Cancer Patient Cancer cell line Cell Cycle Cell Respiration Cell division Cervical Cervix Neoplasms Cessation of life Characteristics Chemotherapy and/or radiation Classification Clinical Clinical Data Clinical Treatment Cluster Analysis Data Development Disease Epithelium Failure Gene Expression Genes Genomics Glycolysis Grant Histology Human Papilloma Virus Vaccination Image In Situ In Vitro Incidence Libraries Ligands Local Therapy Lymph Node Involvement Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of cervix uteri Mass Spectrum Analysis Measures Mesenchymal Metastatic/Recurrent Methods Modality Modeling Molecular Multiomic Data Mutate Nanostructures Neoplasm Metastasis Nodal Organoids Outcome Oxidative Phosphorylation Pathway interactions Patient-Focused Outcomes Patients Pelvis Phenotype Positive Lymph Node Positron-Emission Tomography Pre-Clinical Model Prediction of Radiation Response Prediction of Response to Therapy Prognosis Prognostic Marker Proteins Proteomics Publications Publishing Quantitative Reverse Transcriptase PCR RNA Radiation Oncology Radiogenomics Reaction Reagent Recurrence Recurrent disease Research Resistance Respiration Risk Sampling Science Series Societies Staging Stratification Survival Analysis Survival Rate Testing The Cancer Genome Atlas Time Tissue Microarray Translations Treatment Failure Treatment Protocols Tumor Bank Western Blotting Woman alternative treatment anticancer research biomarker identification cancer diagnosis cancer risk cell type chemoradiation clinical decision-making clinically actionable clinically significant complex data crowdsourcing deep learning druggable target experience genomic data high dimensionality improved improved outcome inhibitor insight lymph nodes metabolomics mortality multiple omics novel novel marker nuclear factor-erythroid 2 outcome prediction palliative patient stratification personalized medicine predicting response predictive marker predictive modeling prognostic prognostic model programs radiomics research clinical testing resistance mechanism risk prediction risk prediction model risk stratification serial imaging single-cell RNA sequencing standard of care targeted treatment therapy outcome transcriptome sequencing transcriptomics treatment risk tumor tumor progression

项目摘要

PROJECT SUMMARY/ABSTRACT Cervical cancer is among the most common cancer diagnoses among women, and treatment failure of standard of care chemoradiation therapy (CRT) for locally advanced cervical cancer (LACC) is as high as 30-50%. Since recurrent and metastatic diseases are not curable, there is a pressing need to identify patients at risk of treatment failure as early as possible to allow for personalized treatment, rather than after a failure and progression. While TCGA’s molecular stratification of cervical cancer using genomic data failed to associate to patient outcomes, we recently published on integrating genomic and imaging data to improve LACC risk stratification after CRT. Therefore, in this study we intend to use multi-omics data to define and validate LACC risk groups and identify group-specific treatment targets. Based on our preliminary data that indicate distinct biological mechanisms drive CRT resistance in patients with different levels of lymph node (LN) involvement at presentation, we will stratify patients by LN status to develop and validate novel radiogenomic biomarkers. Prognostic models will be developed using gene expression data from pre-treatment tumor biopsy and radiomic features from pre- treatment PET imaging data. Upstream driver and/or feature genes will be validated at the RNA and protein levels by qRT-PCR, Western blotting, and tissue microarray (TMA). One such gene identified from our preliminary data using a radiogenomic approach is nuclear factor erythroid 2–related factor 2 (NRF2), which has not been previously characterized in LACC, since it is not frequently mutated in cervical cancer. We will perform functional analysis to study NRF2 biology in LACC via clonogenic survival assay and other standard assays. In addition to pre-treatment biomarkers, we will leverage radiomic features from our time course MR images and on-treatment gene expression data to develop novel radiogenomic biomarkers to assess a patient's evolving risk of treatment failure over the course of CRT, informing adjustment of therapy at mid-treatment. The pre-treatment model will be further refined by applying deep learning to identify predictive features for CRT outcome directly from clinical PET images to inform intensified treatment from the beginning. Finally, we will apply multi-omics approaches (scRNA-seq, proteomics, metabolomics) to characterize the biology related to LACC CRT radiogenomic biomarkers. Taken together, we expect fulfillment of these aims will create a series of optimized, validated recurrence biomarkers at presentation and over the course of 6 weeks of CRT treatment, and will indicate targets for personalized alternative treatment regimens. Beyond the specific application to LACC, our proposal will generate novel methods to integrate multi-omics data to improve hypothesis-driven cancer research.

项目摘要/摘要宫颈癌是女性中最常见的癌症诊断之一，也是标准的治疗失败局部晚期宫颈癌（LACC）的护理化学放疗治疗（CRT）高达30-50％。自从经常性和转移性疾病无法治愈，迫切需要识别患有治疗风险的患者尽早失败以允许个性化治疗，而不是在失败和进展之后。尽管 TCGA使用基因组数据对宫颈癌的分子分层未能与患者结局相关，我们最近发表了整合基因组和成像数据，以改善CRT后LACC风险分层。因此，在这项研究中，我们打算使用多摩管数据来定义和验证LACC风险组并确定小组特异性治疗靶标。根据我们的初步数据，该数据指示不同的生物学机制驱动表达时具有不同淋巴结（LN）受累的患者的CRT耐药性，我们将分层通过LN状态患者开发和验证新型放射基因组生物标志物。预后模型将是使用预处理前肿瘤活检和放射性特征的基因表达数据开发处理宠物成像数据。上游驱动器和/或特征基因将在RNA和蛋白质上进行验证 QRT-PCR的水平，蛋白质印迹和组织微阵列（TMA）。从我们的使用放射原性方法的初步数据是核因子红系2相关因子2（NRF2），它具有以前没有在LACC中表征，因为它在宫颈癌中不经常突变。我们将表演通过克隆生存测定法和其他标准测定法研究LACC中NRF2生物学的功能分析。在除了治疗前的生物标志物外，我们还将利用时间课程MR图像和在治疗基因表达数据中开发新的放射基因组生物标志物，以评估患者不断发展的风险在CRT过程中的治疗失败，在中期治疗中调整治疗。预处理通过应用深度学习来直接识别CRT结果的预测特征，将进一步完善模型从临床宠物图像从一开始就为受启发的治疗提供信息。最后，我们将应用多摩ic 方法（SCRNA-SEQ，蛋白质组学，代谢组学）来表征与LACC CRT相关的生物学放射基因组生物标志物。综上所述，我们预计这些目标的实现将创造一系列优化，在演示和6周的CRT治疗过程中验证了复发生物标志物，并将指示个性化替代治疗方案的目标。除了对LACC的具体应用之外，我们的提案将生成新的方法来整合多摩智数据以改善假设驱动的癌症研究。