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

• 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
• 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

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 宫颈癌是女性最常见的癌症诊断之一，标准的治疗失败 放化疗(CRT)治疗局部晚期宫颈癌(LACC)的成功率高达30%-50%。自.以来 复发和转移性疾病是不可治愈的，迫切需要确定有可能接受治疗的患者。 及早失败，允许个体化治疗，而不是在失败和进展后进行治疗。而当 TCGA使用基因组数据对宫颈癌进行的分子分层未能与患者的预后相关联， 我们最近发表了关于整合基因组和成像数据以改善CRT后LACC风险分层的文章。 因此，在这项研究中，我们打算使用多组学数据来定义和验证LACC风险组，并确定 针对特定群体的治疗目标。基于我们的初步数据表明不同的生物机制驱动 CRT抵抗的患者在出现不同程度的淋巴结(LN)受累时，我们会分层 根据LN状态的患者开发和验证新的放射基因组生物标志物。预测模型将是 使用来自治疗前肿瘤活检的基因表达数据和来自治疗前的 处理正电子发射计算机断层成像数据。上游驱动和/或功能基因将在RNA和蛋白质中得到验证 通过qRT-PCR、Western blotting和组织芯片(TMA)进行检测。其中一个这样的基因是从我们的 使用放射基因组学方法的初步数据是核因子红系2相关因子2(NRF2)，它具有 以前在LACC中没有特征，因为它在宫颈癌中不经常突变。我们将表演 功能分析通过克隆存活实验和其他标准实验研究LACC中的NRF2生物学。在……里面 除了治疗前的生物标记物外，我们还将利用我们的时间进程磁共振图像和 治疗中的基因表达数据，以开发新的放射基因组生物标志物来评估患者的演变风险 在CRT治疗过程中的治疗失败，在治疗中期通知治疗调整。前置处理 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PET图像到提示强化治疗从一开始。最后，我们将应用多组学 研究LACC CRT相关生物学特性的方法(scRNA-seq、蛋白质组学、代谢组学) 放射基因组生物标志物。总而言之，我们预计这些目标的实现将创造一系列优化的、 经过验证的复发生物标志物在临床表现和CRT治疗6周过程中，将 指出个性化替代治疗方案的目标。除了对LACC的特定应用之外，我们的 该提案将产生整合多组学数据的新方法，以改善假说驱动的癌症研究。