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.

项目概要/摘要 宫颈癌是女性最常见的癌症诊断之一，标准治疗失败 局部晚期宫颈癌 (LACC) 的放化疗 (CRT) 治疗率高达 30-50%。自从 复发和转移性疾病无法治愈，迫切需要识别有治疗风险的患者 尽早失败，以便进行个性化治疗，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进行分子分层未能与患者结果相关联， 我们最近发表了关于整合基因组和影像数据以改善 CRT 后 LACC 风险分层的文章。 因此，在本研究中，我们打算使用多组学数据来定义和验证 LACC 风险组并识别 特定群体的治疗目标。根据我们的初步数据表明不同的生物机制驱动 就诊时具有不同程度淋巴结 (LN) 受累的患者的 CRT 抵抗，我们将分层 根据 LN 状态对患者进行评估，以开发和验证新型放射基因组生物标志物。预测模型将是 使用治疗前肿瘤活检的基因表达数据和治疗前的放射组学特征开发 治疗 PET 成像数据。上游驱动和/或特征基因将在 RNA 和蛋白质上进行验证 通过 qRT-PCR、蛋白质印迹和组织微阵列 (TMA) 检测水平。从我们的研究中发现了一个这样的基因 使用放射基因组学方法的初步数据是核因子红细胞 2 相关因子 2 (NRF2)，它具有 由于 LACC 在宫颈癌中不常见突变，因此之前未在 LACC 中对其进行表征。我们将表演 通过克隆生存分析和其他标准分析来研究 LACC 中 NRF2 生物学的功能分析。在 除了治疗前生物标志物外，我们还将利用时程 MR 图像中的放射组学特征和 治疗中的基因表达数据，用于开发新型放射基因组生物标志物，以评估患者不断变化的风险 CRT 过程中治疗失败的情况，通知在治疗中期调整治疗。预处理 通过应用深度学习直接识别 CRT 结果的预测特征，模型将得到进一步完善 从临床 PET 图像中获取信息，从一开始就为强化治疗提供信息。最后，我们将应用多组学 表征 LACC CRT 相关生物学的方法（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、 在就诊时和 6 周的 CRT 治疗过程中验证复发生物标志物，并将 指出个性化替代治疗方案的目标。除了 LACC 的具体应用之外，我们的 该提案将产生整合多组学数据的新方法，以改进假设驱动的癌症研究。