Exploiting convergent evolution to design biomarker extraction tools for the prediction of therapeutic response in cancer

利用趋同进化设计生物标志物提取工具来预测癌症治疗反应

• 批准号: 10320353
• 负责人: Jessica Anne Scarborough
• 金额: $ 5.18万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10320353
• 关键词: Ally; Biological Markers; Biological Models; Cancer cell line; Cell Line; Chronic Myeloid Leukemia; Clinical; Clinical Treatment; Collection; Data; Data Set; Databases; Disease Progression; Drug resistance; Epidermal Growth Factor Receptor; Evolution; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genomics; Genotype; Grouping; Imatinib; Individual; Inter-tumoral heterogeneity; Lead; Malignant Neoplasms; Methods; Molecular; Molecular Profiling; Mutation; Network-based; Non-Small-Cell Lung Carcinoma; Oncology; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Philadelphia Chromosome; Prediction of Response to Therapy; Process; Research; Research Personnel; Resistance; Sampling; Seeds; Somatic Mutation; Stochastic Processes; Testing; The Cancer Genome Atlas; Therapeutic; Time; Tissue-Specific Gene Expression; Work; base; bcr-abl Fusion Proteins; cancer cell; cancer gene expression; cancer subtypes; cancer type; chemotherapy; design; differential expression; drug sensitivity; effective therapy; experimental study; gene product; improved; individual patient; interest; machine learning model; novel; novel strategies; open source; personalized approach; personalized care; personalized medicine; precision medicine; pressure; responders and non-responders; response; success; targeted treatment; therapy resistant; tool; treatment planning; treatment response; tumor

PROJECT SUMMARY/ABSTRACT The effective treatment of drug resistant tumors represents one of the greatest unmet needs in oncology research. The evolution of therapeutic resistance in cancer is a dynamic process, shaped by many external forces, including selection pressures, microenvironment, and the timescales of clinical treatments. As tumors evolve under these heterogeneous settings, a variety of genotypes emerge and lead to large differences in drug response phenotypes between patients. By grouping tumors based on their response to treatment, we can exploit principles of convergent evolution, where similar phenotypes evolve independently between individuals. In doing so, this work aims to aid precision medicine by identifying commonalities between tumors with similar drug response phenotypes. Gene expression signatures are a powerful tool that can be used to predict convergent states of drug sensitiv- ity and resistance. Using vast open-source datasets, Aim 1 of this proposal will demonstrate a novel method for extracting and validating gene expression signatures to predict therapeutic response in cancer. Cell lines with the best and worst response to a given drug are pooled and compared using differential gene expression analysis. Genes with increased expression in a state of sensitivity or resistance become seed genes in a co-expression network based on gene expression from tumor samples. From there, only seed genes with strong co-expression within patient samples are extracted to form the final gene expression signature. This novel approach integrates clinical sample data to the signature extraction method in order to increase translational value compared to molec- ular signatures extracted using only cell line datasets. Next, Aim 2 of this proposal investigates the phenomenon of collateral sensitivity, where resistance to one drug aligns with sensitivity to another drug. Because the evo- lution of collateral resistance and sensitivity can be unpredictable, molecular signatures of convergent states of collateral sensitivity and resistance could greatly enhance treatment planning once resistance to first-line ther- apy has evolved. Using EGFR+ non-small cell lung cancer cell lines as a model system, this project aims to identify molecular signatures of evolutionarily convergent collateral sensitivity/resistance phenotypes during the experimental evolution of therapeutic resistance to targeted therapies.

项目总结/摘要 耐药肿瘤的有效治疗代表了肿瘤学中最大的未满足需求之一 research.癌症治疗耐药性的演变是一个动态过程，受许多外部因素的影响。 力，包括选择压力，微环境和临床治疗的时间尺度。如肿瘤 在这些异质性环境下进化，出现了各种基因型，并导致药物的巨大差异。 患者之间的反应表型。通过根据肿瘤对治疗的反应进行分组，我们可以 利用趋同进化的原则，即相似的表型在个体之间独立进化。在 这样做，这项工作的目的是通过识别肿瘤与类似药物之间的共性来帮助精确医学。 反应表型 基因表达特征是一个强大的工具，可用于预测药物敏感性的收敛状态， 性和抵抗。使用大量的开源数据集，本提案的目标1将展示一种新的方法， 提取和验证基因表达特征以预测癌症的治疗反应。细胞系， 将对给定药物的最佳和最差反应合并，并使用差异基因表达分析进行比较。 在敏感或抗性状态下表达增加的基因成为共表达的种子基因 基于肿瘤样本基因表达的网络。从那里，只有种子基因与强共表达 提取患者体内的样本以形成最终的基因表达标记。这种新颖的方法集成了 临床样品数据的签名提取方法，以增加翻译价值相比，molec， 仅使用细胞系数据集提取的ular签名。接下来，本提案的目标2调查了这一现象 即对一种药物的耐药性与对另一种药物的敏感性相一致。因为evo- 间接抗性和敏感性的分离可能是不可预测的， 一旦对一线治疗产生耐药性，侧支敏感性和耐药性可以大大增强治疗计划， 他进化了。本项目使用EGFR+非小细胞肺癌细胞系作为模型系统，旨在 鉴定进化收敛的侧枝敏感性/抗性表型的分子特征， 对靶向治疗的治疗抗性的实验性演变。