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: 10543422
• 负责人: Jessica Anne Scarborough
• 金额: $ 5.27万
• 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/10543422
• 关键词: 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; 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; Shapes; Somatic Mutation; Stochastic Processes; Testing; The Cancer Genome Atlas; Therapeutic; Time; Tissue-Specific Gene Expression; Work; 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 data; personalized approach; personalized care; personalized medicine; precision medicine; predictive tools; 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.

项目概要/摘要 耐药肿瘤的有效治疗是肿瘤学中最大的未满足需求之一 癌症治疗耐药性的演变是一个动态过程，受到许多外部因素的影响。 力量，包括选择压力、微环境和临床治疗的时间尺度。 在这些异质环境下进化，出现了多种基因型并导致药物的巨大差异 通过根据患者对治疗的反应对肿瘤进行分组，我们可以 利用趋同进化的原理，相似的表型在个体之间独立进化。 这项工作旨在通过识别肿瘤与类似药物之间的共性来帮助精准医疗 反应表型。 基因表达特征是一种强大的工具，可用于预测药物敏感性的收敛状态。 该提案的目标 1 将使用大量开源数据集展示一种新颖的方法。 提取和验证基因表达特征以预测癌症细胞系的治疗反应。 使用差异基因表达分析来汇总和比较对给定药物的最佳和最差反应。 在敏感或抗性状态下表达增加的基因成为共表达中的种子基因 基于肿瘤样本基因表达的网络，只有具有强共表达的种子基因。 这种新颖的方法整合了患者样本中的基因表达特征。 与分子生物学相比，将临床样本数据转化为特征提取方法以增加转化价值 接下来，该提案的目标 2 研究了这一现象。 附带敏感性，其中对一种药物的耐药性与对另一种药物的敏感性一致。 附带阻力和敏感性的解决可能是不可预测的，收敛状态的分子特征 一旦对一线治疗产生耐药性，附带敏感性和耐药性可以极大地提高治疗计划 apy 已经发展，使用 EGFR+ 非小细胞肺癌细胞系作为模型系统，该项目旨在 识别进化趋同的附带敏感性/抗性表型的分子特征 对靶向治疗的治疗耐药性的实验进化。