A biomarker-driven strategy to guide the use of radiotherapy in non-small cell lung cancer

指导非小细胞肺癌放疗使用的生物标志物驱动策略

• 批准号: 10518064
• 负责人: Mohamed E. Abazeed
• 金额: $ 35.87万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10518064
• 关键词: Acceleration; Accounting; Adoption; Architecture; BRAF gene; Biological Markers; Biopsy Specimen; Calibration; Cancer Etiology; Cancer Patient; Cancer cell line; Catalogs; Catalytic Domain; Categories; Cell Survival; Cells; Cessation of life; Chemicals; Classification; Clinical; Clinical Research; Clinical Trials; Collection; Complement; Data; Dependence; Drug Combinations; Evolution; Experimental Models; Frequencies; Gene Expression; Genetic; Genetic Determinism; Genetic Variation; Genomics; Geography; Goals; Immunofluorescence Immunologic; Individual; Investigation; Lung Neoplasms; MEKs; Malignant Neoplasms; Malignant neoplasm of lung; Measures; Methods; Minor; Molecular; Mutation; NF-E2-related factor 2; Non-Small-Cell Lung Carcinoma; Operative Surgical Procedures; Outcome; PIK3CA gene; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phosphatidylinositols; Phosphotransferases; Proto-Oncogene Proteins B-raf; Radiation; Radiation Tolerance; Radiation therapy; Radiosensitization; Recurrence; Regimen; Research; Resistance; Role; Sampling; Technology; Testing; Therapeutic; Time; Translations; Tumor Cell Line; United States; Variant; Work; actionable mutation; biomarker driven; cancer cell; cancer type; chemoradiation; chemotherapy; clinical translation; clinically actionable; efficacy evaluation; exome sequencing; experimental study; genetic variant; genomic data; in vivo; individual patient; inhibitor; innovation; kinase inhibitor; lung cancer cell; mathematical model; mortality; mouse model; neoplastic cell; novel therapeutics; outcome prediction; palliative; patient derived xenograft model; programs; radiation resistance; radiation response; radioresistant; response; standard of care; synergism; targeted treatment; tumor; tumor DNA

ABSTRACT There is an urgent need to nominate biomarkers that are likely to predict the efficacy of radiotherapy and accelerate their clinical translation. Efforts thus far have been limited in large part because the genetic features regulating tumor cell survival and their frequency across and within individual cancer types had not been studied on a large-scale. Our group completed the largest profiling effort of survival after radiation in cancer cell lines, comprising a diverse collection of 533 genetically annotated tumor cell lines from 26 cancer types. To complement this work, we recently initiated the systematic profiling of >1000 genetic variants that could potentially contribute to the resistance of cancer cells to radiation. We combined results from our profiling efforts to identify features that predict the resistance of lung cancer cells to radiation. The objective in this investigation is to advance the clinical translation of two of the most important regulators of radiation resistance in lung cancer, Nrf2 and Braf. The Nrf2 pathway is genetically altered in ~28% of patients with non-small cell lung cancer (NSCLC) and cells with mutations in NFE2L2 or KEAP1 are the most highly correlated with resistance to radiation. To identify genetic dependencies of Nrf2-active tumors, we used computational and experimental approaches to demonstrate the frequent co-occurrence between Nrf2 and phosphoinositide 3-kinase (PI3K) alteration in NSCLCs. Using genetic and chemical means we show that antagonizing the catalytic subunit of PI3K, p110 (encoded by PIK3CA), decreases Nrf2 activity and reverses radiation resistance driven by this pathway. These results provide the rationale to advance a radiosensitization strategy for patients with Nrf2-active NSCLC by targeting PI3K. Our profiling efforts also demonstrate a critical role for BRAF, which is genetically altered in ~7% of patients with NSCLC, in the resistance of lung cancer cells to radiation. We show, for the first time, that BRAF kinase domain mutations confer resistance to radiation in lung cancers and that they, unlike Nrf2 pathway alterations, are almost invariably a minor component of the tumor (i.e. they are subclonal). We use mathematical and experimental models to show that clonal architecture has significant implications for the likelihood of response to targeted therapies and radiation. Together, these results provide a compelling rationale to examine the role of Nrf2 and Braf alterations in predicting outcomes after radiotherapy and advance a genomically-guided radiosensitization strategy for patients with these tumors. If these hypotheses are correct, our results will demonstrate that radiotherapeutic sensitizers can be selected based on both the identity and type (clonal v. subclonal) of genetic alterations identified in a patient's cancer, prompting an evolution in the use of radiation from a generic approach to one that is guided by the genetic composition of individual tumors.

摘要 迫切需要提名可能预测放射治疗疗效的生物标志物， 加快临床转化。迄今为止的努力在很大程度上是有限的，因为遗传特征 调节肿瘤细胞存活及其在不同癌症类型之间和内部的频率尚未研究 大规模的。我们的团队完成了对癌细胞系辐射后存活率的最大分析， 包括来自26种癌症类型的533种遗传注释的肿瘤细胞系的多样化集合。到 作为对这项工作的补充，我们最近启动了对超过1000种遗传变异的系统分析， 可能有助于癌细胞抵抗辐射。我们综合了分析结果 以确定预测肺癌细胞对辐射抵抗力的特征。这次调查的目的 是推进肺癌中两种最重要的放射抵抗调节因子的临床转化， Nrf 2和Braf。Nrf 2通路在约28%的非小细胞肺癌患者中发生遗传改变 （NSCLC）和具有NFE 2L 2或KEAP 1突变的细胞与对NSCLC的耐药性最高度相关。 辐射为了确定Nrf 2活性肿瘤的遗传依赖性，我们使用了计算和实验方法， 证明Nrf 2和磷脂酰肌醇3-激酶（PI 3 K）之间频繁共存的方法 非小细胞肺癌的改变。利用遗传和化学手段，我们表明，拮抗的催化亚单位， PI 3 K，p110（由PIK 3CA编码），降低Nrf 2活性并逆转由其驱动的辐射抗性。 通路这些结果为Nrf 2活性患者的放射增敏策略提供了依据。 靶向PI 3 K的NSCLC。我们的分析工作也证明了BRAF的关键作用，它在基因上是 在约7%的NSCLC患者中，肺癌细胞对放射的抵抗力发生了改变。我们首先展示 时间，BRAF激酶结构域突变赋予肺癌对放射的抵抗力，并且它们与 Nrf 2通路改变几乎总是肿瘤的次要组成部分（即它们是亚克隆的）。我们使用 数学和实验模型表明，克隆结构有显着的影响， 对靶向治疗和放射的反应可能性。总之，这些结果提供了一个令人信服的理由 研究Nrf 2和Braf改变在预测放疗后结局中的作用，并提出一种新的治疗方法。 基因组学指导的放射增敏策略。如果这些假设正确， 我们的研究结果将表明，辐射增敏剂可以根据身份和类型进行选择， （克隆v.亚克隆）在患者的癌症中鉴定的遗传改变，促使使用 放射治疗从一般的方法到一个由个体肿瘤的遗传组成指导的方法。

项目成果

Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems.

• DOI: 10.1200/cci.22.00100
• 发表时间: 2023-01
• 期刊: JCO CLINICAL CANCER INFORMATICS
• 影响因子: 4.2
• 作者: Randall, James;Teo, Troy;Lou, Bin;Shah, Jainil;Patel, Jyoti;Kamen, Ali;Abazeed, Mohamed E.
• 通讯作者: Abazeed, Mohamed E.