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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的迫切需要提名可能预测放疗疗效的生物标志物加速其临床转化。迄今为止的努力在很大程度上受到限制，因为遗传特征尚未研究调节肿瘤细胞存活及其在个体癌症类型之间和内部的频率大规模地。我们的团队完成了最大规模的癌细胞系辐射后存活率分析工作，包含来自 26 种癌症类型的 533 个基因注释肿瘤细胞系。到为了补充这项工作，我们最近启动了超过 1000 个遗传变异的系统分析，这些变异可以可能有助于癌细胞对辐射的抵抗力。我们综合了分析工作的结果确定预测肺癌细胞对辐射的抵抗力的特征。本次调查的目的是为了推进肺癌中两种最重要的抗辐射调节因子的临床转化， Nrf2 和 Braf。约 28% 的非小细胞肺癌患者的 Nrf2 通路发生基因改变 (NSCLC) 和 NFE2L2 或 KEAP1 突变的细胞与耐药性最相关辐射。为了确定 Nrf2 活性肿瘤的遗传依赖性，我们使用了计算和实验证明 Nrf2 和磷酸肌醇 3-激酶 (PI3K) 之间频繁共现的方法 NSCLC 的改变。使用遗传和化学手段，我们表明拮抗催化亚基 PI3K、p110（由 PIK3CA 编码）降低 Nrf2 活性并逆转由此驱动的辐射抵抗途径。这些结果为推进 Nrf2 活性患者的放射增敏策略提供了理论依据靶向 PI3K 的非小细胞肺癌。我们的分析工作还证明了 BRAF 的关键作用，它在遗传上具有重要意义。大约 7% 的 NSCLC 患者肺癌细胞对放射的抵抗力发生了变化。我们首先展示当时，BRAF 激酶结构域突变赋予肺癌对辐射的抵抗力，并且它们与 Nrf2 通路的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对对靶向治疗和放射反应的可能性。这些结果共同提供了令人信服的理由研究 Nrf2 和 Braf 改变在预测放疗后结果中的作用并推进针对这些肿瘤患者的基因组引导放射增敏策略。如果这些假设正确的话我们的结果将证明放射治疗敏化剂可以根据其特性和类型进行选择（克隆与亚克隆）在患者癌症中发现的基因改变，促进了使用的演变放射治疗从通用方法转变为以个体肿瘤的遗传组成为指导的方法。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

基于图像的深度神经网络用于个性化放射治疗剂量，可跨卫生系统传输。

DOI：
发表时间：
2023-01
期刊：
影响因子：
4.2
作者：
Randall, James;Teo, P Troy;Lou, Bin;Shah, Jainil;Patel, Jyoti;Kamen, Ali;Abazeed, Mohamed E
通讯作者：
Abazeed, Mohamed E

Clonal selection confers distinct evolutionary trajectories in BRAF-driven cancers.

克隆选择赋予 BRAF 驱动的癌症独特的进化轨迹。

DOI：
发表时间：
2019
期刊：
影响因子：
16.6
作者：
Gopal, Priyanka;Sarihan, Elif Irem;Chie, Eui Kyu;Kuzmishin, Gwendolyn;Doken, Semihcan;Pennell, Nathan A;Raymond, Daniel P;Murthy, Sudish C;Ahmad, Usman;Raja, Siva;Almeida, Francisco;Sethi, Sonali;Gildea, Thomas R;Peacock, Craig D;Adams, Drew
通讯作者：
Adams, Drew

An image-based deep learning framework for individualizing radiotherapy dose

用于个性化放射治疗剂量的基于图像的深度学习框架

DOI：
发表时间：
2024-09-14
期刊：
影响因子：
0
作者：
B. Lou;S. Doken;T. Zhuang;D. Wingerter;M. Gidwani;N. Mistry;Lance Ladic;A. Kamen;Mohamed;E. Abazeed
通讯作者：
E. Abazeed

Cellular and Genetic Determinants of the Sensitivity of Cancer to α-Particle Irradiation.

癌症对α粒子辐照敏感性的细胞和遗传决定因素。

DOI：
发表时间：
2019
期刊：
影响因子：
11.2
作者：
Yard, Brian D;Gopal, Priyanka;Bannik, Kristina;Siemeister, Gerhard;Hagemann, Urs B;Abazeed, Mohamed E
通讯作者：
Abazeed, Mohamed E

Modeling Cellular Response in Large-Scale Radiogenomic Databases to Advance Precision Radiotherapy.

在大规模放射基因组数据库中模拟细胞反应以推进精准放射治疗。

DOI：
发表时间：
2019
期刊：
影响因子：
11.2
作者：
Manem, Venkata Sk;Lambie, Meghan;Smith, Ian;Smirnov, Petr;Kofia, Victor;Freeman, Mark;Koritzinsky, Marianne;Abazeed, Mohamed E;Haibe;Bratman, Scott V
通讯作者：
Bratman, Scott V