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

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.