放射基因组学在精准放疗中的研究进展

基础医学与临床 ›› 2020, Vol. 40 ›› Issue (5): 696-700.

放射基因组学在精准放疗中的研究进展

叶菲, 余盈^*

华中科技大学同济医学院附属协和医院普外科, 湖北武汉 430022

收稿日期:2019-08-26 修回日期:2020-01-17 出版日期:2020-05-05 发布日期:2020-04-30
通讯作者: *52092033@qq.com

Advances in radiogenomics in precision radiotherapy

YE Fei, YU Ying^*

Department of General Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China

Received:2019-08-26 Revised:2020-01-17 Online:2020-05-05 Published:2020-04-30
Contact: *52092033@qq.com

摘要/Abstract

摘要： 在精准医疗的背景下,放射基因组学作为一种将放射组学与基因组学结合在一起进行多组学研究的新型策略,在预测基因组结构及表达的改变、构建放射生物学的物理模型上等方面具有独到的优势。尤其是在精准放疗方面,囊括影像-基因两个维度的具有放射基因组学特点的模型可以提供多组学的信息,进而精准地指导临床决策,在提高患者生存率的同时,减少放疗带来的并发症。

关键词: 放射基因组学, 机器学习, 精准放疗, 个体化医疗

Abstract: In the background of precision medicine, radiogenomics is a new strategy that combines radiomics and genomics for multi-omics research. It has unique advantages in predicting changes of genome expression and structure and establishing the physics model of radiobiology. Especially in terms of precision radiotherapy, a model of radiation genomics that encompasses the two dimensions of images-gene can provide multi-omics information and accurately guide clinical therapy, which increases the rate of patient survival and reduces the radiotherapy band coming complications.

Key words: radiogenomics, machine learning, precision radiotherapy, individualized medicine

中图分类号:

R730.55

叶菲, 余盈. 放射基因组学在精准放疗中的研究进展[J]. 基础医学与临床, 2020, 40(5): 696-700.

YE Fei, YU Ying. Advances in radiogenomics in precision radiotherapy[J]. Basic & Clinical Medicine, 2020, 40(5): 696-700.

参考文献

[1]Andreassen CN, Eriksen JG, Jensen K, et al. IMRT-Biomarkers for dose escalation, dose de-escalation and personalized medicine in radiotherapy for head and neck cancer[J]. Oral Oncol, 2018. 86: 91-99.
[2]Qian ZH, Li YM, Sun ZY, et al. Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction[J]. Aging-Us, 2018. 10: 2884-2899.
[3]Naqa El, Napel S, Zaidi H. Radiogenomics is the future of treatment response assessment in clinical oncology[J]. Med Phys, 2018, 45: 4325-4328.
[4]Kerns SL, Chuang KH, Hall W, et al. Radiation biology and oncology in the genomic era[J]. Br J Radiol, 2018: 91.doi: 10.1259/bjr.20170949.
[5]Kickingereder P, Bonekamp D, Nowosielski M, et al. Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR Imaging features[J]. Radiology, 2016, 281: 907-918.
[6]Hu LS, Ning SL, Eschbacher JM, et al. Radiogenomics to characterize regional genetic heterogeneity in glio-blastoma[J]. Neuro-Oncol, 2017, 19: 128-137.
[7]Kang J, Rancati T, Lee S, et al. Machine learning and radiogenomics: lessons learned and future directions[J]. Front Oncol, 2018: 8. doi: 10.3389/fonc.2018.00228.
[8]Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study[J]. Lancet Oncol, 2018, 19: 1180-1191.
[9]Kerns SL, Fachal L, Dorling L, et al. Radiogenomics consortium genome-wide association study meta-analysis of late toxicity after prostate cancer radiotherapy[J]. J Natl Cancer Inst, 2019. doi: 10.1093/jnci/djz075.
[10]Seibold P, Behrens S, Schmezer P, XRCC1 polymor-phism associated with late toxicity after radiation therapy in breast cancer patients[J]. Int J Radiat Oncol Biol Phys, 2015, 92: 1084-1092.
[11]Coates J, Jeyaseelan AK, Ybarra N, et al. Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer[J]. Radiother Oncol, 2015, 115: 107-113.
[12]Zhang YY, He Q, Hu Z, et al. Long noncoding RNA LINP1 regulates repair of DNA double-strand breaks in triple-negative breast cancer[J]. Nat Struct Mol Biol, 2016, 23: 522-530.
[13]Manem VS, Dhawan A. RadiationGeneSigDB: a data-base of oxic and hypoxic radiation response gene signa-tures and their utility in pre-clinical research[J]. Br J Radiol, 2019: 92.doi: 10.1259/bjr.20190198.
[14]Yard BD, Adams DJ, Chie EK, et al. A genetic basis for the variation in the vulnerability of cancer to DNA damage[J]. Nat Commun, 2016: 7.doi: 10.1038/ncomms11428.
[15]Annede P, Cosset JM, Van Limbergen E, et al. Radiobiology: foundation and new Insights in modeling brachytherapy effects[J]. Semin Radiat Oncol, 2020, 30: 4-15.
[16]Wu J, Tha KK, Xing L, et al. Radiomics and radiogenomics for precision radiotherapy[J]. J Radiat Res, 2018, 59: 125-131.
[17]Giraud P, Giraud P, Gasnier A, et al. Radiomics and machine learning for radiotherapy in head and neck cancers[J]. Front Oncol, 2019: 9.doi: 10.3389/fonc.2019.00174.
[18]Marcu LG, Forster JC, Bezak E, The potential role of radiomics and radiogenomics in patient stratification by tumor hypoxia status[J]. J Am Coll Radiol, 2019, 16: 1329-1337.
[19]Lambin P, van Stiphout RGPM, Starmans MHW, et al. Predicting outcomes in radiation oncology-multifactorial decision support systems[J]. Nat Rev Clin Oncol, 2013, 10: 27-40.
[20]Jeon SH, Chie EK, Kim YJ, et al. Targeted next-generation DNA sequencing identifies Notch signaling pathway mutation as a predictor of radiation response[J]. Int J Radiat Biol, 2019.doi: 10.1080/09553002.2019.1665212.
[21]El Naqa I, Kerns SL, Coates J, et al. Radiogenomics and radiotherapy response modeling[J]. Phys Med Biol, 2017, 62: R179-R206.
[22]Jang BS, IA Kim. A radiosensitivity gene signature and PD-L1 status predict clinical outcome of patients with invasive breast carcinoma in The Cancer Genome Atlas (TCGA) dataset[J]. Radiother Oncol, 2017, 124: 403-410.
[23]Pershad Y, Govindan S, Hara AK, et al. Using naive bayesian analysis to determine imaging characteristics of KRAS mutations in metastatic colon cancer[J]. Diagnostics, 2017: 7.doi: 10.3390/diagnostics7030050.
[24]El Naqa I, Pandey G, Aerts H, et al. Radiation therapy outcomes models in the era of radiomics and radiogenomics: uncertainties and validation[J]. Int J Radiat Oncol Biol Phys, 2018, 102: 1070-1073.
[25]Redig AJ,Janne PA. Basket trials and the evolution of clinical trial design in an era of genomic medicine[J]. J Clin Oncol, 2015, 33: 975-977.