Advances in radiogenomics in precision radiotherapy

Abstract

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

CLC Number:

R730.55

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

References

[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.