The Prediction of Radiotherapy Toxicity Using Single Nucleotide Polymorphism-Based Models: A Step Toward Prevention

Seminars in Radiation Oncology

Volumn 25, Issue 4, 2015, Pages 281-291

  Kerns, Sarah L ^a,b   Kundu, Suman ^c   Oh, Jung Hun ^d   Singhal, Sandeep K ^e   Janelsins, Michelle ^a   Travis, Lois B ^a   Deasy, Joseph O ^d   Janssens, A Cecile J E ^f   Ostrer, Harry ^g   Parliament, Matthew ^e   Usmani, Nawaid ^e   Rosenstein, Barry S ^b,h  

^a UNIVERSITY OF ROCHESTER MEDICAL CENTER   (United States)

^b MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^c ERASMUS MEDICAL CENTER   (Netherlands)

^d MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^e UNIVERSITY OF ALBERTA   (Canada)

^f EMORY UNIVERSITY   (United States)

^g ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

^h NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CANCER RADIOTHERAPY; DOSIMETRY; DYSPHAGIA; ESOPHAGITIS; FIBROSIS; GENETIC ASSOCIATION; GENETIC RISK; GENOMICS; HUMAN; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR; MACHINE LEARNING; PENALIZED REGRESSION METHOD; PENETRANCE; PNEUMONIA; PRIORITY JOURNAL; RADIATION INJURY; RADIOGENOMICS; RADIOSENSITIVITY; RECTUM HEMORRHAGE; REGRESSION ANALYSIS; REVIEW; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; NEOPLASMS; RADIATION INJURIES; RADIATION RESPONSE;

HUMANS; NEOPLASMS; POLYMORPHISM, SINGLE NUCLEOTIDE; RADIATION INJURIES;

EID: 84941554669     PISSN: 10534296     EISSN: 15329461     Source Type: Journal    
DOI: 10.1016/j.semradonc.2015.05.006     Document Type: Review

References (53)

1. Travis L.B., Ng A.K., Allan J.M., et al. Second malignant neoplasms and cardiovascular disease following radiotherapy. J Natl Cancer Inst 2012, 104:357-370.
2. Ringborg U., Bergqvist D., Brorsson B., et al. The Swedish Council on Technology Assessment in Health Care (SBU) systematic overview of radiotherapy for cancer including a prospective survey of radiotherapy practice in Sweden 2001-summary and conclusions. Acta Oncol 2003, 42:357-365.
3. Ohri N., Dicker A.P., Showalter T.N. Late toxicity rates following definitive radiotherapy for prostate cancer. Can J Urol 2012, 19:6373-6380.
4. Zelefsky M.J., Chan H., Hunt M., et al. Long-term outcome of high dose intensity modulated radiation therapy for patients with clinically localized prostate cancer. J Urol 2006, 176:1415-1419.
5. American Cancer Society Cancer Treatment and Survivorship Facts & Figures 2014-2015 2014, American Cancer Society, Atlanta, GA.
6. Bentzen S.M., Constine L.S., Deasy J.O., et al. Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): An introduction to the scientific issues. Int J Radiat Oncol Biol Phys 2010, 76:S3-S9.
7. West C., Rosenstein B.S. Establishment of a radiogenomics consortium. Radiother Oncol 2010, 94:117-118.
8. West C., Rosenstein B.S., Alsner J., et al. Establishment of a radiogenomics consortium. Int J Radiat Oncol Biol Phys 2010, 76:1295-1296.
9. Edvardsen H., Landmark-Hoyvik H., Reinertsen K.V., et al. SNP in TXNRD2 associated with radiation-induced fibrosis: A study of genetic variation in reactive oxygen species metabolism and signaling. Int J Radiat Oncol Biol Phys 2013, 86:791-799.
10. Fachal L., Gómez-Caamaño A., Barnett G.C., et al. A three stage genome wide association study reveals susceptibility for late radiotherapy toxicity at the 2q24.1 (TANC1) locus. Nat Genet 2014, 46(8):891-894.
11. Guerra J.L., Gomez D., Wei Q., et al. Association between single nucleotide polymorphisms of the transforming growth factor beta1 gene and the risk of severe radiation esophagitis in patients with lung cancer. Radiother Oncol 2012, 105:299-304.
12. Kerns S.L., Stock R.G., Stone N.N., et al. Genome-wide association study identifies a region on chromosome 11q14.3 associated with late rectal bleeding following radiation therapy for prostate cancer. Radiother Oncol 2013, 107:372-376.
13. Lopez Guerra J.L., Wei Q., Yuan X., et al. Functional promoter rs2868371 variant of HSPB1 associates with radiation-induced esophageal toxicity in patients with non-small-cell lung cancer treated with radio(chemo)therapy. Radiother Oncol 2011, 101:271-277.
14. Pang Q., Wei Q., Xu T., et al. Functional promoter variant rs2868371 of HSPB1 is associated with risk of radiation pneumonitis after chemoradiation for non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2013, 85:1332-1339.
15. Talbot C.J., Tanteles G.A., Barnett G.C., et al. A replicated association between polymorphisms near TNFalpha and risk for adverse reactions to radiotherapy. Br J Cancer 2012, 107:748-753.
16. Andreassen CN, Barnett GC, Kerns SL, et al: Analysis of 5434 patients shows a link between the ATM codon 1853 SNP and the risk of radiation-induced toxicity, in European Society for Radiotherapy and Oncology (ESTRO) 33. Vienna, Austria, 2014.
17. Kerns S.L., CM L.W., Andreassen C.N., et al. Radiogenomics: The search for genetic predictors of radiotherapy response. Future Oncol 2014, 10:2391-2406.
18. Barnett G.C., Thompson D., Fachal L., et al. A genome wide association study (GWAS) providing evidence of an association between common genetic variants and late radiotherapy toxicity. Radiother Oncol 2014, 111(2):178-185.
19. Anderson C.A., Pettersson F.H., Clarke G.M., et al. Data quality control in genetic case-control association studies. Nat Protoc 2010, 5:1564-1573.
20. 1000 Genomes Project Consortium, Abecasis G.R., Auton A., et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012, 491:56-65.
21. Andreassen C.N., Barnett G.C., Langendijk J.A., et al. Conducting radiogenomic research-Do not forget careful consideration of the clinical data. Radiother Oncol 2012, 105:337-340.
22. Nelder J., Wedderburn R. Generalized linear models. J R Stat Soc 1972, 135:370-384.
23. Higgins J., Thompson S., Deeks J., et al. Statistical heterogeneity in systematic reviews of clinical trials: A critical appraisal of guidelines and practice. J Health Serv Res Policy 2002, 7:51-61.
24. Petitti D.B. Approaches to heterogeneity in meta-analysis. Stat Med 2001, 20:3625-3633.
25. Good P.I., Hardin J.W. Common errors in statistics (and how to avoid them) 2009, Wiley, Hoboken, N.J. 3rd ed.
26. Janssens A.C., Aulchenko Y.S., Elefante S., et al. Predictive testing for complex diseases using multiple genes: Fact or fiction?. Genet Med 2006, 8:395-400.
27. Kundu S., Aulchenko Y.S., van Duijn C.M., et al. PredictABEL: An R package for the assessment of risk prediction models. Eur J Epidemiol 2011, 26:261-264.
28. Kong F.M., Hayman J.A., Griffith K.A., et al. Final toxicity results of a radiation-dose escalation study in patients with non-small-cell lung cancer (NSCLC): Predictors for radiation pneumonitis and fibrosis. Int J Radiat Oncol Biol Phys 2006, 65:1075-1086.
29. Tucker S.L., Dong L., Cheung R., et al. Comparison of rectal dose-wall histogram versus dose-volume histogram for modeling the incidence of late rectal bleeding after radiotherapy. Int J Radiat Oncol Biol Phys 2004, 60:1589-1601.
30. Marzi S., Iaccarino G., Pasciuti K., et al. Analysis of salivary flow and dose-volume modeling of complication incidence in patients with head-and-neck cancer receiving intensity-modulated radiotherapy. Int J Radiat Oncol Biol Phys 2009, 73:1252-1259.
31. Eeles R.A., Olama A.A., Benlloch S., et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet 2013, 45(385-91):91e1-92e.
32. Michailidou K., Hall P., Gonzalez-Neira A., et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet 2013, 45(353-61):61e1-62e.
33. McCormack M., Alfirevic A., Bourgeois S., et al. HLA-A*3101 and carbamazepine-induced hypersensitivity reactions in Europeans. N Engl J Med 2011, 364:1134-1143.
34. Ozeki T., Mushiroda T., Yowang A., et al. Genome-wide association study identifies HLA-A*3101 allele as a genetic risk factor for carbamazepine-induced cutaneous adverse drug reactions in Japanese population. Hum Mol Genet 2011, 20:1034-1041.
35. Ge D., Fellay J., Thompson A.J., et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 2009, 461:399-401.
36. Ochi H., Maekawa T., Abe H., et al. IL-28B predicts response to chronic hepatitis C therapy--fine-mapping and replication study in Asian populations. J Gen Virol 2011, 92:1071-1081.
37. Suppiah V., Gaudieri S., Armstrong N.J., et al. IL28B, HLA-C, and KIR variants additively predict response to therapy in chronic hepatitis C virus infection in a European Cohort: A cross-sectional study. PLoS Med 2011, 8:e1001092.
38. Tanaka Y., Kurosaki M., Nishida N., et al. Genome-wide association study identified ITPA/DDRGK1 variants reflecting thrombocytopenia in pegylated interferon and ribavirin therapy for chronic hepatitis C. Hum Mol Genet 2011, 20:3507-3516.
39. Defraene G., Van den Bergh L., Al-Mamgani A., et al. The benefits of including clinical factors in rectal normal tissue complication probability modeling after radiotherapy for prostate cancer. Int J Radiat Oncol Biol Phys 2012, 82:1233-1242.
40. El Naqa I., Bradley J., Blanco A.I., et al. Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors. Int J Radiat Oncol Biol Phys 2006, 64:1275-1286.
41. Appelt A.L., Vogelius I.R., Farr K.P., et al. Towards individualized dose constraints: Adjusting the QUANTEC radiation pneumonitis model for clinical risk factors. Acta Oncol 2013, 53(5):605-612.
42. Cella L., D'Avino V., Liuzzi R., et al. Multivariate normal tissue complication probability modeling of gastrointestinal toxicity after external beam radiotherapy for localized prostate cancer. Radiat Oncol 2013, 8:221.
43. Tucker S.L., Li M., Xu T., et al. Incorporating single-nucleotide polymorphisms into the Lyman model to improve prediction of radiation pneumonitis. Int J Radiat Oncol Biol Phys 2013, 85:251-257.
44. Guy R.T., Santago P., Langefeld C.D. Bootstrap aggregating of alternating decision trees to detect sets of SNPs that associate with disease. Genet Epidemiol 2012, 36:99-106.
45. Zhang Y. A novel bayesian graphical model for genome-wide multi-SNP association mapping. Genet Epidemiol 2012, 36:36-47.
46. Abraham G., Tye-Din J.A., Bhalala O.G., et al. Accurate and robust genomic prediction of celiac disease using statistical learning. PLoS Genet 2014, 10:e1004137.
47. Manor O., Segal E. Predicting disease risk using bootstrap ranking and classification algorithms. PLoS Comput Biol 2013, 9:e1003200.
48. Wei Z., Wang W., Bradfield J., et al. Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease. Am J Hum Genet 2013, 92:1008-1012.
49. Oh JH, Kerns SL, Ostrer H, et al: A machine learning method demonstrates that a large number of SNPs contribute to clinical radiosensitivity, in European Societry for Radiotherapy and Oncology (ESTRO), Barcelona, Spain, 2015.
50. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc 1996, 58:267-288.
51. De Ruyck K., Duprez F., Werbrouck J., et al. A predictive model for dysphagia following IMRT for head and neck cancer: Introduction of the EMLasso technique. Radiother Oncol 2013, 107:295-299.
52. De Langhe S., De Meerleer G., De Ruyck K., et al. Integrated models for the prediction of late genitourinary complaints after high-dose intensity modulated radiotherapy for prostate cancer: Making informed decisions. Radiother Oncol 2014, 112:95-99.
53. Sabbe N., Thas O., Ottoy J.P. EMLasso: Logistic lasso with missing data. Stat Med 2013, 32:3143-3157.