Background Radiation is an effective anti-cancer therapy but leads to severe late radiation toxicity in 5%–10% of patients. Assuming that genetic susceptibility impacts this risk, we hypothesized that the cellular response of normal tissue to X-rays could discriminate patients with and without late radiation toxicity. Methods and Findings Prostate carcinoma patients without evidence of cancer 2 y after curative radiotherapy were recruited in the study. Blood samples of 21 patients with severe late complications from radiation and 17 patients without symptoms were collected. Stimulated peripheral lymphocytes were mock-irradiated or irradiated with 2-Gy X-rays. The 24-h radiation response was analyzed by gene expression profiling and used for classification. Classification was performed either on the expression of separate genes or, to augment the classification power, on gene sets consisting of genes grouped together based on function or cellular colocalization. X-ray irradiation altered the expression of radio-responsive genes in both groups. This response was variable across individuals, and the expression of the most significant radio-responsive genes was unlinked to radiation toxicity. The classifier based on the radiation response of separate genes correctly classified 63% of the patients. The classifier based on affected gene sets improved correct classification to 86%, although on the individual level only 21/38 (55%) patients were classified with high certainty. The majority of the discriminative genes and gene sets belonged to the ubiquitin, apoptosis, and stress signaling networks. The apoptotic response appeared more pronounced in patients that did not develop toxicity. In an independent set of 12 patients, the toxicity status of eight was predicted correctly by the gene set classifier. Conclusions Gene expression profiling succeeded to some extent in discriminating groups of patients with and without severe late radiotherapy toxicity. Moreover, the discriminative power was enhanced by assessment of functionally or structurally related gene sets. While prediction of individual response requires improvement, this study is a step forward in predicting susceptibility to late radiation toxicity.
References
[1]
Turesson I, Carlsson J, Brahme A, Glimelius B, Zackrisson B, et al. (2003) Biological response to radiation therapy. Acta Oncol 42: 92–106.
[2]
Zelefsky MJ, Fuks Z, Hunt M, Lee HJ, Lombardi D, et al. (2001) High dose radiation delivered by intensity modulated conformal radiotherapy improves the outcome of localized prostate cancer. J Urol 166: 876–881.
[3]
Hanks GE, Hanlon AL, Epstein B, Horwitz EM (2002) Dose response in prostate cancer with 8–12 years' follow-up. Int J Radiat Oncol Biol Phys 54: 427–435.
[4]
Pollack A, Zagars GK, Starkschall G, Antolak JA, Lee JJ, et al. (2002) Prostate cancer radiation dose response: Results of the M. D. Anderson phase III randomized trial. Int J Radiat Oncol Biol Phys 53: 1097–1105.
[5]
Peeters STK, Heemsbergen WD, Van Putten WLJ, Slot A, Tabak H, et al. (2005) Acute and late complications after radiotherapy for prostate cancer: Results of a multicenter randomized trial comparing 68 Gy to 78 Gy. Int J Radiat Oncol Biol Phys 61: 1019–1034.
[6]
Safwat A, Bentzen SM, Turesson I, Hendry JH (2002) Deterministic rather than stochastic factors explain most of the variation in the expression of skin telangiectasia after radiotherapy. Int J Radiat Oncol Biol Phys 52: 198–204.
[7]
Tucker SL, Turesson I, Thames HD (1992) Evidence for individual differences in the radiosensitivity of human skin. Eur J Cancer 28A: 1783–1791.
[8]
Turesson I, Nyman J, Holmberg E, Oden A (1996) Prognostic factors for acute and late skin reactions in radiotherapy patients. Int J Radiat Oncol Biol Phys 36: 1065–1075.
[9]
Borgmann K, Roper B, El-Awady R, Brackrock S, Bigalke M, et al. (2002) Indicators of late normal tissue response after radiotherapy for head and neck cancer: Fibroblasts, lymphocytes, genetics, DNA repair, and chromosome aberrations. Radiother Oncol 64: 141–152.
[10]
Hoeller U, Borgmann K, Bonacker M, Kuhlmey A, Bajrovic A, et al. (2003) Individual radiosensitivity measured with lymphocytes may be used to predict the risk of fibrosis after radiotherapy for breast cancer. Radiother Oncol 69: 137–144.
[11]
Baumann M, Holscher T, Begg AC (2003) Towards genetic prediction of radiation responses: ESTRO's GENEPI project. Radiother Oncol 69: 121–125.
[12]
Russell NS, Begg AC (2002) Editorial radiotherapy and oncology 2002: Predictive assays for normal tissue damage. Radiother Oncol 64: 125–129.
[13]
Burnet NG, Nyman J, Turesson I, Wurm R, Yarnold JR, et al. (1992) Prediction of normal-tissue tolerance to radiotherapy from in-vitro cellular radiation sensitivity. Lancet 339: 1570–1571.
[14]
Barber JBP, West CML, Kiltie AE, Roberts SA, Scott D (2000) Detection of individual differences in radiation-induced apoptosis of peripheral blood lymphocytes in normal individuals, ataxia telangiectasia homozygotes and heterozygotes, and breast cancer patients after radiotherapy. Radiat Res 153: 570–578.
[15]
Dikomey E, Borgmann K, Peacock J, Jung H (2003) Why recent studies relating normal tissue response to individual radiosensitivity might have failed and how new studies should be performed. Int J Radiat Oncol Biol Phys 56: 1194–1200.
[16]
Lee TK, Allison RR, O'Brien KF, Johnke RM, Christie KI, et al. (2003) Lymphocyte radiosensitivity correlated with pelvic radiotherapy morbidity. Int J Radiat Oncol Biol Phys 57: 222–229.
[17]
Begley TJ, Rosenbach AS, Ideker T, Samson LD (2002) Damage recovery pathways in revealed by genomic phenotyping and interactome mapping. Mol Cancer Res 1: 103–112.
[18]
Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, et al. (2004) Genetic analysis of genome-wide variation in human gene expression. Nature 430: 743–747.
[19]
Rieger KE, Hong WJ, Tusher VG, Tang J, Tibshirani R, et al. (2004) Toxicity from radiation therapy associated with abnormal transcriptional responses to DNA damage. Proc Natl Acad Sci U S A 101: 6635–6640.
[20]
Amundson SA, Do KT, Shahab S, Bittner M, Meltzer P, et al. (2000) Identification of potential mRNA biomarkers in peripheral blood lymphocytes for human exposure to ionizing radiation. Radiat Res 154: 342–346.
[21]
Jen KY, Cheung VG (2003) Transcriptional response of lymphoblastoid cells to ionizing radiation. Genome Res 13: 2092–2100.
[22]
Rieger KE, Chu G (2004) Portrait of transcriptional responses to ultraviolet and ionizing radiation in human cells. Nucleic Acids Res 32: 4786–4803.
[23]
van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530–536.
[24]
Dobbin KK, Beer DG, Meyerson M, Yeatman TJ, Gerald WL, et al. (2005) Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays. Clin Cancer Res 11: 565–572.
[25]
Michiels S, Koscielny S, Hill C (2005) Prediction of cancer outcome with microarrays: A multiple random validation strategy. Lancet 365: 488–492.
[26]
Frantz S (2005) An array of problems. Nat Rev Drug Discov 4: 362–363.
Said MR, Begley TJ, Oppenheim AV, Lauffenburger DA, Samson LD (2004) Global network analysis of phenotypic effects: Protein networks and toxicity modulation in . Proc Natl Acad Sci U S A 101: 18006–18011.
[32]
Pavy JJ, Denekamp J, Letschert J, Littbrand B, Mornex F, et al. (1995) EORTC Late Effects Working Group. Late effects toxicity scoring: The SOMA scale. Int J Radiat Oncol Biol Phys 31: 1043–1091.
[33]
Tates AD, van Dam FJ, van Mossel H, Schoemaker H, Thijssen JC, et al. (1991) Use of the clonal assay for the measurement of frequencies of HPRT mutants in T-lymphocytes from five control populations. Mutat Res 253: 199–213.
[34]
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004) Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol 5: R80.
[35]
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249–264.
[36]
Chu TM, Weir B, Wolfinger R (2002) A systematic statistical linear modeling approach to oligonucleotide array experiments. Math Biosci 176: 35–51.
[37]
Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95: 14863–14868.
[38]
Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100: 9440–9445.
[39]
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene Ontology: Tool for the unification of biology. Nat Genet 25: 25–29.
[40]
Izaks GJ, Remarque EJ, Becker SV, Westendorp RG (2003) Lymphocyte count and mortality risk in older persons. The Leiden 85-Plus Study. J Am Geriatr Soc 51: 1461–1465.
[41]
Everitt BS (1993) Cluster analysis, 3rd edition. London: Edward Arnold. 170 p.
[42]
Bond GL, Hu WW, Bond EE, Robins H, Lutzker SG, et al. (2004) A single nucleotide polymorphism in the MDM2 promoter attenuates the p53 tumor suppressor pathway and accelerates tumor formation in humans. Cell 119: 591–602.
[43]
Fraser HB (2005) Modularity and evolutionary constraint on proteins. Nat Genet 37: 351–352.
[44]
Svensson JP, de Menezes RX, Turesson I, Giphart-Gassler M, Vrieling H (2005) Dissecting systems-wide data using mixture models: Application to identify affected cellular processes. BMC Bioinformatics 6: 177.
[45]
Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, et al. (2005) Standardizing global gene expression analysis between laboratories and across platforms . Nat Methods 2: 351–356.
