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Germ line polymorphisms as predictive markers for pre-surgical radiochemotherapy in locally advanced rectal cancer: a 5-year literature update and critical review

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Abstract

Purpose

Locally advanced rectal cancer is currently treated with pre-surgical radiotherapy and chemotherapy. Approximately one-half of patients obtain a relevant shrinkage/disappearance of tumour, with major clinical advantages. The remaining patients, in contrast, show no benefit and possibly need alternative treatment. To provide the best therapeutic option for each individual patient, predictive markers have been widely researched. This review was undertaken to evaluate recent progress made in this field.

Methods

A systematic literature search was performed using PubMed and Scopus database, focused on germ line gene polymorphisms as biomarkers and response and toxicity as outcomes. Because an exhaustive previous review was available describing findings up to 2008, we restricted our analysis to the last 5 years.

Results

Ten original research articles were found, reporting promising results for some candidate genes in drug metabolism (TYMS, MTHFR), DNA repair (XRCC1, OGG1, CCND1) and inflammation (SOD2, TGFB1)/immunity (IL13) pathways, but with no firm conclusion. All the studies had small sample size and were defined as exploratory. This review highlights pivotal molecular, clinical, genetic and statistical issues in the investigation of genetic polymorphisms as outcome predictors for rectal cancer and offers suggestions for future development.

Conclusions

What emerges is a clear need for new proposals, especially in view of the increasing evidence for tumour-host and gene-gene interactions during anticancer treatment, together with stronger adherence to proper methodological requirements.

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Abbreviations

5-FU:

5-Fluorouracil

5-MTHF:

5-Methyl-tetrahydrofolate

5,10-MTHF:

5,10-Methylenetetrahydrofolate

CART:

Classification and regression tree

CT:

Chemotherapy

DFS:

Disease-free survival

GWAS:

Genome-wide association studies

MAF:

Minor allele frequency

MTHFR:

Methylenetetrahydrofolate reductase

OGG1:

Oxoguanine DNA glycosylase

OS:

Overall survival

q-RT-PCR:

Quantitative, retro-transcription polymerase chain reaction

RC:

Rectal cancer

RCT:

Radiochemotherapy

RR:

Response rate

RT:

Radiotherapy

SNP:

Single nucleotide polymorphism

SOD:

Superoxide dismutase

TGF:

Transforming growth factor

TNF-α:

Tumour necrosis factor alpha

TYMS/TS:

Thymidylate synthase gene/protein

UTR:

Untranslated region

XRCC1:

X-ray cross-complementing group 1

References

  1. Trakarnsanga A, Ithimakin S, Weiser MR (2012) Treatment of locally advanced rectal cancer: controversies and questions. World J Gastroenterol 39:5521–5532

    Article  Google Scholar 

  2. Chen ET, Mohiuddin M, Brodovsky H, Fishbein G, Marks G (1994) Downstaging of advanced rectal cancer following combined preoperative chemotherapy and high dose radiation. Int J Radiat Oncol Biol Phys 30:169–175

    Article  CAS  PubMed  Google Scholar 

  3. De Caluwé L, Van Nieuwenhove Y, Ceelen WP (2013) Preoperative chemoradiation versus radiation alone for stage II and III resectable rectal cancer. Cochrane Database Syst Rev Issue 2. Art. No.: CD006041. doi: 10.1002/14651858.CD006041.pub3

  4. Coate L, Cuffe S, Horgan A, Hung RJ, Christiani D, Liu G (2010) Germline genetic variation, cancer outcome, and pharmacogenetics. J Clin Oncol 28:4029–4037. doi:10.1200/JCO.2009.27.2336

    Article  CAS  PubMed  Google Scholar 

  5. Pesenti C, Gusella M, Sirchia SM, Miozzo M (2015) Germline oncopharmacogenetics, a promising field in cancer therapy. Cell Oncol (Dordr)

  6. Ludwig JA, Weinstein JN (2005) Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 5:845–856

    Article  CAS  PubMed  Google Scholar 

  7. Astier A (2010) Recent developments of pharmacogenomics in the treatment of colorectal cancers. Ann Pharmacother 68:233–253

    CAS  Google Scholar 

  8. Kuremsky JG, Tepper JE, McLeod HL (2009) Biomarkers for response to neoadjuvant chemoradiation for rectal cancer. Int J Radiat Oncol Biol Phys 74:673–688

    Article  CAS  PubMed  Google Scholar 

  9. Agostini M, Crotti S, Bedin C, Cecchin E, Maretto I, D’Angelo E, Pucciarelli S, Nitti D (2014) Predictive response biomarkers in rectal cancer neoadjuvant treatment. Front Biosci (Schol Ed) 6:110–119

    Article  Google Scholar 

  10. Milgrom SA, Garcia-Aguilar J (2013) Molecular biomarkers as predictors of response to neoadjuvant chemoradiation therapy in rectal cancer. Semin Colon Rectal Surg 24:119–124

    Article  Google Scholar 

  11. Grimminger PP, Brabender J, Warnecke-Eberz U, Narumiya K, Wandhöfer C, Drebber U, Bollschweiler E, Hölscher AH, Metzger R, Vallböhmer D (2010) XRCC1 gene polymorphism for prediction of response and prognosis in the multimodality therapy of patients with locally advanced rectal cancer. J Surg Res 164:e61–e66

    Article  CAS  PubMed  Google Scholar 

  12. Lamas MJ, Duran G, Gomez A, Balboa E, Anido U, Bernardez B, Rana-Diez P, Lopez R, Carracedo A, Barros F (2012) X-ray cross-complementing group 1 and thymidylate synthase polymorphisms might predict response to chemoradiotherapy in rectal cancer patients. Int J Radiat Oncol Biol Phys 82:138–144

    Article  CAS  PubMed  Google Scholar 

  13. Balboa E, Duran G, Lamas MJ, Gomez-Caamaño A, Celeiro-Muñoz C (2010) Pharmacogenetic analysis in neoadjuvant chemoradiation for rectal cancer: high incidence of somatic mutations and their relation with response. Pharmacogenomics 11:747–761

    Article  CAS  PubMed  Google Scholar 

  14. Ho-Pun-Cheung A, Assenat E, Bascoul-Mollevi C, Bibeau F, Boissière-Michot F, Thezenas S, Cellier D, Azria D, Rouanet P, Senesse P, Ychou M, Lopez-Crapez E (2011) A large-scale candidate gene approach identifies SNPs in SOD2 and IL13 as predictive markers of response to preoperative chemoradiation in rectal cancer. Pharmacogenomics J 11:437–443

    Article  CAS  PubMed  Google Scholar 

  15. Cecchin E, Agostani M, Pucciarelli S, De Paoli A, Canzonieri V, Sigon R, De Mattia E, Friso ML, Biason P, Visentin M, Nitti D, Toffoli G (2011) Tumor response is predicted by patient genetic profile in rectal cancer patients treated with neo-adjuvant chemo-radiotherapy. Pharmacogenomics J 11:214–226

    Article  CAS  PubMed  Google Scholar 

  16. Garcia-Aguilar J, Chen Z, Smith DD, Li W, Madoff RD, Cataldo P, Marcet J, Pastor C (2011) Identification of a biomarker profile associated with resistance to neoadjuvant chemoradiation therapy in rectal cancer. Ann Surg 254:486–492. doi:10.1097/SLA.0b013e31822b8cfa

    Article  PubMed Central  PubMed  Google Scholar 

  17. Páez D, Paré L, Altés A, Sancho-Poch FJ, Petriz L, Garriga J, Monill JM, Salazar J, del Rio E, Barnadas A, Marcuello E, Baiget M (2010) Thymidylate synthase germline polymorphisms in rectal cancer patients treated with neoadjuvant chemoradiotherapy based on 5-fluorouracil. J Cancer Res Clin Oncol 136:1681–1689

    Article  PubMed  Google Scholar 

  18. Páez D, Salazar J, Paré L, Pertriz L, Targarona E, del Rio E, Barnadas A, Marcuello E, Baiget M (2011) Pharmacogenetic study in rectal cancer patients treated with preoperative chemoradiotherapy: polymorphisms in thymidylate synthase, epidermal growth factor receptor, GSTP1, and DNA repair genes. Int J Radiat Oncol Biol Phys 81:1319–1327

    Article  PubMed  Google Scholar 

  19. Schirmer MA, Mergler CP, Rave-Fränk M, Herrmann MK, Hennies S, Gaedcke J, Conradi LC, Jo P, Beissbarth T, Hess CF, Becker H, Ghadimi M, Brockmöller J, Christiansen H, Wolff HA (2012) Acute toxicity of radiochemotherapy in rectal cancer patients: a risk particularly for carriers of the TGFB1 Pro25 variant. Int J Radiat Oncol Biol Phys 83:149–157

    Article  CAS  PubMed  Google Scholar 

  20. Thomas F, Motsinger-Reif AA, Hoskins JM, Dvorak A, Roy S, Alyasiri A, Myerson RJ, Fleshman JW, Tan BR, McLeod HL (2011) Methylenetetrahydrofolate reductase genetic polymorphisms and toxicity to 5-FU-based chemoradiation in rectal cancer. Br J Cancer 105:1654–1662

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Parliament MB, Murray D (2010) Single nucleotide polymorphisms of DNA repair genes as predictors of radioresponse. Semin Radiat Oncol 20(4):232–240. doi:10.1016/j.semradonc.2010.05.003, Review

    Article  PubMed  Google Scholar 

  22. Borchiellini D, Etienne-Grimaldi MC, Thariat J, Milano G (2012) The impact of pharmacogenetics on radiation therapy outcome in cancer patients. A focus on DNA damage response genes. Cancer Treat Rev 38(6):737–759. doi:10.1016/j.ctrv.2012.02.004, Review

    Article  CAS  PubMed  Google Scholar 

  23. Nishioka M, Ueno K, Hazama S, Okada T, Sakai K, Suehiro Y, Okayama N, Hirata H, Oka M, Imai K, Dahiya R, Hinoda Y (2013) Possible involvement of Wnt11 in colorectal cancer progression. Mol Carcinog 52:207–217. doi:10.1002/mc.21845

    Article  PubMed  Google Scholar 

  24. Rimkus C, Friederichs J, Boulesteix AL, Theisen J, Mages J, Becker K, Nekarda H, Rosenberg R, Janssen KP, Siewert JR (2008) Microarray-based prediction of tumor response to neoadjuvant radiochemotherapy of patients with locally advanced rectal cancer. Clin Gastroenterol Hepatol 6:53–61. doi:10.1016/j.cgh.2007.10.022

    Article  CAS  PubMed  Google Scholar 

  25. Watanabe T, Kobunai T, Akiyoshi T, Matsuda K, Ishihara S, Nozawa K (2014) Prediction of response to preoperative chemoradiotherapy in rectal cancer by using reverse transcriptase polymerase chain reaction analysis of four genes. Dis Colon Rectum 57:23–31

    Article  PubMed  Google Scholar 

  26. Chen MB, Wu XY, Yu R, Li C, Wang LQ, Shen W, Lu PH (2012) P53 status as a predictive biomarker for patients receiving neoadjuvant radiation-based treatment: a meta-analysis in rectal cancer. PLoS One 7(9):e45388. doi:10.1371/journal.pone.0045388

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Leong KJ, Beggs A, James J, Morton DG, Matthews GM, Bach SP (2014) Biomarker-based treatment selection in early-stage rectal cancer to promote organ preservation. Br J Surg 101(10):1299–1309. doi:10.1002/bjs.9571

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. McAllister SS, Weinberg RA (2010) Tumor-host interactions: a far-reaching relationship. J Clin Oncol 28:4022–4028. doi:10.1200/JCO.2010.28.4257

    Article  PubMed  Google Scholar 

  29. Chargari C, Clemenson C, Martins I, Perfettini JL, Deutsch E (2013) Understanding the functions of tumor stroma in resistance to ionizing radiation: emerging targets for pharmacological modulation. Drug Resist Updat 16:10–21

    Article  CAS  PubMed  Google Scholar 

  30. Kamochi N, Nakashima M, Aoki S, Uchihashi K, Sugihara H, Toda S, Kudo S (2008) Irradiated fibroblast-induced bystander effects on invasive growth of squamous cell carcinoma under cancer-stromal cell interaction. Cancer Sci 99:2417–2427. doi:10.1111/j.1349-7006.2008.00978.x

    Article  CAS  PubMed  Google Scholar 

  31. Chiba N, Comaills V, Shiotani B, Takahashi F, Shimada T, Tajima K, Winokur D, Hayashida T, Willers H, Brachtel E, Vivanco MD, Haber DA, Zou L, Maheswaran S (2011) Homeobox B9 induces epithelial-to-mesenchymal transition-associated radioresistance by accelerating DNA damage responses. Proc Natl Acad Sci U S A 109:2760–2765. doi:10.1073/pnas.1018867108

    Article  PubMed Central  PubMed  Google Scholar 

  32. Trusolino L, Bertotti A, Comoglio PM (2010) MET signalling: principles and functions in development, organ regeneration and cancer. Nat Rev Mol Cell Biol 11:834–848. doi:10.1038/nrm3012

    Article  CAS  PubMed  Google Scholar 

  33. De Bacco F, Luraghi P, Medico E, Reato G, Girolami F, Perera T, Gabriele P, Comoglio PM, Boccaccio C (2011) Induction of MET by ionizing radiation and its role in radioresistance and invasive growth of cancer. J Natl Cancer Inst 103:645–661. doi:10.1093/jnci/djr093

    Article  PubMed  Google Scholar 

  34. Orimo A, Gupta PB, Sgroi DC, Arenzana-Seisdedos F, Delaunay T, Naeem R, Carey VJ, Richardson AL, Weinberg RA (2005) Stromal fibroblasts present in invasive human breast carcinomas promote tumor growth and angiogenesis through elevated SDF-1/CXCL12 secretion. Cell 121:335–348

    Article  CAS  PubMed  Google Scholar 

  35. Armstrong T, Packham G, Murphy LB, Bateman AC, Conti JA, Fine DR, Johnson CD, Benyon RC, Iredale JP (2004) Type I collagen promotes the malignant phenotype of pancreatic ductal adenocarcinoma. Clin Cancer Res 10:7427–7437

    Article  CAS  PubMed  Google Scholar 

  36. Aravindan N, Aravindan S, Pandian V, Khan FH, Ramraj SK, Natt P, Natarajan M (2014) Acquired tumor cell radiation resistance at the treatment site is mediated through radiation-orchestrated intercellular communication. Int J Radiat Oncol Biol Phys 88:677–685. doi:10.1016/j.ijrobp.2013.11.215

    Article  PubMed Central  PubMed  Google Scholar 

  37. Berger SH, Jenh CH, Johnson LF, Berger FG (1985) Thymidylate synthase overproduction and gene amplification in fluorodeoxyuridine-resistant human cells. Mol Pharmacol 28:461–467

    CAS  PubMed  Google Scholar 

  38. Sohn KJ, Croxford R, Yates Z, Lucock M, Kim YI (2004) Effect of the methylenetetrahydrofolate reductase C677T polymorphism on chemosensitivity of colon and breast cancer cells to 5-fluorouracil and methotrexate. J Natl Cancer Inst 96:134–144

    Article  CAS  PubMed  Google Scholar 

  39. Wang Y, Spitz MR, Zhu Y, Dong Q, Shete S, Wu X (2003) From genotype to phenotype: correlating XRCC1 polymorphisms with mutagen sensitivity. DNA Repair (Amst) 2:901–908

    Article  CAS  Google Scholar 

  40. Yamane A, Kohno T, Ito K, Sunaga N, Aoki K, Yoshimura K, Murakami H, Nojima Y, Yokota J (2004) Differential ability of polymorphic OGG1 proteins to suppress mutagenesis induced by 8-hydroxyguanine in human cell in vivo. Carcinogenesis 25:1689–1694

    Article  CAS  PubMed  Google Scholar 

  41. Fisher CJ, Goswami PC (2008) Mitochondria-targeted antioxidant enzyme activity regulates radioresistance in human pancreatic cancer cells. Cancer Biol Ther 7:1271–1279

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Terabe M, Park JM, Berzofsky JA (2004) Role of IL-13 in regulation of anti-tumor immunity and tumor growth. Cancer Immunol Immunother 53:79–85

    Article  CAS  PubMed  Google Scholar 

  43. Gauderman WJ, Morrison JM (2006) QUANTO 1.1: a computer program for power and sample size calculations for genetic-epidemiology studies. http://hydra.usc.edu/gxe

  44. Wright S (1992) Adjusted p-values for simultaneous inference. Biometrics 48:1005–1013

    Article  Google Scholar 

  45. Verhoeven KJF, Simonsen KL, McIntyre ML (2005) Implementing false discovery rate control: increasing your power. OIKOS 108:643–647

    Article  Google Scholar 

  46. NCI-NHGRI Working Group on Replication in Association Studies, Chanock SJ, Manolio T, Boehnke M, Boerwinkle E, Hunter DJ, Thomas G, Hirschhorn JN, Abecasis G, Altshuler D, Bailey-Wilson JE, Brooks LD, Cardon LR, Daly M, Donnelly P, Fraumeni JF Jr, Freimer NB, Gerhard DS, Gunter C, Guttmacher AE, Guyer MS, Harris EL, Hoh J, Hoover R, Kong CA, Merikangas KR, Morton CC, Palmer LJ, Phimister EG, Rice JP, Roberts J, Rotimi C, Tucker MA, Vogan KJ, Wacholder S, Wijsman EM, Winn DM, Collins FS (2007) Replicating genotype-phenotype associations. Nature 447:655–660

    Article  Google Scholar 

  47. Andreassen CN, Alsner J (2009) Genetic variants and normal tissue toxicity after radiotherapy: a systematic review. Radiother Oncol 92:299–309. doi:10.1016/j.radonc.2009.06.015

    Article  CAS  PubMed  Google Scholar 

  48. Guchelaar HJ, Gelderblom H, van der Straaten T, Schellens JH, Swen JJ (2014) Pharmacogenetics in the cancer clinic: from candidate gene studies to next-generation sequencing. Clin Pharmacol Ther 95(4):383–385. doi:10.1038/clpt.2014.13

    Article  CAS  PubMed  Google Scholar 

  49. Hong H, Xu L, Liu J, Jones WD, Su Z, Ning B, Perkins R, Ge W, Miclaus K, Zhang L, Park K, Green B, Han T, Fang H, Lambert CG, Vega SC, Lin SM, Jafari N, Czika W, Wolfinger RD, Goodsaid F, Tong W, Shi L (2012) Technical reproducibility of genotyping SNP arrays used in genome-wide association studies. PLoS One 7:e44483. doi:10.1371/journal.pone.0044483

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  50. Kim RY, Xu H, Myllykangas S, Ji H (2011) Genetic-based biomarkers and next-generation sequencing: the future of personalized care in colorectal cancer. Perinat Med 8(3):331–345

    Google Scholar 

  51. Spencer CC, Su Z, Donnelly P, Marchini J (2009) Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet 5:e1000477. doi:10.1371/journal.pgen.1000477

    Article  PubMed Central  PubMed  Google Scholar 

  52. Robert J, Le Morvan V, Giovannetti E, Peters GJ, PAMM Group of EORTC (2014) On the use of pharmacogenetics in cancer treatment and clinical trials. Eur J Cancer 50:2532–2543. doi:10.1016/j.ejca.2014.07.013

    Article  CAS  PubMed  Google Scholar 

  53. Cordell HJ (2002) Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet 11:2463–2468

    Article  CAS  PubMed  Google Scholar 

  54. Motsinger AA, Ritchie MD (2006) Multifactor dimensionality reduction: an analysis strategy for modelling and detecting gene-gene interactions in human genetics and pharmacogenomics studies. Hum Genomics 2:318–328

    PubMed Central  CAS  PubMed  Google Scholar 

  55. Hahn LW, Ritchie MD, Moore JH (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19:376–382

    Article  CAS  PubMed  Google Scholar 

  56. García-Magariños M, López-de-Ullibarri I, Cao R, Salas A (2009) Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction. Ann Hum Genet 73:360–369. doi:10.1111/j.1469-1809.2009.00511.x

    Article  PubMed  Google Scholar 

Further reading

  1. PubMed, U.S. National Institute of Health, National Library of Medicine. http://www.ncbi.nlm.nih.gov/pubmed. Accessed 10 July 2014

  2. Scopus, Elsevier. http://www.scopus.com. Accessed 27 Sep 2014

  3. Vanderbilt University, Department of Biostatistic. Power and sample size calculation. http://biostat.mc.vanderbilt.edu/wiki/Main/PowerSampleSize. Accessed 15 Aug 2014

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Acknowledgments

We thank Dr. Eros Ferrazzi for the critical analysis, Dr. Felice Pasini for support to this paper, Dr. Stephen D. Skaper for linguistic input and Roberta Sato for search on web database. This work was supported in part by 5x1000 funds from Lega Italiana per la Lotta contro i Tumori.

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The authors declare that they have no conflict of interest.

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Correspondence to Milena Gusella.

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Pezzolo, E., Modena, Y., Corso, B. et al. Germ line polymorphisms as predictive markers for pre-surgical radiochemotherapy in locally advanced rectal cancer: a 5-year literature update and critical review. Eur J Clin Pharmacol 71, 529–539 (2015). https://doi.org/10.1007/s00228-015-1824-0

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