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Proteomic and metabolic prediction of response to therapy in gastrointestinal cancers

Nature Reviews Gastroenterology & Hepatology volume 6, pages 170–183 (2009)Cite this article

Abstract

Despite substantial improvements in the diagnosis and treatment of many gastrointestinal cancers, particularly colorectal cancer, numerous patients are only diagnosed in advanced stages of disease, which can preclude curative treatment. Screening and early diagnosis of high-risk individuals might be the most promising approach to improve prognosis; however, molecular biomarkers for early diagnosis of most gastrointestinal cancers are not yet available. The prognosis of patients with advanced gastrointestinal cancers has improved through the development of multimodal treatments and the introduction of targeted therapies. Nonetheless, not all patients benefit equally from these treatment approaches, and toxicity can be substantial. The ability to predict whether a patient will respond to therapy early in their treatment for gastrointestinal cancer may be of particular value to stratify and individualize patient treatment strategies. Despite improvement in the understanding of cancer pathogenesis and progression at the molecular level, the molecular changes that underlie treatment response and/or drug resistance are still largely unknown. PET is the first technique to show promise in prediction of response to therapy, and has resulted in promising advancements, particularly in esophageal and gastric cancers. Tissue-based and blood-based molecular biomarkers are still subject to validation. Prediction of response to treatment could ultimately lead to an overall improvement in prognosis.

Key Points

  • The ability to predict which patients will respond positively to a particular cancer therapy early in the treatment course has the potential to improve individualization of management strategies

  • Identification of cancer-specific biomarkers (with which to assess a patient's prognosis and predict their response to treatment) is a key area of current research

  • Proteomic analysis techniques, including mass spectrometry, have enabled the identification of potential cancer-specific proteins that might be useful markers of response to treatment early in the course of therapy; however, validation of these markers is pending

  • Imaging mass spectrometry uses bioinformatics and statistical approaches to combine mass spectrometry protein profiles with histological information, and thus improves biomarker discovery

  • Metabolic imaging approaches aim to follow changes in the metabolism of the tumor after initiation of treatment, and may facilitate prediction of which patients will successfully respond to a treatment

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Figure 1: Principles of imaging mass spectrometry.
Figure 2: Use of imaging mass spectrometry to determine the location of a spectrum in relation to tumorous or nontumorous tissue.
Figure 3: Hierarchical clustering of spectra.
Figure 4: Early metabolic response evaluation of a patient with locally advanced gastric cancer undergoing neoadjuvant chemotherapy.

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References

  1. Ferlay J et al. (2007) Estimates of the cancer incidence and mortality in Europe in 2006. Ann Oncol 18: 581–592

    Article  CAS  PubMed  Google Scholar 

  2. Chan AT et al. (2007) Aspirin and the risk of colorectal cancer in relation to the expression of COX-2. N Engl J Med 356: 2131–2142

    Article  CAS  PubMed  Google Scholar 

  3. Kelloff GJ et al. (2006) Progress in chemoprevention drug development: the promise of molecular biomarkers for prevention of intraepithelial neoplasia and cancer—a plan to move forward. Clin Cancer Res 12: 3661–3697

    Article  CAS  PubMed  Google Scholar 

  4. Seewald S et al. (2008) Detection and endoscopic therapy of early esophageal adenocarcinoma. Curr Opin Gastroenterol 24: 521–529

    Article  PubMed  Google Scholar 

  5. Curvers W et al. (2008) Chromoendoscopy and narrow-band imaging compared with high-resolution magnification endoscopy in Barrett esophagus. Gastroenterology 134: 670–679

    Article  PubMed  Google Scholar 

  6. Kiesslich R et al. (2007) Chromoscopy-guided endomicroscopy increases the diagnostic yield of intraepithelial neoplasia in ulcerative colitis. Gastroenterology 132: 874–882

    Article  PubMed  Google Scholar 

  7. Kleeff J and Friess H (2008) Nonmetastatic pancreatic cancer: many trials, little progress. J Clin Oncol 26: 3100–3101

    Article  PubMed  Google Scholar 

  8. Moore MJ et al. (2007) National Cancer Institute of Canada Clinical Trials Group. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol 25: 1960–1966

    Article  CAS  PubMed  Google Scholar 

  9. Hurwitz HI et al. (2005) Bevacizumab in combination with fluorouracil and leucovorin: an active regimen for first-line metastatic colorectal cancer. J Clin Oncol 23: 3502–3508

    Article  CAS  PubMed  Google Scholar 

  10. Theisen J et al. (2008) Predictors of response and survival for neoadjuvant treated patients with esophageal adenocarcinoma. Dis Esophagus 21: 601–606

    Article  CAS  PubMed  Google Scholar 

  11. Hodgson NC and Gulenchyn KY (2008) Is there a role for positron emission tomography in breast cancer staging? J Clin Oncol 26: 712–720

    Article  PubMed  Google Scholar 

  12. van Heijl M et al. (2008) Neoadjuvant therapy monitoring with PET and CT in esophageal cancer (NEOPEC trial). BMC Med Phys 8: 3

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kato H et al. (2008) Head and neck squamous cell carcinoma: usefulness of diffusion-weighted MR imaging in the prediction of a neoadjuvant therapeutic effect. Eur Radiol [10.1007/s00330-008-1108-5]

  14. Poettgen C et al. (2007) Correlation of PET/CT findings and histopathology after neoadjuvant therapy in non-small cell lung cancer. Oncology 73: 316–323

    Article  CAS  PubMed  Google Scholar 

  15. von Minckwitz G et al. (2008) Clinical response after two cycles compared to HER2, Ki-67, p53, and bcl-2 in independently predicting a pathological complete response after preoperative chemotherapy in patients with operable carcinoma of the breast. Breast Cancer Res 10: R30

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Juhász M et al. (2004) Helicobacter pylori and molecular mechanisms of gastric carcinogenesis: targets for prevention and therapy. Expert Rev Anticancer Ther 4: 97–103

    Article  PubMed  Google Scholar 

  17. Schneider G et al. (2005) Pancreatic cancer: basic and clinical aspects. Gastroenterology 128: 1606–1625

    Article  CAS  PubMed  Google Scholar 

  18. Cappell MS (2008) Pathophysiology, clinical presentation, and management of colon cancer. Gastroenterol Clin North Am 37: 1–24

    Article  PubMed  Google Scholar 

  19. Hanahan D and Weinberg RA (2000) The hallmarks of cancer. Cell 100: 57–70

    Article  CAS  PubMed  Google Scholar 

  20. Woerner SM et al. (2006) Microsatellite instability in the development of DNA mismatch repair deficient tumors. Cancer Biomark 2: 69–86

    Article  CAS  PubMed  Google Scholar 

  21. Issa JP (2004) CpG island methylator phenotype in cancer. Nat Rev Cancer 12: 988–993

    Article  CAS  Google Scholar 

  22. Bergamaschi A et al. (2008) Extracellular matrix signature identifies breast cancer subgroups with different clinical outcome. J Pathol 214: 357–367

    Article  CAS  PubMed  Google Scholar 

  23. Bertucci F and Goncalves A (2008) Clinical proteomics and breast cancer: strategies for diagnostic and therapeutic biomarker discovery. Future Oncol 4: 271–287

    Article  CAS  PubMed  Google Scholar 

  24. Cornett DS et al. (2007) MALDI imaging mass spectrometry: molecular snapshots of biochemical systems. Nat Methods 4: 828–833

    Article  CAS  PubMed  Google Scholar 

  25. Veenstra TD et al. (2005) Biomarkers: mining the biofluid proteome. Mol Cell Proteomics 4: 409–418

    Article  CAS  PubMed  Google Scholar 

  26. Röcken C et al. (2008) Application of proteome analysis to the assessment of prognosis and response prediction in clinical oncology. Curr Cancer Drug Targets 8: 141–145

    Article  PubMed  Google Scholar 

  27. Herosimczyk A et al. (2006) Plasma proteome analysis: 2D gels and chips. J Physiol Pharmacol 57: 81–93

    PubMed  Google Scholar 

  28. López JL (2007) Two-dimensional electrophoresis in proteome expression analysis. J Chromatogr B Analyt Technol Biomed Life Sci 849: 190–202

    Article  PubMed  CAS  Google Scholar 

  29. Karas M and Hillenkamp F (1988) Laser desorption ionization of proteins with molecular masses exceeding 10,000 Daltons. Anal Chem 60: 2299–2301

    Article  CAS  PubMed  Google Scholar 

  30. Chaurand P et al. (2004) Integrating histology and imaging mass spectrometry. Anal Chem 76: 1145–1155

    Article  CAS  PubMed  Google Scholar 

  31. Reid GE and McLuckey SA (2002) Top down protein characterization via tandem mass spectrometry. J Mass Spectrom 37: 663–675

    Article  CAS  PubMed  Google Scholar 

  32. Leman ES et al. (2008) Evaluation of colon cancer-specific antigen 2 as a potential serum marker for colorectal cancer. Clin Cancer Res 14: 1349–1354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wu CC et al. (2008) Identification of collapsin response mediator protein-2 as a potential marker of colorectal carcinoma by comparative analysis of cancer cell secretomes. Proteomics 8: 316–332

    Article  CAS  PubMed  Google Scholar 

  34. Ward DG et al. (2006) Identification of serum biomarkers for colon cancer by proteomic analysis. Br J Cancer 94: 1898–1905

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kim H et al. (2006) Suppression of human selenium-binding protein 1 is a late event in colorectal carcinogenesis and is associated with poor survival. Proteomics 6: 3466–3476

    Article  PubMed  Google Scholar 

  36. Xia Q et al. (2005) Proteomics-based identification of DEAD-box protein 48 as a novel autoantigen, a prospective serum marker for pancreatic cancer. Biochem Biophys Res Commun 6: 526–532

    Article  CAS  Google Scholar 

  37. Ebert MP et al. (2006) Advances, challenges, and limitations in serum-proteome-based cancer diagnosis. J Proteome Res 5: 19–25

    Article  CAS  PubMed  Google Scholar 

  38. Marko-Varga G and Fehniger TE (2004) Proteomics and disease—the challenges for technology and discovery. J Proteome Res 3: 167–178

    Article  CAS  PubMed  Google Scholar 

  39. Fung ET et al. (2005) Classification of cancer types by measuring variants of host response proteins using SELDI serum assays. Int J Cancer 115: 783–789

    Article  CAS  PubMed  Google Scholar 

  40. Su Y et al. (2007) Diagnosis of gastric cancer using decision tree classification of mass spectral data. Cancer Sci 98: 37–43

    Article  CAS  PubMed  Google Scholar 

  41. Qian HG et al. (2005) Preliminary study on proteomics of gastric carcinoma and its clinical significance. World J Gastroenterol 11: 6249–6253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lim JY et al. (2007) Diagnostic application of serum proteomic patterns in gastric cancer patients by ProteinChip surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Int J Biol Markers 22: 281–286

    Article  CAS  PubMed  Google Scholar 

  43. Poon TC et al. (2006) Diagnosis of gastric cancer by serum proteomic fingerprinting. Gastroenterology 130: 1858–1864

    Article  CAS  PubMed  Google Scholar 

  44. Hayashida Y et al. (2005) Possible prediction of chemoradiosensitivity of esophageal cancer by serum protein profiling. Clin Cancer Res 11: 8042–8047

    Article  CAS  PubMed  Google Scholar 

  45. Liu XP et al. (2006) A serum proteomic pattern for the detection of colorectal adenocarcinoma using surface enhanced laser desorption and ionization mass spectrometry. Cancer Invest 24: 747–753

    Article  CAS  PubMed  Google Scholar 

  46. Smith FM et al. (2007) Combination of SELDI-TOF-MS and data mining provides early-stage response prediction for rectal tumors undergoing multimodal neoadjuvant therapy. Ann Surg 245: 259–266

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bhattacharyya S et al. (2004) Diagnosis of pancreatic cancer using serum proteomic profiling. Neoplasia 6: 674–686

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Yu Y et al. (2005) Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification. Oncology 68: 79–86

    Article  CAS  PubMed  Google Scholar 

  49. Koopmann J et al. (2004) Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry. Clin Cancer Res 10: 860–868

    Article  CAS  PubMed  Google Scholar 

  50. Mitani Y et al. (2007) REGIV is a serum biomarker for gastric cancer patients and predicts response to 5-fluorouracil-based chemotherapy. Oncogene 26: 4383–4393

    Article  CAS  PubMed  Google Scholar 

  51. Liu W et al. (2007) Down-regulated expression of complement factor I: a potential suppressive protein for gastric cancer identified by serum proteome analysis. Clin Chim Acta 377: 119–126

    Article  CAS  PubMed  Google Scholar 

  52. Chen CD et al. (2007) Overexpression of CLIC1 in human gastric carcinoma and its clinicopathological significance. Proteomics 7: 155–167

    Article  CAS  PubMed  Google Scholar 

  53. Ren H et al. (2006) Analysis of variabilities of serum proteomic spectra in patients with gastric cancer before and after operation. World J Gastroenterol 12: 2789–2792

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ebert MP et al. (2005) Overexpression of cathepsin B in gastric cancer identified by proteome analysis. Proteomics 5: 1693–1704

    Article  CAS  PubMed  Google Scholar 

  55. Fujita Y et al. (2008) Proteomics-based identification of autoantibody against heat shock protein 70 as a diagnostic marker in esophageal squamous cell carcinoma. Cancer Lett 263: 280–290

    Article  CAS  PubMed  Google Scholar 

  56. Fu L et al. (2007) Identification of alpha-actinin 4 and 67 kDa laminin receptor as stage-specific markers in esophageal cancer via proteomic approaches. Cancer 110: 2672–2681

    Article  CAS  PubMed  Google Scholar 

  57. Fujita Y et al. (2006) Proteomics-based approach identifying autoantibody against peroxiredoxin VI as a novel serum marker in esophageal squamous cell carcinoma. Clin Cancer Res 12: 6415–6420

    Article  CAS  PubMed  Google Scholar 

  58. Melle C et al. (2005) Discovery and identification of alpha-defensins as low abundant, tumor-derived serum markers in colorectal cancer. Gastroenterology 129: 66–73

    Article  CAS  PubMed  Google Scholar 

  59. Albrethsen J et al. (2005) Upregulated expression of human neutrophil peptides 1, 2 and 3 (HNP 1–3) in colon cancer serum and tumours: a biomarker study. BMC Cancer 5: 8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Bloomston M et al. (2006) Fibrinogen gamma overexpression in pancreatic cancer identified by large-scale proteomic analysis of serum samples. Cancer Res 66: 2592–2599

    Article  CAS  PubMed  Google Scholar 

  61. Hwang TL et al. (2006) Overexpression and elevated serum levels of phosphoglycerate kinase 1 in pancreatic ductal adenocarcinoma. Proteomics 6: 2259–2272

    Article  CAS  PubMed  Google Scholar 

  62. Sun ZL et al. (2007) Serum proteomic-based analysis of pancreatic carcinoma for the identification of potential cancer biomarkers. Biochim Biophys Acta 1774: 764–771

    Article  CAS  PubMed  Google Scholar 

  63. Melle C et al. (2007) Protein profiling of microdissected pancreas carcinoma and identification of HSP27 as a potential serum marker. Clin Chem 53: 629–635

    Article  CAS  PubMed  Google Scholar 

  64. Melle C et al. (2005) Characterization of pepsinogen C as a potential biomarker for gastric cancer using a histoproteomic approach. J Proteome Res 4: 1799–1804

    Article  CAS  PubMed  Google Scholar 

  65. de Roos B et al. (2008) Proteomic methodological recommendations for studies involving human plasma, platelets, and peripheral blood mononuclear cells. J Proteome Res 7: 2280–2290

    Article  PubMed  Google Scholar 

  66. Callesen AK et al. (2008) Reproducibility of mass spectrometry based protein profiles for diagnosis of breast cancer across clinical studies: a systematic review. J Proteome Res 7: 1395–1402

    Article  CAS  PubMed  Google Scholar 

  67. Cecconi D et al. (2005) Proteomic analysis of pancreatic ductal carcinoma cells after combined treatment with gemcitabine and trichostatin A. J Proteome Res 4: 1909–1916

    Article  CAS  PubMed  Google Scholar 

  68. Mori-Iwamoto S et al. (2007) Proteomics finding heat shock protein 27 as a biomarker for resistance of pancreatic cancer cells to gemcitabine. Int J Oncol 31: 1345–1350

    CAS  PubMed  Google Scholar 

  69. Moncada V and Srivastava S (2008) Biomarkers in oncology research and treatment: early detection research network: a collaborative approach. Biomark Med 2: 181–195

    Article  PubMed  Google Scholar 

  70. Stoeckli M et al. (2001) Imaging mass spectrometry: a new technology for the analysis of protein expression in mammalian tissues. Nat Med 7: 493–496

    Article  CAS  PubMed  Google Scholar 

  71. Walch A et al. (2008) MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology. Histochem Cell Biol 130: 421–434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Fournier I et al. (2008) Tissue imaging using MALDI-MS: a new frontier of histopathology proteomics. Expert Rev Proteomics 5: 413–424

    Article  CAS  PubMed  Google Scholar 

  73. Wisztorski M et al. (2008) Molecular MALDI imaging: an emerging technology for neuroscience studies. Dev Neurobiol 68: 845–858

    Article  CAS  PubMed  Google Scholar 

  74. Schwamborn K et al. (2007) Identifying prostate carcinoma by MALDI-Imaging. Int J Mol Med 20: 155–159

    CAS  PubMed  Google Scholar 

  75. McCombie G et al. (2005) Spatial and spectral correlations in MALDI mass spectrometry images by clustering and multivariate analysis. Anal Chem 77: 6118–6124

    Article  CAS  PubMed  Google Scholar 

  76. Turillazzi S et al.2007) Comparison of the medium molecular weight venom fractions from five species of common social wasps by MALDI-TOF spectra profiling. J Mass Spectrom 42: 199–205

    Article  CAS  PubMed  Google Scholar 

  77. Yanagisawa K et al. (2003) Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet 362: 433–439

    Article  CAS  PubMed  Google Scholar 

  78. Lemaire R et al. (2007) Specific MALDI imaging and profiling for biomarker hunting and validation: fragment of the 11S proteasome activator complex, Reg alpha fragment, is a new potential ovary cancer biomarker. J Proteome Res 6: 4127–4134

    Article  CAS  PubMed  Google Scholar 

  79. Schwartz SA et al. (2004) Protein profiling in brain tumors using mass spectrometry: feasibility of a new technique for the analysis of protein expression. Clin Cancer Res 10: 981–987

    Article  CAS  PubMed  Google Scholar 

  80. Li L et al. (2000) Single-cell MALDI: a new tool for direct peptide profiling. Trends Biotechnol 18: 151–160

    Article  CAS  PubMed  Google Scholar 

  81. Xu BJ et al. (2002) Direct analysis of laser capture microdissected cells by MALDI mass spectrometry. J Am Soc Mass Spectrom 13: 1292–1297

    Article  CAS  PubMed  Google Scholar 

  82. Herring KD et al. (2007) Direct tissue analysis by matrix-assisted laser desorption ionization mass spectrometry: application to kidney biology. Semin Nephrol 27: 597–608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Chaurand P et al. (2004) Proteomics in diagnostic pathology: profiling and imaging proteins directly in tissue sections. Am J Pathol 165: 1057–1068

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Warburg O (1956) On respiratory impairment in cancer cells. Science 124: 269–270

    CAS  PubMed  Google Scholar 

  85. Weber WA et al. (1999) Reproducibility of metabolic measurements in malignant tumors using FDG PET. J Nucl Med 40: 1771–1777

    CAS  PubMed  Google Scholar 

  86. Weber WA and Wieder H (2006) Monitoring chemotherapy and radiotherapy of solid tumors. Eur J Nucl Med Mol Imaging 33: 27–37

    Article  PubMed  Google Scholar 

  87. Weber WA and Figlin R (2007) Monitoring cancer treatment with PET/CT: does it make a difference? J Nucl Med 48: 36S–44S

    Article  CAS  PubMed  Google Scholar 

  88. Weber WA et al. (2001) Prediction of response to preoperative chemotherapy in adenocarcinomas of the esophagogastric junction by metabolic imaging. J Clin Oncol 19: 3058–3065

    Article  CAS  PubMed  Google Scholar 

  89. Ott K et al. (2006) Metabolic imaging predicts response, survival, and recurrence in adenocarcinomas of the esophagogastric junction. J Clin Oncol 24: 4692–4698

    Article  PubMed  Google Scholar 

  90. Lordick F et al. (2007) PET to assess early metabolic response and to guide treatment of adenocarcinoma of the oesophagogastric junction: the MUNICON phase II trial. Lancet Oncol 8: 797–805

    Article  PubMed  Google Scholar 

  91. Wieder HA et al. (2004) Time course of tumor metabolic activity during chemoradiotherapy of esophageal squamous cell carcinoma and response to treatment. J Clin Oncol 22: 900–908

    Article  CAS  PubMed  Google Scholar 

  92. Ott K et al. (2003) Prediction of response to preoperative chemotherapy in gastric carcinoma by metabolic imaging: results of a prospective trial. J Clin Oncol 21: 4604–4610

    Article  CAS  PubMed  Google Scholar 

  93. Ott K et al. (2008) Early metabolic response evaluation by fluorine-18 fluorodeoxyglucose positron emission tomography allows in vivo testing of chemosensitivity in gastric cancer: long term results of a prospective study. Clin Cancer Res 14: 2012–2018

    Article  CAS  PubMed  Google Scholar 

  94. Shah MA et al. (2007) A phase II study of preoperative chemotherapy with irinotecan and cisplatin for gastric cancer: FDG-PET/CT predicts patient outcome. J Clin Oncol 25: 4502

    Article  CAS  Google Scholar 

  95. Cascini GL et al. (2006) 18F-FDG PET is an early predictor of pathologic tumor response to preoperative radiochemotherapy in locally advanced rectal cancer. J Nucl Med 47: 1241–1248

    CAS  PubMed  Google Scholar 

  96. De Geus-Oei LF et al. (2008) Chemotherapy response evaluation with FDG-PET in patients with colorectal cancer. Ann Oncol 19: 348–352

    Article  CAS  PubMed  Google Scholar 

  97. Amthauer H et al. (2004) Response prediction by FDG-PET after neoadjuvant radiochemotherapy and combined regional hyperthermia of rectal cancer: correlation with endorectal ultrasound and histopathology. Eur J Nucl Med Mol Imaging 31: 811–819

    Article  PubMed  Google Scholar 

  98. Brucher BL et al. (2001) Neoadjuvant therapy of esophageal squamous cell carcinoma: response evaluation by positron emission tomography. Ann Surg 233: 300–309

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Flamen P et al. (2002) Positron emission tomography for assessment of the response to induction radiochemotherapy in locally advanced oesophageal cancer. Ann Oncol 13: 361–368

    Article  CAS  PubMed  Google Scholar 

  100. Guillem JG et al. (2004) Sequential preoperative fluorodeoxyglucose-positron emission tomography assessment of response to preoperative chemoradiation: a means for determining longterm outcomes of rectal cancer. J Am Coll Surg 199: 1–7

    Article  PubMed  Google Scholar 

  101. Kim MK et al. (2007) Value of complete metabolic response by (18)F-fluorodeoxyglucose–positron emission tomography in oesophageal cancer for prediction of pathologic response and survival after preoperative chemoradiotherapy. Eur J Cancer 43: 1385–1391

    Article  PubMed  Google Scholar 

  102. Port JL et al. (2007) Positron emission tomographic scanning predicts survival after induction chemotherapy for esophageal carcinoma. Ann Thorac Surg 84: 393–400

    Article  PubMed  Google Scholar 

  103. Swisher SG et al. (2004) Utility of PET, CT, and EUS to identify pathologic responders in esophageal cancer. Ann Thorac Surg 78: 1152–1160

    Article  PubMed  Google Scholar 

  104. Smithers BM et al. (2008) Positron emission tomography and pathological evidence of response to neoadjuvant therapy in adenocarcinoma of the esophagus. Dis Esophagus 21: 151–158

    Article  CAS  PubMed  Google Scholar 

  105. Lordick F et al. (2008) European Organization of Research and Treatment of Cancer (EORTC) Gastrointestinal Cancer Working Group: workshop on the role of metabolic imaging in the neoadjuvant treatment of gastrointestinal cancer. Eur J Cancer 44: 1807–1819

    Article  PubMed  Google Scholar 

  106. Westerterp M et al. (2008) 18FDG uptake in oesophageal adenocarcinoma: linking biology and outcome. J Cancer Res Clin Oncol 134: 227–236

    Article  PubMed  Google Scholar 

  107. Herrmann K et al. (2007) Imaging gastric cancer with PET and the radiotracers 18F-FLT and 18F-FDG: a comparative analysis. J Nucl Med 48: 1945–1950

    Article  CAS  PubMed  Google Scholar 

  108. Rosenberg R et al. (2008) The predictive value of metabolic response to preoperative radiochemotherapy in locally advanced rectal cancer measured by PET/CT. Int J Colorectal Dis [10.1007/s00384-008-0616-8]

  109. Downey RJ et al. (2003) Whole body 18FDG-PET and the response of esophageal cancer to induction therapy: results of a prospective trial. J Clin Oncol 21: 428–432

    Article  PubMed  Google Scholar 

  110. Calvo FA et al. (2004) 18F-FDG positron emission tomography staging and restaging in rectal cancer treated with preoperative chemoradiation. Int J Radiat Oncol Biol Phys 58: 528–535

    Article  PubMed  Google Scholar 

  111. Capirci et al. (2007) Sequential FDG-PET/CT reliably predicts response of locally advanced rectal cancer to neo-adjuvant chemo-radiation therapy. Eur J Nucl Med Mol Imaging 34: 1583–1593

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

MPA Ebert and A Walch are supported by a grant from the Deutsche Forschungsgemeinschaft (SFB 824/TP B1), the Deutsche Krebshilfe (107885) and the MolBioMed Program (EndoMed 01EZ0802) of the Federal Ministry of Education and Research (BMBF), Germany.

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  1. BJ Krause is an Associate Professor and M Schwaiger is Head of the Department of Nuclear Medicine, RM Schmid is Head of Department and MPA Ebert is a Professor of Clinical and Molecular Gastroenterology at the Department of Medicine II, H Höfler is Head of the Department of Pathology and H Friess is Head of the Department of Surgery at Klinikum rechts der Isar, K Herrmann is a Resident, B Balluff and M Tänzer are Postgraduate Students, all at the Technische Universität München, München.,

    Ken Herrmann, Benjamin Balluff, Marc Tänzer, Heinz Höfler, Bernd J Krause, Markus Schwaiger, Helmut Friess, Roland M Schmid & Matthias PA Ebert

  2. A Walch is an Associate Professor at the Institute of Pathology, Helmholtz-Zentrum München, München, Germany.,

    Axel Walch

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Herrmann, K., Walch, A., Balluff, B. et al. Proteomic and metabolic prediction of response to therapy in gastrointestinal cancers. Nat Rev Gastroenterol Hepatol 6, 170–183 (2009). https://doi.org/10.1038/ncpgasthep1366

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