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Proteomic and metabolic prediction of response to therapy in gastrointestinal cancers

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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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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Correspondence to Matthias PA Ebert.

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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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