Skip to main content

Functional Metabolomics and Chemoproteomics Approaches Reveal Novel Metabolic Targets for Anticancer Therapy

  • Chapter
  • First Online:
Cancer Metabolomics

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1280))

Abstract

Cancer cells exhibit different metabolic patterns compared to their normal counterparts. Although the reprogrammed metabolism has been indicated as strong biomarkers of cancer initiation and progression, increasing evidences suggest that metabolic alteration tuned by oncogenic drivers contributes to the occurrence and development of cancers rather than just being a hallmark of cancer. With this notion, targeting cancer metabolism holds promise as a novel anticancer strategy and is embracing its renaissance during the past two decades. Herein we have summarized the most recent developments in omics technology, including both metabolomics and proteomics, and how the combined use of these analytical tools significantly impacts this field by comprehensively and systematically recording the metabolic changes in cancer and hence reveals potential therapeutic targets that function by modulating the disrupted metabolic pathways.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Vogelstein, B., & Kinzler, K. W. (2004). Cancer genes and the pathways they control. Nature Medicine, 10(8), 789–799.

    Article  CAS  PubMed  Google Scholar 

  2. Hanahan, D., & Weinberg, R. A. (2000). The hallmarks of cancer. Cell, 100, 57–70.

    Article  CAS  PubMed  Google Scholar 

  3. Thompson, C. B. (2011). Rethinking the regulation of cellular metabolism. Cold Spring Harbor Symposia on Quantitative Biology, 76, 23–29.

    Article  CAS  PubMed  Google Scholar 

  4. Kawaguchi, T., Takenoshita, M., Kabashima, T., & Uyeda, K. (2001). Glucose and cAMP regulate the L-type pyruvate kinase gene by phosphorylation/dephosphorylation of the carbohydrate response element binding protein. Proceedings of the National Academy of Sciences of the United States of America, 98(24), 13710–13715.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Geiger, R., Rieckmann, J. C., Wolf, T., Basso, C., Feng, Y., Fuhrer, T., Kogadeeva, M., Picotti, P., Meissner, F., Mann, M., Zamboni, N., Sallusto, F., & Lanzavecchia, A. (2016). L-arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell, 167(3), 829–842.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Moellering, R. E., & Cravatt, B. F. (2013). Functional lysine modification by an intrinsically reactive primary glycolytic metabolite. Science, 341(6145), 549–553.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Parsons, D. W., Jones, S., Zhang, X., Lin, J. C., Leary, R. J., Angenendt, P., Mankoo, P., Carter, H., Siu, I.-M., Gallia, G. L., Olivi, A., McLendon, R., Rasheed, B. A., Keir, S., Nikolskaya, T., Nikolsky, Y., Busam, D. A., Tekleab, H., Diaz, L. A., Hartigan, J., Smith, D. R., Strausberg, R. L., Marie, S. K. N., Shinjo, S. M. O., Yan, H., Riggins, G. J., Bigner, D. D., Karchin, R., Papadopoulos, N., Parmigiani, G., Vogelstein, B., Velculescu, V. E., & Kinzler, K. W. (2008). An integrated genomic analysis of human glioblastoma multiforme. Science, 321, 1807–1812.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Stein, E. M., DiNardo, C. D., Fathi, A. T., Pollyea, D. A., Stone, R. M., Altman, J. K., Roboz, G. J., Patel, M. R., Collins, R., Flinn, I. W., Sekeres, M. A., Stein, A. S., Kantarjian, H. M., Levine, R. L., Vyas, P., MacBeth, K. J., Tosolini, A., VanOostendorp, J., Xu, Q., Gupta, I., Lila, T., Risueno, A., Yen, K. E., Wu, B., Attar, E. C., Tallman, M. S., & de Botton, S. (2019). Molecular remission and response patterns in patients with mutant-IDH2 acute myeloid leukemia treated with enasidenib. Blood, 133(7), 676–687.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mullard, A. (2017). FDA approves first-in-class cancer metabolism drug. Nature Reviews. Drug Discovery, 16(9), 593.

    PubMed  Google Scholar 

  10. Dwarakanath, B., Singh, D., Banerji, A. K., Sarin, R., Venkataramana, N., Jalali, R., Vishwanath, P., Mohanti, B., Tripathi, R., Kalia, V., & Jain, V. (2009). Clinical studies for improving radiotherapy with 2-deoxy-D-glucose: Present status and future prospects. Journal of Cancer Research and Therapeutics, 5(9), 21–26.

    Article  CAS  Google Scholar 

  11. Jelonek, K., & Widłak, P. (2018). Metabolome-based biomarkers: Their potential role in the early detection of lung cancer. Contemporary Oncology, 22(3), 135–140.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. McCartneya, A., Vignolib, A., Biganzolia, L., Lovec, R., Tenorib, L., Luchinatb, C., & Leoa, A. D. (2018). Metabolomics in breast cancer: A decade in review. Cancer Treatment Reviews, 67, 88–96.

    Article  Google Scholar 

  13. Kdadra, M., Höckner, S., Leung, H., Kremer, W., & Schiffer, E. (2019). Metabolomics biomarkers of prostate cancer: A systematic review. Diagnostics, 9(1), 1–44.

    Article  CAS  Google Scholar 

  14. Beckonert, O., Keun, H. C., Ebbels, T. M. D., Bundy, J., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2007). Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nature Protocols, 2(11), 2692–2703.

    Article  CAS  PubMed  Google Scholar 

  15. Alvarez-Sanchez, B., Priego-Capote, F., & Castro, L. (2010). Metabolomics analysis I. Selection of biological samples and practical aspects preceding sample preparation. Trends in Analytical Chemistry, 29(2), 111–119.

    Article  CAS  Google Scholar 

  16. Liu, L., Aa, J., Wang, G., Yan, B., Zhang, Y., Wang, X., Zhao, C., Cao, B., Shi, J., Li, M., Zheng, T., Zheng, Y., Hao, G., Zhou, F., Sun, J., & Wu, Z. (2010). Differences in metabolite profile between blood plasma and serum. Analytical Biochemistry, 406(2), 105–112.

    Article  CAS  PubMed  Google Scholar 

  17. Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vazquez-Fresno, R., Sajed, T., Johnson, D., Li, C., Karu, N., Sayeeda, Z., Lo, E., Assempour, N., Berjanskii, M., Singhal, S., Arndt, D., Liang, Y., Badran, H., Grant, J., Serra-Cayuela, A., Liu, Y., Mandal, R., Neveu, V., Pon, A., Knox, C., Wilson, M., Manach, C., & Scalbert, A. (2018). HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Research, 46, D608–D617.

    Article  CAS  PubMed  Google Scholar 

  18. Nicholson, J. K., Buckingham, M. J., & Sadler, P. J. (1983). High resolution 1H n.m.r. studies of vertebrate blood and plasma. The Biochemical Journal, 211, 605–615.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bothwell, J. H. F., & Griffin, J. L. (2011). An introduction to biological nuclear magnetic resonance spectroscopy. Biological Reviews, 86(2), 493–510.

    Article  PubMed  Google Scholar 

  20. Emwas, A.-H. M. (2015). The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods in Molecular Biology, 1277, 161–193.

    Article  CAS  PubMed  Google Scholar 

  21. Au, A., Cheng, K.-K., & Wei, L. K. (2017). Metabolomics, lipidomics and pharmacometabolomics of human hypertension. Advances in Experimental Medicine and Biology, 956, 599–613.

    Article  PubMed  Google Scholar 

  22. Beckonert, O., Coen, M., Keun, H. C., Wang, Y., Ebbels, T. M. D., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2010). High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nature Protocols, 5(6), 1019–1032.

    Article  CAS  PubMed  Google Scholar 

  23. Furusho, A., Koga, R., Akita, T., Mita, M., Kimura, T., & Hamase, K. (2019). Three-dimensional high-performance liquid chromatographic determination of Asn, Ser, Ala, and Pro enantiomers in the plasma of patients with chronic kidney disease. Analytical Chemistry, 91, 11569. https://doi.org/10.1021/acs.analchem.1029b01615.

    Article  CAS  PubMed  Google Scholar 

  24. Ibáñez, C., Simó, C., Barupal, D. K., Fiehn, O., Kivipelto, M., Cedazo-Mínguez, A., & Cifuentes, A. (2013). A new metabolomic workflow for early detection of Alzheimer’s disease. Journal of Chromatography A, 1302, 65–71.

    Article  PubMed  CAS  Google Scholar 

  25. Hu, S., Wang, J., Ji, E. H., Christison, T., Lopez, L., & Huang, Y. (2015). Targeted metabolomic analysis of head and neck cancer cells using high performance ion chromatography coupled with a Q exactive HF mass spectrometer. Analytical Chemistry, 87(12), 6371–6379.

    Article  CAS  PubMed  Google Scholar 

  26. Cui, L., Liu, J., Yan, X., & Hu, S. (2017). Identification of metabolite biomarkers for gout using capillary ion chromatography with mass spectrometry. Analytical Chemistry, 89(21), 11737–11743.

    Article  CAS  PubMed  Google Scholar 

  27. Wen, C., Lin, F., Huang, B., Zhang, Z., Wang, X., Ma, J., Lin, G., Chen, H., & Hu, L. (2019). Metabolomics analysis in acute paraquat poisoning patients based on UPLC-Q-TOF-MS and machine learning approach. Chemical Research in Toxicology, 32(4), 629–637.

    Article  CAS  PubMed  Google Scholar 

  28. Hilaire, P. B. S., Hohenester, U. M., Colsch, B., Tabet, J.-C., Junot, C., & Fenaille, F. (2018). Evaluation of the high-field orbitrap fusion for compound annotation in metabolomics. Analytical Chemistry, 90(5), 3030–3035.

    Article  CAS  Google Scholar 

  29. Damen, C. W. N., Isaac, G., Langridge, J., Hankemeier, T., & Vreeken, R. J. (2014). Enhanced lipid isomer separation in human plasma using reversed-phase UPLC with ion-mobility/high-resolution MS detection. Journal of Lipid Research, 55(8), 1772–1783.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wang, L., Su, B., Zeng, Z., Li, C., Zhao, X., Lv, W., Xuan, Q., Ouyang, Y., Zhou, L., Yin, P., Peng, X., Lu, X., Lin, X., & Xu, G. (2018). Ion-pair selection method for pseudotargeted metabolomics based on SWATH MS acquisition and its application in differential metabolite discovery of type 2 diabetes. Analytical Chemistry, 90(19), 11401–11408.

    Article  CAS  PubMed  Google Scholar 

  31. Li, H., Cai, Y., Guo, Y., Chen, F., & Zhu, Z.-J. (2016). MetDIA: Targeted metabolite extraction of multiplexed MS/MS spectra generated by data-independent acquisition. Analytical Chemistry, 88(17), 8757–8764.

    Article  CAS  PubMed  Google Scholar 

  32. Ye, H., Zhu, L., Sun, D., Luo, X., Lu, G., Wang, H., Wang, J., Cao, G., Xiao, W., Wang, Z., Wang, G., & Hao, H. (2016). Nontargeted diagnostic ion network analysis (NINA): A software to streamline the analytical workflow for untargeted characterization of natural medicines. Journal of Pharmaceutical and Biomedical Analysis, 131, 40–47.

    Article  CAS  PubMed  Google Scholar 

  33. Ye, H., Wang, L., Zhu, L., Sun, D., Luo, X., Wang, H., Wang, G., & Hao, H. (2016). Stepped collisional energy MSAll: An analytical approach for optimal MS/MS acquisition of complex mixture with diverse physicochemical properties. Journal of Mass Spectrometry, 51(5), 328–341.

    Article  CAS  PubMed  Google Scholar 

  34. Ye, H., Zhu, L., Wang, L., Liu, H., Zhang, J., Wu, M., Wang, G., & Hao, H. (2016). Stepped MSAll relied transition (SMART): An approach to rapidly determine optimal multiple reaction monitoring mass spectrometry parameters for small molecules. Analytica Chimica Acta, 907, 60–68.

    Article  CAS  PubMed  Google Scholar 

  35. Wang, L., Ye, H., Sun, D., Meng, T., Cao, L., Wu, M., Zhao, M., Wang, Y., Chen, B., Xu, X., Wang, G., & Hao, H. (2017). Metabolic pathway extension approach for metabolomic biomarker identification. Analytical Chemistry, 89(2), 1229–1237.

    Article  CAS  PubMed  Google Scholar 

  36. Luo, P., Dai, W., Yin, P., Zeng, Z., Kong, H., Zhou, L., Wang, X., Chen, S., Lu, X., & Xu, G. (2015). Multiple reaction monitoring-ion pair finder: A systematic approach to transform nontargeted mode to pseudotargeted mode for metabolomics study based on liquid chromatography-mass spectrometry. Analytical Chemistry, 87(10), 5050–5055.

    Article  CAS  PubMed  Google Scholar 

  37. Shen, X., Wang, R., Xiong, X., Yin, Y., Cai, Y., Ma, Z., Liu, N., & Zhu, Z.-J. (2019). Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics. Nature Communications, 10(1), 1516.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Huan, T., Tang, C., Li, R., Shi, Y., Lin, G., & Li, L. (2015). MyCompoundID MS/MS search: Metabolite identification using a library of predicted fragment-ion-spectra of 383,830 possible human metabolites. Analytical Chemistry, 87(20), 10619–10626.

    Article  CAS  PubMed  Google Scholar 

  39. Kang, S. W., Lee, S., & Lee, E. K. (2015). ROS and energy metabolism in cancer cells: Alliance for fast growth. Archives of Pharmacal Research, 38, 338–345.

    Article  CAS  PubMed  Google Scholar 

  40. Wishart, D. S. (2016). Emerging applications of metabolomics in drug discovery and precision medicine. Nature Reviews. Drug Discovery, 15(7), 473–484.

    Article  CAS  PubMed  Google Scholar 

  41. Kumar, N., Shahjaman, M. N. H., Islam, S., & Hoque, A. (2017). Serum and plasma metabolomic biomarkers for lung cancer. Bioinformation, 13(6), 202–208.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Siegel, R., Ma, J., Zou, Z., & Jemal, A. (2014). Cancer statistics, 2014. CA: A Cancer Journal for Clinicians, 64(1), 9–29.

    Google Scholar 

  43. Mu, Y., Zhou, Y., Wang, Y., Li, W., Zhou, L., Lu, X., Gao, P., Gao, M., Zhao, Y., Wang, Q., Wang, Y., & Xu, G. (2019). Serum metabolomics study of nonsmoking female patients with non-small cell lung cancer using gas chromatography-mass spectrometry. Journal of Proteome Research, 18(5), 2175–2184.

    Article  CAS  PubMed  Google Scholar 

  44. Rocha, C. M., Carrola, J., Barros, A. S., Gil, A. M., Goodfellow, B. J., Carreira, I. M., Bernardo, J., Gomes, A., Sousa, V., Carvalho, L., & Duarte, I. F. (2011). Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma. Journal of Proteome Research, 10(9), 4314–4324.

    Article  CAS  PubMed  Google Scholar 

  45. Ni, J., Xu, L., Li, W., Zheng, C., & Wu, L. (2019). Targeted metabolomics for serum amino acids and acylcarnitines in patients with lung cancer. Experimental and Therapeutic Medicine, 18(1), 188–198.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Mayers, J. R., Torrence, M. E., Danai, L. V., Papagiannakopoulos, T., Davidson, S. M., Bauer, M. R., Lau, A. N., Ji, B. W., Dixit, P. D., Hosios, A. M., Muir, A., Chin, C. R., Freinkman, E., Jacks, T., Wolpin, B. M., Vitkup, D., & Heiden, M. G. V. (2016). Tissue of origin dictates branched-chain amino acid metabolism in mutant Kras-driven cancers. Science, 353(6304), 1161–1165.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. McGranahan, N., & Swanton, C. (2017). Clonal heterogeneity and tumor evolution: Past, present, and the future. Cell, 168(4), 613–628.

    Article  CAS  PubMed  Google Scholar 

  48. Cros, J., Raffenne, J., Couvelard, A., & Poté, N. (2018). Tumor heterogeneity in pancreatic adenocarcinoma. Pathobiology, 85, 64–71.

    Article  CAS  PubMed  Google Scholar 

  49. Marusyk, A., & Polyak, K. (2010). Tumor heterogeneity: Causes and consequences. Biochimica et Biophysica Acta, 1805(1), 105–117.

    CAS  PubMed  Google Scholar 

  50. Prat, A., & Perou, C. M. (2011). Deconstructing the molecular portraits of breast cancer. Molecular Oncology, 5(1), 5–23.

    Article  CAS  PubMed  Google Scholar 

  51. Conforti, R., Boulet, T., Tomasic, G., Taranchon, E., Arriagada, R., Spielmann, M., Ducourtieux, M., Soria, J. C., Tursz, T., Delaloge, S., Michiels, S., & Andre, F. (2007). Breast cancer molecular subclassification and estrogen receptor expression to predict efficacy of adjuvant anthracyclines-based chemotherapy: A biomarker study from two randomized trials. Annals of Oncology, 18(9), 1477–1483.

    Article  CAS  PubMed  Google Scholar 

  52. Cao, M. D., Lamichhane, S., Lundgren, S., Bofin, A., Fjøsne, H., Giskeødegård, G. F., & Bathen, T. F. (2014). Metabolic characterization of triple negative breast cancer. BMC Cancer, 14, 941–952.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Jin, N., Bi, A., Lan, X., Xu, J., Wang, X., Liu, Y., Wang, T., Tang, S., Zeng, H., Chen, Z., Tan, M., Ai, J., Xie, H., Zhang, T., Liu, D., Huang, R., Song, Y., Leung, E. L.-H., Yao, X., Ding, J., Geng, M., Lin, S.-H., & Huang, M. (2019). Identification of metabolic vulnerabilities of receptor tyrosine kinases-driven cancer. Nature Communications, 10(1), 2701.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Ge, S., Xia, X., Ding, C., Zhen, B., Zhou, Q., Feng, J., Yuan, J., Chen, R., Li, Y., Ge, Z., Ji, J., Zhang, L., Wang, J., Li, Z., Lai, Y., Hu, Y., Li, Y., Li, Y., Gao, J., Chen, L., Xu, J., Zhang, C., Jung, S. Y., Choi, J. M., Jain, A., Liu, M., Song, L., Liu, W., Guo, G., Gong, T., Huang, Y., Qiu, Y., Huang, W., Shi, T., Zhu, W., Wang, Y., He, F., Shen, L., & Qin, J. (2018). A proteomic landscape of diffuse-type gastric cancer. Nature Communications, 9(1), 1–16.

    CAS  Google Scholar 

  55. Aoun, F., Peltier, A., & van Velthoven, R. (2014). A comprehensive review of contemporary role of local treatment of the primary tumor and/or the metastases in metastatic prostate cancer. BioMed Research International, 2014, 1–12.

    Article  Google Scholar 

  56. Siegel, R. L., Miller, K. D., & Jemal, A. (2018). Cancer statistics, 2018. CA: A Cancer Journal for Clinicians, 68(1), 7–30.

    Google Scholar 

  57. Ross, R. W., Xie, W., Regan, M. M., Pomerantz, M., Nakabayashi, M., Daskivich, T. J., Sartor, O., Taplin, M. E., Kantoff, P. W., & Oh, W. K. (2008). Efficacy of androgen deprivation therapy (ADT) in patients with advanced prostate cancer: Association between Gleason score, prostate-specific antigen level, and prior ADT exposure with duration of ADT effect. Cancer, 112(6), 1247–1253.

    Article  PubMed  Google Scholar 

  58. Sreekumar, A., Poisson, L. M., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R. J., Li, Y., Nyati, M. K., Ahsan, A., Kalyana-Sundaram, S., Han, B., Cao, X., Byun, J., Omenn, G. S., Ghosh, D., Pennathur, S., Alexander, D. C., Berger, A., Shuster, J. R., Wei, J. T., Varambally, S., Beecher, C., & Chinnaiyan, A. M. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457(7231), 910–914.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Eniu, D. T., Romanciuc, F., Moraru, C., Goidescu, I., Eniu, D., Staicu, A., Rachieriu, C., Buiga, R., & Socaciu, C. (2019). The decrease of some serum free amino acids can predict breast cancer diagnosis and progression. Scandinavian Journal of Clinical and Laboratory Investigation, 79(1–2), 17–24.

    Article  CAS  PubMed  Google Scholar 

  60. Wang, J. H., Chen, W. L., Li, J. M., Wu, S. F., Chen, T. L., Zhu, Y. M., Zhang, W. N., Li, Y., Qiu, Y. P., Zhao, A. H., Mi, J. Q., Jin, J., Wang, Y. G., Ma, Q. L., Huang, H., Wu, D. P., Wang, Q. R., Li, Y., Yan, X. J., Yan, J. S., Li, J. Y., Wang, S., Huang, X. J., Wang, B. S., Jia, W., Shen, Y., Chen, Z., & Chen, S. J. (2013). Prognostic significance of 2-hydroxyglutarate levels in acute myeloid leukemia in China. Proceedings of the National Academy of Sciences of the United States of America, 110(42), 17017–17022.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Mathe, E. A., Patterson, A. D., Haznadar, M., Manna, S. K., Krausz, K. W., Bowman, E. D., Shields, P. G., Idle, J. R., Smith, P. B., Anami, K., Kazandjian, D. G., Hatzakis, E., Gonzalez, F. J., & Harris, C. C. (2014). Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Research, 74(12), 3259–3270.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Huang, F., Ni, M., Chalishazar, M. D., Huffman, K. E., Kim, J., Cai, L., Shi, X., Cai, F., Zacharias, L. G., Ireland, A. S., Li, K., Gu, W., Kaushik, A. K., Liu, X., Gazdar, A. F., Oliver, T. G., Minna, J. D., Hu, Z., & DeBerardinis, R. J. (2018). Inosine monophosphate dehydrogenase dependence in a subset of small cell lung cancers. Cell Metabolism, 28(3), 369–382.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Wang, Z., Yip, L. Y., Lee, J. H. J., Wu, Z., Chew, H. Y., Chong, P. K. W., Teo, C. C., Ang, H. Y., Peh, K. L. E., Yuan, J., Ma, S., Choo, L. S. K., Basri, N., Jiang, X., Yu, Q., Hillmer, A. M., Lim, W. T., Lim, T. K. H., Takano, A., Tan, E. H., Tan, D. S. W., Ho, Y. S., Lim, B., & Tam, W. L. (2019). Methionine is a metabolic dependency of tumor-initiating cells. Nature Medicine, 25(5), 825–837.

    Article  CAS  PubMed  Google Scholar 

  64. Yuan, R., Hou, Y., Sun, W., Yu, J., Liu, X., Niu, Y., Lu, J.-J., & Chen, X. (2017). Natural products to prevent drug resistance in cancer chemotherapy: A review. Annals of the New York Academy of Sciences, 1401(1), 19–27.

    Article  PubMed  Google Scholar 

  65. Bosc, C., Selak, M. A., & Sarry, J.-E. (2017). Resistance is futile: Targeting mitochondrial energetics and metabolism to overcome drug resistance in cancer treatment. Cell Metabolism, 26(5), 705–707.

    Article  CAS  PubMed  Google Scholar 

  66. Kominsky, D. J., Klawitter, J., Brown, J. L., Boros, L. G., Melo, J. V., Eckhardt, S. G., & Serkova, N. J. (2009). Abnormalities in glucose uptake and metabolism in imatinib-resistant human BCR-ABL-positive cells. Clinical Cancer Research, 15(10), 3442–3450.

    Article  CAS  PubMed  Google Scholar 

  67. Ruprecht, B., Zaal, E. A., Zecha, J., Wu, W., Berkers, C. R., Kuster, B., & Lemeer, S. (2017). Lapatinib resistance in breast cancer cells is accompanied by phosphorylation-mediated reprogramming of glycolysis. Cancer Research, 77(8), 1842–1853.

    Article  CAS  PubMed  Google Scholar 

  68. Tewey, K. M., Rowe, T. C., Yang, L., Halligan, B. D., & Liu, L. F. (1984). Adriamycin-induced DNA damage mediated by mammalian DNA topoisomerase II. Science, 226(4673), 3.

    Article  Google Scholar 

  69. Cagel, M., Grotz, E., Bernabeu, E., Moretton, M. A., & Chiappetta, D. A. (2017). Doxorubicin: Nanotechnological overviews from bench to bedside. Drug Discovery Today, 22(2), 270–281.

    Article  CAS  PubMed  Google Scholar 

  70. Chen, T., Shen, H. M., Deng, Z. Y., Yang, Z. Z., Zhao, R. L., Wang, L., Feng, Z. P., Liu, C., Li, W. H., & Liu, Z. J. (2017). A herbal formula, SYKT, reverses doxorubicin-induced myelosuppression and cardiotoxicity by inhibiting ROS-mediated apoptosis. Molecular Medicine Reports, 15(4), 2057–2066.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Koleini, N., & Kardami, E. (2017). Autophagy and mitophagy in the context of doxorubicin-induced cardiotoxicity. Oncotarget, 8(28), 46663–46680.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Shao, C., Lu, W., Wan, N., Wu, M., Bao, Q., Tian, Y., Lu, G., Wang, N., Hao, H., & Ye, H. (2019). Integrative omics analysis revealed that metabolic intervention combined with metronomic chemotherapy selectively kills cancer cells. Journal of Proteome Research, 18(6), 2643–2653.

    Article  CAS  PubMed  Google Scholar 

  73. Celiktas, M., Tanaka, I., Tripathi, S. C., Fahrmann, J. F., Aguilar-Bonavides, C., Villalobos, P., Delgado, O., Dhillon, D., Dennison, J. B., Ostrin, E. J., Wang, H., Behrens, C., Do, K. A., Gazdar, A. F., Hanash, S. M., & Taguchi, A. (2017). Role of CPS1 in cell growth, metabolism and prognosis in LKB1-inactivated lung adenocarcinoma. Journal of the National Cancer Institute, 109(3), 1–9.

    Article  PubMed  CAS  Google Scholar 

  74. Cai, Z., Zhao, J.-S., Li, J.-J., Peng, D.-N., Wang, X.-Y., Chen, T.-L., Qiu, Y.-P., Chen, P.-P., Li, W.-J., Xu, L.-Y., Li, E.-M., Tam, J. P. M., Qi, R. Z., Jia, W., & Xie, D. (2010). A combined proteomics and metabolomics profiling of gastric cardia cancer reveals characteristic dysregulations in glucose metabolism. Molecular & Cellular Proteomics, 9(12), 2617–2628.

    Article  CAS  Google Scholar 

  75. Dougan, J., Hawsawi, O., Burton, L. J., Edwards, G., Jones, K., Zou, J., Nagappan, P., Wang, G., Zhang, Q., Danaher, A., Bowen, N., Hinton, C., & Odero-Marah, V. A. (2019). Proteomics-metabolomics combined approach identifies peroxidasin as a protector against metabolic and oxidative stress in prostate cancer. International Journal of Molecular Sciences, 20(12), 3046.

    Article  CAS  PubMed Central  Google Scholar 

  76. Wettersten, H. I., Hakimi, A. A., Morin, D., Bianchi, C., Johnstone, M. E., Donohoe, D. R., Trott, J. F., Aboud, O. A., Stirdivant, S., Neri, B., Wolfert, R., Stewart, B., Perego, R., Hsieh, J. J., & Weiss, R. H. (2015). Grade-dependent metabolic reprogramming in kidney cancer revealed by combined proteomics and metabolomics analysis. Cancer Research, 75(12), 2541–2552.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Shender, V. O., Pavlyukov, M. S., Ziganshin, R. H., Arapidi, G. P., Kovalchuk, S. I., Anikanov, N. A., Altukhov, I. A., Alexeev, D. G., Butenko, I. O., Shavarda, A. L., Khomyakova, E. B., Evtushenko, E., Ashrafyan, L. A., Antonova, I. B., Kuznetcov, I. N., Gorbachev, A. Y., Shakhparonov, M. I., & Govorun, V. M. (2014). Proteome-metabolome profiling of ovarian cancer ascites reveals novel components involved in intercellular communication. Molecular & Cellular Proteomics, 13(12), 3558–3571.

    Article  CAS  Google Scholar 

  78. Wu, M., Ye, H., Shao, C., Zheng, X., Li, Q., Wang, L., Zhao, M., Lu, G., Chen, B., Zhang, J., Wang, Y., Wang, G., & Hao, H. (2017). Metabolomics–proteomics combined approach identifies differential metabolism-associated molecular events between senescence and apoptosis. Journal of Proteome Research, 16(6), 2250–2261.

    Article  CAS  PubMed  Google Scholar 

  79. Hulce, J. J., Cognetta, A. B., Niphakis, M. J., Tully, S. E., & Cravatt, B. F. (2013). Proteome-wide mapping of cholesterol-interacting proteins in mammalian cells. Nature Methods, 10(3), 259–264.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Moraru, A., Wiederstein, J., Pfaff, D., Fleming, T., Miller, A. K., Nawroth, P., & Teleman, A. A. (2018). Elevated levels of the reactive metabolite methylglyoxal recapitulate progression of type 2 diabetes. Cell Metabolism, 27(4), 926–934.e928.

    Article  CAS  PubMed  Google Scholar 

  81. Qin, W., Qin, K., Zhang, Y., Jia, W., Chen, Y., Cheng, B., Peng, L., Chen, N., Liu, Y., Zhou, W., Wang, Y.-L., Chen, X., & Wang, C. (2019). S-glycosylation-based cysteine profiling reveals regulation of glycolysis by itaconate. Nature Chemical Biology, 15, 983–991.

    Article  CAS  PubMed  Google Scholar 

  82. Fu, X., Chin, R. M., Vergnes, L., Hwang, H., Deng, G., Xing, Y., Pai, M. Y., Li, S., Ta, L., Fazlollahi, F., Chen, C., Prins, R. M., Teitell, M. A., Nathanson, D. A., Lai, A., Faull, K. F., Jiang, M., Clarke, S. G., Cloughesy, T. F., Graeber, T. G., Braas, D., Christofk, H. R., Jung, M. E., Reue, K., & Huang, J. (2015). 2-Hydroxyglutarate inhibits ATP synthase and mTOR signaling. Cell Metabolism, 22(3), 508–515.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Chin, R. M., Fu, X., Pai, M. Y., Vergnes, L., Hwang, H., Deng, G., Diep, S., Lomenick, B., Meli, V. S., Monsalve, G. C., Hu, E., Whelan, S. A., Wang, J. X., Jung, G., Solis, G. M., Fazlollahi, F., Kaweeteerawat, C., Quach, A., Nili, M., Krall, A. S., Godwin, H. A., Chang, H. R., Faull, K. F., Guo, F., Jiang, M., Trauger, S. A., Saghatelian, A., Braas, D., Christofk, H. R., Clarke, C. F., Teitell, M. A., Petrascheck, M., Reue, K., Jung, M. E., Frand, A. R., & Huang, J. (2014). The metabolite α-ketoglutarate extends lifespan by inhibiting ATP synthase and TOR. Nature, 510(7505), 397–401.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Li, Q., Cao, L., Tian, Y., Zhang, P., Ding, C., Lu, W., Jia, C., Shao, C., Liu, W., Wang, D., Ye, H., & Hao, H. (2018). Butyrate suppresses the proliferation of colorectal cancer cells via targeting pyruvate kinase M2 and metabolic reprogramming. Molecular & Cellular Proteomics, 17(8), 1531–1545.

    Article  CAS  Google Scholar 

  85. Huber, K. V., Olek, K. M., Muller, A. C., Tan, C. S., Bennett, K. L., Colinge, J., & Superti-Furga, G. (2015). Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nature Methods, 12(11), 1055–1057.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Reinhard, F. B., Eberhard, D., Werner, T., Franken, H., Childs, D., Doce, C., Savitski, M. F., Huber, W., Bantscheff, M., Savitski, M. M., & Drewes, G. (2015). Thermal proteome profiling monitors ligand interactions with cellular membrane proteins. Nature Methods, 12(12), 1129–1131.

    Article  CAS  PubMed  Google Scholar 

  87. Diether, M., & Sauer, U. (2017). Towards detecting regulatory protein-metabolite interactions. Current Opinion in Microbiology, 39, 16–23.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This study was financially supported by the National Key R&D Program ofChina (2018YFD0901101), the National Natural Science Foundation of China (grants No. 81872838, 81720108032), the Natural Science Foundation of Jiangsu Province (BK20180079), the Project of State Key Laboratory of Natural Medicines in ChinaPharmaceutical University (SKLNMZZCX201817), the Double First-Rate University project (CPU2018GY09, CPU2018GF09), and the Project for Major New Drugs Innovation and Development (2018ZX09711001-002-003,2018ZX09711002-001-004, 2017ZX09301013).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Haiping Hao or Hui Ye .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shao, C., Lu, W., Hao, H., Ye, H. (2021). Functional Metabolomics and Chemoproteomics Approaches Reveal Novel Metabolic Targets for Anticancer Therapy. In: Hu, S. (eds) Cancer Metabolomics. Advances in Experimental Medicine and Biology, vol 1280. Springer, Cham. https://doi.org/10.1007/978-3-030-51652-9_9

Download citation

Publish with us

Policies and ethics