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Intracellular pH dynamics and charge-changing somatic mutations in cancer

  • Katharine A. White
  • Kyle Kisor
  • Diane L. BarberEmail author
Article

Abstract

An unresolved question critical for understanding cancer is how recurring somatic mutations are retained and how selective pressures drive retention. Increased intracellular pH (pHi) is common to most cancers and is an early event in cancer development. Recent work shows that recurrent somatic mutations can confer an adaptive gain in pH sensing to mutant proteins, enhancing tumorigenic phenotypes specifically at the increased pHi of cancer. Newly identified amino acid mutation signatures in cancer suggest charge-changing mutations define and shape the mutational landscape of cancer. Taken together, these results support a new perspective on the functional significance of somatic mutations in cancer. In this review, we explore existing data and new directions for better understanding how changes in dynamic pH sensing by somatic mutation might be conferring a fitness advantage to the high pH of cancer.

Keywords

Intracellular pH dynamics Oncogenes pH sensing Somatic mutations 

Notes

Funding

This study is supported by a National Institute of Health grant CA197855 (D.L.B.) and startup funds from the University of Notre Dame (K.A.W.).

References

  1. 1.
    Webb, B. A., Chimenti, M., Jacobson, M. P., & Barber, D. L. (2011). Dysregulated pH: a perfect storm for cancer progression. Nature Reviews Cancer, 11, 671–677.CrossRefGoogle Scholar
  2. 2.
    White, K. A., Grillo-Hill, B. K., & Barber, D. L. (2017). Cancer cell behaviors mediated by dysregulated pH dynamics at a glance. Journal of Cell Science, 130, 663–669.CrossRefGoogle Scholar
  3. 3.
    Pedersen, S. F., & Stock, C. (2013). Ion channels and transporters in cancer: pathophysiology, regulation, and clinical potential. Cancer Research, 73, 1658–1661.CrossRefGoogle Scholar
  4. 4.
    Parks, S. K., Chiche, J., & Pouyssegur, J. (2013). Disrupting proton dynamics and energy metabolism for cancer therapy. Nature Reviews Cancer, 13, 611–623.CrossRefGoogle Scholar
  5. 5.
    Reshkin, S. J., et al. (2000). Na+/H+ exchanger-dependent intracellular alkalinization is an early event in malignant transformation and plays an essential role in the development of subsequent transformation-associated phenotypes. The FASEB Journal, 14, 2185–2197.CrossRefGoogle Scholar
  6. 6.
    Grillo-Hill, B. K., Choi, C., Jimenez-Vidal, M., & Barber, D. L. (2015). Increased H(+) efflux is sufficient to induce dysplasia and necessary for viability with oncogene expression. Elife, 4.Google Scholar
  7. 7.
    Nowell, P. C. (1976). The clonal evolution of tumor cell populations. Science, 194, 23–28.CrossRefGoogle Scholar
  8. 8.
    Greaves, M., & Maley, C. C. (2012). Clonal evolution in cancer. Nature, 481, 306–313.CrossRefGoogle Scholar
  9. 9.
    Gillies, R. J., Verduzco, D., & Gatenby, R. A. (2012). Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nature Reviews Cancer, 12, 487–493.CrossRefGoogle Scholar
  10. 10.
    Ward, S. G., & Mrsny, R. (2009). New insights into mechanisms of gastrointestinal inflammation and cancer. Current Opinion in Pharmacology, 9, 677–679.CrossRefGoogle Scholar
  11. 11.
    Ramachandran, S., Ient, J., Gottgens, E. L., Krieg, A. J., & Hammond, E. M. (2015). Epigenetic therapy for solid tumors: highlighting the impact of tumor hypoxia. Genes Basel, 6, 935–956.CrossRefGoogle Scholar
  12. 12.
    Brahimi-Horn, M. C., Bellot, G., & Pouyssegur, J. (2011). Hypoxia and energetic tumour metabolism. Current Opinion in Genetics & Development, 21, 67–72.CrossRefGoogle Scholar
  13. 13.
    Pickup, M. W., Mouw, J. K., & Weaver, V. M. (2014). The extracellular matrix modulates the hallmarks of cancer. EMBO Reports, 15, 1243–1253.CrossRefGoogle Scholar
  14. 14.
    Su, A. I., et al. (2001). Molecular classification of human carcinomas by use of gene expression signatures. Cancer Research, 61, 7388–7393.Google Scholar
  15. 15.
    Wood, L. D., Parsons, D. W., Jones, S., Lin, J., Sjoblom, T., Leary, R. J., Shen, D., Boca, S. M., Barber, T., Ptak, J., Silliman, N., Szabo, S., Dezso, Z., Ustyanksky, V., Nikolskaya, T., Nikolsky, Y., Karchin, R., Wilson, P. A., Kaminker, J. S., Zhang, Z., Croshaw, R., Willis, J., Dawson, D., Shipitsin, M., Willson, J. K. V., Sukumar, S., Polyak, K., Park, B. H., Pethiyagoda, C. L., Pant, P. V. K., Ballinger, D. G., Sparks, A. B., Hartigan, J., Smith, D. R., Suh, E., Papadopoulos, N., Buckhaults, P., Markowitz, S. D., Parmigiani, G., Kinzler, K. W., Velculescu, V. E., & Vogelstein, B. (2007). The genomic landscapes of human breast and colorectal cancers. Science, 318, 1108–1113.CrossRefGoogle Scholar
  16. 16.
    Alexandrov, L. B., et al. (2013). Signatures of mutational processes in human cancer. Nature, 500, 415–421.CrossRefGoogle Scholar
  17. 17.
    Watanabe, N., Okochi, E., Mochizuki, M., Sugimura, T., & Ushijima, T. (2001). The presence of single nucleotide instability in human breast cancer cell lines. Cancer Research, 61, 7739–7742.Google Scholar
  18. 18.
    Bignell, G. R., Greenman, C. D., Davies, H., Butler, A. P., Edkins, S., Andrews, J. M., Buck, G., Chen, L., Beare, D., Latimer, C., Widaa, S., Hinton, J., Fahey, C., Fu, B., Swamy, S., Dalgliesh, G. L., Teh, B. T., Deloukas, P., Yang, F., Campbell, P. J., Futreal, P. A., & Stratton, M. R. (2010). Signatures of mutation and selection in the cancer genome. Nature, 463, 893–898.CrossRefGoogle Scholar
  19. 19.
    Szpiech, Z. A., Strauli, N. B., White, K. A., Ruiz, D. G., Jacobson, M. P., Barber, D. L., & Hernandez, R. D. (2017). Prominent features of the amino acid mutation landscape in cancer. PLoS One, 12, e0183273.CrossRefGoogle Scholar
  20. 20.
    Anoosha, P., Sakthivel, R., & Michael Gromiha, M. (2016). Exploring preferred amino acid mutations in cancer genes: applications to identify potential drug targets. Biochimica et Biophysica Acta, 1862, 155–165.CrossRefGoogle Scholar
  21. 21.
    Tan, H., Bao, J., & Zhou, X. (2015). Genome-wide mutational spectra analysis reveals significant cancer-specific heterogeneity. Scientific Reports, 5, 12566.CrossRefGoogle Scholar
  22. 22.
    Weber, C. C., & Whelan, S. (2019). Physicochemical amino acid properties better describe substitution rates in large populations. Molecular Biology and Evolution.  https://doi.org/10.1093/molbev/msz003.
  23. 23.
    Pollock, D. D., Thiltgen, G., & Goldstein, R. A. (2012). Amino acid coevolution induces an evolutionary stokes shift. Proceedings of the National Academy of Sciences, 109, E1352–E1359.CrossRefGoogle Scholar
  24. 24.
    White, K. A., Ruiz, D. G., Szpiech, Z. A., Strauli, N. B., Hernandez, R. D., Jacobson, M. P., & Barber, D. L. (2017). Cancer-associated arginine-to-histidine mutations confer a gain in pH sensing to mutant proteins. Science Signaling, 10, eaam9931.CrossRefGoogle Scholar
  25. 25.
    Frantz, C., Barreiro, G., Dominguez, L., Chen, X., Eddy, R., Condeelis, J., Kelly, M. J. S., Jacobson, M. P., & Barber, D. L. (2008). Cofilin is a pH sensor for actin free barbed end formation: role of phosphoinositide binding. The Journal of Cell Biology, 183, 865–879.CrossRefGoogle Scholar
  26. 26.
    Srivastava, J., Barreiro, G., Groscurth, S., Gingras, A. R., Goult, B. T., Critchley, D. R., Kelly, M. J. S., Jacobson, M. P., & Barber, D. L. (2008). Structural model and functional significance of pH-dependent talin-actin binding for focal adhesion remodeling. Proceedings of the National Academy of Sciences of the United States of America, 105, 14436–14441.CrossRefGoogle Scholar
  27. 27.
    Yun, C. H., Boggon, T. J., Li, Y., Woo, M. S., Greulich, H., Meyerson, M., & Eck, M. J. (2007). Structures of lung cancer-derived EGFR mutants and inhibitor complexes: mechanism of activation and insights into differential inhibitor sensitivity. Cancer Cell, 11, 217–227.CrossRefGoogle Scholar
  28. 28.
    Forbes, S. A., Beare, D., Boutselakis, H., Bamford, S., Bindal, N., Tate, J., Cole, C. G., Ward, S., Dawson, E., Ponting, L., Stefancsik, R., Harsha, B., Kok, C. Y., Jia, M., Jubb, H., Sondka, Z., Thompson, S., de, T., & Campbell, P. J. (2017). COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Research, 45, D777–D783.CrossRefGoogle Scholar
  29. 29.
    Joerger, A. C., & Fersht, A. R. (2010). The tumor suppressor p53: from structures to drug discovery. Cold Spring Harbor Perspectives in Biology, 2, a000919.CrossRefGoogle Scholar
  30. 30.
    Shi, Y., & Massagué, J. (2003). Mechanisms of TGF-β signaling from cell membrane to the nucleus. Cell, 113, 685–700.CrossRefGoogle Scholar
  31. 31.
    Singh, P., Srinivasan, R., & Wig, J. D. (2012). SMAD4 genetic alterations predict a worse prognosis in patients with pancreatic ductal adenocarcinoma. Pancreas, 41, 541–546.CrossRefGoogle Scholar
  32. 32.
    Papageorgis, P., Cheng, K., Ozturk, S., Gong, Y., Lambert, A. W., Abdolmaleky, H. M., Zhou, J. R., & Thiagalingam, S. (2011). Smad4 inactivation promotes malignancy and drug resistance of colon cancer. Cancer Research, 71, 998–1008.CrossRefGoogle Scholar
  33. 33.
    Schiro, M. M., Stauber, S. E., Peterson, T. L., Krueger, C., Darnell, S. J., Satyshur, K. A., Drinkwater, N. R., Newton, M. A., & Hoffmann, F. M. (2011). Mutations in protein-binding hot-spots on the hub protein Smad3 differentially affect its protein interactions and Smad3-regulated gene expression. PLoS One, 6, e25021.CrossRefGoogle Scholar
  34. 34.
    Linder, P., & Jankowsky, E. (2011). From unwinding to clamping—the DEAD box RNA helicase family. Nature Reviews Molecular Cell Biology, 12, 505–516.CrossRefGoogle Scholar
  35. 35.
    Northcott, P. A., Jones, D. T. W., Kool, M., Robinson, G. W., Gilbertson, R. J., Cho, Y. J., Pomeroy, S. L., Korshunov, A., Lichter, P., Taylor, M. D., & Pfister, S. M. (2012). Medulloblastomics: the end of the beginning. Nature Reviews. Cancer, 12, 818–834.CrossRefGoogle Scholar
  36. 36.
    Floor, S. N., Condon, K. J., Sharma, D., Jankowsky, E., & Doudna, J. A. (2016). Autoinhibitory Interdomain interactions and subfamily-specific extensions redefine the catalytic core of the human DEAD-box protein DDX3. The Journal of Biological Chemistry, 291, 2412–2421.CrossRefGoogle Scholar
  37. 37.
    Sengoku, T., Nureki, O., Nakamura, A., Kobayashi, S., & Yokoyama, S. (2006). Structural basis for RNA unwinding by the DEAD-box protein Drosophila Vasa. Cell, 125, 287–300.CrossRefGoogle Scholar
  38. 38.
    Valentin-Vega, Y. A., Wang, Y. D., Parker, M., Patmore, D. M., Kanagaraj, A., Moore, J., Rusch, M., Finkelstein, D., Ellison, D. W., Gilbertson, R. J., Zhang, J., Kim, H. J., & Taylor, J. P. (2016). Cancer-associated DDX3X mutations drive stress granule assembly and impair global translation. Scientific Reports, 6.Google Scholar
  39. 39.
    Jones, R. G., & Thompson, C. B. (2009). Tumor suppressors and cell metabolism: a recipe for cancer growth. Genes & Development, 23, 537–548.CrossRefGoogle Scholar
  40. 40.
    Welcker, M., & Clurman, B. E. (2008). FBW7 ubiquitin ligase: a tumour suppressor at the crossroads of cell division, growth and differentiation. Nature Reviews Cancer, 8, 83–93.CrossRefGoogle Scholar
  41. 41.
    Akhoondi, S., Sun, D., von der Lehr, N., Apostolidou, S., Klotz, K., Maljukova, A., Cepeda, D., Fiegl, H., Dofou, D., Marth, C., Mueller-Holzner, E., Corcoran, M., Dagnell, M., Nejad, S. Z., Nayer, B. N., Zali, M. R., Hansson, J., Egyhazi, S., Petersson, F., Sangfelt, P., Nordgren, H., Grander, D., Reed, S. I., Widschwendter, M., Sangfelt, O., & Spruck, C. (2007). FBXW7/hCDC4 is a general tumor suppressor in human cancer. Cancer Research, 67, 9006–9012.CrossRefGoogle Scholar
  42. 42.
    White, K. A., Grillo-Hill, B. K., Esquivel, M., Peralta, J., Bui, V. N., Chire, I., & Barber, D. L. (2018). β-Catenin is a pH sensor with decreased stability at higher intracellular pH. The Journal of Cell Biology, 217, 3965–3976.CrossRefGoogle Scholar
  43. 43.
    Isom, D. G., Castaneda, C. A., Cannon, B. R., & Garcia-Moreno, B. (2011). Large shifts in pKa values of lysine residues buried inside a protein. Proceedings of the National Academy of Sciences of the United States of America, 108, 5260–5265.CrossRefGoogle Scholar
  44. 44.
    Castaneda, C. A., et al. (2009). Molecular determinants of the pKa values of Asp and Glu residues in staphylococcal nuclease. Proteins, 77, 570–588.CrossRefGoogle Scholar
  45. 45.
    Fang, Y., Liu, Z., Chen, Z., Xu, X., Xiao, M., Yu, Y., Zhang, Y., Zhang, X., du, Y., Jiang, C., Zhao, Y., Wang, Y., Fan, B., Terheyden-Keighley, D., Liu, Y., Shi, L., Hui, Y., Zhang, X., Zhang, B., Feng, H., Ma, L., Zhang, Q., Jin, G., Yang, Y., Xiang, B., Liu, L., & Zhang, X. (2017). Smad5 acts as an intracellular pH messenger and maintains bioenergetic homeostasis. Cell Research, 27, 1083–1099.CrossRefGoogle Scholar
  46. 46.
    Vercoulen, Y., Kondo, Y., Iwig, J. S., Janssen, A. B., White, K. A., Amini, M., Barber, D. L., Kuriyan, J., & Roose, J. P. (2017). A histidine pH sensor regulates activation of the Ras-specific guanine nucleotide exchange factor RasGRP1. Elife, 6, e29002.CrossRefGoogle Scholar
  47. 47.
    Isakoff, S. J., Engelman, J. A., Irie, H. Y., Luo, J., Brachmann, S. M., Pearline, R. V., Cantley, L. C., & Brugge, J. S. (2005). Breast cancer-associated PIK3CA mutations are oncogenic in mammary epithelial cells. Cancer Research, 65, 10992–11000.CrossRefGoogle Scholar
  48. 48.
    Miller, M. S., et al. (2014). Structural basis of nSH2 regulation and lipid binding in PI3Kalpha. Oncotarget, 5, 5198–5208.CrossRefGoogle Scholar
  49. 49.
    Huang, C. H., Mandelker, D., Gabelli, S. B., & Amzel, L. M. (2008). Insights into the oncogenic effects of PIK3CA mutations from the structure of p110alpha/p85alpha. Cell Cycle, 7, 1151–1156.CrossRefGoogle Scholar
  50. 50.
    Mandelker, D., Gabelli, S. B., Schmidt-Kittler, O., Zhu, J., Cheong, I., Huang, C. H., Kinzler, K. W., Vogelstein, B., & Amzel, L. M. (2009). A frequent kinase domain mutation that changes the interaction between PI3Kalpha and the membrane. Proceedings of the National Academy of Sciences of the United States of America, 106, 16996–17001.CrossRefGoogle Scholar
  51. 51.
    Miled, N., Yan, Y., Hon, W. C., Perisic, O., Zvelebil, M., Inbar, Y., Schneidman-Duhovny, D., Wolfson, H. J., Backer, J. M., & Williams, R. L. (2007). Mechanism of two classes of cancer mutations in the phosphoinositide 3-kinase catalytic subunit. Science, 317, 239–242.CrossRefGoogle Scholar
  52. 52.
    Zhao, L., & Vogt, P. K. (2010). Hot-spot mutations in p110alpha of phosphatidylinositol 3-kinase (pI3K): differential interactions with the regulatory subunit p85 and with RAS. Cell Cycle, 9, 596–600.CrossRefGoogle Scholar
  53. 53.
    Hatsell, S. J., Idone, V., Wolken, D. M. A., Huang, L., Kim, H. J., Wang, L., Wen, X., Nannuru, K. C., Jimenez, J., Xie, L., Das, N., Makhoul, G., Chernomorsky, R., D’Ambrosio, D., Corpina, R. A., Schoenherr, C. J., Feeley, K., Yu, P. B., Yancopoulos, G. D., Murphy, A. J., & Economides, A. N. (2015). ACVR1R206H receptor mutation causes fibrodysplasia ossificans progressiva by imparting responsiveness to activin A. Science Translational Medicine, 7, 303ra137–303ra137.CrossRefGoogle Scholar
  54. 54.
    Haupt, J., Stanley, A., McLeod, C. M., Cosgrove, B. D., Culbert, A. L., Wang, L., Mourkioti, F., Mauck, R. L., & Shore, E. M. (2018). ACVR1R206H FOP mutation alters mechanosensing and tissue stiffness during heterotopic ossification. Molecular Biology of the Cell (MBoC), 30, 17–29.  https://doi.org/10.1091/mbc.E18-05-0311.CrossRefGoogle Scholar
  55. 55.
    Buczkowicz, P., Hoeman, C., Rakopoulos, P., Pajovic, S., Letourneau, L., Dzamba, M., Morrison, A., Lewis, P., Bouffet, E., Bartels, U., Zuccaro, J., Agnihotri, S., Ryall, S., Barszczyk, M., Chornenkyy, Y., Bourgey, M., Bourque, G., Montpetit, A., Cordero, F., Castelo-Branco, P., Mangerel, J., Tabori, U., Ho, K. C., Huang, A., Taylor, K. R., Mackay, A., Bendel, A. E., Nazarian, J., Fangusaro, J. R., Karajannis, M. A., Zagzag, D., Foreman, N. K., Donson, A., Hegert, J. V., Smith, A., Chan, J., Lafay-Cousin, L., Dunn, S., Hukin, J., Dunham, C., Scheinemann, K., Michaud, J., Zelcer, S., Ramsay, D., Cain, J., Brennan, C., Souweidane, M. M., Jones, C., Allis, C. D., Brudno, M., Becher, O., & Hawkins, C. (2014). Genomic analysis of diffuse intrinsic pontine gliomas identifies three molecular subgroups and recurrent activating ACVR1 mutations. Nature Genetics, 46, 451–456.CrossRefGoogle Scholar
  56. 56.
    Papadopoulos, T., Schemm, R., Grubmüller, H., & Brose, N. (2015). Lipid binding defects and perturbed synaptogenic activity of a collybistin R290H mutant that causes epilepsy and intellectual disability. The Journal of Biological Chemistry, 290, 8256–8270.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Katharine A. White
    • 1
    • 2
  • Kyle Kisor
    • 2
  • Diane L. Barber
    • 2
    Email author
  1. 1.Harper Cancer Research Institute, Department of Chemistry and BiochemistryUniversity of Notre DameSouth BendUSA
  2. 2.Department of Cell and Tissue Biology, University of California San FranciscoSan FranciscoUSA

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