Skip to main content
SpringerLink
Log in

Novel frontiers in detecting cancer metastasis

Clinical & Experimental Metastasis Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Cite this article

Abstract

Cancer microenvironment is the critical battle ground between the cancer cells and host response. Thus, more emphasis is directed to study the relationship between cancer cells and the stromal cells. Multiplex microscopy is an emerging technique in which multiple cell populations within the cancer microenvironment may be stained so that spatial relationship between cancer cells and, in particular, the immune cells may be studied during different stages of cancer development. Recent discovery of mutational burden and neoantigens in cancer has opened new landscapes in the interaction of host immune cells and cancer neoantigens. The emerging role of miRNAs may become an added dimension to study cancer beyond traditional pathway of DNA directed RNA being associated with the malignant behavior of cancer. Circulating tumor cells, cancer markers and ctDNA can be used as markers for circulating cancer cells in the blood. Further studies are needed to validate if liquid biopsy of cancer may become a routine clinical tool to screen cancer or follow patients for recurrence or responses to treatment.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

References

  1. Pardo DM (2012) Multiple co-stimulatory and inhibitory interactions regulate T cell responses. Nat Rev Cancer 12:252

    Article  Google Scholar 

  2. Schumacher TN, Schreiber RD (2015) Neoantigens in cancer immunotherapy. Science 348:69–74

    Article  CAS  Google Scholar 

  3. Ito A, Kitano S et al (2015) Cancer neoantigens: a promising source of immunogens for cancer immunotherapy. J Clin Cell Immunol 6(322):2

    Google Scholar 

  4. Virchow R (1863) Cellular pathology as based upon physiological and pathological histology. Philadelphia: J.B. Lippincott

  5. Handley WS (1907) The pathology of melanotic growths in relation to their operative treatment. Lancet 169:996–1003

    Article  Google Scholar 

  6. Bethmann D, Feng Z, Fox BA (2017) Immunoprofiling as a predictor of patient’s response to cancer therapy-promises and challenges. Curr Opin Immunol 45:60–72

    Article  CAS  Google Scholar 

  7. Galon J, Costes A, Sanchez-Cabo F et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313:1960–1964

    Article  CAS  Google Scholar 

  8. Galon J, Pages F, Marincola FM et al (2012) Cancer classification using the Immunoscore: a worldwide task force. J Transl Med 10:205

    Article  Google Scholar 

  9. Pages F, Mlecnik B, Marliot F et al (2018) International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet 391(10135):2128–2139

    Article  Google Scholar 

  10. Cheever MA, Allison JP, Ferris AS et al (2009) The prioritization of cancer antigens: a national cancer institute pilot project for the acceleration of translational research. Clin Cancer Res 15:5323–5337

    Article  Google Scholar 

  11. Page DB, Hulett TW, Hilton TL et al (2016) Glimpse into the future: harnessing autophagy to promote anti-tumor immunity with the DRibbles vaccine. J Immunother Cancer 4:25

    Article  Google Scholar 

  12. Mlecnik B, Van den Eynde M, Bindea G et al (2018) Comprehensive intrametastatic immune quantification and major impact of immunoscore on survival. J Natl Cancer Inst 110

  13. Hirsch FR, McElhinny A, Stanforth D et al (2017) PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the blueprint PD-L1 IHC assay comparison project. J Thorac Oncol 12:208–222

    Article  Google Scholar 

  14. Yuan J (2016) Circulating protein and antibody biomarker for personalized cancer immunotherapy. J Immunother Cancer 4:46

    Article  Google Scholar 

  15. Feng Z, Puri S, Moudgil T et al (2015) Multispectral imaging of formalin-fixed tissue predicts ability to generate tumor-infiltrating lymphocytes from melanoma. J Immunother Cancer 3:47

    Article  Google Scholar 

  16. LaCelle MG, Jensen SM, Fox BA (2009) Partial CD4 depletion reduces regulatory T cells induced by multiple vaccinations and restores therapeutic efficacy. Clin Cancer Res 15:6881–6890

    Article  CAS  Google Scholar 

  17. Feng Z, Bethmann D, Kappler M et al (2017) Multiparametric immune profiling in HPV-oral squamous cell cancer. JCI Insight 2

  18. Riaz N, Havel JJ, Makarov V et al (2017) Tumor and microenvironment evolution during immunotherapy with Nivolumab. Cell 171:934–949

    Article  CAS  Google Scholar 

  19. Zaretsky JM, Garcia-Diaz A, Shin DS et al (2016) Mutations associated with acquired resistance to PD-1 blockade in melanoma, N Engl J Med 375(9):819–829

    Article  CAS  Google Scholar 

  20. Hulett TW, Jensen SM, Wilmarth PA et al (2018) Coordinated responses to individual tumor antigens by IgG antibody and CD8+ T cells following cancer vaccination. J ImmunoTher Cancer 6(1):27. https://doi.org/10.1186/s40425-018-0331-0

    Article  PubMed  PubMed Central  Google Scholar 

  21. Snyder A, Makarov V, Merghoub T et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371:2189–2199

    Article  Google Scholar 

  22. Rizvi NA, Hellmann MD, Snyder A et al (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348:124–128

    Article  CAS  Google Scholar 

  23. Robbins PF, Lu YC, El-Gamil M et al (2013) Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat Med 19:747–752

    Article  CAS  Google Scholar 

  24. van Rooij N, van Buuren MM, Philips D et al (2013) Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol 31:e439–e442

    Article  Google Scholar 

  25. Bassani-Sternberg M, Braunlein E, Klar R et al (2016) Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat Commun 7:13404

    Article  CAS  Google Scholar 

  26. Karpanen T, Olweus J (2017) The potential of donor T-cell repertoires in neoantigen-targeted cancer immunotherapy. Front Immunol 8:1718

    Article  Google Scholar 

  27. Abelin JG, Keskin DB, Sarkizova S et al (2017) Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46:315–326

    Article  CAS  Google Scholar 

  28. Vita R, Overton JA, Greenbaum JA et al (2015) The immune epitope database (IEDB) 3.0. Nucleic Acids Res 43:D405–D412

    Article  CAS  Google Scholar 

  29. Andreatta M, Nielsen M (2016) Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32:511–517

    Article  CAS  Google Scholar 

  30. Rubinsteyn A, O’Donnell T, Damaraju N, Hammerbacher J (2016) Predicting peptide-MHC binding affinities with imputed training data. bioRxiv. https://doi.org/10.1101/054775

    Article  Google Scholar 

  31. Bassani-Sternberg M, Pletscher-Frankild S, Jensen LJ et al (2015) Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation. Mol Cell Proteomics 14:658–673

    Article  CAS  Google Scholar 

  32. Yadav M, Jhunjhunwala S, Phung QT et al (2014) Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515:572–576

    Article  CAS  Google Scholar 

  33. Pauken KE, Wherry EJ (2015) Overcoming T cell exhaustion in infection and cancer. Trends Immunol 36:265–276

    Article  CAS  Google Scholar 

  34. Stronen E, Toebes M, Kelderman S et al (2016) Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352:1337–1341

    Article  CAS  Google Scholar 

  35. Sahin U, Derhovanessian E, Miller M et al (2017) Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547:222–226

    Article  CAS  Google Scholar 

  36. Kochenderfer JN, Somerville RPT, Lu T et al (2017) Lymphoma remissions caused by anti-CD19 chimeric antigen receptor T cells are associated with high serum interleukin-15 levels. J Clin Oncol 35:1803–1813

    Article  CAS  Google Scholar 

  37. Lee DW, Kochenderfer JN, Stetler-Stevenson M et al (2015) T cells expressing CD19 chimeric antigen receptors for acute lymphoblastic leukaemia in children and young adults: a phase 1 dose-escalation trial. Lancet 385:517–528

    Article  CAS  Google Scholar 

  38. Stevanovic S, Draper LM, Langhan MM et al (2015) Complete regression of metastatic cervical cancer after treatment with human papillomavirus-targeted tumor-infiltrating T cells. J Clin Oncol 33:1543–1550

    Article  CAS  Google Scholar 

  39. Ott PA, Hu Z, Keskin DB et al (2017) An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547:217–221

    Article  CAS  Google Scholar 

  40. Mensah VA, Gueye A, Ndiaye M et al (2016) Safety, immunogenicity and efficacy of prime-boost vaccination with ChAd63 and MVA encoding ME-TRAP against Plasmodium falciparum infection in adults in senegal. PLoS ONE 11:e0167951

    Article  Google Scholar 

  41. Reddy KB (2015) MicroRNA (miRNA) in cancer. Cancer Cell Int 15:38

    Article  Google Scholar 

  42. Hayes J, Peruzzi PP, Lawler S (2014) MicroRNAs in cancer: biomarkers, functions and therapy. Trends Mol Med 20:460–469

    Article  CAS  Google Scholar 

  43. Peng Y, Croce CM (2016) The role of MicroRNAs in human cancer. Signal Transduct Target Ther 1:15004

    Article  Google Scholar 

  44. Lee YS, Dutta A (2009) MicroRNAs in cancer. Annu Rev Pathol 4:199–227

    Article  CAS  Google Scholar 

  45. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674

    Article  CAS  Google Scholar 

  46. Weinberg RA (2007) The biology of cancer; Garland Science

  47. Cristofanilli M, Budd GT, Ellis MJ et al (2004) Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 351:781–791

    Article  CAS  Google Scholar 

  48. Zhang L, Riethdorf S, Wu G et al (2012) Meta-analysis of the prognostic value of circulating tumor cells in breast cancer. Clin Cancer Res 18:5701–5710

    Article  Google Scholar 

  49. Cristofanilli M, Hayes DF, Budd GT et al (2005) Circulating tumor cells: a novel prognostic factor for newly diagnosed metastatic breast cancer. J Clin Oncol 23:1420–1430

    Article  Google Scholar 

  50. de Bono JS, Scher HI, Montgomery RB et al (2008) Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin Cancer Res 14:6302–6309

    Article  Google Scholar 

  51. Cohen SJ, Punt CJ, Iannotti N et al (2008) Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. J Clin Oncol 26:3213–3221

    Article  Google Scholar 

  52. Koyanagi K, O’Day SJ, Boasberg P et al (2010) Serial monitoring of circulating tumor cells predicts outcome of induction biochemotherapy plus maintenance biotherapy for metastatic melanoma. Clin Cancer Res 16:2402–2408

    Article  CAS  Google Scholar 

  53. Zhang L, Riethdorf S, Wu G et al (2012) Metaanalysis of the prognostic value of circulating tumor cells in breast cancer. Clin Cancer Res 18:5701–5710

    Article  Google Scholar 

  54. Hayashi N, Yamauchi H (2012) Role of circulating tumor cells and disseminated tumor cells in primary breast cancer. Breast Cancer 19:110–117

    Article  Google Scholar 

  55. Ignatiadis M, Kallergi G, Ntoulia M et al (2008) Prognostic value of the molecular detection of circulating tumor cells using a multimarker reverse transcription-PCR assay for cytokeratin 19, mammaglobin A, and HER2 in early breast cancer. Clin Cancer Res 14:2593–2600

    Article  CAS  Google Scholar 

  56. Xenidis N, Ignatiadis M, Apostolaki S et al (2009) Cytokeratin-19 mRNA-positive circulating tumor cells after adjuvant chemotherapy in patients with early breast cancer. J Clin Oncol 27:2177–2184

    Article  CAS  Google Scholar 

  57. Peach G, Kim C, Zacharakis E, Purkayastha S, Ziprin P (2010) Prognostic significance of circulating tumour cells following surgical resection of colorectal cancers: a systematic review. Br J Cancer 102:1327–1334

    Article  CAS  Google Scholar 

  58. Rahbari NN, Aigner M, Thorlund K et al (2010) Meta-analysis shows that detection of circulating tumor cells indicates poor prognosis in patients with colorectal cancer. Gastroenterology 138:1714–1726

    Article  Google Scholar 

  59. Hoshimoto S, Faries MB, Morton DL et al (2012) Assessment of prognostic circulating tumor cells in a phase III trial of adjuvant immunotherapy after complete resection of stage IV melanoma. Ann Surg 255:357–362

    Article  Google Scholar 

  60. Merker JD, Oxnard GR, Compton C et al (2018) Circulating tumor DNA analysis in patients with cancer: American Society of Clinical oncology and college of American pathologists joint review. J Clin Oncol 36:1–11

    Article  Google Scholar 

Download references

Funding

Sebastian Marwitz has been funded by Deutsche Forschungsgemeinschaft (MA 7800/1-1).

Author information

Authors and Affiliations

  1. California Pacific Medical Center and Research Institute, San Francisco, CA, 94115, USA

    Stanley P. Leong

  2. Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Cancer Institute, Portland, OR, 97213, USA

    Carmen Ballesteros-Merino, Shawn M. Jensen, Sebastian Marwitz & Bernard A. Fox

  3. Translational Digital and Molecular Pathology, Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Cancer Institute, Portland, OR, 97213, USA

    Carlo Bifulco

  4. Regional Pathology, Providence Health and Services, Portland, OR, 97213, USA

    Carlo Bifulco

  5. Department of Molecular Microbiology & Immunology, Oregon Health & Science University, Portland, OR, 97239, USA

    Bernard A. Fox

  6. Gritstone Oncology, Cambridge, MA, 02139, USA

    Mojca Skoberne

Authors
  1. Stanley P. Leong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carmen Ballesteros-Merino
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shawn M. Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sebastian Marwitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carlo Bifulco
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bernard A. Fox
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mojca Skoberne
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stanley P. Leong.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leong, S.P., Ballesteros-Merino, C., Jensen, S.M. et al. Novel frontiers in detecting cancer metastasis. Clin Exp Metastasis 35, 403–412 (2018). https://doi.org/10.1007/s10585-018-9918-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10585-018-9918-6

Keywords

search

Navigation