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D/2 Predictors of Favorable Outcome in Cancer

  • Zoltán Pós
  • Jérôme Galon
Chapter

Abstract

Prediction of disease outcome in cancer is usually achieved by histological evaluation of tissue samples obtained during surgical extirpation of the primary tumor, mostly focusing on histological characteristics of cancer cells in the tumor mass, such as the extent of atypical cell morphology, of tissue integrity, aberrant expression of protein markers or malignant transformation, senescence and proliferation, various characteristics of the invasive margin and surrounding tumor stroma, depth of invasion, and the extent of vascularization. In addition, histological or radiological analysis of both tumor draining- and distant lymph nodes and remote organs can be carried out looking for evidence of metastases. Based on these data, evaluation of cancer progression can be performed and serve as an estimate of patient prognosis. This is done on the basis of statistical data available of patients exhibiting similar progression characteristics and their actual outcome parameters, such as average disease-free (DFS), disease-specific (DSS) and overall survival (OS). To this end, several dozens of tumor-type specific staging and grading systems have been developed such as Clark’s and Breslow’s indexes for melanoma, Gleason’s score for prostate cancer, Duke’s for colorectal cancer, Boden-Gibb’s staging for testicular cancer, the Evans staging system for neuroblastoma, etc., and also more universal ones such as the TNM system, which summarizes data on tumor burden (T), presence of cancer cells in draining and distant lymph nodes (N) and evidence for metastases (M). With the large body of statistical data available on cancer patients’ survival with a given progression stage, such approaches have been shown to be valuable and in many cases of acceptable accuracy in estimating disease outcome in cancer.

Keywords

Vascular Endothelial Growth Factor Overall Survival Renal Cell Carcinoma Renal Cell Cancer Immune Marker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aggarwal, B.B., Shishodia, S., Sandur, S.K., Pandey, M.K. & Sethi, G. Inflammation and cancer: how hot is the link? Biochem. Pharmacol. 72, 1605–1621 (2006).PubMedCrossRefGoogle Scholar
  2. Atkins, M. et al. Carbonic anhydrase IX expression predicts outcome of interleukin 2 therapy for renal cancer. Clin Cancer Res. 11, 3714–3721 (2005).PubMedCrossRefGoogle Scholar
  3. Balkwill, F. & Mantovani, A. Inflammation and cancer: back to Virchow? Lancet 357, 539–545 (2001).PubMedCrossRefGoogle Scholar
  4. Ben Baruch, A. Inflammation-associated immune suppression in cancer: the roles played by cytokines, chemokines and additional mediators. Semin. Cancer Biol. 16, 38–52 (2006).PubMedCrossRefGoogle Scholar
  5. Brichard, V.G. & Lejeune, D. Cancer immunotherapy targeting tumour-specific antigens: towards a new therapy for minimal residual disease. Expert. Opin. Biol. Ther. 8, 951–968 (2008).PubMedCrossRefGoogle Scholar
  6. Bui, J.D. & Schreiber, R.D. Cancer immunosurveillance, immunoediting and inflammation: independent or interdependent processes? Curr. Opin. Immunol. 19, 203–208 (2007).PubMedCrossRefGoogle Scholar
  7. Camus, M. et al. Coordination of intratumoral immune reaction and human colorectal cancer recurrence. Cancer Res. 69, 2685–2693 (2009).PubMedCrossRefGoogle Scholar
  8. Coussens, L.M. & Werb, Z. Inflammation and cancer. Nature 420, 860–867 (2002).PubMedCrossRefGoogle Scholar
  9. Critchley-Thorne, R.J. et al. Down-regulation of the interferon signaling pathway in T lymphocytes from patients with metastatic melanoma. PLoS. Med. 4, e176 (2007).PubMedCrossRefGoogle Scholar
  10. Denardo, D.G., Johansson, M. & Coussens, L.M. Immune cells as mediators of solid tumor metastasis. Cancer Metastasis Rev. 27, 11–18 (2008).PubMedCrossRefGoogle Scholar
  11. Dhodapkar, M.V., Dhodapkar, K.M. & Palucka, A.K. Interactions of tumor cells with dendritic cells: balancing immunity and tolerance. Cell Death. Differ. 15, 39–50 (2008).PubMedCrossRefGoogle Scholar
  12. Dieu-Nosjean, M.C. et al. Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures. J Clin Oncol 26, 4410–4417 (2008).PubMedCrossRefGoogle Scholar
  13. Dong, H.P. et al. NK- and B-cell infiltration correlates with worse outcome in metastatic ovarian carcinoma. Am. J Clin Pathol. 125, 451–458 (2006).PubMedGoogle Scholar
  14. Donskov, F. & von der, M.H. Impact of immune parameters on long-term survival in metastatic renal cell carcinoma. J Clin Oncol 24, 1997–2005 (2006).PubMedCrossRefGoogle Scholar
  15. Dunn, G.P., Bruce, A.T., Ikeda, H., Old, L.J. & Schreiber, R.D. Cancer immunoediting: from immunosurveillance to tumor escape. Nat. Immunol. 3, 991–998 (2002).PubMedCrossRefGoogle Scholar
  16. Dunn, G.P., Old, L.J. & Schreiber, R.D. The immunobiology of cancer immunosurveillance and immunoediting. Immunity 21, 137–148 (2004).PubMedCrossRefGoogle Scholar
  17. Finak, G. et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat. Med. 14, 518–527 (2008).PubMedCrossRefGoogle Scholar
  18. Fu, J. et al. Increased regulatory T cells correlate with CD8 T-cell impairment and poor survival in hepatocellular carcinoma patients. Gastroenterology 132, 2328–2339 (2007).PubMedCrossRefGoogle Scholar
  19. Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).PubMedCrossRefGoogle Scholar
  20. Gao, Q. et al. Intratumoral balance of regulatory and cytotoxic T cells is associated with prognosis of hepatocellular carcinoma after resection. J Clin Oncol 25, 2586–2593 (2007).PubMedCrossRefGoogle Scholar
  21. Grabenbauer, G.G., Lahmer, G., Distel, L. & Niedobitek, G. Tumor-infiltrating cytotoxic T cells but not regulatory T cells predict outcome in anal squamous cell carcinoma. Clin Cancer Res. 12, 3355–3360 (2006).PubMedCrossRefGoogle Scholar
  22. Guminski, A.D. & Thompson, J.F. Predicting response to IL-2 therapy for metastatic melanoma. Expert. Rev. Anticancer Ther. 9, 1571–1575 (2009).PubMedCrossRefGoogle Scholar
  23. Ino, K. et al. Inverse correlation between tumoral indoleamine 2,3-dioxygenase expression and tumor-infiltrating lymphocytes in endometrial cancer: its association with disease progression and survival. Clin Cancer Res. 14, 2310–2317 (2008).PubMedCrossRefGoogle Scholar
  24. Jensen, H.K. et al. Presence of intratumoral neutrophils is an independent prognostic factor in localized renal cell carcinoma. J Clin Oncol 27, 4709–4717 (2009).PubMedCrossRefGoogle Scholar
  25. Kawai, O. et al. Predominant infiltration of macrophages and CD8(+) T Cells in cancer nests is a significant predictor of survival in stage IV nonsmall cell lung cancer. Cancer 113, 1387–1395 (2008).PubMedCrossRefGoogle Scholar
  26. Kirkwood, J.M. & Tarhini, A.A. Biomarkers of therapeutic response in melanoma and renal cell carcinoma: potential inroads to improved immunotherapy. J. Clin. Oncol. 27, 2583–2585 (2009).PubMedCrossRefGoogle Scholar
  27. Kirkwood, J.M. et al. Next generation of immunotherapy for melanoma. J Clin Oncol 26, 3445–3455 (2008).PubMedCrossRefGoogle Scholar
  28. Klatte, T. et al. The chemokine receptor CXCR3 is an independent prognostic factor in patients with localized clear cell renal cell carcinoma. J Urol. 179, 61–66 (2008).PubMedCrossRefGoogle Scholar
  29. Krambeck, A.E. et al. B7-H4 expression in renal cell carcinoma and tumor vasculature: associations with cancer progression and survival. Proc. Natl. Acad. Sci. U. S. A 103, 10391–10396 (2006).PubMedCrossRefGoogle Scholar
  30. Leffers, N. et al. Prognostic significance of tumor-infiltrating T-lymphocytes in primary and metastatic lesions of advanced stage ovarian cancer. Cancer Immunol. Immunother. 58, 449–459 (2009).PubMedCrossRefGoogle Scholar
  31. Louahed, J. et al. Expression of defined genes identified by pretreatment tumor profiling: Association with clinical responses to the GSK MAGE- A3 immunotherapeutic in metastatic melanoma patients (EORTC 16032-18031). J. Clin. Oncol. (Meeting Abstracts) 26, 9045 (2008).Google Scholar
  32. Mager, D.L. Bacteria and cancer: cause, coincidence or cure? A review. J. Transl. Med. 4, 14 (2006).PubMedCrossRefGoogle Scholar
  33. Mlecnik, B. et al. Biomolecular network reconstruction identifies T cell homing factors associated with survival in colorectal cancer. Gastroenterology 138, 1429–1440 (2010).PubMedCrossRefGoogle Scholar
  34. Pages, F. et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N. Engl. J. Med. 353, 2654–2666 (2005).PubMedCrossRefGoogle Scholar
  35. Rosenberg,S.A., Restifo,N.P., Yang,J.C., Morgan,R.A. & Dudley,M.E. Adoptive cell transfer: a clinical path to effective cancer immunotherapy. Nat. Rev. Cancer 8, 299–308 (2008).PubMedCrossRefGoogle Scholar
  36. Roth, T.J. et al. B7-H3 ligand expression by prostate cancer: a novel marker of prognosis and potential target for therapy. Cancer Res. 67, 7893–7900 (2007).PubMedCrossRefGoogle Scholar
  37. Sabatino, M. et al. Serum vascular endothelial growth factor and fibronectin predict clinical response to high-dose interleukin-2 therapy. J. Clin. Oncol. 27, 2645–2652 (2009).PubMedCrossRefGoogle Scholar
  38. Schon, M.P. & Schon, M. TLR7 and TLR8 as targets in cancer therapy. Oncogene 27, 190–199 (2008).PubMedCrossRefGoogle Scholar
  39. Shen, X. et al. Persistence of tumor infiltrating lymphocytes in adoptive immunotherapy correlates with telomere length. J. Immunother. 30, 123–129 (2007).PubMedCrossRefGoogle Scholar
  40. Strieter, R.M. et al. Cancer CXC chemokine networks and tumour angiogenesis. Eur. J. Cancer 42, 768–778 (2006).PubMedCrossRefGoogle Scholar
  41. Tahara, H. et al. Emerging concepts in biomarker discovery; the US-Japan Workshop on Immunological Molecular Markers in Oncology. J. Transl. Med. 7, 45 (2009).PubMedCrossRefGoogle Scholar
  42. Talmadge, J.E., Donkor, M. & Scholar, E. Inflammatory cell infiltration of tumors: Jekyll or Hyde. Cancer Metastasis Rev. 26, 373–400 (2007).PubMedCrossRefGoogle Scholar
  43. Taylor, R.C., Patel, A., Panageas, K.S., Busam, K.J. & Brady, M.S. Tumor-infiltrating lymphocytes predict sentinel lymph node positivity in patients with cutaneous melanoma. J Clin Oncol 25, 869–875 (2007).PubMedCrossRefGoogle Scholar
  44. Thompson, R.H. et al. Serum-soluble B7x is elevated in renal cell carcinoma patients and is associated with advanced stage. Cancer Res. 68, 6054–6058 (2008).PubMedCrossRefGoogle Scholar
  45. Unitt, E. et al. Tumour lymphocytic infiltrate and recurrence of hepatocellular carcinoma following liver transplantation. J. Hepatol. 45, 246–253 (2006).PubMedCrossRefGoogle Scholar
  46. Vansteenkiste, J.F. et al. Association of gene expression signature and clinical efficacy of MAGE-A3 antigen-specific cancer immunotherapeutic (ASCI) as adjuvant therapy in resected stage IB/II non-small cell lung cancer (NSCLC). J. Clin. Oncol. (Meeting Abstracts) 26, 7501 (2008).Google Scholar
  47. Wang, W. et al. Modulation of signal transducers and activators of transcription 1 and 3 signaling in melanoma by high-dose IFNalpha2b. Clin Cancer Res. 13, 1523–1531 (2007).PubMedCrossRefGoogle Scholar
  48. Yurkovetsky, Z.R. et al. Multiplex analysis of serum cytokines in melanoma patients treated with interferon-alpha2b. Clin Cancer Res. 13, 2422–2428 (2007).PubMedCrossRefGoogle Scholar
  49. Zhou, J. et al. Telomere length of transferred lymphocytes correlates with in vivo persistence and tumor regression in melanoma patients receiving cell transfer therapy. J. Immunol. 175, 7046–7052 (2005).PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Infectious Disease and Immunogenetics Section, Department of Transfusion Medicine, Clinical Center, and Center for Human ImmunologyNational Institutes of HealthBethesdaUSA

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