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Translating metastasis-related biomarkers to the clinic—progress and pitfalls

Abstract

In the context of metastatic disease, preclinical models have been used primarily to decipher different steps of the metastatic cascade. Numerous molecular processes operate in these model systems, but none of these has been successfully translated to the clinic. We discuss some of the successes and failures of preclinical models in metastasis research and suggest some of the clues for more clinically relevant research. These potential avenues of research include: the use of adequate statistical methods and well-annotated cohorts in biomarker discovery; an objective assessment of the level of evidence provided by each biomarker; the development of robust molecular or cellular surrogates of metastasis in patients; and original designs for clinical trials.

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Figure 1: Breast cancer DTC and 5-year metastasis-free survival.
Figure 2: Biological and clinical issues related to CTC and DTC assessment.
Figure 3: The CirCe T-DM1 trial—an adaptive trial for biomarker-driven treatment.

References

  1. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Kishimoto, H. et al. In vivo imaging of lymph node metastasis with telomerase-specific replication-selective adenovirus. Nat. Med. 12, 1213–1219 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Beerling, E., Ritsma, L., Vrisekoop, N., Derksen, P. W. & van Rheenen, J. Intravital microscopy: new insights into metastasis of tumors. J. Cell Sci. 124, 299–310 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Tsai, J. H., Donaher, J. L., Murphy, D. A., Chau, S. & Yang, J. Spatiotemporal regulation of epithelial-mesenchymal transition is essential for squamous cell carcinoma metastasis. Cancer Cell 22, 725–736 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Denoix, P. Enquete permanente dans les centres anticancereux. Bull. Inst. Natl Hyg. 1, 12–17 (1946).

    CAS  PubMed  Google Scholar 

  6. Cogen, A. et al. TNM-classification for lung cancer: from the 7th to the 8th edition. Acta Chir. Belg. 111, 389–392 (2011).

    Article  PubMed  Google Scholar 

  7. Sobin, L. H., Gospodarowicz, M. K. & Wittekind, C. TNM Classification of Malignant Tumours, 7th edn (Wiley Blackwell, Hoboken, 2009).

    Google Scholar 

  8. Mego, M., Mani, S. A. & Cristofanilli, M. Molecular mechanisms of metastasis in breast cancer-clinical applications. Nat. Rev. Clin. Oncol. 7, 693–701 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Valastyan, S. & Weinberg, R. A. Tumor metastasis: molecular insights and evolving paradigms. Cell 147, 275–292 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sethi, N. & Kang, Y. Unravelling the complexity of metastasis - molecular understanding and targeted therapies. Nat. Rev. Cancer 11, 735–748 (2011).

    Article  CAS  PubMed  Google Scholar 

  11. Kleer, C. G. et al. RhoC GTPase expression as a potential marker of lymph node metastasis in squamous cell carcinomas of the head and neck. Clin. Cancer Res. 12, 4485–4490 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. Wang, W. et al. Genomic analysis reveals RhoC as a potential marker in hepatocellular carcinoma with poor prognosis. Br. J. Cancer 90, 2349–2355 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kondo, T. et al. Expression of RHOC is associated with metastasis of gastric carcinomas. Pathobiology 71, 19–25 (2004).

    Article  PubMed  CAS  Google Scholar 

  14. Kamai, T. et al. Significant association of Rho/ROCK pathway with invasion and metastasis of bladder cancer. Clin. Cancer Res. 9, 2632–2641 (2003).

    CAS  PubMed  Google Scholar 

  15. Kleer, C. G. et al. Characterization of RhoC expression in benign and malignant breast disease: a potential new marker for small breast carcinomas with metastatic ability. Am. J. Pathol. 160, 579–584 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hu, G. et al. MTDH activation by 8q22 genomic gain promotes chemoresistance and metastasis of poor-prognosis breast cancer. Cancer Cell 15, 9–20 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Li, J. et al. Astrocyte elevated gene-1 is a novel prognostic marker for breast cancer progression and overall patient survival. Clin. Cancer Res. 14, 3319–3326 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Prat, A., Ellis, M. J. & Perou, C. M. Practical implications of gene-expression-based assays for breast oncologists. Nat. Rev. Clin. Oncol. 9, 48–57 (2012).

    Article  CAS  Google Scholar 

  19. van 't Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    Article  CAS  PubMed  Google Scholar 

  20. Foekens, J. A. et al. Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J. Clin. Oncol. 24, 1665–1671 (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817–2826 (2004).

    Article  CAS  PubMed  Google Scholar 

  22. Fan, C. et al. Concordance among gene-expression-based predictors for breast cancer. N. Engl. J. Med. 355, 560–569 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Liu, R. et al. The prognostic role of a gene signature from tumorigenic breast-cancer cells. N. Engl. J. Med. 356, 217–226 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Sørlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869–10874 (2001).

    Article  PubMed  PubMed Central  Google Scholar 

  25. van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002).

    Article  CAS  PubMed  Google Scholar 

  26. Buyse, M. et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J. Natl Cancer Inst. 98, 1183–1192 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Paik, S. et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J. Clin. Oncol. 24, 3726–3734 (2006).

    Article  CAS  PubMed  Google Scholar 

  28. Albain, K. S. et al. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol. 11, 55–65 (2010).

    Article  CAS  PubMed  Google Scholar 

  29. Cuzick, J. et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer. J. Clin. Oncol. 29, 4273–4278 (2011).

    Article  PubMed  Google Scholar 

  30. Bogaerts, J. et al. Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial. Nat. Clin. Pract. Oncol. 3, 540–551 (2006).

    Article  CAS  PubMed  Google Scholar 

  31. Sotiriou, C. & Pusztai, L. Gene-expression signatures in breast cancer. N. Engl. J. Med. 360, 790–800 (2009).

    Article  CAS  PubMed  Google Scholar 

  32. Rutgers, E. et al. The EORTC 10041/BIG 03–04 MINDACT trial is feasible: Results of the pilot phase. Eur. J. Cancer 47, 2742–2749 (2011).

    Article  PubMed  Google Scholar 

  33. Mook, S. et al. Daily clinical practice of fresh tumour tissue freezing and gene expression profiling; logistics pilot study preceding the MINDACT trial. Eur. J. Cancer 45, 1201–1208 (2009).

    Article  CAS  PubMed  Google Scholar 

  34. Bernards, R. & Weinberg, R. A. A progression puzzle. Nature 418, 823 (2002).

    Article  CAS  PubMed  Google Scholar 

  35. Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Millar, E. K. et al. Prediction of local recurrence, distant metastases, and death after breast-conserving therapy in early-stage invasive breast cancer using a five-biomarker panel. J. Clin. Oncol. 27, 4701–4708 (2009).

    Article  PubMed  Google Scholar 

  38. Smid, M. et al. Subtypes of breast cancer show preferential site of relapse. Cancer Res. 68, 3108–3114 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Desmedt, C., Ruíz-García, E. & André, F. Gene expression predictors in breast cancer: current status, limitations and perspectives. Eur. J. Cancer 44, 2714–2720 (2008).

    Article  CAS  PubMed  Google Scholar 

  40. Ng, C., Weigelt, B., Grigoriadis, A. & Reis-Filho, J. S. Prognostic signatures in breast cancer: correlation does not imply causation. Breast Cancer Res. 14, 313 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Michiels, S., Koscielny, S. & Hill, C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365, 488–492 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Ein-Dor, L., Zuk, O. & Domany, E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl Acad. Sci. USA 103, 5923–5928 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Weigelt, B. et al. Breast cancer molecular profiling with single sample predictors: a retrospective analysis. Lancet Oncol. 11, 339–349 (2010).

    Article  CAS  PubMed  Google Scholar 

  44. Mackay, A. et al. Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement. J. Natl Cancer Inst. 103, 662–673 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Vignot, S., Besse, B., André, F., Spano, J. P. & Soria, J. C. Discrepancies between primary tumor and metastasis: A literature review on clinically established biomarkers. Crit. Rev. Oncol. Hematol. (2012).

  46. Amir, E. et al. Prospective study evaluating the impact of tissue confirmation of metastatic disease in patients with breast cancer. J. Clin. Oncol. 30, 587–592 (2012).

    Article  PubMed  Google Scholar 

  47. Nelson, P. S. Predicting prostate cancer behavior using transcript profiles. J. Urol. 172, S28–S32 (2004).

    CAS  PubMed  Google Scholar 

  48. Markert, E. K., Mizuno, H., Vazquez, A. & Levine, A. J. Molecular classification of prostate cancer using curated expression signatures. Proc. Natl Acad. Sci. USA 108, 21276–21281 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sørensen, K. D. & Ørntoft, T. F. Discovery of prostate cancer biomarkers by microarray gene expression profiling. Expert Rev. Mol. Diagn. 10, 49–64 (2010).

    Article  PubMed  Google Scholar 

  50. Agulló-Ortuño, M. T., López-Ríos, F. & Paz-Ares, L. Lung cancer genomic signatures. J. Thorac. Oncol. 5, 1673–1691 (2010).

    Article  PubMed  Google Scholar 

  51. Boutros, P. C. et al. Prognostic gene signatures for non-small-cell lung cancer. Proc. Natl Acad. Sci. USA 106, 2824–2828 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Salazar, R. et al. Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J. Clin. Oncol. 29, 17–24 (2011).

    Article  PubMed  Google Scholar 

  53. Gray, R. G. et al. Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer. J. Clin. Oncol. 29, 4611–4619 (2011).

    Article  PubMed  Google Scholar 

  54. Zhang, Y. et al. Copy number alterations that predict metastatic capability of human breast cancer. Cancer Res. 69, 3795–3801 (2009).

    Article  CAS  PubMed  Google Scholar 

  55. Berns, E. M. & Bowtell, D. D. The changing view of high-grade serous ovarian cancer. Cancer Res. 72, 2701–2704 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Roessler, S. et al. Integrative genomic identification of genes on 8p associated with hepatocellular carcinoma progression and patient survival. Gastroenterology 142, 957–966.e912 (2012).

    Article  CAS  PubMed  Google Scholar 

  57. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shah, S. P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 999–1005 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Yu, Y. Y. & Zhu, Z. G. Significance of biological resource collection and tumor tissue bank creation. World J. Gastrointest. Oncol. 2, 5–8 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Reshmi, G., Sona, C. & Pillai, M. R. Comprehensive patterns in microRNA regulation of transcription factors during tumor metastasis. J. Cell. Biochem. 112, 2210–2217 (2011).

    Article  CAS  PubMed  Google Scholar 

  64. Lovat, F., Valeri, N. & Croce, C. M. MicroRNAs in the pathogenesis of cancer. Semin. Oncol. 38, 724–733 (2011).

    Article  CAS  PubMed  Google Scholar 

  65. Hurst, D. R., Edmonds, M. D. & Welch, D. R. Metastamir: the field of metastasis-regulatory microRNA is spreading. Cancer Res. 69, 7495–7498 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Nair, V. S., Maeda, L. S. & Ioannidis, J. P. Clinical outcome prediction by microRNAs in human cancer: a systematic review. J. Natl Cancer Inst. 104, 528–540 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Paget, S. The distribution of secondary growths in cancer of the breast. Lancet 1, 571–573 (1889).

    Article  Google Scholar 

  68. Smid, M. et al. Genes associated with breast cancer metastatic to bone. J. Clin. Oncol. 24, 2261–2267 (2006).

    Article  CAS  PubMed  Google Scholar 

  69. Nguyen, D. X., Bos, P. D. & Massague, J. Metastasis: from dissemination to organ-specific colonization. Nat. Rev. Cancer 9, 274–284 (2009).

    Article  CAS  PubMed  Google Scholar 

  70. Peinado, H. et al. Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat. Med. 18, 883–891 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  72. Muller, V., Alix-Panabieres, C. & Pantel, K. Insights into minimal residual disease in cancer patients: implications for anti-cancer therapies. Eur. J. Cancer 46, 1189–1197 (2010).

    Article  PubMed  CAS  Google Scholar 

  73. Vincent-Salomon, A., Bidard, F. C. & Pierga, J. Y. Bone marrow micrometastasis in breast cancer: review of detection methods, prognostic impact and biological issues. J. Clin. Pathol. 6, 570–576 (2008).

    Article  Google Scholar 

  74. Joosse, S. A. et al. Changes in keratin expression during metastatic progression of breast cancer: impact on the detection of circulating tumor cells. Clin. Cancer Res. 18, 993–1003 (2012).

    Article  CAS  PubMed  Google Scholar 

  75. Müller, V. et al. Prognostic impact of circulating tumor cells assessed with the CellSearch System and AdnaTest Breast in metastatic breast cancer patients: the DETECT study. Breast Cancer Res. 14, R118 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Braun, S. et al. A pooled analysis of bone marrow micrometastasis in breast cancer. N. Engl. J. Med. 353, 793–802 (2005).

    Article  CAS  PubMed  Google Scholar 

  77. Janni, W. et al. Persistence of disseminated tumor cells in the bone marrow of breast cancer patients predicts increased risk for relapse--a European pooled analysis. Clin. Cancer Res. 17, 2967–2976 (2011).

    Article  PubMed  Google Scholar 

  78. Husemann, Y. et al. Systemic spread is an early step in breast cancer. Cancer Cell 13, 58–68 (2008).

    Article  PubMed  CAS  Google Scholar 

  79. Stoecklein, N. H. et al. Direct genetic analysis of single disseminated cancer cells for prediction of outcome and therapy selection in esophageal cancer. Cancer Cell 13, 441–453 (2008).

    Article  CAS  PubMed  Google Scholar 

  80. Pantel, K., Alix-Panabieres, C. & Riethdorf, S. Cancer micrometastases. Nat. Rev. Clin. Oncol. 6, 339–351 (2009).

    Article  CAS  PubMed  Google Scholar 

  81. Aguirre-Ghiso, J. A. Models, mechanisms and clinical evidence for cancer dormancy. Nat. Rev. Cancer 7, 834–846 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Uhr, J. W. & Pantel, K. Controversies in clinical cancer dormancy. Proc. Natl Acad. Sci. USA 108, 12396–12400 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Bidard, F. C. et al. Prognosis of women with stage IV breast cancer depends on detection of circulating tumor cells rather than disseminated tumor cells. Ann. Oncol. 19, 496–500 (2008).

    Article  PubMed  Google Scholar 

  84. Riethdorf, S., Wikman, H. & Pantel, K. Review: Biological relevance of disseminated tumor cells in cancer patients. Int. J. Cancer 123, 1991–2006 (2008).

    Article  CAS  PubMed  Google Scholar 

  85. Rahbari, N. N. et al. Meta-analysis shows that detection of circulating tumor cells indicates poor prognosis in patients with colorectal cancer. Gastroenterology 138, 1714–1726 (2010).

    Article  PubMed  Google Scholar 

  86. Lucci, A. et al. Circulating tumour cells in non-metastatic breast cancer: a prospective study. Lancet Oncol. 13, 688–695 (2012).

    Article  PubMed  Google Scholar 

  87. Miller, M. C., Doyle, G. V. & Terstappen, L. W. Significance of circulating tumor cells detected by the CellSearch system in patients with metastatic breast colorectal and prostate cancer. J. Oncol. 2010, 617421 (2010).

    Article  PubMed  Google Scholar 

  88. Pierga, J. Y. et al. High independent prognostic and predictive value of circulating tumor cells compared with serum tumor markers in a large prospective trial in first-line chemotherapy for metastatic breast cancer patients. Ann. Oncol. 23, 618–624 (2012).

    Article  PubMed  Google Scholar 

  89. Rack, B. et al. Prognostic relevance of circulating tumor cells (CTCs) in peripheral blood of breast cancer patients before and after adjuvant chemotherapy: The German SUCCESS-Trial [abstract]. J. Clin. Oncol. 26 (Suppl.), a503 (2008).

    Article  Google Scholar 

  90. Molloy, T. J. et al. The prognostic significance of tumor cell detection in the peripheral blood versus the bone marrow in 733 early-stage breast cancer patients. Breast Cancer Res. 13, R61 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Hayes, D. F. et al. in AJCC Cancer Staging Manual 7th edn (eds Edge, S. B. et al.) 347–376 (Springer, New York, 2009).

    Google Scholar 

  92. Coumans, F. A., Ligthart, S. T., Uhr, J. W. & Terstappen, L. W. Challenges in the enumeration and phenotyping of CTC. Clin. Cancer Res. 18, 5711–5718 (2012).

    Article  PubMed  Google Scholar 

  93. Saliba, A. E. et al. Microfluidic sorting and multimodal typing of cancer cells in self-assembled magnetic arrays. Proc. Natl Acad. Sci. USA 107, 14524–14529 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Tan, S. J. et al. Versatile label free biochip for the detection of circulating tumor cells from peripheral blood in cancer patients. Biosens. Bioelectron. 26, 1701–1705 (2010).

    Article  CAS  PubMed  Google Scholar 

  95. Autebert, J. et al. Microfluidic: an innovative tool for efficient cell sorting. Methods 57, 297–307 (2012).

    Article  CAS  PubMed  Google Scholar 

  96. Higgins, M. J. et al. Detection of tumor PIK3CA status in metastatic breast cancer using peripheral blood. Clin. Cancer Res. 18, 3462–3469 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Madic, J. et al. Pyrophosphorolysis-activated polymerization detects circulating tumor DNA in metastatic uveal melanoma. Clin. Cancer Res. 18, 3934–3941 (2012).

    Article  CAS  PubMed  Google Scholar 

  98. Diaz, L. A. Jr et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537–540 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Alix-Panabieres, C., Schwarzenbach, H. & Pantel, K. Circulating tumor cells and circulating tumor DNA. Annu. Rev. Med. 63, 199–215 (2012).

    Article  CAS  PubMed  Google Scholar 

  100. Schwarzenbach, H. et al. Loss of heterozygosity at tumor suppressor genes detectable on fractionated circulating cell-free tumor DNA as indicator of breast cancer progression. Clin. Cancer Res. 18, 5719–5730 (2012).

    Article  CAS  PubMed  Google Scholar 

  101. Weigelt, B. et al. No common denominator for breast cancer lymph node metastasis. Br. J. Cancer 93, 924–932 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Ellsworth, R. E. et al. Differential gene expression in primary breast tumors associated with lymph node metastasis. Int. J. Breast Cancer 2011, 142763 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Naume, B. et al. Presence of bone marrow micrometastasis is associated with different recurrence risk within molecular subtypes of breast cancer. Mol. Oncol. 1, 160–171 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Woelfle, U. et al. Molecular signature associated with bone marrow micrometastasis in human breast cancer. Cancer Res. 63, 5679–5684 (2003).

    CAS  PubMed  Google Scholar 

  105. Reyal, F. et al. Circulating tumor cell detection and transcriptomic profiles in early breast cancer patients. Ann. Oncol. 22, 1458–1459 (2011).

    Article  CAS  PubMed  Google Scholar 

  106. Meng, S. et al. HER-2 gene amplification can be acquired as breast cancer progresses. Proc. Natl Acad. Sci. USA 101, 9393–9398 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Powell, A. A. et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS ONE 7, e33788 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Schmidt-Kittler, O. et al. From latent disseminated cells to overt metastasis: genetic analysis of systemic breast cancer progression. Proc. Natl Acad. Sci. USA 100, 7737–7742 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Ligthart, S. T. et al. Unbiased quantitative assessment of Her-2 expression of circulating tumor cells in patients with metastatic and non metastatic breast cancer. Ann. Oncol. 10.1093/annonc/mds625 (2012).

  110. Chambers, A. F., Groom, A. C. & MacDonald, I. C. Dissemination and growth of cancer cells in metastatic sites. Nat. Rev. Cancer 2, 563–572 (2002).

    Article  CAS  PubMed  Google Scholar 

  111. International Cancer Genome Consortium. ICGC Cancer Genome Projects [online], (2012).

  112. Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993–998 (2010).

    Article  CAS  PubMed  Google Scholar 

  113. Gray, J. & Druker, B. Genomics: The breast cancer landscape. Nature 486, 328–329 (2012).

    Article  CAS  PubMed  Google Scholar 

  114. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  115. Banerji, S. et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature 486, 405–409 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Hartmaier, R. J., Priedigkeit, N. & Lee, A. V. Who's driving anyway? Herculean efforts to identify the drivers of breast cancer. Breast Cancer Res. 14, 323 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Pushkarev, D., Neff, N. F. & Quake, S. R. Single-molecule sequencing of an individual human genome. Nat. Biotechnol. 27, 847–852 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Yerushalmi, R., Woods, R., Ravdin, P. M., Hayes, M. M. & Gelmon, K. A. Ki67 in breast cancer: prognostic and predictive potential. Lancet Oncol. 11, 174–183 (2010).

    Article  CAS  PubMed  Google Scholar 

  119. Thiolloy, S. & Rinker-Schaeffer, C. W. Thinking outside the box: using metastasis suppressors as molecular tools. Semin. Cancer Biol. 21, 89–98 (2011).

    Article  CAS  PubMed  Google Scholar 

  120. Steeg, P. S. Perspectives on classic article: metastasis suppressor genes. J. Natl Cancer Inst. 96, E4 (2004).

    Article  PubMed  Google Scholar 

  121. Shoushtari, A. N., Szmulewitz, R. Z. & Rinker-Schaeffer, C. W. Metastasis-suppressor genes in clinical practice: lost in translation? Nat. Rev. Clin. Oncol. 8, 333–342 (2011).

    Article  CAS  PubMed  Google Scholar 

  122. McShane, L. M. et al. Reporting recommendations for tumor marker prognostic studies (REMARK). Nat. Clin. Pract. Oncol. 2, 416–422 (2005).

    CAS  PubMed  Google Scholar 

  123. Abdi, H. The Bonferonni and Šidák Corrections for Multiple Comparisons in Encyclopedia of Measurement and Statistics (ed. Salkind, N. J.) 103–107 (Thousand Oaks, CA, Sage, 2007).

    Google Scholar 

  124. André, F. et al. Biomarker studies: a call for a comprehensive biomarker study registry. Nat. Rev. Clin. Oncol. 8, 171–176 (2011).

    Article  PubMed  Google Scholar 

  125. Hayes, D. F. et al. Tumor marker utility grading system: a framework to evaluate clinical utility of tumor markers. J. Natl Cancer Inst. 88, 1456–1466 (1996).

    Article  CAS  PubMed  Google Scholar 

  126. Simon, R. M., Paik, S. & Hayes, D. F. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J. Natl Cancer Inst. 101, 1446–1452 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Hsieh, S. M., Look, M. P., Sieuwerts, A. M., Foekens, J. A. & Hunter, K. W. Distinct inherited metastasis susceptibility exists for different breast cancer subtypes: a prognosis study. Breast Cancer Res. 11, R75 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Esserman, L. J. et al. Biologic markers determine both the risk and the timing of recurrence in breast cancer. Breast Cancer Res. Treat. 129, 607–616 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Saghatchian, M. et al. Characterization of breast cancer distant metastasis based on outcome over time using a gene expression profiling approach and identification of pathway activities of late relapse. 71 (Suppl. 3), Cancer Res. S1–S6 (2011).

    Google Scholar 

  130. Bidard, F. C. et al. Time to metastatic relapse and breast cancer cells dissemination in bone marrow at metastatic relapse. Clin. Exp. Metastasis 25, 871–875 (2008).

    Article  CAS  PubMed  Google Scholar 

  131. Paez, D. et al. Cancer dormancy: a model of early dissemination and late cancer recurrence. Clin. Cancer Res. 18, 645–653 (2012).

    Article  PubMed  Google Scholar 

  132. Desmedt, C. et al. Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin. Cancer Res. 13, 3207–3214 (2007).

    Article  CAS  PubMed  Google Scholar 

  133. Wang, Y. et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679 (2005).

    Article  CAS  PubMed  Google Scholar 

  134. Kienast, Y. et al. Real-time imaging reveals the single steps of brain metastasis formation. Nat. Med. 16, 116–122 (2010).

    Article  CAS  PubMed  Google Scholar 

  135. Tentler, J. J. et al. Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338–350 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Ocaña, A., Pandiella, A., Siu, L. L. & Tannock, I. F. Preclinical development of molecular-targeted agents for cancer. Nat. Rev. Clin. Oncol. 8, 200–209 (2010).

    Article  PubMed  CAS  Google Scholar 

  137. André, T. et al. Improved overall survival with oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment in stage II or III colon cancer in the MOSAIC trial. J. Clin. Oncol. 27, 3109–3116 (2009).

    Article  PubMed  CAS  Google Scholar 

  138. Early Breast Cancer Trialists' Collaborative Group. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365, 1687–1717 (2005).

  139. Hayes, D. F., Trock, B. & Harris, A. L. Assessing the clinical impact of prognostic factors: when is “statistically significant” clinically useful? Breast Cancer Res. Treat. 52, 305–319 (1998).

    Article  CAS  PubMed  Google Scholar 

  140. Davies, C. et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 378, 771–784 (2011).

    Article  CAS  PubMed  Google Scholar 

  141. de Bono, J. S. et al. Potential applications for circulating tumor cells expressing the insulin-like growth factor-I receptor. Clin. Cancer Res. 13, 3611–3616 (2007).

    Article  CAS  PubMed  Google Scholar 

  142. Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

    Article  CAS  PubMed  Google Scholar 

  143. Steeg, P. S. Perspective: The right trials. Nature 485, S58–59 (2012).

    Article  CAS  PubMed  Google Scholar 

  144. Stopeck, A. T. et al. Denosumab compared with zoledronic acid for the treatment of bone metastases in patients with advanced breast cancer: a randomized, double-blind study. J. Clin. Oncol. 28, 5132–5139 (2010).

    Article  CAS  PubMed  Google Scholar 

  145. Coleman, R., Gnant, M., Morgan, G. & Clezardin, P. Effects of bone-targeted agents on cancer progression and mortality. J. Natl Cancer Inst. 104, 1059–1067 (2012).

    Article  CAS  PubMed  Google Scholar 

  146. Bidard, F. C. et al. Clinical application of circulating tumor cells in breast cancer: overview of the current interventional trials. Cancer Metastasis Rev. http://dx.doi.org/10.1007/s10555-012-9398-0.

  147. Tournoux-Facon, C., De Rycke, Y. & Tubert-Bitter, P. How a new stratified adaptive phase II design could improve targeting population. Stat. Med. 30, 1555–1562 (2011).

    Article  PubMed  Google Scholar 

  148. Glimelius, B. & Lahn, M. Window-of-opportunity trials to evaluate clinical activity of new molecular entities in oncology. Ann. Oncol. 22, 1717–1725 (2011).

    Article  CAS  PubMed  Google Scholar 

  149. Alonso, D. F., Ripoll, G. V., Garona, J., Iannucci, N. B. & Gomez, D. E. Metastasis: recent discoveries and novel perioperative treatment strategies with particular interest in the hemostatic compound desmopressin. Curr. Pharm. Biotechnol. 12, 1974–1980 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Pivot, X. et al. CEREBEL (EGF111438): An open label randomized phase III study comparing the incidence of CNS metastases in patients (pts) with HER2+ Metastatic Breast Cancer (MBC), treated with Lapatinib plus Capecitabine (LC) versus Trastuzumab plus Capecitabine (TC) [abstract]. Ann. Oncol. 23, LBA11 (2012).

    Google Scholar 

  151. Kim, E. S. et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 1, 44–53 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

F.-C. Bidard received a fellowship from the Fondation pour la Recherche Nuovo-Soldati. The CirCe TDM-1 trial was designed at the ECCO-AACR-EORTC-ESMO Workshop on Methods in Clinical Cancer Research (“Flims Workshop”) in 2011, with major contributions of Susan G. Hilsenbeck (Baylor College of Medicine, Houston, TX) and Johann de Bono (Royal Marsden, London, UK). J. P. Thiery is supported by core grants from A*STAR and School of Medicine NUS.

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F.-C. Bidard, J.-Y. Pierga and J. P. Thiery made contributions to researching the data, discussion of content, and writing, reviewing and editing of the manuscript before submission. J.-C. Soria contributed to discussion of content and reviewing the manuscript.

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Correspondence to François-Clément Bidard.

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Bidard, FC., Pierga, JY., Soria, JC. et al. Translating metastasis-related biomarkers to the clinic—progress and pitfalls. Nat Rev Clin Oncol 10, 169–179 (2013). https://doi.org/10.1038/nrclinonc.2013.4

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