PERSONALIZED   MEDICINE   AND   MOLECULAR   CHARACTERIZATION

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1   INTRODUCTION TO CANCER BIOLOGY

1.5   PERSONALIZED   MEDICINE   AND   MOLECULAR   CHARACTERIZATION

Understanding the underlying mechanisms of cancer and disease progression is the key to develop effective treatment and deploy to most responsive patients. Cancer exhibits certain common hallmarks, however it may be caused by different factors and have different molecular signatures. This changes the way it is diagnosed and how it is treated. Hence the best way of eradicating cancer is accounting all the differences at molecular, genetic and immunologic level and to apply the treatment in more ‘personalized’ way targeting the specific signatures rather than using a more generic treatment for all types of cancer. This approach relies on the identification of molecular pathways and target markers which are unique to each cancer types and even each patient.

Different strategies have been incorporated to develop therapeutics such as78 revealing the molecular profiling of the tumor, defining single or multi-gene expressions for response or resistance to a particular drug, development of targeted therapies to inhibit certain molecular pathways, and vaccine therapies79 treating the disease by patients own immune system.

As discussed before, tumor heterogeneity is suggested to be results of CSC model and/or clonal evolution. Heterogeneity has already been reported in many cancers between patients with the same type of cancer or between tumours of different tissue and cell types, (intertumor heterogeneity)80 and also within the same patient between primary and matched metastatic tumor or within the same tumor of an individual(intratumor heterogeneity)81. These distinct subtypes are different in disease progression, response and tolerance to the treatment. Hence accurate identification of subtypes with distinct molecular profile is crucial by measuring biomarkers that can relate to outcome of the disease or of the treatment. There are different kinds of biomarkers: prognostic, predictive and pharmacodynamic (Figure 5). Prognostic biomarkers are used to evaluate the disease-free or overall survival and risk or recurrence82. Predictive markers are able to assess the response to the given therapy82. On the other hand pharmacodynamic biomarkers can measure the effect of the drug on the disease. These markers should be extensively analyzed to confirm their clinical relevance and analytical validation. By selecting the patients who would likely benefit from particular therapy based on these biomarkers will not only increase the efficacy of the treatment but also help to avoid unnecessary adverse effect of drugs. Moreover, biomarker based drug selection has a substantial influence on cost. For example, it has been reported that erlotinib treatment was much more cost-effective if it is used on patients having high EGFR (Epidermal growth factor receptor)

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copy number83. Though there is still a debate whether overall cost will be reduced due to the high cost of identification of biomarkers and development of targeted therapies.

FIGURE 5 IDENTIFICATION OF BIOMARKERS (IMAGE COPIED FROM REF 87)

Breast cancer is known to be very heterogeneous in terms of histological subtypes, treatment response and patient outcome. Thanks to microarray technologies, analyses of gene expression profile have been used to classify intrinsic subtypes of breast cancer:

luminal A, luminal B, HER2-enriched and basal like81 subtypes that can be primarily distinguished by the positive or negative expressions of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2).

Further studies have shown two more subtypes under the basal like (triple negative for ER, PR and HER2) subtype in which claudin-low tumors show a gene expression profile similar to mammary stem cell with EMT positive markers and molecular apocrine tumors are positive for androgen receptor (AR). All these six subtypes have different sensitivity against treatments but these behaviors have to be clinically validated. For example, HER2 positive tumors are found in %20-25 of the breast cancer; they are identified by the amplification of Her2 gene. This category of patients are associated with poor prognosis84 and the patients are more likely to benefit from the targeted therapy called Trastuzumab85.

To distinguish subtypes, different analytical approaches were used by Cancer Genome Atlas (TCGA) such as genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, and microRNA sequencing and reverse-phase protein arrays86. This covers different molecular profiles at genomic, epigenetic and proteomic level present in each subtype so that specific protein expression and signaling pathways could be used for a therapeutic target. Moreover a breakthrough in development of sequencing methods; next-generation sequencing, has unveiled variety of recurrent point mutations, translocations, small

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insertion or deletions, duplications or amplification in DNA at a large scale that can be attained to predict prognosis or drug response87.

By accurately identifying the biomarkers and having molecular characterization of cancer relating to the distinct subtypes of cancer, several different strategies could be used for treatment. For example, therapies could be combined to target more than one pathways, one inhibiting the primary progression driver and the other inhibiting the upfront signaling mediating the resistance for the treatment81. An alternative way is that the simultaneous adaptation is made to the therapy after development of the resistant clone or any treatment related perturbations occurs, which is termed as adaptive therapy81. Another way could be by targeting the tumor environment such as inhibition of angiogenic switch or interaction between stroma and cancer cells.

A critical aspect of personalized medicine is how the patient responds to the targeted therapy at the single person level. All these identified genomic and proteomic signatures do not directly reflect the phenotype of the tumor or the activity of the drug and this limitation is addressed by assays termed as ‘next-generation functional diagnostics88. To predict the behavior of the drug response, one should consider all the components of the complex system of cancer such as proteins, metabolites, genes, interactions of gene-RNA, protein-protein and RNA-protein-protein and this cannot be provided by the only genomic information.

Already studies are performed to yield the resistance patterns by analysis on biopsies before and after treatment or monitoring the circulating tumor DNA during the treatment, yet they do not provide immediate response against the administered agents. These functional assays are now emerging to screen cytotoxicity, efficacy of potential targeted therapies, tumor responses such as FACS (Fluorescence-activated cell sorting)for measuring multiple pathways simultaneously, 3D organoid derived from patients to screen cancer-relevant drugs and CTCs for monitoring the treatment response and drug screening88. One of the promising examples of these assays is patient-derived xenograft (PDX) mouse models in which tumor biopsy from the patient is implanted into an immunodeficient mouse and expanded which gives the advantage to have some of the histological properties, gene expressions and somatic genetics from the patient. These models allow testing the drug efficacy and also started to be used in selecting therapies. Hence in the ideal world, functional assays will be used on the patients’ samples before the therapy achieving a best

‘personalized’ treatment. However, each technique still has its own limitations and has to be further tested for analytical and clinical validation and clinical utility.

33 REFERENCES

1. Society, T. A. C. Early History of Cancer. (2014). at

<http://www.cancer.org/acs/groups/cid/documents/webcontent/002048-pdf.pdf>

2. National Cancer Institut. Risk Factors for Cancer. at <http://www.cancer.gov/about-cancer/causes-prevention/risk>

3. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–74 (2011).

4. Aylon, Y. & Oren, M. New plays in the p53 theater. Curr. Opin. Genet. Dev. 21, 86–92 (2011).

5. Rivlin, N., Brosh, R., Oren, M. & Rotter, V. Mutations in the p53 Tumor Suppressor Gene: Important Milestones at the Various Steps of Tumorigenesis. Genes Cancer 2, 466–474 (2011).

6. Sigal, A. & Rotter, V. Oncogenic Mutations of the p53 Tumor Suppressor: The Demons of the Guardian of the Genome. Cancer Res 53, 6788–6793 (2000).

7. Brosh, R. & Rotter, V. When mutants gain new powers: news from the mutant p53 field. Nat. Rev. Cancer 9, 701–713 (2009).

8. Brown, J. M. & Attardi, L. D. The role of apoptosis in cancer development and treatment response. Nat. Rev. Cancer 5, 231–237 (2005).

9. Adams, J. M. & Cory, S. The Bcl-2 apoptotic switch in cancer development and therapy. Oncogene 26, 1324–1337 (2007).

10. Attardi, L. D. The role of p53-mediated apoptosis as a crucial anti-tumor response to genomic instability: Lessons from mouse models. Mutat. Res. - Fundam. Mol.

Mech. Mutagen. 569, 145–157 (2005).

11. Blasco, M. Telomeres and human disease: ageing, cancer and beyond. Nat. Rev.

Genet. 6, 611–22 (2005).

12. Yancopoulos, G. D. et al. Vascular-specific growth factors and blood vessel formation. Nature 407, 242–8 (2000).

13. Kerbel, R. S. Tumor angiogenesis: past, present and the near future. Carcinogenesis 21, 505–515 (2000).

34

14. Grivennikov, S. I., Greten, F. R. & Karin, M. Immunity, Inflammation, and Cancer.

Cell 140, 883–899 (2010).

15. Dougan, M. & Dranoff, G. Immune Therapy for Cancer. Annu. Rev. Immunol 27, 83–117 (2009).

16. Nowell, P. The clonal evolution of tumor cell populations. Science (80-. ). 194, 23–28 (1976).

17. Marusyk, A., Almendro, V. & Polyak, K. Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12, 323–34 (2012).

18. Marusyk, A. & Polyak, K. Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805, 1–28 (2011).

19. Swanton, C. Intratumor heterogeneity: Evolution through space and time. Cancer Res.

72, 4875–4882 (2012).

20. Tabassum, D. P. & Polyak, K. Tumorigenesis: it takes a village. Nat. Rev. Cancer 15, 473–83 (2015).

21. Calbo, J. et al. A Functional Role for Tumor Cell Heterogeneity in a Mouse Model of Small Cell Lung Cancer. Cancer Cell 19, 244–256 (2011).

22. Caldas, C. Cancer sequencing unravels clonal evolution. Nat. Biotechnol. 30, 408–

410 (2012).

23. Chaffer, C. L. & Weinberg, R. a. A perspective on cancer cell metastasis. Science 331, 1559–1564 (2011).

24. Talmadge, J. E. & Fidler, I. J. The Biology of Cancer Metastasis: Historical Perspective. Cancer Res. 70, 5649–5669 (2010).

25. Butler, T. P. & Gullino, P. M. Quantitation of Cell Shedding into Efferent Blood of Mammary Adenocarcinoma. Cancer Res. 35, 512–516 (1975).

26. Wong, C. W. et al. Apoptosis: An Early Event in Metastatic Inefficiency. Cancer Res. 61, 333–338 (2001).

27. Luzzi, K. J. et al. Multistep Nature of Metastatic Inefficiency. Am. J. Pathol. 153, 865–873 (1998).

35

28. Braun, S. et al. A Pooled Analysis of Bone Marrow Micrometastasis in Breast Cancer. N. Engl. J. Med. 353, 793–802 (2005).

29. Labelle, M., Begum, S. & Hynes, R. O. Direct Signaling between Platelets and Cancer Cells Induces an Epithelial-Mesenchymal-Like Transition and Promotes Metastasis. Cancer Cell 20, 576–590 (2011).

30. Sosa, M. S., Bragado, P. & Aguirre-Ghiso, J. A. Mechanisms of disseminated cancer cell dormancy: an awakening field. Nat. Rev. Cancer 14, 611–22 (2014).

31. Giancotti, F. G. Mechanisms governing metastatic dormancy and reactivation. Cell 155, 750–764 (2013).

32. Massagué, J. & Obenauf, A. C. Metastatic colonization by circulating tumour cells.

Nature 529, 298–306 (2016).

33. Scheel, C. & Weinberg, R. A. Cancer stem cells and epithelial–mesenchymal transition: Concepts and molecular links. Semin. Cancer Biol. 22, 396–403 (2012).

34. Li, F., Tiede, B., Massagué, J. & Kang, Y. Beyond tumorigenesis: cancer stem cells in metastasis. Cell Res. 17, 3–14 (2007).

35. Paget, S. The distribution of secondary growths in cancer of the breast. 1889.

Cancer Metastasis Rev. 8, 98–101 (1989).

36. Alix-Panabières, C., Riethdorf, S. & Pantel, K. Circulating tumor cells and bone marrow micrometastasis. Clin. Cancer Res. 14, 5013–5021 (2008).

37. Schlüter, K. et al. Organ-specific metastatic tumor cell adhesion and extravasation of colon carcinoma cells with different metastatic potential. Am. J. Pathol. 169, 1064–

1073 (2006).

38. Kaplan, R. N. et al. VEGFR1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche. Nature 438, 820–7 (2005).

39. Kim, M.-Y. et al. Tumor self-seeding by circulating cancer cells. Cell 139, 1315–26 (2009).

40. Nicolson, G. L. Generation of phenotypic diversity and progression in metastatic tumor cells. Cancer Metastasis Rev. 3, 25–42 (1984).

41. Raser, J. M. Noise in Gene Expression: Origins, Consequences, and Control.

Science (80-. ). 309, 2010–2013 (2005).

36

42. Mani, S. A. et al. The Epithelial-Mesenchymal Transition Generates Cells with Properties of Stem Cells. Cell 133, 704–715 (2008).

43. Meacham, C. E. & Morrison, S. J. Tumour heterogeneity and cancer cell plasticity.

Nature 501, 328–37 (2013).

44. Diehn, M. et al. Association of reactive oxygen species levels and radioresistance in cancer stem cells. Nature 458, 780–783 (2009).

45. Lapidot, T. et al. c. Nature 367, 645–648 (1994).

46. Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J. & Clarke, M. F.

Prospective identification of tumorigenic breast cancer cells. Proc. Natl. Acad. Sci. U.

S. A. 100, 3983–8 (2003).

47. Li, C. et al. Identification of pancreatic cancer stem cells. Cancer Res. 67, 1030–

1037 (2007).

48. Lu, X. & Kang, Y. in (eds. Dittmar, T. & Zänker, S. K.) 129–140 (Springer Netherlands, 2011). doi:10.1007/978-94-007-0782-5_6

49. Maenhaut, C., Dumont, J. E., Roger, P. P. & van Staveren, W. C. G. Cancer stem cells: a reality, a myth, a fuzzy concept or a misnomer? An analysis.

Carcinogenesis 31, 149–158 (2010).

50. Dalerba, P., Cho, R. W. & Clarke, M. F. Cancer stem cells: models and concepts.

Annu. Rev. Med. 58, 267–284 (2007).

51. Teicher, B. A. Tumor models for efficacy determination. Mol. Cancer Ther. 5, 2435–

2443 (2006).

52. Eppert, K. et al. Stem cell gene expression programs influence clinical outcome in human leukemia. Nat Med 17, 1086–1093 (2011).

53. Medema, J. P. Cancer stem cells: the challenges ahead. Nat. Cell Biol. 15, 338–

44 (2013).

54. Beier, D. et al. CD133+ and CD133- glioblastoma-derived cancer stem cells show differential growth characteristics and molecular profiles. Cancer Res. 67, 4010–4015 (2007).

55. Shackleton, M., Quintana, E., Fearon, E. R. & Morrison, S. J. Heterogeneity in Cancer: Cancer Stem Cells versus Clonal Evolution. Cell 138, 822–829 (2009).

37

56. Nguyen, L. V., Vanner, R., Dirks, P. & Eaves, C. J. Cancer stem cells: an evolving concept. Nat. Rev. Cancer 12, 133–143 (2012).

57. Sarrio, D. et al. Epithelial-Mesenchymal Transition in Breast Cancer Relates to the Basal-like Phenotype. Cancer Res. 68, 989–997 (2008).

58. Aktas, B. et al. Stem cell and epithelial-mesenchymal transition markers are frequently overexpressed in circulating tumor cells of metastatic breast cancer patients.

Breast Cancer Res. 11, R46 (2009).

59. Lamouille, S., Xu, J. & Derynck, R. Molecular mechanisms of epithelial–

mesenchymal transition. Nat. Rev. Mol. Cell Biol. 15, 178–196 (2014).

60. Thiery, J. P., Acloque, H., Huang, R. Y. J. & Nieto, M. A. Epithelial-Mesenchymal Transitions in Development and Disease. Cell 139, 871–890 (2009).

61. Thiery, J. P. Epithelial-mesenchymal transitions in tumour progression. Nat. Rev.

Cancer 2, 442–54 (2002).

62. Kalluri, R. & Weinberg, R. A. The basics of epithelial-mesenchymal transition. 119, (2009).

63. Fischer, K. R. et al. Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance. Nature 527, 472–476 (2015).

64. Theveneau, E. & Mayor, R. Cadherins in collective cell migration of mesenchymal cells. Curr. Opin. Cell Biol. 24, 677–684 (2012).

65. Onder, T. T. et al. Loss of E-cadherin promotes metastasis via multiple downstream transcriptional pathways. Cancer Res. 68, 3645–3654 (2008).

66. Mendez, M. G., Kojima, S. I. & Goldman, R. D. Vimentin induces changes in cell shape, motility, and adhesion during the epithelial to mesenchymal transition. FASEB J. 24, 1838–1851 (2010).

67. Willipinski-Stapelfeldt, B. et al. Changes in cytoskeletal protein composition indicative of an epithelial-mesenchymal transition in human micrometastatic and primary breast carcinoma cells. Clin. Cancer Res. 11, 8006–8014 (2005).

68. Koenig, A., Mueller, C., Hasel, C., Adler, G. & Menke, A. Collagen type I induces disruption of E-cadherin-mediated cell-cell contacts and promotes proliferation of pancreatic carcinoma cells. Cancer Res. 66, 4662–4671 (2006).

38

69. Polyak, K. & Weinberg, R. A. Transitions between epithelial and mesenchymal states: acquisition of malignant and stem cell traits. Nat Rev Cancer 9, 265–273 (2009).

70. Bastid, J. EMT in carcinoma progression and dissemination: Facts, unanswered questions, and clinical considerations. Cancer Metastasis Rev. 31, 277–283 (2012).

71. Tarin, D. The fallacy of epithelial mesenchymal transition in neoplasia. Cancer Res.

65, 5996–6000 (2005).

72. Vargo-Gogola, T. & Rosen, J. M. Modelling breast cancer: one size does not fit all. Nat. Rev. Cancer 7, 659–672 (2007).

73. Hugo, H. et al. Epithelial—mesenchymal and mesenchymal—epithelial transitions in carcinoma progression. J. Cell. Physiol. 213, 374–383 (2007).

74. Gao, D. et al. Myeloid progenitor cells in the premetastatic lung promote metastases by inducing mesenchymal to epithelial transition. Cancer Res. 72, 1384–1394 (2012).

75. Morel, A.-P. et al. Generation of Breast Cancer Stem Cells through Epithelial-Mesenchymal Transition. PLoS One 3, e2888 (2008).

76. Guo, W. et al. Slug and Sox9 Cooperatively Determine the Mammary Stem Cell State. Cell 148, 1015–1028 (2012).

77. Pinto, C. a., Widodo, E., Waltham, M. & Thompson, E. W. Breast cancer stem cells and epithelial mesenchymal plasticity - Implications for chemoresistance. Cancer Lett. 341, 56–62 (2013).

78. Schilsky, R. L. Personalized medicine in oncology: the future is now. Nat. Rev.

Drug Discov. 9, 363–366 (2010).

79. Guo, C. et al. in 18, 421–475 (2013).

80. Burrell, R. a, McGranahan, N., Bartek, J. & Swanton, C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–45 (2013).

81. Zardavas, D., Irrthum, A., Swanton, C. & Piccart, M. Clinical management of breast cancer heterogeneity. Nat. Rev. Clin. Oncol. 1–14 (2015).

doi:10.1038/nrclinonc.2015.73

39

82. Cianfrocca, M. Prognostic and Predictive Factors in Early-Stage Breast Cancer.

Oncologist 9, 606–616 (2004).

83. Bradbury, P. A. et al. Economic Analysis: Randomized Placebo-Controlled Clinical Trial of Erlotinib in Advanced Non-Small Cell Lung Cancer. JNCI J. Natl. Cancer Inst. 102, 298–306 (2010).

84. Slamon, D. et al. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science (80-. ). 235, 177–182 (1987).

85. Dawood, S., Broglio, K., Buzdar, A. U., Hortobagyi, G. N. & Giordano, S. H.

Prognosis of women with metastatic breast cancer by HER2 status and trastuzumab treatment: An institutional-based review. J. Clin. Oncol. 28, 92–98 (2010).

86. Koboldt, D. C. et al. Comprehensive molecular portraits of human breast tumours.

Nature 490, 61–70 (2012).

87. Simon, R. & Roychowdhury, S. Implementing personalized cancer genomics in clinical trials. Nat. Rev. Drug Discov. 12, 358–69 (2013).

88. Friedman, A. a., Letai, A., Fisher, D. E. & Flaherty, K. T. Precision medicine for cancer with next-generation functional diagnostics. Nat. Rev. Cancer 15, 747–756 (2015).

89. Broad Institute. Transcription Factor. (2016). at

<https://www.broadinstitute.org/education/glossary/transcription-factor>

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2. CIRCULATING TUMOR CELLS AND

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