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Molecular screening for breast cancer prevention, early detection, and treatment planning: Combining biomarkers from DNA, RNA, and protein

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Abstract

The completion of the human genome project, along with the ancillary technologies derived from this effort, provides the ability to comprehensively analyze patient tumors as well as the individual patient’s own genetic make-up at the DNA, RNA, and protein level. As a result, novel molecular screening techniques have the potential to push the boundaries of detection to even smaller tumors and also to allow accurate risk assessment, cancer prevention, and treatment planning in individual women. This review focuses on advances over the past 2 years in the use of molecular signatures and circulating tumor cells for early breast cancer detection and for prediction of response to therapy.

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References and Recommended Reading

  1. Fisher B, Costantino JP, Wickerham DL, et al.: Tamoxifen for the prevention of breast cancer: current status of the National Surgical Adjuvant Breast and Bowel Project P-1 study [see comment]. J Natl Cancer Inst 2005, 97:1652–1662.

    Article  PubMed  CAS  Google Scholar 

  2. Barrett-Connor EL, Mosca L, et al.: Effects of raloxifene on cardiovascular events and breast cancer in postmenopausal women. N Engl J Med 2006, 355:125–137.

    Article  PubMed  CAS  Google Scholar 

  3. Gail MH, Costantino JP, Bryant J, et al.: Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer [see comment][erratum in J Natl Cancer Inst 2000 Feb 2;92(3):275]. J Natl Cancer Inst 1999, 91:1829–1846.

    Article  PubMed  CAS  Google Scholar 

  4. Ozanne EM, Klemp JR, Esserman LJ: Breast cancer risk assessment and prevention: a framework for shared decision-making consultations [see comment]. Breast J 2006, 12:103–113.

    Article  PubMed  Google Scholar 

  5. Berry DA, Iversen ES Jr, Gudbjartsson DF, et al.: BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes [see comment]. J Clin Oncol 2002, 20:2701–2712.

    Article  PubMed  CAS  Google Scholar 

  6. Calderon-Margalit R, Paltiel O: Prevention of breast cancer in women who carry BRCA1 or BRCA2 mutations: a critical review of the literature. Int J Cancer 2004, 112:357–364.

    Article  PubMed  CAS  Google Scholar 

  7. Prevalence and penetrance of BRCA1 and BRCA2 mutations in a population-based series of breast cancer cases. Anglian Breast Cancer Study Group. Br J Cancer 2000, 83:1301–1308.

    Article  Google Scholar 

  8. Ford D, Easton DF, Stratton M, et al.: Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. The Breast Cancer Linkage Consortium. Am J Hum Genet 1998, 62:676–689.

    Article  PubMed  CAS  Google Scholar 

  9. Easton DF, Ford D, Bishop DT: Breast and ovarian cancer incidence in BRCA1-mutation carriers. Breast Cancer Linkage Consortium. Am J Hum Genet 1995, 56:265–271.

    PubMed  CAS  Google Scholar 

  10. Walsh T, Casadei S, Coats KH, et al.: Spectrum of mutations in BRCA1, BRCA2, CHEK2, and TP53 in families at high risk of breast cancer. JAMA 2006, 295:1379–1388.

    Article  PubMed  CAS  Google Scholar 

  11. Dapic V, Carvalho MA, Monteiro ANA: Breast cancer susceptibility and the DNA damage response. Cancer Control 2005, 12:127–136.

    PubMed  Google Scholar 

  12. Thompson D, Easton D: The genetic epidemiology of breast cancer genes. J Mammary Gland Biol Neoplasia 2004, 9:221–236.

    Article  PubMed  Google Scholar 

  13. Johnson N, Fletcher O, Naceur-Lombardelli C, et al.: Interaction between CHEK2*1100delC and other low-penetrance breast-cancer susceptibility genes: a familial study. Lancet 2005, 366:1554–1557.

    Article  PubMed  CAS  Google Scholar 

  14. Kammerer U, Thanner F, Kapp M, et al.: Expression of tumor markers on breast and ovarian cancer cell lines. Anticancer Res 2003, 23:1051–1055.

    PubMed  Google Scholar 

  15. Ellis NA, Kirchhoff T, Mitra N, et al.: Localization of breast cancer susceptibility loci by genome-wide SNP linkage disequilibrium mapping. Genet Epidemiol 2006, 30:48–61.

    Article  PubMed  Google Scholar 

  16. Meijers-Heijboer H, van Geel B, van Putten WL, et al.: Breast cancer after prophylactic bilateral mastectomy in women with a BRCA1 or BRCA2 mutation [see comment]. N Engl J Med 2001, 345:159–164.

    Article  PubMed  CAS  Google Scholar 

  17. Rebbeck TR: Prophylactic oophorectomy in BRCA1 and BRCA2 mutation carriers. Eur J Cancer 2002, 38(Suppl 6):S15-S17.

    Article  PubMed  CAS  Google Scholar 

  18. Balmana J, Sanz J, Bonfill X, et al.: Genetic counseling program in familial breast cancer: analysis of its effectiveness, cost and cost-effectiveness ratio. Int J Cancer 2004, 112:647–652.

    Article  PubMed  CAS  Google Scholar 

  19. Paradiso A, Muggia F: Familial breast cancer screening: ethical and social implications [comment]. Ann Oncol 2004, 15(Suppl 1):I5-I6.

    Article  PubMed  Google Scholar 

  20. Moller P: Costs and benefits of diagnosing familial breast cancer [see comment]. Ann Oncol 2004, 15(Suppl 1):I55-I59.

    Article  PubMed  Google Scholar 

  21. Cipollini G, Tommasi S, Paradiso A, et al.: Genetic alterations in hereditary breast cancer [see comment]. Ann Oncol 2004, 15(Suppl 1):I7-I13.

    Article  PubMed  Google Scholar 

  22. Gasco M, Argusti A, Bonanni B, Decensi A: SERMs in chemoprevention of breast cancer. Eur J Cancer 2005, 41:1980–1989.

    Article  PubMed  CAS  Google Scholar 

  23. Tibes R, Qiu Y, Lu Y, et al.: Reverse phase protein array (RPPA): validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cell. Mol Cancer Ther 2006, in press.

  24. Jager D, Knuth A: Antibodies and vaccines—hope or illusion? Breast 2005, 14:631–635.

    Article  PubMed  Google Scholar 

  25. Sauer G, Kurzeder C, Heilmann V, et al.: Immunotherapy and cancer vaccines in the management of breast cancer [see comment]. Curr Pharm Des 2005, 11:3475–3483.

    Article  PubMed  CAS  Google Scholar 

  26. Mittendorf EA, Gurney JM, Storrer CE, et al.: Vaccination with a HER2/neu peptide induces intra- and inter-antigenic epitope spreading in patients with early stage breast cancer. Surgery 2006, 139:407–418.

    Article  PubMed  Google Scholar 

  27. Peoples GE, Gurney JM, Hueman MT, et al.: Clinical trial results of a HER2/neu (E75) vaccine to prevent recurrence in high-risk breast cancer patients [see comment]. J Clin Oncol 2005, 23:7536–7545.

    Article  PubMed  CAS  Google Scholar 

  28. Houssami N, Cuzick J, Dixon JM: The prevention, detection, and management of breast cancer. Med J Aust 2006, 184:230–234.

    PubMed  Google Scholar 

  29. Kuhl CK, Kuhn W, Schild H: Management of women at high risk for breast cancer: new imaging beyond mammography. Breast 2005, 14:480–486.

    Article  PubMed  CAS  Google Scholar 

  30. Ayers M, Symmans WF, Stec J, et al.: Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer [see comment]. J Clin Oncol 2004, 22:2284–2293. This study uses breast tissue samples obtained from needle biopsies prior to treatment and attempts to predict response to various chemotherapy agents. This type of analysis is the goal in personalized medicine. In all three cases [30••-32••], the authors conclude that it is possible to predict clinical responses.

    Article  PubMed  CAS  Google Scholar 

  31. Chang JC, Wooten EC, Tsimelzon A, et al.: Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer [see comment]. Lancet 2003, 362:362–369. See reference [30••].

    Article  PubMed  CAS  Google Scholar 

  32. Iwao-Koizumi K, Matoba R, et al.: Prediction of docetaxel response in human breast cancer by gene expression profiling. J Clin Oncol 2005, 23:422–431. See reference [30••].

    Article  PubMed  CAS  Google Scholar 

  33. Wang Y, Klijn JGM, Zhang Y, et al.: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer [see comment]. Lancet 2005, 365:671–679. Genome-wide expression profiles from 286 lymph node-negative patients who had not received adjuvant systemic treatment were used to identify patterns of gene activity that subclassify tumors for patients at high risk of distant recurrence. Relapse scores have been calculated, and pathway analysis is presented.

    PubMed  CAS  Google Scholar 

  34. Callagy G, Pharoah P, Chin S, et al.: Identification and validation of prognostic markers in breast cancer with the complementary use of array-CGH and tissue microarrays. J Pathol 2005, 205:388–396. The researchers combine genomic approaches with proteomic approaches to validate prognostic markers.

    Article  PubMed  CAS  Google Scholar 

  35. Rhodes DR, Chinnaiyan AM: Integrative analysis of the cancer transcriptome. Nat Genet 2005, 37(Suppl):S31–37. An excellent review of the novel computational, analytical approaches to understanding of the molecular heterogeneity of the cancer transcriptome.

    Article  PubMed  CAS  Google Scholar 

  36. Rhodes DR, Yu J, Shanker K, et al.: Large-scale metaanalysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A 2004, 101:9309–9314. A large-scale analysis of gene expression data from over 3700 cancer samples to characterize a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation.

    Article  PubMed  CAS  Google Scholar 

  37. Buckhaults P, Zhang Z, Chen Y-C, et al.: Identifying tumor origin using a gene expression-based classification map. Cancer Res 2003, 63:4144–4149.

    PubMed  CAS  Google Scholar 

  38. Linden HM, Stekhova SA, Link JM, et al.: Quantitative fluoroestradiol positron emission tomography imaging predicts response to endocrine treatment in breast cancer. J Clin Oncol 2006, 24:2793–2799.

    Article  PubMed  CAS  Google Scholar 

  39. Romond EH, Perez EA, Bryant J, et al.: Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer [see comment]. N Engl J Med 2005, 353:1673–1684.

    Article  PubMed  CAS  Google Scholar 

  40. Rouzier R, Pusztai L, Delaloge S, et al.: Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. J Clin Oncol 2005, 23:8331–8339.

    Article  PubMed  Google Scholar 

  41. Paik S, Shak S, Tang G, et al.: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer [see comment]. N Engl J Med 2004, 351:2817–2826.

    Article  PubMed  CAS  Google Scholar 

  42. van ’t Veer LJ, Dai H, van de Vijver MJ, et al.: Gene expression profiling predicts clinical outcome of breast cancer [see comment]. Nature 2002, 415:530–536.

    Article  Google Scholar 

  43. Jeffrey SS, Lonning PE, Hillner BE: Genomics-based prognosis and therapeutic prediction in breast cancer. J Natl Compr Cancer Netw 2005, 3:291–300. Excellent review of high-throughput technologies applied to breast cancer.

    Google Scholar 

  44. Espinosa E, Redondo A, Vara JAF, et al.: High-throughput techniques in breast cancer: a clinical perspective. European J Cancer 2006, 42:598–607.

    Article  Google Scholar 

  45. Reis-Filho JS, Milanezi F, Carvalho S, et al.: Metaplastic breast carcinomas exhibit EGFR, but not HER2, gene amplification and overexpression: immunohistochemical and chromogenic in situ hybridization analysis. Breast Cancer Res 2005, 7:R1028-R1035.

    Article  PubMed  CAS  Google Scholar 

  46. Cronin M, Pho M, Dutta D, et al.: Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol 2004, 164:35–42.

    PubMed  CAS  Google Scholar 

  47. Paik S, Kim C-y, Song Y-k, Kim W-s: Technology insight: Application of molecular techniques to formalin-fixed paraffin-embedded tissues from breast cancer. Nat Clin Pract Oncol 2005, 2:246–254. A good comparison of the technologies for the use of RNA from formalin-fixed, paraffin-embedded tissues. The study presents the current methodologies available in the field. This is a good guide to understanding the limitations on the use of paraffin-embedded tissues.

    Article  PubMed  CAS  Google Scholar 

  48. Segal E, Friedman N, Kaminski N, et al.: From signatures to models: understanding cancer using microarrays. Nat Genet 2005, 37(Suppl):S38-S45.

    Article  PubMed  CAS  Google Scholar 

  49. Hayes DF: Prognostic and predictive factors revisited. Breast 2005, 14:493–499.

    Article  PubMed  Google Scholar 

  50. Bild AH, Yao G, Chang JT, et al.: Oncogenic pathway signatures in human cancers as a guide to targeted therapies [see comment]. Nature 2006, 439:353–357. The authors use a systems biology approach to identify gene expression signatures that reflect the activation state of key pathways. Combining signature-based predictions across several pathways, the authors identify coordinated patterns of pathway deregulation that distinguish between specific cancers and tumor subtypes.

    Article  PubMed  CAS  Google Scholar 

  51. Mendrinos S, Nolen JDL, Styblo T, et al.: Cytologic findings and protein expression profiles associated with ductal carcinoma of the breast in ductal lavage specimens using surface-enhanced laser desorption and ionization-time of flight mass spectrometry. Cancer 2005, 105:178–183.

    Article  PubMed  Google Scholar 

  52. Ricolleau G, Charbonnel C, Lode L, et al.: Surfaceenhanced laser desorption/ionization time of flight mass spectrometry protein profiling identifies ubiquitin and ferritin light chain as prognostic biomarkers in node-negative breast cancer tumors. Proteomics 2006, 6:1963–1975.

    Article  PubMed  CAS  Google Scholar 

  53. Hudelist G, Singer CF, Pischinger KID, et al.: Proteomic analysis in human breast cancer: Identification of a characteristic protein expression profile of malignant breast epithelium. Proteomics 2006, 6:1989–2002. An excellent proteomic analysis used to find expression profiles.

    Article  PubMed  CAS  Google Scholar 

  54. Dirix L, Van Dam P, Vermeulen P: Genomics and circulating tumor cells: promising tools for choosing and monitoring adjuvant therapy in patients with early breast cancer? Curr Opin Oncol 2005, 17:551–558.

    Article  PubMed  CAS  Google Scholar 

  55. Seregni E, Coli A, Mazzucca N, Italian Group Ria-Irma Test IAoNM: Circulating tumour markers in breast cancer. Eur J Nucl Med Mol Imaging 2004, 31(Suppl 1):S15–22.

    Article  PubMed  Google Scholar 

  56. Duffy MJ: Serum tumor markers in breast cancer: are they of clinical value? Clin Chem 2006, 52:345–351.

    Article  PubMed  CAS  Google Scholar 

  57. Cristofanilli M, Hayes DF, Budd GT, et al.: Circulating tumor cells: a novel prognostic factor for newly diagnosed metastatic breast cancer [erratum in J Clin Oncol 2005 23:4808]. J Clin Oncol 2005, 23:1420–1430.

    Article  PubMed  Google Scholar 

  58. Cristofanilli M, Budd GT, Ellis MJ, et al.: Circulating tumor cells, disease progression, and survival in metastatic breast cancer [see comment]. N Engl J Med 2004, 351:781–791. This study tests and validates the hypothesis that the level of circulating tumor cells can predict survival in metastatic breast cancer. The number of circulating tumor cells before treatment was found to be an independent predictor of progression-free survival and overall survival. This study led to other recent studies of the use of circulating tumor cells in molecular screening.

    Article  PubMed  CAS  Google Scholar 

  59. Reinholz MM, Nibbe A, Jonart LM, et al.: Evaluation of a panel of tumor markers for molecular detection of circulating cancer cells in women with suspected breast cancer [see comment]. Clin Cancer Res 2005, 11:3722–3732.

    Article  PubMed  CAS  Google Scholar 

  60. Masuda T-A, Kataoka A, Ohno S, et al.: Detection of occult cancer cells in peripheral blood and bone marrow by quantitative RT-PCR assay for cytokeratin-7 in breast cancer patients. Int J Oncol 2005, 26:721–730.

    PubMed  CAS  Google Scholar 

  61. Stathopoulos EN, Sanidas E, Kafousi M, et al.: Detection of CK-19 mRNA-positive cells in the peripheral blood of breast cancer patients with histologically and immunohistochemically negative axillary lymph nodes. Ann Oncol 2005, 16:240–246.

    Article  PubMed  CAS  Google Scholar 

  62. Muller V, Stahmann N, Riethdorf S, et al.: Circulating tumor cells in breast cancer: correlation to bone marrow micrometastases, heterogeneous response to systemic therapy and low proliferative activity. Clin Cancer Res 2005, 11:3678–3685.

    Article  PubMed  Google Scholar 

  63. Becker FF, Wang XB, Huang Y, et al.: Separation of human breast cancer cells from blood by differential dielectric affinity. Proc Natl Acad Sci U S A 1995, 92:860–864.

    Article  PubMed  CAS  Google Scholar 

  64. Ding C, Chiu RWK, Lau TK, et al.: MS analysis of single-nucleotide differences in circulating nucleic acids: Application to noninvasive prenatal diagnosis. Proc Natl Acad Sci U S A 2004, 101:10762–10767.

    Article  PubMed  CAS  Google Scholar 

  65. Sheen-Chen S-M, Liu Y-W, et al.: Serum levels of hepatocyte growth factor in patients with breast cancer. Cancer Epidemiol Biomarkers Prev 2005, 14:715–717.

    Article  PubMed  CAS  Google Scholar 

  66. Furstenberger G, von Moos R, Lucas R, et al.: Circulating endothelial cells and angiogenic serum factors during neoadjuvant chemotherapy of primary breast cancer. Br J Cancer 2006, 94:524–531.

    Article  PubMed  CAS  Google Scholar 

  67. Rigolin GM, Fraulini C, Ciccone M, et al.: Neoplastic circulating endothelial cells in multiple myeloma with 13q14 deletion. Blood 2006, 107:2531–2535.

    Article  PubMed  CAS  Google Scholar 

  68. Houghton J, Stoicov C, Nomura S, et al.: Gastric cancer originating from bone marrow-derived cells [see comment]. Science 2004, 306:1568–1571.

    Article  PubMed  CAS  Google Scholar 

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  1. Kleberg Center for Molecular Markers, Department of Molecular Therapeutics-Unit 950, University of Texas M.D. Anderson Cancer Center, 7435 Fannin Street, 77054, Houston, TX, USA

    Rahul Mitra PhD

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  1. Katherine Stemke-Hale PhD
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  2. Bryan Hennessy MD
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  3. Gordon B. Mills MD, PhD
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  4. Rahul Mitra PhD
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Correspondence to Rahul Mitra PhD.

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Stemke-Hale, K., Hennessy, B., Mills, G.B. et al. Molecular screening for breast cancer prevention, early detection, and treatment planning: Combining biomarkers from DNA, RNA, and protein. Curr Oncol Rep 8, 484–491 (2006). https://doi.org/10.1007/s11912-006-0078-5

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