logo

Journal of Healthcare Engineering

PDF
Journal of Healthcare Engineering / 2012 / Article

Research Article | Open Access

Volume 3 |Article ID 579482 | https://doi.org/10.1260/2040-2295.3.4.503

Alicia D. Powers, Sean P. Palecek, "Protein Analytical Assays for Diagnosing, Monitoring, and Choosing Treatment for Cancer Patients", Journal of Healthcare Engineering, vol. 3, Article ID 579482, 32 pages, 2012. https://doi.org/10.1260/2040-2295.3.4.503

Protein Analytical Assays for Diagnosing, Monitoring, and Choosing Treatment for Cancer Patients

Alicia D. Powers1 and Sean P. Palecek1

1Department of Chemical and Biological Engineering, University of Wisconsin-Madison, USA

Received01 Jun 2012
Accepted01 Aug 2012

Abstract

Cancer treatment is often hindered by inadequate methods for diagnosing the disease or insufficient predictive capacity regarding therapeutic efficacy. Targeted cancer treatments, including Bcr-Abl and EGFR kinase inhibitors, have increased survival for some cancer patients but are ineffective in other patients. In addition, many patients who initially respond to targeted inhibitor therapy develop resistance during the course of treatment. Molecular analysis of cancer cells has emerged as a means to tailor treatment to particular patients. While DNA analysis can provide important diagnostic information, protein analysis is particularly valuable because proteins are more direct mediators of normal and diseased cellular processes. In this review article, we discuss current and emerging protein assays for improving cancer treatment, including trends toward assay miniaturization and measurement of protein activity.

References

  1. L. A. Pollack, J. Li, Z. Berkowitz et al., “Melanoma survival in the United States, 1992 to 2005,” Journal of the American Academy of Dermatology, pp. S78–S86, 2011. View at: Google Scholar
  2. S. E. Bohndiek and K. M. Brindle, “Imaging and, “omic” methods for the molecular diagnosis of cancer,” Expert review of molecular diagnostics, vol. 10, pp. 417–434, 2010. View at: Google Scholar
  3. R. Guo, C. Pan, J. Shen, and C. Liu, “New serum biomarkers for detection of esophageal carcinoma using Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry,” Journal of cancer research and clinical oncology, vol. 137, pp. 513–519, 2011. View at: Google Scholar
  4. N. T. Zinkin, F. Grall, K. Bhaskar et al., “Serum proteomics and biomarkers in hepatocellular carcinoma and chronic liver disease,” Clinical cancer research, vol. 14, pp. 470–477, 2008. View at: Google Scholar
  5. A. Vlahou, C. Laronga, L. Wilson et al., “A novel approach toward development of a rapid blood test for breast cancer,” Clinical breast cancer, vol. 4, pp. 203–209, 2003. View at: Google Scholar
  6. N. Galanina, V. Bossuyt, and L. N. Harris, “Molecular predictors of response to therapy for breast cancer,” Cancer journal (Sudbury, Mass.), vol. 17, pp. 96–103, 2011. View at: Google Scholar
  7. A. C. Wolff, M. E. H. Hammond, J. N. Schwartz et al., “American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer,” Archives of pathology & laboratory medicine, vol. 131, pp. 18–43, 2007. View at: Google Scholar
  8. G. Hudelist, M. Pacher-Zavisin, C. F. Singer et al., “Use of high-throughput protein array for profiling of differentially expressed proteins in normal and malignant breast tissue,” Breast cancer research and treatment, vol. 86, pp. 281–291, 2004. View at: Google Scholar
  9. L. Gutiérrez-Xicoténcatl, T. Plett-Torres, C. L. Madrid-González, and V. Madrid-Marina, “Molecular diagnosis of human papillomavirus in the development of cervical cancer,” Salud pública de México, vol. 51, Suppl 3, pp. S479–S488, 2009. View at: Google Scholar
  10. J. Ingvarsson, C. Wingren, A. Carlsson et al., “Detection of pancreatic cancer using antibody microarray-based serum protein profiling,” Proteomics, vol. 8, pp. 2211–2219, 2008. View at: Google Scholar
  11. S. Darmanis, R. Y. Nong, M. Hammond et al., “Sensitive plasma protein analysis by microparticle-based proximity ligation assays,” Molecular & cellular proteomics, vol. 9, pp. 327–335, 2010. View at: Google Scholar
  12. I. Visintin, Z. Feng, G. Longton et al., “Diagnostic markers for early detection of ovarian cancer,” Clinical cancer research, vol. 14, pp. 1065–1072, 2008. View at: Google Scholar
  13. D. Lin, S. Feng, J. Pan et al., “Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis,” Optics express, vol. 19, pp. 13565–13577, 2011. View at: Google Scholar
  14. J. Lin, R. Chen, S. Feng et al., “A novel blood plasma analysis technique combining membrane electrophoresis with silver nanoparticle-based SERS spectroscopy for potential applications in noninvasive cancer detection,” Nanomedicine, vol. 7, pp. 655–663, 2011. View at: Google Scholar
  15. J. R. Fromm, A. Thomas, and B. L. Wood, “Flow cytometry can diagnose classical hodgkin lymphoma in lymph nodes with high sensitivity and specificity,” American journal of clinical pathology, vol. 131, pp. 322–332, 2009. View at: Google Scholar
  16. H. Yaziji, C. R. Taylor, N. S. Goldstein et al., “Consensus recommendations on estrogen receptor testing in breast cancer by immunohistochemistry,” Applied immunohistochemistry & molecular morphology, vol. 16, pp. 513–520, 2008. View at: Google Scholar
  17. E. D. Hsi, K. J. Kopecky, F. R. Appelbaum et al., “Prognostic significance of CD38 and CD20 expression as assessed by quantitative flow cytometry in chronic lymphocytic leukaemia,” British journal of haematology, vol. 120, pp. 1017–1025, 2003. View at: Google Scholar
  18. M. Schmitt, A. S. Sturmheit, A. Welk, C. Schnelldorfer, and N. Harbeck, “Procedures for the quantitative protein determination of urokinase and its inhibitor, PAI-1, in human breast cancer tissue extracts by ELISA,” Methods in molecular medicine, vol. 120, pp. 245–265, 2006. View at: Google Scholar
  19. S. Uhrmacher, C. Schmidt, F. Erdfelder et al., “Use of the receptor tyrosine kinase-like orphan receptor 1 (ROR1) as a diagnostic tool in chronic lymphocytic leukemia (CLL),” Leukemia research, vol. 35, pp. 1360–1366, 2011. View at: Google Scholar
  20. N. L. Anderson, “The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum,” Clinical chemistry, vol. 56, pp. 177–185, 2010. View at: Google Scholar
  21. M. S. Kim, T. Kim, S.-Y. Kong et al., “Breast cancer diagnosis using a microfluidic multiplexed immunohistochemistry platform,” PloS one, vol. 5, e10441, 2010. View at: Google Scholar
  22. C. Decaestecker, X. M. Lopez, N. D'Haene et al., “Requirements for the valid quantification of immunostains on tissue microarray materials using image analysis,” Proteomics, vol. 9, pp. 4478–4494, 2009. View at: Google Scholar
  23. D. T. Ross, C.-Y. Kim, G. Tang et al., “Chemosensitivity and stratification by a five monoclonal antibody immunohistochemistry test in the NSABP B14 and B20 trials,” Clinical cancer research, vol. 14, pp. 6602–6609, 2008. View at: Google Scholar
  24. B. Z. Ring, R. S. Seitz, Ra. Beck et al., “A novel five-antibody immunohistochemical test for subclassification of lung carcinoma,” Modern pathology, vol. 22, pp. 1032–1043, 2009. View at: Google Scholar
  25. M. Kashani-Sabet, J. Rangel, S. Torabian et al., “A multi-marker assay to distinguish malignant melanomas from benign nevi,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, pp. 6268–6272, 2009. View at: Google Scholar
  26. S. J. Crabb, C. D. Bajdik, S. Leung et al., “Can clinically relevant prognostic subsets of breast cancer patients with four or more involved axillary lymph nodes be identified through immunohistochemical biomarkers? A tissue microarray feasibility study,” Breast cancer research, vol. 10, R6, 2008. View at: Google Scholar
  27. L. Grimwade, E. Gudgin, D. Bloxham, Ma. Scott, and W. N. Erber, “PML protein analysis using imaging flow cytometry,” Journal of clinical pathology, vol. 64, pp. 447–450, 2011. View at: Google Scholar
  28. A. Demurtas, G. Accinelli, D. Pacchioni et al., “Utility of flow cytometry immunophenotyping in fine-needle aspirate cytologic diagnosis of non-Hodgkin lymphoma: A series of 252 cases and review of the literature,” Applied immunohistochemistry & molecular morphology: AIMM / official publication of the Society for Applied Immunohistochemistry, vol. 18, pp. 311–322, 2010. View at: Google Scholar
  29. G. D'Arena, P. Musto, N. Cascavilla, M. Dell'Olio, N. Di Renzo, and M. Carotenuto, “Quantitative flow cytometry for the differential diagnosis of leukemic B-cell chronic lymphoproliferative disorders,” American journal of hematology, vol. 64, pp. 275–281, 2000. View at: Google Scholar
  30. J. Sun, M. D. Masterman-Smith, Na. Graham et al., “A microfluidic platform for systems pathology: multiparameter single-cell signaling measurements of clinical brain tumor specimens,” Cancer research, vol. 70, pp. 6128–6138, 2010. View at: Google Scholar
  31. C. M. Ferrier, H. H. de Witte, H. Straatman et al., “Comparison of immunohistochemistry with immunoassay (ELISA) for the detection of components of the plasminogen activation system in human tumour tissue,” British journal of cancer, vol. 79, pp. 1534–1541, 1999. View at: Google Scholar
  32. W. Tan, L. Sabet, Y. Li et al., “Optical protein sensor for detecting cancer markers in saliva,” Biosensors & bioelectronics, vol. 24, pp. 266–271, 2008. View at: Google Scholar
  33. S. Wang, X. Zhao, I. Khimji et al., “Integration of cell phone imaging with microchip ELISA to detect ovarian cancer HE4 biomarker in urine at the point-of-care,” Lab on a chip, vol. 11, pp. 3411–3418, 2011. View at: Google Scholar
  34. A. M. Attallah, Ha. Sakr, H. Ismail, E.-S. K. Abdel-Hady, and I. El-Dosoky, “An office-based immunodiagnostic assay for detecting urinary nuclear matrix protein 52 in patients with bladder cancer,” BJU international, vol. 96, pp. 334–339, 2005. View at: Google Scholar
  35. S. Wang, L. Ge, X. Song et al., “Paper-based chemiluminescence ELISA: lab-on-paper based on chitosan modified paper device and wax-screen-printing,” Biosensors & bioelectronics, vol. 31, pp. 212–218, 2012. View at: Google Scholar
  36. C. S. M. Ferreira, K. Papamichael, G. Guilbault, T. Schwarzacher, J. Gariepy, and S. Missailidis, “DNA aptamers against the MUC1 tumour marker: design of aptamer-antibody sandwich ELISA for the early diagnosis of epithelial tumours,” Analytical and bioanalytical chemistry, vol. 390, pp. 1039–1050, 2008. View at: Google Scholar
  37. Y. Liu, H. Wang, J. Huang, J. Yang, B. Liu, and P. Yang, “Microchip-based ELISA strategy for the detection of low-level disease biomarker in serum,” Analytica chimica acta, vol. 650, pp. 77–82, 2009. View at: Google Scholar
  38. L. F. Cheow, S. H. Ko, S. J. Kim, K. H. Kang, and J. Han, “Increasing the sensitivity of enzyme-linked immunosorbent assay using multiplexed electrokinetic concentrator,” Analytical chemistry, vol. 82, pp. 3383–3388, 2010. View at: Google Scholar
  39. F. Zhou, L. Yuan, H. Wang, D. Li, and H. Chen, “Gold Nanoparticle Layer: A Promising Platform for Ultra-Sensitive Cancer Detection,” Langmuir: the ACS journal of surfaces and colloids, pp. 2155–2158, 2011. View at: Google Scholar
  40. V. Gau and D. Wong, “Oral fluid nanosensor test (OFNASET) with advanced electrochemical-based molecular analysis platform,” Annals of the New York Academy of Sciences, vol. 1098, pp. 401–410, 2007. View at: Google Scholar
  41. J.-H. Maeng, B.-C. Lee, Y.-J. Ko et al., “A novel microfluidic biosensor based on an electrical detection system for alpha-fetoprotein,” Biosensors & bioelectronics, vol. 23, pp. 1319–1325, 2008. View at: Google Scholar
  42. S. Rauser, C. Marquardt, B. Balluff et al., “Classification of HER2 receptor status in breast cancer tissues by MALDI imaging mass spectrometry,” Journal of proteome research, vol. 9, pp. 1854–1863, 2010. View at: Google Scholar
  43. Q. Hu, Y. Huang, Z. Wang et al., “Application of Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Coupled With an Artificial Neural Network Model for the Diagnosis of Hepatocellular Carcinoma,” Hepato-gastroenterology, vol. 59, 2011. View at: Google Scholar
  44. M.-C. W. Gast, C. H. V. A. N. Gils, L. F. A. Wessels et al., “Serum protein profiling for diagnosis of breast cancer using SELDI-TOF MS,” Oncology Reports, vol. 22, pp. 205–213, 2009. View at: Google Scholar
  45. C. Liu, “The application of SELDI-TOF-MS in clinical diagnosis of cancers,” Journal of biomedicine & biotechnology, vol. 2011, 245821, 2011. View at: Google Scholar
  46. I. Szanto, L. Mark, A. Bona et al., “High-throughput screening of saliva for early detection of oral cancer: a pilot study,” Technology in cancer research & treatment, vol. 11, pp. 181–188, 2012. View at: Google Scholar
  47. D. S. Cornett, M. L. Reyzer, P. Chaurand, and R. M. Caprioli, “MALDI imaging mass spectrometry: molecular snapshots of biochemical systems,” Nature methods, vol. 4, pp. 828–833, 2007. View at: Google Scholar
  48. M. Walter, C. Heinze, T. Steiner et al., “Immunochemotherapy-associated protein patterns in tumour tissue and serum of patients with metastatic renal cell carcinoma,” Archives of physiology and biochemistry, vol. 116, pp. 197–207, 2010. View at: Google Scholar
  49. K.-L. Tang, T.-H. Li, W.-W. Xiong, and K. Chen, “Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data,” BMC bioinformatics, vol. 11, 109, 2010. View at: Google Scholar
  50. A. K. Callesen, W. Vach, P. E. Jørgensen et al., “Reproducibility of mass spectrometry based protein profiles for diagnosis of breast cancer across clinical studies: a systematic review,” Journal of proteome research, vol. 7, pp. 1395–1402, 2008. View at: Google Scholar
  51. I. Cadron, T. Van Gorp, F. Amant et al., “The use of laser microdissection and SELDI-TOF MS in ovarian cancer tissue to identify protein profiles,” Anticancer research, vol. 29, pp. 1039–1045, 2009. View at: Google Scholar
  52. A. Blokzijl, M. Friedman, F. Pontén, and U. Landegren, “Profiling protein expression and interactions: proximity ligation as a tool for personalized medicine,” Journal of internal medicine, vol. 268, pp. 232–245, 2010. View at: Google Scholar
  53. E. Schallmeiner, E. Oksanen, O. Ericsson et al., “Sensitive protein detection via triple-binder proximity ligation assays,” Nature methods, vol. 4, pp. 135–137, 2007. View at: Google Scholar
  54. X. Jiang, S. Cheng, W. Chen, L. Wang, F. Shi, and C. Zhu, “Comparison of oligonucleotide-labeled antibody probe assays for prostate-specific antigen detection,” Analytical biochemistry, vol. 424, pp. 1–7, 2012. View at: Google Scholar
  55. Y. Shi, W. Huang, Y. Tan et al., “A novel proximity assay for the detection of proteins and protein complexes: quantitation of HER1 and HER2 total protein expression and homodimerization in formalin-fixed, paraffin-embedded cell lines and breast cancer tissue,” Diagnostic molecular pathology: the American journal of surgical pathology, part B, vol. 18, pp. 11–21, 2009. View at: Google Scholar
  56. W. Huang, M. Reinholz, J. Weidler et al., “Comparison of central HER2 testing with quantitative total HER2 expression and HER2 homodimer measurements using a novel proximity-based assay,” American journal of clinical pathology, vol. 134, pp. 303–311, 2010. View at: Google Scholar
  57. R. Dua, J. Zhang, G. Parry, and E. Penuel, “Detection of hepatocyte growth factor (HGF) ligand-c-MET receptor activation in formalin-fixed paraffin embedded specimens by a novel proximity assay,” PloS one, vol. 6, e15932, 2011. View at: Google Scholar
  58. R. L. Woodbury, S. M. Varnum, and R. C. Zangar, “Elevated HGF levels in sera from breast cancer patients detected using a protein microarray ELISA,” Journal of proteome research, vol. 1, pp. 233–237, 2002. View at: Google Scholar
  59. J. C.-H. Lin, “Protein microarrays for cancer diagnostics and therapy,” Medical principles and practice, vol. 19, pp. 247–254, 2010. View at: Google Scholar
  60. A. Carlsson, C. Wingren, J. Ingvarsson et al., “Serum proteome profiling of metastatic breast cancer using recombinant antibody microarrays,” European journal of cancer, vol. 44, pp. 472–480, 2008. View at: Google Scholar
  61. S. Strömberg, M. G. Björklund, C. Asplund et al., “A high-throughput strategy for protein profiling in cell microarrays using automated image analysis,” Proteomics, vol. 7, pp. 2142–2150, 2007. View at: Google Scholar
  62. C. Schröder, A. Jacob, S. Tonack et al., “Dual-color proteomic profiling of complex samples with a microarray of 810 cancer-related antibodies,” Molecular & cellular proteomics, vol. 9, pp. 1271–1280, 2010. View at: Google Scholar
  63. C. Fang, Y. Wang, N. T. Vu et al., “Integrated microfluidic and imaging platform for a kinase activity radioassay to analyze minute patient cancer samples,” Cancer research, vol. 70, pp. 8299–8308, 2010. View at: Google Scholar
  64. A. A. Adams, P. I. Okagbare, J. Feng et al., “Highly efficient circulating tumor cell isolation from whole blood and label-free enumeration using polymer-based microfluidics with an integrated conductivity sensor,” Journal of the American Chemical Society, vol. 130, pp. 8633–8641, 2008. View at: Google Scholar
  65. Z. Du, N. Colls, K. H. Cheng, M. W. Vaughn, and L. Gollahon, “Microfluidic-based diagnostics for cervical cancer cells,” Biosensors & bioelectronics, vol. 21, pp. 1991–1995, 2006. View at: Google Scholar
  66. M. S. Kim, S. Kwon, T. Kim, E. S. Lee, and J.-K. Park, “Quantitative proteomic profiling of breast cancers using a multiplexed microfluidic platform for immunohistochemistry and immunocytochemistry,” Biomaterials, vol. 32, pp. 1396–1403, 2011. View at: Google Scholar
  67. O. Y. Henry, A. Fragoso, V. Beni et al., “Design and testing of a packaged microfluidic cell for the multiplexed electrochemical detection of cancer markers,” Electrophoresis, vol. 30, pp. 3398–3405, 2009. View at: Google Scholar
  68. A. Sukhanova and I. Nabiev, “Fluorescent nanocrystal-encoded microbeads for multiplexed cancer imaging and diagnosis,” Critical reviews in oncology/hematology, vol. 68, pp. 39–59, 2008. View at: Google Scholar
  69. C. Xie and G. Wang, “Development of simultaneous detection of total prostate-specific antigen (tPSA) and free PSA with rapid bead-based immunoassay,” Journal of clinical laboratory analysis, vol. 25, pp. 37–42, 2011. View at: Google Scholar
  70. Y. Long, Z. Zhang, X. Yan et al., “Multiplex immunodetection of tumor markers with a suspension array built upon core-shell structured functional fluorescence-encoded microspheres,” Analytica chimica acta, vol. 665, pp. 63–68, 2010. View at: Google Scholar
  71. K.-S. Shin, S. W. Lee, K.-C. Han et al., “Amplification of fluorescence with packed beads to enhance the sensitivity of miniaturized detection in microfluidic chip,” Biosensors & bioelectronics, vol. 22, pp. 2261–2267, 2007. View at: Google Scholar
  72. Z. Fu, C. Hao, X. Fei, and H. Ju, “Flow-injection chemiluminescent immunoassay for alpha-fetoprotein based on epoxysilane modified glass microbeads,” Journal of immunological methods, vol. 312, pp. 61–67, 2006. View at: Google Scholar
  73. A. Retnakumari, J. Jayasimhan, P. Chandran et al., “CD33 monoclonal antibody conjugated Au cluster nano-bioprobe for targeted flow-cytometric detection of acute myeloid leukaemia,” Nanotechnology, vol. 22, 285102, 2011. View at: Google Scholar
  74. S. Ray, P. J. Reddy, S. Choudhary, D. Raghu, and S. Srivastava, “Emerging nanoproteomics approaches for disease biomarker detection: a current perspective,” Journal of proteomics, vol. 74, pp. 2660–2681, 2011. View at: Google Scholar
  75. J. C. Y. Kah, K. W. Kho, C. G. L. Lee et al., “Early diagnosis of oral cancer based on the surface plasmon resonance of gold nanoparticles,” International journal of nanomedicine, vol. 2, pp. 785–798, 2007. View at: Google Scholar
  76. X. Yu, B. Munge, V. Patel et al., “Carbon nanotube amplification strategies for highly sensitive immunodetection of cancer biomarkers,” Journal of the American Chemical Society, vol. 128, pp. 11199–11205, 2006. View at: Google Scholar
  77. J. Lin, C. He, L. Zhang, and S. Zhang, “Sensitive amperometric immunosensor for alpha-fetoprotein based on carbon nanotube/gold nanoparticle doped chitosan film,” Analytical biochemistry, vol. 384, pp. 130–135, 2009. View at: Google Scholar
  78. N. Y. Ji, Y. H. Kang, M.-Y. Park et al., “Development of a fluorescent microsphere immunoassay for cystatin B, (CSTB) in serum of patients with hepatocellular carcinoma,” Clinical chemistry and laboratory medicine, vol. 49, pp. 151–155, 2011. View at: Google Scholar
  79. C. Xie, H. J. Kim, J. G. Haw et al., “A novel multiplex assay combining autoantibodies plus PSA has potential implications for classification of prostate cancer from non-malignant cases,” Journal of translational medicine, vol. 9, 43, 2011. View at: Google Scholar
  80. E. Dekking, V. H. J. van der Velden, S. Böttcher et al., “Detection of fusion genes at the protein level in leukemia patients via the flow cytometric immunobead assay,” Best practice & research. Clinical haematology, vol. 23, pp. 333–345, 2010. View at: Google Scholar
  81. J. V. Jokerst, A. Raamanathan, N. Christodoulides et al., “Nano-bio-chips for high performance multiplexed protein detection: determinations of cancer biomarkers in serum and saliva using quantum dot bioconjugate labels,” Biosensors & bioelectronics, vol. 24, pp. 3622–3629, 2009. View at: Google Scholar
  82. K. H. Lee, J. F. Galloway, J. Park et al., “Quantitative molecular profiling of biomarkers for pancreatic cancer with functionalized quantum dots,” Nanomedicine: nanotechnology, biology, and medicine, 2012. View at: Google Scholar
  83. B. Barat, S. J. Sirk, K. E. McCabe et al., “Cys-diabody quantum dot conjugates (immunoQdots) for cancer marker detection,” Bioconjugate chemistry, vol. 20, pp. 1474–1481, 2009. View at: Google Scholar
  84. H. Zheng, G. Chen, L. A. DeLouise, and Z. Lou, “Detection of the cancer marker CD146 expression in melanoma cells with semiconductor quantum dot label,” Journal of biomedical nanotechnology, vol. 6, pp. 303–311, 2010. View at: Google Scholar
  85. M. Hu, J. Yan, Y. He et al., “Ultrasensitive, multiplexed detection of cancer biomarkers directly in serum by using a quantum dot-based microfluidic protein chip,” ACS nano, vol. 4, pp. 488–494, 2010. View at: Google Scholar
  86. J. C. Y. Kah, M. C. Olivo, C. G. L. Lee, and C. J. R. Sheppard, “Molecular contrast of EGFR expression using gold nanoparticles as a reflectance-based imaging probe,” Molecular and cellular probes, vol. 22, pp. 14–23, 2008. View at: Google Scholar
  87. X. Liu, Q. Dai, L. Austin et al., “A one-step homogeneous immunoassay for cancer biomarker detection using gold nanoparticle probes coupled with dynamic light scattering,” Journal of the American Chemical Society, vol. 130, pp. 2780–2782, 2008. View at: Google Scholar
  88. W. Lu, S. R. Arumugam, D. Senapati et al., “Multifunctional Oval-Shaped Gold-Nanoparticle-Based Selective Detection of Breast Cancer Cells Using Simple Colorimetric and Highly Sensitive Two- Photon Scattering Assay,” ACS nanoano, vol. 4, pp. 1739–1749, 2010. View at: Google Scholar
  89. J. Ladd, H. Lu, A. D. Taylor, V. Goodell, M. L. Disis, and S. Jiang, “Direct detection of carcinoembryonic antigen autoantibodies in clinical human serum samples using a surface plasmon resonance sensor,” Colloids and surfaces. B, Biointerfaces, vol. 70, pp. 1–6, 2009. View at: Google Scholar
  90. S. Choi and J. Chae, “A microfluidic biosensor based on competitive protein adsorption for thyroglobulin detection,” Biosensors & bioelectronics, vol. 25, pp. 118–123, 2009. View at: Google Scholar
  91. K. Lee, V. P. Drachev, and J. Irudayaraj, “DNA-gold nanoparticle reversible networks grown on cell surface marker sites: application in diagnostics,” ACS nano, vol. 5, pp. 2109–2117, 2011. View at: Google Scholar
  92. W.-C. Law, K.-T. Yong, A. Baev, and P. N. Prasad, “Sensitivity improved surface plasmon resonance biosensor for cancer biomarker detection based on plasmonic enhancement,” ACS nano, vol. 5, pp. 4858–4864, 2011. View at: Google Scholar
  93. C. Yu, H. Nakshatri, and J. Irudayaraj, “Identity profiling of cell surface markers by multiplex gold nanorod probes,” Nano letters, vol. 7, pp. 2300–2306, 2007. View at: Google Scholar
  94. M. Piliarik, M. Bocková, and J. Homola, “Surface plasmon resonance biosensor for parallelized detection of protein biomarkers in diluted blood plasma,” Biosensors & bioelectronics, vol. 26, pp. 1656–1661, 2010. View at: Google Scholar
  95. S. Kazuno, T. Fujimura, T. Arai et al., “Multi-sequential surface plasmon resonance analysis of haptoglobin-lectin complex in sera of patients with malignant and benign prostate diseases,” Analytical biochemistry, vol. 419, pp. 241–249, 2011. View at: Google Scholar
  96. S. Mallidi, T. Larson, J. Tam et al., “Multiwavelength photoacoustic imaging and plasmon resonance coupling of gold nanoparticles for selective detection of cancer,” Nano letters, vol. 9, pp. 2825–2831, 2009. View at: Google Scholar
  97. X. Qian, X.-H. Peng, D. O. Ansari et al., “In vivo tumor targeting and spectroscopic detection with surface-enhanced Raman nanoparticle tags,” Nature biotechnology, vol. 26, pp. 83–90, 2008. View at: Google Scholar
  98. B. Lutz, C. Dentinger, L. Sun et al., “Raman nanoparticle probes for antibody-based protein detection in tissues,” The journal of histochemistry and cytochemistry, vol. 56, pp. 371–379, 2008. View at: Google Scholar
  99. G. Wang, R. J. Lipert, M. Jain et al., “Detection of the potential pancreatic cancer marker MUC4 in serum using surface-enhanced Raman scattering,” Analytical chemistry, vol. 83, pp. 2554–2561, 2011. View at: Google Scholar
  100. K. K. Maiti, U. S. Dinish, C. Y. Fu et al., “Development of biocompatible SERS nanotag with increased stability by chemisorption of reporter molecule for in vivo cancer detection,” Biosensors & bioelectronics, vol. 26, pp. 398–403, 2010. View at: Google Scholar
  101. O. Aydin, M. Altaş, M. Kahraman, O. F. Bayrak, and M. Culha, “Differentiation of healthy brain tissue and tumors using surface-enhanced Raman scattering,” Applied spectroscopy, vol. 63, pp. 1095–1100, 2009. View at: Google Scholar
  102. K. K. Maiti, A. Samanta, M. Vendrell, K.-S. Soh, M. Olivo, and Y.-T. Chang, “Multiplex cancer cell detection by SERS nanotags with cyanine and triphenylmethine Raman reporters,” Chemical communications, vol. 47, pp. 3514–3516, 2011. View at: Google Scholar
  103. H. Chon, S. Lee, S.-Y. Yoon, S.-I. Chang, D. W. Lim, and J. Choo, “Simultaneous immunoassay for the detection of two lung cancer markers using functionalized SERS nanoprobes,” Chemical communications, vol. 47, pp. 12515–12517, 2011. View at: Google Scholar
  104. M. Y. Sha, H. Xu, M. J. Natan, and R. Cromer, “Surface-enhanced Raman scattering tags for rapid and homogeneous detection of circulating tumor cells in the presence of human whole blood,” Journal of the American Chemical Society, vol. 130, pp. 17214–17215, 2008. View at: Google Scholar
  105. F. Domenici, A. R. Bizzarri, and S. Cannistraro, “SERS-based nanobiosensing for ultrasensitive detection of the p53 tumor suppressor,” International journal of nanomedicine, vol. 6, pp. 2033–2042, 2011. View at: Google Scholar
  106. B. V. Chikkaveeraiah, A. Bhirde, R. Malhotra, V. Patel, J. S. Gutkind, and J. F. Rusling, “Single-wall carbon nanotube forest arrays for immunoelectrochemical measurement of four protein biomarkers for prostate cancer,” Analytical chemistry, vol. 81, pp. 9129–9134, 2009. View at: Google Scholar
  107. R. Malhotra, V. Patel, J. P. Vaqué, J. S. Gutkind, and J. F. Rusling, “Ultrasensitive electrochemical immunosensor for oral cancer biomarker IL-6 using carbon nanotube forest electrodes and multilabel amplification,” Analytical chemistry, vol. 82, pp. 3118–3123, 2010. View at: Google Scholar
  108. K.-J. Huang, D.-J. Niu, W.-Z. Xie, and W. Wang, “A disposable electrochemical immunosensor for carcinoembryonic antigen based on nano-Au/multi-walled carbon nanotubes-chitosans nanocomposite film modified glassy carbon electrode,” Analytica chimica acta, vol. 659, pp. 102–108, 2010. View at: Google Scholar
  109. D. Yeung and T. Hughes, “Therapeutic targeting of BCR-ABL: prognostic markers of response and resistance mechanism in chronic myeloid leukaemia,” Critical reviews in oncology, 2012. View at: Google Scholar
  110. P. Saintigny and J. A. Burger, “Recent Advances in Non-small Cell Lung Cancer Biology and Clinical Management,” Discovery Medicine. n.d., vol. 13, pp. 287–297. View at: Google Scholar
  111. G. Ghosh, X. Yan, A. G. Lee, S. J. Kron, and S. P. Palecek, “Quantifying the sensitivities of EGF receptor (EGFR) tyrosine kinase inhibitors in drug resistant non-small cell lung cancer (NSCLC) cells using hydrogel-based peptide array,” Biosensors & bioelectronics, vol. 26, pp. 424–431, 2010. View at: Google Scholar
  112. T. Mizutani, T. Kondo, S. Darmanin et al., “A novel FRET-based biosensor for the measurement of BCR-ABL activity and its response to drugs in living cells,” Clinical cancer research, vol. 16, pp. 3964–3975, 2010. View at: Google Scholar
  113. M. Jarvius, J. Paulsson, I. Weibrecht et al., “In situ detection of phosphorylated platelet-derived growth factor receptor beta using a generalized proximity ligation method,” Molecular & cellular proteomics, vol. 6, pp. 1500–1509, 2007. View at: Google Scholar
  114. G. Ghosh, A. G. Lee, and S. P. Palecek, “Hydrogel-based protein array for quantifying epidermal growth factor receptor activity in cell lysates,” Analytical biochemistry, vol. 393, pp. 205–214, 2009. View at: Google Scholar
  115. A. G. Lee, D. J. Beebe, and S. P. Palecek, “Quantification of kinase activity in cell lysates via photopatterned macroporous poly(ethylene glycol) hydrogel arrays in microfluidic channels,” Biomedical microdevices, vol. 14, pp. 247–257, 2012. View at: Google Scholar
  116. P. Kim, X. Liu, T. Lee et al., “Highly sensitive proximity mediated immunoassay reveals HER2 status conversion in the circulating tumor cells of metastatic breast cancer patients,” Proteome science, vol. 9, 75, 2011. View at: Google Scholar
  117. I. H. Khan, J. Zhao, P. Ghosh et al., “Microbead arrays for the analysis of ErbB receptor tyrosine kinase activation and dimerization in breast cancer cells,” Assay and drug development technologies, vol. 8, pp. 27–36, 2010. View at: Google Scholar
  118. S. Lu and Y. Wang, “Fluorescence resonance energy transfer biosensors for cancer detection and evaluation of drug efficacy,” Clinical cancer research, vol. 16, pp. 3822–3824, 2010. View at: Google Scholar
  119. S. B. Lowe, Ja. G. Dick, B. E. Cohen, and M. M. Stevens, “Multiplex sensing of protease and kinase enzyme activity via orthogonal coupling of quantum dot-peptide conjugates,” ACS nano, vol. 6, pp. 851–857, 2012. View at: Google Scholar
  120. R. Toita, T. Mori, Y. Naritomi et al., “Fluorometric detection of protein kinase Cα activity based on phosphorylation-induced dissociation of a polyion complex,” Analytical biochemistry, vol. 424, pp. 130–136, 2012. View at: Google Scholar
  121. A. Fujiwara, E. Shibata, H. Terashima et al., “Evaluation of matrix metalloproteinase-2 (MMP-2) activity with film in situ zymography for improved cytological diagnosis of breast tumors,” Breast cancer, vol. 13, pp. 272–278, 2006. View at: Google Scholar
  122. M. R. Darragh, E. L. Schneider, J. Lou et al., “Tumor detection by imaging proteolytic activity,” Cancer research, vol. 70, pp. 1505–1512, 2010. View at: Google Scholar
  123. J. H. Ryu, A. Lee, S. Lee et al., ““One-step” detection of matrix metalloproteinase activity using a fluorogenic peptide probe-immobilized diagnostic kit,” Bioconjugate chemistry, vol. 21, pp. 1378–1384, 2010. View at: Google Scholar
  124. G. L. Liu, F. F. Chen, Ja. Ellman, and L. P. Lee, “Peptide-nanoparticle hybrid SERS probe for dynamic detection of active cancer biomarker enzymes,” in Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 795–798, IEEE Engineering in Medicine and Biology Society. Conference, 2006. View at: Google Scholar
  125. J. Fajkus, “Detection of telomerase activity by the TRAP assay and its variants and alternatives,” Clinica chimica acta, vol. 371, pp. 25–31, 2006. View at: Google Scholar
  126. J. Rothacker, R. G. Ramsay, D. Ciznadija et al., “A novel magnetic bead-based assay with high sensitivity and selectivity for analysis of telomerase in exfoliated cells from patients with bladder and colon cancer,” Electrophoresis, vol. 28, pp. 4435–4446, 2007. View at: Google Scholar
  127. K. Fizazi, L. Morat, L. Chauveinc et al., “High detection rate of circulating tumor cells in blood of patients with prostate cancer using telomerase activity,” Annals of oncology, vol. 18, pp. 518–521, 2007. View at: Google Scholar
  128. T. Xu, B. Lu, Y.-C. Tai, and A. Goldkorn, “A cancer detection platform which measures telomerase activity from live circulating tumor cells captured on a microfilter,” Cancer research, vol. 70, pp. 6420–6426, 2010. View at: Google Scholar
  129. X. Zuo, F. Xia, A. Patterson, H. T. Soh, Y. Xiao, and K. W. Plaxco, “Two-step, PCR-free telomerase detection by using exonuclease III-aided target recycling,” Chembiochem, vol. 12, pp. 2745–2747, 2011. View at: Google Scholar
  130. X. Zhou, D. Xing, D. Zhu, and L. Jia, “Magnetic bead and nanoparticle based electrochemiluminescence amplification assay for direct and sensitive measuring of telomerase activity,” Analytical chemistry, vol. 81, pp. 255–261, 2009. View at: Google Scholar

Copyright © 2012 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

PDF Download Citation Citation
Order printed copiesOrder
Views764
Downloads539
Citations

Related articles