[go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

Molecular Screening of Cancer

The Future is Here

  • Cancer
  • Published:
Molecular Diagnosis & Therapy Aims and scope Submit manuscript

Abstract

The remarkable growth in our knowledge of the biology of cancer is leading to the identification of previously elusive pathways and networks involved in cancer causation. The development of technologies has played a pivotal role in furthering this understanding and appreciation of the complexity of tumorigenesis, and is advancing efforts to fully grasp the biology and exploit the knowledge for improvements in cancer detection, prevention, and therapy. The future of molecular screening, i.e. detection of risk or cancer via molecular determinants, has never been so close to a reality. Molecular assays employed in cancer detection and therapy are likely to revolutionize cancer treatment through individual-based diagnosis and treatment, i.e. personalized medicine. A number of detection techniques, such as the detection of aberrant DNA and RNA, the presence of auto-antibodies in serum or plasma, and protein profiling, are already in limited use for patient stratification for clinical trials and for predicting drug response.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Table I
Table II
Fig. 1

Similar content being viewed by others

References

  1. Weir H, Thun M, Hankey B, et al. Annual report to the nation on the status of cancer, 1975–2000. J Natl Cancer Inst 2003; 95(17): 1276–99

    Article  PubMed  Google Scholar 

  2. Eyre HJ, Smith RA, Mettlin CJ. Cancer Medicine. 5th ed. Hamilton, Ontario: BC Decker Inc., 2002

    Google Scholar 

  3. Swensen SJ, Jett JR, Sloan JA, et al. Screening for lung cancer with low-dose spiral computed tomography. Am J Respir Crit Care Med 2002; 165(4): 508–13

    PubMed  Google Scholar 

  4. Palmisano WA, Divine KK, Saccomanno G, et al. Predicting lung cancer by detecting aberrant promoter methylation in sputum. Cancer Res 2000 Nov 1; 60(21): 5954–8

    PubMed  CAS  Google Scholar 

  5. Campa MJ, Wang MZ, Howard B, et al. Protein expression profiling identifies macrophage migration inhibitory factor and cyclophilin a as potential molecular targets in non-small cell lung cancer. Cancer Res 2003; 63(7): 1652–6

    PubMed  CAS  Google Scholar 

  6. Wagner P, Verma M, Srivastava S. Challenges for biomarkers in cancer detection. New York: New York Academy of Science, 2004

    Google Scholar 

  7. Fasouliotis SJ, Schenker JG. BRCA and BRCA2 gene mutations: decision making dilemmas concerning testing and management. Obstet Gynecol Surv 2000; 55(6): 373–84

    Article  PubMed  CAS  Google Scholar 

  8. Ludwig JA, Weinstein J. Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 2005; 5(11): 845–85

    Article  PubMed  CAS  Google Scholar 

  9. Feng Z, Prentice R, Srivastava S. Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistic perspective. Pharmacogenomics 2004; 5(6): 709–19

    Article  PubMed  CAS  Google Scholar 

  10. Sidransky D, Tokino T, Hamilton SR, et al. Identification of ras oncogene mutations in the stool of patients with curable colorectal tumors. Science 1992; 256(5053): 102–5

    Article  PubMed  CAS  Google Scholar 

  11. Villa E, Dugani A, Rebecchi AM, et al. Identification of subjects at risk for colorectal carcinoma through a test based on K-ras determination in the stool. Gastroenterology 1996; 110(5): 1346–53

    Article  PubMed  CAS  Google Scholar 

  12. Nollau P, Moser C, Weinland G, et al. Detection of K-ras mutations in stools of patients with colorectal cancer by mutant-enriched PCR. Int J Cancer 1996; 66(3): 332–6

    Article  PubMed  CAS  Google Scholar 

  13. Su YH, Wang M, Aiamkitsumrit B, et al. Detection of a K-ras mutation in urine of patients with colorectal cancer. Disease Markers. In press

  14. Umar A, editor. Lynch syndrome (HNPCC) and microsatellite instability. Amsterdam: IOS Press, 2004

    Google Scholar 

  15. Verma M, Srivastava S. Epigenetics in cancer: implications for early detection and prevention. Lancet Oncol 2003; 3(12): 755–62

    Article  Google Scholar 

  16. Ohtani-Fujita N, Dryja TP, Rapaport JM, et al. Hypermethylation in the retinoblastoma gene is associated with unilateral, sporadic retinoblastoma. Cancer Genet Cytogenet 1997 Oct 1; 98(1): 43–9

    Article  PubMed  CAS  Google Scholar 

  17. Esteller M, Silva JM, Dominguez G, et al. Promoter hypermethylation and BRCA1 inactivation in sporadic breast and ovarian tumors. J Natl Cancer Inst 2000; 92(7): 515–7

    Article  Google Scholar 

  18. Esteller M, Sparks A, Toyota M, et al. Analysis of adenomatous polyposis coli promoter hypermethylation in human cancer. Cancer Res 2000; 60(16): 4366–71

    PubMed  CAS  Google Scholar 

  19. Battagli C, Uzzo R, Dulaimi E, et al. Tumor suppressor gene hypermethylation in urine DNA from kidney cancer patients. Cancer Res 2003; 63(24): 8695–9

    PubMed  CAS  Google Scholar 

  20. Toyooka S, Toyooka KO, Maruyama R, et al. DNA methylation profiles of lung tumors. Mol Cancer Ther 2001; 1(1): 61–7

    PubMed  CAS  Google Scholar 

  21. Topaloglu O, Hoque MO, Tokumaru Y, et al. Detection of promoter hypermethylation of multiple genes in the tumor and bronchoalveolar lavage of patients with lung cancer. Clin Cancer Res 2004; 10(7): 2284–8

    Article  PubMed  CAS  Google Scholar 

  22. Dunning AM, Healey CS, Pharoah PD-P, et al. A systematic review of genetic polymorphisms and breast cancer risk. Cancer Epidemiol Biomarkers Prev 1999; 8(10): 843–54

    PubMed  CAS  Google Scholar 

  23. Takakura S, Kohno T, Shimizu K, et al. Somatic mutations and genetic polymorphisms of the PPP1R3 gene in patients with several types of cancers. Oncogene 2000 Feb 10; 19(6): 836–40

    Article  PubMed  CAS  Google Scholar 

  24. O’Keefe LV, Richards RI. Common chromosomal fragile sites and cancer: focus on FRA16D. Cancer Lett 2006 Jan 28; 232(1): 37–47

    Article  PubMed  Google Scholar 

  25. Srinivas PR, Verma M, Zhao Y, et al. Proteomics for cancer biomarker discovery. Clin Chem 2002; 48(8): 1160–9

    PubMed  CAS  Google Scholar 

  26. Fung E, Diamond D, Simonsesn AH, et al. The use of SELDI ProteinChip array technology in renal disease research. Methods Mol Med 2003; 86: 295–312

    PubMed  CAS  Google Scholar 

  27. Fung ET, Enderwick C. ProteinChip clinical proteomics: computational challenges and solutions. Biotechniques 2002 Mar; Suppl. 34-8: 40–1

    Google Scholar 

  28. Petricoin EF, Ardekani AM, Hitt BA, et al. Use of serum patterns to identify ovarian cancer. Lancet 2002; 359(9306): 572–7

    Article  PubMed  CAS  Google Scholar 

  29. Zhang Z, Bast RC, Yu Y, et al. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res 2004; 64(16): 5882–90

    Article  PubMed  CAS  Google Scholar 

  30. Vlahou A, Laronga C, Wilson L, et al. A novel approach toward development of a rapid blood test for breast cancer. Clin Breast Cancer 2003; 4(3): 203–9

    PubMed  CAS  Google Scholar 

  31. Adam BL, Qu Y, Davis JW, et al. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res 2002; 62(13): 3609–14

    PubMed  CAS  Google Scholar 

  32. Liotta LA, Espina V, Mehta AI, et al. Protein microarrays: meeting analytical challenges for clinical applications. Cancer Cell 2003; 3(4): 317–25

    Article  PubMed  CAS  Google Scholar 

  33. Sreekumar A, Chinnaiyan AM. Protein microarrays: a powerful tool to study cancer. Curr Opin Mol Ther 2002; 4(6): 587–93

    PubMed  CAS  Google Scholar 

  34. Miller JC, Zhou H, Kwekel J, et al. Antibody microarray profiling of human prostate cancer sera: antibody screening and identification of potential biomarkers. Proteomics 2003; 3(1): 56–63

    Article  PubMed  CAS  Google Scholar 

  35. Hanash S. Harnessing immunity for cancer marker discovery. Nat Biotechnol 2003; 21(1): 37–8

    Article  PubMed  CAS  Google Scholar 

  36. Wang X, Yu J, Sreekumar A, et al. Autoantibody signatures in prostate cancer. N Engl J Med 2005; 353(12): 1224–35

    Article  PubMed  CAS  Google Scholar 

  37. Ransohoff D. Bias as a threat to the validity of cancer molecular-marker research. Nat Rev Cancer 2005; 5(2): 142–9

    Article  PubMed  CAS  Google Scholar 

  38. Ransohoff D. Rules of evidence for cancer molecular-marker discovery and validation. Nat Rev Cancer 2004; 4(4): 309–14

    Article  PubMed  CAS  Google Scholar 

  39. Pepe MS, Etzioni R, Feng Z, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst 2001; 93(4): 1054–61

    Article  PubMed  CAS  Google Scholar 

  40. National Cancer Institute Early Detection Research Network [online]. Available from URL: http://edrn.nci.nih.gov/ [Accessed 2006 Jun 13]

  41. Steiner G, Schoenberg MP, Linn JF, et al. Detection of bladder cancer recurrence by microsatellite analysis of urine. Nat Med 1997; 3(6): 621–4

    Article  PubMed  CAS  Google Scholar 

  42. Fujiyama S, Izuno K, Gohshi K, et al. Clinical usefulness of des-carboxy prothrombin assay in early diagnosis of hepatocellular carcinoma. Dig Dis Sci 1991; 36(12): 1787–92

    Article  PubMed  CAS  Google Scholar 

  43. Marrero JA, Su GL, Wei W, et al. Des-gamma carboxyprotbrombin can differentiate hepatocellular carcinoma from nonmalignant chronic liver disease in American patients. Hepatology 2003; 37(5): 1114–21

    Article  PubMed  CAS  Google Scholar 

  44. Xie Y, Liu J, Zhang J, et al. Method for the comparative glycomic analyses of O-linked, mucin-type oligosaccharides. Anal Chem 2004; 76(17): 5186–97

    Article  PubMed  CAS  Google Scholar 

  45. Kobata A, Amano J. Altered glycosylation of proteins produced by malignant cells, and application for the diagnosis and immunotherapy of tumours. Immunol Cell Biol 2005; 83(4): 429–39

    Article  PubMed  CAS  Google Scholar 

  46. Rudd PM, Elliott T, Cresswell P, et al. Glycosylation and the immune system. Science 2001; 291(5512): 2370–6

    Article  PubMed  CAS  Google Scholar 

  47. Terasawa K, Furumoto H, Kamada M, et al. Expression of Tn and Sialyl-Tn antigens in the neoplastic transformation of uterine cervical epithelial cells. Cancer Res 1996; 56(9): 2229–32

    PubMed  CAS  Google Scholar 

  48. Nakamori S, Furukawa H, Hiratsuka M, et al. Expression of carbohydrate antigen sialyl Le(a): a new functional prognostic factor in gastric cancer. J Clin Oncol 1997; 15(2): 816–25

    PubMed  CAS  Google Scholar 

  49. Lee JS, Ro JY, Sahin AA, et al. Expression of blood-group antigen A: a favorable prognostic factor in non-small-cell lung cancer. N Engl J Med 1991; 324(16): 1084–90

    Article  PubMed  CAS  Google Scholar 

  50. Li L, Neaves WB. Normal stem cells and cancer stem cells: the niche matters. Cancer Res 2006; 66(9): 4553–7

    Article  PubMed  CAS  Google Scholar 

  51. National Cancer Institute. The cancer genome atlas [online]. Available from URL: http://cancergenome.nih.gov [Accessed 2006 June 14]

Download references

Acknowledgements

The author would like to acknowledge Dr Mark Shoenberg and Dr Jorge Marrero for their leadership in the microsatellite analysis for bladder cancer and des-gamma carboxyprothrombin for hepatocellular carcinoma studies, respectively. The author is a government employee and therefore no grant application number applies.

The author has no conflicts of interest that are directly relevant to the content of this article.

Author information

Authors and Affiliations

  1. Cancer Biomarkers Research Group, National Cancer Institute, Rockville, Maryland, USA

    Sudhir Srivastava

Authors
  1. Sudhir Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sudhir Srivastava.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Srivastava, S. Molecular Screening of Cancer. Mol Diag Ther 10, 221–230 (2006). https://doi.org/10.1007/BF03256460

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03256460

Keywords

Search

Navigation