Topics:

Proteomics to Diagnose Human Tumors and Provide Prognostic Information

Proteomics to Diagnose Human Tumors and Provide Prognostic Information

Biomedical research is in the midst of unprecedented transformation stemming from the overall impact of molecular biology on medical research, including the emerging high-throughput genomicsbased technologies. These new paradigms are leading to better definition of the disease state as well as more precise and less toxic therapeutic strategies. But even as we begin to understand the implications of gene-based information on the genesis, pathophysiology, and progression of disease and on the development of novel therapeutic approaches, the dawn of the era of proteomics is heralding even more radical changes. Drs. Ornstein and Petricoin have produced a concise yet excellent review of currently existing proteomics technologies that aim to define and characterize the various proteins and peptides associated with biologic processes and/ or disease states. These new proteomics technologies hold promise for providing disease biomarkers, including cancer markers that would be valuable to both patients and clinicians. Proteomic Analytic Tools
The authors discuss both classical and emerging gel-based proteomic analytic tools such as two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and digital in-gel electrophoresis (DIGE). Although improvements such as immobilized pH gradients, image analysis software, and better fluorescent-based dyes have improved reproducibility, 2D-PAGE remains a comparatively low-throughput method of proteome interrogation requiring a relatively large amount of biologic sample when clinical assay development is concerned.[1] The importance and limitations of relatively novel technologies such as isotope-coded affinity tagging, laser capture microdissection, tissue microarrays, and reverse phase protein arrays are also adequately discussed. Protein-based arrays have a clear advantage over cDNA arrays in that the latter cannot address the various features of protein alterations and posttranslational modifications that occcur during the disease process. However, the dynamic range exhibited by the human proteome presents an equally formidable challenge for arrays that rely on traditional antibody-staining protocols.[ 1] The limitation of antibody specificity has sparked the development of novel arrays employing new classes of protein-capture agents that include aptamers,[2] ribozymes,[3] modified binding proteins, and partial- molecule imprints.[4] Role of Mass Spectrometry
Mass spectrometry-based proteomics is providing the impetus for advancement and is proving to be an indispensable tool for molecular and cellular protein studies, making it the method of choice for analyzing complex biologic samples.[5] The constant invention of higher platforms of mass spectrometers exhibiting an exponential increase in dynamic range and resolution further increases the functionality of this technology, not only in discovery but in translational research. Aside from the utility illustrated in the article by Ornstein and Petricoin, other mass spectrometry-based concepts are being developed. For example, mass spectrometric tissue imaging[6] employs tissue sections subjected to matrix-assisted laser desorption/ ionization (MALDI), and the diagnostic protein profiles contained in the tissue sections are generated by "imaging" the sample with an array of mass spectra. The authors have emphasized the utility of surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry using Ciphergen's ProteinChip Biology System II (with its ProteinChip Reader) and Applied Biosystems' QSTAR Pulsar LC/MS/MS System. The diagnostic utility of this technology in recognizing the serum protein patterns diagnostic of ovarian cancer or prostate cancer has been highlighted.[7-10] Although mass spectrometry equipment has the potential to provide desirable diagnostic biomarkers, the cost and level of expertise required to perform the spectrometric analysis is of concern. The high dimensionality of data generated from proteomic investigations requires powerful bioinformatics tools to uncover biomarkers and hidden patterns that are characteristic of the disease state. Genetic algorithms, self-organized cluster analysis, and decision tree algorithms described by the authors represent some of the analytic tools employed. Artificial neural networks[ 11-12] and discrete wavelet transform data reduction[13] are other methods currently under investigation. Recent successes have emphasized the importance of bioinformatics. However, key issues affecting data analysis, such as biologic variability, consideration of preanalytic variables, and analytic reproducibility, need to be addressed.[14] Conclusions
The issues surrounding both the promising potential and evident limitations of proteomics and its medical applications are testaments to the developmental stage of this emerging technology. Investigators in cancer research or other disease-focused research will increasingly need to rely on technologic advancements in genomics and proteomics to achieve their goals; ie, discovering the pathways that lead to malignancy and taking advantage of gene or protein targets for diagnosis, prognostication, therapy, or prevention of disease. The further development of the proteomic innovations discussed here and the invention of novel ones will certainly prove beneficial in overcoming present limitations and hopefully will provide robust, practical platforms for the clinical setting.

Disclosures

The authors have no significant financial interest or other relationship with the manufacturers of any products or providers of any service mentioned in this article.

References

1. Hanash S: Disease proteomics. Nature 422:226-232, 2003.
2. Smith D, Collins BD, Heil J, et al: Sensitivity and specificity of photoaptamer probes. Mol Cell Proteomics 2:11-18, 2003.
3. McCauley TG, Hamaguchi N, Stanton M: Aptamer-based biosensor arrays for detection and quantification of biological macromolecules. Anal Biochem 319:244-250, 2003.
4. Xu L, Aha P, Gu K, et al: Directed evolution of high-affinity antibody mimics using mRNA display. Chem Biol 9:933-942, 2002.
5. Aebersold R, Mann M: Mass spectrometry- based proteomics. Nature 422:198-207, 2003.
6. Stoeckli M, Chaurand P, Hallahan DE, et al: Imaging mass spectrometry: A new technology for the analysis of protein expression in mammalian tissues. Nat Med 7:493-496, 2001.
7. Petricoin EF III, Ardekani AM, Hitt BA, et al: Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359:572-577, 2002.
8. Adam BL, Qu Y, Davis JW, et al: Serum protein fingerprinting coupled with a patternmatching algorithm distinguishes prostate cancer from benign hyperplasia and healthy men. Cancer Res 62:3609-3614, 2002.
9. Petricoin EF III, Ornstein DK, Paweletz CP, et al: Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst 94:1576-1578, 2002.
10. Bañez LL, Prasanna P, Sun L, et al: Diagnostic potential of serum proteomic patterns in prostate cancer. J Urol 170:442-446, 2003.
11. Rogers MA, Clarke P, Noble J, et al: Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: Identification of key issues affecting potential clinical utility. Cancer Res 63:6971-6983, 2003.
12. Mian S, Ball G, Hornbuckle J, et al: A prototype methodology combining surfaceenhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to paclitaxel and doxorubicin under in vitro conditions. Proteomics 3:1725-1737, 2003.
13. Qu Y, Adam BL, Thornquist M, et al: Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data. Biometrics 59:143-151, 2003.
14. Boguski MS, McIntosh MW: Biomedical informatics for proteomics. Nature 422:233- 237, 2003.
 
Loading comments...

By clicking Accept, you agree to become a member of the UBM Medica Community.