Point: Proteomic patterns in biological fluids: do they represent the future of cancer diagnostics?

Writing on the future of cancer diagnostics, this author has predicted that multiparametric biomarker analysis, in combination with artificial neural networks and pattern recognition, will likely represent one of the most promising methodologies for diagnosing and monitoring cancer (1)(2). Over the last few years, we have witnessed publication of many reports dealing with proteomic patterns in biological fluids, and especially serum, by using the so-called “SELDI-TOF” technique (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry), in combination with artificial intelligence (3)(4)(5)(6)(7). The reported sensitivities and specificities of this method for ovarian, prostate, and breast cancer diagnosis are clearly impressive, and they are superior to the sensitivities and specificities obtained with current serologic cancer biomarkers (8)(9)(10)(11)(12). In particular, these techniques appear to detect early as well as advanced disease with similar efficiency, making them candidate tools for cancer screening, an application that is not currently recommended, by utilizing the classical cancer biomarkers, e.g., CA125, carcinoembryonic antigen (CEA), and α-fetoprotein (AFP) (1). In addition to scientific journals, these reports have also been presented in international news media and have attracted public attention. Despite of some important shortcomings of these methodologies, criticism has been minimal (13)(14). It seems that the impressive bottom line (very high diagnostic sensitivity and specificity) overshadows potential problems. The recent publication of three reports, from two different research groups, on the use of this technology in the diagnosis of prostate cancer allows for comparison of the data and the methodology and for the presentation of some important questions that have not been adequately addressed. In the following paragraphs, I will focus on some critical questions and provide discussion that could form the basis for further investigations. I will concentrate only …

[1]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[2]  K Chapman,et al.  The ProteinChip Biomarker System from Ciphergen Biosystems: a novel proteomics platform for rapid biomarker discovery and validation. , 2001, Biochemical Society transactions.

[3]  E. Petricoin,et al.  Serum proteomic patterns for detection of prostate cancer. , 2002, Journal of the National Cancer Institute.

[4]  D. Katsaros,et al.  Human kallikrein 6 (hK6): a new potential serum biomarker for diagnosis and prognosis of ovarian carcinoma. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  P. Schellhammer,et al.  Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. , 2002, Cancer research.

[6]  Emanuel F Petricoin,et al.  Mass spectrometry-based diagnostics: the upcoming revolution in disease detection. , 2003, Clinical chemistry.

[7]  E. Diamandis,et al.  Tumor Markers: Physiology, Pathobiology, Technology, and Clinical Applications , 2002 .

[8]  S. Gutteridge,et al.  Molecular mass determination for prostate‐specific antigen and α1‐antichymotrypsin complexed in vitro , 1998, Biotechnology and applied biochemistry.

[9]  D. Chan,et al.  Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. , 2002, Clinical chemistry.

[10]  E. Petricoin,et al.  Clinical proteomics: translating benchside promise into bedside reality , 2002, Nature Reviews Drug Discovery.

[11]  Mukesh Verma,et al.  Proteomics for Cancer Biomarker Discovery , 2002 .

[12]  E. Diamandis,et al.  Re: Serum proteomic patterns for detection of prostate cancer. , 2003, Journal of the National Cancer Institute.

[13]  D. Chace,et al.  The application of tandem mass spectrometry to neonatal screening for inherited disorders of intermediary metabolism. , 2002, Annual review of genomics and human genetics.

[14]  B. Rockhill Proteomic patterns in serum and identification of ovarian cancer , 2002, The Lancet.

[15]  D. Bruns,et al.  Cancer Diagnostics: Discovery and Clinical Applications—Introduction , 2002 .

[16]  R. Berkowitz,et al.  Osteopontin as a potential diagnostic biomarker for ovarian cancer. , 2002, JAMA.

[17]  G. Wright,et al.  Proteinchip® surface enhanced laser desorption/ionization (SELDI) mass spectrometry: a novel protein biochip technology for detection of prostate cancer biomarkers in complex protein mixtures , 1999, Prostate Cancer and Prostatic Diseases.

[18]  S Hanash,et al.  Proteomics in early detection of cancer. , 2001, Clinical chemistry.

[19]  N. Anderson,et al.  The Human Plasma Proteome , 2002, Molecular & Cellular Proteomics.

[20]  A. Partin,et al.  Human Kallikrein 2 (hK2) and prostate-specific antigen (PSA): two closely related, but distinct, kallikreins in the prostate. , 1998, Critical reviews in clinical laboratory sciences.

[21]  A W Partin,et al.  Use of the percentage of free prostate-specific antigen to enhance differentiation of prostate cancer from benign prostatic disease: a prospective multicenter clinical trial. , 1998, JAMA.

[22]  Thomas P Conrads,et al.  The SELDI-TOF MS approach to proteomics: protein profiling and biomarker identification. , 2002, Biochemical and biophysical research communications.

[23]  P. Schellhammer,et al.  Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. , 2002, Clinical chemistry.

[24]  A. Vlahou,et al.  Proteomic approaches to biomarker discovery in prostate and bladder cancers , 2001, Proteomics.

[25]  J. Ward,et al.  Identification of needs in biomarker research. , 1996, Environmental health perspectives.

[26]  D. Chace Mass spectrometry in the clinical laboratory. , 2001, Chemical reviews.