Diagnostic potential of serum proteomic patterns in prostate cancer

Identification of serum biomarkers for colon cancer by proteomic analysis

Colorectal cancer (CRC) is often diagnosed at a late stage with concomitant poor prognosis. Early detection greatly improves prognosis; however, the invasive, unpleasant and inconvenient nature of current diagnostic procedures limits their applicability. No serum-based test is currently of sufficient sensitivity or specificity for widespread use. In the best currently available blood test, carcinoembryonic antigen exhibits low sensitivity and specificity particularly in the setting of early disease. Hence, there is great need for new biomarkers for early detection of CRC. We have used surface-enhanced laser desorbtion/ionisation (SELDI) to investigate the serum proteome of 62 CRC patients and 31 noncancer subjects. We have identified proteins (complement C3a des-arg, α1-antitrypsin and transferrin) with diagnostic potential. Artificial neural networks trained using only the intensities of the SELDI peaks corresponding to identified proteins were able to classify the patients used in this study with 95% sensitivity and 91% specificity.

Prostate cancer detection strategies

Prostate cancer is the most common malignancy in men and, as a result, there has been a nationwide emphasis on screening and detection. With the widespread use of the prostate-specific antigen (PSA), prostate cancer screening effectively detects localized prostate cancer. However, recent reports have identified a significant proportion of prostate cancer in men with low PSA levels. Many of these cancers are higher-grade malignancies. Consequently, PSA may function more effectively as a screening tool when applied over a continuum that is associated with degree of risk, rather than a binary measure. Other markers are currently being investigated. Ideally, a marker will identify the malignancy that is a clinical threat, thereby avoiding intervention for indolent disease. Prevention strategies may be employed for higher-risk patients, and these strategies eventually may be tailored to genetic or other risks.

Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment

Radiotherapy is one of the mainstays of glioblastoma (GBM) treatment. This study aims to investigate and characterise differences in protein expression patterns in brain tumour tissue following radiotherapy, in order to gain a more detailed understanding of the biological effects. Rat BT4C glioma cells were implanted into the brain of two groups of 12 BDIX-rats. One group received radiotherapy (12 Gy single fraction). Protein expression in normal and tumour brain tissue, collected at four different time points after irradiation, were analysed using surface enhanced laser desorption/ionisation – time of flight – mass spectrometry (SELDI-TOF-MS). Mass spectrometric data were analysed by principal component analysis (PCA) and partial least squares (PLS). Using these multivariate projection methods we detected differences between tumours and normal tissue, radiation treatment-induced changes and temporal effects. 77 peaks whose intensity significantly changed after radiotherapy were discovered. The prompt changes in the protein expression following irradiation might help elucidate biological events induced by radiation. The combination of SELDI-TOF-MS with PCA and PLS seems to be well suited for studying these changes. In a further perspective these findings may prove to be useful in the development of new GBM treatment approaches.

Processing and classification of protein mass spectra

Among the many applications of mass spectrometry, biomarker pattern discovery from protein mass spectra has aroused considerable interest in the past few years. While research efforts have raised hopes of early and less invasive diagnosis, they have also brought to light the many issues to be tackled before mass-spectra-based proteomic patterns become routine clinical tools. Known issues cover the entire pipeline leading from sample collection through mass spectrometry analytics to biomarker pattern extraction, validation, and interpretation. This study focuses on the data-analytical phase, which takes as input mass spectra of biological specimens and discovers patterns of peak masses and intensities that discriminate between different pathological states. We survey current work and investigate computational issues concerning the different stages of the knowledge discovery process: exploratory analysis, quality control, and diverse transforms of mass spectra, followed by further dimensionality reduction, classification, and model evaluation. We conclude after a brief discussion of the critical biomedical task of analyzing discovered discriminatory patterns to identify their component proteins as well as interpret and validate their biological implications.

Standardization of calibration and quality control using surface enhanced laser desorption ionization-time of flight-mass spectrometry

BACKGROUND

Protein profiling by surface enhanced laser desorption ionization-time of flight-mass spectrometry (SELDI-TOF-MS) is gaining importance as a diagnostic tool for a whole range of diseases. This report describes a QC procedure, which acts prospectively by checking the calibration before starting profiling experiments.

METHODS

A well-defined protocol for calibration of the Protein Biosystem IIc instrument was established, using a commercial QC sample containing independent certified standards and by determination of acceptance criteria. Instrument calibration was performed externally every week with the standards provided by the manufacturer. QC was performed for the period of 5 months.

RESULTS

According to the acceptance criteria defined in this study, data points should be in the established range of the process mean+/-2 standard deviations for the mass-to-charge ratios (m/z values), peak intensities, signal-to-noise ratios (S/N), and peak resolutions for insulin and apomyoglobin in the QC sample. Moreover, it was demonstrated that the pipetting variability in the handling of the QC sample significantly contributed to systematic errors and that spotting of a larger volume of QC sample resulted in a better reproducibility.

CONCLUSIONS

Stringent quality control of the calibration part of SELDI-TOF-MS experiments prevents unreliable data acquisition from the very start.

Possible detection of pancreatic cancer by plasma protein profiling

The survival rate of pancreatic cancer patients is the lowest among those with common solid tumors, and early detection is one of the most feasible means of improving outcomes. We compared plasma proteomes between pancreatic cancer patients and sex- and age-matched healthy controls using surface-enhanced laser desorption/ionization coupled with hybrid quadrupole time-of-flight mass spectrometry. Proteomic spectra were generated from a total of 245 plasma samples obtained from two institutes. A discriminating proteomic pattern was extracted from a training cohort (71 pancreatic cancer patients and 71 healthy controls) using a support vector machine learning algorithm and was applied to two validation cohorts. We recognized a set of four mass peaks at 8,766, 17,272, 28,080, and 14,779 m/z, whose mean intensities differed significantly (Mann-Whitney U test, P < 0.01), as most accurately discriminating cancer patients from healthy controls in the training cohort [sensitivity of 97.2% (69 of 71), specificity of 94.4% (67 of 71), and area under the curve value of 0.978]. This set discriminated cancer patients in the first validation cohort with a sensitivity of 90.9% (30 of 33) and a specificity of 91.1% (41 of 45), and its discriminating capacity was further validated in an independent cohort at a second institution. When combined with CA19-9, 100% (29 of 29 patients) of pancreatic cancers, including early-stage (stages I and II) tumors, were detected. Although a multi-institutional large-scale study will be necessary to confirm clinical significance, the biomarker set identified in this study may be applicable to using plasma samples to diagnose pancreatic cancer.

Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer

There has been an impressive emergence of mass spectrometry based technologies applied toward the study of proteins. Equally notable is the rapid adaptation of these technologies to biomedical approaches in the realm of clinical proteomics. Concerted efforts toward the elucidation of the proteomes of organ sites or specific disease state are proliferating and from these efforts come the promise of better diagnostics/prognostics and therapeutic intervention. Prostate cancer has been a focus of many such studies with the promise of improved care to patients via biomarkers derived from these proteomic approaches. The newer technologies provide higher analytical capabilities, employ automated liquid handling systems, fractionation techniques and bioinformatics tools for greater sensitivity and resolving power, more robust and higher throughput sample processing, and greater confidence in analytical results. In this prospects, we summarize the proteomic technologies applied to date in prostate cancer, along with their respective advantages and disadvantages. The development of newer proteomic strategies for use in future applications is also discussed.

Mass Spectrometry: Uncovering the Cancer Proteome for Diagnostics

Despite impressive scientific achievements over the past few decades, cancer is still a leading cause of death. One of the major reasons is that most cancer patients are diagnosed with advanced disease. This is clearly illustrated with ovarian cancer in which the overall 5-year survival rates are only 20-30%. Conversely, when ovarian cancer is detected early (stage 1), the 5-year survival rate increases to 95%. Biomarkers, as tools for preclinical detection of cancer, have the potential to revolutionize the field of clinical diagnostics. The emerging field of clinical proteomics has found applications across a wide spectrum of cancer research. This chapter will focus on mass spectrometry as a proteomic technology implemented in three areas of cancer: diagnostics, tissue imaging, and biomarker discovery. Despite its power, it is also important to realize the preanalytical, analytical, and postanalytical limitations currently associated with this methodology. The ultimate endpoint of clinical proteomics is individualized therapy. It is essential that research groups, the industry, and physicians collaborate to conduct large prospective, multicenter clinical trials to validate and standardize this technology, for it to have real clinical impact.

Clinico-toxinological characterization of the acute effects of the venom of the marine snail, Conus loroisii

The venom of the marine snail, Conus loroisii, was studied to assess its risk and lethal factors in regard of human welfare. The lethality of the crude venom (LD50-5.0 mg/kg via i.p.) in mice was associated with reduced motor activity, asphyxiation, followed by respiratory failure. The effects on vital tissues revealed vascular congestion and inflammatory cell infiltration around the portal triad of the liver, spongiosis of the brain, hemorrhages/congested blood vessels in lung and endothelial cells of the renal tubule. Repeated measures of hematological profiles indicated that the venom significantly reduced erythrocytes (P<0.001, GLM repeated measures), followed associated with depletion of hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin and platelet count. Serum enzymes such as, glutamic-oxaloacetic transaminase, glutamic-pyruvic transaminase, lactate dehydrogenase and alkaline and acid phosphatases were altered significantly (P<0.05, Friedman test), which in turn confirmed the damage of vital organ tissues. Dual effect of the venom on the activity of mouse brain acetylcholinesterase stand for concentration specific, whereas maximal inhibition (60.41%, P<0.05, Wilcoxon signed rank test) in erythrocyte acetylcholinesterase did not show the dual activity observed in brain. The Ciphergen ProteinChip analysis of the envenomed serum further revealed that the venom causes changes in definite molecules involved in inflammatory process and ionic transport. In all, the venom of C. loroisii is potentially lethal to mammals, through its rapid action on the central and peripheral nervous systems by blocking neurotransmission with selective interference of ionic channels/receptors.

Establishment of serum protein pattern model for screening rectal carcinoma by SELDI-TOF-MS

AIM: To establish a serum protein pattern model for screening rectal carcinoma.

METHODS: The proteomic spectra of patients with rectal carcinoma, rectal polypus, and healthy people were obtained by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELD-TOF-MS) on WCX-2 chips. The collected data were compared and analyzed by Biomaker Wizard software (BPS) to set up the primary serum protein pattern model for screening rectal carcinoma. Then the pattern was evaluated by masked test.

RESULTS: A total of 26 protein was significantly different between the rectal carcinoma and normal controls (P < 0.05), among which 4 (m/z 9 295, 3 730, 3 938, and 4 095) were selected to set up an optimal serum protein biomarker pattern model. And the correct rate of this model was 96.8% (93/96). Its sensitivity and specificity was 95.0% (38/40) and 93.4% (45/48), respectively, when tested by masked samples.

CONCLUSION: The discovered serum protein pattern model can efficiently identify patients with and without rectal carcinoma. SELDI-TOF-MS plays a valuable role in the diagnosis of rectal carcinoma and the discovery of new tumor-specific protein biomarkers.

Diagnostic potential of tear proteomic patterns in Sjögren's syndrome

Histological and functional changes of the lacrimal gland might be reflected in proteomic patterns in tear fluids. In this study, we carried out a determination of the disease biomarkers in tear fluid for Sjögren's syndrome (SS) and a performance of noninvasive diagnostic test based on the proteomic patterns. Thirty-one SS patients and 57 control subjects were enrolled to this study. Their details were 23 cases with primary SS, 8 with secondary SS, 14 with dry eyes, 22 with miscellaneous ocular diseases, and 21 of healthy volunteers. Protein profiling in tear fluids was identified by surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Multiple protein changes were reproducibly detected in the primary SS group, including 10 potential novel biomarkers. Seven of the biomarkers (2094, 2743, 14191, 14702, 16429, 17453, 17792 m/z) were down-regulated and 3 biomarkers (3483, 4972, 10860 m/z) were up-regulated in primary SS group, comparing to the protein profiles of control subjects. When cutoff value of SS down-score was set less than 0.5, this result yielded 87% sensitivity and 100% specificity. The positive predictive value for this sample set was 100%. There was a significant inverse correlation between SS down-scores and epithelial damages of the ocular surface in primary SS patients. These findings support the potential of proteomic pattern technology in tear fluids as the noninvasive diagnostic test for primary SS.

Proteomics in prostate cancer

PURPOSE OF REVIEW

State-of-the-art proteomics technologies are currently being assessed for utility in the study of prostatic malignancy. This review aims to provide background information on the current proteomics techniques employed in prostate cancer research, recent reports showing the potential application of proteomics in urological practice, and the future direction of proteomics in prostate cancer research and management.

RECENT FINDINGS

Proteomic profiling of serum as a diagnostic tool and a platform for biomarker discovery in prostate cancer continues to draw favorable attention as well as close scrutiny as technological enhancements and multi-center study results are reported. In-vitro studies on prostate cell lines provide positive proof-of-principle results. The application of proteomics to query prostate tissue specimens yields novel prostate cancer biomarkers requiring further validation. The integration of proteomics with immunology also yields promising findings that may translate into clinically relevant biological assays.

SUMMARY

The study of proteomics is an emerging research field, and current studies continue to display potential future usage in prostate cancer management. Succeeding scientific investigations will probably yield new diagnostic and prognostic tools for prostate cancer, provide insights into its underlying biology, and contribute to the development of novel treatment strategies.

Protéomique clinique : vers la détection précoce des cancers ?

A key challenge in clinical proteomic of cancer is the identification of biomarkers that would allow early detection, diagnosis and monitor progression of the disease to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and surfaced-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry is one of the most interesting new approaches for cancer diagnostic using proteomic profiling. This review aims to summarize the results of studies that have used this new technology method for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems and still requires some efforts to be standardised and validated.

Classification of cancer types by measuring variants of host response proteins using SELDI serum assays

Protein expression profiling has been increasingly used to discover and characterize biomarkers that can be used for diagnostic, prognostic or therapeutic purposes. Most proteomic studies published to date have identified relatively abundant host response proteins as candidate biomarkers, which are often dismissed because of an apparent lack of specificity. We demonstrate that 2 host response proteins previously identified as candidate markers for early stage ovarian cancer, transthyretin and inter-alpha trypsin inhibitor heavy chain 4 (ITIH4), are posttranslationally modified. These modifications include proteolytic truncation, cysteinylation and glutathionylation. Assays using Surface Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) may provide a means to confer specificity to these proteins because of their ability to detect and quantitate multiple posttranslationally modified forms of these proteins in a single assay. Quantitative measurements of these modifications using chromatographic and antibody-based ProteinChip array assays reveal that these posttranslational modifications occur to different extents in different cancers and that multivariate analysis permits the derivation of algorithms to improve the classification of these cancers. We have termed this process host response protein amplification cascade (HRPAC), since the process of synthesis, posttranslational modification and metabolism of host response proteins amplifies the signal of potentially low-abundant biologically active disease markers such as enzymes.

Proteome analysis of prostate cancer

In this paper, we briefly review cancer proteomics in general, with particular attention to our proteome analyses of prostate cancer. Our efforts include development of new tools and novel approaches to discovering proteins potentially useful as cancer diagnostic and/or prognostic biomarkers or as therapeutic targets. To this end, we analyzed prostate cancer proteomes using two-dimensional gel electrophoresis employing agarose gels for the initial isoelectric focusing step (agarose 2-DE), with mass spectrometry used for protein identification. Agarose 2-DE offers advantages over the more widely used immobilized pH gradient 2-DE for separating high molecular mass proteins (15-500 kDa), thereby increasing its power to detect changes in the cancer's high-molecular mass proteomes.

Biomarkers for the detection and prognosis of prostate cancer

Recent studies have cast doubt on the reliability of serum total prostate-specific antigen as a biomarker for the detection and prognosis of prostate cancer. Biomarkers that can identify those men at risk for clinically significant prostate cancer are desperately needed. The search for biomarkers that may improve the detection of biologically consequential prostate cancer is one of the most active areas under current investigation. In this review, we highlight some of these ongoing efforts. Proper validation of newly discovered biomarkers is of paramount importance.

Prostate cancer screening

Prostate cancer is the leading noncutaneous cancer in men of the Western world. Because of its prevalence and ability to cause morbidity and mortality,prostate cancer screening continues to be an important area of focus in health care. This article covers the sensitivity and specificity of prostate-specific antigen and current techniques used to improve the test's validity, the importance of detecting clinically important cancers with screening, as well as the downward stage migration, decreased disease-specific mortality, and decreased metastases rate seen inpatients screened and treated for prostate cancer.

Evaluation of matrix-assisted laser desorption/ionization-time of flight mass spectrometry proteomic profiling: identification of alpha 2-HS glycoprotein B-chain as a biomarker of diet

Biomarkers have the potential to impact a wide range of public health concerns, including early detection of diseases, drug discovery, and improved accuracy of monitoring effects of interventions. Given new technological developments, broad-based screening approaches will likely advance biomarker discovery at an accelerated pace. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) allows for the elucidation of individual protein masses from a complex mixture with high throughput. We have developed a method for identifying serum biomarkers using MALDI-TOF and statistical analysis. However, before applying this approach to screening of complex diseases, we evaluated the approach in a controlled dietary intervention study. In this study, MALDI-TOF spectra were generated using samples from a randomized controlled trial. During separate feeding periods, 38 participants ate a basal diet devoid of fruits and vegetables and a basal diet supplemented with cruciferous (broccoli) family vegetables. Serum samples were obtained at the end of each 7-day feeding period and treated to remove large, abundant proteins. MALDI-TOF spectra were analyzed using peak picking algorithms and logistic regression models. Our bioinformatics methods identified two significant peaks at m/z values of 2740 and 1847 that could classify participants based on diet (basal vs. cruciferous) with 76% accuracy. The 2740 m/z peak was identified as the B-chain of alpha 2-HS glycoprotein, a serum protein previously found to vary with diet and be involved in insulin resistance and immune function.

Diagnosis of pancreatic cancer using serum proteomic profiling

In the United States, mortality rates from pancreatic cancer (PCa) have not changed significantly over the past 50 years. This is due, in part, to the lack of early detection methods for this particularly aggressive form of cancer. The objective of this study was to use high-throughput protein profiling technology to identify biomarkers in the serum proteome for the early detection of resectable PCa. Using surface-enhanced laser desorption/ionization mass spectrometry, protein profiles were generated from sera of 49 PCa patients and 54 unaffected individuals after fractionation on an anion exchange resin. The samples were randomly divided into a training set (69 samples) and test set (34 samples), and two multivariate analysis procedures, classification and regression tree and logistic regression, were used to develop classification models from these spectral data that could distinguish PCa from control serum samples. In the test set, both models correctly classified all of the PCa patient serum samples (100% sensitivity). Using the decision tree algorithm, a specificity of 93.5% was obtained, whereas the logistic regression model produced a specificity of 100%. These results suggest that high-throughput proteomics profiling has the capacity to provide new biomarkers for the early detection and diagnosis of PCa.

Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification

OBJECTIVE

In order to improve the prognosis of pancreatic cancer patients, it is crucial to explore novel tools for its early diagnosis. Here, we attempted to screen serum biomarkers to distinguish pancreatic cancer from non-cancer individuals.

METHODS

47 serum samples from pancreatic cancer patients, 39 of whom had small surgically resectable cancers, were collected before surgery, and an additional 53 serum samples from age- and sex-matched individuals without cancer were used as controls. The surface-enhanced laser desorption/ionization (SELDI) ProteinChip was applied to analyze serum protein profiling. 54 samples (27 with pancreatic cancer and 27 controls) were analyzed in the training set by a decision tree algorithm to be able to separate pancreatic cancer from controls. A double-blind test was used to determine the sensitivity and specificity of the classification model.

RESULTS

A panel of six biomarkers was selected to set up a decision tree as the classification model. The model separated effectively pancreatic cancer from control samples, achieving a sensitivity of 88.9% and a specificity of 74.1%. The double-blind test challenged the model with a sensitivity of 80% and a specificity of 84.6%.

CONCLUSION

The SELDI ProteinChip combined with an artificial intelligence classification algorithm shows great potential for the diagnosis of pancreatic cancer.

Mass Spectrometry-based Expression Profiling of Clinical Prostate Cancer

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.

Workshop on Cancer Biometrics: Identifying Biomarkers and Surrogates of Cancer in Patients

The current excitement about molecular targeted therapies has driven much of the recent dialog in cancer diagnosis and treatment. Particularly in the biologic therapy of cancer, identifiable antigenic T-cell targets restricted by MHC molecules and the related novel stress molecules such as MICA/B and Letal allow a degree of precision previously unknown in cancer therapy. We have previously held workshops on immunologic monitoring and angiogenesis monitoring. This workshop was designed to discuss the state of the art in identification of biomarkers and surrogates of tumor in patients with cancer, with particular emphasis on assays within the blood and tumor. We distinguish this from immunologic monitoring in the sense that it is primarily a measure of the tumor burden as opposed to the immune response to it. Recommendations for intensive investigation and targeted funding to enable such strategies were developed in seven areas: genomic analysis; detection of molecular markers in peripheral blood and lymph node by tumor capture and RT-PCR; serum, plasma, and tumor proteomics; immune polymorphisms; high content screening using flow and imaging cytometry; immunohistochemistry and tissue microarrays; and assessment of immune infiltrate and necrosis in tumors. Concrete recommendations for current application and enabling further development in cancer biometrics are summarized. This will allow a more informed, rapid, and accurate assessment of novel cancer therapies.

Tree analysis of mass spectral urine profiles discriminates transitional cell carcinoma of the bladder from noncancer patient

BACKGROUND

Recent advances in proteomic profiling technologies, such as surface-enhanced laser desorption/ionization mass spectrometry (SELDI), have allowed preliminary profiling and identification of tumor markers in biological fluids in several cancer types and establishment of clinically useful diagnostic computational models. We developed a bioinformatics tool and used it to identify proteomic patterns in urine that distinguish transitional cell carcinoma (TCC) from noncancer.

METHODS

Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionization). A preliminary "training" set of spectra derived from analysis of urine from 46 TCC patients, 32 patients with benign urogenital diseases (BUD), and 40 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that identified a fine-protein mass pattern that discriminated cancer from noncancer effectively. A blinded test set, including 38 new cases, was used to determine the sensitivity and specificity of the classification system.

RESULTS

The algorithm identified a cluster pattern that, in the training set, segregated cancer from noncancer with sensitivity of 84.8% and specificity of 91.7%. The discriminatory pattern correctly identified. A sensitivity of 93.3% and a specificity of 87.0% for the blinded test were obtained when comparing the TCC vs. noncancer.

CONCLUSIONS

These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for bladder cancer in high-risk and general populations.

Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I. Assessment of platform reproducibility

BACKGROUND

Protein expression profiling for differences indicative of early cancer has promise for improving diagnostics. This report describes the first stage of a National Cancer Institute/Early Detection Research Network-sponsored multiinstitutional evaluation and validation of this approach for detection of prostate cancer.

METHODS

Two sequential experimental phases were conducted to establish interlaboratory calibration and standardization of the surface-enhanced laser desorption (SELDI) instrumental and assay platform output. We first established whether the output from multiple calibrated Protein Biosystem II SELDI-ionization time-of-flight mass spectrometry (TOF-MS) instruments demonstrated acceptable interlaboratory reproducibility. This was determined by measuring mass accuracy, resolution, signal-to-noise ratio, and normalized intensity of three m/z "peaks" present in a standard pooled serum sample. We next evaluated the ability of the calibrated and standardized instrumentation to accurately differentiate between selected cases of prostate cancer and control by use of an algorithm developed from data derived from a single site 2 years earlier.

RESULTS

When the described standard operating procedures were established at all laboratory sites, the across-laboratory measurements revealed a CV for mass accuracy of 0.1%, signal-to-noise ratio of approximately 40%, and normalized intensity of 15-36% for the three pooled serum peaks. This was comparable to the intralaboratory measurements of the same peaks. The instrument systems were then challenged with sera from a selected group of 14 cases and 14 controls. The classification agreement between each site and the established decision algorithm were examined by use of both raw peak intensity boosting and ranked peak intensity boosting. All six sites achieved perfect blinded classification for all samples when boosted alignment of raw intensities was used. Four of six sites achieved perfect blinded classification with ranked intensities, with one site passing the criteria of 26 of 28 correct and one site failing with 19 of 28 correct.

CONCLUSIONS

These results demonstrate that "between-laboratory" reproducibility of SELDI-TOF-MS serum profiling approaches that of "within-laboratory" reproducibility as determined by measuring discrete m/z peaks over time and across laboratories.

Serum Levels of an Isoform of Apolipoprotein A-II as a Potential Marker for Prostate Cancer

Purpose: We recently showed that protein expression profiling of serum using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) has potential as a diagnostic approach for detection of prostate cancer. As a parallel effort, we have been pursuing the identification of the protein(s) comprising the individual discriminatory “peaks” and evaluating their utility as potential biomarkers for prostate disease.

Experimental Design: We employed liquid chromatography, gel electrophoresis and tandem mass spectroscopy to isolate and identify a protein that correlates with observed SELDI-TOF MS mass/charge (m/z) values. Immunodepletion, immunoassay, and Western analysis were used to verify that the identified protein generated the observed SELDI peak. Subsequent immunohistochemistry was used to examine the expression of the proteins in prostate tumors.

Results: An 8,946 m/z SELDI-TOF MS peak was found to retain discriminatory value in each of two separate data sets with an increased expression in the diseased state. Sequence identification by liquid chromatography-MS/MS and subsequent immunoassays verified that an isoform of apolipoprotein A-II (ApoA-II) is the observed 8,946 m/z SELDI peak. Immunohistochemistry revealed that ApoA-II is overexpressed in prostate tumors. SELDI-based immunoassay revealed that an 8.9-kDa isoform of ApoA-II is specifically overexpressed in serum from individuals with prostate cancer. ApoA-II was also overexpressed in the serum of individuals with prostate cancer who have normal prostate-specific antigen (0-4.0 ng/mL).

Conclusions: We have identified an isoform of ApoA-II giving rise to an 8.9K m/z SELDI “peak” that is specifically overexpressed in prostate disease. The ability of ApoA-II to detect disease in patients with normal prostate-specific antigen suggests potential utility of the marker in identifying indolent disease.

Proteomic patterns: their potential for disease diagnosis

Alterations in proteins abundance, structure, or function, act as useful indicators of pathological abnormalities prior to development of clinical symptoms and as such are often useful diagnostic and prognostic biomarkers. The underlying mechanism of diseases such as cancer are, however, quite complicated in that often multiple dysregulated proteins are involved. It is for this reason that recent hypotheses suggest that detection of panels of biomarkers may provide higher sensitivities and specificities for disease diagnosis than is afforded with single markers. Recently, a novel approach based on the analysis of protein patterns has emerged that may provide a more effective means to diagnose diseases, such as ovarian and prostate cancer. The method is based on the use of surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry (TOF-MS) to detect differentially captured proteins from clinical samples, such as serum and plasma. This analysis results in the detection of "proteomic" patterns that have been shown in recent investigations to distinguish diseased and unaffected subjects to varying degrees. This review will discuss the basics of SELDI protein chip technology and highlight its recent applications in disease biomarker discovery with emphasis on cancer diagnosis.

Molecular Markers and Prostate Cancer Prognosis

Prostate cancer is the most common malignancy among American men and is the second-leading cause of cancer-related mortality. Although radical prostatectomy and radiation therapy offer hope for cure for the majority of men with localized tumors, we continue to lack the tools to definitively determine which cancers need to be treated, which cancers will recur after treatment, and which cancers will behave aggressively when they have metastasized. Recent breakthroughs in molecular biology have led to the identification of a number of potential biomarkers for prostate cancer, many of which have been suggested to have prognostic significance. Eventually, combinations of these markers will hopefully enable us to more rationally facilitate counseling and direct management for men with prostate cancer.

Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer

Early detection remains the most promising approach to improve long-term survival of patients with ovarian cancer. In a five-center case-control study, serum proteomic expressions were analyzed on 153 patients with invasive epithelial ovarian cancer, 42 with other ovarian cancers, 166 with benign pelvic masses, and 142 healthy women. Data from patients with early stage ovarian cancer and healthy women at two centers were analyzed independently and the results cross-validated to discover potential biomarkers. The results were validated using the samples from two of the remaining centers. After protein identification, biomarkers for which an immunoassay was available were tested on samples from the fifth center, which included 41 healthy women, 41 patients with ovarian cancer, and 20 each with breast, colon, and prostate cancers. Three biomarkers were identified as follows: (a) apolipoprotein A1 (down-regulated in cancer); (b) a truncated form of transthyretin (down-regulated); and (c) a cleavage fragment of inter-alpha-trypsin inhibitor heavy chain H4 (up-regulated). In independent validation to detect early stage invasive epithelial ovarian cancer from healthy controls, the sensitivity of a multivariate model combining the three biomarkers and CA125 [74% (95% CI, 52-90%)] was higher than that of CA125 alone [65% (95% CI, 43-84%)] at a matched specificity of 97% (95% CI, 89-100%). When compared at a fixed sensitivity of 83% (95% CI, 61-95%), the specificity of the model [94% (95% CI, 85-98%)] was significantly better than that of CA125 alone [52% (95% CI, 39-65%)]. These biomarkers demonstrated the potential to improve the detection of early stage ovarian cancer.

Applicability of biomarkers in the early diagnosis of prostate cancer

Early diagnosis of prostate cancer can increase the curative success rate for this disease. Although serum prostate-specific antigen measurement is regarded as the best conventional tumor marker available, there is little doubt that it has great limitations. The threshold above which biopsies are indicated has now decreased to a serum prostate-specific antigen value of 3 ng/ml, which results in a negative biopsy rate of 70-80%. This can readily be explained by the fact that prostate-specific antigen is not specific for prostate cancer. Clinicians need more sensitive tools to help diagnose prostate cancer and monitor progression of the disease. Molecular oncology is playing an increasing role in the fields of diagnosis and therapy for prostate cancer and has already been instrumental in elucidating many of the basic mechanisms underlying the development and progression of this disease. The identification of new prostate cancer-specific genes, such as DD3PCA3, would represent a considerable advance in the improvement of diagnostic tests for prostate cancer. This could subsequently lead to a reduction of the number of unnecessary biopsies.

Interplay of transcriptomics and proteomics

Despite the obvious attractions of parallel profiling of transcripts and proteins on a global 'omic' scale, there are practical and biological differences involved in their application. Transcriptomics is now a robust, high-throughput, cost-effective technology capable of simultaneously quantifying tens of thousands of defined mRNA species in a miniaturized, automated format. Conversely, proteomic analysis is currently much more limited in breadth and depth of coverage owing to variations in protein abundance, hydrophobicity, stability, size and charge. Nevertheless, transcriptomic and proteomic data can be compared and contrasted provided the studies are carefully designed and interpreted. Differential splicing, post-translational modifications and data integration are among some of the future challenges to tackle.

New serum biomarkers for detection of HBV-induced liver cirrhosis using SELDI protein chip technology

AIM

To find new serum biomarkers for liver cirrhosis (LC) in chronic carriers of hepatitis B virus (HBV).

METHODS

Surface enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry was used to discover biomarkers for differentiating HBV induced LC from non-cirrhotic cohorts. A training population of 25 patients with HBV-induced LC, 20 patients with HCC, and 25 closely age-matched healthy men, was studied.

RESULTS

Two biomarkers with M(r) 7 772 and 3 933 were detected in sera of non-cirrhotic cohorts, but not in patients with HBV-induced LC. A sensitivity of 80% for all LC patients, a specificity of 81.8% for all non-cirrhotic cohorts and a positive predictive value of 75% for the study population were obtained.

CONCLUSION

These two serum biomarkers for HBV-induced LC might be used for diagnosis and assessment of disease progression.

Screening for prostate cancer: current recommendations

Recent studies have questioned the efficacy of PSA as a marker for the early detection of prostate cancer, but techniques are being investigated to improve the sensitivity and specificity of screening. It is hoped that new methods can differentiate between lethal and nonlethal cancers, thereby avoiding lead-time bias. Even with the current limitations of PSA, the combination of stage migration seen with screening, the recent Scandinavian study showing decrease of disease progression following surgical extirpation, and the known mortality in patients presenting with advanced disease help support PSA screening for prostate cancer. It is hoped that prospective, randomized, long-term screening studies, such as the PLCO and ERSCP trials, will show improved survival using the admittedly imperfect PSA marker in prostate cancer screening.

The Use of Plasma Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Proteomic Patterns for Detection of Head and Neck Squamous Cell Cancers

PURPOSE

Our study was undertaken to determine the utility of plasma proteomic profiling using surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry for the detection of head and neck squamous cell carcinomas (HNSCCs).

EXPERIMENTAL DESIGN

Pretreatment plasma samples from HNSCC patients or controls without known neoplastic disease were analyzed on the Protein Biology System IIc SELDI-TOF mass spectrometer (Ciphergen Biosystems, Fremont, CA). Proteomic spectra of mass:charge ratio (m/z) were generated by the application of plasma to immobilized metal-affinity-capture (IMAC) ProteinChip arrays activated with copper. A total of 37356 data points were generated for each sample. A training set of spectra from 56 cancer patients and 52 controls were applied to the "Lasso" technique to identify protein profiles that can distinguish cancer from noncancer, and cross-validation was used to determine test errors in this training set. The discovery pattern was then used to classify a separate masked test set of 57 cancer and 52 controls. In total, we analyzed the proteomic spectra of 113 cancer patients and 104 controls.

RESULTS

The Lasso approach identified 65 significant data points for the discrimination of normal from cancer profiles. The discriminatory pattern correctly identified 39 of 57 HNSCC patients and 40 of 52 noncancer controls in the masked test set. These results yielded a sensitivity of 68% and specificity of 73%. Subgroup analyses in the test set of four different demographic factors (age, gender, and cigarette and alcohol use) that can potentially confound the interpretation of the results suggest that this model tended to overpredict cancer in control smokers.

CONCLUSIONS

Plasma proteomic profiling with SELDI-TOF mass spectrometry provides moderate sensitivity and specificity in discriminating HNSCC. Further improvement and validation of this approach is needed to determine its usefulness in screening for this disease.

Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management

Recent technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available. In this paper, we discuss how the exploratory data analysis techniques of machine learning and high-dimensional visualization can be applied to extract clinically useful knowledge from a heterogeneous assortment of molecular data. After an introductory overview of machine learning and visualization techniques, we describe two proprietary algorithms (PURS and RadViz) that we have found to be useful in the exploratory analysis of large biological data sets. We next illustrate, by way of three examples, the applicability of these techniques to cancer detection, diagnosis, and management using three very different types of molecular data. We first discuss the use of our exploratory analysis techniques on proteomic mass spectroscopy data for the detection of ovarian cancer. Next, we discuss the diagnostic use of these techniques on gene expression data to differentiate between squamous and adenocarcinoma of the lung. Finally, we illustrate the use of such techniques in selecting from a database of chemical compounds those most effective in managing patients with melanoma versus leukemia.

MONITORING THE SEROLOGICAL PROTEOME: THE LATEST MODALITY IN PROSTATE CANCER DETECTION

PURPOSE

Various strategies have recently emerged to improve the diagnostic prediction of prostate cancer (CaP). One such strategy includes the mass profiling of serum protein fractions selectively adsorbed onto chemically modified probes. In the current study we further validated this approach, while offering a more versatile, less expensive and yet equally predictive alternative to existing technologies.

MATERIALS AND METHODS

A solid core lipophilic C-18 resin was used to extract and enrich the low molecular weight protein fraction from patient serum for further analysis by mass spectrometry. Mass spectra generated from a 48 patient training set were data mined using multivariate analysis to identify diagnostically significant protein peaks. These peaks were then used to test a blinded study set comprising 168 patients with common statistical algorithms and commercially available software packages.

RESULTS

A total of 36 peaks generated from the training set were used to test the combined set of 168 serum samples obtained from 98 healthy individuals and 70 patients with CaP. We report a sensitivity of 94.1% and a specificity of 99.0% with 1 false-positive, 4 false-negative and 5 nondiagnosed cases.

CONCLUSIONS

Our results further indicate that mass profiling of serological proteins provides a means for the accurate detection of CaP. In addition, our approach was found to be superior to chip based protocols, generating rich, sharp, highly reproducible spectra attainable in a high throughput manner and at minimal cost. This technique is also scaleable for subsequent protein characterization using multidimensional protein identification technologies. Finally, analyses of mass spectra with commercially available statistical applications was found to be highly effective in generating highly discriminatory m/z values for CaP diagnosis.

Screening for Prostate Cancer

Prostate cancer is a highly prevalent disease in the Western world. In the United States alone, prostate cancer affects approximately 230,000 men and causes the death of 30,000 American men annually. Several theoretical health care measures may be implemented to decrease the morbidity and mortality of any disease. These measures include prevention, screening, improved curative treatment, and the transformation of an acute lethal disease to a chronic, tolerable one. This summary focuses on the screening aspects of prostate cancer.

Detection of Prostate Cancer Using Serum Proteomics Pattern in a Histologically Confirmed Population

PURPOSE

We retrospectively identified a panel of serum proteins that can discriminate between men with prostate cancer (clinically organ confined) and men with benign prostate disease.

MATERIALS AND METHODS

A contemporary set of 345 men who had an archival serum sample available were included in this study. The cancer group consisted of 246 men who underwent radical prostatectomy at the Johns Hopkins Hospital between March 1999 and April 2001. The noncancer group included 99 men with no histological evidence of prostate cancer on biopsy between April 1997 and April 2001 at the same institution. Serum proteomics mass spectra of these patients were generated using ProteinChip arrays and a ProteinChip Biomarker System II surface enhanced laser desorption/ionization time of flight mass spectrometer (Ciphergen Biosystems, Inc., Fremont, California). The cases and controls were randomly split into training and testing groups by a stratified sampling procedure. A combination of bioinformatics tools including ProPeak (3Z Informatics, Charleston, South Carolina) was used to reveal the optimal panel of biomarkers for maximum separation of the prostate cancer and the benign prostate disease cohorts.

RESULTS

A panel of 3 proteins (PC-1, PC-2 and PC-3) was selected using the training data. Performance of each of the protein markers and a linear regression derived composite index (PC-com3) were evaluated on the testing data. The area under the curve for prostate specific antigen (PSA), PC-1, PC-2, PC-3 and PC-com3 was 0.542, 0.585, 0.600, 0.636 and 0.643, respectively. Improvement of PC-com3 compared to PSA is observed at specificity range 30% to 80%. At a selected specificity of 45% the sensitivity of PC-com3 is 76%, significantly better than the PSA sensitivity of 57% (p <0.0001).

CONCLUSIONS

Serum proteomics patterns may potentially aid in the early detection of prostate cancer.

Proteomics in pathology, research and practice

Using more reliable and sophisticated protein biochemical techniques, it is possible to perform large scale, partly high-throughput characterization of the human proteome. Two-dimensional electrophoresis (2-DE) and mass spectrometry largely contribute to the identification of proteins and peptides. 2-DE has been used to study differential expression of peptides and proteins in various disease entities, searching for new diagnostic and therapeutic targets. However, 2-DE usually requires large amounts of starting material, is time-consuming, and reveals only a fraction of the proteins present in a given sample. More recently, the ProteinChip technology coupled with bioinformatics has gained considerable attention. This technique uses surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF/MS) to screen any protein source for putative disease biomarkers in a spectrum from 2 to 20 kDa. Between 15,500 (low resolution SELDI TOF) and > 400,000 peptides and proteins (high-resolution SELDI-TOF) can be resolved from a small sample volume (microl-range). Several studies have provided evidence that ProteinChip technology is capable of detecting early stage cancer by its unique cancer-specific proteomic finger prints, with sensitivities and specificities reaching far beyond well established serum-based tumor markers. In this review, we summarize the recent developments of proteomics in research and pathology, and critically discuss putative limitations and future applications of disease-specific biomarkers. Special emphasis is put on the former Human Protein Index project.

Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations

Serum proteomic profiling, by using surfaced-enhanced laser desorption/ionization-time-of-flight mass spectrometry, is one of the most promising new approaches for cancer diagnostics. Exceptional sensitivities and specificities have been reported for some cancer types such as prostate, ovarian, breast, and bladder cancers. These sensitivities/specificities are far superior to those obtained by using classical cancer biomarkers. In this review, I concentrate more on questions that cast doubt on the results reported and propose experiments to investigate these questions in detail, before the technique is used at the clinic. It is clear that the method needs to be externally and thoroughly validated before clinical implementation is warranted.