Impact of Elotuzumab Plus Pomalidomide/Dexamethasone on Health-related Quality of Life for Patients With Relapsed/Refractory Multiple Myeloma: Final Data From the Phase 2 ELOQUENT-3 Trial.

Triplet regimens containing immunomodulatory drugs and proteasome inhibitors (PIs) have improved outcomes and extended survival for patients with relapsed/refractory multiple myeloma (RRMM). We evaluated updated health-related quality of life (HRQoL) findings from the phase 2 ELOQUENT-3 clinical trial (NCT02654132) after 4 years of treatment with elotuzumab plus pomalidomide and dexamethasone (EPd) and assessed the impact of the addition of elotuzumab on patients' HRQoL. HRQoL was assessed as an exploratory endpoint using the MD Anderson Symptom Inventory for Multiple Myeloma (MDASI-MM), which evaluates symptom severity, symptom interference, and HRQoL, and the 3-level EQ-5D, a patient-reported measure of health utility and general health. Statistical analyses included descriptive responder, longitudinal mixed-model, and time-to-first-deterioration (TTD) analyses using prespecified minimally important differences and responder definitions. Of 117 randomized patients, 106 (EPd, n = 55; pomalidomide and dexamethasone [Pd], n = 51) were eligible for inclusion in HRQoL analyses. Completion rates at almost all on-treatment visits were ≥80%. The proportion of patients treated with EPd who improved or maintained stable HRQoL until cycle 13 ranged from 82% to 96% for MDASI-MM total symptom score and 64% to 85% for MDASI-MM symptom interference. Across measurements, there were no clinically meaningful differences in changes from baseline between treatment arms, and TTD was not significantly different for EPd versus Pd. In conclusion, HRQoL was not impacted by the addition of elotuzumab to Pd and did not significantly deteriorate in patients with RRMM previously treated with lenalidomide and a PI in ELOQUENT-3.

Proteomic biomarkers apolipoprotein A1, truncated transthyretin and connective tissue activating protein III enhance the sensitivity of CA125 for detecting early stage epithelial ovarian cancer.

OBJECTIVE: The low prevalence of ovarian cancer demands both high sensitivity (>75%) and specificity (99.6%) to achieve a positive predictive value of 10% for successful early detection. Utilizing a two stage strategy where serum marker(s) prompt the performance of transvaginal sonography (TVS) in a limited number (2%) of women could reduce the requisite specificity for serum markers to 98%. We have attempted to improve sensitivity by combining CA125 with proteomic markers. METHODS: Sera from 41 patients with early stage (I/II) and 51 with late stage (III/IV) epithelial ovarian cancer, 40 with benign disease and 99 healthy individuals, were analyzed to measure 7 proteins [Apolipoprotein A1 (Apo-A1), truncated transthyretin (TT), transferrin, hepcidin, ß-2-microglobulin (ß2M), Connective Tissue Activating Protein III (CTAPIII), and Inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4)]. Statistical models were fit by logistic regression, followed by optimization of factors retained in the models determined by optimizing the Akaike Information Criterion. A validation set included 136 stage I ovarian cancers, 140 benign pelvic masses and 174 healthy controls. RESULTS: In a training set analysis, the 3 most effective biomarkers (Apo-A1, TT and CTAPIII) exhibited 54% sensitivity at 98% specificity, CA125 alone produced 68% sensitivity and the combination increased sensitivity to 88%. In a validation set, the marker panel plus CA125 produced a sensitivity of 84% at 98% specificity (P=0.015, McNemar's test). CONCLUSION: Combining a panel of proteomic markers with CA125 could provide a first step in a sequential two-stage strategy with TVS for early detection of ovarian cancer.

A novel proteomic biomarker panel as a diagnostic tool for patients with ovarian cancer.

BACKGROUND: Previous reports have shown that the proteomic markers apolipoprotein A1, hepcidin, transferrin, inter-alpha trypsin IV internal fragment, transthyretin, connective-tissue activating protein 3 and beta-2 microglobulin may discriminate between a benign pelvic mass and ovarian cancer (OC). The aim was to determine if these serum proteomic biomarkers alone as well as in combination with age and serum CA125, could be helpful in triage of women with a pelvic mass. METHODS: We included prospectively 144 patients diagnosed with (OC), 40 with a borderline tumor and 469 with a benign tumor. Surface-enhanced laser desorption/ionization time of flight-mass spectrometry was used for analyses. The Danish Index (DK-Index) based on the proteomic data, age and CA125 was developed using logistic regression models. RESULTS: Multivariate logistic regression analysis demonstrated that the selected proteomic markers, CA125 and age were independent predictors of OC and the combination of these is proposed as the DK-index. A sensitivity (SN) of 99% had a specificity (SP) of 57% for DK-index and 49% for CA125. At a SN of 95%, the SP increased to 81% for DK-index compared to 68% for CA125 alone. For stage I+II the SP was 58% for DK-index and 49% for CA125. For stage III+IV the corresponding values were 94% and 86% respectively. CONCLUSIONS: The DK-index warrants further evaluation in independent cohorts.

Psychometric evaluation of the Advanced Systemic Mastocytosis Symptom Assessment Form (AdvSM-SAF).

BACKGROUND: The Advanced Systemic Mastocytosis Symptom Assessment Form (AdvSM-SAF) was developed to evaluate symptoms of advanced systemic mastocytosis (AdvSM). This study aimed to psychometrically evaluate AdvSM-SAF scores and provide score interpretation guidelines. METHODS: The 10-item AdvSM-SAF was administered daily (scored as a seven-day average) in EXPLORER, an open-label Phase 1 study in AdvSM. Score distribution, reliability, construct-related validity, sensitivity to change, and interpretation guidelines were evaluated for AdvSM-SAF items, gastrointestinal symptom score (GSS), skin symptom score (SSS), and total symptom score (TSS). RESULTS: Thirty-one patients contributed to the analyses. At Baseline, the GSS, SSS, and TSS had adequate internal consistency (α > 0.7) and test-retest reliability (intraclass correlation coefficients >0.7). AdvSM-SAF scores were moderately to strongly correlated with variables as expected, and distinguished among clinically distinct groups. Observed relationships between change scores in the AdvSM-SAF and other assessments reflect evidence that AdvSM-SAF scores change in concert with other assessments designed to measure similar constructs. The magnitude of AdvSM-SAF weekly TSS mean change scores based on different anchor groupings was as expected (improvement > stable > worsening). Candidate clinically meaningful between-group difference estimates (GSS = 2-4, SSS = 2-3, and TSS = 4-7 points) and within-person change estimates (GSS = 6-9, SSS = 1-4, TSS = 9-14) for AdvSM-SAF weekly scores were generated. CONCLUSION: The AdvSM-SAF produced reliable, construct-valid, and sensitive scores when administered in the target patient population. These results, along with its strong development history and evidence of content validity, indicate that the AdvSM-SAF is fit for the purpose of measuring treatment benefit in individuals with AdvSM.

A proteomics panel for predicting optimal primary cytoreduction in stage III/IV ovarian cancer.

The objective of this prospective study was to evaluate CA-125 and a 7-marker panel as predictors of incomplete primary cytoreduction in patients with stage III/IV ovarian cancer (OC). From September 2004 to January 2008, serum from 201 patients referred to surgery for a pelvic tumor was analyzed for CA-125. In addition, serum was analyzed for 7 biomarkers using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. These biomarkers were combined into a single-valued ovarian-cancer-risk index (OvaRI). CA-125 and OvaRI were evaluated as predictors of cytoreduction in 75 stage III/IV patients using receiver operating characteristic curves. Complete primary cytoreduction (no macroscopic residual disease) was achieved in 31% (23/75) of the patients. The area under the receiver operating characteristic curve was 0.66 for CA-125 and 0.75 for OvaRI. The sensitivity and specificity of CA-125 for predicting incomplete cytoreduction were 71% (37/52) and 57% (13/23), respectively (P = 0.04). The sensitivity and specificity of OvaRI for predicting incomplete cytoreduction were 73% (38/52) and 70% (16/23), respectively (P = 0.001). In conclusion, CA-125 and an index of 7 biomarkers were found to be predictors of cytoreduction. However, future studies of biomarkers are anticipated to promote early diagnosis and provide prognostic information to guide treatment of OC patients. In addition, new biomarkers might also play a role in predicting outcome from primary surgery in OC patients.

Serum prohepcidin is associated with soluble transferrin receptor-1 but not ferritin in healthy post-menopausal women.

Hepcidin is a 25-amino-acid iron peptide hormone originated from its two precursors of prohepcidin (60-amino-acid) and preprohepcidin (84-amino-acid). Serum prohepcidin levels have been widely used to evaluate iron overload in clinical and preclinical studies. However, its usefulness is often questioned and its stepwise conversion mechanism remains largely unknown. Using New York University Women's Health Study subjects, we measured serum levels of prohepcidin with ELISA and hepcidin with mass spectrometry as well as ferritin and soluble transferrin receptor 1 (sTfR1) in 45 normal healthy post-menopausal women over a 1-year period with 2 samples per subject. We found that serum prohepcidin levels are correlated with the serum sTfR1 levels (r=0.45, p<0.01) but not to ferritin levels (r=0.08, p=0.60), suggesting that serum prohepcidin is not a biomarker of iron overload that was originally thought and designed for. Interestingly, serum hepcidin levels are associated with serum ferritin levels (r=0.64, p<0.0001) but not with sTfR1 levels (r=0.06, p=0.70), indicating that hepcidin is a measure of iron overload. Although hepcidin is a downstream product of prohepcidin, the amounts of hepcidin and prohepcidin are not related to each other (r=-0.007, p=0.90) under normal physiological conditions. The interrelationships between sTfR1 and prohepcidin or between ferritin and hepcidin suggest that ferritin- and sTfR1-sensed hepcidin conversion system exist in human body and maybe regulated at the post-translational level.

Application of ProteinChip array profiling in serum biomarker discovery for patients suffering from severe acute respiratory syndrome.

A new strain of coronavirus has caused an outbreak of severe acute respiratory syndrome (SARS) from 2002 to 2003 resulting in 774 deaths worldwide. By protein chip array profiling technology, a number of serum biomarkers that might be useful in monitoring the clinical course of SARS patients were identified. This book chapter describes how the protein chip array profiling was carried out for this study. Briefly, SARS patients' serum samples were first fractionated in Q Ceramic HyperD ion exchange sorbent beads by buffers at different pH. Serum protein fractions thus obtained were then bound onto a copper (II) immobilized metal affinity capture (IMAC30 Cu [II]) ProteinChip Array or a weak cation-exchange (CM10) ProteinChip Array. After washing and addition of sinapinic acid, the chips were read in a Protein Biological System (PBS) IIc mass spectrometer. Ions were generated by laser shots and flied in a time of flight mode to the ion detector according to their mass over charge (m/z) ratio. The serum profiling spectra in SARS patients were acquired, baseline subtracted and analyzed in parallel with those from the control subjects by Ciphergen ProteinChip Software 3.0.2 with their peak intensities compared by a nonparametric two sample Mann-Whitney-U test. More than twelve peaks were differentially expressed in SARS patients with one at m/z of 11,695 (later identified to be serum amyloid A protein), which had increase in peak intensity correlating with the extent of SARS-coronavirus induced pneumonia as defined by a serial chest X-ray opacity score. The remaining biomarkers could also be useful in the study of other clinical parameters in SARS patients.

Deep proteome profiling of sera from never-smoked lung cancer patients.

Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.

Evaluation of apolipoprotein A1 and posttranslationally modified forms of transthyretin as biomarkers for ovarian cancer detection in an independent study population.

BACKGROUND: Although overall 5-year survival rates for ovarian cancer are poor (10-30%), stage I/IIa patients have a 95% 5-year survival. New biomarkers that improve the diagnostic performance of existing tumor markers are critically needed. A previous study by Zhang et al. reported identification and validation of three biomarkers using proteomic profiling that together improved early-stage ovarian cancer detection. METHODS: To evaluate these markers in an independent study population, postdiagnostic/pretreatment serum samples were collected from women hospitalized at the Mayo Clinic from 1980 to 1989 as part of the National Cancer Institute Immunodiagnostic Serum Bank. Sera from 42 women with ovarian cancer, 65 with benign tumors, and 76 with digestive diseases were included in this study. Levels of various posttranslationally forms of transthyretin and apolipoprotein A1 were measured in addition to CA125. RESULTS: Mean levels of five of the six forms of transthyretin were significantly lower in cases than in controls. The specificity of a model including transthyretin and apolipoprotein A1 alone was high [96.5%; 95% confidence interval (95% CI), 91.9-98.8%] but sensitivity was low (52.4%; 95% CI, 36.4-68.0%). A class prediction algorithm using all seven markers, CA125, and age maintained high specificity (94.3%; 95% CI, 89.1-97.5%) but had higher sensitivity (78.6%; 95% CI, 63.2-89.7%). CONCLUSIONS: We were able to replicate the findings reported by Zhang et al. in an independently conducted blinded study. These results provide some evidence that including age of patient and these markers in a model may improve specificity, especially when CA125 levels are >/=35 units/mL. Influences of sample handling, subject characteristics, and other covariates on biomarker levels require further consideration in discovery and replication or validation studies.

Identification of serum amyloid a protein as a potentially useful biomarker to monitor relapse of nasopharyngeal cancer by serum proteomic profiling.

PURPOSE: Nasopharyngeal cancer (NPC) is a common cancer in Hong Kong, and relapse can occur frequently. Using protein chip profiling analysis, we aimed to identify serum biomarkers that were useful in the diagnosis of relapse in NPC. EXPERIMENTAL DESIGN: Profiling analysis was performed on 704 sera collected from 42 NPC patients, 39 lung cancer patients, 30 patients with the benign metabolic disorder thyrotoxicosis (TX), and 35 normal individuals (NM). Protein profile in each NPC patient during clinical follow up was correlated with the relapse status. RESULTS: Profiling analysis identified two biomarkers with molecular masses of 11.6 and 11.8 kDa, which were significantly elevated in 22 of 31 (71%) and 21 of 31 (68%) NPC patients, respectively, at the time of relapse (RP) as compared with 11 patients in complete remission (CR; RP versus CR, P = 0.009), 30 TX (RP versus TX, P < 0.001), or 35 NM (RP versus NM, P < 0.001). The markers were also elevated in 16 of 39 (41%) lung cancer patients at initial diagnosis. By tryptic digestion, followed by tandem mass spectrometry fragmentation, the markers were identified as two isoforms of serum amyloid A (SAA) protein. Monitoring the patients longitudinally for SAA level both by protein chip and immunoassay showed a dramatic SAA increase, which correlated with relapse and a drastic fall correlated with response to salvage chemotherapy. Serum SAA findings were compared with those of serum Epstein-Barr virus DNA in three relapsed patients showing a similar correlation with relapse and chemo-response. CONCLUSIONS: SAA could be a useful biomarker to monitor relapse of NPC.

Serum protein-expression profiling using the ProteinChip biomarker system.

Protein-expression profiling of serum is a common approach to the discovery of potential diagnostic and therapeutic markers of disease. Like any other proteome, the serum proteome is characterized by protein expression across a large dynamic range. This single facet requires the employment of fractionation procedures prior to detection of protein. The authors use a combination of conventional column chromatography with array-based chromatography to simplify the serum proteome into subproteomes, thus providing a greater representation of the serum proteome. Robotics is employed to increase the throughput of sample processing. These procedures result in large amounts of data that are analyzed through a series of preprocessing and postprocessing steps. A well-designed serum profiling project can therefore result in the discovery of statistically sound, clinically meaningful protein biomarkers.

Early inflammatory response in epithelial ovarian tumor cyst fluids.

Mortality rates for epithelial ovarian cancer (EOC) are high, mainly due to late-stage diagnosis. The identification of biomarkers for this cancer could contribute to earlier diagnosis and increased survival rates. Given that chronic inflammation plays a central role in cancer initiation and progression, we selected and tested 15 cancer-related cytokines and growth factors in 38 ovarian cyst fluid samples. We used ovarian cyst fluid since it is found in proximity to the pathology and mined it for inflammatory biomarkers suitable for early detection of EOC. Immunoprecipitation and high-throughput sample fractionation were obtained by using tandem antibody libraries bead and mass spectrometry. Two proteins, monocyte chemoattractant protein-1 (MCP-1/CCL2) and interleucin-8 (IL-8/CXCL8), were significantly (P < 0.0001) higher in the malignant (n = 16) versus benign (n = 22) tumor cysts. Validation of MCP-1, IL-8, and growth-regulated protein-α (GROα/CXCL1) was performed with ELISA in benign, borderline, and malignant cyst fluids (n = 256) and corresponding serum (n = 256). CA125 was measured in serum from all patients and used in the algorithms performed. MCP-1, IL-8, and GROα are proinflammatory cytokines and promoters of tumor growth. From 5- to 100-fold higher concentrations of MCP-1, IL-8 and GROα were detected in the cyst fluids compared to the serum. Significant (P < 0.001) cytokine response was already established in borderline cyst fluids and stage I EOC. In serum a significant (P < 0.01) increase of IL-8 and GROα was found, but not until stage I and stage III EOC, respectively. These findings confirm that early events in tumorigenesis can be analyzed and detected in the tumor environment and we conclude that ovarian cyst fluid is a promising source in the search for new biomarkers for early ovarian tumors.

Ovarian cyst fluid is a rich proteome resource for detection of new tumor biomarkers.

BACKGROUND: We aimed to investigate the use of ovarian cyst fluid as a source for biomarker discovery and to find novel biomarkers for use in the diagnosis of epithelial ovarian tumors. RESULTS: Ovarian cyst fluids from 218 women were collected and 192 (benign n = 129, malignant n = 63) were analyzed using mass spectrometry. 1180 peaks were detected, 221 of which were differently expressed between benign and malignant ovarian tumors. Seventeen peaks had receiver operating curve and area under the curve values >0.70; the majority of these represented peaks for apolipoproteins C-III and C-I (ApoC-I), transthyretin (TTR), serum amyloid A4 (SAA4), and protein C inhibitor (PCI). ApoC-III, PCI, and serum CA125, with an ROC AUC 0.94 was the best combination for diagnosing epithelial ovarian cancer. ApoC-III and PCI was analyzed with ELISA in the original cohort (n = 40) and in 40 new cyst fluid samples for confirmation with an independent method and validation. Results from MS and ELISA for ApoC-III correlated well (p = 0.04). In the validation set, ApoC-III was significantly (p = 0.001) increased in the malignant epithelial ovarian cancers. CONCLUSIONS: Fluid from ovarian cysts connected directly to the primary tumor harbor many possible new tumor-specific biomarkers. Biomarkers found in ovarian cyst fluid may be used as molecular imaging targets for early diagnostics and prediction of therapy. Plasma abundant proteins are also influencing the cystic fluid proteome. Methods for isolating less frequent cyst fluid proteins are needed.

Proteomic biomarkers for overall and progression-free survival in ovarian cancer patients.

PURPOSE: To determine if the level of apolipoprotein A1, hepcidin, transferrin, inter-α trypsin IV internal fragment, transthyretin (TT), connective-tissue activating protein 3 (CTAP3), serum amyloid A1, ß-2 microglobulin (B2M) might have impact on overall and progression-free survival for ovarian cancer (OC) patients. EXPERIMENTAL DESIGN: Serum from 150 OC patients was tested using SELDI-TOF-MS. RESULTS: A proteomic prognostic index (xb-pro) was constructed using the regression coefficients based on inter-α trypsin IV internal fragment, B2M and TT. A multivariable Cox survival analysis including the xb-pro index showed that xb-pro (p<0.0001, HR=2.50, 95% CI: 1.65-3.79), residual tumor after primary surgery (p=0.0005), age (p=0.01) and chemotherapy (p=0.0002) are of independent prognostic value for overall survival. International Federation of Gynecology and Obstetrics stage, performance status, histological type of tumor and serum CA125 were found of no independent value. A proteomic index (xb-pfs) based on B2M and CTAP3 was found to predict progression-free survival (xb-pfs: p=0.008, HR=1.77, 95% CI: 1.17-2.70 together with type of surgery, age and chemotherapy. CONCLUSIONS AND CLINICAL RELEVANCE: We found an index with three proteomic biomarkers (xb-pro) to be of independent prognostic value for overall survival and an index with two proteomic biomarkers (xb-pfs) with evidence of independent prognostic value for progression-free survival.

Comparison of proteomic biomarker panels in urine and serum for ovarian cancer diagnosis.

PURPOSE: The purposes of this study were to confirm previously found candidate epithelial ovarian cancer biomarkers in urine and to compare a paired serum biomarker panel and a urine biomarker panel from the same study cohort with regard to the receiver operating characteristic curve (ROC) area under the ROC curve (AUC) values. EXPERIMENTAL DESIGN: Four significant urine biomarkers were confirmed among 130 pelvic mass patients in the present study. The four biomarkers form a potential urine biomarker panel. From the same study cohort, the potential urine biomarker panel was compared to a serum biomarker panel, consisting of seven proteins/peptides, OvaRI. RESULTS: Multivariate analysis of the urine panel demonstrated a significant differentiation (p<0.0001) between epithelial ovarian cancer patients and patients with benign ovarian pelvic masses. The ROC AUC of the urine panel was 0.84 and the ROC AUC of OvaRI was 0.83. Combining the urine panel with OvaRI demonstrated a significant contribution from both, for urine peaks, OR=2.12 and for OvaRI, OR=1.39; the ROC AUC of this model was 0.88. CONCLUSIONS AND CLINICAL RELEVANCE: We demonstrated that both urine and serum can be used individually or in combination to potentially aid in ovarian cancer diagnostics. Urine proteomic profiling could provide biomarkers for the non-invasive test required in clinical practice.

ProteinChip array profiling for identification of disease- and chemotherapy-associated biomarkers of nasopharyngeal carcinoma.

BACKGROUND: We previously used ProteinChip array profiling analysis to discover a serum biomarker associated with nasopharyngeal carcinoma (NPC). In this study, we used the same method to examine other biomarkers associated with NPC and response to chemotherapy (CT) in NPC patients. METHODS: We performed ProteinChip array analysis in 209 serum samples from 66 relapsed patients before and after salvage CT with gemcitabine and cisplatin or etoposide and cisplatin combinations, 11 patients in remission, and 35 healthy individuals. Intensities of the biomarker peaks were correlated with CT response of the patients and other clinical parameters. RESULTS: We discovered 13 candidate biomarkers associated with different clinical parameters. Two biomarkers (2803 and 3953 Da) were significantly increased in patients compared with controls at all stages of disease. Analysis of pre- and post-CT paired serum samples revealed 7 biomarkers correlated with impact of CT. Of these 7 biomarkers, 2 (2509 and 2756 Da) were significantly increased and 5 (7588, 7659, 7765, 7843, and 8372 Da) were significantly decreased post-CT in either 1 or both CT cohorts. Four biomarkers from pre-CT sera were correlated with CT response, with 3 (2950, 13 510, and 14 855 Da) being significantly decreased and 1 (6701 Da) significantly increased in patients who did not respond to CT. Tandem mass spectrometric sequencing and/or immunoaffinity capture assay identified the 3953 Da biomarker as a fragment of interalpha-trypsin inhibitor precursor and 7765 Da biomarker as platelet factor-4. CONCLUSIONS: Treatment-associated serum biomarkers found might serve to triage NPC patients for appropriate CT treatment.

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.

Protein chip array profiling analysis in patients with severe acute respiratory syndrome identified serum amyloid a protein as a biomarker potentially useful in monitoring the extent of pneumonia.

BACKGROUND: A new strain of coronavirus (CoV) has caused an outbreak of severe acute respiratory syndrome (SARS), with 8098 individuals being infected and 774 deaths worldwide. We carried out protein chip array profiling analysis in an attempt to identify biomarkers that might be useful in monitoring the clinical course of SARS patients. METHODS: We performed surface-enhanced laser desorption ionization time-of-flight mass spectrometry on 89 sera collected from 28 SARS patients, 72 sera from 51 control patients with various viral or bacterial infections, and 10 sera from apparently healthy individuals. RESULTS: Nine significantly increased and three significantly decreased serum biomarkers were discovered in the SARS patients compared with the controls. Among these biomarkers, one (11,695 Da) was identified to be serum amyloid A (SAA) protein by peptide mapping and tandem mass spectrometric analysis. When we monitored the SAA concentrations longitudinally in 45 sera from four SARS patients, we found a good correlation of SAA concentration with the extent of pneumonia as assessed by a serial chest x-ray opacity score. Increased SAA occurred in three of four patients at the time of extensive pneumonia as indicated by high x-ray scores. Over the course of gradual recovery in two patients, as assessed clinically and radiologically, SAA concentrations gradually decreased. In the third patient, the concentrations were initially increased, but were further increased with superimposed multiple bacterial infections. SAA was not markedly increased in the fourth patient, who had low x-ray scores and whose clinical course was relatively mild. CONCLUSIONS: Protein chip array profiling analysis could be potentially useful in monitoring the severity of disease in SARS patients.

Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes.

BACKGROUND: Detection of hepatocellular carcinoma (HCC) in patients with chronic liver disease (CLD) is difficult. We investigated the use of comprehensive proteomic profiling of sera to differentiate HCC from CLD. METHODS: Proteomes in sera from 20 CLD patients with alpha-fetoprotein (AFP) <500 microg/L (control group) and 38 HCC patients (disease group) were profiled by anion-exchange fractionation (first dimension), two types (IMAC3 copper and WCX2) of ProteinChip Arrays (second dimension), and time-of-flight mass spectrometry (third dimension). Bioinformatic tests were used to identify tumor-specific proteomic features and to estimate the values of the tumor-specific proteomic features in the diagnosis of HCC. Cross-validation was performed, and we also validated the models with pooled sera from the control and disease groups, serum from a CLD patient with AFP >500 microg/L, and postoperative sera from two HCC patients. RESULTS: Among 2384 common serum proteomic features, 250 were significantly different between the HCC and CLD cases. Two-way hierarchical clustering differentiated HCC and CLD cases. Most HCC cases with advanced disease were clustered together and formed two subgroups that contained significantly more cases with lymph node invasion or distant metastasis. For differentiation of HCC and CLD by an artificial network (ANN), the area under the ROC curve was 0.91 (95% confidence interval, 0.82-1.01; P <0.0005) for all cases and 0.954 (95% confidence interval, 0.881-1.027; P <0.0005) for cases with nondiagnostic serum AFP (<500 microg/L). At a specificity of 90%, the sensitivity was 92%. Both cluster analysis and ANN correctly classified the pooled serum samples, the CLD serum sample with increased AFP, and the HCC patient in complete remission. CONCLUSION: Tumor-specific proteomic signatures may be useful for detection and classification of hepatocellular cancers.