Liping Chung

Discovery of serum biomarkers for pancreatic adenocarcinoma using proteomic analysis

Background and aims:The serum/plasma proteome was explored for biomarkers to improve the diagnostic ability of CA19-9 in pancreatic adenocarcinoma (PC).Methods:A Training Set of serum samples from 20 resectable and 18 stage IV PC patients, 54 disease controls (DCs) and 68 healthy volunteers (HVs) were analysed by surface-enhanced laser desorption and ionisation time-of-flight mass spectrometry (SELDI-TOF MS). The resulting protein panel was validated on 40 resectable PC, 21 DC and 19 HV plasma samples (Validation-1 Set) and further by ELISA on 33 resectable PC, 28 DC and 18 HV serum samples (Validation-2 Set). Diagnostic panels were derived using binary logistic regression incorporating internal cross-validation followed by receiver operating characteristic (ROC) analysis.Results:A seven-protein panel from the training set PC vs DC and from PC vs HV samples gave the ROC area under the curve (AUC) of 0.90 and 0.90 compared with 0.87 and 0.91 for CA19-9. The AUC was greater (0.97 and 0.99, P<0.05) when CA19-9 was added to the panels and confirmed on the validation-1 samples. A simplified panel of apolipoprotein C-I (ApoC-I), apolipoprotein A-II (ApoA-II) and CA19-9 was tested on the validation-2 set by ELISA, in which the ROC AUC was greater than that of CA19-9 alone for PC vs DC (0.90 vs 0.84) and for PC vs HV (0.96 vs 0.90).Conclusions:A simplified diagnostic panel of CA19-9, ApoC-I and ApoA-II improves the diagnostic ability of CA19-9 alone and may have clinical utility.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.

Tissue biomarkers of breast cancer and their association with conventional pathologic features.

Background:Tissue protein expression profiling has the potential to detect new biomarkers to improve breast cancer (BC) diagnosis, staging, and prognostication. This study aimed to identify tissue proteins that differentiate breast cancer tissue from healthy breast tissue using protein chip mass spectrometry and to examine associations with conventional pathological features.Methods:To develop a training model, 82 BC and 82 adjacent unaffected tissue (AT) samples were analysed on cation-exchange protein chips by time-of-flight mass spectrometry. For validation, 89 independent BC and AT sample pairs were analysed.Results:From the protein peaks that were differentially expressed between BC and AT by univariate analysis, binary logistic regression yielded two peaks that together classified BC and AT with a ROC area under the curve of 0.92. Two proteins, ubiquitin and S100P (in a novel truncated form), were identified by liquid chromatography/tandem mass spectrometry and validated by immunoblotting and reactive-surface protein chip immunocapture. The combined marker panel was positively associated with high histologic grade, larger tumour size, lymphovascular invasion, ER and PR positivity, and HER2 overexpression, suggesting that it may be associated with a HER2-enriched molecular subtype of breast cancer.Conclusion:This independently validated protein panel may be valuable in the classification and prognostication of breast cancer patients.

Proteomic profiling of growth hormone-responsive proteins in human peripheral blood leukocytes.

CONTEXT GH is a known modulator of the immune system, but the effect of exogenous GH administration on white blood cell proteins has not been investigated. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a powerful platform for the study of GH effects on immune system proteins. OBJECTIVE Our objective was to explore a novel approach for the detection of GH-responsive proteins in human leukocytes by proteomic analysis using SELDI-TOF MS. DESIGN We conducted a randomized double-blind, placebo-controlled GH administration study of 8 wk treatment followed by 6 wk washout. Pre- and posttreatment samples from 30 subjects were used for biomarker discovery. SETTING The study was performed at a clinical research facility. PARTICIPANTS We studied 30 recreationally trained healthy athletes. INTERVENTION Subjects received either recombinant human GH (2 mg/d sc; n = 22) or placebo (n = 8) for 8 wk. MAIN OUTCOME MEASURES Proteomic profiles were determined using CM10 weak cation-exchange protein chips, and some GH-regulated proteins were purified and identified by mass spectrometry and/or immunoblotting. RESULTS SELDI-TOF analysis revealed a number of GH-regulated peptides/proteins in the 3- to 22-kDa range that are either up- or down-regulated by GH. Several of these may be useful as biomarkers of GH action. The calcium-binding, proinflammatory calgranulins S100A8, S100A9, and S100A12 were all significantly down-regulated in response to GH treatment. CONCLUSION This study illustrates the novel use of human leukocyte proteomic profiling by SELDI-TOF MS and reveals the negative regulation of proinflammatory S100 proteins by GH in human white blood cells.

Discovery of diagnostic biomarkers for pancreatic cancer in immunodepleted serum by SELDI-TOF MS

MOTIVATION Reports of serum pancreatic cancer (PC) biomarkers using SELDI-TOF MS have been inconsistent because different chip surfaces and interference with high-abundant proteins. This study examines the influence of these factors on the detection of discriminating diagnostic biomarkers. METHODS Serum from fourteen from patients with PC, disease controls (DC, n = 14) and healthy volunteers (HV, n = 14) were evaluated by SELDI using H50, IMAC, Q10 and CM10 chips. A further evaluation was undertaken after depletion of seven high-abundant proteins using spin cartridges. RESULTS More protein peaks were detected in whole serum than in depleted serum for IMAC, H50 and Q10 chips: 60 vs 39, 56 vs 48 and 69 vs 65, respectively, while the CM10 found less peaks in serum (27 vs 47 peaks). However, there were more differentially expressed peaks in the depleted serum samples for PC vs DC and PC vs HV samples using the H50, Q10 and CM10 ProteinChip arrays, whereas for IMAC arrays, more discriminating peaks were seen in non-depleted serum. The highly significant peaks observed on Q10, CM10 and H50 are consistent with the previous finding of ApoA-I (m/z 27,910-28000) and ApoA-II (m/z 8758 and 17,240). In addition, a number of new discriminating protein peaks were found on different ProteinChip arrays, notably peaks at m/z 4280 and 7763 on IMAC arrays. CONCLUSION This study confirms the diagnostic value of ApoA-I&II and identifies further potential diagnostic biomarkers for pancreatic cancer when multiple chip surfaces are used with depletion of the most highly-abundant proteins.

Detection of growth hormone responsive proteins using SELDI-TOF mass spectrometry

The detection of growth hormone (GH) doping is a significant problem in elite sports. GH is secreted in a pulsatile pattern from the anterior pituitary, influenced by a variety of normal and pathophysiological conditions. Exogenous recombinant hGH is virtually indistinguishable from the predominant naturally occurring isoform and is cleared from the body within 24h. Although GH is on the World Anti-doping Agency list of banned substances, the detection of GH abuse remains challenging. This article gives an overview of the potential application of surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry to examine proteomic changes following GH administration, using both serum and white blood cell extracts as samples for analysis. Results to date indicate that proteomic changes observed following GH administration have the potential to yield novel biomarker sets for the detection of GH abuse.

A novel truncated form of S100P predicts disease-free survival in patients with lymph node positive breast cancer

The calcium-binding protein S100P is overexpressed in various cancers and may contribute to the oncogenic phenotype. This study used mass spectrometry to characterize a novel 9.2-kDa C-terminally truncated form of S100P (t-S100P), and to investigate its potential prognostic value in breast cancer. Univariate analysis demonstrated the association between breast tissue t-S100P levels (n = 148) and conventional pathological markers. Across all tumor samples, high t-S100P was strongly prognostic for poor disease-free survival (P = 0.005), its efficacy confined to lymph node-positive tumors (n = 74, P = 0.007). Matrix-assisted laser desorption/ionization imaging mass spectrometry confirmed differential t-S100P abundance between breast cancer and unaffected adjacent tissue. t-S100P was exclusively located in the cell nucleus of breast cancer tissue, and full-length S100P was essentially undetectable by mass spectrometry. We conclude that t-S100P is the predominant form of S100P in breast cancer tissue and is strongly prognostic for disease-free survival in women with lymph node-positive disease.

Serum apolipoprotein C-II is prognostic for survival after pancreatic resection for adenocarcinoma

Background:Pancreaticoduodenectomy remains a major undertaking. A preoperative blood test, which could confidently predict the benefits of surgery would improve the selection of pancreatic cancer patients for surgery. This study aimed to identify protein biomarkers prognostic for long-term survival and to validate them with clinico-pathological information.Methods:Serum from 40 preoperative patients was used to train for predictive biomarkers using surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI), and the results were verified on 21 independent samples. Two predictive proteins were identified by tryptic peptide mass fingerprinting and sequencing, and validated on serum from another 57 patients by enzyme-linked immunosorbent assay (ELISA). The influence of these proteins on growth and invasion of two cancer cell lines was tested in-vitro.Results:The SELDI panel of m/z 3700, 8222 and 11 522 peaks predicted <12 months’ survival (ROC AUC: 0.79, 0.64–0.90; P<0.039). When CA19-9 was added, the ROC AUC increased to 0.95 (0.84–0.99; P<0.0001). The six subjects in the verification group who died within 12 months were correctly classified. The m/z 8222 and 11 522 proteins were identified as Serum ApoC-II and SAA-1, respectively. In the validation samples, ELISA results confirmed that ApoC-II was predictive of survival (Kaplan–Meier P<0.009), but not SAA-I. ApoC-II, CA19-9 and major-vessel involvement independently predicted survival. ApoC-II and SAA-1 increased cell growth and invasion of both cancer cell lines.Conclusion:Serum ApoC-II, CA19-9 and major-vessel invasion independently predict survival and improves selection of patients for pancreaticoduodenectomy.

Abstract 5094: Protein biomarker discovery in glioblastoma using Seldi-Tof-MS

Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL Glioblastoma (GBM) is an aggressive brain tumor with a poor prognosis. Generally, patients with GBM show a modest response to all therapeutic modalities. The unsatisfactory response to treatment resides in our lack of ability to identify patients who are most likely to benefit from chemotherapy, in particular targeted therapy. Biomarkers, with high sensitivity and specificity to identify favorable response to treatment are urgently needed to improve survival times. SELDI-TOF MS was used to examine the differential expression of proteins in GBM patients dichotomized according to response to the standard treatment schedule of temozolomide combined with radiotherapy (chemoradiotherapy). Patients with newly diagnosed GBM were recruited from Royal North Shore Hospital (Sydney, Australia). For uniformity, only patients who received chemoradiotherapy were included in this study. Patient response to chemoradiotherapy was defined according to Progression Free Survival (PFS) ≥ 8 months and/or Overall Survival (OS) > 24 months. A known marker of response, MGMT promoter methylation was also measured in all patients. Proteins extracted from 42 fresh-frozen GBM tumour samples were directly analyzed on four different chip surfaces; H50 (hydrophobic), IMAC (metal affinity), Q10 (anion exchange) and CM10 (cation exchange). Total number of 27 patients, PFS < 8 months (n=15) and PFS ≥ 8 months (n=12), separated from the patient cohort. In 54 spectra from 27 samples (performed in duplicate) 61, 47, 84 and 70 common peaks were identified for H50, IMAC, Q10 and CM10 chips, respectively. In combination of data from all chip surfaces, 35 significantly differentially expressed proteins were detected between the two PFS groups. These proteins were evaluated individually and in combination using binary logistic regression (BLR) analysis. Combined analysis of three upregulated proteins in PFS ≥ 8 months group showed a stronger area under the curve (AUC=1.000) when compared with their individual AUCs. Joint BLR analysis of nine significantly upregulated proteins in PFS < 8 months group demonstrated a powerful predictability by AUC=1.000. 39 out of 42 patients could be classified into Long Term Survived (LTS) (n=7) and Short Term Survived (STS) (n=32) patient subgroups. 70 peaks with H50 and 73 with CM10 were identified common to both patient groups. 13 protein peaks were found differentially expressed between groups. Two upregulated proteins identified in the LTS patient group were detected by BLR analysis as the most potent biomarkers to discriminate between the two OS groups with an improved AUC=0.87 in combination. In conclusion, proteomics is an effective method for identifying potential candidate biomarkers associated with response to treatment. Further experiments will be performed for identification and validation of explored peptides. These findings will provide new insights into understanding response of GBM patients to the standard therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5094. doi:10.1158/1538-7445.AM2011-5094