Glycosylation plays an important
role in epithelial cancers, including
pancreatic ductal adenocarcinoma. However, little is known about the
glycoproteome of the human pancreas or its alterations associated
with pancreatic tumorigenesis. Using quantitative glycoproteomics
approach, we investigated protein N-glycosylation in pancreatic tumor
tissue in comparison with normal pancreas and chronic pancreatitis
tissue. The study lead to the discovery of a roster of glycoproteins
with aberrant N-glycosylation level associated with pancreatic cancer,
including mucin-5AC (MUC5AC), carcinoembryonic antigen-related cell
adhesion molecule 5 (CEACAM5), insulin-like growth factor binding
protein (IGFBP3), and galectin-3-binding protein (LGALS3BP). Pathway
analysis of cancer-associated aberrant glycoproteins revealed an emerging
phenomenon that increased activity of N-glycosylation was implicated
in several pancreatic cancer pathways, including TGF-β, TNF,
NF-kappa-B, and TFEB-related lysosomal changes. In addition, the study
provided evidence that specific N-glycosylation sites within certain
individual proteins can have significantly altered glycosylation occupancy
in pancreatic cancer, reflecting the complexity of the molecular mechanisms
underlying cancer-associated glycosylation events.
pancreatic cancer; proteomics; glycoproteomics; glycosylation; glycoprotein; pancreatitis; mass spectrometry; pancreas; glycosylation
Isobaric tags for relative and absolute quantitation (iTRAQ) is a prominent mass spectrometry technology for protein identification and quantification that is capable of analyzing multiple samples in a single experiment. Frequently, iTRAQ experiments are carried out using an aliquot from a pool of all samples, or “masterpool”, in one of the channels as a reference sample standard to estimate protein relative abundances in the biological samples and to combine abundance estimates from multiple experiments. In this manuscript, we show that using a masterpool is counterproductive. We obtain more precise estimates of protein relative abundance by using the available biological data instead of the masterpool and do not need to occupy a channel that could otherwise be used for another biological sample. In addition, we introduce a simple statistical method to associate proteomic data from multiple iTRAQ experiments with a numeric response and show that this approach is more powerful than the conventionally employed masterpool-based approach. We illustrate our methods using data from four replicate iTRAQ experiments on aliquots of the same pool of plasma samples and from a 406-sample project designed to identify plasma proteins that covary with nutrient concentrations in chronically undernourished children from South Asia.
Mass spectrometry; iTRAQ; statistical analysis; experimental design
Metabolomic profiles were used to characterize the effects of consuming a high-phytochemical diet compared to a diet devoid of fruits and vegetables in a randomized trial and cross-sectional study. In the trial, 8 h fasting urine from healthy men (n=5) and women (n=5) was collected after a 2-week randomized, controlled trial of 2 diet periods: a diet rich in cruciferous vegetables, citrus and soy (F&V), and a fruit- and vegetable-free (basal) diet. Among the ions found to differentiate the diets, 176 were putatively annotated with compound identifications, with 46 supported by MS/MS fragment evidence. Metabolites more abundant in the F&V diet included markers of dietary intervention (e.g., crucifers, citrus and soy), fatty acids and niacin metabolites. Ions more abundant in the basal diet included riboflavin, several acylcarnitines, and amino acid metabolites. In the cross-sectional study, we compared participants based on tertiles of crucifers, citrus and soy from 3 d food records (3DFR; n=36) and food frequency questionnaires (FFQ; n=57); intake was separately divided into tertiles of total fruit and vegetable intake for FFQ. As a group, ions individually differential between the experimental diets differentiated the observational study participants. However, only 4 ions were significant individually, differentiating the third vs. first tertile of crucifer, citrus and soy intake based on 3FDR. One of these was putatively annotated: proline betaine, a marker of citrus consumption. There were no ions significantly distinguishing tertiles by FFQ. Metabolomics assessment of controlled dietary interventions provides a more accurate and stronger characterization of diet than observational data.
Longitudinal algorithms incorporate change over time in biomarker levels to individualize screening decision rules. Compared with a single-threshold (ST) rule, smaller deviations from baseline biomarker levels are required to signal disease. We demonstrated improvement in ovarian cancer early detection by using a longitudinal algorithm to monitor annual CA125 levels.
Patients and Methods
We retrospectively evaluated serial preclinical serum CA125 values measured annually in 44 incident ovarian cancer cases identified from participants in the PLCO (Prostate Lung Colorectal and Ovarian) Cancer Screening Trial to determine how frequently and to what extent the parametric empirical Bayes (PEB) longitudinal screening algorithm identifies ovarian cancer earlier than an ST rule.
The PEB algorithm detected ovarian cancer earlier than an ST rule in a substantial proportion of cases. At 99% specificity, which corresponded to the ST-rule CA125 cutoff ≥ 35 U/mL that was used in the PLCO trial, 20% of cases were identified earlier by using the PEB algorithm. Among these cases, the PEB signaled abnormal CA125 values, on average, 10 months earlier and at a CA125 concentration 42% lower (20 U/mL) than the ST-rule cutoff. The proportion of cases detected earlier by the PEB algorithm and the earliness of detection increased as the specificity of the screening rule was reduced.
The PEB longitudinal algorithm identifies ovarian cancer earlier and at lower biomarker concentrations than an ST screening algorithm adjusted to the same specificity. Longitudinal biomarker assessment by using the PEB algorithm may have application for screening other solid tumors in which biomarkers are available.
Acute myeloid leukemia (AML) encompasses a heterogeneous group of diseases, and novel biomarkers for risk refinement and stratification are needed to optimize patient care. To identify novel risk factors, we performed transcriptome sequencing on 68 diagnostic AML samples and identified 2 transcript variants (−E2 and −E2/3) of the α-subunit (ITGA5) of the very late antigen-5 integrin. We then quantified expression of ITGA5 and these splice variants in specimens from participants of the AAML03P1 trial. We found no association between ITGA5 expression and clinical outcome. In contrast, patients with the highest relative expression (Q4) of the −E2/3 ITGA5 splice variant less likely had low-risk disease than Q1–3 patients (21% vs. 38%, P=0.027). Q4 patients had worse response to chemotherapy with a higher proportion having persistent minimal residual disease (50% vs. 23%, P=0.003) and inferior overall survival (at 5 years: 48% vs. 67%, P=0.015); the latter association was limited to low-risk patients (Q4 vs. Q1–3: 56% vs. 85%, P=0.043) and was not seen in standard-risk (51% vs. 60%, P=0.340) or high-risk (33% vs. 38%, P=0.952) patients. Our exploratory studies indicate that transcriptome sequencing is useful for biomarker discovery, as exemplified by the identification of ITGA5 −E2/3 splice variant as potential novel adverse prognostic marker for low-risk AML that, if confirmed, could serve to further risk-stratify this patient subset.
acute myeloid leukemia; ITGA5; prognostication; splice variants; transcriptome sequencing
Biomarkers that detect pancreatic cancer at earlier stages could improve the outcome of this deadly disease.
We investigated a dozen biomarker candidates for their potential as pancreatic cancer blood biomarkers using ELISA assays.
Among them, the MIF and OPN blood tests were nearly perfect in distinguishing pancreatic cancer cases from healthy controls (100% and 95% sensitivity respectively at 100% specificity). Five biomarker candidates were then tested on an expanded set of diseased controls which included sera from pancreatitis patients. The sensitivity dropped significantly for all five candidate markers.
Our results suggest that biomarker marker candidates could fail in various steps of biomarker development. Earlier knowledge of candidate biomarker flaws could lead to strategies to overcome the flaw, or alternatively lead to earlier termination of biomarkers that are prone to failure in the later phases of validation testing.
Pancreatic cancer; biomarker; early detection; pancreatitis
Clinical oncology is hampered by a lack of tools to accurately assess a patient’s response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not-responding to a therapy could be usefully incorporated into tools for monitoring response. Here we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study we use Stable Isotope Labeling of Amino acids in Culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGFR targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information and a subset consisting of  proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and demonstrated that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response.
NSCLC; EGFR; Gefitinib; Proteomics; SILAC
We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens.
Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias.
In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue.
Conclusions and clinical relevance
These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
Breast cancer; Her2; mouse; proteome; transcriptome
Xenografts of human colorectal cancer (CRC) in immune-deficient mice have great potential for accelerating the study of tumor biology and therapy. We evaluated xenografts established in NOD/scid/IL2Rγ-null mice from the primary or metastatic tumors of 27 patients with CRC to estimate their capacity for expanding tumor cells for in vitro studies and to assess how faithfully they recapitulated the transcriptional profile of their parental tumors. RNA-seq analysis of parental human CRC tumors and their derivative xenografts demonstrated that reproducible transcriptional changes characterize the human tumor to murine xenograft transition. In most but not all cases, the human stroma, vasculature, and hematopoietic elements were systematically replaced by murine analogues while the carcinoma component persisted. Once established as xenografts, human CRC cells that could be propagated by serial transplantation remained transcriptionally stable. Three histologically atypical xenografts, established from patients with peritoneal metastases, contained abundant human stromal elements and blood vessels in addition to human tumor cells. The transcriptomes of these mixed tumor/stromal xenografts did not closely resemble those of their parental tumors, and attempts to propagate such xenografts by serial transplantation were unsuccessful. Stable expression of numerous genes previously identified as high priority targets for immunotherapy was observed in most xenograft lineages. Aberrant expression in CRC cells of human genes that are normally only expressed in hematopoietic cells was also observed. Our results suggest that human CRC cells expanded in murine xenografts have great utility for studies of tumor immunobiology and targeted therapies such as immunotherapy but also identify potential limitations.
Disease-associated aberrant glycosylation may be protein specific and glycosylation site specific. Quantitative assessment of glycosylation changes at a site-specific molecular level may represent one of the initial steps for systematically revealing the glycosylation abnormalities associated with a disease or biological state. Comparative quantitative profiling of glycoproteome to provide accurate quantification of site-specific glycosylation occupancy has been a challenging task, requiring a concerted approach drawing from a variety of techniques. In this report, we present a quantitative glycoproteomics method that allows global scale identification and comparative quantification of glycosylation site occupancy using mass spectrometry. We further demonstrated this approach by quantitatively characterizing the N-glycoproteome of human pancreas.
Human epididymis protein 4 (HE4) is approved for clinical use with CA125 to predict epithelial ovarian cancer (EOC) in women with a pelvic mass or in remission after chemotherapy. Previously reported reference ranges for HE4 are inconsistent.
We report positivity thresholds yielding 90%, 95%, 98% and 99% specificity for age-defined populations of healthy women for HE4, CA125 and Risk of Malignancy Algorithm (ROMA), a weighted average of HE4 and CA125. HE4 and CA125 were measured in 1780 samples from 778 healthy women aged >25 years with a documented deleterious mutation, or aged >35 years with a significant family history. Effects on marker levels of a woman’s age, ethnicity and epidemiologic characteristics were estimated, as were the population-specific means, variances and within- and between-woman variances used to generate longitudinal screening algorithms for these markers.
CA125 levels were lower with Black ethnicity (p=0.008). Smoking was associated with higher HE4 (p=0.007) and ROMA (p<0.019). Continuous oral contraceptive use decreased levels of CA125 (p=0.041), and ROMA (p=0.12). CA125 was lower in women age ≥55, and HE4 increased with age (p<0.01), particularly among women age ≥55.
Due to the strong effect of age on HE4, thresholds for HE4 are best defined for women of specific ages. Age-specific population thresholds for HE4 for 95% specificity ranged from 41.4 pmol/L for women age 30 to 82.1 pmol/L for women age 80.
Incorporation of serial marker values from screening history reduces personalized thresholds for CA125 and HE4 but is inappropriate for ROMA.
ovarian cancer; screening; longitudinal algorithm; HE4; CA125; early detection; high-risk
In screening for epithelial ovarian cancer, unnecessary surgery can be reduced by limiting use of transvaginal ultrasound (TVU) to women with increasing CA125 serum levels. Replacing or augmenting TVU with measurement of a serum marker specific for malignancy might further improve screening performance. Serum samples from 112 invasive ovarian cancer patients and 706 matched control subjects from the Prostate, Lung, Colorectal, and Ovarian trial were used to evaluate human epididymis protein 4 (HE4), mesothelin, matrix metalloproteinase 7 (MMP7), SLPI, Spondin2, and insulin-like growth factor binding protein 2 (IGFBP2) for their potential use in screening. TVU results were available for a subset of 84 patients and 516 control subjects used to compare the best marker with TVU. HE4 was found to perform better than TVU as a second-line screen, confirming 27 of 39 cancers with increasing CA125 serum levels compared with 17 cancers confirmed by TVU (P = .03). Serum HE4 levels were found to increase with age and smoking status, suggesting that a longitudinal algorithm might improve its performance.
Establishing a cancer screening biomarker’s intended performance requires “phase III” specimens obtained in asymptomatic individuals before clinical diagnosis rather than “phase II” specimens obtained from symptomatic individuals at diagnosis. We used specimens from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial to evaluate ovarian cancer biomarkers previously assessed in phase II sets.
Phase II specimens from 180 ovarian cancer cases and 660 benign disease or general population controls were assembled from four Early Detection Research Network (EDRN) or Ovarian Cancer Specialized Program of Research Excellence (SPORE) sites and used to rank 49 biomarkers. Thirty-five markers, including 6 additional markers from a fifth site, were then evaluated in PLCO proximate specimens from 118 women with ovarian cancer and 474 matched controls.
Top markers in phase II specimens included CA125, HE4, transthyretin, CA15.3, and CA72.4 with sensitivity at 95% specificity ranging from 0.73 to 0.40. Except for transthyretin, these markers had similar or better sensitivity when moving to phase III specimens that had been drawn within six months of the clinical diagnosis. Performance of all markers declined in phase III specimens more remote than 6 months from diagnosis.
Despite many promising new markers for ovarian cancer, CA125 remains the single-best biomarker in the phase II and phase III specimens tested in this study.
Ovarian neoplasms; CA125; HE4; Screening tests; CA72.4
A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used pre-diagnostic samples to assess the potential of the panels for early detection. We conducted a multi-site systematic evaluation of biomarker panels using pre-diagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial.
Using a nested case-control design, levels of 28 biomarkers were measured laboratory-blinded in 118 serum samples obtained before cancer diagnosis and 951 serum samples from matched controls. Five predictive models, each containing 6–8 biomarkers, were evaluated according to a pre-determined analysis plan. Three sequential analyses were conducted: blinded validation of previously established models (Step 1); simultaneous split-sample discovery and validation of models (Step 2); and exploratory discovery of new models (Step 3). Sensitivity, specificity, sensitivity at 98% specificity, and AUC were computed for the models and CA125 alone among 67 cases diagnosed within one year of blood draw and 476 matched controls. In Step 1, one model showed comparable performance to CA125, with sensitivity, specificity and AUC at 69.2%, 96.6% and 0.892, respectively. Remaining models had poorer performance than CA125 alone. In Step 2, we observed a similar pattern. In Step 3, a model derived from all 28 markers failed to show improvement over CA125.
Thus, biomarker panels discovered in diagnostic samples may not validate in pre-diagnostic samples; utilizing pre-diagnostic samples for discovery may be helpful in developing validated early detection panels.
Early Detection; Screening; Biomarkers; Validation; Study Design
High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.
Chronic pancreatitis is a chronic inflammatory disorder of the pancreas. The etiology is multi-fold, but all lead to progressive scarring and loss of pancreatic function. Early diagnosis is difficult; and the understanding of the molecular events that underlie this progressive disease is limited. In this study, we investigated differential proteins associated with mild and severe chronic pancreatitis in comparison with normal pancreas and pancreatic cancer. Paraffin-embedded formalin-fixed tissues from five well-characterized specimens each of normal pancreas (NL), mild chronic pancreatitis (MCP), severe chronic pancreatitis (SCP) and pancreatic ductal adenocarcinoma (PDAC) were subjected to proteomic analysis using a “label-free” comparative approach. Our results show that the numbers of differential proteins increase substantially with the disease severity, from mild to severe chronic pancreatitis, while the number of dysregulated proteins is highest in pancreatic adenocarcinoma. Important functional groups and biological processes associated with chronic pancreatitis and cancer include acinar cell secretory proteins, pancreatic fibrosis/stellate cell activation, glycoproteins, and inflammatory proteins. Three differential proteins were selected for verification by immunohistochemistry, including collagen 14A1, lumican and versican. Further canonical pathway analysis revealed that acute phase response signal, prothrombin activation pathway, and pancreatic fibrosis/pancreatic stellate cell activation pathway were the most significant pathways involved in chronic pancreatitis, while pathways relating to metabolism were the most significant pathways in pancreatic adenocarcinoma. Our study reveals a group of differentially expressed proteins and the related pathways that may shed light on the pathogenesis of chronic pancreatitis and the common molecular events associated with chronic pancreatitis and pancreatic adenocarcinoma.
Applying advanced proteomic technologies to prospectively collected specimens from large studies is one means of identifying preclinical changes in plasma proteins that are potentially relevant to the early detection of diseases like breast cancer. We conducted fourteen independent quantitative proteomics experiments comparing pooled plasma samples collected from 420 estrogen receptor positive (ER+) breast cancer patients ≤17 months prior to their diagnosis and matched controls. Based on the over 3.4 million tandem mass spectra collected in the discovery set, 503 proteins were quantified of which 57 differentiated cases from controls with a p-value<0.1. Seven of these proteins, for which quantitative ELISA assays were available, were assessed in an independent validation set. Of these candidates, epidermal growth factor receptor (EGFR) was validated as a predictor of breast cancer risk in an independent set of preclinical plasma samples for women overall [odds ratio (OR)=1.44, p-value=0.0008], and particularly for current users of estrogen plus progestin (E+P) menopausal hormone therapy (OR=2.49, p-value=0.0001). Among current E+P users EGFR's sensitivity for breast cancer risk was 31% with 90% specificity. While EGFR's sensitivity and specificity are insufficient for a clinically useful early detection biomarker, this study suggests that proteins that are elevated preclinically in women who go on to develop breast cancer can be discovered and validated using current proteomic technologies. Further studies are warranted to both examine the role of EGFR and to discover and validate other proteins that could potentially be used for breast cancer early detection.
Breast cancer; epidermal growth factor receptor; menopausal hormone therapy
Tumor-derived, circulating proteins are potentially useful as biomarkers for detection of cancer, for monitoring of disease progression, regression and recurrence, and for assessment of therapeutic response. Here we interrogated how a protein's stability, cellular localization, and abundance affect its observability in blood by mass-spectrometry-based proteomics techniques. We performed proteomic profiling on tumors and plasma from two different xenograft mouse models. A statistical analysis of this data revealed protein properties indicative of the detection level in plasma. Though 20% of the proteins identified in plasma were tumor-derived, only 5% of the proteins observed in the tumor tissue were found in plasma. Both intracellular and extracellular tumor proteins were observed in plasma; however, after normalizing for tumor abundance, extracellular proteins were seven times more likely to be detected. Although proteins that were more abundant in the tumor were also more likely to be observed in plasma, the relationship was nonlinear: Doubling the spectral count increased detection rate by only 50%. Many secreted proteins, even those with relatively low spectral count, were observed in plasma, but few low abundance intracellular proteins were observed. Proteins predicted to be stable by dipeptide composition were significantly more likely to be identified in plasma than less stable proteins. The number of tryptic peptides in a protein was not significantly related to the chance of a protein being observed in plasma. Quantitative comparison of large versus small tumors revealed that the abundance of proteins in plasma as measured by spectral count was associated with the tumor size, but the relationship was not one-to-one; a 3-fold decrease in tumor size resulted in a 16-fold decrease in protein abundance in plasma. This study provides quantitative support for a tumor-derived marker prioritization strategy that favors secreted and stable proteins over all but the most abundant intracellular proteins.
To evaluate the impact of ovarian cancer risk on the performance of the serum biomarkers mesothelin, HE4 and CA125.
We measured mesothelin, HE4 and CA125 levels from women with invasive ovarian cancer (n=143), benign gynecological conditions (n=124) and healthy women (n=344). Demographic, epidemiologic, reproductive, medical and family history data were collected using a standardized questionnaire. Pedigree and BRCA 1/2 test results were used to stratify women into average and high risk groups. The diagnostic accuracy of each biomarker was characterized using receiver operating characteristic (ROC) curve methods.
Baseline characteristics did not vary by risk or case status. The distribution of stage and histology was similar in average and high risk women. All three markers discriminated ovarian cancer cases from risk-matched healthy and benign controls. Marker performance did not vary by risk status. The sensitivity at 95% specificity for discriminating cases from risk-matched healthy control women in the average and high risk groups respectively was 53.9% and 39.0% for mesothelin, 80.4% and 87.8% for HE4, and 79.4% and 82.9% for CA125. The performance of the markers was not as robust when cases were compared to benign controls. AUC values for cases vs. healthy and benign controls did not vary by risk status.
The ability of serum mesothelin, HE4 and CA 125 levels to discriminate ovarian cancer cases from healthy and benign controls is not influenced by risk status. Our findings support the pursuit of additional studies evaluating the early detection potential of these markers in high-risk populations.
ovarian cancer screening; biomarkers; high-risk; molecular diagnosis and prognosis; molecular markers in prevention research; gynecological cancers: ovarian
The recent advance in technology for mass spectrometry-based targeted protein quantification has opened new avenues for a broad range of proteomic applications in clinical research. The major breakthroughs are highlighted by the capability of using a “universal” approach to perform quantitative assays for a wide spectrum of proteins with minimum restrictions, and the ease of assembling multiplex detections in a single measurement. The quantitative approach relies on the use of synthetic stable isotope labeled peptides or proteins, which precisely mimic their endogenous counterparts and act as internal standards to quantify the corresponding candidate proteins. This report reviews recently developed platform technologies for emerging applications of clinical proteomics and biomarker development.
proteomics; absolute quantification; mass spectrometry; biomarker; MRM; MALDI TOF/TOF; AQUA; SISCAPA; stable isotope dilution
The vast majority of clinical tissue samples are formalin-fixed and paraffin-preserved. This type of preservation has been considered an obstacle to protein extraction from these tissues. However, these are the very tissue samples that have associated patient histories, diagnoses and outcomes — ideal samples in the quest to translate bench research into clinical applications. Thus, until recently, these valuable specimens have been unavailable for proteomic analysis.
Over the last decade, researchers have been exploring efficient methods to undo protein cross-linking caused by standard tissue fixatives and extract proteins from archived tissue specimens. These methods have been applied in different clinical proteomic studies. In this report, we attempt to review the development of these techniques, summarize the proteomic findings, and discuss the impact on future clinical proteomics.
proteomics; protein extraction; fixed tissue; paraffin; formalin; Hollandes; laser capture microdissection
Perhaps the greatest barrier to translation of serum biomarker discoveries is the inability to evaluate putative biomarkers in high throughput validation studies. Here we report on the development, production, and implementation of a high-density antibody microarray used to evaluate large numbers of candidate ovarian cancer serum biomarkers. The platform was shown to be useful for evaluation of individual antibodies for comparative analysis, such as with disease classification, and biomarker validation and discovery. We demonstrate its performance by showing that known tumor markers behave as expected. We also identify several promising biomarkers from a candidate list and generate hypotheses to support new discovery studies.
We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.
Strategies to discover circulating protein markers of ovarian cancer are urgently needed. We developed a novel technology that permits us to isolate recombinant antibodies directed against the potential serum biomarkers, to facilitate the further development of affinity reagents necessary to construct diagnostic tests.
This study presents a novel discovery approach based on serum immunoprecipitation with cancer-specific in vivo biotinylated recombinant antibodies (biobodies) derived from differentially selected yeast-display scFv, and analysis of the eluted serum proteins by electrophoresis and/or mass spectrometry.
Using this strategy we identified catabolic fragments of complement factors, EMILIN2, Von Willebrand factor and phosphatidylethanolamine-binding protein 1 (PEBP1 or RKIP) in patient sera. To our knowledge, this is the first report of a soluble form of PEBP1 in human. Independent evidence for ovarian cancer-specific expression of PEBP1 in patient sera was found by ELISA assays and antibody arrays with anti-PEBP1 antibodies. PEBP1 was detected in 29 out of 30 ascites samples and discriminated ovarian cancer sera from controls (p = 0.02). Finally, we confirmed by western blots the presence of a 21–23 kDa fragment corresponding to the expected size of PEBP1 but we also showed additional bands of 38 kDa and 50–52 kDa in various tissues and cell lines.
We conclude that the novel strategy described here allows the identification of candidate biomarkers that can be variants of normally expressed proteins or that display cancer-specific post-translational modifications.
Epithelial ovarian cancer is a significant cause of mortality both in the United States and worldwide, due largely to the high proportion of cases that present at a late stage, when survival is extremely poor. Early detection of epithelial ovarian cancer, and of the serous subtype in particular, is a promising strategy for saving lives. The low prevalence of ovarian cancer makes the development of an adequately sensitive and specific test based on blood markers very challenging. We evaluated the performance of a set of candidate blood markers and combinations of these markers in detecting serous ovarian cancer.
Methods and Findings
We selected 14 candidate blood markers of serous ovarian cancer for which assays were available to measure their levels in serum or plasma, based on our analysis of global gene expression data and on literature searches. We evaluated the performance of these candidate markers individually and in combination by measuring them in overlapping sets of serum (or plasma) samples from women with clinically detectable ovarian cancer and women without ovarian cancer. Based on sensitivity at high specificity, we determined that 4 of the 14 candidate markers-MUC16, WFDC2, MSLN and MMP7-warrant further evaluation in precious serum specimens collected months to years prior to clinical diagnosis to assess their utility in early detection. We also reported differences in the performance of these candidate blood markers across histological types of epithelial ovarian cancer.
By systematically analyzing the performance of candidate blood markers of ovarian cancer in distinguishing women with clinically apparent ovarian cancer from women without ovarian cancer, we identified a set of serum markers with adequate performance to warrant testing for their ability to identify ovarian cancer months to years prior to clinical diagnosis. We argued for the importance of sensitivity at high specificity and of magnitude of difference in marker levels between cases and controls as performance metrics and demonstrated the importance of stratifying analyses by histological type of ovarian cancer. Also, we discussed the limitations of studies (like this one) that use samples obtained from symptomatic women to assess potential utility in detection of disease months to years prior to clinical detection.