Neutrophils dominate the immune cell composition in non-small cell lung cancer.

The response rate to immune checkpoint inhibitor therapy for non-small-cell lung cancer (NSCLC) is just 20%. To improve this figure, several early phase clinical trials combining novel immunotherapeutics with immune checkpoint blockade have been initiated. Unfortunately, these trials have been designed without a strong foundational knowledge of the immune landscape present in NSCLC. Here, we use a flow cytometry panel capable of measuring 51 immune cell populations to comprehensively identify the immune cell composition and function in NSCLC. The results show that the immune cell composition is fundamentally different in lung adenocarcinoma as compared with lung squamous cell carcinoma, and that neutrophils are the most prevalent immune cell type. Using T-cell receptor-ß sequencing and tumour reactivity assays, we predict that tumour reactive T cells are frequently present in NSCLC. These results should help to guide the design of clinical trials and the direction of future research in this area.

Expression and functional characterization of CD33 transcript variants in human acute myeloid leukemia.

With the demonstration of improved survival of some acute myeloid leukemia (AML) patients with the CD33 antibody-drug conjugate, gemtuzumab ozogamicin (GO), CD33 has been validated as a target for antigen-specific immunotherapy. Since previous studies identified a CD33 splice variant missing exon 2 (CD33∆E2) and, consequently, the immune-dominant membrane-distal V-set domain, we investigated the expression and functional characteristics of CD33 transcript variants in AML. In primary AML specimens, we not only found full-length CD33 (CD33FL) and CD33∆E2 but also corresponding variants containing an alternate exon 7 predicted to encode a CD33 protein lacking most of the intracellular domain (CD33E7a and, not previously described, CD33∆E2,E7a) in almost all cases. In acute leukemia cell sublines engineered to express individual CD33 splice variants, all splice variants had endocytic properties. CD33FL and CD33E7a mediated similar degrees of GO cytotoxicity, whereas CD33∆E2 and CD33∆E2,E7a could not serve as target for GO. Co-expression of CD33∆E2 did not interfere with CD33FL endocytosis and did not impact CD33FL-mediated GO cytotoxicity. Together, our findings document a greater-than-previously thought complexity of CD33 expression in human AML. They identify CD33 variants that lack exon 2 and are not recognized by current CD33-directed therapeutics as potential target for future unconjugated or conjugated antibodies.

Proteins associated with pancreatic cancer survival in patients with resectable pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a dismal prognosis. However, while most patients die within the first year of diagnosis, very rarely, a few patients can survive for >10 years. Better understanding the molecular characteristics of the pancreatic adenocarcinomas from these very-long-term survivors (VLTS) may provide clues for personalized medicine and improve current pancreatic cancer treatment. To extend our previous investigation, we examined the proteomes of individual pancreas tumor tissues from a group of VLTS patients (survival ≥10 years) and short-term survival patients (STS, survival <14 months). With a given analytical sensitivity, the protein profile of each pancreatic tumor tissue was compared to reveal the proteome alterations that may be associated with pancreatic cancer survival. Pathway analysis of the differential proteins identified suggested that MYC, IGF1R and p53 were the top three upstream regulators for the STS-associated proteins, and VEGFA, APOE and TGFß-1 were the top three upstream regulators for the VLTS-associated proteins. Immunohistochemistry analysis using an independent cohort of 145 PDAC confirmed that the higher abundance of ribosomal protein S8 (RPS8) and prolargin (PRELP) were correlated with STS and VLTS, respectively. Multivariate Cox analysis indicated that 'High-RPS8 and Low-PRELP' was significantly associated with shorter survival time (HR=2.69, 95% CI 1.46-4.92, P=0.001). In addition, galectin-1, a previously identified protein with its abundance aversely associated with pancreatic cancer survival, was further evaluated for its significance in cancer-associated fibroblasts. Knockdown of galectin-1 in pancreatic cancer-associated fibroblasts dramatically reduced cell migration and invasion. The results from our study suggested that PRELP, LGALS1 and RPS8 might be significant prognostic factors, and RPS8 and LGALS1 could be potential therapeutic targets to improve pancreatic cancer survival if further validated.

Quantitative glycoproteomics analysis reveals changes in N-glycosylation level associated with pancreatic ductal adenocarcinoma.

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-ß, 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.

Phenotypic and transcriptional fidelity of patient-derived colon cancer xenografts in immune-deficient mice.

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.

Metabolomic profiling of urine: response to a randomised, controlled feeding study of select fruits and vegetables, and application to an observational study.

Metabolomic profiles were used to characterise the effects of consuming a high-phytochemical diet compared with a diet devoid of fruits and vegetables (F&V) in a randomised 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 randomised, controlled trial of two diet periods: a diet rich in cruciferous vegetables, citrus and soya (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 forty-six supported by MS/MS fragment evidence. Metabolites more abundant in the F&V diet included markers of the dietary intervention (e.g. crucifers, citrus and soya), 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 the participants based on the tertiles of crucifers, citrus and soya from 3 d food records (n 36) and FFQ (n 57); intake was separately divided into the 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 four ions were significant individually, differentiating the third v. first tertile of crucifer, citrus and soya intake based on 3 d food records. One of these ions was putatively annotated: proline betaine, a marker of citrus consumption. There were no ions significantly distinguishing tertiles by FFQ. The metabolomic assessment of controlled dietary interventions provides a more accurate and stronger characterisation of the diet than observational data.

Statistical inference from multiple iTRAQ experiments without using common reference standards.

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.

Longitudinal screening algorithm that incorporates change over time in CA125 levels identifies ovarian cancer earlier than a single-threshold rule.

PURPOSE: 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. RESULTS: 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. CONCLUSION: 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.

Interpretation of single and serial measures of HE4 and CA125 in asymptomatic women at high risk for ovarian cancer.

BACKGROUND: Human epididymis protein 4 (HE4) is approved for clinical use with CA125 to predict epithelial ovarian cancer in women with a pelvic mass or in remission after chemotherapy. Previously reported reference ranges for HE4 are inconsistent. METHODS: We report positivity thresholds yielding 90%, 95%, 98%, and 99% specificity for age-defined populations of healthy women for HE4, CA125, and Risk of Ovarian Malignancy Algorithm (ROMA), a weighted average of HE4 and CA125. HE4 and CA125 were measured in 1,780 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. RESULTS: 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. CONCLUSIONS: Because of 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. IMPACT: Incorporation of serial marker values from screening history reduces personalized thresholds for CA125 and HE4 but is inappropriate for ROMA.

Discovery and preliminary confirmation of novel early detection biomarkers for triple-negative breast cancer using preclinical plasma samples from the Women's Health Initiative observational study.

Triple-negative breast cancer is a particularly aggressive and lethal breast cancer subtype that is more likely to be interval-detected rather than screen-detected. The purpose of this study is to discover and initially validate novel early detection biomarkers for triple-negative breast cancer using preclinical samples. Plasma samples collected up to 17 months before diagnosis from 28 triple-negative cases and 28 matched controls from the Women's Health Initiative Observational Study were equally divided into a training set and a test set and interrogated by a customized antibody array. Data were available on 889 antibodies; in the training set, statistically significant differences in case versus control signals were observed for 93 (10.5 %) antibodies at p < 0.05. Of these 93 candidates, 29 were confirmed in the test set at p < 0.05. Areas under the curve for these candidates ranged from 0.58 to 0.79. With specificity set at 98 %, sensitivity ranged from 4 to 68 % with 20 candidates having a sensitivity ≥ 20 % and 6 having a sensitivity ≥ 40 %. In an analysis of KEGG gene sets, the pyrimidine metabolism gene set was upregulated in cases compared to controls (p = 0.004 in the testing set) and the JAK/Stat signaling pathway gene set was downregulated (p = 0.003 in the testing set). Numerous potential early detection biomarkers specific to triple-negative breast cancer in multiple pathways were identified. Further research is required to followup on promising candidates in larger sample sizes and to better understand their potential biologic importance as our understanding of the etiology of triple-negative breast cancer continues to grow.

Large-scale quantitative glycoproteomics analysis of site-specific glycosylation occupancy.

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.

Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition.

Clinical oncology is hampered by 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 EGF receptor (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 400 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 showed 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.

Proteomics portrait of archival lesions of chronic pancreatitis.

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.

Potential role of HE4 in multimodal screening for epithelial ovarian cancer.

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.

Impact of protein stability, cellular localization, and abundance on proteomic detection of tumor-derived proteins in plasma.

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.

A targeted proteomics-based pipeline for verification of biomarkers in plasma.

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.

Ovarian cancer biomarker performance in prostate, lung, colorectal, and ovarian cancer screening trial specimens.

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 or Ovarian Cancer Specialized Program of Research Excellence 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 6 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.

A framework for evaluating biomarkers for early detection: validation of biomarker panels for ovarian cancer.

A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used prediagnostic samples to assess the potential of the panels for early detection. We conducted a multisite systematic evaluation of biomarker panels using prediagnostic 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 to 8 biomarkers, were evaluated according to a predetermined 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 prediagnostic samples; utilizing prediagnostic samples for discovery may be helpful in developing validated early detection panels.

Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling.

Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis, and nonpancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the nonpancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.