Serum Biomarker Panels for the Detection of Pancreatic Cancer

Brand, Randall E.; Nolen, Brian M.; Zeh, Herbert J.; Allen, Peter J.; Eloubeidi, Mohamad A.; Goldberg, Michael J.; Elton, Eric; Arnoletti, J. Pablo; Christein, John D.; Vickers, Selwyn M.; Langmead, Christopher J.; Landsittel, Douglas; Whitcomb, David C.; Grizzle, William E.; Lokshin, Anna

doi:10.1158/1078-0432.ccr-10-0248

Cited by 211 publications

(210 citation statements)

References 44 publications

(46 reference statements)

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“…Increased gene expression, and/or secretion of TIMP1, has been previously observed in PDAC and found to induce tumor cell proliferation (26,31,32). Although elevated circulating TIMP1 levels have been mostly associated with PDAC (13,14,(33)(34)(35)(36), increased levels have also been found in other epithelial tumor types (37)(38)(39). A role for LRG1 has been suggested in promoting angiogenesis through activation of the TGF-b pathway (40).…”

Section: Discussionmentioning

confidence: 96%

“…Although promising PDAC plasma protein biomarkers have been previously reported (11,12,14), this is the first study where a proteomics-based discovery, performed using both human prediagnostic and mouse early-stage PDAC plasma samples, was followed by sequential validation of the identified biomarker candidates in multiple independent sets of samples from resectable PDAC patients and matched controls. By applying a rigorous statistical method, we were able to build a model with statistically significantly improved performance than CA19-9 alone, which was blindly tested in an independent sample set.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, there is substantial ongoing effort to identify additional serological biomarkers that complement CA19-9 for the detection of early-stage PDAC. Markers investigated include proteins (9)(10)(11)(12)(13)(14), metabolites (15)(16)(17), autoantibodies (18,19), miRNAs (20)(21)(22), markers with aberrant glycosylation (CA19-9 and Tn antigens) (6,23,24), and exosomes (25). In view of the wide array of potential biomarkers, there is a need for systematic evaluation of candidates using CA19-9 as an anchor marker given its established performance.…”

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confidence: 99%

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Sequential Validation of Blood-Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer

Capello

Bantis

Scélo

et al. 2016

JNCI J Natl Cancer Inst

123

125

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Background: CA19-9, which is currently in clinical use as a pancreatic ductal adenocarcinoma (PDAC) biomarker, has limited performance in detecting early-stage disease. We and others have identified protein biomarker candidates that have the potential to complement CA19-9. We have carried out sequential validations starting with 17 protein biomarker candidates to determine which markers and marker combination would improve detection of early-stage disease compared with CA19-9 alone. Methods: Candidate biomarkers were subjected to enzyme-linked immunosorbent assay based sequential validation using independent multiple sample cohorts consisting of PDAC cases (n ¼ 187), benign pancreatic disease (n ¼ 93), and healthy controls (n ¼ 169). A biomarker panel for early-stage PDAC was developed based on a logistic regression model. All statistical tests for the results presented below were one-sided. Results: Six out of the 17 biomarker candidates and CA19-9 were validated in a sample set consisting of 75 PDAC patients, 27 healthy subjects, and 19 chronic pancreatitis patients. A second independent set of 73 early-stage PDAC patients, 60 healthy subjects, and 74 benign pancreatic disease patients (combined validation set) yielded a model that consisted of TIMP1, LRG1, and CA19-9. Additional blinded testing of the model was done using an independent set of plasma samples from 39 resectable PDAC patients and 82 matched healthy subjects (test set). The model yielded areas under the curve (AUCs) of 0.949 (95% confidence interval [CI] ¼ 0.917 to 0.981) and 0.887 (95% CI ¼ 0.817 to 0.957) with sensitivities of 0.849 and 0.667 at 95% specificity in discriminating early-stage PDAC vs healthy subjects in the combined validation and test sets, respectively. The performance of the biomarker panel was statistically significantly improved compared with CA19-9 alone (P < .001, combined validation set; P ¼ .008, test set).

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Sequential Validation of Blood-Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer

Capello

Bantis

Scélo

et al. 2016

JNCI J Natl Cancer Inst

123

125

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“…The specificity for all cancers was at least 98% using SVM. The low sensitivity for PC may be due to general immune suppression in later stages of this disease (16,25,26).…”

Section: Resultsmentioning

confidence: 99%

Immunosignature system for diagnosis of cancer

Stafford

Cichacz

Woodbury

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Although the search for disease biomarkers continues, the clinical return has thus far been disappointing. The complexity of the body's response to disease makes it difficult to represent this response with only a few biomarkers, particularly when many are present at low levels. An alternative to the typical reductionist biomarker paradigm is an assay we call an "immunosignature." This approach leverages the response of antibodies to diseaserelated changes, as well as the inherent signal amplification associated with antigen-stimulated B-cell proliferation. To perform an immunosignature assay, the antibodies in diluted blood are incubated with a microarray of thousands of random sequence peptides. The pattern of binding to these peptides is the immunosignature. Because the peptide sequences are completely random, the assay is effectively disease-agnostic, potentially providing a comprehensive diagnostic on multiple diseases simultaneously. To explore the ability of an immunosignature to detect and identify multiple diseases simultaneously, 20 samples from each of five cancer cohorts collected from multiple sites and 20 noncancer samples (120 total) were used as a training set to develop a reference immunosignature. A blinded evaluation of 120 blinded samples covering the same diseases gave 95% classification accuracy. To investigate the breadth of the approach and test sensitivity to biological diversity further, immunosignatures of >1,500 historical samples comprising 14 different diseases were examined by training with 75% of the samples and testing the remaining 25%. The average accuracy was >98%. These results demonstrate the potential power of the immunosignature approach in the accurate, simultaneous classification of disease.cancer diagnostic | immunodiagnostic | antibody biomarker | peptide microarray

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“…Using biomarker patterns, instead of single molecular markers, for interpretation and prediction of phenotypic variation has been evolving in conjunction with analytical developments [1][2][3] . This trend is still at an early stage and a consensus on how to extract and statistically evaluate such biomarker patterns is still to be reached.…”

Section: Introductionmentioning

confidence: 99%

Statistical loadings and latent significance simplify and improve interpretation of multivariate projection models

Jonsson

Björkblom

Chorell

et al. 2018

Preprint

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Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a versatile multivariate projection method for analysis of correlation, discrimination and effect changes. However, currently OPLS is not fully using its multivariate potential since orthogonal systematic variation is not considered in model interpretation, resulting in univariate interpretation of variable significance. We present a strategy for improved interpretation of OPLS models based upon a post-hoc linear regression analysis that can be used with or without the orthogonal OPLS score(s) as a covariate to make the interpretation multivariate or univariate respectively. By selecting the observed response y or estimated response yhat as a one of the factors in the linear regression the results are related to either of the OPLS loadings w or p. Furthermore, converting the OPLS loading values to statistical t-values creates a direct link to statistical significance. Finally, by applying three different Boolean loadings W, P and W∧P variable significance can be summarized based on three criteria. W and P reveal if the values in w or p respectively are outside the statistical limits with W∧P being the logical conjunction of W and P (significant if outside limits in both W and P). Two examples are used to verify the proposed strategy. First, a synthetic example, simulating a mix of mass spectra, and second a clinical metabolomics study of a dietary intervention. In the simulated example we show that multivariate interpretation gives higher accuracy for estimation of true differences, mainly due to higher true positive rate. Furthermore, we highlight how application of W∧P for summarizing variable significance leads to higher accuracy. For the metabolomics example, we show that a more detailed interpretation, i.e. larger number of significant metabolites of relevance, is obtained using the multivariate interpretation. In summary, the suggested strategy provides means for facilitated interpretation of OPLS models, beyond univariate statistics, and offers a multivariate tool for discovery of biomarker patterns, i.e. latent biomarkers.. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Serum Biomarker Panels for the Detection of Pancreatic Cancer

Cited by 211 publications

References 44 publications

Sequential Validation of Blood-Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer

Sequential Validation of Blood-Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer

Immunosignature system for diagnosis of cancer

Statistical loadings and latent significance simplify and improve interpretation of multivariate projection models

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